Bernard Chatelain

Étienne Farvaque

Luisa Lambertini

Marc-Alexandre Senegas

Camélia Turcu

Claudio Araujo

Samuel Guérineau

Grégoire Rota-Graziosi

Ekrame Boubtane

Francesca Marchetta

Martine Audibert

Sonia Schwartz

Jean-François Brun

Pascale Motel-Combes

Marie-Françoise Renard

Florent Bresson

Cather- Ine Araujo

Michael Goujon

Marie-Eliette Dury

Un

'équipe Administrative, Hassen

Johan Chantal

Flora

Christelle Estelle Sandra

Charles Collyns

Jung Kim

Joshua Wojnilower

Reginald Darius

Kwang Lee

Hasan Toprak

Hippolyte

Charles

Moussé Gomez

Aicha Camara

Assi Nestor

Gildas

Ali Alou

Camille Adil

Axelle, Ibrahim Muhammad

Pepy Pierre Hugues

Naimatou, Yoro, Kabinet, Fatoumata, Awa, Afrika, Mohamed Jules Hamid

Kwami, Mahamady Abdoul-Akim Faycal

Camille Un

Traoré, Seydou, Aissata, Éric Sanogo

Hermann

Yeo

Valéry, Marilyne Yolande Michael

leurs efforts, leurs encouragements, leurs assurances et leurs prières.

Un grand merci à mes frères et soeurs, Stéphanie, Joscelyne,

R

Cette thèse étudie deux phénomènes qui ont impacté la trajectoire de développement de plusieurs pays dans le monde : les crises financières et les poussées de croissance. La première partie de cette thèse, composée de deux chapitres (chapitres 2 et 3), analyse les coûts commerciaux et les contractions économiques associés aux crises financières dans les pays en développement et émergents. Elle examine également les canaux de transmission de ces effets et le rôle de l'espace budgétaire dans la relance économique d'après crise. La seconde partie, elle aussi composée de deux chapitres (chapitres 4 et 5), analyse les déterminants des poussées de croissance et le rôle du Fonds Monétaire International dans leur initiation. Cette thèse contribue significativement à la littérature existante sur ces deux phénomènes. Le chapitre 2 étudie les effets des crises de la dette, bancaire et de change sur le commerce des biens agricoles, miniers, manufacturiers et des services dans 41 pays émergents sur la période allant de 1980 à 2018. Il révèle que les crises génèrent une baisse prononcée et persistante du commerce international (exportations et importations), portée principalement par la contraction du commerce des biens manufacturiers, et dans une certaine mesure par la baisse du commerce des services, des produits miniers, alors que les biens agricoles apparaissent plus résilients, notamment à la suite des crises de la dette. En outre, la baisse du commerce est beaucoup plus accentuée pour les crises combinées. Les crises induisent cette baisse à travers des effets de composition (la structure et la diversification du commerce), de demande (baisse de la demande de biens et services), et d'offre (baisse de l'offre du crédit, des flux de capitaux entrants et du développement financier).

Le chapitre 3 étudie le rôle de l'espace budgétaire sur les effets récessifs des crises financières et la politique de relance économique dans 35 pays en développement et 56 pays émergents sur la période 1985-2017. Il montre que la disponibilité de l'espace budgétaire avant la crise génère une dualité. Dans les pays qui ont un espace budgétaire suffisant, les coûts des crises sont plus faibles voir nuls et les gouvernements mènent des politiques de relance, supportées par une hausse de la consommation, des investissements et des flux nets de capitaux. Dans les pays avec un espace budgétaire faible, les gouvernements renoncent à leurs politiques de relance et mènent des politiques de consolidations budgétaires pour accroître la soutenabilité des finances publiques ; dans ce cas, la consommation, les investissements et les flux nets de capitaux baissent, et la récession est accentuée et persistante. Le chapitre 4 s'intéresse aux déterminants des poussées de croissance économique. Il identifie 132 épisodes de croissance soutenue dans 117 pays sur la période 1980-2010. Il montre que les améliorations de la stabilité macroéconomique et des conditions externes et dotations en ressources augmentent plus la probabilité des poussées de croissance. Elles sont suivies par les vagues de réformes structurelles, les gains d'investissements, de travail et de productivité, l'amélioration de la diversification et la qualité du commerce, et enfin par l'amélioration des facteurs institutionnels. De plus, il montre que la probabilité d'avoir des poussées de croissance augmente significativement quand les améliorations de la stabilité macroéconomique et des conditions externes et dotations en ressources interviennent, d'une part, et les autres facteurs, d'autre part. Ces deux premiers facteurs apparaissent donc comme des facteurs dominants. Le chapitre 5 évalue le rôle du FMI dans l'initiation des périodes de croissance soutenue et contribue à la littérature très controversée sur l'efficacité des politiques du FMI. Il montre que le FMI a significativement contribué à générer des périodes de croissance soutenue, notamment à travers ses programmes PRGT.

Ces effets positifs ont été plus accentués dans les années 2000 et effectifs dans plusieurs pays indépendamment de leur localisation ou niveau de développement. Ces gains proviennent de l'amélioration de la stabilité macroéconomique, de l'implémentation des réformes structurelles, et de la création d'un climat favorable pour l'investissement, le travail, la productivité et les conditions externes et de dotations.

S

This dissertation studies two phenomena that have been widespread in many countries of the world through history and have huge implications for development, namely the financial crises and growth surges. The first part, comprising two chapters (chapters 2 and 3), analyzes the sectoral trade and output costs of financial crises in the context of developing and emerging countries. It also examines the channels by which financial crises affect trade and output and assess the role of fiscal policy and space to alleviate the output costs. The second part, comprising also two chapters (chapters 4 and 5), turns our attention to the determinants of growth surges in countries and the International Monetary Fund's role in igniting growth surges. Chapter 2 studies the response of different types of trade (i.e. agricultural, mining, and manufactured goods, and services) following various types of financial crises (i.e. debt, banking, and currency crises) in 41 emerging countries over the period 1980-2018. It reveals that the collapse of total trade in the aftermath of financial crises is long-lasting and mainly driven by the fall of manufacturing trade. Also, trade in both mining goods and services declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. These trade costs are reinforced for combined crises and can be explained by compositional and structural (trade structure and diversification), demand-side (fall in demand for goods and services), and supply-side channels (disruption of financial development, fall of net capital inflows and deterioration of credit ratings). Chapter 3 studies how fiscal policy space shapes the dynamics of output losses in the aftermath of financial crises and normal recessions in a sample of 35 developing and 56 emerging countries over the period 1985-2017. It reveals that the availability of fiscal space in the aftermath of financial crises and normal recessions generates a mixed fiscal environment with different output losses of shocks.

In countries with enough fiscal space, governments can enact credible fiscal policy expansion by increasing their deficit and using their fiscal space to alleviate the costs of financial crises and normal recessions. In such a situation, private consumption and investment, as well as net capital inflows, increase, which favors a rapid recovery. In countries with limited fiscal space, the story is different and painful; governments immediately trade output stabilization goals out to address the debt sustainability issues while implementing fiscal consolidations, which deepens the recessionary forces. Besides, in these countries, private consumption and investment, as well as net capital inflows, are depressed, and recovery, if any, is a distant and uncertain prospect.

Chapter 4 studies the determinants of growth surges. It identifies 132 episodes of growth surges in 117 countries over the period 1980-2010 and finds that improvements in macroeconomic stability and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. Besides, it shows that countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments, on one hand, and other determinants, on the other hand. Moreover, significant changes in macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges. Chapter 5 engages and contributes to the debate on the effectiveness of the IMF in promoting growth. It concludes that IMF-supported programs (more PRGT than GRA programs) have significantly and positively contributed to boosting mediumto long-term growth in countries, particularly in the 2000s than previous decades, and in all countries around the world, regardless of their geographical position and levels of development.

It has done so by pursuing macroeconomic stability and implementing structural reforms, but also creating the pre-conditions to boost investments, labor, and productivity and benefit more from favorable external and endowments conditions.

3.2

Illustration of the signals approach method . . . . . . . . . . . . . . . . . . . . . 3.3 Thresholds and relative weights of fiscal indicators . . . . . . . . . . . . . . . . 3.4 Difference in characteristics between treated and control groups . . . . . . . . . . 3.5 Endogeneity check of fiscal space . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 ATT estimates of the effects of shocks on output dynamics, unconditional paths . 3.7 ATT estimates of the effects of shocks and fiscal space on output dynamics, conditional paths . . B.2 summary statistics, both treated and control groups . . . . . . . . . . . . . . . . B.3 Balance diagnostics between the treated and control groups after weighting, financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.4 Balance diagnostics between the treated and control groups after weighting, normal recessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.5 OLS estimates of the effects of financial crises on output dynamics, unconditional paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.6 OLS estimates of the effects of normal recessions on output dynamics, unconditional paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.7 OLS estimates of the effects of financial crises and fiscal space on output dynamics, conditional paths . B.11 Misspecification error and relevance of fiscal space, financial crises, ATT estimates B.12 Misspecification error and relevance of fiscal space, normal recessions, ATT estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.13 Alternative measures of fiscal space, financial crises, ATT estimates . . . . . . . B.14 Alternative measures of fiscal space, normal recessions, ATT estimates . . . . . . D.15 Predicting growth surges, robustness checks, alternative horizons of growth determinants, average over the post-7 years of growth surges . . . . . . . . . . . . . . D.16 Predicting growth surges, robustness checks, alternative horizons of growth determinants, average over the post-3 years of growth surges . . . . . . . . . . . . . . D.17 Predicting growth surges, robustness checks, Probit estimator . . . . . . . . . . . D.18 Predicting growth surges, robustness checks, Tobit estimator . . . . . . . . . . . D.19 Predicting growth surges, robustness checks, alternative set of growth surges, Hausmann et al. (2005)

L F

2.1

Financial crises over time and by regions . . . . . . . . . . . . . . . . . . . . . . 2.2 Survival models of the duration between the onset of different financial crises . .

2.3

Evolution of the average international trade in financial crises . . . . . . . . . . . 2.4 Cumulative trade costs over five years after financial crises . . . . . . . . . . . . 2.5 Cumulative trade costs over five years after financial crises, total and sectoral level A.1 Sample of countries and the starting date of the various financial crises since 1980 A.2 Kernel density of the distribution of the propensity scores for the treated and control groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3 Robustness, cumulative trade costs over five years after combined financial crises A.4 Robustness, cumulative trade costs over five years after non-combined financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.5 Robustness, Drop the period 2008 onwards, cumulative trade costs over five years after financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.6 Robustness, Maximum weight set to 20, cumulative trade costs over five years after financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.7 Robustness, Maximum weight set to 5, cumulative trade costs over five years after financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.8 Robustness, Alternative sources and definitions of crises, cumulative trade costs over five years after financial crises . . . . . . . . . . . . . . . . . . . . . . . . . A.9 Robustness, Inverse Probability Weighted (IPW) estimator, cumulative trade costs over five years after financial crises . . . . . . . . . . . . . . . . . . . . . . . . . A.10 Channels, cumulative trade costs over five years after financial crises with and without a higher share of manufacturing exports . . . . . . . . . . . . . . . . . . A.11 Channels, cumulative trade costs over five years after financial crises with and without a higher export diversification . . . . . . . . . . . . . . . . . . . . . . . A.12 Channels, cumulative trade costs over five years after financial crises with and without a higher export diversification . . . . . . . . . . . . . . . . . . . . . . . A.13 Channels, cumulative trade costs over five years after financial crises with and without a higher trading partners' growth . . . . . . . . . . . . . . . . . . . . . . A.14 Channels, cumulative trade costs over five years after financial crises with and without a higher financial development . . . . . . . . . . . . . . . . . . . . . . . A.15 Channels, cumulative trade costs over five years after financial crises with and without higher gross capital inflows . . . . . . . . . . . . . . . . . . . . . . . . A.16 Channels, cumulative trade costs over five years after financial crises with and without a higher investors' credit rating risks . . . . . . . . . . . . . . . . . . . .

3.1

Historical and recent trend of fiscal space index and fiscal crises . . . . . . . . . 3.2 Fiscal space index and the costs of financial crises and normal recessions . . . . . 3.3 Fiscal space dynamics around shocks . . . . . . . . . . . . . . . . . . . . . . . . 3.4 OLS estimates, effects of shocks and fiscal space on output growth and recovery . 3.5 ATT estimates, effects of shocks and fiscal space on output growth and recovery . 3.6 Non-linearity of fiscal space . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4.1

Illustration of identified growth surges: cases of Chile, China, Ghana, and India . 4.2 Paired average marginal effects of the six broad determinants of growth surges . .

C.1

Average marginal effects of the six broad determinants of growth surges . . . . .

5.1

Stylized facts on growth surges and their association with IMF-supported programs by decades, regions, and levels of development . . . . . . . . . . . . . . . . . . . D.1 Average marginal effects of the broad determinants of growth surges, with and without all IMF-supported programs . . . . . . . . . . . . . . . . . . . . . . . . D.2 Average marginal effects of the broad determinants of growth surges, with and without GRA programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.3 Average marginal effects of the broad determinants of growth surges, with and without PRGT programs . Argentina, 2001 andIndia, 1991, two countries, two different stories that are quite appealing for this dissertation. One illustrates the story of several countries that have gone through the detrimental consequences of crises, the other illustrates the success stories of sustained economic growth periods and their benefits in many countries.

The 2001 Argentina's crisis

Starting in 2001, Argentina experienced one of the worst economic crises in history. Argentina halted payments on bonds worth $81 billion in December 2001, two-third of the nation's GDP wiped out in four years, riots erupted claiming that "all of them must go!" and "we are dying of hunger!", the country had five presidents in two weeks, inflation reignited, the banking system was paralyzed, unemployment rose above 20 percent, millions of lives were impoverished, and the currency board collapses. The year running to the crisis, Argentina was praised and cited as a model of successful economic reform that has weathered the storm of the Tequila crisis. As it was common in Latin America, Argentina 2001 crisis was not rooted in large money financed deficits or hyperinflation, but in an excessive build-up of external debt and a persistent deficit of the public sector, coupled with high off-budget activities. Indeed, public debt increased at a higher pace, by 18 percentage points of GDP in the five years preceding the 2001 default, fueled by higher public deficits over the same period. With the collapse of the economy, public debt reached 152.2% of GDP and public deficit plummeted to 25% of GDP in 2002. Argentina 20 Chapter . General introduction requested an IMF Stand-By Arrangement of $14 billion and an international support package of $40 billion and renegotiated its debt with its creditors in 2005.

India's growth surges

Since its large economic reforms of the 1990s, India's long-term economic growth has steadily accelerated by 4.4% per year during the 1990s, and further to 6.5% per year between 2000 and 2017. Its income per capita in PPP terms was multiplied by 4.9 over this period. At the same time, India's growth has become more stable and more resilient to shocks. This contributed to a significant reduction of poverty that went from around 45 to 22% between 1991 and 2012. What India did? India's success was rooted in large economic reforms towards openness and liberalization. This marked a turning point for India and its people. Following the macroeconomic crisis of 1991-92, in the same vein of the liberalization reforms of the 1980s, India undertook a wave of reforms in two keys areas: industry and external trade. Its reforms encompassed deregulation of industry, tariff cuts, capital account openness, currency depreciation to boost competitiveness, liberalization of trade, banking, and NTIC sectors, and attraction of foreign direct investments. As a result of reforms, growth has accelerated the fastest in services, followed by industry. It was driven by an increasing share of investment and exports, with a large contribution from consumption, and an increase in trade diversification, labor, and total factor productivity.

Of course, these two stories far from being specific to Argentina and India have been widespread phenomena in different countries, regardless of their geographical location and levels of development. They raise questions about the causes and consequences of financial crises and growth surges. This dissertation provides some answers to these questions and enriches our knowledge of financial crises and growth surges. To do so, it is divided into two parts. The first part, comprising two chapters (chapters 2 and 3), provides analyses of the sectoral trade and output costs of financial crises in the context of developing and emerging countries. It also examines the channels by which financial crises affect trade and output and assess the role of fiscal policy and space to alleviate the output costs of crises. The second part, comprising also two chapters (chapters 4 and 5), turns our attention to the determinants of growth surges in countries and the International Monetary Fund's role in igniting growth surges. It draws lessons from the past and provides some guidance on how to ignite growth surges. Moreover, it sheds light on the potential role played by the IMF in the initiation of growth surges. [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF][START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]Atsebi et al. 2019). At the micro-level, following the great trade collapse, several contributions explain the contraction of trade following financial crises through two mechanisms, the demand-side channel and the supply-side channel. However, this literature may be developed on several grounds. First and more importantly, this literature has overlooked the cross-sectoral differences of the effects of financial crises, i.e. the differentiated costs of crises on the different types of traded goods and services. Second, most of them treat financial crises as exogenous, therefore, they may not capture a causal effect. Third, they generally analyze the effects of each

Financial crises and international trade

Chapter . General introduction financial crisis separately without controlling for other crises, therefore, they may suffer from overestimation bias since the different crises may be interrelated.

Against this backdrop, chapter 2 studies the response of different types of trade (i.e. agricultural, mining, and manufactured goods, and services) following various types of financial crises (i.e. debt, banking, and currency crises) in 41 emerging countries over the period 1980-2018.

It uses a combination of impact assessment and local projections to capture a causal dynamic effect running from financial crises to the trade activity. It reveals that the collapse of total trade in the aftermath of financial crises is long-lasting and mainly driven by the fall of manufacturing trade. However, the impact of financial crises on the other types of traded goods and especially on services is far from being negligible. Trade in both mining goods and services also declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. When looking at the costs of combined crises, it shows that they exert a significant and higher decline of trade, compared to crises occurring without any other crisis in the years around. Also, financial crises exert an adverse effect on total and sectoral trade through compositional and structural, demand-side, and supply-side channels. In detail, about the compositional and structural channel, this chapter sustains that financial crises may act as an impediment of structural transformation as they hurt more manufacturing exports in countries where the share of manufacturing exports is relatively lower. Also, by diversifying their exports and partners, countries will increase their resilience to financial crises. About the demand-side channel, it shows that financial crises associated with a lower demand of goods and services from trading partners will have more adverse trade costs; therefore they can generate an unprecedented collapse of international trade when they are generalized within regions and at the global level as witnessed in the post-GFC period, which is of particular interest in this time of Covid-19 pandemic. Finally, it supports the idea that the supply-side channel is critical to understand the way financial crises shape the dynamics of international trade. When associated with a deterioration of the domestic financial development and external financial conditions, and sudden stops, financial crises will exert a significant and detrimental collapse on international trade.

Financial crises and economic growth

Financial crises have also generated long-lasting and protracted output losses in many countries (see, e.g., Kaminsky and Reinhart 1999;Bordo et al. 2003;Tomz and Wright 2007;Cerra and Saxena 2008;Reinhart and Rogoff 2009;Fatás and Mihov 2013;Borensztein and Panizza 2014;Jordà et al. 2013Jordà et al. , 2016;;Jordà and Taylor 2016;[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]Kuvshinov and Zimmermann 2019;Trebesch and Zabel 2017;Laeven and Valencia 2018;Romer and Romer . . Financial crises and economic growth 23 2018;[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]Atsebi et al. 2020). However, the size of the output collapse and the pace at which the recovery may happen crucially depend on the availability of fiscal space that is the "room in a government's budget that allows it to provide resources for the desired purpose without jeopardizing the sustainability of its financial position or the stability of the economy" as defined by Heller (2005). Indeed, in the aftermath of the global financial crisis (GFC) of 2008-09, many governments around the world enacted large fiscal stimulus plans to boost their sagging economies. These plans were based on the Keynesian theory that sustains that deficit spending by governments can stimulate their economy by supporting the aggregate demand. This is of particular interest since the evidence of larger fiscal multipliers in recessions than in expansions. However, at the same time, many other countries were forced to implement large fiscal consolidations to dissipate fiscal sustainability issues and restore external viability, and this at the worst possible time, which exacerbated the recessionary forces of the crisis. One noticeable difference between countries that were implementing loose and contractionary fiscal policy lies in the availability of fiscal space they had in the run-up of the crisis. For instance, Romer and Romer (2018) analyze the effects of fiscal and monetary space on output dynamics in the aftermath of financial distress and show that the output losses are less than 1% when a country has both types of policy space, but almost 10% when it has neither. One of the channels is that governments can use monetary and fiscal policy more aggressively when policy space is available. However, they focus exclusively on advanced countries for which in the post-WWII, crises and recessions have been less recurrent and severe compared to developing and emerging countries. Besides, Bohn (2002) and Mendoza and Ostry (2008) show that fiscal policy tends to be on average more expansionary when government debt is low. Giavazzi and Pagano (1990); Blanchard (1993); [START_REF] Perotti | [END_REF]; [START_REF] Minea | [END_REF]; [START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF]Ilzetzki et al. (2013) show that expansionary fiscal policy is more effective and has Keynesian effects at low levels of debt or deficit, and non-Keynesian effects in the opposite circumstances [START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF], and [START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF] reveal that fiscal multipliers are larger during recessions and financial crises. Altogether, these papers show that fiscal policy may be more effective in alleviating the size of recessions when countries have enough fiscal space that allows them to enact stimulus packages without deterioration their fiscal position and the market sentiment.

Taking stock of this existing literature, chapter 3 studies how fiscal policy space shapes the dynamics of output losses in the aftermath of financial crises and normal recessions in a sample of 35 developing and 56 emerging countries over the period 1985-2017. It builds a new index

Chapter . General introduction of fiscal space and applies a combination of local projections models and impact assessment to identify a causal effect. It reveals that the availability of fiscal space in the aftermath of financial crises and normal recessions generates a mixed fiscal environment with different output losses of shocks. In countries with enough fiscal space, governments can enact credible fiscal policy expansion by increasing their deficit and using their fiscal space to alleviate the costs of financial crises and normal recessions. In such a situation, private consumption and investment, as well as net capital inflows, increase, which favors a rapid recovery. In countries with limited fiscal space, the story is different and painful; governments immediately trade output stabilization goals out to address the debt sustainability issues while implementing fiscal consolidations, which deepens the recessionary forces. Besides, in these countries, private consumption and investment, as well as net capital inflows, are depressed, and recovery, if any, is a distant and uncertain prospect. Just like in physics, i.e., momentum naturally winds down rather than up unless outside energy is applied, countries that neglect the right disciplines will not only fall but will slope there unless they have fiscal space that allows them to boost their economy in downturns. This chapter suggests that governments and policymakers need to be more than proactive to learn lessons from the past, fix the roof while the sun is shining, build fiscal buffers, reduce debt and deficit, increase tax base and revenues, and lock the drinks cabinet when the economy is starting to improve substantially to be able to appropriately respond to the next crisis looming on the horizon.

Financial crises and growth surges

A broad range of political and economic factors can explain why and when growth surges happen or not; one these, which is analyzed in part I, is the presence of a crisis. This is known as the "crisis-induces-reform" hypothesis. Indeed, financial crises unveil the non-performing macroeconomic policies, political, economic, and social dysfunctions of the economies, which allows considering serious and well-targeted macroeconomic and structural reforms to increase the country's resilience to crises, but more importantly to embark on a tour of sustained growth.

For instance, IMF (2019) shows that crises foster trade liberalization and, to a lesser extent, labor market and financial deregulation over the medium term. Moreover, Lora and Olivera (2004) show that collapse in domestic demand may lower opposition to trade liberalization from industries that usually rely on domestic demand. Similarly, Duval et al. (2018) finds that periods of high unemployment may increase pressure on governments to enact reforms that ease labor market regulation in the hope of boosting employment. Mian et al. (2014) show that after a financial crisis resulting from a period of deregulation, governments are inclined to re-regulate the financial sector and the economy. These results are supportive of the "crisis-induces-reform" hypothesis and suggest that there is hope to jump-start growth in the aftermath of financial crises if the appropriate policy and reforms are undertaken. Moreover, financial crises are not the only determinant of growth surges and many others may matter.

Growth surges and its determinants

The existing literature on the determinants of growth surges is very inconclusive. Among others, Hausmann et al. (2005) concluded that investment and trade, real exchange rate depreciation, political regime changes, external factors, and economic reforms, "on the whole, [...] do a very poor job of predicting the turning points. [...] growth accelerations are caused predominantly by idiosyncratic, and often small-scale changes" and [START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] pointed that "growth accelerations are extremely hard to engineer with a high degree of certainty [...] roughly 9 out of 10 instances failed to ignite a take-off". Jong-A-Pin and de Haan (2011) highlighted the important role of economic liberalizations while they found that a move toward more democracy reduces the likelihood of growth surges. [START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF] pointed out the critical role of macroeconomic stability and trade diversification to ignite and sustain growth.

Chapter 4 attempts to reconcile the existing papers. To do so, it identifies 132 episodes of growth surges in 117 countries over the period 1980-2010 and finds that many growth determinants have a significant and positive effect on the probability of initiating growth. Specifically, improvements in macroeconomic stability and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. When looking at the two-way interactions of growth determinants, it shows that countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments, on one hand, and other determinants, on the other hand. Besides, it reveals that significant changes in macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges. Therefore, macroeconomic policies and structural reforms work. Unfortunately, we have witnessed a significant decrease in the pace of structural reforms and an increase in deregulations leading to excessive build-up of risks in countries since the 2000s. With the recommendation of chapter 3 in mind, fix the roof when

Chapter . General introduction the sun is shining, countries have to engage in large reforms programs following the Covid-19 crisis to strengthen their resilience to shocks, jump-start sustained growth and reconnect with economic wealth.

Growth surges and the IMF

The International Monetary Fund (IMF) described as the "lender of last resort" or the "financial firefighter", both criticized and lauded for its effort to promote financial stability, continues to find itself at the forefront of global economic crises management. As stated in the IMF's

Guidelines on Conditionality (2002), "Fund-supported programs should be directed primarily toward the following macroeconomic goals: (a) solving the member's balance of payments problem without recourse to measures destructive of national or international prosperity; and (b) achieving medium-term external viability while fostering sustainable economic growth".

However, lackluster growth under IMF-supported programs relative to non-program countries or periods has often been criticized as indicative of an excessive tightening bias and resulted in a perceived stigma, potentially discouraging the use of IMF financing and challenging the Fund's reputation. Meanwhile, IMF's economists argue that restoring macroeconomic stability even painful in the short-term will create the conditions for higher medium to longer-term growth.

This controversy debate on the IMF's effectiveness, particularly on promoting growth, has led to several analyses in the literature. Not surprising, this literature is very inconclusive, reflecting in part significant empirical challenges involved in identifying appropriate counterfactuals and isolating the impact of programs on growth from influences of other factors, and because of varying data and methods employed by the researchers. The class of papers highlighting a positive effect of IMF-supported programs on growth encompasses, e.g. Dicks-Mireaux et al. (2000); Hutchison (2004); Atoyan and Conway (2006); Bas and Stone (2014); Bal [START_REF] Gündüz | Ils sont contredits par Przeworski and Vreeland[END_REF] and Bird and Rowlands (2017). There are contradicted by Przeworski and Vreeland (2000); IEO and IMF (2002); [START_REF] Hutchison | [END_REF]; [START_REF] Barro | [END_REF]; [START_REF] Butkiewicz | Sachant que le chapitre 4 a révélé que la stabilité macroéconomique est l'une des conditions préalables aux poussées de croissance, le chapitre 5 contribue au débat sur l'efficacité du FMI à promouvoir la croissance, notamment car il a été impliqué dans la restauration de la stabilité macroéconomique dans plusieurs pays. Il conclut que les programmes soutenus par le FMI (plus les programmes PRGT que GRA) ont contribué de manière significative et positive à stimuler la croissance à moyen et long terme dans les pays, en particulier dans les années 2000 7 Historiquement, depuis sa création en[END_REF]; [START_REF] Sixth | finds that none of the top 20 recipients of repeated IMF-supported programs over 1980-99 were to achieve reasonable growth and macroeconomic stability[END_REF] and Dreher (2006).

7 Also, the IMF (2019) shows that "reforms take several years to deliver, and some of them [...] may entail greater short-term costs when carried out in bad times; these are best implemented under favorable economic conditions and early in authorities' electoral mandate. Reform gains also tend to be larger when governance and access to credit-two binding constraints on growth-are strong, and where labor market informality is higher-because reforms help reduce it. 8 Historically, since its inception in 1944, the IMF has been assisting more than 150 countries through 1,300 IMF-supported programs. This includes the reconstruction of the international system payments system in the post-world war II, the transition of Former Soviet Union nations to market-based economies, and the management of the diverse crises in countries affecting by the 1970s' oil shocks, the 1980s' Latin American and African debt crises, the 1990s' Asian financial crisis, the European debt crisis in the Aftermath of the 2008-09 global financial crisis, and the 2019-2020 Covid-19 Pandemic crisis.

Introduction

"Manufacture, don't just trade. There is money in manufacturing even though it is capital intensive. To achieve a big breakthrough, I had to start manufacturing the same product I was trading on; which is commodities."

-Aliko Dangote, Nigerian businessman, philanthropist, founder and chairman of Dangote Group, an industrial conglomerate in Africa.

The recent 2008-09 crisis can be qualified as the "Great Trade Collapse" due to its profound effects on international trade. Indeed, according to the WTO and IMF, the drop in world trade flows (around 12% of world GDP in 2009) exceeded that of world GDP (about 5% in 2009).

1 Baldwin (2011) reports that global trade fell for at least three quarters only in three of the worldwide recessions that occurred between 1965 and 2008: the oil-shock recession of 1974-75, the inflation-defeating recession of 1982-83, and the Tech-Wreck recession of 2001-02. However, the "Great Trade Collapse" of 2008-09 is by far the largest trade collapse since the WWII. Given the worldwide benefits of trade, this severe downturn brought back into the spotlight the issue of the trade costs of financial crises.

By adopting a macroeconomic perspective, most existing studies focus on gravity models estimated on data of bilateral trade of goods between countries. In a panel of 150 countries, Rose (2005) finds a negative effect of debt crises on the trade between a debtor (defaulting country) and its creditors (the countries affected by the default), a result extended by Martinez and Sandleris (2011) to all trading partners of a defaulting country (i.e. both creditors and non-creditors) and confirmed more recently by [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF] in a treatment effect analysis. Such a detrimental effect on trade is equally emphasized for banking and currency crises, with some exceptions. Indeed, [START_REF] Ma | Martinez and Sandleris[END_REF] reveal that imports decline following both banking and currency crises, while exports decrease (increase) following banking (currency) crises.

Besides, [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF] conclude that debt and banking crises do not significantly affect exports, while they induce a sharp and long-lasting decline of imports. Altogether, despite some exceptions for exports, there exists a fairly strong consensus on the detrimental consequences of financial crises on trade at the macroeconomic level. However, this literature may be developed on several grounds. First, most of the existing papers use trade data only for goods and do not account for trade in services, which may have greater resilience to financial crises according to Borchert and Mattoo (2010) and Ariu (2016). Second, since financial crises are likely not exogenous, they may not capture a causal effect with the notable exception of Asonuma et al. (2016). Third, given the focus on the costs of each crisis taken separately without controlling for other crises, the effects may be overestimated since the different crises may be interrelated.

Fourth and more importantly, one of the shortcomings of this literature is the lack of evidence on the cross-sectoral differences of the effects of financial crises. Indeed, the different types of goods and services have different natures (demand elasticity, reliance on external financing, use as intermediate goods, vertical linkages, etc.) that make them more or less vulnerable to financial crises.

More recently, following the great trade collapse, several contributions explain the contraction of trade following financial crises through two mechanisms, the income channel and the disruption channel. Focusing on the demand side, the income channel suggests that financial crises reduce trade through their recessionary effect on income (see Reinhart and Rogoff 2009), which leads to a fall in consumption, investment, and imports. For example, Freund (2009) finds that the income elasticity of trade increased from under 2 in the 1960s to over 3.5 in recent years, meaning that nowadays trade could fall about 3.5 times more than GDP. Such a disproportionate fall of the demand, and particularly of durable and investment goods, is indeed at work following the 2008-09 great trade collapse (see e.g. Bricongne et al. 2012;[START_REF] Behrens | [END_REF][START_REF] Eaton | À l'inverse, en se concentrant sur l'offre, le canal de perturbation est soutenu par Iacovone and Zavaka[END_REF]). Conversely, focusing on the supply side, the disruption channel is supported by Iacovone and Zavaka (2009); [START_REF] Amiti | [END_REF]; [START_REF] Minetti | [END_REF]; [START_REF] Chor | qui insistent sur le rôle des conditions de crédit (par exemple, la faiblesse du développement financier) et le crédit commercial (par exemple, la dépendance vis-à-vis des finances extérieures) pour expliquer le déclin du commerce international suite aux crises financières[END_REF]; Zymek (2012), and[START_REF] Chor | qui insistent sur le rôle des conditions de crédit (par exemple, la faiblesse du développement financier) et le crédit commercial (par exemple, la dépendance vis-à-vis des finances extérieures) pour expliquer le déclin du commerce international suite aux crises financières[END_REF], who insist on the role of credit conditions (for example, financial development weakness) and trade credit (for example, external finance dependency) for explaining the decline of international trade following financial crises, while Bems et al. (2011); [START_REF] Altomonte | la littérature où de nombreux auteurs se sont appuyés sur un seul indicateur, notamment la dette en pourcentage du PIB ou des recettes fiscales[END_REF] and Ariu (2016) point out the role played by the disruption of global value chains. Although these studies focus on the within-manufacturing comparison of industries over the recent period (i.e. following the great trade collapse of 2008-09), they suggest that not all types of goods and services may be equally affected by financial crises, due to differences in their demand elasticity, external financial needs, vertical linkages through value chains, and their different perception by customers and investors.

Taking stock of the existing literature, the goal of our paper is to assess the trade costs of financial crises by adopting a sectoral perspective. Indeed, except for the aggregate trade of goods and trade in manufactured goods, the literature has so far remained fairly silent regarding the patterns of trade in agricultural or mining goods, or services, following historical financial crises. Moreover, compared with the recent literature that mainly focuses on the 2008-09 crisis, we draw upon a wide sample of 41 emerging countries over the period 1980-2018 to analyze the trade effects of several types of financial crises, namely 38 debt crises, 34 banking crises, and 36 currency crises. To treat potential endogeneity issues and provide a dynamic view of the trade costs of financial crises, we employ a novel method that combines local projections à la Jordà (2005) and impact assessment with the Augmented Inverse Propensity Weighted estimator.

Moreover, we investigate the demand-side and supply-side channels by which financial crises affect both total and sectoral international trade. We supplement these channels by a third channel that looks at the composition and structure of the trade.

Our results are as follows. First, consistent with the existing literature, we find that aggregate exports and imports fall by 5.6 and 11 percentage points (pp.) of pre-crisis GDP following debt crises, 8.9 and 14 pp. following banking crises, and 7.7 and 9.1 pp. following currency crises, respectively, over five years.

Second, we go beyond existing studies and disaggregate trade costs by type of goods and services. We find that manufacturing goods are the most affected by financial crises. However, the impact of financial crises on the other types of traded goods and especially on services is far from being negligible. Trade in both mining goods and services (particularly after banking crises) also declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. When looking at the costs of combined crises, we find that they exert a significant and higher decline of trade, compared to crises occurring without any other crisis in the years around.

Robust to a wide variety of specifications, including the use of alternative samples, maximum weights in the treatment models, sources and definitions of crises, and estimators, our findings are explained by (i) compositional and structural, demand, (ii) supply effects. From a compositional and structural perspective, our findings sustain that financial crises may act as an impediment of structural transformation as they hurt more manufacturing exports in countries where the share of manufacturing exports is relatively lower. Also, by diversifying their exports and partners, countries will increase their resilience to financial crises.

From a demand perspective, our findings show that financial crises associated with lower demand for goods and services from trading partners will have more adverse trade costs, particularly for manufacturing trade. Indeed, agricultural and mining goods and services may have a lower income elasticity of demand compared with manufactured goods, which makes them more resilient to crises. For instance, the demand for agricultural goods may not contract too much when income decreases, since they are often necessary for subsistence (relative to other goods that may be reduced), and they are usually low-priced; in the same vein, Borchert and Mattoo (2010) outline that the focus on the trade of goods has obscured the quiet resilience of the trade of services during the recent crisis, which may be explained according to Ariu (2016) by their lower GDP growth elasticity compared with the elasticity of exports of goods (and also by the fact that services are intangible products that cannot be stored and used as collateral for requesting financing, and essential inputs for maintaining a production activity). On the contrary, the income elasticity of demand is high for manufactured goods, and particularly for durable and investment goods: [START_REF] Eaton | À l'inverse, en se concentrant sur l'offre, le canal de perturbation est soutenu par Iacovone and Zavaka[END_REF] show that the decline of demand for "postponable" (durable and non-durable) manufactured goods drives the overall collapse in trade, and plays a role in the contagion to other countries consistent with the findings of Levchenko et al. (2010) of a strong decline in the trade in durable and intermediate inputs following the 2008-09 crisis.

Finally, our findings support the idea that the supply-side channel is critical to understand the way financial crises shape the dynamics of international trade. When associated with a deterioration of the domestic financial development and external financial conditions, and sudden stops, financial crises will exert a significant and detrimental collapse on international trade, which is mainly driven by the fall of manufacturing and services trade. Indeed, the stronger decline in the trade of manufactured goods during periods of credit crunch or deterioration in external financial conditions associated with financial crises may be related to the fact that their production and transport rely relatively more on external finance compared with agricultural Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries and mining goods. Indeed, by severely limiting external finance, a credit crunch reduces firms' production and export capacities; for example, Iacovone and Zavaka (2009); [START_REF] Amiti | [END_REF]; [START_REF] Minetti | [END_REF]; Zymek (2012) discuss such effects in the industry sector. Conversely, the production of agricultural and mining goods and services may require less external financing; for instance, mining goods are produced by large companies (often multinationals) that may rely on self-financing. Besides, since most global value chains concern the production of manufactured goods, the interruption of a link in an international production chain and trade credit, due to a crisis in a country, can lead to the destruction of the entire chain, and further to a larger decline of the trade of manufactured goods. Altogether, these rich and detailed results unveil the panorama of the trade costs of financial crises.

The rest of this paper is structured as follows. Section 2.2 details the methodology, Section 2.3 describes the data, Section 2.4 presents the main results, Section 2.5 analyzes their robustness, Section 2.6 discusses potential channels, and Section 2.7 concludes the paper.

Methodology

The causal effect going from financial crises to international trade is likely to be polluted by endogeneity, arising from different characteristics between countries that experience or not financial crises, or from reverse causality between trade and financial crises. We tackle these issues using a combined method of impact assessment methodology (IAM) and local projections (LP) à la Jordà (2005), following [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]; Forni et al. (2016); Jordà et al. (2016) and Kuvshinov and Zimmermann (2019), which consists of three steps. First, we estimate the likelihood of financial crises (i.e. the propensity score) based on their determinants. Second, we fit an outcome model in which changes in trade flows at each horizon scaled by pre-crisis GDP are explained by some factors. Third, we compute a semi-parametric estimator of the average treatment effect (ATE), namely the Augmented Inverse Propensity Weighted (AIPW), using the predicted propensity scores obtained from the first stage, and the observed and the potential (predicted in the second stage) values of the change in trade flows. In the following, we describe the LP model and the AIPW estimator.

Local projection model

LP was extensively used to estimate fiscal multipliers, the effects of fiscal consolidations, and the consequences of financial crises, see e.g. Auerbach and Gorodnichenko (2012a,b); Owyang et al. (2013); [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]Forni et al. (2016); Jordà et al. (2016); Kuvshinov and Zimmermann (2019), and its popularity is supported by several aspects. First, being a flexible, semi-parametric method to estimate dynamic effects, it captures both the direct and indirect (i.e. through changes in fundamentals) effect of financial crises on trade. Second, LP easily accounts for a nonlinear response of trade, which may be potentially at work in our analysis devoted to the effects of financial crises. Third, it can be estimated through standard regression models, and easily combined with IAM. Based on the standard setup in the literature, we estimate the following LP model , +ℎ = , ,ℎ , + , ,ℎ , + , ,ℎ , + ,ℎ 1 , -1 + ,ℎ 2 , -2 + , +ℎ ,ℎ + , +ℎ (2.1)

for the time-horizon ℎ ∈ 0; 5 , where , +ℎ = ( , +ℎ -, -1 )/ -1 × 100 is the cumulative change between -1 and + ℎ in 100 times the trade flows of variable of country scaled by pre-crisis GDP. denotes exports/imports of agricultural, mining, and manufactured goods, and services.

, , , , and , are dummies of debt, banking, and currency crises, respectively. These dummies equal to 1 at the start of each financial crises, and 0 in non-crises years. Their effects at each horizon ℎ are captured through , ,ℎ , , ,ℎ , , ,ℎ , respectively.

, -1 and , -2 are respectively the change in the trade flows (of trade variable ) one and two years prior to the financial crisis. Finally, , +ℎ is a set of control variables, and , +ℎ is the error term.

The augmented inverse propensity weighted (AIPW) estimator

Our impact assessment considers that financial crises are the treatment variable, and changes in trade flows at each horizon ℎ are the outcome variable. Simplifying the algebra by dropping the indexes for the different dependent variables, and , , and for financial crises, the average treatment effect (ATE) is defined as In this study, we use the AIPW estimator that requires estimating two models, namely the treatment and the outcome models. Regarding the former, we estimate a covariate balancing propensity score (CBPS) model, introduced by Imai and Ratkovic ( 2014), for each crisis on variables , , and obtain the propensity score for country at time to be in the treated, ˆ , = 1 ( ; ˆ ), and control, 1 -ˆ , = 0 ( ; ˆ ), group. Indeed, the CBPS model has several good performances over the traditional logit and probit models; it ensures the perfect balancing of covariates between the treated and control groups compared to probit and logit models, and it also limits the bias due to misspecification in the treatment model (see, tables A.3 to A.5).

= ℎ = E[ , +ℎ (1) -, -1 | , = 1] -E[ , +ℎ (1) -, -1 | , = 0], ∀ ℎ. ( 2
Introduced by Rosenbaum and Rubin (1983), the propensity score is particularly appealing for our analysis to eliminate the biases between the treated and the control group, and we use weighting by propensity scores to mimic a situation where financial crises happen randomly.

Regarding the latter, the outcome model eq. ( 2.1) is estimated separately on both treated and control groups, and we predict the potential outcome E[ , +ℎ -, -1 | , = ; , ]; ∀ ∈ {0, 1}

for the entire sample, based on the characteristics of each group. This provides the potential trade for countries in the treated (control) group if they have not (have) experienced crises, conditional on the set of control variables , . Following the general expression of the AIPW provided by Lunceford and Davidian (2004), we compute the estimated ATE of financial crises on international trade for ℎ year-horizon as

ℎ = 1 , ( , +ℎ -, -1 ) ˆ , - (1 -, )( , +ℎ -, -1 ) 1 -ˆ , - , -ˆ , ˆ , (1 -ˆ , ) ×[(1 -ˆ , ) E[ , +ℎ -, -1 | , = 1; , ] + ˆ , E[ , +ℎ -, -1 | , = 0; , ] .
(2.4) This semi-parametric estimator has the distinctive property of being the most efficient doubly robust estimators, namely, it is unbiased when at least the outcome or the treatment model is 7 Consequently, we use the propensity score predicted using the CBPS method in the rest of the paper. 8 Following Imbens (2004) and Cole and Hernán (2008), we truncated the maximum weight, defined by ˆ -1

, for the treated group and (1 -ˆ , ) -1 for the control group, to 10. In the robustness analysis we change the maximum weight to 5 and 20. 9 Following [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]Jordà et al. (2016), andKuvshinov andZimmermann (2019), we use a larger set of controls in the treatment model compared with the outcome model; indeed, Lunceford and Davidian (2004) suggests including as many variables as collected in the treatment model. correctly specified (see e.g. Leon et al. 2003;Imbens 2004;Lunceford and Davidian 2004;Tsiatis and Davidian 2007;Wooldridge 2007;Kreif et al. 2013). Besides, compared with the inverse propensity weighted (IPW) estimator, it includes an additional adjustment term consisting of the weighted average of the two predicted potential outcomes, which stabilizes the estimator when the propensity scores get close to zero or one, and has expectation zero when either the treatment or the outcome model is correctly specified (see, Glynn andQuinn 2009). Finally, Glynn andQuinn (2009) conclude that the AIPW estimator displays comparable or lower mean square error than competing estimators when the treatment and outcome models are both properly specified and outperforms them when one of these models is misspecified.

Data, and preliminaries

Data

Our unbalanced panel covers 38 debt crises, 34 banking crises, and 36 currency crises in 41 emerging countries that experienced at least one of these crises during the period 1980-2018.

This sample is restricted by the availability of data used in this analysis. We focus on emerging countries for several reasons. First, trade has increased more in these countries over the past decades and represents today a large proportion of world trade. Second, the way international trade reacts to financial crises depends on the levels of development and the structure of trade; therefore, focusing on emerging countries increases the homogeneity of the effects of financial crises. Third, emerging countries have been more affected by all types of crises than low-income and developed countries.

Regarding financial crises, data for debt crises come from Reinhart and Rogoff (2009), data for banking crises are from Laeven and Valencia (2018), and data for currency crises are built using the definition of Frankel and Rose (1996). Debt crises are defined as the failure of the government to meet a principal or interest payment on the due date and/or the episodes of post-default debt restructuring. Banking crises are defined as events where there are signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations) and/or banking policy intervention measures in response to significant losses in the banking system. Currency crises are defined as a nominal depreciation of the local currency against trading partners' currencies of at least 25% that is also 2.1) to predict the changes in trade at each horizon ℎ for each type of good and for services, namely: (i) the change of trade flows one and two years before the onset of financial crises, (ii) other crises, and (iii) the exchange rate regimes at horizon ℎ. The sources and summary statistics are provided in section A. 1.3 and section A.2, respectively. 11 According to Borchert and Mattoo (2010), trade in services accounts for over one-fifth of global cross-border trade, and up to one-third of exports in some large countries (including US or India); and Miroudot et al. (2009) conclude that roughly three-fourth of trade in services in OECD are intermediate inputs. 12 The pairwise correlations between our aggregate trade dataset with existing datasets on aggregate trade (UN Comtrade or IMF Direction of Trade Statistics) are higher than 0.95. This indicates that our data are properly compiled using disaggregate trade. 13 In the channel section, we focus on potential mechanisms by which financial crises affect international trade and include more variables in the analysis.

A preliminary look at the data

In this section, we discuss three features of financial crises: their occurrence, the connections between different types of financial crises, and their link with international trade.

The occurrence of financial crises

The evolution of financial crises during the period 1980-2014 can be summarized by the charts reported in Figure 2.1. According to (A), all types of crises are recurrent in emerging countries and occurred by clusters (e.g., the debt crises in Latin America and Africa in the 1980s, the banking and currency crises in Asia, Latin America, and Eastern Europe of the 1990s).

The number of crises follows a downward trend since the beginning of the 2000s (the Great moderation period), and the 2008-09 contraction has been characterized by less incidence of debt, banking, and currency crises in emerging countries compared to advanced economies.

Moreover, as shown by (B), crises are long-lasting, and emerging countries were suffering debt crises (especially in the 1980s and 1990s) more than banking and currency crises. Finally, (C) suggests that financial crises strike the economies by clusters and spread within the regions, with emerging countries in Africa, Latin America, and Middle-East being more affected by debt crises, emerging countries in Latin America, Europe, and Central Asia, and East Asia and Pacific more affected by banking crises, and countries in Europe and Central Asia, Latin and America more affected by currency crises.

The connections between financial crises

We analyze potential connections between financial crises using the standard nonparametric Kaplan-Meier estimator. after a currency crisis, a debt crisis follows in one-quarter of cases in two years, and a banking crisis in four years. Consequently, the takeaway for the design of our empirical analysis is that when estimating the effect of a crisis one should systematically control for other crises to avoid overestimating its trade costs.

Financial crises and international trade

As a foretaste of the potential trade costs of crises, fig. 2.3 plots the cumulative change of trade flows from the year before the onset of each crisis to 5-year ahead, scaled by pre-crisis GDP. The overall picture supports the collapse of international trade. Total exports and imports decline sharply during all types of financial crises (for example, exports and imports decline respectively by between 11 and 14 percentage points of pre-crisis GDP following all types of crises), mainly driven by the contraction of trade in manufactured goods, followed by the one in services, mining goods, and agricultural goods. In sum, the trade costs of financial crises seem important. However, various issues may lead to an overestimation of these costs. Consequently, we develop in the following a formal econometric analysis to provide a robust estimation of the trade costs of financial crises. Besides, it shows that trade in emerging countries consists mainly of exports and imports in manufactured goods, followed by trade in services, trade in mining goods, and finally trade in agricultural goods.

Results

Estimation of propensity scores

As previously indicated, the first step of our analysis is devoted to the estimation of propensity scores (PS) for each crisis. we weighted the covariates using PS. As shown by tables A.3 to A.5, there are many significant differences between countries with and without countries in the unweighted sample, with less favorable macroeconomic, external and institutional conditions in countries hit by crises. More importantly, we show, according to the criteria of Rubin (2002), that weighting the covariates by the estimated PS obtained from the CBPS model perfectly eliminates the differences in covariates between the treated and the control group (which is less true for traditional pooled probit model). Since our weighting strategy mimics a situation where financial crises occur randomly, it allows us to properly identify the ATE of crises.

Financial crises and aggregated trade

We first focus on aggregated trade, namely exports and imports, and then look at the trade balance. Next, we present the sectoral trade costs of financial crises. Later, we show the robustness of our findings. Finally, we reveal the potential channels by which financial crises affect both aggregate and sectoral international trade. The ATE-AIPW estimates of the effects of financial crises on aggregated exports and imports are reported in table A.7, and their cumulative impulse responses are depicted in Panel I and II of fig. 2.4, respectively. 14 As a benchmark, we also estimate a pooled model whose results are reported in table A.6. In a nutshell, estimations show that: the likelihood of currency crises is increasing with the occurrence of past-5 years debt and banking crises and decreasing with the level of development. Debt crises are more likely when the level of public debt, financial openness, and the US interest rate increase. Banking crises are more likely when the levels of domestic credit and trading partners' growth increase and less likely when the level of liquid liabilities increases. The likelihood of debt and currency crises is decreasing with the level of foreign reserves and increasing in countries with floating exchange rate regimes. Debt crises are less likely when trading partners' growth increases. Currency crises are less likely when trade openness increases. Besides, standard diagnostic tests reported at the bottom of the table show that our models present a large classification power (above 94%) and Area Under Receiver Operating Characteristic curve (around 0.8 or more). 15 Following Imbens (2004) and Cole and Hernán (2008), we truncate the maximum weight to 10 to reduce the influence of outliers on our ATE estimates. In the robustness, we use a maximum weight of 5 or 20. Notes: Conditional cumulative change of total exports and imports from the start of the various crises (debt, banking, and currency). Each colored path shows local projections of the cumulative change relative to the year before the onset of the crisis for years 1-5 after the onset of the crisis. These costs describe the difference in the change of trade between the treated and control groups after re-randomization using the predicted propensity scores. The thinner and thicker bands are 90% and 95% confidence intervals, respectively. The top (bottom) row refers to the costs for exports (imports).

Exports

The findings show that all types of financial crises reduce exports both on impact and cumulated over five years in countries affected by crises compared with those unaffected. As shown by Panel I of fig. 2.4, export costs are relatively small just after the occurrence of crises (except for currency crises), but then intensify and follow an L-shape. Export recovery from crises, if any, is a distant and uncertain prospect. Finally, the magnitude of this negative effect over 5 years is economically meaningful and equal to 5.6 percentage points (pp.) of pre-crisis GDP for debt crises, 7.7 pp. for currency crises and 8.9 pp. for banking crises.

Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries

Imports

Our findings confirm that imports are equally negatively affected by financial crises and do not recover over five years. As shown by Panel II of fig. 2.4, all types of crises exert significantly negative cumulated effects from the beginning of financial crises to five years later. Compared to exports, the magnitude of the adverse effects of financial crises on imports is more important and estimated at 9.1 pp. for currency crises, 11 pp. for debt crises, and 14 pp. for banking crises.

Trade balance

We look at the costs of financial crises on the trade balance by comparing their costs on exports and imports (see the bottom of table A.7 for the mean difference tests). We find that the trade balance increases for all years in the aftermath of debt and banking crises by more than 5 pp. of pre-crisis GDP, due to the stronger decrease in imports compared with exports. We find similar results for currency crises for the years 1-4 after the crises hit. However, currency crises are not found to significantly affect the trade balance the year 5 after their burst. Indeed, countries following crises will tend to reduce significantly imports than exports to restore or maintain their external viability.

Summing up, at the aggregated level we find that financial crises reduce both the exports and imports of countries over five years, consistent with previous empirical findings. Nevertheless, while some studies, see e.g. [START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF] and Kuvshinov and Zimmermann (2019), find no effect of debt and banking crises on exports, we reveal that exports sharply decline following these financial crises in line with [START_REF] Ma | Martinez and Sandleris[END_REF] and Asonuma et al. (2016). Conversely to [START_REF] Ma | Martinez and Sandleris[END_REF], we find that currency crises also decrease exports, showing that the volume effect stemming from a gain of competitiveness due to the local currency depreciation does not suffice to overcome the negative price effect. Besides, currency crises tend to be associated with sudden stops in capital inflows which are necessary to trade (see, Bordo 2006;Reinhart and Rogoff 2009;Mendoza 2010). Finally, there are several differences between crises:

(i) banking crises exert the highest negative effect on both exports and imports, (ii) debt and banking crises induce a higher reduction in imports than exports, which increases the trade balance; (iii) currency crises have comparable costs on exports and imports in the year 5 after they occur. Keeping these results in mind as a benchmark, we now look at the effects of financial crises at a more disaggregated level.

The sectoral trade costs of financial crises

We now look at the costs of financial crises on the trade of agricultural, mining, and manufactured goods, and services. As detailed in the introduction, this is, as far as we are aware, the first analysis that disentangles the aggregate trade costs of financial crises on all categories of goods and services traded. The estimated cumulative ATE over five years for exports and imports are reported in table A.7,and Panel I and II of fig. 2.5 provide a graphical illustration, respectively.

Agricultural trade

We find that both exports and imports of agricultural goods are the least affected by financial crises an effect may be supported by the relatively lower income elasticity of demand for agricultural goods, which may equally require less external financing to be produced and traded.

Mining trade

We reveal that, except for the positive effects of debt crises, the other financial crises significantly reduce the exports of mining goods. Indeed, five years after debt crises, exports of mining goods increase by 1.1 pp. of pre-crisis GDP. In the aftermath of banking and currency crises, exports of mining goods decrease both on impact and over the five years to reach a collapse of 1.6 pp. and 2.9 pp., respectively. After all types of crises hit the emerging countries, imports of mining goods over five years are also significantly reduced by 1.9 pp. for debt crises and 3 pp.

for both banking and currency crises. Overall, the collapse of mining goods in the aftermath of financial crises is higher than the one of agriculture goods and sometimes lower sometimes higher than the one of trade in services, but more often largely lower than the collapse of the trade in manufactured goods.

Manufacturing trade

Our results point to a systematic and large reduction of the trade in manufactured goods in the aftermath of financial crises. The costs of financial crises on manufacturing exports are fairly important over five years and correspond to 6.1, 4 and 2.3 pp. of pre-crisis GDP for debt, currency, and banking crises, respectively. They are even higher for imports and equal to 4.6, 5.4, and 7 pp. for currency, debt, and banking crises, respectively. Besides, we note that except for the effects of banking crises on manufacturing exports, the collapse of manufactured trade contributes the most to the total drop of international trade in the aftermath of financial crises in emerging countries. These contributions equal to 107, 51, and 26% of the total collapse of exports for debt, currency, and banking crises, respectively, and around 50% of the total collapse of imports for all types of crises. Moreover, as manufacturing imports fall more than manufacturing exports, the adverse effects of financial crises on the trade in manufactured goods are driven by the increase in overall trade balance highlighted for debt and banking crises.

These findings are consistent with previous micro-level studies using data of manufacturing [START_REF] Eaton | À l'inverse, en se concentrant sur l'offre, le canal de perturbation est soutenu par Iacovone and Zavaka[END_REF] on the income channel. However, despite revealing large effects of the occurrence of financial crises, the analysis of the trade of manufactured goods leaves unexplained a fairly large proportion of the trade costs at the aggregated level.

Services trade

Finally, similar to manufacturing trade, services trade is significantly reduced by all types of financial crises. We find that the trade costs of financial crises on trade in services are larger for exports and following banking crises; the costs reach 3.5 pp. of pre-crisis GDP over five years, which represents a contribution of 39% of the total export collapse. Also, exports are contracted in the aftermath of debt and currency crises by 2.9 and 0.9 pp. over five years. We find similar patterns for imports of services that are reduced by 2.6 pp. over five years following debt and banking crises and 0.9 pp. following currency crises. Therefore, compared with studies that focus on the trade of services following the global financial crises of 2008-09 (see e.g.

Borchert and

Mattoo 2010; Ariu 2016), we find that trade in services may also decline during crises. However, the trade of services presents a greater resilience compared with the trade of manufactured goods, except in the aftermath of banking crises, in line with the arguments of lower-income elasticity of demand and lower external financial dependence.

To summarize, our sectoral analysis reveals that manufactured traded goods are the most affected in terms of magnitude in the aftermath of financial crises. However, the impact of financial crises on the other types of traded goods and services is far from being negligible.

Trade in both mining goods and services also declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. In the following sections, we present the robustness of our findings before moving to explain the channels by which financial crises exert a negative effect on total and sectoral trade costs.

Robustness

We further investigate the robustness of our findings using a wide variety of alternative samples, maximum weights in the treatment models, sources and definitions of crises, and specifications.

Alternative samples

The trade costs of combined financial crises

The analysis performed so far focused on the effect of each financial crisis when controlling for the other types of crises in the prediction of the potential outcome and the computation of propensity scores. Given that financial crises seem to be connected (see the previous section),

we now look at the trade effects of both combined and non-combined crises. Following Glick and Hutchison (2001) and Hutchison and Noy (2005), we define a combined crisis as a crisis occurring in a two-year band around a financial crisis of another type, i.e. a combined crisis occurs at time if another type of crisis occurs in any of the years spanning between -2 and + 2. Similarly, a non-combined crisis is a crisis that occurs without any other crises in the years around. The results reported in table A.8 and fig. A.3 show that combined financial crises trigger more significant and of a higher magnitude aggregated and sectoral trade costs, except for imports following debt crises. Also, as shown by table A.9 and fig. A.4, the total and sectoral trade costs of non-combined financial crises are quite lower (more for exports than imports), except for imports following debt crises. Consequently, combined crises unveil more severe trade costs than non-combined crises, and studies that focus exclusively on a type of crisis without controlling for others may suffer from an overestimated bias. Finally, our benchmark findings are robust regardless of the trade costs of combined financial crises or not.

Drop the post-GFC period

We drop the post-GFC period (2008 onwards), given the collapse in international trade. The results are reported in table A.10 and fig. A.5. Removing this period leads to both qualitatively and quantitatively similar results for the total and sectoral export costs of banking crises and currency crises while the costs of debt crises are significantly reduced. Besides, while qualitatively the same, the trade costs of financial crises on total and sectoral imports have a lower magnitude.

Alternative maximum weights set in the treatment models

Compared to the maximum weight of 10 for our treated and control groups used in the benchmark model, we now use a maximum weight of 20 in table A.11 and fig. A.6 and 5 in table A.12 and fig. A.7. The choice of lower weights reduces the influence of country-year observations in the treated (control) group that receive a low (high) likelihood of financial crises. The results confirm the robustness of the significance and the size of the effect of financial crises on total and sectoral trade. Besides, the use of a maximum weight of 5 leads to both qualitatively and quantitatively similar results as in our benchmark findings. In contrast, the use of a maximum weight of 20 is associated with a somewhat lower magnitude of the trade costs for some types of goods and financial crises. Overall, our main findings are robust to the choice of alternative maximum as recommended by Imbens (2004) and Cole and Hernán (2008).

Alternative sources and definitions of crises

We consider alternative sources and definitions of financial crises.

ATE-IPW estimator

Compared with our benchmark analysis that draws upon the Augmented Inverse Propensity Weighted (AIPW) estimator, we use the Inverse Propensity Weighted (IPW) estimator that is more popular in the existing literature. The results are presented in table A.14 and fig. A.9.

They are like our benchmark findings but highlight a lower magnitude in the total and sectoral trade costs of financial crises, especially for imports.

As previously emphasized, our results are confirmed by several robustness tests. In the next section, we analyze the channels by which financial crises exert a negative effect on trade.

Channels

The existing literature on the channels by which financial crises impact international trade can be summarized into demand-side and supply-side factors. First, the demand-side argument sustains that the fall in income following crises hurts the demand of traded goods and services, especially in the aftermath of GFC (see, e.g. Freund 2009;Levchenko et al. 2010;Bems et al. 2011;Bricongne et al. 2012;[START_REF] Behrens | [END_REF][START_REF] Eaton | À l'inverse, en se concentrant sur l'offre, le canal de perturbation est soutenu par Iacovone and Zavaka[END_REF][START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF][START_REF] Altomonte | la littérature où de nombreux auteurs se sont appuyés sur un seul indicateur, notamment la dette en pourcentage du PIB ou des recettes fiscales[END_REF]Ariu 2016) 2002) and the standard assumption in international trade theory to assume that unlike agricultural and mining goods, manufactured goods exhibit increasing returns to scale.

Moreover, these products are relatively more credit intensive, vertically integrated into global value chains and their income elasticity is also higher, making them particularly more vulnerable to financial crises. Similarly, as shown by Miroudot et al. (2009), trade in services are mainly intermediate inputs. They may also require higher external finance compared to primary goods; therefore, they may also suffer more from financial crises.

To assess the potential role of the different channels in shaping the trade costs of financial crises, we split our initial dummies of financial crises in two identical parts along with these variables used as proxies for the channels. We use the median of these variables at the start of each financial crisis to have enough number of observations for both groups. First, we proxy the compositional and structural channel by the share of manufactured exports in total exports, export diversification index, and trading partners diversification index the year before the crises.

Second, we proxy the demand-side channel by the evolution of trading partners' growth rate over the five years following crises. Third, we proxy the supply-side channel by the evolution of financial development, gross capital inflows, and investors credit rating risks over the five years following crises. The evolution of the variable over the five years following financial crises is computed as

+5, -1 = 1 5 5 =0 ( + -+1 ) (2.5)
As in the benchmark model, we estimate treatment models for the likelihood of financial crises as well as the outcome models for the financial crises identified above or below the median of the channel variables, separately. After that, we compute the ATE-AIPW estimates of the effects of financial identified above or below the median of the channel variables. For simplicity, our interpretations focus on the 5-year cumulated effects of financial crises, although we present the dynamics of the trade costs over five years.

The Compositional and structural channel

As previously indicated, we first analyze the costs of financial crises in countries with a higher and lower share of manufacturing exports the year before the beginning of crises. This allows us to capture the differentiated effects of financial crises in countries with different export structure.

Indeed, given the previous discussion, we expect a higher adverse effect of financial crises on total and manufacturing trade (both exports and imports) in countries that predominantly export manufactured goods compared to primary goods and services. The results are reported in fig. A.10 and tables A.15a to A.15c. We find that both total, manufacturing, and services exports and imports fall more following crises in countries with a lower share of manufacturing exports. The trade of agricultural and mining goods highlights similar patterns for the two groups of countries. Consequently, countries with a higher share of manufacturing exports are more resilient to financial crises. Financial crises act as an impediment to structural transformation for countries reliant on primary goods and reinforce their comparative advantage in primary goods by disrupting more their manufacturing and services trade structure. Besides, our benchmark results, namely a total trade collapse driven by the fall of manufacturing and services trade, remain valid.

Second, we investigate the role of export diversification in shaping the trade costs of financial crises. We, therefore, create two groups of financial crises with higher and lower export diversification the year before crises. The results are presented in fig. A.11 and tables A.16a to A.16c. We find that financial crises generally lead to higher contraction of total, manufacturing, and services trade in countries with lower export diversification. These contractions are largely driven by the fall of manufacturing trade, except for exports following banking crises.

Therefore, countries with a more diversified export structure will suffer less from financial crises.

Third, we study the effects of financial crises in countries with higher and lower trading partners diversification the year before crises. By doing so, we check whether having a diversified number of trading partners' help to alleviate the trade costs of financial crises. The results are shown in fig. A.12 and tables A.17a to A.17c. Except for the collapse of trade that exhibits similar trend in our two groups following banking crises, we reveal that countries with a higher diversified number of trading partners experience a lower adverse collapse of their trade in the aftermath of debt and currency crises, compared to countries trading with a smaller fraction of countries. Consequently, diversifying its partners may help to significantly reduce the trade costs of financial crises. Finally, as previously stated, the fall of the manufacturing and services trade is leading the total trade collapse following debt and currency crises, and banking crises, respectively.

Overall, the proxy variables show that the composition of trade, its diversification in terms of exported products, and trading partners matter for the costs of financial crises on trade. We find that trade is more reduced in countries with a lower share of manufacturing exports, diversified exported products, and trading partners. These findings sustain that financial crises may act as an impediment to structural transformation as they hurt more manufacturing exports in countries where the share of manufacturing exports is relatively lower. Besides, by diversifying their exports and partners, countries will increase their resilience to financial crises.

The demand-side channel

We explore the demand-side channel using as a proxy the trading partners' growth. We identify two sets of financial crises with stronger and lower trading partners' growth in their aftermath and study their effects on total and sectoral trade costs, respectively. We expect higher trading partners' growth to be negatively associated with the collapse of both exports and imports (as the contraction of income may be lower in this case). We report the results in fig. A.13 and tables A.18a to A.18c. Not surprisingly, we find that financial crises when associated with higher trading partners' growth have either a milder or no adverse effect on international trade, except for banking crises. This is remarkable following debt crises for which no effect is found for both exports and imports because of a large increase of agriculture and mining exports which overcome the collapse of manufacturing and services trade, and a small fall of agriculture and services imports which is balanced by the small increase of mining imports. In the aftermath of currency crises, the higher collapse of exports and imports in countries with lower trading partners' growth is driven by the fall of all types of goods and services, mainly manufacturing and mining goods, expect for agriculture exports. In contrast, we find that banking crises lead to similar or slightly higher trade costs when associated with higher trading partners' growth.

In sum, these findings show that financial crises associated with lower demand for goods and services from trading partners will have more adverse trade costs; therefore can generate an unprecedented collapse of international trade when they are generalized within regions and at the global level as witnessed in the post-GFC period.

The supply-side channel

We complete our investigation of the channels by which financial crises exert a negative cost on international trade with the supply-side channel which is related to the availability of external financing that more needed during periods of financial turmoil. We first explore the supply-side or credit channel using the evolution of financial development in the aftermath of financial crises. We create two types of financial crises with and without a higher increase in financial development and analyze their trade costs. The results are presented in fig. A.14 and tables A.19a to A.19c. Our results suggest that the evolution of financial development in the aftermath of financial crises matter for the total and sectoral trade costs of financial crises. We find that all types of financial crises associated with a lower increase or a contraction of financial development lead to a significant decline of trade compared to financial crises with expanding financial development. These findings reinforce the idea that financial crises drive the collapse of trade because of their disruptive effects on the monetary and financial sector.

Second, aside from domestic financial development, international trade also depends on the ability to issue external or trade credit from other countries or having new foreign direct expected, all types of financial crises associated with a large reduction of gross capital inflows generate a significant collapse of both total and all sectoral trade compared to crises with milder reduction or increase in gross capital inflows where the collapse is milder. This collapse of trade is driven by the fall of manufacturing exports for debt and currency crises, service exports for banking crises, and manufacturing imports for all crises. This shows that the supply-side channel is quite important when analyzing the trade costs of financial crises, particularly for the manufacturing and services trade.

Finally, we assess the role of financial crises using the evolution of investors' credit ratings risks as a proxy of the international financial conditions and costs of borrowing. We present the results in fig. A.16 and tables A.20a to A.20c. As for financial development and gross capital inflows, we reveal that financial crises when associated with a higher deterioration of investors' sentiment generally lead to a significant and detrimental collapse of total and sectoral trade, compared to crises with a somewhat lower reduction or increase in investors' credit ratings risks where sometimes no effect or milder trade contraction is found. This higher collapse is mainly driven by the fall of the manufacturing and services trade. This shows that deterioration of investors' or market sentiment in the aftermath of financial crises, and therefore, the supply-side channel, is one of the key reasons why financial crises are associated with trade collapse.

Overall, these findings support the idea that the supply-side channel is critical to understand the way financial crises shape the dynamics of international trade. When associated with a deterioration of the domestic financial development and external financial conditions, and

Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries sudden stops, financial crises will exert a significant and detrimental collapse on international trade, which is mainly driven by the fall of manufacturing and services trade. Our paper contributes to the existing literature on the trade costs of financial crises by highlighting the differentiated sectoral trade costs of crises and their channels while focusing on the context of emerging countries.

Concluding remarks

This paper assesses the sectoral trade costs of financial crises. Compared with the existing literature that mainly focuses on the total trade of goods and, in the context of the recent great recession, on manufacturing trade, we look at the response of different types of trade (i.e.

agricultural, mining, and manufactured goods, and services) following various types of financial crises (i.e. debt, banking, and currency crises). To this end, we draw upon a methodology that combines impact assessment and local projections to capture a causal dynamic effect running from financial crises to the trade activity. We also analyze the channels by which financial crises impact international trade by looking at the compositional and structural, demand-side, and supply-side channels.

While we confirm that aggregate exports and imports significantly decrease following most financial crises, our analysis reveals interesting patterns at the disaggregated level. Manufacturing goods are the most affected by financial crises. However, the impact of financial crises on the other types of traded goods and especially on services is far from being negligible. Trade in both mining goods and services also declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. When looking at the costs of combined crises, we find that they exert a significant and higher decline of trade, compared to crises occurring without any other crisis in the years around. These findings are robust to a wide variety of alternative samples, maximum weights in the treatment models, sources and definitions of crises, and estimators.

Besides, we find that financial crises exert an adverse effect on total and sectoral trade through compositional and structural, demand-side, and supply-side channels. In detail, about the compositional and structural channel, our findings sustain that financial crises may act as an impediment of structural transformation as they hurt more manufacturing exports in countries where the share of manufacturing exports is relatively lower. Also, by diversifying their exports and partners, countries will increase their resilience to financial crises. Moreover, about the demand-side channel, our findings show that financial crises associated with a lower demand of goods and services from trading partners will have more adverse trade costs; therefore they can generate an unprecedented collapse of international trade when they are generalized within

A.1.2 List of countries included in regressions analyses

Albania, Algeria, Argentina, Armenia, Bolivia, Brazil, Chile, China, Colombia, Costa Rica, Croatia, Dominican Republic, Ecuador, Egypt, Arab Rep., Gabon, Georgia, Guatemala, Hungary, India, Indonesia, Jamaica, Jordan, Lebanon, Mexico, Morocco, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russian Federation, South Africa, Sri Lanka, Thailand, Tunisia, Turkey, Ukraine, Uruguay, Venezuela. Notes: About the cutpoint on the absolute value of the standardized difference to define imbalance, Rubin ( 2002) suggests a cut-off of 0.25. As such, if the absolute value of the standardized difference for a variable is higher than 0.25, then there is a significant difference between the treated and control group for this variable. Moreover, Rubin (2002) proposes the use of the ratio of treated and control variances as a balance measure of the second moment, where balance is defined by values close to 1.0 and variables are out of balance if the variance ratio is greater than 2.0 or less than 0.5. The CBPS model eliminates all the differences in characteristics between treated and control groups, which is less the case for the pooled probit model.

A.1.3 Data description and sources

Table A.4: Balance diagnostics between the treated and control groups, Banking crises Notes: About the cutpoint on the absolute value of the standardized difference to define imbalance, Rubin (2002) suggests a cut-off of 0.25. As such, if the absolute value of the standardized difference for a variable is higher than 0.25, then there is a significant difference between the treated and control group for this variable. Moreover, Rubin (2002) proposes the use of the ratio of treated and control variances as a balance measure of the second moment, where balance is defined by values close to 1.0 and variables are out of balance if the variance ratio is greater than 2.0 or less than 0.5. The CBPS model eliminates all the differences in characteristics between treated and control groups, which is less the case for the pooled probit model. Notes: About the cutpoint on the absolute value of the standardized difference to define imbalance, Rubin (2002) suggests a cut-off of 0.25. As such, if the absolute value of the standardized difference for a variable is higher than 0.25, then there is a significant difference between the treated and control group for this variable. Moreover, Rubin (2002) proposes the use of the ratio of treated and control variances as a balance measure of the second moment, where balance is defined by values close to 1.0 and variables are out of balance if the variance ratio is greater than 2.0 or less than 0.5. The CBPS model eliminates all the differences in characteristics between treated and control groups, which is less the case for the pooled probit model. Notes: The predicted propensity scores in (1), (2), and (3) are obtained after applying the CBPS estimator. The dependent variable equals to 1 at the start of financial crises, 0 in country-years observations without crises (unfolding). We use the largest set of control variables described in the data section and included with lags to reduce potential issues of endogeneity. These figures display a high probability of financial crises for the treated groups (especially for debt and currency crises) and a low probability for their counterparts. More importantly, they show a significant overlap between the treated and control groups.

A.3.2 Overlap check

Since some observations receive a large weight, we set the maximum weight to 10 for the ATE-AIPW estimates.

A.4 Supplementary graphs and tables

A.4.2.2 Benchmark and robustness results

Table A.7 The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 10.

A.4.2.3 Channels

Table A.15a: Channels, Cumulative trade costs over five years after debt crises with higher and lower manufacturing exports -3.200 -3.200 -3.200 -3.200 -3.200 -3.200 Cutoff of channel variable, median - 3.200 -3.200 -3.200 -3.200 -3.200 -3.200 Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Introduction

"The best time to repair the roof is when the sun is shining" -John F. Kennedy, Former President of the United States, 1961States, -1963 In the aftermath of the global financial crisis (GFC) of 2008-09, many governments around the world enacted large fiscal stimulus plans to boost their sagging economies. These plans were based on the Keynesian theory that sustains that deficit spending by governments can stimulate their economy by supporting the aggregate demand. This is of particular interest since the evidence of larger fiscal multipliers in recessions than in expansions. However, at the same time, many other countries were forced to implement large fiscal consolidations to dissipate fiscal sustainability issues, and this at the worst possible time, which exacerbated the recessionary forces of the crisis. One noticeable difference between countries that were implementing loose and contractionary fiscal policy lies in the availability of fiscal space they had in the run-up of the crisis. To be effective and credibly conducted, fiscal stabilization should rely on enough who respectively show that more credit-intensive expansions, higher public debt and lower bound central bank interest rate, and severe sudden stops amplify the output costs of crises and prevent recovery to happen, the rest of the papers show little evidence of the channels through which financial crises or recessions exert a negative effect on the economic development of countries.

Second, this paper is closely linked to Romer and Romer (2018) who analyze the effects of fiscal and monetary space on output dynamics in the aftermath of financial distress. They show that the output losses are less than 1% when a country has both types of policy space, but almost 10% when it has neither. One of the channels is that governments can use monetary and fiscal policy more aggressively when policy space is available. However, they focus exclusively on advanced countries for which in the post-WWII, crises and recessions have been less recurrent and severe compared to developing and emerging countries. Although this question became of great interest for advanced countries in the aftermath of the GFC that reignites the role of fiscal and monetary policy responses, it is also quite appealing for both developing and emerging countries as they have had a long history of pro-cyclical fiscal policy (e.g., Alesina et al. 2008;

Ilzetzki and Vegh 2008), even if, over the last two decades, a growing share of fiscal policies in these countries had graduated and become countercyclical (e. g., Frankel 2011;Frankel et al. 2013;[START_REF] Grauwe | Par conséquent, notre indice comble les lacunes des mesures utilisées dans les études précédentes en incorporant autant d'informations compte tenu de la disponibilité des données pour mesurer le concept multidimensionnel d'espace budgétaire. Quatrièmement, notre article est également lié à la littérature sur les multiplicateurs budgétaires et la cyclicité de la politique budgétaire[END_REF]). We go beyond these limitations by analyzing how fiscal space reduces the losses of financial crises and normal recessions in the context of developing and emerging countries. Besides, their analysis suffers from endogeneity issues for two reasons.

On the one hand, financial distress is endogenous to the country's key characteristics making countries during normal times potentially different from those with financial distress. In this

Chapter . Fiscal space and stabilization policy in developing and emerging countries case, by comparing countries in normal times with those in financial distress, their output losses could be biased and overestimated. On the other hand, the country's pre-shock policy space is correlated with the country's key structural characteristics and may be affected by a shock that reduces the policy space and generates a protracted recession. In such a situation, the benefits of fiscal space may be underestimated. In this paper, we deal with these endogeneity issues by estimating the average treatment effects on the treated (ATT) of shocks and fiscal space on output dynamics after re-randomization (i.e. eliminating the differences in the characteristics between countries with and without shocks).

Third, our study falls into the literature that examines the different measures of fiscal space.

A frequent concern with fiscal space is the lack of clarity about it and how to measure it. 2018). We share the latter argument as fiscal space is a multidimensional concept that is difficult to measure using a single indicator. In this paper, we use four indicators including debt to average tax revenues ratio, fiscal balance to average tax revenues ratio, short-term external debt in percentage of the total, and debt services in the percentage of international reserves. We aggregate them into an index of fiscal space using the signals approach à la Kaminsky et al.

(1998). Then, our index overcomes the shortcomings of the measures used in previous studies by incorporating as much information given the availability of data to measure the multidimensional concept of fiscal space.

Fourth, our paper is also related to the literature on fiscal multipliers and the cyclicality of fiscal policy. For example, Bohn (2002) and Mendoza and Ostry (2008) show that fiscal policy tends to be on average more expansionary when government debt is low. show that fiscal policy may be more effective in alleviating the size of recessions when countries have enough fiscal space that allows them to enact stimulus packages without deterioration their fiscal position and the market sentiment.

Taking stock of the existing literature, we contribute to it in many ways. First, we build a new index of fiscal space that we use to analyze the output losses of financial crises and normal recessions and show how fiscal space in the run-up of shocks helps to alleviate their costs.

Second, this paper is in the context of emerging and developing countries in which financial crises and normal recessions have been more recurrent, and where fiscal stabilization has had a long history of procyclicality. The literature has overlooked the role of fiscal space in these countries. Third, we differentiate between the costs of financial crises (banking and currency)

and normal recessions that are not associated with a crisis. Fourth, we provide a treatment of endogeneity to identify a causal effect running from fiscal space and shocks to the output dynamics in the aftermath of financial crises and normal recessions. Fifth, we seek to provide the potential channels through which the effects of fiscal space operate. We then study how the availability of pre-shock fiscal space affects the dynamics of primary fiscal balance, fiscal space itself, private consumption and investment, and net capital inflows in the aftermath of financial crises and normal recessions.

Our benchmark results show that financial crises and normal recessions induce persistent and large output losses. Indeed, for financial crises, output falls by 1.5 percentage points (pp.) in year 1, 5.1 pp. in year 2 and around 4 pp. in subsequent years. For normal recessions, the output contraction is much larger in year 1 and stands at 5.6 pp., before lowering to around 2.5 pp. in subsequent years. To put these findings into perspective, knowing that the median Real GDP growth in our sample is 4.8% per year so that the median economy would have grown by 24% over five years; a single episode of a financial crisis and normal recession would cost about 16 and 10% of that economy's long-term growth, respectively. Besides, the results show that, also in the context of developing and emerging countries, fiscal space still matters and significantly reduces the output losses of financial crises and normal recessions. We find that an increase of pre-shock fiscal space by one standard deviation is associated with a maximum reduction of the output losses of 4.6 and 3.9 percentage points in the aftermath of financial crises and normal recessions, respectively, which is enough to completely offset their output losses. These findings show that there is a significant gain to fix the roof when the sun is shining, i.e. to build-up fiscal buffers, reduce debt and deficit, increase tax base and revenues, and lock the drinks cabinet when the economy is booming for weathering the storm in recessions; otherwise, there may be no way out but to adjust at the worst possible time. These results remain unchanged after several robustness checks. About the channels, our results reveal that in countries with higher fiscal space, governments enact stimulus packages by using fiscal space and loosening fiscal policy to alleviate the output losses of financial crises and normal recessions. Although fiscal space is used and depressed, it remains strong and above that of countries with limited fiscal space that implement fiscal consolidations to address their fiscal issues. Besides, we find that higher

Chapter . Fiscal space and stabilization policy in developing and emerging countries fiscal space is associated with an increase of private consumption and investment, supporting the Keynesian view, and an increase of net capital flows necessary to finance the recovery. The opposite results are found in countries with limited fiscal space where both private consumption and investment and net capital inflows are strongly depressed. Our results add clarity to that time where following the GFC of 2008-09, many governments are drowning into large amounts of debt, persisted fiscal deficit, and depleted fiscal buffers whereas another crisis may be looming on the horizon. The previous experiences have taught us valuable lessons; some of them are old forgotten ones. This paper revives the benefits of fiscal space and the role of fiscal policy stabilization in the aftermath of financial crises and normal recessions and calls for efforts to consolidate public finances in booms to be well prepared for the next recessions.

The rest of this paper proceeds as follows. Section 3.2 presents data and definitions. Section 3.3 describes how we build our fiscal space index and some stylized facts on it. Section 3.4 details the methodology used to analyze the output losses of financial crises and normal recessions as well as the benefits of fiscal space. Section 3.5 reports our key findings. Section 3.6 discusses the channels through which fiscal space may operate. Section 3.7 shows the robustness of our benchmark findings. Section 3.8 concludes the paper.

Data, and definitions

Our data set covers 91 countries (whose 35 developing and 56 emerging countries according to the IMF WEO classification) over the period 1985-2017 at annual frequency. Regarding the macroeconomic shocks, we differentiate between financial crises and normal recessions.

Financial crises encompass banking crises obtained from Laeven and Valencia (2018) and currency crises that are built using the definition of Frankel and Rose (1996). Banking crises are defined as events where there are signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations) and/or banking policy intervention measures in response to significant losses in the banking system. Currency crises are defined as at least a 25% depreciation of the nominal effective exchange rate that is also at least a 10% increase in the rate of depreciation. Normal recessions are recessions that coincide with neither financial crises nor major conflicts in a window of five years centered around the shock. We derive them by applying the Bry and Boschan (1971) algorithm on real GDP. Section B.1.1 describes the list of countries, and section B.1.2 shows the list of financial crises and normal recessions used in our analysis.

As shown in table 3.1, given We also use data for fiscal crises from Medas et al. (2018) to compute our fiscal space index as described in the following section. Finally, in line with the existing literature on the output costs of financial crises and recessions, we use a set of control variables including macroeconomic and political variables as determinants of shocks and/or the dynamics of output. Recall that we go beyond the literature and use a larger set of variables in this analysis to limit the omitted variable bias. The data and their sources are reported in Table B.1 in section B.1.3, and table B.2 in section B.1.4 presents the summary statistics.

Measuring Fiscal space

This section describes how we build the fiscal space index. We draw upon the signals approach Chapter . Fiscal space and stabilization policy in developing and emerging countries of debt or the ratio of debt to average tax revenues, we use four indicators that we combine in a way that accounts for their predictive power of fiscal crises. Then, our index overcomes the shortcomings of the measures used in previous studies by incorporating as much information given the availability of data to measure the multidimensional concept of fiscal space. Conditional on data availability in developing and emerging countries, we select four fiscal indicators as early warning indicators of fiscal crises: (i) (minus) debt to average tax revenues ratio, (ii) fiscal balance to average tax revenues ratio, (iii) (minus) short-term external debt in percentage of the total, (iv) (minus) debt services in percentage of international reserves. we multiply some of the indicators by minus (-) so that lower values of each indicator are associated with higher risks of fiscal crises.

Fiscal crises and early warning indicators

Signals approach

To identify critical values or thresholds of early warning indicators of fiscal space, we follow The results of the signaling analysis can be summarized in a matrix as in table 3.2 in which fiscal crisis occurrence and the signal issuance of each indicator are measured against each other.

We choose the period between the signal and fiscal crises to be equal to one, two or three years

( (ℎ) = 3
) and retain the one that gives the highest predictive power. The type I error or share of false alarms among non-fiscal crises is defined as /( + ), and the type II error or share of missed crises among fiscal crises is defined as /( + ). Then, the total misspecification error is the sum of type I and II errors, i.e. = /( + ) + /( + ), and the predictive power is one minus the total misspecification error, i.e. = 1 -. We can also define two other performance indicators: the effectiveness defined as the share of true positive among all signals, i.e effectiveness= /( + ), and the incidence defined as the share of true positive among all fiscal crises, i.e incidence= /( + ).

Fiscal space index

For each of our four early warning indicators , we define two signal variables: (i) ℎ = , (see, eq. ( 3.1a)) and (ii) = , (see, eq. (3.1b)) based on a country-specific threshold ℎ , and an income group-specific threshold ℎ , , respectively. For example, if the threshold based on the signal approach is equal to the 20 ℎ percentile, ℎ , will be the corresponding value specific to each country while ℎ , will be the corresponding value specific to each income group (developing countries and emerging countries). By doing so, we identify fiscal distress by comparing a country with itself at other periods and with its income group counterparts at all periods. For example, fiscal distress is aggravating with an increase of the fiscal deficit of a country relative to its historical values that also tends to be higher than the fiscal deficit of its income group counterparts at all periods.

ℎ = , = 1 ≤ ℎ , 0 ℎ (3.1a) = , = 1 ≤ ℎ , 0 ℎ (3.1b)
Chapter . Fiscal space and stabilization policy in developing and emerging countries

We then construct three indexes of fiscal space. The first two indexes and are defined as one minus the composite early warning indicator of fiscal crises, based on the country-specific (see, eq. ( 3.2a)) and the income group-specific thresholds (see, eq. (3.2b)), respectively. The third index is our preferred index of fiscal space as it allows for both within and between comparisons in the calculation of fiscal space by computing the average of the indexes and

(see, eq. ( 3.2c)). For each signal variable , or , , the corresponding weight is given by the predictive power ( = 1 -) for the relevant early warning indicator.

= 1 - 4 =1 × , (3.2a) = 1 - 4 =1 × , (3.2b)
= 1 2

+ (3.2c)

Results of the signals approach

We apply the signals approach on three different samples including (i) a full sample, (ii) a sample of developing countries, and (iii) emerging countries using a period of one, two and three years between the signals of early warning indicators and fiscal crises. Table 3.3 reports the results for a period of three years as this horizon provides the highest predictive power of the early warning indicators. First, our strategy leads to lower type I errors and higher type II errors. Second, we also find that signals tend to be followed by few fiscal crises (the effectiveness is between 8% and 20%) while fiscal crises are generally preceded by a signal (the incidence is between 36% and 74%). Third, we show that the variables fiscal balance to average tax revenues and debt services in the percentage of international reserves are good predictors of fiscal crises in both developing and emerging countries whereas debt to average tax revenues and short-term external debt in percentage of the total are exclusively good predictors of fiscal crises in developing countries and emerging countries, respectively. Fourth, the thresholds of the early warning indicators differ between developing and emerging countries.

We define the fiscal space index as described in section 3.3.3 based on the results and thresholds obtained for developing and emerging countries in table 3.3. Relying on different thresholds for each level of development allows us to maximize the predictive power of the early warning indicator and account for the structural differences between countries at different stages of development. Notes: This table presents the results of the signals approach when the time span between signals and the onset of fiscal crises is set to be three years. This time span gives the higher predictive power of our early warning indicators. The results when using a time span of one and two years can be obtained upon request. The fiscal space index uses the results and thresholds obtained when applying the signals approach to the different samples of developing and emerging countries.

Stylized facts on fiscal space index

In fig. 3.1, we plot the historical trend of average fiscal space index and incidence of fiscal crises The positive slopes of the two fitted lines indicate that the larger the fiscal space one year before the shock the higher is output growth and recovery in the aftermath of both financial crises and normal recessions. This confirms our intuition about the role of fiscal space that needs to be Chapter . Fiscal space and stabilization policy in developing and emerging countries .9 1 Fiscal space in 2017 . 3 .4

. 5 .6 .7 .8 .9 1 Fiscal space in 2007 Notes: This figure shows that higher fiscal space one year before financial crises and normal recessions is associated with higher output growth and recovery in the aftermath of these shocks. Year indicates the start of financial crises or normal recessions.

The sample used for this figure is similar to the one used in the regressions below.

Empirical methodology

The question that we ultimately want to answer is if and how the availability of fiscal space in the run-up of financial crises and normal recessions influences their output costs. To answer it, first, we rely on local projections methods by Jordà (2005) to draw a dynamic of the effects of financial crises, normal recessions, and fiscal space over a horizon of five years after the shocks.

Second, we combine both local projections and the inverse propensity weighted with regression adjustment (IPWRA) estimation and obtain dynamic ATT estimates which establish a causal effect running from shocks and fiscal space to output growth. One technical innovation of this paper is that it accounts for the possibility that shocks and fiscal space are endogenous to output dynamics. We will first show the advantages of this methodology over OLS estimates, and then move to analyze whether higher fiscal space is associated with lower output losses and rapid output recovery in the aftermath of shocks.

Local projections

To quantify the overall cumulative effect (both direct and indirect) of shocks and country's prior fiscal space on output dynamics, while controlling for the dynamic feedback from other variables, we employ the Jordà local projections (LPs) method described for the unconditional path as follows

, +ℎ = ℎ + ,ℎ , + ,ℎ , + ℎ 1 , -1 + ℎ 2 , -2 + ,ℎ , + , -1 ℎ + , +ℎ (3.3)
where ℎ ∈ 0; 5 denotes the time-horizon.

, +ℎ = ( , +ℎ -, -1 )/ , -1 × 100 is the cumulative change between -1 and + ℎ in 100 times the real GDP of country .

, and , are dummies equal to 1 if country has a financial crisis (banking or currency crisis) or normal recession at time (the onset), respectively. Then, the effects of financial crises and normal recessions at each horizon relative to normal times are captured by ,ℎ and ,ℎ , respectively.

, -1 and , -2 are the real GDP growth rate at one and two years prior to the onset of the shock, respectively.

, captures other shocks including conflicts and the number of financial crises and normal recessions over the past three years.

, -1 is a set of lagged control (macroeconomic and political) variables. We control for (i) the log of the real GDP to capture both the resilience to shocks and the convergence theory, (ii) terms of trade that affects both the likelihood of financial crisis and normal recession as well as fiscal space, (iii) fiscal rules Chapter . Fiscal space and stabilization policy in developing and emerging countries used as a signal of a sound fiscal policy and a reduced discretion of governments, (iv) political rights as a proxy for the quality of institutions, (v) presence of an IMF program as a signal of a balance of payment crisis and a support from the IMF, and (vi) world growth to account for global shocks. These control variables are used at time -1, the year before the onset of the shock. Finally, ℎ stands for country fixed effects used to control for unobserved heterogeneity, and , +ℎ is the error term.

After describing the output costs of shocks, our interest is to show to what extent fiscal space prior to shocks affects the trajectory of output growth in the aftermath of these shocks. Here is where the role of fiscal space comes into our model. It is captured by introducing in eq. (3.3) an interaction term between our dummy of shock and our fiscal space index as follows

, +ℎ = ℎ + ,ℎ , + ,ℎ , × -1 + ,ℎ -1 + ... + , +ℎ where = { , } (3.4)
Here the coefficients of interest are ,ℎ and ,ℎ . They capture the effects of shock of type on output modulated by fiscal space. In the case where fiscal space helps to alleviate the output losses following shocks, ,ℎ will be positive. We also include the level of fiscal space to account for any systematic difference between countries with and without space in normal times. We estimate both eqs. (3.3) and (3.4) separately for horizons 0 to 5 (that is, up to five years after time ) and derive the unconditional and conditional on country's prior fiscal space impulse response function of output in the aftermath of shocks.

Treating endogeneity, combining local projections and IPWRA

The endogeneity of financial crises, normal recessions, and fiscal space

This paper takes new steps to address the endogeneity issue that arises when analyzing the effects of shocks and fiscal space on output dynamics, contrary to Romer and Romer (2018).

We identify two mains sources of endogeneity.

First, as shown in table 3.4, countries hit by shocks (treated group) differ from countries in normal times (control group) in many aspects. Indeed, countries hit by shocks have less distress in the years leading to the shocks (as we consider repeated shocks in a window of three years as a unique shock). They also experience a decline in output growth one and two years before financial crises and an increase in output growth two years before normal recessions. Also, in the run-up of the shocks, the economy is overheating as output is above its potential, REER is over-valuated, and the current account deficit increases. We also find that financial crises are more likely in less developed countries and less likely in countries with fiscal rules. Finally, shocks happen in countries with weak institutions, and when global growth is halting and weak. Second, although relying on fiscal space the year before the onset of the crises reduces the endogeneity of fiscal space with respect to future output and current fiscal policy, it does not prevent our OLS estimates to be biased for two reasons. Indeed, the country's prior fiscal space may be correlated with the deterioration of the country's economic conditions and key structural characteristics in the run-up of the shock, therefore the benefits of fiscal space may be underestimated. We conduct two analysis checks. We first depict the dynamics of fiscal space ten years around the start of shocks in fig. 3.3. It shows that fiscal space remains stable in the run-up of shocks, and then declines after the shocks strike the economy. This general trend sustains that fiscal space before shocks may be exogenous, while fiscal space after the shocks is affected by the shocks and/or is used to alleviate their output losses. However, this general trend does not mean that all countries do not encounter a fall in their fiscal space in the run-up of shocks. To check that, we estimate in table 3.5 a panel fixed effects model on the Chapter . Fiscal space and stabilization policy in developing and emerging countries sample of shocks where fiscal space before the shock is explained by past shocks, lagged and current growth, output gap, and terms of trade. The findings show that countries with lower fiscal space one year before financial crises have lower growth, output gap, and terms of trade as well as suffer past-recessions. Those with lower fiscal space one year before normal recessions have lower growth and terms of trade. These results sustain that fiscal space before the shocks may be affected by other shocks that have implications for both fiscal space and future output growth. This leads us to the second reason why fiscal space may be endogenous. Countries that endure a severe decline of fiscal space the year before shock may be the ones with lower resilience and weak macroeconomic policies that make them more prone to protracted and large recessions. As we are interested in the recovery resulting from the use of fiscal policy, the benefits of fiscal space in the latter case may be underestimated as the output losses also depend on the characteristics of countries rather than the use of the fiscal policy. .6

.7

.8

.9

1 Fiscal space index, average

Normal recessions

Notes: In the run-up of shocks, fiscal space is quite stable, and then collapse when the shocks strike. The solid lines depict the median of fiscal space, and the areas show the interquartile range.

In sum, the OLS estimates may suffer from endogeneity issues for (i) a selection on observables between countries hit by shocks and those in normal times, and (ii) a selection on observables between countries with higher and limited fiscal space in the run-up of financial crises or normal recessions. To address these issues, we included, in eqs. (3.3) and (3.4), country fixed-effects as well as many of the macroeconomic and political variables for which countries differ, but more accurately we estimate an average treatment effect on the treated (ATT) of shocks and fiscal space on output dynamics after re-randomization (i.e. eliminating the difference in the , +ℎ , are the outcome variables. Simplifying the algebra for the type of shock, , the ATT of shocks is obtained as follows Hainmueller (2012), and (iii) a pooled logit. Indeed, (i) and (ii) ensure the perfect balancing of covariates between the treated and control groups compared to (iii), and they also limit the bias due to misspecification in the treatment model (see, tables B.3 and B.4).

= ℎ = E[ , +ℎ (1)| , = 1] -E[ , +ℎ (0)| , = 1], ∀ ℎ. ( 3
Second, we fit an outcome model for each horizon ℎ as in eq. ( 3.3) with weights derived from the first-stage to mimic a situation where the shocks occurred randomly. We do so over the treated group only ( , = 1) and obtain the predicted potential outcomes for the whole sample 8 In fact, many of these papers use the Augmented Inverse Propensity Weighted (AIPW) estimation instead of the IPWRA, which however provides only the Average treatment effects (ATE). As we are interested in the ATT, we estimate the IPWRA, which as the AIPW, falls into the class of doubly robust estimators of treatment effects. See, for instance, Imbens (2004); Lunceford andDavidian (2004), andStuart (2010) for a comprehensive review of these estimations. 9 Consequently, we use the propensity score predicted using the method (i) in the rest of the paper. Our results remain robust when using methods (ii) and (iii). For ATT estimates, we use as weights, 1 , = ˆ , = 1 for the treated group, and 0 , = ˆ , /(1 -ˆ , ) for the control groups.

based on the treated group characteristics, ˆ ℎ 1 ( , ). We repeat the same operation over the control group only ( , = 0), and obtain the predicted potential outcomes for the whole sample based on the control group characteristics, ˆ ℎ 0 ( , ). Third, we estimate the following equations only over the sample of shocks at the start to obtain the unconditional ATT estimates of shocks on output dynamics (see, eq. (3.6a)), and the conditional ATT estimates of shocks on pre-shock fiscal space (see, eq. ( 3.6b)).

ˆ ℎ 1 ( , ) -ˆ ℎ 0 ( , ) = ℎ , , ∀ ℎ , = 1 (3.6a) ˆ ℎ 1 ( , ) -ˆ ℎ 0 ( , ) = ℎ , + ℎ -1 , ∀ ℎ , = 1 (3.6b)
The IPWRA estimator falls into the class of doubly robust estimators (see, e.g., Imbens 2004;Lunceford and Davidian 2004;Imbens and Wooldridge 2008;Stuart 2010). This means that it is unbiased when either the treatment model or the outcome model is correctly specified.

Benchmark results

In this section, we present our benchmark results. We first discuss briefly the advantages of the ATT estimates over the OLS estimates. Second, we show the unconditional paths of output growth in financial crises and normal recessions. Third, we incorporate in the analysis the role of fiscal space and describe the paths of output growth conditional on pre-shock fiscal space.

OLS estimates and the advantages of ATT estimates

The unconditional OLS estimates of the effects of financial crises and normal recessions are shown in tables B.5 and B.6 in section B.2.2, and fig. 3.4 (A) and (B) depict them graphically.

They are obtained by estimating eq. ( 3.3) and show the difference in output dynamics between countries hit by shocks and countries in normal times. The coefficients are statistically significant and quantitatively large. On average and relative to the pre-shock level, after financial crises, output falls by 2.2 pp. at the onset and decline further to 7.3 pp. in year 5; after normal recessions, the collapse is even greater at the onset at 7.2 pp., which rises to 9.5 pp. in year 5.

These paths do not show a recovery of output in the aftermath of shocks as output persistently remains below its pre-shock level.

The average output losses in the aftermath of financial crises and normal recessions may obscure a different trajectory of output in countries with and without fiscal space. To reveal the effects of fiscal space, we estimate eq. ( 3.4) and report the conditional effects of shocks on pre-shock fiscal space in tables B.7 and B.8 in section B.2.2. The tables show that an increase

Chapter . Fiscal space and stabilization policy in developing and emerging countries of fiscal space by one standard deviation is associated with a maximum reduction in the output losses of shocks by 2.4 and 1.5 pp. in the aftermath of financial crises and normal recessions, respectively. Graphically, the dynamics for countries with higher fiscal space when the fiscal space index is at its 75 ℎ percentile, and for countries with limited fiscal space when the fiscal space index is at its 25 ℎ percentile are presented in fig. 3.4 (C) and (D) for financial crises and normal recessions, respectively. We adopt this convention throughout the paper to differentiate between the dynamics of countries with higher and limited fiscal space. The findings imply that higher pre-shock fiscal space is associated with lower output losses. The estimated gain is around 5.5 pp. for both financial crises and normal recessions in year 5 after the shock.

However, as discussed in section 3.4.2.1 above, the OLS estimates may overestimate the effects of shocks and underestimate the benefits of fiscal space. To check this assumption, we compare the results of the OLS estimates with the ATT estimates after eliminating the differences in characteristics between countries hit by shocks and countries in normal times.

As shown below, our assumptions about the bias of the OLS estimates are verified and could be attributed to the differences in characteristics between countries. Consequently, the OLS estimates may provide us with the upper bound of the costs of shocks, and the lower bound of the benefits of fiscal space.

Conditional paths

Higher FS Limited FS

Notes: OLS estimates. Conditional cumulative changes in output from the onset of financial crises and normal recessions. The solid path shows local projection point estimates of the output losses for years 1-5 after the onset of the shocks. These losses describe the difference between the changes of output in financial crises or normal recessions relative to normal times at each horizon. For figures (A) and (B), the thinner and thicker bands are 90% and 95% confidence intervals, respectively. For figures (C) and (D), the dashed lines are 90% confidence intervals; the Higher FS and Lower FS paths correspond to the 75 ℎ percentile and 25 ℎ percentile of the fiscal space index, respectively.

The role of fiscal space in accounting for the variation of output dynamics in the aftermath of shocks

In this section, we compute the ATT estimates of the effects of shocks and fiscal space after re-randomization as described in section 3.4.2.2 to address the endogeneity issues. The results for the unconditional and conditional paths are presented in tables 3.6 and 3.7, respectively. In Recall that the paper aims at assessing whether fiscal space in the run-up of financial crises and normal recessions shapes the dynamics of their output costs. Indeed, some financial crises or normal recessions are less detrimental to growth because countries can enact fiscal stimulus to smooth the economy over the business cycle, or they can continue to assess external financing.

The results of the conditional paths of output in the aftermath of financial crises and normal recessions modulating by pre-shock fiscal space are presented in table 3.7. Figure 3.5 (C) and (D) depict them graphically. The findings show that, also in the context of developing and emerging countries, fiscal space still matters and significantly reduces the output losses of financial crises and normal recessions. We find that an increase of pre-shock fiscal space by one standard deviation is associated with a maximum reduction of the output losses of 4.6 and 3.9 pp. in the aftermath of financial crises and normal recessions, respectively, which is enough to completely offset their output losses. Putting differently, if pre-shock fiscal space increases by one standard deviation, the long-term growth of the median economy will not be affected by a financial crisis or normal recession. These results can be seen graphically in Figure 3.5 (C) and (D) where we depict the dynamics of output in the aftermath of shocks for countries with higher and limited fiscal space (as previously defined). In countries with limited fiscal space (equals the 25 ℎ percentile), output collapses by 1.8% at the onset of financial crises, and the B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times. The second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request.

decline peaks in year 2 at 5.5% and remains persistent around 4.5% in subsequent years. After normal recessions, the decline in output is severe at the onset at 5.5% and remains persistent but reduced around 4.5% in subsequent years. For countries with higher fiscal space (equals the 75 ℎ percentile), output falls in the first two years following financial crises and normal recessions before recovering in subsequent years. More generally, besides being non significantly different from zero two years after the shocks, the average dynamics of output in countries with higher fiscal space in the run-up of shocks are established well above the ones in countries with limited fiscal space.

Our results confirm the findings of Romer and Romer (2018) give them strong evidence of the benefits they may enjoy when moving to a countercyclical fiscal policy. These findings show that there is a significant gain to fix the roof when the sun is shining, i.e., to build-up fiscal buffers, reduce debt and deficit, increase tax base and revenues, the cumulative changes of output from the start of shocks to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. Conditional effects of shocks on pre-shock fiscal space. The first-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times.

The second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request.

Chapter . Fiscal space and stabilization policy in developing and emerging countries and lock the drinks cabinet when the economy is booming for weathering the storm in recessions;

otherwise, there may be no way out but to adjust at the worst possible time.

Channels

Our benchmark results reveal that output falls considerably in countries with limited fiscal space while it remains relatively unchanged or even increases in countries with higher fiscal space in the aftermath of financial crises and normal recessions. However, very little is known about the transmission channels through which fiscal space could alleviate the output costs of financial crises and normal recessions.

In this paper, we test three main channels of the effects of fiscal space on output dynamics in the aftermath of shocks. First, the primary fiscal balance channel analyzes the behavior of the primary fiscal balance in the aftermath of shocks. When fiscal space is high, countries could effort to run significant deficits without raising concerns about debt sustainability or deteriorating the market sentiment. On the contrary, when fiscal space is limited, countries immediately trade output stabilization goals out to address the debt sustainability issues while implementing fiscal consolidations. This is particularly of interest since the literature highlighted that fiscal multipliers are higher in downturns (e.g., [START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF]Fazzari et al. 2015). Besides, as shown by DeLong and Summers (2012); Jordà and Taylor (2016) and Fatás and Summers (2018), fiscal consolidations may be self-defeating in downturns as they depress growth and investment further, then failing to reduce and stabilize debt levels.

Based on these findings, in downturns, fiscal space may be self-sustaining as the increase in fiscal deficit today supports future growth and tends to increase the levels of future fiscal space. We also present the dynamic of fiscal space in the aftermath of shocks conditional on the pre-shock fiscal space. This is done to support the idea of the use of fiscal space in the aftermath of shocks and check whether fiscal consolidations implemented in countries with low fiscal space are enough to achieve higher levels of fiscal space.

Second, we further test whether a Keynesian mechanism is driving our findings and that there is no Neoclassical or Ricardian view that may undermine the benefits of a fiscal expansion when fiscal space is available. Under a Keynesian view, a discretionary increase in the fiscal deficit or a fiscal policy expansion will lead to higher aggregate demand and higher output growth.

The effectiveness of the fiscal expansion will depend, among others, on whether the expansion is associated with a crowding-out effect of private investment or an increase in precautionary savings, as predicted by neoclassical view or a Ricardian equivalence. This leads us to the private 10 Romer and Romer (2018) find that higher fiscal space is associated with an increase in the government's deficit in advanced countries; nevertheless, they do not provide any further analysis of the channels.

absorption channel in which we analyze the behavior of private consumption and investment in the aftermath of shocks, and conditional on the pre-shock fiscal space. If we find that countries with higher fiscal space can loose fiscal policy and simultaneously have an increase in private absorption in the aftermath of shocks, then the Keynesian view is likely to drive our findings while the evidence of Neoclassical and Ricardian views may be weakened.

Third, we look at the behavior of net capital inflows in the aftermath of shocks, and conditional on the pre-shock fiscal space. There is clear evidence that abrupt declines in net capital inflows or sudden stops following financial crises or recessions tend to exacerbate the contraction of output (see e.g., Bordo 2006;Hutchison and Noy 2006;Mendoza 2010;Reinhart and Rogoff 2009;[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]). We guess that higher fiscal space can prevent capital flows to pull back from the country and maintain an external source of deficit financing in downturns. Then, fiscal space increases the available funds the government may tap on to finance its loose fiscal policy by preventing a sudden stop in capital flows and preserving a good market sentiment. In countries with limited fiscal space, capital flows are likely to be pro-cyclical. This means that they contribute to the build-up of risks in booms and flow away in downturns where they are needed, deepening the recessionary forces.

To test these channels, we employ the same strategy as in section 3.4.2.2 and rely on ATT estimates. One issue that arises here is that we want to capture the behavior of our channel variables adjusted for business cycles. For example, when analyzing the impacts of shocks and fiscal space on primary fiscal balance, we want to focus on the discretionary policy of the governments and purge the effects of the automatic stabilizers. To do so, we sightly modify the outcome models estimated in the second stage of the IPWRA procedure to add as explanatory variable the difference between the output gap at each horizon ℎ after the shock and the pre-shock output gap as follows

, +ℎ = ℎ + ,ℎ , + ,ℎ , + ℎ 1 , -1 + ℎ 2 , -2 + ℎ , +ℎ - , -1 +...+ , +ℎ (3.
7) ℎ captures the effects of the changes in business cycles at the horizon ℎ.

, +ℎ represents the cumulative changes of the dependent channel variables (fiscal deficit, fiscal space, private absorption, and net capital flows) between -1 and + ℎ after the shocks.

The results of the transmission channels in the aftermath of financial crises and normal recessions are reported in tables B.9 and B.10, respectively. They reveal that in countries with higher fiscal space, governments enact stimulus packages by using fiscal space and loosening fiscal policy to alleviate the output losses of financial crises and normal recessions. Although fiscal space is used and depressed, it remains strong and above that of countries with limited fiscal space that implement fiscal consolidations to address their fiscal issues. Besides, we find that higher fiscal space is associated with an increase of private consumption and investment, supporting the Keynesian view, and an increase of net capital flows necessary to finance the recovery. The opposite results are found in countries with limited fiscal space where both private absorption and net capital inflows are strongly depressed.

Going into detail, first, we find in Panel A that an increase of pre-shock fiscal space by one standard deviation leads to a decrease of the primary fiscal balance between 1.4 and 2.1 pp. for financial crises and between 1.1 and 1.8 pp. for normal recessions for each year after the shock.

This also leads to a use and decrease of fiscal space for all years after the shocks except for year 5 after normal recessions that peaks at 0.14 in year 5 after financial crises, and 0.09 in year 4 after normal recessions as shown in Panel B. However, one can notice that although fiscal space decreases in countries with higher fiscal space and increases in countries with limited fiscal space, it remains strong and higher in countries with higher fiscal space for all years after the shock. This result shows a strong link between the availability of fiscal space and the cyclicality of fiscal policy in line with [START_REF] Grauwe | Par conséquent, notre indice comble les lacunes des mesures utilisées dans les études précédentes en incorporant autant d'informations compte tenu de la disponibilité des données pour mesurer le concept multidimensionnel d'espace budgétaire. Quatrièmement, notre article est également lié à la littérature sur les multiplicateurs budgétaires et la cyclicité de la politique budgétaire[END_REF]. In countries with higher fiscal space, fiscal policy is countercyclical and dissipates the recessionary forces, while in countries with limited fiscal space, fiscal policy is procyclical and intensify the recessionary forces in the aftermath of shocks. Second, in Panel C, we find that an increase of pre-shock fiscal space by one standard deviation favors an increase of private consumption and investment for all years after the shocks that peaks at 5.1 pp. in year 5 after financial crises and 3.6 pp. in year 2 and remains close of 3.2 pp. in subsequent years after normal recessions. In that case, fiscal space that allows fiscal deficits to increase in downturn helps to achieve sufficient demand and output in line with the Keynesian view. This result shows indirectly the Neoclassical and Ricardian views sustaining that deficits will crowd-out private investment and will induce an increase of precautionary savings in the prevention of future higher taxes are of the least concern. Third, we show that an increase of pre-shock fiscal space by one standard deviation induces an increase in net capital flows that peaks at 6.3 pp. in year 5 after financial crises and at 4.5 pp. in year 3 after normal recessions. This finding reveals that fiscal space helps to preserve an external financing source of fiscal deficit.

Altogether, based on these channels, we find that fiscal space is necessary to conduct a credible countercyclical fiscal policy that increases output growth and support private consumption and investment. It also helps to maintain an external financing source that governments may tap on to finance their recovery. As the experience of some successful examples of crisis and recession management in Latin American and Asian countries have shown, it is possible to pursue countercyclical policies to mitigate output losses; however, this depends crucially on the availability of fiscal space, that is on the room in a government's budget that allows it to provide resources for a fiscal stabilization without jeopardizing the sustainability of its financial position or the stability of the economy.

Robustness checks

In this section, we check the robustness of our benchmark results. To do so, we first check the misspecification of the IPWRA estimation when fiscal space and the interaction term between fiscal space and shocks are not included as explanatory variables in models. Second, we use different alternative measures of fiscal space. Third, we estimate the baseline models on a sample of shocks not followed by subsequent shocks in the next five years.

Relevance of fiscal space for the dynamics of output, misspecification errors

We employ a strategy to show the misspecification error when fiscal space and the interaction term between fiscal space and shock are not included in the IPWRA estimation. This indirectly shows the relevance of fiscal space for explaining the variation of the output dynamics in the aftermath of financial crises and normal recessions. More specifically, this strategy requires estimating a test equation on the sample of shocks where we regress each of the observed dependent variables at each horizon ℎ on their predicted values based on the different variants of IPWRA models and the measure of fiscal space. If the coefficient associated with fiscal space is significant, then there is a misspecification error. The results are reported in tables B.11 and B.12 for financial crises and normal recessions, respectively. First, In Panel A and B, we do not include fiscal space and an interaction term between fiscal space and shock in the IPWRA model. When comparing Panel A and B of each table, one can notice that the explanatory power increases by around 3 pp. when fiscal space is included as regressors in the test equation.

This means that fiscal space explains a variation of output dynamics that is not captured by the Panel A and B IPWRA model. Also, the coefficients associated with fiscal space are statistically significant and positive, which raises an issue of misspecification related to fiscal space that is positively associated with output in the aftermath of shocks. Second, in Panel C, we include as regressors fiscal space in the IPWRA models; however, this does not resolve the misspecification as the coefficients associated with fiscal space are still statistically significant. Third, in Panel D, we include as regressors both fiscal space and an interaction term between fiscal space and shock in the IPWRA models. Here, the coefficients associated with fiscal space turn into insignificant, which shows that the misspecification issue is lifted. Based on these model specification checks, we show that fiscal space is an important variable that explains output dynamics in the aftermath of financial crises and normal recessions; therefore, our benchmark models (in Panel D) are well-performing compared to other specifications which do not account for the role of fiscal space.

Alternative measures of fiscal space

We check the robustness of our benchmark results to the use of alternative measures of fiscal space. First, although our strategy helps to address the endogeneity of fiscal space, we use an average of one and two-year lags in fiscal space instead of a one-year lag in the benchmark models. By doing so, we reduce the possibility that an exogenous shock will simultaneously affect both fiscal space and future growth. Second, we use the fiscal space measure that is computed based on the income group-specific thresholds as described in eq. (3.2b); this index allows for better comparison between countries within the same income groups. Indeed, based on this index, countries with limited fiscal space will be the ones with the worst early warning indicators in comparison with their counterparts. Third, we assume that fiscal space may be a non-linear function of our computed fiscal measure as shown in fig. 3.6 and in line with the concept of fiscal fatigue (i.e., there are limits to the government's ability to raise the primary surplus in response to higher debt) in Ghosh et al. (2013). To capture this non-linearity, we draw upon the cumulative normal distribution with mean and standard deviation set at the mean and standard deviation of our initial measure. The new index is a non-linear function of the initial (old) measure; it is essentially zero at a value of the old index below 0.4, 0.5 at 0.75, the mean of the old index, and close to 0.9 at a value of the old index close to 1. The results are reported in tables B.13 and B.14 for financial crises and normal recessions, respectively. They reveal that our benchmark results are quite robust to the use of alternative measures of fiscal space.

Shocks not followed by subsequent shocks in the next five years

Throughout this paper, we consider that financial crises and normal recessions that are overlapping with other similar shocks in a window of three years constitute a unique shock. This helps to reduce the bias of our estimates. However, among the 55 financial crises and 58 normal recessions identified, 10 and 15 are followed by subsequent shocks in the next five years, respectively. This means that for these types of shocks, our ATT estimates of the benefits of fiscal space may be underestimated as the subsequent shocks may offset the benefits of fiscal space. To deal with these potential biases, we remove from our initial sample, the shocks that are followed by subsequent shocks. The results are reported in table B.15. They reinforce our benchmark findings and show that our assumptions about the bias generated by subsequent shocks are verified. The benefits of fiscal space for output recovery in the aftermath of financial crises and normal recessions are even larger and significant (especially for normal recessions). An increase of fiscal space by one standard deviation leads to a reduction of output losses by around 4.9 and 5.4 pp. for financial crises and normal recessions, respectively, compared to 4.6 and 3.9 pp. in the benchmark results. Consequently, there is no doubt that fiscal space is beneficial for output recovery, especially when shocks are not followed by subsequent shocks.

Concluding remarks

This paper reveals that the availability of fiscal space in the aftermath of financial crises and normal recessions generates a mixed fiscal environment with different output losses of shocks.

In countries with enough fiscal space, governments can enact credible fiscal policy expansion by increasing their deficit and using their fiscal space to alleviate the costs of financial crises and normal recessions. In such a situation, private consumption and investment, as well as net capital inflows, increase, which favors a rapid recovery. In countries with limited fiscal space, the story is different and painful; governments immediately trade output stabilization goals out to address the debt sustainability issues while implementing fiscal consolidations, which deepens the recessionary forces. Besides, in these countries, private consumption and investment, as well as net capital inflows, are depressed, and recovery, if any, is a distant and uncertain prospect.

Quantitatively, we find that an increase of pre-shock fiscal space by one standard deviation is associated with a maximum reduction of the output losses of 4.6 and 3.9 pp. in the aftermath of financial crises and normal recessions, respectively, which is enough to completely offset their output losses. (i) the effect of a one standard deviation increase of fiscal space on each dependent variable, (ii) the critical point of fiscal space for which the derivative of the dependent variable with respect to shock is null; (iii) the percentage of shocks for which fiscal space is above its critical value; and (iv) the difference of fiscal space between countries with decreasing fiscal space and increasing fiscal space, and its associated p-value. Sample restricted to be exactly the same in all regressions. Conditional effects of shocks on pre-shock fiscal space. First-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times. Second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the difference in output gap between t+h and t-1 as well as the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request. (i) the effect of a one standard deviation increase of fiscal space on each dependent variable, (ii) the critical point of fiscal space for which the derivative of the dependent variable with respect to shock is null; (iii) the percentage of shocks for which fiscal space is above its critical value; and (iv) the difference of fiscal space between countries with decreasing fiscal space and increasing fiscal space, and its associated p-value. Conditional effects of shocks on pre-shock fiscal space. First-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times. Second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the difference in output gap between t+h and t-1 as well as the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request. The explanatory variables are the predicted dependent variables based on the IPWRA estimator and fiscal space. There is a misspecification error in the model when the coefficient associated with fiscal space is significant. Notes: ATT estimates. Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of shocks to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. Conditional effects of shocks on pre-shock fiscal space. The first-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times. The second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request. Notes: ATT estimates. Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of shocks to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. Conditional effects of shocks on pre-shock fiscal space. The first-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables B.3 and B.4, weighting the determinants of shocks by the propensity score predicted in the first-stage models perfectly eliminate differences in characteristics between countries hit by shocks and countries in normal times. The second-stage outcome models are separately estimated for countries hit by shocks and countries in normal times using the weights from the first-stage models, and predict the potential outcomes based on the characteristics of each sample after re-randomization. They include as control variables the same variables in the first-stage models except the output gap, t-1, REER gap, t-1, and current account, t-1. The results from the first-and second-stages models can be obtained upon request. 1956) and the "second generation" endogenous growth theories (see, e.g. [START_REF] Romer | [END_REF]Romer , 1987;;Lucas 1988;Barro 1991) on long-run growth averages for a country may hide distinct periods of success and failure.

B.2.2 OLS estimates

Panel B: Normal recessions

(1) (2) (3) (4) (5)
Moving away from explaining long-run growth averages to explaining growth surges is crucial; however, not straightforward. It necessitates the identification of the timing of growth surges and their determinants. First, three types of approaches have been used to identify growth surges: (i) a filter-based approach, (ii) a statistical structural breaks approach (see, e. Second, turning to the determinants of growth surges, the literature found inconclusive results. Among others, Hausmann et al. (2005) concluded that investment and trade, real exchange rate depreciation, political regime changes, external factors, and economic reforms, "on the whole, [...] do a very poor job of predicting the turning points. [...] growth accelerations are caused predominantly by idiosyncratic, and often small-scale changes" and [START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] pointed that "growth accelerations are extremely hard to engineer with a high degree of certainty [...] roughly 9 out of 10 instances failed to ignite a take-off". Jong-A-Pin and de Haan (2011) highlighted the important role of economic liberalizations while they found that a move toward more democracy reduces the likelihood of growth surges. [START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF] surges is that, each country as its realities, and then its impediments to growth. For some, the concerns may be a high level of inflation, debt, or deficit, for other the lack of infrastructures or a corrupted political system, and so on. Therefore, my determinants should account for country characteristics. Finally, I use a more comprehensive empirical analysis where growth surges are explained by all the six broad determinants.

My results are as follows. First, all growth determinants have a significant and positive effect on the probability of initiating growth. However, improvements in macroeconomic stability, and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. Second, when looking at the two-way interactions of growth determinants, I show that countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments, on one hand, and other determinants, on the other hand. Besides, I find that significant changes in either macroeconomic stability or external factors are needed to have a higher and positive effect of the other broad determinants on growth surges. Third, there is a notable difference between decades, regions, and levels of development. This calls for careful tailoring of policies aiming at igniting growth surges to local conditions. Fourth, one clear message from this analysis is that the capacity to ignite growth surges relies on the capacity to restore or preserve macroeconomic stability, although other determinants also matter.

I have several contributions to the literature. First, to the best of my knowledge, this is the first paper of growth surges that fully covers and characterizes the growth surges of the 2000s. In this decade, all the determinants turn to have a higher, positive, and significant effect on the probability of a growth surge compared to the 1980s and 1990s. Second, I use an extensive list of growth determinants that I combine in different broad categories to create a good narrative. Third, I employ an optimization process to identify the threshold beyond which significant changes or improvements in the broad determinants occurred in a way that accounts for country-specific characteristics. Fourth, I show that growth surges exhibit a significant predictive component and that disproportionate changes of determinants, when they are accurately identified, can significantly impact the likelihood of growth surges, contrary to the existing literature (see, e.g. Hausmann et al. 2005;Gupta et al. 2005; Peruzzi and Terzi 2018). I, therefore, solve the "poor predictive power" problem of the literature. Fifth, there exists combinations of growth determinants that may help to maximize the probability of growth surges. Sixth, macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges.

The remainder of the paper is organized as follows. Section 4.2 identifies growth surges.

Section 4.3 presents the growth determinants and the strategy used to determine significant changes of these determinants, as well as results of the noise-to-signal ratio as a prerequisite for the comprehensive regression analysis. Section 4.4 describes the empirical analysis, baseline results, robustness checks, and sensitivity analysis. Section 4.5 presents the concluding remarks.

Growth surges identification and stylized facts

Growth surges identification

The first step in my analysis is to identify growth surges, i.e. sustained periods of relatively high growth. To do so, I employ a filter-based approach introduced by Hausmann et al. (2005) and used, e.g., by [START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF], Aizenman andSpiegel (2010), and[START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF].

Following the seminal work of Pritchett (2000), three types of approaches have been used to identify growth surges : (i) a filter-based approach, (ii) a statistical structural breaks approach (see, e.g. Kerekes 2007; Jones and Olken 2008;[START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF], and (iii) a combination of the two previous approaches (see, e.g. Kar et al. 2013a,b;Munro 2020). This literature on how to determine turning points in growth dynamics highlighted the main limitation of the filter-based approach -the use of filters pre-determined by the researcher is ad-hoc and may lead to lack of consistency across studies that use this method. However, the second approach or Bai and Perron (1998) method has also several potential issues. First, this method may capture "growth spells" following a period of sharply negative growth where the level of per capita income fails to reach its previous height. Second, this method may capture "growth spells" where the average growth rate during the spell differs relatively little from the growth rate before the spell. Third, and more importantly, the Bai-Perron test used in this approach has a low power so that it is unable to identify genuine breaks in highly volatile series (the "true negative" problem). Besides, the papers that sustained improving the two other methods by combining them may also bear their shortcomings, and generally leads to similar identification than the filter-based approach when applying the same criteria.

Against this backdrop, the filter-based approach remains the parsimonious and easiest way of detecting growth surges and having a clear understanding of the process that leads to their determination by setting reasonable criteria. Also, it allows me to set different criteria and identify multiple sets of growth surges to be used in the robustness checks. In line with most of the papers in the literature, I assume that a growth surge lasts a minimum of eight years (i.e., = 7). My criteria are as follows 1) , + ≥ 3.5 pp., i.e., growth is rapid: the average annual growth rate of real income per capita over the next eight years is at least 3.5 percentage points.

2) , = , + --, ≥ 2.0 pp., i.e, growth accelerates: the average annual growth rate over the next eight years is at least 2 percentage points above the one of the previous eight years period.

3) -1 >= ( -, ..., -1 ), i.e, the level of income per capita one year before the start of the growth surge is the peak of the pre-episode period.

A few comments are in order here. First, my first-two criteria are like those in Hausmann et al. (2005), but the third one is different. Indeed, Hausmann et al. (2005) set the criteria to be + >= ( -, ..., -1 ), i.e., the income level the eighth year after the surge exceeds its pre-episode peak. By doing so, they may capture episodes in which the first years are "pure recovery" from previous bad shocks (like natural disasters, major political upheavals, or wars).

The more stringent criterion used in this analysis helps avoid identifying growth surges capturing recovery from bad shocks. Second, when these criteria are met in consecutive years, I deem the first instance to be the "best" starting date. Third, I do not allow countries to have overlapping growth surges. If these criteria are met within an overlapping period of eight years, then I extend the end date of the surge. To check the robustness of my results, I employ the same criteria than Hausmann et al. (2005), or "stricter" or "looser" filters.

Stylized facts on growth surges

Country illustration

Application of this procedure using real income per capita at PPP values from the Penn World Tables for 169 countries (eliminating countries that do not have enough data series) identifies 132 episodes of growth surges in 117 countries over the period 1980-2010. I also find that 102 countries experience one growth surge and 15 countries experience two growth surges over the full period, indicating nonetheless that growth surges are a surprisingly widespread phenomenon. Figure 4.1 shows typical episodes of growth surges identified by the filters. I present the cases of Chile, China, Ghana, and India (some of them are well-known by growth Chapter . Igniting Growth Surges Lessons from the Past economists). One can notice that during episodes of growth surges the average growth rate over the last past eight years spikes at the start of the episodes and remains higher for several years until the end of the episodes; it looks like a mountain. At the same time, income per capita continuously increases to reach very high levels never encounter in the countries (except if they had a dramatic collapse as we can see in less developed economies). Indeed, Chile had two episodes of growth surges between 1989 and 1998, and 2005 and 2013; where the average growth was 4.1 and 5.8%, respectively. Since the start of the first episode, income per capita has tripled to reach 22291 USD PPP. Similarly, China also had two episodes. The first one began in 1981 and lasted nine years in which the average growth rate was 3.8%. The second started three years later the first, i.e. in 1992 and lasted 21 years (this is the second-largest duration of growth surges after 1980, after the Bolivia case from 1992-2013). Since the first growth surge, China's income per capita went from 1826.9 to 10596.5 USD PPP. As the case of Ghana illustrates, growth surges have also occurred in low-income countries. It started in 2001 until 2015, in which the average growth rate was 3.9% and where income per capita was multiplied by 1.8 to reach 4875 USD PPP. Finally, India has two growth surges starting in 1989 and 2000, and lasting 9 and 14 years, respectively. The average growth rate during these episodes was 3.9 and 6.8%, which helps to boost income per capita that went from 1232 to 4975.8 USD PPP at the end of the last episode. Table C.1 shows the full list of growth surges and related information as identified by my criteria.

Statistics and unconditional probability

The 132 growth surges episodes identified occurred mainly in the decade 2000-2010 (65 episodes, i.e. 49.2%), followed by the decade 1990-1999 (40 episodes, i.e. 30.3%), and less in the decade 1980-1989 (27 episodes, i.e. 20.4%). Besides, the region that has had the largest number of episodes was Europe with 34 cases (25.8%); it was followed closely by Asia and the Pacific and Africa regions with 28 and 26 cases (around 20%), respectively. The Middle East and Central Asia, and the Americas and Caribbean regions had 22 cases each (16.7%).

Finally, the growth surges were largely concentrated in current emerging economies in which 79 cases (59.9%) were identified, followed by low-income countries with 34 cases (25.8%), and advanced economies with 19 cases (14.4%). To get a better sense of the distribution of growth surges, I look at the unconditional probability by decades, regions, and income levels.

The unconditional probability is defined as the ratio of episodes over the number of years where an episode may occur. I exclude the non-starting years of growth surges because an episode cannot take place then. Table 4.1 reports the unconditional probabilities. I find that the average unconditional probability in my sample is 3.6% (slightly above the 2.8% found in Hausmann et al. (2005) over the period 1957-1992). Saying otherwise, it means that a typical country would have 36% chance of experiencing a growth surge in a decade. Looking at the unconditional probabilities by decades, it was almost twice higher in 2000-2010 (5.7%) compared with the decades 1990-1990 (3.3%) and 1980-1989 (2.1%). There is no doubt that growth surges have significantly increased over the decades. Looking by regions, the unconditional probability was almost three-times higher in Asia and the Pacific (6.4%), and twice higher in the Middle East and Central Asia (5.0%) compared to the Americas and Caribbean (2.8%) and Africa (2.2%).

In all these regions, the unconditional probability has increased over the decades. Looking at the levels of development, emerging economies are dominating the occurrence of growth surges with an unconditional probability of 4.8% compared to around 2.7% in low-income countries, and 2.5% in advanced economies. The low performance for low-income countries is driven by the low unconditional probability in the decades 1980-1989 and 1990-1999, which substantially increase in the decade 2000-2010. This fact is also observed in emerging economies while I find a diminution of the unconditional probability in advanced economies over time.

Growth determinants

To paraphrase Lucas (1988), "What, exactly" can countries do to initiate an episode of growth surge? This question has always been central to all economists. In this section, I develop a new strategy to identify significant changes in many factors that are essential for growth. As in Rodrik ( 2019) and [START_REF] Hausmann | [END_REF], my theoretical understanding of the initiation Chapter . Igniting Growth Surges Lessons from the Past of growth surges is that, each country as its realities, and then its impediments to growth.

For some, the concerns may be a high level of inflation, debt, or deficit, for other the lack of infrastructures or a corrupted political system, and so on. Therefore, my determinants should be country-specific and account for country characteristics. Given that, if a country can eliminate its "binding constraints", growth may accelerate. In this country, I should expect to capture significant changes in growth determinants in the run-up of the take-off. This means that while the changes should be country-specific, I can identify a set of standard growth determinants that the literature found to be crucial for predicting medium to long-term growth, and check how they evolve in the run-up of the take-off in a way that accounts for the country characteristics.

In other words, first, I start by identifying the main determinants of growth without looking at the specific economic situation of a country. Second, I identify significant changes at the country-level for all the determinants and focus on the period before the growth surge (over five years before the start). Third, if I find that in the run-up of a growth surge, there were significant changes or improvements in one determinant, I assume that this determinant was potentially a bottleneck to growth in this country. Consequently, this strategy allows identifying ex-post potential country-specific bottlenecks to growth surge in each country. As one will notice in the results section, this strategy solves the "poor predictive power" problem of the literature.

Categorization of growth determinants

To identify growth determinants, I rely on the existing growth surges literature (see, e. [START_REF] Edwards | [END_REF]Easterly et al. 1993;Barro 2003;Hamann and Prati 2002;Gupta et al. 2005) and natural resources endowments (see, e.g. Manzano and Rigobon 2001;[START_REF] Mehlum | Jong-A-Pin and de Haan[END_REF]Sala-i Martin et al. 2004;Mideksa 2013) for growth. This broad determinant captures the effects of "good luck" (favorable external conditions or discoveries of natural resources). I, therefore, include in this category:

(i) trading partners' growth, (ii) terms of trade, (iii) nominal US FED interest rate, (iv) volatility of S&P 500 index returns, and (v) total natural resources rents.

2) Institutions: Institutional factors have proven to be central and critical for growth through its direct effects on growth or its conditional effects on other growth determinants (see, e.g. [START_REF] Dornbusch | Bruno and Easterly[END_REF]Hamann and Prati 2002;Barro 2003;Gupta et al. 2005;[START_REF] Mehlum | Jong-A-Pin and de Haan[END_REF]; Jong-A-Pin and de Haan 2011; [START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF][START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]).

However, the literature that focuses on the political regime (democracy versus autocracy) is very inconclusive and sometimes points toward a higher likelihood of growth surges in autocracy or democracy. I think that beyond everything, what matters for growth is not the political regime, but rather the quality of institutions (accountability, corruption, civil liberties, economic freedom, rule of law) and how they organize or affect the economic choices. Thus, I rely on different indexes that measure: (i) government's accountability, (ii) civil liberties, (iii) political corruption, and (iv) the rule of law.

3) Macroeconomic stability: Does an economy may grow faster when debt exceeds 90% or when inflation is greater than 40%? Indeed, macroeconomic stability is a prerequisite to sustained growth, a view that is cherished by multilateral institutions (see, e.g. Mussa and

Savastano 1999; [START_REF] Collier | Many papers have highlighted the significant role of macroeconomic stability for growth[END_REF][START_REF] Sixth | finds that none of the top 20 recipients of repeated IMF-supported programs over 1980-99 were to achieve reasonable growth and macroeconomic stability[END_REF]). Many papers have highlighted the significant role of macroeconomic stability for growth (see, e.g. [START_REF] Dornbusch | Bruno and Easterly[END_REF]Bruno and Easterly 1998;[START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF]Darvas 2012;[START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF]).

For countries with significant macroeconomic stability, demand-restraining measures, sometimes combined with exchange rate depreciation may be needed before take-off takes place. Consequently, I include in this category: (i) end of financial crises and normal recessions, (ii) public debt, (iii) current account, (iv) inflation, and (v) change of real effective exchange rate.

4) Structural Reforms:

Igniting growth surges heavily depends on the structure of the economy and the capacity to implement structural reforms to eliminate the bottlenecks of the economy (see, e.g. Ahluwalia 2002;Panagariya 2004;Gupta et al. 2005;[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]Prati et al. 2013;IMF 2019;[START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF]). Moreover, while achieving macroeconomic stability (which is not without any cost for the economy), structural reforms may be needed to significantly increase the allocation of resources and their effectiveness that would have a medium to long-term effect on growth; this has been the strategy adopted in IMF-supported programs (see, e.g. Mussa and Savastano 1999). I therefore include in this category different sets of reforms including: (i) agriculture-, (ii) product market-, (iii) labor market-, (iv) financial-, (v) trade-tariff-, (vi) current account-, and (vii) capital account reforms.

5)

Trade diversification and quality: Many countries achieved a higher level of devel-opment because they succeeded the diversification of their exports and trading' partners while improving the quality of the products they export and import (machinery and highly technological products) (see, e.g. Hausmann et al. 2007[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF][START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF][START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]). Consequently, I use in this category: (i) export and (ii) import quality, (iii) product, and (iv) partner diversification.

6) Investments, labor and productivity: The neoclassical models of long-term growth stressed out the importance of investments in determining growth (see, e.g. Solow 1956;Swan 1956;Gupta et al. 2005) while the endogenous-growth models stressed out the importance of human capital and productivity (see, e.g. [START_REF] Romer | [END_REF]Romer , 1987;;Lucas 1988;Barro 1991;Joshua 2015;Gupta et al. 2005). I, therefore, include in this category: (i) domestic investment (both private and public investments), (ii) foreign direct investment, welfare-relevant total factor productivity, and (iii) human capital index. These factors are more direct determinants of growth, and may also be determined by the other determinants 1) to 5).

My list of the six broad growth determinants is quite extensive and will allow identifying what matters for igniting growth surges. Table C.2 reports the full list of growth determinants and their sources, and table C.3 presents the summary statistics of all variables.

Identifying significant changes or improvements of growth determinants

As said in the chapeau of this section, the second step in my strategy consists of identifying significant country-specific changes or improvements in each of the growth determinants.

= =1 × ℎ = 1 0 ℎ (4.1)
One can notice that the indexes of each broad determinant of growth surges are increasing with significant changes or improvements in the sub-determinants of this broad determinant. For

Chapter . Igniting Growth Surges Lessons from the Past instance, if there is a significant reduction of the level of debt, deficit, and inflation, coupled with a depreciation of real exchange rate to boost competitiveness, and an end of a financial crisis, the macroeconomic stability index will increase. The results of the noise-to-signal approach, as well as the assumption on the direction of effects for the sub-determinants, are reported in table C.4.

These results show that almost all the sub-determinants have a good predictive power (see, incidence and effectiveness indicators, and total misspecification errors) of growth surges. This means that I have identified good determinants and that my optimization strategy is capturing significant changes or improvements in determinants related to growth surges. Hereafter, I will focus on the results of the broad determinants.

Advanced stylized facts on growth surges

Now that I identify significant changes for each of the six broad determinants of growth surges, I can characterize growth surges by looking at the incidence or the probability that growth surges were preceded by significant changes of determinants, and the effectiveness or the probability that significant changes were followed by growth surges. From the theoretical standpoint, one can think of incidence as a necessary condition to ignite growth surges while the effectiveness relates to a sufficient condition. As in the unconditional probability analysis, I drop non-starting years of growth surges as growth surges cannot occur in these years when analyzing the performance of growth determinants.

Table 4.3 displays the incidence and effectiveness of all the six broad determinants. I find that the probability that growth surges were preceded by significant changes in each broad determinant is very high for all broad categories (it varies between 84 and 98.5%). This shows that many of growth determinants should significantly improve to create favorable conditions to jump-start growth in countries. Saying differently, all the different growth strategies found in the literature seem vindicated. This should not surprise given that the analysis focuses on the significant country-specific changes endogenously identified through an optimization process.

Some caveats need to be done at this stage. These findings are challenging the existing results in the literature while reconciling them. For instance, Hausmann et al. (2005) found that "most growth accelerations are not preceded [. . . ] and endowments (98.5%) come first (as found by Easterly et al. 1993, who attested that "good luck" matters more macroeconomic and political stability); macroeconomic stability (91%), investments, labor and productivity (91%), and trade diversification and quality (89%) come in the second position, followed by institutions (86%) and structural reforms (84%). However, as already stated above, an analysis of the effectiveness is needed to have a broader view of the performance of all the six broad determinants. When comparing the values found for incidence and effectiveness, I show that while many of the growth surges were preceded by a significant improvement in all of the six broad determinants, around 65% of the signals for each of broad determinants were not followed by a growth surge.

However, the effectiveness found here remains quite higher compared to other papers (see, e.g.

Peruzzi and Terzi 2018), perhaps because they set ad-hoc values for the thresholds while I find them through an optimization process. In a nutshell, I find that 37.2% of the significant improvements in macroeconomic stability were followed by a growth surge; this figure stands at 34.5% for structural reforms, around 30% for investments, labor and productivity, external factors and endowments, institutions, and trade diversification and quality. As a complementary analysis, I present how the general findings highlighted in this section vary by decades, regions, and levels of development (see, Tables C.5 to C.7).

When looking at the incidence by decades, regions, and levels of development, the general picture found above remains true, i.e., almost all the growth surges were preceded by significant changes or improvements in each of my six broad determinants. In contrast, there is a notable variation of the effectiveness of the six broad determinants by decades, regions, and levels of development (in line with the unconditional probabilities shown above). First, the effectiveness of all the six broad determinants has increased over decades; especially for structural reforms, trade diversification and quality, and investments, labor and productivity that more than doubled between 1980-1989 and 1990-1999 and were multiplied by more than 3 between 1980-1989 and 2000-2010. The other determinants also increased and were multiplied by around 1.5 between 1980-1989 and 1990-1999, and more than doubled between 1980-1989 and 2000-2010. Second, the effectiveness was quite similar in Europe, the Middle East and Central Asia, and Asia and the Pacific, with minor exception, and stands between 36 and 60% in these regions. Unfortunately, it was importantly lower in Africa, and the Americas and Caribbean, between 16 and 23%, depending on the determinants, compared to the other regions. Finally, when looking at effectiveness by levels of development, I find that it was higher in emerging economies, between 36 and 45% while it was relatively lower in low-income countries and advanced economies (between 20 and 34%).

Here, two caveats are worth noting. First, while growth surges are rare phenomena (unconditional probability is low and is equal to 3.6%), significant improvements of the broad determinants of growth lead to a relatively good probability of igniting growth surges. Again, achieving significant improvements in the growth determinants may not be an easy task for policymakers. Second, the noise-to-signal ratio analyzes each of the six broad determinants and does not account for the possible correlation between them (especially as I show that many of the growth surges were preceded by almost all the broad determinants). This states that a more comprehensive analysis is needed to fully weight the power of each of determinants one against another before making conclusive recommendations on how to ignite growth surges. and assume that time is needed to ignite growth surges. Finally, I also include dummies for decades, regions, and levels of development to capture the heterogeneities found in the previous section. This empirical setting is exempted from a reserve causality issue as I am looking at the forward-looking effects of significant changes in growth determinants.

Empirical analysis

Baseline results

Table 4.4 shows the baseline results, and fig. C.1 depicts the average marginal effects graphically.

First, my models have a good predictive performance as presented in the bottom of the table (classification power equals around 90%, Area under the ROC is close to 0.8, and Pseudo R 2 equals 0.18 in my preferred model). Here one notable difference with the existing literature is worth noting. While I find here that the predictive power is quite high, the literature generally highlighted a very poor predictive power. I solve this "poor predictive power" problem of the literature by accounting for country-specificity and focusing on significant improvements of the growth surge determinants using an optimization process. Also, the BIC criterion suggests that removing all non-starting years of growth surges improves the specification.

Second, I find that all growth determinants have a significant and positive effect on the probability of initiating growth, which is robust across the two specifications. This finding is in contrast with many papers in the literature that generally stressed out the beneficial effects of few determinants while others have no significant effects (see, e.g. Hausmann et al. 2005;

Jong-A-Pin and de Haan 2011; [START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF]). My identification strategy, which consists of grouping determinants in six broad determinants and identifying their country-specific and endogenous significant improvements, allows me to reconcile the findings in the literature: all determinants matter, but in different orders.

Third, I find that improvements in macroeconomic stability and external factors and endowments favor a higher probability of growth surge. A one-unit increase in each of these 1), the non-starting period of growth surges are not deleted for robustness checks. Column (2) shows my preferred results were country-years observations in which growth surges may be occurred are removed.

determinants raises the probability of a growth surge by around 40%. They are followed by structural reforms; investments, labor, and productivity; and trade diversification and quality, for which a one-unit increase leads to a probability of a growth surge of 26.3, 17.3, and 14.7%, respectively. Last, improvements in institutions also increase the probability of growth surges, but in a lower magnitude: a one-unit increase is associated with a probability of growth surge of 8.6%.

Fourth, as suggested by the descriptive analysis, I show that the likelihood of growth surges was higher in Asia and the Pacific, Europe, and the Middle East and Central Asia as opposed to Africa and the Americas and Caribbean. Besides, it increases in the decade 2000-2010 compared to the period 1980-1999. It was also higher in emerging countries than in low-income and advanced countries. Below, I analyze the sensitivity of the findings by decades, regions, and levels of development.

In sum, in the eyes of a policymaker of a typical country with issues in many of the growth determinants seeking to ignite growth surge, the best strategies will be to restore macroeconomic stability and benefit from favorable external factors and endowments. Next, implementing structural reforms, increasing investments, human capital, and productivity, and diversifying its production and trading partners will also be a good option. Finally, fighting corruption, and improving government accountability, civil liberties and rule of law may also be needed, but less efficient to ignite growth surge.

Robustness checks

I check the robustness of my baseline results to alternative timing conventions, estimation techniques, and sets of growth surges. First, I modify the timing needed for a growth determinant to ignite growth surge from five years, to seven (column 2) and three (column 3) years. The results are reported in table C.8. My results are qualitatively identical but slightly different in terms of magnitude. The average marginal effects increase, especially for external factors and endowments, and except for investments, labor, and productivity determinant, when I raise the timing convention from five to seven years. In contrast, the average marginal effects decrease when I consider a smaller duration of three years. This shows that it may take some years after the significant improvements in growth determinants to ignite growth surges and that five years seems reasonable (the BIC criterion suggests using this timing convention).

Second, I employ different estimation techniques. I estimate a pooled probit (column 2), a tobit (column 3) treating non-growth surges period as censored at zero, and Random-effects logit (column 4) and probit (column 5). The results presented in table C.9 in all cases are not only qualitatively, but also quantitatively similar. This is reassuring and suggests that the logit results reported in the baseline are broadly representative.

Third, I test the validity of my results to the modification of the filters used in the identification of growth surges. By changing the parameters, I get a different set of growth surges. In column 2 of table C.10, I apply the same criteria as in Hausmann et al. (2005) and identify 159 growth surges. In columns 3 and 4, I set the minimum duration of growth surge to 6 (144 growth surges) and 10 (125 growth surges) years, respectively, instead of 8 years. In columns 5 and 6, I change the "growth is rapid" criterion from 2 pp. to 1 (135 growth surges) and 3 (97 growth surges) pp., respectively. In columns 7 and 8, I change the growth accelerates criterion from 3.5 pp. to 2.5 (141 growth surges) and 4.5 (104 growth surges) pp., respectively. Indeed, the number of growth surges identified is reduced when the minimum duration of growth surge, the "growth is rapid", and the "growth accelerates" criteria are set to a high value. In general, the main results hold, but some remarks are needed. Compared to the Hausmann et al. 2005 criteria, mine are less likely to capture recovery from bad shocks like crises or natural disasters. I show that using their initial criteria, the average marginal effects of macroeconomic stability is almost doubled signaling the need to restore macroeconomic stability. However, this finding is contrary to their conclusion as they found little effect of good policies as opposed to good luck. There is little change in other determinants. Besides, as in the baseline, significant improvements in macroeconomic stability, external factors and endowments, and structural reforms are associated with the highest probability of growth surges while improvements in institutions have the lowest power to ignite growth surges and sometimes have no significant effects. Overall, the robustness checks confirm the solidity of my findings.

Paired growth determinants

In the previous section, I weighted the predictive power of one broad determinant of growth surge to the others, contrary to what was done using the Noise-to-Signal ratio. To dig deeper into the analysis, I study the two-way interaction between all the six broad determinants. This analysis will allow me to answer two critical questions. Do joint improvements in two broad determinants raise the likelihood of a growth surge? Does the growth-enhancing effect of one determinant conditional to improvements in another? In order words, I want to check whether joint and significant improvements in external factors and macroeconomic stability raise the likelihood of a growth surge and whether countries could experience a growth surge following significant improvements in institutions when they struggle to achieve macroeconomic stability (concerns do not come alone; a country that needs improvements in institutions may also need to clean its macro-economy). The results of the average marginal effects of two-way interactions are reported in fig. 4 Notes: The figures show the average marginal effects of the interaction of each pair of broad determinants of growth surges.

To answer the first question, I find that countries can maximize the likelihood to ignite growth surges if they jointly achieve significant improvements in macroeconomic stability, on one hand, and trade diversification and quality, institutions, external factors and endowments, structural reforms, and investments, labor, and productivity, on the other hand. Also, they can significantly improve their institutions, implement reforms, increase investments, human capital, and productivity while experiencing good and favorable external conditions and endowments.

In all the previous cases, the likelihood of a growth surge may reach 40-60%. In contrast, the likelihood of a growth surge when combining significant changes in institutions, on one hand, and structural reforms, and trade diversification and quality, in the other hand, or investments, labor, and productivity, on one hand, and structural reforms, and trade diversification and quality, in the other hand, cannot exceed 20%.

To answer the second question, I find that significant changes in either macroeconomic Chapter . Igniting Growth Surges Lessons from the Past stability or external factors are needed to have a positive effect on many of the other broad determinants. For instance, even if institutions and trade diversification and quality improve significantly and even reach their highest values without any improvement in macroeconomic stability, the likelihood of having a growth surge is lower than 10%. Similarly, for significant improvements in institutions, structural reforms, and investments, labor, and productivity when there are no favorable external conditions or endowments. In sum, macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges.

Sensitivity

Before concluding, I present in this section, the sensitivity of my results by running the main model by decades, regions, and levels of development, as the growth determinants may have played a different role over the decades, across regions, and levels of development.

First, when looking at the results by decades, I show that the role of significant changes in broad determinants has significantly evolved. Out of the six determinants, only macroeconomic stability was consistently higher and significant over the decades (see, table C.11). In the 1980s, only significant changes in macroeconomic stability and institutions were associated with a positive and significant probability of a growth surge, respectively 52 and 12.1% following a one-unit increase in each determinant. In the 1990s, I find that a one-unit increase in macroeconomic stability, structural reforms, and trade diversification and quality raise the probability of a growth surge by 36.8, 18.2, and 14%, respectively, while the other determinants display a negative or non-significant effect (which is quite intriguing) maybe because all the pre-conditions were not fulfilled. In the 2000s, all the determinants turn to have a higher, positive, and significant effect on the probability of a growth surge. This is in line with the literature on the Great moderation.

One key lesson emanating from this finding is that significant changes should occur in many of the determinants that are needed, or the pre-conditions of take-off should be reunited to ignite a growth surge.

Second, when looking at the results by regions (see, table C.12), I find that not all significant changes in broad determinants have raised the likelihood of growth surge in all regions. In Africa, except for institutions, and trade diversification and quality, significant changes in all other determinants were followed by a positive and significant increase in the probability of a growth surge. In this region, external factors and endowments have the highest power to ignite growth surge (49.6%), followed by macroeconomic stability (34.4%), and structural reforms (29.1%). In Asia and the Pacific, what counted was structural reforms and trade diversification and quality (as illustrated, e.g. by the case of China, India, and Korea). A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 96.8 and 28.5%, respectively. In Europe, macroeconomic stability, and external factors and endowments mattered the most (as illustrated by the case of transition economies in Eastern Europe). A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 98.4% and 30.7%, respectively. In the Middle East and Central Asia, significant improvements in macroeconomic stability, trade diversification and quality, and institutions were effective to increase the likelihood of a growth surge. A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 60.4, 33.3, and 26.3%, respectively. In the Americas and Caribbean, growth surges were mainly determined by significant improvements in external factors and endowments, followed by macroeconomic stability and institutions. A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 65.9, 16.4, and 11.2%, respectively. Indeed, the constraints and realities of the regions are quite different. These findings suggest that policymakers should carefully target the main concerns of countries while adjusting the policies to their realities.

Third, when looking at the results by levels of development (see, table C.13), a clear difference between low-income countries and emerging markets, on one hand, and advanced economies, on the other hand, is striking, showing that different policies may be needed to boost growth at different levels of development. In low-income countries, what counted was significant improvements in external factors and endowments, followed by macroeconomic stability, structural reforms, and investments, labor, and productivity. A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 54.7, 48.8, 36.9, and 23.5%, respectively. In emerging markets, all of the determinants were associated with an increase of the likelihood of a growth surge, and the most important were external factors and endowments (60.2%) and macroeconomic stability (46%), followed by structural reforms (33.3%), trade diversification and quality (24.6%), investments, labor and productivity (21.2%), and finally institutions (13.3%). In Advanced economies, growth surges were mainly determined by significant improvements of macroeconomic stability, trade diversification and quality, and institutions. A one-unit increase in each of the determinants is associated with an increase in the likelihood of a growth surge of 93.2, 34.2, and 24.1%, respectively.

Concluding remarks

Since the seminal paper of Pritchett (2000), many researchers have been searching for the anatomy of growth surges to characterize them, understand their determinants, and make policy recommendations to jump-start growth in countries. This paper falls into this agenda. I identified 132 growth surges in 117 countries over the period 1980-2010. The unconditional probability of growth surges was higher in the decade 2000-2010, in emerging countries, in Asia and the Pacific, the Middle East and Central Asia, and Europe.

In this paper, I develop a strategy to answer the question: "What, exactly" can countries do to initiate an episode of growth surge? First, it consists of identifying growth determinants in the literature, then calculating significant changes of these determinants in a way that accounts for country specificity through an optimization process, and constructing six indexes used as broad determinants of growth surges. These determinants include (i) external factors and endowments, (ii) institutions, (iii) macroeconomic stability, (iv) structural reforms, (v) trade diversification and quality, and (vi) investments, labor, and productivity. Second, it further analyzes how the significant changes in these determinants relate to growth surges. This strategy allows me to solve the "poor predictive power" problem of the literature by accounting for country-specificity and focusing on significant improvements of the growth surge determinants using an optimization process (see, for the "poor predictive power" problem, Hausmann et al. 2005;Gupta et al. 2005;[START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF]. Indeed, the most striking different results of this paper compared to the literature may come from this improved strategy.

First, I find that the probability that growth surges were preceded by significant changes in each broad determinant is very high (it varies between 84 and 98.5%). This shows that many of growth determinants should significantly improve to jump-start growth in countries. This finding contradicts the results of Hausmann et al. (2005) and [START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] who find that "growth accelerations are not preceded or accompanied by major changes in economic policies, institutional arrangements, political circumstances, or external conditions" or "most successful growth accelerations follow the relaxation of few binding constraints in key dimensions, rather than jointly unlocking several growth channels". This difference may come from the fact that they define an ad-hoc threshold to identify disproportionate changes in growth determinants and/or focus on non-aggregated determinants.

Second, while growth surges are somewhat rare phenomena (unconditional probability is low and is equal to 3.6%), significant improvements of the broad determinants of growth lead to a good probability of igniting growth surges (around 30%). This also contradicts the findings of Hausmann et al. (2005), Gupta et al. (2005) and [START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] that states that "on the whole, those determinants do a very poor job of predicting the turning points [...] growth accelerations are caused predominantly by idiosyncratic, and often small-scale changes", "many acceleration episodes occur when the explanatory factors in the model would not predict an acceleration, and many times, even though the variables associated with accelerations are conducive, an episode does not take place. There are clearly factors the model is not capturing, as well as country-level idiosyncratic factors that warrant further investigation in order to better guide policy. Predicting the timing, or onset, of growth accelerations is even more difficult."

and "growth accelerations are extremely hard to engineer with a high degree of certainty [...] roughly 9 out of 10 instances failed to ignite a take-off", respectively.

Third, when applying a more comprehensive empirical analysis, I find that all growth determinants have a significant and positive effect on the probability of initiating growth, in line with the non-parametric analysis. However, improvements in macroeconomic stability, and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. When looking at the two-way interactions of growth determinants, I show that countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments, on one hand, and other determinants, on the other hand. Besides, I find that significant changes in macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges. This is in contrast with [START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] that found "no strictly dominant strategy to ignite a growth acceleration". My results are robust to alternative assumptions on the timing needed to ignite growth surges, filters criteria applied to identify surges, and estimations techniques.

Fourth, there is a notable difference between decades, regions, and levels of development.

Out of the six determinants, only macroeconomic stability was consistently higher and significant over the decades. In the 2000s, all the determinants turn to have a higher, positive, and significant effect on the probability of a growth surge, in line with the literature on the Great moderation.

Besides, I find that not all significant changes in broad determinants have raised the likelihood of growth surge in all regions. Finally, the difference between low-income countries and emerging markets, on one hand, and advanced economies, on the other hand, is striking. This calls for careful tailoring of policies aiming at igniting growth surges to local conditions, which is also found in the literature. One clear takeaway from this analysis is that the capacity to ignite growth surges relies on the capacity to restore or preserve macroeconomic stability, although other determinants also matter. 2251.304 2251.304 2251.304 Notes: Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. All country-years observations in which growth surges may be occurred are removed.

C.3.4 Sensitivity

T I M F '

R I G S

This chapter is joint work with Joshua WOJNILOWER (IMF).

Introduction

"But such IMF pressure is very much helpful for me to push such a, you know, reform. So in this sense, I think the IMF is very much helpful for alien society."

-Kim Dae-Jung, Former President of the Republic of Korea, 1998Korea, -2003 "There were times when there were riots in Africa, demonstrations against the IMF because of the policy advice they were giving, the conditionalities they were imposing, and the difficulties that arose out of the implementation of those conditionalities."

-Jakaya M. Kikwete, Former President of the United Republic of Tanzania, 2005-2015

The International Monetary Fund (IMF) described as the "lender of last resort" or the "financial firefighter", both criticized and lauded for its effort to promote financial stability, continues to find itself at the forefront of global economic crises management, especially following the 2008-09 Global financial crisis and 2019-20 Coronavirus disease (Covid-19) pandemic. As 2 Historically, since its inception in 1944, the IMF has been assisting more than 150 countries through 1,300 IMF-supported programs. This includes the reconstruction of the international system payments system in the post-world war II, the transition of Former Soviet Union nations to market-based economies, and the management of the diverse crises in countries affecting by the 1970s' oil shocks, the 1980s' Latin American and African debt crises, the 1990s' Asian financial crisis, the European debt crisis in the Aftermath of the 2008-09 global financial crisis, and the 2019-2020 Covid-19 Pandemic crisis. stated in the IMF's Guidelines on Conditionality (2002), "Fund-supported programs should be directed primarily toward the following macroeconomic goals: (a) solving the member's balance of payments problem without recourse to measures destructive of national or international prosperity; and (b) achieving medium-term external viability while fostering sustainable economic growth". However, lackluster growth under IMF-supported programs relative to non-program countries or periods has often been criticized as indicative of an excessive tightening bias and resulted in a perceived stigma, potentially discouraging the use of IMF financing and challenging the Fund's reputation. Meanwhile, IMF's economists argue that restoring macroeconomic stability even painful in the short-term will create the conditions for higher medium to longer-term growth.

In practice, the IMF has been reinventing itself to pay more attention to growth in its lending programs. For instance, it creates the Extended Fund Facility (EFF) in 1974 provided scope for structural policies over longer program and repayment periods to support deeper adjustment and achieve greater growth impact; the Enhanced Structural Adjustment Facility (ESAF) in 1987, renamed the Poverty Reduction and Growth Trust (PRGT) in 1999 provided a greater emphasis on growth and poverty reduction in low-income countries through concessional loans and structural reforms. These greater emphases on growth outcomes-both during the program period and afterward-implied increasing attention to growth-friendly policies such as protection of public investment, growth-enhancing structural reforms, and debt operations to alleviate the extent of fiscal adjustment needed to achieve viability.

However, others dismiss the suggestion that the IMF's approach changed. They sustain that the IMF's remedy has always been straight out of the structural adjustment playbook: cut salaries and benefits, privatize state-owned enterprises, reduce public spending, reduce minimum wages, and restrict collective bargaining. In other words, the IMF has put too much effort into adjustments and relegates growth to a secondary objective. Indeed, renowned economist and Nobel Prize winner Joseph E. Stiglitz severely criticized the IMF's work in its book Globalization and its Discontents (2002), which looks more like "the IMF and its discontents".

He denounced the IMF as a primary culprit in the failed development policies implemented in some countries. He argues that many of the economic reforms the IMF required as conditions for its lending-fiscal austerity, high-interest rates, trade liberalization, privatization, and open capital markets-have often been counterproductive for target economies and devastating for local populations.

This controversy debate on the IMF's effectiveness, particularly on promoting growth, has led to several analyses in the literature. Not surprising, this literature is very inconclusive, reflecting in part significant empirical challenges involved in identifying appropriate counterfactuals and isolating the impact of programs on growth from influences of other factors, and because of varying data and methods employed by the researchers.

The class of papers highlighting a positive effect of IMF-supported programs on growth encompasses, e.g. Dicks-Mireaux et al. (2000); Hutchison (2004); Atoyan and Conway (2006); Bas and Stone (2014); Bal [START_REF] Gündüz | Ils sont contredits par Przeworski and Vreeland[END_REF] and Bird andRowlands (2017). First, Dicks-Mireaux et al. (2000) focus on a sample of low-income countries that engaged in the IMF's Enhanced Structural Adjustment Facility (ESAF) over the period 1986-1991. They reveal significant beneficial effects of IMF support on output growth and debt but no effects on inflation. Second, Hutchison (2004), using a variety of matching methods, also shows a positive association between growth and IMF-supported programs. He argues that the sample selection bias is mainly responsible for the common perception that real output growth declines because countries choose to participate in IMF programs. Third, Atoyan and Conway (2006) find little statistical support that IMF programs contemporaneously improve real economic growth in participating countries but stronger evidence of an improvement in economic growth in years following a program. Fourth, Bas and Stone (2014), after addressing the selection bias problem, show that countries benefit from IMF programs on average in terms of higher growth rates. This positive effect is pronounced in long-term users than among short-term users' countries; in contrast with the previous literature. Fifth, Bal [START_REF] Gündüz | Ils sont contredits par Przeworski and Vreeland[END_REF] find that the short-term IMF engagement is positively associated with a wide range of macroeconomic outcomes. Notably, the impact on short-term growth is the greatest and becomes significant only for low-income countries facing substantial macroeconomic imbalances or large exogenous shocks. Finally, Bird and Rowlands (2017) adopt a propensity score matching method and show that concessional programs have had a generally positive effect on growth for up to two years after agreements were signed in the context of low-income countries.

In contrast, the second class of papers rather sustains a negative effect of the IMF-supported program on growth. It encompasses Przeworski and Vreeland (2000); IEO and IMF (2002); [START_REF] Hutchison | [END_REF]; [START_REF] Barro | [END_REF]; [START_REF] Butkiewicz | Sachant que le chapitre 4 a révélé que la stabilité macroéconomique est l'une des conditions préalables aux poussées de croissance, le chapitre 5 contribue au débat sur l'efficacité du FMI à promouvoir la croissance, notamment car il a été impliqué dans la restauration de la stabilité macroéconomique dans plusieurs pays. Il conclut que les programmes soutenus par le FMI (plus les programmes PRGT que GRA) ont contribué de manière significative et positive à stimuler la croissance à moyen et long terme dans les pays, en particulier dans les années 2000 7 Historiquement, depuis sa création en[END_REF]; Easterly (2005) and Dreher (2006). First, Przeworski andVreeland (2000) reveal that program participation lowers growth rates for as long as countries remain under a program. Once countries leave the program, they grow faster than if they had remained, but not faster than they would have without participation. Second, the same vein, IEO and IMF's first evaluation report on the prolonged use of IMF resources (2002) finds more adverse effects of IMF-supported programs on growth for prolonged users than for "temporary" users. The adverse consequences for the growth of prolonged use appear to be concentrated in programs supported under general resources, and not in those under concessional facilities. Third, [START_REF] Hutchison | [END_REF] focus on the IMF experience in Latin American countries. They reveal higher short-run output costs of IMF-supported programs in this region compared to other regions. Fourth, Barro and Lee Chapter . The International Monetary Fund's Role in Igniting Growth Surges on the IMF's role in igniting periods of growth surges.

One key takeaway is that all the analyzes carried out in this paper show that having an IMF-supported program is enhancing the probability of igniting a growth surge. They support that the IMF has contributed to boosting medium-to long-term growth in countries while pursuing macroeconomic stability and implementing structural reforms, but also creating the pre-conditions to boost investments, labor, and productivity and benefit more from favorable external and endowments conditions.

In detail, the non-parametric analyses show that 28% of all IMF-supported programs (starting outside the episodes of growth surges) were followed by a growth surge (25.1% for GRA programs, and 32.7% PRGT programs); out of the 132 growth surges identified in 117 countries, 56% occurred during or 2 years after IMF-supported programs in 72 countries (32.6% for GRA programs, and 26.5% for PRGT programs); the average unconditional probability of growth surges is higher for growth surges associated with an IMF-supported program (4.7%) compared to growth surges without an IMF-supported program (2.8%); the occurrence and magnitude of the significant improvements in growth surges determinants were higher during periods of IMF-supported programs than in periods without IMF-supported programs. Besides, the analysis of the effectiveness reveals that the probability that significant improvements in growth surges determinants were followed by a growth surge is between 1.4 and 1.8 times higher when these improvements occur during IMF-supported programs (both GRA and PRGT programs). Moreover, these findings sustain some disparities across decades, regions, and levels of development.

Expectedly, there are few changes when shifting the analysis from non-parametric to parametric methods. For example, the non-parametric analysis shows that having an IMF-supported program similarly accentuates and magnifies the predictive power of all the growth surges determinants whereas the parametric estimation, accounting for the link between the determinants, shows that these benefits of an IMF-supported program are different in magnitude and significance. Indeed, from the biggest to the smallest effect, improvements in external factors and endowments, macroeconomic stability, structural reforms and investments, labor, and productivity associated with an IMF-supported program help to jump-start growth in countries. When looking at the benefits of having either a GRA or PRGT program, we globally reveal that the benefits of having a PRGT than a GRA program are higher because the significant improvements in growth determinants occurring during a PRGT program have a higher intensity and further increase the likelihood of igniting a growth surge. This is not surprising as PRGT programs have a longer duration than GRA programs and provide concessional assistance better tailored to the diversity and needs of low-income countries. At a more granular level, we find that the IMF may have been more efficient in triggering growth surges in the 2000s than previous decades, in line with its reinvention to pay more attention to growth as stated in the IMF's Guidelines on Conditionality (2002). Also, it has played a role in igniting growth surges in all countries around the world, regardless of their geographical location and levels of development.

Some caveats are worth noting. First and most importantly, the IMF's role in igniting growth surges may also capture the action of other multilateral institutions like the World Bank, the EU, and Regional Development Banks as they have intertwined and joint interventions in countries. Therefore, the IMF's role here may be overestimated. Nevertheless, this issue may be less important for the growth determinants that are part of the IMF's core activities such as macroeconomic stability and policies and some areas of structural reforms. Second, our findings may be subject to the "selection bias" problem because periods with and without an IMF-supported program are importantly different, and the IMF has been routinely identified with economic hardship and political ferment. However, we have two reasons to believe this problem is of the least concern for our study. Primarily, we focus on the medium-to the long-term effect of IMF's interventions rather than their short-term effect, at the time of the crisis. Secondary, as IMF-supported programs have consistently been associated with economic and financial turmoil and their consequences, we believe that it may be more difficult to engineer higher growth in this time; therefore the positive IMF's role in igniting growth surges found here may serve as a lower-bound effect. Third, we capture the role of the IMF using the presence of an IMF-supported program or not. However, this does not tell anything about the structure and depth of the IMF conditionalities (e.g., fiscal and growth-oriented), the compliance of countries with these conditionalities, whether the programs went off-track or not. These analyses are left for further research. Finally, the positive results may not be fully attributed to the IMF as igniting growth surges is also a matter of country ownership as well as domestic political interests and institutional constraints. Therefore, the benefits found here are the result of both the IMF and the countries' coordination. Nevertheless, while focusing on disproportionate improvements in growth surges determinants, we do not think that country ownership would have caused an upward bias in the IMF's effect of igniting growth because they must be strong to achieve these changes regardless of the IMF presence.

The remainder of this paper proceeds as follows. Section 5.2 identifies growth surges. Section 5.3 presents the growth determinants and the strategy used to determine significant changes in these determinants as well as the non-parametric analyzes of the IMF's role in igniting growth surges. Section 5.4 describes the empirical analysis, baseline results, robustness checks, and sensitivity analysis. Section 5.5 concludes and provides some recommendations.

Chapter . The International Monetary Fund's Role in Igniting Growth Surges

Growth surges identification and stylized facts

Growth surges identification

Our main goal is to assess the IMF's role in igniting growth surges. Therefore, we first identify growth surges and then separate them into two groups: (i) those associated with an IMF-supported program and (ii) those not associated with an IMF-supported program. We define a growth surge associated with an IMF-supported program as a growth surge that occurs during an IMF-supported program or 2 years after the completion of an IMF-supported program.

In the literature, three types of approaches have been used to identify growth surges : (i) a filter-based approach (see, e.g. Hausmann et al. 2005;Gupta et al. 2005;Aizenman and Spiegel 2010;[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF][START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF]; Atsebi 2020), (ii) a statistical structural breaks approach (see, e.g. Kerekes 2007; Jones and Olken 2008;[START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF], and (iii) a combination of the two previous approaches (see, e.g. Kar et al. 2013a,b;Munro 2020). None of these approaches is perfect, and each of them has some drawbacks. First, the filter-based approach is said to be ad-hoc and may lead to a lack of consistency across studies that use this method. Second, the statistical structural breaks approach or Bai and Perron (1998) method may capture "growth spells" following a period of sharply negative growth where the level of per capita income fails to reach its previous height, "growth spells" where the average growth rate during the spell differs relatively little from the growth rate before the spell and has a low power so that it is unable to identify genuine breaks in highly volatile series (the "true negative" problem). Third, the method that combines these two previous approaches may have their shortcomings (i.e., being ad-hoc and cannot identify genuine breaks) and generally leads to similar identification than the filter-based approach when applying the same criteria.

Against this backdrop, the filter-based approach remains the parsimonious and easiest way of detecting growth surges and having a clear understanding of the process that leads to their determination by setting reasonable criteria. Also, it allows us to set different criteria and identify multiple sets of growth surges to be used in robustness checks. In line with most of the papers in the literature, we assume that a growth surge lasts a minimum of 8 years (i.e., = 7). Moreover, growth surges are identified by the level of 8-year forward-looking per capita income growth rate, a comparison of 8-year backward-and forward-looking per capita income growth rates, and an additional criterion on the level of per capita income to avoid capturing pure recovery from past-shocks. We employ the same criteria as in Atsebi (2020); they are as follows 1) , + ≥ 3.5 pp., i.e., growth is rapid: the average annual growth rate of real income per capita over the next 8 years is at least 3.5 percentage points.

2) , = , + --, ≥ 2.0 pp., i.e., growth accelerates: the average annual growth rate over the next 8 years is at least 2 percentage points above the one of the previous 8 years period.

3) -1 >= ( -, ..., -1 ), i.e., the level of income per capita one year before the start of the growth surge is the peak of the pre-episode period.

A few comments are in order here. First, our first-two criteria are like those in Hausmann et al. (2005), but the third one is different. Indeed, Hausmann et al. (2005) set the criteria to be + >= ( -, ..., -1 ), i.e., the income level the eighth year after the surge exceeds its pre-episode peak. By doing so, they may capture episodes in which the first years are "pure recovery" from previous bad shocks (like natural disasters, major political upheavals, or wars).

The more stringent criterion used in this analysis helps avoid identifying growth surges capturing recovery from bad shocks. Second, when these criteria are met in consecutive years, we deem the first instance to be the "best" starting date. Third, we do not allow countries to have overlapping growth surges. If these criteria are met within an overlapping period of 8 years, then we extend the end date of the surge. To check the robustness of our results, we employ the same criteria than Hausmann et al. (2005), or "stricter" or "looser" filters.

Stylized facts on growth surges associated with an IMF-supported program

Overview of growth surges associated with an IMF-supported program

After applying our filters on the real income per capita at PPP values from the Penn World Tables for 169 countries (eliminating countries that do not have enough data series), we identify 132 episodes of growth surges in 117 countries over the period 1980-2010. Out of them, 74 (56%) occurred during or 2 years after IMF-supported programs in 72 countries. This figure stands at 43 (32.6%) for GRA programs and 35 (26.5%) for PRGT programs (some GRA and PRGT programs are blended or consecutive programs) in 42 and 34 countries, respectively.

Chapter . The International Monetary Fund's Role in Igniting Growth Surges in the 1990s, and 66.1% in the 2000s. Second, a considerable number of growth surges in Africa, and the Americas and Caribbean were associated with an IMF-supported program, 76.9 and 63.6%, respectively. This is also true in Europe and the Middle East and Central Asia in which 50% of the growth surges were associated with an IMF-supported program, and in Asia and Pacific were 42.8% of growth surges were associated with an IMF-supported program.

Third, growth surges have been more associated with an IMF-supported program in low-income countries (79.4%) and emerging markets (54.4%) than in advanced countries (21%). In sum, the IMF has been involved in growth surges, more in the 2000s compared to previous decades, in all regions of the world, and particularly in low-income countries and emerging markets.

Unconditional probability of growth surges

As a first insight of the IMF's role in igniting growth surges, we present in table 5. exclude the non-starting years of growth surges because an episode cannot take place then. We find that the average unconditional probability is higher for growth surges associated with an IMF-supported program (4.7%) compared to growth surges without an IMF-supported program (2.8%). Saying differently, it means that the probability of a typical country to experience a growth surge in a decade is higher by 19 percentage points if the IMF is present in all the years of the decade. Although quite informative, this general picture hides some disparities by decades, regions, and levels of development. Indeed, the unconditional probability of growth surges was remarkably higher when associated with an IMF-supported program in the 2000s (9.3% versus 3.3%), which was less true in the 1980s (2.4% versus 1.9%) and the 1990s (3.2% versus 3.4%). This finding is confirmed across regions and levels of development and pronounced in Europe, the Middle East and Central Asia, Asia and the Pacific, and in emerging markets and advanced countries. Besides, the unconditional probability of growth surges with an IMF-supported program was higher by more than 7 pp. in Europe, and the Middle East and Central Asia, 2.9 pp. in Asia and the Pacific, and less than 2 pp. in the Americas and Caribbean and Africa compared to the probability of growth surges without an IMF-supported program. Finally,

Chapter . The International Monetary Fund's Role in Igniting Growth Surges the gains of the probability of growth surge when associated with an IMF-supported program decrease with the levels of development. The unconditional probability of growth surges with an IMF-supported program was higher by 7.9 pp. in advanced economies, 3.3 pp. in emerging markets, and only 0.9 pp. in low-income countries. Overall, this non-parametric analysis of the unconditional probability of growth surges shows that the IMF may have played a role in igniting growth surges, particularly in the 2000s, and more in Europe, the Middle East and Central Asia, and the Asia and Pacific than the Americas and Caribbean and Africa, and more in emerging markets and advanced countries than in low-income countries. Using these stylized facts as a clue of the IMF's role in igniting growth surges, we pursue our analysis by employing a non-parametric noise-to-signal ratio method to identify disproportionate improvements in determinants of growth surges and how they affect the probabilities of growth surges. We also analyze whether the IMF-supported programs contribute to achieving the needed improvements in the run-up of the growth surges, and how they influence the predictive power of growth surges determinants. This non-parametric analysis is supplemented by an empirical strategy using binary outcome models that account for the correlation among all the determinants of the growth surges.

Determinants of growth surges

Following closely the strategy in Atsebi (2020), and as in Rodrik (2019) and Hausmann et al.

(2008), we consider that a growth surge may occur in one country if the country can significantly eliminate its "binding constraints". These bindings constraints may be either a high level of inflation, debt, or deficit for some countries, or the lack of infrastructures or a corrupted political system, and so on, for other countries. Consequently, the significant improvements in the growth surges determinants should be country-specific and could systematically be identified into a set of standard growth determinants that the literature found to be crucial for predicting medium to long-term growth. In other words, first, we start by identifying the main determinants of growth without looking at the specific economic situation of a country. Second, we identify significant changes at the country-level for all the determinants and focus on the period before the growth surge (five years before the start). Third, if we find that in the run-up of a growth surge, there were significant changes or improvements in one determinant, we assume that this determinant was potentially an impediment to growth in this country. Briefly, this strategy allows identifying ex-post potential country-specific bottlenecks to growth surge in each country. Finally, after identifying the significant improvements in the growth surges determinants, we look at whether they happened during an IMF-supported program or not or how the IMF contributes to creating the conditions necessary to jump-start growth.

Chapter . The International Monetary Fund's Role in Igniting Growth Surges measures, sometimes combined with exchange rate depreciation may be needed before take-off takes place. Consequently, we include in this category: (i) end of financial crises and normal recessions, (ii) public debt, (iii) current account, (iv) inflation, and (v) change of real effective exchange rate.

4) Structural Reforms: Igniting growth surges heavily depends on the structure of the economy and the capacity to implement structural reforms to eliminate the bottlenecks of the economy (see, e.g. Ahluwalia 2002;Panagariya 2004;Gupta et al. 2005;[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]Prati et al. 2013;IMF 2019;[START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF]; Atsebi 2020). Moreover, while achieving macroeconomic stability (which is not without any cost for the economy), structural reforms may be needed to significantly increase the allocation of resources and their effectiveness that would have a medium to long-term effect on growth; this has been the strategy adopted in IMF-supported programs (see, e.g. Mussa and Savastano 1999). We therefore include in this category different sets of reforms including: (i) agriculture-, (ii) product market-, (iii) labor market-, (iv) financial-, (v) trade-tariff-, (vi) current account-, and (vii) capital account reform.

5)

Trade diversification and quality: Many countries achieved a higher level of development because they succeeded the diversification of their exports and trading' partners while improving the quality of the products they export and import (machinery and highly technological products) (see, e.g. Hausmann et al. 2007[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF][START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF][START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]; Atsebi 2020). Therefore, we use in this category: (i) export and (ii) import quality, (iii) product, and (iv) partner diversification.

6) Investments, labor and productivity:

The neoclassical models of long-term growth stressed out the importance of investments in determining growth (see, e.g. Solow 1956;Swan 1956;Gupta et al. 2005) while the endogenous-growth models stressed out the importance of human capital and productivity (see, e.g. [START_REF] Romer | [END_REF]Romer , 1987;;Lucas 1988;Barro 1991;Joshua 2015;Gupta et al. 2005; Atsebi 2020). We, therefore, include in this category: (i) domestic investment (both private and public investments), (ii) foreign direct investment, welfare-relevant total factor productivity, and (iii) human capital index. These factors are more direct determinants of growth and may also be determined by the other determinants 1) to 5).

Our list of the six broad growth determinants is quite extensive and will allow identifying what matters for igniting growth surges. Table D.2 reports the full list of growth determinants and their sources, and table D.3 presents the summary statistics of all variables. As illustrated in the case of Africa by Gupta et al. (2005) and based on our assumptions, we posit that if the IMF has played any role, it should have been through restoring macroeconomic stability in countries facing debt, banking or currency crises or affecting by bad external shocks, and implementing comprehensive structural reforms in diverse sectors of the economy. Also, the IMF may have pushed for or accompanied changes in economic institutions towards more neoliberal policies.

Identifying significant changes or improvements of growth determinants

After identifying the set of growth surges determinants, we now proceed with the identification of the significant country-specific changes or improvements in each of the growth determinants.

To do so, we employ the noise-to-signal ratio as in Atsebi (2020). More specifically, for each indicator and country, we draw the country-specific percentile distribution of the change of each indicator and identify thresholds in the upper tail of the distribution beyond which a signal is issued (higher changes of the determinants). Thus, the distribution is divided into two parts, with and without a signal. The threshold that divides the distribution is a percentile that is endogenously determined by minimizing the total misspecification errors (sum of type I and II errors) and ensuring a balance of the two types of statistical errors. The use of percentiles to define thresholds, instead of absolute values, takes into consideration structural differences across countries and identifies significant changes in a country-specific fashion, based on the country's history. No Growth surge (T) Growth surge (T) No signal (T-h,T) A (true negative) B (missed or error type II )

Signal (T-h,T) C (false alarms or error type I ) D (true positive)

The results of the signaling analysis can be summarized in a matrix as in table 5.2 in which the occurrence of growth surges and the issuance of signals of each indicator are measured against each other. We assume that it may take time to initial growth surge, and then we consider that a genuine signal is the one occurring in the 5 years before the growth surge (i.e., (ℎ) = 5).

The error type I or share of false alarms among non-growth surges is defined as /( + ), and the error type II or share of missed growth surges among growth surges is defined as /( + ). Then, the total misspecification error is the sum of errors type I and II, i.e. TME= /( + )+ /( + ), and the predictive power is one minus the total misspecification error, i.e. = 1 -. We can also define two other performance indicators: the effectiveness defined as the share of true positive among all signals, i.e. effectiveness= /( + ), and the incidence defined as the share of true positive among all growth surges, i.e. incidence= /( + ). After identifying optimal thresholds beyond which signals are issued (i.e., significant changes of growth determinants occurred), we construct six indexes of broad determinants of growth as presented in the previous section. These indexes are the weighted average of all signals in sub-determinants where the weights are the predictive powers (1-TME), i.e.:

= =1 × ℎ = 1 0 ℎ (5.1)
One can notice that the indexes of each broad determinant of growth surges are increasing when there are significant changes or improvements in the sub-determinants of this broad determinant. For instance, if there is a significant reduction of the level of debt, deficit, and inflation, coupled with a depreciation of real exchange rate to boost competitiveness, and an end of a financial crisis, the macroeconomic stability index will increase. The results of the noise-to-signal approach, as well as the assumption on the direction of effects for the sub-determinants, are reported in table D.4. These results show that almost all the sub-determinants have a good predictive power (see, incidence and effectiveness indicators, and total misspecification errors) of growth surges. This means that we have identified good determinants and that our optimization strategy is capturing significant changes or improvements in determinants related to growth surges. Hereafter, we will focus on the results of the broad determinants.

Advanced stylized facts on growth surges determinants

Unconditional probability of significant improvements in broad growth surges determinants

Compared to Atsebi (2020), in this paper, we go one step forward to analyze the IMF's role in igniting growth surges. To do so, after identifying the significant changes in growth surges determinants, we disentangle them between significant changes occurring during an IMF-supported program and those that do not and look at their unconditional probability as reported in table 5.3. Data of IMF-supported programs are from the IMF Strategy and Policy Review department that follows all the programs since 1952. Surprisingly good, it is more likely that significant improve-ments in all the six broad determinants occur during periods of IMF-supported programs than in periods without IMF-supported programs. Indeed, the unconditional probability of significant changes of the growth surges determinants when associated with an IMF-supported program is higher by 11.3 pp. for structural reforms, 9.3 pp. for macroeconomic stability, 9.0 pp. for external factors and endowments, 8.2 pp. for institutions, 7.9 pp. for investments, labor, and productivity, and 7.1 pp. for trade diversification and quality, than when not associated with an IMF-supported program. While there is no evident reason that this finding holds for external factors and endowments compared to other determinants, the higher numbers observed for structural reforms, macroeconomic stability, and institutions reveal that the IMF may have played an important role to create an environment suitable to jump-start growth through restoring macroeconomic stability, implementing structural reforms, and pushing for institutional improvements.

Also, these improvements may have supported other improvements in investments, labor and productivity, and trade diversification and quality. For instance, Gupta et al. (2005) show that an increase in total factor productivity growth in the run-up of growth acceleration in Africa was significantly influenced by improvements in countries with on-track IMF-supported programs.

Besides, we do not find any significant difference between the unconditional probability of the significant changes in the growth surges determinants for GRA and PRGT programs. Notes: The unconditional probability are calculated as the number of significant changes of growth surges determinants (associated with or without an IMF-supported program) over the number of years (with and without an IMF-supported program) excluding years of non-starting growth surges.

Incidence and Effectiveness by association with an IMF-supported program or not

However, the occurrence of the significant improvements in growth surges determinants is not the whole story. Another supplementary analysis is to look at how often the growth surges were preceded by the significant improvements in growth surges determinants or the incidence, or how often the significant improvements in growth surges determinants were followed by a growth surge or the effectiveness. We then analyze the incidence and effectiveness of each of the broad

Chapter . The International Monetary Fund's Role in Igniting Growth Surges determinants when associated with an IMF-supported program or not. Table 5.4 displays the results of this analysis. First, the analysis of the incidence shows that the probability that growth surges were preceded by significant changes in each broad determinant is very high for all broad categories (between 79 and 100%), and this independently of the presence of an IMF-supported program. This finding shows that, in general, significant improvements in almost all the broad determinants may be required to jump-start growth, and this does not vary significantly with the presence of IMF-supported programs. Second, the analysis of the effectiveness reveals that the probability that significant improvements in growth surges determinants were followed by a growth surges is between 1.4 and 1.8 times higher when these improvements occur during IMF-supported programs. This finding is verified for both GRA and PRGT programs. In other words, the effectiveness of the significant improvements in growth surges determinants is magnified when occurring during an IMF-supported program. Third, we find also that 28% of all IMF-supported programs (starting outside the episodes of growth surges) were followed by a growth surge. This stands at 25.1% for GRA programs, and 32.7% PRGT programs. These findings show that not only the significant changes in the growth surges determinants occur more often during an IMF-supported program but also these more frequent changes are more likely to be followed by an IMF-supported program. Besides, we look at the relative importance of each of the determinants by ranking them by their effectiveness. For significant changes occurring during an IMF-supported program, structural reforms (44.3%) and macroeconomic stability (42.6%) have the highest effectiveness. They are closely followed by investments, labor, and productivity (41.7%) and external factors and endowments (39.9%), and finally by trade diversification and quality and institutions (around 35% each). For significant changes occurring in periods without an IMF-supported program, macroeconomic stability also takes the lead (with 29.4%), followed by other determinants (between 23 and 25%).

As a complementary analysis, we present how the general findings highlighted in this section vary by decades, regions, and levels of development (see for further detail, tables D.5 to D.7 for incidence and tables D.8 to D.10 for effectiveness,). Briefly, the findings for incidence remain valid across decades, regions, and levels of development. In contrast, there is a notable variation of the effectiveness by decades, regions, and levels of development (in line with the unconditional probabilities of growth surges). First, both the effectiveness of all determinants and the gains when they are associated with an IMF-supported program increase over decades. In the 1980s, the association with IMF-supported programs has no significant effects. It turns to be positive for structural reforms, macroeconomic stability, and investments, labor and productivity in the recommendations. We, therefore, check the consistency of these findings using an empirical strategy.

Empirical analysis

Methodology

Our empirical investigation sought to verify the consistency of our previous analysis, assess the IMF's role in igniting growth surges, and tell a story on how it helps countries to jump-start growth in a sustained manner for a prolonged period. To do so, we employ binary outcomes models (mainly logit in the benchmark and many others as robustness checks).

As it is common in the literature (see e. and zero otherwise (we further split the analysis by GRA and PRGT programs), the average of each of the six broad determinants constructed in the previous section calculated over the previous 5 years, and six interaction terms between the IMF-supported program dummy and all the six broad determinants. These interaction terms will capture the probability of starting a growth surge following the significant changes in each of the broad determinants when occurring during an IMF-supported program or 2 years after or not. Finally, we also include dummies for decades, regions, and levels of development to capture the heterogeneities found in the previous section. Many robustness checks are also undertaken to check the validity of our main findings to alternative samples, timing conventions, estimators, sets of growth surges based on alternative filters. Also, the sensitivity of our findings across decades, regions, levels of development are presented.

Benchmark results

Our benchmark findings are reported in table 5.5. It shows the average marginal effects of all the broad determinants when their significant changes occur during an IMF-supported program (column 2, 4, and 5) or do not (column 1, 3, and 4). Figures D.1 to D.3 depict these average marginal effects graphically for more accurate interpretation (indeed, our indexes of broad determinants never reach the maximum value of one). First, our models have a good predictive performance as presented in the bottom of the table (classification power above 90%, Area under the ROC close to 0.8, and Pseudo R 2 around 0.18). Also, the BIC criterion suggests that carrying out the analysis by GRA and PRGT programs brings more information than in the specification when all IMF-supported programs are analyzed altogether.

Second, our empirical analysis sustains a notable difference in the predictive power of the growth surges determinants when their significant improvements occur during an IMF-supported program. The presence of an IMF-supported program accentuates and magnifies the positive association between growth surges and significant improvements in external factors and endowments, structural reforms, investments, labor and productivity, and macroeconomic stability. In contrast, it does not affect trade diversification and quality, and institutions for which significant changes occurring outside of an IMF-supported program are more effective to predict growth surges. In detail, a one-unit increase in external factors and endowments raises the probability of a growth surge by 19.6% when not associated with an IMF-supported program, which is quite magnified to 67.3% percent when associated with an IMF-supported program. While structural reforms and investments, labor, and productivity changes do not significantly raise the probability of a growth surge (although positive) when not associated with an IMF-supported program, they turn out to have a significant and positive effect when occurring under an IMF-supported 13 Here one notable difference with the existing literature is worth noting. While we find here that predictive power is quite high, the literature generally highlighted a very poor predictive power. Indeed, Hausmann et al. (2005) sustained that "a lot of takeoffs take place when [...] conditions appear not to be particularly favorable [...] And growth takeoffs typically fail to materialize when the conditions are indeed favorable". Gupta et al. (2005) reinforced by stating that "The in-sample predictive power of both the acceleration and sustained acceleration models, however, is relatively poor. Thus, many acceleration episodes occur when the explanatory factors in the model would not predict an acceleration, and many times, even though the variables associated with accelerations are conducive, an episode does not take place. There are factors the model is not capturing, as well as country-level idiosyncratic factors that warrant further investigation to better guide policy. Predicting the timing, or onset, of growth accelerations is even more difficult.", and Peruzzi and Terzi (2018) concluded that "growth accelerations are extremely hard to engineer with a high degree of certainty [...] roughly 9 out of 10 instances failed to ignite a take-off". While we cherish the precautionary conclusion of Gupta et al. (2005), overall, these findings are completely misleading. Indeed, their studies fall short to identify growth surges determinants while accounting for the specificity of each country. Furthermore, they focus on the level of the growth determinants or set ad-hoc thresholds to identify favorable conditions while we identify the significant improvements beyond a certain threshold of the growth surges determinants through an optimization process. Briefly, accounting for country-specificity and focusing on significant and endogenous improvements of the growth surges determinants solve the "poor predictive power" of the literature.

Chapter . The International Monetary Fund's Role in Igniting Growth Surges program; in this case, a one-unit increase of these indexes are associated with a probability of growth surge of 33.1 and 23.4%, respectively. Finally, a one-unit increase in the macroeconomic stability index raises the probability of growth surges by 49.8% when occurring during an IMF-supported program and by 40.8% when not associated with an IMF-supported program. In contrast to non-parametric findings, changes in trade diversification and quality and institutions when occurring during an IMF-supported program have no significant effect on the probability of growth surges; a one-unit increase of these determinants is associated with a probability of 19 and 9.7% when not associated with an IMF-supported program, respectively. These findings are in line with Gupta et al. (2005) that shows that, in the context of Africa, improvements in macroeconomic and structural policies and total factor productivity were more pronounced in countries with on-track IMF-supported programs, and they generally lead to faster growth.

When looking at the benefits of having either a GRA or PRGT program, we globally reveal that the benefits of having a PRGT than a GRA program are higher because the significant improvements in growth determinants occurring during a PRGT program have a higher intensity and further increase the likelihood of igniting a growth surge. This is not surprising as PRGT programs have a longer duration than GRA programs and provide concessional assistance better tailored to the diversity and needs of low-income countries to achieve their objectives of solving balance of payments problems, reaching higher growth, and reducing poverty.

Third, the analysis of the relative importance of each of the growth surges determinants shows that countries, when they have no IMF-supported programs, can maximize their probability of having a growth surge by privileging improvements in macroeconomic stability, and trade diversification and quality as well as by benefiting from favorable external and endowments conditions. When countries are under an IMF-supported program, they can achieve a higher probability of growth surges if they benefit from favorable external and endowments conditions and significantly improve their macroeconomic stability, implement structural reforms, and increase investments, labor and productivity. Overall, all the analysis carried out in this paper, from the non-parametric analyses of unconditional probability, incidence, effectiveness to the parametric estimations show that having an IMF-supported is somewhat enhancing the probability of igniting a growth surge. Expectedly, there are few changes when shifting the analysis from non-parametric to parametric methods.

For example, the non-parametric analysis shows that having an IMF-supported program similarly accentuates and magnifies the predictive power of all the growth surges determinants whereas the parametric estimation that accounts for the link between the determinants shows that these benefits of an IMF-supported program are different in magnitude and significance. Indeed, from the biggest to the smallest effect, improvements in external factors and endowments, macroeconomic stability, structural reforms and investments, labor, and productivity associated with an IMF-supported program help to jump-start growth in countries. This supports that the IMF may have contributed to boosting medium-to long-term growth in countries while pursuing macroeconomic stability and implementing structural reforms, but also creating the pre-conditions to boost investments, labor, and productivity and benefit more from favorable external and endowments conditions.

The intensity of the significant improvements in growth surges determinants under an IMF-supported program

One possible explanation of the benefits of having significant improvements of growth surges determinants during an IMF-supported program may be that these improvements, even if they are already disproportionately large, may be of higher intensity when executing under the IMF lending and assistance. To check that, we compute the mean difference of the 5-year average of the broad growth surges determinants when occurring during an IMF-supported program or not at the start of growth surges in table 5.6. We found that except for trade diversification and quality for all types of programs, and investments, labor, and productivity for GRA programs, all other significant improvements in growth surges determinants have a higher intensity when occurring during an IMF-supported program or 2 years after (both GRA and PRGT programs).

The highest difference in intensity is found for institutions (driven mainly by GRA programs) and structural reforms (both GRA and PRGT programs). They are followed by macroeconomic stability (both GRA and PRGT programs), investments, labor, and productivity (only for PRGT programs), and finally by external factors and endowments (mainly driven by PRGT programs).

In other words, this finding shows that in the 5 years running up to growth surges while we focus on significant improvements in growth surges determinants regardless of the presence of the IMF, when the IMF is there, these significant changes will more recurrent and intense. 2237.996 2237.996 1858.329 1858.329 1673.873 1673.873 Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

Discussion on the endogeneity of IMF's role in igniting growth surges

Throughout the paper, we implicitly assume that the IMF's role in igniting growth surges to be exogenous. However, this assumption may be weakened in different manners. First and most importantly, our effect may also capture the action of other multilateral institutions like the World Bank, the EU, and Regional Development Banks as they have intertwined and joint interventions in countries. Therefore, the IMF's role here may be overestimated. Nevertheless, this may be of a least concern for the growth determinants that are part of the IMF's core activities such Table 5.6: Mean difference between broad growth surges determinants occurring during an IMF-supported program or not at the start of growth surges as macroeconomic stability and policies and some structural reforms in the product market, financial market, trade, and capital and current account.

Second, one can advance that periods with and without periods of an IMF-supported program are importantly different. Indeed, countries that come to the IMF for requesting a program face some difficulties in the balance of payments emanating from structural problems or financial crises or external and natural disaster shocks. This is one reason the IMF has been routinely identified with economic hardship and political ferment, which makes the IMF's evaluation a very difficult task. In econometrics, this concerns raised here is known as the "selection bias" problem. We have two reasons to believe this problem is of the least concern for our study.

Primarily, we focus on the medium-to the long-term effect of IMF interventions rather than their short-term effect at the time of the crisis. Secondary, as IMF-supported programs have consistently been associated with economic and financial turmoil and their consequences, we believe that it may be more difficult to engineer higher growth in this time; therefore the positive IMF's role in igniting growth surges found here may serve as a lower-bound effect.

Third, we capture the role of the IMF using the presence of an IMF-supported program or not. However, this does not tell anything about the structure and depth of the IMF conditionalities (e.g., fiscal and growth-oriented), the compliance of countries with these conditionalities, whether the programs went off-track or not. These analyses are left for further research.

Finally, the positive results may not be fully attributed to the IMF as igniting growth surges is also a matter of country ownership as well as domestic political interests and institutional constraints. The IMF cannot simply impose its agenda on program countries. Its programs are conditional on countries' commitment to carry out the agreed program of economic policies.

Therefore, the benefits found here are the result of both the IMF and the countries' coordination.

8 years. In table D.22 and table D.23, we change the "growth is rapid" criterion from 2 pp. to 1 (135 growth surges) and 3 (97 growth surges) pp., respectively. In table D.24 and table D.25, we change the growth accelerates criterion from 3.5 pp. to 2.5 (141 growth surges) and 4.5 (104 growth surges) pp., respectively. Overall, our main results are not altered using alternative sets of growth surges.

Sensitivity

The descriptive analysis highlighted the disparities of growth surges across decades, regions, and levels of development. We now turn to analyze whether the IMF's role in igniting growth surges was also different according to this disaggregation.

First, the IMF's role in igniting growth surges has positively evolved over the decades (see, table D.26). In the 1980s, only the effects of the significant changes in external factors and endowments and macroeconomic stability were magnified by GRA-supported programs. We find no beneficial effect for structural reforms and investments, labor, and productivity as in the benchmark results. In the 1990s, the benefits of having an IMF-supported program was true for only macroeconomic stability and structural reforms. In the 2000s, all the determinants were positively and significantly associated with growth surges, with higher amplitude than in the benchmark results, except for the trade diversification and quality under an IMF-supported program. This shows that this decade was particularly different from the 1980s and 1990s in line with the Great moderation literature. More importantly, over this decade, the benefits of having an IMF-supported program was even accentuated and magnified for external factors and endowments, macroeconomic stability, and structural reforms. As a result, the IMF may have been more efficient in triggering growth surges in the 2000s, in line with its reinvention to pay more attention to growth as stated in the IMF's Guidelines on Conditionality (2002).

Second, we look at the results by regions (see, table D.27). We find that both the effects of IMF and the significant changes in growth determinants in igniting growth surges vary across regions. In Africa, we find that IMF-supported programs accentuate and magnify the positive association between significant improvements in external factors and endowments, macroeconomic stability, structural reforms (only PRGT programs), and investments, labor, and productivity. The other determinants have no significant effect regardless of the IMF presence.

These results for the case of Africa are quite in line with our benchmark results. In Asia and the Pacific, we find that significant changes in structural reforms, macroeconomic stability, and trade diversification were the driving force of growth surges. The predictive power of structural reforms and macroeconomic stability was reinforced under IMF-supported programs (only PRGT for macroeconomic stability) while only significant changes in trade diversification and quality carried out without an IMF intervention paid off. In Europe, we find qualitatively similar results as in Africa and the benchmark findings, except for the non-significance of investments, labor, and productivity. IMF-supported programs accentuate and magnify the positive association between significant improvements in external factors and endowments, macroeconomic stability, and structural reforms. The other determinants have no significant effects (even if positive). In the Middle East and Central Asia, we show all determinants except for structural reforms and investments, labor and productivity are driving force of growth surges.

Importantly, as in other regions, we find that association of the significant improvements in external factors and endowments and macroeconomic stability with an IMF-supported program induce a greater likelihood of growth surges. Finally, in the Americas and Caribbean, we find that external factors and endowments, macroeconomic stability, and institutions were the drivers of growth surges. The effects of two first determinants were magnified when occurring IMF-supported programs. One key takeaway is that the IMF may have played a role in igniting growth surges, especially through macroeconomic stability in all regions, except in Asia and the Pacific, structural reforms in different areas in Africa, Asia, and the Pacific and Europe, creating good conditions while benefiting from favorable external factors and endowments in all regions, except in Asia and the Pacific, and pursue investments, labor and productivity policies in Africa.

Third, we present the results by levels of development (see, table D.28). We show the varying association between the significant changes in growth determinants with and without IMF and growth surges across levels of development. In low-income countries and emerging markets, the results are like our benchmark results but of different magnitudes. Indeed, the positive effects of the significant improvements in external factors and endowments, macroeconomic stability, structural reforms, and investments, labor, and productivity are magnified and accentuated by IMF-supported programs. The positive gains of having an IMF-supported program are higher in emerging countries than low-income countries, except for structural reforms. Besides, the effects of institutions and trade diversification and quality are non-significant regardless of the presence of the IMF, except for the positive and significant association between trade diversification and quality and growth surges in emerging markets outside IMF-supported programs (as in the benchmark results). In advanced countries, only the effects of significant changes in macroeconomic stability were reinforced when achieving under an IMF-supported program (higher than in low-income countries and less than in emerging markets). Also, Institutions and trade diversification and quality induce an increase of the likelihood of growth surges of the same magnitude regardless of the IMF presence. In sum, the IMF may have played a significant role in igniting growth surges through restoring macroeconomic stability in all countries regardless of their levels of development, implementing structural reforms and policies enhancing investments, labor, and productivity, and creating conditions to jump-start growth when favorable external and endowments conditions happen (except for advanced countries).

Concluding remarks

This paper contributes to the debate on the effectiveness of the IMF in promoting growth by taking a different route. While most of the papers in the literature focus on the short-term effects of the IMF-supported programs; therefore, confronting to the "selection bias" issue, we focus more on the medium-to long-term effect. Also, while they focus on the annual growth rate, we choose to focus on the initiation of periods of growth surges, i.e., periods of sustained growth for a prolonged period. We also make sure to explain how the IMF has played a role by looking at its demand-management and supply-side policies. To do so, we use longitudinal data comprising 169 countries and spanning 1980-2010. Our strategy consisted of identifying growth surges applying filters method as in Hausmann et al. (2005), identifying growth determinants in the literature, setting an optimization process to identify significant improvements in growth surges determinants by accounting for country-specificity, and analyzing the effects of these improvements when occurring during an IMF-supported program or 2 years after or not through various sets of non-parametric and empirical methods. To the best of our knowledge, this is the first analysis that exclusively focuses on the IMF's role in igniting periods of growth surges. Our results enrich the literature and establish a panorama of the IMF's role in igniting growth surges.

One key takeaway is that all the analyzes carried out in this paper show that having an IMF-supported program is enhancing the probability of igniting a growth surge. They support that the IMF has contributed to boosting medium-to long-term growth in countries while pursuing macroeconomic stability and implementing structural reforms, but also creating the pre-conditions to boost investments, labor, and productivity and benefit more from favorable external and endowments conditions. At a more granular level, we find that the IMF has been more efficient in triggering growth surges in the 2000s than in previous decades, in line with its reinvention to pay more attention to growth as stated in the IMF's Guidelines on Conditionality (2002). Also, it has played a role in igniting growth surges in all countries around the world, regardless of their geographical location and levels of development. However, it was through different determinants.

Since its inception in 1944, the IMF has been amid a major and perpetual reinvention to pay more attention to growth by reforming its governance, its objectives, and operational works in its member countries. Our study finds that these improvements have paid off and lead to a pronounced role of the IMF's in igniting growth surges in all regions of the world, and more recent decades. This shows that the IMF as a learning organization has a lot to gain in its role as a "financial firefighter" when reinventing its policies and objectives. While macroeconomic stability matters for growth surges, structural reforms and investments, labor, and productivity are also valuable tools. Therefore, this paper acknowledges that IMF's structural conditionalities must be designed to address structural concerns of countries and be of higher depth while achieving macroeconomic stability and enhancing investments, labor, and productivity to support medium-to longer-term growth. Also, our study calls for careful tailoring of policies aiming at igniting growth surges to country-specific conditions. Besides, the success of IMF-supported programs in igniting growth surges is highly dependent on the country ownership; therefore, the IMF should be attention to country's urgent economic needs and includes their policies into integrated national reform plan while maintaining better coordination with other international financial institutions. .

3

.4

.5

Pr(GS=1) 0 .33 .17 Index [T-5,T] With IMF Without IMF

Structural reforms

No signal 10% higher signal .

3

.4

.5

Pr(GS=1) 0 .48 .25 Index [T-5,T] With IMF Without IMF

Investments, labor and productivity

Notes: The figures show average marginal effects based on the column (1) and (2) of table 5.4.

.5

Pr(GS=1) 0 .31 .18 Index [T-5,T] With IMF Without IMF

External factors and endowments

No signal 10% higher signal

.5

Pr(GS=1) 0 .64 .32 Index [T-5,T] With IMF Without IMF

Institutions

No signal 10% higher signal

.5

Pr(GS=1) 0 .41 .21 Index [T-5,T] With IMF Without IMF

Macroeconomic stability

No signal 10% higher signal

.5

Pr(GS=1) 0 .33 .17 Index [T-5,T] With IMF Without IMF

Structural reforms

No signal 10% higher signal

.5

Pr(GS=1) 0 .51 .28 Index [T-5,T] With IMF Without IMF

Trade diversification and quality

No signal 10% higher signal

.5

Pr(GS=1) 0 .48 .24 Index [T-5,T] With IMF Without IMF

Investments, labor and productivity

Notes: The figures show average marginal effects based on the column ( 3) and (4) of table 5.4. .824 BIC 1581.824 BIC .439 1581.824 BIC .439 1346.824 BIC .260 1346.824 BIC .260 1074.824 BIC .304 1074.304 .304 Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs. 2232.549 2232.549 1765.156 1765.156 1580.903 1580.903 Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs. 2362.890 2362.890 1963.763 1963.763 1788.261 1788.261 Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs. Table D.25: Predicting growth surges, robustness checks, alternative set of growth surges, post-7 years of growth surges per capita income growth exceeds 4. [START_REF]approfondie de ces deux canaux[END_REF] .816 BIC 1903.816 BIC .412 1903.816 BIC .412 1621.816 BIC .958 1621.816 BIC .958 1418.953 .953 2259.240 2259.240 1901.365 1901.365 1691.199 C

D.3.3.2 Alternative association of signals with IMF-supported programs

D.3.3.3 Alternative horizons of growth determinants

6

G C

We have come to the end of a journey that has taken us through the episodes of financial crises, normal recessions, and growth surges, that are widespread phenomena in different countries, regardless of their geographical location and levels of development. We have learned that what makes financial crises and recessions spectacular events is the direct connection they have with the real economy, in particular by inducing protracted and long-lasting disruption of international trade and economic growth. From a micro-perspective, this thesis states that financial crises while exerting a large contraction of a variety of traded goods and protracted loss of economic growth is not only a nightmare for policymakers and political leaders but more importantly a serious thread at the doors of everyone, even of small-farmers in small regions of the world. Besides, rather than having only a dark-side, financial crises unveil the non-performing macroeconomic policies, political, economic, and social dysfunctions of the economies, which allows considering serious and well-targeted macroeconomic and structural reforms to increase the country's resilience to crises, but more importantly to embark on a tour of sustained growth. Indeed, the history as shown that igniting growth surges is not easy, but not impossible. and local projections to capture a causal dynamic effect running from financial crises to the trade activity. It reveals that the collapse of total trade in the aftermath of financial crises is long-lasting and mainly driven by the fall of manufacturing trade. However, the impact of financial crises on the other types of traded goods and especially on services is far from being negligible. Trade in both mining goods and services also declines following several types of financial crises, while trade in agricultural goods seems to benefit from a possible substitution effect particularly following debt crises. When looking at the costs of combined crises, it shows that they exert a significant and higher decline of trade, compared to crises occurring without any other crisis in the years around. Also, financial crises exert an adverse effect on total and sectoral trade through compositional and structural, demand-side, and supply-side channels. In detail, about the compositional and structural channel, this chapter sustains that financial crises may act as an impediment of structural transformation as they hurt more manufacturing exports in countries where the share of manufacturing exports is relatively lower. Also, by diversifying their exports and partners, countries will increase their resilience to financial crises. About the demand-side channel, it shows that financial crises associated with a lower demand of goods and services from trading partners will have more adverse trade costs; therefore they can generate an unprecedented collapse of international trade when they are generalized within regions and at the global level as witnessed in the post-GFC period, which is of particular interest in this time of Covid-19 pandemic. Finally, it supports the idea that the supply-side channel is critical to understand the way financial crises shape the dynamics of international trade. When associated with a deterioration of the domestic financial development and external financial conditions, and sudden stops, financial crises will exert a significant and detrimental collapse on international trade.

Chapter 3 would predict that (i) some countries while pursuing a bailout of their economy to overcome the adverse consequences of the Covid-19 crisis may rapidly trade their stabilization policies out to address debt sustainability and external viability issues, at the worse possible time, and (ii) while fiscal space is constrained and monetary policy is accommodative but also constrained in many countries around the world, e. reforms, but also creating the pre-conditions to boost investments, labor, and productivity and benefit more from favorable external and endowments conditions. In this difficult time, in the words of the IMF's Managing Director, Kristalina Georgieva, "the IMF has secured $1 trillion in lending capacity, serving our members and responding fast to an unprecedented number of emergency financing requests-from over 90 countries so far". This is indeed a turning point for the IMF policy recommendations and its support to countries in times of hardship, and a great challenge to reinvent itself, learn from its past mistakes, and take the opportunity to contribute to economic prosperities in countries while pursuing its role of "financial firefighter".

This dissertation provides opportunities for interesting research in various ways. It is not the end of the journey, but the beginning of an avenue for future research in macroeconomic and general topics that have demonstrated to be quite valuable to improve the well-being of people around the world.

Les poussées de croissance de l'Inde

A la suite des grandes réformes économiques des années 90, la croissance économique à long terme de l'Inde s'est régulièrement accélérée de 4,4% par an au cours des années 90, puis de 6,5% (voir, par exemple Solow 1956;Swan 1956;[START_REF] Romer | [END_REF][START_REF] Edwards | [END_REF]Romer 1987;Barro 1991;Easterly et al. 1993;[START_REF] Dornbusch | Bruno and Easterly[END_REF]Bruno and Easterly 1998;[START_REF] Dornbusch | Bruno and Easterly[END_REF]Ahluwalia 2002;Hamann and Prati 2002;Panagariya 2004;Barro 2003;Sala-i Martin et al. 2004;Hausmann et al. 2007;[START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF][START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF][START_REF] Hausmann | We engage and contribute to this debate on the effectiveness of the IMF in promoting growth by taking a different route[END_REF]IMF 2019).

Nous nous engageons et contribuons à ce débat sur l'efficacité du FMI à promouvoir la croissance en empruntant une voie différente. Alors que la plupart des articles de la littérature se Bien que l'avenir soit difficilement prédictible ; en effet, la prévision des crises n'a jusqu'à

  Keywords: Trade costs; Output losses; Financial crises; Fiscal policy space; Growth surges; Macroeconomic stability; Structural reforms; International Monetary Fund JEL Codes: F14; F4; G01; E6; H6; O1; O4 crises and normal recessions . . . . . . . . . . . . . . . . .

  Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . D.20 Predicting growth surges, robustness checks, alternative set of growth surges, minimum duration of growth surges sets to 6 years . . . . . . . . . . . . . . . . D.21 Predicting growth surges, robustness checks, alternative set of growth surges, minimum duration of growth surges sets to 10 years . . . . . . . . . . . . . . . . D.22 Predicting growth surges, robustness checks, alternative set of growth surges, per capita income growth accelerates by at least 1 percentage point . . . . . . . . . . D.23 Predicting growth surges, robustness checks, alternative set of growth surges, per capita income growth accelerates by at least 3 percentage points . . . . . . . . . D.24 Predicting growth surges, robustness checks, alternative set of growth surges, post-7 years of growth surges per capita income growth exceeds 2.5 percentage points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.25 Predicting growth surges, robustness checks, alternative set of growth surges, post-7 years of growth surges per capita income growth exceeds 4.5 percentage points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.26 Predicting growth surges, sensitivity by decades . . . . . . . . . . . . . . . . . . D.27 Predicting growth surges, sensitivity by regions . . . . . . . . . . . . . . . . . . D.28 Predicting growth surges, sensitivity by levels of development . . . . . . . . . . .

Financial

  crises have generally been associated with trade contraction. Baldwin (2011) reports that global trade fell for at least three quarters only in three of the worldwide recessions that occurred between 1965 and 2008: the oil-shock recession of 1974-75, the inflation-defeating recession of 1982-83, and the Tech-Wreck recession of 2001-02. However, the "Great Trade Collapse" of 2008-09 is by far the largest trade collapse since WWII. Indeed, according to the WTO and IMF, the drop in world trade flows (around 12% of world GDP in 2009) exceeded that of world GDP (about 5% in 2009). Given the worldwide benefits of trade, this severe downturn brought back into the spotlight the issue of the trade costs of financial crises. The empirical literature, despite some exceptions for exports, has converged to a strong consensus on the detrimental consequences of financial crises on trade at the macroeconomic level (Rose 2005; Ma and Cheng 2005; Martinez and Sandleris 2011;

  .2) Since E[ , +ℎ (1) -, -1 | , = 0] is not observable, we use a counterfactual. Under the independence assumption [ , +ℎ ( ) -, -1 ] ⊥ , | , ; ∀ ℎ ; ∈ {0, 1}, i.e. an independent Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries financial crises allocation of potential outcomes conditional on a set of covariates , , we estimate the ATE by comparing trade in countries with and without financial crises conditional on the set of variables , = ℎ = E[ , +ℎ (1) -, -1 | , = 1; , ] -E[ , +ℎ (0) -, -1 | , = 0; , ] ; ∀ ℎ. (2.3)

  Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries at least a 10% increase in the rate of depreciation. Alternative definitions and sources for crises are considered in the robustness analysis.Trade data on goods come from UN Comtrade, via the World Trade Integrated Solution (WITS)-World Bank, which provides exports and imports at the 3-digit code of the Standard International Trade Classification (SITC). We classify this disaggregated data into three types of goods, namely agricultural, mining, and manufactured goods, following the WTO classification.Compared with most studies that focus exclusively on the export of goods, we also consider the import of goods, which can improve firms' productivity and export competitiveness. Besides, we equally consider the trade of services (data comes from United Nations Conference on Trade and Development-UNCTAD), which represents as large as one-quarter of total exports and imports in our sample; besides, since they mostly concern intermediate inputs, their decrease may have strong (negative) effects on the economy. Total trade is obtained by aggregating the four categories of goods and services (agriculture, mining, manufacturing, and services), and deflated by export/import prices. Finally, we consider two sets of control variables in line with the existing literature that we extend further. The first set is used in the treatment model and includes those variables that influence the likelihood of financial crises and are correlated with international trade, namely, following the related literature: (i) number of past-5 years financial crises except the one of interest, (ii) intensity of conflicts, (iii) log. of Real GDP, (iv) log. of public debt to GDP, (v) log. of domestic credit to GDP, (vi) log. of liquid liabilities to GDP, (vii) net capital inflows to GDP, (viii) log. of foreign reserves to GDP, (ix) current account to GDP, (x) financial openness index, (xi) log of trade openness to GDP, (xii) terms of trade growth, (xiii) floating exchange rate regime, (xiv) government accountability index, (xv) corruption index, (xvi) trading partners' growth, and (xvii) US interest rate on gov.'s securities. These predictors of financial crises are included one-year lagged. The second set of control variables is used in the outcome model eq. (

Figure

  Figure 2.1: Financial crises over time and by regions

Figure 2 . 2 :

 22 Figure 2.2: Survival models of the duration between the onset of different financial crises

Figure 2 . 3 :

 23 Figure 2.3: Evolution of the average international trade in financial crises

Figure 2 . 4 :

 24 Figure 2.4: Cumulative trade costs over five years after financial crises

Figure 2 . 5 :

 25 Figure 2.5: Cumulative trade costs over five years after financial crises, total and sectoral level

  investments. In addition, financial crises are very often associated with sudden stops or capital reversals (see, e.g. Bordo 2006; Reinhart and Rogoff 2009; Mendoza 2010). We, therefore, analyze the role played by the evolution of gross capital inflows in shaping the dynamics of the trade costs of financial crises. The results are shown in fig. A.15 and tables A.20a to A.20c. As

FAppendixFigure A. 1 :

 1 Figure A.1: Sample of countries and the starting date of the various financial crises since 1980

Figure A. 2 :

 2 Figure A.2: Kernel density of the distribution of the propensity scores for the treated and control groups

  standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, and services exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Maximum weights truncated at 10.

Figure A. 4 :

 4 Figure A.4: Robustness, cumulative trade costs over five years after non-combined financial crises

Figure A. 5 :

 5 Figure A.5: Robustness, Drop the period 2008 onwards, cumulative trade costs over five years after financial crises

  standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, and services exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Maximum weights truncated at 10.

Figure A. 6 :

 6 Figure A.6: Robustness, Maximum weight set to 20, cumulative trade costs over five years after financial crises

Figure

  Figure A.7: Robustness, Maximum weight set to 5, cumulative trade costs over five years after financial crises

  standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, and services exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Maximum weights truncated at 10.

  Figure A.8: Robustness, Alternative sources and definitions of crises, cumulative trade costs over five years after financial crises

Figure

  Figure A.9: Robustness, Inverse Probability Weighted (IPW) estimator, cumulative trade costs over five years after financial crises

  standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, and services exports and imports relative to the year before the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Maximum weights truncated at 10.

Figure A. 11 :

 11 FigureA.11: Channels, cumulative trade costs over five years after financial crises with and without a higher export diversification

Figure A. 13 :

 13 FigureA.13: Channels, cumulative trade costs over five years after financial crises with and without a higher trading partners' growth

Figure A. 15 :

 15 FigureA.15: Channels, cumulative trade costs over five years after financial crises with and without higher gross capital inflows

  160) ATE-AIPW Mining -0.414*** -1.211*** -1.476*** -1.760*** -2.339*** -1.938*** ATE-AIPW Mining -0.350*** -0.596*** -0.951*** -1.367*** -1.670*** -1Manufacturing -0.659*** -1.733*** -2.339*** -2.712*** -3.316*** -4.311*** ATE-AIPW Manufacturing -2.153*** -4.167*** -4.845*** -5.182*** -4.906*** -5Services -0.335*** -0.986*** -1.333*** -1.470*** -1.830*** -1.922*** ATE-AIPW Services -0.345*** -0.827*** -1.123*** -1.075*** -1.483*** -1.887*

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 10.

Notes:

  Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  995*** -9.200*** -10.062*** -9.171*** -9.966*** -10.131*

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  183*** -4.744*** -4.650*** -4.192*** -5.084*** -4.638*

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  793*** -6.716*** -7.603*** -8.189*** -9.440*** -10.110*

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

  is on the "room in a government's budget that allows it to provide resources for the desired purpose without jeopardizing the sustainability of its financial position or the stability of the economy" as defined byHeller (2005).This paper builds a new measure of fiscal space and studies its effect on output dynamics in the aftermath of financial crises and normal recessions (hereafter denominated as shocks) in the context of developing and emerging countries over the period 1985-2017. It contributes to various strands of literature. First, it is related to the literature that assesses the output costs of financial crises, in which there is a consensus on the contractionary role of financial crises.This strand of literature that uses conventional panel regressions, local projections and/or impact assessment methodology includes, e.g.,Kaminsky and Reinhart (1999); Bordo et al. (2003); Tomz and Wright (2007); Cerra and Saxena (2008); Reinhart and Rogoff (2009); Fatás and Mihov (2013); Borensztein and Panizza (2014); Jordà et al. (2013, 2016); Jordà and Taylor (2016); Asonuma et al. (2016); Trebesch and Zabel (2017); Laeven and Valencia (2018); Romer and Romer (2018); Kuvshinov and Zimmermann (2019), and Asonuma et al. (2019). With the notable exception of Jordà et al. (2013);[START_REF] Romer | [END_REF] Romer (2018), and[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF]

  Giavazzi and Pagano (1990);Blanchard (1993);[START_REF] Perotti | [END_REF];[START_REF] Minea | [END_REF];[START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF] Ilzetzki et al. (2013) show that expansionary fiscal policy is more effective and has Keynesian effects at low levels of debt or deficit, and non-Keynesian effects in the opposite circumstances.[START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF], and[START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF] reveal that fiscal multipliers are larger during recessions and financial crises. Altogether, these papers

  proposed byKaminsky et al. (1998). This methodology selects a set of indicators as early warning indicators and determines the threshold values for each variable beyond which signals are issued indicating that a crisis is likely to happen shortly. Contrary to previous studies on fiscal space including, e.g.,Aizenman and Jinjarak (2010);[START_REF] Aizenman | [END_REF];Romer and Romer (2018), and[START_REF] Grauwe | Par conséquent, notre indice comble les lacunes des mesures utilisées dans les études précédentes en incorporant autant d'informations compte tenu de la disponibilité des données pour mesurer le concept multidimensionnel d'espace budgétaire. Quatrièmement, notre article est également lié à la littérature sur les multiplicateurs budgétaires et la cyclicité de la politique budgétaire[END_REF] that use as indicator of fiscal space either the level

  Data on fiscal crises are taken fromMedas et al. (2018). Their definition of fiscal crises encompasses public debt default and near-default events, as well as severe deteriorations in the fiscal solvency risk outlook leading to fiscal sustainability risks. They identify 439 episodes of fiscal crises in 188 countries over the period 1970-2015 by employing four distinctive criteria: (i) credit events associated with sovereign debt (e.g., outright defaults and restructuring); (ii) recourse to large-scale IMF financial support; (iii) implicit domestic public default (e.g., via high inflation rates); and (iv) loss of market confidence in the sovereign. These criteria are complementary, as individual indicators may not capture all fiscal crises.

  Balducci et al. (2011); Berti et al. (2012), and Cerovic et al. (2018), and first construct a composite early warning indicator of fiscal crises. The index of fiscal space will be equal to one minus the composite early warning indicator of fiscal crises. More specifically, for each indicator and country, we draw the country-specific percentile distribution of each indicator and identify thresholds in the lower tail of the distribution by minimizing the total misspecification errors (sum of type I and II errors) and ensuring a balance of the two types of statistical errors.The use of percentiles to define thresholds, instead of absolute values, takes into consideration structural differences across countries and identifies fiscal distress in a country-specific fashion, based on the country's history. Table 3.2: Illustration of the signals approach method No fiscal crises (t+1 to t+h) Fiscal crises (t+1 to t+h)

(

  left figure), and the recent development of the fiscal space index, notably around the GFC of 2008-09 (right figure). First, fiscal space has increased in developing and emerging countries between the periods 1985-2000 and 2000-2007, and then decreased in the aftermath of the GFC of 2008-09. Indeed, following the GFC, many developing and emerging countries have enacted stimulus packages to smooth their economy over the business cycle or have increased their external and domestic public debt, and fiscal deficit, especially thanks to lower interest rates and accommodative monetary policy. Second, fiscal crises peaked around 1990-91 when many transitions countries moved from command to market economies; around 1998 characterized by the Asian financial crises and Russian crisis; around 2002 marked by the Argentina crisis; around 2006 when many sub-Saharan countries recourse to large IMF financing; and finally, around the GFC. Third, fiscal space index tends to remarkably decrease in the run-up of large episodes of fiscal crises signaling a deterioration in the fiscal position of governments, that intensifies after the crises strike.

Figure 3 .

 3 Figure 3.2 is an illustration of the role of fiscal space in the output dynamics in the aftermath of financial crises and normal recessions. It shows the deviation of real GDP growth five years after the shocks from the pre-shock level against the fiscal space index in the pre-shock year.

Figure 3 . 1 :

 31 Figure 3.1: Historical and recent trend of fiscal space index and fiscal crises

  of fiscal space index between 2007 and 2017 Notes: Figure3.1 (A) shows the historical trend of fiscal space and fiscal crises, and fig.3.1 (B) presents the recent developments in the fiscal space index one year before and ten years after the GFC of 2008-09. further analyzed.

Figure 3 . 2 :

 32 Figure 3.2: Fiscal space index and the costs of financial crises and normal recessions

Figure 3 . 3 :

 33 Figure 3.3: Fiscal space dynamics around shocks

Figure 3 . 4 :

 34 Figure 3.4: OLS estimates, effects of shocks and fiscal space on output growth and recovery

Figure 3 . 5

 35 Figure 3.5 depicts these results graphically.

Figure 3 . 5 :

 35 Figure 3.5: ATT estimates, effects of shocks and fiscal space on output growth and recovery

Figure 3

 3 Figure 3.6: Non-linearity of fiscal space

  g. Kerekes 2007; Jones and Olken 2008;[START_REF] Berg | Bien qu'aucune des méthodes ne soit parfaite, l'approche basée sur les filtres reste la façon la plus parcimonieuse et la plus simple de détecter les poussées de croissance et d'avoir une compréhension claire du processus qui conduit à leur détermination en fixant des critères raisonnables. En outre, il permet de définir différents critères pour identifier plusieurs ensembles de poussées de croissance à utiliser comme contrôles de robustesse[END_REF], and (iii) a combination of the two previous approaches (see, e.g.Kar et al. 2013a,b; Munro 2020). Although none of the methods is perfect, the filter-based approach remains the parsimonious and easiest way of detecting growth surges and having a clear understanding of the process that leads to their determination by setting reasonable criteria. Also, it allows setting different criteria to identify multiple sets of growth surges to be used as robustness checks. I, therefore, employ it and identify 132 episodes of growth surges in 117 countries over the period 1980-2010. I also find that the unconditional probability of growth surges was higher in the decade 2000-2010, in emerging countries, in Asia and the Pacific, the Middle East and Central Asia, and Europe.

  pointed out Chapter . Igniting Growth Surges Lessons from the Past the critical role of macroeconomic stability and trade diversification to ignite and sustain growth. In this paper, I attempt to reconcile the existing papers. To do so, after identifying the growth determinants in the growth surge literature and general literature on growth, I classify them in six broad categories of determinants: (i) external factors and endowments, (ii) institutions, (iii) macroeconomic stability, (iv) structural reforms, (v) trade diversification and quality, and (vi) investments, labor, and productivity. I further analyze how significant changes in these determinants are related to growth surges. Then, I develop a new strategy and employ the non-parametric noise-to-signal ratio method introduced by Kaminsky et al. (1998) to identify significant changes in many of these determinants that are essential for growth. As in Rodrik (2019) and Hausmann et al. (2008), my theoretical understanding of the initiation of growth

Figure 4 . 1 :

 41 Figure 4.1: Illustration of identified growth surges: cases of Chile, China, Ghana, and India

  g. Hausmann et al. 2005; Gupta et al. 2005; Hausmann et al. 2008; Kerekes 2007; Jones and Olken 2008; Aizenman and Spiegel 2010; Jong-A-Pin and de Haan 2011; Berg et al. 2012; Peruzzi and Terzi 2018) and general literature on growth (see below). I classify them in six broad categories of determinants. 1) External factors and Endowments: many papers have shown the importance of external factors (see, e.g.

  To do so, I employ the noise-to-signal ratio introduced by Kaminsky et al. (1998) to predict currency crises, and used, e.g. by Balducci et al. (2011); Berti et al. (2012); Cerovic et al. (2018); Atsebi et al. (2020) to predict fiscal crises and construct an index of fiscal space. More specifically, for each indicator and country, I draw the country-specific percentile distribution of the change of each indicator and identify thresholds in the upper tail of the distribution beyond which a signal is issued (higher changes of the determinants). Thus, the distribution is divided into two parts, with and without a signal. The threshold that divides the distribution is a percentile that is endogenously determined by minimizing the total misspecification errors (sum of type I and II errors) and ensuring a balance of the two types of statistical errors. The use of percentiles to define thresholds, instead of absolute values, takes into consideration structural differences across countries and identifies significant changes in a country-specific fashion, based on the countryThe results of the signal analysis can be summarized in a matrix as in table 4.2 in which the occurrence of growth surges and the issuance of signals of each indicator are measured against each other. I assume that it may take time to initial growth surge, and then I consider that a genuine signal is the one occurring in the five years before the growth surge (i.e., (ℎ) = 5). The error type I or share of false alarms among non-growth surges is defined as /( + ), and the error type II or share of missed growth surges among growth surges is defined as /( + ). Then, the total misspecification error is the sum of errors type I and II, i.e. = /( + ) + /( + ), and the predictive power is one minus the total misspecification error, i.e. = 1 -. I can also define two other performance indicators: the effectiveness defined as the share of true positive among all signals, i.e. effectiveness= /( + ), and the incidence defined as the share of true positive among all growth surges, i.e. incidence= /( + ). After identifying optimal thresholds beyond which signals are issued (i.e., significant changes of growth determinants occurred), I construct six indexes of broad determinants of growth as presented in the previous section. These indexes are the weighted average of all signals in sub-determinants where the weights are the predictive powers (1 -), i.e.:

  by major changes in economic policies", in contrast to what I find here. Indeed, their findings were already questioned by Jong-A-Pin and de Haan (2011); Berg et al. (2012); Prati et al. (2013); IMF (2019); Peruzzi and Terzi (2018); Libman et al. (2019) who highlighted the critical role of macroeconomic stability. If one wants to highlight any difference between the broad determinants in terms of incidence, external factors,

  to a more comprehensive analysis of the growth surges determinants by employing binary outcomes models (logit in baseline and many others as robustness checks). My dependent is a dummy that takes the value one the three years centered on the first year of the growth surges identified by my filters and zero otherwise. The years were a growth surge cannot take place are excluded, i.e. I exclude all the non-starting years of the growth surges because I am interested in their initiation. For robustness purposes, I also present the baseline results where they are not excluded. Given that, my comparison group includes all country-years observations where a growth surge did not occur, including all countries that never experienced a surge. These practices are common in the literature (see e.g., Hausmann et al. 2005; Jong-A-Pin and de Haan 2011; Libman et al. 2019). The sample period covers the years 1980-2010, as restrained by my filters. The explanatory variables are the average of each of the six broad determinants constructed in the previous section calculated over the previous five years. I also use different horizons in the robustness checks. I, therefore, account for the size of the significant changes

Figure 5 . 1 :

 51 Figure 5.1: Stylized facts on growth surges and their association with IMF-supported programs by decades, regions, and levels of development

Figure 5 .

 5 Figure 5.1 presents the numbers of all growth surges and growth surges associated with an IMF-supported program (further split by GRA and PRGT programs) by decades, regions, and levels of development. First, growth surges have increasingly been associated with an IMF-supported program over decades, from 44.4% of growth surges in the 1980s to 47.5%

  1 the unconditional probability of igniting growth surges associated with an IMF-supported program (Panel A) and without an IMF-supported program (Panel B) by decades, regions, and levels of development. The unconditional probability of growth surges with an IMF-supported program is the number of growth surges associated with an IMF-supported program over the number of years of IMF-supported programs excluding non-starting years of growth surges. Similarly, the unconditional probability of growth surges without an IMF-supported program is the number of growth surges without an IMF-supported program over the number of years where an IMF-supported program was not in place excluding non-starting years of growth surges. We

  g., Hausmann et al. 2005; Jong-A-Pin and de Haan 2011;[START_REF] Libman | The colonial origins of comparative development: An empirical investigation[END_REF]; Atsebi 2020), our dependent is a dummy that takes the value one the 3 years centered on the first year of the growth surges identified by our filters and zero otherwise, given the uncertainty on the exact timing of the start of growth surges. The years were a growth surge cannot take place are excluded, i.e. we exclude all the non-starting years of the growth surges because we are interested in their initiation. Given that, our comparison group includes all country-years observations where a growth surge did not occur, including all countries that never experienced a surge. The sample period covers the years 1980-2010, as restrained by our filters. Our set of explanatory variables includes the dummy that takes the value one if the country is under an IMF-supported program or 2 years after one is completed,

Fourth

  , as suggested by descriptive analysis, we show the likelihood of growth surges was higher in Asia and the Pacific, Europe, and the Middle East and Central Asia as opposed to Africa and the Americas and Caribbean. Besides, it increases in the decade 2000-2010 compared to the period 1980-1999. It was also higher in emerging countries than in low-income and advanced countries. Below, we analyze the sensitivity of the results by decades, regions, and levels of development.

  endowments index, average [T-5,T] 0.027*** (0.008) 0.019** (0.008) 0.035*** (0.010) Institutions index, average [T-5,T] 0.077*** (0.011) 0.091*** (0.013) 0.058*** (0.013) Macroeconomic stability index, average [T-5,T] 0.035*** (0.008) 0.035*** (0.009) 0.035*** (0.010) Structural reforms index, average [T-5,T] 0.054*** (0.008) 0.060*** (0.009) 0.051*** (0.009) Trade diversification and quality index, average [T-5,T] 0

Figure D. 1 :

 1 Figure D.1: Average marginal effects of the broad determinants of growth surges, with and without all IMF-supported programs

Figure D. 2 :

 2 Figure D.2: Average marginal effects of the broad determinants of growth surges, with and without GRA programs

  en 2001 et L'Inde en 1991, deux pays, deux histoires différentes qui sont assez attrayantes pour cette thèse. L'un illustre l'histoire de plusieurs pays qui ont subi les conséquences néfastes des crises, l'autre illustre les réussites des poussées de croissance économique soutenue et leurs avantages dans de nombreux pays.0.1.1 La crise de l'Argentine en 2001À partir de 2001, l'Argentine a connu l'une des pires crises économiques de l'histoire. L'Argentine a suspendu ses paiements d'obligations d'une valeur de 81 milliards de dollars en décembre 2001, les deux tiers du PIB du pays ont été perdus en quatre ans, des émeutes ont éclaté, les populations clamaient « tous doivent partir! » et « nous mourons de faim! », le pays a eu cinq présidents en deux semaines, l'inflation est repartie à la hausse, le système bancaire a été paralysé, le chômage a dépassé les 20%, des millions de vies ont été appauvries et la caisse d'émission ou office de stabilisation des changes s'est effondrée. L'année précédant la crise, l'Argentine a été saluée et citée comme un modèle de réforme économique réussie qui a résisté à la tempête de la crise de la Tequila. Comme il était courant en Amérique latine, la crise de l'Argentine de 2001 n'était pas due à d'importants déficits financés par le seigneuriage ou à l'hyperinflation, mais à une accumulation excessive de la dette extérieure et à un déficit persistant du secteur public, couplée à des activités hors budget élevées (notamment des entreprises étatiques avec une contrainte budgétaire souple). En effet, la dette publique a augmenté à un Chapter . Résumé extensif en français rythme plus rapide, de 18 points de pourcentage du PIB au cours des cinq années précédant le défaut de paiement de 2001, alimentée par des déficits publics plus élevés sur la même période.Avec l'effondrement de l'économie, la dette publique a atteint 152,2% du PIB et le déficit public a chuté à 25% du PIB en 2002. L'Argentine a demandé un appui du FMI à travers un program et un financement de 14 milliards de dollars et un programme de soutien international de 40 milliards de dollars. Elle a également renégocié sa dette avec ses créanciers en 2005.

0 . 3 «

 03 par an entre 2000 et 2017. Son revenu par habitant en termes de parité de pouvoir d'achat a été multiplié par 4,9 sur cette période. Dans le même temps, la croissance de l'Inde est devenue plus stable et plus résistante aux chocs. Cela a contribué à une réduction significative de la pauvreté qui est passée d'environ 45 à 22% entre 1991 et 2012. Qu'est-ce qui explique cette poussée de croissance en Inde ? Le succès de l'Inde reposait sur d'importantes réformes économiques en faveur de l'ouverture et de la libéralisation. Cela a marqué un tournant pour l'Inde et son peuple. À la suite de la crise macroéconomiquede 1991-1992, dans la même veine des réformes de libéralisation des années 80, l'Inde a entrepris une vague de réformes dans deux domaines clés : l'industrie et le commerce extérieur. Ces réformes comprenaient la déréglementation . . Les crises financières et la croissance économique 307 détail, concernant le canal de la composition et de la structure du commerce, ce chapitre soutient que les crises financières peuvent constituer un obstacle à la transformation structurelle, car elles affectent davantage les exportations manufacturières dans les pays où la part des exportations manufacturières est relativement plus faible. De plus, en diversifiant leurs exportations et leurs partenaires, les pays pourraient augmenter leur résilience aux crises financières. Concernant le canal de la demande, il montre que les crises financières associées à une baisse de la demande de biens et services des partenaires commerciaux auront des coûts commerciaux plus importants ; par conséquent, elles peuvent générer un effondrement sans précédent du commerce international lorsqu'elles se généralisent au niveau régional ou mondial, comme en témoigne la période post-crise financière globale. Enfin, il soutient l'idée que le canal de l'offre est essentiel pour comprendre comment les crises financières affectent la dynamique du commerce international.Lorsqu'elles sont associées à une détérioration du développement financier intérieur et des conditions financières extérieures, et à une baisse brutale des flux de capitaux entrants, les crises financières entraînent un effondrement plus important du commerce international. Les crises financières et la croissance économiqueLes crises financières ont également généré des contractions et récessions économiques durables et prolongées dans de nombreux pays(voir, par exemple, Kaminsky and Reinhart 1999; Bordo et al. 2003; Tomz and Wright 2007; Cerra and Saxena 2008; Reinhart and Rogoff 2009; Fatás and Mihov 2013; Borensztein and Panizza 2014; Jordà et al. 2013 Jordà et al. , 2016;; Jordà and Taylor 2016;[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF] Trebesch and Zabel 2017; Laeven and Valencia 2018; Romer and Romer 2018;[START_REF] Abiad | de chaque crise financière séparément sans tenir compte des autres crises, par conséquent, elles peuvent souffrir d'un biais de surestimation car les différentes crises sont liées. Dans ce contexte, le chapitre 2 étudie l'évolution de différents types de commerce (à savoir les produits agricoles, miniers et manufacturés et les services) à la suite de divers types de crises financières (à savoir les crises de la dette, bancaires et de changes) dans 41 pays émergents sur la période[END_REF] Kuvshinov and Zimmermann 2019; Atsebi et al. 2020). Cependant, l'ampleur de l'effondrement de la production et le rythme auquel la reprise peut se produire dépendent de manière cruciale de la disponibilité de l'espace budgétaire définit comme la marge de manoeuvre dans le budget d'un gouvernement qui lui permet de fournir des ressources pour un objectif souhaité sans compromettre la soutenabilité de sa situation financière ou la stabilité de son économie (Heller 2005). En effet, à la suite de la crise financière mondiale de 2008-2009, de nombreux gouvernements ont adopté de vastes plans de relance budgétaire pour stimuler l'activité économique et renouer avec la croissance. Ces plans étaient basés sur la théorie keynésienne selon laquelle les déficits des gouvernements peuvent stimuler leur économie en soutenant la demande globale. Ceci est particulièrement intéressant étant donné la preuve de multiplicateurs budgétaires plus importants dans les récessions que dans les expansions.A contrario, de nombreux autres pays ont été contraints de mettre en oeuvre d'importantes consolidations budgétaires afin d'assurer la soutenabilité de leurs finances publiques, et cela au pire moment possible, ce qui a exacerbé les forces récessives de la crise. Une différence Chapter . Résumé extensif en français notable entre les pays qui appliquaient une politique budgétaire expansionniste et restrictive réside dans la disponibilité de l'espace budgétaire dont ils disposaient avant la survenance de la crise.Par exemple, Romer and Romer (2018) en analysant les effets de l'espace budgétaire et monétaire sur la dynamique de croissance à la suite d'une crise financière montrent que les pertes de croissance sont inférieures à 1% lorsqu'un pays dispose des deux types d'espaces, mais atteignent 10% quand il n'a ni l'un ni l'autre. L'un des canaux est que les gouvernements peuvent utiliser la politique monétaire et budgétaire de manière plus agressive lorsque lorsqu'ils ont plus de marge de manoeuvre. Cependant, ils se concentrent exclusivement sur les pays avancés pour lesquels, après la Seconde Guerre mondiale, les crises et les récessions ont été moins récurrentes et moins graves que dans les pays en développement et émergents.Par ailleurs, Bohn (2002) etMendoza and Ostry (2008) montrent que la politique budgétaire a tendance à être en moyenne plus expansionniste lorsque la dette publique est faible.Giavazzi and Pagano (1990); Blanchard (1993);[START_REF] Perotti | [END_REF];[START_REF] Minea | [END_REF];[START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF]) et Ilzetzki et al. (2013) montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées.[START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF] et[START_REF] Corsetti | montrent que la politique budgétaire expansionniste est plus efficace et a des effets keynésiens à des niveaux d'endettement ou de déficit faibles et des effets non keynésiens dans les circonstances opposées[END_REF] révèlent que les multiplicateurs budgétaires sont plus importants pendant les récessions et les crises financières. Dans l'ensemble, ces analyses montrent que la politique budgétaire peut être plus efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et la confiance des marchés financiers.Etant donné cette littérature, le chapitre 3 étudie comment l'espace budgétaire affecte la dynamique des contractions ou récessions à la suite des crises financières et des récessions normales sur 35 pays en développement et 56 pays émergents au cours de la période 1985-2017.Il construit un nouvel indice d'espace budgétaire et applique une combinaison de modèles de projections locales et d'évaluation d'impact pour identifier un effet causal. Il révèle que la disponibilité de l'espace budgétaire à la suite des crises financières et des récessions normales génère un environnement mixte en termes de croissance. Dans les pays disposant de suffisamment d'espace budgétaire, les gouvernements peuvent adopter une expansion crédible de la politique budgétaire en augmentant leur déficit et en utilisant leur espace budgétaire pour alléger les coûts des crises financières et des récessions normales. Dans une telle situation, la consommation et l'investissement privés ainsi que les flux nets de capitaux entrants augmentent, ce qui favorise une reprise rapide des activités économiques. Dans les pays où l'espace budgétaire est limité, l'histoire est différente et plus douloureuse ; les gouvernements échangent immédiatement leurs objectifs de stabilisation de l'activité pour résoudre les problèmes de soutenabilité de la dette à travers des consolidations budgétaires, ce qui renforce les forces récessives. En outre, dans ces pays, la consommation et l'investissement privés ainsi que les flux nets de capitaux entrants baissent et la reprise, le cas échéant, est une perspective lointaine et incertaine. Tout comme en physique, les objets tombent à moins qu'une énergie extérieure ne soit appliquée, les pays qui négligent les bonnes disciplines vont connaitre des récessions plus accentuées à moins d'avoir un espace budgétaire qui leur permette de stimuler leur économie en période de ralentissement. Ce chapitre suggère que les gouvernements et les décideurs doivent être plus que proactifs pour tirer les leçons du passé, réparer le toit pendant que le soleil brille, construire des « buffers » budgétaires, réduire la dette et le déficit, augmenter l'assiette fiscale et les revenus lorsque l'économie commence à s'améliorer sensiblement pour être en mesure de répondre de manière appropriée à la prochaine crise qui se profile à l'horizon.0.4 Les Crises financières et les poussées de croissancePlusieurs facteurs politiques et économiques peuvent expliquer pourquoi et quand les poussées de croissance se produisent ou non ; l'une d'entre elles, qui est analysée dans la partie I, est la présence d'une crise. Cette liaison est connue sous l'hypothèse de « la crise induit une réforme ». En effet, les crises financières révèlent les politiques macroéconomiques non performantes, les dysfonctionnements politiques, économiques et sociaux des économies, ce qui permet d'envisager des réformes macroéconomiques et structurelles sérieuses et bien ciblées pour accroître la résilience du pays aux crises, mais surtout pour renouer avec une croissance soutenue. Par exemple, IMF (2019) montre que les crises favorisent la libéralisation des échanges, du marché du travail ainsi que la déréglementation financière à moyen terme. En outre, Lora and Olivera (2004) montrent que l'effondrement de la demande intérieure due à une crise peut réduire les oppositions à la libéralisation, notamment celles des industries qui dépendent généralement de la demande intérieure. De même, Duval et al. (2018) constatent que les périodes de chômage élevé peuvent accroître la pression sur les gouvernements pour qu'ils adoptent des réformes qui assouplissent la réglementation du marché du travail dans l'espoir de stimuler l'emploi. Mian et al. (2014) montrent qu'après une crise financière résultant d'une période de déréglementation, les gouvernements sont enclins à reréguler le secteur financier et l'économie. Ces résultats soutiennent l'hypothèse de «la crise induit une réforme » et suggèrent qu'il est possible de relancer la croissance au lendemain des crises financières si les politiques et les réformes appropriées sont entreprises. De plus, les crises financières ne sont pas le seul déterminant des poussées de croissance et de nombreux autres facteurs sont tout aussi Chapter . Résumé extensif en français financiers externes, les liens verticaux à travers les chaînes de valeur et leur perception différente par les clients et les investisseurs. En faisant le point sur la littérature existante, l'objectif de notre article est d'évaluer les coûts commerciaux des crises financières en adoptant une perspective sectorielle. En effet, à l'exception du commerce global des marchandises et du commerce des produits manufacturés, la littérature est jusqu'à présent restée assez silencieuse concernant la structure des échanges de biens ou de services agricoles ou miniers, à la suite des crises financières historiques. Par ailleurs, par rapport à la littérature récente qui porte principalement sur la crise de 2008-09, nous nous appuyons sur un large échantillon de 41 pays émergents sur la période 1980-2018 pour analyser les effets commerciaux de plusieurs types de crises financières, à savoir 38 crises de la dette, 34 crises bancaires et 36 crises de change. Pour traiter les problèmes d'endogénéité potentiels et fournir une vue dynamique des coûts commerciaux des crises financières, nous utilisons une nouvelle méthode qui combine les projections locales à la Jordà (2005) et l'évaluation d'impact avec l'estimateur augmenté pondéré à propension inverse. De plus, nous étudions les canaux de l'offre et de la demande par lesquels les crises financières affectent le commerce international total et sectoriel. Nous complétons ces canaux par un troisième canal qui examine la composition et la structure du commerce. Nos résultats sont les suivants. Premièrement, conformément à la littérature existante, nous constatons que les exportations et les importations agrégées chutent sur cinq ans de 5,6 et 11 points de pourcentage (pp.) du PIB d'avant la crise après les crises de la dette, 8,9 et 14 pp. après les crises bancaires, et 7,7 et 9,1 pp. après les crises de change, respectivement. Deuxièmement, nous allons au-delà des études existantes et ventilons les coûts commerciaux par type de biens et services. Nous constatons que les produits manufacturés sont les plus touchés par les crises financières. Cependant, l'impact des crises financières sur les autres types de biens échangés et notamment sur les services est loin d'être négligeable. Le commerce des biens et services miniers (en particulier après les crises bancaires) diminue également à la suite de plusieurs types de crises financières, tandis que le commerce des produits agricoles semble bénéficier d'un éventuel effet de substitution, notamment à la suite des crises de la dette. Lorsque nous examinons les coûts des crises combinées, nous constatons qu'elles exercent une baisse importante du commerce, par rapport aux crises non combinées. Robustes selon une grande variété de spécifications, y compris l'utilisation d'échantillons alternatifs, les poids maximaux dans les modèles de traitement, les sources et définitions des crises et les estimateurs, nos résultats sont expliqués par les canaux (i) de la composition et la structure du commerce, de la demande, et (ii) de l'offre. Du point de vue de la composition et de la structure, nos résultats soutiennent que les crises financières peuvent constituer un obstacle à la transformation structurelle car elles nuisent à davantage d'exportations manufacturières dans . . Chapitre les coûts commerciaux sectoriels des crises financières dans les pays émergents 315 les pays où la part des exportations manufacturières est relativement plus faible. De plus, en diversifiant leurs exportations et leurs partenaires, les pays augmenteront leur résilience aux crises financières. Du point de vue de la demande, nos résultats montrent que les crises financières associées à une baisse de la demande de biens et services des partenaires commerciaux auront des coûts commerciaux plus défavorables, en particulier pour le commerce manufacturier. En effet, les biens et services agricoles et miniers peuvent avoir une plus faible élasticité-revenu de la demande par rapport aux produits manufacturés, ce qui les rend plus résistants aux crises. Par exemple, la demande de produits agricoles peut ne pas trop se contracter lorsque le revenu diminue, car ils sont souvent nécessaires à la subsistance (par rapport à d'autres biens qui peuvent être réduits), et ils sont généralement à bas prix; dans le même ordre d'idées, Borchert and Mattoo (2010) soulignent que l'accent mis sur le commerce des biens a obscurci la résilience tranquille du commerce des services au cours de la récente crise, ce qui peut s'expliquer selon Ariu (2016) par leur élasticité plus faible par rapport à l'élasticité des exportations de biens et aussi par le fait que les services sont des produits intangibles qui ne peuvent pas être stockés et utilisés comme garantie pour demander un financement, et des intrants essentiels pour maintenir une activité de production. Au contraire, l'élasticité-revenu de la demande est élevée pour les produits manufacturés, et en particulier pour les biens durables et d'investissement: Eaton et al. (2016) montrent que la baisse de la demande de produits manufacturés « ajournables » (durables et non durables) entraîne l'effondrement global du commerce et joue un rôle dans la contagion à d'autres pays, conformément aux conclusions de Levchenko et al. (2010) d'une forte baisse du commerce des intrants durables et intermédiaires après la crise de 2008-09.Enfin, nos résultats soutiennent l'idée que le canal de l'offre est essentiel pour comprendre comment les crises financières façonnent la dynamique du commerce international. Lorsqu'elles sont associées à une détérioration du développement financier intérieur et des conditions financières extérieures, et à des arrêts brusques de flux de capitaux, les crises financières vont entraîner un effondrement important et préjudiciable du commerce international, qui est principalement dû à la chute du commerce manufacturier et des services. En effet, la baisse plus forte du commerce des produits manufacturés pendant les périodes de resserrement du crédit ou la détérioration des conditions financières extérieures associées aux crises financières peut être liée au fait que leur production et leur transport dépendent relativement plus de financements extérieurs par rapport aux produits agricoles et miniers. En effet, en limitant fortement les financements extérieurs, un resserrement du crédit réduit les capacités de production et d'exportation des entreprises ; par exemple, Iacovone and Zavaka (2009); Amiti and Weinstein (2011); Minetti and Zhu (2011); Zymek (2012) discutent de tels effets dans le secteur industriel. Inversement, la production de biens et services agricoles et miniers peut nécessiter moins de financement extérieur ; par exemple, les biens miniers sont produits par de grandes entreprises (souvent des multinationales) qui peuvent compter sur l'autofinancement. De plus, comme la plupart des chaînes de valeur mondiales concernent la production de biens manufacturés, l'interruption d'un maillon d'une chaîne de production internationale et du crédit commercial, du fait d'une crise dans un pays, peut conduire à la destruction de l'ensemble de la chaîne, et une baisse plus importante du commerce des produits manufacturés. Globalement, ces résultats riches et détaillés dévoilent le panorama des coûts commerciaux des crises financières. Au lendemain de la crise financière mondiale (GFC) de 2008-2009, de nombreux gouvernements du monde entier ont adopté de vastes plans de relance budgétaire pour stimuler leurs économies en déclin. Ces plans étaient basés sur la théorie keynésienne qui soutient que les dépenses déficitaires des gouvernements peuvent stimuler leur économie en soutenant la demande globale. Cela présente un intérêt particulier étant donné la preuve de multiplicateurs budgétaires plus importants dans les récessions que dans les expansions. Cependant, dans le même temps, de nombreux autres pays ont été contraints de procéder à d'importantes consolidations budgétaires pour dissiper les problèmes de viabilité budgétaire, et ce au pire moment possible, ce qui a exacerbé les forces récessives de la crise. Une différence notable entre les pays qui appliquaient une politique budgétaire souple et restrictive réside dans la disponibilité de l'espace budgétaire dont ils disposaient au cours de la crise. Pour être efficace et menée de manière crédible, la stabilisation budgétaire devrait reposer sur un espace budgétaire suffisant qui est « la place dans le budget d'un gouvernement qui lui permet de fournir des ressources pour l'objectif souhaité sans compromettre la viabilité de sa situation financière ou la stabilité de l'économie » tel que défini par Heller (2005). Cet article construit une nouvelle mesure de l'espace budgétaire et étudie son effet sur la dynamique de la production au lendemain des crises financières et des récessions normales (ci-après dénommées chocs) dans le contexte des pays en développement et émergents au cours de la période 1985-2017. Il contribue à divers volets de la littérature. Premièrement, elle est liée à la littérature qui évalue les coûts de production des crises financières, dans laquelle il existe un consensus sur le rôle de contraction des crises financières. Ce volet de la littérature qui utilise des régressions de panel conventionnelles, des projections locales et / ou une méthodologie d'évaluation d'impact inclut, par exemple, Kaminsky and Reinhart (1999); Bordo et al. (2003); . . Chapitre L'espace budgétaire et les politiques de stabilisation macroéconomique dans les pays en développement et émergents 317 Tomz and Wright (2007); Cerra and Saxena (2008); Reinhart and Rogoff (2009); Fatás and Mihov (2013); Borensztein and Panizza (2014); Jordà et al. (2013, 2016); Jordà and Taylor (2016); Asonuma et al. (2016); Trebesch and Zabel (2017); Laeven and Valencia (2018); Romer and Romer (2018); Kuvshinov and Zimmermann (2019) et Asonuma et al. (2019). À l'exception notable de Jordà et al. (2013); Romer and Romer (2018) et Asonuma et al. (2019) qui montrent respectivement que les expansions à plus forte intensité de crédit, la dette publique plus élevée et le taux d'intérêt de la banque centrale à la baisse, et les arrêts brusques des flux de capitaux sévères amplifient les récessions causées par les crises et empêchent la reprise économique de se produire, le reste des analyses montre peu de preuves des canaux par lesquels les crises financières ou les récessions normales exercent un effet négatif sur le développement économique des pays. Deuxièmement, cet article est étroitement lié à Romer and Romer (2018) qui analysent les effets de l'espace budgétaire et monétaire sur la dynamique de la production au lendemain d'une détresse financière. Ils montrent que les pertes de production sont inférieures à 1% lorsqu'un pays dispose des deux types d'espace, mais presque 10% lorsqu'il n'en a aucun. L'un des canaux est que les gouvernements peuvent utiliser la politique monétaire et budgétaire de manière plus agressive lorsqu'ils sont suffisamment disponibles. Cependant, ils se concentrent exclusivement sur les pays avancés pour lesquels, après la Seconde Guerre mondiale, les crises et récessions ont été moins récurrentes et moins graves que les pays en développement et les pays émergents. Bien que cette question soit devenue d'un grand intérêt pour les pays avancés à la suite de la crise financière globale de 2008-09 qui relance le rôle des réponses de politique budgétaire et monétaire, elle est également très attrayante pour les pays en développement et émergents car ils ont une longue histoire de politique budgétaire procyclique (par exemple, Alesina et al. 2008; Ilzetzki and Vegh 2008), même si, au cours des deux dernières décennies, une part croissante des politiques budgétaires dans ces pays a progressé et est devenue contracyclique (par exemple, Frankel 2011; Frankel et al. 2013; Aizenman et al. 2019). Nous allons au-delà de ces limites en analysant comment l'espace budgétaire réduit les coûts des crises financières et des récessions normales dans le contexte des pays en développement et émergents. En outre, leur analyse souffre de problèmes d'endogénéité pour deux raisons. D'une part, la détresse financière est endogène aux caractéristiques clés du pays, ce qui rend les pays en temps normal potentiellement différents de ceux qui souffrent de détresse financière. Dans ce cas, en comparant les pays en temps normal avec ceux en détresse financière, leurs pertes de production pourraient être biaisées et surestimées. D'un autre côté, l'espace de politique de relance du pays avant le choc est corrélé avec les principales caractéristiques structurelles du pays et peut être affecté par un choc qui réduit l'espace lui-même et génère une récession prolongée. Dans une telle situation, les avantages de l'espace budgétaire peuvent être sous-estimés. Dans cet article, nous traitons ces . . Chapitre L'espace budgétaire et les politiques de stabilisation macroéconomique dans les pays en développement et émergents 319 mièrement, nous construisons un nouvel indice de l'espace budgétaire que nous utilisons pour analyser les pertes de production des crises financières et des récessions normales et montrer comment l'espace budgétaire dans la période précédant les chocs contribue à réduire leurs coûts. Deuxièmement, cette analyse se situe dans le contexte des pays émergents et en développement dans lesquels les crises financières et les récessions normales ont été plus récurrentes et où la stabilisation budgétaire a eu une longue histoire de procyclicité. La littérature a négligé le rôle de l'espace budgétaire dans ces pays. Troisièmement, nous distinguons les coûts des crises financières (bancaires et de change) et des récessions normales qui ne sont pas associées à une crise. Quatrièmement, nous proposons un traitement de l'endogénéité pour identifier un effet causal allant de l'espace budgétaire et des chocs à la dynamique de la production au lendemain des crises financières et des récessions normales. Cinquièmement, nous cherchons à fournir les canaux potentiels par lesquels les effets de l'espace budgétaire opèrent. Nous étudions ensuite comment la disponibilité de l'espace budgétaire avant le choc affecte la dynamique du solde budgétaire primaire, l'espace budgétaire lui-même, la consommation et l'investissement privés et les entrées nettes de capitaux au lendemain des crises financières et des récessions normales. Nos résultats de référence montrent que les crises financières et les récessions normales entraînent des pertes de production importantes et persistantes. En effet, pour les crises financières, la production baisse de 1,5 point de pourcentage (pp.) la première année, 5,1 pp. la deuxième année et environ 4 pp. les années suivantes. Pour les récessions normales, la contraction de la production est beaucoup plus importante au cours de la première année et se situe à 5,6 points de pourcentage, avant de retomber à environ 2,5 points de pourcentage les années suivantes. Pour mettre ces résultats en perspective, sachant que la croissance médiane du PIB réel de notre échantillon est de 4,8 % par an, de sorte que l'économie médiane aurait crû de 24 % sur cinq ans ; un seul épisode de crise financière et de récession normale coûterait respectivement environ 16 et 10 % de la croissance à long terme de cette économie. En outre, les résultats montrent que, également dans le contexte des pays en développement et émergents, l'espace budgétaire est toujours important et réduit considérablement les pertes de production des crises financières et des récessions normales. Nous constatons qu'une augmentation de l'espace budgétaire avant le choc d'un écart-type est associée à une réduction maximale des pertes de production de 4,6 et 3,9 points de pourcentage respectivement après les crises financières et les récessions normales, ce qui est suffisant pour compenser complètement leurs pertes de production liées aux crises. Ces résultats montrent qu'il y a un gain important à réparer le toit quand le soleil brille, c'est-à-dire à accumuler des espaces budgétaires; sinon, il ne peut y avoir d'autre issue que de faire de la consolidation budgétaire au pire moment possible. Ces résultats restent inchangés après plusieurs contrôles de robustesse. En ce qui concerne les canaux, nos résultats révèlent que dans les pays où l'espace budgétaire est plus important, les gouvernements adoptent des plans de relance en Chapter . Résumé extensif en français utilisant l'espace budgétaire et en relançant leur économie pour atténuer les pertes de production des crises financières et des récessions normales. Bien que l'espace budgétaire soit utilisé et déprimé, il reste solide et supérieur à celui des pays disposant d'un espace budgétaire limité qui mettent en oeuvre des consolidations budgétaires pour résoudre leurs problèmes budgétaires. En outre, nous constatons qu'un espace budgétaire plus élevé est associé à une augmentation de la consommation et des investissements privés, soutenant la vision keynésienne, et à une augmentation des flux nets de capitaux nécessaires pour financer la reprise. Les résultats opposés sont trouvés dans les pays à espace budgétaire limité où la consommation et l'investissement privés et les entrées nettes de capitaux sont fortement déprimés. Nos résultats clarifient cette époque où, après la crise financière globale de 2008-2009, de nombreux gouvernements se noient dans de grandes quantités de dette, persistent dans le déficit budgétaire et épuisent les espaces budgétaires alors qu'une autre crise pourrait se profiler à l'horizon. Les expériences précédentes nous ont appris de précieuses leçons ; certains d'entre eux sont de vieux oubliés. Cette étude fait revivre les avantages de l'espace budgétaire et le rôle de la stabilisation de la politique budgétaire au lendemain des crises financières et des récessions normales et demande que les efforts de consolidation des finances publiques en plein essor soient bien préparés pour les prochaines récessions. Quoi exactement l'Inde peut faire pour croitre rapidement ? » (Lucas 1988). Je ne pense pas que cette question soit dépassée, même pour l'Inde qui a connu deux épisodes de croissance soutenue depuis 1989 ; parce que de nombreux pays dans le monde n'ont pas réussi à croître plus rapidement sur une période prolongée, et ceux qui l'ont fait avec succès doivent encore atteindre des pics de croissance pour relever les défis mondiaux de cette époque. En effet, le déclenchement de poussées de croissance soutenue peut avoir des implications quantitativement énormes pour le revenu national, la pauvreté et, plus largement, pour le bien-être des populations.Pritchett et al. (2016) estiment que les 20 premières accélérations des six dernières décennies avaient une valeur actuelle nette de 30 billions de dollars : deux fois la taille du PIB américain, et Chen and Ravallion (2010) et Radelet (2016) montrent que les poussées de croissance en Chine et en Inde, ainsi qu'en Indonésie, au Pakistan et au Vietnam ont contribué à la réduction de la pauvreté dans le monde depuis 1981. souligné le rôle essentiel de la stabilité macroéconomique et de la diversification des échanges pour stimuler et soutenir la croissance. Dans cet article, j'essaie de concilier les articles existants. Pour ce faire, après avoir identifié les déterminants de la croissance dans la littérature sur les poussées de croissance et la littérature générale sur la croissance, je les classe en six grandes catégories de déterminants : (i) facteurs externes et dotations, (ii) institutions, (iii) stabilité macroéconomique, (iv) réformes structurelles, (v) diversification et qualité du commerce, et (vi) investissements, travail et productivité. J'analyse plus en détail la façon dont les changements importants de ces déterminants sont liés aux poussées de croissance. Ensuite, je développe une nouvelle stratégie et j'utilise la méthode non paramétrique de rapport bruit / signal introduite par Kaminsky et al. (1998) pour identifier des changements significatifs dans plusieurs de ces déterminants qui sont essentiels pour la croissance. Comme dans Rodrik (2019) et Hausmann et al. (2008), ma compréhension théorique de l'initiation des poussées de croissance est que, chaque pays a ses réalités et obstacles à la croissance. Pour certains, les préoccupations peuvent être un niveau élevé d'inflation, de dette ou de déficit, pour d'autres le manque d'infrastructures ou un système politique corrompu, etc. Par conséquent, les déterminants devraient tenir compte des caractéristiques des pays. Enfin, j'utilise une analyse empirique plus complète où les poussées de croissance sont expliquées par les six grands déterminants. Mes résultats sont les suivants. Premièrement, tous les déterminants de la croissance ont un effet significatif et positif sur la probabilité d'amorcer la croissance. Cependant, l'amélioration de la stabilité macroéconomique et les facteurs externes et dotations favorisent une probabilité plus élevée de poussée de croissance. Ils sont suivis des réformes structurelles, des investissements, du travail et de la productivité, de la diversification et de la qualité des échanges, et enfin des institutions. Deuxièmement, lorsque j'examine les interactions bidirectionnelles des déterminants de la croissance, je montre que les pays peuvent maximiser la probabilité de déclencher des poussées de croissance s'ils parviennent conjointement à améliorer sensiblement la stabilité macroéconomique et bénéficient des conditions externes et dotations favorables, d'une part, et d'autres déterminants, d'autre part. En outre, je trouve que des changements importants dans la stabilité macroéconomique ou les facteurs externes sont nécessaires pour avoir un effet plus élevé et positif des autres grands déterminants sur les poussées de croissance. Troisièmement, il existe une différence notable entre les décennies, les régions et les niveaux de développement.Cela nécessite une adaptation minutieuse des politiques visant à déclencher les poussées de croissance en fonction des conditions locales. Quatrièmement, un message clair de cette analyse est que la capacité à déclencher des poussées de croissance dépend de la capacité de restaurer ou de préserver la stabilité macroéconomique, bien que d'autres déterminants soient également importants. J'ai plusieurs contributions à la littérature. Premièrement, à ma connaissance, il s'agit de la première étude sur les poussées de croissance qui couvre et caractérise entièrement . . Chapitre le rôle du Fonds Monétaire International dans l'initiation des poussées de croissance 323 les poussées de croissance des années 2000. Au cours de cette décennie, tous les déterminants ont eu un effet plus élevé, positif et significatif sur la probabilité d'une poussée de croissance par rapport aux années 80 et 90. Deuxièmement, j'utilise une longue liste de déterminants de la croissance que je combine dans différentes grandes catégories pour créer un bon récit. Troisièmement, j'utilise un processus d'optimisation pour identifier le seuil au-delà duquel des changements ou des améliorations significatifs des grands déterminants se sont produits d'une manière qui tient compte des caractéristiques spécifiques au pays. Quatrièmement, je montre que les poussées de croissance présentent une composante prédictive importante et que des changements disproportionnés des déterminants, lorsqu'ils sont identifiés avec précision, peuvent avoir un impact significatif sur la probabilité de poussées de croissance, contrairement à la littérature existante (voir, par exemple Hausmann et al. 2005; Gupta et al. 2005; Peruzzi and Terzi 2018). Par conséquent, je résous le problème du « faible pouvoir prédictif » de la littérature. Cinquièmement, il existe des combinaisons de déterminants de la croissance qui peuvent aider à maximiser la probabilité des poussées de croissance. Sixièmement, la stabilité macroéconomique et, dans une certaine mesure, les facteurs externes et les dotations peuvent être considérés comme des stratégies dominantes pour déclencher une poussée de croissance, car aucune amélioration de ces déterminants empêche généralement les autres déterminants d'avoir un effet moindre sur les poussées de croissance.0.10 Chapitre 5: le rôle du Fonds Monétaire International dans l'initiation des poussées de croissance Le Fonds monétaire international (FMI) décrit comme le « prêteur de dernier recours » ou le « pompier financier », à la fois critiqué et salué pour ses efforts en faveur de la stabilité financière, continue de se trouver à la pointe de la gestion des crises économiques mondiales, en particulier après la crise financière mondiale de 2008-2009 et la pandémie de coronavirus (Covid-19) en 2019-2020. Comme indiqué dans le guide sur les conditionnalités du FMI (IMF 2002), « les programmes soutenus par le Fonds devraient viser principalement les objectifs macroéconomiques suivants : (a) résoudre le problème de la balance des paiements du membre sans recourir à des mesures destructrices des politiques nationales ou internationales la prospérité ; et (b) réaliser la viabilité extérieure à moyen terme tout en favorisant une croissance économique Chapter . Résumé extensif en français durable ». Cependant, une croissance médiocre dans le cadre des programmes soutenus par le FMI par rapport aux pays ou périodes hors programme a souvent été critiquée comme révélatrice d'un biais de resserrement excessif et a entraîné une stigmatisation du FMI qui pourrait décourager l'utilisation de ses financements et remettre en cause sa réputation. Parallèlement, les économistes du FMI soutiennent que le rétablissement de la stabilité macroéconomique, même douloureuse à court terme, créera les conditions d'une croissance plus élevée à moyen et à long terme. Dans la pratique, le FMI s'est réinventé pour accorder plus d'attention à la croissance dans ses programmes de prêt. Par exemple, il crée la Facilité élargie de financement (FEP) en 1974, offrant la possibilité de politiques structurelles sur des périodes de programme et de remboursement plus longues pour soutenir un ajustement plus profond et obtenir un plus grand impact sur la croissance; la Facilité d'ajustement structurel renforcée (FASR) en 1987, rebaptisée « Poverty Reduction and Growth Trust » (PRGT) en 1999, a mis davantage l'accent sur la croissance et la réduction de la pauvreté dans les pays à faible revenu par le biais de prêts concessionnels et de réformes structurelles. Ces efforts accrus sur les résultats de la croissanceà la fois pendant la période de programmation et par la suite -impliquaient une attention accrue aux politiques favorables à la croissance telles que la protection des investissements publics, les réformes structurelles favorisant la croissance et les opérations de la dette pour atténuer l'ampleur de l'ajustement budgétaire nécessaire pour atteindre la viabilité. Cependant, d'autres rejettent la suggestion selon laquelle l'approche du FMI a changé. Ils soutiennent que le remède du FMI a toujours été directement sorti du livre d'ajustement structurel : réduire les salaires et les avantages sociaux, privatiser les entreprises publiques, réduire les dépenses publiques, réduire les salaires minimums et restreindre la négociation collective. En d'autres termes, le FMI a consacré trop d'efforts aux ajustements et a relégué la croissance à un objectif secondaire. En effet, l'économiste de renom et lauréat du prix Nobel Joseph E. Stiglitz a sévèrement critiqué le travail du FMI dans son livre « Globalization and its Discontents » (2002), qui ressemble plus à « The IMF and its Discontents ». Il a dénoncé le FMI comme l'un des principaux responsables de l'échec des politiques de développement mises en oeuvre dans certains pays. Il fait valoir que bon nombre des réformes économiques que le FMI exigeait comme conditions de son prêt -austérité budgétaire, taux d'intérêt élevés, libéralisation des échanges, privatisation et l'ouverture des marchés financiers -ont souvent été contre-productifs pour les économies cibles et dévastatrices pour les populations locales. Ce débat controversé sur l'efficacité du FMI, notamment sur la promotion de la croissance, a conduit à plusieurs analyses dans la littérature. Il n'est pas surprenant que cette littérature ne soit pas concluante, reflétant en partie d'importants défis empiriques impliqués dans l'identification des contrefactuels appropriés et l'isolement de l'impact des programmes sur la croissance des . . Chapitre le rôle du Fonds Monétaire International dans l'initiation des poussées de croissance 325 influences d'autres facteurs, et en raison des données et des méthodes variées utilisées par les chercheurs. La classe d'articles soulignant un effet positif des programmes soutenus par le FMI sur la croissance comprend, par exemple : Dicks-Mireaux et al. (2000); Hutchison (2004); Atoyan and Conway (2006); Bas and Stone (2014); Bal Gündüz (2016) et Bird and Rowlands (2017). Premièrement, Dicks-Mireaux et al. (2000) se concentrent sur un échantillon de pays à faible revenu qui ont participé à la Facilité d'ajustement structurel renforcée (FASR) du FMI au cours de la période 1986-1991. Ils révèlent des effets bénéfiques importants du soutien du FMI sur la croissance de la production et la dette, mais aucun effet sur l'inflation. Deuxièmement, Hutchison (2004), utilisant une variété de méthodes d'appariement, montre également une association positive entre la croissance et les programmes soutenus par le FMI. Il soutient que le biais de sélection de l'échantillon est principalement responsable de la perception commune que la croissance de la production réelle diminue parce que les pays choisissent de participer aux programmes du FMI. Troisièmement, Atoyan and Conway (2006) trouvent peu de preuves statistiques que les programmes du FMI améliorent simultanément la croissance économique réelle dans les pays participants, mais des preuves plus solides d'une amélioration de la croissance économique au cours des années suivant un programme. Quatrièmement, Bas and Stone (2014), après avoir résolu le problème du biais de sélection, montrent que les pays bénéficient en moyenne des programmes du FMI en termes de taux de croissance plus élevés. Cet effet positif est plus prononcé chez les utilisateurs à long terme que chez les utilisateurs à court terme ; contrairement à la littérature précédente. Cinquièmement, Bal Gündüz (2016) constate que l'engagement à court terme du FMI est positivement associé à un large éventail de résultats macroéconomiques. En particulier, l'impact sur la croissance à court terme est le plus important et ne devient significatif que pour les pays à faible revenu confrontés à des déséquilibres macroéconomiques importants ou à des chocs exogènes importants. Enfin, Bird and Rowlands (2017) adoptent une méthode d'appariement par score de propension et montrent que les programmes concessionnels ont eu un effet généralement positif sur la croissance jusqu'à deux ans après la signature des accords dans le contexte des pays à faible revenu. En revanche, la deuxième catégorie d'articles met en exergue plutôt un effet négatif des programmes soutenus par le FMI sur la croissance. Elle comprend Przeworski and Vreeland(2000);IEO and IMF (2002);[START_REF] Hutchison | [END_REF];[START_REF] Barro | [END_REF]; Butkiewicz and Yanikkaya (2005); Easterly (2005) et Dreher (2006). Premièrement, Przeworski and Vreeland (2000) révèlent que la participation au programme diminue les taux de croissance tant que les pays restent sous-programme. Une fois que les pays quittent le programme, ils croissent plus vite que s'ils étaient restés, mais pas plus vite qu'ils ne l'auraient fait sans participation. Deuxièmement, dans la même veine, le premier rapport d'évaluation de IEO and IMF (2002) Chapter . Résumé extensif en français sur l'utilisation prolongée des ressources du FMI trouve plus d'effets négatifs des programmes soutenus par le FMI sur la croissance pour les utilisateurs prolongés que pour les utilisateurs « temporaires ». Les conséquences néfastes pour la croissance de l'utilisation prolongée semblent être concentrées dans les programmes soutenus par les ressources générales, et non dans ceux bénéficiant de facilités concessionnelles. Troisièmement, Hutchison and Noy (2003) se concentrent sur l'expérience du FMI dans les pays d'Amérique latine. Ils montrent que la baisse de l'activité économique à court terme dans les programmes soutenus par le FMI dans cette région est plus élévée par rapport à d'autres régions. Quatrièmement, Barro and Lee (2005) montrent que la politique de prêt du FMI est sensible aux variables d'économie politique comme la proximité des pays avec les États-Unis et les principaux pays européens. En utilisant une stratégie instrumentale, ils soulignent que des taux de participation aux prêts plus élevés du FMI réduisent la croissance économique, ce qui s'explique en partie par leur association négative avec l'État de droit et leurs effets non significatifs sur l'investissement, l'inflation, la consommation publique et l'ouverture internationale. Cinquièmement, Butkiewicz and Yanikkaya (2005) maintiennent un effet négatif ou neutre des programmes de prêt du FMI en raison de leur impact négatif sur les investissements publics et privés. Parallèlement, ils constatent que les prêts de la Banque mondiale stimulent la croissance dans certains cas, principalement en augmentant les investissements publics. Sixièmement, Easterly (2005) constate qu'aucun des 20 principaux bénéficiaires de programmes répétés soutenus par le FMI au cours de la période 1980-99 n'aurait atteint une croissance raisonnable et une stabilité macroéconomique. Enfin, Dreher (2006) soutient l'impact négatif des programmes soutenus par le FMI sur un panel de 98 pays sur la période 1970-2000. Il montre néanmoins que le respect des conditionnalités du FMI contribue à atténuer cet effet négatif. Outre la littérature sur l'évaluation du FMI, notre analyse s'inscrit également dans la littérature sur les poussées de croissance et ses déterminants (voir, par exemple Hausmann et al. 2005; Gupta et al. 2005; Hausmann et al. 2008; Kerekes 2007; Jones and Olken 2008; Aizenman and Spiegel 2010; Jong-A-Pin and de Haan 2011; Berg et al. 2012; Peruzzi and Terzi 2018; Atsebi 2020). Cette littérature ne parvient pas à un consensus sur les déterminants des poussées de croissance dans les pays, et Atsebi (2020) tente de les réconcilier. En outre, il se situe dans la littérature générale sur la croissance

..

  Chapitre le rôle du Fonds Monétaire International dans l'initiation des poussées de croissance 327 concentrent sur les effets à court terme des programmes soutenus par le FMI ; par conséquent, face au problème du biais de sélection, nous nous concentrons davantage sur l'effet à moyen et long terme. Aussi, alors qu'ils se concentrent sur le taux de croissance annuel, nous choisissons de nous concentrer sur le déclenchement de périodes de poussées de croissance, c'est-à-dire, périodes de croissance soutenue pendant une période prolongée. Nous veillons également à documenter comment le FMI a joué un rôle dans le déclenchement des poussées de croissance. Pour ce faire, nous utilisons des données longitudinales comprenant 169 pays et couvrant la période 1980-2010. Notre stratégie a consisté à identifier d'abord les poussées de croissance en appliquant la méthode des filtres comme dans Hausmann et al. (2005), à identifier les déterminants de la croissance dans la littérature, et à définir un processus d'optimisation pour identifier les améliorations significatives des déterminants des poussées de croissance en tenant compte de la spécificité du pays et en analysant les effets de ces améliorations lorsqu'elles se produisent pendant une programme soutenu par le FMI ou non par le biais de divers ensembles de méthodes non paramétriques et empiriques. À notre connaissance, il s'agit de la première analyse qui se concentre exclusivement sur le rôle du FMI dans l'initiation des périodes de poussées de croissance. L'un des points clés à retenir est que toutes les analyses effectuées dans cette étude montrent que le fait d'avoir un programme soutenu par le FMI augmente la probabilité de déclencher une poussée de croissance. Elles soutiennent que le FMI a contribué à stimuler la croissance à moyen et long terme dans les pays tout en poursuivant la stabilité macroéconomique et en mettant en oeuvre des réformes structurelles, mais aussi en créant les conditions préalables pour stimuler les investissements, la main-d'oeuvre et la productivité et bénéficier davantage des conditions externes et de dotations favorables. En détail, les analyses non paramétriques montrent que 28% de tous les programmes soutenus par le FMI (commençant en dehors des épisodes de poussées de croissance) ont été suivis d'une poussée de croissance (25,1% pour les programmes GRA et 32,7% pour les programmes PRGT); sur les 132 poussées de croissance identifiées dans 117 pays, 56 % se sont produites pendant ou 2 ans après les programmes soutenus par le FMI dans 72 pays (32,6% pour les programmes GRA et 26,5% pour les programmes PRGT); la probabilité inconditionnelle moyenne des poussées de croissance est plus élevée pour les poussées de croissance associées à un programme soutenu par le FMI (4,7%) que pour les poussées de croissance sans programme soutenu par le FMI (2,8%); l'occurrence et l'ampleur des améliorations significatives des déterminants des poussées de croissance étaient plus élevées pendant les périodes de programmes soutenus par le FMI que pendant les périodes sans programmes soutenus par le FMI. En outre, l'analyse de l'efficacité révèle que la probabilité que des améliorations significatives des déterminants des poussées de croissance soient suivies d'une poussée de croissance est entre 1,4 et 1,8 fois plus élevée lorsque Chapter . Résumé extensif en français ces améliorations se produisent pendant les programmes soutenus par le FMI (programmes GRA et PRGT). En outre, ces résultats entretiennent certaines disparités entre les décennies, les régions et les niveaux de développement. De manière attendue, il y a peu de changements lors du passage de l'analyse des méthodes non paramétriques aux méthodes paramétriques. Par exemple, l'analyse non paramétrique montre que le fait d'avoir un programme soutenu par le FMI accentue et amplifie de manière similaire le pouvoir prédictif de tous les déterminants des poussées de croissance, tandis que l'estimation paramétrique, qui tient compte du lien entre les déterminants, montre que les avantages des programmes soutenus par le FMI sont différents en ampleur et en importance selon les déterminants. En effet, du plus grand au plus petit effet, l'amélioration des facteurs externes et des dotations, la stabilité macroéconomique, les réformes structurelles et les investissements, la main-d'oeuvre et la productivité associés à un programme soutenu par le FMI contribuent à relancer la croissance dans les pays. Lorsque nous examinons les avantages d'avoir un programme GRA ou PRGT, nous révélons globalement que les avantages d'avoir un PRGT qu'un programme GRA sont plus élevés parce que les améliorations significatives des déterminants de la croissance qui se produisent pendant un programme PRGT ont une intensité plus élevée et augmentent encore plus la probabilité de déclencher une poussée de croissance. Cela n'est pas surprenant car les programmes PRGT ont une durée plus longue que les programmes GRA et fournissent une assistance concessionnelle mieux adaptée à la diversité et aux besoins des pays à faible revenu. À un niveau plus granulaire, nous constatons que le FMI a peut-être été plus efficace pour déclencher des poussées de croissance dans les années 2000 que les décennies précédentes, conformément à sa réinvention pour accorder plus d'attention à la croissance, comme indiqué dans les directives sur les conditionnalités du FMI (IMF 2002). En outre, il a joué un rôle dans le déclenchement de poussées de croissance dans tous les pays du monde, quels que soient leur emplacement géographique et leur niveau de développement. Certaines mises en garde méritent d'être notées. Premièrement et surtout, le rôle du FMI dans le déclenchement des poussées de croissance peut également saisir l'action d'autres institutions multilatérales telles que la Banque mondiale, l'UE et les banques régionales de développement, car elles ont des interventions conjointes dans les pays. Par conséquent, le rôle du FMI ici peut être surestimé. Néanmoins, cette question peut être moins importante pour les déterminants de la croissance qui font partie des activités principales du FMI telles que la stabilité et les politiques macroéconomiques et certains domaines de réformes structurelles. Deuxièmement, nos constatations peuvent être sujettes au problème du « biais de sélection » parce que les périodes 14Les programmes GRA représentent les programmes du compte de ressources générales qui comprennent une variété de programmes de prêt avec différents calendriers de décaissement et échéances en fonction des besoins de la balance des paiements du membre. Les programmes PRGT sont des programmes de « Poverty Reduction Growth Trust » qui représentent des programmes de prêt fournissant un soutien financier concessionnel aux pays à faible revenu.

..

  Quelques applications dans le cadre de la crise de la pandémie du Covid-329 avec et sans programme soutenu par le FMI sont très différentes, et le FMI a été régulièrement associé à des difficultés économiques et une agitation politique dans les pays. Cependant, nous avons deux raisons de penser que ce problème est le moins préoccupant pour notre étude. Nous nous concentrons principalement sur l'effet à moyen et long terme des interventions du FMI plutôt que sur leur effet à court terme, au moment de la crise. Secondaire, comme les programmes soutenus par le FMI ont toujours été associés aux turbulences économiques et financières et à leurs conséquences, nous pensons qu'il peut être plus difficile de concevoir une croissance plus élevée en ce moment ; par conséquent, le rôle positif du FMI dans la stimulation des poussées de croissance constatées ici peut servir de borne inférieure. Troisièmement, nous saisissons le rôle du FMI en utilisant ou non un programme soutenu par le FMI, mais cela ne dit rien sur la structure et la profondeur des conditionnalités du FMI, la conformité des pays à ces conditionnalités, que les programmes aient ou non échoué. Ces analyses sont laissées pour de nouvelles recherches. Enfin, les résultats positifs ne doivent pas être entièrement attribués au FMI, car la relance des poussées de croissance est également une question d'appropriation par les pays ainsi que d'intérêts politiques nationaux et de contraintes institutionnelles. Par conséquent, les avantages trouvés ici sont le résultat de la coordination entre le FMI et ses pays membres. Néanmoins, tout en nous concentrant sur des améliorations disproportionnées des déterminants des poussées de croissance, nous ne pensons pas que l'appropriation par les pays aurait entraîné un biais à la hausse dans l'effet du FMI de déclencher la croissance, car ils doivent être solides pour réaliser ces changements indépendamment de la présence du FMI.

0. 11

 11 Quelques applications dans le cadre de la crise de la pandémie du Covid-19Cette thèse arrive à une période où, suite à la pandémie de Covid-19, les mesures de confinement nécessaires pèsent sur les entreprises et les emplois, l'incertitude augmente et les marchés financiers paniquent, les flux de capitaux reculent, les prix des matières premières diminuent, les pays s'engagent dans les grands plans de relance s'élevant à environ 8000 milliards de dollars (selon les estimations du FMI), malgré un espace budgétaire inférieur à celui d'avant la crise financière de 2008-09, et les politiques monétaires sont contraintes. Dans l'ensemble, nous sommes arrivés à un moment où la crise qui se profile à l'horizon est plus proche que ce que nous pensons. En effet, ce « temps est différent » ; il s'agit d'une grave menace pour l'économie mondiale. Et, tout comme la crise sanitaire frappe le plus durement les personnes vulnérables, la crise économique qui pourrait en découler devrait frapper plus durement les pays vulnérables.

  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.8 OLS estimates of the effects of normal recessions and fiscal space on output dynamics, conditional paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.9 ATT estimates, dynamics of fiscal balance, fiscal space, private absorption, and net capital inflows in the aftermath of financial crises, conditional on pre-shock fiscal space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.10 ATT estimates, dynamics of fiscal balance, fiscal space, private absorption, and net capital inflows in the aftermath of normal recessions, conditional on pre-shock fiscal space . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

C 1 G 1.1 Argentina, 2001 and India, 1991, two different stories

  Journal of International Economics, 98, 138-149. Labor and Product Market Reforms in Advanced Countries?" IMF Working Papers, 18, 1. E , W. (2005): "What did structural adjustment adjust? The association of policies and growth with repeated IMF and World Bank adjustment loans," Journal of Development Economics, 76, 1-22.

	B (2014): "How does trade evolve in the aftermath of financial crises?" IMF Economic Review, 62, 213-247. , A., P. M , P. T A , A. (2016): "Crisis-proof services: Why trade in services did not suffer during the A , C., V. M , D. X (2015): "Competition, markups, and the gains from international trade," American Economic Review, 105, 3183-3221. E , S. (1998): "Openness, productivity and growth: What do we really know?" The 2008-2009 collapse," E Economic Journal, 108, 383-398.

# of starting crises

	1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Years Banking Crisis Currency Crisis (A) Debt Crisis	0 5 10 15 20 25 Number of countries in crises	1980 (B) Debt Crisis 1985	1990	1995 Years 2000 Banking Crisis	2005 Currency Crisis 2010 2015

# of countries experiencing crises

				0.105
	So ut h A si a M id dl e E as t & N o rt h A fr ic a L at in A m er ic a & C ar ib b ea n E ur o p e & C en tr al A si a E as t A si a & P ac if ic	0.000	0.057 0.069 0.060 0.094 0.087 0.033 0.119 0.136 0.128 0.126	0.211 0.182	0.337	0.406
	Su b -S ah ar an A fr ic a	0	0.040	0.086 .1	.2	.3	.4
	Unconditional probability of experiencing crises over 1980-2014

(C) Unconditional probability of experiencing crises

	Debt crisis	Banking crisis	Currency crisis
	Notes: Sample: 1980-2014. Authors' calculations based on data and definitions from Frankel and Rose (1996); Reinhart and
	Rogoff (2009) and Laeven and Valencia (2018), World Development Indicators.

  Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries occur significantly quicker after a crisis of another type: (i) after a debt crisis hits a country, a banking crisis follows in one-quarter of cases in five years, and a currency crisis in one year; (ii) after a banking crisis, a debt or currency crisis follows in one-quarter of cases in two years;(iii)

The main message of fig.

2

.2 is that financial crises of a new type

  Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries ensures a perfect balancing of covariates between countries with and without financial crises by using an optimization process (see, Imai and Ratkovic 2014, for more details on the model and its advantages over traditional binary models). Based on this model, fig. A.2 in sectionA.3 illustrates the smooth kernel density of the distribution of the PS for the treated and control groups, for each financial crisis. Given the high classification power, countries in the treated (control) group receive a high (low) likelihood of financial crises, while countries in the treated (control) group with PS close to zero (one) receive higher weights. Besides, fig. A.2 also shows considerable overlaps between the distributions of PS for the treated and control groups; thus,

We use the CBPS model (a kind of machine learning program) which

  Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries 2.2 pp. of pre-crisis GDP over five years, compared with countries unaffected by crises. In contrast, exports of agricultural goods are left unchanged in the aftermath of currency crises or significantly decrease by 1.5 pp. over five years after the banking crises. Regarding agricultural imports, they are significantly reduced by between 0.6 and 1.5 pp. for all types of crises. These findings suggest that trade in agricultural goods exhibits greater resilience during financial crises and can even intensify, which may signal a substitution effect in favor of agricultural goods. Such

. Countries that experience debt crises present larger exports of agricultural goods by

  Following Cruces and Trebesch (2013), debt crises now exclusively capture preemptive and post-default debt restructurings with private creditors (i.e. we drop restructurings with official creditors). Banking crises have the same definition but now come from the dataset of Reinhart and Rogoff (2009) (instead

of

Laeven and Valencia, 2018)

. Currency crises are redefined based on

Reinhart and Rogoff (2009)

, namely by at least a 20% nominal depreciation of the local currency against the US dollar. Based on these new sources and definitions, we study 41 debt crises, 44 banking crises, and 69 currency crises compared to 38 debt crises, 34 banking crises, and 36 currency crises in the benchmark model. The results are reported in table

A.13 and fig. A.8

. They generally confirm the robustness of our findings, even if sometimes the trade costs of financial crises are lower in magnitude, especially for total and sectoral imports following debt and currency crises.

  Chapter . The Sectoral Trade Costs of Financial Crises in Emerging Countries mining, manufactured goods, and services, given their different income elasticity and degree of vertical linkages through global value chains. This channel may be pronounced for imports and during times of generalized financial turmoil. Second, the supply-side argument suggests that financial crises are associated with significant reductions in the availability of external and trade finance (see, e.g. Iacovone and Zavaka 2009; Amiti and Weinstein 2011;[START_REF] Minetti | [END_REF][START_REF] Chor | qui insistent sur le rôle des conditions de crédit (par exemple, la faiblesse du développement financier) et le crédit commercial (par exemple, la dépendance vis-à-vis des finances extérieures) pour expliquer le déclin du commerce international suite aux crises financières[END_REF][START_REF] Chor | qui insistent sur le rôle des conditions de crédit (par exemple, la faiblesse du développement financier) et le crédit commercial (par exemple, la dépendance vis-à-vis des finances extérieures) pour expliquer le déclin du commerce international suite aux crises financières[END_REF] Zymek 2012). Consequently, financial crises will exert different costs on the types of traded goods and services, given their different external financial needs. Overall, we believe that the two traditional channels can be supplemented by a third channel, namely the compositional and structural effect of financial crises on trade. We follow Beck (

. In this case, financial crises will differently affect the trade of agricultural,

Balance and overlap checks A.3.1 Balance Checks

	A.3 Table A.3: Balance diagnostics between the treated and control groups, Debt crises
	(1) Non weighted	(2) Weighted
	(21)
	Table A.1: Data sources and descriptions

Covariate Balancing propensity score (22) Pooled probit Variables Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio

	# of Banking crises over past-5 years (t-5,t)	0.316	0.147	0.405	1.767	0.211	0.211	0.000	1.026	0.238	0.156	0.205	1.413
	# of Currency crises over past-5 years (t-5,t)	0.474	0.148	0.687	2.245	0.247	0.247	0.000	0.944	0.320	0.167	0.337	1.671
	Intensity of conflicts (t-1)	0.816	1.251	-0.219	0.533	1.161	1.161	0.000	1.042	1.109	1.232	-0.059	0.732
	Log of Real GDP per capita (t-1)	5.490	7.356	-0.427	1.994	6.388	6.388	0.000	3.001	6.418	7.233	-0.174	2.484
	Log. of public debt to GDP (t-1)	3.985	3.657	0.507	0.785	3.832	3.832	0.000	0.760	3.823	3.678	0.236	0.591
	Log. of domestic credit to GDP (t-1)	3.308	3.486	-0.297	0.634	3.326	3.326	0.000	0.673	3.251	3.472	-0.384	0.509
	Log. of liquid liabilities to GDP (t-1)	3.361	3.655	-0.517	0.968	3.470	3.470	0.000	0.604	3.417	3.637	-0.440	0.497
	Net capital inflows to GDP (t-1)	3.733	2.443	0.247	1.120	3.788	3.789	0.000	1.272	2.516	2.544	-0.005	1.075
	Log. of foreign reserves to GDP (t-1)	1.269	2.276	-1.147	1.294	1.747	1.747	0.000	0.508	1.675	2.211	-0.642	0.893
	Current account to GDP (t-1) -3.434	-2.426	-0.192	1.250	-3.585	-3.585	0.000	1.479	-2.305	-2.512	0.040	1.207
	Financial openness index (t-1)	0.392	0.480	-0.257	1.097	0.440	0.440	0.000	1.067	0.517	0.474	0.124	1.096
	Log of trade openness to GDP (t-1)	3.885	4.031	-0.283	1.237	3.980	3.980	0.000	0.635	3.849	4.024	-0.357	0.931
	Terms of trade growth (t-1)	0.009	0.005	0.069	2.131	0.016	0.016	0.000	0.516	0.013	0.006	0.136	1.023
	Floating exchange rate regime (t-1)	0.395	0.091	0.751	2.969	0.269	0.269	0.000	1.026	0.225	0.111	0.305	1.807
	Government accountability index	0.379	0.683	-0.338	1.499	0.381	0.381	0.000	1.354	0.630	0.655	-0.029	1.295
	Corruption index -0.593	-0.589	-0.016	1.170	-0.632	-0.632	0.000	1.041	-0.637	-0.591	-0.187	0.903
	Trading partners' growth (t-1)	2.761	3.702	-0.496	0.799	3.153	3.153	0.000	0.924	3.338	3.633	-0.158	0.717
	US interest rate on gov.'s securities (t-1)	8.409	4.442	0.916	1.570	7.299	7.299	0.000	0.435	6.878	4.786	0.501	0.908

Covariate Balancing propensity score (22) Pooled probit Variables Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio

					Banking crises
			(1) Non weighted					(2) Weighted
	(21) # of Debt crises over past-5 years (t-5,t) 0.147 0.149 -0.005 1.266 0.154	0.154	0.000	1.431	0.173	0.149	0.051	1.778
	# of Currency crises over past-5 years (t-5,t)	0.206	0.163	0.106	1.114	0.161	0.161	0.000	0.943	0.210	0.166	0.109	1.118
	Intensity of conflicts (t-1)	1.353	1.222	0.053	1.471	1.184	1.184	0.000	1.593	1.142	1.230	-0.036	1.379
	Log of Real GDP per capita (t-1)	6.890	7.277	-0.099	1.258	7.238	7.238	0.000	1.708	7.286	7.259	0.007	1.268
	Log. of public debt to GDP (t-1)	3.551	3.680	-0.161	1.835	3.582	3.582	0.000	1.087	3.668	3.673	-0.007	1.434
	Log. of domestic credit to GDP (t-1)	3.617	3.470	0.235	0.818	3.546	3.546	0.000	0.515	3.554	3.477	0.131	0.598
	Log. of liquid liabilities to GDP (t-1)	3.438	3.650	-0.374	0.933	3.592	3.592	0.000	0.469	3.583	3.639	-0.112	0.516
	Net capital inflows to GDP (t-1)	3.389	2.467	0.198	0.646	2.583	2.584	0.000	0.638	2.402	2.514	-0.023	0.798
	Log. of foreign reserves to GDP (t-1)	1.809	2.244	-0.503	1.073	2.136	2.136	0.000	0.779	2.050	2.222	-0.198	1.039
	Current account to GDP (t-1) -3.308	-2.438	-0.191	0.640	-2.473	-2.474	0.000	0.625	-2.259	-2.485	0.048	0.759
	Financial openness index (t-1)	0.367	0.480	-0.347	0.911	0.483	0.483	0.000	1.038	0.482	0.475	0.021	1.089
	Log of trade openness to GDP (t-1)	3.822	4.033	-0.372	1.768	4.009	4.009	0.000	1.623	4.057	4.023	0.059	1.755
	Terms of trade growth (t-1)	0.013	0.005	0.181	0.539	0.010	0.010	0.000	0.517	0.009	0.005	0.073	0.461
	Floating exchange rate regime (t-1)	0.176	0.103	0.211	1.616	0.115	0.115	0.000	1.029	0.110	0.107	0.010	1.056
	Government accountability index	0.537	0.673	-0.157	1.287	0.713	0.713	0.000	1.010	0.713	0.666	0.056	1.132
	Corruption index -0.581	-0.589	0.035	0.914	-0.553	-0.553	0.000	1.161	-0.524	-0.588	0.235	1.327
	Trading partners' growth (t-1)	4.225	3.625	0.356	0.384	3.984	3.984	0.000	0.357	3.983	3.655	0.191	0.446
	US interest rate on gov.'s securities (t-1)	6.973	4.535	0.567	1.434	4.984	4.984	0.000	0.312	5.393	4.669	0.204	0.521

Table A .

 A 5: Balance diagnostics between the treated and control groups,

Currency crises Currency crises (1) Non weighted (2) Weighted (21) Covariate Balancing propensity score (22) Pooled probit Variables Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio Treated Control Std. mean Var. ratio

	# of Debt crises over past-5 years (t-5,t)	0.500	0.131	0.735	2.773	0.220	0.220	0.000	1.231	0.212	0.148	0.152	1.399
	# of Banking crises over past-5 years (t-5,t)	0.472	0.139	0.767	2.134	0.327	0.327	0.000	1.027	0.264	0.162	0.248	1.468
	Intensity of conflicts (t-1)	0.944	1.243	-0.149	0.554	0.994	0.994	0.000	0.653	0.509	1.219	-0.394	0.297
	Log of Real GDP per capita (t-1)	5.308	7.360	-0.490	1.707	6.914	6.915	0.000	1.319	7.130	7.262	-0.035	1.174
	Log. of public debt to GDP (t-1)	3.749	3.670	0.114	1.039	3.764	3.764	0.000	1.276	3.983	3.678	0.427	1.167
	Log. of domestic credit to GDP (t-1)	3.374	3.482	-0.167	0.913	3.474	3.474	0.000	1.125	3.603	3.477	0.197	0.872
	Log. of liquid liabilities to GDP (t-1)	3.432	3.651	-0.375	1.078	3.622	3.622	0.000	0.868	3.790	3.640	0.262	0.981
	Net capital inflows to GDP (t-1)	3.975	2.434	0.336	0.591	3.604	3.603	0.000	0.745	3.617	2.554	0.220	0.788
	Log. of foreign reserves to GDP (t-1)	1.252	2.274	-1.314	0.750	1.688	1.688	0.000	0.302	1.816	2.205	-0.467	0.749
	Current account to GDP (t-1) -3.531	-2.424	-0.243	0.631	-3.158	-3.157	0.000	0.736	-3.259	-2.499	-0.163	0.756
	Financial openness index (t-1)	0.327	0.483	-0.504	0.702	0.401	0.401	0.000	1.061	0.496	0.473	0.064	1.362
	Log of trade openness to GDP (t-1)	3.665	4.042	-0.740	1.230	3.840	3.840	0.000	0.591	3.912	4.018	-0.223	0.797
	Terms of trade growth (t-1)	0.013	0.005	0.154	1.399	0.009	0.009	0.000	0.829	0.005	0.006	-0.005	0.669
	Floating exchange rate regime (t-1)	0.389	0.092	0.733	2.923	0.213	0.213	0.000	1.027	0.140	0.109	0.092	1.270
	Government accountability index	0.455	0.678	-0.256	1.304	0.326	0.326	0.000	0.793	0.234	0.648	-0.486	1.077
	Corruption index -0.579	-0.589	0.040	1.150	-0.598	-0.598	0.000	1.550	-0.652	-0.588	-0.230	1.480
	Trading partners' growth (t-1)	3.501	3.661	-0.090	0.532	3.436	3.435	0.000	0.506	3.263	3.638	-0.216	0.483
	US interest rate on gov.'s securities (t-1)	7.388	4.507	0.669	1.466	6.088	6.088	0.000	1.265	5.335	4.718	0.139	1.432

A.4.1 Graphs A.4.1.1 Benchmark and robustness results

	Figure A.3: Robustness, cumulative trade costs over five years after combined financial crises
			(A) Debt Crises					(B) Banking Crises					(C)
	-5 0	-1.2 0.2 -0.9 -0.5	-0.8 0.1 -1.9 -0.7	-0.6 0.4 -3.0 -1.0	0.3 -1.8 -5.0	0.1 -1.1 -7.2	-0.2 1.3 -8.4	-5 0	0.1 -0.1 -0.2 -0.5	-0.3 -1.4 -4.2	-2.4 -0.6 -0.1 -3.7	-2.7 -0.2 -0.2 -3.0	-4.4 0.4 -2.1	-6.2 0.3 -2.1	-5 0	-1.6 -4.0 -0.2 -1.0	-1.3 -2.8 -1.2 -0.3	-1.5 -2.0 -1.8 0.1	-2.3 -2.7 -1.6 0.7	-4.2 -2.5 0.6	-5.1 -3.6 1.0
					-1.1					-2.7
													-3.6							-2.7
						-1.5
	-10						-2.4	-10						-4.2	-10						-3.3
	-15							-15							-15
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5

Currency Crises Panel I: Exports (% of pre-crisis GDP)

			(A) Debt Crises					(B) Banking Crises
	0	-0.4 -0.4 -2.5 -1.6	-0.7 -0.8 -4.9	-0.6 -1.2 -4.2	-0.7 -1.3 -3.8	-0.9 -0.2 -4.8	-0.5 -0.5 -3.5	0	-0.4 -0.0 -0.8 -1.9	-1.7 -0.9	-1.6 -1.3	-1.1 -1.4	-1.6 -1.6	-2.7 -1.8
	-5						-1.2	-5
												-7.3
			-1.9	-2.4	-2.5	-2.1				-8.6	-8.7		-7.3
														-8.5
	-10							-10
												-2.2
										-1.7	-2.2		-2.5
	-15							-15						-2.7
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5

A.4.2 Tables A.4.2.1 First stage model, pooled probit

	Table A.6: Treatment models predicting the likelihood of financial crises, pooled probit, average
	marginal effects
		(1)	(2)	(3)
		Debt	Banking Currency
	# of Debt crises over past-5 years (t-5,t)		-0.012	0.034**
			(0.022)	(0.014)
	# of Banking crises over past-5 years (t-5,t)	0.026		0.060***
		(0.016)		(0.016)
	# of Currency crises over past-5 years (t-5,t)	0.018	0.010
		(0.016)	(0.023)
	Intensity of conflicts (t-1)	-0.004	-0.002	-0.006
		(0.004)	(0.004)	(0.004)
	Log of real GDP (t-1)	-0.001	-0.002	-0.005**
		(0.002)	(0.002)	(0.002)
	Log. of public debt to GDP (t-1) 0.042***	0.004	-0.002
		(0.015)	(0.013)	(0.012)
	Log. of domestic credit to GDP (t-1)	0.010	0.049***	0.010
		(0.015)	(0.019)	(0.014)
	Log. of liquid liabilities to GDP (t-1)	-0.026	-0.051**	0.019
		(0.019)	(0.022)	(0.019)
	Net capital inflows to GDP (t-1)	0.002	0.001	0.002
		(0.005)	(0.007)	(0.005)
	Log. of foreign reserves to GDP (t-1) -0.038***	-0.013	-0.034***
		(0.010)	(0.012)	(0.011)
	Current account to GDP (t-1)	0.003	-0.001	-0.002
		(0.005)	(0.007)	(0.005)
	Financial openness index (t-1)	0.051*	-0.035	-0.002
		(0.027)	(0.026)	(0.026)
	Log of trade openness to GDP (t-1)	0.008	-0.015	-0.038**
		(0.017)	(0.018)	(0.017)
	Terms of trade growth (t-1)	0.000	0.107	0.050
		(0.101)	(0.132)	(0.104)
	Floating exchange rate regime (t-1) 0.054***	-0.028	0.047**
		(0.020)	(0.030)	(0.018)
	Government accountability index	-0.000	-0.000	-0.009
		(0.012)	(0.012)	(0.011)
	Corruption index	-0.008	0.002	0.003
		(0.034)	(0.036)	(0.035)
	Trading partners' growth (t-1) -0.010** 0.010**	0.001
		(0.004)	(0.005)	(0.004)
	US interest rate on gov.'s debt securities (t-1) 0.004**	0.002	-0.001
		(0.002)	(0.002)	(0.002)
	Observations	732	732	732
	# of crises	38	34	36
	Classification	94.262	95.219	95.082
	Model AUC	0.922	0.769	0.915
	s.e. AUC	0.016	0.038	0.019
	pseudoR2	0.338	0.121	0.343
	Notes: Robust standard errors clustered at the country-level in parenthe-
	ses. * < 0.10, * * < 0.05, * * * < 0.01. Pooled probit model. The
	coefficients are the average marginal effects at the mean. AUC denotes
	Area Under Receiver Operating Characteristic curve.

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

  : Benchmark results, Cumulative trade costs over five years after financial crises

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total	-0.134	-3.415*** -3.348*** -5.925*** -6.413*** -5.639***	ATE-AIPW Total -4.397*** -8.365*** -9.358*** -10.403*** -11.188*** -11.012***
		(0.342)	(0.466)	(0.597)	(0.860)	(1.093)	(1.514)		(0.471)	(0.649)	(1.004)	(1.322)	(1.668)	(2.246)
	ATE-AIPW Agriculture	-0.146	-0.048	0.382**	0.364	0.846**	2.248***	ATE-AIPW Agriculture -0.375*** -0.822*** -0.810*** -1.069*** -1.363*** -1.078***
		(0.122)	(0.141)	(0.184)	(0.293)	(0.395)	(0.623)		(0.073)	(0.081)	(0.134)	(0.159)	(0.188)	(0.245)
	ATE-AIPW Mining 1.420***	-0.404*	-0.013	-0.639*	-0.263	1.068*	ATE-AIPW Mining -0.644*** -1.492*** -1.664*** -2.103*** -1.934*** -1.927***
		(0.215)	(0.226)	(0.260)	(0.351)	(0.425)	(0.590)		(0.093)	(0.156)	(0.215)	(0.298)	(0.381)	(0.531)
	ATE-AIPW Manufacturing -0.862*** -2.026*** -2.605*** -4.057*** -5.098*** -6.065***	ATE-AIPW Manufacturing -2.265*** -4.432*** -4.456*** -4.730*** -5.207*** -5.388***
		(0.145)	(0.257)	(0.327)	(0.418)	(0.540)	(0.736)		(0.305)	(0.414)	(0.612)	(0.800)	(1.011)	(1.358)
	ATE-AIPW Services -0.546*** -0.936*** -1.113*** -1.592*** -1.898*** -2.890***	ATE-AIPW Services -1.113*** -1.619*** -2.429*** -2.502*** -2.685*** -2.619***
		(0.082)	(0.128)	(0.187)	(0.246)	(0.293)	(0.380)		(0.114)	(0.153)	(0.209)	(0.241)	(0.290)	(0.346)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	38	38	38	38	38	38	# of Crises	38	38	38	38	38	38
	# of Countries 41 ATE-AIPW Total -1.274*** -4.957*** -5.561*** -5.642*** -8.392*** -8.943*** 41 41	ATE-AIPW Total -3.208*** -7.119*** -9.158*** -9.746*** -11.466*** -14.059***
		(0.321)	(0.519)	(0.618)	(0.747)	(0.960)	(1.130)		(0.484)	(0.840)	(0.959)	(1.069)	(1.388)	(1.571)
	ATE-AIPW Agriculture	-0.086	-0.403*** -0.724*** -0.885*** -1.071*** -1.530***	ATE-AIPW Agriculture -0.248***	0.027	-0.417*** -0.732*** -0.962*** -1.493***
		(0.070)	(0.097)	(0.133)	(0.158)	(0.192)	(0.212)		(0.081)	(0.139)	(0.149)	(0.149)	(0.180)	(0.173)
	ATE-AIPW Mining	-0.156	-0.857*** -0.824*** -1.185*** -2.262*** -1.604***	ATE-AIPW Mining -0.666*** -1.310*** -1.669*** -1.938*** -2.421*** -2.934***
		(0.165)	(0.238)	(0.280)	(0.315)	(0.353)	(0.480)		(0.112)	(0.163)	(0.185)	(0.222)	(0.271)	(0.307)
	ATE-AIPW Manufacturing -0.362** -1.853*** -1.830*** -1.399*** -2.065*** -2.326***	ATE-AIPW Manufacturing -1.880*** -4.767*** -5.380*** -5.140*** -5.748*** -7.038***
		(0.152)	(0.305)	(0.385)	(0.480)	(0.599)	(0.728)		(0.271)	(0.509)	(0.578)	(0.644)	(0.849)	(0.989)
	ATE-AIPW Services -0.669*** -1.844*** -2.183*** -2.174*** -2.994*** -3.484***	ATE-AIPW Services -0.414*** -1.070*** -1.692*** -1.937*** -2.334*** -2.594***
		(0.102)	(0.170)	(0.238)	(0.318)	(0.394)	(0.503)		(0.108)	(0.134)	(0.187)	(0.219)	(0.262)	(0.293)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	34	34	34	34	34	34	# of Crises	34	34	34	34	34	34
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P

-value Total Exports= Total Imports

	0.019	0.000	0.002	0.250	0.008	0.011	P

-value Total Exports= Total Imports

	0.019	0.000	0.002	0.250	0.008	0.011

Panel C: Currency Crises Panel C: Currency Crises ATE-AIPW Total

  Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 10.

		-4.435*** -4.198*** -4.901*** -5.135*** -6.757*** -7.702***	ATE-AIPW Total -8.533*** -9.463*** -9.255*** -10.500*** -10.630*** -9.105***
		(0.356)	(0.440)	(0.616)	(0.776)	(0.896)	(1.113)		(0.506)	(0.629)	(0.871)	(1.098)	(1.308)	(1.640)
	ATE-AIPW Agriculture	-0.037	-0.257**	-0.234	-0.261	-0.079	0.039	ATE-AIPW Agriculture -0.761*** -0.764*** -0.838*** -1.080*** -0.831*** -0.667***
		(0.076)	(0.123)	(0.230)	(0.250)	(0.235)	(0.317)		(0.084)	(0.078)	(0.105)	(0.118)	(0.144)	(0.170)
	ATE-AIPW Mining -0.898*** -1.053*** -1.591*** -1.548*** -2.404*** -2.896***	ATE-AIPW Mining -1.765*** -1.508*** -1.952*** -2.263*** -2.978*** -2.957***
		(0.204)	(0.176)	(0.263)	(0.296)	(0.336)	(0.402)		(0.112)	(0.144)	(0.209)	(0.250)	(0.282)	(0.389)
	ATE-AIPW Manufacturing -2.578*** -2.282*** -2.584*** -3.127*** -3.852*** -3.959***	ATE-AIPW Manufacturing -4.930*** -5.963*** -5.179*** -5.839*** -5.801*** -4.557***
		(0.195)	(0.256)	(0.314)	(0.416)	(0.507)	(0.616)		(0.311)	(0.392)	(0.514)	(0.642)	(0.778)	(0.948)
	ATE-AIPW Services -0.923*** -0.605*** -0.491**	-0.199	-0.422	-0.886**	ATE-AIPW Services -1.077*** -1.229*** -1.286*** -1.318*** -1.021*** -0.925***
		(0.089)	(0.150)	(0.211)	(0.315)	(0.352)	(0.407)		(0.087)	(0.131)	(0.191)	(0.244)	(0.281)	(0.312)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	36	36	36	36	36	36	# of Crises	36	36	36	36	36	36
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P-value Total Exports= Total Imports	0.093	0.000	0.000	0.000	0.004	0.879	P-value Total Exports= Total Imports	0.093	0.000	0.000	0.000	0.004	0.879
	Notes:

Table A .

 A 8: Robustness, Cumulative trade costs over five years after combined financial crises

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total -2.398*** -3.242*** -4.211*** -7.640*** -9.691*** -9.805***	ATE-AIPW Total -4.958*** -8.270*** -8.344*** -8.271*** -8.116*** -5.690***
		(0.308)	(0.498)	(0.652)	(0.918)	(1.077)	(1.328)		(0.481)	(0.739)	(1.072)	(1.366)	(1.539)	(1.819)
	ATE-AIPW Agriculture -1.190*** -0.780*** -0.609***	0.292	0.137	-0.249	ATE-AIPW Agriculture -0.446*** -0.684*** -0.594*** -0.716*** -0.928***	-0.502**
		(0.119)	(0.133)	(0.187)	(0.297)	(0.318)	(0.342)		(0.066)	(0.093)	(0.138)	(0.192)	(0.179)	(0.216)
	ATE-AIPW Mining	0.173	0.061	0.400	-1.849*** -1.124***	1.272**	ATE-AIPW Mining -0.398*** -0.773*** -1.190*** -1.344***	-0.239	-0.480
		(0.119)	(0.230)	(0.272)	(0.345)	(0.405)	(0.575)		(0.093)	(0.160)	(0.213)	(0.294)	(0.379)	(0.514)
	ATE-AIPW Manufacturing -0.865*** -1.866*** -2.984*** -4.990*** -7.223*** -8.395***	ATE-AIPW Manufacturing -2.519*** -4.879*** -4.157*** -3.758*** -4.817*** -3.520***
		(0.150)	(0.273)	(0.347)	(0.450)	(0.562)	(0.673)		(0.310)	(0.464)	(0.655)	(0.799)	(0.870)	(0.970)
	ATE-AIPW Services -0.517*** -0.658*** -1.019*** -1.093*** -1.481*** -2.433***	ATE-AIPW Services -1.596*** -1.933*** -2.403*** -2.453*** -2.131*** -1.188***
		(0.089)	(0.144)	(0.205)	(0.260)	(0.303)	(0.378)		(0.113)	(0.160)	(0.211)	(0.247)	(0.281)	(0.314)
	Observations	689	689	689	689	689	689	Observations	689	689	689	689	689	689
	# of Crises	24	24	24	24	24	24	# of Crises	24	24	24	24	24	24
	# of Countries	40	40	40	40	40	40	# of Countries	40	40	40	40	40	40
	P-

value Total Exports= Total Imports

	0.000	0.000	0.000	0.461	0.087	0.000	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.461	0.087	0.000

Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total

		-0.561	-8.605*** -6.842*** -6.090*** -9.662*** -12.192***	ATE-AIPW Total -3.082*** -12.940*** -13.704*** -11.994*** -12.902*** -15.745***
		(0.378)	(0.488)	(0.686)	(0.780)	(0.910)	(1.054)		(0.586)	(1.003)	(1.262)	(1.328)	(1.350)	(1.677)
	ATE-AIPW Agriculture	0.140*	-0.299*** -0.627***	-0.209	0.405*	0.291	ATE-AIPW Agriculture -0.382*** -0.907*** -1.271*** -1.427*** -1.606*** -1.840***
		(0.077)	(0.091)	(0.137)	(0.172)	(0.213)	(0.229)		(0.078)	(0.145)	(0.172)	(0.162)	(0.158)	(0.188)
	ATE-AIPW Mining	-0.066	-1.362***	-0.090	-0.229	-2.077*** -2.056***	ATE-AIPW Mining	-0.008	-1.687*** -1.592*** -1.065*** -1.558*** -2.685***
		(0.154)	(0.219)	(0.314)	(0.351)	(0.417)	(0.542)		(0.128)	(0.199)	(0.242)	(0.275)	(0.295)	(0.347)
	ATE-AIPW Manufacturing	-0.155	-4.234*** -3.686*** -3.002*** -4.403*** -6.214***	ATE-AIPW Manufacturing -1.882*** -8.617*** -8.660*** -7.272*** -7.263*** -8.524***
		(0.177)	(0.283)	(0.354)	(0.449)	(0.581)	(0.703)		(0.340)	(0.590)	(0.738)	(0.767)	(0.786)	(1.016)
	ATE-AIPW Services -0.481*** -2.710*** -2.439*** -2.650*** -3.586*** -4.214***	ATE-AIPW Services -0.810*** -1.730*** -2.179*** -2.230*** -2.475*** -2.697***
		(0.114)	(0.184)	(0.264)	(0.328)	(0.422)	(0.541)		(0.109)	(0.141)	(0.195)	(0.235)	(0.252)	(0.276)
	Observations	689	689	689	689	689	689	Observations	689	689	689	689	689	689
	# of Crises	17	17	17	17	17	17	# of Crises	17	17	17	17	17	17
	# of Countries	40	40	40	40	40	40	# of Countries	40	40	40	40	40	40
	P

-value Total Exports= Total Imports

	0.000	0.000	0.000	0.000	0.001	0.003	P

-value Total Exports= Total Imports

	0.000	0.000	0.000	0.000	0.001	0.003

Panel C: Currency Crises Panel C: Currency Crises ATE-AIPW Total -

	6.901*** -5.610*** -5.217*** -5.917*** -8.798*** -11.019*** (0.325) (0.475) (0.651) (0.877) (0.972) (1.201) ATE-AIPW Agriculture -0.187** -0.257** 0.088 0.674*** 0.608** 0.953*** (0.079) (0.130) (0.240) (0.258) (0.238) (0.322) ATE-AIPW Mining -1.031*** -1.226*** -1.844*** -1.611*** -2.519*** -3.648*** (0.115) (0.220) (0.289) (0.299) (0.356) (0.509) ATE-AIPW Manufacturing -4.049*** -2.782*** -1.980*** -2.704*** -4.183*** -5.050*** (0.192) (0.272) (0.344) (0.446) (0.574) (0.723) ATE-AIPW Services -1.634*** -1.344*** -1.482*** -2.277*** -2.705*** -3.274*** (0.108) (0.166) (0.240) (0.506) (0.479) (0.476) Observations 689 689 689 689 689 689 # of Crises 24 24 24 24 24 24 # of Countries 40 40 40 40 40 40 40 Table A.9: Robustness, Cumulative trade costs over five years after non-combined financial crises ATE-AIPW Total -11.025*** -10.675*** -9.179*** -10.436*** -12.926*** -13.957*** (0.495) (0.653) (0.852) (1.251) (1.401) (1.697) ATE-AIPW Agriculture -0.791*** -0.677*** -0.836*** -1.018*** -1.041*** -1.241*** (0.085) (0.085) (0.102) (0.173) (0.156) (0.193) ATE-AIPW Mining -2.005*** -1.546*** -1.128*** -1.537*** -2.557*** -3.148*** (0.101) (0.143) (0.186) (0.260) (0.290) (0.411) ATE-AIPW Manufacturing -6.593*** -6.633*** -5.223*** -5.535*** -6.925*** -7.226*** (0.308) (0.420) (0.527) (0.702) (0.795) (0.944) ATE-AIPW Services -1.635*** -1.820*** -1.993*** -2.346*** -2.404*** -2.341*** (0.095) (0.122) (0.179) (0.345) (0.384) (0.356) Observations 689 689 689 689 689 689 # of Crises 24 24 24 24 24 24 # of Countries 40

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total 2.186*** -3.367*** -2.720*** -4.850*** -4.808*** -3.521*	ATE-AIPW Total -4.211*** -10.576*** -10.066*** -12.441*** -14.970*** -16.934***
		(0.433)	(0.601)	(0.880)	(1.331)	(1.572)	(1.899)		(0.464)	(0.662)	(1.140)	(1.468)	(2.040)	(2.776)
	ATE-AIPW Agriculture 0.658*** 0.375*** 0.950*** 0.827*** 1.417*** 3.495***	ATE-AIPW Agriculture -0.408*** -1.204***	-0.994***	-1.448***	-1.759***	-1.679***
		(0.075)	(0.121)	(0.163)	(0.215)	(0.313)	(0.390)		(0.115)	(0.122)	(0.164)	(0.185)	(0.243)	(0.317)
	ATE-AIPW Mining 2.403*** -1.013***	-0.450	-0.211	-0.239	0.666	ATE-AIPW Mining -0.712*** -2.037***	-1.914***	-2.991***	-3.302***	-3.217***
		(0.252)	(0.268)	(0.346)	(0.432)	(0.597)	(0.606)		(0.107)	(0.153)	(0.255)	(0.334)	(0.446)	(0.601)
	ATE-AIPW Manufacturing -0.452** -1.633*** -1.917*** -3.110*** -3.241*** -3.648***	ATE-AIPW Manufacturing -2.301*** -5.782***	-4.867***	-5.891***	-7.210***	-8.958***
		(0.183)	(0.322)	(0.454)	(0.722)	(0.793)	(0.993)		(0.276)	(0.420)	(0.673)	(0.855)	(1.187)	(1.617)
	ATE-AIPW Services -0.422*** -1.096*** -1.303*** -2.356*** -2.745*** -4.035***	ATE-AIPW Services -0.791*** -1.553***	-2.291***	-2.110***	-2.699***	-3.080***
		(0.162)	(0.195)	(0.283)	(0.388)	(0.442)	(0.557)		(0.094)	(0.136)	(0.210)	(0.282)	(0.355)	(0.451)
	Observations	670	670	670	670	670	670	Observations	670	670	670	670	670	670
	# of Crises	13	13	13	13	13	13	# of Crises	13	13	13	13	13	13
	# of Countries	40	40	40	40	40	40	# of Countries	40	40	40 0.000	0.000	0.000	0.000

Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total

		-1.570*** -4.914*** -5.442*** -5.659*** -8.415*** -7.794***	ATE-AIPW Total -4.305*** -8.532***	-8.706*** -10.003*** -12.822*** -13.706***
		(0.403)	(0.548)	(0.651)	(0.833)	(1.199)	(1.225)		(0.524)	(1.021)	(1.163)	(1.462)	(2.028)	(2.020)
	ATE-AIPW Agriculture	-0.037	-0.490*** -0.578*** -0.848*** -1.333*** -1.901***	ATE-AIPW Agriculture -0.360*** -0.577***	-0.639***	-0.912***	-1.401***	-1.545***
		(0.076)	(0.128)	(0.146)	(0.170)	(0.200)	(0.211)		(0.085)	(0.119)	(0.133)	(0.177)	(0.247)	(0.237)
	ATE-AIPW Mining	-0.117	-1.071*** -1.285*** -1.546*** -2.359*** -1.226**	ATE-AIPW Mining -1.018*** -1.693***	-2.009***	-2.313***	-3.181***	-3.337***
		(0.241)	(0.185)	(0.219)	(0.295)	(0.362)	(0.481)		(0.134)	(0.231)	(0.256)	(0.308)	(0.433)	(0.384)
	ATE-AIPW Manufacturing -0.531*** -1.537*** -1.525*** -1.280** -1.843**	-1.453*	ATE-AIPW Manufacturing -2.356*** -4.814***	-4.436***	-5.011***	-5.923***	-6.444***
		(0.194)	(0.332)	(0.421)	(0.531)	(0.725)	(0.783)		(0.300)	(0.597)	(0.662)	(0.817)	(1.127)	(1.209)
	ATE-AIPW Services -0.885*** -1.817*** -2.054*** -1.986*** -2.879*** -3.215***	ATE-AIPW Services -0.571*** -1.448***	-1.622***	-1.767***	-2.316***	-2.380***
		(0.105)	(0.186)	(0.269)	(0.373)	(0.493)	(0.594)		(0.099)	(0.171)	(0.239)	(0.295)	(0.362)	(0.362)
	Observations	681	681	681	681	681	681	Observations	681	681	681	681	681	681
	# of Crises	17	17	17	17	17	17	# of Crises	17	17	17	17	17	17
	# of Countries 41 ATE-AIPW Total -2.264*** -1.741*** -4.252*** -3.546*** -2.154** -2.988** 41 41	ATE-AIPW Total -6.517*** -9.494*** -12.343*** -12.761*** -9.287***	-6.618***
		(0.318)	(0.478)	(0.600)	(0.811)	(0.954)	(1.183)		(0.438)	(0.763)	(1.090)	(1.265)	(1.490)	(1.802)
	ATE-AIPW Agriculture	0.112*	-0.490*** -0.635*** -1.096*** -0.996*** -1.036***	ATE-AIPW Agriculture -0.640*** -1.054***	-1.334***	-1.411***	-0.832***	-0.562***
		(0.059)	(0.094)	(0.113)	(0.149)	(0.176)	(0.223)		(0.058)	(0.094)	(0.138)	(0.140)	(0.163)	(0.197)
	ATE-AIPW Mining -1.807*** -1.095*** -1.635*** -2.340*** -2.483*** -2.801***	ATE-AIPW Mining -2.042*** -2.197***	-4.469***	-4.466***	-4.708***	-4.350***
		(0.216)	(0.286)	(0.268)	(0.341)	(0.393)	(0.559)		(0.120)	(0.165)	(0.287)	(0.343)	(0.383)	(0.453)
	ATE-AIPW Manufacturing -0.423*** -0.607** -2.625*** -2.407*** -2.039*** -2.206***	ATE-AIPW Manufacturing -3.440*** -5.908***	-5.938***	-6.410***	-4.226***	-3.079***
		(0.151)	(0.252)	(0.331)	(0.440)	(0.525)	(0.628)		(0.262)	(0.469)	(0.645)	(0.745)	(0.877)	(1.028)
	ATE-AIPW Services	-0.145	0.451**	0.642**	2.297*** 3.365*** 3.056***	ATE-AIPW Services -0.394***	-0.336**	-0.602***	-0.475*	0.479	1.372***
		(0.116)	(0.180)	(0.257)	(0.348)	(0.383)	(0.449)		(0.091)	(0.151)	(0.192)	(0.250)	(0.293)	(0.336)
	Observations	681	681	681	681	681	681	Observations	681	681	681	681	681	681
	# of Crises

Table A .

 A 10: Robustness, Drop the period 2008 onwards, Cumulative trade costs over five years after financial crises

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total	-0.070	-1.142**	-0.504	-2.795** -2.855**	-2.373	ATE-AIPW Total -4.044*** -6.069*** -5.434*** -6.101*** -6.073*** -8.638***
		(0.452)	(0.567)	(0.756)	(1.128)	(1.374)	(1.830)		(0.649)	(0.850)	(1.300)	(1.576)	(1.858)	(2.300)
	ATE-AIPW Agriculture -0.444***	-0.179	0.528**	0.544	0.584	0.929**	ATE-AIPW Agriculture -0.293** -0.636*** -0.414** -0.637** -0.662*** -0.600**
		(0.155)	(0.151)	(0.225)	(0.395)	(0.419)	(0.415)		(0.148)	(0.140)	(0.200)	(0.252)	(0.211)	(0.270)
	ATE-AIPW Mining 1.261*** 0.812*** 0.864***	-0.201	0.247	1.634*	ATE-AIPW Mining -0.540*** -0.489***	-0.356	-0.732** -0.871** -1.751***
		(0.303)	(0.312)	(0.333)	(0.447)	(0.580)	(0.909)		(0.122)	(0.156)	(0.217)	(0.318)	(0.416)	(0.544)
	ATE-AIPW Manufacturing -0.487*** -1.118*** -1.414*** -2.449*** -2.896*** -3.589***	ATE-AIPW Manufacturing -2.178*** -3.678*** -3.045*** -3.162*** -3.106*** -4.758***
		(0.150)	(0.305)	(0.430)	(0.574)	(0.729)	(0.963)		(0.419)	(0.549)	(0.817)	(0.939)	(1.116)	(1.330)
	ATE-AIPW Services -0.400*** -0.657***	-0.481*	-0.689**	-0.790** -1.348***	ATE-AIPW Services -1.032*** -1.266*** -1.619*** -1.570*** -1.435*** -1.528***
		(0.106)	(0.173)	(0.265)	(0.305)	(0.364)	(0.463)		(0.170)	(0.219)	(0.295)	(0.314)	(0.383)	(0.449)
	Observations	409	409	409	409	409	409	Observations	409	409	409	409	409	409
	# of Crises	33	33	33	33	33	33	# of Crises	33	33	33	33	33	33
	# of Countries	39	39	39	39	39	39	# of Countries	39	39	39	39	39	39
	P-

value Total Exports= Total Imports

	0.000	0.000	0.000	0.002	0.001	0.000	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.002	0.001	0.000

Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total

		-1.009*** -2.124*** -2.521*** -2.977*** -5.977*** -7.152***	ATE-AIPW Total -2.645*** -3.893*** -4.564*** -4.511*** -5.715*** -8.990***
		(0.341)	(0.511)	(0.668)	(0.925)	(1.226)	(1.485)		(0.535)	(0.754)	(0.992)	(1.344)	(1.993)	(2.723)
	ATE-AIPW Agriculture	0.070	-0.020	-0.136	-0.281	-0.494* -0.741***	ATE-AIPW Agriculture -0.226**	-0.040	-0.166	-0.398*	-0.533**	-0.871**
		(0.095)	(0.131)	(0.182)	(0.204)	(0.257)	(0.251)		(0.107)	(0.125)	(0.151)	(0.204)	(0.250)	(0.347)
	ATE-AIPW Mining	-0.066	-0.018	0.176	-0.306	-1.912*** -1.305**	ATE-AIPW Mining -0.278*** -0.234** -0.394*** -0.647*** -1.140*** -1.881***
		(0.135)	(0.195)	(0.273)	(0.315)	(0.365)	(0.628)		(0.081)	(0.113)	(0.144)	(0.212)	(0.354)	(0.522)
	ATE-AIPW Manufacturing -0.391**	-0.789** -1.119**	-1.095*	-1.591** -2.419**	ATE-AIPW Manufacturing -1.576*** -2.831*** -2.971*** -2.405*** -2.544** -4.279***
		(0.165)	(0.310)	(0.469)	(0.635)	(0.794)	(1.022)		(0.326)	(0.495)	(0.641)	(0.839)	(1.224)	(1.642)
	ATE-AIPW Services -0.622*** -1.297*** -1.442*** -1.295*** -1.981*** -2.686***	ATE-AIPW Services -0.566*** -0.789*** -1.034*** -1.062*** -1.498*** -1.959***
		(0.152)	(0.219)	(0.301)	(0.418)	(0.514)	(0.632)		(0.150)	(0.162)	(0.241)	(0.289)	(0.370)	(0.438)
	Observations	409	409	409	409	409	409	Observations	409	409	409	409	409	409
	# of Crises	31	31	31	31	31	31	# of Crises	31	31	31	31	31	31
	# of Countries	39	39	39	39	39	39	# of Countries	39	39	39	39	39	39
	P

-value Total Exports= Total Imports

	0.002	0.003	0.012	0.119	0.837	0.342	P

-value Total Exports= Total Imports

	0.002	0.003	0.012	0.119	0.837	0.342

Panel C: Currency Crises Panel C: Currency Crises ATE-AIPW Total

  Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 10.

		-1.166***	-0.930*	-2.269*** -2.112*	-4.091*** -7.208***	ATE-AIPW Total -4.687*** -5.187*** -5.589*** -5.694*** -5.854*** -7.014***
		(0.424)	(0.511)	(0.833)	(1.105)	(1.337)	(1.736)		(0.650)	(0.789)	(1.123)	(1.474)	(1.874)	(2.376)
	ATE-AIPW Agriculture 0.244**	0.148	0.171	-0.159	0.347	0.542*	ATE-AIPW Agriculture -0.549*** -0.318*** -0.358** -0.441***	-0.202	-0.174
		(0.115)	(0.169)	(0.360)	(0.344)	(0.287)	(0.315)		(0.125)	(0.115)	(0.141)	(0.158)	(0.172)	(0.198)
	ATE-AIPW Mining	-0.365	-0.479** -1.041*** -0.906** -2.210*** -3.164***	ATE-AIPW Mining -0.888*** -0.445*** -1.317*** -1.256*** -1.780*** -2.058***
		(0.347)	(0.219)	(0.343)	(0.369)	(0.407)	(0.525)		(0.136)	(0.157)	(0.270)	(0.296)	(0.380)	(0.449)
	ATE-AIPW Manufacturing -0.564***	-0.603* -1.686*** -2.070*** -2.865*** -4.210***	ATE-AIPW Manufacturing -2.608*** -3.841*** -3.345*** -3.483*** -3.557*** -4.313***
		(0.163)	(0.312)	(0.447)	(0.653)	(0.817)	(1.038)		(0.413)	(0.529)	(0.701)	(0.951)	(1.224)	(1.555)
	ATE-AIPW Services -0.482***	0.004	0.288	1.022***	0.636	-0.377	ATE-AIPW Services -0.642*** -0.582*** -0.569**	-0.514*	-0.314	-0.469
		(0.106)	(0.172)	(0.279)	(0.393)	(0.458)	(0.598)		(0.124)	(0.171)	(0.245)	(0.301)	(0.359)	(0.442)
	Observations	409	409	409	409	409	409	Observations	409	409	409	409	409	409
	# of Crises	33	33	33	33	33	33	# of Crises	33	33	33	33	33	33
	# of Countries	39	39	39	39	39	39	# of Countries	39	39	39	39	39	39
	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.000	0.086	0.869	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.000	0.086	0.869
	Notes:

Table A .

 A 11: Robustness, Maximum weight set to 20, Cumulative trade costs over five years after financial crises

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total	-0.080	-3.186*** -2.749*** -4.965*** -5.193*** -4.224*	ATE-AIPW Total -3.748*** -8.087*** -7.590*** -8.127*** -8.633***	-8.839**
		(0.548)	(0.646)	(0.849)	(1.291)	(1.693)	(2.526)		(0.760)	(0.935)	(1.596)	(2.012)	(2.515)	(3.475)
	ATE-AIPW Agriculture	-0.239	0.069	0.595**	0.730	1.240*	2.661**	ATE-AIPW Agriculture -0.352*** -0.783*** -0.594** -0.896*** -1.113***	-0.845**
		(0.220)	(0.244)	(0.301)	(0.504)	(0.706)	(1.181)		(0.117)	(0.118)	(0.233)	(0.218)	(0.271)	(0.333)
	ATE-AIPW Mining 1.544***	-0.291	0.188	-0.361	0.203	1.851*	ATE-AIPW Mining -0.622*** -1.464*** -1.491*** -1.850*** -1.648***	-1.656**
		(0.362)	(0.370)	(0.401)	(0.548)	(0.686)	(1.003)		(0.126)	(0.239)	(0.334)	(0.468)	(0.590)	(0.842)
	ATE-AIPW Manufacturing -0.837*** -2.029*** -2.474*** -3.849*** -4.870*** -5.933***	ATE-AIPW Manufacturing -1.834*** -4.289*** -3.342*** -3.281***	-3.542**	-4.011*
		(0.186)	(0.308)	(0.413)	(0.539)	(0.731)	(1.128)		(0.492)	(0.595)	(0.968)	(1.231)	(1.573)	(2.202)
	ATE-AIPW Services -0.547*** -0.936*** -1.057*** -1.485*** -1.766*** -2.803***	ATE-AIPW Services -0.939*** -1.551*** -2.164*** -2.099*** -2.330*** -2.327***
		(0.103)	(0.155)	(0.249)	(0.326)	(0.358)	(0.483)		(0.188)	(0.238)	(0.332)	(0.353)	(0.427)	(0.501)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	38	38	38	38	38	38	# of Crises	38	38	38	38	38	38
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P-

value Total Exports= Total Imports

	0.000	0.000	0.000	0.021	0.015	0.029	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.021	0.015	0.029

Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total

		-1.507*** -5.105*** -5.351*** -5.129*** -7.698*** -7.614***	ATE-AIPW Total -3.827*** -8.575*** -9.925*** -10.447*** -11.951*** -13.271***
		(0.432)	(0.658)	(0.723)	(0.934)	(1.229)	(1.429)		(0.644)	(1.061)	(1.137)	(1.190)	(1.660)	(1.891)
	ATE-AIPW Agriculture	-0.131	-0.407*** -0.639*** -0.811*** -1.004*** -1.383***	ATE-AIPW Agriculture -0.350*** -0.488*** -0.683*** -0.956*** -1.244*** -1.518***
		(0.092)	(0.130)	(0.184)	(0.207)	(0.259)	(0.286)		(0.115)	(0.149)	(0.170)	(0.159)	(0.193)	(0.192)
	ATE-AIPW Mining	-0.259	-1.041*** -0.919*** -1.153*** -2.258*** -1.252*	ATE-AIPW Mining -0.802*** -1.425*** -1.742*** -2.041*** -2.414*** -2.775***
		(0.232)	(0.276)	(0.320)	(0.404)	(0.433)	(0.658)		(0.139)	(0.193)	(0.209)	(0.253)	(0.318)	(0.361)
	ATE-AIPW Manufacturing -0.347*	-1.664*** -1.508***	-0.948	-1.470*	-1.559	ATE-AIPW Manufacturing -2.142*** -5.332*** -5.675*** -5.466*** -5.927*** -6.406***
		(0.193)	(0.439)	(0.528)	(0.646)	(0.802)	(0.997)		(0.372)	(0.685)	(0.722)	(0.768)	(1.084)	(1.280)
	ATE-AIPW Services -0.769*** -1.994*** -2.285*** -2.217*** -2.967*** -3.419***	ATE-AIPW Services -0.533*** -1.331*** -1.825*** -1.983*** -2.365*** -2.572***
		(0.148)	(0.248)	(0.342)	(0.466)	(0.545)	(0.684)		(0.152)	(0.166)	(0.252)	(0.281)	(0.328)	(0.369)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	34	34	34	34	34	34	# of Crises	34	34	34	34	34	34
	# of Countries 41 ATE-AIPW Total -4.289*** -3.923*** -4.340*** -4.417*** -6.314*** -7.098*** 41 41	ATE-AIPW Total -8.398*** -9.289*** -8.674*** -9.452*** -9.760*** -8.094***
		(0.497)	(0.620)	(0.918)	(1.114)	(1.285)	(1.619)		(0.790)	(0.903)	(1.243)	(1.544)	(1.869)	(2.475)
	ATE-AIPW Agriculture	0.042	-0.127	0.011	-0.037	0.120	0.451	ATE-AIPW Agriculture -0.684*** -0.693*** -0.744*** -0.976*** -0.730***	-0.502**
		(0.113)	(0.211)	(0.422)	(0.454)	(0.396)	(0.550)		(0.145)	(0.117)	(0.156)	(0.162)	(0.202)	(0.256)
	ATE-AIPW Mining -0.867*** -1.007*** -1.485*** -1.407*** -2.445*** -2.810***	ATE-AIPW Mining -1.751*** -1.403*** -1.668*** -1.866*** -2.679*** -2.593***
		(0.249)	(0.252)	(0.405)	(0.408)	(0.442)	(0.548)		(0.157)	(0.206)	(0.308)	(0.366)	(0.385)	(0.606)
	ATE-AIPW Manufacturing -2.511*** -2.195*** -2.434*** -2.924*** -3.789*** -4.206***	ATE-AIPW Manufacturing -4.875*** -5.985*** -5.068*** -5.458*** -5.487*** -4.359***
		(0.296)	(0.351)	(0.401)	(0.565)	(0.675)	(0.807)		(0.490)	(0.566)	(0.727)	(0.898)	(1.121)	(1.400)
	ATE-AIPW Services -0.954*** -0.594***	-0.433	-0.049	-0.200	-0.533	ATE-AIPW Services -1.088*** -1.208*** -1.194*** -1.151***	-0.864**	-0.639
		(0.120)	(0.217)	(0.286)	(0.423)	(0.505)	(0.590)		(0.116)	(0.182)	(0.257)	(0.330)	(0.387)	(0.430)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	36	36	36	36	36	36	# of Crises	36	36	36	36	36	36
	# of Countries	41	41	41

Table A .

 A 12: Robustness, Maximum weight set to 5, Cumulative trade costs over five years after financial crises

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP)

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total	-0.404	-3.766*** -3.717*** -6.236*** -6.823*** -6.020***	ATE-AIPW Total -4.413*** -9.211***	-9.623*** -10.631*** -11.542*** -11.879***
		(0.250)	(0.364)	(0.479)	(0.668)	(0.824)	(1.058)		(0.333)	(0.511)	(0.725)	(0.979)	(1.244)	(1.639)
	ATE-AIPW Agriculture -0.163**	-0.058	0.356***	0.359*	0.820*** 2.174***	ATE-AIPW Agriculture -0.423*** -0.941***	-0.870***	-1.092***	-1.364***	-1.181***
		(0.074)	(0.093)	(0.126)	(0.187)	(0.241)	(0.352)		(0.055)	(0.067)	(0.092)	(0.126)	(0.147)	(0.192)
	ATE-AIPW Mining 1.175*** -0.659***	-0.301	-0.882*** -0.580*	0.756*	ATE-AIPW Mining -0.685*** -1.626***	-1.836***	-2.378***	-2.224***	-2.277***
		(0.150)	(0.162)	(0.200)	(0.271)	(0.319)	(0.411)		(0.077)	(0.119)	(0.156)	(0.212)	(0.268)	(0.368)
	ATE-AIPW Manufacturing -0.913*** -2.142*** -2.721*** -4.163*** -5.252*** -6.116***	ATE-AIPW Manufacturing -2.296*** -5.006***	-4.604***	-4.822***	-5.380***	-5.893***
		(0.124)	(0.214)	(0.283)	(0.366)	(0.461)	(0.578)		(0.211)	(0.325)	(0.447)	(0.591)	(0.741)	(0.964)
	ATE-AIPW Services -0.503*** -0.907*** -1.052*** -1.550*** -1.811*** -2.834***	ATE-AIPW Services -1.009*** -1.637***	-2.312***	-2.339***	-2.575***	-2.528***
		(0.073)	(0.115)	(0.163)	(0.214)	(0.260)	(0.329)		(0.075)	(0.110)	(0.151)	(0.185)	(0.226)	(0.272)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	38	38	38	38	38	38	# of Crises	38	38	38	38	38	38
	# of Countries	41	41	41	41	41	41	# of Countries 0.000	0.000	0.000	0.000

Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total

		-1.558*** -5.542*** -6.014*** -5.958*** -8.742*** -9.041***	ATE-AIPW Total -3.602*** -8.758*** -10.466*** -10.817*** -12.541*** -14.174***
		(0.259)	(0.423)	(0.546)	(0.658)	(0.824)	(0.965)		(0.365)	(0.669)	(0.824)	(0.963)	(1.234)	(1.415)
	ATE-AIPW Agriculture	-0.050	-0.409*** -0.713*** -0.863*** -1.051*** -1.413***	ATE-AIPW Agriculture -0.357*** -0.537***	-0.760***	-1.032***	-1.339***	-1.565***
		(0.057)	(0.079)	(0.110)	(0.134)	(0.156)	(0.178)		(0.053)	(0.085)	(0.099)	(0.112)	(0.142)	(0.157)
	ATE-AIPW Mining -0.392*** -1.175*** -0.996*** -1.300*** -2.419*** -1.659***	ATE-AIPW Mining -0.696*** -1.478***	-1.860***	-2.103***	-2.604***	-3.084***
		(0.129)	(0.207)	(0.260)	(0.282)	(0.323)	(0.413)		(0.093)	(0.141)	(0.169)	(0.203)	(0.249)	(0.281)
	ATE-AIPW Manufacturing -0.339** -1.953*** -1.927*** -1.456*** -2.142*** -2.371***	ATE-AIPW Manufacturing -1.996*** -5.431***	-5.991***	-5.652***	-6.143***	-6.881***
		(0.135)	(0.238)	(0.314)	(0.398)	(0.501)	(0.600)		(0.211)	(0.405)	(0.494)	(0.569)	(0.729)	(0.856)
	ATE-AIPW Services -0.777*** -2.006*** -2.379*** -2.339*** -3.130*** -3.597***	ATE-AIPW Services -0.553*** -1.312***	-1.855***	-2.031***	-2.455***	-2.644***
		(0.081)	(0.132)	(0.188)	(0.250)	(0.315)	(0.388)		(0.073)	(0.113)	(0.156)	(0.196)	(0.235)	(0.256)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	34	34	34	34	34	34	# of Crises	34	34	34	34	34	34
	# of Countries	41	41	41 663***
		(0.274)	(0.370)	(0.502)	(0.642)	(0.758)	(0.935)		(0.386)	(0.514)	(0.697)	(0.881)	(1.078)	(1.310)
	ATE-AIPW Agriculture -0.097*	-0.331*** -0.328**	-0.350**	-0.175	-0.005	ATE-AIPW Agriculture -0.819*** -0.798***	-0.908***	-1.140***	-0.906***	-0.752***
		(0.054)	(0.082)	(0.138)	(0.156)	(0.160)	(0.207)		(0.054)	(0.061)	(0.084)	(0.095)	(0.118)	(0.138)
	ATE-AIPW Mining -1.054*** -1.154*** -1.687*** -1.779*** -2.614*** -3.177***	ATE-AIPW Mining -1.875*** -1.600***	-2.004***	-2.377***	-3.141***	-3.263***
		(0.131)	(0.149)	(0.211)	(0.260)	(0.299)	(0.360)		(0.088)	(0.115)	(0.155)	(0.195)	(0.234)	(0.304)
	ATE-AIPW Manufacturing -2.690*** -2.398*** -2.672*** -3.211*** -4.060*** -4.451***	ATE-AIPW Manufacturing -5.328*** -6.348***	-5.718***	-6.368***	-6.468***	-5.713***
		(0.156)	(0.220)	(0.283)	(0.367)	(0.454)	(0.555)		(0.238)	(0.324)	(0.425)	(0.525)	(0.641)	(0.762)
	ATE-AIPW Services -0.944*** -0.643*** -0.526***	-0.248	-0.472*	-0.731**	ATE-AIPW Services -1.156*** -1.320***	-1.422***	-1.459***	-1.176***	-0.934***
		(0.077)	(0.122)	(0.173)	(0.259)	(0.285)	(0.330)		(0.072)	(0.104)	(0.147)	(0.197)	(0.231)	(0.256)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	36	36	36	36	36	36	# of Crises	36	36	36	36	36	36
	# of Countries	41	41	41

ATE-AIPW

Total -4.785*** -4.526*** -5.213*** -5.588*** -7.321*** -8.365*** ATE-AIPW Total -9.178*** -10.066*** -10.053*** -11.343*** -11.691*** -10.

Table A .

 A 13: Robustness, Alternative sources and definitions of crises, Cumulative trade costs over five years after financial crises

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total -1.408*** -2.015*** -3.179*** -4.189*** -5.069*** -5.063***	ATE-AIPW Total -3.102*** -5.427*** -5.799*** -4.693*** -4.553***	-2.668
		(0.384)	(0.551)	(0.769)	(1.102)	(1.256)	(1.611)		(0.464)	(0.702)	(1.044)	(1.385)	(1.582)	(1.806)
	ATE-AIPW Agriculture	0.151	0.212	0.574***	0.401	0.466	1.195***	ATE-AIPW Agriculture -0.286*** -0.422*** -0.429*** -0.318*	-0.359**	-0.191
		(0.115)	(0.160)	(0.216)	(0.338)	(0.353)	(0.432)		(0.078)	(0.092)	(0.112)	(0.191)	(0.161)	(0.184)
	ATE-AIPW Mining -0.589** -0.961*** -1.262*** -1.445*** -1.474*** -1.393**	ATE-AIPW Mining -0.296*** -0.614*** -0.905*** -1.289*** -1.556*** -1.478***
		(0.239)	(0.251)	(0.349)	(0.481)	(0.518)	(0.672)		(0.084)	(0.128)	(0.198)	(0.259)	(0.303)	(0.471)
	ATE-AIPW Manufacturing -0.743*** -1.010*** -2.167*** -2.953*** -3.993*** -4.949***	ATE-AIPW Manufacturing -1.642*** -3.224*** -3.412*** -2.116**	-1.952*	-0.854
		(0.131)	(0.269)	(0.367)	(0.493)	(0.633)	(0.786)		(0.305)	(0.461)	(0.663)	(0.878)	(0.997)	(1.059)
	ATE-AIPW Services -0.227***	-0.257*	-0.325	-0.192	-0.068	0.084						686**	-0.145
		(0.082)	(0.133)	(0.199)	(0.240)	(0.312)	(0.398)		(0.111)	(0.163)	(0.230)	(0.249)	(0.288)	(0.315)
	Observations	590	590	590	590	590	590	Observations	590	590	590	590	590	590
	# of Crises	41	41	41	41	41	41	# of Crises	41	41	41	41	41	41
	# of Countries	31	31	31	31	31	31	# of Countries	31	31	31	31	31	31
	P-value Total Exports= Total Imports	0.000	0.000	0.001	0.552	0.559	0.014	P-value Total Exports= Total Imports	0.000	0.000	0.001	0.552	0.559	0.014
														*
		(0.340)	(0.474)	(0.589)	(0.789)	(1.044)	(1.252)		(0.529)	(0.761)	(0.952)	(1.048)	(1.325)	(1.577)
	ATE-AIPW Agriculture	-0.117	-0.196	-0.325*	-0.349	-0.574**	-0.300							*
		(0.093)	(0.131)	(0.195)	(0.219)	(0.257)	(0.354)		(0.113)	(0.118)	(0.133)	(0.139)	(0.139)	(0.

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP) ATE-AIPW Services -0.878*** -1.168*** -1.053*** -0.970*** -0.Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total -1.525*** -4.126*** -5.473*** -6.291*** -8.058*** -8.470*** ATE-AIPW Total -3.179*** -6.036*** -7.383*** -8.324*** -8.729*** -10.438**ATE-AIPW Agriculture -0.331*** -0.446*** -0.464*** -0.699*** -0.670*** -0.908**

value Total Exports= Total Imports

														**
		(0.102)	(0.168)	(0.220)	(0.264)	(0.336)	(0.392)		(0.095)	(0.114)	(0.180)	(0.205)	(0.229)	(0.263)
	Observations	590	590	590	590	590	590	Observations	590	590	590	590	590	590
	# of Crises	44	44	44	44	44	44	# of Crises	44	44	44	44	44	44
	# of Countries	31	31	31	31	31	31	# of Countries	31	31	31	31	31	31
	P-0.001	0.001	0.006	0.004	0.379	0.063	P-value Total Exports= Total Imports	0.001	0.001	0.006	0.004	0.379	0.063
	Panel C: Currency Crises							Panel C: Currency Crises
	ATE-AIPW Total	-0.609	-3.360*** -4.102*** -3.490*** -4.686*** -5.157***	ATE-AIPW Total	-0.548	-5.051*** -5.245*** -3.995*** -5.357*** -4.713**
		(0.462)	(0.656)	(0.727)	(0.945)	(1.332)	(1.739)		(0.512)	(0.762)	(0.958)	(1.163)	(1.473)	(2.081)
	ATE-AIPW Agriculture	0.003	-0.317*	-0.475*** -0.507** -0.616*** -0.584**	ATE-AIPW Agriculture	-0.092	-0.484*** -0.508*** -0.408*** -0.653*** -0.475**
		(0.168)	(0.175)	(0.182)	(0.222)	(0.229)	(0.296)		(0.060)	(0.083)	(0.108)	(0.140)	(0.156)	(0.207)
	ATE-AIPW Mining	-0.394	-1.075*** -1.147*** -1.115*** -1.933*** -2.186***	ATE-AIPW Mining	0.028	-0.861*** -0.953*** -0.968*** -1.219*** -1.239***
		(0.248)	(0.275)	(0.348)	(0.407)	(0.478)	(0.577)		(0.124)	(0.149)	(0.175)	(0.217)	(0.258)	(0.361)
	ATE-AIPW Manufacturing	0.075	-0.994*** -1.309***	-0.894	-1.150	-0.984	ATE-AIPW Manufacturing	-0.424	-3.112*** -2.973*** -1.941** -2.783***	-2.315*
		(0.220)	(0.383)	(0.473)	(0.656)	(0.934)	(1.262)		(0.331)	(0.533)	(0.668)	(0.823)	(1.023)	(1.398)
	ATE-AIPW Services -0.294** -0.974*** -1.171*** -0.975*** -0.988** -1.403***	ATE-AIPW Services	-0.059	-0.594*** -0.811*** -0.679*** -0.702**	-0.684*
		(0.121)	(0.194)	(0.245)	(0.318)	(0.432)	(0.462)		(0.111)	(0.129)	(0.181)	(0.218)	(0.291)	(0.370)
	Observations	590	590	590	590	590	590	Observations	590	590	590	590	590	590
	# of Crises	69	69	69	69	69	69	# of Crises	69	69	69	69	69	69
	# of Countries	31	31	31	31	31	31	# of Countries	31	31	31	31	31	31
	P-value Total Exports= Total Imports	0.898	0.004	0.154	0.590	0.557	0.715	P-value Total Exports= Total Imports	0.898	0.004	0.154	0.590	0.557	0.715

Table A .

 A 14: Robustness, Inverse Probability Weighted (IPW) estimator, Cumulative trade costs over five years after financial crises

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt Crises							Panel A: Debt Crises
	ATE-AIPW Total -1.217*** -2.667*** -3.576*** -5.509*** -5.567*** -4.068***	ATE-AIPW Total -5.012*** -5.826*** -6.225*** -7.082*** -5.982*** -3.581***
		(0.284)	(0.898)	(1.112)	(1.164)	(1.066)	(0.850)		(0.838)	(1.027)	(1.407)	(2.219)	(1.460)	(0.280)
	ATE-AIPW Agriculture	-0.246	0.134	0.488*** 0.940***	1.325**	2.702*	ATE-AIPW Agriculture -0.405*** -0.588*** -0.795*** -1.074***	-0.823** -0.396***
		(0.300)	(0.241)	(0.186)	(0.224)	(0.549)	(1.387)		(0.096)	(0.167)	(0.228)	(0.318)	(0.327)	(0.067)
	ATE-AIPW Mining	0.474*	-0.029	-0.252	-1.069***	-0.461	0.602	ATE-AIPW Mining -0.501** -0.733*** -1.183*** -1.607*** -1.472*** -0.441***
		(0.253)	(0.493)	(0.380)	(0.256)	(0.398)	(0.702)		(0.199)	(0.253)	(0.333)	(0.282)	(0.335)	(0.099)
	ATE-AIPW Manufacturing -0.793*** -1.551*** -2.414*** -3.646*** -4.360*** -4.986***	ATE-AIPW Manufacturing -3.042*** -3.047*** -2.735***	-2.826**	-2.125** -1.960***
		(0.235)	(0.358)	(0.555)	(0.675)	(0.921)	(1.067)		(0.520)	(0.624)	(0.747)	(1.120)	(0.903)	(0.209)
	ATE-AIPW Services -0.652*** -1.221*** -1.398*** -1.735*** -2.071*** -2.387***	ATE-AIPW Services -1.064*** -1.458*** -1.512***	-1.574**	-1.562** -0.784***
		(0.142)	(0.302)	(0.368)	(0.595)	(0.740)	(0.780)		(0.241)	(0.254)	(0.474)	(0.658)	(0.631)	(0.082)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	38	38	38	38	38	38	# of Crises	38	38	38	38	38	38
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P-value Total Exports= Total Imports	0.027	0.053	0.119	0.690	0.361	0.434	P-value Total Exports= Total Imports	0.053	0.119	0.690	0.361	0.434	0.027
														*
		(0.440)	(1.868)	(1.819)	(1.843)	(2.405)	(2.647)		(2.975)	(3.369)	(3.722)	(4.541)	(4.912)	(0.525)
														*
		(0.060)	(0.178)	(0.215)	(0.206)	(0.279)	(0.276)		(0.244)	(0.302)	(0.349)	(0.453)	(0.502)	(0.070)
	ATE-AIPW Mining -0.282**	-1.073*	-0.964*										*
		(0.127)	(0.550)	(0.551)	(0.514)	(0.635)	(0.835)		(0.582)	(0.577)	(0.584)	(0.722)	(0.873)	(0.121)
	ATE-AIPW Manufacturing	-0.198	-1.767**	-1.639**	-1.012*	-1.711**	-1.713*	ATE-AIPW Manufacturing -5.798*** -5.978***	-5.402**	-5.924**	-6.206** -1.868***
		(0.236)	(0.835)	(0.640)	(0.599)	(0.867)	(0.888)		(1.842)	(2.001)	(2.153)	(2.629)	(2.775)	(0.315)
														416***
		(0.105)	(0.352)	(0.483)	(0.637)	(0.762)	(0.794)		(0.329)	(0.512)	(0.659)	(0.763)	(0.795)	(0.130)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	34	34	34	34	34	34	# of Crises	34	34	34	34	34	34
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P-

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP) Panel B: Banking Crises Panel B: Banking Crises ATE-AIPW Total -1.344*** -5.400*** -5.681*** -5.263*** -7.923*** -7.786*** ATE-AIPW Total -9.309*** -10.412*** -10.230*** -11.879*** -12.939*** -3.315**ATE-AIPW Agriculture -0.119** -0.530*** -0.722*** -0.819*** -0.961*** -1.304*** ATE-AIPW Agriculture -0.521** -0.655** -0.869** -1.149** -1.354*** -0.361**-1.213** -2.262*** -1.447* ATE-AIPW Mining -1.611*** -1.832*** -1.920*** -2.354*** -2.779*** -0.669**ATE-AIPW Services -0.745*** -2.031*** -2.357*** -2.219*** -2.989*** -3.321*** ATE-AIPW Services -1.379*** -1.947*** -2.040*** -2.452*** -2.600*** -0.

value Total Exports= Total Imports

	0.024	0.000	0.000	0.000	0.003	0.002	P-value Total Exports= Total Imports	0.000	0.000	0.000	0.003	0.002	0.024

Panel C: Currency Crises Panel C: Currency Crises ATE-AIPW Total

														451***
		(1.387)	(1.256)	(1.254)	(1.526)	(1.600)	(1.248)		(2.038)	(2.072)	(2.678)	(3.115)	(2.305)	(1.924)
	ATE-AIPW Agriculture	0.106	-0.176	0.279	0.106	0.136	0.550*	ATE-AIPW Agriculture -0.644*** -0.749*** -0.857***	-0.736**	-0.634** -0.584***
		(0.174)	(0.157)	(0.313)	(0.193)	(0.192)	(0.282)		(0.192)	(0.240)	(0.304)	(0.336)	(0.293)	(0.144)
	ATE-AIPW Mining	-0.487	-1.046210***
		(0.297)	(0.297)	(0.318)	(0.418)	(0.424)	(0.366)		(0.372)	(0.324)	(0.377)	(0.503)	(0.376)	(0.356)
	ATE-AIPW Manufacturing -1.427** -1.872*** -2.436*** -2.911*** -3.557*** -3.922***	ATE-AIPW Manufacturing -4.759*** -4.541*** -4.631*** -5.008*** -4.520*** -3.723***
		(0.705)	(0.577)	(0.429)	(0.542)	(0.686)	(0.560)		(1.209)	(1.278)	(1.580)	(1.800)	(1.326)	(1.197)
	ATE-AIPW Services -0.879***	-0.506	-0.440	-0.292	-0.318	-0.931*	ATE-AIPW Services -1.105*** -1.100***	-1.139**	-1.005*	-1.121** -0.934***
		(0.242)	(0.309)	(0.451)	(0.575)	(0.531)	(0.505)		(0.300)	(0.366)	(0.505)	(0.533)	(0.521)	(0.253)
	Observations	732	732	732	732	732	732	Observations	732	732	732	732	732	732
	# of Crises	36	36	36	36	36	36	# of Crises	36	36	36	36	36	36
	# of Countries	41	41	41	41	41	41	# of Countries	41	41	41	41	41	41
	P-value Total Exports= Total Imports	0.000	0.000	0.004	0.004	0.028	0.376	P-value Total Exports= Total Imports	0.000	0.004	0.004	0.028	0.376	0.000

-2.687* -3.601*** -4.047*** -4.871*** -6.220*** -6.749*** ATE-AIPW Total -7.923*** -8.230*** -8.786*** -9.370*** -8.338*** -6.*** -1.449*** -1.775*** -2.481*** -2.447*** ATE-AIPW Mining -1.415*** -1.839*** -2.158*** -2.620*** -2.063*** -1.Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates.

Panel d: Manufacturing Panel d: Manufacturing ATE-AIPW High

													280***	-3.274**
		(0.300)	(0.480)	(0.620)	(0.812)	(1.002)	(1.285)		(0.505)	(0.641)	(0.937)	(1.123)	(1.399)	(1.633)
	ATE-AIPW Low	-0.649*	-3.375*** -4.900*** -7.863*** -7.811*** -7.610***	ATE-AIPW Low	-3.935*** -6.851*** -7.775*** -9.486*** -10.617*** -10.384***
		(0.336)	(0.455)	(0.620)	(0.873)	(1.091)	(1.437)		(0.540)	(0.692)	(1.014) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.030	6.212	9.802	13.553	17.101	21.167	Avg.(trade) in countries above cutoff	3.678	7.557	11.744	16.187	20.486	25.232
	Avg.(trade) in countries below cutoff	2.844	5.659	8.548	11.798	14.526	17.317	Avg.(trade) in countries below cutoff	3.305	6.505	9.622	13.326	17.041	20.623
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.645***	-0.090	-0.103	0.229	0.246	1.188***	ATE-AIPW High	0.009	-0.310*** -0.376*** -0.494*** -0.593***	-0.349**
		(0.111)	(0.112)	(0.156)	(0.210)	(0.237)	(0.278)		(0.096)	(0.088)	(0.135)	(0.133)	(0.159)	(0.174)
	ATE-AIPW Low	0.075	0.081	0.354*	0.553**	0.756*	1.901***
		(0.102)	(0.134)	(0.181)	(0.281)	(0.393)	(0.631)		(0.101)	(0.088)	(0.138)	(0.133)	(0.160)	(0.182)
	P-value High=Low	0.000	0.181	0.003	0.230	0.166	0.252	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.368	0.728	1.160	1.597	2.034	2.546	Avg.(trade) in countries above cutoff	0.330	0.691	1.072	1.495	1.928	2.415
	Avg.(trade) in countries below cutoff	0.422	0.937	1.471	2.103	2.536	3.161	Avg.(trade) in countries below cutoff	0.329	0.634	0.951	1.325	1.653	1.951
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.232*	-0.212	-0.397	-0.598*	-0.683*	-0.598	ATE-AIPW High	-0.683*** -0.599*** -0.713*** -1.283***	-0.552*	-0.276
		(0.127)	(0.195)	(0.247)	(0.321)	(0.401)	(0.483)		(0.087)	(0.149)	(0.194)	(0.269)	(0.329)	(0.423)
	ATE-AIPW Low	0.646***	0.228	-0.232	-1.717*** -0.713*	0.338	ATE-AIPW Low	-0.175** -0.558*** -0.871*** -1.519*** -2.261*** -2.406***
		(0.210)	(0.242)	(0.276)	(0.365)	(0.426)	(0.591)		(0.088)	(0.170)	(0.213)	(0.268)	(0.326)	(0.430)
	P-value High=Low	0.000	0.018	0.305	0.000	0.915	0.036	P-value High=Low	0.000	0.750	0.268	0.230	0.000	0.000
	Avg.(trade) in countries above cutoff	0.341	0.761	1.246	1.756	2.230	2.730	Avg.(trade) in countries above cutoff	0.642	1.346	2.126	2.960	3.709	4.479
	Avg.(trade) in countries below cutoff	0.975	1.833	2.767	3.778	4.560	5.295	Avg.(trade) in countries below cutoff	0.444	0.902	1.356	1.839	2.191	2.551
													384***	-2.205**
		(0.147)	(0.262)	(0.319)	(0.409)	(0.514)	(0.670)		(0.317)	(0.405)	(0.573)	(0.661)	(0.815)	(0.921)
	ATE-AIPW Low													966***
		(0.136)	(0.222)	(0.308)	(0.401)	(0.509)	(0.663)		(0.342)	(0.427)	(0.618)	(0.761)	(0.972)	(1.305)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.000	0.000	0.000	0.002	0.086
	Avg

* ATE-AIPW Low -0.293*** -0.688*** -0.742*** -1.099*** -1.434*** -1.226**-0.573*** -0.857*** -1.480*** -2.883*** -3.455*** -3.561*** ATE-AIPW High -1.036*** -1.763*** -1.717*** -1.478** -2.-0.851*** -2.074*** -2.963*** -4.102*** -5.047*** -5.975*** ATE-AIPW Low -2.371*** -4.014*** -3.951*** -4.481*** -4.544*** -3.

.(trade) in countries above cutoff

		1.502	3.078	4.808	6.585	8.254	10.168	Avg.(trade) in countries above cutoff	2.093	4.242	6.542	8.920	11.275	13.933
	Avg.(trade) in countries below cutoff	0.648	1.295	1.865	2.645	3.324	4.003	Avg.(trade) in countries below cutoff	1.949	3.816	5.627	7.811	10.218	12.546
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.171** -0.590***	-0.223	-0.533** -0.882*** -0.722**	ATE-AIPW High	-0.312*** -0.802*** -0.717*** -0.655*** -0.751***	-0.444
		(0.084)	(0.135)	(0.195)	(0.243)	(0.291)	(0.353)		(0.106)	(0.131)	(0.174)	(0.207)	(0.251)	(0.298)
	ATE-AIPW Low	-0.520787***
		(0.078)	(0.132)	(0.191)	(0.244)	(0.290)	(0.349)		(0.113)	(0.143)	(0.200) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.819	1.645	2.587	3.614	4.583	5.723	Avg.(trade) in countries above cutoff	0.614	1.277	2.004	2.812	3.573	4.404
	Avg.(trade) in countries below cutoff	0.799	1.594	2.445	3.272	4.106	4.857	Avg.(trade) in countries below cutoff	0.582	1.154	1.688	2.349	2.980	3.576
	Observations	709	709	709	709	709	709	Observations	709	709	709	709	709	709
	# of Crises	20	20	20	20	20	20	# of Crises	20	20	20	20	20	20
	Cutoff of channel variable, median	17.031	17.031	17.031	17.031	17.031	17.031	Cutoff of channel variable, median	17.031	17.031	17.031	17.031	17.031	17.031

*** -1.610*** -2.060*** -2.597*** -2.807*** -3.874*** ATE-AIPW Low -1.095*** -1.590*** -2.212*** -2.387*** -2.378*** -2.

Table A .

 A 15b: Channels, Cumulative trade costs over five years after banking crises with higher and lower manufacturing exports

Table A .

 A 15c: Channels, Cumulative trade costs over five years after currency crises with higher and lower manufacturing exports

		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-2.029*** -3.126*** -4.221*** -3.771*** -3.391*** -2.809***	ATE-AIPW High	-5.643*** -7.081*** -7.551*** -8.797***	-8.196***	-6.686***
		(0.297)	(0.411)	(0.547)	(0.734)	(0.859)	(1.060)		(0.422)	(0.608)	(0.819)	(1.041)	(1.266)	(1.563)
	ATE-AIPW Low	-1.504*** -3.195*** -4.446*** -6.511*** -9.557*** -10.517***	ATE-AIPW Low	-4.482*** -6.917*** -9.503*** -10.529*** -11.507*** -10.802***
		(0.298)	(0.410)	(0.617)	(0.743)	(0.848)	(1.076)		(0.403)	(0.553)	(0.789)	(1.009)	(1.209)	(1.513)
	P-value High=Low	0.000	0.788	0.577	0.000	0.000	0.000	P-value High=Low	0.000	0.646	0.000	0.002	0.000	0.000
	Avg.(trade) in countries above cutoff	3.147	6.441	10.080	13.956	17.656	21.730	Avg.(trade) in countries above cutoff	3.838	7.895	12.320	16.948	21.328	26.069
	Avg.(trade) in countries below cutoff	2.714	5.462	8.487	11.692	14.429	17.597	Avg.(trade) in countries below cutoff	3.165	6.288	9.352	12.987	16.753	20.699
	Panel b: Agriculture							Panel b: Agriculture
												969***	-0.872***	-0.883***
		(0.056)	(0.098)	(0.134)	(0.172)	(0.212)	(0.289)		(0.081)	(0.074)	(0.095)	(0.110)	(0.137)	(0.154)
	ATE-AIPW Low	0.377***	0.068	0.677***	0.281	0.099	-0.065	ATE-AIPW Low	-0.444*** -0.600*** -0.905*** -1.055***	-0.937***	-0.728***
		(0.071)	(0.106)	(0.210)	(0.214)	(0.199)	(0.235)		(0.082)	(0.070)	(0.095)	(0.109)	(0.137)	(0.169)
	P-value High=Low	0.000	0.000	0.000	0.000	0.001	0.257	P-value High=Low	0.094	0.198	0.001	0.064	0.331	0.193
	Avg.(trade) in countries above cutoff	0.339	0.708	1.114	1.531	1.964	2.444	Avg.(trade) in countries above cutoff	0.326	0.672	1.065	1.509	1.951	2.412
	Avg.(trade) in countries below cutoff	0.451	0.904	1.450	2.055	2.495	3.135	Avg.(trade) in countries below cutoff	0.336	0.681	0.998	1.354	1.701	2.097
	Panel c: Mining							Panel c: Mining
													-2.700***	-2.042***
		(0.196)	(0.150)	(0.201)	(0.252)	(0.302)	(0.352)		(0.082)	(0.137)	(0.182)	(0.237)	(0.272)	(0.374)
	ATE-AIPW Low													633***
		(0.210)	(0.182)	(0.273)	(0.310)	(0.340)	(0.411)		(0.088)	(0.135)	(0.172)	(0.209)	(0.247)	(0.303)
	P-value High=Low	0.000	0.060	0.339	0.031	0.000	0.079	P-value High=Low	0.000	0.016	0.000	0.011	0.464	0.050
	Avg.(trade) in countries above cutoff	0.305	0.639	1.024	1.436	1.796	2.167	Avg.(trade) in countries above cutoff	0.674	1.457	2.329	3.266	4.059	4.933
	Avg.(trade) in countries below cutoff	0.839	1.699	2.656	3.669	4.537	5.407	Avg.(trade) in countries below cutoff	0.454	0.862	1.269	1.694	2.098	2.418

Panel I: Exports (% of pre-crisis GDP) Panel II: Imports (% of pre-crisis GDP) ATE-AIPW High -0.219*** -0.570*** -0.739*** -0.722*** -0.538** -0.356 ATE-AIPW High -0.498*** -0.654*** -0.763*** -0.ATE-AIPW High -0.199 -0.825*** -1.192*** -1.705*** -1.989*** -2.092*** ATE-AIPW High -1.124*** -1.361*** -2.256*** -2.586*** -0.493** -1.026*** -1.369*** -1.308*** -2.664*** -2.479*** ATE-AIPW Low -0.643*** -1.150*** -1.746*** -2.172*** -2.580*** -2.

Panel d: Manufacturing Panel d: Manufacturing ATE-AIPW High

												087***	-3.588***	-2.937***
		(0.137)	(0.237)	(0.314)	(0.410)	(0.493)	(0.604)		(0.260)	(0.371)	(0.485)	(0.616)	(0.761)	(0.926)
	ATE-AIPW Low													545***
		(0.123)	(0.208)	(0.285)	(0.371)	(0.460)	(0.563)		(0.247)	(0.333)	(0.457)	(0.595)	(0.713)	(0.882)
	P-value High=Low	0.231	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.511	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	1.667	3.417	5.312	7.286	9.163	11.236	Avg.(trade) in countries above cutoff	2.183	4.400	6.813	9.253	11.612	14.180
	Avg.(trade) in countries below cutoff	0.645	1.300	1.963	2.734	3.383	4.187	Avg.(trade) in countries below cutoff	1.850	3.693	5.475	7.619	10.005	12.576
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.840*** -0.848*** -0.829***	-0.252	-0.309	-0.779**	ATE-AIPW High	-0.810*** -0.955*** -0.957*** -1.154***	-1.036***	-0.824***
		(0.077)	(0.129)	(0.184)	(0.264)	(0.304)	(0.363)		(0.075)	(0.120)	(0.172)	(0.208)	(0.243)	(0.276)
	ATE-AIPW Low	-0.541*** -0.578*** -1.007*** -1.643*** -2.067*** -2.268***	ATE-AIPW Low	-0.711*** -1.205*** -1.441*** -1.600*** -1.647*** -1.897***
		(0.078)	(0.134)	(0.183)	(0.243)	(0.295)	(0.373)		(0.077)	(0.120)	(0.181)	(0.214)	(0.246)	(0.286)
	P-value High=Low	0.000	0.004	0.165	0.000	0.000	0.000	P-value High=Low	0.053	0.001	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.835	1.677	2.631	3.703	4.733	5.882	Avg.(trade) in countries above cutoff	0.655	1.365	2.113	2.920	3.707	4.544
	Avg.(trade) in countries below cutoff	0.779	1.559	2.419	3.235	4.014	4.869	Avg.(trade) in countries below cutoff	0.525	1.051	1.610	2.319	2.948	3.608
	Observations	712	712	712	712	712	712	Observations	712	712	712	712	712	712
	# of Crises	19	19	19	19	19	19	# of Crises	19	19	19	19	19	19
	Cutoff of channel variable, median	24.364	24.364	24.364	24.364	24.364	24.364	Cutoff of channel variable, median	24.364	24.364	24.364	24.364	24.364	24.364

-0.771*** -0.882*** -1.462*** -1.092*** -0.555 0.418 ATE-AIPW High -3.211*** -4.111*** -3.575*** -4.-0.847*** -1.658*** -2.747*** -3.841*** -4.924*** -5.705*** ATE-AIPW Low -2.684*** -3.961*** -5.412*** -5.702*** -6.343*** -5.

Table A .

 A 16a: Channels, Cumulative trade costs over five years after debt crises with higher and lower export diversification

Panel d: Manufacturing Panel d: Manufacturing ATE-AIPW High

														**
		(0.277)	(0.470)	(0.639)	(0.843)	(1.031)	(1.271)		(0.459)	(0.625)	(0.924)	(1.146)	(1.356)	(1.605)
	ATE-AIPW Low	-1.104*** -3.529*** -5.243*** -7.874*** -8.565*** -8.603***	ATE-AIPW Low	-4.309*** -6.566*** -8.070*** -11.081*** -12.402*** -13.761***
		(0.322)	(0.443)	(0.636)	(0.896)	(1.042)	(1.232)		(0.516)	(0.680)	(0.992) (1.186) 0.000 0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.217	6.663	10.494	14.540	18.218	22.318	Avg.(trade) in countries above cutoff	3.849	7.770	12.059	16.650	21.042	25.598
	Avg.(trade) in countries below cutoff	2.355	4.456	6.711	9.156	11.463	14.078	Avg.(trade) in countries below cutoff	2.883	5.931	8.759	12.002	15.415	19.295
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.631*** -0.356*** -0.325**	-0.387**	-0.173	0.549*	ATE-AIPW High	0.002	-0.455*** -0.295**	-0.266	-0.461***	-0.342*
		(0.111)	(0.107)	(0.156)	(0.195)	(0.235)	(0.292)		(0.116)	(0.107)	(0.139)	(0.177)	(0.161)	(0.188)
	ATE-AIPW Low	-0.267***	-0.127	0.119	0.706**	0.032	0.094	ATE-AIPW Low	-0.357483***
		(0.082)	(0.102)	(0.159)	(0.274)	(0.290)	(0.284)		(0.121)	(0.109)	(0.143)	(0.181)	(0.162)	(0.190)
	P-value High=Low	0.000	0.007	0.000	0.000	0.411	0.092	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.371	0.766	1.213	1.653	2.107	2.602	Avg.(trade) in countries above cutoff	0.340	0.691	1.087	1.509	1.933	2.380
	Avg.(trade) in countries below cutoff	0.394	0.795	1.293	1.911	2.239	2.793	Avg.(trade) in countries below cutoff	0.308	0.634	0.909	1.290	1.642	2.027
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.287***	-0.273	-0.323	-0.412	-0.440	-0.403	ATE-AIPW High	-0.579*** -0.627*** -1.045*** -1.228***	-0.432	-0.001
		(0.109)	(0.191)	(0.269)	(0.344)	(0.431)	(0.503)		(0.090)	(0.157)	(0.207)	(0.277)	(0.326)	(0.423)
	ATE-AIPW Low	0.675***	0.303	-0.273	-1.781***	-0.860*	0.154	ATE-AIPW Low	-0.377256***
		(0.200)	(0.238)	(0.297)	(0.388)	(0.455)	(0.611)		(0.093)	(0.174)	(0.214)	(0.263)	(0.315)	(0.370)
	P-value High=Low	0.000	0.002	0.754	0.000	0.111	0.210	P-value High=Low	0.000	0.654	0.067	0.001	0.000	0.000
	Avg.(trade) in countries above cutoff	0.409	0.908	1.515	2.157	2.718	3.256	Avg.(trade) in countries above cutoff	0.654	1.341	2.095	2.924	3.671	4.403
	Avg.(trade) in countries below cutoff	0.793	1.434	2.029	2.677	3.204	3.837	Avg.(trade) in countries below cutoff	0.416	0.915	1.448	1.942	2.316	2.745
														466***
		(0.141)	(0.261)	(0.323)	(0.413)	(0.512)	(0.624)		(0.287)	(0.386)	(0.558)	(0.662)	(0.778)	(0.894)
	ATE-AIPW Low													684***
		(0.129)	(0.225)	(0.316)	(0.407)	(0.502)	(0.610)		(0.321)	(0.415)	(0.597) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	1.567	3.224	5.004	6.901	8.624	10.625	Avg.(trade) in countries above cutoff	2.230	4.450	6.843	9.398	11.911	14.567
	Avg.(trade) in countries below cutoff	0.496	0.941	1.399	1.855	2.397	2.863	Avg.(trade) in countries below cutoff	1.601	3.242	4.783	6.422	8.338	10.520
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.031	-0.677***	-0.221	-0.515** -1.079*** -1.505***	ATE-AIPW High	-0.366*** -1.008*** -0.832*** -0.633*** -0.867*** -0.829***
		(0.077)	(0.128)	(0.189)	(0.237)	(0.284)	(0.340)		(0.086)	(0.119)	(0.164)	(0.198)	(0.232)	(0.276)
	ATE-AIPW Low	-0.584*** -1.564*** -2.059*** -2.614*** -2.780*** -3.301***	ATE-AIPW Low	-1.076*** -1.366*** -2.052*** -2.291*** -2.164*** -2.338***
		(0.074)	(0.127)	(0.189)	(0.243)	(0.285)	(0.343)		(0.103)	(0.137)	(0.198)	(0.227)	(0.275)	(0.303)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.001	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.869	1.764	2.762	3.829	4.769	5.835	Avg.(trade) in countries above cutoff	0.626	1.288	2.034	2.819	3.527	4.248
	Avg.(trade) in countries below cutoff	0.673	1.286	1.990	2.713	3.624	4.585	Avg.(trade) in countries below cutoff	0.558	1.139	1.620	2.348	3.119	4.003
	Observations	707	707	707	707	707	707	Observations	707	707	707	707	707	707
	# of Crises	19	19	19	19	19	19	# of Crises	19	19	19	19	19	19
	Cutoff

*** -0.661*** -0.948*** -1.303*** -1.634*** -1.*** -0.684*** -0.776*** -1.846*** -2.552*** -3.-0.652*** -0.822*** -1.458*** -2.985*** -3.864*** -4.319*** ATE-AIPW High -1.240*** -2.653*** -2.478*** -2.065*** -3.324*** -3.-0.928*** -2.141*** -3.031*** -4.186*** -4.956*** -5.551*** ATE-AIPW Low -2.499*** -3.855*** -4.293*** -5.642*** -6.051*** -6.

of channel variable, median

Table A .

 A 16b: Channels, Cumulative trade costs over five years after banking crises with higher and lower export diversification

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel B: Banking crises							Panel B: Banking crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.991*** -6.176*** -5.784*** -4.618*** -6.971*** -7.303***	ATE-AIPW High	-2.628*** -10.660*** -11.084*** -10.113*** -11.891*** -12.633***
		(0.294)	(0.477)	(0.575)	(0.730)	(0.893)	(1.113)		(0.421)	(0.757)	(0.949)	(1.136)	(1.448)	(1.766)
	ATE-AIPW Low	-1.383*** -4.750*** -6.592*** -8.249*** -11.364*** -12.734***	ATE-AIPW Low	-2.252*** -6.143***	-9.148*** -11.568*** -13.811*** -18.793***
		(0.293)	(0.479)	(0.580)	(0.730)	(0.901)	(1.084)		(0.447)	(0.721)	(0.925)	(1.113)	(1.440)	(1.700)
	P-value High=Low	0.108	0.000	0.001	0.000	0.000	0.000	P-value High=Low	0.310	0.000	0.000	0.002	0.007	0.000
	Avg.(trade) in countries above cutoff	3.742	7.485	11.588	15.922	19.878	24.287	Avg.(trade) in countries above cutoff	3.906	7.937	12.360	16.945	21.150	25.699
	Avg.(trade) in countries below cutoff	2.528	5.211	8.193	11.371	14.286	17.555	Avg.(trade) in countries below cutoff	3.395	6.870	10.444	14.451	18.529	22.795
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.107*	-0.521517***
		(0.056)	(0.084)	(0.113)	(0.145)	(0.177)	(0.201)		(0.054)	(0.090)	(0.111)	(0.136)	(0.173)	(0.204)
	ATE-AIPW Low	-0.009	-0.559*** -0.984*** -1.195*** -1.568*** -2.182***	ATE-AIPW Low	-0.013	0.293***	-0.156	-0.600***	-0.883***	-1.857***
		(0.063)	(0.093)	(0.131)	(0.150)	(0.165)	(0.179)		(0.073)	(0.108)	(0.128)	(0.144)	(0.180)	(0.205)
											0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.420	0.849	1.308	1.808	2.304	2.872	Avg.(trade) in countries above cutoff	0.278	0.575	0.919	1.274	1.634	2.002
	Avg.(trade) in countries below cutoff	0.352	0.729	1.191	1.673	2.046	2.524	Avg.(trade) in countries below cutoff	0.362	0.736	1.110	1.554	1.985	2.452
	Panel c: Mining							Panel c: Mining
														*
		(0.137)	(0.222)	(0.242)	(0.268)	(0.320)	(0.401)		(0.101)	(0.159)	(0.191)	(0.232)	(0.287)	(0.344)
	ATE-AIPW Low	0.041	-0.602**	-0.422	-0.968*** -1.904***	-0.737	ATE-AIPW Low	-0.663*** -1.317***	-2.087*** -2.632*** -3.120*** -3.787***
		(0.165)	(0.235)	(0.272)	(0.313)	(0.352)	(0.478)		(0.096)	(0.157)	(0.190)	(0.228)	(0.285)	(0.340)
	P-value High=Low	0.000	0.000	0.000	0.000	0.001	0.000	P-value High=Low	0.008	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.382	0.773	1.206	1.633	1.959	2.286	Avg.(trade) in countries above cutoff	0.741	1.542	2.417	3.327	4.125	4.902
	Avg.(trade) in countries below cutoff	0.593	1.218	1.924	2.696	3.383	4.088	Avg.(trade) in countries below cutoff	0.498	1.036	1.621	2.256	2.809	3.383
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.048	-2.576*** -1.749***	-0.557	-1.365**	-1.184*	ATE-AIPW High	-1.439*** -6.470***	-6.507*** -5.627*** -6.151*** -6.037***
		(0.159)	(0.256)	(0.323)	(0.427)	(0.533)	(0.666)		(0.251)	(0.465)	(0.573)	(0.670)	(0.857)	(1.067)
	ATE-AIPW Low													*
		(0.141)	(0.263)	(0.377)	(0.477)	(0.539)	(0.644)		(0.254)	(0.420)	(0.535)	(0.639)	(0.826)	(0.979)
	P-value High=Low	0.000	0.000	0.010	0.000	0.000	0.000	P-value High=Low	0.804	0.000	0.000	0.128	0.033	0.000
	Avg.(trade) in countries above cutoff	2.070	4.176	6.469	8.908	11.156	13.706	Avg.(trade) in countries above cutoff	2.272	4.538	7.019	9.582	11.941	14.602
	Avg.(trade) in countries below cutoff	0.800	1.661	2.559	3.505	4.400	5.405	Avg.(trade) in countries below cutoff	1.931	3.872	5.842	7.993	10.338	12.785
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.416*** -1.850*** -1.921*** -1.772*** -2.571*** -2.647***	ATE-AIPW High	-0.359*** -1.569***	-2.088*** -2.064*** -2.602*** -2.740***
		(0.089)	(0.138)	(0.198)	(0.282)	(0.358)	(0.491)		(0.073)	(0.129)	(0.166)	(0.212)	(0.257)	(0.291)
	ATE-AIPW Low	-0.774*** -2.088*** -2.776*** -2.932*** -3.867*** -4.755***	ATE-AIPW Low	-0.084	-0.864*** -1.577*** -2.218*** -2.569*** -3.005***
		(0.099)	(0.165)	(0.230)	(0.298)	(0.378)	(0.448)		(0.110)	(0.123)	(0.182)	(0.222)	(0.273)	(0.314)
	P-value High=Low	0.000	0.031	0.000	0.000	0.000	0.000	P-value High=Low	0.008	0.000	0.000	0.253	0.825	0.103
	Avg.(trade) in countries above cutoff	0.870	1.687	2.605	3.574	4.459	5.422	Avg.(trade) in countries above cutoff	0.614	1.282	2.005	2.762	3.450	4.193
	Avg.(trade) in countries below cutoff	0.784	1.602	2.519	3.496	4.456	5.538	Avg.(trade) in countries below cutoff	0.603	1.227	1.871	2.648	3.396	4.174
	Observations	710	710	710	710	710	710	Observations	710	710	710	710	710	710
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-2.445	-2.445	-2.445	-2.445	-2.445	-2.445	Cutoff of channel variable, median	-2.445	-2.445	-2.445	-2.445	-2.445	-2.445

*** -0.668*** -0.638*** -0.617*** -0.780*** ATE-AIPW High -0.310*** -0.835*** -0.947*** -1.102*** -1.282*** -1.ATE-AIPW High -0.421*** -1.229*** -1.447*** -1.651*** -2.418*** -2.692*** ATE-AIPW High -0.520*** -1.786*** -1.542*** -1.320*** -1.857*** -2.339**-0.641*** -1.501*** -2.410*** -3.154*** -4.026*** -5.059*** ATE-AIPW Low -1.491*** -4.254*** -5.328*** -6.119*** -7.238*** -10.144**

Table A .

 A 16c: Channels, Cumulative trade costs over five years after currency crises with higher and lower export diversification

Table A .

 A 17a: Channels, Cumulative trade costs over five years after debt crises with higher and lower partners' diversification

Panel d: Manufacturing Panel d: Manufacturing ATE-AIPW High

														**
		(0.273)	(0.438)	(0.601)	(0.786)	(0.995)	(1.371)		(0.390)	(0.594)	(0.894)	(1.086)	(1.382)	(1.759)
	ATE-AIPW Low	-0.819*** -3.430*** -5.488*** -8.469*** -10.352*** -11.499***	ATE-AIPW Low	-3.227*** -5.393*** -6.527*** -9.592*** -11.627*** -12.841***
		(0.316)	(0.414)	(0.580)	(0.824)	(0.995)	(1.342)		(0.452)	(0.647)	(0.941)	(1.095)	(1.397)	(1.694)
	P-value High=Low	0.000	0.067	0.000	0.000	0.000	0.000	P-value High=Low	0.074	0.001	0.034	0.017	0.001	0.002
	Avg.(trade) in countries above cutoff	3.408	6.906	10.623	14.548	18.010	21.646	Avg.(trade) in countries above cutoff	3.969	7.939	12.020	16.432	20.518	24.673
	Avg.(trade) in countries below cutoff	2.498	5.122	8.162	11.422	14.550	18.278	Avg.(trade) in countries below cutoff	3.157	6.540	10.233	14.240	18.398	22.999
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.649*** -0.394***	-0.022	-0.332*	-0.348	0.410	ATE-AIPW High	-0.389*** -0.710*** -0.822*** -0.932*** -1.148***	-1.131***
		(0.111)	(0.108)	(0.172)	(0.199)	(0.247)	(0.320)		(0.073)	(0.076)	(0.124)	(0.121)	(0.162)	(0.187)
	ATE-AIPW Low	-0.244***	-0.056	-0.096	0.609**	-0.027	-0.019	ATE-AIPW Low	-0.017	-0.464*** -0.535*** -0.966*** -1.402*** -1.110***
		(0.079)	(0.097)	(0.151)	(0.269)	(0.280)	(0.278)		(0.064)	(0.077)	(0.125)	(0.125)	(0.161)	(0.183)
	P-value High=Low	0.000	0.000	0.591	0.000	0.216	0.150	P-value High=Low	0.000	0.000	0.000	0.594	0.000	0.816
	Avg.(trade) in countries above cutoff	0.444	0.926	1.461	2.018	2.498	3.046	Avg.(trade) in countries above cutoff	0.330	0.700	1.079	1.493	1.899	2.306
	Avg.(trade) in countries below cutoff	0.302	0.606	0.981	1.390	1.740	2.210	Avg.(trade) in countries below cutoff	0.334	0.654	1.001	1.406	1.807	2.265
	Panel c: Mining							Panel c: Mining
														*
		(0.110)	(0.175)	(0.230)	(0.304)	(0.392)	(0.472)		(0.092)	(0.148)	(0.198)	(0.278)	(0.350)	(0.474)
	ATE-AIPW Low	0.847***	0.348	-0.234	-1.860*** -1.028**	-0.022	ATE-AIPW Low	-0.432*** -0.697*** -1.030*** -1.900*** -1.903***	-2.524***
		(0.197)	(0.227)	(0.261)	(0.350)	(0.413)	(0.580)		(0.088)	(0.165)	(0.207)	(0.256)	(0.316)	(0.397)
	P-value High=Low	0.000	0.000	0.004	0.000	0.964	0.021	P-value High=Low	0.055	0.603	0.669	0.076	0.906	0.027
	Avg.(trade) in countries above cutoff	0.573	1.149	1.764	2.415	2.977	3.436	Avg.(trade) in countries above cutoff	0.700	1.425	2.161	2.955	3.563	4.132
	Avg.(trade) in countries below cutoff	0.449	0.942	1.530	2.164	2.706	3.387	Avg.(trade) in countries below cutoff	0.466	1.001	1.643	2.316	3.000	3.739
													*	-5.660***
		(0.147)	(0.253)	(0.326)	(0.418)	(0.542)	(0.739)		(0.244)	(0.364)	(0.537)	(0.624)	(0.789)	(0.968)
	ATE-AIPW Low												093***	-6.815***
		(0.137)	(0.224)	(0.318)	(0.412)	(0.536)	(0.732)		(0.296)	(0.411)	(0.578)	(0.656)	(0.831)	(0.979)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.057	0.000	0.000	0.435	0.019	0.013
	Avg.(trade) in countries above cutoff	1.506	3.075	4.661	6.321	7.771	9.344	Avg.(trade) in countries above cutoff	2.297	4.536	6.786	9.185	11.553	13.961
	Avg.(trade) in countries below cutoff	1.010	2.072	3.309	4.654	6.009	7.602	Avg.(trade) in countries below cutoff	1.788	3.670	5.742	7.941	10.269	12.914
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.344*** -1.096*** -0.844*** -1.256*** -1.344***	-1.435***	ATE-AIPW High	-0.639*** -1.315*** -1.332*** -1.007*** -1.266***	-1.444***
		(0.081)	(0.134)	(0.194)	(0.247)	(0.296)	(0.379)		(0.090)	(0.129)	(0.170)	(0.201)	(0.240)	(0.305)
	ATE-AIPW Low	-0.479*** -1.394*** -1.794*** -2.318*** -2.876***	-3.874***	ATE-AIPW Low	-0.945*** -1.200*** -1.901*** -2.295*** -2.230***	-2.391***
		(0.078)	(0.131)	(0.188)	(0.244)	(0.290)	(0.377)		(0.103)	(0.143)	(0.199)	(0.227)	(0.275)	(0.323)
	P-value High=Low	0.001	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.277	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.885	1.756	2.737	3.794	4.764	5.820	Avg.(trade) in countries above cutoff	0.642	1.278	1.993	2.799	3.504	4.273
	Avg.(trade) in countries below cutoff	0.737	1.502	2.343	3.214	4.096	5.079	Avg.(trade) in countries below cutoff	0.569	1.215	1.848	2.577	3.322	4.081
	Observations	708	708	708	708	708	708	Observations	708	708	708	708	708	708
	# of Crises	20	20	20	20	20	20	# of Crises	20	20	20	20	20	20
	Cutoff of channel variable, median	-2.647	-2.647	-2.647	-2.647	-2.647	-2.647	Cutoff of channel variable, median	-2.647	-2.647	-2.647	-2.647	-2.647	-2.647

ATE-AIPW

High -0.426*** -0.435** -0.686*** -0.990*** -1.016*** -1.037** ATE-AIPW High -0.547*** -0.764*** -0.965*** -1.551*** -1.925*** -1.875**-0.703*** -1.007*** -1.654*** -2.928*** -3.255*** -3.510*** ATE-AIPW High -2.218*** -3.927*** -4.484*** -4.700*** -5.101**-0.944*** -2.327*** -3.363*** -4.899*** -6.421*** -7.584*** ATE-AIPW Low -1.834*** -3.033*** -3.060*** -4.431*** -6.

Table A .

 A 17b: Channels, Cumulative trade costs over five years after banking crises with higher and lower partners' diversification

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel B: Banking crises							Panel B: Banking crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.586	-6.705*** -6.833*** -5.696*** -8.003*** -9.403***	ATE-AIPW High	-3.632*** -11.387*** -11.124*** -9.891*** -12.012*** -13.812***
		(0.368)	(0.512)	(0.619)	(0.758)	(0.998)	(1.131)		(0.539)	(0.817)	(0.987)	(1.209)	(1.599)	(1.633)
	ATE-AIPW Low	-1.773*** -3.846*** -4.900*** -5.836*** -9.015*** -9.904***	ATE-AIPW Low	-1.576*** -5.140***	-7.882*** -9.455*** -11.356*** -14.855***
		(0.291)	(0.501)	(0.643)	(0.781)	(1.028)	(1.149)		(0.421)	(0.774)	(0.990)	(1.216)	(1.637)	(1.665)
	P-value High=Low	0.000	0.000	0.000	0.729	0.042	0.349	P-value High=Low	0.000	0.000	0.000	0.377	0.372	0.197
	Avg.(trade) in countries above cutoff	3.323	6.806	10.544	14.475	17.794	21.415	Avg.(trade) in countries above cutoff	3.740	7.721	11.843	16.362	20.229	24.397
	Avg.(trade) in countries below cutoff	2.673	5.405	8.500	11.824	15.120	18.851	Avg.(trade) in countries below cutoff	3.452	6.888	10.586	14.539	18.889	23.430
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.019	-0.399*** -0.554*** -0.498***	-0.336*	-0.554***	ATE-AIPW High	-0.419*** -0.598***	-0.684*** -0.866*** -1.156***	-1.193***
		(0.072)	(0.091)	(0.116)	(0.149)	(0.189)	(0.211)		(0.065)	(0.086)	(0.105)	(0.140)	(0.211)	(0.200)
	ATE-AIPW Low	-0.082	-0.554*** -0.954*** -1.190*** -1.580*** -2.190***	ATE-AIPW Low	-0.012	0.248**	-0.116	-0.481*** -0.732*** -1.598***
		(0.065)	(0.095)	(0.128)	(0.148)	(0.161)	(0.176)		(0.074)	(0.104)	(0.119) (0.218) 0.000 (0.145) 0.000 0.000	0.000
	Avg.(trade) in countries above cutoff	0.444	0.938	1.496	2.055	2.533	3.077	Avg.(trade) in countries above cutoff	0.311	0.683	1.074	1.510	1.902	2.313
	Avg.(trade) in countries below cutoff	0.317	0.630	1.000	1.423	1.790	2.272	Avg.(trade) in countries below cutoff	0.351	0.675	1.014	1.400	1.813	2.263
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	0.063	-1.384983***	-2.755***
		(0.180)	(0.260)	(0.291)	(0.318)	(0.381)	(0.470)		(0.134)	(0.207)	(0.240)	(0.294)	(0.367)	(0.328)
	ATE-AIPW Low	-0.486*** -0.624** -0.639** -1.407*** -2.674*** -1.556***	ATE-AIPW Low	-0.655*** -1.358***	-1.944*** -2.358*** -2.829***	-3.146***
		(0.162)	(0.266)	(0.310)	(0.312)	(0.357)	(0.507)		(0.108)	(0.203)	(0.240)	(0.273)	(0.355)	(0.322)
	P-value High=Low	0.000	0.000	0.000	0.415	0.000	0.006	P-value High=Low	0.029	0.000	0.000	0.000	0.000	0.001
	Avg.(trade) in countries above cutoff	0.536	1.112	1.726	2.360	2.898	3.336	Avg.(trade) in countries above cutoff	0.695	1.446	2.222	3.046	3.611	4.210
	Avg.(trade) in countries below cutoff	0.496	0.998	1.590	2.241	2.806	3.482	Avg.(trade) in countries below cutoff	0.495	1.028	1.643	2.302	3.017	3.710
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	0.023	-2.973*** -2.611*** -1.537*** -2.704*** -3.280***	ATE-AIPW High	-2.023*** -7.348***	-6.772*** -5.672*** -6.491***	-7.555***
		(0.177)	(0.273)	(0.325)	(0.439)	(0.564)	(0.647)		(0.300)	(0.473)	(0.557)	(0.657)	(0.851)	(0.941)
	ATE-AIPW Low	-0.648*** -0.985*** -1.147*** -1.138** -1.613*** -1.748**	ATE-AIPW Low	-1.051*** -3.232***	-4.261*** -4.591*** -5.258***	-7.048***
		(0.147)	(0.269)	(0.390)	(0.493)	(0.598)	(0.705)		(0.233)	(0.434)	(0.555)	(0.674)	(0.897)	(0.991)
	P-value High=Low	0.000	0.000	0.000	0.255	0.004	0.002	P-value High=Low	0.000	0.000	0.000	0.001	0.020	0.402
	Avg.(trade) in countries above cutoff	1.461	2.954	4.508	6.153	7.485	9.038	Avg.(trade) in countries above cutoff	2.119	4.316	6.547	8.991	11.217	13.583
	Avg.(trade) in countries below cutoff	1.104	2.290	3.594	4.986	6.458	8.068	Avg.(trade) in countries below cutoff	2.004	3.963	6.071	8.251	10.712	13.371
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.652*** -1.949*** -2.290*** -2.384*** -3.057*** -3.262***	ATE-AIPW High	-0.723*** -1.621***	-1.982*** -1.961*** -2.383***	-2.310***
		(0.102)	(0.147)	(0.208)	(0.285)	(0.388)	(0.437)		(0.097)	(0.135)	(0.179)	(0.228)	(0.292)	(0.305)
	ATE-AIPW Low	-0.556*** -1.683*** -2.160*** -2.100*** -3.149*** -4.409***	ATE-AIPW Low	0.143	-0.798*** -1.562*** -2.026*** -2.536***	-3.063***
		(0.097)	(0.167)	(0.250)	(0.338)	(0.435)	(0.468)		(0.088)	(0.123)	(0.192)	(0.246)	(0.302)	(0.315)
	P-value High=Low	0.275	0.019	0.413	0.181	0.687	0.000	P-value High=Low	0.000	0.000	0.001	0.634	0.305	0.000
	Avg.(trade) in countries above cutoff	0.882	1.802	2.813	3.906	4.878	5.964	Avg.(trade) in countries above cutoff	0.614	1.277	2.000	2.816	3.499	4.291
	Avg.(trade) in countries below cutoff	0.756	1.488	2.316	3.174	4.065	5.029	Avg.(trade) in countries below cutoff	0.602	1.223	1.857	2.585	3.346	4.086
	Observations	710	710	710	710	710	710	Observations	710	710	710	710	710	710
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-2.570	-2.570	-2.570	-2.570	-2.570	-2.570	Cutoff of channel variable, median	-2.570	-2.570	-2.570	-2.570	-2.570	-2.570

*** -1.377*** -1.277*** -1.906*** -2.307*** ATE-AIPW High -0.467*** -1.820*** -1.685*** -1.393*** -1.Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 17c: Channels, Cumulative trade costs over five years after currency crises with higher and lower partners' diversification

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.906*** -2.691*** -4.702*** -5.056*** -4.832*** -3.864***	ATE-AIPW High	-4.646*** -5.541*** -6.528***	-7.934***	-7.824*** -5.638***
		(0.284)	(0.390)	(0.543)	(0.719)	(0.863)	(1.180)		(0.415)	(0.556)	(0.784)	(1.023)	(1.248)	(1.638)
	ATE-AIPW Low	-1.910*** -3.838*** -4.368*** -5.609*** -8.273*** -9.555***	ATE-AIPW Low	-6.056*** -8.521*** -10.785*** -11.023*** -11.202*** -11.450***
		(0.308)	(0.422)	(0.612)	(0.756)	(0.847)	(0.989)		(0.419)	(0.590)	(0.805)	(1.012)	(1.214)	(1.389)
	P-value High=Low	0.981	0.000	0.407	0.253	0.000	0.000	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.400	6.948	10.694	14.608	18.135	21.811	Avg.(trade) in countries above cutoff	3.961	7.986	12.116	16.485	20.600	24.793
	Avg.(trade) in countries below cutoff	2.482	5.023	8.011	11.265	14.308	17.989	Avg.(trade) in countries below cutoff	3.144	6.446	10.070	14.116	18.242	22.809
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.107** -0.426*** -0.399*** -0.336*	-0.147	-0.109	ATE-AIPW High	-0.501*** -0.479*** -0.572***	-0.868***	-0.730*** -0.538***
		(0.053)	(0.087)	(0.122)	(0.172)	(0.205)	(0.284)		(0.083)	(0.070)	(0.094)	(0.110)	(0.139)	(0.173)
	ATE-AIPW Low	0.237***	-0.097	0.306	-0.161	-0.333*	-0.437**	ATE-AIPW Low	-0.580*** -0.680*** -0.996***	-1.050***	-0.927***	-0.921***
		(0.072)	(0.105)	(0.214)	(0.215)	(0.193)	(0.223)		(0.081)	(0.074)	(0.097)	(0.109)	(0.135)	(0.149)
	P-value High=Low	0.000	0.001	0.001	0.407	0.311	0.214	P-value High=Low	0.019	0.000	0.000	0.000	0.004	0.001
	Avg.(trade) in countries above cutoff	0.437	0.921	1.452	2.001	2.481	3.028	Avg.(trade) in countries above cutoff	0.328	0.701	1.083	1.486	1.896	2.311
	Avg.(trade) in countries below cutoff	0.306	0.604	0.979	1.393	1.740	2.209	Avg.(trade) in countries below cutoff	0.337	0.651	0.994	1.412	1.807	2.259
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.155	-0.579*** -0.968*** -1.269*** -1.498*** -1.378***	ATE-AIPW High	-0.832*** -1.046*** -1.694***	-1.999***	-1.890*** -1.315***
		(0.196)	(0.153)	(0.210)	(0.272)	(0.323)	(0.392)		(0.079)	(0.134)	(0.173)	(0.234)	(0.270)	(0.389)
	ATE-AIPW Low	-0.593*** -1.431*** -1.805*** -2.032*** -3.390*** -3.460***	ATE-AIPW Low	-0.974*** -1.464*** -2.366***	-2.797***	-3.390*** -3.457***
		(0.211)	(0.182)	(0.264)	(0.298)	(0.324)	(0.377)		(0.091)	(0.134)	(0.173) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.564	1.137	1.755	2.395	2.958	3.418	Avg.(trade) in countries above cutoff	0.696	1.427	2.180	2.959	3.586	4.166
	Avg.(trade) in countries below cutoff	0.457	0.950	1.535	2.182	2.720	3.407	Avg.(trade) in countries below cutoff	0.464	0.988	1.606	2.294	2.958	3.687
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.952*** -1.044*** -2.038*** -2.204*** -2.152*** -1.244*	ATE-AIPW High	-2.512*** -3.078*** -3.225***	-3.851***	-3.888*** -2.562***
		(0.128)	(0.211)	(0.295)	(0.396)	(0.498)	(0.649)		(0.253)	(0.333)	(0.457)	(0.598)	(0.730)	(0.924)
	ATE-AIPW Low	-0.713*** -1.498*** -2.212*** -2.729*** -3.262*** -3.808***	ATE-AIPW Low	-3.578*** -5.107*** -5.905***	-5.647***	-5.591*** -5.686***
		(0.135)	(0.235)	(0.308)	(0.399)	(0.483)	(0.579)		(0.261)	(0.370)	(0.482)	(0.605)	(0.727)	(0.843)
	P-value High=Low	0.001	0.000	0.241	0.013	0.000	0.000	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	1.510	3.092	4.702	6.371	7.869	9.484	Avg.(trade) in countries above cutoff	2.290	4.569	6.844	9.240	11.616	14.045
	Avg.(trade) in countries below cutoff	0.991	2.024	3.224	4.549	5.844	7.387	Avg.(trade) in countries below cutoff	1.782	3.606	5.644	7.842	10.158	12.786
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.692*** -0.643*** -1.296*** -1.247*** -1.034*** -1.134***	ATE-AIPW High	-0.801*** -0.938*** -1.037***	-1.215***	-1.316*** -1.222***
		(0.081)	(0.143)	(0.187)	(0.252)	(0.304)	(0.392)		(0.075)	(0.116)	(0.178)	(0.211)	(0.249)	(0.299)
	ATE-AIPW Low	-0.840*** -0.811*** -0.657*** -0.687*** -1.288*** -1.850***	ATE-AIPW Low	-0.924*** -1.271*** -1.518***	-1.529***	-1.294*** -1.385***
		(0.075)	(0.122)	(0.183)	(0.266)	(0.307)	(0.353)		(0.076)	(0.123)	(0.176)	(0.217)	(0.248)	(0.274)
	P-value High=Low	0.005	0.082	0.000	0.003	0.291	0.018	P-value High=Low	0.021	0.000	0.000	0.016	0.898	0.469
	Avg.(trade) in countries above cutoff	0.890	1.798	2.785	3.841	4.827	5.881	Avg.(trade) in countries above cutoff	0.647	1.288	2.008	2.801	3.502	4.272
	Avg.(trade) in countries below cutoff	0.728	1.445	2.274	3.141	4.003	4.986	Avg.(trade) in countries below cutoff	0.561	1.201	1.826	2.568	3.319	4.077
	Observations	710	710	710	710	710	710	Observations	710	710	710	710	710	710
	# of Crises	19	19	19	19	19	19	# of Crises	19	19	19	19	19	19
	Cutoff of channel variable, median	-2.659	-2.659	-2.659	-2.659	-2.659	-2.659	Cutoff of channel variable, median	-2.659	-2.659	-2.659	-2.659	-2.659	-2.659

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 18a: Channels, Cumulative trade costs over five years after debt crises with higher and lower trading partners' growth

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt crises							Panel A: Debt crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.172	-1.326*** -2.185*** -4.365*** -2.954***	0.138	ATE-AIPW High	-1.994*** -2.942*** -2.033**	-2.356*	-3.811**	-0.008
		(0.309)	(0.466)	(0.606)	(0.810)	(1.048)	(1.376)		(0.414)	(0.620)	(0.950)	(1.273)	(1.758)	(2.514)
	ATE-AIPW Low	-2.507*** -4.443*** -5.493*** -8.602*** -11.205*** -13.270***	ATE-AIPW Low	-5.291*** -8.481*** -11.274*** -13.819*** -15.840*** -19.211***
		(0.265)	(0.397)	(0.572)	(0.798)	(0.963)	(1.170)		(0.410)	(0.624)	(0.926) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.398	7.038	10.886	14.885	18.763	22.794	Avg.(trade) in countries above cutoff	4.046	8.612	13.261	18.035	22.883	27.790
	Avg.(trade) in countries below cutoff	1.239	1.969	3.337	5.137	6.293	7.925	Avg.(trade) in countries below cutoff	1.536	1.762	2.502	4.440	5.702	7.410
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	0.502*** 0.609*** 0.562*** 0.753***	1.796***	4.327***	ATE-AIPW High	-0.095	-0.220*** -0.285**	-0.599*** -0.932***	-0.541*
		(0.084)	(0.131)	(0.168)	(0.214)	(0.352)	(0.624)		(0.064)	(0.078)	(0.135)	(0.164)	(0.219)	(0.300)
	ATE-AIPW Low	-0.973*** -0.594***	-0.020	0.096	-0.696*** -0.980***	ATE-AIPW Low	-0.363*** -0.903*** -0.973***	-1.290*** -1.640*** -1.735***
		(0.100)	(0.092)	(0.153)	(0.251)	(0.258)	(0.227)		(0.066)	(0.075)	(0.132) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.438	0.907	1.389	1.897	2.403	2.981	Avg.(trade) in countries above cutoff	0.354	0.774	1.188	1.632	2.098	2.580
	Avg.(trade) in countries below cutoff	0.131	0.218	0.525	0.791	0.900	1.191	Avg.(trade) in countries below cutoff	0.219	0.273	0.424	0.690	0.832	1.064
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	0.488**	0.478**	0.524**	0.106	1.599***	3.184***	ATE-AIPW High	-0.244***	0.005	0.077	-0.083	0.045	0.957*
		(0.205)	(0.237)	(0.257)	(0.324)	(0.398)	(0.535)		(0.085)	(0.169)	(0.211)	(0.287)	(0.374)	(0.535)
	ATE-AIPW Low	-0.112	-0.621*** -1.420*** -3.027*** -3.506*** -4.160***	ATE-AIPW Low	-0.599*** -1.073*** -1.682***	-2.655*** -3.361*** -4.206***
		(0.117)	(0.163)	(0.225)	(0.319)	(0.373)	(0.442)		(0.081)	(0.136)	(0.183) (0.253) 0.000 0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.674	1.385	2.089	2.910	3.670	4.399	Avg.(trade) in countries above cutoff	0.691	1.508	2.345	3.203	3.988	4.785
	Avg.(trade) in countries below cutoff	-0.098	-0.249	-0.043	-0.086	-0.287	-0.413	Avg.(trade) in countries below cutoff	0.154	0.062	0.115	0.316	0.384	0.336
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.684*** -1.246*** -2.044*** -3.457*** -4.317*** -4.737***	ATE-AIPW High	-1.124*** -1.749***	-0.641	-0.423	-1.560	0.609
		(0.139)	(0.260)	(0.326)	(0.409)	(0.515)	(0.629)		(0.271)	(0.379)	(0.582)	(0.779)	(1.073)	(1.535)
	ATE-AIPW Low	-0.944*** -1.876*** -2.578*** -3.785*** -4.767*** -5.246***	ATE-AIPW Low	-3.070*** -4.905*** -6.402***	-7.558*** -8.197*** -10.014***
		(0.135)	(0.227)	(0.318)	(0.403)	(0.503)	(0.613)		(0.265)	(0.396)	(0.560)	(0.689)	(0.933)	(1.249)
	P-value High=Low	0.000	0.000	0.000	0.003	0.003	0.010	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	1.405	2.936	4.546	6.147	7.723	9.355	Avg.(trade) in countries above cutoff	2.328	4.894	7.475	10.074	12.796	15.565
	Avg.(trade) in countries below cutoff	0.704	1.146	1.655	2.702	3.438	4.293	Avg.(trade) in countries below cutoff	0.859	0.994	1.461	2.604	3.514	4.724
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.478*** -1.167*** -1.227*** -1.767*** -2.032*** -2.635***	ATE-AIPW High	-0.530*** -0.977*** -1.184***	-1.250*** -1.364*** -1.032***
		(0.086)	(0.136)	(0.199)	(0.257)	(0.328)	(0.427)		(0.090)	(0.137)	(0.189)	(0.227)	(0.282)	(0.351)
	ATE-AIPW Low	-0.478*** -1.352*** -1.476*** -1.886*** -2.236*** -2.883***	ATE-AIPW Low	-1.259*** -1.599*** -2.218***	-2.315*** -2.642*** -3.256***
		(0.084)	(0.136)	(0.192)	(0.264)	(0.329)	(0.424)		(0.103)	(0.139)	(0.183)	(0.229)	(0.294)	(0.357)
	P-value High=Low	0.994	0.001	0.013	0.342	0.124	0.126	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.881	1.810	2.863	3.931	4.967	6.059	Avg.(trade) in countries above cutoff	0.672	1.436	2.253	3.125	4.001	4.861
	Avg.(trade) in countries below cutoff	0.501	0.854	1.201	1.730	2.242	2.854	Avg.(trade) in countries below cutoff	0.305	0.434	0.501	0.829	0.971	1.287
	Observations	698	698	698	698	698	698	Observations	698	698	698	698	698	698
	# of Crises	19	19	19	19	19	19	# of Crises	19	19	19	19	19	19
	Cutoff of channel variable, median	7.041	7.041	7.041	7.041	7.041	7.041	Cutoff of channel variable, median	7.041	7.041	7.041	7.041	7.041	7.041

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 18b: Channels, Cumulative trade costs over five years after banking crises with higher and lower trading partners' growth

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel B: Banking crises							Panel B: Banking crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.192*** -4.281*** -4.924*** -5.350*** -8.152*** -8.547***	ATE-AIPW High	-3.316*** -8.238*** -10.201*** -10.742*** -11.746*** -12.816***
		(0.317)	(0.516)	(0.575)	(0.697)	(0.916)	(1.126)		(0.455)	(0.745)	(0.812)	(0.956)	(1.226)	(1.428)
	ATE-AIPW Low	-1.372*** -4.084*** -5.190*** -5.163*** -6.886*** -7.061***	ATE-AIPW Low	-2.543*** -3.910*** -5.419***	-6.725***	-8.888*** -11.218***
		(0.304)	(0.515)	(0.573)	(0.740)	(0.974)	(1.135)		(0.479)	(0.736)	(0.816)	(0.993)	(1.281)	(1.485)
	P-value High=Low	0.387	0.382	0.294	0.600	0.007	0.010	P-value High=Low	0.024	0.000	0.000	0.000	0.000	0.046
	Avg.(trade) in countries above cutoff	3.566	7.497	11.648	15.781	19.801	23.915	Avg.(trade) in countries above cutoff	4.403	9.231	14.243	19.193	24.208	29.261
	Avg.(trade) in countries below cutoff	1.547	2.546	4.018	6.215	7.919	10.127	Avg.(trade) in countries below cutoff	1.528	2.544	3.707	6.150	8.179	10.579
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.200*** -0.509*** -0.635*** -0.773*** -1.037*** -1.352***	ATE-AIPW High	-0.363*** -0.592*** -0.771***	-1.098***	-1.349*** -1.550***
		(0.062)	(0.092)	(0.119)	(0.148)	(0.176)	(0.211)		(0.053)	(0.093)	(0.098)	(0.110)	(0.140)	(0.157)
	ATE-AIPW Low	-0.050	-0.413*** -0.648*** -0.860*** -1.135*** -1.660***	ATE-AIPW Low	-0.087	0.622***	0.276**	-0.035	-0.244	-1.051***
		(0.063)	(0.093)	(0.113)	(0.130)	(0.152)	(0.182)		(0.073)	(0.107)	(0.111)	(0.122)	(0.157)	(0.169)
	P-value High=Low	0.000	0.108	0.884	0.366	0.384	0.009	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.469	0.975	1.506	2.034	2.537	3.151	Avg.(trade) in countries above cutoff	0.372	0.798	1.248	1.714	2.216	2.706
	Avg.(trade) in countries below cutoff	0.159	0.283	0.534	0.832	1.075	1.376	Avg.(trade) in countries below cutoff	0.223	0.378	0.531	0.805	0.969	1.261
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	0.009	-0.679***	-0.428*	-0.577*	-1.508*** -1.441***	ATE-AIPW High	-0.585*** -1.156*** -1.559***	-1.615***	-1.878*** -2.244***
		(0.171)	(0.226)	(0.245)	(0.311)	(0.406)	(0.463)		(0.101)	(0.142)	(0.152)	(0.179)	(0.232)	(0.277)
	ATE-AIPW Low	-0.090	-0.518** -1.177*** -1.831*** -2.266*** -1.374***	ATE-AIPW Low	-0.708*** -1.027*** -1.392***	-2.011***	-2.464*** -2.873***
		(0.153)	(0.220)	(0.223)	(0.278)	(0.383)	(0.494)		(0.098)	(0.145)	(0.154)	(0.184)	(0.228)	(0.270)
	P-value High=Low	0.309	0.050	0.000	0.000	0.000	0.812	P-value High=Low	0.001	0.033	0.009	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.739	1.537	2.358	3.221	4.035	4.789	Avg.(trade) in countries above cutoff	0.778	1.661	2.609	3.521	4.370	5.213
	Avg.(trade) in countries below cutoff	0.002	-0.071	0.025	0.164	0.148	0.239	Avg.(trade) in countries below cutoff	0.129	0.176	0.229	0.517	0.666	0.784
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.429*** -0.993*** -1.162*** -1.199** -2.120*** -2.465***	ATE-AIPW High	-2.092*** -5.195*** -5.912***	-5.831***	-6.120*** -6.584***
		(0.159)	(0.288)	(0.380)	(0.465)	(0.532)	(0.647)		(0.271)	(0.448)	(0.494)	(0.583)	(0.742)	(0.861)
	ATE-AIPW Low	-0.448*** -1.742*** -1.791*** -1.070** -1.254**	-0.938	ATE-AIPW Low	-1.324*** -2.894*** -3.242***	-3.435***	-4.327*** -5.187***
		(0.151)	(0.263)	(0.323)	(0.429)	(0.557)	(0.690)		(0.281)	(0.443)	(0.492)	(0.596)	(0.771)	(0.900)
	P-value High=Low	0.846	0.000	0.015	0.704	0.018	0.002	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.013
	Avg.(trade) in countries above cutoff	1.442	3.063	4.762	6.430	8.053	9.666	Avg.(trade) in countries above cutoff	2.517	5.229	7.964	10.650	13.449	16.297
	Avg.(trade) in countries below cutoff	0.845	1.429	2.087	3.160	4.057	5.205	Avg.(trade) in countries below cutoff	0.899	1.477	2.273	3.682	4.996	6.525
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.572*** -2.101*** -2.699*** -2.802*** -3.488*** -3.290***	ATE-AIPW High	-0.275*** -1.295*** -1.959***	-2.199***	-2.398*** -2.438***
		(0.091)	(0.157)	(0.224)	(0.297)	(0.380)	(0.500)		(0.105)	(0.143)	(0.184)	(0.221)	(0.264)	(0.302)
	ATE-AIPW Low	-0.784*** -1.412*** -1.575*** -1.402*** -2.231*** -3.089***	ATE-AIPW Low	-0.424*** -0.612*** -1.062***	-1.244***	-1.853*** -2.107***
		(0.096)	(0.144)	(0.197)	(0.274)	(0.350)	(0.415)		(0.095)	(0.135)	(0.170)	(0.215)	(0.255)	(0.291)
	P-value High=Low	0.005	0.000	0.000	0.000	0.000	0.545	P-value High=Low	0.141	0.000	0.000	0.000	0.000	0.058
	Avg.(trade) in countries above cutoff	0.916	1.922	3.021	4.096	5.175	6.310	Avg.(trade) in countries above cutoff	0.736	1.542	2.423	3.308	4.174	5.045
	Avg.(trade) in countries below cutoff	0.541	0.905	1.373	2.059	2.639	3.308	Avg.(trade) in countries below cutoff	0.277	0.514	0.674	1.146	1.548	2.011
	Observations	700	700	700	700	700	700	Observations	700	700	700	700	700	700
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	7.984	7.984	7.984	7.984	7.984	7.984	Cutoff of channel variable, median	7.984	7.984	7.984	7.984	7.984	7.984

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 18c: Channels, Cumulative trade costs over five years after currency crises with higher and lower trading partners' growth

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.364*** -2.490*** -2.713*** -2.881*** -4.422*** -3.300***	ATE-AIPW High	-5.765*** -7.089*** -8.831*** -9.538*** -9.656*** -5.608***
		(0.269)	(0.412)	(0.540)	(0.706)	(0.838)	(1.054)		(0.422)	(0.580)	(0.807)	(0.994)	(1.207)	(1.532)
	ATE-AIPW Low	-2.102*** -3.787*** -5.713*** -7.148*** -8.791*** -11.604***	ATE-AIPW Low	-4.109*** -6.614*** -7.946*** -8.878*** -9.760*** -11.549***
		(0.267)	(0.393)	(0.587)	(0.698)	(0.823)	(0.956)		(0.382)	(0.538)	(0.749)	(0.951)	(1.176)	(1.365)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.171	0.059	0.208	0.865	0.000
	Avg.(trade) in countries above cutoff	3.443	7.127	11.100	15.111	18.992	23.077	Avg.(trade) in countries above cutoff	4.130	8.756	13.541	18.397	23.253	28.205
	Avg.(trade) in countries below cutoff	1.297	2.162	3.353	5.326	6.735	8.418	Avg.(trade) in countries below cutoff	1.498	1.947	2.607	4.545	6.128	7.998
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.266*** -0.516*** -0.697*** -0.707*** -0.538***	-0.153	ATE-AIPW High	-0.467*** -0.566*** -0.829*** -1.061*** -0.886*** -0.451***
		(0.056)	(0.088)	(0.115)	(0.154)	(0.182)	(0.257)		(0.085)	(0.076)	(0.099)	(0.112)	(0.136)	(0.167)
	ATE-AIPW Low	0.516*** 0.296*** 1.023*** 0.686*** 0.531***	0.355	ATE-AIPW Low	-0.455*** -0.584*** -0.788*** -0.823*** -0.870*** -0.993***
		(0.069)	(0.102)	(0.209)	(0.209)	(0.188)	(0.229)		(0.080)	(0.069)	(0.094)	(0.105)	(0.134)	(0.149)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.024	P-value High=Low	0.727	0.691	0.385	0.000	0.804	0.000
	Avg.(trade) in countries above cutoff	0.448	0.923	1.411	1.916	2.395	2.980	Avg.(trade) in countries above cutoff	0.359	0.780	1.207	1.655	2.128	2.609
	Avg.(trade) in countries below cutoff	0.128	0.231	0.534	0.835	1.071	1.367	Avg.(trade) in countries below cutoff	0.217	0.301	0.433	0.704	0.854	1.113
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	0.037	-0.379**	-0.005	-0.034	-1.040*** -0.792**	ATE-AIPW High	-1.004*** -1.233*** -1.988*** -2.078*** -2.468*** -1.543***
		(0.143)	(0.163)	(0.234)	(0.283)	(0.316)	(0.392)		(0.084)	(0.133)	(0.190)	(0.228)	(0.272)	(0.352)
	ATE-AIPW Low	-1.004*** -1.810*** -2.992*** -3.482*** -4.174*** -4.685***	ATE-AIPW Low	-0.658*** -1.200*** -1.803*** -2.331*** -2.760*** -3.008***
		(0.156)	(0.169)	(0.232)	(0.264)	(0.315)	(0.362)		(0.087)	(0.130)	(0.158)	(0.194)	(0.231)	(0.264)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.675	0.183	0.087	0.064	0.000
	Avg.(trade) in countries above cutoff	0.690	1.402	2.152	2.978	3.757	4.498	Avg.(trade) in countries above cutoff	0.705	1.524	2.394	3.271	4.058	4.866
	Avg.(trade) in countries below cutoff	-0.075	-0.146	-0.049	-0.025	-0.195	-0.276	Avg.(trade) in countries below cutoff	0.158	0.148	0.167	0.368	0.499	0.493
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.551*** -1.110*** -1.571*** -1.698*** -2.124*** -1.582***	ATE-AIPW High	-3.478*** -4.286*** -4.923*** -5.114*** -5.130*** -2.706***
		(0.134)	(0.233)	(0.307)	(0.409)	(0.497)	(0.591)		(0.252)	(0.345)	(0.465)	(0.588)	(0.721)	(0.894)
	ATE-AIPW Low	-0.824*** -1.503*** -2.657*** -3.251*** -3.952*** -5.156***	ATE-AIPW Low	-2.260*** -3.615*** -3.973*** -4.395*** -4.776*** -5.786***
		(0.127)	(0.210)	(0.283)	(0.366)	(0.459)	(0.550)		(0.238)	(0.341)	(0.457)	(0.573)	(0.707)	(0.823)
	P-value High=Low	0.000	0.002	0.000	0.000	0.000	0.000	P-value High=Low	0.000	0.003	0.000	0.017	0.310	0.000
	Avg.(trade) in countries above cutoff	1.420	2.970	4.631	6.245	7.832	9.484	Avg.(trade) in countries above cutoff	2.379	4.982	7.641	10.281	12.994	15.786
	Avg.(trade) in countries below cutoff	0.724	1.207	1.648	2.709	3.490	4.354	Avg.(trade) in countries below cutoff	0.831	1.076	1.491	2.638	3.750	5.036
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.583*** -0.485*** -0.441**	-0.442*	-0.720**	-0.772**	ATE-AIPW High	-0.816*** -1.003*** -1.091*** -1.284*** -1.173*** -0.908***
		(0.085)	(0.145)	(0.192)	(0.265)	(0.309)	(0.385)		(0.078)	(0.122)	(0.172)	(0.203)	(0.242)	(0.290)
	ATE-AIPW Low	-0.791*** -0.770*** -1.087*** -1.101*** -1.197*** -2.117***	ATE-AIPW Low	-0.735*** -1.216*** -1.383*** -1.329*** -1.354*** -1.761***
		(0.072)	(0.119)	(0.180)	(0.251)	(0.295)	(0.338)		(0.064)	(0.109)	(0.155)	(0.201)	(0.240)	(0.267)
	P-value High=Low	0.000	0.004	0.000	0.001	0.046	0.000	P-value High=Low	0.135	0.005	0.007	0.738	0.292	0.000
	Avg.(trade) in countries above cutoff	0.886	1.833	2.905	3.972	5.008	6.116	Avg.(trade) in countries above cutoff	0.687	1.469	2.299	3.190	4.073	4.944
	Avg.(trade) in countries below cutoff	0.521	0.870	1.220	1.807	2.369	2.973	Avg.(trade) in countries below cutoff	0.292	0.421	0.517	0.835	1.026	1.356
	Observations	701	701	701	701	701	701	Observations	701	701	701	701	701	701
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	7.295	7.295	7.295	7.295	7.295	7.295	Cutoff of channel variable, median	7.295	7.295	7.295	7.295	7.295	7.295

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 19a: Channels, Cumulative trade costs over five years after debt crises with higher and lower evolution of financial development

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt crises							Panel A: Debt crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.330*** -3.509*** -4.022*** -6.209*** -5.852*** -4.147***	ATE-AIPW High	-3.726*** -6.091*** -5.929*** -6.136*** -6.831***	-3.282
		(0.330)	(0.434)	(0.602)	(0.849)	(1.066)	(1.356)		(0.464)	(0.644)	(0.962)	(1.227)	(1.528)	(2.021)
	ATE-AIPW Low	-1.688*** -1.924*** -3.107*** -6.051*** -6.711*** -7.029***	ATE-AIPW Low	-2.995*** -4.646*** -5.097*** -6.569*** -7.457*** -11.367***
		(0.277)	(0.402)	(0.555)	(0.699)	(0.874)	(1.057)		(0.378)	(0.598)	(0.863)	(1.057)	(1.302)	(1.550)
	P-value High=Low	0.153	0.000	0.001	0.753	0.179	0.001	P-value High=Low	0.013	0.000	0.098	0.533	0.470	0.000
	Avg.(trade) in countries above cutoff	3.150	6.576	10.282	14.141	17.839	21.941	Avg.(trade) in countries above cutoff	3.934	8.125	12.488	17.125	21.423	26.126
	Avg.(trade) in countries below cutoff	2.338	4.155	6.369	8.905	10.716	12.419	Avg.(trade) in countries below cutoff	2.346	4.351	6.563	9.195	12.348	15.096
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	0.045	0.262*	0.742*** 1.197*** 1.675*** 3.321***	ATE-AIPW High	-0.438*** -0.497*** -0.642*** -0.804*** -1.036*** -0.603***
		(0.102)	(0.135)	(0.179)	(0.278)	(0.388)	(0.635)		(0.079)	(0.081)	(0.128)	(0.134)	(0.157)	(0.194)
	ATE-AIPW Low	-0.665*** -0.222** -0.418*** -0.686*** -0.738***	-0.243	ATE-AIPW Low	0.018	-0.657*** -0.329*** -0.681*** -0.827*** -1.004***
		(0.110)	(0.095)	(0.139)	(0.163)	(0.197)	(0.215)		(0.064)	(0.079)	(0.120) 0.000	0.043	0.002	0.000
	Avg.(trade) in countries above cutoff	0.400	0.822	1.319	1.783	2.225	2.779	Avg.(trade) in countries above cutoff	0.355	0.720	1.117	1.561	1.970	2.440
	Avg.(trade) in countries below cutoff	0.312	0.616	0.932	1.420	1.786	2.220	Avg.(trade) in countries below cutoff	0.249	0.527	0.781	1.059	1.422	1.709
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	0.113	-0.273	-0.338	-1.314***	-0.070	1.207**	ATE-AIPW High	-0.281*** -0.750*** -0.659*** -0.967*** -1.037***	-0.070
		(0.209)	(0.223)	(0.256)	(0.350)	(0.411)	(0.558)		(0.091)	(0.144)	(0.199)	(0.282)	(0.359)	(0.466)
	ATE-AIPW Low	-0.049	-0.118	-0.646*** -1.597*** -2.032*** -2.266***	ATE-AIPW Low	-0.644***	-0.007	-0.693*** -1.345*** -1.511*** -2.662***
		(0.107)	(0.155)	(0.215)	(0.265)	(0.324)	(0.384)		(0.087)	(0.168)	(0.213)	(0.263)	(0.323)	(0.380)
	P-value High=Low	0.372	0.356	0.037	0.225	0.000	0.000	P-value High=Low	0.000	0.000	0.810	0.038	0.009	0.000
	Avg.(trade) in countries above cutoff	0.577	1.166	1.863	2.598	3.304	4.039	Avg.(trade) in countries above cutoff	0.662	1.394	2.175	2.972	3.649	4.457
	Avg

.(trade) in countries below cutoff

		0.297	0.633	0.917	1.266	1.323	1.269	Avg.(trade) in countries below cutoff	0.334	0.658	1.041	1.554	1.997	2.018
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.958*** -2.107*** -2.691*** -3.832*** -4.828*** -5.195***	ATE-AIPW High	-2.031*** -3.310*** -2.649*** -2.357*** -2.630***	-0.742
		(0.136)	(0.227)	(0.315)	(0.407)	(0.512)	(0.619)		(0.283)	(0.392)	(0.578)	(0.748)	(0.954)	(1.291)
	ATE-AIPW Low	-0.701*** -0.786*** -1.714*** -3.129*** -3.562*** -4.017***	ATE-AIPW Low	-1.988*** -3.492*** -3.546*** -3.969*** -4.441*** -6.536***
		(0.142)	(0.235)	(0.316)	(0.405)	(0.505)	(0.608)		(0.232)	(0.359)	(0.509)	(0.610)	(0.747)	(0.880)
	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000	P-value High=Low	0.818	0.426	0.004	0.001	0.005	0.000
	Avg.(trade) in countries above cutoff	1.361	2.889	4.392	6.009	7.542	9.202	Avg.(trade) in countries above cutoff	2.287	4.671	7.090	9.619	12.048	14.642
	Avg.(trade) in countries below cutoff	0.935	1.534	2.561	3.588	4.383	5.257	Avg.(trade) in countries below cutoff	1.245	2.255	3.521	4.966	6.834	8.824
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.530*** -1.391*** -1.735*** -2.259*** -2.629*** -3.480***	ATE-AIPW High	-0.976*** -1.534*** -1.979*** -2.009*** -2.127*** -1.867***
		(0.080)	(0.135)	(0.196)	(0.250)	(0.294)	(0.353)		(0.112)	(0.149)	(0.201)	(0.229)	(0.265)	(0.301)
	ATE-AIPW Low	-0.273*** -0.798*** -0.329* -0.639***	-0.379	-0.503	ATE-AIPW Low	-0.382*** -0.490*** -0.530*** -0.574*** -0.679*** -1.164***
		(0.078)	(0.135)	(0.183)	(0.237)	(0.278)	(0.334)		(0.085)	(0.119)	(0.157) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.812	1.698	2.708	3.752	4.768	5.921	Avg.(trade) in countries above cutoff	0.631	1.339	2.107	2.973	3.756	4.587
	Avg.(trade) in countries below cutoff	0.794	1.373	1.959	2.632	3.224	3.673	Avg.(trade) in countries below cutoff	0.517	0.912	1.221	1.616	2.096	2.545
	Observations	702	702	702	702	702	702	Observations	702	702	702	702	702	702
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-0.008	-0.008	-0.008	-0.008	-0.008	-0.008	Cutoff of channel variable, median	-0.008	-0.008	-0.008	-0.008	-0.008	-0.008

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 19b: Channels, Cumulative trade costs over five years after banking crises with higher and lower evolution of financial development

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel B: Banking crises							Panel B: Banking crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-2.306*** -5.461*** -6.304*** -5.320*** -5.970*** -5.898***	ATE-AIPW High	-5.496*** -9.162*** -9.787*** -8.626*** -7.680*** -8.499***
		(0.278)	(0.473)	(0.554)	(0.696)	(0.878)	(1.082)		(0.422)	(0.727)	(0.851)	(1.001)	(1.266)	(1.521)
	ATE-AIPW Low	-0.014	-5.316*** -5.057*** -5.012*** -9.209*** -9.637***	ATE-AIPW Low	-0.131	-8.313*** -9.721*** -10.532*** -14.917*** -16.622***
		(0.305)	(0.475)	(0.567)	(0.701)	(0.858)	(1.059)		(0.398)	(0.695)	(0.837)	(0.946)	(1.202)	(1.469)
	P-value High=Low	0.000	0.535	0.000	0.409	0.000	0.000	P-value High=Low	0.000	0.073	0.887	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.591	7.423	11.452	15.652	19.711	24.007	Avg.(trade) in countries above cutoff	4.294	9.036	13.876	18.734	23.349	28.106
	Avg.(trade) in countries below cutoff	1.895	3.640	5.876	8.340	10.264	12.566	Avg.(trade) in countries below cutoff	2.345	4.250	6.480	9.508	12.560	15.974
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.221*** -0.674*** -0.696*** -0.689*** -0.764*** -0.883***	ATE-AIPW High	-0.724*** -0.773*** -0.892*** -0.937*** -0.854*** -1.083***
		(0.063)	(0.087)	(0.119)	(0.148)	(0.170)	(0.204)		(0.056)	(0.087)	(0.102)	(0.112)	(0.145)	(0.167)
	ATE-AIPW Low	0.171*** -0.185** -0.404*** -0.626*** -0.822*** -1.433***	ATE-AIPW Low	0.048	-0.246** -0.348*** -0.709*** -1.383*** -1.629***
		(0.058)	(0.086)	(0.112)	(0.132)	(0.160)	(0.185)		(0.061)	(0.098)	(0.110)	(0.115)	(0.132)	(0.162)
	P-value High=Low	0.000	0.000	0.002	0.557	0.649	0.000	P-value High=Low	0.000	0.000	0.000	0.001	0.000	0.000
	Avg.(trade) in countries above cutoff	0.429	0.890	1.378	1.835	2.334	2.927	Avg.(trade) in countries above cutoff	0.374	0.793	1.246	1.737	2.169	2.638
	Avg.(trade) in countries below cutoff	0.296	0.580	0.980	1.472	1.769	2.182	Avg.(trade) in countries below cutoff	0.257	0.477	0.688	0.952	1.293	1.651
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.274	-1.332*** -1.537*** -1.705*** -2.237*** -2.542***	ATE-AIPW High	-0.976*** -1.524*** -1.861*** -2.005*** -2.203*** -2.723***
		(0.167)	(0.223)	(0.240)	(0.292)	(0.358)	(0.428)		(0.094)	(0.146)	(0.169)	(0.199)	(0.252)	(0.308)
	ATE-AIPW Low	-0.269* -0.902*** -0.794*** -1.079*** -2.342***	-0.802*	ATE-AIPW Low	-0.204** -1.578*** -1.605*** -1.463*** -2.098*** -2.636***
		(0.143)	(0.226)	(0.258)	(0.282)	(0.327)	(0.461)		(0.101)	(0.151)	(0.169)	(0.206)	(0.250)	(0.300)
	P-value High=Low	0.953	0.000	0.000	0.000	0.492	0.000	P-value High=Low	0.000	0.398	0.000	0.000	0.318	0.469
	Avg.(trade) in countries above cutoff	0.671	1.270	2.107	2.972	3.751	4.520	Avg.(trade) in countries above cutoff	0.690	1.483	2.341	3.161	3.903	4.751
	Avg.(trade) in countries below cutoff	0.244	0.662	0.864	1.139	1.325	1.519	Avg.(trade) in countries below cutoff	0.412	0.791	1.197	1.779	2.202	2.460
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.709*** -1.461*** -1.967*** -1.295***	-0.965*	-1.065*	ATE-AIPW High	-2.825*** -5.402*** -5.341*** -4.256*** -2.897*** -3.075***
		(0.126)	(0.229)	(0.307)	(0.417)	(0.530)	(0.624)		(0.252)	(0.445)	(0.519)	(0.606)	(0.774)	(0.928)
	ATE-AIPW Low	0.351** -2.218*** -1.257***	-0.543	-2.244*** -2.308***	ATE-AIPW Low	-0.327	-5.505*** -5.886*** -6.033*** -8.596*** -9.124***
		(0.169)	(0.292)	(0.378)	(0.465)	(0.526)	(0.674)		(0.231)	(0.417)	(0.501)	(0.565)	(0.716)	(0.883)
	P-value High=Low	0.000	0.000	0.006	0.026	0.000	0.008	P-value High=Low	0.000	0.751	0.084	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	1.519	3.256	4.845	6.576	8.259	9.971	Avg.(trade) in countries above cutoff	2.530	5.233	7.899	10.521	13.122	15.688
	Avg.(trade) in countries below cutoff	0.829	1.430	2.494	3.553	4.375	5.467	Avg.(trade) in countries below cutoff	1.232	2.228	3.517	5.222	7.013	9.286
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-1.101*** -1.993*** -2.104*** -1.631*** -2.003*** -1.408***	ATE-AIPW High	-0.971*** -1.463*** -1.693*** -1.428*** -1.726*** -1.619***
		(0.092)	(0.144)	(0.194)	(0.271)	(0.336)	(0.460)		(0.087)	(0.134)	(0.167)	(0.203)	(0.237)	(0.271)
	ATE-AIPW Low	-0.267*** -2.011*** -2.602*** -2.764*** -3.802*** -5.093***	ATE-AIPW Low	0.353*** -0.984*** -1.882*** -2.327*** -2.841*** -3.232***
		(0.090)	(0.157)	(0.224)	(0.288)	(0.353)	(0.412)		(0.089)	(0.115)	(0.167)	(0.198)	(0.240)	(0.281)
	P-value High=Low	0.000	0.870	0.001	0.000	0.000	0.000	P-value High=Low	0.000	0.000	0.121	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.972	2.007	3.121	4.269	5.367	6.589	Avg.(trade) in countries above cutoff	0.699	1.527	2.390	3.314	4.156	5.029
	Avg.(trade) in countries below cutoff	0.526	0.968	1.538	2.177	2.795	3.397	Avg.(trade) in countries below cutoff	0.444	0.754	1.078	1.555	2.052	2.577
	Observations	705	705	705	705	705	705	Observations	705	705	705	705	705	705
	# of Crises	17	17	17	17	17	17	# of Crises	17	17	17	17	17	17
	Cutoff of channel variable, median	0.006	0.006	0.006	0.006	0.006	0.006	Cutoff of channel variable, median	0.006	0.006	0.006	0.006	0.006	0.006

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 19c: Channels, Cumulative trade costs over five years after currency crises with higher and lower evolution of financial development

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.746*** -2.530*** -3.852*** -4.669*** -6.155*** -7.517***	ATE-AIPW High	-4.780*** -6.537*** -7.660*** -8.646*** -9.468*** -8.439***
		(0.286)	(0.407)	(0.583)	(0.755)	(0.864)	(1.100)		(0.413)	(0.546)	(0.780)	(1.015)	(1.236)	(1.578)
	ATE-AIPW Low	-3.067*** -4.724*** -4.861*** -5.566*** -7.669*** -9.094***	ATE-AIPW Low	-6.698*** -8.322*** -9.583*** -10.429*** -10.760*** -11.804***
		(0.384)	(0.426)	(0.568)	(0.714)	(0.830)	(1.004)		(0.523)	(0.625)	(0.839)	(1.014)	(1.222)	(1.454)
	P-value High=Low	0.000	0.000	0.010	0.054	0.004	0.039	P-value High=Low	0.000	0.000	0.000	0.001	0.049	0.002
	Avg.(trade) in countries above cutoff	3.157	6.498	10.061	13.818	17.344	21.302	Avg.(trade) in countries above cutoff	3.949	8.017	12.196	16.687	20.890	25.421
	Avg.(trade) in countries below cutoff	2.126	3.955	6.467	9.158	11.308	13.106	Avg.(trade) in countries below cutoff	1.922	3.976	6.522	9.347	12.662	15.733
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.009	-0.041	0.393*	0.130	-0.294	-0.498*	ATE-AIPW High	-0.533*** -0.498*** -0.562*** -0.917*** -0.843*** -0.708***
		(0.059)	(0.106)	(0.216)	(0.227)	(0.217)	(0.272)		(0.083)	(0.074)	(0.101)	(0.112)	(0.140)	(0.172)
	ATE-AIPW Low	0.060	-0.479*** -0.292**	-0.290*	-0.008	0.054	ATE-AIPW Low	-0.502*** -0.601*** -0.890*** -0.909*** -0.749*** -0.821***
		(0.076)	(0.090)	(0.120)	(0.148)	(0.170)	(0.225)		(0.082)	(0.077)	(0.105)	(0.114)	(0.138)	(0.153)
	P-value High=Low	0.282	0.000	0.001	0.039	0.087	0.016	P-value High=Low	0.393	0.029	0.000	0.874	0.155	0.351
	Avg.(trade) in countries above cutoff	0.400	0.821	1.313	1.773	2.200	2.735	Avg.(trade) in countries above cutoff	0.358	0.718	1.105	1.536	1.934	2.384
	Avg.(trade) in countries below cutoff	0.290	0.573	0.868	1.383	1.796	2.290	Avg.(trade) in countries below cutoff	0.211	0.495	0.756	1.060	1.457	1.792
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.401** -0.785*** -1.102*** -1.406*** -1.856*** -2.017***	ATE-AIPW High	-0.695*** -0.984*** -1.313*** -1.605*** -1.959*** -1.679***
		(0.180)	(0.153)	(0.210)	(0.268)	(0.321)	(0.395)		(0.078)	(0.128)	(0.162)	(0.218)	(0.255)	(0.352)
	ATE-AIPW Low	-0.417** -1.181*** -1.408*** -1.490*** -2.713*** -2.721***	ATE-AIPW Low	-1.511*** -1.807*** -2.606*** -3.114*** -3.725*** -4.008***
		(0.198)	(0.178)	(0.265)	(0.293)	(0.324)	(0.377)		(0.121)	(0.140)	(0.199)	(0.220)	(0.256)	(0.290)
	P-value High=Low	0.857	0.000	0.094	0.625	0.000	0.001	P-value High=Low	0.000	0.000	0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.565	1.149	1.808	2.503	3.167	3.876	Avg.(trade) in countries above cutoff	0.693	1.392	2.120	2.890	3.566	4.292
	Avg.(trade) in countries below cutoff	0.289	0.590	0.946	1.385	1.483	1.366	Avg.(trade) in countries below cutoff	0.120	0.499	1.027	1.596	1.989	2.200
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.554*** -1.034*** -1.955*** -2.285*** -2.967*** -3.105***	ATE-AIPW High	-2.785*** -4.159*** -4.640*** -4.924*** -5.384*** -4.596***
		(0.138)	(0.233)	(0.308)	(0.416)	(0.502)	(0.601)		(0.250)	(0.326)	(0.459)	(0.599)	(0.734)	(0.910)
	ATE-AIPW Low	-1.792*** -2.261*** -2.633*** -3.120*** -3.757*** -4.651***	ATE-AIPW Low	-3.739*** -4.501*** -4.538*** -4.835*** -4.971*** -5.450***
		(0.213)	(0.250)	(0.303)	(0.379)	(0.473)	(0.581)		(0.326)	(0.402)	(0.507)	(0.606)	(0.737)	(0.889)
	P-value High=Low	0.000	0.000	0.000	0.000	0.002	0.000	P-value High=Low	0.000	0.205	0.717	0.782	0.290	0.150
	Avg.(trade) in countries above cutoff	1.355	2.832	4.289	5.868	7.354	8.946	Avg.(trade) in countries above cutoff	2.255	4.582	6.922	9.375	11.738	14.293
	Avg.(trade) in countries below cutoff	0.866	1.481	2.606	3.665	4.501	5.505	Avg.(trade) in countries below cutoff	1.149	2.103	3.459	4.994	7.035	9.060
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.782*** -0.671*** -1.188*** -1.108*** -1.037*** -1.897***	ATE-AIPW High	-0.766*** -0.896*** -1.145*** -1.200*** -1.282*** -1.456***
		(0.083)	(0.144)	(0.190)	(0.282)	(0.322)	(0.395)		(0.077)	(0.118)	(0.174)	(0.221)	(0.259)	(0.293)
	ATE-AIPW Low	-0.919*** -0.802*** -0.528*** -0.667** -1.190*** -1.776***	ATE-AIPW Low	-0.945*** -1.413*** -1.549*** -1.572*** -1.315*** -1.525***
		(0.085)	(0.132)	(0.200)	(0.305)	(0.329)	(0.382)		(0.081)	(0.122)	(0.179)	(0.228)	(0.265)	(0.300)
	P-value High=Low	0.035	0.201	0.000	0.037	0.536	0.708	P-value High=Low	0.003	0.000	0.000	0.006	0.850	0.773
	Avg.(trade) in countries above cutoff	0.837	1.695	2.650	3.674	4.622	5.746	Avg.(trade) in countries above cutoff	0.642	1.324	2.048	2.885	3.652	4.452
	Avg.(trade) in countries below cutoff	0.681	1.311	2.047	2.726	3.527	3.944	Avg.(trade) in countries below cutoff	0.442	0.878	1.279	1.698	2.182	2.682
	Observations	705	705	705	705	705	705	Observations	705	705	705	705	705	705
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-0.015	-0.015	-0.015	-0.015	-0.015	-0.015	Cutoff of channel variable, median	-0.015	-0.015	-0.015	-0.015	-0.015	-0.015

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 20a: Channels, Cumulative trade costs over five years after debt crises with higher and lower evolution of gross capital inflows

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt crises							Panel A: Debt crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.284	-1.984*** -2.188*** -4.005*** -6.220*** -5.370***	ATE-AIPW High	-1.111** -3.563*** -2.538**	-3.576**	-5.443***	-5.047**
		(0.391)	(0.551)	(0.703)	(1.051)	(1.494)	(1.986)		(0.495)	(0.698)	(0.985)	(1.408)	(1.918)	(2.531)
	ATE-AIPW Low	-2.969*** -4.344*** -6.274*** -11.091*** -11.031*** -13.326***	ATE-AIPW Low	-7.441*** -8.027*** -12.078*** -15.083*** -16.385*** -19.098***
		(0.275)	(0.474)	(0.699)	(1.001)	(1.459)	(1.942)		(0.513)	(0.702)	(0.998) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	3.157	6.913	10.928	15.150	18.932	22.777	Avg.(trade) in countries above cutoff	3.770	8.651	13.650	18.749	23.812	28.669
	Avg.(trade) in countries below cutoff	2.571	4.552	6.909	9.276	11.957	14.748	Avg.(trade) in countries below cutoff	2.684	4.068	5.793	7.984	10.379	13.319
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	-0.606***	-0.068	0.582***	1.002***	0.657**	0.958***	ATE-AIPW High	-0.096	-0.561*** -0.326**	-0.327*	-0.563***	-0.381*
		(0.136)	(0.122)	(0.194)	(0.313)	(0.324)	(0.301)		(0.132)	(0.130)	(0.129)	(0.175)	(0.170)	(0.205)
	ATE-AIPW Low	-0.061	-0.381*** -0.558*** -1.059*** -1.219*** -0.708***	ATE-AIPW Low	-0.569*** -0.571*** -1.008*** -1.483*** -1.644*** -1.602***
		(0.066)	(0.089)	(0.148)	(0.171)	(0.219)	(0.230)		(0.123)	(0.117)	(0.127) 0.000 005	0.000 0.427	0.000 0.001	0.000 0.000
	Avg.(trade) in countries above cutoff Avg.(trade) in countries above cutoff	0.334 0.348	0.836 0.857	1.347 1.328	1.870 1.847	2.339 2.279	2.875 2.780	Avg.(trade) in countries above cutoff Avg.(trade) in countries above cutoff	0.305 0.328	0.696 0.719	1.155 1.113	1.648 1.598	2.131 2.019	2.685 2.543
	Avg.(trade) in countries below cutoff Avg.(trade) in countries below cutoff	0.360 0.336	0.559 0.391	0.885 0.744	1.238 1.041	1.600 1.455	1.990 1.876	Avg.(trade) in countries below cutoff Avg.(trade) in countries below cutoff	0.328 0.278	0.566 0.454	0.715 0.648	0.968 0.820	1.207 1.122	1.474 1.350
	Panel c: Mining Panel c: Mining							Panel c: Mining Panel c: Mining
	ATE-AIPW High ATE-AIPW High	1.222*** -0.381	0.135 -0.527*** -0.887*** -1.576*** -2.799*** -2.372*** 0.016 -0.543 -0.284 1.031	ATE-AIPW High ATE-AIPW High	-0.345*** -0.735*** -0.924*** -0.680*** -1.024*** -1.721***	-1.202*** -2.005***	-1.273*** -2.324***	-1.067** -2.337***
		(0.234) (0.241)	(0.299) (0.194)	(0.305) (0.273)	(0.399) (0.339)	(0.511) (0.402)	(0.677) (0.494)		(0.096) (0.147)	(0.155) (0.184)	(0.198) (0.249)	(0.279) (0.364)	(0.381) (0.489)	(0.497) (0.472)
	ATE-AIPW Low ATE-AIPW Low	-1.272*** -0.650** -1.548*** -3.413*** -2.674*** -3.749*** 0.560** -0.905*** -0.815*** -0.951*** -1.725*** -2.172***	ATE-AIPW Low ATE-AIPW Low	-0.887*** -0.560*** -1.180*** -0.609*** -1.489*** -1.911***	-2.465*** -2.181***	-2.881*** -2.944***	-4.205*** -3.306***
	(0.151) (0.254) 0.000 0.715 0.284 -0.716*** -1.330*** -2.226*** -1.927*** (0.260) (0.286) (0.385) (0.191) (0.252) (0.337) 0.000 0.541 0.000 1.397 2.186 2.977 0.659 1.068 1.663 (0.185) (0.267) (0.343) (0.487) -0.619*** -1.223*** -0.813* -0.589 (0.199) (0.311) (0.421) (0.523) 0.310 0.584 0.000 0.000 1.353 2.999 4.567 6.171 1.097 1.726 2.770 3.752 -0.701*** -1.063*** -1.376*** -0.947*** -1.216*** (0.462) (0.423) 0.000 3.660 2.296 -1.290* (0.674) -1.796*** (0.648) 0.240 7.753 4.612 (0.094) (0.151) (0.229) (0.353) (0.462) -0.231** -2.085*** -2.692*** -3.086*** -4.256*** -5.159*** (0.624) (0.503) 0.192 4.303 2.898 -1.371* (0.777) -1.332* (0.792) 0.945 9.386 5.475 -0.831 (0.603) (0.094) (0.175) (0.266) (0.371) (0.476) (0.543) 0.699 1.518 2.466 3.496 4.434 5.500 0.727 1.183 1.679 2.140 2.851 3.328 (0.188) # of Crises (0.104) (0.143) (0.188) P-value High=Low P-value High=Low 0.078 0.000 Avg.(trade) in countries above cutoff Avg.(trade) in countries above cutoff 0.592 1.486 Avg.(trade) in countries below cutoff Avg.(trade) in countries below cutoff 0.616 0.676 Panel d: Manufacturing Panel d: Manufacturing ATE-AIPW High ATE-AIPW High -2.043*** -2.315*** -3.172*** (0.250) 0.001 2.348 1.022 (0.294) (0.500) (0.703) ATE-AIPW Low ATE-AIPW Low -1.063*** -5.205*** -6.101*** (0.313) (0.526) (0.722) P-value High=Low P-value High=Low 0.000 0.000 0.000 Avg.(trade) in countries above cutoff Avg.(trade) in countries above cutoff 2.235 4.833 7.330 Avg.(trade) in countries below cutoff Avg.(trade) in countries below cutoff 1.025 1.258 2.134 Panel e: Services Panel e: Services ATE-AIPW High ATE-AIPW High -0.433*** -0.449*** -0.605*** (0.085) (0.143) (0.212) ATE-AIPW Low ATE-AIPW Low 0.223* -1.303*** -2.183*** (0.119) (0.153) (0.218) 0.000 Avg.(trade) in countries above cutoff Avg.(trade) in countries above cutoff 0.570 1.278 2.073 Avg.(trade) in countries below cutoff Avg.(trade) in countries below cutoff 0.438 0.682 0.830 15 15 15 15 15 15 # of Crises 15 15 15 Observations 579 579 579 579 579 579 Observations 579 579 579 Cutoff of channel variable, median -1.017 -1.017 -1.017 -1.017 -1.017 -1.017 Cutoff of channel variable, median -1.017 -1.017 -1.017 # of Crises 14 14 14 14 14 14 # of Crises 14 14 14 Cutoff of channel variable, median -2.225 -2.225 -2.225 -2.225 -2.225 -2.225 Cutoff of channel variable, median -2.225 -2.225 -2.225	(0.363) 0.002 3.268 1.266 -3.225*** (1.088) -5.920*** (1.091) 0.000 9.976 2.946 -0.883*** (0.280) -2.714*** 0.000 2.952 1.045 15 579 -1.017 14 -2.225	(0.485) 0.000 4.082 1.549 -2.537* (1.405) -7.729*** (1.397) 0.000 12.614 4.301 -1.038*** (0.385) -3.229*** 0.000 3.742 1.456 15 579 -1.017 14 -2.225	(0.464) 0.000 4.793 1.825 -2.430** (1.166) -8.712*** (1.172) 0.000 15.169 6.157 -1.016*** (0.339) -3.269*** 0.000 4.571 1.872 579 15 14 -1.017 -2.225

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20. 0.

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 20c: Channels, Cumulative trade costs over five years after currency crises with higher and lower evolution of gross capital inflows

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-1.391*** -3.473*** -4.683*** -5.104*** -5.993*** -5.173***	ATE-AIPW High	-5.233*** -6.613*** -8.501*** -9.139*** -8.097*** -4.712***
		(0.314)	(0.473)	(0.672)	(0.812)	(0.978)	(1.212)		(0.522)	(0.685)	(1.013)	(1.150)	(1.397)	(1.747)
	ATE-AIPW Low	-3.920*** -3.825*** -3.451*** -4.278*** -7.463*** -11.400***	ATE-AIPW Low	-7.220*** -8.368*** -8.167*** -8.152*** -9.938*** -13.246***
		(0.443)	(0.529)	(0.694)	(0.843)	(0.974)	(1.172)		(0.620)	(0.718)	(0.951)	(1.152)	(1.447)	(1.704)
	P-value High=Low	0.000	0.280	0.002	0.095	0.010	0.000	P-value High=Low	0.000	0.000	0.540	0.081	0.007	0.000
	Avg.(trade) in countries above cutoff	3.116	6.885	10.831	14.932	18.658	22.547	Avg.(trade) in countries above cutoff	3.736	8.616	13.472	18.537	23.372	28.228
	Avg.(trade) in countries below cutoff	2.594	4.403	6.742	9.167	11.856	14.477	Avg.(trade) in countries below cutoff	2.651	3.743	5.446	7.450	10.028	12.806
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	0.022	-0.453***	-0.216*	-0.548*** -0.352**	-0.523**	ATE-AIPW High	-0.516*** -0.702*** -0.881*** -1.061*** -0.786*** -0.478**
		(0.058)	(0.080)	(0.110)	(0.137)	(0.169)	(0.213)		(0.100)	(0.092)	(0.144)	(0.138)	(0.168)	(0.209)
	ATE-AIPW Low	-0.152**	-0.157	0.508**	0.547**	0.191	-0.144	ATE-AIPW Low	-0.486*** -0.446*** -0.647*** -0.617*** -0.734*** -0.971***
		(0.069)	(0.116)	(0.250)	(0.242)	(0.208)	(0.227)		(0.099)	(0.089)	(0.137)	(0.136)	(0.172)	(0.190)
	P-value High=Low	0.000	0.002	0.002	0.000	0.000	0.010	P-value High=Low	0.373	0.000	0.000	0.000	0.461	0.000
	Avg.(trade) in countries above cutoff	0.331	0.850	1.350	1.871	2.325	2.859	Avg.(trade) in countries above cutoff	0.324	0.720	1.161	1.660	2.112	2.670
	Avg.(trade) in countries below cutoff	0.368	0.510	0.841	1.183	1.563	1.942	Avg.(trade) in countries below cutoff	0.296	0.513	0.668	0.888	1.163	1.400
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.050	-0.916*** -1.732*** -1.444*** -2.146*** -2.086***	ATE-AIPW High	-1.311*** -1.649*** -2.671*** -2.818*** -2.972*** -2.470***
		(0.170)	(0.186)	(0.297)	(0.364)	(0.425)	(0.497)		(0.117)	(0.167)	(0.253)	(0.283)	(0.347)	(0.410)
	ATE-AIPW Low	-0.792*** -1.139*** -1.147*** -2.281*** -3.745*** -4.119***	ATE-AIPW Low	-1.337*** -1.571*** -1.514*** -2.131*** -2.994*** -3.550***
		(0.172)	(0.191)	(0.275)	(0.349)	(0.390)	(0.462)		(0.134)	(0.162)	(0.191)	(0.237)	(0.288)	(0.350)
	P-value High=Low	0.000	0.002	0.003	0.000	0.000	0.000	P-value High=Low	0.812	0.337	0.000	0.000	0.914	0.000
	Avg.(trade) in countries above cutoff	0.722	1.424	2.247	3.125	3.864	4.460	Avg.(trade) in countries above cutoff	0.603	1.550	2.502	3.501	4.334	5.057
	Avg.(trade) in countries below cutoff	0.371	0.782	1.216	1.702	2.246	2.942	Avg.(trade) in countries below cutoff	0.591	0.748	1.055	1.311	1.684	2.037
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.669*** -1.470*** -2.130*** -2.236*** -2.385*** -1.582**	ATE-AIPW High	-2.766*** -3.239*** -3.645*** -3.967*** -3.393***	-1.120
		(0.166)	(0.268)	(0.358)	(0.441)	(0.532)	(0.633)		(0.315)	(0.402)	(0.554)	(0.660)	(0.794)	(0.975)
	ATE-AIPW Low	-2.052*** -2.146*** -2.463*** -2.967*** -4.076*** -5.641***	ATE-AIPW Low	-4.405*** -5.394*** -5.114*** -4.667*** -5.449*** -7.367***
		(0.272)	(0.320)	(0.369)	(0.458)	(0.556)	(0.672)		(0.375)	(0.436)	(0.565)	(0.688)	(0.853)	(0.998)
	P-value High=Low	0.000	0.003	0.112	0.008	0.000	0.000	P-value High=Low	0.000	0.000	0.000	0.036	0.000	0.000
	Avg.(trade) in countries above cutoff	1.359	3.053	4.692	6.315	7.855	9.512	Avg.(trade) in countries above cutoff	2.242	5.021	7.664	10.315	13.014	15.730
	Avg.(trade) in countries below cutoff	1.145	1.922	2.961	4.052	5.153	6.151	Avg.(trade) in countries below cutoff	1.292	1.745	2.736	3.959	5.502	7.232
	Panel e: Services							Panel e: Services
	ATE-AIPW High	-0.694*** -0.634*** -0.605*** -0.875*** -1.110*** -0.982**	ATE-AIPW High	-0.641*** -1.024*** -1.305*** -1.293*** -0.947***	-0.644*
		(0.100)	(0.163)	(0.234)	(0.296)	(0.346)	(0.440)		(0.090)	(0.144)	(0.196)	(0.215)	(0.277)	(0.347)
	ATE-AIPW Low	-0.924*** -0.384***	-0.349	0.423	0.168	-1.496***	ATE-AIPW Low	-0.992*** -0.957*** -0.892*** -0.736*** -0.761*** -1.358***
		(0.102)	(0.145)	(0.216)	(0.296)	(0.349)	(0.414)		(0.099)	(0.148)	(0.187)	(0.221)	(0.271)	(0.304)
	P-value High=Low	0.001	0.043	0.155	0.000	0.000	0.175	P-value High=Low	0.000	0.463	0.001	0.000	0.387	0.015
	Avg.(trade) in countries above cutoff	0.704	1.557	2.542	3.621	4.614	5.716	Avg.(trade) in countries above cutoff	0.567	1.324	2.145	3.060	3.912	4.771
	Avg.(trade) in countries below cutoff	0.710	1.190	1.724	2.230	2.894	3.443	Avg.(trade) in countries below cutoff	0.473	0.737	0.988	1.292	1.679	2.137
	Observations	579	579	579	579	579	579	Observations	579	579	579	579	579	579
	# of Crises	15	15	15	15	15	15	# of Crises	15	15	15	15	15	15
	Cutoff of channel variable, median	-1.371	-1.371	-1.371	-1.371	-1.371	-1.371	Cutoff of channel variable, median	-1.371	-1.371	-1.371	-1.371	-1.371	-1.371

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 21a: Channels, Cumulative trade costs over five years after debt crises with higher and lower evolution of investors' credit ratings risks

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel A: Debt crises							Panel A: Debt crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.921*** -1.214*** -1.386** -3.828*** -3.383***	-2.395*	ATE-AIPW High	-1.601*** -2.344***	-1.690*	-0.923	-1.036	1.391
		(0.321)	(0.416)	(0.610)	(0.859)	(1.111)	(1.398)		(0.426)	(0.580)	(0.896)	(1.197)	(1.470)	(1.977)
	ATE-AIPW Low	-0.812*** -3.913*** -6.050*** -8.587*** -9.814*** -10.174***	ATE-AIPW Low	-4.820*** -10.537*** -12.064*** -14.338*** -16.217*** -17.600***
		(0.282)	(0.412)	(0.607)	(0.861)	(1.056)	(1.277)		(0.536)	(0.619)	(0.941) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.905	1.822	2.873	3.963	5.024	6.183	Avg.(trade) in countries above cutoff	0.696	1.463	2.247	3.072	3.900	4.775
	Avg.(trade) in countries below cutoff	0.055	0.124	0.324	0.708	1.128	1.706	Avg.(trade) in countries below cutoff	-0.188	-0.449	-0.551	-0.230	0.054	0.521
	Observations	627	627	627	627	627	627	Observations	627	627	627	627	627	627
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-5.567	-5.567	-5.567	-5.567	-5.567	-5.567	Cutoff of channel variable, median	-5.567	-5.567	-5.567	-5.567	-5.567	-5.567

Table A .

 A 21b: Channels, Cumulative trade costs over five years after banking crises with higher and lower evolution of investors' credit ratings risks

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel B: Banking crises							Panel B: Banking crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.580	-2.438*** -3.889*** -3.848*** -5.909*** -5.329***	ATE-AIPW High	-1.111** -2.971*** -4.883*** -6.376***	-7.466***	-7.985***
		(0.400)	(0.492)	(0.576)	(0.788)	(0.962)	(1.190)		(0.542)	(0.736)	(0.943)	(1.213)	(1.520)	(1.773)
	ATE-AIPW Low	0.005	-6.781*** -6.752*** -6.508*** -10.713*** -12.620***	ATE-AIPW Low	-2.091*** -12.004*** -13.464*** -13.384*** -16.763*** -19.355***
		(0.440)	(0.514)	(0.601)	(0.800)	(0.910)	(1.144)		(0.569)	(0.735)	(0.922) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.990	1.974	3.180	4.440	5.621	6.978	Avg.(trade) in countries above cutoff	0.743	1.576	2.528	3.514	4.468	5.500
	Avg.(trade) in countries below cutoff	0.406	0.860	1.271	1.746	2.315	2.850	Avg.(trade) in countries below cutoff	0.260	0.498	0.602	0.907	1.270	1.672
	Observations	628	628	628	628	628	628	Observations	628	628	628	628	628	628
	# of Crises	17	17	17	17	17	17	# of Crises	17	17	17	17	17	17
	Cutoff of channel variable, median	0.267	0.267	0.267	0.267	0.267	0.267	Cutoff of channel variable, median	0.267	0.267	0.267	0.267	0.267	0.267

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Maximum weights truncated at 20.

Table A .

 A 21c: Channels, Cumulative trade costs over five years after currency crises with higher and lower evolution of investors' credit ratings risks

	Panel I: Exports (% of pre-crisis GDP)							Panel II: Imports (% of pre-crisis GDP)
		(1)	(2)	(3)	(4)	(5)	(6)		(1)	(2)	(3)	(4)	(5)	(6)
		h=0	h=1	h=2	h=3	h=4	h=5		h=0	h=1	h=2	h=3	h=4	h=5
	Panel C: Currency crises							Panel C: Currency crises
	Panel a: Total							Panel a: Total
	ATE-AIPW High	-0.686** -2.269*** -3.408*** -3.764*** -5.295*** -6.311***	ATE-AIPW High	-3.086*** -4.540*** -6.405*** -8.495***	-8.893***	-6.775***
		(0.325)	(0.432)	(0.578)	(0.763)	(0.882)	(1.115)		(0.463)	(0.612)	(0.882)	(1.078)	(1.283)	(1.580)
	ATE-AIPW Low	-3.485*** -3.967*** -4.424*** -6.777*** -10.375*** -13.722***	ATE-AIPW Low	-7.322*** -9.207*** -9.981*** -10.584*** -13.025*** -17.237***
		(0.405)	(0.454)	(0.663)	(0.819)	(0.952)	(1.175)		(0.571)	(0.684)	(0.885)	(1.151)	(1.388)	(1.753)
	P-value High=Low	0.000	0.000	0.024	0.000	0.000	0.000	P-value High=Low	0.000	0.000	0.000	0.001	0.000	0.000
	Avg.(trade) in countries above cutoff	2.992	6.470	10.572	14.938	19.333	24.126	Avg.(trade) in countries above cutoff	3.852	8.450	13.474	18.622	23.806	29.408
	Avg.(trade) in countries below cutoff	0.938	1.257	2.425	4.075	5.444	7.228	Avg.(trade) in countries below cutoff	-0.059	-1.225	-1.161	0.337	2.221	4.302
	Panel b: Agriculture							Panel b: Agriculture
	ATE-AIPW High	0.187**	-0.222**	-0.108	-0.394***	-0.163	-0.146	ATE-AIPW High	-0.286*** -0.437*** -0.631*** -0.873***	-0.686***	-0.344**
		(0.075)	(0.089)	(0.119)	(0.150)	(0.187)	(0.245)		(0.093)	(0.083)	(0.108)	(0.123)	(0.145)	(0.173)
	ATE-AIPW Low	0.005	-0.093	0.566**	0.450*	-0.096	-0.421	ATE-AIPW Low	-0.517*** -0.584*** -0.851*** -1.001***	-1.103***	-1.581***
		(0.066)	(0.130)	(0.253)	(0.267)	(0.265)	(0.341)		(0.091)	(0.076)	(0.103)	(0.118)	(0.146)	(0.161)
	P-value High=Low	0.009	0.291	0.006	0.000	0.756	0.359	P-value High=Low	0.000	0.004	0.000	0.032	0.000	0.000
	Avg.(trade) in countries above cutoff	0.364	0.799	1.292	1.783	2.315	2.970	Avg.(trade) in countries above cutoff	0.345	0.734	1.205	1.700	2.183	2.727
	Avg.(trade) in countries below cutoff	0.181	0.253	0.593	1.065	1.405	1.855	Avg.(trade) in countries below cutoff	0.006	0.006	0.050	0.175	0.359	0.541
	Panel c: Mining							Panel c: Mining
	ATE-AIPW High	-0.004	-0.794*** -0.971*** -1.087*** -1.980*** -2.426***	ATE-AIPW High	-0.826*** -1.169*** -2.179*** -2.677***	-3.228***	-3.155***
		(0.233)	(0.185)	(0.242)	(0.309)	(0.360)	(0.444)		(0.104)	(0.145)	(0.202)	(0.234)	(0.277)	(0.331)
	ATE-AIPW Low	-0.497** -0.655*** -1.218*** -1.888*** -3.295*** -3.343***	ATE-AIPW Low	-1.143*** -1.211*** -1.270*** -1.816***	-2.713***	-3.251***
		(0.222)	(0.161)	(0.266)	(0.303)	(0.353)	(0.406)		(0.124)	(0.152)	(0.186)	(0.248)	(0.287)	(0.424)
	P-value High=Low	0.000	0.206	0.196	0.000	0.000	0.000	P-value High=Low	0.004	0.687	0.000	0.000	0.010	0.794
	Avg

.(trade) in countries above cutoff

	0.461	1.050	1.847	2.766	3.732	4.686	Avg.(trade) in countries above cutoff	0.546	1.275	2.128	3.074	4.044	5.094
	Avg

.(trade) in countries below cutoff

		-0.099	-0.153	-0.065	-0.019	-0.073	0.047	Avg.(trade) in countries below cutoff	0.013	-0.190	-0.033	0.293	0.499	0.635
	Panel d: Manufacturing							Panel d: Manufacturing
	ATE-AIPW High	-0.554*** -1.074*** -2.147*** -2.499*** -3.146*** -3.571***	ATE-AIPW High	-1.538*** -2.314*** -2.946*** -4.364***	-4.606***	-3.002***
		(0.133)	(0.242)	(0.322)	(0.426)	(0.519)	(0.622)		(0.282)	(0.368)	(0.511)	(0.639)	(0.764)	(0.932)
	ATE-AIPW Low	-1.673*** -1.916*** -2.219*** -3.191*** -4.404*** -5.756***	ATE-AIPW Low	-4.558*** -5.753*** -6.005*** -5.682***	-6.911***	-9.234***
		(0.221)	(0.245)	(0.313)	(0.428)	(0.542)	(0.671)		(0.344)	(0.418) 0.000	0.000	0.000	0.000
	Avg.(trade) in countries above cutoff	0.918	1.877	2.972	4.102	5.199	6.412	Avg.(trade) in countries above cutoff	0.707	1.511	2.341	3.209	4.082	5.002
	Avg.(trade) in countries below cutoff	0.236	0.342	0.578	0.976	1.406	1.896	Avg.(trade) in countries below cutoff	0.008	-0.137	-0.202	0.060	0.287	0.660
	Observations	627	627	627	627	627	627	Observations	627	627	627	627	627	627
	# of Crises	18	18	18	18	18	18	# of Crises	18	18	18	18	18	18
	Cutoff of channel variable, median	-3.509	-3.509	-3.509	-3.509	-3.509	-3.509	Cutoff of channel variable, median	-3.509	-3.509	-3.509	-3.509	-3.509	-3.509

  Different measures have been proposed in the literature where many authors relied on a single indicator including debt in percentage of GDP or tax revenues(Aizenman and Jinjarak 2010;[START_REF] Aizenman | [END_REF][START_REF] Grauwe | Par conséquent, notre indice comble les lacunes des mesures utilisées dans les études précédentes en incorporant autant d'informations compte tenu de la disponibilité des données pour mesurer le concept multidimensionnel d'espace budgétaire. Quatrièmement, notre article est également lié à la littérature sur les multiplicateurs budgétaires et la cyclicité de la politique budgétaire[END_REF][START_REF] Grauwe | Par conséquent, notre indice comble les lacunes des mesures utilisées dans les études précédentes en incorporant autant d'informations compte tenu de la disponibilité des données pour mesurer le concept multidimensionnel d'espace budgétaire. Quatrièmement, notre article est également lié à la littérature sur les multiplicateurs budgétaires et la cyclicité de la politique budgétaire[END_REF]) while others recommend to the use of a dashboard of indicators(Botev et al. 2016; Kose et al. 2017; Cheng and Pitterle

  the data availability, our analysis includes 55 financial crises and 58 normal recessions lasting, on average, 2.7 and 1.2 years. Also, 43% of normal recessions occurred in 2008-09, which stands at only 11% for financial crises. This means that despite being not affected by banking and currency crises as we saw in many advanced countries, developing and emerging countries suffered a recession linked to the global economic development in 2008-09. Then, the normal recessions shocks are likely to capture the dynamics of output in developing and emerging countries in the aftermath of the GFC.

Table 3 .

 3 1: Statistics on financial crises and normal recessions This tables presents the set of financial crises and normal recessions used in our regressions given the data availability that constrained our sample of shocks.

		Financial crises Normal recessions
	Full sample
	# of episodes	55	58
	Average duration of episodes (in years)	2.7	1.2
	# of episodes between 2008 and 2009	6	25
	# of countries	43	40
	Low-income countries
	# of episodes	22	23
	Average duration of episodes (in years)	2.4	1.2
	# of countries	15	15
	Emerging countries
	# of episodes	33	35
	Average duration of episodes (in years)	2.8	1.2
	# of countries	28	25
	Notes:

Table 3 .

 3 3: Thresholds and relative weights of fiscal indicators

	Variables	Thres-holds	Effect-iveness	Inci-dence	Type I error	Type II error	TME Weights	# of fiscal crises	# of non crises	# of signals
	Full sample
	(minus) debt to average tax revenues ratio	25	0.108 0.423 0.285	0.577 0.862	0.176	208	2554	816
	fiscal balance to average tax revenues ratio	15	0.154 0.435 0.187	0.565 0.752	0.316	161	2055	454
	(minus) short-term external debt in percentage of total	30	0.108 0.457 0.301	0.543 0.845	0.198	208	2614	883
	(minus) debt service in percentage of international reserves	25	0.137 0.559 0.316	0.441 0.756	0.311	177	1984	725
	Developing countries
	(minus) debt to average tax revenues ratio	16	0.193 0.400 0.174	0.600 0.774	0.265	105	1009	218
	fiscal balance to average tax revenues ratio	15	0.207 0.440 0.178	0.560 0.737	0.308	84	798	179
	(minus) short-term external debt in percentage of total	30	0.112 0.366 0.271	0.634 0.905	0.112	112	1204	367
	(minus) debt service in percentage of international reserves	25	0.196 0.570 0.301	0.430 0.731	0.315	93	724	271
	Emerging countries
	(minus) debt to average tax revenues ratio	22	0.078 0.356 0.270	0.644 0.914	0.103	101	1585	464
	fiscal balance to average tax revenues ratio	10	0.151 0.364 0.120	0.636 0.757	0.291	77	1313	186
	(minus) short-term external debt in percentage of total	33	0.104 0.604 0.357	0.396 0.753	0.296	96	1397	557
	(minus) debt service in percentage of international reserves	37	0.095 0.738 0.480	0.262 0.742	0.309	84	1228	651

Table 3 .

 3 . . Empirical methodology 135 The choice of the control variables draws upon Asonuma et al. (2016); Forni et al. (2016); Jordà and Taylor (2016); Kuvshinov and Zimmermann (2019), and Asonuma et al. (2019). As a result, OLS estimates of the costs of shocks that do not account for differences in characteristics between the treated (bad characteristics) and control (good characteristics) group are biased and overestimated. 4: Difference in characteristics between treated and control groups

		(1) Financial Crises	(2) Normal Recessions
	Variables	Coeff	SE	Coeff	SE	Obs.
	# of financial crises, t-3 to t-1 -0.143*** (0.016)	-0.039***	(0.012)	928
	# of normal recessions, t-3 to t-1 -0.070*** (0.019)	-0.167***	(0.033)	928
	Intensity of conflicts, t-1 to t+1	0.037	(0.030)	-0.028	(0.045)	928
	Real GDP growth, t-1 -1.311*** (0.179)	-0.011	(0.364)	928
	Real GDP growth, t-2 -1.270*** (0.212)	0.755***	(0.188)	928
	Output gap, t-1	0.007*	(0.004)	0.035***	(0.003)	928
	REER gap, t-1 0.086*** (0.011)	0.015***	(0.004)	928
	Current account, t-1 -2.746*** (0.385)	-1.008***	(0.244)	916
	Log of Real GDP, t-1 -0.178*** (0.025)	0.057*	(0.030)	928
	Terms of trade, t-1 -2.717*** (0.528)	-0.615	(1.595)	928
	Presence of fiscal rule, t-1 -0.095*** (0.027)	-0.009	(0.027)	928
	Political rights, t-1 0.287*** (0.075)	0.124***	(0.035)	928
	Presence of IMF program, t-1	-0.018	(0.033)	0.068	(0.042)	928
	World growth, t-1 -0.378*** (0.127)	-0.654**	(0.281)	928

Notes: Driscoll-Kraay standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tables describes the difference in country's characteristics in the run-up of shocks between the treated group (countries with financial crises and normal recessions) and the control group (countries in normal times). Here, the difference is country specific and is obtained by regressing on each variable the dummies of shocks using a panel fixed-effects model. Hodrick-Prescott filter are used to calculate the output gap and REER gap. The Political Right index ranges between 1 (high) and 7 (weak).

Table 3 .

 3 5: Endogeneity check of fiscal space Chapter . Fiscal space and stabilization policy in developing and emerging countries Zimmermann (2019), and Atsebi et al. (2019). Then, our impact assessment considers that financial crises and normal recessions, , , are the treatment and cumulative changes in output at each horizon ℎ,

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Financial crisis			Normal recession
	Dependent variable: Fiscal space index at time t-1 (t=start date of the shocks)
	# of financial crises, t-5 to t-1				0.002
					(0.077)
	# of normal recessions, t-5 to t-1 -0.347***
	(0.089)
	Growth, t-2	-0.003				-0.010***
		(0.007)				(0.003)
	Growth, t-1	0.036***				0.022***
		(0.008)				(0.005)
	Output gap, t-2		-8.231***				-1.236**
			(1.102)				(0.600)
	Output gap, t-1		7.510***				0.861
			(1.101)				(1.077)
	Terms of trade, t-2			0.005***				0.001*
				(0.002)				(0.000)
	Terms of trade, t-1			0.009*				0.000
				(0.005)				(0.001)
	Constant 0.741*** 0.607*** 0.712*** 0.715***	0.795*** 0.753*** 0.798*** 0.790***
	(0.019)	(0.043)	(0.019)	(0.012)	(0.016)	(0.032)	(0.029)	(0.013)

  .5) Since E[ , +ℎ[START_REF]ont souligné le rôle essentiel de la stabilité macroéconomique et de la diversification des échanges pour stimuler et soutenir la croissance. Le chapitre 4 tente de réconcilier les analyses existantes. Pour ce faire, il identifie 132 épisodes de poussées de croissance dans 117 pays au cours de la période 1980-2010 et constate que plusieurs déterminants ont un effet significatif et positif sur la probabilité d'amorcer une croissance soutenue. Plus précisément, les améliorations de la stabilité macroéconomique et des conditions externes et des dotations en ressources sont associées à une probabilité plus élevée des poussées de croissance[END_REF]| , = 1] is not observable, we use a counterfactual.Rosenbaum and Rubin (1983) show that under unconfoundedness, , +ℎ ( ) ⊥ , | , ; ∀ ℎ ; ∈ {0, 1}, i.e. an independence between potential outcomes and shocks, conditional on a set of covariates , , all biases can be removed by adjusting for differences in covariates between our treated and control groups. This means that under unconfoundedness, we can estimate an unbiased ATT by comparing output changes in countries with and without financial crises or normal recessions, after weighting by propensity scores based on the set of covariates , .

More practically, our methodology consists of three steps. First, we estimate a treatment model by explaining the shock , by a set of determinants , , and we predict the propensity score or the likelihood for country at time to be in the treated, ˆ , = 1 ( ), and control, 1 -ˆ , = 0 ( ), groups. As proposed by

Rosenbaum and Rubin (1983)

, we use the predicted propensity score to eliminate the difference in observables between the treated and the control groups. The set of determinants , is presented in table

3.4

above. Ideally, any predictor of shocks should be included, regardless of whether that predictor is a fundamental variable in a macroeconomic model

(Lunceford and Davidian 2004; Jordà et al. 2016

). We estimate the propensity score using three different strategies: (i) a covariate balancing propensity score (CBPS) introduced by Imai and Ratkovic (2014), (ii) an entropy balancing (EB) proposed by

  line with the existing literature that analyzes the output costs of financial crises and normal recessions (e.g., Reinhart and Rogoff 2009; Fatás and Mihov 2013; Jordà et al. 2013, 2016; Jordà and Taylor 2016; Trebesch and Zabel 2017; Laeven and Valencia 2018; Romer and Romer 2018; Asonuma et al. 2019; Kuvshinov and Zimmermann 2019), we find that financial crises and

normal recessions lead to large and protracted output losses, and that recovery, if any, is a distant and uncertain prospect (see, table

3.6 and fig. 3.5 (A)

and (B)). Indeed, for financial crises, output falls by 1.5 pp. in year 1, 5.1 pp. in year 2 and around 4 pp. in subsequent years. For normal recessions, the output contraction is much larger in year 1 and stands at 5.6 pp., before lowering to around 2.5 pp. in subsequent years. To put these findings into perspective, knowing that the median Real GDP growth in our sample is 4.8% per year so that the median economy would have grown by 24% over five years; a single episode of a financial crisis and normal recession would cost about 16 and 10% of that economy's long-term growth, respectively.

  Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of shocks to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. The first-stage treatment models used to predict the propensity scores are estimated using the covariate balancing propensity score algorithm and include as predictors of shocks

	Panel B: Normal recessions
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -5.688*** -2.262*** -2.229*** -3.172*** -2.534***	-2.498**
		(0.398)	(0.437)	(0.627)	(0.718)	(0.886)	(1.118)
	Observations	58	58	58	58	58	58
	Notes: ATT estimates. Driscoll-

: # of financial crises, t-3 to t-1, # of normal recessions, t-3 to t-1, intensity of conflicts, t-3 to t-1, growth, t-1, growth, t-2, output gap, t-1, REER gap, t-1, current account, t-1, log of Real GDP, t-1, terms of trade, t-1, presence of fiscal rule, t-1, political rights, t-1,presence of IMF program, t-1, and world growth, t-1. As shown in tables

Table 3 .

 3 7: ATT estimates of the effects of shocks and fiscal space on output dynamics, conditional paths

	Panel A: Financial crises
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Financial crisis, t -7.796*** -13.052*** -15.530*** -18.584*** -21.884***	-21.707***
		(1.606)	(1.718)	(2.572)	(2.909)	(3.177)	(4.325)
	Financial crisis, t X FS, t-1 8.705*** 11.000*** 15.963*** 21.564*** 24.593***	24.814***
		(2.498)	(2.099)	(4.253)	(4.920)	(4.665)	(6.392)
	Observations	55	55	55	55	55	55
	Output gain when FS increases by one std. dev.	1.614	2.040	2.960	3.999	4.560	4.601
	Panel B: Normal recessions
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -4.329*** -8.194*** -12.383** -14.168*** -17.447***	-19.085***
		(1.106)	(3.040)	(4.951)	(4.912)	(5.940)	(6.286)
	Normal recession, t X FS, t-1	-1.707	7.454*	12.760*	13.817**	18.740**	20.843**
		(1.759)	(4.013)	(6.663)	(6.683)	(8.082)	(8.626)
	Observations	58	58	58	58	58	58
	Output gain when FS increases by one std. dev.	-	1.381	2.364	2.560	3.472	3.862

Notes: ATT estimates. Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are

Table B .

 B 5: OLS estimates of the effects of financial crises on output dynamics, unconditional paths

		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Financial crisis, t -2.233*** -6.265*** -7.057*** -6.759*** -7.617***	-7.345***
		(0.188)	(0.555)	(0.692)	(0.883)	(0.996)	(0.975)
	Normal recession, t -7.227*** -7.252*** -7.651*** -9.081*** -9.313***	-9.825***
		(0.326)	(0.690)	(1.275)	(1.597)	(1.405)	(1.598)
	Real GDP growth, t-1 0.189*** 0.235*** 0.400*** 0.540***	0.554***	0.432***
		(0.012)	(0.028)	(0.033)	(0.041)	(0.063)	(0.087)
	Real GDP growth, t-2	0.002	0.041	0.102***	0.051	-0.021	0.108
		(0.016)	(0.029)	(0.034)	(0.036)	(0.057)	(0.101)
	# of normal recessions, t-3 to t-1 -0.564***	-0.922	-1.095	-1.581	-2.380**	-2.524
		(0.158)	(0.629)	(0.861)	(1.180)	(1.187)	(1.565)
	Intensity of conflicts, t-1 to t+1	-0.240	-0.497*	-0.815** -1.182*** -1.567***	-2.077***
		(0.154)	(0.293)	(0.326)	(0.299)	(0.235)	(0.438)
	Log of Real GDP, t-1	-0.923*	-1.928*	-3.266**	-5.062**	-8.396*** -11.607***
		(0.471)	(1.133)	(1.642)	(2.166)	(2.427)	(2.693)
	Terms of trade, t-1	-0.002	0.009*	0.001	0.000	-0.005	-0.023*
		(0.003)	(0.005)	(0.008)	(0.009)	(0.011)	(0.012)
	Presence of fiscal rule, t-1 0.590***	0.408	0.674	1.356***	2.726***	3.235***
		(0.220)	(0.379)	(0.408)	(0.479)	(0.585)	(0.641)
	Political rights, t-1	0.123	0.189	-0.077	-0.435*	-0.983***	-1.536***
		(0.118)	(0.190)	(0.230)	(0.231)	(0.239)	(0.189)
	Presence of IMF program, t-1 0.302**	0.853**	1.245*** 1.149***	0.931**	1.182**
		(0.122)	(0.419)	(0.315)	(0.325)	(0.406)	(0.482)
	World growth, t-1	0.279*	-0.153	-0.714*** -0.996*** -1.532***	-1.202***
		(0.157)	(0.180)	(0.186)	(0.237)	(0.358)	(0.380)
	Constant 12.598** 28.981** 49.842*** 75.940*** 120.422*** 160.583***
		(4.982)	(12.219)	(17.918)	(23.201)	(26.263)	(28.773)
	Observations	928	928	928	928	928	928
	R2 within	0.305	0.151	0.131	0.132	0.146	0.156
	# of countries	91	91	91	91	91	91
	# of financial crises	55	55	55	55	55	55

Notes: Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of financial crises to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions.

Table B

 B Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of normal recessions to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions.

	.6: OLS estimates of the effects of normal recessions on output dynamics, unconditional
	paths
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -7.166*** -7.151*** -7.536*** -8.908*** -9.050***	-9.555***
		(0.339)	(0.644)	(1.202)	(1.496)	(1.342)	(1.482)
	Financial crisis, t -2.309*** -6.385*** -7.222*** -6.962*** -7.915***	-7.702***
		(0.179)	(0.627)	(0.771)	(1.008)	(1.083)	(1.060)
	Real GDP growth, t-1 0.196*** 0.247*** 0.415*** 0.561***	0.585***	0.464***
		(0.012)	(0.023)	(0.032)	(0.036)	(0.049)	(0.067)
	Real GDP growth, t-2	0.009	0.054	0.115***	0.072**	0.011	0.141
		(0.015)	(0.037)	(0.043)	(0.035)	(0.052)	(0.089)
	# of financial crises, t-3 to t-1 -0.809*** -1.301*** -1.675*** -2.213*** -3.284***	-3.724***
		(0.242)	(0.488)	(0.473)	(0.627)	(0.602)	(0.572)
	Intensity of conflicts, t-1 to t+1	-0.231	-0.483	-0.798** -1.157*** -1.530***	-2.037***
		(0.160)	(0.305)	(0.346)	(0.328)	(0.262)	(0.437)
	Log of Real GDP, t-1 -1.034**	-2.107*	-3.489**	-5.366**	-8.850*** -12.109***
		(0.516)	(1.201)	(1.702)	(2.241)	(2.545)	(2.796)
	Terms of trade, t-1	-0.001	0.010**	0.003	0.003	-0.001	-0.018*
		(0.003)	(0.005)	(0.008)	(0.009)	(0.011)	(0.011)
	Presence of fiscal rule, t-1 0.553**	0.349	0.596	1.255***	2.577***	3.064***
		(0.227)	(0.383)	(0.401)	(0.458)	(0.550)	(0.593)
	Political rights, t-1	0.116	0.176	-0.090	-0.456**	-1.015***	-1.567***
		(0.122)	(0.183)	(0.220)	(0.211)	(0.228)	(0.182)
	Presence of IMF program, t-1 0.334***	0.904**	1.312*** 1.235***	1.059***	1.330***
		(0.126)	(0.400)	(0.282)	(0.281)	(0.339)	(0.469)
	World growth, t-1	0.278*	-0.155	-0.716*** -0.999*** -1.537***	-1.208***
		(0.157)	(0.181)	(0.186)	(0.237)	(0.353)	(0.374)
	Constant 13.692** 30.748** 52.066*** 78.953*** 124.911*** 165.575***
		(5.503)	(13.050)	(18.720)	(24.245)	(27.786)	(30.184)
	Observations	928	928	928	928	928	928
	R2 within	0.306	0.152	0.132	0.133	0.148	0.158
	# of countries	91	91	91	91	91	91
	# of normal recessions	58	58	58	58	58	58
	Notes: Driscoll-

Table B

 B Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of financial crises to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. The effects of financial crises on output conditional on pre-shock fiscal space.

	.7: OLS estimates of the effects of financial crises and fiscal space on output dynamics,
	conditional paths
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Financial crisis, t -5.611*** -11.129*** -14.550*** -15.128*** -16.121*** -16.341***
		(0.629)	(1.668)	(1.995)	(2.720)	(3.325)	(3.525)
	Financial crisis, t X FS, t-1 4.721***	6.809***	10.519*** 11.830*** 12.101*** 12.760***
		(0.684)	(1.718)	(2.099)	(2.658)	(3.178)	(3.551)
	FS, t-1 0.585**	1.057**	2.179***	3.896***	5.485***	5.027***
		(0.269)	(0.438)	(0.611)	(0.778)	(1.107)	(1.545)
	Normal recession, t -7.207*** -7.220*** -7.592*** -8.990***	-9.194***	-9.712***
		(0.322)	(0.690)	(1.272)	(1.592)	(1.401)	(1.587)
	Real GDP growth, t-1 0.180***	0.222***	0.377***	0.505***	0.511***	0.390***
		(0.013)	(0.029)	(0.033)	(0.046)	(0.071)	(0.097)
	Real GDP growth, t-2	-0.006	0.029	0.078**	0.015	-0.068	0.064
		(0.016)	(0.029)	(0.036)	(0.040)	(0.063)	(0.108)
	# of normal recessions, t-3 to t-1 -0.519***	-0.849	-0.961	-1.376	-2.112	-2.270
		(0.163)	(0.648)	(0.900)	(1.254)	(1.286)	(1.635)
	Intensity of conflicts, t-1 to t+1	-0.242	-0.502	-0.829**	-1.214***	-1.617***	-2.121***
		(0.162)	(0.304)	(0.348)	(0.323)	(0.240)	(0.418)
	Log of Real GDP, t-1 -1.006**	-2.070*	-3.542**	-5.526**	-9.030*** -12.195***
		(0.475)	(1.151)	(1.703)	(2.259)	(2.533)	(2.745)
	Terms of trade, t-1	-0.002	0.008*	0.001	-0.000	-0.005	-0.023**
		(0.003)	(0.005)	(0.008)	(0.009)	(0.010)	(0.011)
	Presence of fiscal rule, t-1 0.614***	0.452	0.766*	1.527***	2.971***	3.457***
		(0.223)	(0.387)	(0.433)	(0.487)	(0.577)	(0.627)
	Political rights, t-1	0.100	0.153	-0.136	-0.513**	-1.075***	-1.627***
		(0.101)	(0.167)	(0.194)	(0.198)	(0.211)	(0.194)
	Presence of IMF program, t-1 0.261**	0.792*	1.144***	1.018**	0.780	1.031*
		(0.122)	(0.466)	(0.396)	(0.437)	(0.553)	(0.557)
	World growth, t-1	0.261	-0.182	-0.763*** -1.065***	-1.618***	-1.286***
		(0.165)	(0.187)	(0.193)	(0.247)	(0.370)	(0.385)
	Constant 13.240*** 30.002**	51.665*** 78.640*** 123.860*** 163.867***
		(5.043)	(12.426)	(18.395)	(24.003)	(27.257)	(29.525)
	Observations	928	928	928	928	928	928
	R2 within	0.311	0.155	0.138	0.141	0.157	0.164
	# of countries	91	91	91	91	91	91
	# of financial crises	55	55	55	55	55	55
	Output gain when FS increases by one std. dev.	0.875	1.263	1.951	2.194	2.244	2.366
	Notes: Driscoll-

Table B

 B Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the cumulative changes of output from the start of normal recessions to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions. The effects of normal recessions on output conditional on pre-shock fiscal space.

	.8: OLS estimates of the effects of normal recessions and fiscal space on output dynamics, conditional paths (1) (2) (3) (4) (5) (6) t+0 t+1 t+2 t+3 t+4 t+5 Normal recession, t -4.975*** -8.195*** -12.311*** -13.224*** -14.967*** -15.934*** (0.570) (1.700) (2.016) (1.282) (1.993) (2.005) Normal recession, t X FS, t-1 -2.732*** 1.333 6.030 5.481* 7.511** 8.085** (1.029) (2.652) (3.729) (2.958) (3.615) (3.657) FS, t-1 1.171*** 1.671*** 2.925*** 4.813*** 6.378*** 5.958*** (0.386) (0.537) (0.728) (0.799) (1.061) (1.536) Financial crisis, t -2.279*** -6.317*** -7.085*** -6.758*** -7.644*** -7.443*** (0.171) (0.649) (0.805) (1.059) (1.162) (1.153) Real GDP growth, t-1 0.191*** 0.237*** 0.395*** 0.530*** 0.544*** 0.426*** (0.012) (0.023) (0.030) (0.039) (0.054) (0.074) Real GDP growth, t-2 -0.000 0.041 0.095** 0.038 -0.035 0.099 (0.015) (0.037) (0.045) (0.038) (0.057) (0.093) # of financial crises, t-3 to t-1 -0.810*** -1.272** -1.603*** -2.120*** -3.159*** -3.601*** (0.253) (0.502) (0.495) (0.666) (0.662) (0.640) Intensity of conflicts, t-1 to t+1 -0.249 -0.501* -0.825** -1.208*** -1.596*** -2.098*** (0.157) (0.301) (0.345) (0.331) (0.263) (0.431) Log of Real GDP, t-1 -1.149** -2.280* -3.798** -5.868** -9.515*** -12.732*** (0.499) (1.182) (1.699) (2.266) (2.586) (2.786) Terms of trade, t-1 -0.001 0.010** 0.004 0.003 0.000 -0.017 (0.003) (0.005) (0.008) (0.009) (0.010) (0.011) Presence of fiscal rule, t-1 0.599*** 0.431 0.752* 1.497*** 2.899*** 3.368*** (0.220) (0.391) (0.417) (0.454) (0.523) (0.554) Political rights, t-1 0.112 0.163 -0.120 -0.499** -1.072*** -1.622*** (0.115) (0.172) (0.204) (0.193) (0.208) (0.185) Presence of IMF program, t-1 0.330** 0.880** 1.256*** 1.159*** 0.958** 1.231** (0.128) (0.430) (0.334) (0.373) (0.459) (0.525) World growth, t-1 0.266 -0.172 -0.744*** -1.046*** -1.600*** -1.266*** (0.161) (0.184) (0.192) (0.248) (0.378) (0.396) Constant 14.116** 31.475** 53.424*** 81.085*** 127.743*** 168.246*** (5.503) (13.100) (18.929) (24.678) (28.384) (30.542) Observations 928 928 928 928 928 928 R2 within 0.309 0.154 0.137 0.140 0.157 0.165 # of countries 91 91 91 91 91 91 # of normal recessions 58 58 58 58 58 58 Output gain when FS increases by one std. dev. -0.506 --1.016 1.392 1.498 (1) (2) (3) (4) (5) (6) t+0 t+1 t+2 t+3 t+4 t+5 Financial, t 5.632*** 10.241*** 8.069*** 9.158*** 7.193*** 6.530*** (1.337) (1.441) (1.093) (0.734) (2.155) (1.100) Financial, t X FS, t-1 -7.731*** -11.589*** -9.050*** -11.163*** -9.445*** -8.819*** (1.661) (1.944) (1.724) (0.977) (2.738) (1.431) Observations 44 44 44 44 44 44 Evolution of primary fiscal balance when FS increases by one std. dev. -1.433 -2.149 -1.678 -2.070 -1.751 -1.635 Threshold of FS, t-1 ; critical point 0.729 0.884 0.892 0.820 0.762 0.740 % of shocks with a decrease of primary fiscal balance 50.000 25.000 22.727 38.636 43.182 45.455 Panel B: Dependent: Fiscal space (1) (2) (3) (4) (5) (6) t+0 t+1 t+2 t+3 t+4 t+5 Financial, t 0.100 0.424*** 0.485*** 0.501*** 0.369*** 0.635*** (0.089) (0.101) (0.073) (0.049) (0.103) (0.075) Financial, t X FS, t-1 -0.233** -0.513*** -0.597*** -0.605*** -0.458*** -0.762*** (0.091) (0.137) (0.105) (0.069) (0.118) (0.107) Observations 43 43 43 43 43 43 Evolution of fiscal space when FS increases by one std. dev. -0.043 -0.095 -0.111 -0.112 -0.085 -0.141 Threshold of FS, t-1 ; critical point 0.429 0.827 0.812 0.828 0.805 0.833 % of shocks with a decrease in fiscal space 97.674 39.535 39.535 39.535 41.860 39.535 Mean difference of FS between countries with dec. and inc. FS 0.354 0.145 0.145 0.145 0.136 0.145 P-value Mean difference -0.019 0.019 0.019 0.026 0.019 Panel C: Dependent: Private Absorption (1) (2) (3) (4) (5) (6) t+0 t+1 t+2 t+3 t+4 t+5 Financial, t -2.302 -7.320 -14.460*** -11.623* -19.256*** -20.644*** (3.005) (4.884) (5.096) (6.517) (6.789) (6.441) Financial, t X FS, t-1 4.570 10.166* 18.898*** 16.934** 27.189*** 27.480*** (4.397) (5.836) (6.033) (7.683) (8.175) (7.916) Observations 39 39 39 39 39 39 Evolution of private absorption when FS increases by one std. dev. -1.885 3.504 3.140 5.041 5.095 Threshold of FS, t-1 ; critical point -0.720 0.765 0.686 0.708 0.751 % of shocks with an increase in private absorption -48.718 41.026 71.795 48.718 43.590 Panel D: Dependent: Net capital inflows (1) (2) (3) (4) (5) (6) t+0 t+1 t+2 t+3 t+4 t+5 Financial, t -8.422*** -14.018*** -14.517** -16.529** -19.729*** -27.742*** (1.661) (4.736) (6.541) (8.229) (6.287) (6.950) Financial, t X FS, t-1 9.062*** 14.961** 16.547* 19.863* 25.179*** 34.122*** (2.152) (5.959) (8.358) (10.450) (7.880) (8.279) Observations 41 41 41 41 41 41 Evolution of net capital inflows when FS increases by one std. dev. 1.680 2.774 3.068 3.683 4.668 6.326 Threshold of FS, t-1 ; critical point 0.929 0.937 0.877 0.832 0.784 0.813 Notes: Driscoll-Panel A: Dependent: Primary Fiscal balance % of shocks with an increase in net capital inflows 21.951 21.951 24.390 36.585 46.341 36.585

Notes: ATT estimates. Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Conditional cumulative changes of output from the start of shocks. The table also reports:

Table B .

 B 10: ATT estimates, dynamics of fiscal balance, fiscal space, private absorption, and net capital inflows in the aftermath of normal recessions, conditional on pre-shock fiscal space

	Panel A: Dependent: Primary Fiscal balance
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	NRecession, t 6.927***	5.417*** 5.302***	5.564**	6.899***	5.916***
		(1.766)	(1.950)	(1.332)	(2.161)	(1.778)	(1.537)
	NRecession, t X FS, t-1 -9.906*** -7.260*** -7.198***	-6.012**	-8.006***	-6.639***
		(1.457)	(1.695)	(1.636)	(2.558)	(1.595)	(1.181)
	Observations	51	51	51	51	51	51
	Evolution of primary fiscal balance when FS increases by one std. dev.	-1.837	-1.346	-1.334	-1.115	-1.484	-1.231
	Threshold of FS, t-1 ; critical point	0.699	0.746	0.737	0.926	0.862	0.891
	% of shocks with a decrease of primary fiscal balance	72.549	56.863	56.863	21.569	25.490	21.569
	Panel B: Dependent: Fiscal space
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	NRecession, t 0.196***	0.246*** 0.358***	0.406***	0.410***	0.330***
		(0.044)	(0.084)	(0.047)	(0.054)	(0.070)	(0.092)
	NRecession, t X FS, t-1 -0.243*** -0.347*** -0.439*** -0.475***	-0.482***	-0.415***
		(0.060)	(0.093)	(0.053)	(0.059)	(0.084)	(0.146)
	Observations	54	54	54	54	54	54
	Evolution of fiscal space when FS increases by one std. dev.	-0.045	-0.064	-0.081	-0.088	-0.089	-0.077
	Threshold of FS, t-1 ; critical point	0.808	0.709	0.815	0.855	0.849	0.795
	% of shocks with a decrease in fiscal space	55.556	62.963	53.704	29.630	44.444	59.259
	Mean difference of FS between countries with dec. and inc. FS	0.152	0.153	0.146	0.212	0.154	0.145
	P-value Mean difference	0.001	0.001	0.001	0.000	0.001	0.002
	Panel C: Dependent: Private Absorption
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	NRecession, t -14.454***	-0.527	-14.912**	-13.551*	-14.599***	-17.052***
		(2.757)	(3.277)	(6.594)	(7.401)	(5.346)	(5.163)
	NRecession, t X FS, t-1 15.995***	1.858	19.499**	17.620**	16.984***	17.288**
		(3.068)	(3.297)	(7.726)	(7.749)	(6.305)	(6.840)
	Observations	46	46	46	46	46	46
	Evolution of private absorption when FS increases by one std. dev.	2.965	-	3.615	3.267	3.149	3.205
	Threshold of FS, t-1 ; critical point	0.904	-	0.765	0.769	0.860	0.986
	% of shocks with an increase in private absorption	23.913	-	54.348	54.348	26.087	13.043
	Panel D: Dependent: Net capital inflows
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	NRecession, t -7.067**	-3.437	-10.518* -21.433*** -13.227***	-8.095
		(3.283)	(8.846)	(6.037)	(6.217)	(4.673)	(7.193)
	NRecession, t X FS, t-1	3.607	3.618	14.022** 24.060*** 15.386***	10.215
		(3.456)	(10.554)	(6.727)	(6.844)	(5.240)	(8.413)
	Observations	30	30	30	30	30	30
	Evolution of net capital inflows when FS increases by one std. dev.	-	-	2.600	4.461	2.853	-
	Threshold of FS, t-1 ; critical point	-	-	0.750	0.891	0.860	-
	% of shocks with an increase in net capital inflows	-	-	70.000	36.667	43.333	-

Notes: ATT estimates. Driscoll-Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Conditional cumulative changes of output from the start of shocks. The table also reports:

Table B .

 B 12: Misspecification error and relevance of fiscal space, normal recessions, ATT estimates

	Panel A:

IPWRA model without fiscal space and interaction term between fiscal space and normal recessions

		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Predicted dependent at horizon t+h 0.879*** 0.421*** 0.583*** 0.713*** 0.725***	0.801***
		(0.113)	(0.159)	(0.143)	(0.104)	(0.104)	(0.078)
	Constant	-0.306	0.796*** 1.983*** 2.197*** 3.244***	3.072***
		(0.219)	(0.264)	(0.598)	(0.575)	(0.536)	(0.714)
	Observations	58	58	58	58	58	58
	R-squared	0.536	0.219	0.328	0.453	0.502	0.569
	Panel B:

IPWRA model without fiscal space and interaction term between fiscal space and normal recessions, fiscal space as explanatory

		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Predicted dependent at horizon t+h 0.873*** 0.424*** 0.580*** 0.715*** 0.723***	0.800***
		(0.110)	(0.159)	(0.128)	(0.094)	(0.094)	(0.073)
	Fiscal Space, t-1	-0.566	2.503	6.668	8.050**	8.981*	8.036
		(0.702)	(3.314)	(4.068)	(3.124)	(4.594)	(5.284)
	Constant	0.130	-1.200	-3.308	-4.224*	-3.872	-3.313
		(0.736)	(2.564)	(3.004)	(2.171)	(3.618)	(4.244)
	Observations	58	58	58	58	58	58
	R-squared	0.538	0.231	0.357	0.479	0.528	0.585
	Panel C:

IPWRA model with fiscal space and without interaction term between fiscal space and normal recessions, fiscal space as explanatory

		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Predicted dependent at horizon t+h 0.862*** 0.419*** 0.571*** 0.702*** 0.710***	0.790***
		(0.112)	(0.158)	(0.127)	(0.097)	(0.097)	(0.082)
	Fiscal Space, t-1	-1.126	2.278	5.758	5.881*	6.399	4.601
		(0.804)	(3.262)	(3.997)	(3.044)	(4.499)	(5.275)
	Constant	0.549	-1.014	-2.540	-2.406	-1.671	-0.417
		(0.825)	(2.521)	(2.923)	(2.099)	(3.554)	(4.308)
	Observations	58	58	58	58	58	58
	R-squared	0.535	0.227	0.350	0.476	0.525	0.585

Panel D: IPWRA model with fiscal space and interaction term between fiscal space and normal recessions, fiscal space as explanatory

  Kraay standards errors in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. Dependent variables are the observed cumulative changes of output from the start of shocks to each horizon 1-5 after the shock. Sample restricted to be exactly the same in all regressions.

		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Predicted dependent at horizon t+h 0.869*** 0.421*** 0.584*** 0.716*** 0.720***	0.795***
		(0.114)	(0.158)	(0.124)	(0.098)	(0.099)	(0.085)
	Fiscal Space, t-1	-0.187	1.241	2.948	2.182	2.682	1.699
		(0.631)	(2.981)	(3.697)	(2.833)	(4.297)	(5.235)
	Constant	-0.181	-0.192	-0.368	0.436	1.168	1.816
		(0.679)	(2.301)	(2.668)	(1.938)	(3.424)	(4.305)
	Observations	58	58	58	58	58	58
	R-squared	0.540	0.224	0.355	0.485	0.529	0.586
	Notes: ATT estimates. Driscoll-

Table B .

 B 14: Alternative measures of fiscal space, normal recessions, ATT estimates

	Panel A: Average fiscal space over two years prior to shock
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -4.569*** -8.339*** -12.417*** -12.432*** -17.850***	-22.231***
		(0.882)	(1.744)	(4.121)	(4.068)	(4.493)	(4.774)
	Normal recession, t X FS, t-1	-1.403	7.628***	12.788**	11.624**	19.224***	24.766***
		(1.186)	(2.233)	(5.425)	(5.493)	(6.019)	(6.391)
	Observations	58	58	58	58	58	58
	Output gain when FS increases by one std. dev.	-	1.308	2.193	1.993	3.297	4.247
	Panel B: Fiscal space computed using income group specific threshold
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -4.964*** -6.638**	-9.843** -12.689*** -16.363***	-18.579***
		(0.641)	(2.938)	(4.088)	(3.755)	(5.014)	(4.901)
	Normal recession, t X FS, t-1	-0.950	5.749	10.003*	12.503**	18.170**	21.127***
		(1.169)	(3.966)	(5.680)	(5.265)	(6.962)	(6.910)
	Observations	58	58	58	58	58	58
	Output gain when FS increases by one std. dev.	-	1.256	2.186	2.732	3.971	4.617
	Panel C: Fiscal space accounting for non-linearity
		(1)	(2)	(3)	(4)	(5)	(6)
		t+0	t+1	t+2	t+3	t+4	t+5
	Normal recession, t -5.016*** -4.629*** -6.426*** -7.782***	-9.086***	-10.167***
		(0.341)	(1.195)	(1.903)	(1.881)	(2.295)	(2.410)
	Normal recession, t X FS, t-1	-1.162	4.091*	7.256*	7.968**	11.328**	13.258***
		(0.930)	(2.295)	(3.847)	(3.914)	(4.735)	(5.017)
	Observations	58	58	58	58	58	58
	Output gain when FS increases by one std. dev.	-	1.153	2.045	2.246	3.193	3.737

  (2010) andRadelet (2016) show that growth surges in China and India, and also in Indonesia, Pakistan, and Vietnam contributed to the reduction in global poverty since 1981.AsEasterly et al. (1993) first highlighted and many others have confirmed since, growth process tends to be highly unstable and exhibit multiple structural breaks (see, e.g. Pritchett 2000; Hausmann et al. 2005; Jones and Olken 2008; Aizenman and Spiegel 2010; Jong-A-Pin and de Haan 2011; Pritchett et al. 2016; Peruzzi and Terzi 2018). The more typical pattern is that countries experience phases of growth, stagnation, or decline of varying length. Therefore, the focus of the "first-generation" neoclassical growth models (see, e.g. Solow 1956; Swan

							4
	P	I	II:	G	S	: L
	I		G	S	: W	P	,
			, C C		?
	W 4.1 Introduction R		IMF?
						-Lucas (1988), p. 5
	"What exactly?" I do not think that this question is outdated, even for India that experienced
	two episodes of sustained growth since 1989; because many countries around the world failed
	to grow faster over a prolonged period, and those that successfully did so still need to achieve
	growth surges to address the global challenges of this era. Indeed, igniting periods of sustained
	growth can have quantitatively huge implications for national income, poverty, and more broadly,
	for population well-being. Pritchett et al. (2016) estimate that the top 20 acceleration in the last

"Is there some action a government of India could take that would lead the Indian economy to grow like Indonesia's or

Egypt's? If so, what, exactly? If not, what

is it about the nature of India that makes it so? Once one starts to think about them, it is hard to think about anything

else."

six decades had a net present value of 30 trillion dollars: twice the size of US GDP, and Chen and Ravallion

Table 4 .

 4 1: Unconditional probability, by decades, regions, and levels of development

Decades 1980-1989 1990-1999 2000-2010 Total

	Regions
	Africa	0.008	0.016	0.045	0.022
	Asia and Pacific	0.051	0.060	0.085	0.064
	Europe	0.029	0.051	0.050	0.044
	Middle East and Central Asia	0.012	0.056	0.100	0.050
	Americas and Caribbean	0.021	0.015	0.049	0.028
	Levels of development
	LICs	0.007	0.018	0.062	0.027
	EMs	0.025	0.044	0.085	0.048
	AEs	0.036	0.035	0.010	0.025
	Total	0.021	0.033	0.057	0.036

Table 4 .

 4 3: Incidence and Effectiveness by broad determinants of growth surges Notes: Numbers of GSs in the table are those preceded by a signal in each broad determinant. Similarly, number of signals are those followed by a growth surge.

	Broad determinants of Growth surges # of GSs Incidence # of signals Effectiveness
	External factors and endowments	130	0.985	307	0.315
	Institutions	113	0.856	272	0.304
	Macroeconomic stability	120	0.909	301	0.372
	Structural reforms	111	0.841	291	0.345
	Trade diversification and quality	118	0.894	292	0.292
	Investments, labor and productivity	120	0.909	243	0.321
	Unexplained	0	0.000	-	-
	Total # of GSs		132

Table 4

 4 Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. In column (

	.4: Predicting growth surges, baseline results
		(1)	(2)
		All	Drop GSs period
	External factors and endowments index, average [T-5,T] 0.341***	0.403***
		(0.066)	(0.083)
	Institutions index, average [T-5,T] 0.053*	0.086**
		(0.029)	(0.038)
	Macroeconomic stability index, average [T-5,T] 0.325***	0.439***
		(0.048)	(0.063)
	Structural reforms index, average [T-5,T] 0.179***	0.263***
		(0.053)	(0.066)
	Trade diversification and quality index, average [T-5,T] 0.152***	0.147***
		(0.038)	(0.048)
	Investments, labor and productivity index, average [T-5,T] 0.082**	0.173***
		(0.038)	(0.050)
	Africa (Base level)
	Asia and Pacific 0.057***	0.111***
		(0.013)	(0.019)
	Europe	0.021*	0.053***
		(0.013)	(0.017)
	Middle East and Central Asia 0.026**	0.042***
		(0.013)	(0.016)
	Americas and Caribbean	0.000	-0.002
		(0.010)	(0.011)
	1980-1989 (Base level)
	1990-1999	0.001	0.003
		(0.009)	(0.011)
	2000-2010 0.030***	0.079***
		(0.011)	(0.015)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.019	0.034**
		(0.013)	(0.015)
	Emerging Economies 0.045***	0.090***
		(0.011)	(0.014)
	Observations	4832	3763
	# of GSs	132	132
	Pseudo R2	0.118	0.181
	Classification power 91.867	90.088
	AUROC	0.752	0.794
	BIC 2547.718	2200.353
	Notes:

  .2.Figure 4.2: Paired average marginal effects of the six broad determinants of growth surges

	External factors and endowments	0 .1 .2 .3	0	.2	.4	.6	External factors and endowments	0 .1 .2 .3	0	.1	.2	.3	.4	External factors and endowments	0 .1 .2 .3	0	.1	.2	.3	External factors and endowments	0 .1 .2 .3	0	.1	.2	.3	.4	.5	External factors and endowments	0 .1 .2 .3	0	.1	.2	.3	.4	.5
				Institutions					Macroeconomic stability					Economic reforms				Trade diversification and quality			Investments, labor and productivity
								.6							.6						.6								.6
							Institutions	.2 .4						Institutions	.2 .4					Institutions	.2 .4							Institutions	.2 .4
								0							0						0								0							.6
									0	.1	.2	.3	.4			0	.1	.2	.3			0	.1	.2	.3	.4	.5			0	.1	.2	.3	.4	.5
										Macroeconomic stability					Economic reforms				Trade diversification and quality			Investments, labor and productivity	.5
														Macroeconomic stability	0 .1 .2 .3 .4					Macroeconomic stability	0 .1 .2 .3 .4							Macroeconomic stability	0 .1 .2 .3 .4							.2 .3 .4
																0	.1 Economic reforms .2	.3			0 Trade diversification and quality .1 .2 .3 .4 .5			0 Investments, labor and productivity .1 .2 .3 .4 .5	.1
																					.3								.3							0
																				Economic reforms	.1 .2							Economic reforms	.1 .2							Pr(GS=1)
																					0								0
																						0	.1	.2	.3	.4	.5			0	.1	.2	.3	.4	.5
																						Trade diversification and quality			Investments, labor and productivity
																												Trade diversification and quality	0 .1 .2 .3 .4 .5
																														0	.1	.2	.3	.4	.5
																														Investments, labor and productivity

Table C .

 C 1: List of growth surges and their broad determinants

	Country	ISO	Start	End	Dur.	Avg. Growth	Income per capita (start)	Income per capita (end)	External and Endow.	Insti-tution	Macro. stabiliy	Structural Reforms	Trade Div. Qual.	Labor Inv. Prod.	Unex-plained	# of deter-minants
	Albania	ALB	1997	2013	17	5.78	3750.85	9744.54	1	1	1	1	1	1	0
	Argentina	ARG	1984	1999	16	6.88	4539.50	13165.25	1	1	1	1	1	1	0
	Armenia	ARM	2001	2011	11	9.05	3299.73	8561.05	1	1	1	1	1	1	0
	Antigua and Barbuda	ATG	1980	1992	13	6.09	5525.09	11922.56	1	0	1	1	1	1	0
	Antigua and Barbuda	ATG	1999	2009	11	3.44	13606.24	19736.20	1	0	0	0	1	1	0
	Austria	AUT	1984	1995	12	3.96	18864.98	30071.43	1	1	1	1	1	1	0
	Azerbaijan	AZE	2002	2013	12	15.72	2885.62	16638.21	1	1	1	1	1	1	0
	Benin	BEN	1998	2006	9	3.66	1296.16	1790.50	1	1	1	1	1	1	0
	Bangladesh	BGD	2005	2016	12	6.87	1461.44	3244.94	1	1	0	1	0	1	0
	Bulgaria	BGR	2001	2011	11	5.77	8595.34	15924.35	1	0	1	1	1	1	0
	Bahrain	BHR	1994	2008	15	6.86	16157.99	43683.36	1	1	1	1	1	0	0
	Belarus	BLR	2001	2013	13	5.63	8563.25	17451.27	1	1	1	1	1	1	0
	Bolivia	BOL	1992	2013	22	4.47	2318.15	6064.79	1	1	1	1	1	1	0
	Brazil	BRA	1988	2000	13	2.53	6229.40	8617.32	1	1	1	1	1	1	0
	Brazil	BRA	2006	2015	10	4.27	9515.43	14450.07	1	1	0	1	1	1	0
	Bhutan	BTN	1980	1988	9	3.78	1588.82	2218.84	1	1	1	1	0	1	0
	Bhutan	BTN	1994	2002	9	5.81	2858.74	4751.92	1	1	1	0	0	1	0
	Chile	CHL	1989	1998	10	4.09	7580.75	11316.55	1	1	1	1	1	1	0
	Chile	CHL	2005	2013	9	5.81	13403.45	22291.56	1	0	1	1	0	0	0
	China	CHN	1981	1989	9	3.78	1826.95	2551.46	1	1	0	1	1	1	0
	China	CHN	1992	2012	21	6.51	2820.92	10596.49	0	1	1	1	1	1	0
	Cote d'Ivoire	CIV	2010	2017	9	3.71	2596.71	3605.62	1	1	1	1	1	1	0
	Democratic Republic of Congo	COD	2008	2016	9	3.21	593.80	789.17	1	1	0	0	1	1	0
	Congo	COG	2001	2015	15	3.76	2150.79	3742.83	1	1	1	1	1	1	0
	Colombia	COL	2005	2014	10	5.12	7615.52	12551.27	1	1	0	1	1	1	0
	Cape Verde	CPV	1993	2005	13	4.85	2195.28	4065.04	1	1	1	1	1	1	0
	Czech Republic	CZE	2002	2010	9	3.28	21373.60	28586.14	1	1	1	1	1	1	0
															Continued on next page

C.

3 Supplementary results C.3.1 Results of Noise-to-Signal ratioTable C .

 C 4: Noise to Signal ratio, optimizing the predictions of growth surges

Variables' labels Direction NSR Threshold Effectiveness Incidence Error Type I Error Type II TME # of GSs # of signals

	Trading partners' growth (%)	+	0.164	75	0.258	0.942	0.154	0.058	0.212	920
	Terms of trade (export price / import price)	+	0.215	74	0.217	0.964	0.207	0.036	0.243	1173
	Nominal US FED interest rate (%)	-	0.087	88	0.246	0.935	0.081	0.065	0.146	476
	Volatility of S&P 500 index returns	-	0.165	79	0.268	0.978	0.162	0.022	0.183	979
	Total natural resources rents (% of GDP)	+	0.206	71	0.263	0.914	0.189	0.086	0.275	1134
	Accountability index	+	0.271	70	0.230	0.856	0.232	0.144	0.376	1336
	Civil liberties index	+	0.215	72	0.239	0.856	0.184	0.144	0.328	1074
	Political corruption index	+	0.162	71	0.277	0.568	0.092	0.432	0.524	566
	Rule of law index	+	0.200	70	0.268	0.791	0.158	0.209	0.367	956
	Financial crises at end	+	0.156	70	0.277	0.669	0.104	0.331	0.435	639
	Normal recession at end	+	0.150	70	0.227	0.266	0.040	0.734	0.774	229
	Public debt (% of GDP)	-	0.203	71	0.282	0.935	0.190	0.065	0.255	1174
	Current Account (% of GDP)	+	0.228	72	0.244	0.935	0.213	0.065	0.278	1248
	Inflation (%)	-	0.254	71	0.243	0.863	0.220	0.137	0.356	1286
	Change of REER (%) (+ means depreciation)	+	0.234	71	0.224	0.892	0.209	0.108	0.316	1191
	Agriculture reform	+	0.091	70	0.310	0.072	0.007	0.928	0.935	42
	Product market reform	+	0.086	88	0.309	0.345	0.030	0.655	0.684	191
	Labor market reform	+	0.064	90	0.312	0.187	0.012	0.813	0.825	77
	Financial reform	+	0.176	77	0.301	0.475	0.084	0.525	0.609	529
	Trade-Tariff reform	+	0.235	73	0.267	0.712	0.167	0.288	0.455	1012
	Current account reform	+	0.156	74	0.265	0.374	0.058	0.626	0.684	351
	Capital account reform	+	0.149	85	0.251	0.453	0.067	0.547	0.614	399
	Export Quality Index	+	0.246	71	0.212	0.871	0.214	0.129	0.343	1203
	Average Quality Index of Importers	+	0.244	71	0.228	0.885	0.216	0.115	0.331	1241
	Product diversification index	+	0.256	73	0.224	0.827	0.212	0.173	0.385	1210
	Partner diversification index	+	0.276	70	0.222	0.842	0.232	0.158	0.391	1322
	Domestic investment (% of GDP)	+	0.219	74	0.234	0.899	0.197	0.101	0.297	1137
	Foreign Direct Investment (% of GDP)	+	0.186	76	0.277	0.871	0.162	0.129	0.291	990
	Welfare-relevant TFP index (USA=1)	+	0.214	74	0.205	0.640	0.137	0.360	0.497	764
	Human Capital index	+	0.382	75	0.229	0.446	0.170	0.554	0.724	979

Table C .

 C 5: Incidence and Effectiveness by broad determinants of growth surges, and by decades

Panel A: by decades Broad determinants of Growth surges # of GSs Incidence # of signals Effectiveness 1980-1989 External factors and endowments

			27	1.000	43	0.149
		Institutions	21	0.778	52	0.197
		Macroeconomic stability	26	0.963	55	0.233
		Structural reforms	26	0.963	34	0.161
		Trade diversification and quality	24	0.889	63	0.154
		Investments, labor and productivity	25	0.926	33	0.152
		# of GSs		27
		External factors and endowments	38	0.950	67	0.269
		Institutions	36	0.900	108	0.267
		Macroeconomic stability	39	0.975	126	0.356
	1990-1999	Structural reforms	34	0.850	136	0.343
		Trade diversification and quality	37	0.925	127	0.343
		Investments, labor and productivity	37	0.925	106	0.324
		# of GSs		40
		External factors and endowments	65	1.000	197	0.449
		Institutions	56	0.862	112	0.496
		Macroeconomic stability	55	0.846	120	0.548
	2000-2010	Structural reforms	51	0.785	121	0.513
		Trade diversification and quality	57	0.877	102	0.462
		Investments, labor and productivity	58	0.892	104	0.491
		# of GSs		65

Table C .

 C 6: Incidence and Effectiveness by broad determinants of growth surges, and by regions

Broad determinants of Growth surges # of GSs Incidence # of signals Effectiveness

		External factors and endowments	26	1.000	49	0.172
		Institutions	24	0.923	51	0.165
		Macroeconomic stability	25	0.962	52	0.212
	Africa	Structural reforms	21	0.808	60	0.223
		Trade diversification and quality	23	0.885	48	0.161
		Investments, labor and productivity	25	0.962	54	0.231
		# of GSs		26
		External factors and endowments	28	1.000	54	0.432
		Institutions	24	0.857	47	0.435
		Macroeconomic stability	24	0.857	44	0.468
	Asia and Pacific	Structural reforms	26	0.929	54	0.607
		Trade diversification and quality	22	0.786	57	0.479
		Investments, labor and productivity	26	0.929	41	0.461
		# of GSs		28
		External factors and endowments	33	0.971	103	0.419
		Institutions	32	0.941	92	0.451
		Macroeconomic stability	34	1.000	117	0.563
	Europe	Structural reforms	31	0.912	113	0.483
		Trade diversification and quality	31	0.912	100	0.415
		Investments, labor and productivity	31	0.912	86	0.457
		# of GSs		34
		External factors and endowments	21	0.955	55	0.444
		Institutions	20	0.909	47	0.416
		Macroeconomic stability	21	0.955	53	0.530
	Middle East and Central Asia	Structural reforms	18	0.818	33	0.471
		Trade diversification and quality	21	0.955	51	0.367
		Investments, labor and productivity	17	0.773	34	0.362
		# of GSs		22
		External factors and endowments	22	1.000	46	0.235
		Institutions	13	0.591	35	0.219
		Macroeconomic stability	16	0.727	35	0.216
	Americas and Caribbean	Structural reforms	15	0.682	31	0.170
		Trade diversification and quality	21	0.955	36	0.179
		Investments, labor and productivity	21	0.955	28	0.185
		# of GSs		22

Table C .

 C 7: Incidence and Effectiveness by broad determinants of growth surges, and by levels of income

Broad determinants of Growth surges # of GSs Incidence # of signals Effectiveness LICs External factors and endowments

	Institutions Macroeconomic stability Structural reforms Trade diversification and quality Investments, labor and productivity # of GSs External factors and endowments Institutions Macroeconomic stability Structural reforms Trade diversification and quality Investments, labor and productivity # of GSs External factors and endowments Institutions Macroeconomic stability Structural reforms Trade diversification and quality Investments, labor and productivity # of GSs C.3.3 Robustness checks EMs AEs	34 30 31 28 29 33 78 64 70 65 71 69 18 19 19 18 18 18	1.000 0.882 0.912 0.824 0.853 0.971 0.987 0.810 0.886 0.823 0.899 0.873 0.947 1.000 1.000 0.947 0.947 0.947	34 79	90 68 77 83 75 73 178 161 172 155 167 129 39 43 52 53 50 41	0.272 0.205 0.279 0.268 0.219 0.292 0.396 0.398 0.451 0.449 0.359 0.376 0.199 0.274 0.342 0.280 0.260 0.252

Table C .

 C 8: Predicting growth surges, robustness checks, alternative timing convention for growth determinants Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges.All country-years observations in which growth surges may be occurred are removed.

		(1)	(2)	(3)
		Baseline H=[T-5,T]	H=[T-7,T] H=[T-3,T]
	External factors and endowments index, average H 0.403***	0.679***	0.207***
		(0.083)	(0.104)	(0.063)
	Institutions index, average H	0.086**	0.116***	0.074**
		(0.038)	(0.042)	(0.033)
	Macroeconomic stability index, average H 0.439***	0.490***	0.347***
		(0.063)	(0.072)	(0.053)
	Structural reforms index, average H 0.263***	0.276***	0.183***
		(0.066)	(0.074)	(0.059)
	Trade diversification and quality index, average H 0.147***	0.170***	0.050
		(0.048)	(0.054)	(0.043)
	Investments, labor and productivity index, average H 0.173***	0.133**	0.244***
		(0.050)	(0.057)	(0.041)
	Africa (Base level)
	Asia and Pacific 0.111***	0.111***	0.108***
		(0.019)	(0.018)	(0.018)
	Europe 0.053***	0.068***	0.058***
		(0.017)	(0.018)	(0.017)
	Middle East and Central Asia 0.042***	0.044***	0.046***
		(0.016)	(0.016)	(0.016)
	Americas and Caribbean	-0.002	-0.001	0.000
		(0.011)	(0.011)	(0.011)
	1980-1989 (Base level)
	1990-1999	0.003	-0.002	0.002
		(0.011)	(0.011)	(0.010)
	2000-2010 0.079***	0.064***	0.091***
		(0.015)	(0.015)	(0.015)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.034**	0.040***	0.038**
		(0.015)	(0.015)	(0.015)
	Emerging Economies 0.090***	0.097***	0.089***
		(0.014)	(0.013)	(0.013)
	Observations	3763	3763	3763
	# of GSs	132	132	132
	Pseudo R2	0.181	0.179	0.171
	Classification power	90.088	90.221	89.769
	AUROC	0.794	0.792	0.794
	BIC 2200.353	2204.954	2225.273
	Notes:

Table C

 C Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. All country-years observations in which growth surges may be occurred are removed.

	.9: Predicting growth surges, robustness checks, alternative estimators
		(1)	(2)	(3)	(4)	(5)
		Baseline: Logit	Pooled Probit	Tobit	RE Logit RE Probit
	External factors and endowments index, average [T-5,T] 0.403*** 0.418*** 0.546*** 0.454*** 0.437***
		(0.083)	(0.085)	(0.089)	(0.094)	(0.092)
	Institutions index, average [T-5,T] 0.086**	0.081** 0.094**	0.065	0.066
		(0.038)	(0.038)	(0.040)	(0.044)	(0.043)
	Macroeconomic stability index, average [T-5,T] 0.439*** 0.489*** 0.579*** 0.641*** 0.646***
		(0.063)	(0.064)	(0.067)	(0.077)	(0.076)
	Structural reforms index, average [T-5,T] 0.263*** 0.257*** 0.403*** 0.212**	0.213**
		(0.066)	(0.069)	(0.077)	(0.084)	(0.084)
	Trade diversification and quality index, average [T-5,T] 0.147*** 0.140*** 0.137*** 0.138**	0.142**
		(0.048)	(0.048)	(0.050)	(0.058)	(0.056)
	Investments, labor and productivity index, average [T-5,T] 0.173*** 0.181*** 0.219*** 0.252*** 0.251***
		(0.050)	(0.051)	(0.056)	(0.059)	(0.058)
	Africa (Base level)
	Asia and Pacific 0.111*** 0.117*** 0.135*** 0.145*** 0.146***
		(0.019)	(0.018)	(0.017)	(0.042)	(0.041)
	Europe 0.053*** 0.065*** 0.107*** 0.086**	0.092**
		(0.017)	(0.018)	(0.020)	(0.039)	(0.039)
	Middle East and Central Asia 0.042*** 0.043*** 0.059***	0.068*	0.069*
		(0.016)	(0.016)	(0.018)	(0.037)	(0.037)
	Americas and Caribbean	-0.002	-0.003	-0.012	-0.003	-0.003
		(0.011)	(0.011)	(0.016)	(0.026)	(0.026)
	1980-1989 (Base level)
	1990-1999	0.003	0.000	-0.019	0.003	0.000
		(0.011)	(0.011)	(0.012)	(0.012)	(0.012)
	2000-2010 0.079*** 0.075*** 0.084*** 0.105*** 0.099***
		(0.015)	(0.015)	(0.013)	(0.017)	(0.016)
	Advanced Economies (Base level)
	Low-Income Developing Countries 0.034**	0.037** 0.093***	0.042	0.041
		(0.015)	(0.016)	(0.020)	(0.029)	(0.029)
	Emerging Economies 0.090*** 0.098*** 0.142*** 0.109*** 0.109***
		(0.014)	(0.014)	(0.017)	(0.027)	(0.027)
	Observations	3763	3763	3763	3763	3763
	# of GSs	132	132	132	132	132
	Pseudo R2	0.181	0.182	0.309	-	-
	Classification power	90.088	90.088	-	-	-
	AUROC	0.794	0.795	0.791	0.794	0.794
	BIC 2200.353 2198.787 1373.410 2074.058 2068.716
	Random effects	No	No	No	Yes	Yes
	Notes:

Table C .

 C 10: Predicting growth surges, robustness checks, alternative growth surges identification Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. All country-years observations in which growth surges may be occurred are removed.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Baseline	Hausmann et al. (2005)	5-year horizon	10-year horizon	Difference =1 ppa.	Difference =3 ppa.	Post-acc growth =2.5 ppa	Post-acc growth =4.5 ppa
	External factors and endowments index, average [T-5,T] 0.403***	0.421***	0.281*** 0.421*** 0.232***	0.211*** 0.367*** 0.408***
		(0.083)	(0.114)	(0.080)	(0.085)	(0.090)	(0.059)	(0.088)	(0.072)
	Institutions index, average [T-5,T] 0.086**	0.029	0.043	0.036	0.079*	0.063**	0.109***	0.044
		(0.038)	(0.052)	(0.035)	(0.039)	(0.040)	(0.028)	(0.039)	(0.029)
	Macroeconomic stability index, average [T-5,T] 0.439***	0.852***	0.399*** 0.540*** 0.522***	0.284*** 0.427*** 0.245***
		(0.063)	(0.097)	(0.062)	(0.074)	(0.071)	(0.051)	(0.066)	(0.051)
	Economic reforms index, average [T-5,T] 0.263***	0.184*	0.294*** 0.189**	0.245***	0.167*** 0.211*** 0.174***
		(0.066)	(0.106)	(0.073)	(0.078)	(0.069)	(0.053)	(0.067)	(0.054)
	Trade diversification and quality index, average [T-5,T] 0.147***	0.267***	0.178*** 0.191*** 0.214***	0.088**	0.169***	0.055
		(0.048)	(0.070)	(0.050)	(0.053)	(0.053)	(0.037)	(0.051)	(0.036)
	Investments, labor and productivity index, average [T-5,T] 0.173***	0.191***	0.039	0.127**	0.188***	0.177*** 0.222*** 0.179***
		(0.050)	(0.071)	(0.047)	(0.052)	(0.055)	(0.038)	(0.052)	(0.036)
	Africa (Base level)
	Asia and Pacific 0.111***	0.133***	0.063*** 0.099*** 0.086***	0.054*** 0.118*** 0.075***
		(0.019)	(0.025)	(0.019)	(0.021)	(0.021)	(0.014)	(0.020)	(0.014)
	Europe 0.053***	0.123***	0.012	0.044**	0.070***	0.005	0.067***	0.021*
		(0.017)	(0.028)	(0.017)	(0.019)	(0.021)	(0.011)	(0.018)	(0.011)
	Middle East and Central Asia 0.042***	0.044**	0.001	0.012	0.022	0.038***	0.038** 0.048***
		(0.016)	(0.021)	(0.016)	(0.016)	(0.016)	(0.014)	(0.016)	(0.013)
	Americas and Caribbean	-0.002	-0.002	-0.029**	-0.009	-0.011	-0.006	0.008	0.007
		(0.011)	(0.016)	(0.013)	(0.013)	(0.012)	(0.009)	(0.012)	(0.008)
	1980-1989 (Base level)
	1990-1999	0.003	0.058***	-0.006	-0.008	-0.005	0.008	-0.002	0.006
		(0.011)	(0.015)	(0.010)	(0.011)	(0.011)	(0.008)	(0.011)	(0.008)
	2000-2010 0.079***	0.097***	0.086*** 0.074*** 0.085***	0.061*** 0.088*** 0.064***
		(0.015)	(0.019)	(0.015)	(0.015)	(0.016)	(0.011)	(0.016)	(0.012)
	Advanced Economies (Base level)
	Low-Income Developing Countries 0.034**	0.071***	-0.017	0.025	0.032*	0.002	0.047***	0.021*
		(0.015)	(0.022)	(0.015)	(0.016)	(0.017)	(0.011)	(0.016)	(0.011)
	Emerging Economies 0.090***	0.146***	0.067*** 0.103*** 0.106***	0.060*** 0.100*** 0.069***
		(0.014)	(0.019)	(0.015)	(0.015)	(0.015)	(0.011)	(0.014)	(0.010)
	Observations	3763	2981	3896	3413	3534	4094	3682	4031
	# of GSs	132	159	144	125	135	97	141	104
	Unconditional probablity	0.035	0.053	0.037	0.037	0.038	0.024	0.038	0.026
	Mean(duration of GSs) 11.356	13.906	9.313	13.784	12.630	10.835	11.362	10.942
	Mean(Avg. growth during GSs)	5.900	5.220	6.310	5.636	5.606	6.711	5.664	6.557
	Pseudo R2	0.181	0.139	0.127	0.165	0.155	0.171	0.164	0.198
	Classification power 90.088	85.039	89.605	90.097	89.304	93.405	89.109	92.955
	AUROC	0.794	0.755	0.741	0.778	0.765	0.791	0.773	0.806
	BIC 2200.353	2359.462	2482.337 2048.995 2244.434	1864.434 2322.122 1890.496
	Notes:

Table C .

 C 11: Predicting growth surges, sensitivity by decades Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. All country-years observations in which growth surges may be occurred are removed.

		(1)	(2)	(3)
		1980-1989 1990-1999 2000-2010
	External factors and endowments index, average [T-5,T]	0.214	-0.291**	0.970***
		(0.160)	(0.130)	(0.121)
	Institutions index, average [T-5,T]	0.121**	-0.087*	0.363***
		(0.059)	(0.051)	(0.089)
	Macroeconomic stability index, average [T-5,T] 0.520***	0.368***	0.437***
		(0.132)	(0.079)	(0.117)
	Structural reforms index, average [T-5,T]	-0.104	0.182**	0.606***
		(0.139)	(0.077)	(0.137)
	Trade diversification and quality index, average [T-5,T]	-0.038	0.170**	0.254**
		(0.071)	(0.066)	(0.115)
	Investments, labor and productivity index, average [T-5,T]	0.050	0.076	0.278***
		(0.088)	(0.068)	(0.098)
	Africa (Base level)
	Asia and Pacific 0.090***	0.091***	0.142***
		(0.018)	(0.018)	(0.026)
	Europe	0.032**	0.032**	0.056**
		(0.014)	(0.014)	(0.024)
	Middle East and Central Asia	0.023*	0.023*	0.041*
		(0.012)	(0.012)	(0.021)
	Americas and Caribbean	-0.002	-0.002	-0.004
		(0.009)	(0.009)	(0.017)
	1980-1989 (Base level)
	1990-1999	-0.001	-0.001	-0.001
		(0.012)	(0.012)	(0.012)
	2000-2010 0.075***	0.075***	0.075***
		(0.016)	(0.016)	(0.016)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.020	0.020	0.036
		(0.013)	(0.012)	(0.022)
	Emerging Economies 0.070***	0.070***	0.114***
		(0.014)	(0.013)	(0.021)
	Observations	3763	3763	3763
	# of GSs	27	40	65
	Pseudo R2	0.212	0.212	0.212
	Classification power	90.566	90.566	90.566
	AUROC	0.816	0.816	0.816
	BIC 2220.634	2220.634	2220.634
	Notes:

Table C .

 C Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges. All country-years observations in which growth surges may be occurred are removed.

	12: Predicting growth surges, sensitivity by regions
		(1)	(2)	(3)	(4)	(5)
			Asia		Middle East	Americas
		Africa	and	Europe	and	and
			Pacific		Central Asia	Caribbean
	External factors and endowments index, average [T-5,T] 0.496***	-0.044	0.307*	0.291	0.659***
		(0.125)	(0.297)	(0.172)	(0.183)	(0.165)
	Institutions index, average [T-5,T]	0.013	0.068	0.111	0.263**	0.112**
		(0.053)	(0.137)	(0.106)	(0.123)	(0.054)
	Macroeconomic stability index, average [T-5,T] 0.344***	0.267	0.984***	0.604***	0.164*
		(0.085)	(0.269)	(0.188)	(0.167)	(0.085)
	Structural reforms index, average [T-5,T] 0.291** 0.968***	0.203	0.309	-0.079
		(0.115)	(0.273)	(0.131)	(0.210)	(0.119)
	Trade diversification and quality index, average [T-5,T]	0.000	0.285*	0.233	0.333***	0.000
		(0.071)	(0.163)	(0.146)	(0.119)	(0.077)
	Investments, labor and productivity index, average [T-5,T] 0.211***	0.198	0.138	0.036	0.107
		(0.072)	(0.193)	(0.125)	(0.149)	(0.086)
	Africa (Base level)
	Asia and Pacific 0.111*** 0.111*** 0.111***	0.111***	0.111***
		(0.020)	(0.020)	(0.020)	(0.020)	(0.020)
	Europe 0.056*** 0.056*** 0.056***	0.056***	0.056***
		(0.019)	(0.019)	(0.019)	(0.019)	(0.019)
	Middle East and Central Asia 0.045** 0.045** 0.045**	0.045**	0.045**
		(0.019)	(0.019)	(0.019)	(0.019)	(0.019)
	Americas and Caribbean	-0.004	-0.004	-0.004	-0.004	-0.004
		(0.012)	(0.012)	(0.012)	(0.012)	(0.012)
	1980-1989 (Base level)
	1990-1999	0.003	0.008	0.005	0.005	0.003
		(0.008)	(0.020)	(0.013)	(0.013)	(0.007)
	2000-2010 0.050*** 0.126*** 0.085***	0.082***	0.051***
		(0.012)	(0.028)	(0.019)	(0.020)	(0.012)
	Advanced Economies (Base level)
	Low-Income Developing Countries 0.021**	0.056*	0.037*	0.035*	0.021*
		(0.010)	(0.029)	(0.022)	(0.019)	(0.011)
	Emerging Economies 0.056*** 0.142*** 0.096***	0.092***	0.057***
		(0.011)	(0.027)	(0.023)	(0.016)	(0.010)
	Observations	3763	3763	3763	3763	3763
	# of GSs	26	28	34	22	22
	Pseudo R2	0.206	0.206	0.206	0.206	0.206
	Classification power 90.167	90.167	90.167	90.167	90.167
	AUROC	0.809	0.809	0.809	0.809	0.809
	BIC 2334.679 2334.679 2334.679	2334.679	2334.679
	Notes:

Table C .

 C 13: Predicting growth surges, sensitivity by levels of development

		(1)	(2)	(3)
		LICs	EMs	AEs
	External factors and endowments index, average [T-5,T] 0.547*** 0.602***	-0.085
		(0.126)	(0.153)	(0.166)
	Institutions index, average [T-5,T]	0.011	0.133*	0.241**
		(0.068)	(0.068)	(0.110)
	Macroeconomic stability index, average [T-5,T] 0.488*** 0.460*** 0.932***
		(0.107)	(0.112)	(0.186)
	Structural reforms index, average [T-5,T] 0.369*** 0.333**	0.134
		(0.125)	(0.130)	(0.119)
	Trade diversification and quality index, average [T-5,T]	-0.017	0.246*** 0.342***
		(0.085)	(0.093)	(0.115)
	Investments, labor and productivity index, average [T-5,T] 0.235*** 0.212**	0.048
		(0.089)	(0.100)	(0.111)
	Africa (Base level)
	Asia and Pacific 0.100*** 0.164*** 0.091***
		(0.021)	(0.027)	(0.017)
	Europe 0.046** 0.077*** 0.043***
		(0.019)	(0.027)	(0.014)
	Middle East and Central Asia 0.032** 0.053**	0.030**
		(0.015)	(0.022)	(0.014)
	Americas and Caribbean	-0.005	-0.009	-0.005
		(0.010)	(0.018)	(0.010)
	1980-1989 (Base level)
	1990-1999	0.009	0.015	0.009
		(0.010)	(0.016)	(0.010)
	2000-2010 0.083*** 0.136*** 0.076***
		(0.015)	(0.023)	(0.016)
	Advanced Economies (Base level)
	Low-Income Developing Countries	-0.003	-0.003	-0.003
		(0.020)	(0.020)	(0.020)
	Emerging Economies 0.055*** 0.055*** 0.055***
		(0.018)	(0.018)	(0.018)
	Observations	3763	3763	3763
	# of GSs 34.000	79.000	19.000
	Pseudo R2	0.200	0.200	0.200
	Classification power 90.699	90.699	90.699
	AUROC	0.805	0.805	0.805
	BIC

Table 5 .

 5 1: Unconditional probability, by decades, regions, and levels of development

	Panel A:

With IMF-supported programs Panel B: Without IMF-supported programs Decades Decades 1980-1989 1990-1999 2000-2010 Total 1980-1989 1990-1999 2000-2010 Total

	Regions
	Africa	0.008	0.017	0.055	0.026	0.006	0.015	0.026	0.015
	Asia and Pacific	0.050	0.060	0.176	0.083	0.052	0.060	0.053	0.054
	Europe	0.032	0.075	0.196	0.105	0.029	0.039	0.020	0.028
	Middle East and Central Asia	0.074	0.059	0.207	0.103	0.000	0.055	0.062	0.033
	Americas and Caribbean	0.027	0.016	0.072	0.037	0.014	0.015	0.030	0.019
	Levels of development
	LICs	0.004	0.017	0.068	0.030	0.013	0.019	0.041	0.021
	EMs	0.043	0.047	0.141	0.069	0.013	0.042	0.056	0.036
	AEs	0.063	0.143	0.100	0.100	0.034	0.027	0.007	0.021
	Total	0.024	0.032	0.093	0.047	0.019	0.034	0.033	0.028

Table 5 .

 5 2: Illustration of the signals approach method

Table 5 .

 5 3: Unconditional probability of the significant improvements in growth surges determinants

	(1) GSs and no IMF	(2) GSs and IMF	(3) GSs and GRA	(4)

GSs and PRGT Broad determinants of Growth surges # Signals Unc Prob # Signals Unc Prob # Signals Unc Prob # Signals Unc Prob

	External factors and endowments	120	0.043	187	0.133	111	0.104	86	0.115
	Institutions	104	0.038	168	0.120	107	0.100	70	0.093
	Macroeconomic stability	113	0.041	188	0.134	120	0.113	81	0.108
	Structural reforms	88	0.032	203	0.145	129	0.121	91	0.121
	Trade diversification and quality	128	0.046	164	0.117	102	0.096	77	0.103
	Investments, labor and productivity	88	0.032	155	0.110	88	0.083	74	0.099
	# of candidate years	2767		1403		1065		750

Table 5 .

 5 5: Predicting growth surges, baseline results, with and without IMF-supported programs

	(1)	(2)	(3)	(4)	(5)	(6)
	All programs	GRA programs	PRGT programs
	No IMF	IMF	No IMF	GRA	No IMF	PRGT

Panel B: Asia and Pacific External factors and endowments

	--0.052 0.594 0.095 0.329 0.048 0.499 0.238 0.098 -0.005 0.968 -0.005 0.968 -0.000 0.000 0.084 0.489 0.039 0.737 0.175 0.182 0.087 0.425 -0.006 0.952 0.000 0.000 0.286 0.356 0.429 0.217 0.000 0.000 -0.028 0.321 0.083 0.391 0.028 0.726 0.139 0.129 0.056 0.460 0.000 1.000 0.068 -0.079 0.114 1.000 0.900 1.000 1.000 0.900 0.900 1.000 0.857 0.857 0.857 0.905 0.857 1.000 10 1.000 1.000 3 1.000 0.833 0.889 0.889 0.917 0.861 0.058 -0.088 0.075 0.109 -0.068 0.258 0.123 -0.053 0.379 0.069 -0.043 0.312 0.083 -0.106 0.109 5 -0.486 0.139 0.167 0.545 0.224 0.038 0.591 0.216 0.086 0.667 0.188 0.137 0.457 0.008 0.940 0.625 0.264 0.057 0.303 0.145 0.019 0.269 0.140 0.037 0.268 0.118 0.075 0.239 0.124 0.038 0.195 0.043 0.434 # of GSs 1.000 Panel B: 1990-1999 External factors and endowments 19 0.905 19 1.000 0.095 0.176 10 Institutions 20 0.952 16 0.842 -0.110 0.257 9 Macroeconomic stability 20 0.952 19 1.000 0.048 0.348 10 Structural reforms 16 0.762 18 0.947 0.185 0.106 10 Trade diversification and quality 19 0.905 18 0.947 0.043 0.620 9 Investments, labor and productivity 19 0.905 18 0.947 0.043 0.620 9 # of GSs 21 19 -Panel C: 2000-2010 External factors and endowments 22 1.000 43 1.000 0.000 0.000 21 Institutions 17 0.773 39 0.907 0.134 0.142 18 Macroeconomic stability 18 0.818 37 0.860 0.042 0.661 18 Structural reforms 15 0.682 36 0.837 0.155 0.154 18 Trade diversification and quality 18 0.818 39 0.907 0.089 0.310 19 Investments, labor and productivity 19 0.864 39 0.907 0.043 0.601 18 Structural reforms 5 0.714 23 0.852 0.138 0.410 3 Trade diversification and quality 4 0.571 25 0.926 0.355 0.018 3 Investments, labor and productivity 7 1.000 26 0.963 -0.037 0.618 3 # of GSs 7 27 -Panel B: Emerging markets External factors and endowments 35 0.972 43 1.000 0.028 0.277 36 Institutions 27 0.750 37 0.860 0.110 0.218 30 Macroeconomic stability 31 0.861 39 0.907 0.046 0.529 32 Structural reforms 27 0.750 38 0.884 0.134 0.124 32 Trade diversification and quality 31 0.861 40 0.930 0.069 0.317 33 Investments, labor and productivity 31 0.861 38 0.884 0.023 0.767 31 # of GSs 36 43 External factors and endowments 13 0.148 36 0.183 0.035 0.471 5 Institutions 14 0.146 37 0.174 0.028 0.543 4 Macroeconomic stability 14 0.177 38 0.229 0.052 0.357 7 Structural reforms 12 0.176 48 0.239 0.062 0.288 9 Trade diversification and quality 11 0.112 37 0.184 0.072 0.113 6 Investments, labor and productivity 14 0.189 40 0.250 0.061 0.307 4 # of GSs 6 20 -26 0.347 28 0.560 0.213 0.018 17 Institutions 18 0.321 29 0.558 0.236 0.013 18 Macroeconomic stability 21 0.375 23 0.605 0.230 0.028 13 Structural reforms 22 0.478 32 0.744 0.266 0.010 16 Trade diversification and quality 31 0.449 26 0.520 0.071 0.450 16 -Panel A: Africa Investments, labor and productivity 22 0.361 19 0.679 0.318 0.005 10 # of GSs 16 12 -Panel E: Americas and Caribbean External factors and endowments 15 0.158 31 0.307 0.149 0.014 27 Institutions 8 0.129 27 0.276 0.146 0.029 25 Macroeconomic stability 11 0.151 24 0.270 0.119 0.068 22 Structural reforms 9 0.115 22 0.212 0.096 0.089 22 Trade diversification and quality 16 0.152 20 0.208 0.056 0.304 17 Investments, labor and productivity 9 0.136 19 0.224 0.087 0.174 15 0.214 0.078 0.237	8 9 31 22 21 23 24 10 10 10 9 9 8 33 31 40 31 36 11 12 10 17 10 9 4 3 4 2 5 5	25 15	0 10 0.880 1.000 0.800 1.000 0.900 1.000 1.000 1.000 0.960 0.880 0.840 0.880 0.880 0.840 0.920 0.960 1.000 1.000 1.000 0.900 0.900 0.800 0.235 0.199 0.256 0.267 0.238 0.303 0.733 0.545 0.588 0.850 0.526 0.750 0.286 0.250 0.308 0.095 -0.020 0.797 -0.095 0.329 -0.152 0.192 0.048 0.499 0.138 0.380 0.095 0.329 0.095 0.329 -0.000 0.000 0.187 0.057 0.062 0.563 0.158 0.210 0.062 0.563 0.016 0.870 -0.120 0.352 0.126 0.468 0.349 0.025 -0.040 0.605 -0.028 0.604 0.250 0.081 0.139 0.221 0.150 0.320 0.039 0.753 -0.061 0.643 0.087 0.115 0.053 0.283 0.079 0.193 0.090 0.149 0.126 0.015 0.113 0.081 -0.387 0.005 0.224 0.069 0.213 0.123 0.372 0.004 0.077 0.556 0.389 0.012 0.128 0.243 0.121 0.287 0.157 0.173 0.455 0.302 0.013 0.278 0.141 0.157
	# of GSs

D.3.3 Robustness checks D.3.3.1 Alternative samples

  Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

	Table D.11: Predicting growth surges, robustness checks, drop countries without any
	IMF-supported programs
		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.029** 0.029**	0.013	0.013	0.040**	0.040**
		(0.012)	(0.012)	(0.012)	(0.012)	(0.018)	(0.018)
	External factors and endowments index, average [T-5,T] 0.364*** 0.591*** 0.358*** 0.446*** 0.331*** 0.582***
		(0.126)	(0.126)	(0.119)	(0.148)	(0.113)	(0.170)
	Institutions index, average [T-5,T]	0.049	0.081	0.047	0.111*	0.036	-0.035
		(0.064)	(0.056)	(0.060)	(0.058)	(0.054)	(0.088)
	Macroeconomic stability index, average [T-5,T] 0.384*** 0.556*** 0.365*** 0.462*** 0.298*** 0.602***
		(0.095)	(0.101)	(0.089)	(0.119)	(0.082)	(0.141)
	Structural reforms index, average [T-5,T] 0.218** 0.378*** 0.196*	0.340*** 0.199** 0.434***
		(0.109)	(0.099)	(0.103)	(0.109)	(0.093)	(0.149)
	Trade diversification and quality index, average [T-5,T] 0.345***	0.100	0.303***	0.078	0.316***	0.051
		(0.078)	(0.079)	(0.075)	(0.090)	(0.071)	(0.107)
	Investments, labor and productivity index, average [T-5,T]	0.085	0.227***	0.084	0.078	0.079	0.365***
		(0.075)	(0.083)	(0.071)	(0.096)	(0.065)	(0.119)
	Africa (Base level)
	Asia and Pacific 0.103*** 0.126*** 0.076*** 0.085*** 0.143*** 0.187***
		(0.020)	(0.024)	(0.022)	(0.025)	(0.024)	(0.033)
	Europe	0.027*	0.034*	0.024	0.027	0.045**	0.065**
		(0.016)	(0.020)	(0.018)	(0.020)	(0.018)	(0.026)
	Middle East and Central Asia 0.039** 0.049** 0.040**	0.046**	0.007	0.011
		(0.018)	(0.022)	(0.020)	(0.023)	(0.013)	(0.020)
	Americas and Caribbean	-0.003	-0.004	-0.003	-0.003	0.003	0.004
		(0.011)	(0.014)	(0.013)	(0.015)	(0.010)	(0.015)
	1980-1989 (Base level)
	1990-1999	-0.003	-0.003	-0.009	-0.010	0.010	0.015
		(0.010)	(0.013)	(0.010)	(0.012)	(0.011)	(0.016)
	2000-2010 0.103*** 0.126*** 0.083*** 0.093*** 0.098*** 0.133***
		(0.018)	(0.022)	(0.020)	(0.024)	(0.020)	(0.026)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.006	0.008	-0.005	-0.005	0.022	0.033
		(0.017)	(0.022)	(0.018)	(0.021)	(0.016)	(0.022)
	Emerging Economies 0.070*** 0.087*** 0.067*** 0.075*** 0.101*** 0.138***
		(0.017)	(0.019)	(0.018)	(0.018)	(0.017)	(0.026)
	Observations	3081	3081	2454	2454	2193	2193
	# of GSs	110	110	79	79	71	71
	# of Countries	139	139	138	138	135	135
	Pseudo R2	0.234	0.234	0.212	0.212	0.257	0.257
	Classification power 90.230	90.230	90.872	90.872	91.701	91.701
	AUROC	0.831	0.831	0.821	0.821	0.837	0.837
	BIC 1787.348 1787.348 1410.098 1410.098 1221.804 1221.804
	Notes:

Table D .

 D 12: Predicting growth surges, robustness checks, drop countries without any IMF-supported programs and top 10% countries with higher number of IMF-supported programs

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.027** 0.027**	0.019	0.019	0.035*	0.035*
		(0.013)	(0.013)	(0.013)	(0.013)	(0.019)	(0.019)
	External factors and endowments index, average [T-5,T] 0.367*** 0.635*** 0.357*** 0.529*** 0.330*** 0.566***
		(0.130)	(0.144)	(0.128)	(0.173)	(0.114)	(0.196)
	Institutions index, average [T-5,T]	0.053	0.120*	0.055	0.118*	0.036	0.074
		(0.066)	(0.064)	(0.065)	(0.068)	(0.053)	(0.109)
	Macroeconomic stability index, average [T-5,T] 0.374*** 0.426*** 0.371*** 0.466*** 0.280*** 0.360**
		(0.097)	(0.117)	(0.095)	(0.137)	(0.080)	(0.174)
	Structural reforms index, average [T-5,T] 0.228** 0.396*** 0.225** 0.386*** 0.195**	0.414**
		(0.112)	(0.110)	(0.111)	(0.123)	(0.092)	(0.180)
	Trade diversification and quality index, average [T-5,T] 0.341***	0.124	0.325***	0.126	0.292***	0.019
		(0.080)	(0.095)	(0.080)	(0.102)	(0.069)	(0.149)
	Investments, labor and productivity index, average [T-5,T]	0.098	0.167*	0.097	0.073	0.091	0.282*
		(0.077)	(0.095)	(0.076)	(0.111)	(0.065)	(0.146)
	Africa (Base level)
	Asia and Pacific 0.103*** 0.127*** 0.078*** 0.091*** 0.135*** 0.183***
		(0.021)	(0.025)	(0.023)	(0.026)	(0.023)	(0.036)
	Europe 0.035** 0.044**	0.026	0.030	0.053*** 0.076***
		(0.017)	(0.021)	(0.019)	(0.022)	(0.019)	(0.028)
	Middle East and Central Asia 0.045** 0.057** 0.045**	0.053**	0.014	0.020
		(0.019)	(0.023)	(0.022)	(0.026)	(0.013)	(0.020)
	Americas and Caribbean	0.002	0.003	-0.001	-0.001	0.007	0.011
		(0.012)	(0.016)	(0.015)	(0.018)	(0.011)	(0.016)
	1980-1989 (Base level)
	1990-1999	-0.004	-0.005	-0.011	-0.013	0.008	0.012
		(0.011)	(0.014)	(0.012)	(0.014)	(0.012)	(0.017)
	2000-2010 0.098*** 0.120*** 0.090*** 0.104*** 0.084*** 0.117***
		(0.019)	(0.024)	(0.021)	(0.026)	(0.020)	(0.028)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.018	0.022	0.008	0.009	0.031*	0.045**
		(0.018)	(0.023)	(0.020)	(0.024)	(0.017)	(0.021)
	Emerging Economies 0.069*** 0.086*** 0.064*** 0.075*** 0.097*** 0.135***
		(0.017)	(0.019)	(0.018)	(0.019)	(0.016)	(0.028)
	Observations	2529	2529	2173	2173	1823	1823
	# of GSs 97.000	97.000	76.000	76.000	61.000	61.000
	# of Countries 118.000 118.000 117.000	117.000	114.000	114.000
	Pseudo R2	0.223	0.223	0.200	0.200	0.246	0.246
	Classification power 89.719	89.719	90.244	90.244	91.882	91.882
	AUROC	0.818	0.818	0.809	0.809	0.824	0

Table D .

 D 13: Predicting growth surges, robustness checks, signals associated with IMF-supported programs are those occurring during programs period only Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (no years after included)	0.016	0.016	0.004	0.004	0.037**	0.037**
		(0.011)	(0.011)	(0.011)	(0.011)	(0.018)	(0.018)
	External factors and endowments index, average [T-5,T] 0.226** 0.660*** 0.228** 0.554*** 0.237** 0.701***
		(0.102)	(0.124)	(0.100)	(0.147)	(0.096)	(0.176)
	Institutions index, average [T-5,T] 0.104*	0.054	0.086*	0.087	0.085*	-0.080
		(0.054)	(0.055)	(0.052)	(0.055)	(0.048)	(0.094)
	Macroeconomic stability index, average [T-5,T] 0.433*** 0.471*** 0.388*** 0.391*** 0.373*** 0.574***
		(0.081)	(0.099)	(0.077)	(0.113)	(0.075)	(0.143)
	Structural reforms index, average [T-5,T] 0.160* 0.350***	0.144	0.272***	0.123	0.440***
		(0.094)	(0.094)	(0.091)	(0.101)	(0.084)	(0.148)
	Trade diversification and quality index, average [T-5,T] 0.190***	0.061	0.163***	0.035	0.175***	0.030
		(0.061)	(0.077)	(0.059)	(0.086)	(0.056)	(0.111)
	Investments, labor and productivity index, average [T-5,T] 0.120* 0.226*** 0.110*	0.114	0.095	0.369***
		(0.064)	(0.083)	(0.062)	(0.094)	(0.059)	(0.122)
	Africa (Base level)
	Asia and Pacific 0.109*** 0.117*** 0.085*** 0.085*** 0.135*** 0.163***
		(0.019)	(0.021)	(0.020)	(0.021)	(0.021)	(0.029)
	Europe 0.044*** 0.049*** 0.040**	0.041** 0.069*** 0.089***
		(0.017)	(0.018)	(0.019)	(0.019)	(0.020)	(0.026)
	Middle East and Central Asia 0.037** 0.041**	0.032*	0.033*	0.017	0.024
		(0.016)	(0.018)	(0.017)	(0.017)	(0.013)	(0.018)
	Americas and Caribbean	-0.006	-0.007	-0.008	-0.009	-0.001	-0.001
		(0.011)	(0.012)	(0.012)	(0.013)	(0.010)	(0.014)
	1980-1989 (Base level)
	1990-1999	0.003	0.004	0.002	0.002	0.017	0.023
		(0.010)	(0.011)	(0.010)	(0.011)	(0.011)	(0.015)
	2000-2010 0.079*** 0.085*** 0.058*** 0.058*** 0.068*** 0.086***
		(0.015)	(0.017)	(0.016)	(0.017)	(0.016)	(0.020)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.016	0.018	0.008	0.008	0.023	0.031
		(0.017)	(0.018)	(0.017)	(0.017)	(0.016)	(0.020)
	Emerging Economies 0.078*** 0.084*** 0.075*** 0.075*** 0.095*** 0.119***
		(0.015)	(0.016)	(0.016)	(0.015)	(0.015)	(0.023)
	Observations	3763	3763	3172	3172	2958	2958
	# of GSs	132	132	106	106	99	99
	# of Countries	169	169	168	168	166	166
	Pseudo R2	0.188	0.188	0.160	0.160	0.190	0.190
	Classification power 90.433	90.433	90.763	90.763	91.176	91.176
	AUROC	0.795	0.795	0.778	0.778	0.792	0.792
	BIC 2240.909 2240.909 1889.442 1889.442 1723.421 1723.421
	Notes:

Table D .

 D 14: Predicting growth surges, robustness checks, signals associated with IMF-supported programs are those occurring during programs period or four years after program completion

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 4 years after) 0.019*	0.019*	0.005	0.005	0.030*	0.030*
		(0.012)	(0.012)	(0.011)	(0.011)	(0.018)	(0.018)
	External factors and endowments index, average [T-5,T]	0.089	0.697***	0.139	0.560***	0.118	0.650***
		(0.108)	(0.116)	(0.103)	(0.145)	(0.100)	(0.170)
	Institutions index, average [T-5,T]	0.071	0.077	0.060	0.084	0.048	-0.078
		(0.058)	(0.050)	(0.054)	(0.054)	(0.051)	(0.089)
	Macroeconomic stability index, average [T-5,T] 0.429*** 0.461*** 0.396*** 0.375*** 0.373*** 0.535***
		(0.088)	(0.089)	(0.082)	(0.110)	(0.081)	(0.137)
	Structural reforms index, average [T-5,T] 0.198* 0.286*** 0.176*	0.264***	0.175*	0.412***
		(0.105)	(0.087)	(0.098)	(0.099)	(0.094)	(0.141)
	Trade diversification and quality index, average [T-5,T] 0.185***	0.092	0.137**	0.029	0.168***	0.026
		(0.064)	(0.071)	(0.060)	(0.084)	(0.058)	(0.105)
	Investments, labor and productivity index, average [T-5,T]	0.061	0.257***	0.073	0.110	0.059	0.349***
		(0.069)	(0.075)	(0.065)	(0.091)	(0.063)	(0.117)
	Africa (Base level)
	Asia and Pacific 0.104*** 0.113*** 0.079*** 0.079*** 0.133*** 0.156***
		(0.020)	(0.021)	(0.021)	(0.022)	(0.022)	(0.029)
	Europe 0.039** 0.044** 0.042**	0.043** 0.080*** 0.098***
		(0.017)	(0.018)	(0.019)	(0.019)	(0.023)	(0.028)
	Middle East and Central Asia 0.035** 0.040** 0.036**	0.037**	0.016	0.022
		(0.015)	(0.018)	(0.017)	(0.018)	(0.012)	(0.017)
	Americas and Caribbean	-0.008	-0.010	-0.005	-0.005	-0.002	-0.003
		(0.010)	(0.012)	(0.012)	(0.013)	(0.010)	(0.013)
	1980-1989 (Base level)
	1990-1999	0.004	0.005	0.001	0.001	0.016	0.021
		(0.010)	(0.011)	(0.011)	(0.011)	(0.011)	(0.014)
	2000-2010 0.078*** 0.086*** 0.049*** 0.049*** 0.065*** 0.079***
		(0.015)	(0.017)	(0.016)	(0.017)	(0.016)	(0.019)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.010	0.012	0.007	0.007	0.037**	0.048**
		(0.016)	(0.019)	(0.018)	(0.018)	(0.018)	(0.020)
	Emerging Economies 0.074*** 0.082*** 0.074*** 0.074*** 0.107*** 0.128***
		(0.016)	(0.016)	(0.016)	(0.015)	(0.016)	(0.024)
	Observations	3763	3763	2959	2959	2698	2698
	# of GSs	132	132	97	97	89	89
	# of Countries	169	169	168	168	159	159
	Pseudo R2	0.191	0.191	0.155	0.155	0.184	0.184
	Classification power 90.194	90.194	90.875	90.875	91.253	91.253
	AUROC	0.796	0.796	0.772	0.772	0.784	0.784
	BIC

Table D .

 D 15: Predicting growth surges, robustness checks, alternative horizons of growth determinants, average over the post-7 years of growth surges Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.024** 0.024**	0.014	0.014	0.029*	0.029*
		(0.011)	(0.011)	(0.012)	(0.012)	(0.017)	(0.017)
	External factors and endowments index, average [T-7,T] 0.344*** 1.090*** 0.376*** 0.933*** 0.320*** 1.008***
		(0.127)	(0.157)	(0.120)	(0.199)	(0.119)	(0.211)
	Institutions index, average [T-7,T] 0.115*	0.092	0.098*	0.058	0.092*	0.056
		(0.061)	(0.062)	(0.055)	(0.069)	(0.054)	(0.091)
	Macroeconomic stability index, average [T-7,T] 0.461*** 0.553*** 0.422*** 0.444*** 0.403*** 0.578***
		(0.092)	(0.113)	(0.085)	(0.136)	(0.086)	(0.157)
	Structural reforms index, average [T-7,T]	0.121	0.413***	0.106	0.300**	0.110	0.578***
		(0.102)	(0.106)	(0.095)	(0.121)	(0.093)	(0.159)
	Trade diversification and quality index, average [T-7,T] 0.193***	0.100	0.155**	0.037	0.191***	0.087
		(0.067)	(0.086)	(0.063)	(0.102)	(0.061)	(0.115)
	Investments, labor and productivity index, average [T-7,T]	0.061	0.191**	0.066	0.100	0.052	0.310**
		(0.073)	(0.093)	(0.069)	(0.112)	(0.067)	(0.131)
	Africa (Base level)
	Asia and Pacific 0.105*** 0.118*** 0.086*** 0.094*** 0.133*** 0.148***
		(0.019)	(0.021)	(0.020)	(0.023)	(0.021)	(0.027)
	Europe 0.053*** 0.062*** 0.051*** 0.057*** 0.085*** 0.099***
		(0.017)	(0.019)	(0.019)	(0.020)	(0.022)	(0.026)
	Middle East and Central Asia 0.039** 0.046** 0.035**	0.039**	0.018	0.023
		(0.015)	(0.018)	(0.016)	(0.018)	(0.012)	(0.016)
	Americas and Caribbean	-0.006	-0.007	-0.006	-0.007	-0.003	-0.005
		(0.010)	(0.012)	(0.011)	(0.013)	(0.009)	(0.012)
	1980-1989 (Base level)
	1990-1999	-0.001	-0.002	-0.001	-0.001	0.012	0.015
		(0.010)	(0.012)	(0.010)	(0.012)	(0.011)	(0.014)
	2000-2010 0.063*** 0.071*** 0.042*** 0.046*** 0.060*** 0.070***
		(0.015)	(0.017)	(0.015)	(0.017)	(0.016)	(0.018)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.015	0.018	0.009	0.010	0.031*	0.039**
		(0.016)	(0.019)	(0.017)	(0.019)	(0.017)	(0.018)
	Emerging Economies 0.078*** 0.089*** 0.075*** 0.082*** 0.107*** 0.122***
		(0.015)	(0.016)	(0.015)	(0.015)	(0.015)	(0.022)
	Observations	3763	3763	3136	3136	2875	2875
	# of GSs	132	132	101	101	93	93
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.190	0.190	0.158	0.158	0.191	0.191
	Classification power 90.221	90.221	90.657	90.657	91.409	91.409
	AUROC	0.796	0.796	0.777	0.777	0.792	0.792
	BIC 2236.553 2236.553 1866.295 1866.295 1671.191 1671.191
	Notes:

Table D .

 D 16: Predicting growth surges, robustness checks, alternative horizons of growth determinants, average over the post-3 years of growth surges Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.021*	0.021*	0.005	0.005	0.037**	0.037**
		(0.012)	(0.012)	(0.011)	(0.011)	(0.018)	(0.018)
	External factors and endowments index, average [T-3,T]	0.145	0.429*** 0.185** 0.362***	0.156*	0.473***
		(0.089)	(0.107)	(0.086)	(0.122)	(0.083)	(0.155)
	Institutions index, average [T-3,T] 0.098*	0.053	0.086*	0.089*	0.076*	-0.090
		(0.052)	(0.051)	(0.048)	(0.051)	(0.046)	(0.089)
	Macroeconomic stability index, average [T-3,T] 0.341*** 0.481*** 0.314*** 0.352*** 0.292*** 0.574***
		(0.076)	(0.090)	(0.071)	(0.102)	(0.069)	(0.132)
	Structural reforms index, average [T-3,T]	0.144	0.249***	0.128	0.231**	0.113	0.279**
		(0.093)	(0.088)	(0.087)	(0.093)	(0.083)	(0.140)
	Trade diversification and quality index, average [T-3,T] 0.174***	0.011	0.138**	-0.015	0.158***	-0.038
		(0.059)	(0.072)	(0.055)	(0.081)	(0.053)	(0.107)
	Investments, labor and productivity index, average [T-3,T] 0.148** 0.274*** 0.142**	0.144*	0.129** 0.378***
		(0.060)	(0.076)	(0.056)	(0.085)	(0.055)	(0.115)
	Africa (Base level)
	Asia and Pacific 0.107*** 0.122*** 0.083*** 0.085*** 0.134*** 0.168***
		(0.019)	(0.022)	(0.020)	(0.022)	(0.021)	(0.030)
	Europe 0.044*** 0.052*** 0.040**	0.042** 0.075*** 0.100***
		(0.016)	(0.019)	(0.018)	(0.018)	(0.021)	(0.028)
	Middle East and Central Asia 0.039** 0.046** 0.035**	0.036**	0.019	0.028
		(0.015)	(0.018)	(0.017)	(0.018)	(0.012)	(0.018)
	Americas and Caribbean	-0.005	-0.007	-0.005	-0.006	-0.001	-0.001
		(0.010)	(0.013)	(0.012)	(0.013)	(0.010)	(0.014)
	1980-1989 (Base level)
	1990-1999	0.003	0.004	0.000	0.000	0.015	0.021
		(0.010)	(0.011)	(0.010)	(0.010)	(0.011)	(0.015)
	2000-2010 0.087*** 0.100*** 0.060*** 0.062*** 0.074*** 0.097***
		(0.016)	(0.018)	(0.016)	(0.017)	(0.016)	(0.021)
	Advanced Economies (Base level)
	Low-Income Developing Countries	0.015	0.018	0.004	0.004	0.031*	0.044**
		(0.016)	(0.019)	(0.017)	(0.018)	(0.016)	(0.020)
	Emerging Economies 0.074*** 0.085*** 0.072*** 0.074*** 0.100*** 0.130***
		(0.015)	(0.016)	(0.016)	(0.015)	(0.015)	(0.024)
	Observations	3763	3763	3136	3136	2875	2875
	# of GSs	132	132	101	101	93	93
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.183	0.183	0.157	0.157	0.184	0.184
	Classification power 90.194	90.194	90.784	90.784	91.061	91.061
	AUROC	0.794	0.794	0.778	0.778	0.789	0.789
	BIC 2254.189 2254.189 1866.805 1866.805 1684.705 1684.705
	Notes:

D.

3.3.4 Alternative estimatorsTable D .

 D 17: Predicting growth surges, robustness checks, Probit estimator Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after)=1 0.022*	0.022*	0.006	0.006	0.035**	0.035**
		(0.012)	(0.012)	(0.011)	(0.011)	(0.018)	(0.018)
	External factors and endowments index, average over [T-5,T] 0.184* 0.723*** 0.229** 0.622*** 0.203** 0.713***
		(0.105)	(0.125)	(0.102)	(0.153)	(0.100)	(0.173)
	Institutions index, average over [T-5,T]	0.085	0.053	0.071	0.092	0.071	-0.089
		(0.056)	(0.054)	(0.053)	(0.056)	(0.051)	(0.092)
	Macroeconomic stability index, average over [T-5,T] 0.459*** 0.527*** 0.426*** 0.416*** 0.411*** 0.576***
		(0.083)	(0.098)	(0.078)	(0.114)	(0.078)	(0.141)
	Structural reforms index, average over [T-5,T]	0.132	0.340***	0.123	0.280***	0.100	0.442***
		(0.099)	(0.097)	(0.093)	(0.106)	(0.091)	(0.151)
	Trade diversification and quality index, average over [T-5,T] 0.158***	0.084	0.129**	0.043	0.151***	0.059
		(0.060)	(0.076)	(0.057)	(0.087)	(0.056)	(0.111)
	Investments, labor and productivity index, average over [T-5,T] 0.117* 0.236*** 0.115*	0.115	0.113*	0.361***
		(0.064)	(0.083)	(0.061)	(0.097)	(0.060)	(0.121)
	APD 0.111*** 0.120*** 0.090*** 0.090*** 0.140*** 0.160***
		(0.019)	(0.021)	(0.020)	(0.021)	(0.021)	(0.028)
	EUR 0.053*** 0.059*** 0.049*** 0.050*** 0.090*** 0.107***
		(0.018)	(0.019)	(0.019)	(0.019)	(0.022)	(0.028)
	MCD 0.039** 0.043** 0.036**	0.036**	0.016	0.021
		(0.015)	(0.017)	(0.016)	(0.017)	(0.013)	(0.017)
	WHD	-0.007	-0.008	-0.007	-0.008	-0.002	-0.003
		(0.010)	(0.012)	(0.012)	(0.012)	(0.010)	(0.013)
	decade=2	0.001	0.001	-0.000	-0.000	0.013	0.016
		(0.010)	(0.011)	(0.010)	(0.011)	(0.011)	(0.014)
	decade=3 0.073*** 0.079*** 0.050*** 0.050*** 0.063*** 0.075***
		(0.015)	(0.016)	(0.015)	(0.016)	(0.015)	(0.018)
	Low-Income Developing Countries	0.017	0.019	0.009	0.009	0.035**	0.045**
		(0.017)	(0.018)	(0.017)	(0.017)	(0.017)	(0.019)
	Emerging Economies 0.083*** 0.090*** 0.080*** 0.080*** 0.111*** 0.131***
		(0.016)	(0.016)	(0.016)	(0.015)	(0.016)	(0.023)
	Observations	3763	3763	3136	3136	2875	2875
	# of GSs	132	132	101	101	93	93
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.190	0.190	0.163	0.163	0.189	0.189
	Classification power 90.300	90.300	90.689	90.689	91.096	91.096
	AUROC	0.797	0.797	0.782	0.782	0.793	0.793
	BIC 2235.462 2235.462 1855.712 1855.712 1674.777 1674.777
	Notes:

Table D .

 D 18: Predicting growth surges, robustness checks, Tobit estimator Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.025** 0.025**	0.014	0.014	0.036**	0.036**
		(0.012)	(0.012)	(0.013)	(0.013)	(0.017)	(0.017)
	External factors and endowments index, average [T-5,T]	0.182	1.046*** 0.236** 1.033*** 0.237** 1.052***
		(0.115)	(0.128)	(0.115)	(0.171)	(0.113)	(0.171)
	Institutions index, average [T-5,T] 0.119**	0.028	0.101*	0.105	0.107*	-0.147*
		(0.057)	(0.056)	(0.056)	(0.069)	(0.056)	(0.084)
	Macroeconomic stability index, average [T-5,T] 0.543*** 0.611*** 0.530*** 0.543*** 0.510*** 0.663***
		(0.089)	(0.099)	(0.087)	(0.130)	(0.087)	(0.138)
	Structural reforms index, average [T-5,T] 0.216* 0.511*** 0.204*	0.474***	0.195*	0.619***
		(0.110)	(0.107)	(0.109)	(0.132)	(0.108)	(0.159)
	Trade diversification and quality index, average [T-5,T] 0.187***	0.044	0.172***	0.011	0.178***	0.031
		(0.064)	(0.075)	(0.064)	(0.097)	(0.063)	(0.107)
	Investments, labor and productivity index, average [T-5,T] 0.133* 0.289*** 0.129*	0.228**	0.143*	0.354***
		(0.075)	(0.085)	(0.073)	(0.113)	(0.073)	(0.118)
	Africa (Base level)
	Asia and Pacific 0.131*** 0.131*** 0.101*** 0.101*** 0.176*** 0.176***
		(0.017)	(0.017)	(0.019)	(0.019)	(0.019)	(0.019)
	Europe 0.094*** 0.094*** 0.072*** 0.072*** 0.140*** 0.140***
		(0.020)	(0.020)	(0.022)	(0.022)	(0.023)	(0.023)
	Middle East and Central Asia 0.054*** 0.054*** 0.041**	0.041**	0.037*	0.037*
		(0.019)	(0.019)	(0.020)	(0.020)	(0.020)	(0.020)
	Americas and Caribbean	-0.019	-0.019	-0.029	-0.029	0.001	0.001
		(0.016)	(0.016)	(0.018)	(0.018)	(0.019)	(0.019)
	1980-1989 (Base level)
	1990-1999	-0.016	-0.016	-0.012	-0.012	0.004	0.004
		(0.012)	(0.012)	(0.013)	(0.013)	(0.014)	(0.014)
	2000-2010 0.085*** 0.085*** 0.072*** 0.072*** 0.078*** 0.078***
		(0.013)	(0.013)	(0.014)	(0.014)	(0.015)	(0.015)
	Advanced Economies (Base level)
	Low-Income Developing Countries 0.060*** 0.060*** 0.047**	0.047** 0.097*** 0.097***
		(0.022)	(0.022)	(0.023)	(0.023)	(0.025)	(0.025)
	Emerging Economies 0.120*** 0.120*** 0.106*** 0.106*** 0.163*** 0.163***
		(0.018)	(0.018)	(0.018)	(0.018)	(0.020)	(0.020)
	Observations	3763	3763	3136	3136	2875	2875
	# of GSs	132	132	101	101	93	93
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.336	0.336	0.313	0.313	0.368	0.368
	AUROC	0.791	0.791	0.775	0.775	0.786	0.786
	BIC 1382.418 1382.418 1063.798 1063.798 938.579	938.579
	Notes:

D.

3.3.5 Alternative sets of growth surges (based on different criteria of filters)Table D .

 D 19: Predicting growth surges, robustness checks, alternative set of growth surges, Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs. Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

	Hausmann et al. (2005) criteria
		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after)	-0.013	-0.013	-0.034** -0.034**	0.029	0.029
		(0.016)	(0.016)	(0.015)	(0.015)	(0.024)	(0.024)
	External factors and endowments index, average [T-5,T] 0.416*** 0.661*** 0.504*** 0.514*** 0.482*** 0.937***
	(0.151) 0.122 (0.075) Macroeconomic stability index, average [T-5,T] 0.895*** 0.921*** 0.810*** 0.885*** 0.791*** 0.934*** (0.159) (0.150) (0.175) (0.137) (0.257) Institutions index, average [T-5,T] -0.049 0.117 -0.090 0.094 -0.126 (0.070) (0.072) (0.076) (0.066) (0.117) (0.136) (0.129) (0.132) (0.149) (0.124) (0.206) Structural reforms index, average [T-5,T] 0.086 0.309** 0.083 0.253* 0.066 0.524** (0.163) (0.134) (0.157) (0.139) (0.147) (0.217) Trade diversification and quality index, average [T-5,T] 0.361*** 0.122 0.290*** -0.091 0.293*** 0.433*** (0.094) (0.098) (0.092) (0.109) (0.085) (0.151) Investments, labor and productivity index, average [T-5,T] -0.009 0.387*** 0.002 0.222* -0.003 0.280* (0.097) (0.109) (0.094) (0.132) (0.086) (0.160) Africa (Base level) Asia and Pacific 0.138*** 0.122*** 0.134*** 0.102*** 0.137*** 0.146*** (0.026) (0.024) (0.028) (0.024) (0.026) (0.031) Europe 0.115*** 0.102*** 0.132*** 0.100*** 0.193*** 0.201*** (0.030) (0.027) (0.032) (0.025) (0.040) (0.045) Middle East and Central Asia 0.035 0.031 0.035 0.027 0.000 0.000 (0.022) (0.020) (0.023) (0.018) (0.018) (0.021) Americas and Caribbean -0.009 -0.008 0.009 0.007 -0.007 -0.008 (0.017) (0.016) (0.019) (0.014) (0.016) (0.019) 1980-1989 (Base level) 1990-1999 0.062*** 0.055*** 0.056*** 0.042*** 0.067*** 0.073*** (0.016) (0.015) (0.018) (0.014) (0.017) (0.019) 2000-2010 0.101*** 0.089*** 0.059*** 0.044*** 0.061*** 0.066*** Advanced Economies (Base level) (0.020) (0.018) (0.021) (0.017) (0.019) (0.021) Low-Income Developing Countries 0.065** 0.058*** 0.058** 0.044** 0.089*** 0.100*** (0.027) (0.022) (0.028) (0.020) (0.028) (0.027) Emerging Economies 0.141*** 0.124*** 0.146*** 0.111*** 0.186*** 0.199*** (0.023) (0.020) (0.023) (0.018) (0.024) (0.033) Observations 2981 2981 2515 2515 2288 2288 # of GSs 159 159 128 128 119 119 Unconditional probability 0.053 0.053 0.053 0.053 0.053 0.053 Mean(duration of GSs) 13.906 13.906 13.906 13.906 13.906 13.906 Mean(Avg. growth during GSs) 5.220 5.220 5.220 5.220 5.220 5.220 # of Countries 168 168 167 167 161 161 Pseudo R2 0.163 0.163 0.162 0.162 0.146 0.146 Classification power 85.340 85.340 85.646 85.646 85.490 85.490 AUROC 0.772 0.772 0.775 0.775 0.762 0.762 BIC 2353.763 2353.763 1968.490 1968.490 1853.077 1853.077 0.014 0.014 0.004 0.004 0.020 0.020 (0.011) (0.011) (0.011) (0.011) (0.017) (0.017) External factors and endowments index, average [T-5,T] 0.149 0.474*** 0.154 0.468*** 0.240** 0.385** (0.097) (0.123) (0.098) (0.151) (0.096) (0.178) Institutions index, average [T-5,T] 0.091* -0.011 0.084* 0.022 0.067 -0.145 (0.050) (0.052) (0.049) (0.059) (0.046) (0.091) Macroeconomic stability index, average [T-5,T] 0.317*** 0.429*** 0.320*** 0.486*** 0.279*** 0.295** (0.084) (0.091) (0.082) (0.111) (0.079) (0.136) Structural reforms index, average [T-5,T] 0.151 0.341*** 0.143 0.262** 0.105 0.393** (0.109) (0.097) (0.108) (0.107) (0.103) (0.159) Trade diversification and quality index, average [T-5,T] 0.190*** 0.138* 0.168*** 0.007 0.178*** 0.211* (0.065) (0.076) (0.065) (0.092) (0.061) (0.113) Investments, labor and productivity index, average [T-5,T] -0.085 0.244*** -0.074 0.193** -0.070 0.282*** (0.065) (0.069) (0.065) (0.084) (0.061) (0.105) Africa (Base level) Asia and Pacific 0.063*** 0.066*** 0.035* 0.033* 0.084*** 0.095*** (0.019) (0.020) (0.021) (0.020) (0.021) (0.026) Europe 0.001 0.001 0.001 0.001 0.029 0.034 (0.016) (0.018) (0.020) (0.019) (0.020) (0.023) Middle East and Central Asia -0.003 -0.003 -0.005 -0.005 -0.019 -0.023 (0.016) (0.017) (0.018) (0.018) (0.014) (0.017) Americas and Caribbean -0.032*** -0.036** -0.036** -0.036** -0.027** -0.032** (0.012) (0.014) (0.015) (0.015) (0.013) (0.016) 1980-1989 (Base level) 1990-1999 -0.004 -0.004 -0.008 -0.008 0.008 0.009 (0.010) (0.011) (0.011) (0.010) (0.012) (0.014) 2000-2010 0.077*** 0.082*** 0.068*** 0.065*** 0.048*** 0.055*** Advanced Economies (Base level) (0.015) (0.016) (0.016) (0.017) (0.015) (0.017) Low-Income Developing Countries -0.036** -0.039** -0.030* -0.030 -0.006 -0.007 (0.016) (0.019) (0.018) (0.019) (0.016) (0.020) Emerging Economies 0.048*** 0.050*** 0.045** 0.043*** 0.079*** 0.090*** (0.017) (0.017) (0.018) (0.016) (0.017) (0.022) Observations 3896 3896 3238 3238 2951 2951 # of GSs 144 144 116 116 101 101 Unconditional probability 0.037 0.037 0.037 0.037 0.037 0.037 Mean(duration of GSs) 9.313 9.313 9.313 9.313 9.313 9.313 Mean(Avg. growth during GSs) 6.310 6.310 6.310 6.310 6.310 6.310 # of Countries 169 169 169 169 166 166 Pseudo R2 0.141 0.141 0.127 0.127 0.102 0.102 Classification power 90.092 90.092 90.426 90.426 90.003 90.003 Notes: IMF-supported programs dummy (include 2 years after) AUROC 0.742 0.742 0.722 0.722 0.711 0.711
	BIC 2501.914 2501.914 2096.050 2096.050 1932.626 1932.626
	Notes:

Table D .

 D 21: Predicting growth surges, robustness checks, alternative set of growth surges, minimum duration of growth surges sets to 10 years Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after)	0.010	0.010	-0.001	-0.001	0.023	0.023
		(0.011)	(0.011)	(0.011)	(0.011)	(0.018)	(0.018)
	External factors and endowments index, average [T-5,T] 0.283** 0.547*** 0.311*** 0.409*** 0.312*** 0.760***
		(0.113)	(0.114)	(0.109)	(0.128)	(0.101)	(0.191)
	Institutions index, average [T-5,T]	0.052	0.028	0.041	0.036	0.024	-0.094
		(0.057)	(0.053)	(0.053)	(0.055)	(0.050)	(0.094)
	Macroeconomic stability index, average [T-5,T] 0.449*** 0.587*** 0.416*** 0.505*** 0.384*** 0.639***
		(0.099)	(0.107)	(0.093)	(0.121)	(0.089)	(0.166)
	Structural reforms index, average [T-5,T]	-0.026	0.264***	-0.025	0.187*	-0.028	0.337**
		(0.122)	(0.101)	(0.115)	(0.109)	(0.108)	(0.160)
	Trade diversification and quality index, average [T-5,T] 0.210*** 0.152** 0.170***	0.162*	0.181***	0.028
		(0.068)	(0.076)	(0.065)	(0.086)	(0.061)	(0.115)
	Investments, labor and productivity index, average [T-5,T]	0.083	0.239***	0.079	0.177**	0.059	0.296**
		(0.068)	(0.078)	(0.065)	(0.087)	(0.061)	(0.123)
	Africa (Base level)
	Asia and Pacific 0.098*** 0.097*** 0.067*** 0.062*** 0.123*** 0.132***
		(0.022)	(0.022)	(0.022)	(0.021)	(0.023)	(0.030)
	Europe	0.034*	0.034*	0.023	0.021	0.064*** 0.072***
		(0.019)	(0.019)	(0.021)	(0.019)	(0.023)	(0.028)
	Middle East and Central Asia	0.009	0.009	0.004	0.004	-0.008	-0.010
		(0.016)	(0.016)	(0.017)	(0.016)	(0.013)	(0.015)
	Americas and Caribbean	-0.012	-0.012	-0.011	-0.011	-0.012	-0.015
		(0.013)	(0.013)	(0.015)	(0.014)	(0.012)	(0.014)
	1980-1989 (Base level)
	1990-1999	-0.008	-0.008	-0.005	-0.005	0.007	0.008
		(0.011)	(0.011)	(0.011)	(0.010)	(0.012)	(0.014)
	2000-2010
		0.070*** 0.069*** 0.049*** 0.046*** 0.044*** 0.050***
	Advanced Economies (Base level) (0.016)	(0.016)	(0.016)	(0.016)	(0.015)	(0.017)
	Low-Income Developing Countries	0.015	0.015	-0.002	-0.002	0.031*	0.037**
		(0.017)	(0.017)	(0.018)	(0.017)	(0.017)	(0.018)
	Emerging Economies 0.089*** 0.089*** 0.083*** 0.077*** 0.118*** 0.131***
		(0.017)	(0.016)	(0.018)	(0.016)	(0.017)	(0.025)
	Observations	3413	3413	2862	2862	2599	2599
	# of GSs	125	125	100	100	87	87
	Unconditional probability	0.037	0.037	0.037	0.037	0.037	0.037
	Mean(duration of GSs) 13.784	13.784	13.784	13.784	13.784	13.784
	Mean(Avg. growth during GSs)	5.636	5.636	5.636	5.636	5.636	5.636
	# of Countries	169	169	165	165	160	160
	Pseudo R2	0.174	0.174	0.145	0.145	0.169	0.169
	Classification power 90.214	90.214	90.321	90.321	90.997	90.997
	AUROC	0.779	0.779	0.760	0.760	0.776	0.776
	BIC 2085.833 2085.833 1788.677 1788.677 1563.211 1563.211
	Notes:

Table D .

 D 22: Predicting growth surges, robustness checks, alternative set of growth surges, per capita income growth accelerates by at least 1 percentage point Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after)	0.007	0.007	-0.008	-0.008	0.036*	0.036*
		(0.012)	(0.012)	(0.011)	(0.011)	(0.020)	(0.020)
	External factors and endowments index, average [T-5,T]	0.044	0.597***	0.108	0.429***	0.075	0.723***
		(0.116)	(0.121)	(0.108)	(0.132)	(0.109)	(0.198)
	Institutions index, average [T-5,T]	0.091	0.049	0.070	0.073	0.070	-0.081
		(0.060)	(0.053)	(0.054)	(0.052)	(0.053)	(0.099)
	Macroeconomic stability index, average [T-5,T] 0.386*** 0.528*** 0.347*** 0.383*** 0.346*** 0.682***
		(0.098)	(0.100)	(0.089)	(0.110)	(0.089)	(0.160)
	Structural reforms index, average [T-5,T] 0.240** 0.330*** 0.211** 0.272*** 0.240** 0.446***
		(0.110)	(0.086)	(0.101)	(0.088)	(0.100)	(0.159)
	Trade diversification and quality index, average [T-5,T] 0.243*** 0.154** 0.201***	0.081	0.212***	0.148
		(0.069)	(0.075)	(0.064)	(0.082)	(0.063)	(0.123)
	Investments, labor and productivity index, average [T-5,T] 0.155**	0.159*	0.131*	0.087	0.140**	0.235*
		(0.076)	(0.081)	(0.070)	(0.093)	(0.070)	(0.130)
	Africa (Base level)
	Asia and Pacific 0.084*** 0.082*** 0.047**	0.041** 0.116*** 0.139***
		(0.022)	(0.021)	(0.022)	(0.020)	(0.024)	(0.032)
	Europe 0.050** 0.050**	0.035	0.031	0.081*** 0.100***
		(0.021)	(0.020)	(0.023)	(0.020)	(0.025)	(0.032)
	Middle East and Central Asia	0.016	0.016	0.003	0.002	0.005	0.007
		(0.017)	(0.017)	(0.018)	(0.016)	(0.014)	(0.019)
	Americas and Caribbean	-0.017	-0.018	-0.025*	-0.022*	-0.013	-0.018
		(0.013)	(0.013)	(0.015)	(0.013)	(0.012)	(0.016)
	1980-1989 (Base level)
	1990-1999	-0.004	-0.004	-0.005	-0.004	-0.000	-0.000
		(0.011)	(0.011)	(0.011)	(0.010)	(0.012)	(0.016)
	2000-2010
		0.079*** 0.077*** 0.051*** 0.044*** 0.058*** 0.072***
	Advanced Economies (Base level) (0.017)	(0.017)	(0.017)	(0.016)	(0.017)	(0.021)
	Low-Income Developing Countries	0.017	0.017	-0.005	-0.005	0.024	0.032
		(0.019)	(0.018)	(0.019)	(0.017)	(0.018)	(0.022)
	Emerging Economies 0.096*** 0.094*** 0.093*** 0.081*** 0.129*** 0.156***
		(0.018)	(0.016)	(0.019)	(0.016)	(0.019)	(0.029)
	Observations	3534	3534	2936	2936	2692	2692
	# of GSs	135	135	103	103	95	95
	Unconditional probability	0.038	0.038	0.038	0.038	0.038	0.038
	Mean(duration of GSs) 12.630	12.630	12.630	12.630	12.630	12.630
	Mean(Avg. growth during GSs)	5.606	5.606	5.606	5.606	5.606	5.606
	# of Countries	168	168	167	167	163	163
	Pseudo R2	0.167	0.167	0.140	0.140	0.155	0.155
	Classification power 89.360	89.360	89.952	89.952	90.045	90.045
	AUROC	0.768	0.768	0.745	0.745	0.756	0.756
	BIC 2271.786 2271.786 1885.306 1885.306 1731.230 1731.230
	Notes:

Table D .

 D 23: Predicting growth surges, robustness checks, alternative set of growth surges, per capita income growth accelerates by at least 3 percentage points Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The tableshows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after) 0.021** 0.021**	0.012	0.012	0.035**	0.035**
		(0.008)	(0.008)	(0.009)	(0.009)	(0.014)	(0.014)
	External factors and endowments index, average [T-5,T]	0.098	0.337***	0.105	0.282***	0.106*	0.352**
		(0.067)	(0.092)	(0.068)	(0.108)	(0.059)	(0.140)
	Institutions index, average [T-5,T] 0.084**	0.021	0.079**	0.056	0.059*	-0.104
		(0.039)	(0.042)	(0.038)	(0.044)	(0.032)	(0.086)
	Macroeconomic stability index, average [T-5,T] 0.183*** 0.362*** 0.179*** 0.373*** 0.147*** 0.340**
		(0.061)	(0.084)	(0.060)	(0.100)	(0.052)	(0.138)
	Structural reforms index, average [T-5,T] 0.148* 0.244*** 0.148*	0.201**	0.123*	0.280**
		(0.079)	(0.073)	(0.078)	(0.080)	(0.067)	(0.128)
	Trade diversification and quality index, average [T-5,T]	0.041	0.169***	0.033	0.035	0.021	0.272***
		(0.046)	(0.058)	(0.046)	(0.067)	(0.039)	(0.101)
	Investments, labor and productivity index, average [T-5,T] 0.092** 0.252*** 0.096**	0.193**	0.081**	0.245**
		(0.046)	(0.066)	(0.046)	(0.076)	(0.040)	(0.116)
	Africa (Base level)
	Asia and Pacific 0.048*** 0.059***	0.020	0.022	0.059*** 0.093***
		(0.013)	(0.017)	(0.015)	(0.017)	(0.013)	(0.024)
	Europe	-0.005	-0.006	-0.017	-0.020	0.022*	0.037*
		(0.010)	(0.013)	(0.012)	(0.015)	(0.012)	(0.020)
	Middle East and Central Asia 0.033** 0.042**	0.022	0.024	0.020**	0.033*
		(0.013)	(0.017)	(0.015)	(0.017)	(0.010)	(0.018)
	Americas and Caribbean	-0.010	-0.013	-0.021** -0.025*	-0.002	-0.003
		(0.008)	(0.011)	(0.011)	(0.013)	(0.007)	(0.012)
	1980-1989 (Base level)
	1990-1999	0.008	0.011	0.004	0.004	0.005	0.009
		(0.007)	(0.009)	(0.007)	(0.009)	(0.008)	(0.013)
	2000-2010
		0.055*** 0.069*** 0.046*** 0.052*** 0.030*** 0.048***
	Advanced Economies (Base level) (0.011)	(0.013)	(0.011)	(0.014)	(0.010)	(0.016)
	Low-Income Developing Countries	-0.014	-0.018	-0.020	-0.024	0.010	0.018
		(0.011)	(0.015)	(0.012)	(0.016)	(0.010)	(0.016)
	Emerging Economies 0.043*** 0.053*** 0.037*** 0.041*** 0.078*** 0.122***
		(0.012)	(0.014)	(0.013)	(0.014)	(0.012)	(0.025)
	Observations	4094	4094	3418	3418	3123	3123
	# of GSs	97	97	77	77	66	66
	Unconditional probability	0.024	0.024	0.024	0.024	0.024	0.024
	Mean(duration of GSs) 10.835	10.835	10.835	10.835	10.835	10.835
	Mean(Avg. growth during GSs)	6.711	6.711	6.711	6.711	6.711	6.711
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.189	0.189	0.156	0.156	0.181	0.181
	Classification power 93.454	93.454	93.739	93.739	94.012	94.012
	AUROC	0.796	0.796	0.774	0.774	0.798	0.798
	BIC 1886.153 1886.153 1605.683 1605.683 1394.186 1394.186
	Notes:

Table D .

 D 24: Predicting growth surges, robustness checks, alternative set of growth surges, post-7 years of growth surges per capita income growth exceeds 2.5 percentage points

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	IMF-supported programs dummy (include 2 years after)	0.015	0.015	-0.003	-0.003	0.039**	0.039**
		(0.012)	(0.012)	(0.011)	(0.011)	(0.019)	(0.019)
	External factors and endowments index, average [T-5,T] 0.232** 0.545*** 0.269**	0.353**	0.256** 0.658***
		(0.112)	(0.123)	(0.108)	(0.141)	(0.105)	(0.182)
	Institutions index, average [T-5,T] 0.124**	0.081	0.115**	0.103*	0.105**	-0.056
		(0.058)	(0.054)	(0.054)	(0.053)	(0.052)	(0.099)
	Macroeconomic stability index, average [T-5,T] 0.365*** 0.522*** 0.341*** 0.461*** 0.315*** 0.618***
		(0.086)	(0.100)	(0.081)	(0.116)	(0.079)	(0.152)
	Structural reforms index, average [T-5,T]	0.119	0.245***	0.106	0.249**	0.102	0.271*
		(0.100)	(0.093)	(0.094)	(0.099)	(0.090)	(0.151)
	Trade diversification and quality index, average [T-5,T] 0.249***	0.050	0.208***	0.080	0.229***	-0.091
		(0.066)	(0.077)	(0.063)	(0.085)	(0.060)	(0.118)
	Investments, labor and productivity index, average [T-5,T] 0.135* 0.303*** 0.134**	0.062	0.123*	0.488***
		(0.069)	(0.081)	(0.066)	(0.092)	(0.064)	(0.124)
	Africa (Base level)
	Asia and Pacific 0.118*** 0.124*** 0.100*** 0.094*** 0.148*** 0.180***
		(0.020)	(0.022)	(0.021)	(0.022)	(0.022)	(0.031)
	Europe 0.055*** 0.060*** 0.055*** 0.052*** 0.082*** 0.105***
		(0.018)	(0.019)	(0.020)	(0.018)	(0.022)	(0.029)
	Middle East and Central Asia 0.030**	0.033*	0.030*	0.029*	0.011	0.015
		(0.015)	(0.017)	(0.016)	(0.016)	(0.012)	(0.017)
	Americas and Caribbean	0.002	0.002	0.007	0.007	0.006	0.009
		(0.011)	(0.013)	(0.013)	(0.012)	(0.011)	(0.016)
	1980-1989 (Base level)
	1990-1999	-0.000	-0.000	-0.002	-0.002	0.006	0.008
		(0.011)	(0.012)	(0.011)	(0.010)	(0.012)	(0.016)
	2000-2010
		0.089*** 0.094*** 0.066*** 0.062*** 0.072*** 0.090***
	Advanced Economies (Base level) (0.016)	(0.018)	(0.017)	(0.017)	(0.017)	(0.021)
	Low-Income Developing Countries	0.027	0.030	0.018	0.017	0.038**	0.051**
		(0.018)	(0.019)	(0.019)	(0.017)	(0.018)	(0.021)
	Emerging Economies 0.088*** 0.094*** 0.086*** 0.081*** 0.117*** 0.147***
		(0.016)	(0.016)	(0.016)	(0.014)	(0.016)	(0.025)
	Observations	3682	3682	3068	3068	2828	2828
	# of GSs	141	141	109	109	102	102
	Unconditional probability	0.038	0.038	0.038	0.038	0.038	0.038
	Mean(duration of GSs) 11.362	11.362	11.362	11.362	11.362	11.362
	Mean(Avg. growth during GSs)	5.664	5.664	5.664	5.664	5.664	5.664
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.170	0.170	0.145	0.145	0.173	0.173
	Classification power 89.272	89.272	89.668	89.668	90.559	90.559
	AUROC	0.775	0.775	0.757	0.757	0.770	0.770
	BIC

  1418.953 Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

	IMF-supported programs dummy (include 2 years after)	0.012	0.012	0.000	0.000	0.024	0.024
		(0.010)	(0.010)	(0.010)	(0.010)	(0.017)	(0.017)
	External factors and endowments index, average [T-5,T] -0.366***	-0.189	-0.302**	-0.123	-0.391***	-0.319
		(0.137)	(0.160)	(0.146)	(0.163)	(0.149)	(0.227)
	Institutions index, average [T-5,T]	-0.086	-0.112	-0.050	-0.028	-0.058	-0.259**
		(0.058)	(0.069)	(0.058)	(0.062)	(0.059)	(0.129)
	Macroeconomic stability index, average [T-5,T] 0.269*** 0.451*** 0.287*** 0.346*** 0.248*** 0.586***
		(0.087)	(0.106)	(0.091)	(0.110)	(0.091)	(0.170)
	Structural reforms index, average [T-5,T]	0.042	0.231**	0.045	0.219**	0.002	0.249
		(0.100)	(0.093)	(0.102)	(0.093)	(0.106)	(0.158)
	Trade diversification and quality index, average [T-5,T] 0.217***	0.126	0.169**	0.114	0.232***	0.012
		(0.074)	(0.084)	(0.077)	(0.093)	(0.078)	(0.113)
	Investments, labor and productivity index, average [T-5,T]	0.034	0.117	0.012	0.014	0.026	0.275**
		(0.074)	(0.089)	(0.078)	(0.091)	(0.076)	(0.131)
	# of GSs	21	19	21	10	21	10
	Panel C: 2000-2010
	IMF-supported programs dummy (include 2 years after) 0.036**	0.036**	0.013	0.013	0.037	0.037
		(0.016)	(0.016)	(0.015)	(0.015)	(0.022)	(0.022)
	External factors and endowments index, average [T-5,T] 0.768*** 1.217*** 0.674*** 0.960*** 0.855*** 1.255***
		(0.151)	(0.164)	(0.144)	(0.207)	(0.148)	(0.220)
	Institutions index, average [T-5,T] 0.328*** 0.336*** 0.250**	0.287** 0.488*** 0.291**
		(0.111)	(0.102)	(0.109)	(0.115)	(0.120)	(0.136)
	Macroeconomic stability index, average [T-5,T] 0.365*** 0.622*** 0.297**	0.396**	0.242*	0.652***
		(0.131)	(0.155)	(0.129)	(0.184)	(0.130)	(0.187)
	Structural reforms index, average [T-5,T] 0.441*** 0.775*** 0.425*** 0.707*** 0.338** 0.740***
		(0.162)	(0.174)	(0.159)	(0.198)	(0.160)	(0.222)
	Trade diversification and quality index, average [T-5,T] 0.309**	0.132	0.322**	0.235	0.330**	-0.046
		(0.135)	(0.136)	(0.144)	(0.164)	(0.140)	(0.163)
	Investments, labor and productivity index, average [T-5,T]	0.195*	0.337**	0.205*	0.208	0.067	0.424***
		(0.112)	(0.132)	(0.109)	(0.154)	(0.115)	(0.162)
	# of GSs	22	43	22	21	22	25
	Observations	3763	3763	3136	3136	2875	2875
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.220	0.220	0.188	0.188	0.232	0.232
	Classification power	90.539	90.539	91.231	91.231	91.652	91.652
	AUROC	0.820	0.820	0.800	0.800	0.821	0.821
	BIC

  Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

							1691.199
	Decades dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Regions dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Levels of development dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Notes:

Table D .

 D 27: Predicting growth surges, sensitivity by regions

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	Panel A: Africa
	IMF-supported programs dummy (include 2 years after)	0.020**	0.020**	0.008	0.008	0.029***	0.029***
		(0.009)	(0.009)	(0.010)	(0.010)	(0.011)	(0.011)
	External factors and endowments index, average [T-5,T]	0.316**	0.656***	0.338**	0.584***	0.356***	0.620***
		(0.129)	(0.149)	(0.172)	(0.217)	(0.119)	(0.160)
	Institutions index, average [T-5,T]	0.011	0.006	0.018	0.052	0.038	-0.027
		(0.062)	(0.061)	(0.082)	(0.091)	(0.052)	(0.064)
	Macroeconomic stability index, average [T-5,T]	0.293***	0.410***	0.199	0.239	0.134*	0.371***
		(0.098)	(0.101)	(0.123)	(0.147)	(0.081)	(0.106)
	Structural reforms index, average [T-5,T]	0.208	0.330**	0.252	0.332*	0.078	0.254*
		(0.130)	(0.129)	(0.189)	(0.198)	(0.108)	(0.132)
	Trade diversification and quality index, average [T-5,T]	0.008	-0.029	0.005	-0.034	-0.044	-0.069
		(0.080)	(0.083)	(0.109)	(0.123)	(0.066)	(0.086)
	Investments, labor and productivity index, average [T-5,T]	0.183**	0.271***	0.267***	0.288**	0.098	0.318***
		(0.079)	(0.091)	(0.101)	(0.137)	(0.062)	(0.098)
	# of GSs	6	20	6	5	6	15
	Panel B: Asia and Pacific
	IMF-supported programs dummy (include 2 years after)	0.020	0.020	-0.005	-0.005	0.057*	0.057*
		(0.021)	(0.021)	(0.020)	(0.020)	(0.034)	(0.034)
	External factors and endowments index, average [T-5,T]	-0.343	0.297	-0.375	0.148	-0.114	0.095
		(0.319)	(0.330)	(0.318)	(0.317)	(0.379)	(0.469)
	Institutions index, average [T-5,T]	0.056	0.044	0.036	0.105	-0.022	-0.250
		(0.157)	(0.156)	(0.151)	(0.140)	(0.191)	(0.253)
	Macroeconomic stability index, average [T-5,T]	0.194	0.343	0.289	0.342	0.237	0.697*
		(0.283)	(0.308)	(0.278)	(0.295)	(0.333)	(0.410)
	Structural reforms index, average [T-5,T]	0.829***	1.053***	0.564*	0.683**	1.717***	2.142***
		(0.297)	(0.305)	(0.291)	(0.305)	(0.319)	(0.370)
	Trade diversification and quality index, average [T-5,T]	0.277	0.196	0.237	0.139	0.458**	0.466
		(0.170)	(0.205)	(0.162)	(0.191)	(0.184)	(0.296)
	Investments, labor and productivity index, average [T-5,T]	0.178	0.308	0.189	0.203	0.037	0.495
		(0.197)	(0.238)	(0.185)	(0.234)	(0.220)	(0.309)
	# of GSs	16	12	16	5	16	8
	Panel C: Europe
	IMF-supported programs dummy (include 2 years after)	0.021	0.021	0.004	0.004	0.052*	0.052*
		(0.013)	(0.013)	(0.013)	(0.013)	(0.028)	(0.028)
	External factors and endowments index, average [T-5,T]	0.146	0.587***	0.259	0.596***	0.004	0.185
		(0.178)	(0.203)	(0.176)	(0.210)	(0.264)	(0.371)
	Institutions index, average [T-5,T]	0.077	0.075	0.048	0.095	0.268	0.107
		(0.110)	(0.118)	(0.103)	(0.100)	(0.174)	(0.260)
	Macroeconomic stability index, average [T-5,T]	0.831***	0.997***	0.797***	0.827***	1.283***	1.814***
		(0.193)	(0.209)	(0.187)	(0.196)	(0.274)	(0.355)
	Structural reforms index, average [T-5,T]	0.118	0.253*	0.107	0.209	-0.094	0.206
		(0.149)	(0.154)	(0.143)	(0.141)	(0.225)	(0.348)
	Trade diversification and quality index, average [T-5,T]	0.203	0.161	0.166	0.105	0.053	0.053
		(0.142)	(0.166)	(0.135)	(0.150)	(0.205)	(0.301)
	Investments, labor and productivity index, average [T-5,T]	0.076	0.163	0.047	0.063	0.058	0.451
		(0.125)	(0.149)	(0.123)	(0.143)	(0.181)	(0.284)
	# of GSs	17	17	17	13	17	6
						Continued on next page

Table D .27 -continued from previous page

 D Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

	(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	Panel D: Middle East and Central Asia
	IMF-supported programs dummy (include 2 years after)	0.018	0.018	0.002	0.002	0.031*	0.031*
		(0.013)	(0.013)	(0.013)	(0.013)	(0.018)	(0.018)
	External factors and endowments index, average [T-5,T]	0.122	0.562**	0.090	0.434*	0.226	0.403*
		(0.188)	(0.226)	(0.194)	(0.237)	(0.152)	(0.236)
	Institutions index, average [T-5,T]	0.241*	0.253*	0.253**	0.294**	0.305**	0.272
		(0.126)	(0.136)	(0.121)	(0.121)	(0.130)	(0.184)
	Macroeconomic stability index, average [T-5,T]	0.540***	0.681***	0.502***	0.535***	0.430***	0.791***
		(0.168)	(0.203)	(0.163)	(0.190)	(0.150)	(0.260)
	Structural reforms index, average [T-5,T]	0.211	0.355	0.298	0.395	0.044	0.250
		(0.217)	(0.243)	(0.233)	(0.247)	(0.168)	(0.251)
	Trade diversification and quality index, average [T-5,T]	0.342***	0.313**	0.318***	0.251*	0.125	0.156
		(0.120)	(0.151)	(0.121)	(0.149)	(0.099)	(0.179)
	Investments, labor and productivity index, average [T-5,T]	-0.028	0.050	-0.097	-0.076	0.005	0.248
		(0.149)	(0.183)	(0.150)	(0.171)	(0.122)	(0.210)
	# of GSs	11	11	11	8	11	4
	Panel E: Americas and Caribbean
	IMF-supported programs dummy (include 2 years after)	0.017**	0.017**	0.007	0.007	0.023*	0.023*
		(0.008)	(0.008)	(0.008)	(0.008)	(0.012)	(0.012)
	External factors and endowments index, average [T-5,T]	0.452***	0.818***	0.482***	0.730***	0.200	0.389*
		(0.156)	(0.190)	(0.157)	(0.183)	(0.128)	(0.231)
	Institutions index, average [T-5,T]	0.100*	0.114*	0.075	0.110**	-0.068	-0.186
		(0.057)	(0.064)	(0.052)	(0.054)	(0.059)	(0.127)
	Macroeconomic stability index, average [T-5,T]	0.135	0.222**	0.123	0.161*	0.078	0.280*
		(0.083)	(0.106)	(0.080)	(0.094)	(0.063)	(0.161)
	Structural reforms index, average [T-5,T]	-0.131	-0.079	-0.054	0.008	-0.059	0.035
		(0.122)	(0.135)	(0.115)	(0.119)	(0.111)	(0.189)
	Trade diversification and quality index, average [T-5,T]	0.018	-0.016	-0.057	-0.097	0.049	0.073
		(0.076)	(0.089)	(0.077)	(0.084)	(0.063)	(0.120)
	Investments, labor and productivity index, average [T-5,T]	0.041	0.101	-0.001	0.010	0.067	0.264*
		(0.089)	(0.097)	(0.093)	(0.091)	(0.076)	(0.139)
	# of GSs	8	14	8	12	8	2
	Observations	3763	3763	3136	3136	2875	2875
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.211	0.211	0.191	0.191	0.227	0.227
	Classification power	90.114	90.114	90.976	90.976	91.304	91.304
	AUROC	0.81	0.81	0.799	0.799	0.813	0.813
	BIC	2379.062	2379.062	1991.916	1991.916	1796.554	1796.554
	Decades dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Regions dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Levels of development dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Notes:

Table D .

 D 28: Predicting growth surges, sensitivity by levels of development

		(1)	(2)	(3)	(4)	(5)	(6)
		All programs	GRA programs	PRGT programs
		No IMF	IMF	No IMF	GRA	No IMF	PRGT
	Panel A: Low-income countries
	IMF-supported programs dummy (include 2 years after) 0.024**	0.024**	0.011	0.011	0.029**	0.029**
		(0.009)	(0.009)	(0.011)	(0.011)	(0.014)	(0.014)
	External factors and endowments index, average [T-5,T] 0.303** 0.609***	0.098	0.284	0.452** 0.622***
		(0.146)	(0.132)	(0.188)	(0.222)	(0.190)	(0.150)
	Institutions index, average [T-5,T]	-0.004	-0.004	-0.068	-0.060	0.033	-0.056
		(0.077)	(0.071)	(0.110)	(0.119)	(0.093)	(0.083)
	Macroeconomic stability index, average [T-5,T] 0.345*** 0.521*** 0.419*** 0.564*** 0.352** 0.509***
		(0.124)	(0.113)	(0.151)	(0.181)	(0.158)	(0.128)
	Structural reforms index, average [T-5,T]	0.267* 0.361***	0.134	0.171	0.152	0.367***
		(0.143)	(0.129)	(0.198)	(0.218)	(0.178)	(0.140)
	Trade diversification and quality index, average [T-5,T]	0.003	-0.041	0.018	-0.069	-0.105	-0.036
		(0.094)	(0.088)	(0.128)	(0.143)	(0.118)	(0.099)
	Investments, labor and productivity index, average [T-5,T]	0.129	0.257*** 0.235*	0.298*	0.079	0.306***
		(0.100)	(0.095)	(0.136)	(0.164)	(0.114)	(0.108)
	# of GSs	7	27	7	3	7	25
	Panel B: Emerging markets
	IMF-supported programs dummy (include 2 years after) 0.030*	0.030*	0.011	0.011	0.052	0.052
		(0.017)	(0.017)	(0.018)	(0.018)	(0.032)	(0.032)
	External factors and endowments index, average [T-5,T] 0.408** 0.872*** 0.533*** 0.860*** 0.495**	0.638
		(0.176)	(0.215)	(0.180)	(0.222)	(0.195)	(0.443)
	Institutions index, average [T-5,T]	0.099	0.112	0.105	0.130	0.065	-0.107
		(0.091)	(0.094)	(0.090)	(0.089)	(0.098)	(0.233)
	Macroeconomic stability index, average [T-5,T] 0.356*** 0.576*** 0.319** 0.519*** 0.343**	0.493
		(0.128)	(0.170)	(0.128)	(0.171)	(0.140)	(0.376)
	Structural reforms index, average [T-5,T]	0.229	0.322*	0.231	0.281*	0.409**	0.793**
		(0.168)	(0.169)	(0.168)	(0.171)	(0.193)	(0.388)
	Trade diversification and quality index, average [T-5,T] 0.252**	0.204	0.209**	0.058	0.345***	0.595*
		(0.105)	(0.138)	(0.106)	(0.138)	(0.115)	(0.319)
	Investments, labor and productivity index, average [T-5,T]	0.119	0.306**	0.099	0.177	0.124	0.523*
		(0.119)	(0.145)	(0.122)	(0.148)	(0.130)	(0.292)
	# of GSs	36	43	36	36	36	10
	Panel C: Advanced economies
	IMF-supported programs dummy (include 2 years after) 0.021*	0.021*	0.009	0.009	-	-
		(0.012)	(0.012)	(0.011)	(0.011)	-	-
	External factors and endowments index, average [T-5,T]	-0.110	0.132	-0.047	0.131	-	-
		(0.171)	(0.239)	(0.159)	(0.215)	-	-
	Institutions index, average [T-5,T] 0.236**	0.264*	0.211**	0.231*	-	-
		(0.114)	(0.141)	(0.102)	(0.122)	-	-
	Macroeconomic stability index, average [T-5,T] 0.931*** 1.143*** 0.842*** 0.979***	-	-
		(0.191)	(0.244)	(0.179)	(0.239)	-	-
	Structural reforms index, average [T-5,T]	0.062	0.110	0.072	0.100	-	-
		(0.133)	(0.165)	(0.122)	(0.144)	-	-
	Trade diversification and quality index, average [T-5,T] 0.348*** 0.341** 0.303***	0.222	-	-
		(0.118)	(0.158)	(0.110)	(0.140)	-	-
	Investments, labor and productivity index, average [T-5,T]	0.050	0.161	0.052	0.097	-	-
		(0.114)	(0.163)	(0.104)	(0.147)	-	-
	# of GSs	15	4	15	4	-	-
	Observations	3763	3763	3136	3136	2875	2875
	# of Countries	169	169	168	168	165	165
	Pseudo R2	0.204	0.204	0.18	0.18	0.21	0.21
	Classification power 90.539	90.539	91.04	91.04	91.409	91.409
	AUROC	0.806	0.806	0.792	0.792	0.807	0.807
	BIC 2298.035 2298.035 1917.444 1917.444 1731.014 1731.014
	Decades dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Regions dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Levels of development dummies	Yes	Yes	Yes	Yes	Yes	Yes

Notes: Logit estimations. Robust standards errors are in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. The table shows the average marginal effect of all of the six broad determinants of growth surges with and with IMF-supported programs.

  This dissertation comes to a period where, following the Covid-19 pandemic crisis, necessary containment measures put a drag on businesses and jobs, uncertainty is rising and market sentiment is freaking, capital flows are pulling back, commodity prices are decreasing, countries are engaging in large recourse plans amounting to about $8 trillion (as estimated by the IMF), despite a lower fiscal space than the pre-GFC crisis, and monetary policies are constrained by the zero lower bound interest rate. Overall, we have come to a time where the crisis looming on the horizon is closer than what we think. Indeed, this "time is different" as said byReinhart and Rogoff (2009), not the ironical "this time is different" from the Great moderation; this is a serious threat to the global economy. And, just as the health crisis hits vulnerable people hardest, the unfolding economic crisis that it has generated is expected to hit vulnerable countries hardest.Yet, the future is unknowable; indeed forecasting crises has claimed only modest success to date. However, the "Lucas critique" is not always verified, by training and temperament, and with historical analyses, there is a tremendous scope to think ahead. The analyses of this dissertation allow us to think ahead of the possible consequences of the crisis that is materializing. But more importantly, to draw some key takeaways from history.

	294	Chapter . General Conclusion
	Countries when addressing the economic bottlenecks to growth, particularly
	in the aftermath of financial crises can significantly jump-start a sustained period of economic
	growth, which can have quantitatively huge implications for national income, poverty, and more
	broadly, for population well-being.

Chapter 2 suggests that, if not appropriate policies are taken, the "Great Lockdown" crisis may be associated with a far larger collapse of international trade never recorded, in particular of the trade in manufacturing goods and services compared to the 2008-09 GFC crisis. This may happen because this crisis is associated with both a demand-side and supply-side shock while the GFC crisis was only a demand-side shock. More specifically, this chapter studies the response of different types of trade (i.e. agricultural, mining, and manufactured goods, and services) following various types of financial crises (i.e. debt, banking, and currency crises) in 41 emerging countries over the period 1980-2018. It uses a combination of impact assessment

  g. due to higher debt, permanent deficit resulting from the GFC and nominal interest rates close to zero, countries may suffer a prolonged collapse of economic growth than previously witnessed, again if appropriate and coordinated policies not implemented. More specifically, this chapter studies how fiscal policy space shapes the dynamics of output losses in the aftermath of financial crises and normal recessions in a sample of 35 developing and 56 emerging countries over the period 1985-2017. It builds a new index of fiscal space and applies a combination of local projections models and impact assessment to identify a causal effect. It reveals that the availability of fiscal space in the aftermath of financial crises and normal recessions generates a mixed fiscal environment with different output losses of shocks. In countries with enough fiscal space, governments can enact credible fiscal policy expansion by increasing their deficit and using their fiscal space to alleviate the costs of financial crises and normal recessions. In such a situation, private consumption and investment, as well as net capital inflows, increase, which favors a rapid recovery. In countries with limited fiscal space, the story is different and painful; governments immediately trade output stabilization goals out to address the debt sustainability issues while implementing fiscal consolidations, which deepens the recessionary forces. Besides, in these countries, private consumption and investment, as well as net capital inflows, are depressed, and recovery, if any, is a distant and uncertain prospect. Just like in physics, i.e., momentum naturally winds down rather than up unless outside energy is applied, countries that neglect the right disciplines will Chapter . General Conclusion not only fall but will slope there unless they have fiscal space that allows them to boost their economy in downturns. This chapter suggests that governments and policymakers need to be more than proactive to learn lessons from the past, fix the roof while the sun is shining, build fiscal buffers, reduce debt and deficit, increase tax base and revenues, and lock the drinks cabinet when the economy is starting to improve substantially to be able to appropriately respond to the next crisis looming on the horizon.Chapter 4 gives hope in this difficult time; it shows some successful stories of economic growth and discusses what drives them. It identifies 132 episodes of growth surges in 117 countries over the period 1980-2010. It applies non-parametric and binary outcomes model and finds that many growth determinants have a significant and positive effect on the probability of initiating growth. Specifically, improvements in macroeconomic stability and external factors and endowments favor a higher probability of growth surge. They are followed by structural reforms, investments, labor and productivity, trade diversification and quality, and lastly by institutions. When looking at the two-way interactions of growth determinants, it shows that countries can maximize the likelihood of igniting growth surges if they jointly achieve significant improvements in macroeconomic stability and external conditions and endowments, on one hand, and other determinants, on the other hand. Besides, it reveals that significant changes in macroeconomic stability, and to some extent, external factors and endowments may be considered as dominant strategies to ignite a growth surge, as no improvements in these determinants, generally constraint the other determinants to have a smaller effect on growth surges. Therefore, macroeconomic policies and structural reforms work. Unfortunately, we have witnessed a significant decrease in the pace of structural reforms and an increase in deregulations leading to excessive build-up of risks in countries since the 2000s. With the recommendation of chapter 3 in mind, fix the roof when the sun is shining, countries have to engage in large reforms programs following the Covid-19 crisis to strengthen their resilience to shocks and jump-start sustained growth and reconnect with economic wealth.Chapter 5 reveals that the countries' collaboration and joint work with multilateral institutions like the IMF could deliver sizable medium-to long-term output gains. More specifically, this chapter by focusing on the sample and methodology of chapter 4 engages and contributes to the debate on the effectiveness of the IMF in promoting growth. It concludes that IMF-supported programs (more PRGT than GRA programs) have significantly and positively contributed to boosting medium-to long-term growth in countries, particularly in the 2000s than previous decades, and in all countries around the world, regardless of their geographical location and levels of development. It has done so by pursuing macroeconomic stability and designing structural

This is indeed one illustration of various financial crises that have consistently animated the lives of countries around the world since the Great depression of 1932, only to date from then. These crises include, among others, the Suez crisis of the 1950s, the Oil shocks of the 1970s, the International debt crisis of the 1980s or "lost decade", particularly in Latin America, Eastern Europe, and Africa, the Tequila crisis starting in Mexico in the mid-1990s, the Asian financial and Russian crises of the second half of the 1990s, the Global financial crisis of

2008-09, the European debt crisis, and the current Covid-19 pandemiccrisis. 2 Recall that the annual growth rate was only 0.7% per year over the period1960-1990. 3 With the help of multilateral institutions and strong government ownership, India quietly undertook a wave of liberalizations in the 1980s that is known as the "liberalization by stealth". First, India pursued the relaxation of import controls through the Open General Licensing (OGL), mostly accompanied by a reduction of tariffs. Having disappeared, India reintroduced in 1976 the OGL list with 79 capital goods items on it that was expanded to around 1,329 capital goods and 949 intermediate goods in 1990. Second, the liberalization consisted of the reduction of canalized imports, i.e. the reduction of the monopoly rights of the government for the import of certain items. In the 1980s, canalized non-POL (petroleum, oil, and lubricants) imports declined from 44 to 11 percent of the total non-POL imports. Third, several export incentives were introduced (e.g., in the 1985 budget, 50 percent of business profits attributable to exports were made income tax deductibles; this was extended to 100 percent in the 1988 budget.) and exchange rate policies supported the country's openness to trade. The wave of reforms in the 1980s was accompanied by unsustainable borrowing and public expenditure and rising debt and deficit that ended in a macroeconomic crisis in 1991 (e.g., external debt rose from $20.6 billion in 1980-81 to $64.4 billion in 1989-90, the share of non-concessional debt rose from 42 to 54 percent between 1985 and 1990).

Early studies byDollar (1992);Sachs and Warner (1995);Edwards (1998), and Frankel and Romer (1999) suggest that trade increases income, a result confirmed more recently byRodríguez and Rodrik (2000) and Feyrer (2009a,b). Besides, international trade was also found to support overall and firms productivity or real consumption, and to reduce poverty (see e.g.Bernard and Jensen 1999; Pavcnik 2002; Trefler 2004; Burstein and Cravino 2015; Edmond et al. 2015; Johns et al. 2015).

See e.g.Berman and Martin (2012) and Ariu (2016) for an extensive discussion of these two channels.

In the aftermath of the recent global financial crisis of 2008-09, there is a growing work on the fiscal multipliers when monetary space winds up at the zero lower bound on policy rates. In such circumstances,Christiano et al. (2011) find that fiscal multipliers on output exceed two or even three; see also, Woodford (2011);[START_REF] Erceg | [END_REF].

Early studies byDollar (1992);Sachs and Warner (1995);Edwards (1998), and Frankel and Romer (1999) suggest that trade increases income, a result confirmed more recently byRodríguez and Rodrik (2000) and Feyrer (2009a,b). Besides, international trade was also found to support overall and firms productivity or real consumption, and to reduce poverty (see e.g.Bernard and Jensen 1999; Pavcnik 2002; Trefler 2004; Burstein and Cravino 2015; Edmond et al. 2015; Johns et al. 2015).

See e.g.Berman and Martin (2012) and Ariu (2016) for an extensive discussion of these two channels.

Tables A.3 to A.5 in section A.3.1 reveal that countries that experience financial crises present different fundamentals compared with countries that do not.

The literature has by now emphasized that trade may lead to financial crises and play an important role in their contagion; see e.g.Krugman (1979); Eichengreen and Rose (1999);Glick and Rose (1999); Forbes (2001) and[START_REF] Ma | Martinez and Sandleris[END_REF].

We include all the dummies of the financial crises at the same time to account for their correlation and avoid a overestimated bias of financial crises. We do not include country-fixed effects as the variables are included as differences (or growth).

On the export side, the emerging economies' share in world trade has increased from around 19% of world exports in the early 1990s to close to 40% recently. On the import side, the share has increased from 20% to 35% over the same period.

To simplify the exposition, we focus more on the cumulated costs over five years (the dynamics of the costs from the onset of the financial crises until five years ahead are also presented).

Therefore, we can identify financial crises with and without a higher share of manufacturing exports, export diversification, trading partners diversification for the compositional and structural channel; financial crises with or without a higher trading partners' growth for the demand-side channel; financial crises with or without a higher increase of financial development, gross capital inflows, and investors' credit rating risks for the supply-side channel.

This chapter was supported by the LABEX IDGM+ (ANR-10-LABX-14-01) within the program "Investissements d'Avenir" operated by the French National Research Agency (ANR).

In the aftermath of the recent global financial crisis of 2008-09, there is a growing work on the fiscal multipliers when monetary space winds up at the zero lower bound on policy rates. In such circumstances,Christiano et al. (2011) find that fiscal multipliers on output exceed two or even three; see also, Woodford (2011);[START_REF] Erceg | [END_REF].

In fact, due to our local projections methodology, we can only study the costs of financial crises and normal times that occurred between 1985 and 2012 on the output dynamics until 2017.

The average tax revenues over the last five years is computed to smooth for business cycle fluctuations, and it is used as a proxy of the tax base.

A lower threshold (the model sends fewer signals) is associated with an increase of type II errors or missed crises, but at the same time, a decrease of type I errors or false alarms. A higher threshold (the model sends more signals) is associated with a decrease of type II errors or missed crises, but at the same time, an increase of type I errors or false alarms. The thresholds are determined endogenously between the 10 ℎ and 40 ℎ percentiles.

Normal times are obtained as years without any unfolding financial crisis (banking, currency, and fiscal crisis) and normal recession.

There is a literature that shows that growth surges are not the only cause of the reduction of poverty, and sometimes even fail to do so (see, e.g. Munro

2020). However, there is no doubt that it has contributed to growth reduction in many countries.

The use of shorter periods (e.g. between three or five years) may capture pure recovery from a bad shock or business cycles. The use of longer duration may significantly reduce the number of episodes that can be identified. However, I test the robustness of my findings to using episodes of at least six or ten years.

More specifically, if these criteria are met within overlapping periods, then the final year of the latter period has deemed the end of the growth surge (e.g. if the criteria are met in 1980 and 1986 then the end of the growth surge would be 1993).

Due to the nature of filters, episodes can only be obtained over the period 1980-2010. Therefore, my analysis focuses on this period.

This practice is uncommon in the literature and was applied in[START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF]. However, the set of variables that I use in this analysis is quite extensive compared to what is done in the literature.

Clearly, this strategy does not apply to dichotomic variables like the end of financial crises or normal recessions. In this case, I use the year in which the end of the financial crisis or normal recessions occurred as the signal.

Determinants that harm growth such as public debt and inflation are multiplied by minus (-) so that improvements of these determinants also fall in the upper tail of its new distribution. A lower threshold (the model sends fewer signals) is associated with an increase of type II errors or missed crises, but at the same time, a decrease of type I errors or false alarms.

8 A higher threshold (the model sends more signals) is associated with a decrease of type II errors or missed crises, but at the same time, an increase of type I errors or false alarms. The thresholds are determined endogenously between the 70 ℎ

and 90 ℎ percentiles.

Indeed, Hausmann et al. (2005) sustained that "a lot of takeoffs take place when[...] conditions appear not to be particularly favorable[...] And growth takeoffs typically fail to materialize when the conditions are indeed favorable".Gupta et al. (2005) reinforced by stating that "The in-sample predictive power of both the acceleration and sustained acceleration models, however, is relatively poor. Thus, many acceleration episodes occur when the explanatory factors in the model would not predict an acceleration, and many times, even though the variables associated with accelerations are conducive, an episode does not take place. There are factors the model is not capturing, as well as country-level idiosyncratic factors that warrant further investigation to better guide policy. Predicting the timing, or onset, of growth accelerations is even more difficult.", and[START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF] concluded that "growth accelerations are extremely hard to engineer with a high degree of certainty[...] roughly 9 out of

instances failed to ignite a take-off". While I cherish the precautionary conclusion ofGupta et al. (2005), overall, these findings are completely misleading. Indeed, their studies fall short to identify growth surges determinants while accounting for the specificity of each country. Furthermore, they focus on a simple level of the growth determinants or set ad-hoc thresholds to identify favorable conditions while I identify the significant improvements beyond a certain threshold of the growth surges determinants through an optimization process. In a nutshell, accounting for country-specificity and focusing on significant and endogenous improvements of the growth surge determinants solve the "poor predictive power" problem of the literature.10 The lower average marginal effects found when non-starting years of growth surges are included shows that significant changes in growth determinants also occur during the growth surges, which may be necessary to sustain them. I address this research question in a separate paper this is forthcoming.

The lowest beneficial effect found for institutions may be because they are highly inertial and do not vary a lot over time. One other possible explanation may be that we cannot put in place good institutions in five years; it may take more time to alter them while more than five years may be needed after improvements in institutions to ignite growth surges.

For more accurate interpretation, and as my index varies between zero and one, and that the values never reached one, the readers can refer to fig.C.1 that shows the average marginal effects for attainable values of all indexes.

This chapter has been prepared for the IEO's evaluation on Adjustment and Growth in IMF-supported programs. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management. The usual disclaimer applies.

GRA programs stand for General Resources Account programs that comprise a variety of lending programs with different disbursement schedules and maturities depending on the balance of payment needs of the member. PRGT programs stand for Poverty Reduction Growth Trust programs that represent lending programs providing concessional financing support to low-income countries.

The use of shorter periods (e.g. between 3 or

years) may capture pure recovery from a bad shock or business cycles. The use of longer duration may significantly reduce the number of episodes that can be identified. However, we test the robustness of our findings to using episodes of at least 5 or 10 years.

More specifically, if these criteria are met within overlapping periods, then the final year of the latter period has deemed the end of the growth surge (e.g. if the criteria are met in 1980 and 1986 then the end of the growth surge would be 1993).

Recall that the average unconditional probability of all growth surges is 3.6% (slightly above the 2.8% found inHausmann et al. (2005) over the period 1957-1992).

This practice is uncommon in the literature and was applied in Atsebi (2020) and[START_REF] Peruzzi | Atsebi 2020) and general literature on growth[END_REF]. However, the set of variables that we use in this analysis is quite extensive compared to what is done in the literature.

Clearly, this strategy does not apply to dichotomic variables like the end of financial crises or normal recessions. In this case, we use the year in which the end of the financial crisis or normal recessions occurred as the signal.

Determinants that harm growth such as public debt and inflation are multiplied by minus (-) so that improvements of these determinants also fall in the upper tail of its new distribution.

 10 A lower threshold (the model sends fewer signals) is associated with an increase of type II errors or missed crises, but at the same time, a decrease of type I errors or false alarms. A higher threshold (the model sends more signals) is associated with a decrease of type II errors or missed crises, but at the same time, an increase of type I errors or false alarms. The thresholds are determined endogenously between the 70 ℎ and 90 ℎ

percentiles. 11 We use a horizon of 7 and 3 years in the robustness checks.

Note that the incidence is the probability that growth surges were preceded by significant changes of determinants while the effectiveness is the probability that significant changes were followed by growth surges. From the theoretical standpoint, one can think of incidence as a necessary condition to ignite growth surges while the effectiveness relates to a sufficient condition.

This means that the IMF and countries through their agreement may pursue their objective of large improvements in countries' corruption, civil liberties, accountability, and rule of law; large reduction of debt, public deficit, and inflation, exchange rate depreciation to boost competitiveness, and end of financial crises or normal recessions; large structural reforms in agriculture, product market, labor, financial market, trade, and current account openness; and foreign and domestic investments, human capital, and overall productivity.

-21 -25 -

-10

# of GSs

-4 -0 -D.3.2 Baseline : Figure of average marginal effects

-4 -

-12 -2 -

-4 -0 -

[START_REF] Pritchett | sur les moyennes de croissance à long terme pour un pays peuvent masquer des périodes distinctes de succès et d'échec. Il est crucial de ne plus expliquer les moyennes de croissance à long terme pour expliquer les poussées de croissance ; cependant, cela n'est pas simple[END_REF] estimate that the top

acceleration in the last six decades had a net present value of 30 trillion dollars: twice the size of US GDP, and Chen and Ravallion (2010) andRadelet (2016) show that growth surges in China and India, and also in Indonesia, Pakistan, and Vietnam contributed to the reduction in global poverty since 1981.

As shown, e.g. by[START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF] Fazzari et al. (2015), fiscal multipliers are higher in downturns. Besides, as shown, e.g., by DeLong and Summers (2012); Jordà and Taylor (2016) and Fatás and Summers (2018), fiscal consolidations may be self-defeating in downturns as they depress growth and investment further, then failing to reduce and stabilize debt levels.

Also, the IMF (2019) shows that "reforms take several years to deliver, and some of them[...] may entail greater short-term costs when carried out in bad times; these are best implemented under favorable economic conditions and early in authorities' electoral mandate. Reform gains also tend to be larger when governance and access to credit-two binding constraints on growth-are strong, and where labor market informality is higher-because reforms help reduce it.

Il s'agit en effet d'une illustration de diverses crises financières qui ont constamment animé la vie des pays du monde entier depuis la Grande dépression de 1932, jusqu'à ce jour. Ces crises comprennent, entre autres, la crise du canal de Suez des années 1950, les chocs pétroliers des années 1970, la crises internationale de la dette des années 1980 ou la «décennie perdue», notamment en Amérique latine, en Europe de l'Est et en Afrique, la crise de la Tequila commencée au Mexique au milieu des années 90, les crises financière asiatique et russe de la seconde moitié des années 90, la crise financière mondiale de

[2008][2009], la crise de la dette européenne et la crise de la pandémie de Covid-19. 2 Rappelons que le taux de croissance annuel n'était que de 0,7% par an sur la période 1960-1990.3 Avec l'aide des institutions multilatérales et un fort engagement du gouvernement indien, l'Inde a discrètement entrepris une vague de libéralisations dans les années 80, connue sous le nom de "liberalization by stealth". Premièrement, l'Inde a poursuivi l'assouplissement des contrôles à l'importation par le biais de l'Open General Licensing (OGL), principalement accompagné d'une réduction des droits de douane. Ayant disparu, l'Inde a réintroduit en 1976 la liste OGL avec 79 biens d'équipement. La liste a été étendue à environ 1329 biens d'équipement et 949 biens intermédiaires en 1990. Deuxièmement, la libéralisation a consisté en la réduction des importations canalisées, c'est-à-dire la réduction des droits monopolistiques du gouvernement pour l'importation de certains articles. Par exemple, les importations canalisées de gaz, pétrole et lubrifiants sont passées de 44 à 11% du total des importations. Troisièmement, plusieurs incitations à l'exportation ont été introduites (par exemple, en 1985, la loi de finances prévoyait que 50% des bénéfices des entreprises attribuables aux exportations soient déductibles de l'impôt ; ce chiffre a été étendu à 100% dans la loi des finances de 1988) et les politiques de change ont soutenu l'ouverture du pays au commerce à travers une dépréciation favorable à un gain de compétitivité. Cependant, la vague de réformes des années 80 s'est accompagnée d'emprunts et de dépenses publiques insoutenables et d'une augmentation de la dette et du déficit qui ont abouti à une crise macroéconomique en 1991 (par exemple, la dette extérieure est passée de 20,6 milliards de dollars en 1980-1981 à 64,4 milliards de dollars en 1989-1990, la part de la dette non concessionnelle est passée de 42 à 54% entre 1985 et 1990).

A la suite de la récente crise financière mondiale de 2008-2009, il a eu plusieurs travaux sur les multiplicateurs budgétaires lorsque la politique monétaire est contrainte. Dans de telles circonstances, Christiano et al. (2011) constatent que les multiplicateurs budgétaires sur la production dépassent deux, voire trois ; voir aussi, Woodford (2011);[START_REF] Erceg | [END_REF].

Voir par exemple, Berman and Martin (2012) et Ariu (2016) pour une discussion approfondie de ces deux canaux.

Il existe une littérature qui montre que les poussées de croissance ne sont pas la seule cause de la réduction de la pauvreté (voir, par exemple. Munro 2020). Cependant, il ne fait aucun doute qu'elles ont contribué à ralentir la croissance de la pauvreté dans de nombreux pays.

Depuis sa création en 1944, le FMI a aidé plus de 150 pays dans le cadre de 1 300 programmes soutenus par le FMI. Cela comprend la reconstruction du système de paiements du système international dans l'après-Seconde Guerre mondiale, la transition des pays de l'ex-Union soviétique vers des économies de marché et la gestion des diverses crises dans les pays touchés par les chocs pétroliers des années 1970, les crises de la dette latino-américaine et africaine dans les années 1980, les crises financières asiatique et russe des années 90, la crise de la dette européenne au lendemain de la crise financière mondiale de 2008-2009 et la crise pandémique de Covid-19 en 2019-2020.

Remerciements vii

Résumé regions and at the global level as witnessed in the post-GFC period. Finally, our findings support the idea that the supply-side channel is critical to understand the way financial crises shape the dynamics of international trade. When associated with a deterioration of the domestic financial development and external financial conditions, and sudden stops, financial crises will exert a significant and detrimental collapse on international trade, which is mainly driven by the fall of manufacturing and services trade.

Consequently, our paper unveils the panorama of the trade costs of financial crises. Through illustrating the differentiated effects of various financial crises on sectoral and total international trade and by investigating the channels, our analysis contributes to the general understanding of the trade effects of financial crises in emerging countries and provides insightful support for the design and implementation of policies aimed at coping with these effects.

F

This chapter is joint work with Jean-Louis COMBES (CERDI-UCA), Xavier DEBRUN (National Bank of Belgium) and Alexandru MINEA (CERDI-UCA).

Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Notes: Robust standard errors clustered at the country-level in parentheses. * < 0.10, * * < 0.05, * * * < 0.01. AIPW estimates. The dependent variables are 100 times the cumulative change of agricultural, mining, manufacturing, services, and total exports and imports relative to the year prior to the onset of the crisis for years 1-5 after the onset of the crisis, scaled pre-crisis GDP. Accumulated costs over five years. Observations in the treated and control groups are weighted by the propensity scores predicted in the treatment model. Table A.20b: Channels, Cumulative trade costs over five years after banking crises with higher and lower evolution of gross capital inflows characteristics between our treated and control groups). Besides, the occurrence of subsequent shocks may pollute our ATT estimates of the output losses of shocks and the benefits of fiscal space. Indeed, if a subsequent shock occurs in the years following an initial shock, it will intensify the costs of the initial shock and offset the benefits of fiscal space and fiscal policy expansion. Based on that, the output losses of shocks will be overestimated and the benefits of fiscal space underestimated. To account for this possibility, we remove, in robustness checks, all shocks that are followed by subsequent shocks in the five years after they hit the economy.

Inverse Propensity Weighted with Regression Adjustment (IPWRA)

To tackle the endogeneity issues, we estimate an ATT of shocks and fiscal space on output dynamics by combining the inverse propensity weighted with regression adjustment (IPWRA) Romer and Romer (2018) do not provide any treatment of the endogeneity issues; besides, they do not include control variables, as we suggest here. Therefore, their study may suffer from endogeneity.

Chapter . Fiscal space and stabilization policy in developing and emerging countries Chapter . Fiscal space and stabilization policy in developing and emerging countries

These findings show that, also in the context of developing and emerging countries, there is a significant gain to fix the roof when the sun is shining for weathering the storm in recessions;

otherwise, there may be no way out but to adjust at the worst possible time. Just like in physics, i.e., momentum naturally winds down rather than up unless outside energy is applied, countries that neglect the right disciplines will not only fall but will slope there unless they have fiscal space that allows them to boost their economy in downturns. Governments and policymakers need to be more than proactive to learn lessons from the past, fix the roof while the sun is shining, build fiscal buffers, reduce debt and deficit, increase tax base and revenues, and lock the drinks cabinet when the economy is starting to improve substantially to be able to appropriately respond to the next crisis looming on the horizon.

B.1.2 List of financial crises and normal recessions

55 financial crises at start Algeria (1994);Argentina (1995Argentina ( , 2001)); Belarus (2011);Bolivia (1986Bolivia ( , 1994););Brazil (1999);Central African Republic (1994); Colombia (1998); Congo (1992); Costa Rica (1994); Notes: Rubin ( 2002) suggests the use the absolute value of the standardized difference (Sdt-diff) as a balance measure for the first moment, where the balance is defined by absolute values below 0.25. He also suggests the use of the ratio of treated and control variances (Var-ratio) as a balance measure for the second moment, where the balance is defined by values close to 1.0, and variables are out of balance if the variance ratio is greater than 2.0 or less than 0.5.

B.1.3 Data sources of variables

Table B.4: Balance diagnostics between the treated and control groups after weighting, normal recessions Notes: Rubin ( 2002) suggests the use the absolute value of the standardized difference (Sdt-diff) as a balance measure for the first moment, where the balance is defined by absolute values below 0.25. He also suggests the use of the ratio of treated and control variances (Var-ratio) as a balance measure for the second moment, where the balance is defined by values close to 1.0, and variables are out of balance if the variance ratio is greater than 2.0 or less than 0.5.

B.2.3 Channels

Table B.9: ATT estimates, dynamics of fiscal balance, fiscal space, private absorption, and net capital inflows in the aftermath of financial crises, conditional on pre-shock fiscal space

B.2.4 Robustness

Table B.11: Misspecification error and relevance of fiscal space, financial crises, ATT estimates

Panel A: IPWRA model without fiscal space and interaction term between fiscal space and financial crises The explanatory variables are the predicted dependent variables based on the IPWRA estimator and fiscal space. There is a misspecification error in the model when the coefficient associated with fiscal space is significant.

Appendix B. Appendix to Chapter

Table B.13: Alternative measures of fiscal space, financial crises, ATT estimates

Panel A: Average fiscal space over two years prior to shock

Financial crisis, t -7.

C.2 Summary statistics

C.3.2 Baseline : Figure of average marginal effects

Investments, labor and productivity

Notes: The figures show the average marginal effects based on the column (2) of table 4.4. 1990s. In the 2000s, the effectiveness of all the determinants was higher when the significant changes occur during an IMF-supported program (in line with the general picture). Second, the effectiveness of the significant changes in almost all growth surges determinants was particularly higher in Europe, the Middle East and Central Asia, and Asia and the Pacific, with minor exception, when occurring during an IMF-supported program. Smaller gains are also found in the Americas and Caribbean while no gains are found for Africa. Third, we find that the effectiveness gains of the significant changes in growth surges determinants when associated with an IMF-supported program appears only in advanced countries and emerging markets while no gains are found in low-income countries.

Here, two caveats are worth noting. First, the analysis so far has focused on employing non-parametric analyses (the unconditional probability of the growth surges, the unconditional probability, incidence, and effectiveness of the growth surges determinants) to draw general lessons on the IMF's role in igniting growth surge. All of them point towards a significant and positive role of the IMF in contributing to creating favorable conditions necessary to jump-start growth in countries, notably through restoring macroeconomic stability, implementing structural reforms, and pushing for institutional improvements. Moreover, this role tends to be more pronounced in the 2000s, in Europe, the Middle East and Central Asia, and Asia and the Pacific, and emerging markets and advanced countries. Second, all these analyses do not consider the possible correlation between all the broad determinants, especially as we show that many of the significant improvements of these determinants tend to occur simultaneously. This states that a more comprehensive analysis is needed to fully weight the power of each of determinants one against another and assess the IMF's role in igniting growth surges before making conclusive

Chapter . The International Monetary Fund's Role in Igniting Growth Surges Nevertheless, while focusing on disproportionate improvements in growth surges determinants, we do not think that country ownership would have played a critical role in shaping the higher probability of igniting growth surges under an IMF-supported program because they have to be strong regardless of the IMF presence to achieve these changes.

Robustness checks

In this section, we check the validity of our benchmark results to alternative country samples, timing conventions, estimation techniques, and sets of growth surges. First, we increase the homogeneity of our sample by dropping (i) all countries that have never had an IMF-supported program throughout the studied period, and (ii) both countries that have never had an IMF-supported program throughout the studied period and the prolonged user of IMF-supported programs (i.e., countries with the top 10% highest number of IMF-supported programs). The results are reported in table D.11 and table D.12, respectively. Our benchmark results are qualitatively and somewhat quantitatively robust.

Second, while we consider significant improvements in growth surges determinants to be associated with an IMF-supported program when they occur during it or 2 years after its completion, we now define association by (i) occurrence during an IMF-supported program only (see, table D.13), and (ii) occurrence during an IMF-supported program or 4 years after its completion (see, table D.14). These alternatives definitions do not alter our main findings both qualitatively and quantitatively.

Third, we modify the timing needed for a growth determinant to ignite growth surge from 5 years, to 7 and 3 years. The results are reported in table D.15 and table D.16, respectively. Our results are qualitatively identical but slightly different in terms of magnitude. While the gains of the IMF programs remains consistent, we find that the probabilities of igniting growth surges following significant changes in external factors and endowments, macroeconomic stability, and structural reforms are higher, regardless of the presence of the IMF, when we raise the timing convention from 5 to 7 years. In contrast, the probabilities are reduced for these growth surges determinants when reducing the timing convention from 5 to 3 years.

Fourth, we employ different estimation techniques: (i) a pooled probit model (see, table D.17), and (ii) a tobit model (see, table D.18) treating non-growth surges period as censored at zero. The results in all cases are not only qualitatively, but also quantitatively similar.

Fifth, we set different criteria in the determination of growth surges and identify alternative sets of growth surges. In table D.19, we apply the same criteria as in Hausmann et al. (2005) and identify 159 growth surges. In table D.20

D.2 Summary statistics

Macroeconomic stability

No signal 10% higher signal 0 .

2

.4

.6

Pr(GS=1) 0 .33 .17 Index [T-5,T] With IMF Without IMF

Structural reforms

No signal 10% higher signal 0 .

2

.4

.6

Pr(GS=1) 0 .51 .29 Index [T-5,T] With IMF Without IMF

Trade diversification and quality

No signal 10% higher signal 0 .

2

.4

.6

Pr(GS=1) 0 .48 .25 Index [T-5,T] With IMF Without IMF

Investments, labor and productivity

Notes: The figures show average marginal effects based on the column ( 5) and (6) of table 5.4.

Appendix D. Appendix to Chapter

Table D.20 Chapter . Résumé extensif en français présent connu qu'un succès modeste, par la formation et le tempérament, et avec les analyses historiques, on pourrait faire quelques prédictions en se basant sur les résultats de cette thèse.

Le chapitre 2 suggérait que, si des politiques appropriées ne sont pas prises, la crise du « grand confinement » pourrait être associée à un effondrement beaucoup plus important du commerce international jamais enregistré, en particulier du commerce des biens manufacturés et des ser- 15 Comme indiqué, par exemple par [START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF][START_REF] Auerbach | efficace pour atténuer l'ampleur des récessions lorsque les pays disposent de suffisamment d'espace budgétaire pour leur permettre de mettre en oeuvre des plans de relance sans détériorer leur situation budgétaire et le sentiment du marché. Faisant le bilan de la littérature existante, nous y contribuons de multiples façons[END_REF]Fazzari et al. (2015), les multiplicateurs budgétaires sont plus élevés en période de ralentissement. En outre, comme indiqué, par exemple, par DeLong and Summers (2012);Jordà andTaylor (2016) et Fatás andSummers (2018), les consolidations budgétaires peuvent échouer en cas de ralentissement, car elles freinent davantage la croissance et l'investissement, sans pour autant réduire et stabiliser les niveaux d'endettement. 16 De plus, IMF (2019) montre que les réformes nécessitent plusieurs années, et certaines d'entre elles peuvent entraîner des coûts à court terme plus importants lorsqu'ils sont exécutés dans des périodes difficiles de récession par exemple ; il est donc préférable de les mettre en oeuvre lorsque les conditions économiques sont favorables. De plus, Les gains dus aux réformes structurelles tendent également à être plus importants lorsque la gouvernance, l'accès au crédit et l'informalité -trois contraintes à la croissance -sont élevés.