Jean-Noël Senne

Jean-Michel Etienne

Sabine Ferrand-Nagel

Miren Lafourcade

Felipe Starosta De Waldemar

Matthieu Manant

Nathalie Ferriere

Florence Puech

Guo Liang

Enn Je Remercie Maryse

Jan, Izabela, Metin, Uros Thomas

Monaten

Sebastiaan Marta Alky

Jonas Fernando

Nicolas Boileau

L ' Art

pour ces beaux moments que nous avons partagés

J'aimerais tout d'abord remercier mon directeur de thèse José de Sousa, pour m'avoir permis de mener cette thèse à bien. Ses nombreuses remarques m'ont grandement aidée à améliorer mes articles, que ce soit sur la forme ou sur le fond. Accessible, compréhensif et d'une grande gentillesse, il a su m'accompagner et me donner les bons réexes. Il m'a ouvert les portes du monde si particulier de la recherche, me poussant à aller présenter mes recherches en conférence, ou discuter avec d'autres chercheurs, ce que je n'aurais pas forcément fait de moi-même.

I thank the members of my jury, Matthieu Crozet, Inmaculada Martinez-Zarzoso, and in particular my rapporteurs Laura Hering, Isabelle Rabaud and Volker Nitsch, whose reports have been very useful. I am particularly grateful to Volker Nitsch for hosting me for 5 months in his department at TU Darmstadt. He gave me the idea for my second chapter and followed my work. I enjoyed excellent working conditions in Darmstadt, and was able to go to a conference in Florence thanks to the department. De même, je suis tout particulièrement reconnaissante à Matthieu Crozet qui m'a beaucoup aidée tout au long de la thèse, me faisant de nombreuses remarques pertinentes, qui ont modié en profondeur et amélioré grandement mes articles.

Je remercie le laboratoire du RITM pour m'avoir fait bénécier d'excellentes conditions de travail pendant ces 3 années de thèse, et m'avoir permis de partir en conférence toutes les fois où j'en ai fait la demande. Je remercie les professeurs et maîtres de conférence du RITM pour leur accessibilité, leurs encouragements et leur enthousiasme. J'ai particulièrement apprécié le fait que nous mangions tous les midis ensemble à la cantine, sans distinction de statut. Je tiens à remercier en particulier Lisa, d'une gentillesse rare, qui telle une attentive bonne fée est venue tout au long de la thèse vérier que tout allait bien et quand ce n'était pas le cas, mettre tout en ÷uvre pour que cela aille mieux.

Il est très important d'étudier les mécanismes par lesquels le commerce et les unions monétaires s'inuencent mutuellement, car cela permet de concevoir de meilleures politiques publiques.

En eet, savoir quelles sont les caractéristiques des unions monétaires qui importent pour le commerce permet aux politiques publiques de se concentrer sur les bons canaux.

Chapitre 1: Distinguer les eets de l'union monétaire sur le commerce Andrew Rose a relancé l'intérêt pour les unions monétaires avec son célèbre article "One money, One Market" (2000). Il arme que les unions monétaires augmentent le commerce de 300 %.

De nombreux articles ont testé la robustesse de ce résultat, concluant que les unions monétaires accroissent plutôt le commerce de 30 à 90 %, et la zone euro seulement de 2 à 30%. L'un des grands enjeux de cette littérature est de régler un problème d'endogénéité: les pays auraient pu choisir de former une union monétaire parce qu'ils commerçaient déjà beaucoup.

La littérature est très abondante sur l'eet des unions monétaires sur le commerce, mais aucune étude n'a distingué l'eet de chaque caractéristique de l'union monétaire sur l'augmentation du commerce observée. Trois caractéristiques de l'union monétaire pourraient jouer un rôle dans l'augmentation des échanges: la transparence des prix, les coûts de conversion et l'impossibilité de dévaluer. Il est important d'avoir une compréhension subtile des mécanismes en jeu derrière cette augmentation pour savoir quels sont les déterminants du commerce. En eet, cela permettrait aux décideurs politiques de savoir de la transparence des prix et de la xation du taux de xv change, laquelle a favorisé le commerce. Le premier chapitre de cette thèse aborde la question de la distinction de l'impact de chacune des caractéristiques de l'union monétaire sur le commerce.

Dans le premier chapitre de cette thèse, je prote des diérents types de régimes de change Je vérie ensuite empiriquement les prédictions de mon modèle et j'estime l'importance de l'inuence des caractéristiques de chaque union monétaire. Pour ce faire, je compare les eets des diérents régimes sur le commerce à diérentes périodes de maturité an de mesurer l'eet de chaque caractéristique à chaque étape. Je règle le problème des ux commerciaux nuls en utilisant un Poisson Pseudo Maximum Likelihood.

Ma méthodologie est basée sur le modèle de gravité, permettant de prédire le commerce entre deux pays, en fonction de la distance qui les sépare et de leur taille respective. J'inclus les eets xes pays-temps, pour contrôler pour la croissance de l'activité économique. J'inclus les eets xes paire de pays, qui capturent les variables spéciques à une paire invariable dans le temps telles que la contiguïté des frontières, la distance, une langue commune ou des liens coloniaux. Je contrôle pour l'augmentation des échanges commerciaux découlant des traités de libre-échange en ajoutant une variable indiquant un accord de libre-échange. Enn j'inclus une Prenant en compte cette information, les investisseurs et les exportateurs agissent diéremment et cela a un impact sur l'activité économique.

L'eet du régime de change xe un pour un étant endogène, cela signie que la crédibilité des unions monétaires a encore plus d'impact que ce que l'on pensait auparavant, car les eets sur le commerce ne proviennent pas de la xité du taux de change ou de la transparence des prix.

Le fait que l'eet de l'union monétaire sur le commerce découle de la crédibilité de son régime de change xe est la principale contribution de ce chapitre.

Chapitre 2: L'euro a-t-il protégé ses membres de l'eondrement du commerce?

La crise nancière a fait chuter le produit intérieur brut mondial (PIB) de 4% et le commerce mondial de 15% (surréaction de 18 en 2009 contre 1,9 sur la période 1990-2008). [START_REF] Baldwin | The Great Trade Collapse: Causes, Consequences and Prospects[END_REF] a déclaré que "le grand eondrement du commerce a été déclenché -et a contribué à être propagé -par la récession économique mondiale". Tous les pays pour lesquels l'Organisation Mondiale du L'eet de l'euro sur le commerce a été largement étudié dans la littérature, avec une cinquantaine de publications en circulation. Selon ces études, la fourchette de l'eet positif de l'euro oscille entre 2 et 30 % (voir Rose, 2017 pour une analyse récente des métadonnées). Toutefois, à ma connaissance, personne jusqu'à présent n'a étudié l'eet de l'euro pendant une crise économique. Il n'est a priori pas évident de déterminer comment le commerce supplémentaire dû à l'introduction de la monnaie unique réagit à une crise. Est-il plus volatil, plus fragile? L'euro a-t-il protégé ses membres pendant le grand eondrement du commerce? La chute des échanges observée lors de la crise nancière en Europe aurait-elle été plus importante sans l'euro? L'eet de l'euro est-il le même pour tous les secteurs? Pour répondre à ces questions, j'étudie dans mon deuxième chapitre l'eet de l'euro sur le commerce pendant le grand eondrement commercial, qui a le triple avantage d'être exogène par rapport à la zone euro, inattendu et mondial.

Pour évaluer l'impact de l'euro sur le commerce pendant la crise, j'estime un modèle de double diérence, comparant la diérence du commerce avant et après la crise dans la zone euro avec la diérence de commerce à la même période pour les autres pays développés. J'inclus les eets xes typiques des modèles de gravité: eets xes importateur-temps, exportateur-temps et paire de pays. De cette façon, j'absorbe les caractéristiques invariantes dans le temps (distance, langage commun, colonie, etc.), mais aussi les caractéristiques variables dans le temps spéciques aux pays (PIB par habitant, baisse du PIB pendant la crise, etc). Enn, les eets xes me permettent de capturer beaucoup de caractéristiques non observées.

Pour étudier le grand eondrement du commerce, j'ai d'abord borné la crise nancière de 2008 à 2010. Mon groupe de traitement comprend les membres de la zone euro avant 2007 (Allemagne, Autriche, Belgique, Finlande, France, Grèce, Irlande, Italie, Luxembourg, Pays-Bas, Portugal et Espagne). En tant que groupe témoin, j'ai décidé de prendre les pays les plus développés de l'OCDE (Corée du Sud, Australie, Canada, Danemark, États-Unis, Corée du Sud, Islande, Israël, Japon, Norvège, Nouvelle-Zélande, Suède, Suisse et Royaume-Uni).

L'euro a eu un eet positif et signicatif au cours de la grande crise commerciale de 2008-2010 par rapport aux autres pays développés de mon groupe de contrôle. Mes résultats sont robustes à de nombreux contrôles de robustesse (changement de groupe de contrôle, de période, retrait de pays de l'échantillon, etc.). Le commerce entre les membres de la zone euro a moins plongé que pour les autres pays développés, la reprise a également été plus rapide avec un rebond plus fort.

Cependant, si l'on passe à des données sectorielles, il apparaît que l'eet positif et signicatif que j'observe au niveau agrégé est dû à un eet de composition: l'euro n'a protégé que les secteurs qui exportent le plus. En eet, je constate que les secteurs les plus aidés par l'euro sont le secteur des machines (19% du commerce total), le secteur chimique (14% du commerce total), l'industrie métallurgique (11% du commerce total) et l'industrie des transports (15% du commerce total). Il semble que ces grands secteurs sont aussi plus indépendants nancièrement et plus sensibles à la volatilité des taux de change. L'euro a eu un eet positif précisément sur les secteurs présentant de telles caractéristiques. En eet, les banques européennes ont été particulièrement touchées par la crise nancière, les secteurs nancièrement indépendants ont donc relativement mieux résisté.

Deuxièmement, la volatilité des taux de change s'est accrue partout dans le monde pendant la crise, sauf dans la zone euro où elle est restée, par nature, stable. La monnaie unique a permis d'éviter les incertitudes liées au taux de change entre ses membres. C'est particulièrement important pour les industries qui font face à des prix rigides. L'euro a aidé les secteurs utilisant des prix référencés car il a maintenu les prix stables. L'euro a empêché l'incertitude sur les bénéces des entreprises de la zone euro, alors que cette incertitude s'est accrue à l'extérieur.

Par conséquent, l'euro bénécie d'une prime à la stabilité du taux de change en période de crise économique.

Chapitre 3: Eets des chaînes de valeur mondiales sur la transmission des chocs et le taux de change

Introduction

How to measure the eect of monetary and economic integration on trade

Inside the big European Union circle of economic integration, a smaller circle of countries has been developed, keen for a stronger economic integration: the members of the Eurozone. Baldwin (2006) qualies the euro as the world largest economic policy experiment since a common currency strongly modies the equilibrium of the area on many levels (ination, business cycles, trade, interest rate, etc.). Such a disruption has never been done before on such a large scale;

the founding states of the Eurozone represent 20% of world output, 30% of world trade and 300 million people.

My thesis explores the eects of monetary and economic integration on trade. The gravity model allows economists to measure this integration. The gravity model was inspired by astrophysics to measure the gravity force between two planets depending on their size and the distance between them. The idea is the same in economics: the gravity force in this case is trade, the two planets are countries, and their size is their gross domestic product (GDP). Economists rened the model by adding control variables inuencing trade (common language, former colony, common border, island, landlocked country, etc.). It is possible to replace these variables by country-pair, exporter-time and importer-time xed eects to capture unobserved characteristics. The equation predicts trade remarkably well. Economists compare predicted trade with real trade, and try to explain the deviations. For example, adding a free trade agreement (FTA) dummy to the regression allows us to measure the eect of a FTA on trade.

xxiii INTRODUCTION

The gravity model predicts that a region equidistant with two other regions of equal size must have the same level of trade with each of them. [START_REF] Mccallum | National Borders Matter: Canada-U.S. Regional Trade Patterns[END_REF] compares the trade between USA states and Canadian provinces. He found that trade is 20 times higher between two regions within a country compared with a region of the same size at an equal distance but on the other side of the border. This famous result is known as the "border eect". The border eect proves that the market between the USA and Canada is fragmented. This might be due to protectionist barriers (tari or non-tari barriers), home bias of households, rms and administrations, institutional dierences (e.g. dierence in contracts) or transaction costs (dierent currencies, etc.). In the case of the American-Canadian border, since the countries speak a common language, have similar cultural values, and have a free trade agreement, there is a high probablility that the dierent currencies are playing a role. Indeed, currency unions which are closely integrated, represent the level just below federation in terms of integration. According to the literature, a currency union increases trade by between 30% and 60% on average.

Dierent exchange rate regimes

A currency union is a type of exchange rate regime. Economists usually divide exchange rates regimes into two categories, oating and xed. However, the situation is far from being binary since there is a multiplicity of exchange rate regimes. First, there is the free oating regime: the central bank does not intervene in the currency exchange rate. Japan and Australia, for example, have pure oating regimes. Then there is the managed oating regime (or dirty oat): in cases of extreme appreciation or depreciation, central bank intervenes to stabilize the currency. Singapore has opted for this regime. There is also the crawling peg (usually considered a xed exchange rate): it allows depreciation or appreciation to happen gradually. The most famous example is China. Its currency slowly but regularly appreciates against the US dollar. Then there is the peg within horizontal bands: the rate is allowed to uctuate in a xed band (bigger than 1%) around the anchor's currency rate. The European Monetary System applies this exchange rate regime.

There is also the strict xed exchange rate: the currency is xed to an anchor and does not uctuate at all. An example of this is Denmark's currency which is pegged to the euro. Finally, there is the xed exchange rate regime, the most extreme being currency union; since countries xxv use the same currency there is no possibility of deviation from the anchor country. For instance, Greece was unable to undertake a competitive devaluation in 2011 during the sovereign crisis.

To complicate the situation even more, xed exchange rate regimes can decide to peg either to another country, or to a basket of currencies (to be less exposed to a shock aecting only the anchor country).

The choice of exchange rate regime real has consequences for countries in terms of ination management, trade, interest rates, business cycle stabilization, etc. In relation to trade, I found that classical xed exchange rates increase trade only after 15 years. Kosovo and Montenegro (these countries had adopted the German mark then, following Germany, switched to the euro). Another possibility is the ocial agreed currency substitution, followed by Monaco and Andorra in adopting the euro. The countries opting for dollarization have in common either that they are very poor with institutions that lack credibility, or they are very small, with institutions not willing to manage an independent monetary policy. The main advantage of dollarization is to stabilize ination in the case of weak institutions since the currency is credible independently of them. Another advantage is an increase in trade because the countries share a common currency.

Dierent types of currency unions

The other possibility is a multilateral currency union: the countries share seigniorage and have a common central bank. In this case, there is no single country controlling the monetary policy in its interest but rather a shared monetary policy supposed to take into account every member of the currency union to choose the right ination target. Multilateral currency unions member countries are usually bigger than countries in dollarization. The Franc CFA and the euro are two examples of multilateral currency unions.

INTRODUCTION

Benets and drawbacks of sharing a single currency

There are benets and drawbacks to belonging to a currency union. A rst benet is the ending of exchange rate volatility and uncertainty between the members of the currency union (since it is xed irrevocably). Getting rid of uncertainty makes economic forecasting easier, and promotes investment. Consumers traveling within the currency union do not have to change currencies, and therefore face fewer administrative procedures. Currency unions reduce transaction costs: for example, there is no payment commission, and rms do not have to pay hedging costs to insure against currency uctuations. Small rms are aected particularly by exchange rates uctuations and the administrative costs of accountings in the dierent countries. Currency unions create trading blocks, with higher trade among their members and greater negotiating power in trade agreements with the rest of the world. There is also price transparency, allowing consumers to more easily compare the prices of goods. States are supposed to be more responsible for decits since all members collectively would suer from the resulting lack of credibility (resulting in turn, in higher interest rates). There is much less risk of high ination (devaluation is not possible).

However, there are also drawbacks related to currency unions. First, monetary independence is scaried and there is loss of sovereignty over economic aairs. Countries cannot absorb shocks appropriately to their economy in a context of a personalized monetary policy. Furthermore, a single monetary policy is unlikely to satisfy every country, and especially if countries are heterogenous. Countries are necessarily dependent on one another, and the presence of a weak country can destabilize the entire currency union by weakening the currency.

Reasons for entering and exiting a currency union

Entering or exiting a currency union is almost always because of political rather than economic reasons; political glue holds [in place] a currency union (Hochreiter and Siklos, 2004). During the XXth and the beginning of XXI century, more than twenty currency unions were in place.

The biggest currency union is the euro, born from a political will to build something strong together. The second biggest currency union is the American dollar. Most of the countries which have adopted the American dollar are either small islands, or poor and unstable Latin xxvii American countries. The third biggest currency union is the pound sterling, which united a lot of former British colonies before they became independent and left it. The next biggest currency union is the CFA Franc. It is an unusual currency union which unites former French colonies that remained in the currency union after independence. Also, unlike most other currency unions, there is no one hub country in the CFA Franc currency union; e.i. no single or pair of countries driving the monetary policy. Another currency union that lacks a hub is the East Caribbean Dollar which unites a lot of small isolated islands. The Deutschmark is another case of special currency union; Kosovo and Montenegro adopted the Deutschmark to stabilize their economies.

Germany was the biggest economic power in the region which drove these countries'choices.

In conclusion, the reasons for entering and exiting currency unions often have very dierent roots. In my sample, the vast majority of the countries that have left currency unions were former colonies which obtained independence and abandoned their coloniser's currency. On the other hand, many countries in my database that joined a currency union are small (mostly islands)

or poor countries that lacked the credibility or willingness to have a currency of their own (e.g., Liechtenstein, Naru, Kosovo, etc.). Other countries joined currency unions to strength political and economic links.

Trade and currency unions: what mechanisms are involved?

In my thesis, I study in depth the link between currency union and trade, and try to understand the mechanisms behind the signicant eects found in the literature. I study the link in both directions: how currency unions increase trade, and how trade creates the right conditions for an optimal currency union.

First, I try to understand how currency unions promote trade, in general and in times of crisis.

Currency unions aect trade through three potential channels: price transparency, transaction costs, and absence of exchange rate volatility and uncertainty. Price transparency allows citizens to compare prices across borders and buy from where the good is cheapest. The internet is enabling price transparency to have even bigger impact by making ordering goods from abroad much easier. Transaction costs are drastically reduced with a single currency since banks are INTRODUCTION not able to charge customers for changing currency. Changing currency also implies many bureaucratic tasks. We can consider these administrative and nancial transaction costs as xed costs faced by all rms that export to another country with a dierent currency; this tends to exclude small rms from foreign markets. Large rms have the capacity to pay sta to take care of these costs and bureaucratic produced, and are able to buy the foreign currency at the lowest prices, something that small rms cannot aord. Finally, exchange rate volatility forces rms to take insurance against exchange rate risks, making exports in another currency more costly. In addition, since it has an impact on demand the possibility of devaluation creates uncertainty for rms, and therefore making production planning more dicult.

Mundell's trilemma states that a country has to choose among having a xed foreign exchange rate and/or free capital movement (absence of capital controls), and/or an independent monetary policy. However, in the case of an important shock, these regimes can abandon the peg to devaluate, and therefore "switch" to choose free capital movement and an independent monetary policy.

However, currency unions are stuck forever on the "free capital movement-xed foreign exchange rate", and cannot switch in case of shock.

xxix

The aim of my rst paper is to measure the eect of each of these characteristics on trade. I nd that the absence of exchange rate uncertainty has the biggest eect on trade. Indeed, contrary to a xed exchange rate, currency unions commit to keeping exchange rate level irrevocable.

The credibility derived from such a commitment is extremely high since the cost of leaving a currency union and the consequences would be enormous. The example of Greece which needed competitive devaluation but preferred to pay the high price by staying in the Eurozone is a good example of the power of this commitment.

In my second paper, I study the eect of currency unions, and more especially the euro, during economic crises. Among all the characteristics described above, the absence of exchange rate volatility despite market pressures is what makes a dierence during an economic crisis.

Indeed, in time of crisis, exchange rates become three to four times more volatile than usual, and this absence of volatility gives a premium to the members of a currency union.

My last paper takes the reverse view and studies how trade makes the economic environment more compatible with a currency union. [START_REF] Frankel | The Endogeneity of the Optimum Currency Area Criteria[END_REF] show that trade increases business cycle correlation; therefore, since currency unions increase trade, shocks will become more symmetric over time. This nding suggests endogeneity in the optimal currency area (OCA). I study vertical trade specically which implies that the various stages of the production of a good are located in dierent countries. This new production organisation favors even more shocks transmission among the countries involved in the global value chains. The result is that business cycles are more correlated which makes currency unions more attractive. Indeed, as the shock is symmetric among countries, the optimal monetary response is the same on each side of the borders and, therefore, is not a problem when a single central bank is responsible for all these countries. He claims that currency unions increase trade by 300%. Numerous papers have tested the robustness of this result, and nd that currency unions increase trade by between 30% and 90%.

A major issue in this literature is the problem of endogeneity: countries could be in currency union because they trade a lot.

The question

The literature concerning the eect of currency union on trade is very prolic although there are no studies that distinguish which characteristics of the currency union regime drive the increased trade observed. Three characteristics of currency union might play a role in trade increases:

ln Export ijt = β 1 CU ijt + β 2 P eg1to1 ijt + β 3 F ixed ijt + β 4 F T A ijt + λ 1it + λ 2jt + γ ij + ijt
Where lnExport ijt is the (log) of annual export from country i to country j, CU ijt is a dummy for currency unions, P eg1to1 ijt a dummy for pegs one-to-one and F ixed ijt a dummy for classical xed exchange rate. F T A ijt is a dummy for free trade agreements. λ 1it and λ 2jt represent the time-country xed eect on exporter and importer, and γ ij the pair-country xed eect. ijt is the error term.

There is one additional issue that I need to address in order to get a more reliable view on the eect of these dierent exchange rate regimes on trade. These regressions suer from problems of endogeneity: countries, because they trade a lot, decide on a currency union or a peg one-to-one.

xxxiii

The potential endogeneity is not necessarily captured by the xed eects. One way to capture it is to use an instrumental variable.

I use the instrument developed by [START_REF] Barro | Economic Eects of Currency Unions[END_REF]. The instrument uses the huband-spoke organisation of xed exchange rate. The main idea is to use the independent decisions of countries to peg/ be in currency union with a main anchor. The likelihood that they peg to the same anchor is used as an instrument since it is considered to be exogenous.

I adopt the same methodology as [START_REF] Barro | Economic Eects of Currency Unions[END_REF] and estimate the probability that a given country enters a currency union using one of the six main anchors in Barro and Tenreyro (Australia, France, Germany, Japan, the UK and the USA), employing a probit. This probability depends on several factors such as distance, colonial links, common language, etc. I then compute the joint probability that two countries have a common anchor.

The results

Theory My model predicts that currency unions trade more than pegs one-to-one and classical xed exchange rate regimes. The trade under currency union is stable over time, while trade under xed exchange rate regimes increases as their credibility increases. Therefore, the levels of trade between these regimes converge. In addition, to my knowledge, my model is the rst to explain why oating and classical xed exchange rate regime have similar eects on trade in the rst years, but not at steady state. To summarize the model predictions:

• Case 1: if the sensibility to volatility of the rms is high, then trade increases monotonically with peg credibility: currency union should therefore increase trade the most, then peg oneto-one, then the classical xed exchange rate, then the oating exchange rate.

• Case 2: if the sensibility to volatility of the rms is low, the level of trade is the highest for countries linked by a credible peg but it is high also (but less high) for countries with the most volatile exchange rate (oating exchange rate). This is because the increase in utility for the average rm in the country with the devaluation more than compensates for the decreased utility of the average rm in the country with appreciation. The results suggest that pegs one-to-one are special in the xed exchange rate family. The dierence between peg one-to-one and classical xed exchange rate lies in the fact that no price conversion is needed for peg one-to-one, while a classical xed exchange has to pay an additional conversion cost. When a shock occurs, which upsets the trade balance, a country with a simple peg devalues its currency to restore the balance, and most of the time re-pegs to the same currency. However, a country with a peg one-to-one loses its specicity if it devalues. The incentive to keep the peg is much higher. Therefore, the shock would have to be much bigger to cause a devaluation. This explains the dierence in life expectancy between the two types of peg which is 3 years for a classical peg, while pegs one-to-one have a lifespan of 21 years (against 27 years for currency unions). This gap potentially implies fundamental dierences between them. Knowing this, investors and traders act dierently and this has an impact on the economic activity.

As peg one-to-one eect is endogenous, it means that currency unions have an even greater impact than previously thought as the eects on trade do not come from the xity of the exchange rate, or price transparency.

The question

The eect of the euro on trade has been studied in the literature with around 50 papers on the topic. According to these studies, the range of the euro positive eect uctuates between 2% and 30% (see Rose, 2017 for a recent metadata analysis). However, to my knowledge, no work has been done so far on the euro eect during an economic crisis. It is a priori not clear how the additional trade due to introduction of the single currency reacts to a crisis. Is it more volatile, more fragile? Has the euro protected its members during the great trade collapse? Would the trade drop observed during the nancial crisis in Europe have been bigger without the euro? Is the euro eect the same for every sector? To address these questions, I study the eect of the euro on trade during the great trade collapse, a context that provides three advantages: it was exogenous to the Eurozone, was unexpected and was worldwide.

The methodology

ln Export ijt = βEU R ijt * Crisis t + λ 1it + λ 2jt + λ 3ij + ijt (1)
with:

• Export ijt bilateral trade

The question

The expansion of global value chains over the last 20 years is undeniable, and when designing monetary policy rules, it is important to take account of their eect on the transmission of shocks.

In chapter 3 of this thesis, I analyse the eect of global value chains on the international transmission of productivity and monetary shocks in dierent exchange rate regimes and export price currencies, and to discuss optimal monetary policies and monetary coordination.

The methodology

Based on the observation that global value chains increase synchronisation of business cycles, I build a model to identify the consequences for optimal monetary rules.

My model is related to Corsetti and Pesenti (2009). This model has the advantage of simplicity and elegance since it can be solved manually but its predictions are powerful. It studies the eect of trade on business cycle synchronisation among trading partners, and derives optimal monetary policies. I am interested in the same issues, but for a particular form of trade: global value chains. Therefore, I adopt their approach, and change only the production function by As a robustness check, following Chen and Novy (2016), I study the impact of currency unions and pegs one-to-one depending on potential trade between the pairs, to check whether the eect is heterogenous, I nd that it is.

The literature compares the eect of currency unions and xed exchange rate regimes on trade.

To the best of my knowledge, [START_REF] Fielding | Do Currency Unions Deliver More Economic Integration than Fixed Exchange Rates? Evidence from the Franc Zone and the ECCU[END_REF] is the article closest to mine in being the only one to look at the dierence between currency union and peg one-to-one by studying the CFA franc. 2 Once there was sucient data, many economists concentrated on the specic eect of the euro on trade.

The general result is that bilateral trade in the Eurozone has increased signicantly, between 5% and 40% 3 but much less than was estimated for the other currency unions.

Glick and Rose (2016) tested several methodologies to measure the eect of currency union on trade in the world and in the Eurozone and to compare them. They (as I did) found that the results were very sensitive to the econometric methodology, which did not allow a robust estimation. However, they conclude that the Eurozone has had signicant and important eects.

Their estimates are in the same range as previous papers studying other currency unions.

THE MODEL

The literature comparing xed and oating exchange rate is much older and also smaller.

Most of the earliest articles 4 estimate a large negative correlation between nominal variability of exchange rate and trade while studies published in the 1990's 5 report small and almost insignif- icant eects. The recent literature does not settle the question. [START_REF] Nilsson | Exchange Rate Regimes and Export Performance of Developing Countries[END_REF] nd that the more exible the exchange rate, the greater the exports while Fritz-Krockow and Jurzyk (2004) nd that the longer and more credible the peg, the higher the trade. [START_REF] Tenreyro | On the trade impact of nominal exchange rate volatility[END_REF]

Households

The economy consists of households and rms. The representative Home household at period t maximizes expected intertemporal utility from consumption (C ), taking into account expected

consumption at period s, U t = E t ∞ s=t β s-t C 1-γ s 1-γ , 0 ≤ β ≤ 1 being the subjective discount factor
and γ > 0 the inverse of the intertemporal elasticity of substitution. Each rm produces a variety ω, which is an imperfect substitute to all other varieties over a continuum of goods Ω under monopolistic competition. Households consume the basket of goods

C t = ω∈Ω c t (ω) θ-1 θ dω θ θ-1
, with θ > 1 being the symmetric elasticity of substitution across goods. The consumption-based price index at home is

P t = ω∈Ω p t (ω) θ-1 dω 1 θ-1
, p t (ω) being the price of a good ω. Finally, R denotes aggregate expenditure with R t = P t C t = ω∈Ω r t (ω)dω.

Therefore we have c t (ω) = pt(ω) Pt -θ C t and r t (ω) = pt(ω) Pt 1-θ R t .

Firms

Prior to entry, rms face an identical xed entry cost f D,t , paid on a period-by-period basis. 6

Production function is the following C t = LZ t -f E , production requiring only labor L, and aggregate labor productivity being indexed by Z t . Let p D,t being the nominal domestic price of home rm, and ρ D,t the real price, with

ρ D,t (z) ≡ p D,t (z) P t = θ θ -1 w t zZ t , (1.1)
where w t /Z t z is the cost per unit of the consumption good C t , w t being the real wage and z the relative productivity, dierent for each rm.

We therefore have

c t (z 1 ) c t (z 2 ) = z 1 z 2 θ , (1.2) r t (z 1 ) r t (z 2 ) = z 1 z 2 θ-1
.

(1.3) 6 In the model of [START_REF] Melitz | The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity[END_REF], the xed cost f D,t is only paid in the rst period.

Prot and revenues for the rms Domestic prot for home rms expressed in real terms in units of the consumption basket is

d D,t (z) = 1 θ ρ D,t (z) 1-θ C t -f D,t , (1.4)
and domestic revenues (being equal to the sales at home) for home rms

r D,t (z) = ρ D,t (z) 1-θ C t .
(1.5)

In every period, a mass N D,t of rms produces in the home country. These rms have a distribution of productivity level over [z min , ∞] given by G(z). Here is the average productivity level for all producing rm in each country

zD ≡ ∞ z D,min z θ-1 dG(z) 1 θ-1 . (1.6)
From (1.3), we can now calculate the domestic revenue of the average home rm

r D,t (z D ) = rD,t = zD z D,min θ-1 r D,t (z D,min ). (1.7)
Therefore the domestic prot of the average home rm is

dD,t = 1 θ zD z D,min θ-1 r D,t (z D,min ) -f D,t . (1.8)
The zero cuto prot condition, by pinning down the prot of the cuto rm implies

d D,t (z D,min ) = 0 ⇐⇒ r D,t (z D,min ) = θf D,t ,
therefore the domestic prot of the average rm is

dD,t = zD z D,min θ-1 -1 f D,t , (1.9)
and the domestic revenue of the average rm is

rD,t = θf D,t zD z D,min θ-1
.

(1.10)

Foreign market

Firms have a disutility in prot volatility (relative to the initial value and not to the mean) due to exchange rate uctuations, expressed in its export utility function. By contrast, prot made in domestic market is not aected by this disutility because of market segmentation. Firms decide to enter on the foreign market before knowing the exchange rate. The rm sensitivity to volatility is captured by α ≥ 0. I dene α low when α ≤ ᾱ and α high when α > ᾱ. 7

U X,t (z) = E t (d X,t (z)) -α V t (d X,t (z)), (1.11)
with E t the expectation and V t the volatility. In my case, the volatility comes from exchange rate variations. There are evidence rms seek ways to reduce their exposure to exchange rate risk through domestic-currency invoicing and hedging. Devereux et al. (2004) nd that monetary stability increases the attractiveness of a currency to be used as invoicing currency. [START_REF] Döhring | Hedging and invoicing strategies to reduce exchange rate exposure -a euroarea perspective[END_REF] nds that about 50% of euro-area exports to countries outside the European Union (EU) are invoiced in euro, and this share is higher in exports to other EU countries. He also nds that euro area non-nancial blue chip companies systematically use nancial derivatives to hedge transaction risk.

Price of exported goods

Only the most productive rms can export as exporting is costly, involving a melting-iceberg cost τ t ≥ 1 as well as a xed cost f X,t > f D,t (in real terms w t f X,t /Z t ) paid at each period. Let p X,t be the general nominal export price of Home rm, and ρ X,t the real price, the consumption-based real exchange rate is

ρ X,t (z) ≡ p X,t (z) P t = Q -1 t τ t ρ D,t (z), (1.12)
7 Solving the model, I nd that ᾱ = 2.

with Q t ≡ t P * t /P t and t the nominal exchange rate.

The rm's utility function does not modify the price structure, which remains the same as in [START_REF] Melitz | The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity[END_REF]. Owing to ρ D,t (z) shape (mark up being constant), the pass-through is equal to one, increasing the importance of the exchange rate regime.

Let the melting-iceberg cost be dierent if the rm exports to a country with the same currency, in peg one-to-one or in classical xed exchange rate. Indeed, as seen in table 1.1, the potential costs are not the same. Let τ i > 1 with i = {1, 2, 3}, with τ 1 being the transport cost, τ 2 the bank fees on foreign currency change and τ 3 the conversion cost (because of lack of price transparency). This implies -a price for currency union,

ρ cu,X,t (z) ≡ p cu,X,t (z) P t = P t P * t τ 1,t ρ D,t (z),
-a price for peg one-to-one, ρ peg,X,t (z) ≡ p peg,X,t (z)

P t = P * t P t D -1 τ 1,t τ 2,t ρ D,t (z),
-a price for classical xed exchange rate,

ρ f ix,X,t (z) ≡ p f ix,X,t (z) P t = P * t P t D -1 τ 1,t τ 2,t τ 3,t ρ D,t (z),
with D = 1 (no devaluation) with probability P t and for the sake of simplicity D = 1/2 (devaluation of one half ) with probability 1 -P t . The probability P t of no devaluation depends on the credibility φ of the exchange rate regime, and its history (has the peg held for a long time already?).

Therefore, we obtain -a probability to hold for currency union, P t (φ CU ) = 1, since the commitment to hold the peg is extrem.

-a probability to hold for peg one-to-one, P t (φ peg ) = P t (φ peg (φ peg,t-1 )) < P t (φ CU ), since it is not possible to commit as much as the currency union.

-a probability to hold for xed exchange rate, P t (φ f ix ) = P t (φ f ix (φ f ix,t-1 )) < P t (φ peg ), since it is proved in the empirical part that the life expectancy of peg one-to-one is ve times higher than classical xed exchange rate.

8

At steady state, if there has been no devaluation, P t (φ peg ) and P t (φ f ix ) tend towards 1 with the credibility increase. Therefore, the dierence in trade will only be caused by τ 2,t and τ 3,t . It is therefore possible to disentangle the eect of credibility from the eect of price transparency empirically.

The cost of exporting depends on the exchange rate regime. As only rms with a big enough markup can export, there will be now 4 dierent types of rms: those not exporting, those exporting only in the currency union, those exporting in the currency union and with countries being in peg one-to-one, and those exporting everywhere.

Prots and revenues from sales abroad

The total prot of a home rm will be the sum of its prot at home and abroad:

d t (z) = d D,t (z) + d X,t (z) with d X,t (z) being d X,t (z) = Q t θ ρ X,t (z) 1-θ C * t - w t f X,t Z t , (1.13)
and export revenues (being equal to the sales abroad) for Home rms

r X,t (z) = Q t ρ X,t (z) 1-θ C * t .
(1.14)

Among the home rms, there are N X,t = [1-G(z X,t )]N D,t exporting rms. Here is the average productivity level for Home rm exporting

zX ≡ 1 1 -G(z X,t ) ∞ z X,min z θ-1 dG(z) 1 θ-1 . (1.15)
8 I can extend the model easily to include oating exchange rate. The price for a oating exchange rate would be the same than for classical xed exchange rate, but Pt = 0 by denition, since there is no peg. [START_REF] Melitz | The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity[END_REF] and parametrize G(z) the same way with G

THE MODEL I follow

(z) = 1 -(z D,min /z) k with shape parameter k > θ -1. Letting ν ≡ k/[k -(θ -1)] 1/(θ-1)
, we have zD = νz D,min and zX = νz X,min . We now have the share of home exporting rms N X,t /N D,t = νz D,min /z X k . Equation (1.3) remains the same, we can therefore use it the same way as before to calculate the export prot of the average home rm

dX,t = 1 θ zX z X,min θ-1 r X,t (z X,min ) - w t f X,t Z t .
(1.16)

Volatility disutility impact on prot and revenue

The cuto rm productivity can now be calculated. I start by only study the eect of volatility disutility, and I will later add the distinction in trade cost for the three regimes.

Let η > 0 be the change (positive or negative) in the exchange rate from the initial state.

With the new rm utility, the cuto rm has now the following revenue 9

r X,t (z X,min ) = w t f X,t Z t θτ θ-1 t P t (φ) + η θ (1 -P t (φ)) -α(|1 -η θ |) 1 -P t (φ) -1
.

(1.17)

Proposition 1: predicting cuto revenue r X,t (z X,min ) varies with P t (φ), in case of devaluation (1 -η θ < 0) :

• if α low , ∂r X,t (z X,min ) ∂Pt(φ) > 0 until P t (φ) = 1 -α 2 4 , then ∂r X,t (z X,min ) ∂Pt(φ)
< 0. The minimum revenue required to enter on the foreign market is low for P t (φ) = 0 (which means both small and big rms export), increasing until P t (φ) = 1 -α 2 /4 (only big rms can export) and decreasing afterward.

Two dierent mechanisms occur when P t (φ) tends toward 0 and 1. When P t (φ) tends toward 0, the "expectancy" mechanism works: rms prot is higher in case of devaluation 10 and the rms expect it. The gain linked to the prot increase more than compensates the loss due to the volatility disutility. When P t (φ) tends toward 1, the "volatility" mechanism 9 r X,t (z X,min ) being dened as the minimum revenue to export, calculated in domestic currency, with no devaluation.

10 d nodevaluation X,t -d devaluation X,t = 1 -η θ < 0.
works: small rms are more sensitive to the volatility than big rms (which make more prot) and enter on the market if the probability of no devaluation is high enough.

• if α high , then ∂r X,t (z X,min ) ∂Pt(φ)

< 0. The sensitivity to volatility is so high that the gain in prot in case of devaluation does not counterbalance the volatility disutility. Small rms enter only if P t (φ) is high enough.

r X,t (z X,min ) varies with P t (φ) in case of appreciation (1 -η θ < 0) :

• ∀α,

∂r X,t (z X,min ) ∂Pt(φ)
< 0. In case of appreciation, the prot of the rms decrease, therefore "expectancy" mechanism works the other way and the function is monotonously decreasing.

Again, small rms enter only if P t (φ) is high enough.

The classical xed exchange rate having the lowest probability of holding the peg among the xed exchange rate regimes, this regime is less appealing for the rms. There is a hierarchy among the regimes: if a rm can export in a classical xed exchange rate setup, it can also export in a peg one-to-one and currency union set up.

11 Likewise, if a rm can export in a peg one-to-one setup, it can also export in a currency union set up. I would have liked to check empirically that the minimum revenue threshold for currency union is lower than for peg one-to-one than for classical xed exchange rate, but I do not have the data.

Revenue and prot of the average rm in case of no devaluation variate the same way than minimum revenue to export. For the sake of clarity and without loss of generality, all my following equations will be written in the case of no devaluation.

12

rX,t = zX z X,min θ-1 w t f X,t Z t θτ θ-1 t P t (φ) + η θ (1 -P t (φ)) -α(|1 -η θ |) 1 -P t (φ) -1
, (1.18) 11 The only exception being if Pt(φ) < 1 -α 2 /4 and α low at the same time. However empirically, this setup is never observed for xed exchange rate regimes.

12 Writing in expectation terms would not have changed the results.

dX,t = zX z X,min θ-1 τ θ-1 t P t (φ)+η θ (1-P t (φ))-α(|1-η θ |) 1 -P t (φ) -1 -1 w t f X,t Z t . (1.19)
Volatility disutility impact on trade Using aggregation, my micro founded model oers macro prediction on trade that I can check in the empirical part. Trade is dened as the sum of exports (sales on their respective foreign markets) of Home and Foreign rms:

T t = Q t N X,t rX,t + N * X,t r * X,t , T t = Q t N D,t τ -k t θf D,t k θ-1 k k -(θ -1) k θ-1 rX,t 1-k θ-1 +N * D,t τ -k t θf * D,t k θ-1 k k -(θ -1) k θ-1 r * X,t 1-k θ-1 . (1.20)
As the two countries are symmetric (similar size, xed cost of entry on the domestic and foreign market, wage and aggregate labor productivity), the trade without devaluation can be written (K being a constant) as

T t = K P t (φ) + η θ (1 -P t (φ)) -α(|1 -η θ |) 1 -P t (φ) k θ-1 -1 +K P t (φ) + η -θ (1 -P t (φ)) -α(|1 -η -θ |) 1 -P t (φ) k θ-1 -1 . (1.21)
Trade follows the inverse movement of r X,t (z X,min ) in the devaluation case. 13 Indeed, smaller is the minimum revenue required to export, and bigger is the number of rms being able to export, increasing mechanically trade.

The countries being symmetric, if there is a devaluation for one side, it means a appreciation on the other side.

Proposition 2: predicting trade

• If α low , ∂Tt ∂Pt(φ) < 0 until P t (φ) = 1-α 2
to volatility (α low ), when there are high chances of changes in the exchange rate (P t (φ)

close to zero), on one side all the rms will export because they expect a devaluation, and on the other side only the biggest rms will export because they expect a devaluation. In total, trade is higher than when only the biggest rms export (when P t (φ) = 1 -α 2 4 , the uncertainty being the highest). On the contrary, when there are low chances of changes in the exchange rate (P t (φ) is close to one), in both countries all the rms export because the expected disutility of exchange rate volatility is low. The trade will maximum in this case.

• If α high , ∂Tt ∂Pt(φ) > 0. When the rms are very sensible to volatility (α high ), small rms on both sides will only export when they are sure there will be no changes in the exchange rate; trade will be the highest when the peg is very credible (P t (φ) close to one). Trade will be the lowest when the peg is the less credible (P t (φ) close to zero).

Trade is therefore the highest for currency union, then peg one-to-one, and then classical xed exchange rate. 14

The literature does not nd signicant dierences in eect on trade between oating 15 and xed exchange rate (what I also nd). Therefore, regarding the results of my model, it is would imply that we are in the case where α is low. Furthermore, xed exchange rate regimes would always have a probability P t (φ) > 1 -α 2 4 to hold the peg.

Volatility disutility and dierentiated trade costs

Combining volatility disutility and dierentiated trade cost per regime, here are the dierent revenues of the cuto rm for the three regimes:

• revenues of the cuto rm in currency union

r cu,X,t (z X,min ) = w t f X,t Z t θτ θ-1 1,t ,
• revenues of the cuto rm in peg one-to-one

r peg,X,t (z X,min ) = w t f X,t Z t θ(τ 1,t τ 2,t ) θ-1 P t (φ)+η θ τ 1-θ 3,t (1-P t (φ))-α(|1-η θ τ 1-θ 3,t |) 1 -P t (φ) -1 ,
• revenues of the cuto rm in classical xed exchange rate

r f ix,X,t (z X,min ) = w t f X,t Z t θ -1 (τ 1,t τ 2,t τ 3,t ) θ-1 P t (φ)+η θ (1-P t (φ))-α(|1-η θ |) 1 -P t (φ) -1
.

Trade cost increase the tendency observed previously: the higher is the trade cost, and more it increases the minimum revenue required to enter in the foreign market (reducing the number of rms being able to export), and the less the trade is important.

In the long term, P t (φ) = 1 as the credibility increases over time. The only dierence between the regimes is therefore in the trade costs To summarize the model predictions:

T t = N D,t θf D,t k θ-1 k k -(θ -1) w t f X,t Z t θτ θ-1 t 1-k θ-1 +N * D,t θf * D,t k θ-1 k k -(θ -1) w * t f * X,t Z * t θτ θ-1 t 1-k θ-1 , with τ t = τ 1,
16

T t,P=1 = N D,t θf D,t k θ-1 k k -(θ -1) wtf X,t Zt θτ θ-1 t 1-k θ-1 +N * D,t θf * D,t k θ-1 k k -(θ -1) w * t f * X,t Z * t θτ θ-1 t 1-k θ-1 > T t,P=0 = N D,t θf D,t k θ-1 k k -(θ -1) wtf X,t Zt θτ θ-1 t 1 η θ -α(|1 -η θ | 1-k θ-1 +N * D,t θf * D,t k θ-1 k k -(θ -1) w * t f * X,t Z * t θτ θ-1 t 1 η -θ -α(|1 -η -θ | 1-k θ-1
• Case 1: if the sensibility to volatility of the rms is high, then trade increases monotonically with peg credibility: currency union should therefore increase trade the most, then peg oneto-one, then the classical xed exchange rate, then the oating exchange rate.

• Case 2: if the sensibility to volatility of the rms is low, the level of trade is the highest for countries linked by a credible peg but it is high also (but less high) for countries with the most volatile exchange rate (oating exchange rate). This is because the increase in utility for the average rm in the country with the devaluation more than compensates for the decreased utility of the average rm in the country with appreciation. In this case, a oating exchange rate does not necessarily lead to a lower level of trade than the classical xed exchange rate.

• Currency union allows smaller rms to export because the cuto revenue to export is lower than in the other regimes.

• At steady state, when the exchange rate regime gains credibility, only trade cost dierences matter for trade. The model predicts that at steady state, xed exchange rate regimes have a bigger eect on trade than oating exchange rate regimes.

Since I have only aggregate trade data, I am able only to check the macro predictions of my model (proposition 2) not the micro predictions (proposition 1). Trade data at country level have the triple advantage of exhaustiveness (a high number of countries), homogeneity (in the collection and organisation of data) and timespan extension. In the empirical part, I expect to nd a higher level of trade for currency union, then peg one-to-one, compared to a xed exchange rate. I expect to be in case 2 (consistent with the literature) and nd there is no signicant dierence between a oating and a classical xed exchange rate. I also expect trade to increase over time for peg one-to-one and xed exchange rate regimes. At steady state, I expect to nd a dierent eect on trade for oating and classical xed exchange rates. Unfortunately, I do not have the data needed to check the prediction about the size threshold for rms to export. 0.60% of my observations. Moreover, pegs one-to-one dummies represent 9.2% of the total xed exchange rates dummies. Summary statistics are provided in table B1 in the appendix.

It should be noted that countries in peg one-to-one have tight cultural links with their anchor since there is a propensity for a common language (even higher than among members of a currency union) and also a propensity to have colonial links. These countries on average are quite poor compared to currency union members or classical xed exchange rate countries. They have small populations and small surface areas, and are geographically close to the anchor. They are also slightly more open in terms of trade. These characteristics are fairly in line with the determinants identied in the literature 22 for being part of a currency union, rst theorised by Mundell in his paper on optimum currency areas. Furthermore, Ahn, Khandelwal and Wei (2011) highlight the role of intermediaries in facilitating trade for low productivity rms. They show that intermediaries are especially important in markets that are more dicult to penetrate.

As peg one-to-one pairs and currency unions have tighter cultural links, we can speculate that intermediaries should be less importnat, and direct export less costly.

Methodology and rst results

My methodology is based on the gravity model, which is standard in the empirical literature on the determinants of bilateral trade. I follow Glick and Rose (2016) in using Head and Mayer's

ln Export ijt = β 1 CU ijt + β 2 P eg1to1 ijt + β 3 F ixed ijt + β 4 F T A ijt + λ 1it + λ 2jt + γ ij + ijt ,
where lnExport ijt is the (log) of annual export from country i to country j, CU ijt is a dummy for currency unions, P eg1to1 ijt a dummy for pegs one-to-one and F ixed ijt a dummy for classical xed exchange rate. F T A ijt is a dummy for free trade agreements. λ 1it and λ 2jt represent the time-country xed eect on exporter and importer, and γ ij the pair-country xed eect. ijt is the error term.

As the peg one-to-one is a form of xed exchange rate, my dependent variables are linked to each other, and I could have used a nested probit model, with countries facing a decision tree.

However, there are very strong assumptions on the error term, leading (if the model is not well specied) to worse misspecications than in a simple linear model. I therefore prefer a linear model. Econometrically speaking, the fact that the control variables are not independent is not a problem in the linear regression framework.

There is one additional issue that I need to address in order to get a more reliable view on the eect of these dierent exchange rate regimes on trade. These regressions suer from problems of endogeneity: countries, because they trade a lot, decide on a currency union or a peg one-to-one. The potential endogeneity is not necessarily captured by the xed eect. One way to capture it is to use an instrumental variable.

I use the instrument developed by [START_REF] Barro | Economic Eects of Currency Unions[END_REF]. The instrument uses the huband-spoke organisation of xed exchange rate. The main idea is to use the independent decisions of countries to peg/ be in currency union with a main anchor. The likelihood that they peg to the same anchor is used as an instrument since it is considered to be exogenous. For example, Portugal and Estonia are in currency union exogenously: they both wanted to link to the main anchors (Germany and France). Frankel (2010) uses a similar exogenous event in a case study to show the eect on trade. Some former French colonies were pegged to France. When France joined the Eurozone, it created an exogenous peg between the rest of the Eurozone and these former colonies. Frankel shows that trade increased by 76% between the former French colonies and the Eurozone members (France excluded), proving causality. I adopt the same methodology as [START_REF] Barro | Economic Eects of Currency Unions[END_REF] and estimate the probability that a given country enters a currency union using one of the six main anchors in Barro and Tenreyro (Australia, France, Germany, Japan, the UK and the USA), employing a probit that is presented in table A5 in the appendix. This probability depends on several factors such as distance, colonial links, common language, etc. I then compute the joint probability that two countries have a common anchor. The key point is that bilateral relations are not transitive. For example, the distance from the two countries to their common anchor does not give a precise information on the distance between these countries. Similarly, since more than one language is spoken in some countries, the relation is nontransitive.

The joint probability to be in currency union is used as an instrument for the currency union dummy, and the joint probability to be in peg one-to-one is used as an instrument for the peg one-to-one dummy. The SarganHansen test does not reject the instrument. However, I do not instrument the classical xed exchange rate since an f-test rejects my instrument as not being suciently predictive. I also modify Barro and Tenreyro's instrument by extending the number of hubs, adding India, South Africa, Belgium, Portugal, Spain, Russia, and New Zealand. The list of hubs and spokes I provide in the appendix. The estimations do not change signicantly. I report in table 1.2 the results with the simple LSDV and IV. The results discussed so far suggest that pegs one-to-one are special in the xed exchange rate family. The dierence between peg one-to-one and classical xed exchange rate lies in the fact that no price conversion is needed for peg one-to-one, while a classical xed exchange has to pay an additional conversion cost. When a shock occurs which upsets the trade balance, a country with a simple peg devalues its currency to restore the balance, and most of the time re-pegs to the same currency.

the two types of peg which is 3 years 25 for a classical peg, while pegs one-to-one have a lifespan of 21 years (against 27 years for currency unions). This gap potentially implies fundamental dierences between them. Knowing this, investors and traders act dierently and this has an impact on economic activity.

Frankel (2010) nds an increase in trade among the Eurozone members as early as 1997,

showing an anticipation eect of the single currency. In table A7 in the appendix, I check whether there is anticipation before the switch to currency union, peg one-to-one and xed exchange rate. To do that, I use one dummy per year before and after the switch to the new regime. I nd anticipation eects up to six years before the introduction of a currency union, but no eect for peg one-to-one and xed exchange rates. This is unsurprising since changing a currency is more cumbersome, and therefore can be anticipated better. The consequence is that the eect of currency unions is underestimated in table 1.2 column 1 (that is why I have much higher coecients in table A7 column 1).

In line with [START_REF] Barro | Economic Eects of Currency Unions[END_REF], table 1.2 column 3 shows that ordinary least square (OLS) results underestimate the impact of currency union on bilateral trade. However, the impact of peg one-to-one on trade is no longer signicant. This means that the increase in trade is endogenous. Indeed, looking at the characteristics of the countries in peg one-to-one, 26 it appears that their propensity to share a common language with their anchor is very high (even higher than among countries sharing a common currency), and also they have strong colonial links. This may explain the endogeneity.

The instrument variable (IV) also takes care in part of the anticipation eect highlighted in table A7 column 1, since it mainly concerns the main anchor and not the collateral members of the currency union.

My results are in line with Meissner and Oomes (2009) who show that trade was a key determinant in the choice of the anchor for a pegged country while [START_REF] Marchesiani | On The Determinants Of Currency Unions[END_REF] shows that bilateral transactions between two countries are not a useful indicator of their membership in a common 25 This short peg period supports the idea in The Mirage of Fixed Exchange Rates Obstfeld and Rogo (1995) that xed exchange rates constantly adjust and are not that xed: literally only a handful of countries in the world today have continuously maintained tightly xed exchange rates against any currency for ve years or more.

currency area.

Currency unions have an even greater impact than previously thought as the eects on trade do not come from the xity of the exchange rate, or price transparency. The fact that the currency union eect on trade comes from its xed exchange rate regime credibility is the main contribution of this paper.

Robustness checks Additional tests

I check robustness by estimating a traditional gravity equation, without pair-country and timecountry xed eects, adding all the traditional controls as in [START_REF] Barro | Economic Eects of Currency Unions[END_REF]. Table A8 in the appendix presents these results.

This exercise suggests that currency unions increase trade by 127% Therefore, I focus only on the relative dierence between currency unions and pegs one-to-one and not on levels since they uctuate so much.

Nevertheless, my ndings for currency union are in the range of those in the literature.

28 How- ever, they are higher than those obtained by measuring only the eect of the euro on bilateral trade. 29 Glick and Rose (2016) also nd much bigger eects when they include the world in their sample rather than only the Eurozone. 30 The story that emerges from both these tables is that currency union still has a bigger impact on trade than peg one-to-one. [16][17][18][19][20]. My model predicts also that trade is smaller at the beginning for peg oneto-one and xed exchange rate compared to currency union, and increases linearly over time.

However, for peg one-to-one as for currency union, the empirical results show non-linearity for the 11 and 20 year periods (again, the coecient dierences are signicant between ages 6-10 and ages 11-15, but not between ages 11-15 and ages 16-20). The curved shape can be explained by two contradictory mechanisms: on the one side, regime credibility increases and therefore, trade should increase. On the other side, De [START_REF] Berman | Time to Ship during Financial Crises[END_REF] shows that the eect of currency union on trade is decreasing over time, and this might apply also to peg one-to-one.

After 20 years, the dierence between currency union and peg one-to-one is quite small since peg one-to-one has gained credibility. Therefore, the dierence between the gap in the 0-5 year period, and the gap in the +20 year period represents the gain in commitment/credibility. At an innite horizon, the gap between currency union and peg one-to-one would represent the cost of changing between currencies (embodied in τ 2,t in my model) since there would be no longer any dierence in credibility between currency union and peg one-to-one. The classical xed exchange rate eect on trade is not signicant in the rst years, and becomes signicant after 15 years although not very high (around 17%

31 ). This is in range with what my model predicts.

Currency union and peg one-to-one eect varies across country pairs and across blocks

Chen and Novy (2016) nd heterogeneous eects of currency unions on trade, country pairs associated with small import shares beneting currency union a lot, while country pairs associated with large import shares do not benet. I replicate their methodology, 32 adding peg one-to-one.

In the rst stage, I predict the theoretical trade for each country pair by regressing observed (log of ) bilateral trade on exogenous variables (distance, common border, common language, colony, same country) and exporter-year and importer-year xed eects. In the second stage, I regress (log of ) bilateral trade on the currency union dummy, an interaction between the currency union dummy and the predicted trade, peg one-to-one dummy, an interaction between the peg oneto-one dummy and the predicted trade, and exporter-year, importer-year and pair-country xed eects. The results are presented in table 1.4.

31 e 0.158 ≈ 1.17.

32 However I use log of bilateral export as my dependent variable, and not log of bilateral export divided by the GDP of the exporter, divided by the number of dierent product categories exported by each country. Chen and Novy (2016). This means that the eect of currency union on trade is larger for pairs of countries that trade the least, and smaller for countries that trade the most. The heterogeneity of the eect applies also to peg one-to-one but is less marked.

Following Chen and Novy (2016), I split my sample into ten equally-sized intervals of predicted trade ranked by value, creating a dummy for each category. I then estimate the eect of currency union and peg one-to-one on each interval (table A10 in the appendix). I nd that currency union has the biggest impact on the 50th-80th percentile. The eect of peg one-to-one on each decile is more chaotic with no clear pattern. This might be due to the endogeneity of this eect.

Finally, I take the six biggest currency unions with at least three pegs one-to-one attached to them 33 (pound sterling, US dollar, Franc CFA, Indian Rupee, French Franc and Portugal Escudo) and estimate their eect on trade separately. The results are presented in table A11 33 Euro has no peg one-to-one attached.

in the appendix. There is a great deal of heterogeneity in the eect on trade of both currency unions and pegs one-to-one. This is not surprising since these currency unions have such dierent histories and such diverse economic links.

The case of vehicle currency pricing First, looking at the whole sample at the two-digit product disaggregation, it is interesting that the eect of the currency union is still higher than the eect of peg one-to-one, both being smaller than in table 1.2. One reason for this might be that here, my sample here starts in 1995.

De [START_REF] Berman | Time to Ship during Financial Crises[END_REF] shows that the eect of currency union on trade decreases over time, which might be the same for peg one-to-one.

Second, columns 2 and 3 show that peg one-to-one has no eect on goods traded on an organised market while currency union does. This might be because currencies used in currency unions are more powerful, and are more often used as a vehicle currency in organised markets. To sum up, the vehicle currency pricing system decreases the eect of the peg one-to-one.

Entry and exit

In this section, I am interested in comparing the eect of entering and exiting an exchange rate regime. I compare the rst ve years after entry in each exchange rate regime, to the ve years before exit. The results are presented in table 1.6.

Table 1.6: Comparing the eect of an exchange rate regime on trade during its rst 5 years and its last 5 years The eect on trade is always bigger for currency unions than for the other exchange rate regimes, whether we compare the rst or the last years of the regime. The eect is bigger in the last years in the case of both currency union and peg one-to-one. This might be linked to the fact that (as table 1.3 shows) the eect on trade tends to increase over time. My results contradict those in Glick and Rose (2016) who nd a symmetry between the eects of entering and leaving a currency union. The dierence observed between the eects on trade during the rst and last years is featured in the model for peg one-to-one but not for currency unions. Finally, note that the classical xed exchange rate has a negative and signicant impact on trade during the last years before devaluation/ change of regime, which could be one reason for the above nding.

Reasons for entering and exiting currency unions often stem from very dierent roots. In my sample, the majority of extis are brutal. For example, many of the countries leaving currency unions were former colonies that became independent and abandoned their coloniser's currency.

On the other hand, many countries in my database that joined a currency union are small (often islands) or poor countries that lack credibility or the willingness for a currency of their own (e.g., Liechtenstein, Naru, Kosovo, etc.). Other countries joined a currency union to strength political and economical links.

The reasons for entering and exiting a peg one-to-one are also diverse. A lot of formers colonies left currency unions for peg one-to-one, either with their former coloniser, or with their neighbors which were also former colonies (this applies to former French colonies in Sub-Saharan Africa).

The transition from currency union to xed exchange rate is therefore smoother. In contrast, Belgium and Luxemburg left peg one-to-one in 1999 to achieve a tighter link: currency union.

The other scenario is Argentina which in 2001 left peg one-to-one with the United States for a oating exchange rate under pressure from the markets. Similarly, in 1966 Bangladesh, Nepal and Sri Lanka broke with peg one-to-one with India when India decided to devalue the Indian rupee by 57% because of an internal economic crisis.

Finally, reasons for entering and exiting a classical xed exchange rate vary. For example, in 1992 the European Union strengthened the European Monetary System to prepare for the single currency union, allowing very little uctuations in the band. The oil countries decided to peg to the dollar in the 90's to stabilize their revenues. On the other hand, a lot of countries were pegged to the dollar during the gold standard, and abandoned the peg in the subsequent three years. Many Latin American countries faced severe economic crisis with high ination in the 1980s which forced them to abandon the peg with the dollar. After decolonisation, numerous former British colonies switched their peg from the pound sterling to the dollar. Finally, some countries have a xed exchange rate regime with a band, whose width changes over time. My database considers the status of the regime depending on the band width adopted every year, I therefore have a lot of entries and exits depending on the small size of the band of the peg.

Since the reasons for exiting and entering a exchange rate regime are dierent, it is not surprising that the eects are not the same.

Conclusion

To conclude, there is denitely something particular about peg one-to-one; it displayes the characteristics of neither the currency union nor classical exchange rate. Its positive impacts on trade increase over time and tend at steady state, to be close to currency union eects; however, in contrast to currency union, their eects appear to be endogenous. Therefore, peg one-to-one should be studied separately from currency unions, in particular, to understand the reasons of its longevity. This might have important consequences since some researchers pool currency unions and peg one-to-one.

My study conrms the important and positive eects of currency union on trade and shows that they do not come from exchange rate xity or price transparency but rather from credibility.

Indeed, a common currency is seen as a much more serious and durable commitment than other monetary arrangements [START_REF] Mccallum | National Borders Matter: Canada-U.S. Regional Trade Patterns[END_REF], currency union regime credibility is even more important than most researchers thought: credible commitment and trust are the main engine of trade. Even if government changes, monetary policy does not.

The policy implications of this study concern the importance of exchange rate regime credibility for international trade. Government's hand must be tied, and it appears that only currency union so suciently strong enough to achieve this, peg one-to-one being of little help. In addition, price transparency does not play a role in increasing trade. and to include all countries with an iso3 code (today, there are 255 country code assigned). Each currency union and peg one-to-one has to last for more than one year to qualify for inclusion in my database. Regime changes June 30 are not considered for the year.

1.A Appendix: Map

In order to build my currency union database, for each country I searched for the previous ocial currency(ies) for every year. If I found a match between two countries, the pair was classed as in currency union. I used the same methodology for the peg one-to-one database with a de facto classication (and no uctuation band allowed) on the ocial market. The peg one-to-one ended if a devaluation was notied.

There are 35 currency unions in my database, the smallest gathering two countries, and the biggest (pound sterling) gathering 46 countries. Between 1948 and 2016, 142 regions/ countries were at some point involved in a currency union. Peg one-to-one account for 8% of the pegs.

Between 1948 and 2016, 145 regions/ countries at some points were involved in peg one-to-one.

Next page, some summary statistics for each exchange rate regime. Did the Euro Protect its Members during the Great Trade Collapse?

Introduction

The eect of the euro on trade has been studied extensively in the literature with around 50 papers on the topic 1 (as of July 2017). According to these studies, the range of the euro positive eect uctuates between 2% and 30% (see Rose, 2017 for a recent metadata analysis). However, to my knowledge, no work has been done so far on the euro eect during an economic crisis. It is a priori not clear how the additional trade due to introduction of the single currency reacts to a crisis. Is it more volatile, more fragile? Has the euro protected its members during the great trade collapse? Would the trade drop observed during the nancial crisis in Europe have been bigger without the euro? Is the euro eect the same for every sector? To address these questions, I study the eect of the euro on trade during the great trade collapse a context that provides three advantages: it was exogenous to the Eurozone, was unexpected and was worldwide.

The literature identis various factors explaining the great trade collapse. On the supply side, researchers highlight an important trade credit crunch [START_REF] Auboin | The challenges of trade nancing[END_REF] Overall, I expect to nd a negative eect of the euro on trade during the great trade collapse, in view of the causes of that collapse. However, I nd that the euro amortized the great trade collapse. I control for all the causes of the great trade collapse and they do not shrink the positive and signicant coecient associated to the euro eect on trade during the crisis.

The nancial crisis caused world GDP to decrease by 4% and world trade by 15% (in 2009 overshooting by 18 against 1.9 in the 1990-2008 period).

2 [START_REF] Baldwin | The Great Trade Collapse: Causes, Consequences and Prospects[END_REF] states that "the great use of multiple high-dimensional xed eects is demanding but it captures many confounding factors, and provides the cleanest benchmark against which to assess the impact of the euro during the great trade collapse. My results are robust to many robustness checks (changing the control group, and the time period, excluding countries etc.).

However, at the sector level, it appears that the positive and signicant eect I observe at the aggregate level is due to a composition eect: the euro protected only those sectors exporting the most. I nd that the sectors that the euro helped the most are machinery (19% of total trade), chemical (14% of total trade), metal (11% of total trade) and transport (15% of total trade). It appears that these large sectors are also more nancially independent and more sensitive to exchange rate volatility. The euro had a positive eect on precisely such sectors with these characteristics. The European banks were particularly hard hit by the nancial crisis, therefore, the nancially independent sectors did comparatively better. Also, exchange rate volatility during the crisis increased everywhere in the world except in the Eurozone where by nature it remained xed. Therefore, the euro provides a premium for exchange rate stability during economic crises.

In section 2, I study the eects of the euro during the crisis using aggregate data. In section 3, using sector data I check whether the eects observed at the aggregate level are driven by particular sectors. In section 4, I test the causes of the great trade collapse to understand the channels through which the eect of the euro occurs. Section 5 concludes.

ln Export ijt = βEuro ijt * Crisis t + λ 1it + λ 2jt + λ 3ij + ijt , (2.1)
with:

• Export ijt bilateral trade 15 that the eects of the euro on trade in general are much smaller than the eects of the other currency unions in the world. Furthermore, the control group is dierent.

To summarize, the euro had a positive and signicant approximately 10% eect on aggregate trade during the crisis. This result is very robust and has not been highlighted by the literature so far.

So far, I have studied trade only at the country level. The eects of the euro at the aggregate level seem pretty robust. I next try to understand the reasons for this positive eect on trade, studying trade at sector level.

Heterogeneous eect of the euro across sectors

In this section, I work at a disaggregated level. I check whether or not the eect of the euro was homogenous across sectors, and if not, which sectors beneted most from the euro during the crisis.

Descriptive statistics

The Harmonized System (HS) classication includes 21 sectors (1-digit level) and 97 subsections

(2-digit level), details are provided in the appendix. 19 which rely on make-to-order production. Therefore, the impact of the crisis was postponed for a few months, or even a few years (e.g. the airbus order book for the A320 is lled up for the next ten years).

Eect of the euro on each sector during the crisis

To test my hypothesis that the biggest exporting sectors are those impacted the most positively by the euro, I use dummies euro * crisis * sector to measure the impact of the euro on each sector and do not initially add sector xed eects (otherwise this would capture the eect of sector size). It is to be expected that a country, or a group of countries will refocus on those sectors with highest comparative advantage during a crisis. It seems that euro increased this tendency.

The positive and signicant eect on trade found at the aggregate level is driven by the sectors exporting the most, where the euro has the biggest comparative advantage. This result is new in the literature.

Identifying the channels

In this section, I test the dierent causes of the great trade collapse at sector level to understand through which channel the single currency protected the intra Eurozone trade.

Causes of the great trade collapse

The literature identies many dierent causes for the great trade collapse. I test whether they played a role in the positive and signicant eect of the euro observed in the long run in crises [START_REF] Yi | Can Vertical Specialization Explain the Growth of World Trade?[END_REF] was the rst to show how relatively small tari reductions can cause relatively large increases in world trade through global value chains (the small transaction costs adding up each time a border is crossed). Given trans-borders transaction cost reductions, he predicted the increasing international vertical specialization in currency unions. Flam and Nordström (2006) found an increase in the number of global value chains in the Eurozone, trade in the machinery sectors contributing the most due to the single currency.

Since global value chains are considered in the literature as a multiplier eect of the great trade collapse, it might be expected that Eurozone members would not perform as well as the other developed countries. However, some authors argue that global value chains do not play a big part in trade drops. Indeed, Bénassy-Quéré et al. (2009) prove that relative prices held constant, vertical trade should react proportionally to a drop in world GDP. Therefore, global value chains do not explain the observed overshooting of world trade on world GDP. [START_REF] Auboin | The challenges of trade nancing[END_REF] shows that industries that are the biggest users of global value chains, are also those with the most nancial dependency on banks (as shown also by my data). Therefore, the trade drop observed during the nancial crisis is correlated to but not necessarily caused by vertical trade.

Finally, Altomonte and Ottaviano (2009) argue that trade ows within supply chains are more rather than less, resilient to large adverse shocks. Indeed, setting up organized supply chains entails some sunk costs, so rms would rather adjust the entire chain along the intensive as opposed to the extensive margin. for the banking sector crisis within each country. Implementing a Spearman rank test shows a negative relationship between nancial dependency and the euro eect during the crisis (-0.62 for 2008-2010 period and -0.65 for 2008-2015 period). Again this is not surprising since European banks were hit particularly hard by the nancial crisis, and therefore the euro had a positive impact on the nancially independent sectors, and a negative impact otherwise.

Protectionism is not part of the story since, had it played a role, it would have been at the European Union not the Eurozone level. In my specication, my pair-country xed eect absorbs the eect of the European Union (there are no entries to the EU in my sample). comparatively good performance in trade. This result is one of the main contributions of this paper.

The euro, the crisis and uncertainty

Conclusion

To conclude, the euro protected its members during the great trade collapse. However, this protection was not homogenous, since the sectors exporting the most were the ones that beneted the most from this protection. The sectors exporting the most were more sensitive to the uncertainty linked to exchange rates. To my knowledge, the fact that the euro had a positive impact on trade during a major trade crisis has not previously been identied. However, it should be taken into account when measuring the advantages and drawbacks of a single currency. My specication does not allow dierentiation between the impact of the euro and other factors confounded by euro membership (banking union, Sixpack, etc.). These reforms would not be expected to aect sectors dierently, and therefore should not have driven the eect I have highlighted.

My results raise a new question which could be studied in a separate investigation: the eect of currency unions on trade generally during the great trade collapse. I nd that the positive eect of the euro comes mostly from the absence of exchange rate volatility. This characteristic is common to all currency unions. The results in table B11 suggest a positive general eect of currency unions during the crisis. It would be interesting to compare the eect of dierent currency unions during the crisis to understand whether the eect was homogenous. It would help to know whether the eects of the euro stem from the single currency, or laws supporting Eurozone integration (such as the banking union).

• Euro * CrisisS1 : dummy for both countries being in the Eurozone in sector 1, during the crisis • Euro * 2004 : dummy for both countries being in the Eurozone in 2004

• S1 : sector 1 (for more details about sector 1, the HS classication can be found in the appendix)

• CU Crisis08 -10 : dummy for both countries being in currency union between 2008 and 2010

• CU Crisis08 -15 : dummy for both countries being in currency union between 2008 and 2015

• Currency U nion : dummy for both countries being in currency union together (and dollarized economies)

• European U nion : dummy for both countries being in the European Union • N on Durable Good : dummy for non-durable good (Engel and Wang index)

• Real interest rate : lending interest rate adjusted for ination as measured by the GDP deator (World Development Indicators from the World Bank) (1960-2015)

• Domestic credit to private sector : nancial resources provided to the private sector by nancial corporations (World Development Indicators from the World Bank) (1960-2015)

2.B Appendix: Maps The expansion of global value chains over the last 20 years is undeniable, and when designing the monetary policy rules, it is important to take account of their eect on the transmission of shocks.

In this chapter, I analyse the eect of global value chains on international transmission of productivity and monetary shocks in dierent exchange rate regimes and export price currencies. I then discuss optimal monetary policy and monetary coordination.

Based on the observation that global value chains increase synchronisation of business cycles, 1

A long history of studies on optimal monetary policy

Studies on sticky prices, exchange rates and optimal monetary policy started some time ago.

Friedman (1953) called for freely oating exchange rates as the most ecient mechanism to achieve relative price adjustment between countries when prices are sticky, since exible exchange rates can deal with country specic productivity shocks or demand shocks. Currency Pricing, LCP). In contrast to the PCP case, in LCP, monetary policies cannot replicate exible price allocation since the pass-through from the exchange rate is zero. Therefore, the paper concludes that in this case, a xed exchange rate would be optimal. Therefore, an optimal exchange rate regime depends on the currency of the export pricing.

Corsetti and Pesenti (2009) extend [START_REF] Devereux | Monetary Policy in the Open Economy Revisited: Price Setting and Exchange-Rate Flexibility[END_REF] analysis in various directions.

They analyse the transmission of productivity and monetary shocks, the welfare gains from macroeconomic stabilization, the optimal monetary policies and the coordination required to achieve this goal. They highlight the role of exchange rate pass-through, and the choice of exchange rate regime according to the export pricing currency. [START_REF] Devereux | Expenditure Switching vs. Real Exchange Rate Stabilization: Competing Objectives for Exchange Rate Policy[END_REF] analyse the trade-o between the objectives of stabilizing consumptionbased real exchange rates and allowing terms of trade adjustment. They use a vertical-trade model consistent with both evidence of weak exchange rate pass-through to consumer goods prices, and high pass-through to imported goods prices.

Brustein, Kurz and Tesar (2008) use a theoretical model that is close to mine which also predicts that business cycles are more synchronized. However, they provide no public policy implications. [START_REF] Shi | Optimal Monetary Policy with Vertical Production and Trade[END_REF] and [START_REF] Wang | Optimal Monetary Policy Under a Global Dollar Standard: The Eect of Vertical Trade and Production[END_REF] are the closest papers to mine in the sense that they analyse the optimal monetary policy using a vertical-trade model. [START_REF] Shi | Optimal Monetary Policy with Vertical Production and Trade[END_REF] choose sticky prices for intermediate goods which provides dierent results: they nd that the existence of the transborder spillover eect depends on the currency of export price setting. [START_REF] Wang | Optimal Monetary Policy Under a Global Dollar Standard: The Eect of Vertical Trade and Production[END_REF] introduce asymmetry in the pricing behavior of exporters in the Home and Foreign countries (Home exporters set prices in the currency of the producers, Foreign exporters set prices in the currency of the consumers). This implies that the responses of monetary authorities to productivity shocks from the stage of intermediate goods production are symmetric (which I also nd) but asymmetric to productivity shocks from the stage of nal goods production. Furthermore, they analyse optimal monetary policy when there is a shock in both the intermediate and nal stages of production. They nd that gains from cooperation are related to the covariance of productivity shocks in these two stages.

Finally, my work is linked to [START_REF] Mundell | A Theory of Optimum Currency Areas[END_REF] since I examine whether vertical trade can help a geographic area to become an optimal currency area. Mundell explains one of the key aspects as symmetry of the productivity shocks.

The model

Households

Households have an identical utility

U t (j) = ln C t (j) -κ t (j), (3.1)
where is hours worked. κC is the marginal rate of substitution between consumption and leisure. C t (j) is a consumption basket of the Home and Foreign goods

C t (j) = C H,t (j) 1/2 C F,t (j) 1/2 . (3.2)
For each household j in the Home country, the consumption indices of Home and Foreign brands are dened as

C H,t (j) = [ 1 0 C t (h, j) 1-1 θ dh] θ θ-1 , C F,t (j) = [ 1 0 C t (f, j) 1-1 θ dh] θ θ-1 , (3.3)
where θ is the elasticity of substitution between varieties, with θ > 1, and C t (h, j) and C t (f, j)

are respectively consumption of Home brand h and Foreign brand f by Home agent j at time t.

The Foreign households are analogously characterized.

The utility-based CPI P t is dened as

P t = 2P 1/2 H,t P 1/2 F,t , (3.4)
where

P H,t = [ 1 0 p t (h) 1-θ dh] 1 1-θ , P F,t = [ 1 0 p t (f ) 1-θ dh] 1 1-θ , (3.5)
p t (h) and p t (f ) being the Home-currency prices of the varieties.

It follows that the Home-currency individual demand curves for varieties h and f are respectively

C t (h, j) = p t (h) P H,t -θ C H,t (j) , C t (f, j) = p t (f ) P F,t -θ
C F,t (j).

(3.6)

The optimal consumption of nominal spending is P H,t C H,t (j) = P F,t C F,t (j) = 1 2 P t C t (j).

(3.7)

Final-good rms

Households own the portfolio of all rms. Home nal-good rm h produces Y (h) using X H,t and X F,t intermediate goods, according to the following production function

Y t (h) = X H,t (h) α X F,t (h) 1-α , (3.8)
with α being the share of Foreign and Home good used in the production function. Foreign nal-good rm f produces Y (f )

Y t (f ) = X F,t (h) α X H,t (h) 1-α . (3.9)
The nal-good Home rm prot is From the cost-minimization problem, we can derive nal-good rm h's demand for X H,t and

Π H,t (h) = X H,t (h) α X F,t (h)
X F,t X H,t (h) * = p XH p XF 1 -α α α Y t , X F,t (h) * = p XF p XH α 1 -α 1-α Y t .
M C t (h) = p XH,t α α p XF,t 1 -α 1-α . (3.12)
Prices are exible, imperfectly competitive rms set prices by charging an optimal mark-up over their marginal costs

p t (h) f lex = θ θ -1 M C t (h) = θ θ -1 p XH,t α α p XF,t 1 -α 1-α . ( 3
X H,t = Z H,t H,t , (3.14)
where Z H,t is the productivity of labor and H,t is the labor for the good X H,t .

Their prot function is

P t C t = µ t . (3.20)
t is the nominal exchange rate dened as

t = µ t µ * t , (3.21)
and

P t C t = t P * t C * t . (3.22)
The Aggregate Supply "AS" equation relates output to total employment measured in terms of hours worked,

C t = (Z H,t H,t ) α (Z F,t F,t ) 1-α τ,
with τ being an index of international spillover, reecting the macroeconomic impact of uctuations of relative prices and terms of trade on the Home economy. It is dened as

τ = [ P 2 ( 1 P H + 1 P * H )] -1 . (3.23)
2 Natural rate of employment is the employment rate without shocks. At any point in time, the intersection between "AD" and "AS" determines the equilibrium allocation of consumption C t and labor H,t for given values of the exogenous variables µ t , Z H,t and Z F,t , as well as for a given price level P t . Of course, the price level is an endogenous variable in our system. We therefore need to analyze how rms optimally set their prices.

Flexible price case

The optimal price charged by the representative nal-good rm will be

P f lex H,t = P * f lex H,t = θ θ -1 w H,t Z H,t α α w F,t Z F,t 1 -α 1-α , (3.24)
P f lex F,t = P * f lex F,t = θ θ -1 w F,t Z F,t α α 1 w H,t Z H,t 1 -α 1-α .
With a perfectly competitive labor market, the equilibrium wage rate in units of consumption (W/P ) is equal to the marginal rate of substitution (MRS) between consumption and leisure of the representative agent w H = κP t C t . (3.25) From this equation and the exible price equation, we can now calculate the rate of employment The exchange rate is stabilized. The decrease of p XH,t has a bigger impact on the consumer price index than without vertical trade. It also osets expenditure switching eect.

H,t H,t = θ -1 θκτ α α (1 -α)

Rigid price case

We now consider the rigid price case, but only for nal goods. Indeed, according to Clark

= θ θ -1 E t w H,t Z H,t α α w F,t Z F,t 1 -α 1-α , (3.27)
P P CP F,t = P * P CP F,t = θ θ -1 E t w F,t Z F,t α α 1 w H,t Z H,t 1 -α 1-α , and = θ -1 θκτ α α (1 -α) 1-α E t µ t /Z H,t µ * t /Z F,t 1-α .
(3.28)

Consumption moves depending both on Home and Foreign monetary policy

C t = ( µ t P H,t ) 1/2 ( µ * t P F,t ) 1/2 , C t = θ -1 θκ α α (1 -α) 1-α µ t E t ( µt Z H,t ) α ( µt Z F,t ) 1-α 1/2 E t ( µ * t Z F,t ) α ( µ * t Z H,t ) 1-α 1/2 .
(3.29)

We now calculate the optimal policy response to a Z H shock, all else being equal. First without coordination (µ * is given), the government wants to maximize E(U ),

E t (U t ) = E t (ln C t -κ H,t ) = E t (ln µ t ) -E t (ln P 1/2 H,t P 1/2 F,t ) -κ H,t , E t (U t ) = E t (ln µ t ) - 1 2 E t (ln µ t Z α H,t Z 1-α F,t ) + constant, ∂E t (U t ) ∂µ t ≡ µ -1 t - 1 2 Z α H,t Z 1-α F,t E t (µ t /Z α H,t Z 1-α F,t ) = 0. (3.30)
Therefore µ t = βZ α H,t Z 1-α F,t , β being a constant known in advance by the rms.

The government still has an inward-looking policy as his policy does not depends on the Foreign monetary policy. Monetary policy responds to both Home and Foreign shocks: the country responds the same way to Home and Foreign shocks.

The Foreign country being symmetrical, it will have the same monetary policy function. So maximizing an average of the two functions will give the same monetary rules. There is no gain to cooperation. If the Foreign government chooses to implement a monetary expansion (which would increase welfare both at home and abroad), the exchange rate does not change and we have the same eects than in the exible price case.

Local Currency Pricing Firms preset a price in domestic currency for the domestic market, and a price in Foreign currency for the export markets. With LCP, rms optimally set:

P LCP H,t = θ θ -1 E t w H,t Z H,t α α w F,t Z F,t 1 -α 1-α , P * LCP H,t = θ θ -1 E t 1 w H,t Z H,t α α w F,t Z F,t 1 -α 1-α , (3.31)
P LCP F,t = θ θ -1 E t w F,t Z F,t α α 1 w H,t Z H,t 1 -α 1-α , P * LCP F,t = θ θ -1 E t w F,t Z F,t α α 1 w H,t Z H,t 1 -α 1-α , and = θ -1 θκτ α α (1 -α) 1-α E t µ t /Z H,t µ * t /Z F,t 1-α , (3.32)
C t = θ -1 θκ α α (1 -α) 1-α µ 1/2 t µ * 1/2 t E t ( µt Z H,t ) α ( µt Z F,t ) 1-α 1/2 E t ( µ * t Z F,t ) α ( µ * t Z H,t ) 1-α 1/2 .
(3.33)

We now calculate the optimal policy response to a Z H shock, all else being equal. First without coordination (µ * is given), the government wants to maximize E(U ). We can calculate the optimal monetary policy when prices are rigid (Home and Foreign countries are forced to coordinate).

E t (U t ) = E t (ln C t -κ H,t ) = 1 2 E t (ln µ t ) + 1 2 E t (ln µ * t ) -E t (ln P 1/2 H,t P 1/2 F,t ) -κ H,t , = 1 2 E t (ln µ t ) - 1 2 E t (ln µ t Z α H,t Z 1-α F ;t ) - 1 2 E t (ln µ t Z α F,t Z 1-α H,t ) + constant, ∂E t (U t ) ∂µ t ≡ µ -1 t - Z α H,t Z 1-α F,t E t (µ t /Z α H,t Z 1-α F,t ) - Z α F,t Z 1-α H,t E t (µ t /Z α F,t Z 1-α H,t ) = 0. ( 3
E t ( 1 2 U + 1 2 U * t ) = 1 2 E t (ln µ t ) - 1 4 ln E t ( µ t Z α H,t Z 1-α F,t ) - 1 4 ln E t ( µ * t Z α F,t Z 1-α H,t ) + 1 2 E t (ln µ * t ) - 1 4 ln E t ( µ t Z α H,t Z 1-α F,t ) - 1 4 ln E t ( µ * t Z α F,t Z 1-α H,t ) + constant. µ t being equal to βµ * t , ∂E t ( 1 2 U t + 1 2 U * t ) ∂µ t ≡ (1 + β) 1 µ t - Z α H,t Z 1-α F,t E t (µ t /Z α H,t Z 1-α F,t ) - Z α F,t Z 1-α H,t E t (µ t /Z α F,t Z 1-α H,t ) = 0. (3.36)
The optimal policy solves equation 3.36. The countries consider the shock as a weighted average of the impact of the shock in the two countries. Since the exchange rate is xed, the Home and Foreign central banks cannot unilaterally implement a monetary expansion. To increase the welfare of both countries, they would need to coordinate so that the two monetary expansions are of the same size. This way, they would go back to the exible price case. Figure 3.7 shows the mechanisms just described. In this case, it would be easier to have a monetary union, with a single central bank implementing a monetary expansion so that the coordination between the countries is perfect.

However, the size of the expansion is a problem: if the two countries use the same proportion of X H (α = 1/2), then the productivity shock is the same on both side and the two countries need the same level of monetary expansion. However, if the productivity shock is not the same on both sides, then the central bank maximizes the global utility, in one country there will be too much ination, and in the other too much unemployment.

Monetary union

Mundell (1961) explains his concept of optimum currency area. One of his main points is that to have an optimum currency area, you need shocks to be symmetric in the area so that every part of the area needs the same optimal monetary policy. Indeed, if a shock is asymmetric, and there is a shift in demand from a good produced in a region to a good produced in another region, it produces unemployment in the rst region, and ination in the second. Then in the rst region, to correct unemployment, the bank needs to put in place monetary expansion. However, this monetary expansion aggravates inationary pressure in the second region. In this case, a "currency area cannot prevent both unemployment and ination among its members" and the China's ranking in gure C1). Figure C1 shows that Luxemburg, 9 Hungary and Czech Republic are the most vertically specialized, while Brazil, Japan and the USA are the least specialised. 10

My results are in the range of Backer and Yamano (2012).

I calculate vertical specialization by sector in the same way, as the ratio of intermediate good inputs imported by a sector in nal good outputs exported to nal Foreign consumers. Figure C2 shows that mining and quarrying, construction and basic metals and fabricated metal sectors are the most vertically integrated.

11 The fact that their ratio is superior to 1 means that the output of these sectors does not reach nal consumer directly but is used by other industries as intermediate goods. 9 Luxemburg ratio is superior to 1, which means either that it sells most of its nal goods in its own market or it exports its output as intermediate goods.

10 Not surprising considering the size of the countries and the fact that Japan is an island. They can more easily keep all the production stages within country.

11 The sector codings and titles are presented in the appendix 2.

+λ 1ijsr + λ 2ist + λ 3jrt + ijt ,
where corrSectorBusinessCycle ijsrt is the correlation between detrended intermediate good trade from country i sector s to country j sector s and the detrended nal good trade from country j sector r exported to nal consumer everywhere in the world at time t. intensityIntermediateT rade ijst is the ratio between intermediate good from country i sector s used by sector r of country j at time t and the nal goods exported by sectors r and s at time t. intensityF inalT rade ijsrt is the ratio between nal good traded from country i sector r to country j at time t and the nal goods exported by sectors r and s at time t (this way, I control for direct trade). λ 1ijsr , λ 2ist and λ 3jrt are pair-sector-xed eects, exporter-sector-time xed eects and importer-sector-time 12 Frankel andRose (1997, 1999) xed eect, 17 this way I control for time-invariant country pair-specic variables which may have an impact on vertical trade synchronization, and I control for general increase of trade over time.

I plan to also test a specication without exporter-sector-time xed eects and importer-sectortime xed eects together, and adding additional control variables, to take into account if the case where a shock hits the two sectors simustaneously. I plan to add other control variables, as xed exchange rate regime dummies, or nancial integration proxies.

My xed eects in part handle the endogeneity problems but do not get rid of them completely.

I tried two instruments. The rst instrument, following Frankel and Romer (1999) contains distance, GDP per capita, GDP, colonial links, common language, common border, FTA, currency union, land surface, population size, island, landlocked. A test for overidentifying restrictions rejects it. 18 The second instrument contains WTO, FTA and currency union only. An F-test rejects it since it does not have strong predictive power for trade. Currently, I am searching for a new instrument.

3.A.4 Results at sector level

In my most favored specication (OLS), the eect of vertical trade on sectors' business cycle synchronisation is positive but not signicant. Direct trade increases signicantly business cycle sycnhronisation which is in line with the literature. Free trade agreements and currency unions favor a correlation between the sectors' business cycle. WTO does not increase business cycle synchronisation which is not surprising since it includes a wide range of countries. 17 Pol, Fort and Tintelnot (2017) show the importance of importer xed eect, as the size of the country is related to the size of the rms outsourcing, itself being linked to the number of countries they can import from.

18 Indeed, [START_REF] Gruben | How much does international trade aect business cycle synchronization ? Working Paper[END_REF] argue that it is correlate not onlyd with the outcome variable but also to the dependent variable. I then check the eect of vertical trade on the sychnonisation of sector r of country j with its sector trading partners. Table C2 shows the results of this specication, for the sectors using global value chains the most intensively. The results for all the sectors are provided in appendix 3 (tables C4, C5, C6 and C7).

Table C2: OLS: Eect of vertical trade on the sychnonisation of each sector

(1) (2) (3) (4) (5)
(2)

(3) (4) (5) (6) (7) (8)

Conclusion

Overall message

In this thesis, I show the importance of certainty for trade. Indeed, currency union by guaranteeing an irrevocable xed exchange rate promotes trade, both in normal times and during economics crisis. The credibility of a peg, and therefore the level of certainty of the future rate (used by the rms when they take the decision to export) depends on the lenght of time without devaluation, and the type of commitment. Fixed exchange rate regimes have an eect on trade only after 15 years, when the credibility of the peg is sucient. By entering an extremely binding commitment, currency unions do not need to wait 15 years to become credible, they have an eect on trade from the rst year. Indeed, the cost of exit is very uncertain, and potentially very high, preventing countries with economic diculties from leaving in order to make a competing devaluation. Looking at historical examples, it is rare for a country to leave a currency union for economic reasons; it is usually but for political reasons.

Original results

In the rst chapter, I found that currency union increases trade more than peg one-to-one, both being positive and signicant. I found no eect of classical xed exchange rate regimes compared to oating exchange rate regimes. Currency unions have very strong eects from the outset, pegs one-to-one have small eects and classical xed exchange rates have no eect. At steady state, the eects of currency unions (that remain stable over time) and pegs one-to-one tend to converge. Classical xed exchange rate regimes have small eects only after 15 years which is also predicted by my model. However, correcting for endogeneity issues, peg one-to-one 143 the eects of the reforms implemented only by Eurozone members. Furthermore, since I conclude is that the channel through which the euro had a positive eect on trade during the crisis lacks of volatility, the next logical step would be to replicate this for all currency unions during crisis since this would constitute a characteristic common to all of them. It would help me to know whether I have identied the right channel.

In the third chapter, the scope of my model is not wide as all the nal goods consumed by users come from vertical trade; however, in reality this is not the case. The new transmission paths highlighted by my model therefore do not represent very signicant paths for the transmission of shocks. One extension to the model could be to add another tradable sector in each country.

This would provide a more realistic view since most trade is not vertical. In addition, in my model, the forward-looking nature of ination is neglected. My model presents a link between employment and ination, a sort of original form Phillips curve. However, this equation is too simplistic because the agents anticipate ination, which does not appear in my model. It seems that the euro amortized the trade drop observed throughout the world, owing to the absence of exchange rate volatility among its members. Chapter 3 presents a theoretical model to study the transmission of shocks in a vertically specialized economy, and proposes an optimal monetary policy to stabilize the economy. The model promotes symmetrical monetary policies between the two countries sharing a production mode based on global value chains.

Université

  -dierences: robustness checks for the period 2003-2015 . . . . . . . 92 B5 Dierence-in-dierences: excluding countries . . . . . . . . . . . . . . . . . . . . . 93 B6 Dierence-in-dierences: excluding countries . . . . . . . . . . . . . . . . . . . . . 94 B7 PPML: dealing with the zero ows problem . . . . . . . . . . . . . . . . . . . . . 95 B8 Dierence-in-dierences: changing the period of reference . . . . . . . . . . . . . 95 B9 Dierence-in-dierences: changing the period of reference . . . . . . . . . . . . . 96 B10 Dierence-in-dierences: changing the start of the crisis (2009 instead of 2008) . 96 B11 Dierence-in-dierences: eect of currency unions on trade during the crisis . . . 97 B12 Dierence-in-dierences: eect of the euro per sector controlling for size . . . . . 98 C1 Dierent specications, my favorite being the OLS . . . . . . . . . . . . . . . . . 131 C2 OLS: Eect of vertical trade on the sychnonisation of each sector . . . . . . . . . 132 C3 List of sectors in my database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 C4 OLS: eect of vertical trade on business cycle synchronisation -sector 1 to 9 . . . 138 C5 OLS: eect of vertical trade on business cycle synchronisation -sector to 18 . 139 C6 OLS: eect of vertical trade on business cycle synchronisation -sector to 27 . 140 C7 OLS: eect of vertical trade on business cycle synchronisation -sector to 34 . 141 Résumé en français Dans le cadre de cette thèse, j'étudie en profondeur le lien entre les unions monétaires et le commerce, et je détermine les mécanismes sous-jacents aux eets signicatifs découverts par la littérature. J'étudie ce lien dans les deux directions: comment les unions monétaires accroissent le commerce et comment le commerce met en place les conditions appropriées pour une union monétaire optimale. L'idée de mon premier chapitre est de mesurer l'eet de chaque caractéristique de l'union monétaire (transparence des prix, impossibilité de dévaluer et absence de coûts de transaction) sur le commerce. Je constate que l'absence d'incertitude quant au taux de change est la caractéristique ayant le plus grand eet sur le commerce. En eet, contrairement au taux de change xe, les unions monétaires s'engagent à maintenir un niveau du change xe irrévocable. La crédibilité d'un tel engagement est extrêmement élevée, car le coût de la sortie d'une union monétaire et ses conséquences sont énormes. L'exemple de la Grèce, qui aurait eu besoin d'une dévaluation compétitive, mais qui a préféré payer un prix élevé en restant dans la zone euro, est un bon exemple de la force de cet engagement.J'étudie ensuite l'eet des unions monétaires, et plus particulièrement de l'euro, pendant la crise économique de 2008. Parmi toutes les caractéristiques des unions monétaires, l'absence de volatilité des taux de change malgré la pression des marchés est celle qui fait la diérence en période de crise économique. En eet, en temps de crise, les taux de change sont trois à quatre fois plus volatils que d'habitude, et l'absence de volatilité donne une prime aux membres d'une union monétaire. xiii Mon dernier chapitre change de perspective, car il étudie comment le commerce crée un environnement propice aux unions monétaires. J'étudie spéciquement le commerce vertical, qui implique que les diérentes étapes de production d'un bien se situent dans diérents pays. Cette nouvelle organisation de la production favorise la transmission des chocs entre les pays partageant des chaînes de valeur mondiales. Il en résulte que les cycles économiques sont plus corrélés, ce qui rend les unions monétaires plus attrayantes. En eet, comme les chocs sont symétriques entre les pays, les politiques monétaires optimales sont les mêmes de part et d'autre de la frontière, il ne serait donc pas problématique qu'une seule banque centrale opère dans les deux pays.

  xe pour distinguer l'eet de chaque diérence sur le commerce. Tout d'abord, je construis un modèle théorique micro-fondé pour expliquer comment les diérentes caractéristiques des régimes de change (principalement le coût du commerce et la possibilité de dévaluation) inuent sur le commerce. Mon modèle explique les diérences entre les eets à court terme et à long terme sur le commerce de chaque régime en permettant aux entreprises de tenir compte de la longévité d'un ancrage pour évaluer sa crédibilité. Mon modèle est lié à Melitz (2003). Ce modèle a l'avantage majeur d'expliquer simplement et intuitivement pourquoi toutes les entreprises n'exportent pas. J'ai choisi de baser mon modèle sur celui de Melitz parce que les mécanismes mis en évidence pour expliquer les micro-décisions d'une entreprise d'exporter ou non peuvent être utilisés pour expliquer le commerce bilatéral agrégé entre deux pays, en fonction des coûts commerciaux (comme les régimes de change). Je modie la fonction d'utilité de l'entreprise en ajoutant une désutilité à la volatilité des bénéces et en modulant le coût des échanges en fonction du régime de change, pour rendre le modèle plus réaliste. Je compare alors mes résultats à ceux de Melitz. Bien que je n'utilise que des données macroéconomiques dans mon analyse empirique, l'utilisation d'un modèle micro-fondé me semble appropriée car il permet de mieux comprendre les mécanismes impliqués. Mon modèle prédit que les unions monétaires échangent davantage que les régimes de change xe classique et un pour un. Il apparaît que le commerce des unions monétaires est stable au l du temps, tandis que le commerce des régimes de change xe augmente à mesure que leur crédibilité s'accroît. Par conséquent, les niveaux de commerce entre ces régimes convergent. De plus, à ma connaissance, mon modèle est le premier à expliquer pourquoi les régimes de change xe, ottant et classique ont des eets semblables sur le commerce au cours des premières années, mais pas en régime permanent. Pour résumer les prédictions du modèle: Cas 1: si la sensibilité à la volatilité des rmes est élevée, le commerce augmente de façon monotone avec la crédibilité du régime: l'union monétaire devrait donc augmenter le plus le commerce, puis le régime de change xe un pour un, le régime de change xe classique, et enn le régime de change ottant. Cas 2: si la sensibilité à la volatilité des entreprises est faible, le commerce est le plus élevé pour les pays liés par un régime de change xe crédible, mais il est également élevé (mais moins élevé) pour les pays dont le taux de change est le plus volatil (taux de change ottant). La raison est que l'augmentation de l'utilité pour l'entreprise moyenne dans le pays dont la monnaie se déprécie plus que compense la baisse de l'utilité pour l'entreprise moyenne dans le pays dont la monnaie s'apprécie. Dans ce cas, le régime de change ottant n'entraîne pas nécessairement un niveau d'échanges inférieur au régime de change xe classique. L'union monétaire permet aux petites entreprises d'exporter parce que les recettes minimum pour exporter sont plus faibles que pour les autres régimes. En régime permanent, lorsque le régime de change gagne en crédibilité, seules les diérences de coûts commerciaux expliquent les diérences de niveau de commerce. Le modèle prédit qu'à l'état stationnaire, les régimes de change xe ont un eet plus important sur le commerce que les régimes de change ottant.

  variable indiquant les unions monétaires, une variable indiquant les régimes de change xe un pour un et une variable indiquant les régimes de change xe classique. Ces variables indicatrices xvii me permettent de mesurer l'eet de chaque régime de change sur le commerce. Cette régression soure cependant d'un problème d'endogénéité: des pays, parce qu'ils commercent beaucoup, décident d'avoir une union monétaire ou un régime de change xe un pour un. L'endogénéité potentielle n'est pas nécessairement captée par les eets xes. Une façon de la capturer est d'utiliser une variable instrumentale. J'utilise l'instrument développé par Barro et Tenreyro (2007). L'instrument exploite l'organisation en étoile du régime de change xe. L'idée principale est d'utiliser les décisions indépendantes des pays de s'ancrer/être en union monétaire avec un pays principal. La probabilité que deux pays se rattachent au même pays d'ancrage est utilisée comme un instrument car elle est considérée comme exogène. Je suis la même méthodologie que Barro et Tenreyro (2007) et j'estime la probabilité qu'un pays donné entre dans une union monétaire avec l'un des six principaux pays (Australie, France, Allemagne, Japon, Royaume-Uni et Etats-Unis) en utilisant un probit. Cette probabilité dépend de nombreux facteurs, comme la distance, les liens coloniaux, une langue commune, etc. Je calcule ensuite la probabilité conjointe que deux pays aient un point d'ancrage commun. En utilisant la méthode des moindres carrés ordinaire, je constate que l'union monétaire accroît davantage le commerce que le régime de change xe un pour un, leurs eets étant positif et signicatif. Je ne trouve aucun eet du régime de change xe classique par rapport au régime de change ottant. Les résultats sont conformes aux prédictions de mon modèle. Cependant, si l'on corrige les problèmes d'endogénéité, les eets du régime de change xe un pour un deviennent non signicatifs. Par ailleurs, les unions monétaires dès le début ont des eets très forts, alors que les régimes de change xe classiques n'en ont aucun. Toutefois, à l'état d'équilibre, les eets des unions monétaires (qui demeurent stables au l du temps) et des régimes de change xe un pour un tendent à converger. Le régime de change xe classique n'a d'eets (minimes) qu'après 15 ans, ce que mon modèle prédit également. Mes résultats suggèrent que les régimes de change xe un pour un ont une place à part dans la "famille des taux de change xe". La diérence entre le régime de change xe un pour un et le régime de change xe classique réside dans le fait qu'aucune conversion de prix n'est nécessaire pour le régime de change xe un pour un, alors que le régime de change xe classique doit s'acquitter d'un coût de conversion supplémentaire. Lorsqu'un choc perturbe la balance commerciale, un pays avec un simple régime de change xe dévalue sa monnaie pour rétablir l'équilibre, et la plupart du temps, il se ré-ancre à la même monnaie. Cependant, un pays qui a un régime de change xe un pour un perd sa spécicité s'il dévalue. L'incitation est ainsi beaucoup plus forte de conserver l'ancrage. Par conséquent, le choc devrait être beaucoup plus important pour provoquer une dévaluation. Ceci explique la diérence d'espérance de vie entre les deux types d'ancrage: elle est de 3 ans pour un ancrage classique, alors que les régimes de change xe un pour un ont une durée de vie moyenne de 21 ans (contre 27 ans pour les unions monétaires). Cet écart implique potentiellement des diérences fondamentales entre eux.

Commerce a publié des

  données ont enregistré une baisse des importations et des exportations au cours du second semestre de 2008 et du premier semestre de 2009. L'Europe a été touchée en moyenne plus durement que le reste du monde par la grande crise du commerce, avec une chute des échanges de 25% entre 2008 et 2009. Les pays d'Europe de l'Est ont connu la plus forte baisse, en particulier les nouveaux membres de l'est de la zone euro, dont les échanges commerciaux ont chuté de 28 %. Curran (2009) a étudié l'Union européenne lors du grand eondrement commercial xix et Shelburne (2010) les pays d'Europe de l'Est, mais pas spéciquement la zone euro.

Hummels,

  Ishii et Yi (2001) et Escaith, Lindenberg et Miroudot (2010) ont montré l'importance croissante des chaînes de valeur mondiales depuis les années 1970. Hummels, Rapoport et Yi (1998) soulignent l'augmentation du commerce vertical, qui représente plus de 25% de l'augmentation du commerce total dans la plupart des pays de l'OCDE étudiés. Dans certains petits pays, xxi comme les Pays-Bas, la part des échanges verticaux dans le commerce total avoisine les 50%. Le développement des technologies de l'information, de la normalisation internationale des marchandises et l'amélioration des transports et de la logistique encouragent l'expansion du commerce vertical. Il est très probable que les chaînes de valeur mondiales continueront à se répartir entre les industries et les pays. Il en résulte que la politique économique nationale ne doit pas être considérée comme aectant uniquement le territoire national, sans tenir compte de l'intégration plus poussée de l'économie sur le marché mondial. Bems, Yi et Johnson (2009) soutiennent que le commerce vertical est un mécanisme de transmission des chocs intérieurs et qu'il a été l'un des principaux facteurs de l'eondrement synchronisé du commerce en 2009. L'OCDE (2010) arrive à la même conclusion. La prise en compte des nouvelles structures de production et de l'intégration croissante des économies mondiales est un véritable dé pour les concepteurs de politiques monétaires. L'expansion des chaînes de valeur mondiales étant indéniable depuis vingt ans, il est important de tenir compte de ses eets sur la transmission des chocs dans la conception des politiques monétaires. Dans le troisième chapitre de ma thèse, j'analyse l'eet des chaînes de valeur mondiales sur la transmission internationale de la productivité et des chocs monétaires dans diérents régimes de change et devises des prix à l'exportation, et je discute des politiques monétaires optimales ainsi que de la coordination monétaire. Partant du constat que les chaînes de valeur mondiales accroissent la synchronisation des cycles économiques, je construis un modèle pour en tirer les conséquences sur les règles de politique monétaire optimale. Mon modèle est lié à Corsetti et Pesenti (2009). Ce modèle a l'avantage de la simplicité et de l'élégance car il peut être résolu à la main, et il conserve de puissantes capacités de prédiction. Il étudie l'eet du commerce sur la synchronisation des cycles économiques des partenaires commerciaux et en tire des conséquences en terme de politique monétaire optimale. Je m'intéresse à la même question, mais pour une forme particulière de commerce: les chaînes de valeur mondiales. J'adopte donc leur approche, en changeant la fonction de production par l'ajout d'une étape intermédiaire, pour mieux prendre en compte l'évolution du commerce mondial. J'étudie ensuite en quoi mes résultats dièrent des leurs. Mon modèle explique simplement comment les chocs de productivité et monétaires se transmettent d'un pays à l'autre, et plus généralement comment les chaînes de valeur mondiales augmentent la corrélation des cycles économiques. Enn, mon modèle formule des recommandations en matière de politique monétaire et traite de la pertinence de la coordination de la politique monétaire. Dans le cas des échanges verticaux, les économies sont beaucoup plus liées que dans le modèle de Corsetti et Pesenti (2009), les échanges étant seulement horizontaux. En eet, le modèle de Corsetti et Pesenti (2009) néglige un canal de transmission des chocs de productivité, et ainsi sous-estime la propagation des chocs. En eet, leur modèle n'aborde que le canal traditionnel: le bien nal domestique est moins cher, de sorte que l'indice des prix à la consommation à l'étranger diminue. Le deuxième canal que j'explore est inhérent au commerce vertical: le choc de productivité diminue le prix du bien intermédiaire, de sorte que le bien nal étranger devient également moins cher. Par conséquent, l'indice des prix à la consommation étranger diminue également par ce canal. L'impact du choc de productivité sur l'économie nationale et étrangère dépend principalement de la part du bien intermédiaire domestique utilisée pour produire le bien nal. Le commerce vertical a également un impact sur la volatilité du taux de change. Le taux de change est en eet beaucoup moins ottant, plus "visqueux", à mi-chemin entre le taux de change xe et le taux de change ottant. C'est le cas parce que les économies évoluent beaucoup plus ensemble, les chocs étant symétriques. Par conséquent, le taux de change n'a pas à uctuer puisque les variations du taux de change expriment une diérence dans les prix relatifs entre les pays. Si les prix nationaux et étrangers évoluent ensemble, le taux de change reste constant.

  Within the family of currency unions, economists distinguish two types: dollarization/euroization, and multilateral currency unions. Dollarization is the unilateral choice to abandon monetary policy independence completely by adopting the currency of another country -examples being Ecuador, El Salvador and Panama. Examples of euroization (unilaterally adopting the euro) are

Figure 1 :

 1 Figure 1: Mundell trilemma in currency union and in xed exchange rate in case of shock

  price transparency, bank conversion costs, and the impossibility of devaluation. It is important to have a nuanced comprehension of the mechanisms behind this increase, to know what are the determinants of trade. It would help the policy makers to know which among price transparency and xity of change promotes trade. The rst chapter of this thesis disentangles the impact of each of the currency union characteristics on trade. The methodology Theory In the rst chapter of this thesis, I use the dierent types of xed exchange rate regimes to disentangle the eect of each dierence on trade. First, I build a theoretical micro founded model to show explicitly how the dierent characteristics of the exchange rate regimes (mainly trade costs and possibility of devaluation) inuence trade. My model explains the dierences between the short duration and long duration eects on trade of each regime by letting rms take account of the longevity of a peg to value its credibility. My model is related to Melitz (2003). His model has the major advantage of explaining simply and intuitively why not every rms export. I chose to base my model on Melitz's model since INTRODUCTION the mechanism highlighted to explain the rm's micro decisions to export or not can be used to explain the aggregate bilateral trade between two countries, depending on trade cost as in exchange rate regimes. I change the rm's utility function by adding a disutility in prot volatility and by modulating the trade cost depending on the exchange rate regime to match with the empirical evidence. I then see how my results dier from his. Although I use only macro data in my empirical analysis, a micro-founded model seems appropriates because it provides better insights into the mechanisms involved. Empirics I then check empirically the predictions of my model and estimate the size of the inuence of each currency union characteristic. In order this, I compare the eects of the dierent regimes on trade at dierent stages of maturity to disentangle the eect of each characteristic at these dierent stages. I deal with the zero trade ows problem by using Poisson Pseudo Maximum Likelihood (PPML). My methodology is based on the gravity model. I include time-country xed eects, to capture growth of economic activity. I include country-pair xed eects which capture time-invariant pair-specic variables such as borders contiguity, distance, common language or colonial links. I control for trade increases from free trade agreements by including a FTA dummy. The baseline model estimated is the following:

  indicates that nominal exchange rate variability has no signicant impact on trade ows and Dorn and Egger (2013) nd that countries pegged trade more, but only after about 8 years. Berman, Martin and Mayer (2012) analyse the reaction of exporting rms to exchange rate changes. They nd that high productivity rms modify their export prices, while low productivity rms modify their export volume. This explains the weak impact of exchange rate movements on aggregate export volumes. This is in line with my ndings of a non-signicant dierence between the oating and xed exchange rate regime eect on trade. Section two presents my model, disentangling the eects of each currency union characteristic on trade. Section three measures the eect of currency union, peg one-to-one and simple xed exchange rate on trade empirically. Section 4 concludes the paper. 1.2 The model 1.2.1 Domestic market The world consists of two symmetric countries, denoted as the Home country and the Foreign country; Foreign variables being denoted with a superscript star. Prices are exible and in nominal terms. Utility functions are similar at Home and abroad and consumers face a similar consumption basket.

1. 3

 3 Empirical results 1.3.1 The data set Country level trade data are drawn from the Correlates of War Trade database (Barbieri and Keshk, 2012), for more than 200 countries between 1971 and 2009. Two-digit product disaggregation trade data are drawn from BACI (Gaulier and Zignago, 2010), for more than 200 countries between 1995 and 2015. FTA data are drawn from the Design of International Trade Agreements database (Dür, Baccini and Elsig, 2014), xed exchange rate data from Shambaugh Exchange Rate Regime Classication 17 (Shambaugh, 2004), geographic data from the CEPII database "GeoDist" (Mayer and Zignago, 2011); and for the rest I use World Development Indicators from the World Bank (1960-2014).

  18 Shambaugh's database establishes an inventory of all the de facto xed exchange rates since 1971; I restrict my study to the post-1971 period and compare the eects on trade of dierent exchange rate regimes, oat exchange rate being the benchmark. It is less interesting to study the period pre 1971 since the gold standard was still in place. Moreover, I consider an exchange rate as xed only if it has 0% band 19 so that the only dierence with peg one-to-one is the price transparency. If a currency is pegged, faces a devaluation and re-pegs to the same anchor, I consider the year of the devaluation as oating regime (with the anchor but also with all the countries still pegged to the anchor). The pairs of countries linked by a xed exchange rates represent 7% of my observations. I built a database 20 to reference the currency unions and another one for the pegs one-to-one (checking the currency of the countries year by year) since Rose's (2001) database mixes the two. More details on the construction of my database are provided in the appendix. My database diers from Rose's in that I distinguish between currency unions and pegs one-to-one, add currency unions, correct some dates and update the data. My database covers 35 currency unions and 255 countries/ regions from 1971 21 to 2015. Pairs of countries sharing the same currency represent 0.60% of my observations, and pairs of countries linked by a peg one-to-one represent 17 I do not use Ilzetzki, Reinhart and Rogo (2017) exchange rate classication as I need bilateral data, and this classication does not provide the anchor of each currency in peg. 18 Variables description can be found in the appendix. 19 Pegtype=1 in Shambaugh classication. 20 Helped by De Sousa (2012) Currency Unions database. 21 In reality, my database starts in 1948 but I use the years previous to 1971 only to calculate the longevity of currency unions and pegs one-to-one.

  (2014) LSDV (Least Squares with time-varying country Dummy Variables) technique, and include time-country xed eects to capture growth of economic activity. I follow Flam and Nordström (2006) by having one-way trade (exports) instead of two-way trade (exports plus imports). In line with Baldwin and Taglioni's (2007) recommendation, I include country-pair xed eects which capture time-invariant pair-specic variables such as border contiguity, distance, common language or colonial links. My data by construction are clustered by pair-country. The source of identication of the regime eects comes from switches in and out of regimes. I believe the use of these xed eects provides the cleanest benchmark against which to assess the impact of currency unions, peg one-to-one and xed exchange rate on (log) exports. The Hausman test supports the hypothesis of a xed eect. I control for trade increases from free trade agreements 22 Openness for McKinnon (1990), distance, language, colonial heritage and size for Tenreyro (2001) and March- esiani (2011). by including a FTA dummy, following Micco, Stein and Ordoñez (2003) since this can inuence bilateral trade. The estimated baseline model is the following

30

  [START_REF] Frankel | The Estimated Eects of the Euro on Trade: Why Are They Below Historical Eects of Monetary Unions Among Smaller Countries? In Europe and the Euro, university of chicago press[END_REF] tries to explain this discrepancy but fails to charge the three usual suspects: lack of hindsight because of the youth of the euro, dierence in size since euro members on average are bigger, and endogeneity problems.

  the rst 5 years after being in currency union, trade was 34% higher compared to countries sharing a oating exchange rate. In the last 5 years before exiting currency union, trade was 63% higher compared to countries sharing a oating exchange rate. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

Figure A1 :

 A1 Figure A1: Currency unions since 1948, the most recent being represented above

  probability of pair-countries to be linked through a peg or a currency union. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

3 East

 3 trade collapse was triggered by -and helped spread -the global economic slump". Every nation for which the WTO reports data experienced a drop in both imports and exports during the second half of 2008 and the rst half of 2009. On average, Europe has been hit harder than the rest of the world by the great trade collapse, with a trade drop of 25% between 2008 and 2009. European countries experienced the most severe drop, especially the new eastern Eurozone members whose trade fell by 28%. Curran (2009) studied the European Union during the great trade collapse and Shelburne (2010) studied east European countries, but not the Eurozone specically. I contribute to the literature by specically studying the Eurozone during a crisis. Using a dierence-in-dierences methodology on bilateral trade data, with exporter-time, importertime and pair-country xed eects, I nd that the euro had a positive and signicant eect on aggregate trade, not only during the drop, but also during the rebound and in the long run. The 2 Figure B1 in appendix shows the world trade drop and gure B3 shows world overshooting. 3 Figure B2 in the appendix shows the average drop for every European country.

Figure 2 . 1 :

 21 Figure 2.1: Weights of the sectors on total exports 16

Figure

  Figure 2.1 shows that the Eurozone (the treatment group) and the developed countries (the

Finally,

  Berman et al. (2012) show that agents are especially cautious during crisis periods.During the nancial crisis, exchange rates were more volatile than usual[START_REF] Coudert | Exchange rate volatility across nancial crises[END_REF] Morales-Zumaquero and Sosvilla-Rivero, 2014). The single currency prevented uncertainty linked to exchange rates among members. This is especially important for industries with sticky prices.To measure price stickiness, I useRauch's (1999) classication Rauch dierentiates sectors according to the way prices are settled (goods traded on an organized exchange, reference priced and dierentiated products) to create an index for each of my 21 sectors. Using a Spearman rank test, I nd correlations of 0.44 for 2008-2010 and 2008-2015 between the euro eect (using sector xed eects) and the use of referenced prices.21 In contrast, I nd a correlation of -0.42 for use of dierentiated products. Therefore, the euro has helped those sectors using referenced prices by keeping prices stable. Indeed, since the prices are referenced, and therefore stickier, it is dicult for the rms to modify them. The euro removed the uncertainty on prots rms faced inside the Eurozone, while this uncertainty increased outside, exchange rates being more volatile during the crisis. In my data, on average the sectors exporting the most use more referenced prices compared to other sectors. To sum up, since uncertainty is greater in times of economic crisis, currency unions have a premium from the absence of exchange rate volatility.The main channel through which the euro protected intra Eurozone trade is exchange rate stability. The literature identies a wait-and-see mechanism during the crisis, worsened by an important exchange rate volatility. The euro avoided this additional problem, which explains its 21 And a correlation of 0.35 for 2008-2010 and for 2008-2015 between the euro eect not using sector xed eects and the use of referenced prices.

•

  F T A dummy : dummy for Free Trade Agreements (Design of International Trade Agreements database) (1948-2015) • GDP : GDP in current US dollar (World Development Indicators from the World Bank) (1960-2015) • GDP per Capita : GDP per capita in constant 2005 US dollar (World Development Indicators from the World Bank) (1960-2015) • Debt : Central government debt, in percentage of GDP (World Development Indicators from the World Bank) (1960-2016) • Overshooting : trade growth between 2008 and 2009 divided by GDP growth between 2008 and 2009 • T rade openness : (import+export)/GDP, (imports and exports from BACI, GDP from World Bank World Development Indicators database) • Global V alue Chain : weighted average of the sectors global value chains index in 2008 (OECD Global Value Chains indicators)• F inancial Dependency : weighted average of the sectors external nance share (Choi index)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)

Figure B1 :

 B1 Figure B1: Trade drop in the world between 2008 and 2009

( 2001 )

 2001 as occurring when: A. a good is produced in two or more sequential stages, B. two or more countries provide value-added during the production of the good, C. at least one country must use imported inputs in its stage of the production process, and some of the resulting output must be exported. The A380 airbus is a good example of vertical specialization: the wings are made in the United Kingdom, the fuselage in Germany, the tail in Spain and the cockpit in France.More generally, the link between trade and business cycle synchronization has been the subject of numerous studies which highligh a strong positive eect[START_REF] Frankel | Is EMU more justiable ex post than ex ante?[END_REF][START_REF] Clark | Borders and business cycles[END_REF];[START_REF] Kose | Can the Standard International Business Cycle Model Explain the Relation Between Trade and Comovement[END_REF].[START_REF] Baxter | Determinants of business cycle comovement: a robust analysis[END_REF] show that this link is robust, even to the inclusion of gravity variables, suggesting an independent role of trade in transmitting business cycles.Numerous studies investigate the link between vertical trade and business cycles synchonisation. Arkolakis and Ramanarayanan (2009) nd empirically "international business cycle model augmented with vertical specialization -i.e., the production of goods in multiple stages spread across countries and quantitatively assess its ability to generate stronger business cycle synchronization between countries that trade more".Burstein, Kurz and Tesar (2008) reach the same conclusion. Sturgeon and Memedovic (2011) study three industries (electronics, automobiles and motorcycles, and apparel and footwear). They nd evidence of increasing economic integration but the level diers among the three industries. Other authors have studied individual sectors but for a limited number of countries only. Brustein, Kurz and Tesar (2008) nd that the output of American and Mexican rms is more correlated if the Mexican rms supply the American rms with intermediary goods. The intensity of production-sharing seems to be at least as important when accounting for bilateral rm output correlation, as the trade volume. Iossifov (2014) highlights the role of vertical trade in the business cycle synchronization of central and eastern European countries, and the Eurozone. Gangnes, Ma and Van Assche (2012) use Chinese trade data and nd that vertical trade increased the sensitivity of trade to external business cycle shocks due to a composition eect, global value chains being more developed in higher income elasticity goods. Finally, Giovanni, Levchenko and Mejean (2017) using micro data of French rms nd that indirect linkages add signicantly to aggregate correlations in business cycles. Hummels, Ishii and Yi (2001) and Escaith, Lindenberg and Miroudot (2010) demonstrate the growing importance of global value chains since the 1970s. Hummels, Rapoport and Yi (1998) highlight that increases in vertical-specialization-based trade account for more than 25% of the increase in total trade in most of the OECD countries tested. In some smaller countries, such as the Netherlands, the share of vertical trade in total trade approached 50%. The development of information technology, international standardization of goods, and improvements to transport and logistics encourage the expansion of vertical trade. It is likely that global value chains will continue to spread across industries and countries. The consequence is that national economic policy should not be seen as aecting only the national territory but should take account of the deeper integration of the economy on the global market. Bems, Yi and Johnson (2009) argue that vertical trade acts as a transmission mechanism for domestic shocks and is one of the main factors in the synchronized great trade collapse in 2009. OECD (2010) reaches the same conclusion. Taking into account the new production structures and the growing integration of world economies is a problem for designers of monetary policy.

  My model is related toCorsetti and Pesenti (2009). This model has the advantage of simplicity and elegance since it can be solved manually but its predictions are powerful. It studies the eect of trade on business cycle synchronisation among trading partners, and derives optimal monetary policies. I am interested in the same issues, but for a particular form of trade: global value chains since it has an increasing part in the world trade. Therefore, I adopt their approach, and change the production function by adding an intermediate good stage, allowing for a new shock transmission channel. Vertical trade comes from the fact that a nal-good production requires both Home and Foreign intermediate goods. I then explore how my results dier from theirs.The world consists of two countries of equal size, denoted the Home country and the Foreign country; countries are denoted H and F respectively. I adopt the convention that terms denominated in Foreign currency are denoted by a star.The economy consists of households, nal-good rms and intermediate-good rms. Each country has one unit of population. Households and rms are dened over a continuum of unit mass.Households are indexed by j ∈ [0, 1] at home and j * ∈ [0, 1] abroad, nal-good rms are indexed by h ∈ [0, 1] at home and f ∈ [0, 1] abroad.Each country is specialized in two types of tradable good: one type of intermediate good and one type of nal good. In each country, monopolistic competitors produce imperfectly substitutable varieties of the same national nal good, employing a combination of intermediate goods. Finalgood rms sell in the two markets, domestic and foreign.There is perfect competition in the two intermediate-good markets. All X H goods are identical and perfect substitutes, and all X F are identical and perfect substitutes. Countries are perfectly specialized: the Home country produces only the intermediate good X H and the Foreign country produces the intermediate good X F , the two goods are not perfect substitute.Households are immobile across borders and consume only nal goods. Households consume both domestic and foreign nal goods. In both countries, the elasticity of substitution among dierent varieties of the same type of good (θ) is higher than the elasticity of substitution between types of goods H and F, which we posit is equal to 1 (followingCorsetti and Pesenti, 2009).

Figure 3 . 1 :

 31 Figure 3.1: the world economy

(3. 11 )

 11 Each nal-good rm h is price-taker of the intermediate goods, so the marginal cost (M C t ) of one unit is

Figure 3 . 2 :

 32 Figure 3.2: World economy model in steady state

(

  1999), intermediate-good prices are more exible. Therefore, I choose to keep the intermediategood prices exible while the nal-good prices are rigid.

Figure 3 . 3 :

 33 Figure 3.3: International transmission of productivity shocks under exible prices and oating exchange rate

Figure 3 . 4 :

 34 Figure 3.4: International transmission of productivity shocks under PCP and oating exchange rate

Figure 3 .

 3 5 shows the case where only Home country implements a monetary expansion.It is in the Foreign country interest to also implement a monetary expansion. Then the transmission eect is positive.

Figure 3 . 5 :

 35 Figure 3.5: International transmission of productivity shocks under LCP and oating exchange rate

Figure 3 . 6 :

 36 Figure 3.6: International transmission of productivity shocks under exible prices and xed exchange rate

Figure 3 . 7 :

 37 Figure 3.7: International transmission of productivity shocks under xed prices, xed exchange rate, monetary expansion at home and abroad

Figure C1 :

 C1 Figure C1: Vertical specialization by country in 2011

  , Caldero« et al. (2007), Inklaar et al. (2008). 13 I divide my 12 years period in two to be able to add time xed eects.

14 s

 14 and r can be equal. 15 i = j 16 I plan to test alternative detrend methods.

  highest vertical specialization. S12: Basic metals and fabricated metal; S18: Construction; S2: Mining and quarrying; S13: Machinery, nec; S14: Electrical and optical equipment; S11: Other non-metallic mineral. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  , the sector of interest is the one importing intermediate goods and exporting nal goods to the nal consummer. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  , the sector of interest is the one importing intermediate goods and exporting nal goods to the nal consummer. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  , the sector of interest is the one importing intermediate goods and exporting nal goods to the nal consummer. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  , the sector of interest is the one importing intermediate goods and exporting nal goods to the nal consummer. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  Paris-Saclay Espace Technologique / Immeuble Discovery Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France Titre : Trois études portant sur les unions monétaires et le commerce Mots clés : Union monétaire, taux de change fixe, commerce, euro, crise, effondrement du commerceRésumé : Cette thèse relève du commerce international, des politiques monétaires et de macroéconomie internationale. Le premier chapitre étudie les différentes caractéristiques des différents régimes de change fixe et évalue les effets de chacune de ces caractéristiques sur le commerce international. Il apparaît que la transparence des prix et les coûts de transaction liés au changement de monnaie n'ont pas d'effet significatif sur le commerce, seule la crédibilité du régime de change fixe (liée à l'absence de possibilité de dévaluation) augmente significativement le commerce. Cela explique pourquoi seule l'union monétaire augmente le commerce entre ses membres dès les premières années du régime de change.Le second chapitre s'intéresse aux effets de l'euro sur le commerce pendant la crise financière de 2008-2009. Il apparaît que l'euro amortit la chute du commerce observée partout dans le monde, grâce à l'absence de volatilité du taux de change entre ses membres. Le troisième chapitre présente un modèle théorique permettant d'étudier la transmission des chocs dans une économie spécialisée verticalement, et propose une politique monétaire optimale pour stabiliser l'économie. Le modèle promeut des politiques monétaires symétriques entre les deux pays partageant un mode de production basé sur les chaînes globales de valeur.Title : Three Essays on Currency Union and TradeKeywords : Currency union, Fixed exchange rate, trade, euro, crisis, great trade collapse Abstract : This thesis studies international trade, monetary policy and international macroeconomics. Chapter 1 examines the different characteristics of different fixed exchange rate regimes and assesses the effects of each of these characteristics on international trade. It appears that price transparency and transaction costs linked to the currency changes do not have a significant effect on trade; only the credibility of the fixed exchange rate regime (due to the absence of devaluation possibilities) increases trade significantly. This explains why only monetary union increases trade between its members from the first years of the exchange rate regime. Chapter 2 looks at the effects of the euro on trade during the 2008-2009 financial crisis.

  Studying the mechanisms through which trade and currency unions inuence each other is very important as it helps in the design of better public policy by identifying on which characteristics of currency unions it is important to focus.

	The context
	Andrew Rose's (2000) paper One money, One Market revived the interest in currency unions.

[START_REF] Yi | Can Vertical Specialization Explain the Growth of World Trade?[END_REF]

highlights the huge impact on global value chains of a small reduction in cross-border transaction costs. Since global value chains cross many borders, transaction costs add up, and any reductions in these costs has a more than proportional impact on vertical trade. Since currency unions reduce cross-border transaction costs, they promote the formation of global values chains among their members. In turn, transmission of shocks is much higher between countries linked INTRODUCTION by global value chains. Therefore, global value chains increase business cycle synchronization. xxxi Summary Chapter 1: "Disentangling the Eects of Currency Unions on Trade"

  Currency union allows smaller rms to export because the cuto revenue to export is lower than in the other regimes.

	• At steady state, when the exchange rate regime gains credibility, only trade cost dierences
	matter for trade. The model predicts that at steady state, xed exchange rate regimes have
	a bigger eect on trade than oating exchange rate regimes.
	Empirics Using an ordinary least squares (OLS), I nd that currency union increases trade
	more than peg one-to-one, although both are positive and signicant. I nd no eect of classical
	xed exchange rate compared to oating exchange rate. This is in line with my model predictions.
	However, by correcting for endogeneity issues, peg one-to-one eects turn insignicant.
	Furthermore, currency unions from the outset have very strong eects, while classical xed
	exchange rates have none. At steady state, the eects of currency union (which remain stable
	over time) and pegs one-to-one, tend to become more similar. Classical xed exchange rate
	regimes have small eects only after 15 years, also predicted by my model.
	In this case, a
	oating exchange rate does not necessarily lead to a lower level of trade than the classical
	xed exchange rate.

INTRODUCTION

•

Table 1 .

 1 1 presents the dierent types of xed exchange rates the currency union, to classical xed exchange rate, and their characteristics.

	adding an intermediate good stage. Vertical trade comes from the fact that nal-good produc-
	tion requires both Home and Foreign intermediate goods. I then see how my results dier to
	theirs. My model explains simply how productivity and monetary shocks are transmitted from
	one country to another, and more generally how global value chains increase the correlation of
	business cycles. Finally, my model provides monetary policy recommendations, and discusses
	the relevance of monetary policy coordination.
	The results
	With vertical trade, economies are more closely linked than in the Corsetti and Pesenti's (2009)
	case of only horizontal trade. Indeed, the intermediate good productivity shock is transmitted

xed exchange rate regimes have small eects only after 15 years, also predicted by my model, as the regime gains credibility over time. A major issue in this literature is the problem of endogeneity: countries could be in currency union because they trade a lot.

[START_REF] Barro | Economic Eects of Currency Unions[END_REF]

build an instrumental variable (IV) which I use in this paper, in order to deal with this issue. They use the fact that many countries adopt the currency of an anchor country with which they have links. By doing so, they are in currency unions with other countries with which they do not have special links.

[START_REF] Barro | Economic Eects of Currency Unions[END_REF]

use this exogenous link to create their instrumental variable. They nd that Rose (2000) eect remains large, and is not due to endogeneity. In my paper, by correcting for endogeneity issues, peg one-to-one eects turn insignicant. Furthermore, currency unions from the outset, have very strong eects, while pegs one-to-one have small eects and classical xed exchange rates no eect.

Table 1 .

 1

		2: LSDV and IV methods
		(1)	(2)	(3)
	lnExport	LSDV	LSDV without	IV
			xed exchange rate
	Currency Union ijt	0.502***	0.550***	4.774***
		(0.0405)	(0.0327)	(0.680)
	Peg one-to-one ijt	0.216***	0.318***	19.12
		(0.0578)	(0.0544)	(14.73)
	Fixed exchange rate ijt	-0.0113		-0.376*
		(0.0108)		(0.218)
	FTA	0.262***	0.206***	0.250***
		(0.0113)	(0.00944)	(0.0190)
	Pair-country FE	YES	YES	YES
	Exporter-year FE	YES	YES	YES
	Importer-year FE	YES	YES	YES
	Observations	419,348	541,063	359,871
	R-squared	0.879	0.876	0.854
	F-test	20.44	15.71	1.026
	Prob > F	6.17e-06	7.37e-05	0.311

Note: The control group is the oat exchange rate regime. The rst stage of the IV can be found in table A5 in the appendix. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Table

1

.2 column 1 shows that currency union has a bigger impact on trade than peg one-toone (65% 23 increase against 24%, 14 or approximately a 63% dierence). Fixed exchange rate has no signicant impact. This is in range with my model predictions in the case of α low .

  27 e 0.820 ≈ 2.27; e 0.230 ≈ 1.26; e 0.498 ≈ 1.65; e 0.378 ≈ 1.46; e 0.0246 ≈ 1.024.

	28 Persson (2001) nds 61% and Tenreyro (2001) 50%. 29 Micco, Ordoñez and Stein (2003) nds between 6 and 26%; Bun and Klaassen (2007) nds 38%; Flam and
	Nordström (2006) 15%.

  Table A8 shows the biggest eect dierences between currency union and peg one-to-one compared to the LSDV methodology (table 1.2), around an 80% dierence with only time xed-eects, and a 30% dierence with the addition of country-xed eects. The coecient of the xed exchange rate dummies is not signicant which means that a classical xed exchange rate does not aect bilateral trade. My ndings are in line with Tenreyro (2007).Controling for zeros One problem with the logarithm of export is that it leads to the exclusion of observations with zeros values. In order to address this potential bias, I followBarro and

	Table 1.3: First years versus latest years
		(1)	(2)	(3)
	lnExport	Currency union	Peg one-to-one	Fixed exchange rate
	Age 0-5 years	0.459***	0.303***	0.00309
		(0.0386)	(0.0521)	(0.0112)
	Age 6-10 years	0.579***	0.321***	0.00849
		(0.0452)	(0.0593)	(0.0196)
	Age 11-15 years	0.306***	0.124**	0.0459
		(0.0728)	(0.0625)	(0.0343)
	Age 16-20 years	0.246**	0.215***	0.158***
		(0.101)	(0.0585)	(0.0420)
	Age more 20 years	0.547***	0.399***	0.107**
		(0.0934)	(0.0582)	(0.0496)
	Constant	1.117***	1.119***	1.106***
		(0.00155)	(0.00152)	(0.00205)
	Pair-country FE	YES	YES	YES
	Exporter-year FE	YES	YES	YES
	Importer-year FE	YES	YES	YES
	Observations	637,754	637,754	421,083
	R-squared	0.869	0.869	0.880

[START_REF] Tenreyro | On the trade impact of nominal exchange rate volatility[END_REF]

and estimate the gravity equation with a Poisson Pseudo Maximum Likelihood (PPML). The results (table A9 in the appendix) are close to those obtained above and do not reverse the trend for currency union to have a bigger eect than peg one-to-one. This suggests that my coecients are not aected by the exclusion of zero-value observations. Regime duration

Klein and Shambaugh (2006)

establish that the expected duration of a peg increases dramatically if it survives two years.

[START_REF] Klein | The dynamics of exchange rate regimes: Fixes, oats, and ips[END_REF]

calculated the probability of a xed exchange rate given that it was xed the previous year, was 82%; and this conditional survival rate increases with the number of years duration in the spell. Fritz-Krockow and Jurzyk

(2004)

established that the longer the peg and more positive its impact on bilateral trade. According to Dorn and Egger (2013), the eect is signicant after eight years. I investigated whether my estimations are sensitive to the duration of the exchange rate regime by comparing the rst years of a regimes with latest periods. Table

1

.3 presents the eects of currency unions/pegs one-to-one/classical xed exchange rates in the rst 5 years, then between the 5th and 10th years, etc. up to over 20 years.

Note: here is how to read this table, in the rst 5 years, currency unions increase trade by 46%, while when they are between 11 and 15 years old they increase trade by 31%. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) My model predicts that currency unions have a strong eect from the beginning, and are stable over time. However, my empirical results do not conrm the stability (the dierence in the currency union eect between age 6-10 and age 11-15 is signicant, but not between age 11-15 and age

Table 1 .

 1 4: Heterogenous eect of currency union and peg one-to-one

	(1)

Note: two-step method estimating the eect of currency union and peg one-to-one depending on the intensity of the potential trade between two countries. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) I nd a negative and signicant coecient of Currency Union*PredictedTrade, in line with

  Since many nondierentiated goods exchanged on centralised markets are priced in dollars, the currency of the two partner countries does not really matter. I use Rauch's (1999) classication (2-digit product disaggregation) to test for vehicle currency pricing, dividing the sample between goods traded on an organized exchange and others. The results are presented in table 1.5.

		Table 1.5: Eect of vehicle currency pricing
		(1)	(2)	(3)
	lnExport HS 2-digit	All	Goods traded on	Goods not traded
	product disaggregation		an organized exchange	on an organized exchange
	Currency Union	0.144***	0.232***	0.107***
		(0.00867)	(0.0165)	(0.00994)
	Peg one-to-one	0.0862**	-0.0158	0.108**
		(0.0435)	(0.0861)	(0.0493)
	Fixed exchange rate	-0.0181***	-0.0186*	-0.0136**
		(0.00492)	(0.00960)	(0.00560)
	Observations	13,604,591	3,767,913	9,833,080
	R-squared	0.358	0.336	0.398
	Pair-country FE	YES	YES	YES
	Exporter-year FE	YES	YES	YES
	Importer-year FE	YES	YES	YES
	F-test	1.694	8.011	7.82e-05
	Prob > F	0.193	0.00465	0.993

Note: The control group is the oat exchange rate regime. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

Table A12 and

 A12 A13 in the appendix show the conditional probability of being in a regime, depending on the previous regime, or the next one. The switches from xed exchange rate to oat exchange rate, and vice versa, represent the large majority of my observations, these two regimes being the most common. It is interesting that most countries were rst in a xed exchange rate before being in a currency union which indicates the existence of previous monetary links between the pairs. Similarly, most countries in peg one-to-one were previously in a currency union which suggest a willingness to lessen the shock of currency union exit.

I try to study specically the eect of switching from one exchange rate regime to another. I work on a 20-year period: 10 years before and 10 years after a switch. Table

1

.7 presents the results for entries: for example in column 1, among the switches to a oating regime, the coecients represent the eect on trade of previously being in either currency union, peg oneto-one, or xed exchange rate. Table

1

.8 presents the results for exits.

Table 1 .

 1 7: Comparing the last years of each regimes (control group: the rst years of the common regime they switch to)

		(1)	(2)	(3)	(4)
	lnExport	Switching Switching Switching Switching
		to Float	to CU	to Peg	to Fixed
	Last years in Currency Union	0.273**		0.493	3.270***
		(0.109)		(1.134)	(1.219)
	Last years in Peg one-to-one	0.208**	0.0425		-0.364
		(0.0927)	(54,374)		(0.270)
	Last years in Fixed exchange rate -0.0836***	0.00646	3.554
		(0.0236)	(0.120)	(2.213)
	Last years in Float exchange rate		0.0927	-2.765	0.0138
			(0.0941)	(158,016)	(0.0266)
	Pair-country FE	YES	YES	YES	YES
	Exporter-year FE	YES	YES	YES	YES
	Importer-year FE	YES	YES	YES	YES
	Observations	90,663	4,468	1,154	73,058
	R-squared	0.906	0.991	0.989	0.910
	F-test	9.426	0.240	1.296	3.916
	Prob > F	8.06e-05	0.786	0.274	0.0199

Note: switching from a currency union to a oating exchange rate, trade was 27% higher in the last 10 years of currency union compared to the 10 rst years in oat. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

Table 1 .

 1 8: Comparing the rst years of each regimes (control group: the last years of the common regime they switch from)

		(1)	(2)	(3)	(4)
	lnExport	Switching	Switching Switching	Switching
		from Float	from CU	from Peg from Fixed
	First years in Currency Union	0.324***		-14.72	0.365***
		(0.105)		(12,008)	(0.0797)
	First years in Peg one-to-one	-0.119	-0.0307		0.163
		(0.211)	(0.371)		(0.370)
	First years in Fixed exchange rate	-0.00824	-0.146	-0.320*
		(0.0260)	(0.476)	(0.164)
	First years in Float exchange rate		-0.0518	-0.411***	0.0836***
			(0.361)	(0.133)	(0.0237)
	Pair-country FE	YES	YES	YES	YES
	Exporter-year FE	YES	YES	YES	YES
	Importer-year FE	YES	YES	YES	YES
	Observations	75,375	2,431	5,649	86,220
	R-squared	0.913	0.970	0.967	0.910
	F-test	5.305	0.0454	1.42e-06	6.184
	Prob > F	0.00497	0.956	0.999	0.00206

Note: switching from a oating exchange rate to a currency union, trade was 32% higher in the rst 10 years of currency union compared to the 10 last years in oat. Cluster by construction. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

The eect of entering or exiting from a currency union (line 1 of table 1.7 and 1.8) is usually large and signicant with the exception of leaving or entering a peg one-to-one. Therefore, it means that the exchange rate regimes are quite close. However, columns 2 in table 1.7 and 1.8 are never signicant since the eect of peg one-to-one, xed exchange rate and oat are compared to currency union.

The eects of the rst years and last years again are dierent, as in table 1.6. Also, care is needed when comparing the two tables, because the control groups are dierent, the columns in table 1.7 compare only countries entering in a certain regime, while the columns in table 1.8 compare only countries exiting from a certain regime.

Table A1 :

 A1 Summary Statistics 1.C Appendix: List of currency unions and pegs one-to-one

	1.B. APPENDIX: METHODOLOGY FOR THE CONSTRUCTION OF THE DATABASES41
	Variable	Regime	Obs.	Mean	Std. Dev.	Min	Max
	CommonBorder
		All	3336216	0.012	0.109	0	1
		CU	39434	.1080286	.3104205	0	1
		Peg1:1	34430	.0368283	.1883429	0	1
		Fixed	91732	.0431256	.203141	0	1
	CommonLanguage
		All	3336216	0.177	0.381	0	1
		CU	39434	.6588731	.4740939	0	1
		Peg1:1	34430	.701946	.45741	0	1
		Fixed	91732	.3257969	.4686744	0	1
	Colony
		All	3336216	0.01	0.098	0	1
		CU	39434	.0849521	.2788139	0	1
		Peg1:1	34430	.0307871	.1727429	0	1
		Fixed	91732	.0176601	.1317135	0	1
	Distance
		All	3336216	8545.405	4683.881	59.61723	19951.16
		CU	39434	4079.538	4342.446	59.61723	18517.37
		Peg1:1	34430	6102.437	4594.335	111.0933	19227.74
		Fixed	91732	6995.551	4958.834	80.98481	19711.86
	Surface
		All	13032	3709523	1.24e+07	2	1.30e+08
		CU	3982	867813.9	3478862	2	2.74e+07
		Peg1:1	1701	307632.2	410171.6	50	2381740
		Fixed	1194	588083.9	1313594	300	9388250
	Population
		All	13429	1.43e+08	5.50e+08	4279	7.21e+09
		CU	4164	3.45e+07	1.37e+08	5563	1.27e+09
		Peg1:1	1758	5329419	7073528	5563	5.72e+07
		Fixed	1192	2.75e+07	1.30e+08	60704	1.30e+09
	GDPperCapita
		All	9916	1.11e+12	4.48e+12	1.60e+07	5.81e+13
		CU	3010	5.23e+11	2.03e+12	1.60e+07	1.83e+13
		Peg1:1	1326	2.57e+10	6.95e+10	1.12e+08	5.81e+11
		Fixed	1003	6.07e+10	1.66e+11	1.09e+08	2.04e+12

Table A2 :

 A2 List of currency unions in my database

	Currency union CFA Franc XOF Currency union CFP Franc		Hub and spoke Hub and spoke Date	Added/modied compared to Rose Date Added/modied compared to Rose
				Benin	1948 French Polynesia (France)	1958
	Currency union East Caribbean Dollar Escudo	Hub and spoke Burkina Faso New Caledonia (France) Date 1948 Guinea 1948 Wallis and Futuna (France) Anguilla (UK) 1948 2015 1986 1962 Guinea Bissau 1948 Ivory Coast 1948 Portugal 1948	Added/modied Modied compared to Rose Added
				Antigua and Barbuda Mali Azores (Portugal) 1948 2015 1948 1948	Added
				Mali	Barbados Cape Verde 1984	1948 1973 1948	Modied
				Cayman Islands (UK) Mauritania Guinea-Bissau 1948	1952 1965 1948	Added Added
				Niger	Dominica Madeira Islands (Portugal) 1948 2015 1948 1948	Added
	Qatar-Dubai Riyal CFA Franc XAF Peseta	East Caribbean States Grenada Senegal Jamaica Montserrat (UK) St. Kitts and Nevis St. Lucia Togo Qatar 1948 1948 United Arab Emirates 1948 2015 1948 2015 1952 1965 1948 2015 1948 2015 1966 1966 Cameroon 1948 Central African Rep 1948 Spain 1948 1948 2015 St. Vincent 1948 2015 Chad 1948 Andorra 1948	Added Added Modied	Modied Modied
				Trinidad and Tobago Congo Equatorial Guinea 1948 1965 1948 1948	Modied
			Turks and Caicos Islands (UK) 1952 1965 Equatorial Guinea 1948 Morocco 1948	Added	Added
	Virgin Islands (UK) Gabon Western Sahara 1948 East African Currency Area Central America CFA Franc XMCF and the Caribbean Aruba (Netherlands) Comores 1948 Kenya	1948 1958 1948 1948 1985 1948	Added Modied	Added
				Curaçao (Netherlands) Madagascar Uganda 1948	1948 2015 1948
	Sint Maarten (Netherlands) Bonaire, Sint Eustatius and Saba (Netherlands) Mayotte (France) 1948 1975 1948 2015 1948 2010 Tanzania 1948 Reunion (France) 1948 United Kingdom 1948 Australia 1948 2015 Christmas Island (Australia) 1956 2015 Kiribati 1948 2015 St. Pierre and Miquelon (France) Pound Sterling CFA Franc XCFG Australian dollar 1948 Bermuda 1948 British Indian Ocean 1966 South Africa Rand South Africa 1948 Territories (UK)	Added Added Modied Added Modied	Added
				Botswana	Nauru	1948 Cameroon	1948 2015 1948	Added
				Norfolk Island (Australia) Lesotho 1948 Cyprus	1948 2015 1948	Added
				Papua New Guinea Swaziland Falkland Islands (UK) 1948 1975 1948 1948	Added
				Solomon Islands Namibia Gibraltar (UK) 1948	1948 1978 1948
	Malaya Dollar Indian Rupee Danish Krone	Tonga Tuvalu Guernsey (UK) 1948 Maldive India 1948 Nigeria Bahrain Malaysia Singapore Brunei 1948 Sierra Leone Bhutan Christmas Island (Australia) 1948 South Georgia and the 1948 1990 1948 2015 1948 1948 1948 1948 2015 1948 1958 1948 1948 1948 2015 Kuwait Cocos slands (Australia) 1948 South Sandwich Islands (UK) 1948 1959 Nepal 1948 1965 Oman 1948 1958 Sudan 1948 Denmark 1948 Togo 1948	Added Added Added Added Modied	Added Added Modied Added Added Added
		Pakistan Faeroe Islands (Denmark) Trinidad and Tobago 1948 1949 1948 1948	Modied	Added
	Qatar Greenland (Denmark) Virgin Islands (UK) 1948 1948 1958 1948 United Arab Emirates 1948 1958 Yemen Arab Republic 1948 1951 France 1948 United States Dollar French Franc United States 1948 Pakistan Rupee Pakistan 1948 2015 Andorra 1948 American Samoa (USA) 1948 Bangladesh 1948 1971 French Guiana (France) 1948 Bermuda 1971 Guadeloupe (France) 1959 British Indian Ocean 1966 Note: I have updatated and corrected Rose(2000) database, the last column noties the changes (coun-Added Added Modied Modied Modied Added Added Martinique (France) 1959 Territories (UK) tries and currency unions added and dates changed) Mayotte (France) 1976 Added Ecuador 2001 Added
				Monaco	1948 El Salvador	2001	Added
				Morocco	1948 Guam	1948	Modied
			Reunion (France)	1975 Guatemala	2001	Modied	Modied
		St. Pierre and Miquelon (France) Liberia 1973	1948	Modied
				Tunisia	1948 Marshall Island	1948	Modied	Added
						Micronesia	1948	Added

Table A3 :

 A3 List of pegs one-to-one in my database

	Currency Currency United States Dollar Pegs one-to-one Pegs one-to-one Argentina East Caribbean Dollar Bahamas	Date 1992 Date 1971
		Barbados	Belize	1974	1948
		Guyana	Cuba	1948	1948
	Central America and the Caribbean	Trinidad and Tobago Cuba Dominican Republic 1966 Guatemala	1986 1948 2001
		Guyana		1948
		Trinidad and Tobago	1948
	German deutsch mark
		Bosnia and Herzegovina	1948 -
		Montenegro		1994 -
			Serbia		1994 -
	Netherlands Guilder
		Indonesia		1948 -
	Soviet Ruble
		Ukraine		1993
		Uzbekistan		1994
	Egyptian Pound
			Sudan		1958 -

Table A4 :

 A4 List of currencies pegged one-to-one in my database

	with
	• Prex "ln" for logarithm
	• Prex "max" for maximum
	• Prex "min" for minimum
	Currency	Pegs one-to-one
	CFA Franc XOF	Peg with CFA Franc XAF
	CFA Franc XMCF	Peg with CFA Franc XAF
	CFA Franc XCFG	Peg with CFA Franc XAF

Table A5 :

 A5 Propensity to adopt the currency, or to peg, to main anchor

Table A6 :

 A6 First stage of the IV

				Table A8: OLS method
	Table A7: Anticipations of the new regime: eect per year
						(1)	(1)	(2) (2)
	VARIABLES lnExport	(1)	Currency Union (2) Time xed eect Time and country xed eect Peg one-to-one (3)
	lnExport	Currency Union	Peg one-to-one	FixedExchange
	probability Currency Union Currency Union 0.820***	1.578***	-0.0583*** 0.498***
	Year Minus 8			0.127		(0.0891) -0.471 (0.0373)	(0.0101) 0.0441 (0.0345)
	probability Peg one-to-one (0.114) Peg one-to-one 0.230*** (0.389) 0.551***	1.578 (0.0372) 0.378***
	Year Minus 7			0.171		(0.181) -0.216 (0.0426)	(1.172) 0.0227 (0.0410)
	Fixed exchange rate (0.118) Fixed exchange rate		0.0195*** (0.377) 0.0246**	0.0139*** (0.0387) 0.00939
	Year Minus 6	0.402***		(0.000937) -0.235 (0.0124)	(0.000673) -0.0105 (0.0119)
	lnDistance		(0.125)		(0.376) -0.995***	(0.0390) -1.229***
	Observations Year Minus 5 0.287**		359,871 -0.297 (0.00499)	359,871 -0.00559 (0.00513)
	R-squared Common Border	(0.132)		0.787 (0.390) 0.762***	0.843 (0.0387) 0.447***
	Note: Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Year Minus 4 0.270* -0.366 0.0900** (0.0209) (0.0193)
	(0.139) Common Language		(0.420) 0.471***	(0.0384) 0.499***
	Year Minus 3		0.347**		-0.383 (0.00889)	0.109*** (0.00985)
	Colony		(0.145)		(0.420) 1.209***	(0.0385) 1.281***
	Year Minus 2		0.338**		-0.264 (0.0214)	0.0522 (0.0213)
	Current Colony	(0.152)		(0.419) -2.309***	(0.0387) -1.577***
	Year Minus 1	0.435***		-0.00511 (0.186)	0.0649* (0.170)
	FTA dummy		(0.160)		(0.302) 0.479***	(0.0382) 0.298***
	Year 0	0.449***		0.269 (0.0105)	0.0605** (0.0105)
	(0.173) max ln GDP per Capita		(0.269) 0.959***	(0.0300) 0.433***
	Year Plus 1		0.432**		0.864*** (0.00244)	0.0667 (0.00797)
	(0.191) min ln GDP per Capita		(0.213) 0.912***	(0.0435) 0.641***
	Year Plus 2	0.866***		0.859*** (0.00270)	0.0528 (0.00768)
	max ln Population	(0.209)		(0.212) 0.0210***	(0.0441) -0.0427**
	Year Plus 3		0.550**		0.408** (0.00345)	0.0440 (0.0214)
	min ln Population	(0.231)		(0.207) 0.0471***	(0.0435) 0.0401*
	Year Plus 4	0.640***		0.545*** (0.00388)	0.0427 (0.0214)
	max ln Surface	(0.242)		(0.206) -0.127***	(0.0434)
	Year Plus 5		0.598**		0.252 (0.00291)	0.101**
	min ln Surface	(0.253)	(0.194) -0.0563***	(0.0435)
	Year Plus 6	0.693***		0.277 (0.00259)	0.0902**
	Landlocked		(0.262)		(0.193) -0.382***	(0.0435)
	Year Plus 7	0.770***		0.211 (0.00814)	0.0520
	Both Landlocked	(0.271)		(0.191) 0.448***	(0.0435)
	Year Plus 8	0.885***		0.0618 (0.0225)	0.0667
	Island		(0.283)		(0.185) 0.204***	(0.0446)
						(0.00921)
	Observations BothIsland		10,560		4,033 0.878***	127,197
	R-squared			0.976		0.959 (0.0201)	0.888
	Pair-country FE			YES		YES	YES
	Exporter-year FE Year FE			YES		YES	YES	YES YES
	Importer-year FE Importer FE			YES		NO	YES	YES YES
	Note: Control group : countries which will be in the regime studied in more than 8 Exporter FE NO YES
	years. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Observations 351,025 353,887
	R-squared					0.654	0.718
	F-test					119.4	5.850
	Prob > F					0	0.0156

Note: Control group: oating exchange rate. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

Table A9

 A9

	: Poisson Pseudo Maximum Likelihood
		(1)	(2)
	lnExport	Time xed eect	Time and country xed eect
	Currency Union	0.820***	0.498***
		(0.0373)	(0.0345)
	Peg one-to-one	0.230***	0.378***
		(0.0426)	(0.0410)
	Fixed exchange rate	0.0246**	0.00939
		(0.0124)	(0.0119)
	lnDistance	-0.995***	-1.229***
		(0.00497)	(0.00512)
	Common Border	0.762***	0.447***
		(0.0209)	(0.0193)
	Common Language	0.471***	0.499***
		(0.00888)	(0.00985)
	Colony	1.209***	1.281***
		(0.0214)	(0.0212)
	Current Colony	-2.309***	-1.577***
		(0.186)	(0.170)
	FTA dummy	0.479***	0.298***
		(0.0105)	(0.0104)
	max ln GDP per Capita	0.959***	0.433***
		(0.00243)	(0.00796)
	min ln GDP per Capita	0.912***	0.641***
		(0.00270)	(0.00768)
	max ln Population	0.0210***	-0.0427**
		(0.00345)	(0.0214)
	min ln Population	0.0471***	0.0401*
		(0.00388)	(0.0214)
	max ln Surface	-0.127***
		(0.00291)
	min ln Surface	-0.0563***
		(0.00259)
	Landlocked	-0.382***
		(0.00814)
	Both Landlocked	0.448***
		(0.0225)
	Island	0.204***
		(0.00921)
	BothIsland	0.878***
		(0.0201)
	Year FE	YES	YES
	Importer FE	NO	YES
	Exporter FE	NO	YES
	Observations	351,025	353,887
	F-test	119.5	5.856
	Prob > F	0	0.0155
	Note: Control group: oating exchange rate. Standard errors in parentheses (***
	p<0.01, ** p<0.05, * p<0.1)

Table A10 :

 A10 Eect of currency union and peg one-to-one for every decile of trade

		(1)
	VARIABLES	lnExport
	Currency Union*10th rst percentile	-
	Currency Union*10th-20th percentile predicted trade	-0.637
		(0.646)
	Currency Union*20th-30th percentile predicted trade	0.617***
		(0.157)
	Currency Union*30th-40th percentile predicted trade	0.496***
		(0.0515)
	Currency Union*40th-50th percentile predicted trade	0.601***
		(0.0556)
	Currency Union*50th-60th percentile predicted trade	0.946***
		(0.126)
	Currency Union*60th-70th percentile predicted trade	1.381***
		(0.194)
	Currency Union*70th-80th percentile predicted trade	0.677***
		(0.0902)
	Currency Union*80th-90th percentile predicted trade	0.177*
		(0.0974)
	Currency Union*90th-100th percentile predicted trade	0.199**
		(0.0817)
	Peg one-to-one*10th rst percentile predicted trade	0.808***
		(0.111)
	Peg one-to-one*10th-20th percentile predicted trade	-0.0162
		(0.138)
	Peg one-to-one*20th-30th percentile predicted trade	1.259***
		(0.0856)
	Peg one-to-one*30th-40th percentile predicted trade	0.392***
		(0.0671)
	Peg one-to-one*40th-50th percentile predicted trade	0.285**
		(0.128)
	Peg one-to-one*50th-60th percentile predicted trade	-0.290***
		(0.0753)
	Peg one-to-one*60th-70th percentile predicted trade	-1.137***
		(0.167)
	Peg one-to-one*70th-80th percentile predicted trade	0.515***
		(0.115)
	Peg one-to-one*80th-90th percentile predicted trade	-0.133
		(0.112)
	Peg one-to-one*90th-100th percentile predicted trade	0.446***
		(0.0865)
	Observations	635,952
	R-squared	0.868
	Pair-country FE	YES
	Exporter-year FE	YES
	Importer-year FE	YES

Note: Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

Table A11 :

 A11 Eect of each currency union and the pegs one-to-one attached

	(1)
	to it

Table A12 :

 A12 Conditional probability of the previous regime, knowing the actual regime

	Portugal Escudo	1.213**	(0.527) Probability 0.916*** (0.247) before oat	635,952	0.868 Probability YES YES YES before xed	Probability before CU	Probability before peg
	lnExport Pound Sterling US dollar Franc CFA Indian Rupee French Franc Knowing now oat Knowing now xed Knowing now CU Knowing now Peg Knowing now oat Knowing now xed Knowing now CU Knowing now Peg	Currency Union 0.331* 0.792*** 0.294*** 0.717 1.040***	(0.198) (0.107) (0.0813) (0.465) (0.278) Probability Peg one-to-one 0.401*** -0.0624 0.824*** 0.530*** 1.570* (0.0484) (0.0679) (0.0791) (0.111) (0.868) Observations 635,952 635,952 635,952 635,952 635,952 -99% 33% 34% after oat -99% 58% 91%	R-squared 0.868 0.868 0.868 0.868 0.868 Probability Pair-country FE YES YES YES YES YES Exporter-year FE YES YES YES YES YES Importer-year FE YES YES YES YES YES In each column, the dummy variables are equal to one depending on the Note: 96% -56% 15% after xed 99% -2% 3%	0,90% 0,0003% -51% Probability after CU 0,7% currency of interest. Standard errors in parentheses (*** p<0.01, ** p<0.05, * 1% -p<0.1) 6%	3% 0,0001% 12% -Probability after peg 0,04% 0,2% 40% -

Table A13 :

 A13 Conditional probability of the next regime, knowing the actual regime

	Chapter 2

  To evaluate the impact of the euro on trade during the crisis, I estimate a dierence-in-dierence model of logged one-way trade (exports) at annual frequency, comparing the dierence in trade before and after the crisis in the Eurozone with the dierence in trade in the same period for

	pendency index is from Choi (2017) and the global value chain index is calculated based on
	the OECD Global Value Chains indicators database (De Backer and Miroudot, 2013). For the
	remaining data, I use World Development Indicators from the World Bank (1960-2015). The
	variables are described in the appendix.
	2.2.2 Methodology
	2.2 Aggregate eect of the euro
	2.2.1 Data set
	Trade data are drawn from BACI, a world trade database developed by CEPII (Gaulier and
	Zignago, 2010) at the Harmonized System (HS) 6-digit product disaggregation, 4 for more than
	200 countries between 2003 and 2015. Currency union data are drawn from my own database
	(Lebastard, 2017), 5 the durable good index is from Engel and Wang (2011), the nancial de-

the other developed countries. I use dierence-in-dierence rather than matching methods on country level data since the sample is small and the unobserved characteristics too numerous. I include typical gravity xed eects: importer-time, exporter-time and pair-country xed eects, in order to absorb time invariant characteristics (distance, common language, colony, etc.), and also time varying characteristics specic to the countries (GDP per capita, GDP drop during the crisis, etc.). Finally, xed eects allow me to capture several unobserved characteristics. This method which requires multiple high-dimensional xed eects is very demanding but captures many confounding factors; having signicant coecients in these conditions is evidence of a strong eect. I do not include separate control variables for the crisis period and the treatment group since they are absorbed by the xed eects. I start my study in 2003, avoiding a path of trade change over time. I tried other time periods (see table B8 and B9 in the appendix) but the results changed very little. The regression equation is given by

Table 2 .

 2 1: Dierence-in-dierences: eect of the euro during the crisis

		(1)	(2)	(3)	(4)
	lnExport ijt	2003-2010	2003-2015	2003-2010	2003-2015
	Euro*Crisis 2008-2010 ijt	-0.0269*		0.0935***
		(0.0120)		(0.0224)
	Euro*Crisis 2008-2015 ijt		-0.0707***		0.0597***
			(0.0118)		(0.0192)
	Observations	4,416	7,176	4,416	7,176
	R-squared	0.991	0.986	0.993	0.991
	Pair-country FE	YES	YES	YES	YES
	Exporter-time FE	NO	NO	YES	YES
	Importer-time FE	NO	NO	YES	YES
	Exporter FE	YES	YES	NO	NO
	Importer FE	YES	YES	NO	NO
	Time FE	YES	YES	NO	NO
	Note: Dierence-in-dierences approach between the rst 12 Eurozone members
	and the other most developed countries in the world, before and after the nancial
	crisis starting in 2008. Robust standard errors in parentheses (*** p<0.01, **
	p<0.05, * p<0.1)

Not taking into account individual country time trends leads to incorrect conclusions about the euro eect, and negative and signicant coecients (columns 1 and 2 in table 2.1). This is because the exporter-time and importer-time xed eects capture the time-varying eects of a GDP drop and a banking sector crisis. Indeed, the banking crisis was deeper in the Eurozone.

The coecients in columns 1 and 2 do not control for these facts which is why they are negative.

Column 3 in table

2

.1 shows the correct estimation, controlling for individual country time trends as well as time-invariant pair characteristics. The euro now has a positive and signi-

  10 e 0.0935 ≈ 1.098; e 0.0597 ≈ 1.062.

Table 2 .

 2 2: Testing the eect of the euro year by yearThis exercise suggests that trade among the Eurozone members plunged less deeply than in the case of the other developed countries, and that recovery was quicker and exhibited a more powerful rebound. Compared to 2003, the coecients of years 2008 to 2010 are higher than in column 3 in table 2.1 (with years 2003 to 2007 as the benchmark). A potential reason for this stronger rebound is given by Bricongne et al.(2012) who nd that during the great trade collapse, the usual number (not more) of small French rms exited the export market. Only the extensive margin of French trade was aected by the great trade collapse, not the intensive margin. This explains why the trade rebound in France was strong. TableB1shows that on average, Eurozone rms are not as nancially dependent as rms in the other developed countries.It is likely then, that the number of exits of European rms was less important than in the other developed countries for the same reason that French rms were better able to ride the crisis. It might explain the bigger rebound observed in table 2.2. In column 1 my treatment group comprises all the Eurozone members (new members included), my control group is the most developed countries in the world as listed in appendix. In column 2, I withdraw all the countries whose GDP decreased by less than 5%, my treatment group does not change. In column 3, I control for the EU membership, my control group being the OECD countries. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)To account better for the GDP drop during the crisis, I exclude from my sample all countries whose GDP decreased by less than 5%; my treatment group remains the same, only the control group changes. Column 2 in table 2.3 presents the results. The coecients are even higher than before. Indeed, the fact that GDP dropped by more than 5% during the crisis is an indicator of poor nancial health, not necessarily all captured by my country-time xed eects since there 11 Slovenia joined the Eurozone in 2007, Cyprus and Malta in 2008, Slovakia in 2009, Estonia in 2011, Latvia in 2014 and Lithuania in 2015.

		(1)
	lnExport ijt	2003-2015
	Euro*2004 ijt	0.0399
		(0.0506)
	Euro*2005 ijt	0.0350
		(0.0475)
	Euro*2006 ijt	0.0652
		(0.0466)
	Euro*2007 ijt	0.0528
		(0.0513)
	Euro*2008 ijt	0.136***
		(0.0485)
	Euro*2009 ijt	0.114**
		(0.0481)
	Euro*2010 ijt	0.146***
		(0.0490)
	Euro*2011 ijt	0.0776
		(0.0496)
	Euro*2012 ijt	0.0539
		(0.0504)
	Euro*2013 ijt	0.0986*
		(0.0512)
	Euro*2014 ijt	0.0742
		(0.0520)
	Euro*2015 ijt	0.0858
		(0.0544)
	Observations	7,176
	R-squared	0.991
	Pair-country FE	YES
	Exporter-time FE	YES
	Importer-time FE	YES
	Note: The benchmark is the year 2003. The control group is
	the most developed countries in the world (listed in the ap-
	pendix). Robust standard errors in parentheses (*** p<0.01,
	** p<0.05, * p<0.1)
	Compared to the other developed countries, the euro did not have a signicantly dierent
	eect on trade until 2008, when it becomes signicant which is precisely when the nancial crisis
	started.

From year 2011 in table 2.2, the euro continued to have a positive eect on trade but it is no longer signicant. This might be due to the sovereign debt crisis which started in 2011 in the Eurozone. Therefore, the Eurozone has done better than the other developed countries during the great trade collapse, and has not done worse afterwards, despite an internal crisis.

The literature suggests that the overall eect of the euro has been positive and signicant (e.g. Glick and Rose, 2016); however, I nd positive but not signicant eects. The reason for this is that my benchmark is year 2003, so there is no comparison with Eurozone members before they had the euro. In addition, my control group comprises the developed countries, while most studies use the world as the control group.

In column 1 in table 2.3, I add to the equation 2.1 the east European countries after entry to the Eurozone.

11 The coecients remain positive and signicant, although the eect is slightly smaller (7.4% 12 against 9.8% in table 2.1). This small eect is not surprising;

[START_REF] Shelburne | The Global Financial Crisis and Its Impact on Trade: The World and the European Emerging Economies[END_REF]

observed that during the collapse, the rst 15 members of the European Union (EU prior to 2004) did better compared to the 12 new members. He observed that east European countries were the most negatively impacted economies in the world in terms of GDP decline. If I take the OECD countries (including some of the central and eastern Europe countries) as a control group, the eects of the euro are only slightly bigger.

  14 e 0.135 ≈ 1.145; e 0.0798 ≈ 1.083.

	the literature,

Table 2 . 4

 24 2.1 shows that the Eurozone (the treatment group) and the developed countries (the control group) have globally the same structure of exports. For instance, the rst biggest sectors both for my treatment group and my control group are machinery, vehicles and chemicals.

		Weight of	Drop between	Contribution to	Dierence
		the sectors	2008 and 2009	aggregate drop
		Euro	Dev	Euro	Dev	Euro	Dev
	S15 metal	11,34	7,48	37,99	38,84	4,31	2,90	1,41
	S7 plastics	6,31	3,88	22,11	22,75	1,39	0,88	0,51
	S6 chemicals	14,20	13,54	11,65	8,80	1,65	1,19	0,46
	S11 textiles	3,17	1,61	14,21	20,04	0,45	0,32	0,13
	S3 fat	0,75	0,31	25,25	23,83	0,19	0,07	0,12
	S1 animal	3,10	1,89	9,42	11,86	0,29	0,22	0,07
	S13 cement	1,38	0,90	19,02	23,66	0,26	0,21	0,05
	S4 foodstus	4,77	3,08	6,93	9,39	0,33	0,29	0,04
	S8 leather	0,44	0,29	21,35	21,14	0,09	0,06	0,03
	S10 bres	2,82	2,36	16,97	19,24	0,48	0,45	0,03
	S19 arms	0,04	0,14	14,15	2,37	0,006	0,003	0,002
	S2 vegetable	2,79	1,91	13,77	20,98	0,38	0,40	-0,02
	S12 footwear	0,67	0,27	3,40	16,49	0,02	0,04	-0,02
	S9 wood	0,93	0,85	21,70	27,37	0,20	0,23	-0,03
	S20 miscellaneous	1,94	1,47	13,58	22,46	0,26	0,33	-0,07
	S21 art	0,016	0,43	15,95	36,80	0,003	0,16	-0,15
	S18 instruments	2,38	4,64	10,85	13,55	0,26	0,63	-0,37
	S14 precious stones	0,73	3,22	25,26	21,35	0,18	0,69	-0,51
	S17 vehicles	15,06	14,98	21,06	29,26	3,17	4,38	-1,21
	S16 machinery	19,07	22,93	22,07	25,36	4,21	5,82	-1,61
	S5 minerals	8,10	13,85	34,64	38,04	2,81	5,27	-2,46
					sum	20,96	24,56	-3,6

: Each sector's contribution to the aggregate drop

Table 2 .

 2 4 shows the contribution of each sector to the observed drop in aggregate trade between 2008 and 2009 in the Eurozone and in the developed countries. Thus, the metal sector Export ijts = βEuro ijt * Crisis t * Sector s + λ 1its + λ 2jts + λ 3ijs + ijts ,

	(S15) and the machinery sector (S16) together are responsible for more than 40% of the total
	drop in the Eurozone. 17 However, both these sectors do better than the same sectors in the
	16 S1: live animals, animal products; S2: vegetable products; S3: animal or vegetable fats and oils and their
	cleavage products; S4: prepared foodstus, beverages, tobacco; S5: mineral products; S6: chemicals; S7: plastics
	and articles thereof; S8: leather, furskins and articles thereof; S9: wood and articles of wood; S10: pulp of wood
	or of other brous cellulosic material; S11: textiles; S12: footwear, headgear, umbrellas, walking-sticks; S13:
	articles of stone, plaster, cement; S14: natural or cultured pearls, precious or semi-precious stones; S15: base
	metals and articles of base metal; S16: machinery and mechanical appliances, electrical equipment, parts thereof;
	S17: vehicles, aircraft, vessels and associated transport equipment; S18: optical, photographic, cinematographic,
	measuring, checking, precision, medical or surgical instruments and apparatus; clocks and watches; musical instru-
	ments; S19: arms and ammunition; S20: miscellaneous manufactured articles; S21: works of art, collectors'pieces
	and antiques. More details in appendix 1.

ln • Export ijts bilateral trade at the sector level

• Euro ijt * Crisis t * Sector s sectors in the Eurozone (both partners being in the Eurozone) during the crisis dummy • λ 1its exporter-sector-time xed eect • λ 2jts importer-sector-time xed eect • λ 3ijs pair-country-sector xed eect. The results of this regression are presented in table 2.5.

Table 2 .

 2 5: Dierence-in-dierences: sectorial level at the rst and second digit

		(1)	(2)	(3)	(4)
	lnExport ijts	One-digit level	Two-digit level	One-digit level	Two-digit level
		2003-2010	2003-2010	2003-2015	2003-2015
	Euro*Crisis 2008-2010 ijt	0.0323**	0.0340***
		(0.0157)	(0.0102)
	Euro*Crisis 2008-2015 ijt			0.0526***	0.0644***
				(0.0130)	(0.00854)
	Observations	90,768	373,548	147,624	608,389
	R-squared	0.973	0.957	0.968	0.948
	Exporter-sector-time FE	YES	YES	YES	YES
	Importer-sector-time FE	YES	YES	YES	YES
	Pair-country-sector FE	YES	YES	YES	YES
	Note: Regressions are run at the HS 1-digit and 2-digit product disaggregation level. The control group
	is the developed countries listed in the appendix. Robust standard errors in parentheses (*** p<0.01,
	** p<0.05, * p<0.1)

Table 2 .

 2

5

shows that the euro still has a more signicant eect in both the short (column 1 and 2) and long (column 3 and 4) term but much smaller than in table 2.1. This means that the positive and signicant eect observed at the aggregate level is partly a composition eect; controlling for the size of the sector, with sector xed eects, shrinks the eect. One reason for this might be that the sectors exporting the most are machinery and mechanical appliances 18 and vehicles, aircraft, vessels and associated transport equipment,

Table 2

 2

	.6 presents the results.
	18 19% of total Eurozone members' export. 19 15% of total Eurozone members' export.

Table 2 .

 2 run a Spearman rank test between the eects of the euro during the great trade collapse on each sector, and the contribution of each sector to total Eurozone exports. The correlation is 0.95 for 2008-2010, and 0.96 for 2008-2015, the hypothesis that the variables are independent is rejected at the 95% interval. Thus, it is clear that the euro helped the highest exporting sectors. This is in line with the statisticss in table 2.4. In addition, the Eurozone has the biggest comparative advantages in these sectors which are also the most open.2.3.4 Controlling for sector weight in total exportsI control for sector weight in total exports by adding a sector xed eect, to study the eect of the euro independent of export weight.

	6: Dierence-in-dierences: eect of the euro per sector
		(1)	(2)
	lnExport ijts 1-digit	Crisis 2008-2010	Crisis 2008-2015
		period 2003-2010	period 2003-2015
	Euro*Crisis S1 ijts (animal)	0.638***	0.697***
		(0.133)	(0.0849)
	Euro*Crisis S2 ijts (vegetable)	0.196	0.294***
		(0.133)	(0.0849)
	Euro*Crisis S3 ijts (fat)	-1.538***	-1.383***
		(0.134)	(0.0854)
	Euro*Crisis S4 ijts (foodstus)	1.333***	1.383***
		(0.133)	(0.0849)
	Euro*Crisis S5 ijts (minerals)	0.564***	0.663***
		(0.133)	(0.0849)
	Euro*Crisis S6 ijts (chemicals)	2.397***	2.465***
		(0.133)	(0.0849)
	Euro*Crisis S7 ijts (plastics)	1.437***	1.493***
		(0.133)	(0.0849)
	Euro*Crisis S8 ijts (leather)	-1.324***	-1.191***
		(0.133)	(0.0849)
	Euro*Crisis S9 ijts (wood)	-0.710***	-0.779***
		(0.133)	(0.0850)
	Euro*Crisis S10 ijts (bres)	0.654***	0.673***
		(0.133)	(0.0849)
	Euro*Crisis S11 ijts (textiles)	0.980***	0.960***
		(0.133)	(0.0849)
	Euro*Crisis S12 ijts (footwear)	-1.081***	-0.963***
		(0.133)	(0.0849)
	Euro*Crisis S13 ijts (cement)	-0.244*	-0.336***
		(0.133)	(0.0849)
	Euro*Crisis S14 ijts (precious stones)	-1.569***	-1.484***
		(0.133)	(0.0851)
	Euro*Crisis S15 ijts (metal)	1.846***	1.837***
		(0.133)	(0.0849)
	Euro*Crisis S16 ijts (machinery)	2.747***	2.631***
		(0.133)	(0.0849)
	Euro*Crisis S17 ijts (vehicles)	1.623***	1.577***
		(0.133)	(0.0849)
	Euro*Crisis S18 ijts (instruments)	0.594***	0.628***
		(0.133)	(0.0849)
	Euro*Crisis S19 ijts (arm)	-3.872***	-4.092***
		(0.142)	(0.0902)
	Euro*Crisis S20 ijts (miscellaneous)	0.0722	0.0252
		(0.133)	(0.0849)
	Euro*Crisis S21 ijts (art)	-4.964***	-4.942***
		(0.139)	(0.0879)
	Observations	90,806	147,645
	R-squared	0.509	0.530
	Pair-country FE	YES	YES
	Exporter-time FE	YES	YES
	Importer-time FE	YES	YES

Note: The control group is the developed countries as listed in the appendix. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) I

Table 2 .

 2 7: Dierence-in-dierences: eect of the euro per sector with sector xed eect Using a Spearman rank test between the euro eect using sector xed eects 20 and revealed comparative advantage (hypothesis that variables are independent is rejected at the 95% inter-

	(1)	(2)

Note: The control group is the developed countries as listed in the appendix. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

  I tried also to control only for sector export weight; the results are presented in table B12 in the appendix.

	The results did not change.

times. I test separately and then together the eect of global value chain (a proxy constructed by De Backer and Miroudot, 2013), nancial dependency (proxied by external nance share, variable constructed by Choi, 2017) and fall in demand for durable goods (variable constructed by Engel and Wang, 2011). I do not add sector xed eects since they would absorb the variables I use as controls. The results are displayed in table 2.8.

20

Table 2 .

 2 8: Dierence-in-dierences: testing causes of the great trade collapse

		(1)	(2)	(3)	(4)	(5)
	lnExport ijts 2-digit	No control	GVC	Non durable	Dependency	All
		2003-2015	2003-2015	2003-2015	2003-2015	2003-2015
	Euro*Crisis 2008-2015 ijt	0.0553**	0.0580**	0.0555**	0.0553**	0.0589**
		(0.0269)	(0.0255)	(0.0266)	(0.0277)	(0.0262)
	Global value chain s		0.285***			0.310***
			(0.00109)			(0.00119)
	Non durable good s			-0.658***		-0.304***
				(0.00656)		(0.00679)
	Financial dependency s				0.111***	-0.454***
					(0.00764)	(0.00761)
	Observations	609,469	609,469	609,469	578,982	578,982
	R-squared	0.372	0.435	0.383	0.370	0.436
	Pair-country FE	YES	YES	YES	YES	YES
	Exporter-time FE	YES	YES	YES	YES	YES
	Importer-time FE	YES	YES	YES	YES	YES

Note: The control group is the developed countries as listed in the appendix. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Table 2.8 shows few variations in the coecients of Euro * Crisis08-15 in each column, they are all positive and signicant. Therefore, it appears that none of these causes explains the positive eect of the euro. Column 2 in table 2.8 shows that controlling for the intensity of vertical specialization does not suppress my euro eect.

  Bricongne et al. (2012) ndings are in the same direction. This might explain the stronger rebound I observe in the Eurozone after the 2009 trade drop. Using the same data as in table 2.8, and exploiting the results in table 2.7, I implemented a Spearman rank test. I nd a negative relationship between global value chain and the euro eect during the crisis (-0.34 for 2008-2010 period and -0.33 for 2008-2015 period). As expected, vertical trade decreased particularly during the crisis, thus the Eurozone is not an exception. The fact that the results in table 2.8 column 2 do not change much does not contradict the results of the Spearman rank test since this does not take account of the weights of the sectors. Column 3 in table 2.8 shows that controlling for exports of non-durable goods does not suppress my euro eect. This is because my treatment and control groups have the same export structure (gure 2.1), thus the euro eect does not come from exporting more or less durable goods than the other developed countries. Column 4 in table 2.8 shows that controlling for the nancial dependency of sectors does not suppress my euro eect. This is the case because my country-time xed eect already controls

Table B4 :

 B4 Dierence-in-dierences: robustness checks for the period 2003-2015 In column 1 my treatment group comprises all the Eurozone members (new members included), my control group is the most developed countries in the world as listed in appendix. In column 2, I withdraw all the countries whose GDP decreased by less than 5%, my treatment group does not change. In column 3, I control for the EU membership, my control group being the OECD countries. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

	lnExport Euro*Crisis 2008-2010 Euro*Crisis 2008-2011 Euro*Crisis 2008-2012 Euro*Crisis 2008-2013 Euro*Crisis 2008-2014 Euro*Crisis 2008-2015 Observations R-squared Pair-country FE Exporter-time FE lnExport Euro*Crisis 2008-2015 2003-2010 0.0935*** (0.0230) 4,416 0.993 YES YES including new members 2003-2011 2003-2012 0.0799*** (0.0219) 0.0670*** (0.0213) 4,968 5,520 0.993 0.992 YES YES YES YES (1) All Eurozone GDP drop of at 2003-2013 0.0658*** (0.0208) 6,072 0.992 YES YES (2) least 5% in 2009 2003-2014 0.0615*** (0.0205) 6,624 0.991 YES YES Test for EU 2003-2015 0.0597*** (0.0203) 7,176 0.991 YES YES (3) (control group, OECD) 0.0602*** 0.102*** 0.0875*** (0.0179) (0.0252) (0.0193) European Union 0.00830 (0.0400) Observations 6,578 4,940 11,310 R-squared 0.992 0.991 0.989 Pair-country FE YES YES YES Exporter-time FE YES YES YES Importer-time FE YES YES YES (1) lnExport 2003-2010 Table B7: PPML: dealing with the zero ows problem Table B9: Dierence-in-dierences: changing the period of reference Note: Table B5: Dierence-in-dierences: excluding countries Euro*Crisis 2008-2010 no exclusion Table B6: Dierence-in-dierences: excluding countries 0.0935*** (0.0230) Euro*Crisis 2008-2010 exclusion AUT (1) (1) (2) Export PPML 2003-2010 (1) (2) (3) (4) PPML 2003-2015 lnExport 2005-2015 2004-2015 2003-2015 2002-2015 0.116*** lnExport 2003-2015 (0.0245) Euro*Crisis 2008-2010 exclusion BEL 0.0853*** (0.0250) Euro*Crisis 2008-2010 exclusion DEU Euro*Crisis 2008-2015 no exclusion 0.0597*** Euro*Crisis 2008-2010 0.0405*** Euro*Crisis 2008-2015 0.0473** 0.0501** 0.0597*** 0.0571*** (0.0118) Euro*Crisis 2008-2015 (0.0237) (0.0216) (0.0203) (0.0195) 0.0250* (0.0203) 0.0997*** (0.0252) Euro*Crisis 2008-2010 exclusion ESP 0.0927*** Euro*Crisis 2008-2015 exclusion AUT 0.0812*** (0.0217) Euro*Crisis 2008-2015 exclusion BEL 0.0540** (0.0151) Constant 12.45*** Observations 6,072 6,624 7,176 7,728 14.49*** (0.0545) R-squared 0.992 0.991 0.991 0.991 (0.0433) Pair-country FE YES YES YES YES (0.0249) Euro*Crisis 2008-2010 exclusion FIN 0.100*** (0.0244) Euro*Crisis 2008-2010 exclusion FRA 0.0923*** (0.0251) Euro*Crisis 2008-2010 exclusion GRC (0.0220) Euro*Crisis 2008-2015 exclusion DEU 0.0660*** (0.0222) Euro*Crisis 2008-2015 exclusion ESP 0.0656*** Observations 4,416 Exporter-time FE YES YES YES YES 7,176 R-squared 0.999 Importer-time FE YES YES YES YES 0.998 Pair-country FE YES Note: Control group: the most developed countries. Standard errors in parentheses YES Exporter-time FE YES (*** p<0.01, ** p<0.05, * p<0.1) YES (0.0219) 0.0768*** (0.0238) Euro*Crisis 2008-2010 exclusion IRL 0.0929*** Euro*Crisis 2008-2015 exclusion FIN Importer-time FE YES YES 0.0692*** Note: Control group: the most developed countries. Standard errors in (0.0213) Euro*Crisis 2008-2015 exclusion FRA parentheses (*** p<0.01, ** p<0.05, * p<0.1) 0.0598*** (0.0230) Euro*Crisis 2008-2010 exclusion ITA (0.0221) 0.0943*** Euro*Crisis 2008-2015 exclusion GRC 0.0377* (0.0251) Euro*Crisis 2008-2010 exclusion LUX (0.0209) 0.0935*** Euro*Crisis 2008-2015 exclusion IRL 0.0561*** (0.0230) Euro*Crisis 2008-2010 exclusion NLD (0.0208) 0.103*** Euro*Crisis 2008-2015 exclusion ITA 0.0615*** Table B10: Dierence-in-dierences: changing the start of the crisis (2009 instead of 2008) (0.0250) Euro*Crisis 2008-2010 exclusion PRT (0.0220) 0.0758*** (0.0242) Euro*Crisis 2008-2010 exclusion USA Euro*Crisis 2008-2015 exclusion LUX (1) (2) 0.0597*** Table B8: Dierence-in-dierences: changing the period of reference lnExport 2003-2010 2003-2015 (0.0203) 0.112*** (0.0246) Pair-country FE YES Exporter-time FE Euro*Crisis 2008-2015 exclusion NLD 0.0651*** (0.0219) Euro*Crisis 2008-2015 exclusion PRT (1) (2) (3) (4) lnExport 2005-2010 2004-2010 2003-2010 Euro*Crisis 2009-2010 0.0756*** 2002-2010 (0.0257) 0.0406* YES Importer-time FE YES (0.0214) Euro*Crisis 2008-2015 exclusion USA 0.0733*** Euro*Crisis 2008-2010 0.0811*** 0.0839*** 0.0935*** Euro*Crisis 2009-2015 0.0382* 0.0909*** (0.0251) (0.0238) (0.0230) (0.0198) (0.0226) Note: Control group: the most developed countries. Standard (0.0216) errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Pair-country FE YES Exporter-time FE YES Importer-time FE YES Note: Control group: the most developed countries. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) Observations 3,312 3,864 4,416 Observations 4,416 7,176 4,968 R-squared 0.994 0.994 0.993 R-squared 0.993 0.991 0.993 Pair-country FE YES YES YES Pair-country FE YES YES YES Exporter-time FE YES YES YES Exporter-time FE YES YES YES Importer-time FE YES YES YES Importer-time FE YES YES YES Note: Control group: the most developed countries. Standard Note: Control group: the most developed countries. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1) errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)
	Importer-time FE	YES	YES	YES	YES	YES	YES
	Note: Control group: the most developed countries. Standard errors in parentheses (*** p<0.01, **
	p<0.05, * p<0.1)

Table B11 :

 B11 Dierence-in-dierences: eect of currency unions on trade during the crisis

		(1)	(2)
	lnExport	2003-2010	2003-2015
	Currency Union*Crisis 2008-2010	0.135***
		(0.0419)
	Currency Union*Crisis 2008-2015		0.0798**
			(0.0329)
	Observations	205,514	339,992
	R-squared	0.922	0.911
	Pair-country FE	YES	YES
	Exporter-time FE	YES	YES
	Importer-time FE	YES	YES
	Note: Control group: the world. Standard errors in parenthe-
	ses (*** p<0.01, ** p<0.05, * p<0.1)

Table B12 :

 B12 Dierence-in-dierences: eect of the euro per sector controlling for size

		(1)	(2)
	lnExport 1-digit	Crisis 2008-2010	Crisis 2008-2015
		period 2003-2010	period 2003-2015
	Euro*Crisis S1 (animal) Chapter 3	1.072*** (0.105)	1.130*** (0.0679)
	Euro*Crisis S2 (vegetable)	0.706***	0.803***
	(0.105) -0.510*** (0.106) 1.343*** (0.105) -0.269** (0.105) 0.0177 Eects of Global Value Chains on (0.0679) Euro*Crisis S3 (fat) -0.357*** (0.0684) Euro*Crisis S4 (foodstus) 1.392*** (0.0678) Euro*Crisis S5 (minerals) -0.169** (0.0679) Euro*Crisis S6 (chemicals) 0.0910 Shocks Transmission and Exchange Rate
		(0.106)	(0.0683)
	Euro*Crisis S7 (plastics)	1.058***	1.115***
		(0.105)	(0.0679)
	Euro*Crisis S8 (leather)	-0.217**	-0.0857
	Euro*Crisis S9 (wood) 3.1 Introduction	(0.105) 0.272**	(0.0680) 0.201***
		(0.106)	(0.0680)
	Euro*Crisis S10 (bres)	1.158***	1.176***
		(0.105)	(0.0679)
	Euro*Crisis S11 (textiles)	1.395***	1.374***
		(0.105)	(0.0679)
	Euro*Crisis S12 (footwear)	-0.0325	0.0836
		(0.105)	(0.0679)
	Euro*Crisis S13 (cement)	0.626***	0.531***
		(0.105)	(0.0679)
	Euro*Crisis S14 (precious stones)	-0.536***	-0.453***
		(0.105)	(0.0681)
	Euro*Crisis S15 (metal)	0.192*	0.186***
		(0.106)	(0.0681)
	Euro*Crisis S16 (machinery)	-0.864***	-0.974***
		(0.106)	(0.0690)
	Euro*Crisis S17 (vehicles)	-0.973***	-1.013***
		(0.106)	(0.0684)
	Euro*Crisis S18 (instruments)	1.209***	1.241***
		(0.105)	(0.0679)
	Euro*Crisis S19 (arms)	-2.669***	-2.889***
		(0.113)	(0.0722)
	Euro*Crisis S20 (miscellaneous)	0.799***	0.750***
		(0.105)	(0.0679)
	Euro*Crisis S21 (art)	-3.752***	-3.732***
		(0.111)	(0.0704)
	Size	25.33***	25.27***
		(0.110)	(0.0878)
	Observations	90,806	147,645
	R-squared	0.691	0.700
	Pair-country FE	YES	YES
	Exporter-time FE	YES	YES
	Importer-time FE	YES	YES
	Note: Control group: the most developed countries. Standard errors in parentheses
	(*** p<0.01, ** p<0.05, * p<0.1)

  global value chains allow economies to be more closely linked than in Corsetti and Pesenti's (2009) model (with only nal good trade). Intermediate good productivity shocks are transmitted by two channels rather thanone, so shock transmission is much greater. The business cycles of countries are also more correlated since shocks are transmitted symmetrically in the two economies. In addition, the exchange rate is less volatile than in the original model. My model recommends that monetary policies respond in the same way whether the shock comes from home or abroad. There are no gains from coordination.

	I nd that The chapter is organized as follows. Section 2 provides a literature review. Section 3 describes
	the model. Section 4 suggests some implications of the model. Section 5 concludes.
	3.2 Literature review
	3.2.1 Vertical trade and state of the art
	Smith (1776) theorized about task segmentation and specialization to build a nished product;
	vertical trade goes a step further by adding geographic specialization to task segmentation. Ver-
	tical trade or global value chains or vertical specialization, is dened by Hummels, Ishii and Yi
	I build a model to identify the consequences for optimal monetary rules. Following Corsetti and
	Pesenti's (2009) model, I change the production function by adding global value chains. Indeed,
	it represents a increasing part of total world trade, which has important consequences in terms
	of shock transmission. My model explains simply how productivity and monetary shocks are
	transmitted from one country to another, and more generally how global value chains increase
	the correlation of business cycles. Finally, my model provides monetary policy recommendations,
	and discusses the relevance of monetary policy coordination.
	1 e.g. Sturgeon and Memedovic (2011), Brustein et al. (2008), Giovanni et al. (2017).
	99

  Obstfeld and Rogo (2002) worked on optimal monetary policies in open economies, and found that to deliver the best possible outcome, monetary authorities should respond only to domestic shocks; the allocation would be replicated under exible prices. However, these conclusions are based on the assumption that the prices are set in the Producer Currency Pricing framework.

	Devereux and Engel (2003) consider both cases (Producer Currency Pricing, PCP and Local

  1-α p t (h) -X H,t (h)p XH,t -X F,t (h)p XF,t , XH,t is the price of the intermediate good X H,t and p XF,t of the intermediate good X F,t .

	(3.10)
	where p

  Each intermediate-good rm produces a single variety, either X H,t for Home rms or X F,t for Foreign rms, which are not perfect substitute. Labor is the only input in production. Productivity (output per unit of labor) is subject to economy-wide shocks. The labor market is assumed to be perfectly competitive, but there is not labor mobility across countries.

	.13)
	3.3.3 Intermediate-good rms
	Production function of X H,t rms is

Π

  XH,t = X H,t p XH,t -w H,t H,t . C t (j) = C t . C t coincides with aggregate demand in real terms, while P t C t is aggregate nominal spending. Let µ t denote a variable that synthesizes the eect of monetary policy on aggregate nominal spending P t C t . The Aggregate Demand "AD" equation can then be written as:

	and
	p XF,t =	w F,t Z F,t	.	(3.19)
	3.3.4 Structure of the economy
	As in Corsetti and Pesenti (2009), the model can be synthesized by means of three schedules:
	Aggregate Demand [AD], Aggregate Supply [AS], and the Natural Rate (of employment) 2 [NR].
	Agents preferences are homogenous so a common CPI index implies equalization of consump-
	tion across agents:
					(3.15)
	Under perfect competition, prot is equal to zero
	p XH,t =	w H,t Z H,t	.		(3.16)
	In the same way, production function of X F rms is
	X F,t = Z F,t F,t .		(3.17)
	Their prot function is
	Π XF,t = X F,t p XF,t -w F,t F,t ,	(3.18)

  With exible prices, the monetary authorities do not need to intervene since in the absence of rigidities, the market regulates by itself. Consider a positive productivity shock on Home intermediate good (Z H,t increases). Then, marginal cost of Home intermediate good decreases and Home intermediate-good price p XH,t decreases. Since p XH,t decreases, marginal cost of Home nal good also decreases and P H,t decreases (but less than p XH,t ). Therefore, the Home consumer price index P t decreases (but less than P H,t ) and Home consumption C t increases. The term of trade moves against the Home country and the exchange rate depreciates. For the Foreign country, the Home intermediate is cheaper, therefore the Foreign nal good price falls too. Therefore the Foreign consumer price index P * t decreases (since both Foreign nal good and Home nal good are cheaper). C * t increases. The Home term of trade improves and the exchange rate appreciates and comes back to his initial level if α = 1/2. At home, the consumer price index decreases even more since the Foreign nal good is also now cheaper. Figure 3.3 shows the mechanisms just described.

	1-α
	1-α µ t /Z H,t µ * t /Z F,t	.	(3.26)

  If the Home government wants to increase welfare, it can implement a monetary expansion (increase µ). Then Home consumption increases. Exchange rate depreciates and term of trade falls. Foreign goods are more expensive and the Home consumer price index increases: there is ination. Foreign country enjoys lower imported prices.

	If the Foreign government does not implement any monetary expansion, then Foreign nal-
	good price stays the same (only mark-up increases) and Home goods are cheaper: there is
	an expenditure switching eect. Foreign consumer price index decreases, Foreign consumption

Consider a positive productivity shock on Home intermediate good (Z H increases). Then, marginal cost of Home intermediate good decreases and Home intermediate-good price p H decreases (since prices are exible for intermediate goods). Since p H decreases, marginal cost of Home nal good also decreases, and since prices are rigid, the mark-up of nal-good rms increase. For the Foreign country, p H is also cheaper and mark-up of Foreign nal-good rms increases too.

  .34) If α = 1/2, then the government applies exactly the same policy for the PCP and LCP case.Otherwise, he considers the shock as a weighted average of the impact of the shock in the two countries, but still not coordinates.

Consider a positive productivity shock on Home intermediate good (Z H increases). Then, marginal cost of Home intermediate good decreases and Home intermediate-good price p H decreases (since prices are exible for intermediate goods). Since p H decreases, marginal cost of Home nal good also decreases, and since prices are rigid, the mark-up of nal-good rms increases. For the Foreign country, p H is also cheaper and mark-up of Foreign nal-good rms increases too.

If the Home government wants to increase welfare, it can implement a monetary expansion (increase µ). Then Home consumption increases. Exchange rate depreciates but term of trade improves because Home rms mark-up increases (since Foreign-currency prices are preset). It increases even more Home consumption. On the Foreign country side, nal-good rms mark-up increases but prices remain the same. Foreign term of trade falls. The transmission eect is therefore ambiguous.

  H ) and Home consumption C increases. Exchange rate is xed. For the Foreign country, the Home intermediate and nal goods are cheaper, therefore the Foreign nal-good price falls too. Therefore the Foreign consumer price index P * decreases. C * increases. At home, the consumer price index decreases even more since the Foreign nal good is also now cheaper. The result is the same than under exible exchange rate if α = 1/2.Figure 3.6 shows the mechanisms just described. PCP and LCP case With a xed exchange rate, PCP and LCP cases are the same, since the exchange rate is known in advance (there is no uncertainty).

1-α βZ F,t Z H,t 1-α . (3.35) Flexible price case Consider a positive productivity shock on Home intermediate good (Z H increases). Then, marginal cost of Home intermediate good decreases and Home intermediategood price p H decreases. Since p H decreases, marginal cost of Home nal good also decreases and P H decreases (but less than p H ). Therefore, the Home consumer price index P decreases (but less than P

  Consider a positive productivity shock on Home intermediate good (Z H increases). Then, marginal cost of Home intermediate good decreases and Home intermediate-good price p H decreases (since prices are exible for intermediate goods). Since p H decreases, marginal cost of Home nal good also decreases, and since prices are rigid, the mark-up of nal-good rms in-creases. For the Foreign country, p H is also cheaper and mark-up of Foreign nal-good rms increases too.

  3.4 What we learn from this model 3.4.1 Main ndingsWhileCorsetti and Pesenti's (2009) model provides distinct optimal monetary policies depending on whether the prices are producer or local currency prices, my model does not since the distinction between the two is not so relevent in the case of global value chains.Under exible price, the productivity shock is transmitted in exactly in the same way in the two economies. Moreover, the shock has a bigger impact than inCorsetti and Pesenti (2009) since it aects both Foreign and domestic nal goods.The optimal monetary policy is the same in the producer currency pricing (PCP) and the local currency pricing (LCP) case with the share of intermediate foreign product α = 1/2. It is still inward looking, it responds both to a Home and Foreign productivity shock. Coordination and non-coordination still give the same result. If α = 1/2, since the countries are aected in the same way by the productivity shock, they implement the same monetary policy, so the exchange rate does not change.Under xed exchange rate, the monetary policy changes very little compared to the oating case since the countries implement the same policy, which keeps the exchange rate stable. The fact that the exchange rate is xed is not a problem if α = 1/2 since the shock is symmetric.With vertical trade, economies are more closely linked than in the Corsetti and Pesenti's (2009) case of only horizontal trade. Indeed, Corsetti and Pesenti's model neglects a shock transission channel, and therefore underestimate the propagation of shocks. Their model only focuses on the traditional channel: the Home nal good is cheaper, so the Foreign consumer price index decreases. The second channel which I explore is inherent to vertical trade: the productivity shock decreases the price of the intermediate good, so the Foreign nal good also becomes cheaper.Therefore, the Foreign consumer price index also decreases through this channel. The impact of the productivity shock on the Home and Foreign economy depends mainly on α, the share of the Home intermediate good used to produce the nal good.Vertical trade also has an impact on exchange rate volatility. The exchange rate is less oat-ing, more viscous, midway between a xed and a oating exchange rate. This is because the economies are moving together much more, and the shocks are symmetric. Therefore, the exchange rate does not have to move since exchange rate movements express a dierence in the relative prices between the countries. If the Home and Foreign price move together, the exchange rate remains constant. With vertical trade, my employment function diers from Corsetti and Pesenti (2009) since it includes the short term exchange rate (dened as the dierence between the Home and Foreign monetary supply). Therefore, it links employment and ination. This coincides with the Phillips curve and Mundell's (1961) theory. Both works show an inverse relationship between rates of unemployment and ination.

  two regions would be better o with two dierent currencies oating with each other which would give them the mechanisms to regulate asymmetric shocks. Therefore, an optimum currency area can emerge only if regions are suciently homogenous as to be aected by the same shocks thus needing the same optimal monetary policy. My model shows that vertical trade allows the transmission of shocks to be much more ecient between the countries. With vertical trade, shocks are symmetric, which is also what empirical studies nd.My original idea when I began to write this chapter was to shed new light on the cost of transition to the euro. Indeed, economists have highlighted the cost of losing the mechanisms of stabilization linked to oating currencies. However, my model shows that these costs are overestimated since the shocks are less asymmetric than previously thought, so the mechanisms are also less important than rst envisaged. Limits of the model My model has some limitations. First, the model scope is limited since all nal goods consumed by users come from vertical trade, which is not the case in reality. Indeed, in North-North trade, vertical trade represents only 17% of total trade[START_REF] Giovanni | Putting the Parts Together: Trade, Vertical Linkages, and Business Cycle Comovement[END_REF]. Trade of services, representing one-fourth of total world trade in 2017(Loungani et al., 2008) with an increasing share[START_REF] Crozet | Should everybody be in service? the eect of servitization on manufacturing rm performance[END_REF] is not taken into account by my model. Therefore, the share of foreign intermediate goods in nal domestic goods α being equal to one half is not very realistic. α would be very heterogenous depending on the countries. In addition, 50% of the nal-good price is retail costs which again reduces the impact of vertical trade on the nal-good price (and on the consumer price index) and therefore on the transmission of a productivity shock from one country to another.Finally, in my model (as well as inCorsetti and Pesenti, 2009), the forward-looking nature of ination is neglected. Indeed, my model presents a link between employment and ination, a sort of original form Phillips curve. However, this equation is too simplistic as agents anticipate ination, which does not appear in my model.One extension to the model would be to add another tradable sector to each country. This would provide a more realistic view since most trade is not vertical. Another extension would be to make the prices of the intermediate good sticky: indeed, in my model, since intermediate-good prices are exible, wages are exible which contrasts to the data tell us. A nal extension would be to introduce a wage in the nal good production so that the prices of nal goods become even more rigid than intermediate-good prices.[START_REF] Clark | The Responses of Prices at Dierent Stages of Production to Monetary Policy Shocks[END_REF] found prices at early stages of production respond more to a monetary policy shock than do prices at subsequent stages of production. Also, introducing an assembly cost in the nal good would complicate the model a lot since a part of the population would therefore be assigned to this task. A shock in the intermediate stage would have an impact not only on the labor related to the other intermediate good (as in the model) but also that related to the nal-good labor. It would be interesting to study the impact of this change on the results of to my model.Finally, to extend this chapter, using the World Input-Output database, I have started an empirical study to check whether the global value chains promote business cycles synchronisation of trading partners as my model predicts. It is still a preliminary work which suers from endogeneity issues, for which I did not nd yet any good instrument. The draft of this empirical study can be found in appendix. The originality of this empirical study lies in the fact that I on the eects of global value chains. In addition, my methodology (linking output correlation and trade intensity) has never been applied in the context of global value chains. To my knowledge, my study is also the rst one to compare the eects of intermediate good trade and nal good trade on the synchronisation of business cycles.3.5 ConclusionTo conclude, the business cycles of the countries are much more correlated than inCorsetti and Pesenti (2009) since the shocks are transmitted symmetrically between two economies. None of the countries as inCorsetti and Pesenti (2009), reap any advantage from coordinating. However, monetary policies remain inward looking since they depend on Home and Foreign productivity shocks, and not Foreign monetary shocks. If the share of Home intermediate goods used in the nal-good production is the same in the two economies, then the optimal monetary policies are the same in both countries, and the exchange rate remains the same. In this case, a monetary union could be implemented since the symmetric shocks meet the Mundell's optimum currency area criteria.3.A Appendix: Empirical extensionAs a future extension, I would like to include an empirical section. This is the rst draft, potentially plagued by endogeneity issues, for which I have not nd yet a good instrument. The aim of this empirical study is to test my model's predictions, in particular the eect of global Output table provides all the information needed to study vertical trade: inputs imported by each sector in each country, gross output and exports from each sector. The advantage of WIOD is that it allows comparison across sectors, countries and time since the data are consistent.However, the downside of this database is that the data are annual data whereas I would have preferred monthly data because changes to retail prices occur more frequency than annually.8

	Moreover, according to INSEE, the share of Eurozone countries' exports within the Eurozone
	is 67.3% and 63.4% for imports. Eurostat (2011) shows that intra Eurozone trade increases. value chains on business cycles synchronisation.
	Klein and Shambaugh (2006) predicted that: "a large signicant eect of a xed exchange 3.A.1 The data set
	rate on bilateral trade between a base country and a country that pegs to it" (21% increase Trade data are from the World Input-Output Database (Timmer et al., 2015), FTA data are
	in trade). Furthermore, Hummels, Rapoport and Yi (1998) nds that vertical trade represents from the Design of International Trade Agreements database (Dür, Baccini and Elsig, 2014), currency union data are from my own database (Lebastard, 2017), 5 geographic data are from
	34.7% of Netherlands' total trade, 25.2% of Denmark's, 18.7% of France's, 16.3% of Germany's and 19.6% of Italy's total trade in 1990. In absolute values, Ferrarini (2013) explains that the CEPII database "GeoDist"(Mayer and Zignago, 2011). The remaining data are based on World Bank World Development Indicators (1960-2014). 6 I have no missing values. My database includes annual data covering 40 countries and 34 sectors from 1995 to 2011. 7 The
	Germany is considered to dominate the region's processing vertical trade, its network trade being strongest with Austria, France and Italy. WTO and Senat (2010) nd very strong trade growth in intermediate goods in the OECD countries, rising from a base of 100 in 1999 to more than 200 in 2008 (90% processed goods and 10% raw materials). Alfaro, Antràs, Chor and Input-3.A.2 Statistics description
	study 40 countries and 34 sectors at the same time (while the previous studies concentrate on
	a small amount of sectors 3 , or on one or two countries 4 ). I can therefore draw more general
	3 Sturgeon and Memedovic (2011). 4 Brustein, Kurz and Tesar (2008); Gangnes, Ma and Van Assche (2012).

[START_REF] Alfaro | Internalizing Global Value Chains: A Firm-Level Analysis[END_REF]

show that the rule of law promotes global value chains, with rms taking account of contractability when choosing to integrate or not a given stage of production. Therefore, Eurozone institutions should help the spread of global value chains among its members. All these elements show that Eurozone members are ever more dependent on each other, and therefore would be impacted by a shock the same way. Therefore, global chains value should help the Eurozone to synchronise the business cycles of its members, which is one of Mundell's criteria for an optimum currency area.

3.4.3 conclusions I calculated country vertical specialization as the ratio of intermediate good inputs imported

by a country on nal good outputs exported to nal Foreign consumers. The major aw in this technique, highlighted by de Backer and Yamano (2012), is that it does not capture the involvement of the countries that produced the rst stage intermediate good (which explains

5

  Augmented by de Sousa (2012). 6 Variables descriptions are provided in appendix 2. 7 Countries and sectors are listed in the appendix. 8 Imbs et al. (2005).

Table C1 :

 C1 Dierent specications, my favorite being the OLS : Column 1: IV contains distance, GDP per capita, GDP, colonial links, common language, common border, FTA, currency union, surface of the land, population size, island, landlocke. Column 2: IV contains WTO, FTA and currency union. Column 3: no IV. Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

		(1)	(2)	(3)
	corr Sector Business Cycle	IV All control	IV WTO	OLS
	Intensity Intermediate Trade	-107.3	-173.6	0.352
		(101.5)	(184.9)	(0.645)
	Intensity Final Trade	0.418***	0.433***	0.411***
		(0.0658)	(0.0722)	(0.0623)
	FTA		0.0528***	0.0557***
			(0.00499)	(0.00307)
	Currency Union		0.0671***	0.0652***
			(0.00344)	(0.00251)
	WTO			0.00653
				(0.00524)
	Observations	678,610	678,610	678,610
	R-squared	0.850	0.837	0.858
	Pair-country-sector FE	YES	YES	YES
	Exporter-sector-time FE	YES	YES	YES
	Importer-sector-time FE	YES	YES	YES

Note

Table C4 :

 C4 OLS: eect of vertical trade on business cycle synchronisation -sector 1 to 9

Table C5 :

 C5 OLS: eect of vertical trade on business cycle synchronisation -sector 10 to 18

Table C6 :

 C6 OLS: eect of vertical trade on business cycle synchronisation -sector 19 to 27

Table C7 :

 C7 OLS: eect of vertical trade on business cycle synchronisation -sector 28 to 34

	(1)

[START_REF] Abrams | International trade ows under exible exchange rates[END_REF] and[START_REF] Thursby | Bilateral Trade Flows, the Linder Hypothesis, and Exchange Risk[END_REF].

[START_REF] Frankel | Emerging Currency Blocks[END_REF],[START_REF] Eichengreen | The Role of History in Bilateral Trade Flows[END_REF] Irwin (1998), and[START_REF] Frankel | Regional Trade Blocs in the World Economic System[END_REF].

1.2. THE MODEL

and then ∂Tt ∂Pt(φ) > 0. If the rms are not too sensible13 2 -η θ -η -θ < 0 always true.

The only exception being if Pt(φ) < 1 -α 2 /4 and α low at the same time. However empirically, this setup is never observed for xed exchange rate regimes.

For pure oating exchange rate regimes, Pt(φ) = 0.1.2. THE MODEL

However, a country with a peg one-to-one loses its specicity if it devalues. The incentive to keep the peg is much higher. Therefore, the shock would have to be much bigger to cause a devaluation. This explains the dierence in life expectancy between23 e 0.502 ≈ 1.65; e 0.216 ≈ 1.24.24[START_REF] Klein | The dynamics of exchange rate regimes: Fixes, oats, and ips[END_REF].

1.3. EMPIRICAL RESULTS

CHAPTER 1. DISENTANGLING THE EFFECTS OF CURRENCY UNIONS

1.E Appendix: Tables 1.E. APPENDIX: TABLES

However, since the calculations are extremely long at such a disaggregated level, I use only country level trade data, and 1 and 2 digit product disaggregation.

Relying on de Sousa (2012).

3.C Appendix: Additionnal estimations

Remerciements

Finally, I would like to thank the last group which has accompanied me during this PhD

1.D Appendix: Data description

In this appendix, I describe in detail the variables used and give their sources.

• Currency U nion : dummy for currency union and dollarized economies (1948-2015)

• P eg one -to -one : dummy for peg one-to-one • F ixed exchange rate : dummy for xed exchange rate regime (Shambaugh da-tabase) • exit CU : dummy, 1 if there is a currency union exit in the ten years after database) • entry CurrencyU nion : dummy, 1 if there is a currency union entry in the ten years before

• Currency U nion 0 -5 years, Currency U nion 6 -10years, etc : dummy if the currency union is between zero and 5 years old, of between 6 and 10 years old... • Distance : simple distance (most populated cities, km) (CEPII database "GeoDist")

• Common Border : dummy for common frontier (CEPII database "GeoDist")

• Common Language : dummy, 1 if a language is spoken by at least 9% of the population in both countries (CEPII database "GeoDist")

• Colony : dummy, 1 for pairs ever in colonial relationship (CEPII data-base "GeoDist")

• Current Colony : dummy, 1 for pairs currently in colonial relationship (CEPII database "GeoDist")

• P opulation : total population (World Development Indicators from the World Bank) (1960-2014)

• Surf ace : Land area in square kilometer (World Development Indicators from the World Bank)(1960-2014)

• Landlocked : dummy, if one of the two countries is landlocked (CEPII database "GeoDist")

• Both Landlocked : dummy, if the two countries are landlocked (CEPII data-base "GeoDist")

• Island : dummy, if one of the two countries is an island (CEPII database "GeoDist")

• Both Island : dummy, if the two countries are islands (CEPII database "GeoDist")

1.D. APPENDIX: DATA DESCRIPTION

2.A.3 Variables

In this appendix, I describe in detail the variables used and give their sources.

• Export : annual bilateral (one-way) trade ow data in thousands of US dollars (BACI database) (1995-2015)

•

3.B.2 Variables

In this appendix, I describe in detail the variables used and give their sources.

• corrSectorBusinessCycle : correlation calculated on a 6 year period (I have 12 years of data) between detrended intermediate good output from country i sector s and the detrended nal good output from country j sector r exported to nal consumer everywhere in the world, ows being expressed in millions of US dollars (WIOD database) (1995-2011)

• intensityIntermediateT rade : average ratio by period, between intermediate good input from country i sector s used by sector r of country j and the sum of the two sectors r and s nal output, ows being expressed in millions of US dollars (WIOD database) (1995-2011)

• intensityF inalT rade : average ratio by period, between nal good traded from country i sector r to country j and the sum of the two sectors r and s nal output, ows being expressed in millions of US dollars (WIOD database) (1995-2011)

• CurrencyU nion : dummy for both countries being in currency union together (and dollarized economies) during more than 50% of the period • Distance : simple distance (most populated cities, km) (CEPII database "GeoDist")

• CommonBorder : dummy for common frontier (CEPII database "GeoDist")

• CommonLanguage : dummy, 1 if a language is spoken by at least 9% of the population in both countries (CEPII database "GeoDist")

• Colony : dummy, 1 for pairs ever in colonial relationship (CEPII database "GeoDist")

• CurrentColony : dummy, 1 for pairs currently in colonial relationship (CEPII database "GeoDist")

• P opulation : average of total population by period (World Development Indicators from the World Bank) (1960-2014)

• Surf ace : Land area in square kilometer (World Development Indicators from the World Bank)(1960-2014)

• Landlocked : dummy, if one of the two countries is landlocked (CEPII database "GeoDist")

• BothLandlocked : dummy, if the two countries are landlocked (CEPII database "GeoDist")

• Island : dummy, if one of the two countries is an island (CEPII database "GeoDist")

• BothIsland : dummy, if the two countries are islands (CEPII database "GeoDist")

3.B.3 Sector classication description code

Agriculture, hunting, forestry and shing 1

Mining and quarrying 2

Food , beverages and tobacco 3

Textiles and textile 4

Leather, leather and footwear 5

Wood and of wood and cork 6

Pulp, paper, paper , printing and publishing 7

Coke, rened petroleum and nuclear fuel 8 In the second chapter, I found that during the economic crisis, the euro had a positive and signicant eect on aggregate trade, not only during the drop, but also during the rebound and in the long run. My results are robust to many robustness checks (changing the control group and the time period, excluding countries, etc.). During the crisis, exchange rate volatility increased everywhere in the world except in the Eurozone where by nature it remained xed. Therefore, the euro provides a premium for exchange rate stability during economic crises.

In chapter 3, I found that global value chains make economies much more linked than in Corsetti and Pesenti's (2009) model. Indeed, an intermediate good productivity shock is transmitted by two channels instead of one, so shock transmission is much greater. The business cycles of these countries are also more correlated since the shocks are transmitted symmetrically in the two economies. In addition, the exchange rate is less volatile than in the original model.

My model recommends that monetary policies respond in the same way whether the shock comes from home or abroad. There is no gain from coordination.

Limits and Perspectives

In the rst chapter, my model makes a lot of ad-hoc hypotheses, which limit the scope of the predictions obtained. I therefore will have to make them endogenous. In addition, I use a very particular utility function for the rms; I will need instead to introduce a hedging cost and see whether my predictions remain the same. Finally, my model is micro-founded, and therefore the empirical part calls for rm-level data to test all the predictions. Currently, I am searching for data which will allow me to check whether the size distribution of the exporting rms changes when the country enters a currency union. I have these data for European rms for years 2014 and 2015, but to identify my eect, I need a switch otherwise my xed eects absorb the dierences between rms exporting outside the Eurozone and the others.

In chapter 2, I need to control for institutional changes in the Eurozone which are confounded by the presence of a single currency. I therefore cannot disentangle the eects of the euro from