Désiré Avom

Lisa Chauvet

Eugénie W H Maiga

Emmanuel Monsieur Le Docteur

Pinto

Rélwendé Sawadogo

Hippolyte Balima

Éric Dago

Éric Kéré

Axelle Kéré

Yann Tapsoba

Ababacar Gueye

Alexandra Tapsoba

Constantin Compaoré

Aïssata Coulibaly

Nestor P Sawadogo (« M'dawa, Le Sg»)

Jocelyn Okara

« Le Vieux Père Assi

Kady S Keïta

Yashmina Nebié

Kodjo Adandohoin

Cornélie Ayémonna

Hugues Champeaux

Pierre Lesuisse

Lucas Guichard

Épiphane Assouan

Oulimata Ndiaye

Issa Sanou

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Alou A Cerdi

Fayçal Dama

Hamid Sawadogo

Moulaye Silue

Jean-Marc Bamba

Ibrahim Atsebi

Abdoul-Hakim Nana

Mahamady Wandaogo

Ismaël Ouédraogo

Maïmouna Ouédraogo

Macoura Barro

Humaïra Doumbia

Muhammad Pasha

Mohamed Adil

Aïcha Boly

Aale Raza Sanou

Muhammad Rizvi

Fatima Naseem Nasar

Yoro Rizvi

Arouna Diallo

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Naïmatou Okoko

Harouna Ouédraogo

Mouhamed Kinda

Abdramane Zerbo

Achille K Camara

Perin Nzue Sanou

Fawzi Banao Eneme

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Louis- Joël

Dieudonné W Diendéré

Hamed Tondé

La Ouédraogo

Rachelle Gaf

Aussi

Patrick Mabuza

Amadou Boly

Chuku Chuku

Zackary Séogo

Lacina Balma

Linguère Mbaye

Betty Camara

En Particulier Edgard

Sonia Béré

Béatrice Koblavi

Babou Bazié

Aïssata Drabo

Hamadoun Tall

Yassia Gansonré

Sylvestre Sidibé

Inès Ouédraogo

Simon Zanga

Guy Ouédraogo

Etienne Savadogo

Valérie Ouédraogo

Bouraïma Coulibaly

Annick Tiéba

Pierre Noël Sanou

Francis Kaboré

Thierry Ouédraogo

Lionnel Bontogo

Aussi une pensée sympathique

MOREIRA, pour avoir généreusement accepté d'être membres de ce Jury de Thèse malgré le contexte difficile de crise sanitaire, témoignant ainsi leur intérêt pour mes travaux. Vos commentaires et suggestions permettront d'améliorer la qualité de ce travail. Aussi, Émilie Caldeira, je te dis merci. Cette thèse doit également beaucoup

The exploding financing needs in developing countries: the paramount role of taxation…

Financing structural transformation investments in developing countries necessary to embark on a sustainable development path is at the center stage of the international debate in recent years. Developing countries face huge development challenges. Yet, financial resources are quite limited with tight fiscal space. In addition, the ongoing global health crisis ( i.e., Covid-19 pandemic) jeopardizing the hard-won development gains achieved over the past years exacerbates financing needs and the pressure on public finance to mitigate economic and health damages. The World Bank estimates at about $2.5 trillion annually, the financing gap for achieving the Sustainable Development Goals for developing countries. A viable tax system allowing proper taxation would undoubtedly constitute an important source to finance this gap.

"[…]

Until someone comes up with a better idea, taxation is the only practical means of raising the revenue to finance government spending on the goods and services that most of us demand". (Tanzi and Zee, 2001) Taxation is compulsory levies on individuals as well as on corporations imposed by governments. Historically, the main and primary purpose of taxation is to raise revenue to finance public spending and services provision. The bulk of taxes are basically collected to ensure the welfare of taxpayers as a whole. Therefore, the individual taxpayer's liability is independent of any specific compensation, except for a few taxes [START_REF] Mclure | Taxation[END_REF]. For instance, payroll taxes will benefit the taxpayer since they are levied on labor income to finance social security programs, medical payments, retirement benefits, etc. Likewise, fuel taxes are imposed to finance the construction and maintenance of road infrastructure that would benefit no other than road users.

However, the role of taxation could not be conceptually limited to the only purpose of public expenditure. Indeed, in addition, to its financial purpose, taxation was also assigned a role related to social policy to promote social welfare. More precisely, it serves as a regulating factor to lessen inequalities in the distribution of national income and wealth. Last but not the least, the role of taxation in modern economies is that of maintaining economic stability through the implementation of tax policy, to promote price stability and high employment [START_REF] Mclure | Taxation[END_REF].

In the literature, taxation is widely accepted as an important pillar of state erection. The IMF (2011) asserts that taxation is a defining feature of state power, making its improvement a key aspect of state-building. In the same vein, [START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF] emphasize the paramount role of taxation as a plank of state-building and that, through different ways. In fact, as governments rely on taxes and on the prosperity of the people, they have a strong incentive to promote economic growth and engage with the public. This dependence leads to accountability and responsiveness on the part of the state. Moreover, implementing taxation opens avenues to introduce good practices within different parts of government [START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF]. Indeed, through the introduction of a unique taxpayer identification number, tax systems build databases that are essential for broader economic and administrative management.

" Beards, boots, beehives, candles, nuts, hats, horses, chimneys, water -Tsar Peter taxed them all.

But he is still styled 'The Great' in modern histories of Russia, perhaps because of the mighty works his taxes produced." James Harvey Robinson, ed., Readings in European History, 2 Vols. (Boston: Ginn andCompany., 1904-1906), Vol. II: From the opening of the Protestant Revolt to the Present Day, To some extent, taxation has also contributed to the establishment of the politico -institutional power in modern economies [START_REF] Levi | The Predatory Theory of Rule[END_REF](Levi, , 1989;;[START_REF] Brewer | The Sinews of Power: War, Money, and the English State, 1688-1783[END_REF][START_REF] Hoffman | The Political Economy of Warfare and Taxation in Early Modern Europe: Historical Lessons for Economic Development[END_REF].

According to [START_REF] O'brien | Fiscal and Financial Preconditions for the Rise of British Naval Hegemony, 1485-1815[END_REF] for instance, the supremacy of British naval over nearly three hundreds of years was rooted in the superior power to raise taxes. In addition, [START_REF] Hoffman | The Political Economy of Warfare and Taxation in Early Modern Europe: Historical Lessons for Economic Development[END_REF] in their theoretical model to study warfare and taxation in modern Europe consider that a country ruler's decision to join in a fight or to attack another country depends on its capacity to mobilize resources from taxation. Moreover, the "new fiscal sociology" literature emphasizes that implementing taxation fosters state building both by providing a focal point for bargaining between the state and citizenry and through the development of high-quality institutions for tax collection (Bräutigam et al., 2008).

An increasing interest in enhancing domestic revenue mobilization (DRM) in developing countries over recent years: tax revenue as sustainable source of development financing

Since the adoption of the 2030 Agenda, mobilizing sufficient and durable financi al resources has been the priority for the international community. While the major source of development financing in developing countries (DCs) has historically been international development assistance provided by development finance institutions (DFIs) , a flourishing literature underscores the highly volatility of official development assistance (ODA) which compromises its efficacy [START_REF] Bulíř | Aid Volatility: An Empirical Assessment[END_REF][START_REF] Hudson | Aid Volatility, Policy and Development[END_REF][START_REF] Kharas | Measuring the Cost of Aid Volatility[END_REF][START_REF] Chauvet | Aid, Volatility, and Growth Again: When Aid Volatility Matters and When It Does Not[END_REF]Hudson, 2015, Afawubo and[START_REF] Afawubo | The Effectiveness of Aid on Savings and Investment in Sub-Saharan Africa: Do Volatility and Institutional Quality Matter?[END_REF]. Empirical studies at both macro and micro level highlight that volatile ODA hampers economic growth by affecting the level as well as the composition of investment, and the fiscal planning [START_REF] Kharas | Measuring the Cost of Aid Volatility[END_REF]. ODA, in addition to fluctuate over time, is finite and therefore, a chronic and substantial dependence would imply serious uncertainties for recipient countries regarding the sustainability of government expenditures and its implications for future economic growth and development [START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF].

Alternatively, to the ODA, international financial markets also represent an important source for raising consistent financial resources to meet financing needs. This option however is still marginally tapped by developing economies. As stressed by the IMF (2003), accessing international capital markets requires favorable domestic and external conditions including, among others: good macroeconomic stability and performance, fiscal discipline, good external debt management, political stability. Developing countries do not always achieve these conditions.

Moreover, the recent collapse in commodity prices, mainly for oil, has led to colossal losses in export revenues and serious fiscal constraints in resource-rich countries. A vast strand of the empirical literature brightly evidenced the adverse effects of negative commodity prices shocks on various economic variables (see e.g., [START_REF] Deaton | International Commodity Prices, Macroeconomic Performance, and Politics in Sub-Saharan Africa[END_REF][START_REF] Dehn | Commodity Price Uncertainty and Shocks: Implications for Economic Growth[END_REF][START_REF] Brückner | International Commodity Prices, Growth and the Outbreak of Civil War in Sub-Saharan Africa[END_REF] Ciccone 2010; [START_REF] Arezki | Commodity Windfalls, Democracy and External Debt[END_REF][START_REF] Fabrizio | Managing Global Growth Risks and Commodity Price Shocks: Vulnerabilities and Policy Challenges for Low-Income Countries[END_REF][START_REF] Arezki | Boom-Bust Cycle, Asymmetrical Fiscal Response and the Dutch Disease[END_REF][START_REF] Knop | The Sectorial Impact of Commodity Price Shocks in Australia[END_REF][START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF][START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF][START_REF] Eberhardt | Commodity Price Movements and Banking Crises[END_REF][START_REF] Sekine | Effects of Commodity Price Shocks on Inflation: A Cross-Country Analysis[END_REF] underscoring the limitations of extractive revenues dependence.

Hence, domestic resource mobilization, more particularly tax revenues,1 rightly appear to be not only the remaining alternative tool for generating revenues, but also the reliable and sustained sources of government revenues to support sustained and inclusive economic development.

Taxes represent one of the most important sources of government revenue in modern economies [START_REF] Mclure | Taxation[END_REF]. Over recent decades, taxation in developing countries has been receiving important and increasing attention among academics and policymakers. This particular focus on taxation in developing economies results from inter alia, the rapid debt accumulation over the past fifty years in emerging economies and developing countries (Kharas, 2020;Kose et al., 2020;Koh et al., 2020;World Bank, 2020), the decrease and the volatility in the international development aid flows, and most importantly, the wide recognition 2 (e.g., the 2010 G-20 Summit, the 2015 Financing for Development Conference: Addis Tax Initiative (ATI), and the 2017 new European Consensus on Development, etc.) that enhancing domestic tax revenue mobilization in developing countries constitutes a key tool to generate revenues for a sustainable and inclusive economic development financing.

The relation between taxation and growth, and to a broader extent, economic development, has been subject to a substantial literature (e.g., Levi 1988;[START_REF] Myles | Taxation and Economic Growth[END_REF]Bleaney et al., 2001;Yakita, 2003;Johansson et al., 2008;Bräutigam, 2008;Bräutigam, Fjeldstad, and Moore 2008;[START_REF] Besley | The Origins of State Capacity: Property Rights, Taxation, and Politics[END_REF]Persson, 2009-2010;IMF, 2011;Gale and Samwick, 2014;[START_REF] Aghion | Taxation, Corruption, and Growth[END_REF]Gaspar et al., 2016a, 2016b[START_REF] Jaimovich | Nonlinear Effects of Taxation on Growth[END_REF]. Empirical works stressed out that taxes promote economic growth and development. Taxes revenues lead to improved development when they are fully translated into productive and beneficial public spending [START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF]. Some empirical studies also infer the existence of a minimum level of the tax-to-GDP ratio that is associated with a significant acceleration in the process of growth and development. [START_REF] Gaspar | Political Institutions, State Building, and Tax Capacity: Crossing the Tipping Point[END_REF] for instance, drawing on a contemporary database covering

Levels and Composition of Tax Revenue in Developing Countries: Some Stylized Facts

We extracted tax data from the updated and most complete IMF's Government Financial Statistics database (GFS, 2020). The sample covers 102 developed and developing countries 4 over the period 1990-2018. From the panel [A] of Figure 1, it emerges that, on average, the tax ratio stands at respectively 17.7 and 25.0 in developing and developed countries. 5 Although the tax revenue ratio has been non-stable in developing countries over the period (red line, panel [A] of Figure 1), the level has improved on average and remains below that of developed countries. For instance, over the subperiod 2000-2015, the tax revenue ratio in low-income countries increased on average by 3.5 percent of GDP and reaching 16.4 percent in 2015. Source: Authors' calculations using IMF's GFS (2020) Regarding the tax structure, while the major taxes components are income taxes and taxes and goods and services in developed economies, developing countries heavily rely on indirect taxes 6 including mainly taxes on consumption (value-added tax, excises, general sales tax, etc.), 4 Country list by income group is provide in Appendix 1.1. 5 Note that this is a simple average, considering each country as a single observation and treating countries as the same. 6 As a rule of thumb, indirect taxes represent, about 40 percent of total tax revenue in DCs. followed by direct taxes, namely personal and corporate income taxes (Figure 1.2.a). The common feature in the tax composition in both developed and developing countries is the importance of taxes on final consumption, including value-added tax (VAT). VAT represents a modern tax and has been widely adopted across the world since its introduction in the 1960s.

According to the IMF, 160 countries have a VAT as of 2018. 7 Studies in the literature (e.g., [START_REF] Tait | Value-Added Tax: Administrative and Policy Issues[END_REF][START_REF] Le | Value Added Taxation: Mechanism, Design, and Policy Issues[END_REF]Keen and Lockwood, 2010;Keen, 2013;Ulfier, 2014;[START_REF] Akitoby | Tax Revenue Mobilization Episodes in Emerging Markets and Low-Income Countries: Lessons from a New Dataset[END_REF]Acosta-Ormaechea and Morozumi, 2019) underscore the pivotal role of VAT in countries' tax systems.

Indeed, VAT represents an important and solid source of tax revenue in developing countries and presents the advantage to be less distortionary and have the particularity to be selfenforcing [START_REF] Kopczuk | Putting Firms into Optimal Tax Theory[END_REF].

In addition to the VAT, another non-negligible source of tax revenue but which remains underexploited in DCs is property tax. Property tax is widely considered as an equitable and efficient source of raising revenue (Norregaard 2013) with no vertical tax base competition and low compliance cost on taxpayers [START_REF] Bahl | The Property Tax in Developing Countries: Current Practice and Prospects[END_REF]. This tax, however, hardly raises on average less than 1 percent of GDP in developing countries (see e.g., Bahl and Martinez-Vazquez, 2008), partly due to the weaknesses in the design and implementation of the tax combined with the unclear definition of property rights.

Exploring the trend in the composition of taxes in DCs (Figure 1.2.b), data reveal a change in the tax revenue structure over the period. 8 More specifically, Figure 1.2.b depicts a decrease in trade taxes ratio, in line with the global trade liberalization process [START_REF] Devarajan | Trade Liberalization in Developing Countries: Do Imperfect Competition and Scale Economies Matter?[END_REF][START_REF] Ostry | Trade Liberalization in Developing Countries: Initial Trade Distortions and Imported Intermediate Inputs[END_REF][START_REF] Wacziarg | Trade Liberalization and Growth: New Evidence[END_REF]) in these countries. 9 In addition, although the low share of property tax revenue in the GDP, the level is gradually increasing as the result of the more and more resources devoted to identifying, capturing and valuing all relevant properties and upkeeping of fiscal cadaster [START_REF] Akitoby | Tax Revenue Mobilization Episodes in Emerging Markets and Low-Income Countries: Lessons from a New Dataset[END_REF]. Last, taxes 7 https://www.imf.org/external/np/fad/tpaf/pages/vat.htm. 8 We focus on the last two decades. 9 Considering the whole period, trade tax ratio represented about 8 percent of GDP in the 1990s and has considerably dropped to about 2 percent of GDP as of 2018.

Chapter 1: General Introduction and Overview 9 on goods and services and income taxes remain the most important components of total taxes revenues in DCs over the period. In Figure 1.3, we explore the regional disparities in tax revenue ratio in developing countries.

Europe and Central Asia (ECA) is the top-performing region in terms of tax revenue mobilization followed by Sub-Saharan Africa region, while Latin America and Caribbean (LAC) countries as well as South Asian (SA) countries come to be the poor performers of the sample. Source: Authors' calculations using IMF's GFS (2020)

Why do developing countries still tax little?

While rich and extensive works in the literature explore the determinants of countries tax revenue performance (see e.g., [START_REF] Lotz | Measuring 'Tax Effort' in Developing Countries[END_REF][START_REF] Chelliah | Tax Ratios and Tax Effort in Developing Countries, 1969-71[END_REF][START_REF] Leuthold | Tax Shares in Developing Economies A Panel Study[END_REF]Tanzi, 1992;[START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF][START_REF] Grigorian | Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia's Stubbornly Low Tax Collection[END_REF][START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF][START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Feger | Tax Effort Performance in Sub-Sahara Africa and the Role of Colonialism[END_REF]Balima et al., 2016;Belinga et al., 2017;[START_REF] Gnangnon | Impact of Bridging the Internet Gap on Public Revenue Mobilization[END_REF], a parallel strand of the literature rather focuses on the hampering factors and challenges to higher tax ratios in DCs and the persisting tax gaps between advanced and developing economies (see e.g., [START_REF] Andic | Tax Problems of Developing Countries[END_REF][START_REF] Bahl | Tax Policy in Developing Countries: Looking Backand Forward[END_REF][START_REF] Bird | Tax Challenges Facing Developing Countries: A Perspective from Outside the Policy Arena[END_REF][START_REF] Bahl | Tax Policy in Developing Countries: Looking Backand Forward[END_REF][START_REF] Mascagni | Tax Revenue Mobilization In Developing Countries: Issues and Challenges[END_REF][START_REF] Carnahan | Taxation Challenges in Developing Countries[END_REF][START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF]. Countries tax capacity is intrinsically related to their structural and institutional features, including history, the sectoral composition of the economy and the politico-institutional setup. Those factors are difficult to change in the short term. As recognized by [START_REF] Bahl | Tax Policy in Developing Countries: Looking Backand Forward[END_REF], countries' characteristics define their tax systems and their capacity to administer taxes. Several factors in the literature are pointed out to compromise substantial tax revenue mobilization in DCs. First, there is persisting large share of the informal sector 10 in these countries. In fact, informality represents a central challenge to establish efficient tax system and greater tax collection in developing countries. Developing economies are mostly composed of a multitude of small and micro enterprises implying very high administrative costs to identify and to tax [START_REF] Mascagni | Tax Revenue Mobilization In Developing Countries: Issues and Challenges[END_REF]. In addition, the bulk of the workers in DCs are employed in informal enterprises and agriculture and paid in cash. As a result, it is difficult to capture the tax base relying on modern tools of direct taxation fails to yield substantial results [START_REF] Tanzi | Tax Policy for Emerging Markets: Developing Countries[END_REF]. As asserted in [START_REF] Auriol | Taxation Base in Developing Countries[END_REF], tax ratio gaps between DCs and advanced economies mainly explained by the weakness of direct taxation of the informal sector in DCs.

In addition, the ongoing globalization induces a strong growth in the international capital and trade flows, which exacerbates difficulties in taxing multinational companies and transnational transactions in developing countries. Globalization provides to transnational corporations, greater incentives and opportunities to develop aggressive tax optimization and abusive transfer pricing practices, as well as profits shifting [START_REF] Mascagni | Tax Revenue Mobilization In Developing Countries: Issues and Challenges[END_REF]. This combined with the inadequate rules and laws on taxing multinational companies in DCs, as well as the weak tax administrative capacity, inevitably results in important tax revenues losses. 11 Similarly, until a certain period, trade taxes (customs duties) constitute a major tax revenue source in many developing and emerging market economies despite trade liberalization (B ird, 2007;[START_REF] Baunsgaard | Tax Revenue and (or?) Trade Liberalization[END_REF]. However, with the recent trade liberalization waves across the globe, DCs experienced a considerable decline in trade tax revenues leaving them with serious challenges in replacing such revenues [START_REF] Baunsgaard | Tax Revenue and (or?) Trade Liberalization[END_REF].

Moreover, the tightness of the tax base and the less diversified tax sources coupled with generous tax exemptions are common features of tax systems in DCs impeding tax collection.

The literature widely agrees that broadening the tax base is fundamental to raise significant tax revenues (Toyes, 2000;[START_REF] Bird | Introduction to Tax Policy Design and Development; Notes for a Course on Practical Issues of Tax Policy in Developing Countries[END_REF]IMF, 2005IMF, -2006;;[START_REF] Bird | Tax Challenges Facing Developing Countries: A Perspective from Outside the Policy Arena[END_REF][START_REF] Bahl | Tax Policy in Developing Countries: Looking Backand Forward[END_REF]IMF, 2011; 10 About 40 to 60 percent (See e.g., [START_REF] Schneider | Shadow Economies All over the World: New Estimates for 162 Countries from 1999 to 2007[END_REF].

11 About 10 percent (~US$240 billion, annually) of losses in the global corporate income tax revenues [START_REF] Junquera-Varela | Strengthening Domestic Resource Mobilization: Moving from Theory to Practice in Low-and Middle-Income Countries[END_REF].

Chapter 1: General Introduction and Overview [START_REF] Dabla-Norris | Macroeconomic Effects of Tax Rate and Base Changes: Evidence from Fiscal Consolidations[END_REF]. A broad tax base is associated for instance with lower taxation costs and economic inefficiencies, more redistribution (Heady, 2004) and less political opposition [START_REF] Toye | Fiscal Crisis and Fiscal Reform in Developing Countries[END_REF].

Furthermore, the low tax ratios in DCs is the result of low tax effort 12 -itself related to the tax system, the level of compliance and the tax culture. Tax system comprises not only tax administration, but also the tax policy (tax laws) as defined by [START_REF] Slemrod | Tax Systems[END_REF]; the tax administration plays a central role in countries' tax system. [START_REF] Chang | Raising Tax Revenue: How to Get More from Tax Administrations?[END_REF] for example, recently showed that improving the practices and characteristics of tax administration is beneficial to revenue collection agencies. Drawing on the International Survey on Revenue

Administration dataset, the study finds that tax performance is positively and strongly associated with the operational strength of tax administrations. Though , significant progress has been made in reforming and strengthening tax administrations in DCs, more need to be done.

Greater access to financial services in DCs: A potential canal for greater tax revenue?

Parallel to the substantial works on taxation, a flourishing strand of the literature is concerned with financial inclusion and its related implications to the economic environment. An inclusive financial system supposes that individuals as well as businesses, especially those at the bottom of the pyramid, have access to basic financial services in the formal financial system [START_REF] Allen | The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts[END_REF][START_REF] Ozili | Impact of Digital Finance on Financial Inclusion and Stability[END_REF]. The World Bank defines financial inclusion as the process of ensuring that individuals and businesses have access to useful and affordable financial products and services (i.e., transactions, payments, savings, credit and insurance) that meet their needs delivered responsibly and sustainably. Considered as a policy framework for socio-economic development that focuses on getting more people to use and have access to formal banking services [START_REF] Mitchell | Pesos Or Plastic?: Financial Inclusion, Taxation, and Development in South America[END_REF], financial inclusion is increasingly receiving proper attention over recent years. Greater access to financial services is considered as a major strategy and a key enabler to achieve the 2030 development agenda on sustainable development goals. Yet, nearly half of the world's adult population (2 billion adults), is still financially excluded [START_REF] Fu | Financial Inclusion and the 2030 Agenda for Sustainable Development: A Missed Opportunity[END_REF] despite the global rise in mobile and digital banking. As of 2017, about 1.7 billion adults remain unbanked (i.e., without an account at a financial institution or through a mobile money provider) in developing countries, specifically. 13 Previous works on financial inclusion focused mainly on exploring the impact of financial inclusion on economic growth, poverty reduction, employment, and inequality (see e.g., [START_REF] Kpodar | ICT, Financial Inclusion, and Growth : Evidence from African Countries[END_REF][START_REF] Sarma | Financial Inclusion and Development[END_REF][START_REF] Cull | Financial Inclusion and Development: Recent Impact Evidence[END_REF]Sahay et al., 2015a,b;[START_REF] Sharma | Nexus between Financial Inclusion and Economic Growth[END_REF][START_REF] Bayar | Financial Inclusion and Economic Growth: Evidence from Transition Economies of European Union[END_REF][START_REF] Kim | Financial Inclusion and Economic Growth in OIC Countries[END_REF][START_REF] Neaime | Financial Inclusion and Stability in MENA: Evidence from Poverty and Inequality[END_REF]Fouejieu et al., 2020, among others). In developing countries, the bulk of the economy is still operating in the informal sector, dubbed as "hard-to-tax sector". Increased access to and use of formal financial services including banking (credit and savings), insurance, electronic transactions (money transfer and bill payments) is associated to a shift in consumption away from informal to formal markets and a cashless economy, provides opportunities for easy taxation and more tax collection. For instance, [START_REF] Sung | Can Tax Incentives for Electronic Payments Reduce the Shadow Economy? Korea's Attempt to Reduce Underreporting in Retail Businesses[END_REF] show that electronic transactions in South Korea reduced the shadow economy, while [START_REF] Mitchell | Pesos Or Plastic?: Financial Inclusion, Taxation, and Development in South America[END_REF] highlight that cashless economies are prone to higher and more stable VAT-to-GDP ratio in Latin America countries (i.e., Argentina, Brazil, and Chile). Thus, financial inclusion may a priori offer an opportunity for raising more tax revenues in developing countries through, not only a shift in transactions from informal markets into formal markets and less cash use but also a clear identification of taxpayers as well as the traceability of transactions and a good calculation of the tax base.

Surge in number of conflicts and political unrest in developing countries: What impact on the financial sector?

The recent decade has also witnessed a considerable rise in the number conflict-affected of countries across the globe, specifically conflict-affected developing countries. Indeed, the world has incredibly become more violent, with a drastic deterioration of internal unrest in developing countries. This is characterized by different forms of violence including protests (Lebanon, Hong Kong, Iraq, Chile), geopolitical competition (Yemen, Syria), insurgencies (Somalia, Afghanistan), political violence (Mali, Cote d'Ivoire, Cameroon, Guinea) and terrorist

13 World Bank's Global Findex Database, 2017. threat in the Sahel14 (ACLED, 2010). For instance, only in 2019, the Sahel recorded more than 2,100 political violence and protest events resulting in over 5,360 reported fatalities (ACLED, 2010). 15 The negative macroeconomic consequences of conflicts and political instabilities are well established in the literature (see e.g., Alesina and Perotti, 1996;[START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF][START_REF] Collier | On the Economic Consequences of Civil War[END_REF]Hoeffler, 1998,2004;[START_REF] Rho | The Economic Costs of Conflict: A Case-Control Study for the Basque Country[END_REF][START_REF] Neumayer | The Impact of Political Violence on Tourism: Dynamic Cross-National Estimation[END_REF][START_REF] Jong-A-Pin | On the Measurement of Political Instability and Its Impact on Economic Growth[END_REF][START_REF] Aisen | How Does Political Instability Affect Economic Growth?[END_REF][START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF]IMF, 2019;[START_REF] Qureshi | Trade and Thy Neighbor's War[END_REF][START_REF] Huang | The Impacts of Political Instability on Banking Development and Operational Efficiencies[END_REF] as well as their spillover effects (Hegre and Sambanis, 2006). For instance, the IMF (2019) estimates that an increase in conflict intensity in SSA countries is associated with a decrease of about 12 percent in the total revenue, while the fiscal deficit increases of about 1.7 percent of GDP. In addition, conflicts worsen the fiscal balance position in conflict-affected countries. In the first two years of conflict in SSA, the public debt ratio rises about 16 percentage points of GDP, with the effect culminating to almost 20 percent of GDP by the fifth year of conflict (IMF, 2019).

The effect of conflicts and internal unrest on financial variables, surprisingly received much less attention, particularly in developing countries. The existing few studies rather focus more on the United States (see e.g., [START_REF] Willard | Turning Points in the Civil War: Views from the Greenback Market[END_REF][START_REF] Chen | The Effects of Terrorism on Global Capital Markets[END_REF][START_REF] Amihud | Political News and Stock Prices: The Case of Saddam Hussein Contracts[END_REF][START_REF] Schneider | War and the World Economy: Stock Market Reactions to International Conflicts[END_REF]). [START_REF] Rigobon | The Effects of War Risk on US Financial Markets[END_REF], using an heteroskedasticity-based estimation technique explored the impact of the risks associated with the Iraq war on financial indicators in the United States (US). The study finds evidence that rises in war risk result in significant declines in equity prices and treasury yields, a fall in the dollar as well as a widening of lower-grade corporate spreads, and an increase in oil prices. Similarly, [START_REF] Leigh | What Do Financial Markets Think of War in Iraq?[END_REF] analyzed the consequences of the US-Iraq war on an ex-ante assessment framework. The results reveal that the war has large effects on equity markets lowering the value of US equities by around 15 percent. Furthermore, [START_REF] Wolfers | Using Markets to Inform Policy: The Case of the Iraq War[END_REF] point that a 10 percent increase in the probability of war leads to a 1½ percent decline in the S&P 500. This may raise

the empirical question about the potential impact of conflicts and political instabilities on the banking sector in developing countries.

Value Added and theoretical foundations of the thesis

In sum, the aforementioned empirical literature is quite unambiguous that tax revenue constitutes the important source of finance for sustainable development in DCs, and there is imperative need to enhance and strengthen tax revenue mobilization in these countries. In addition, the low tax effort and the non-diversification of tax sources are pointed out as the main impeding factors to greater tax collection in DCs.

Thus, this thesis, drawing essentially on empirical analyses and mainly focusing on developing countries, 16 aims to extend the reflection on the challenges to higher tax revenue mobilization in DCs and improve upon the existing works with particular attention on issues not yet addressed but important. More specifically, the thesis analyses the tax effort in developing countries taking into the natural resources as most of these countries have recently discovered new resources, while examining how these countries may tap more tax revenue from a diversified tax structure and increased access to financial services. The recent proliferation and surge of violence in DCs countries are raising concerns about the effects of conflicts and political instabilities on macroeconomics variables. The thesis also gives the first attention to the consequences on the financial sector, specifically on the banking sector, in conflict-affected developing countries since financial sector has a paramount role in countries capacity to mobilize tax revenues.

However, the thesis, though empirical relates to several strands of the theoretical literature.

First, it relies on the theory of taxation and development [START_REF] Ramsey | A Contribution to the Theory of Taxation[END_REF][START_REF] Kendrick | The Ability-to-Pay Theory of Taxation[END_REF]Kaldor, 1936;[START_REF] Oakland | The Theory of the Value-Added Tax: IA Comparison of Tax Bases[END_REF][START_REF] Diamond | Optimal Taxation and Public Production I: Production Efficiency[END_REF]Feldstein, 1976;[START_REF] Atkinson | The Design of Tax Structure: Direct versus Indirect Taxation[END_REF][START_REF] Mirrlees | Optimal Tax Theory: A Synthesis[END_REF][START_REF] Deaton | Optimal Taxes and the Structure of Preferences[END_REF][START_REF] Bradley | On the Existence of an Optim al Capital Structure: Theory and Evidence[END_REF][START_REF] Bates | A Note on Taxation, Development, and Representative Government[END_REF][START_REF] Chamley | Optimal Taxation of Capital Income in General Equilibrium with Infinite Lives[END_REF][START_REF] Newbery | The Theory of Taxation for Developing Countries[END_REF]Burgess and Stern, 1993;[START_REF] Boadway | Towards a Theory of the Direct-Indirect Tax Mix[END_REF][START_REF] Lemieux | The Effect of Taxes on Labor Supply in the Underground Economy[END_REF]Simpson, 1994;[START_REF] Andreoni | Tax Compliance[END_REF][START_REF] Pirttilä | On Optimal Non-Linear Taxation and Public Good Provision in an Overlapping Generations Economy[END_REF][START_REF] Swank | The New Political Economy of Taxation in Advanced Capitalist Democracies[END_REF][START_REF] Herb | Taxation and Representation[END_REF][START_REF] Cremer | Capital Income Taxation When Inherited Wealth Is Not Observable[END_REF][START_REF] Sandmo | The Theory of Tax Evasion: A Retrospective View[END_REF][START_REF] Emran | On Selective Indirect Tax Reform in Developing Countries[END_REF][START_REF] Auriol | Taxation Base in Developing Countries[END_REF][START_REF] Kaplow | On the Undesirability of Commodity Taxation Even When Income Taxation Is Not Optimal[END_REF][START_REF] Kopczuk | Putting Firms into Optimal Tax Theory[END_REF][START_REF] Besley | The Origins of State Capacity: Property Rights, Taxation, and Politics[END_REF]Kaplow, 2010;[START_REF] Golub | The Theory of Taxation and Public Economics[END_REF]Besley and Persson, 2013;[START_REF] Weinzierl | The Golden Rule of Taxation[END_REF] which emphasizes various issues related to implementing 16 Developing countries include the low-income countries and the middle-income countries, but not exclusively they are more concerned with domestic tax revenue mobilization than developed ones.

Chapter 1: General Introduction and Overview taxation including the level and structure of taxation in developing countries, optimal taxation, tax evasion, capital and income taxation, tax distortions, tax reforms, etc.

Second, the thesis draws upon the theoretical literature on economic diversification [START_REF] Mclaughlin | Industrial Diversification in American Cities[END_REF][START_REF] Tress | Unemployment and the Diversification of Industry 1[END_REF][START_REF] Chenery | Structural Change and Development Policy[END_REF][START_REF] Kort | Regional Economic Instability and Industrial Diversification in the US[END_REF][START_REF] Grossberg | Metropolitan Industrial Mix and Cyclical Employment Stability[END_REF][START_REF] Jackson | An Evaluation of Alternative Measures of Regional Industrial Diversification[END_REF][START_REF] Syrquin | Patterns of Structural Change[END_REF]Scherer and Ross,1990;Kort 1991;[START_REF] Ghosh | Export Instability and the External Balance in Developing Countries[END_REF][START_REF] Siegel | Regional Economic Diversity and Diversification[END_REF][START_REF] Bleaney | The Impact of Terms of Trade and Real Exchange Rate Volatility on Investment and Growth in Sub-Saharan Africa[END_REF]Koren and Tenreyro, 2013 among others) which evidences that diversification is conducive to higher and more stable economic performance and growth [START_REF] Chenery | Structural Change and Development Policy[END_REF][START_REF] Syrquin | Patterns of Structural Change[END_REF]. Economic concentration plays an important role in explaining the volatility of the growth of GDP per capita (Miklûs and Tenreyro, 2007). For instance, focusing on exports, [START_REF] Ghosh | Export Instability and the External Balance in Developing Countries[END_REF], [START_REF] Bleaney | The Impact of Terms of Trade and Real Exchange Rate Volatility on Investment and Growth in Sub-Saharan Africa[END_REF], and [START_REF] Mcmillan | Globalization, Structural Change, and Productivity Growth, with an Update on Africa[END_REF] stress that export diversification could help to stabilize export earnings in the long run and makes countries more resilient to shocks.

Finally, this thesis relates to the finance and growth theories [START_REF] Gurley | Financial Aspects of Economic Development[END_REF][START_REF] Goldsmith | Financial Structure and Development[END_REF][START_REF] Greenwood | Financial Development, Growth, and the Distribution of Income[END_REF][START_REF] Washington | Social Conflict and Growth[END_REF][START_REF] Levine | Finance and Growth: Theory and Evidence[END_REF], as well as the theoretical literature on conflicts (e.g., see [START_REF] Acemoglu | Kleptocracy and Divide-and-Rule: A Model of Personal Rule[END_REF][START_REF] Powell | Bargaining and Learning While Fighting[END_REF][START_REF] Acemoglu | Politics and Economics in Weak and Strong States[END_REF][START_REF] Acemoglu | Economic Origins of Dictatorship and Democracy[END_REF]Blattman and Miguel, 2009;[START_REF] Yared | A Dynamic Theory of War and Peace[END_REF]Acemoglu et al., 2010 among others). Financial intermediaries and more access to financial services encourage high-yield investments and growth, thereby opportunities for taxation, while conflicts are found to be harmful to macroeconomic variables.

Summary and Main Results

This thesis is made up of five chapters. In its first Chapter, the thesis lays the conceptual Second, to analyze the tax effort, it employs a stochastic frontier parametric model which distinguishes countries' structural factors and the tax system. The results show that over the considered period, SSA countries scored an average estimated tax effort of 0.57 corresponding to a tax-to-GDP ratio of 13.2, on average. This result also implies a low use of the tax potential in SSA countries and the existence of room for more tax revenue collection compromised mainly by a weak tax system. More precisely, SSA countries could raise 23.2 percent of GDP in non-resource taxes if they fully used their tax potential. Regarding the pressing financing need in this region, countries will benefit by strengthening their tax system, namely improving tax administration, broadening tax sources, and reducing tax exemptions. Our replication exercises broadly confirm previous analyses on the determinants of tax revenue in DCs, though our results show relative smaller coefficients for some variables suggesting a smaller effect when non-resources tax ratio is used instead of central government tax revenue.

Chapter 3 moves to analyzing the effect of diversified tax sources on tax performance as well as tax revenue stability. Specifically, the chapter tests the idea that relying on a diversified tax structure may enhance resilience to fiscal risks. This chapter is the first of its kind in the literature to explore this avenue by proposing a new cross-country tax revenue diversification index (RDI).

Unlike the few existing tax diversification indices, which are constructed only at the state level for the United States (US), the RDI is computed at the national level, covering a large panel of 17 The reason of focusing on this region in this chapter is twofold: First, the Sub-Saharan Africa region faces the most sizable financing needs compared to other low-income countries region. For instance, to meet the Sustainable Development Goals, the median Sub-Saharan African country additional spending need amounts about 19 percent of GDP compared to 15.4 percent of GDP for low-income countries [START_REF] Gupta | Tax Revenues in Africa Will Be Insufficient to Finance Development Goals[END_REF]. Second, the nature and the availability of the data justifies this choice.

127 countries over the period 2000-2015 and built on the Theil index. We find suggestive evidence that tax revenue diversification reduces tax revenue volatility, thus, comforting the long-held views about the prominence of tax revenue diversification for fiscal resilience strengthening. While exploring the drivers of the RDI, we find that tax revenue diversification is not just a reflection of economic diversification, but also an outcome of macroeconomic, political and institutional factors. Interestingly, a non-monotone relationship is also at play between RDI and economic development. Moreover, countries' portfolio of tax sources is getting more diversified as their economy develops, until a tipping point, where richer countries start finding it harder to diversify further their tax revenue sources.

Chapter 4 analyses how developing countries may tap tax resources from the financial sector, mainly through greater access to formal financial services. It contributes to the existing literature on the drivers of tax revenue by exploring tax revenue opportunities from unlocking access to financial services in developing countries. Evidence based on a sample of 62 developing countries over the period 2004-2017 shows that greater access to financial services captured by the number of ATMs per 100,000 adults increases government non-resources taxto-GDP ratio, and this result is driven by households consumption and business expansion. The chapter provides insights on tax resources-harnessing opportunities from implementing and promoting financial inclusion policies for developing economies.

Based on the finding that the financial sector provides opportunities to reap tax revenue in DCs, the fifth and last Chapter of the thesis, analyzes the consequences of the increasing internal unrest across the world, specifically in DCs, on the financial sector. 18 Unlike the extensive existing literature examining the economic impact of conflict and political instability, of particular importance, the chapter investigates whether rising conflicts and political instabilities lead to increased occurrence of banking crises in developing countries. The analysis is based upon a panel dataset of 92 countries, covering both emerging and developing countries, over the period 1970-2016. The results show a strong evidence that conflicts and political instability are indeed associated with higher probability of systemic banking crises and the primary channel of transmission is the occurrence of fiscal crises following a conflict or political instability. Besides, we find that the duration of a conflict is positively associated with the rising probability of a banking crisis. Interestingly, the chapter also highlights that conflicts and political instability in one country can have negative spillover effects on neighboring countries' banking systems. This chapter clearly emphasizes the need for governments of countries infested by conflict and/or political instability to address their root causes and try to mitigate their negative effects with the appropriate design and implementation of economic policies.

Creating adequate fiscal space in normal times can reduce the likelihood of fiscal crises and in turn lower the probability of systemic banking crises. Our results also suggest that policymakers should pay attention to conflicts in neighboring countries even if they are not conflict-afflicted as their banking systems may suffer negative spillovers from their neighbors. Finally, the thesis concludes and draw some policy recommendations (General Conclusion).

Appendices

Appendix

Introduction

Since the Addis Ababa Conference in July 2015, Domestic Revenue Mobilization (DRM) became one of the main tools of financing Sustainable Development Goals (SDGs). DRM is now a welldiscussed topic to address the issue of economic development (see e.g., Besley and Persson, 2014) and is a privileged tool for donors as for international and regional institutions (African Development Bank, International Monetary Fund, World Bank, European Union Commission).

In this paper, we propose (i) an update and complete version of the tax revenue dataset published in [START_REF] Mansour | A Tax Revenue Dataset for Sub-Saharan Africa: 1980-2010[END_REF], (ii) an estimate of tax effort for these countries, and (iii) some replication analyses of previous tax effort estimations by [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] and Fenechietto and [START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF]. The database covers 42 Sub-Saharan African (SSA) countries over the period 1980-2015. It results from statistical information collected in the African Department of the International Monetary Fund (IMF)-most of which is included in public IMF documents. We distinguish tax revenue from the natural resource extractive industry, from those from other economic sectors.

Tax revenue excluding natural resources is on average 13.2 percent of GDP. The average estimated total tax effort is 0.57. In other words, SSA countries coul d raise on average 23 percent of GDP of non-resource taxes if they fully utilized their potential. We decompose the total tax effort score into time-varying and persistent tax effort and conclude that the total tax effort score is mainly driven by time-varying factors. Moreover, consistent with previous literature, we find that countries' stage of development measured by per-capita income, financial development, and trade openness are important factors improving tax revenue in the region, while natural resource endowment and the importance of the agriculture sector reduce unambiguously the non-resource tax-to-GDP ratio. Regarding the replication exercise, the estimations broadly confirm previous analyses such as Fenechietto and Pessino (2013).

However, our verification test failed to replicate the exact results of [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] in terms of robust coefficients and significance of the variables, which might be caused from the use of less detailed data than we provide here.

Our analysis contributes to the existing literature by providing a new estimation of SSA countries' total tax efforts and their composition. We decompose tax effort in terms of direct taxation (Corporate Income Tax, CIT, and Personal Income Tax, PIT). In addition, beyond the originality of the database itself and the empirical results, our work participates explicitly to the replication principle given its online application developed with R-Shiny. The need for replicability appears highly relevant for tax effort analysis given the primacy of DRM in the agenda of developing countries, donors, and international organizations. The database is dynamic and is hosted on a webpage that allows users to interact with the data and generate new analytical results, including quick descriptive statistics and running alternative specifications of regressions.

The rest of the chapter is organized as follows: Section 2 presents the dataset; Section 3 briefly reviews the literature on the determining factors of tax effort in developing countries; Section 4 describes the empirical methodology and variables. Section 5 presents and discusses the results; section 6 proposes a replication analysis of [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] and Fenechietto and Pessino (2013), and section 7 concludes.

Tax Revenue Dataset for Sub-Saharan Africa over 1980-2015

The study of tax policy in developing countries has long been constrained by the availability and the quality of detailed relevant data. Moreover, extractive industries have played and still play a crucial role in the economic development of SSA countries. More than half of these countries are resource-dependent, that is natural resources represent 25 percent or more of total country's exports. Tax revenues from this sector are usually large and at high risk of being taken out of the source country through various licit or illicit channels, including generous tax incentives provided in mining or petroleum codes and other laws; aggressive tax planning such as the use of thin capitalization, trade mispricing, or plain tax evasion; and doubl e taxation agreements that do not always protect appropriately source countries' taxation rights.

We provide here an updated version of the tax revenue dataset published in [START_REF] Mansour | A Tax Revenue Dataset for Sub-Saharan Africa: 1980-2010[END_REF], which covered the period 1980-2010 for 41 countries (see https://data.cerdi.uca.fr/taxeffort/). 19 It participates to recent efforts to better apprehend tax revenues in Africa, in particular the revenue statistics from the OECD, which cover 26 countries in its last release 20 and the Government Revenue Dataset initiated by the International Centre for Tax and Development (ICTD) and updated by UNU-WIDER. 21 There are three advantages that our dataset provides relative to these two alternatives. First coverage for SSA countries is generally broader, and deeper for each of the tax series. Second, the definition of the variable is consistent across all countries, 22 Finally, the isolation of resource revenue from non-resource (tax) revenue allows for a better understanding of the interaction of these two fundamentally different (economically) sources. 23Distinguishing resource from non-resource revenue is highly relevant to understand countries' tax effort. For instance, [START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF], Crivelli andGupta (2014), and[START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF] emphasize a crowding-out effect between resource revenue and non-resource tax revenue: an increase of the former reduces the latter. [START_REF] Mcguirk | The Illusory Leader: Natural Resources, Taxation and Accountability[END_REF] explains this effect through the strategy of the government to remain in power by reducing its accountability or equivalently the tax pressure. [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF] provide an alternative explanation of the negative relationship between resource and non-resource tax revenue in terms of an inter-ministerial 19 See https://ferdi.fr/publications/a-tax-revenue-dataset-for-sub-saharan-africa-1980-2010. 20 See https://www.oecd.org/ctp/revenue-statistics-in-africa-2617653x.htm.

21 See https://www.wider.unu.edu/project/government-revenue-dataset. 22 The ICTD database, now Government Revenue Dataset (https://www.wider.unu.edu/project/governmentrevenue-dataset), combines revenue data primarily from OECD revenue statistics, IMF staff reports' statistical tables, and IMF GFS. This produces asymmetries in the definition of resource revenues. For instance, ICTD reports no resource tax revenue for Australia and Canada, and only aggregated corporate income tax (CIT), which include profit taxes from the resource sector if the source is OECD of GFS. These asymmetries are less important in SSA countries since the primary source for ICTD for these countries is IMF staff reports, and Keen and Mansour (2009)-and both report a different concept of resource revenue. For instance, the average resource revenue-to-GDP ratio during 1980-2015 in ICTD is 8.16 percent, which is close to the 8.6 percent in our database. However, the average CIT ratio in ICTD is 1.82 percent over the same period, slightly higher than the 1.7 percent in our database-possibly because CIT revenue for SSA taken from OECD revenue statistics for Africa includes some resource revenue.

tax competition: the Minister in charge of Mining and Petroleum can tax partly the same base than the Minister of Finance. The inter-ministerial tax competition reduces total tax revenue and deteriorates the economic development of these countries by favoring a concentration of the economic activity in the extractive industry.

The dataset covers 9 tax series and 42 SSA countries over the period 1980-2015. The series are:

1. Total Taxes; 2. Trade Taxes; 3. Indirect Taxes; 4. VAT with a decomposition for some countries between domestic VAT, VAT collected at the border, and VAT refunds; 5. Excises; 6. Direct Taxes;

7. Personal Income Tax; 8. Corporate Income Tax with additional information for some countries concerning CIT from extractive industry; 9. Other tax revenues.

To isolate the impact of resource revenue on the tax effort, the database reports revenue from extractive activities separately and irrespective of the policy tool used to raise them. As such, resource revenues include royalties and other fees, dividends, and bonuses from extractive activities, the government share of production and related agreements, and (importantly) corporate income taxes. The latter is included because it is similar in design to production sharing, and hence subject to the same extent as volatility in commodity prices. However, resource revenues do not include non-refundable VAT on inputs, which we were not able to identify separately-presumably. This is not very important in aggregate given that extractive companies often seek and obtain an exemption from VAT on input, knowing that VAT refund mechanisms in SSA countries are not very efficient.

Brief Literature Review on Tax Effort

DRM would be a more reliable and sustainable source of financing than its domestic alternative (debt, seigniorage) or international financial flows (i.e., remittances, official development assistance and foreign direct investment). Hence, a non-negligible literature has investigated how countries, specifically developing countries, which face important financial constraints, can levy more domestic resource to finance development and wean themselves from aid.

Several empirical analyses study the macroeconomic and institutional driving factors of countries tax-to-GDP ratio. A first generation of empirical works establishes that agriculture, mining (resource rent), and share of external debt in total debt are significant determinants of countries tax ratios. Agriculture share, which is still important in the least developed economies, is negatively associated with the level of tax revenue [START_REF] Chelliah | Tax Ratios and Tax Effort in Developing Countries, 1969-71[END_REF][START_REF] Leuthold | Tax Shares in Developing Economies A Panel Study[END_REF]Tanzi, 1992;[START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF], while mining and external debt are positively associated with tax revenues (Cheliiah et al., 1975;[START_REF] Tait | Comparaciones internacionales de tributación entre determinados países en desarrollo, 1972-76[END_REF]Tanzi, 1992).

However, the relationship between natural resource sector and tax revenue remains controversial. Indeed, in line with the resource curse debate, recent studies point out a negative association between resource rent and government tax revenue. For instance, Belinga et al.

(2017) highlight a crowding-out effect of resource revenue on non-resource revenue for a panel of 30 resource-rich countries over the period 1992-2012. Natural resource boom is associated with less incentive in tax collection. Brun et al. (2014) consider tax revenue from the natural resource sector as an explanatory variable of the non -resource tax effort. They establish a negative effect of the former on tax revenue potential.

A second generation of empirical works outlined the pivotal role of inflation, institutional quality, education, political stability, external aid, and financial development in addition to the previous economic factors (Tanzi and Davoodi, 1997[START_REF] Grigorian | Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia's Stubbornly Low Tax Collection[END_REF][START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF][START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Feger | Tax Effort Performance in Sub-Sahara Africa and the Role of Colonialism[END_REF].

We provide here a new dataset and focus on the effort of countries to raise tax revenue. We define tax effort as the extent to which the actual tax revenue collected is from the maximum level of tax revenue given their characteristics. These characteristics correspond to the determinant variables previously studied in the literature, which are mainly: the level of development, trade openness, the size of the agricultural sector, natural resource rent, and financial development. Given these characteristics, we compute for each country potential tax revenue. Tax effort results then from the comparison between potential tax revenue and actual collected tax revenue. The closer they are, the greater is the tax effort. We do not study the details of countries' tax code, nor the organization of their revenue administrati on or authorities.

Our approach is then purely economic since it does not rely on countries' tax system 27 but only on economic characteristics. It allows some international comparisons among countries, which share similar economic features. This analysis could be then complemented by some tax policy 27 Gillitzer and Slemrod (2015) define tax system as the combination of tax policy (tax laws) and revenue administration.

Chapter 2: Tax Effort in Sub-Saharan African Countries: Evidence from a New Dataset and revenue administration diagnostics. Indeed, differences in tax effort across countries may result from some distinctions in existing taxes, their statutory rates, their respective tax bases, tax expenditures, the efficiency of revenue administration (organization, IT technology, the number of tax inspectors, and even their remunerations' modalities). 28 Other determinants such as tax morale, the ethnolinguistic fragmentation of the countries, political regimes (presidential or parliamentary), inflation rate are variables, which may be added in our empirical assessment of tax effort. 29 Tax effort is complementary to the tax gap analysis, 30 which measures the difference between expected revenue and collected one. The tax gap approach is legal and microeconomic, while the tax effort analysis relies only on macroeconomic data. Indeed, the computation of expected revenue differs from potential revenue in the tax effort analysis, since the former requires the use of statutory tax rates, tax base's definition, and eventually some assumptions on the behavior of consumers and producers. The tax gap has usually two components: the policy gap and the compliance or administrative gap. The former, roughly equivalent to the cost of tax expenditures, results directly from a political decision to reduce the tax burden of the investor or the consumer. This policy aims at stimulating investments or at protecting the poorest. For instance, investment or sectorial (Petroleum or Mining) codes may provide tax exemptions or reduce tax rates, which would reduce tax revenues. Similarly, one of the main justifications of VAT exemptions or reduced rates is to protect the poorest consumer. For instance, developing countries use to exempt completely the agricultural sector and some SSA countries exempt even from VAT the importations of some foodstuff such as rice and wheat. The rationale is the assumption of a tax incidence close to one, i.e., such exemptions would reduce the price for households. 31 The second element of the gap is the administrative or compliance gap. This gap 28 See for instance [START_REF] Caldeira | The tax effort in Benin: How can tax gaps be reduced?[END_REF] for a detailed analysis of relative tax revenue performance between Benin and Togo, which begins with a tax effort analysis of these two countries and is completed by a review of countries' tax systems. 29 The devoted website to this paper allows adding any variable and running new estimates of tax efforts (see https://data.cerdi.uca.fr/taxeffort/). corresponds to the capacity of tax and customs administration or tax authorities to enforce current tax laws and to the compliance behavior of firms and individuals to pay their taxes.

Empirical Methodology: The Stochastic Frontier Analysis

The literature proposes several approaches to capture the countries' tax effort. The usual indicator to compare countries' tax effort is the tax-to-GDP ratio. However, [START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF] point out that this simple approach is inappropriate to measure the taxable capacity since not all taxes are explicitly linked to income and to its distribution. Using panel data on 42 Sub-Saharan African countries, during the period 1990-1995, they propose another measure of tax effort consisting of the ratio of the actual to the predicted tax share in GDP. They find that countries with high tax shares tend to have a relatively high tax effort index, even though some disparities remain across countries. Following a similar approach, Gupta (2007) computes the tax effort for 105 developing countries over 25 years but clearly recognize some shortcomings related to this approach. [START_REF] Cyan | Measuring Tax Effort: Does the Estimation Approach Matter and Should Effort Be Linked to Expenditure Goals?[END_REF] propose a method of estimating tax effort that closely relies on the revenue adequacy approach. This method consists in looking at the deviations between what a country would like to raise in tax revenues -as revealed by the structural choice of the level of public expenditures -and its actual tax revenue level. This approach corroborates the empirical evidence that changes in expenditures induce changes in tax levels (see [START_REF] Baicker | Health Care Spending Growth and the Future of US Tax Rates[END_REF]. Recently, [START_REF] Yohou | Reassessing Tax Effort in Developing Countries: A Proposal of a Vulnerability-Adjusted Tax Effort Index (VATEI)[END_REF] proposed a Vulnerability-Adjusted Tax Effort Index (VATEI) for a sample of 120 developing countries over 1990-2012. Their approach consists in building the tax effort as the residual of a standard panel regression model (random effects model) of non-resource tax ratio on the economic vulnerabilities and human asset indices, in addition to the usual determinants of tax revenue. This adjusted tax effort index is assumed to measure the willingness and capacity of governments to collect tax beyond the structural factors.

An alternative and increasingly used approach to capture countries' tax effort is the Stochastic Frontier Analysis (SFA) method, which has been followed by [START_REF] Alfirman | Estimating Stochastic Frontier Tax Potential: Can Indonesian Local Governments Increase Tax Revenues under Decentralization? Center for Economic Analysis[END_REF], [START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF], Langford and Ohlenburg (2015), [START_REF] Brun | Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT's Revenue[END_REF]. [START_REF] Aigner | Formulation and Estimation of Stochastic Frontier Production Function Models[END_REF] and [START_REF] Meeusen | Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error[END_REF] developed SFA approach to model firms' production behavior. The rationale is that economic agents cannot exceed an "ideal frontier" of production, which is the optimal level of output given the limited endowment of inputs. In our context, the tax frontier refers to the tax capacity, which is the maximum potential tax revenue, given a country's institutional, demographic, and economic features, while the tax effort is the actual revenue in relation to the frontier. Hence, the closer a country is to that frontier, the greater is its tax effort.

The stochastic frontier approach encompasses parametric and non -parametric models. Data Envelopment Analysis [START_REF] Charnes | Data Envelopment Analysis Theory, Methodology and Applications[END_REF]) and the Free Disposal Hull (Deprins et al. 1984) are the two main and increasingly popular methods used for non -parametric stochastic frontier models. These methods use linear optimization programs to construct the efficiency curve.

They display the advantage that no restrictive assumptions on the production function are necessary (except the standard convexity assumption). However, they remain sensitive to random variations in data and measurement errors. Any variation between production units is therefore likely to be interpreted as inefficiency. Furthermore, the inefficiencies estimated by these models are very sensitive to variations in the sample, to the heterogeneity between the units and to the presence of outliers.

Regarding parametric models in panel data analysis, they are single output-based and categorized into five groups: (i) time-invariant technical inefficiency models [START_REF] Pitt | The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry[END_REF][START_REF] Schmidt | Production Frontiers and Panel Data[END_REF]; (ii) time-varying technical inefficiency models [START_REF] Washington | Production Frontiers with Cross-Sectional and Time-Series Variation in Efficiency Levels[END_REF], [START_REF] Kumbhakar | Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency[END_REF], [START_REF] Battese | Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India[END_REF], Lee and Schmidt (1993), and [START_REF] Kumbhakar | Estimation of Growth Convergence Using a Stochastic Production Frontier Approach[END_REF]; (iii) models that separate firm heterogeneity from inefficiency [START_REF] Greene | Reconsidering Heterogeneity in Panel Data Estimators of the Stochastic Frontier Model[END_REF] Wang and Ho, 2010); (iv) models distinguishing persistent and time-varying inefficiency [START_REF] Kumbhakar | Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976-88[END_REF]; (v) and finally models separating firm effects, persistent inefficiency, and time-varying inefficiency [START_REF] Colombi | Closed-Skew Normality in Stochastic Frontiers with Individual Effects and Long/Short-Run Efficiency[END_REF]. Kumbhakar et al., 2014;[START_REF] Filippini | Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach[END_REF]. In panel data, such models offer the possibility for richer specifications, deal with stochastic noise, and allow testing hypotheses [START_REF] Hjalmarsson | DEA, DFA and SFA: A Comparison[END_REF]Odeck, 2007).

We follow a parametric approach to estimate the tax effort since we focus on a single output: the total non-resource tax-to-GDP ratio. It is, therefore, possible to define the function of the log-likelihood and to deduce from it the MLE of the parameters. However, the complexity of the likelihood function, which, in his form, involves 𝑇 𝑖 integrations, makes very hard the implementation in practice. 34 Hence, [START_REF] Filippini | Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach[END_REF] propose a computation method based on [START_REF] Butler | A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model[END_REF] formulation to simplify the log-likelihood function and subsequently estimate the MLE using a simulationbased optimization. With the same assumptions on the parameters as before, the idea is to obtain the conditional density 𝑓(𝜀 𝑖 /𝜓 𝑖 ), which is defined on 𝜓 𝑖 . Unlike the previous case, the manipulation, then, involves only one integration. To simplify the implementation of the estimation, we use the multi-step procedure of [START_REF] Kumbhakar | Technical Efficiency in Competing Panel Data Models: A Study of Norwegian Grain Farming[END_REF]. 35 The model based on equation 1 is estimated in three stages:

In stage 1, a standard random-effect based regression is used to estimate 𝛽 ̂ and predicts the values of 𝜀 𝑖,𝑡 and 𝜓 𝑖 . We estimate the following equation:

𝜏 𝑖,𝑡 = 𝛼 * + 𝑓( 𝑋 𝑖,𝑡 , 𝛽) + 𝜀 𝑖 ,𝑡 + 𝜓 𝑖 (2.2) 33 We use the logarithm of non-resource tax-to-GDP as dependent. The predictor variables, except the real GDP per capita, are scaled to unit i.e., in percent of GDP and not in logarithm. By doing so, we do not assume implicitly the functional form linking the output to the inputs. The log-log form is the most used in the stochastic frontier literature. Note that our results remain robust to the use of the log-log form.

Where 𝛼 * = 𝛼 -𝐸(𝜂 𝑖 ) -𝐸(𝜑 𝑖𝑡 ), 𝜓 𝑖 = µ 𝑖 -𝜂 𝑖 + 𝐸(𝜂 𝑖 ), 𝜀 𝑖 ,𝑡 = 𝜈 .

In stage 2, by performing a standard stochastic frontier technique, the time -varying tax inefficiency 𝜑 𝑖,𝑡 is estimated using the predicted values of 𝜀 𝑖,𝑡 from the first stage. Following [START_REF] Battese | Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data[END_REF], this procedure gives the prediction of the time-varying tax effort exp (-𝜑 𝑖,𝑡 |𝜀 𝑖,𝑡 ).

In stage 3, we estimate the persistent tax inefficiency component, denoted by 𝜂 𝑖 by performing a stochastic frontier model as in the previous stage. The persistent tax effort is then defined by exp (-𝜂 𝑖 ).

Finally, the overall tax effort is obtained by the product of the time-varying tax effort and the persistent tax effort.

Considering the relevant literature on the determinants of domestic resource mobilization, we identify the following driving factors of government tax revenue and consider them as inputs 𝑋 𝑖,𝑡 :

• The level of development: Countries' tax capacity depends on their l evel of economic development assessed through the level of real GDP per capita. High -income countries should raise more tax revenue than developing countries since they have more efficient tax administration, better institutions, and a higher demand for public goods and services (see [START_REF] Lotz | Measuring 'Tax Effort' in Developing Countries[END_REF]Pessino and Fenechietto, 2010;[START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF].

• Trade openness: Trade liberalization policies implemented in most developing countries starting in the early 1970s and stretching well into the 1990s have substantially increased trade volume in these countries. Therefore, trade openness expressed as total trade (value of imports and exports) as a share of GDP is expected to positively influence tax revenue through households' consumption and domestic corporate profits. This reinforces the role of customs administration in collecting taxes, both the external tariff and domestic taxes, on imported goods (see [START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF]Pessino and Fenechietto, 2010;Gnangnon and Brun, 2018 among others).

• Agriculture value-added as a percent of GDP: Agriculture is often largely tax-exempt in SSA countries from income taxes and other production taxes, and is frequently either tax exempt or out-of-scope of VAT. The first argument in favor of this treatment is that the sector is dominated by small and medium-size farmers that are scattered across geography, and hence hard to tax; and even if taxes can be effectively levied, such farmers cannot be significant contributors to tax revenues. 36 A second explanation is the political will to reduce foodstuff prices in order to limit the risk of malnutrition or even famine. This assumes a tax incidence close to one, which means an almost perfectly competitive market. However, the tax (VAT mainly) exemptions of foodstuffs involve the inability of farmers to deduce the VAT paid on their inputs. The collected VAT on the intermediary consumption of farmer can generate some revenue and may have then an ambiguous effect of total tax revenue.

• Natural resource rent as a percent of GDP: The negative effect of natural resource rent on tax revenue is widely evidenced in the literature. Natural resource endowment is associated with lower tax revenue [START_REF] Sachs | The Curse of Natural Resources[END_REF]Eltony, 2002;Belinga et al., 2007). During commodity prices upswings, governments in resources-rich countries have less incentive to mobilize other tax revenues; resource rent crowds-out tax revenue [START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF], Crivelli and Gupta, 2014[START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF] or an inter-ministerial tax competition occurs [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF].

• Financial development: Financial development may favor higher tax collection (see [START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF]. Combined with improved access to credit, it allows indivi duals and companies to finance profitable projects and improve the national information system on economic activities-hence, favor tax collection. On the other hand, in a presence of an ineffective financial system, firms could successfully evade tax payment by conducting business in cash, which is harder for tax administrations to monitor.

• Aid received: In the literature, empirical studies on the relation between aid and taxes are quite not consensual. While some studies emphasize a positive effect of aid on tax revenue mobilization (see e.g., [START_REF] Brun | Aide et Mobilisation Fiscale Dans Les Pays En Développement[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Morrissey | Aid and Taxation: Exploring the Relationship Using New Data[END_REF] among others), other works including [START_REF] Pack | Is Foreign Aid Fungible? The Case of Indonesia[END_REF], [START_REF] Azam | Aid Dependence Reconsidered[END_REF]

Results

Table 2.1 displays the three-stage estimation results. 37 Dependent variables in column (1) to column ( 4) are total non-resource tax revenue, total income taxes, corporate income tax, and personal income tax, respectively. In line with previous studies [START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF][START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF] all the variables have the expected sign and are statistically significant. The coefficients associated with the level of development (i.e., GDP per capita, logged) and trade openness (logged) are positive and significant at the one-percent level. More precisely, a one percentage increase in per-capita GDP is associated with an increase in the total tax revenue by 0.003 percentage points. Similarly, an increase of one percent in the total trade-to-GDP ratio is associated with a rise in non-resource tax ratio of 0.002 percentage points. Agriculture and natural resources sectors harm tax revenue collection. The coefficients associated with these variables are all negative and statistically significant and are consistent with previous analyses [START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF][START_REF] Sachs | The Curse of Natural Resources[END_REF]Eltony, 2002;Brun et al., 2014;Belinga et al., 2017). These results also hold mostly for tax revenue subcomponents columns ( 2) -( 4).

For the rest of the analysis (stages 2 and 3), we consider the total non-resource tax revenue as the dependent variable (i.e., column 1). Panel A and Panel B of Table 2.1 report the second and third stages. We then deduce the time-varying and persistent tax effort scores, and we compute the total tax effort (Panel C). The higher is the tax effort score, the closer is the country to the "frontier". Table A4 presents the summary of tax effort for columns (2)-( 4). In Figure 2.6, we show the evolution of countries tax effort. Most of SSA countries have improved their tax effort over time, particularly Uganda, Sierra Leone, and Senegal. Table 2.2 provides a country ranking over different sub-periods (1980-1989, 1990-1999, 2000-2009, and 2010-2015) based on total tax effort scores. Focusing on the last sub-period (i.e., [2010][2011][2012][2013][2014][2015], it emerges that Mozambique, Togo, Burundi, Senegal, and The Gambia are top performers with a tax effort score of 0.804, 0.774, 0.769, and 0.755, and 0.742 respectively, while the five poor performers over the sub-period are Congo Republic (0.361), Chad (0.325), Gabon (0.319), Nigeria (0.232) and Equatorial Guinea (0.071). The non-resource tax to GDP has a decreasing trend in Equatorial Guinea, Congo Republic and Gabon. These countries rely a lot and increasingly on revenue from natural resources. As for Nigeria and Chad, they have generally a non-resource tax below 7 percent of GDP over the period 1980-2015.

The poverty level in Mozambique and Burundi paradoxically explains their performance in terms of tax effort. Table 2.1 highlights the crucial role of GDP per capita to determine the tax revenue to GDP ratio. The coefficient of this variable is not only highly significant, but its level is more important. Togo (as Benin) has a substantial transit activity with Nigeria. Given the Nigerian trade policy to foster domestic production especially (for instance, rice and wax fabrics) or for other considerations (such as environmental and security reason for second-hand cars), some goods subject to high tariff rates are imported in Togo and then smuggled to Nigeria. These importations raise revenue in terms of tariffs and VAT even if these goods are not consumed in Togo. Natural resource endowment, especially oil, significantly reduces the computed tax effort (see Figure and7a). The worst performers are resource-rich SSA countries (Nigeria, Equatorial Guinea) given the crowding out effect [START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF], Crivelli and Gupta, 2014[START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF] or the inter-ministerial tax competition weakening the institution in charge of tax policy [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF].

For robustness purpose, specifically, to deal with the omitted variables bias, we control successively for the financial development and for official development assistance. The threestage estimation results are reported in Appendix 2.5 and 2.7, respectively. For each variable, the summary of tax effort for tax subcomponents -columns ( 2)-( 4) -is reported in Appendix 2.6 and Appendix 2.8, respectively.

Focusing on non-resource tax, the average total effort does not change when controlling for financial development: the average tax effort remains 0.572. The top performers in the subperiod 2010-2015 also remain unchanged, namely Togo, Senegal,38 Burundi, and The Gambia.

These countries are resources poor, and their tax potential is particularly low (except for Senegal) given their level of poverty and the share of their agricultural sector in their respective GDP. Moreover, Togo and The Gambia raise significant revenue from the transit activities towards landlocked countries or regions. The average tax effort scores for subcomponents are slightly higher (+0.01) than those from the baseline results.

Controlling for development aid received results in a slight change in the average tax effort:

the average tax effort varies from 0.572 to 0.570. The top performers over the sub -period 2010-2015 are Burundi, Togo, Senegal, and Namibia. 39 The average effort to collect direct tax increases driven by an increase in the effort to collect personal income tax and a decrease in the effort to collect corporate income tax.

To test the sensibility of our results to the change of production technology between the nonresource tax and its determinants, we use the log-log functional form. The average tax effort varies from 0.572 to 0.571 for the baseline estimation (see Table 2.1 and A ppendix 2.9). The average tax efforts are 0.581 and 0.566 when using the log-log specification and controlling for financial development and official development assistance, respectively.

To further test the robustness of our results, we compute the tax effort using some se cond general stochastic frontier models. 40 We first estimate tax effort using the model of [START_REF] Battese | Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India[END_REF]. The average tax effort is 0.573 relying on the baseline.

The average tax effort is 0.626 and 0.576 when controlling respectively for fina ncial development and development assistance and aid. Using the model of [START_REF] Kumbhakar | Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency[END_REF], the average tax effort becomes 0.493 for the baseline model, and 0.626 and 0.587 when controlling for financial development and official development assistance, respectively.

Replications

Our analysis participates explicitly to the replication effort, which ensures the reliability of produced works. Over recent years, there has been a growing interest in replication, particularly in economic research. 41 Following Hamermesch (2007) and Clemens (2017) approach, we undertake a replication of two papers analyzing tax effort: [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] and Fenechietto and [START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF]. This replication approach consists in three stages: verification, reproduction, and robustness. Verification means the use of the same sample, population, and empirical specification, 42 while reproduction uses the same econometric model on different samples from 40 Second-generation models have their limitation in measuring inefficiency, hence our choice of Generalized True Random Effects model. While they allow for time-varying inefficiency, hence improvement over time, the intercept is the same across all countries. Unfortunately, in the presence of time-invariant unobservable factors, they are subject to misspecification bias. Moreover, they are not fitted to deal with heterogeneity between countries. For more discussion, see [START_REF] Wang | Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model[END_REF]), Green (2005), and [START_REF] Belotti | Consistent Inference in Fixed-Effects Stochastic Frontier Models[END_REF]. 41 For instance, the top five Reviews in Economics, for a paper to be accepted and published, request the inputs including dataset and program of the paper for replication purpose (Sukhtankar, 2017) . The American Economic Review particularly has dedicated a whole volume to replication. In addition, Anderson and Kichkha (2017)], after a discussion of the three main methods of research synthesis (i.e., traditional literature surveys, meta-analysis and replication), argue that only pure replication does not contain a substantial judgement.

the same population. 43 Regarding robustness, 44 it consists either in running the same specification on different samples and populations or applying different econometric specifications on the same sample and population. In our replication exercise, 45 we estimate the same specification for the same sample (same countries and period) as in the original paper for the verification. The reproduction consists in running the same specification on the same sample of countries but including all the available observations for the variables used in the author(s)'s specification(s). Finally, for the robustness, we expand the sample and the time period by using all the countries and years on which data are available to test the author(s)'s specification(s). It is worth mentioning that some differences with respect to the original paper on variables characteristics (i.e., mean, standard deviation, minimum and maximum) emerge even though the sources are the same. This could be due to changes and adjustments in dataset over time. These differences could be also related to some minor treatments by the authors during the dataset compilation, which are not reported in the paper. Furthermore, in the case we do not find a variable from the same source as the author, we take one from another source, if applicable. Otherwise, if the variable is not used in the baseline specification,

we do not run the regression for that given specification.

Replication of Gupta (2007)

The author estimates countries' revenue potential for a panel of 105 developing countries over the period 1980-2004 using central government revenue dataset. The estimates explained the ratio of central government revenue (excluding grants) to GDP as a function of a set of structural variables (i.e., the log of per capita GDP, the share of agriculture in GDP, the ratio of imports to GDP, the share of aid and debt in GDP) and politico-institutional variables 43 For Hamermesch (2007), this is called a statistical replication.

44 Hamermesch (2007).

45 Our replication process is applied based on the following conditions: First, the paper must be an empiri cal investigation of countries' tax effort (i.e., employing econometric specification) with an actual computation of tax effort. Second, it must be an international comparison of tax effort among countries. In addition, we choose not to replicate a number of seminal papers on tax effort prior to the 1990s such as [START_REF] Bahl | A Regression Approach to Tax Effort and Tax Ratio Analysis[END_REF]Bahl, ( , 1972)), [START_REF] Chelliah | Trends in Taxation in Developing Countries[END_REF], [START_REF] Chelliah | Tax Ratios and Tax Effort in Developing Countries, 1969-71[END_REF], [START_REF] Leuthold | Tax Shares in Developing Economies A Panel Study[END_REF], Lotz and Morss (1970), [START_REF] Tait | Comparaciones internacionales de tributación entre determinados países en desarrollo, 1972-76[END_REF].

(corruption, law and order, government stability, political stability and economic stability). An important limitation of this paper is the inclusion of natural resources revenue into tax revenue. [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] uses various methods of panel data estimation including Fixed Effect (FE), Random Effects (RE), Common Correlation Coefficient (CCC), Panel Specific Correlation (PSC),

and Dynamic Panel Specification (DPS). The results show that the per capita GDP, trade openness, and the share of agriculture in GDP are statistically significant and strong determinants of countries revenue performance. In addition, certain forms of foreign aid improve revenue performance while external debt does not. Regarding politico-institutional factors, the author found that political and economic stability affect revenue performance positively revenue performance, and corruption significantly and negatively alters revenue performance. The author also emphasizes that countries' tax revenue performance depends on their tax structure. More precisely, countries that depend on indirect taxation as their main source of tax revenue, tend to perform less than countries raising more from direct taxation.

We replicate the key specifications despite a few issues with some variables (economic stability, political stability, and average tariff). The verification test failed to replicate the exact results as in the paper in terms of coefficient and significance of the variables. We have more significant variables than in the original paper (see Appendix 2.6 and 2.7). 46 Moreover, the robustness exercise yields smaller coefficients for all the variables than in the paper, suggesting a smaller effect when non-resources tax is used instead of central government revenue, and when the sample is expanded to all available countries and years (see Appendix 2.8 and 2.9).

Replication of Fenochietto and Pessino (2013)

Fenochietto and Pessino (2013) estimated countries tax capacity and tax effort using a Stochastic Frontier Analysis for 113 countries. They first estimated the tax capacity for 96 nonnatural resources dependent countries and then on the whole sample using tax and pension 46 Although we replicated all the forms of panel data estimations, we present the results for the common correlation coefficient and the panel specific correlation estimations. The reason is that the author expressed his preference for these results in the paper (see Gupta, 2007 p. 26).

contributions revenue collected by central and sub-national governments as percent of GDP from the IMF's WEO. The authors considered a set of structural and institutional variables (level of development, inflation, education, trade, income inequality, corruption, and the ease of tax collection) explaining countries' tax capacity and estimated tax effort using [START_REF] Battese | Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India[END_REF] Half Normal (HN) and Truncated Normal (TN) models incorporating heterogeneity. They also relied on [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] Random Effects Model (REM) to deal with the 'unobserved' heterogeneity.

Our verification test on this paper yield almost the same results: the sign and the magnitude are close. As in the paper, the coefficients for non-resource countries are slightly lower than those for all countries (First two columns of Appendix 2.10 and 2.11).

For the robustness analysis, in addition to broadening the sample to all available countries and year while replacing the dependent variable with the ICTD non-resource tax, we also estimate the parameters of Stochastic Frontier (SF) tax function for ICTD non-resource tax while limiting it to the non-resource dependent countries defined in the paper. The robustness results show stable coefficients for all the variables. Nevertheless, the logarithm of the GDP per capita and the logarithm of the GDP per capita square do not have the expected sign or are not significant.

We went further in robustness analysis, by relaxing the nonlinear relationship assumption between tax revenue and GDP per capita. Thus, we dropped the logarithm of the GDP per capita squared from the specification. Results in last two columns of Appendix 2.10 and 2.11

show that once we relax the nonlinear relationship assumption, all the variables get the expected sign.

Conclusion

In this chapter, we offer a new dataset of tax revenue, which updates and completes the dataset published in [START_REF] Mansour | A Tax Revenue Dataset for Sub-Saharan Africa: 1980-2010[END_REF]. We collect statistical information from the African Department of the International Monetary Fund (IMF), most of which is publicly available, covering 42 SSA countries over the period 1980-2015 and distinguishing resource revenue from non-resource (tax) revenue. This work participates to recent efforts to better apprehend tax revenues in Sub-Saharan Africa, in particular the revenue statistics in Africa from the OECD, the Government Revenue Dataset initiated by the International Centre for Tax and D evelopment (ICTD) and updated by UNU-WIDER.

We provide an estimate of tax effort adopting the Stochastic Frontier Analysis approach. First, we confirm the impact of previously studied factors on countries' DRM capacity such as level of development, financial development, trade openness, natural resource rent, and the size of the agriculture sector. The two latter factors have a negative effect on the domestic revenue mobilization capacity. We estimate on average the total tax effort to be 0.58. Given that nonresource tax revenue amounts to 13.2 percent of GDP, potential tax revenue would be on average 22.75 percent of GDP. Mozambique, Burundi, Togo, Senegal, and The Gambia are top performers with a tax effort score above 0.75, while the five lowest performers are resourcerich countries such as Congo Republic (0.366), Chad (0.333), Gabon (0.327), Nigeria (0.243) and

Equatorial Guinea (0.073). The poverty level in Mozambique and Burundi explains paradoxically their performance in terms of tax effort. Finally, we did some replication analyses of previous works on tax effort, in particular [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF]Fenechietto and[START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF]. We fail to replicate the results of [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF] Net official development assistance is disbursement flows (net of repayment of principal). Net official aid refers to aid flows (net of repayments).

Financial development index

Aggregate of nine indices that summarize how developed financial institutions and financial markets are in terms of their depth, access, and efficiency.

Introduction

Securing stable domestic resources is part of the multiple objectives of tax policy.

Strengthening resilience to fiscal risks arising from government revenue volatility is critical for ensuring a sustainable delivery of public services throughout different phases of the business cycle. A large body of the literature shows that government revenue volatility weighs on economic growth and welfare, including through its adverse effects on the stability of public spending [START_REF] Bleaney | Tax Revenue Instability, with Particular Reference to Sub-Saharan Africa[END_REF][START_REF] Furceri | Is Government Expenditure Volatility Harmful for Growth? A Cross-Country Analysis[END_REF][START_REF] Loayza | Macroeconomic Volatility and Welfare in Developing Countries: An Introduction[END_REF]. Delinking public spending from revenue volatility, through the implementation of rules-based fiscal frameworks, is referred to as a credible option for indirectly strengthening resilience to government revenue volatility (IMF, 2009;[START_REF] Budina | Fisca l Rules in Response to the Crisis: Toward the 'Next-Generation'Rules: A New Dataset[END_REF]. Although not analytically grounded, a long-held intuitive view suggests that tax revenue diversification, that is the reliance on more diversified sources for levying revenue, can serve as an alternative for tackling more directly the root causes of government revenue volatility. The basic tenet is that given the responsiveness to the business cycle fluctuations varies across taxes, relying on a more diversified portfolio of tax streams makes the government's overall tax revenue less subject to as large volatility as compared to relying on a concentrated portfolio of taxes sources.

But is this long-held intuitive view borne out by the data? A few existing studies find evidence supportive of the view that greater tax diversification is conducive to lower revenue shortfalls during recessions (Suyderhound, 1994;[START_REF] Carroll | Are State Governments Prepared for Fiscal Crises? A Look at Revenue Diversification during the 1990s[END_REF] and lower tax revenue volatility (Schunk and Porka, 2005). But other studies found limited evidence supportive of this view in the US during the recent Great Recession [START_REF] Kilby | Does Tax Revenue Diversification Help States Weather Economic Downturns? Evidence from the Great Recession[END_REF]. That said, all these few existing studies relied on Herfindahl-Hirschman (HHI)-based revenue diversification indices computed at the state level for the US. Other studies captured tax diversification indirectly, including through the share of tax revenue coming from the extractive sector (see e.g., IMF, 2016).

This chapter refreshes the literature by proposing a new cross-country tax revenue diversification index (RDI). To the best of our knowledge, this is the first study to construct a homogenous cross-country dataset directly capturing the diversification of tax sources structure. Our proposed RDI is computed at the national level, covering a broad panel of 127 countries over 2000-2015, based on data availability. We focus on tax revenue, leaving non-tax revenues aside, as non-tax revenues are not primarily designed for revenue-enhancing purposes, but rather to get consumers' incentives right. The construction of the RDI rests on six major categories of taxes, as reported in the GFSM 2014, namely corporate income tax (CIT), personal income tax (PIT), property tax, tax on goods and services, tax on international trade, and other taxes. Another novelty of the paper is that our RDI builds on the Theil index (as opposed to the HHI), which offers more interesting properties, notably in terms of stability and robustness to outliers. Finally, this paper sheds light not only on the stability-enhancing role of tax revenue diversification but also on the RDI drivers.

Key stylized facts stand out on the RDI dynamics. On average, AEs relied on more a diversified structure of tax sources than EMEs and LIDCs, by as high as the double in terms of RDI over the period 2000-2015. Resources-rich countries and fragile states exhibit the most concentrated structure of tax sources, reflecting their over-dependence on commodity revenues and weak tax administration capacity, respectively. Regional disparities in the RDI are also noticeable, with North American and EU countries exhibiting the most diversified taxation sources, while GCC, South Asian, Latin American, and Sub-Saharan African countries present the least diversified revenue streams.

We also uncover the following results from our econometric analyses. First, the RDI exhibits high persistency over time, with up to 60-74 percent of the current level of RDI predicted by its lagged value. Second, our empirical investigations suggest that tax revenue diversification is not just a reflection of economic diversification, but also the outcome of macroeconomic, political and institutional factors. A non-monotone relationship is also at play between the RDI and economic development, with countries' portfolio of tax sources getting more diversified as their institutions and tax administration capacity keep improving, until a tipping point, where richer countries start finding it more difficult to diversify further their sources of tax revenue. Third, and not the least, our findings lend support to the long-held view that tax revenue diversification matters a great deal for mitigating government revenue volatility. And it does not stop there: tax revenue diversification also improves tax revenue collection.

The remainder of the chapter is structured as follows. Section 2 introduces the data, while section 3 lays out the detailed steps of the construction of the RDI, and highlights key patterns standing out from the RDI, along with a few pair-wise correlations. Section 4 explores the drivers of the RDI, while section 5 assesses its effects on both volatility and level of government revenue. Section 6 presents some concluding remarks.

Data

The GFS database

Our sample covers 127 countries from all regions and across all income groups, based on data We rely on the IMF's Government Financial Statistics (GFS) dataset to extract tax revenue data.

The GFS dataset provides detailed public finance data in line with international standards (GFSM 2014), thus allowing for comparability across countries and over time [START_REF] Aldasoro | Vertical Fiscal Imbalances and the Accumulation of Government Debt[END_REF]. The GFSM 2014 represents the latest internationally accepted methodology for compiling government finance statistics in a systematic manner, with well-established definitions and classifications.

The GFS presents additional appealing features. First, data from the GFS are actual, not estimates or projections as in the IMF's WEO. Second, unlike alternative databases (WEO, ICTD), the GFS provides the most detailed classification of government's tax revenues for a large coverage across countries and over time. Third, the GFS is compiled by the IMF's Statistics Chapter 3: Fiscal Resilience Building: Insights from a New Tax Revenue Diversification Index 65 Department, which ensures consistency across countries, the quality and the accuracy of data under a common methodology for all countries. 47

Tax revenue components

The GFS provides the most comprehensive and detailed cross-country data in a uniform format. Table 3.1 below provides an overview of tax revenue classification along the GFSM 2014 format. 48 Given data limitations, notably for LIDCs, we restrict data disaggregation to a level that ensures a reasonably large but homogenous sample. We focus on tier-3 of tax revenue disaggregation, which encompasses taxes on income, profits and capital gains, payroll and workforce, on property, goods and services, international trade and transactions, and other taxes. We exclude social contributions and grants, as they do not meet the definition of a tax. 49 Taxes are expressed in percent of GDP and are regrouped in two blocks: (i) direct taxes, which include taxes on income, profits, property, and on capital gains for both individuals and corporations, and (ii) indirect taxes, consisting of taxes on goods and services, taxes on international trade and transactions, and other taxes. 50 47 Despite these differences across databanks, their associated data are highly correlated: the correlation coefficient of total tax revenue between the GFS and the WEO is 0.92, and 0.93 between the GFS and the ICTD. 48 GFSM 2014, pp. 88. 49 Social contributions are actual or imputed revenue receivable by social insurance schemes to make provision for social insurance benefits payable, while grants are transfers receivable by government units, from other resident or nonresident government units or international organizations (GFSM, 2014). and services) stand as the largest tax component, accounting for about 60 percent of total taxes, against 40 percent for direct taxes. This pattern reflects the growing reliance on tax on goods and services over the past two decades (160 countries are currently using some forms of VAT), most likely owing to its relative ease of administration and its economic neutrality. 51 3.2 provides more detailed trends on tax revenue and its associated components. Not surprisingly, on average, tax revenue is higher in AEs (25 percent of GDP), more than twice the level in developing countries. Non-resource-rich countries and non-fragile states mobilize larger tax revenue (20.2 and 19.7 percent of GDP, respectively) compared to their resource-rich and fragile peers (11.6 and 14.8 percent of GDP, respectively). 52 Surprisingly, small states mobilize greater tax revenue than their non-small peers (20.4 and 18.8 percent, respectively). This could be explained by a "size effect", in that smaller states tend to be easier to administer, from a tax collection and administration standpoint. 51 The VAT was first introduced in France in 1954. 52 The low level of tax revenue in non-OECD high-income countries owes much to the fact these are mostly oilexporting countries.

Composition of tax revenues

Construction of the RDI

Methodological Approach

Our RDI is based on the Theil index approach. The Theil's entropy index [START_REF] Theil | With Applications in the Social and Administrative Sciences Number v. 14[END_REF] is preferred to the HHI [START_REF] Hirschman | The Paternity of an Index[END_REF], as it features more appealing properties, notably in terms of stability and robustness to outliers. The Theil index has been proven to be more stable regardless of the level of disaggregation, given it incorporates the within and between components, and is more adapted to grouped data (World Bank, 2014). For instance, in exports diversification analysis, the Theil index can be computed along export lines and split up additively into between-groups and within-groups components [START_REF] Cadot | Export Diversification: What's behind the Hump?[END_REF]. In addition, for income distribution analysis, the Theil index allows decomposing inequality into the part that is due to inequality within areas (e.g., urban, rural) and the part that is due to differences between areas (e.g., the rural-urban income gap). The main drawback of the HHI relates to its instability and sensitivity to the level of disaggregation, as it assigns greater weight to the larger categories. Furthermore, the HHI underestimates the values of small categories, as it uses the square terms, which can be quite problematic for analyzing tax revenue patterns, as any percentage point of additional revenue can make a significant difference in thousands of people's lives. 53 These appealing properties of the Theil index go a long way to explaining its growing popularity in recent studies, including on exports diversification (e.g., [START_REF] Cadot | Export Diversification: What's behind the Hump?[END_REF][START_REF] Papageorgiou | Economic Diversification in LICs: Stylized Facts and Macroeconomic Implications[END_REF].

We use the Theil index formula to calculate the RDI, as follows:

𝑇 = 1 𝑛 ∑ 𝑇𝑎𝑥 𝑖 𝜇 𝑛 𝑖=1 × 𝑙𝑜𝑔 ( 𝑇𝑎𝑥 𝑖 𝜇 ) (3.1)
𝑇 refers to the Theil index; 𝑇𝑎𝑥 𝑖 to a specific direct or indirect tax subcomponent (corporate income tax, personal income tax, or taxes on goods and services), and

𝜇 = 1 𝑛 ∑ 𝑡𝑎𝑥 𝑖 𝑛 𝑖=1
is the average of the tax subcomponent into consideration. 𝑇 is a measure of concentration, with a higher value of 𝑇 referring to a more concentrated structure of tax sources, or a lower diversification of tax revenue. Given the construction of the RDI rests on six categories of taxes, the resulting Theil index will vary between 0 (perfect diversification) and 1.8 (reliance on one type of tax only). 54

Results

Stylized Facts

We highlight key patterns standing out of the RDI. As discussed above, the higher the RDI, the stronger the concentration structure of tax sources. The full sample average RDI stands at 0.51 (Figure 3.2). Japan records the lowest RDI (0.05), while the Kingdom of Bahrain records the 53 For robustness purposes, we compute an HHI-based RDI (see Appendix 3.6), which turns out highly correlated with the Theil-based (correlation coefficient of 0.98). For a comprehensive review of possible approaches for computing concentration indexes, see [START_REF] Roberts | When Bigger Is Better: A Critique of the Herfindahl-Hirschman Index's Use to Evaluate Mergers in Network Industries[END_REF]. 54 The maximum value of the Theil index is ln (n), with n referring to the number of considered tax categories.

highest RDI (1.39), thus standing as the country with the most and least diversified structure of tax sources, respectively. Source: Authors' calculations.

Geographical Distribution of RDI

Significant differences emerge across regions (Figure 3.2.b). NA and EU exhibit the lowest RDI (below the full sample average), while the GCC, LAC and SSA record the highest RDI. This points to lower tax revenue diversification in these latter compared to the former groups.

The RDI also varies by income levels. Figure 3.2.a shows that OECD countries have the most diversified structure of tax sources, followed by middle-income countries. High-income non-OECD and low-income countries record the highest RDI, meaning that they have the most 55 The full RDI-based country ranking can be found in Appendix 3.4. Fragile countries, small states, and resource-rich countries feature more concentrated structure of tax sources. This may stem from the fact that fragile countries face structural impediments, including conflicts, which makes it harder to effectively administer diverse tax revenue streams (Figure 3.2.c). Small States tend to specialize in a few economic activities, thus limiting their ability to diversify their sources of tax revenue (Figure 3.2.c). Small and fragile countries mostly rely on taxes on international trade as a major source of government revenue (see Table 3.2).

Rank

Finally, resource-rich countries have RDI standing above the full sample average, and higher than their non-natural resource rich peers (Figure 3.2.d). This implies that resources-dependent countries have more concentrated portfolios of tax revenue streams, owing, among other factors, to weak incentives to diversify away from the resource bonanza. Tax revenue in these countries mostly comes from corporate income taxes from the resource exploitation.

Putting the RDI in Perspective with Macroeconomic Developments

We provide preliminary correlations between the RDI and key macroeconomic variables, such as total tax revenue and its volatility, spending volatility, growth volatility, income inequality and exports concentration (Figure 3.5). 56 The following patterns stand out:

• Concentrated structure of tax sources is associated with both lower tax revenue (Figure 3.5.a) and greater volatility -tax revenue, growth, and spending (Figure 3.5.b,3.5.c,and 3.5.d,respectively). This seems in line with the intuitive view that a more diversified portfolio of tax revenue streams helps strengthen fiscal resilience to government revenue volatility.

• The RDI is correlated with export diversification (Figure 3.5.f), which also proxies for the level of economic diversification. This may stem from the fact that various taxes from 56 Volatility is captured through the standard deviation of each variable. export-related activities, including from the mining sector, accounts for a big chunk of government revenue in many countries, particularly in LIDCs (Table 3.2).

• Concentrated tax revenue is correlated with income inequality (Figure 3.5.e). A possible explanation is that the more concentrated the tax sources structure, the more likely its incidence gets unequally distributed within the population. This may also suggest that in countries with weak institutions, corrupt leaders may impose highly unequal redistribution of wealth, which in turn translates into more concentrated tax sources structure.

• Tax revenue concentration is negatively associated with tax collection efficiency (Figure 3.5.h) and taxpayer's compliance (Figure 6.g), suggesting that the diversification of tax sources and the capacity to administer tax compliance might be mutually-reinforcing.

Drivers of Tax Revenue Diversification: An Econometric Analysis

We turn now onto assessing the key drivers of cross-country variations in the RDI. We carry out panel regressions linking the RDI to potential explanatory variables, using the full sample over the period 2000-15. The following econometric specification is considered.

𝑅𝐷𝐼 𝑖𝑡 = 𝛼 + 𝛽𝑅𝐷𝐼 𝑖 ,𝑡-1 + 𝛾𝑋 𝑖,𝑡-1 + 𝜆𝐾 𝑖,𝑡-1 + ∑ 𝜑 𝑘 𝑍 𝑘,𝑖 ,𝑡-1 + 𝜂 𝑖 + 𝜀 𝑖𝑡 𝐾 𝑘 =1 (3.2)
Revenue diversification index (RDI) is the dependent variable, and three sets of potential covariates are considered drawing on the existing related literature (e.g., [START_REF] Murphy | Why Is Rent-Seeking so Costly to Growth?[END_REF][START_REF] Acemoglu | Was Prometheus Unbound by Chance? Risk, Diversification, and Growth[END_REF][START_REF] Agosin | Determinants of Export Diversification Around the World: 1962-2000[END_REF]Ahmadov, 2012;[START_REF] Elhiraika | Assessing the Determinants of Export Diversification in Africa[END_REF] factors featuring countries' political and institutional context along with their development status (𝑍 𝑘,𝑖𝑡 ). 57 We run dynamic panel regressions using system-GMM estimators, to better address likely endogeneity problems while accounting for the persistency in the RDI over time.

All covariates are introduced with one-year lag, to account for likely delays in the influence of these variables on the RDI, and to mitigate likely reverse causality bias.

Table 3.4 reports the estimates of the RDI drivers, focusing first on the role of the structure of the economy and the macroeconomic environment. 58 Before going any further, it is worth signaling that the RDI exhibits high persistency over time, as captured by the strongly significant coefficient associated with the lagged RDI variable. Up to 60-74 percent of the current level of tax diversification is predicted by its lagged value, suggesting a strong inertia in the RDI dynamics.

57 Detailed definitions and sources of all variables can be found in Appendix 3.2.

Countries' level of development (proxied by per capita real GDP) has a significant non -linear impact on their ability to diversify their tax revenue sources. There is a significant inverted Ushaped relationship between per capita real GDP and the RDI. The coefficient associated with per capita real GDP is negative, while the coefficient associated with its squared term is positive.

This suggests that countries' level of tax revenue diversification tends to increase as their economy develops. Insofar as they strengthen their institutional framework and improve tax administration capacity, until they reach a tipping point beyond which further diversification of tax revenue becomes harder. 59 This somehow reflects the specialization on a few high skillsbased economic activities that characterize some AE's growth model ( e.g., shifting to an innovation-based growth model).

The structure of the economy matters for shaping a country's tax revenue diversification. First, a less diversified economy, proxied by the export concentration index, is conducive to a more concentrated structure of tax revenue. Columns 2-9 show that higher export concentration goes hand-in-hand with higher tax revenue concentration, as reflected by the positive and significant coefficient associated with the export concentration index. Second, there is also suggestive evidence of some form of "natural resources curse" being at play, as captured by the positive and statistically significant coefficient associated with natural resource rents (column 3). This suggests that countries with larger natural resource endowments face less incentives to diversify their structure of taxation sources. Indeed, most resource-rich countries tend to over-rely on the resource bonanza -the GCC countries for example introduced the VAT for the first time in 2018, amid the recent oil price shocks. Third, the coefficient associated with per capita official development assistance is positive and statisticall y significant (column 4).

This suggests a stronger dependence to donor support weakens policymakers' incentives to diversify taxation sources, bringing to the data long-held views about moral hazard in domestic revenue mobilization in contexts of dependence to public aid, notably unconditional grants [START_REF] Thornton | Does Foreign Aid Reduce Tax Revenue? Further Evidence[END_REF]. Fourth, a larger informal sector makes it harder to identify taxpayers and assess their compliance, thus rendering more arduous any steps to bring taxpayers into the tax net (column 5).

Table 3.4. Macroeconomic and Structural Drivers of RDI, 2000-2015

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Note: Our measure of revenue diversification (RDI) is considered endogenous, along with the GDP per capita, its squared term, and the financial development variables. These endogenous variables are instrumented using their own respective lags. We follow [START_REF] Roodman | How to Do Xtabond2: An Introduction to Difference and System GMM in Stata[END_REF] and collapse the number of instruments to avoid the overidentification problem. In all specifications, we reject the null of the AR (1) test of no autocorrelation in the error terms. Thus, lagged variables can be safely used as instruments. Hansen's p-value robust to heteroskedasticity and autocorrelation validates the over -identification restrictions. The remaining variables are considered exogenous.

Macroeconomic conditions play a role in countries' tax revenue diversification patterns. 60 First, larger trade openness is positively correlated with greater tax revenue diversification. The coefficients associated with trade openness (columns 2-9) are negative, though statistically insignificant in most cases. Second, greater macroeconomic instability (proxied by inflation) is also found to be associated with lower tax diversification (column 6). This may point to the macroeconomic uncertainties brought about by greater instability, which ultimately results in 60 The statistical significance of the coefficients associated with the degree of globalization and human capital (columns 7-8) is weak.

( the instability of the tax revenue, and likely its shrinkage. Third, the coefficient associated with financial development is significantly negative (columns 2 to 9). This suggests that deeper financial systems may allow for greater formalization of the economy, which in turn makes it easier to broaden the portfolio of tax revenue streams [START_REF] Medina | The Informal Economy in Sub-Saharan Africa: Size and Determinants[END_REF]. Fourth, having an IMF-supported program may also help diversify the structure of tax sources. This may reflect countries' efforts to improve revenue collection performance under IMF-supported programs (column 9).

There are significant heterogeneities across income levels and regions (Table 3.5). Compared with AEs, LIDCs and EMEs have more room to diversify further their portfolio of tax revenue streams (column 2), insofar as they strengthen their institutional framework and improve their tax administration capacity [START_REF] Gaspar | Political Institutions, State Building, and Tax Capacity: Crossing the Tipping Point[END_REF]and Akanbi and Akitoby, 2018). Column 1 confirms the regional disparities in RDI, with South Asia, Latin America, and the Middle East and North Africa displaying the least diversified structure of tax sources. Resource -rich countries also exhibit less diversified structure of tax revenue sources compared to other countries (column 3). Political and institutional factors are also at play (Table 3.6). First, deeper democracy seems to foster tax revenue diversification. The coefficient associated with the degree of democracy (the Polity 2 index), is negative and significant (column 1). This finding ma y reflect that stronger democracy, including through greater checks and balances, strengthens the "sincerity" of the social contract between the government and taxpayers, thereby increasing the latter's willingness to pay taxes in exchange for improved quality of public services. Second, polarized political systems (captured either through the government fractionalization or political polarization index, columns 2-3) and stronger political stability (column 4) are conducive to greater diversification of tax revenue. Indeed, a polarized political system may lead to a more diversified portfolio of revenue streams, in that politicians in these contexts have less room to manipulate the tax system disproportionately in favor of given constituencies, thus ending up sharing the tax burden more equally across all segments of the population and of economic activities, consistently with the common pool problem [START_REF] Alesina | Fiscal Expansions and Adjustments in OECD Countries[END_REF]. Stronger political stability makes it easier for the government to focus on implementing its declared policies, including strengthening resilience to revenue volatility through diversifying the taxation sources, instead of embarking on rent-seeking activities. Third, more socialist-oriented governments are more prone to diversifying the taxation sources across all segments of the population and economic activities, as reflected by the negative coefficient associated with the largest government party's orientation (column 5). 61 Fourth, institutional quality, as captured by the quality of bureaucracy (column 6), the rule of law (column 7), government effectiveness (column 8), and government accountability (column 9), strengthens policymakers' ability to diversify tax revenue streams. The coefficients associated with these variables are negative and significant, suggesting that countries with strong institutions have greater capacity to administer compliance on diverse tax instruments. Similarly, stronger control of corruption helps diversify taxation sources (columns 10), as less corruption allows for better tax administration and reduced leakages in tax revenue, hence for greater tax compliance.

Impacts of Tax Revenue Diversification on Tax Revenue Volatility and Collection

This section investigates the potential benefits associated with the diversification of tax revenue sources. We rely on the econometric specification below to assess the influence of the RDI on both tax revenue collection and its volatility.

Y it = α + βY i,t-1 + γRD I it-1 + ∑ φ k Z k,it-1 + η i + π t + ε it K k=1 (3.3)
61 Largest Government Party orientation with respect to economic policy is coded as follows: (i) Right, if the part y is defined as conservative, Christian democratic, or right-wing, and assigned a value of 1; (ii) Center, if the party is defined as centrist or when the party position can best be described as centrist, and assigned a value of 2; (iii) Left if the party is defined as communist, socialist, social democratic, or left-wing, and assigned a value of 3; (iv) the variable equals zero if no information is available (Database on Political Institutions, 2015).

RDI it , which stands for tax revenue diversification, is the explanatory variable of interest. We focus on two outcome variables (Y it ). On the one hand, we investigate the impact of tax revenue diversification on revenue collection performance, as captured by the tax-to-GDP ratio. On the other hand, we assess the effect of revenue diversification on the volatility of tax revenue. 62Subscripts i and t denote the country and time dimensions, respectively. We follow the existing literature and include a set of variables Z k,it in both specifications to isolate the effects of factors that influence revenue collection performances (per capita GDP, trade openness, informality, share of agricultural Value added, natural resource rents, social conflicts and political unrests, the exchange rate, public debt and FDI) and the volatility of tax revenue (GDP per capita, growth volatility, trade openness, natural resource rents, financial development, economic diversification, political stability and polarization, institutional quality, and the presence of fiscal rules), other than the RDI. η i captures the country-specific and time-invariant effects, and 𝜀 𝑖𝑡 is the error term. Time dummies are also included in our specifications to control for common shocks affecting our left-hand-side variables. Equations (3.3) is estimated using the GMM estimators.

We uncover suggestive evidence that greater tax revenue diversifi cation improves non-oil revenue collection (Table 3.7).63 A higher RDI score, which reflects a high level of tax revenue concentration, is associated with lower tax revenue. In other terms, diversifying the portfolio of tax revenue streams improves revenue collection. In terms of magnitude, the results suggest that a 10 percent increase in the RDI score can yield additional tax revenue of up to 0.2-0.4 percentage points of GDP.

Table 3.7. Effects of RDI on tax revenue mobilization

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 pe rcent level, respectively. Standard errors are reported in brackets. Note: The RDI variable, GDP per capita and its squared term are considered endogenous, and instrumented using their own respective lags. The number of internal instruments is restricted to avoid the overfitting problem. All specifications reject the null of the AR (1). Hansen's p-value validates the over-identification restrictions across all specifications.

Tax revenue diversification is also found to be associated with lower tax revenue volatility (Table 3.8). 64 This is reflected in the positive and statistically significant coefficient associated with the RDI (tax revenue concentration), which lends support to the long-held informal view that greater reliance on a diversified portfolio of tax revenue streams mitigates the volatility of tax revenue significantly. Put simply, there is suggestive evidence that countries with the more diversified structure of tax sources are more likely to exhibit stronger resilience to revenue volatility arising from the business cycle fluctuations. In terms of magnitude, the 64 Standard diagnostic tests for the validity of instruments are passed in most cases (except in columns 7 and 9,

where the P-value associated with Hansen's overidentification test did not pass the conventional 5 percent threshold). computed at the states level for the US. In addition, our RDI builds on the Theil index, which features more appealing properties, notably in terms of stability and robustness to outliers.

Key patterns stand out of the RDI. On average, AEs relied on a more diversified structure of tax sources than EMEs and LIDCs, by as high as at least the double in terms of RDI score. Resourcesrich countries and fragile states exhibit the largest tax revenue concentration, reflecting their overdependence on commodity revenues and official development assistance, and their weak tax administration capacity, respectively. From a regional perspective, North American and EU countries record the most diversified structure of tax sources, while GCC, South Asian, Latin American, and Sub-Saharan African have the least diversified portfolio of tax revenue streams.

Empirical investigations suggest that beyond economic diversification, tax revenue diversification is shaped by macroeconomic, political and institutional conditions. On the macroeconomic front, countries' taxation sources get more diversified as their economy develops, insofar as they strengthen their institutions and improve their tax administration capacity, until a tipping point, with richer countries then finding it harder to further diversify their structure of tax sources. Additionally, countries with more concentrated and informal economic structures, stronger dependency to aid, and plagued with macroeconomic instability, are more prone to relying on a concentrated portfolio of tax revenue streams. Political and institutional factors are also at play: deeper democracy makes it easier to diversify the portfolio of tax revenue streams, while greater stability and polarization of the politi cal system are more conducive to greater tax revenue diversification.

Last but not the least, we find evidence supportive of the long-held view that tax revenue diversification matters a great deal for mitigating government revenue volatility. And it does not stop there: tax revenue diversification also improves tax revenue collection. Tax revenue diversification thus stands as a key factor for strengthening resilience to fiscal risks arising from government revenue volatility, critical for ensuring a sustainable delivery of public services throughout different phases of the business cycle. The current coronavirus pandemic adds further credence to this criticality of relying on a diversified portfolio of tax revenue streams for strengthening fiscal policy resilience to large swings to business cycle fluctuations.

Introduction

Over recent years, increasing attention has been paid to financial inclusion among both, policymakers and academics considered as a key factor to achieve the 2030 Sustainable Development Goals (SDGs). Financial inclusion has become a priority of policy agenda in a number of economies across the globe, with more than 50 countries having adopted a National Financial Inclusion Strategy [START_REF] Espinosa-Vega | Measuring Financial Access; 10 Years of the IMF Financial Access Survey[END_REF].

Financial inclusion refers to access to and use of formal financial services by individuals and businesses (Sahay et al., 2015a). 65 A broader definition considers, in addition to the access and use of financial services, further aspects including the quality and range, usefulness and appropriateness, affordability, sustainability, and awareness of financial services and products as well as the consumer protection [START_REF] Grace | Paraguay-National Financial Inclusion Strategy 2014-2018[END_REF][START_REF] Superintendencia De Banca | The Use of Financial Inclusion Data Country Case Study of Peru: Fine-Tuning Regulation Based on Access Indicators[END_REF]BSP 2015;World Bank 2018;[START_REF] Jahan | The Financial Inclusion Landscape in the Asia-Pacific Region: A Dozen Key Findings[END_REF][START_REF] Espinosa-Vega | Measuring Financial Access; 10 Years of the IMF Financial Access Survey[END_REF], making financial inclusion a multifaced concept. The measurement of financial inclusion also remains an important issue in the empirical literature given to its multidimensional aspect. While some components are particularly more easily capturable, other dimensions are not readily observable. 66 For instance, the emergence of informal financial institutions deemed as " shadow banking" in most developing countries is often unrecorded yet provide non-negligible financial assistance to individuals and small businesses.

The consensus in the literature is that access-for-all to financial services is conducive to broader economic and development outcomes, though access to finance still constitutes a major constraint for business creation and expansion in developing countries (World Bank, 2014;Demirgüç-Kunt et al., 2015). Financial inclusion is crucial for inclusive and sustainable economic growth and non-access to formal financial services like bank account entails greater cash transactions is associated with higher transaction fees and financial risks [START_REF] Lusardi | Financial Literacy among the Young[END_REF].

Greater financial inclusion positively affects the GDP, while reducing inequality in developing 65 Financial inclusion is different from financial development. The latter occurs when financial instruments, markets, and intermediaries ameliorate -the effects of information, enforcement, and transactions costs and therefore do a correspondingly better job at providing the five financial functions (Levine, 2005) -though the former is a dimension of the latter (see e.g., Sahay et al., 2015). In addition, more financial development may allow greater access to financial services as emphasized in [START_REF] Rajan | The Great Reversals: The Politics of Financial Development in the Twentieth Century[END_REF].

and emerging (Dabla-Norris et al.,2015). Similarly, focusing on Latin America and the Caribbean (LAC) countries, Dabla-Norris et al. (2015) conclude that easing financial services access and relaxing collateral constraints helps reduce inequality and spur growth. Using a wide sample of 123 countries, Allen et al. (2015) highlight that financial inclusion is associated with stronger legal rights and more politically stable environments.

Micro-level and field experiment studies evidenced that microfinance branch expansion and access to credit facilities is conducive to business expansion and entrepreneurial activities development [START_REF] Bruhn | The Real Impact of Improved Access to Finance: Evidence from Mexico[END_REF][START_REF] Angelucci | Microcredit Impacts: Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco[END_REF][START_REF] Fareed | Financial Inclusion and Women Entrepreneurship: Evidence from Mexico[END_REF]. Similarly, Banerjee et al. (2010) show that access to microcredit increases small business investment and profits of preexisting businesses in India. Focusing on Bosnia, [START_REF] Augsburg | The Impacts of Microcredit: Evidence from Bosnia and Herzegovina[END_REF] also highlight that lower rejection of households' formal loans applications induces higher selfemployment, increases in inventory, and an increase in the labor supply in the household's business. Similarly, in rural Kenya, Dupas and Robinson (2011) provide strong evidence that extending basic banking services (e.g., saving accounts) is associated with an increase in the size of market women businesses. In line with the disciplining effect of group lending, Attanasio et al., (2011) find a positive impact of access to group loans on entrepreneurship in Mongolia.

Access to group loans increases the likelihood of owning an enterprise by 10 percent.

Financial inclusion is found to be an important tool for empowering women and strengthening female decision-making power in developing countries. Relying on a randomized controlled trial, [START_REF] Ashraf | Female Empowerment: Impact of a Commitment Savings Product in the Philippines[END_REF] find that access to basic financial service such as saving accounts is associated with an increase in female decision-making power within the household in the Philippines.

The literature also asserted that financial inclusion positively affects households consumption as well as households income (Banerjee et al. 2010;[START_REF] Karlan | Expanding Credit Access: Using Randomized Supply Decisions to Estimate the Impacts[END_REF]Attanasio et al., 2011;Dupas and Robinson, 2011;[START_REF] Bruhn | The Real Impact of Improved Access to Finance: Evidence from Mexico[END_REF]Zhang and Posso, 2017). Access to financial services allows households to respond to income shocks by raising their current consumption [START_REF] Bhattacharya | Financial Inclusion, Productivity Shocks, and Consumption Volatility in Emerging Economies[END_REF], facilitates households consumption smoothing, and softens output volatility cost [START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF]. In addition, greater access to financial services encourages households savings. Aportela (1999), based on a natural experiment shows that financial inclusion increased the average saving rate of households by Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 100 around 5 percentage points and this effect was even higher for the poorest households in Mexico. Furthermore, access to financial services favors human capital development and increases jobs opportunities (Demirgüç-Kunt et al., 2008). For instance, De [START_REF] Gregorio | Borrowing Constraints, Human Capital Accumulation, and Growth[END_REF] on an overlapping generations model with endogenous growth , highlights that access to financial services is associated with an increase in human capital accumulation and growth in OECD and developing countries.

Subsequent studies explored the impact of access to financial services on the level of poverty (Burgess et al., 2005;Burgess and Pande, 2005;Honohan, 2004a,b;Honohan, 2005Honohan, , 2006;;[START_REF] Karlan | Expanding Credit Access: Using Randomized Supply Decisions to Estimate the Impacts[END_REF][START_REF] Kiendrebeogo | Accès Aux Services Financiers et Réduction de La Pauvreté Dans Les PED[END_REF][START_REF] Bruhn | The Real Impact of Improved Access to Finance: Evidence from Mexico[END_REF][START_REF] Banerjee | The Miracle of Microfinance? Evidence from a Randomized Evaluation[END_REF][START_REF] Rewilak | The Role of Financial Development in Poverty Reduction[END_REF] and find that access to basic financial services is associated with a decrease in the poverty rates. For instance, Coulibaly and Yogo (2018) recently evidenced that improving financial outreach through additional bank branches reduces the number of poor workers in developing countries.

Nevertheless, parallel to this literature, some studies have documented that greater access to financial services without proper financial supervision might be harmful to economic outcomes and result in financial distress [START_REF] Rajan | Fault Lines: How Hidden Fractures Still Threaten the World Economy[END_REF][START_REF] Han | Financial Inclusion for Financial Stability: Access to Bank Deposits and the Growth of Deposits in the Global Financial Crisis[END_REF]Sahay et al., 2015). For instance, high financial inclusion combined with a boom in access to credit is associated with a deterioration in credit quality and thereby leading to financial risks [START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF]. In addition, Dabla-Norris et al. (2015) analyzing factors constraining firms to access to finance find that greater access to credit could result in bank instability by increasing nonperforming loans.

Although [START_REF] Martin | Women in Finance: An Economic Case for Gender Equality[END_REF] postulate that inclusive financial systems can increase the effectiveness of fiscal policy by broadening the tax base, studies exploring the potential effect of accessing financial services on government tax revenue are very scarce (Oz-Yalaman, 2019).

Also, studies particularly focusing on developing countries and using non-resources tax revenue are non-existent. To the best of our knowledge, our paper is, therefore, the first to provide strong and convincing evidence on the nexus between financial inclusion and nonresources tax ratio in developing countries. Our contribution to the literature relies on the use of comparable 67 tax revenue ratios excluding natural resources revenue. As underscored in [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF], distinguishing resource from non-resource revenue is highly relevant to understand tax capacity in developing countries and the literature has well-documented a crowding-out effect between resources revenue and non-resource tax revenue [START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF]James, 2015 among others). In addition, unlike the empirical method employed in Oz-Yalaman ( 2019), we adopted a dynamic specification to account for the inertia in government tax revenue.

Using a sample of 62 developing countries over the period 2004-2017 and drawing on the dynamic generalized method of moments (GMM) to solve the endogeneity and any reverse causality issue, the chapter shows that greater access to financial services captured by the number of ATMs per 100,000 adults increases government non-resources tax-to-GDP ratio.

Looking at the tax revenue structure, the results show that indirect taxes revenue accounts the most sizeable positive effect of increased penetration of ATMs on tax revenue. Explo ring the channels through which financial inclusion influences non -resource tax ratio, our empirical results highlight that the positive effect of greater access to financial services mainly operates through private consumption and business expansion. Our results survived to a battery of robustness exercises including (1) adding more control variable namely the level of education, inflation, the population size, external aid received, domestic financial sector development, remittances inflows and the tax structure, (2) the use of alternative financial inclusion measures to capture the multifaceted aspect of our interest variable and (3) using alternative tax data source.

This chapter provides insights to countries that have implemented or are in the process of implementing financial inclusion policies, on tax revenue-harnessing opportunities from access-for-all financial services. In terms of policy recommendations, this study calls for improved and greater access to financial. Developing countries should promote and guarantee better access to financial services as a key policy objective to increase their tax ratio.

67 Oz-Yalaman (2019) explored the nexus between financial inclusion and tax revenue but using different sources of total tax revenue and tax subcomponents. This entails important shortcomings since the datasets are not strictly comparable (Sahay et al., 2015).

Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries

102

The remainder of the chapter is structured as follows: the second section describes the econometric methodology and introduces the dataset with some stylized facts. Section 3 presents the baseline results from the empirical specification and considers some robustness checks. Section 4 provides concluding remarks and draws policy implications.

Empirical Methodology and Data

Econometric Model

The following dynamic panel model that captures the impact of financial inclusion on nonresources tax revenue was estimated:

NRTax i,t = α+ βNRTax i,t-1 + ψFInc i,t + Z i,t δ+ λ i + ζ t + ε it (4.1)
𝑁𝑅𝑇𝑎𝑥 𝑖,𝑡 is the natural logarithm of non-resource tax-to-GDP ratio for country 𝑖 at time 𝑡. We included the one-period lagged value of the dependent variable -𝑁𝑅𝑇𝑎𝑥 𝑖,𝑡 -because of the inertia in the total tax revenue. 𝐹𝐼𝑛𝑐 𝑖,𝑡 is the financial inclusion measure and 𝒁 𝑖 ,𝑡 is a set of variables that explain the government tax revenue ratio. 𝜆 𝑖 and 𝜁 𝑡 denote time-invariant country-level characteristics and time-varying factors, respectively that could potentially affect the tax ratio. The last term, 𝜀 𝑖𝑡 is an idiosyncratic disturbance.

Taking stock of previous literature on the principal determinants of th e tax share in GDP, the vector 𝒁 𝑖 ,𝑡 includes inter alia the level of development, the sectoral composition of value-added (i.e., agriculture and natural resources), the trade openness and the quality of institutions. The real GDP per capita is commonly used to capture countries' overall level of development.

Countries' tax capacity is intrinsically related to their level of development [START_REF] Lotz | Measuring 'Tax Effort' in Developing Countries[END_REF]Tanzi 1983;[START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF]. High-income countries are expected to raise more tax revenue than developing countries due to the more efficient and strong tax administration, higher degree of economic and institutional sophistication. To capture the non-linearity effect of the level of development and tax capacity, we include the squared of this variable in the specification.

Trade openness refers to the volume of international trade in the GDP. Substantial increase in trade volume makes it more amenable to taxation through domestic consumption and corporate profits [START_REF] Chelliah | Tax Ratios and Tax Effort in Developing Countries, 1969-71[END_REF][START_REF] Leuthold | Tax Shares in Developing Economies A Panel Study[END_REF]Tanzi, 1992;[START_REF] Stotsky | Tax Effort in Sub-Saharan Africa[END_REF][START_REF] Agbeyegbe | Trade Liberalization, Exchange Rate Changes, and Tax Revenue in Sub-Saharan Africa[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Gnangnon | Impact of Bridging the Internet Gap on Public Revenue Mobilization[END_REF].

The effect of natural resources rents on tax revenue ratio is widely evidenced in the literature but remains controversial. While pioneering studies evidenced a positive effect o f natural resource rent on tax revenue (Cheliiah et al., 1975 andTanzi, 1992), recent resource curse literature highlights a negative association between natural resources rents and government tax revenue, suggesting a crowding-out effect [START_REF] Sachs | The Curse of Natural Resources[END_REF]Eltony, 2002;[START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF][START_REF] Mcguirk | The Illusory Leader: Natural Resources, Taxation and Accountability[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF][START_REF] Mehrotra | Financial Inclusion-Issues for Central Banks[END_REF]Belinga et al. 2017). Therefore, the effect of natural resources rents on tax revenue is a priori ambiguous.

The share of agriculture in the GDP is found to be negatively associated with non-resource tax ratio (Cheliiah et al., 1975;and Tanzi, 1992;[START_REF] Agbeyegbe | Trade Liberalization, Exchange Rate Changes, and Tax Revenue in Sub-Saharan Africa[END_REF]. Developing countries are still featured by large shares of the subsistence agricultural sector which is not generating taxable surpluses. In addition, this sector is more often subject to tax exemptions considered as sector providing food for subsistence [START_REF] Agbeyegbe | Trade Liberalization, Exchange Rate Changes, and Tax Revenue in Sub-Saharan Africa[END_REF].

We proxy the quality of institutions by the polity2 index assessing the degree of democracy.

High and strong economic and political institutions are expected to promote tax administrations allowing more tax revenue collection (Davoodi and [START_REF] Grigorian | Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia's Stubbornly Low Tax Collection[END_REF]; [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF][START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Feger | Tax Effort Performance in Sub-Sahara Africa and the Role of Colonialism[END_REF], while the lower quality of institutions portrayed by higher corruption is a threat for tax revenue collection as it affects tax administration and tax officers and occasioning tax evasion.

Equation (4.1) is a dynamic specification since the non-resource tax-to-GDP ratio at period 𝑡 depends on its past realizations. Thus, relying on classical linear Ordinary Least Squares (OLS)

estimator would lead to inconsistent and biased results (Nickel, 1981;Wooldridge, 2002). The appropriate estimator for dynamic panel data models appears to be the popular Generalized Method of Moments (GMM). The GMM estimator is designed for dynamic specifications with "small-T, large-N" 68 panels and allows to tackle potential issues of endogeneity, simultaneous and omitted variables bias. Hence, this estimator will allow, not only to correct the possible endogeneity of our interest variable -financial access-but also to correct for endogeneity of all right-hand side variables by using the lagged values as instruments (one to two lags). Our analysis specifically relied on the system-GMM proposed by [START_REF] Blundell | Initial Conditions and Moment Restrictions in Dynamic Panel Data Models[END_REF] with the two-step estimator. However, the validity of the GMM estimation relies on the main assumption that instruments are exogenous [START_REF] Roodman | How to Do Xtabond2: An Introduction to Difference and System GMM in Stata[END_REF]. Therefore, we resort to Hansen's test for over-identifying restrictions to check the validity of the instruments. Another condition that validates the GMM estimator is the absence of second-order serial correlation in the residuals in difference. Accordingly, the Arellano-Bond's test is used to check that condition.

Data and Some Stylized Facts

The study is conducted on a sample of 62 developing countries over the period 2004-2017, based on data availability. 69 The dataset consists in a yearly unbalanced panel because of Dataset 71 (henceforth ICTD dataset). This dataset combines several major international 68 Meaning few time periods and many individuals, which is the case with our sample. 69 The developing countries category considered in this paper refers to the World Bank income classification, hence including low-income and middle-income countries. The complete country list by region is provided in Appendix 4.1.

missing
70 See Appendix 4.2 for complete definition and sources of variables. 71 The database is available at https://www.ictd.ac/dataset/grd/.

Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 105 databases, as well as drawing on data compiled from all available International Monetary Fund (IMF) Article IV reports [START_REF] Prichard | ICTD Government Revenue Dataset ICTD Working Paper 19[END_REF]. ICTD dataset has the advantage to be available for a large number of developing countries which is the focus of this study. More importantly, unlike alternative databases, ICTD dataset has the particularity to exclude natural resource revenue tax revenue, 72 then providing a non-resource tax revenue data. Non-resource tax revenue as a percentage of GDP refers to total tax (i.e., revenue excluding grants and social contributions) from which resource tax revenue is subtracted. In other words, non-resource tax ratio is the share of tax revenue that is collected out of the extractive sector. [START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF] asserted that companies operating in the resource sector are very often major contributors to corporate income tax. Hence, for instance, corporate income tax as a subcomponent of the non-resource total tax ratio, will not include the tax on profits levied on those companies. Many studies in the literature show that natural resource rents have crowded out incomes from taxation [START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF]Ossowski and Gonzáles-Castillo, 2012;[START_REF] Mcguirk | The Illusory Leader: Natural Resources, Taxation and Accountability[END_REF][START_REF] Thomas | Resource Dependence and Fiscal Effort in Sub-Saharan Africa[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF]. Distinguishing revenue from non-resource revenue is of particular importance when it comes to analyze countries' tax effort [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF]. In addition, non-resource tax revenue has the key advantage to be less volatile due to its non-dependence on revenues from the extractive sector which instability is widely documented in the literature. Furthermore, non -resource tax revenue data also provide more accurate information and give a clear and consistent picture of tax re venue collected out of extractive sectors across countries [START_REF] Prichard | ICTD Government Revenue Dataset ICTD Working Paper 19[END_REF] Alternatively, a competing rich non-resource tax revenue dataset was developed by Mansour (2010Mansour ( ) covering 1980Mansour ( -2010 and recently updated to 2015 (see [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF]. However, this database only focuses on 42 Sub-Saharan African countries. We, therefore, relied on ICTD dataset for coverage purpose. Tax data used in this paper cover six tax series namely: (i) total tax revenue;

(ii) indirect taxes; (ii) direct taxes; (iv) income taxes; (v) taxes on goods and services, and (vi) value-added tax, all expressed as percentage of GDP.

Data on financial inclusion are extracted from the IMF's Financial Access Survey database 73 and include 8 measures of financial access : (i) the number of automated teller machines (ATMs) 72 See [START_REF] Prichard | ICTD Government Revenue Dataset ICTD Working Paper 19[END_REF] and [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF] for further discussion.

73 Data available at https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C. (ECA) and Latin America and Caribbean (LAC) countries have the higher level of financial access in the sample, with on average 44 and 40 ATMs for 100,000 adults, respectively. At the same time, those countries appear to be the top performers in terms of tax revenue collection with a non-resource tax-to-GDP ratio of 20.1 and 15.3 per cent on average, respectively. In contrast, it comes out that the level of access to financial services is the lowest in South Asia (SA) and Sub-Saharan Africa (SSA) countries with on average, 9 and 11 ATMs for 100,000 adults, respectively. SA is the lower performer region of the sample in terms of tax revenue raising, with an average tax-to-GDP ratio of 11.9. SSA countries have been making tremendous efforts to collect tax revenue (14.9 percent of GDP on average) over recent years as a result of important tax policy and administration reforms [START_REF] Mansour | Tax Coordination, Tax Competition, and Revenue Mobilization in the West African Economic and Monetary Union[END_REF][START_REF] Ebeke | The Power to Tax in Sub-Saharan Africa: LTUs, VATs, and SARAs[END_REF] but still have room for greater tax collection. For insta nce, [START_REF] Caldeira | Inter-Ministerial Tax Competition: The Case of Resource Rich Developing Countries[END_REF] stressed that SSA countries could raise up to 22.75 percent of GDP in non -resource taxes if they fully exploited their tax potential.

Results

Baseline results

Table 4.1 reports the system GMM-based estimates of the effect of financial inclusion on the non-resource tax-to-GDP ratio in developing countries as specified in equation (4.1). Column [START_REF]General Introduction and Overview[END_REF] shows the results for the total non-resources tax ratio, while the remaining columns (Columns [2]- [START_REF]Chapter[END_REF]) display the results for different tax revenue subcomponents as mentioned above. Resorting to the number of ATMs as a proxy for access to financial services, our empirical results evidence a positive relationship between financial inclusion and non -resources tax revenue. Indeed, the coefficient associated with financial inclusion is positive and strongly significant at 1 percent level for total non-resource tax (Column [START_REF]General Introduction and Overview[END_REF]). Thus, financial inclusion increases tax revenue mobilization. More precisely, a 1 percentage increase in the number of ATMs leads to an unconditional rise in the non-resource tax revenue ratio by 0.42 percent.

Besides, statistical tests do validate our econometric method and the significance (1 percent) in the coefficients associated with the lagged dependent variable underscore an inertia effect that legitimates the choice of dynamic panel specification. The p-values of the Hansen test and the Arellano-Bond tests for serial correlation (AR [START_REF]General Introduction and Overview[END_REF] and AR ( 2)) are reported at the bottom of the table and confirm all the validity of our econometric approach. 74 Regarding, the composition of tax revenue, the results also support a positive relationship between financial access and all subcomponents of total tax revenue (Column [2] - [START_REF]Chapter[END_REF]). More specifically, the coefficient associated with financial inclusion is more sizeable for indirect taxes (Column [2]) compared to the one for direct taxes (Column [3]). This might suggest that more access to financial services allows for greater total tax revenue mobilization through increased consumption and thereby more consumption taxes to collect. This is confirmed by the positive and statistically positive coefficient of financial inclusion on taxes on goods and services (Column [5]), as well as on value-added taxes (Column [START_REF]Chapter[END_REF]).

Turning to the control variables, the results indicate that the level of development measured by the real GDP per capita, and the quality of the institution appear to be relevant determinants of the non-resources tax ratio in developing countries and are positively and significantly associated with tax ratio. These findings are consistent with previous evidence [START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF][START_REF] Grigorian | Tax Potential vs. Tax Effort: A Cross-Country Analysis of Armenia's Stubbornly Low Tax Collection[END_REF][START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF][START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF][START_REF] Feger | Tax Effort Performance in Sub-Sahara Africa and the Role of Colonialism[END_REF]) that strong and good institutional quality is favorable to greater tax revenue collection, and countries' tax capacity is positively related to their level of development. The results also confirm our hypothesis of non-linearity between 74 See [START_REF] Roodman | How to Do Xtabond2: An Introduction to Difference and System GMM in Stata[END_REF] for a complete discussion on GMM method.

Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 110 the level of development and the capacity captured by the negative a nd significant coefficient associated with the squared real GDP per capita.

Table 4.1. Baseline Results

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors are reported in brackets. Note: The number of instruments is strongly limited-starting with the second lag of the dependent variable and the first lag of the control variables-to avoid the over-fitting problem. In all specifications, the null hypothesis for lack of first-order (AR ( 1)) serial correlation in the first-differenced error terms is rejected, while not rejected for the second-order (AR ( 2)). In addition, the robust (to heteroskedasticity and autocorrelation) Hansen's p -value validates the over-identification restrictions. All of these statistical tests validate the econometric method, and the lagged variables can be safely used as instruments. This applies to all regressions in the chapter.

In line with previous findings (Tanzi, 1992;[START_REF] Agbeyegbe | Trade Liberalization, Exchange Rate Changes, and Tax Revenue in Sub-Saharan Africa[END_REF][START_REF] Bornhorst | Natural Resource Endowments and the Domestic Revenue Effort[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Mcguirk | The Illusory Leader: Natural Resources, Taxation and Accountability[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF], natural resources rents, trade openness and agriculture value-added are also determining factors of non-resources tax subcomponents (columns [2], [3] an [4]), while they appear to be nonsignificant on the total tax ratio.

Transmission channel

This section explores the main channels through which financial inclusion influences nonresource tax ratio. As stressed above, financial inclusion leads to increased household consumption and business development. Relying on this literature, we expect financial inclusion to affect tax revenue through increased business development and private consumption. Easy access to financial services allows both households and firms (e.g., SMEs)

with credit facilities to finance productive investments and increase their consumption. That in turn generates, both, income 75 and consumption tax opportunities for government. Besides, financial inclusion would positively affect tax revenue through business development based on the rational that business expansion is associated with job creation 76 (i.e., decrease in the unemployment rate) and income generation for households, thereby income taxes to collect.

We proxy the households' consumption by the private consumption per capita, while the business development induced-effect of financial inclusion is captured through the unemployment rate. However, since these transmission channels might be direct or indirect, we proceeded in two steps: first, we estimate the effects of financial access on each channel, then we estimate the effects of each variable on the total non-resource tax ratio. 77 The first step estimation results are reported in Table 4.2. The results show that more access to financial services is associated with greater private consumption (column [START_REF]General Introduction and Overview[END_REF]) and a lower unemployment rate (column [2]), confirming previous findings. 75 Taxes on income, profits, & capital gains: corporations.

76 [START_REF] Teima | Scaling-up SME Access to Financial Services in the Developing World[END_REF] highlighted that SMEs contribute up to 45 percent of employment in developing countries. It is also worth mentioning that the employment effect from greater access to financial services could be dived into two sub-effects: (i) new job creation and (ii) an increase in labor demand from existing businesses. However, data unavailability on such a variables does not allow to distinguish these specific effects in the analysis that could be of policy-relevant recommendations.

77 An alternative popular approach in the literature to test whether the effect of financial inclusion on non-resource tax-to-GDP ratio would transmit through the considered channels, consists of running our baseline regression interacting the financial inclusion measure with each transmission channel (see e.g., [START_REF] Caballero | Do Surges in International Capital Inflows Influence the Likelihood of Banking Crises?[END_REF]Compaore et al., 2020 among other). If the coefficients associated with financial inclusion measure works out to be non-significant when the interactive terms and the transmission channels are included, we then concl ude that the effect financial inclusion on tax revenue can be assumed to operate through private consumption and job creation as a result of business development and expansion. Based on this approach, the results also confirm our hypothesis.

Table 4.2. Transmission Channels

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Robust standar d errors are reported in brackets. Note: fixed effects are included.

In Table 4.3, we report the results of the effect of the transmission channels on our tax variable.

In line with our main hypothesis, households private consumption is positively and significantly associated with total non-resources tax ratio, as well as different tax subcomponents (Panel [A],

columns [START_REF]General Introduction and Overview[END_REF]- [START_REF]Chapter[END_REF]). Greater private consumption means an increase in consumption tax revenue such as the VAT. It is worth noting that value-added tax has become one of the most important tools of revenue mobilization in the developing world about one-quarter of total tax revenue is raised through value-added tax. [START_REF] Keen | Is the VAT a Money Machine?[END_REF], 2016, Gerard and Naritomi, 2018). The value-added tax also presents the advantage to be less distortionary in addition to its self-enforcing properties [START_REF] Kopczuk | Putting Firms into Optimal Tax Theory[END_REF].

In Panel [B] of Table 3, we present the results using the unemployment rate. It emerges that the unemployment rate is negatively linked to total tax ratio (column [START_REF]General Introduction and Overview[END_REF]), as well as income taxes and value-added tax (columns [START_REF]General Introduction and Overview[END_REF] and [START_REF]Chapter[END_REF], respectively). Financial inclusion reduces

113

unemployment by increasing business and enhancing income-generating activities, hence providing opportunities to collect income taxes both on individuals and corporates.

Overall, our results confirm that financial inclusion is conducive to higher tax revenue collection through private consumption and business expansion.

Robustness Checks

In this section, we undertake three robustness exercises to check the validity of our findings.

First, we test the sensitivity of the results from the baseline specification to additional control variables, including the level of education, inflation, the population size, external aid received, domestic financial sector development, remittances inflows and the tax structure. 78

A higher level of education is expected to increase tax compliance. Similarly, a large population, a well-functioning financial sector and remittances flows received are positively associated with 78 These variables are considered in the literature as possible determinants of tax revenue (see e.g., [START_REF] Tanzi | Inflation, Lags in Collection, and the Real Value of Tax Revenue[END_REF], Gupta et al. 2014[START_REF] Gordon | Tax Structures in Developing Countries: Many Puzzles and a Possible Explanation[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF][START_REF] Clist | Aid and Tax Revenue: Signs of a Positive Effect since the 1980s[END_REF][START_REF] Ebeke | Remittances, Value Added Tax and Tax Revenue in Developing Countries[END_REF][START_REF] Benedek | Foreign Aid and Revenue: Still a Crowding Out Effect?[END_REF][START_REF] Feger | Tax Effort Performance in Sub-Sahara Africa and the Role of Colonialism[END_REF], and Compaore et al., 2020 among others). Results are reported in Table 4.4 and are similar to the baseline findings. The coefficients associated with the population size, financial sector development and tax diversification index are positive and significant (columns [3], [5] and [7], respectively). A large population constitutes a potential for tax collection. In addition, a well-developed financial sector and greater access to credit allow individuals and corporates to finance profitable projects, which in turn favor tax contribution. Having a diversified tax base is associated with greater tax revenue collection. The results also show that inflation harms tax revenue mobilization (column [4]), corroborating the Tanzi effect. Second, up to now, we used the number of ATMs per 100,000 adults to measure financial inclusion in developing countries. However, financial inclusion is a multifaceted concept which et al., 2020). In addition, the access to banking services widely varies across countries and is changing over time. For instance, developing countries are increasingly shifting from traditional banking toward digital banking and finance with an important use of mobile money. Hence, relying on traditional banking may poorly capture the real state of financial inclusion. 79 Furthermore, as stressed in Coulibaly and Yogo (2019), ATMs as well as bank branches in developing countries may be unevenly distributed within countries and tend to be more often concentrated in large cities (Guerineau and Jacolin, 2014). Hence, people in rural areas will not get access to financial services. Finally, our baseline financial inclusion measure does take into account microfinance institutions (MFIs), credit unions or financial cooperatives which play a pivotal role in providing financial services in developing countries. We, therefore, paid particular attention to these shortcomings by considering several alternative measures to capture financial access in a more comprehensive way. The results are reported in Appendix 4.4 and overall, they support those presented in Table 4.1.

Dependent variable: Non-resource tax ratio

Tot. Tax Indirect Direct Income Goods & Serv. VAT (1) (2) (3) (4) (5) (6)
Dependent variable: Total non-resource tax-to-GDP ratio (1) (2) (3) (4) (5) (6) (7)
We finally exploit an alternative data source to test the validity of our findings. As mentioned above, several tax data sources have been used in the literature. In this robustness exercise, we use the IMF's GFS tax database which also provides detailed classification of government's tax revenues. Appendix 4.5 of appendices reports the estimation results. The results show that the coefficients associated financial inclusion are positive and strongly significant at the 1 percent level for total tax revenue (column [START_REF]General Introduction and Overview[END_REF]) and some tax subcomponents (columns [3], [4] and [START_REF]Chapter[END_REF]).

Therefore, our baseline findings remain valid.

Conclusion

The present chapter adds to the literature on tax revenue mobilization, by empirically examining the relationship between access to financial services and non-resources tax-to-GDP ratio in developing countries. Relying on the popular system-GMM estimator of [START_REF] Blundell | Initial Conditions and Moment Restrictions in Dynamic Panel Data Models[END_REF], this chapter is the first, to the best of our knowledge, to take into account the 79 We point out this fact, but data scarcity does not allow us include a financial inclusion measure capturing mobile and digital banking (e.g., the number of registered and active mobile money agent outlets).

Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 116 inertia in the tax revenue ratio and the possible endogeneity of financial inclusion as well all control variables. Based on panel data of 62 developing countries over the period 2004-2017, the chapter finds that financial inclusion is positively and significantly associated with nonresources tax revenue. Exploring the effect on the tax structure, the results show that access to financial services has a more sizeable effect on indirect taxes than the rest of the total tax subcomponents. The chapter also provides empirical evidence that financial inclusion is inducive to higher tax revenue, mainly through increased private consumption and business expansion. Furthermore, in line with previous evidence, our results show that the level of development as well as the quality of institutions are important determinants of tax ratio and are positively associated with tax revenue performance in developing countries. However, the relationship between the level of overall development turns out to be non -linear, suggesting the existence of a tipping point.

In terms of policy recommendations, our study concurs with previous findings and call for improved and greater access to financial services in developing countries. More specifically, the paper highlights that developing countries should promote and guarantee better access to financial services as a key policy objective to increase their tax ratio. In view of the pressing financing needs to finance structural investments in the developing world, our paper provides insights to countries that have implemented or are in the process of implementing financial inclusion policies, on tax resources harnessing opportunities from better access to financial services. In addition, in the current particular context of coronavirus (Covid -19) pandemic causing severe economic despair across the world and requiring important financial resources for a timely and appropriate response, unlocking access to financial services will help to better cope with the income shock and to smooth households consumption.

Introduction

There has been a marked proliferation of violence and conflicts across the developing countries over the past two decades, especially in the wake of the Arab spring from 2011 (Figure 5.1).

The nature of the violence is diverse and includes ethnic and religious conflicts, terrorism, postelectoral conflicts, civil wars, and most importantly armed conflicts. Violence has undoubted deep socio-economic impact on affected countries and their neighbors. For instance, the World Bank (2017) estimates at more than 400,000 the death toll and US$200-300 billion the loss in GDP in Syria since the conflict started in 2011. Conflicts and violence have severe negative consequences on the affected economies, and which can spill over to their neighboring countries. In addition to the loss of lives, human displacement and the material destruction caused, conflicts can result in d eep economic recession stemming from high inflation, worsened fiscal and financial positions, and lower institutional quality [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF]. In addition, internal instability entails a decline in investor and consumer confidence, and trade disruption [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF]. According to the 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 IMF (2019), on average, in conflict-affected countries annual real GDP growth is 3 percentage points lower and the cumulative impact on per capita GDP increases over time. Furthermore, internal conflicts have negative spillovers on neighboring countries, whose GDP growth typically declines by about 1 percentage point on average.

In this chapter, we explore the impact of conflicts on the probability of banking crises, a channel that has hitherto received little attention in the literature. The literature has largely focused on the potential consequences of risks of instability on other socio-economic outcomes. It is welldocumented that instability has adverse effects on countries' long-run economic performance [START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF]Alesina and Perotti, 1996;[START_REF] Jong-A-Pin | On the Measurement of Political Instability and Its Impact on Economic Growth[END_REF][START_REF] Aisen | How Does Political Instability Affect Economic Growth?[END_REF]Rother et al., 2016, Murdoch and[START_REF] Murdoch | Economic Growth, Civil Wars, and Spatial Spillovers[END_REF], public investment [START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF], IMF 2019), trade [START_REF] Qureshi | Trade and Thy Neighbor's War[END_REF], tourism [START_REF] Neumayer | The Impact of Political Violence on Tourism: Dynamic Cross-National Estimation[END_REF] and fiscal outcomes (IMF 2019). Surprisingly, to the best of our knowledge, there is no empirical study on the potential impact of conflict on banking crises. Although IMF (2019) and [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF] allude to the fact that conflicts can lead to lower performance in the banking sector, they do not provide empirical evidence on whether conflicts and political instability can trigger actual systemic banking crises.

Conflicts and political instability can indeed be associated with a greater risk of systemic banking crisis. Conceptually, there are several channels through which conflicts can lead to banking crises. These include lower economic growth, higher bank non -performing loans, lower bank deposits and liquidity, and fiscal channels. [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF] emphasiz ed that conflicts weaken the performance of the financial sector and deteriorate banks' ability to sustain financial intermediation and payment systems. A recent study by [START_REF] Huang | The Impacts of Political Instability on Banking Development and Operational Efficiencies[END_REF] found that political instability decreases banks' balances, liabilities and assets. [START_REF] Beim | What Triggers a Systemic Banking Crisis[END_REF] enumerated several cases of systemic banking crises that occurred in times of conflict and political instability. For instance, in 1995, during the civil war in Sierra Leone, 40 to 50 percent of banking system loans were non-performing (NPLs) and a license of one of the banks was suspended in 1994. [START_REF] Gobat | Syria's Conflict Economy[END_REF] found that the Syrian conflict deeply affected the banking sector by causing deposit and assets runs and raising NPLs from less than 5 to 35% over 2010-2013.

This chapter fills the gap in the literature by rigorously studying the potential impact of conflict and political instability on systemic banking crisis in 92 developing countries over the period 1970-2016. First, it explores this by using various measures of conflict and political instability on the probability of banking crises. Second, the paper analyzes spillovers of conflict and political instability from one country to another. Third, it examines whether the duration of conflict and political instability increases the probability of banking crises. Fourth, it explores the channel through which conflict and political instability affect the likelihood of banking crises.

The chapter has three main results. First, it shows that conflicts and political insta bility are indeed associated with higher probability of systemic banking crises. Specifically, it finds that the odds of a banking crisis are 2.5 times greater when a country is affected by a conflict.

Second, conflicts and political instability in neighboring countries do increase the likelihood of banking crises in a given country, although the spillover effects are less impactful than primary channels. Third, the duration of a conflict is positively associated with rising probability of a banking crisis. In terms of magnitude of the impact, the probability of experiencing a banking crisis is 25 percent when the conflict lasts 10 years, against 16.4 percent when it lasts two years.

The chapter provides evidence that the likely channel of transmission is the occurrence of fiscal crises following a conflict or political instability. The findings are robust to the use of alternative conflict and political instability indicators from 10 different sources, alternative empirical strategy, and the inclusion of additional covariates. This paper contributes to the vast literature on the adverse effects of conflict and political instability. It is the first to provide a comprehensive empirical study about the impact of conflict and political instability on the likelihood of banking crises in developing countries. Previous studies have provided several claims on the specific cases of some countries [START_REF] Beim | What Triggers a Systemic Banking Crisis[END_REF][START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF], but they lacked strong empirical evidence on a large sample of countries to back up or substantiate the claims.

The rest of the chapter is organized as follows. Section 2 briefly reviews the literature on the determinants of banking crises and the potential mechanisms through which conflicts can provoke banking crises. Section 3 describes the data and provides some stylized facts, and Section 4 discusses the empirical strategy. Section 5 presents the results from the empirical analysis. Section 6 undertakes an extensive battery of robustness tests. Section 7 provides some concluding remarks.

Review of the Literature

There is an increasing literature on the economic consequences of conflicts and political instability, with a particular emphasis on economic growth, income inequality and poverty [START_REF] Collier | On the Economic Consequences of Civil War[END_REF]Murdoch and Sandler, 2004;[START_REF] Lai | The Effect of Civil War on Education, 1980-97[END_REF][START_REF] Polachek | Does Conflict Disrupt Growth? Evidence of the Relationship between Political Instability and National Economic Performance[END_REF][START_REF] Mueller | The Economic Costs of Conflict[END_REF]. For instance, [START_REF] Gates | Development Consequences of Armed Conflict[END_REF] showed that armed conflicts led to development gaps and compromised the progress in meeting the United Nation's Millennium

Development Goals (MDGs) by undermining the efforts to reduce poverty, hunger and infant mortality, improve life expectancy as well as access to potable water. [START_REF] Ghobarah | Civil Wars Kill and Maim People-Long after the Shooting Stops[END_REF] also emphasized the adverse long-lasting effects of conflicts on development outcomes.

Beyond the impact on the economy at the aggregate level, some papers looked at the change in the structure of economies affected by conflict. Depetris [START_REF] Chauvin | The Effects of Conflict on the Structure of the Economy[END_REF] found that the manufacturing sector is the most affected in conflict-affected countries, while natural resource sector appears to be over-exploited in times of conflict.

Another wave of the literature has focused on the fiscal implications of conflicts and political instability (IMF 2019; [START_REF] Gupta | Fiscal Consequences of Armed Conflict and Terrorism in Low-and Middle-Income Countries[END_REF][START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF]. Internal instability impedes on government revenue by disrupting economic activity, destroying the tax base, and lowering the efficiency of tax administration (IMF 2019). [START_REF] Barrett | The Fiscal Cost of Conflict: Evidence from Afghanistan 2005-2016[END_REF] revealed that the conflict in Afghanistan led to a total revenue loss of about $3 billion between 2005 and 2016, resulting mainly from a significant decline in revenue collection efficiency. Similarly, [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF] emphasized that central government revenue collapsed by about 60 percent following the outbreak of the conflict in Yemen in 2015. They also argued that the decline in both internal revenue collection and external financing combined with the increase in government spending have resulted in worsened fiscal positions in the Middle East and North African countries in conflict.

Focusing on sub-Saharan Africa, IMF (2019) found that conflicts entail, on average, a loss of tax revenue by about 2 percent of GDP, affect the composition of government expenditures and worsen the fiscal balance. Using an intertemporal model, [START_REF] Pasten | The Political Economy of Unsustainable Fiscal Deficits[END_REF] highlighted that political instability result in fiscal deficits, and this happens because political instability gives the government an incentive to implement a myopic fiscal policy in order to increase its chances of remaining in office.

However, there is a paucity of studies about the effects of conflicts and political instability on the banking sector. [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF] argued that conflicts weaken the performance of the financial sector and deteriorate bank's ability to sustain financial intermediation and payment systems, but they did not provide any empirical findings. Recently, IMF (2019) found that conflicts result in lower credit to the private sector. Huang (2019) investigated the impact of political instability on banking sector development on a panel of 49 countries over 1960-2004.

The paper found that political instability deteriorates banks' balance sheets, generates inefficiencies in the operational management of banks and affects asset and liability allocation.

Hasanov and Bhattacharya (2019) explored the effect of political factors on the likelihood of a banking crisis using a sample of OECD countries. They shed light that countries with higher government stability tend to have lower likelihood of a banking crisis. [START_REF] Gobat | Syria's Conflict Economy[END_REF] asserted that the Syrian conflict deeply affected the banking sector by causing deposit and assets runs, and rising NPLs from less than 5 to 35% over 2010-2013.

As described above, several papers have found that conflict and political instability often lead to a deterioration of government fiscal positions. We draw on the literature about the transmission of crises from the government fiscal positions to the banking sector [START_REF] Hagen | Money Market Pressure and the Determinants of Banking Crises[END_REF][START_REF] Kaminsky | The Twin Crises: The Causes of Banking and Balance-of-Payments Problems[END_REF][START_REF] Dornbusch | Currency Crises and Collapses[END_REF]. According to this literature, banking crises often happen after a fiscal crisis. Worsened fiscal positions can trigger a banking crisis due to the balance-sheet linkages and banks' direct portfolio exposures [START_REF] Caprio | Banking Crises[END_REF][START_REF] Caruana | Sovereign Creditworthiness and Financial Stability: An International Perspective[END_REF] and the potential impact of debt defaults on the economy (lower growth, high non-performing loans, etc.) [START_REF] Gertler | Financial Intermediation and Credit Policy in Business Cycle Analysis[END_REF].

Budgetary pressures erode the government ability to pay its bills, which can cause an accumulation of arrears to commercial enterprises and banks and increasing banks nonperforming loans. Moreover, sovereign debt is often used by banks as collateral to secure wholesale funding. Higher sovereign risk can reduce the eligibility of collateral, and hence banks' funding capacity and increase banks vulnerabilities [START_REF] Popov | The Impact of Sovereign Debt Exposure on Bank Lending: Evidence from the European Debt Crisis[END_REF].

Data Sources and Stylized Facts

Data Sources

The dataset consists of yearly data for 92 emerging and developing countries during the period 1970-2016. The selection of the sample is exclusively based on data availability.

We first focus on the dependent variables. The data on systemic banking crisis are from [START_REF] Laeven | Systemic Banking Crises Revisited[END_REF]. The authors define a banking crisis as an event that meets two conditions:

(i) significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations); (ii) significant banking policy intervention measures in response to significant losses in the banking system. On the second condition, [START_REF] Laeven | Systemic Banking Crises Revisited[END_REF] territory with the use of armed force between two parties, of which at least one is the government of a state. The database provides an intensity-scaled measure of internal armed conflicts, which takes the value of 1 if the internal conflict's related death toll in a given ye ar is 25-999, 2 if it is 1000 or more, and 0 otherwise. Based on this definition, we also construct an additional binary variable equal to 1 if a civil conflict happens in the country and 0 otherwise as in [START_REF] Miguel | Economic Shocks and Civil Conflict: An Instrumental Variables Approach[END_REF]Holder and[START_REF] Hodler | Economic Shocks and Civil Conflict at the Regional Level[END_REF].

Second, we extract some indicators of political instability from Banks and Wilson (2019)'s Cross-National Time-Series Data Archive. We use 6 indicators from this database that have been widely used in the literature as proxies of political instability ( see [START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF][START_REF] Aisen | How Does Political Instability Affect Economic Growth?[END_REF][START_REF] Neumayer | The Impact of Political Violence on Tourism: Dynamic Cross-National Estimation[END_REF]):

(i) Government cabinet changes. Represents the number of time in a year that a new premier minister is named and/or 50% of the cabinet posts are assumed by new ministers;

(ii) Changes in effective executive. Measures the number of times in a year that effective control of executive power changes hands. Such a change requires that the new executive be independent of his predecessor. This variable addresses one of the drawbacks of the indicator related to major government changes as some cabinet changes may not entail change in executive power;

(iii) Anti-government demonstrations. Captures any peaceful public gathering of at least 100 people for the primary purpose of displaying or voicing thei r opposition to government policies or authority, excluding demonstrations of a distinctly anti -foreign nature;

(iv) Major government crises. Denotes any rapidly developing situation that threatens to bring the downfall of the present regime -excluding situations of revolt aimed at such overthrow;

(v) General strikes. Measures any strike of 1,000 or more industrial or service workers that involves more than one employer and that is aimed at national government policies or authority; and

(vi) Political assassinations. Represents any politically motivated murder or attempted murder of a high government official or politician.

These indicators are the most used in the literature, and we will use them in our baseline estimates. Appendix 5.1 presents the correlations between the different conflicts and political variables. While some variables are highly correlated, the vast majority of them have low degrees of correlation (less than 0.3), providing some comfort that they provide additional information when used in different equations. This also allows us to cover several dimensions of conflict and political instability.

Third, in robustness checks, we use several other sources including: the International Country Risk Guide (ICRG), Correlates of Wars (COW), the Political Terror Scale of Amnesty International, the Global Terrorism Database (GTD), The Major Episodes of Political Violence Database [START_REF] Marshall | Major Episodes of Political Violence (MEPV) and Conflict regions, 1946-2016[END_REF], the Coup d'État Events Database (Marshall and Marshall, 2018) and the State Fragility Index [START_REF] Marshall | Conflict, Governance, and State Fragility[END_REF] (see Appendix 5.2).

Regarding the remaining control variables, they are from different sources. We extract the real exchange rate, inflation rate, external debt in percentage of GDP, GDP per capita, real GDP growth, and terms of trade from the IMF's World Economic Outlook database. The data on M2/reserves and credit growth are from the World Bank's World Development Indicators. We finally draw the data on the degree of democracy from the Polity IV Project (Marshall and Gurr, 2018). and provides evidence that major waves of conflict tend to be associated with a higher rate of occurrence of banking crises.

Stylized Facts

Figure 5.2. Number of Countries in Conflict and Experiencing Banking Crises

Source: Uppsala Conflict Data Program, [START_REF] Laeven | Systemic Banking Crises Revisited[END_REF] and authors' calculations.

Table 5.1 presents the unconditional and conditional probabilities of a banking crisis for all conflict and political instability variables included in our baseline estimates. For each variable, we present the number of observations, the number of banking crises and the probability of a banking crisis. Column (3) considers the sample for all country-year observations and describes the unconditional probability of a banking crisis, which is the proportion of country-year observations identified with the start of a banking crisis. In columns ( 4) and ( 5), we report the conditional probability of a banking crisis, which is the proportion of country-year observations during which a banking crisis occurred in the absence of conflict and political instability (column 4), and the proportion of conflict and political instability that ended up in a banking crisis (column 5). In column [START_REF]Chapter[END_REF], we compute the difference in the conditional probability of a banking crisis in years without a conflict and political instability and years of conflict and political instability, while the p-value of the T-test about the significance of the difference is reported in column (7). In the last column, we report the Pearson chi-squared statistic about the independence of the occurrence of banking crises and conflict or political instability. The Pearson test shows that there a is statistically significant relationship between banking crises and conflict or political instability. As can be observed, the conditional probability of a banking crisis is higher when conflict and political instability occur than in the absence of conflict and political instability. For instance, the conditional probability of a banking crisis in a year without a conflict is 5.3 percent; th at probability almost doubles in years of conflict (10 percent). The difference is even stronger if we consider general strikes and political assassinations: the probability of a banking crisis increases from 5.7 percent in years without general strikes an d political assassinations to 12.5 and 11.7 percent, respectively, in years of general strikes and political assassinations. The t-test in column (7) shows that the differences are statistically different. The unconditional probability of a banking crisis is around 6.4 percent regardless of the variable considered.

( 1 (the small differences are due to missing data). In column ( 4) and ( 5), we have the conditional probabilities of a banking crisis following a conflict without the occurrence of a fiscal crisis (column 4) and with the occurrence of a fiscal crisis (column 5).

Table 5.2 shows that the conditional probability of a banking crisis following the simultaneous occurrence of a conflict or political instability and a fiscal crisis is at least three tim es higher than the conditional probability of a banking crisis following a conflict or political instability but without the occurrence of a fiscal crisis. For instance, the conditional probability of a banking crisis after a joint occurrence of a conflict and a fiscal crisis is 16.7 percent, while that probability declines significantly to only 4.2 percent if a fiscal crisis does not materialize following the conflict. Furthermore, the conditional probability of a banking crisis following the

Change in effective executive

Anti-government demonstrations

Government crises

General strikes occurrence of a conflict or political instability is higher than the conditional probability of a banking crisis after a conflict when a fiscal crisis does not occur (column 3, Table 2) and the unconditional probability of a banking crisis (column 3, Table 5.1).

Empirical Strategy

The empirical specification used in this paper consists of a binary outcome model given that the dependent variable equals to 1 for all observations in the data for which a banking crisis happens, and 0 for the remaining ones (non-occurrence of a banking crisis). The binary response model is written as follows:

𝑃𝑟(𝑌 𝑖𝑡 = 1|𝑋 𝑖𝑡-1 , 𝐶 𝑖 ) = 𝐹(𝑋 𝑖𝑡-1 𝛽 + 𝐶 𝑖 ) = 𝑋 𝑖𝑡-1 𝛽 + 𝐶 𝑖 + 𝜀 𝑖𝑡 (5.1) where 𝑌 𝑖𝑡 is a binary response variable taking the value of 1 if there is a banking crisis in a given country 𝑖 at time 𝑡; 𝑋 𝑖𝑡-1 is a vector of observed explanatory variables including conflict and political instability; 𝛽 is a vector of parameters, 𝐶 𝑖 is an unobserved time-invariant country fixed effect, and 𝜀 𝑖𝑡 is the error term with a zero-mean residual uncorrelated with all the terms on the right-hand side. We lag all control variable by one year to avoid the problem of simultaneity and endogeneity. 80 The composite error term in equation (5.1), 𝐶 𝑖 + 𝜀 𝑖𝑡 , is an important feature of panel data models. 𝐶 𝑖 , also called country-specific heterogeneity, includes historical factors that can affect the probability of experiencing a banking crisis. The key issue is whether the unobserved heterogeneity can be assumed to be independent, or at least uncorrelated, with the observed covariates 𝑋 𝑖𝑡 . A usual assumption is that the set of explanatory variables 𝑋 𝑖𝑡 is contemporaneously exogenous conditional on 𝐶 𝑖 : 𝐸(𝜀 𝑖𝑡 | 𝑋 𝑖𝑡 , 𝐶 𝑖 )= 0 , t = 1,..., T. However, this assumption is difficult to be proven valid. In fact, country-specific factors such as religion, language, regulatory framework (common or civil law), and ethnic diversity have been widely

shown to affect the degree of economic development and growth (see [START_REF] Barro | Religion and Economic Growth[END_REF]Campante and Yanagizawa-Drot, 2015;[START_REF] Mahoney | The Common Law and Economic Growth: Hayek Might Be Right[END_REF][START_REF] Alesina | Ethnic Diversity and Economic Performance[END_REF][START_REF] Montalvo | Ethnic Polarization, Potential Conflict, and Civil Wars[END_REF]. Given that economic growth is among the explanatory variables, thus the uncorrelation hypothesis between the ti me-invariant factors and the explanatory variables is violated. Moreover, treating the time-invariant factors 𝐶 𝑖 as parameters to estimate causes inconsistency in 𝛽 because of the incidental parameters problem [START_REF] Neyman | Consistent Estimates Based on Partially Consi stent Observations[END_REF][START_REF] Lancaster | The Incidental Parameter Problem since 1948[END_REF].

The fixed effects approach could be used to estimate equation ( 5.1). The most important appealing reason is that by controlling out the time invariant variables, the model accounts for biases that occur with omitted and unobserved variables. Unfortunately, the power of the fixed effects approach results in an undesirable consequence: even where we do have data for time invariant variables, that information is excluded from the model. In addition, as noted by Caballero (2014), [START_REF] Eberhardt | Commodity Price Movements and Banking Crises[END_REF], and [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF], all countries that have not experienced banking crises will be excluded from the estimates. In our sample, 40 percent of countries (37 out of 92) have not experienced at all banking crises over our study period 1970-2017. Excluding these countries in the estimates raises the issue of selection bias and inconsistent results. As argued by [START_REF] Bell | Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data[END_REF], the fixed effects models are only modelling one part of the data structure, the within-country effects at the expense of betweencountry effects. [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] (5.2) In this chapter, we employ the random effects-Mundlak model by including the means of all time-varying covariates for the countries in the estimates. These averages have the same value for a given country across years but vary across countries. By including the vector of timeaveraged variables, we still control for time-constant unobserved heterogeneity, as with fixed effects, while avoiding the problem of incidental parameters in nonlinear models. At the same time, the Mundlak model allows measurement of the effects of time-constant independent variables, just as in a traditional random effects model [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. Therefore, by taking care of all country-specific and time-invariant characteristics that may affect the likelihood of a crisis or the occurrence of conflict and political instability, or both, the Mu ndlak model allows for different within and between-country effects (Caballero, 2014). Contrary to the simple fixed effects model which excludes all countries that have not experienced banking crises from the sample, the random effects-Mundlak model takes into account all these countries in the estimates.

In robustness checks, we will use the traditional probit and logit models, as well as the probit fixed-effects model of [START_REF] Fernández-Val | Individual and Time Effects in Nonlinear Panel Models with Large N, T[END_REF]. The approach by Fernández-Val and

Weidner (2016) accounts for the bias arising from the inclusion of country fixed -effects and corrects for the incident parameter bias problem by subtracting from the maximum likelihood estimator a plug-in estimator of the bias. As explained above, the main drawback of this method is that it excludes all countries that have not experienced banking crises.

Relying on the extensive literature on the determinants of banking crises, we control for a number of variables:

• Real effective exchange rate: the literature shows that a sharp decline in the real exchange rate is associated with a greater risk of banking system distress (De [START_REF] Bock | Bank Asset Quality in Emerging Markets: Determinants and Spillovers[END_REF][START_REF] Kaminsky | The Twin Crises: The Causes of Banking and Balance-of-Payments Problems[END_REF][START_REF] Reinhart | Assessing Financial Vulnerability, an Early Warning System for Emerging Markets: Introduction[END_REF]. For instance, De [START_REF] Bock | Bank Asset Quality in Emerging Markets: Determinants and Spillovers[END_REF] found that exchange rate depreciation implies increasing rates of NPLs and banking turmoil on a sample of 25 emerging markets over 1996-2010. Hence, we expect a depreciation of the real exchange rate to be positively associated with an increase in the likelihood of a banking crisis.

• M2/reserves: it measures banks' exposure to foreign exchange risk and a country's vulnerability to currency crises which often coincide with banking crises [START_REF] Davis | Corporate Financial Structure and Financial Stability[END_REF][START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF]. Thus, we expect a positive correlation between M2/reserves and banking crises.

• Inflation: we include this variable to capture the macroeconomic mismanagement as previous studies clearly evidenced that high rates of inflation are associated with banking crises (Demirgüç-Kunt andDetragiache, 1998-2000;[START_REF] Davis | Should Multivariate Early Warning Systems for Banking Crises Pool across Regions?[END_REF][START_REF] Joyce | Financial Globalization and Banking Crises in Emerging Markets[END_REF]. High inflation tends to undermine long-run economic growth and distorts macroeconomic and financial stability. Therefore, a positive correlation between inflation and banking crises is expected.

• Credit growth: an important body of the literature argues that high credit growth is conductive to banking sector problems (Cihák, 2007;[START_REF] Joyce | Financial Globalization and Banking Crises in Emerging Markets[END_REF]Acosta -Gonzalez et al., 2011). For instance, [START_REF] Beck | Bank Concentration, Competition, and Crises: First Results[END_REF] underline that a credit boom could induce an asset price bubble that may cause a crisis when it bursts. Moreover, [START_REF] Büyükkarabacak | The Role of Household and Business Credit in Banking Crises[END_REF] provided evidence that a rapid credit boom generates vulnerabilities that increase the probability of a banking crisis. However, a few studies including Von [START_REF] Hagen | Money Market Pressure and the Determinants of Banking Crises[END_REF] and [START_REF] Rose | Cross-Country Causes and Consequences of the Crisis: An Update[END_REF] do not find evidence that a boom in the credit-to-GDP ratio is associated with greater probability of a banking crisis.

• External debt: high debt-to-GDP ratio indicates greater tighter financial conditions and reduced fiscal space [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF] and is likely to lead a banking crisis. In countries where banks are the main holders of government debt, worsened financial conditions or sovereign debt defaults would undoubtedly weaken banks' balance sheets.

Moreover, heavily-indebted economies are more likely to face high-risk premium in international capital markets. As a result, government capacity to intervene in case of banking liquidity shortage becomes very limited. We expect a positive correlation between external debt and the likelihood of a banking crisis.

• GDP per capita: it captures the level of development in a country. Some studies (Demirgüç-Kunt andDetragiache, 2000-2005;[START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF] found that banking crises are negatively associated with real GDP per capita.

• GDP growth: according to the literature, deteriorating growth prospects are associated with greater risk of a banking crisis as lower economic growth negatively affects banks' balance sheets by increasing the share of non-performing loans [START_REF] Klomp | Causes of Banking Crises Revisited[END_REF]. Some studies found that in most cases, banking crises followed an episode of growth slowdown (Demirgüç-Kunt andDetragiache, 1998-2005;[START_REF] Hagen | Money Market Pressure and the Determinants of Banking Crises[END_REF][START_REF] Angkinand | Exchange Rate Regimes and Banking Crises: The Channels of Influence Investigated[END_REF]. We thus expect a negative association between economic growth and banking crises.

• Terms of trade: a deterioration of the terms of trade reduces the ability of bank's customers to service their financial commitments, leading to an increase of NPLs and rendering banking crises more likely [START_REF] Goldstein | Banking Crises in Emerging Economies: Origins and Policy Options[END_REF][START_REF] Caprio | Bank Insolvencies: Cross-Country Experience[END_REF]. Hence, we expect a negative correlation between terms of trade and banking crises.

• Degree of democracy: it refers to the quality of the politico-institutional environment and is expected to be negatively associated with the occurrence of a banking crisis.

Countries with good institutions and governance tend to implement sound financial regulations to promote banking system stability that can potentially, in turn, reduces the probability of banking crisis [START_REF] Francis | Governance and Financial Fragility: Evidence from a Cross-Section of Countries[END_REF][START_REF] Beck | Bank Concentration, Competition, and Crises: First Results[END_REF]. In addition, financial fraud and the excessive risk-taking in weak institutional countries increase the vulnerability of the banking sector and result in banking collapses [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF].

A negative correlation between the degree of democracy and the likelihood of a banking crisis is expected.

Empirical Results

Baseline Results

The baseline evidence on the relationship between conflicts, political instability and banking crises is reported in Table 5.3. We present in each column the results obtained through the estimates of equation ( 2) employing the random effect model of [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] and using several indicators of conflicts and political instability. The first two columns are about the effects of conflicts, while the remaining columns deal with the effects of political instability. In column [START_REF]General Introduction and Overview[END_REF], we use a binary variable taking the value of 1 if the country experiences a conflict and 0 otherwise. The results show that the coefficient associated with this binary variable is positive and statistically significant at the 1 percent level. This finding suggests that being in conflict affects positively the likelihood of occurrence of banking crises.

The test statistics suggest that the [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] approach used in the estimates is accurate and that the model classifies properly the group of countries that experienced banking cri ses and those that did not experience banking crises. We report at the bottom of the table the area under the ROC curve (AUROC) statistics and their standard errors to test the goodness of fit of the model. The AUROC statistic is between 0 and 1, with higher values representing a strong performance of the model. In Table 3, the AUROC statistic is above 0.73 in all the columns.

To give an idea about the magnitude, we follow Caballero (2014) in analyzing our results in terms of odds ratios. Given that we are using a logit model, the odds ratios are the exponentiated values of the coefficients reported in Table 5.3. Therefore, based on the results in column ( 1), the odds of a banking crisis are 2.5 times greater when a country is affected by a conflict. The probability of experiencing a banking crisis raises from 6.3 percent (unconditional probability) to 13.5 percent when a country is in conflict. 81 In column ( 2), we use the intensity of conflict instead of the binary variable used in column [START_REF]General Introduction and Overview[END_REF]. The results remain consistent as the coefficient associated with conflict is positive and significant at the 1 percent level.

Turning to the effects of political instability, we present in column 3 the results when we use the change in government cabinet as an indicator of political instability following [START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF] and [START_REF] Aisen | How Does Political Instability Affect Economic Growth?[END_REF]. We find that the coefficient associated with the variable cabinet changes is positive and highly significant at the 1 percent level. That said, political instability is correlated with a higher occurrence of banking crises. Quantitatively, an increase in the number of cabinet changes from zero to four (which is the maximum observed in the 81 The odds are the ratio of the probability of a positive outcome to the probability of no positive outcome: odds = p/q, where q=1-p and p = Pr(Y = 1|X). In our sample, the unconditional probability of a crisis is 6.3 percent (191 crises out of 3,026 observations), which implies odds(crisis) = 0.0631. In column 1 of Table 5.3, the odds of a crisis, conditional on the occurrence of a conflict, increase by 2.5 times (this is the exponentiated coefficient associated with conflict: 0.9097). Then, the estimated conditional probability of a crisis is 0.1355 = (2. ) is associated with an increase in the likelihood of banking crises to 21.17 percent, from the unconditional probability of experiencing a banking crisis of 6.3 percent.

Table 5.3. Baseline Results

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors are reported in brackets.

In column 4, we use the number of changes in effective executive as a proxy of political instability [START_REF] Alesina | Politi cal Instability and Economic Growth[END_REF]. We find a positive correlation between the number of changes in effective executive and the occurrence of banking crises. In the remaining columns, we use the number of anti-governmental demonstrations, government crises, general strikes and political assassinations as proxies of political instability. We still find that the coefficients associated with these variables are positive and significant, although the level of significance differs between columns. Regarding the remaining control variables, with a few exceptions, they are significant and consistent with the literature. We find that the coefficients associated with M2/reserves, inflation, credit growth, and external debt are all positively correlated with banking crises. It has been shown that an increase in broad money compared to the level of reserves is positively associated with a high occurrence of banking crises (Demirgüç-Kunt andDetragiache, 2000-2005;[START_REF] Davis | Corporate Financial Structure and Financial Stability[END_REF][START_REF] Hagen | Money Market Pressure and the Determinants of Banking Crises[END_REF][START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF]. High inflation rates negatively affect the banking sector stability [START_REF] Davis | Should Multivariate Early Warning Systems for Banking Crises Pool across Regions?[END_REF][START_REF] Joyce | Financial Globalization and Banking Crises in Emerging Markets[END_REF], while amounting debt level is often considered as a predictor of banks failures.

On the other hand, the coefficients associated with exch ange rate, economic growth, and the degree of democracy are negatively associated with banking crises. As shown in the literature, a depreciation of the exchange rate can potentially lead to a banking crisis [START_REF] Reinhart | Assessing Financial Vulnerability, an Early Warning System for Emerging Markets: Introduction[END_REF][START_REF] Duttagupta | Anatomy of Banking Crises in Developing and Emerging Market Countries[END_REF][START_REF] Bock | Bank Asset Quality in Emerging Markets: Determinants and Spillovers[END_REF]. A sound politicoinstitutional environment is less favorable to the occurrence of banking turmoil [START_REF] Beck | Bank Concentration, Competition, and Crises: First Results[END_REF][START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF]. However, as in Demirgüç-Kunt and Detragiache (2000), the level of development measured by the GDP per capita is not statistically significant. Similarly, the terms of trade are found to be a non-significant determinant of banking crisis.

Do Conflicts and Political Instability in Neighboring Countries Matter?

In this subsection, we assess whether conflicts and political instability in neighboring countries affect the likelihood of experiencing a banking crisis in a given country. Such spillover effects can occur as banks perform their activities in bordering countries in search of portfolio diversification and the last two decades have been marked by an increase in financial globalization [START_REF] Mishkin | Is Financial Globalization Beneficial?[END_REF]Kose et al. 2006). Previous studies have shown that conflicts in bordering countries matter. For instance, [START_REF] Qureshi | Trade and Thy Neighbor's War[END_REF] found a significant negative effect of both intrastate and international conflicts on the bilateral trade of neighboring countries that may not be directly involved in any conflict. Murdoch and Sandler (2004) and De [START_REF] Groot | The Spillover Effects of Conflict on Economic Growth in Neighbouring Countries in Africa[END_REF] highlighted that conflicts have negative spillover effects on neighboring countries by inducing a significant decline in output growth in the short-run.

We define the variables of conflicts and political instability in neighboring countries as follows.

For conflict, we define two variables: one being the number of bordering countries in conflict and another being the simple average of conflict intensity in bordering countries. For political instability variables, we generate the simple average of the number of cabinet changes, changes in effective executive, anti-government demonstrations, government crises, general strikes and political assassinations in bordering countries. We then run the same regressions as in Table 5.3.

The results are reported in Table 5.5. We find that the coefficients associated with our variables of interest are positive and statistically significant in columns 1-4, although the spillover effect is generally lower than the direct one. This suggests that conflicts and political instability in neighboring countries increase the likelihood of banking crises in a given country. More specifically, for a given country, an increase in the number and intensity of conflict, and the number of changes in government cabinet and effective executive and the number of general strikes in bordering countries are associated with an increase in its probability to experience a banking crisis.

For instance, if we focus on column (1), a rise in the number of bordering countries affected by conflict from 0 to 3 (which is the median number of bordering countries in conflict) would result in an increase of the likelihood of banking crises from 6.3 percent t o 11.2 percent. On the other hand, we find the coefficient associated with the number of antigovernment demonstrations, government crises and political assassinations in bordering countries have no significant spillover effects.

Table 5.5. Effect of Conflict and Political Instability in Neighboring Countries

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors a re reported in brackets.

Duration of Conflict and Political Instability

We explore whether the duration of conflict and political instability matter. For each variable, we redefine a new variable taking the value of 1 if the conflict or political instability lasts 1 year, or at least 2 years, 3 years, and up to 10 years. We then estimate equation ( 2) using the [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] estimator. The results are reported in Table 5. We find that conflict and political instability that last only one year has no significant effect on the occurrence of banking crises. However, when the conflict lasts longer, its impact on the occurrence of banking crises become apparent and stronger.

Table 5.6 shows that the coefficient associated with conflict is positive and significant at the 1 percent level when the conflict lasts at least two years. We can also observe that the coefficient is higher when the conflict lasts 10 years than when it lasts only 2 years. In terms of magnitude of the impact, the probability of experiencing a banking crisis is 25 percent when the conflict lasts 10 years, against 16.4 percent when it lasts two years. This finding can be explained by the fact that when the conflict is becoming prolonged, its adverse impact on the economy and the banking sector intensifies. We find similar results in columns 3, 4, 6 and 8, suggesting that the probability of a banking crisis increases when political instability persists. The coefficient associated with antigovernment demonstrations and general strikes become insignificant when they last more than 2 and 3 years, respectively, due to the significant reduction in the number of cases.

Transmission Channels

In this subsection, we explore the channel through which conflict and political instability influence the likelihood of banking crises. As outlined above, we assert th at conflict and political instability affect the likelihood of banking crises by creating some fiscal pressures, which in turn transmit to the banking sector. To test this hypothesis, we extract the data on fiscal crises from [START_REF] Gerling | Fiscal Crises[END_REF]. Fiscal crises are defined as episodes of extreme fiscal distress. Our variable fiscal crisis is a binary variable taking the value of 1 if the country is under tight budgetary conditions and 0 otherwise.

We then include an interactive variable between conflict or political instability and fiscal crisis and the latter itself as additional variables. This allows us to test whether the effect of conflict and political instability on banking crises partly or totally transmit through the occurrence of fiscal crises. If the coefficients associated with conflict and political instability remain highly significant and their magnitudes do not change, thus conflict and political instability influence the likelihood of banking crises even in the absence of fiscal crises. However, if the coefficients associated with conflict and political instability become insignificant when the interactive term and fiscal crisis are included, then the effect of conflict and political instability on banking crisis can be assumed to operate through a simultaneous occurrence of fiscal crises.

The results are reported in Table 5.7. They show that the effect of conflict and political instability operates mainly through a simultaneous fiscal crisis. Indeed, the coefficients associated with the different variables of conflict and political instability become insignificant in all columns when the interactive term and fiscal crisis are included, suggesting that the budgetary constraints are key determinants of banking crises, and that some fiscal crises take place simultaneously with the occurrence of conflict and political instability. The coefficient associated with fiscal crisis is positive and significant in all columns. This finding is in line with our main hypothesis.

Table 5.7. Transmission Channels

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors a re reported in brackets.

Robustness Checks

We now estimate a set of different specifications to test the robustness of our results.

Use of Alternative Data Sources

As highlighted in Section (2019). In this database, the minimum threshold to be qualified as conflict (500 related deaths) is higher than in our baseline database (25 related deaths). We use a binary variable taking a value of 1 if the country experiences a civil war and 0 otherwise in column [START_REF]General Introduction and Overview[END_REF]. Marshall (2017) also defines some scores reflecting the intensity of civil war and civil violence based on an eleven-point scale score (0-10), with higher values representing extreme civil war and violence. In column

(2), we use the score of civil war, while the score of civil violence is used in column (3). In column (4), we use the aggregate index of total violence, which is the simple average of civil war and civil violence scores. The results show that all four variables are positive and strongly significant at the 1 percent level. Therefore, our baseline findings remain unchanged.

In column (5), our indicator of conflict is from the Correlates of Wars (COW) dataset. In this database, the threshold to be considered as civil war is high as the minimum of conflict-related deaths is 1000 deaths, compared to only 25 in UCDP database used in our baseline specification. The COW database is used in some papers [START_REF] Bazzi | Economic Shocks and Conflict: Evidence from Commodity Prices[END_REF]. Appendix 5.3 shows that using this data source does not change our findings. The coefficient associated with conflict is highly significant and higher than that of column (1) in Table 5.3. With this database, being in conflict raises the probability of a banking crisis from 6.3 percent to 17.7 percent.

In the baseline results in Table 5.3, we used the number of political assassinations as proxy indicator of political instability. We now use a very similar indicator from Marshall and Marshall (2018) in column [START_REF]Chapter[END_REF], which focuses on the assassinations of the ruling executives. This variable takes the value of 1 if the ruling executive is assassinated and 0 otherwise. We still fin d that the coefficient with our variable of interest is positive and strongly significant, suggesting that the assassinations of the ruling executive is a predictor of banking crises.

We now look at the cases of terrorism. One data source widely used to ca pture the occurrence of terrorism attacks is the Global Terrorism Database [START_REF] Asongu | The Impact of Terrorism on Governance in African Countries[END_REF][START_REF] Lis | The Impact of Armed Conflict and Terrorism on Foreign Aid: A Sector-Level Analysis[END_REF]. Recent years have been marked by an increase in terrorist attacks in the world, particularly in sub-Saharan Africa (see IMF 2019), leading to severe macroeconomic consequences as infrastructure and human capital are being damaged , businesses delay investment decisions and increase unemployment [START_REF] Rother | The Economic Impact of Conflicts and the Refugee Crisis in the Middle East and North Africa[END_REF]. In columns (7) we use a binary variable taking the value of 1 if a terrorist attack occurs in the country and 0 otherwise.

The results show that terrorism-related attacks are positively associated with higher occurrence of banking crises. The coefficient associated with the variable is strongly significant at the 1 percent level.

As in [START_REF] Neumayer | The Impact of Political Violence on Tourism: Dynamic Cross-National Estimation[END_REF], we use the indicator of political terror from the Political Terror Scale (PTS) database in column (8). This variable captures the violations of basic human rights and includes torture and cruel treatment and punishment, killings and unlawful use of deadly force, political assassinations, kidnappings, forced disappearances, and many other forms of treatments. Given that the source of this database is Amnesty International, the database provides an assessment of political instability made by the humanitarian community, which is very important as they often work closely with the conflict-affected populations. The coefficient associated with the variable PTS is positive and significant at the 10 percent level.

Some authors use coups d'état as an indicator of political instability [START_REF] Fosu | Political Instability and Economic Growth: Implications of Coup Events in Sub-Saharan Africa[END_REF]. Several countries have been subject to repetitive military coups, particularly in sub-Saharan Africa [START_REF] Fosu | Political Instability and Economic Growth: Implications of Coup Events in Sub-Saharan Africa[END_REF][START_REF] Mcgowan | African Military Coups d'Etat, 1956-2001: Frequency, Trends and Distribution[END_REF]. Following these studies, we use the number of coups d'état in column [START_REF] Bird | Introduction to Tax Policy Design and Development; Notes for a Course on Practical Issues of Tax Policy in Developing Countries[END_REF]. We find that the coefficient associated with this variable is statistically not significant.

Finally, we use the index of state fragility from [START_REF] Marshall | Conflict, Governance, and State Fragility[END_REF] and the share of deaths caused by conflict in columns ( 10) and ( 11). The state fragility index captures the degree to which a country is vulnerable to political violence. The use of the proportion of the population killed during conflict aims at taking into account the size of countries, in line with IMF (2019). As shown in column ( 10) and ( 11), the coefficients associated with these two variables are positive and significant at the 5 percent level, and thus our core finding still holds.

Including More Covariates

To avoid the problem of omitted variables, we include several additional control variables in Appendix 5.4. In the first two panels, we check whether controlling for the global conditions will change our results. To this end, we include the S&P 500 index in panel A and the US 3years bond yields in panel B. Given the dominance of the US economy and financial sector in the world, there is no doubt that what is happening in the US affect developing countries.

Previous literature on the contagion effects and market transmission from US markets shows that what happens in US markets affects the markets in other countries [START_REF] Bekaert | Global Crises and Equity Market Contagion[END_REF].

The results in panel A and B show that the coefficient associated with conflict and political instability remains broadly positive and strongly significant even if we control for global conditions.

We control for the role of natural resource endowments in panels C and D. [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF],

and [START_REF] Eberhardt | Commodity Price Movements and Banking Crises[END_REF] have found that commodity price fluctuations can lead to banking crises. To capture this potential effect, we include in panel C the index of commodity prices as in [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF] and in panel D the total rents from natural resources as percentage of GDP. The results in these two panels are consistent with our baseline findings in Table 5.3.

In panel E, we include portfolio investment, net inflows, as percentage of GDP, while in panel F we include the real interest rate. Some studies have found that short-term flows (such as portfolio flows) (Caballero, 2014;[START_REF] Ghosh | When Do Capital Inflow Surges End in Tears?[END_REF] are positively associated with the likelihood of a banking crisis. Furthermore, an increase in the real interest rate is a proxy for a tightening of financial conditions which is likely to squeeze banks' balance sheets and increase the probability of a banking crisis [START_REF] Duttagupta | Anatomy of Banking Crises in Developing and Emerging Market Countries[END_REF]. After controlling for these important covariates, we still find that conflict and political instability increases the likelihood of banking crises, even if the level of significance drops when the interest rate is included. This is due to the significant reduction in the number of observations because of the lack of data on real interest rate.

Finally, we include control of corruption, the degree of exports diversification and financial development in panels G, H and I, respectively. Previous studies stress the importance of institutions that enforce and secure property rights for financial development and the probability of financial fragility being positively associated with weaker institutions (Demirgüç-Kunt and Detragiache 1998). [START_REF] Barth | Corruption in Bank Lending to Firms: Cross-Country Micro Evidence on the Beneficial Role of Competition and Information Sharing[END_REF][START_REF] Beck | Bank Concentration, Competition, and Crises: First Results[END_REF] have shown that when bank supervisors or bank controlling shareholders abuse their power and get involved in corrupted activities, the likelihood of bank failure increases. Regarding exports diversification, some studies have found that countries with relatively low export diversification are more susceptible to banking crises [START_REF] Kinda | Do Commodity Price Shocks Weaken the Financial Sector?[END_REF]Hausmann and Rojas-Suárez, 1996), other studies found that the level of financial development matters [START_REF] Mathonnat | Financial Development and the Occurrence of Banking Crises[END_REF].

We find that controlling for these variables does not alter our baseline findings.

Alternative Econometric Methods

In this section, we use the simple probit and logit models, and the profit fixed effects model as robustness checks. As we underlined in Section 4, although the random-effects of [START_REF] Mundlak | On the Pooling of Time Series and Cross Section Data[END_REF] is our preferred econometric method, the other methods are also used in some papers either as baseline specification or as robustness check [START_REF] Caballero | Do Surges in International Capital Inflows Influence the Likelihood of Banking Crises?[END_REF][START_REF] Ghosh | When Do Capital Inflow Surges End in Tears?[END_REF]. We estimate equation ( 2) using these three empirical estimators. The results are reported in Appendix 5.5. We find that the coefficients associated with our variable of interest (conflict and political instability) are all positive and significant in all panels. Therefore, our baseline results still hold regardless of the econometric method used.

Concluding Remarks

Against the background of rising conflict and political instability over the past several decades, the paper investigates whether this phenomenon has led to increased occurrence of banking crises. While there is an extensive literature examining the economic impact of conflict and political instability, surprisingly there have been few studies on their impact on the probability of banking crises. This paper has attempted to fill this void.

The chapter provides strong evidence that conflicts and political instability are indeed associated with higher probability of systemic banking crises. Unsurprisingly, it also finds that the duration of a conflict is positively associated with rising probability of a banking crisis.

Interestingly, the paper finds that conflicts and political instability in one country can have negative spillover effects in neighboring countries, by raising the probability of banking crises, albeit with lower likelihood.

The paper finds that the primary channel of transmission is the occurrence of fiscal crises following a conflict or political instability. Conflicts and political instability can have a negative impact on the productive capacity of a country and this in turn can reduce government revenue and increase military or other unproductive spending, leading to fiscal crises. Mo re generally, this can generally lead to government dysfunctionality and weakening of institutions.

In terms of policy implications, it is obvious that conflict and political instability have deleterious and far-reaching socio-economic impacts. We concur with Aisen and Veiga (2013) that governments facing conflict and/or political instability need to address their root causes and try to mitigate their negative effects with the appropriate design and implementation of economic policies. Creating adequate fiscal space in normal times can reduce the likelihood of fiscal crises and in turn lower the probability of systemic banking crises. Our results also suggest that policy makers should pay attention to conflicts in neighboring countries even if they themselves are not conflict-afflicted as their banking systems may suffer negative spillovers from their neighbors. perpetrator; (ii) the incident must entail some violence or threat of violence, including violence against property or/and against people; and (iii) the perpetrator of the incident should be sub-national actors. We use the number of terrorism-related incidents per year.

• Marshall (2017)'s major episodes of political violence database (Center for Systemic Peace). Major episodes of political violence are defined by the systematic and sustained use of lethal violence by organized groups that result in at least 500 directly-related deaths over the course of the episode. We define a binary variable taking the value of 1 if such event occurs and 0 otherwise. The database also contains an eleven-point scale score (0-10) providing an assessment of the intensity of civil war and civil violence. We also use these two scores and an additional aggregate score for total violence, which is the simple average of the civil war and civil violence scores.

• Marshall and Marshall (2018)'s coup d'état events database (Center for Systemic Peace).

We use two variables from this database capturing whether a military coup occurred in the country and whether the ruling executive was assassinated. A coup d'état is defined as a forceful seizure of executive authority and office by a dissident/opposition faction within the country's ruling or political elites that results in a substantial change in the executive leadership and the policies of the prior regime. We generate a binary variable taking the value of 1 if a coup d'état occurs and 0 otherwise. We also define a binary variable equal to 1 if the ruling executive is assassinated and 0 otherwise.

• [START_REF] Marshall | Conflict, Governance, and State Fragility[END_REF]'s state fragility index (Center for Systemic Peace). The state fragility index is a composite index based on four dimensions: security, political, economic and social and measures the degree to which a country is vulnerable to political and social violence. The variable ranges between 0 and 25, with higher values meaning extreme fragility.

Tax revenue mobilization emerged as a main tool to finance the 2030 international development agenda since the United Nation General Assembly on Financing for Development in 2015. Alternatively, to the international development assistance -the historical source of financing in developing countries-, which the literature points out some limitations (e.g., perverse effects, volatility, etc.), tax revenues constitute a strong and sustainable source of development financing in emerging markets economies. However, despite of the pressing financing needs, developing countries still face important impediments in raising significant tax revenues to finance essential public expenditures. Developing countries are typically collecting between 10 to 20 percent of GDP, while advanced economies collect on average about the double, 40 percent. In addition to the pressing policy challenge that represents tax revenue mobilization, developing countries face not only a limited access to formal financial services which may be a factor of greater tax revenue collection, but also a rise in violence and internal unrest with devastating macroeconomic consequences.

This thesis was concerned with tax revenue mobilization and the consequences of conflict and political instability on the financial sector in developing countries. More specifically, four important issues are empirically addressed in this thesis: (i) What is the tax effort in Sub-Saharan Africa countries over the past decades? (ii) Does the reliance on diversified tax structure enhance resilience to fiscal risks? (iii) Does greater access to formal financial services in developing countries favors more tax revenue collection? (iv) What are the consequences of the increasing conflicts and political instabilities on the financial sector -particularly, the banking sector-in developing countries?

In Chapter 2, the thesis provided new evidence on tax effort based on a new and original database of tax revenue covering 42 Sub-Saharan African countries over 1980-2015, while replicating some previous empirical works on tax effort. The stochastic frontier analysis shows a tax effort score of 0.57 over the period in SSA countries. This corresponds to an average taxto-GDP ratio of 13.2, suggesting a low tax effort and the presence of room for more tax revenue collection. In line with previous analyses, the chapter also found that countries' stage of development measured by per-capita income, financial development and trade openness are important factors improving tax revenue in the region, while natural resource endowment and the importance of the agricultural sector reduce unambiguously the non-resource tax-to-GDP ratio. Finally, the replication exercises broadly confirm previous analyses on the determinants of tax revenue in DCs. Though our results display relative smaller coefficients for some variables suggesting a smaller effect when non-resource tax ratio is used instead of the central government tax revenue.

Chapter 3 was devoted to providing strong evidence that relying on a diversified tax structure contributes to higher tax revenue mobilization and may enhance resilience to fiscal risks. To the best of our knowledge, this chapter is the first of its kind in the empirical literature to propose such a new cross-country tax revenue diversification index (RDI). We find that diversifying the portfolio of tax revenue streams improves revenue collection. In terms of magnitude, the results suggest that a 10 percent increase in the RDI score can yield additional tax revenue of up to 0.2-0.4 percentage points of GDP. The results also evidenced that tax revenue diversification reduces tax revenue volatility, thus bringing to the data long-held views about the prominence of tax revenue diversification for fiscal resilience strengthening.

Furthermore, focusing on the potential drivers of the RDI, we find that tax revenue diversification is not just a reflection of economic diversification, but also an outcome of macroeconomic, political and institutional factors. Interestingly, a non -monotone relationship is also at play between the RDI and economic development, with countries' portfolio of tax sources getting more diversified as their economy develops, until a tipping point, where richer countries start finding it harder to diversify further their tax revenue sources.

Chapter 4 empirically assessed the effect unlocking access to financial services on tax capacity in developing countries. We find that greater access to financial services captured by the number of ATMs per 100,000 adults increases government non -resource tax-to-GDP ratio.

More precisely, an increase of 1 in the log of the number of ATMs per 100,000 adults increases the total non-resources tax ratio by 0.4. This result is driven by household's consumption and business expansion. Greater access to formal financial and banking services favors household's consumption and thereby VAT to collect. Furthermore, easy access to banking services encourages entrepreneurship and income-generating activities which are potential opportunities for taxation. The chapter also extends the analysis to tax revenue composition and finds that financial inclusion is associated with greater indirect taxes mobilization, compared direct taxes.

Finally, Chapter 5 analyzed the impact of conflict and political instability on the probability of banking crises. It shows that conflicts and political instability indeed significantly increase the probability of systemic banking crises in developing countries. In terms of magnitude, the chapter highlights that the odds of a banking crisis are 2.5 times greater when a country is affected by a conflict. Interestingly, the results show that conflicts and political instability in neighboring countries also increase the likelihood of banking crises in a given country.

Moreover, the probability of experiencing a banking crisis is 25 percent when the conflict lasts 10 years, against 16.4 percent when it lasts two years. Finally, fiscal crises turned out to be the primary channel through which conflict and political instability lead to a higher likelihood of turmoil of the banking sector.

The analysis of tax effort in the Chapter 2 of the thesis shows that Sub-Saharan Africa countries, and developing countries in general, still have considerable untapped room for substantial tax revenue collection. This calls for urgent adequate fiscal policies and reforms to improve and strengthen the tax system and reinforce compliance.

The chapter 3 brightly provided evidence that diversifying tax revenue sources matters a great deal for improving tax revenue collection and mitigating government revenue volatility. Tax revenue diversification stands as a key factor to strengthen resilience to fiscal risks arising from government revenue volatility, critical to ensure sustainable delivery of public services throughout different phases of the business cycle in developing countries. The current coronavirus pandemic, for instance, requiring additional public expenditures to cope with its adverse social and economic impact, adds further credence to the criticality of relying on a diversified portfolio of tax revenue streams for strengthening fiscal policy resilience to large swings to business cycle fluctuations.

In chapter 4, the thesis points out that unlocking access to formal financial services is an effective means of raising tax revenue in developing countries. This chapter provides useful insights on tax revenue-harnessing opportunities from pursuing, implementing and reinforcing financial inclusion strategies and policies for developing economies. Beyond the tax opportunities that financial inclusion offers, it may also serve as a social safety net for the ongoing Covid-19 through supporting populations to withstand better the income losses and the consumption decline.

Chapter 5 reveals that the increasing violence and internal unrest in developing countries characterized by conflicts and political instabilities may have disastrous consequences on the banking sector, therefore calling for strong and firm policy action from non-conflict affected countries to prevent from them. In addition, policymakers should pay great attention to conflicts in neighboring countries even if, they are not conflict-afflicted as their banking systems may suffer negative spillovers from their neighbors given that banks operate across borders.

Furthermore, once broken out, governments in conflict-affected countries need to address, as quickly as possible, their root causes and try to mitigate their negative effects with the appropriate design and implementation of economic policies. It finally calls for building adequate fiscal space in normal times can reduce the likelihood of fiscal crises an d in turn lower the probability of systemic banking crises.

The present thesis also provides and opens avenues for possible interesting extensions and future research. First, in the estimate of tax effort, for instance, one possible extension might be considering the characteristics and performance of tax administrations in developing countries by taking advantage of tax administration information database recently developed, including the International Survey on Revenue Administration (ISORA). Besides, the tax effort estimation provided in the thesis is subject to some caveats in addition to the low coverage of the dataset which only covers SSA countries. Indeed, additional explanatory variables, in particular regarding political regimes, may be taken into account in the estimation, which could then modify the ranking of countries. Fully aware of that, the thesis provides a R-Shiny-based website including several additional variables allowing users not only to download the original dataset but also to replicate our empirical analysis and run their own regressions based on alternative specifications with more covariates. Besides, data on GDP across countries significantly change over the years due to the regular update in the base year necessary to their calculation. Second, future research related to tax revenue diversification could take the analysis further by delving into the causal links behind the empirical regularity observed in the data between per capita GDP and tax revenue diversification, along with its transmission channels. The influence of tax revenue diversification on income inequality as well as on policymakers' leeway for implementing countercyclical fiscal policies is additional interesting avenues for future research. Drawing essentially on empirical analyses and mainly focusing on developing countries, the present thesis is concerned with tax revenue mobilization (Chapter 2, Chapter 3, and Chapter 4) and conflicts (Chapter 5) and provides key policy messages. Chapter 2 estimates the tax effort in Sub-Saharan Africa (SSA) based on a new and original non-resources tax revenue database over 1980-2015. It finds an average tax effort score of 0.57 over the period in SSA countries, corresponding to an average non-resources tax-to-GDP ratio of 13.2. This result suggests a low tax effort and the existence of room for more tax revenue collection. SSA countries could raise up to 23.2 percent of GDP in taxes in full-use their tax potential through an improved tax system. In Chapter 3, the thesis explores the impact of relying on a diversified tax structure on tax revenue mobilization and the fiscal resilience, while proposing a new and the first cross-country tax revenue diversification index (RDI). Results show that diversifying the portfolio of tax revenue streams improves revenue collection. Interestingly, the results suggest that tax revenue diversification reduces tax revenue volatility, thus bringing to the data long-held views about the prominence of tax revenue diversification for fiscal resilience strengthening. Finally, we find that tax revenue diversification is not just a reflection of economic diversification, but also an outcome of macroeconomic, political and institutional factors. Chapter 4 studies the impact of unlocking access to financial services on tax capacity. Its finds strong evidence that greater access to financial services increases non-resources tax revenue, highlighting tax revenue-harnessing opportunities from a more inclusive financial sector for developing countries. In the fifth and last Chapter, the thesis analyses the impact of conflict and political instability on the probability of crises in the banking sector -a key sector for domestic development financing. It shows that conflicts and political instability indeed significantly increase the probability of systemic banking crises in developing countries. Interestingly, this chapter finds that conflicts and political instability in neighboring countries also increase the likelihood of banking crises in a given country , highlighting the spillover-effects of conflicts and political instability. En utilisant pleinement leur potentiel fiscal, les pays d'Afrique Subsaharienne pourraient mobiliser un ratio de taxe de l'ordre de 23,2 pour cent du PIB. Dans le chapitre 3, la thèse analyse la diversification des recettes fiscales comme, non seulement un facteur de résilience budgétaire, mais aussi un moyen de mobilisation accrue des recettes fiscales. Ce chapitre développe le tout premier indicateur de diversification des recettes fiscales (IDR) couvrant un large échantillon de pays qui puisse exister dans la littérature économique. Les résultats empiriques montrent que la diversification des recettes fiscales accroit significativement la perception des recettes et réduit la volatilité des revenus du gouvernements constituant donc un important facteur de résilience budgétaire. Aussi, l'environnement macroéconomique, politique et institutionnel ainsi que le niveau de développement constituent les principaux déterminants de la diversification des recettes fiscales dans les pays. Le chapitre 4 étudie l'impact de l'accès aux services financiers sur la pression fiscale. Il révèle qu'un plus grand accès aux services financiers améliore considérablement la mobilisation des recettes fiscales mettant ainsi en évidence les opportunités potentielles de revenus fiscaux liées à l'inclusion financière. Dans le cinquième et dernier chapitre, la thèse examine l'impact des conflits et de l'instabilité politique sur l'occurrence de crises dans le secteur bancaire -un secteur clé pour le financement domestique. Les résultats montrent que les conflits et l'instabilité politique alimentent significativement la probabilité de crises bancaires systémiques dans les pays en développement. Fait intéressant, ce chapitre souligne que les conflits et l'instabilité politique dans les pays voisins augmentent également la probabilité de crises bancaires dans un pays donné mettant ainsi en évidence les effets de débordement des conflits.

List of Figures

Figure 1 . 1 .

 11 Figure 1.1. Tax Ratio and Trend: DCs Vs Developed Countries

Figure 1 . 2 .

 12 Figure 1.2. Tax Revenue Structure

  Figure 1.3. Regional Disparities (Average, 1990-2018)

  framework and provides an overview of taxation in modern economies with special attention to developing economies. More precisely, Chapter 1 provides a comprehensive definition and describes the various role and importance of implementing tax policy. It also identifies the different factors impeding greater tax revenue mobilization in developing countries with a detailed review of the related existing evidence. From this conceptual and empirical background, the next three chapters of the thesis (Chapter 2, Chapter 3, and Chapter 4) focus on tax revenue mobilization in developing countries. More precisely, Chapter 2 examines the Chapter 1: General Introduction and Overview tax effort in Sub-Saharan African (SSA) region 17 by introducing a new database of tax revenue, while challenging some previous evidence through replication exercises. The database is compiled from statistical information of the African Department of the International Monetary Fund covering 42 SSA countries over the period 1980-2015. This chapter is innovative in two ways: first, the dataset compiled allows to distinguish tax revenue from the natural resource sector from the other sectors (hereafter, non-resource taxes), in line with recent works in the literature emphasizing a crowding-out effect of natural resource bonanza on tax revenue.

Figure 2 .

 2 Figure 2.1.a. displays the evolution of the average tax revenue in Africa, in percent of GDP. 24 Note that the volatility of commodity prices explains a large share of revenue variations over the period. However, an improvement of non-resource tax revenue is perceptible since 2000: This revenue stagnated around 12.5 percent of GDP from 1980 to 2000 and reached 15 percent by 2015.

Figure 2 . 1 .

 21 Figure 2.1. Tax Revenue (percent of GDP) in SSA Over Time (1980-2015) 2.1.a. Total tax revenue 2.1.b. Tax composition

Figure 2 .

 2 Figure 2.2 shows the tax performance (i.e., total tax revenue collected) across SSA countries in 1980 (Figure 2.2.a), 1990 (Figure 2.2.b) and 2015 (Figure 2.2.c). 25 Total tax revenue amounted to 91, 117, and 259 billion USD 26 in 1980, 1990, and 2015, respectively. Exploring the countrybased tax performance, it emerges that South Africa and Nigeria are the top performers. More precisely, South Africa collected in percent of total revenue (including resource revenue) 32, 39, and 40 percent of tax revenue, while Nigeria raised 46, 36.2, and 13 percent of taxes in the concerned years, respectively. This highlights the main role of the natural resource sector in aggregate for SSA and the sharp decrease of total tax revenue collected in Nigeria. The variation of total tax revenue over the period 2000-2015 displays contrasting results: while tax revenue decreases by 49 percent in Nigeria, they increase significantly in Mozambique by 615 percent reaching 3.1 billon USD in 2015, in Rwanda by 403 percent (1,2 billon USD), in Chad by 376 percent (1.1 billon USD), and in Ghana by 341 percent (8 billon USD).

Figure 2 . 2 .

 22 Figure 2.2. Total Tax Revenue in SSA

Figure 2 . 4 .

 24 Figure 2.4. Tax Effort over Time (Average Values)

Figure 2 . 5 .

 25 Figure 2.5. Evolution of Countries Tax Effort Over Time

  availability over the period 2000-15. It is made up of 47 advanced economies (AEs), 31 Emerging Market Economies (EMEs), and 49 low-income developing countries (LIDCs). 25 are from Sub-Saharan Africa (SSA), two from North America (NA), 7 from South Asia (SA), 19 from Latin America & the Caribbean (LAC), 14 from the Middle East & North Africa (MENA) of which 5 from the Gulf Cooperation Council (GCC), 21 from East Asia & Pacific (EAP), and 39 from Europe & Central Asia (ECA) (See Appendix 3.1).

Figure 3 .

 3 Figure 3.1 below provides a snapshot of the different tax categories, along with their relative share during 2000-2015 (full sample average values). Indirect taxes (notably taxes on goods

Figure 3 . 1 .

 31 Figure 3.1. Composition of Tax Revenues, 2000-15

Figure 3 . 2 .Figure 3 . 3 .

 3233 Figure 3.2. RDI by Region, Income Group, Fragility Status and Size (Average Values)

Figure 3 . 4 .

 34 Figure 3.4. RDI Over Time

   Vs. Final period

Figure 3 . 5 .

 35 Figure 3.5. Correlation between the RDI and Key Macroeconomic Variables, 2000-2015

Chapter 4 :

 4 Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 101

Chapter 4 :

 4 Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 104

  observations and is compiled from various sources including the World Bank' Worldwide Development Indicators (WDI), the International Monetary Fund's World Economic Outlook (WEO), Penn World Tables (PWT9.1) and the Polity4 project. 70 Though there are currently several available sources of cross-country tax revenue data including the World Bank, the Organization for Economic Co-operation and Development (OECD), and the Comisión Económica para América Latina y el Caribe (CEPAL) , tax revenue data used in this study come from the most recent ICTD/UNU-WIDER, Government Revenue

Figure 4 Figure 4 . 2 .

 442 Figure 4.1. Financial Inclusion Over Time (Average Number of ATMs)

Figure 4 .

 4 Figure 4.2 depicts countries' tax collection performance measured by the non -resource tax-to-GDP ratio as well as the level of financial access proxied by the number of ATMs per 100,000 adults across regions (Panel [A] and [B], respectively). It emerges th at Europe and Central Asia

Chapter 4 :

 4 Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 108 In figure 4.3 we provide the correlation between to financial access measures -the number of ATMs and branches per 100,000 adults -and the total non-resource tax-to-GDP ratio. It clearly appears that more access to financial services is associated with greater tax revenue mobilization.

Figure 4 . 3 .

 43 Figure 4.3. Correlation Between the Number of ATMs and Branches and Non-resource Tax Ratio

Chapter 4 :

 4 Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries 109

Figure 5 . 1 .

 51 Figure 5.1. Number of Countries in Conflict

  consider policy interventions in the banking sector to be significant if at least three out of the following six measures have been used: (a) deposit freezes and/or bank holidays; (b) significant bank nationalizations; (c) bank restructuring fiscal co sts (at least 3 percent of GDP); (d) extensive liquidity support (at least 5 percent of deposits and liabilities to non-residents); (e) significant guarantees put in place; and (f) significant asset purchases (at least 5 percent of GDP). Our sample covers 191 episodes of banking crises. Regarding the data on conflict and political instability, we collected a range of indicators from several sources, covering most of those that have been used in the literature. First, we extract the data on civil wars from the Uppsala Conflict Data Program (UCDP) provided by the Department of Peace and Conflict Research, Uppsala University. In this database, internal armed conflicts are defined as a contested incompatibility concerning government and/or

Figure 5 .

 5 Figure 5.2 displays the relationship between the number of countries in banking crises and conflict. It shows a positive relationship between the occurrence of conflict and banking crises

  in conflict Number of countries in banking crises Fitted values

Figure 5 .

 5 Figure 5.3 presents the predicted values of the likelihood of banking crises for different levels of conflict probability and intensity, and political instability. The predicted values are obtained from the regressions in Table 5.3. The blue lines represent the predicted probability of a banking crisis given the probability of a conflict (panel 3.A), the intensity of conflict (panel 3. B) or the intensity of political instability (panel 3. C-H). The dashed lines indicate the 95 confidence

Figure 1 .

 1 Figure 1.1. Tax ratio and trend: DCs Vs Developed Countries ......................................................................7 Figure 1.2. Tax Revenue Structure .............................................................................................................................8 Figure 1.3. Regional Disparities (average, 1990-2018) .....................................................................................9

1 . 7 Figure 1 . 2 .Figure 1 . 3 . 9 Figure 2 . 1 .Figure 2 . 5 .Figure 3 . 1 .Figure 3 . 3 .Figure 4 . 1 .Figure 5 . 1 .

 1712139212531334151 Figure 1.1. Tax ratio and trend: DCs Vs Developed Countries ......................................................................7 Figure 1.2. Tax Revenue Structure .............................................................................................................................8 Figure 1.3. Regional Disparities (average, 1990-2018) .....................................................................................9

Keywords:

  Tax Effort, Non-resource tax revenue, Sub-Saharan Africa, Revenue Diversification Index (RDI), Fiscal Resilience, Financial Inclusion, Conflict, Political Instability, Developing Countries Résumé: Cette thèse s'intéresse à la mobilisation des recettes fiscales principalement dans les pays en développement en s'appuyant sur des analyses empiriques (chapitres 2, 3 et 4). Elle aborde également la question des conflits (chapitre 5) et propose d'importantes recommandations de politiques économiques. Le chapitre 2 évalue l'effort fiscal dans les pays d'Afrique au Sud du Sahara (ASS) entre 1980 et 2015 en utilisant une base de données nouvelle et originale des recettes fiscales (hors ressources naturelles) développée à cet effet. Sur la période considérée, les pays d'Afrique au Sud du Sahara ont enregistré un score moyen d'effort fiscal de 0,57 correspondant à une pression fiscale moyenne de 13,2 pour cent du PIB. Ce résultat révèle un faible effort fiscal dans ces pays et indique l'existence de possibilités d'accroitre d'avantage le niveau de recettes fiscales.

  Mots clés: Effort Fiscal, Recettes Fiscales hors Ressources, Afrique Subsaharienne, Indice de Diversification des Revenus (IDR), Résilience Fiscale, Inclusion Financière, Conflit, Instabilité Politique, Pays en Développement

1.1. Country List

	Developing		Developed countries
	Afghanistan, Islamic Republic of Mauritius	Argentina	Japan
	Albania	Mexico	Australia	Korea, Republic of
	Armenia, Republic of	Moldova	Austria	Latvia
	Azerbaijan, Republic of	Mongolia	Barbados	Lithuania
	Belarus	Morocco	Belgium	Luxembourg
	Bolivia	Myanmar	Canada	Malta
	Bosnia and Herzegovina	North Macedonia, Republic of	Chile	Nauru
	Brazil	Paraguay	China, P.R.: Hong Kong Netherlands
	Bulgaria	Peru	China, P.R.: Macao	New Zealand
	Cabo Verde	Philippines	Croatia	Norway
	China, P.R.: Mainland	Romania	Cyprus	Poland
	Colombia	Russian Federation	Czech Republic	Portugal
	Congo, Republic of	Rwanda	Denmark	San Marino
	Costa Rica	Samoa	Estonia	Seychelles
	Egypt	Senegal	Finland	Singapore
	El Salvador	Serbia, Republic of	France	Slovak Republic
	Georgia	South Africa	Germany	Slovenia
	Honduras	Tajikistan	Greece	Spain
	Indonesia	Tanzania	Hungary	Sweden
	Jordan	Thailand	Iceland	Switzerland
	Kazakhstan	Timor-Leste, Dem. Rep. of	Ireland	United Arab Emirates
	Kenya	Tonga	Israel	United Kingdom
	Kiribati	Tunisia	Italy	United States
	Kosovo, Republic of	Turkey
	Kyrgyz Republic	Uganda
	Lesotho	Ukraine
	Malaysia	Uzbekistan
	Maldives	Yemen, Republic of

countries Chapter 2. Tax Effort in Sub-Saharan African Countries: Evidence from a New Dataset*

*This chapter was written with E. Caldeira, A. A. Dama, M. Mansour, and G. Rota-Graziosi and a version of it has been published in the journal Revue d'Économie du Développement.

  32 More precisely, we use the model that separates the 𝜎 𝜈 /𝜎 𝜑 and 𝜎 = 𝜎 𝜈 ² + 𝜎 𝜑 ². Similarly, the error 𝜓 𝑖 = µ 𝑖 -𝜂 𝑖 has an asymmetric normal distribution with parameters 𝜆 = 𝜎 µ /𝜎 𝜂 and 𝜎 = 𝜎 µ ² + 𝜎 𝜂 ². The two-term error 𝜀 𝑖 = 𝜀 𝑖,𝑡 + 𝜓 𝑖 is the sum of two asymmetric normal distributions and then admits a known density.

	inefficiencies respectively, while µ 𝑖 and 𝜈 𝑖,𝑡 represent the random effects and the stochastic
	noise, respectively. 33
	error into four components: Generalized True Random Effects model (GTRE). This model was
	introduced by Colombi et al. (2014), Kumbahkar et al. (2014), and Tsionas and Kumbahkar Starting from hypotheses on the distribution of the four errors, the MLE approach of Colombi
	(2014). It presents several advantages: (i) it takes into account random shocks; (ii) it is robust et al. (2014) makes it possible to obtain a form of the log-likelihood. Indeed, assuming that 𝜈 𝑖,𝑡
	to the presence of heterogeneity within the panel; (iii) it allows distinguishing country is independent and identically distributed (iid) with a normal distribution and 𝜑 𝑖,𝑡 is iid with a
	heterogeneity, and persistent and time-varying factors affecting countries' tax effort. Persistent half-normal distribution, the error 𝜀 𝑖,𝑡 = 𝜈 𝑖,𝑡 -𝜑 𝑖 ,𝑡 has an asymmetric normal distribution with
	(i.e., structural) factors are for instance colonial history, culture, geography, the economic parameters 𝜆 =
	structure of the country, which have a long-lasting influence on the tax effort. The time-varying
	factors are both country-and time-specific. They include tax policy, tax administration
	performance, natural resources discoveries, and commodity price cycles. For example,
	countries might improve their tax administration performance by cl amping down on tax
	evasion, training their tax officers, or using more sophisticated tax tools. Also, countries' tax
	effort might change following the discovery of natural resources or a boom in commodity
	prices.
	Different methods are proposed in the literature to estimate the parameters of the GTRE model:
	Colombi et al. (2014) use a maximum likelihood estimator (MLE); Kumbahakar et al. (2014)
	propose a multi-step procedure; Tsionas and Kumbahakar (2014) develop a Bayesian approach;
	Filipini and Greene (2016) use a simulated maximum likelihood approach.
	We estimate the following model:
	𝜏 𝑖 ,𝑡 = 𝛼 + 𝑓( 𝑋 𝑖,𝑡 , 𝛽) + µ 𝑖 + 𝜈 𝑖 ,𝑡 -𝜂 𝑖 -𝜑 𝑖 ,𝑡	(2.1)
	The dependent variable, 𝜏 𝑖,𝑡 , represents the logarithm of the total non-resource tax-to-GDP, the
	subscripts i and t denote respectively country and time dimensions and 𝑋 𝑖,𝑡 is a vector of
	covariates explaining countries tax ratio. 𝜂 𝑖 > 0 and 𝜑 𝑖,𝑡 > 0 are the persistent and time-varying

  𝑖,𝑡 -𝜑 𝑖 ,𝑡 + 𝐸(𝜑 𝑖,𝑡 ), 𝐸(𝜂 𝑖 ) =

	√ 𝜋𝜎 𝜂 2	2 𝜋𝜎 𝜑 , and 𝐸(𝜑 𝑖,𝑡 ) = √

Figure 2.3. Correlation between Total Non-resource Tax Revenue and Covariates

		et al. (2003), and Benedek et al. (2012) find a negative relationship. For instance, Diakité et
		al. (2019), focusing on conflict-affected countries, show that aid granted during a period of
		conflict has a positive impact on revenue collection, and this effect is important when aid
		is coupled with technical assistance and appears to be non-linear.
	•
	Descriptive statistics and more details on variables' sources and definitions are provided in
	Appendix 2.2 and 2.3, respectively. Figure 2.3 displays scatter a plot of total non-resource tax
	revenue for each of the explanatory variables used in the baseline specification. These graphs
	tend to confirm the expected relationships.
				2.3.a. GDP Per Capita (Log)				2.3.b. Trade (% GDP)
		50.00							50.00
		40.00				Correlation = 0.35		40.00
	NRTax-to-GDP ratio	20.00 30.00						NRTax-to-GDP ratio	20.00 30.00		Correlation = 0.34
		10.00							10.00
		0.00							0.00
			5	6	7	8	9	10		2	3	4	5	6
				GDPPC (Log & in const. 2010 USD)					Log Trade (% GDP)
				NRTax-to-GDP	95% CI	Fitted values				NRTax-to-GDP	95% CI	Fitted values
				2.3.c. Agriculture (% GDP)		2.3.d. Natural Resource Rents (% GDP)
		50.00							50.00
		40.00							40.00
	NRTax-to-GDP ratio	10.00 20.00 30.00				Correlation = -0.55	NRTax-to-GDP	10.00 20.00 30.00			Correlation = -0.39
		0.00	0	20	40	60		80	0.00	0	20	40	, Pivovarsky 60
				Agriculture, value added (% GDP)					Natural resources rents (% GDP)
				NRTax-to-GDP ratio	95% CI	Fitted values			NRTax-to-GDP ratio	95% CI	Fitted values
	36 The improvement in technologies for farming, including in SSA, and the increase of large farming firms over the
	past two decades, weaken such arguments. Nevertheless, countries have been very slow in rethinking the taxation of agriculture. Source: Tax revenue Dataset for SSA, World Development Indicators, and authors' calculations.

Table 2 .1. The three-stage Estimation Results

 2

	Dependent variable: NRTax ratio
		NRTAX	Direct	CIT	PIT
		(1)	(2)	(3)	(4)
	GDP per capita (log)(-1)	0.265***	0.407***	0.331***	0.410***
		(0.0301)	(0.0450)	(0.0596)	(0.0695)
	Total trade (% GDP)(-1)	0.002***	0.005***	0.008***	0.004***
		(0.0004)	(0.0006)	(0.0010)	(0.0011)
	Agriculture value added (% GDP)(-1)	-0.003**	-0.007***	0.002	-0.022***
		(0.0014)	(0.0021)	(0.0029)	(0.0033)
	Total natural resource rent (% GDP)(-1)	-0.002*	-0.002	-0.001	-0.008**
		(0.0014)	(0.0020)	(0.0029)	(0.0035)
	Constant	0.579**	-1.832***	-2.622***	-2.265***
		(0.2299)	(0.3435)	(0.4537)	(0.5285)
	Observations	1165	1,165	1,086	1,081
	R-squared	0.163	0.240	0.142	0.183
	Number of countries	39	39	38	38
	Panel

A: Stage 2 -Estimation of the Time-varying Tax Inefficiency (Stochastic Frontier)

					Number of obs.	1165
					Wald chi2(1)		317.84
	Log likelihood = 104.87				Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.215	0.012	17.830	0.000	0.191	0.239
	usigmas (_cons)	-2.597	0.106	-24.520	0.000	-2.804	-2.389
	vsigma (_cons)	-3.759	0.100	-37.690	0.000	-3.954	-3.563
	Panel

B: Stage 3 -Estimation of the Persistent Tax Inefficiency (Stochastic Frontier)

					Number of obs.	1165
					Wald chi2(1)		1400.00
	Log likelihood = -371.66				Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.446	0.012	37.420	0.000	0.423	0.470
	usigmas (_cons)	-1.275	0.060	-21.340	0.000	-1.392	-1.158
	vsigma (_cons)	-3.790	0.116	-32.630	0.000	-4.018	-3.

563 Panel C: Summary of Tax Effort Estimation Results

		Chapter 2: Tax Effort in Sub-Saharan African Countries: Evidence from a New Dataset
	economic features, SSA countries would raise on average 23.16 percent of GDP of non -resource
	taxes if they fully used their tax potential, rather than the actual 13.22 percent. Furthermore, it
	is worth underscoring that time-varying factors account for only 36 percent of the total tax
	effort. Thus, SSA countries would gain significant additional tax revenue by addressing issues
	related to time-varying factors. In the sample, the minimum tax effort score is 0.057 (Equatorial
	Guinea in 2011) and the maximum is 0.847 (Burundi in 1998). Note that the tax effort has
	improved slightly over the period (Figure 2.5)-from 0.57 during 1980-1989 to 0.58 during the
	most recent period. An important result is that the number of countries that have improved
	their tax effort over time is significantly higher than those for which the tax effort has declined
	(Figure 2.6).
		Obs.	Mean	Std. Dev.	Min	Max
	Time-varying tax effort	1165	0.817	0.092	0.318	0.967
	Persistent tax effort	1165	0.698	0.167	0.101	0.942
	Total tax effort	1165	0.572	0.153	0.057	0.847
	*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported
	in brackets. Note: NRTAX: Total non-resource tax revenue; Direct: Total income tax revenue; CIT: Corp orate income tax revenue;
	PIT: Personal income tax revenue.
	The full sample average stands at 0.817 and 0.698 for time-varying and persistent tax effort,
	respectively over 1980-2015. The average total tax effort score is equal to 0.572, suggesting
	that SSA countries mobilize 57 percent of their tax potential. In other words, given their

Table 2 .2. Full Sample Tax Effort-based Ranking (Baseline Specification)

 2

	Source: Tax revenue Dataset for SSA and authors' calculations

Country Tax Effort Rank Tax Effort Rank Tax Effort Rank Tax Effort Rank Average Tax Ratio

		1980-1989	1990-1999	2000-2009		2010-2015
	Angola	-	-	0.265	34 0.279	36	-	-	8.197
	Benin	0.616	14	0.627	12 0.691	8	0.696		14.672
	Botswana	0.390	28	0.361	30 0.411	30	0.455		20.963
	Burkina Faso	0.611	15	0.660	10 0.689	9	0.702		13.150
	Burundi	0.757	5	0.806	1 0.792	1	0.769	3	13.402
	Cabo Verde	0.543	19	0.605	16 0.619	19	0.589		18.706
	Cameroon	0.474	24	0.482	25 0.532	26	0.548		12.745
	Central African Republic 0.607	16	0.560	21 0.569	23	0.557		7.795
	Chad	0.241	32	0.312	31 0.314	32	0.325		5.994
	Comoros	0.557	18	0.605	15 0.593	21	0.603		11.469
	Congo, Dem. Rep.	-	-	-	--	-	-	-	9.142
	Congo, Rep.	0.422	26	0.375	29 0.307	34	0.361		10.867
	Cote d'Ivoire	0.730	6	0.682	9 0.645	13	0.624		14.019
	Equatorial Guinea	-	-	-	-0.082	37	0.071		2.794
	Ethiopia	-	-	-	--	-	0.724	8	12.010
	Gabon	-	-	-	-0.310	33	0.319		11.584
	Gambia, The	-	-	-	-0.735	3	0.742	5	14.933
	Ghana	0.391	27	0.480	26 0.534	25	0.550		14.820
	Guinea	0.494	22	0.422	28 0.521	27	0.570		13.383
	Guinea-Bissau	0.382	29	0.303	32 0.380	31	0.422		8.147
	Kenya	0.674	8	0.704	6 0.702	7	0.714		15.608
	Lesotho	0.808	1	-	--	-	-	-	40.971
	Madagascar	0.638	11	0.574	19 0.626	18	0.607		9.797
	Malawi	0.793	3	0.752	3 0.686	10	0.740	6	14.017
	Mali	0.620	13	0.585	17 0.634	17	0.617		10.846
	Mauritius	0.541	20	0.492	24 0.461	29	0.462		18.276
	Mozambique	0.702	7	0.685	8 0.663	12	0.804	1	19.952
	Namibia	0.634	12	0.717	5 0.708	5	0.734	7	31.133
	Niger	0.604	17	0.546	22 0.640	14	0.689		12.585
	Nigeria	0.259	31	0.269	33 0.298	35	0.232		4.867
	Rwanda	0.659	9	0.623	14 0.703	6	0.717	9	13.704
	Sao Tome and Principe -	-	-	--	-	-	-	15.403
	Senegal	0.658	10	0.689	7 0.739	2	0.755	4	19.041
	Seychelles	0.784	4	0.767	2 0.635	16	0.583		30.933
	Sierra Leone	0.318	30	0.448	27 0.517	28	0.516		9.327
	South Africa	0.491	23	0.573	20 0.585	22	0.587		22.829
	Swaziland	0.519	21	0.542	23 0.606	20	0.633		25.526
	Tanzania	-	-	0.649	11 0.558	24	0.605		10.821
	Togo	0.799	2	0.625	13 0.729	4	0.774	2	15.895
	Uganda	0.452	25	0.578	18 0.638	15	0.616		11.278
	Zambia	-	-	0.719	4 0.671	11	0.623		14.583

Appendix 2.2. Data Sources and Definition

  Angola, Benin, Botswana, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d'Ivoire, Equatorial Guinea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, South Africa, Swaziland, São Tomé and Principe, Tanzania, Togo, Uganda, Zambia, Zimbabwe

		Appendices
		Appendix 2.1. Country List
	Variables	Definition	Sources
	Total non-resource tax (% GDP)	Total tax revenues excluding resource rent
	Corporate income tax (% GDP)		Tax revenue dataset,
			Mansour
			(2019)
	GDP PC (constant 2010
	US $)
			World
			Development
			Indicators,
		Index measuring the extent to which the distribution of	World Bank
	Gini index	income (or, in some cases, consumption expenditure) among individuals or households within an economy
		deviates from a perfectly equal distribution
	Net official development
	assistance and official
	aid received (% GDP)

in terms of robust coefficients and significance of the variables. Some explanatory variables are missing. However, we confirm broadly the analysis of Fenechietto and

[START_REF] Fenochietto | Understanding Countries' Tax Effort[END_REF]

.

Tax imposed on corporate income in countries that have a corporate tax

Total direct taxes (% GDP)

Taxes on all income sources (i.e., business profits, wages, portfolio income, income from real property, capital gains, etc.) Personal income taxes (% GDP)

Taxes on individual income Total resource rent (% GDP)

Sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents over GDP.

Sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products Total Trade (% GDP) Volume of imports and exports over GDP Agriculture, value added (% GDP)

Net output of forestry, hunting, and fishing, as well as cultivation of crops and livestock production after adding up all outputs and subtracting intermediate inputs, divided by GDP

Summary of Tax Effort Estimation Results -Baseline Estimation (2)-(4) Dependent Tax Effort Components Obs. Mean Std. Dev. Min Max

		Appendix 2.3. Descriptive Statistics
	Variables		Obs.	Mean	Std. Dev.	Min	Max
	Total Taxes		1473	16.19	8.97	0.57	53.33
	Total non-resource taxes (% GDP)	1473	13.22	7.09	0.55	50.81
	Total income taxes	1473	3.89	2.74	0.18	18.69
	Corporate Income Tax	1373	1.64	1.24	0.00	9.06
	Personal Income Tax	1368	1.84	1.79	0.00	13.33
	GDP per capita (constant 2010 US$)	1474	1892.50	2780.36	131.65 20333.94
	Total trade (% of GDP)	1323	71.3	36.48	6.32	265.98
	Agriculture, value added (% of GDP)	1345	27.42	15.70	0.89	72.03
	Total natural resources rents (% of GDP)	1431	11.61	11.92	0.00	89.17
	Financial development (% of GDP)	1435	0.11	0.08	0.00	0.64
	Net ODA and aid received (% of GDP)	1448	10.79	10.49	-0.25	94.44
		Time-varying tax effort	1165	0.756	0.109	0.223	0.936
	Direct	Persistent tax effort	1165	0.620	0.157	0.064	0.866
		Total tax effort	1165	0.470	0.139	0.031	0.755
		Time-varying tax effort	1086	0.701	0.114	0.092	0.919
	CIT	Persistent tax effort	1086	0.443	0.221	0.045	1.000
		Total tax effort	1086	0.311	0.165	0.019	0.834
		Time-varying tax effort	1081	0.648	0.148	0.031	0.940
	PIT	Persistent tax effort	1081	0.539	0.182	0.035	0.834
		Total tax effort	1081	0.350	0.142	0.006	0.676
						Svirydzenka
						(2016)

Source: Authors' calculations Appendix 2.4.

Source: Authors' calculations Appendix 2.5.

The Three-stage Estimation Results: Controlling for Financial Development

	Dependent variable: NRTax ratio
				NRTAX	Direct	CIT	PIT
				(1)	(2)	(3)	(4)
	GDP per capita (log)(-1)			0.228***	0.337***	0.288***	0.339***
				(0.0309)	(0.0459)	(0.0617)	(0.0716)
	Total trade (%GDP)(-1)			0.001***	0.004***	0.008***	0.004***
				(0.0004)	(0.0006)	(0.0010)	(0.0011)
	Agriculture value added (%GDP) (-1)		-0.003**	-0.007***	0.002	-0.021***
				(0.0014)	(0.0021)	(0.0029)	(0.0033)
	Total natural resource rent (%GDP) (-1)		-0.001	-0.000	-0.000	-0.007*
				(0.0014)	(0.0021)	(0.0029)	(0.0035)
	Financial development (-1)			0.776***	1.259***	0.666	1.070**
				(0.1821)	(0.2718)	(0.4225)	(0.4803)
	Constant			0.739***	-1.500***	-2.411***	-1.912***
				(0.2303)	(0.3418)	(0.4534)	(0.5273)
	Observations			1165	1165	1,086	1,081
	R-squared			0.170	0.245	0.140	0.181
	Number of countries			39	39	38	38
					Number of obs.		1165
					Wald chi2(1)		318.74
	Log likelihood = 111.52				Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.214	0.012	17.850	0.000	0.191	0.238
	usigmas (_cons)	-2.605	0.106	-24.640	0.000	-2.813	-2.398
	vsigma (_cons)	-3.773	0.100	-37.740	0.000	-3.969	-3.577

Panel A: Stage 2 -Estimation of the Time-varying Tax Inefficiency (Stochastic Frontier)

Panel B: Stage 3 -Estimation of the Persistent Tax Inefficiency (Stochastic Frontier)

					Number of obs.		1165
					Wald chi2(1)		1568.67
	Log likelihood = -339.46				Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.445	0.011	39.610	0.000	0.423	0.467
	usigmas (_cons)	-1.278	0.058	-22.180	0.000	-1.391	-1.165
	vsigma (_cons)	-4.016	0.125	-32.070	0.000	-4.261	-3.770
	Panel C: Summary of Tax Effort Estimation Results
			Obs.	Mean	Std. Dev.	Min	Max
	Time-varying tax effort		1165	0.818	0.091	0.322	0.968
	Persistent tax effort		1165	0.698	0.171	0.108	0.946
	Total tax effort		1165	0.572	0.156	0.061	0.848

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors are reported in brackets. Note: NRTAX: Total non-resource tax revenue; Direct: Total income tax revenue; CIT: Corporate income tax revenue; PIT: Personal income tax revenue.

Appendix 2.6.

Summary of Tax Effort Estimation Results -Robustness Check: Controlling for Financial Development (2)-(4) Dependent Tax Effort Components Obs. Mean Std. Dev. Min Max

		Time-varying tax effort	1165	0.757 0.108	0.209	0.941
	Direct	Persistent tax effort	1165	0.637 0.152	0.080	0.874
		Total tax effort	1165	0.483 0.136	0.038	0.766
		Time-varying tax effort	1086	0.700 0.115	0.088	0.919
	CIT	Persistent tax effort	1086	0.456 0.217	0.054	1.000
		Total tax effort	1086	0.320 0.164	0.019	0.840
		Time-varying tax effort	1081	0.646 0.149	0.030	0.941
	PIT	Persistent tax effort	1081	0.555 0.177	0.045	0.838
		Total tax effort	1081	0.360 0.141	0.006	0.676

Sources: Authors' calculations

Appendix 2.7.

The Three-stage Estimation Results: Controlling for ODA and Aid

	Dependent variable: NRTax ratio
				NRTAX	Direct	CIT	PIT
				(1)	(2)	(3)	(4)
	GDP per capita (log)(-1)			0.253***	0.367***	0.280***	0.324***
				(0.0325)	(0.0483)	(0.0643)	(0.0755)
	Total trade (% GDP)(-1)			0.002***	0.005***	0.009***	0.004***
				(0.0004)	(0.0006)	(0.0010)	(0.0012)
	Agriculture value added (%GDP)(-1)		-0.003**	-0.007***	0.001	-0.022***
				(0.0014)	(0.0021)	(0.0030)	(0.0034)
	Total natural resource rent (% GDP)(-1)		-0.002	-0.001	-0.000	-0.006*
				(0.0014)	(0.0020)	(0.0029)	(0.0035)
	ODA (% GDP)(-1)			-0.001	-0.006***	-0.008***	-0.009***
				(0.0011)	(0.0017)	(0.0025)	(0.0027)
	Constant			0.669***	-1.539***	-2.209***	-1.603***
				(0.2491)	(0.3696)	(0.4939)	(0.5760)
	Observations			1137	1137	1062	1057
	R-squared			0.159	0.251	0.155	0.175
	Number of countries			39	39	38	38
				Number of obs.		1137
				Wald chi2(1)		305.96
	Log likelihood = 106.30			Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.213	0.012	17.490	0.000	0.189	0.237
	usigmas (_cons)	-2.617	0.108	-24.270	0.000	-2.828	-2.406
	vsigma (_cons)	-3.752	0.099	-37.750	0.000	-3.947	-3.557

Panel A: Stage 2 -Estimation of the Time-varying Tax Inefficiency (Stochastic Frontier)

Panel B: Stage 3 -Estimation of the Persistent Tax Inefficiency (Stochastic Frontier)

					Number of obs.		1137
					Wald chi2(1)		1387.37
	Log likelihood = -376.46				Prob > chi2		0.0000
		Coef.	Std. Err.	z	P>|z|	[95% Conf. Interval]
	frontier (one)	0.451	0.012	37.250	0.000	0.427	0.475
	usigmas (_cons)	-1.233	0.060	-20.640	0.000	-1.350	-1.116
	vsigma (_cons)	-3.819	0.120	-31.760	0.000	-4.055	-3.583
	Panel C: Summary of Tax Effort Estimation Results
			Obs.	Mean	Std. Dev.	Min	Max
	Time-varying tax effort		1137	0.819	0.090	0.319	0.967
	Persistent tax effort		1137	0.694	0.170	0.099	0.941
	Total tax effort		1137	0.570	0.155	0.056	0.842

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors are reported in brackets. Note: NRTAX: Total non-resource tax revenue; Direct: Total income tax revenue; CIT: Corporate income tax revenue; PIT: Personal income tax revenue.

Appendix 2.8.

Summary of Tax Effort Estimation Results -Robustness Check: Controlling for ODA and Aid (2)-(4) Dependent Tax Effort Components Obs. Mean Std. Dev. Min Max

		Time-varying tax effort	1137 0.763	0.104	0.232 0.935
	Direct	Persistent tax effort	1137 0.619	0.155	0.066 0.863
		Total tax effort	1137 0.474	0.137	0.033 0.758
		Time-varying tax effort	1062 0.699	0.115	0.089 0.917
	CIT	Persistent tax effort	1062 0.419	0.214	0.042 1.000
		Total tax effort	1062 0.294	0.160	0.018 0.823
		Time-varying tax effort	1057 0.647	0.148	0.030 0.940
	PIT	Persistent tax effort	1057 0.554	0.172	0.045 0.829
		Total tax effort	1057 0.360	0.138	0.006 0.655

Source: Authors' calculations Appendix 2.9.

The three-stage Estimation Results: Baseline, Controlling for Financial Development and Official Development Assistance and Aid -Log-Log Specification

	Dependent variable: NRTax ratio
				NRTAX
		(1)		(2)		(3)
	GDP per capita (log)(-1)	0.329***	(0.0350)	0.287***	(0.0349)	0.358***	(0.0370)
	Total trade (% GDP)(-1)	0.279***	(0.0289)	0.282***	(0.0294)	0.296***	(0.0301)
	Agriculture value added (%GDP)(-1)	0.068**	(0.0339)	0.098***	(0.0335)	0.098***	(0.0349)
	Total natural resource rent (% GDP)(-1)	-0.006	(0.0148)	-0.000	(0.0151)	0.004	(0.0149)
	Financial development (-1)			0.204***	(0.0292)
	ODA and Aid (% GDP)(-1)					-0.001	(0.0112)
	Constant	-1.222*** (0.3231)	-0.580*	(0.3404)	-1.600*** (0.3393)
	Observations	1155		1126		1125
	R-squared	0.229		0.261		0.238
	Number of countries	39		39		39
	Panel A:

Stage 2 -Estimation of the Time-varying Tax Inefficiency (Stochastic Frontier)

									(1)	(2)	(3)
							Number of obs.	1155	1126	1125
			(1)	(2)	(3)		Wald chi2(1)	226.97 254.58	224.92
	Log likelihood		141.42 169.20 149.50		Prob > chi2	0.0000 0.0000 0.0000
			(1)			(2)		(3)
	frontier (one)	0.192***	(0.0127)	0.194***	(0.0121)	0.1896***	(0.0126)
	usigmas (_cons)	-2.828***	(0.1254)	-2.812***	(0.1184)	-2.852***	(0.1258)
	vsigma (_cons)	-3.680***	(0.0943)	-3.794***	(0.0981)	-3.6974***	(0.0940)
									(1)	(2)	(3)
						Number of obs.	1155	1126	1125
		(1)	(2)	(3)		Wald chi2(1)	1511.43	1382.52	1515.57
	Log likelihood	-452.54	-385.76	-473.43	Prob > chi2	0.0000	0.0000	0.0000
				(1)			(2)		(3)
	frontier (one)		0.478***	(0.0123)	0.447***	(0.0120)	0.488***	(0.0125)
	usigmas (_cons)		-1.125***	(0.0581)	-1.240*** (0.0597)	-1.056*** (0.0580)
	vsigma (_cons)		-3.661***	(0.1092)	-3.710*** (0.1089)	-3.638*** (0.1098)

Panel B: Stage 3 -Estimation of the Persistent Tax Inefficiency (Stochastic Frontier)

Panel C: Summary of Tax Effort Estimation Results Tax Effort Components Obs. Mean Std. Dev. Min Max

		Time-varying tax effort	1155	0.834	0.078	0.379	0.965
	(1)	Persistent tax effort	1155	0.683	0.175	0.090	0.940
		Total tax effort	1155	0.571	0.158	0.056	0.865
		Time-varying tax effort	1126	0.833	0.082	0.365	0.965
	(2)	Persistent tax effort	1126	0.696	0.169	0.113	0.941
		Total tax effort	1126	0.581	0.154	0.070	0.866
		Time-varying tax effort	1125	0.836	0.078	0.382	0.963
	(3)	Persistent tax effort	1125	0.676	0.179	0.085	0.939
		Total tax effort	1125	0.566	0.161	0.054	0.866

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors are reported in brackets.

Appendix 2.

10. Replication results of Gupta (2007) -Common Correlation Coefficients (Verification)

	Dependent variable: Tax ratio
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GDP per capita (log)	3.624***	3.874***	3.080***	3.360***
		(0.398)	(0.363)	(0.401)	(1.103)
	Agriculture (% GDP)					-0.208*** -0.181*** -0.125*** -0.243***
						(0.0362)	(0.0382)	(0.0352)	(0.0754)
	Import (% GDP)		0.0330	0.112***	0.0484*		0.0338** 0.0821*** 0.0741**
			(0.0213)	(0.0207)	(0.0260)		(0.0158)	(0.0212)	(0.0310)
	Aid (% GDP)			-0.00778	0.0371			-0.00711	0.0419
				(0.0252)	(0.0645)			(0.0442)	(0.0771)
	Debt (% GDP)			0.00512	0.0308*			-0.0111**	0.00927
				(0.00332) (0.0164)			(0.00485)	(0.0153)
	Government stability				0.231				0.297*
					(0.173)				(0.170)
	Corruption				-0.305				-0.433
					(0.553)				(0.539)
	Law and Order				0.348				0.476
					(0.352)				(0.321)
	Tax on Goods and Services				0.337**				0.0674
					(0.169)				(0.181)
	Tax on Income				0.521***				0.441**
					(0.171)				(0.179)
	Tax on Trade				1.151***				0.852***
					(0.199)				(0.191)
	Constant	-8.578*** -12.16*** -9.976***	-21.63*	25.51***	23.50***	20.82***	12.97***
		(3.270)	(2.907)	(3.014)	(11.09)	(0.678)	(0.843)	(1.039)	(2.104)
	Observations	954	926	595	120	1,046	1,022	677	118
	R-squared	0.419	0.443	0.534	0.662	0.312	0.311	0.364	0.614
	Number of countries	93	89	62	20	85	83	59	19

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets.

Appendix 2.

11. Replication results of Gupta (2007) -Panel Specific Correlation Coefficients (Verification)

	Dependent variable: Tax ratio
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GDP per capita (log)	3.668***	4.304***	3.259***	3.543***
		(0.365)	(0.354)	(0.395)	(1.216)
	Agriculture (% GDP)					-0.215*** -0.188*** -0.174*** -0.299***
						(0.0301)	(0.0343)	(0.0313)	(0.0686)
	Import (% GDP)		0.0306	0.128***	0.0268		0.0153	0.0624**	0.0436
			(0.0220)	(0.0209)	(0.0230)		(0.0163)	(0.0297)	(0.0267)
	Aid (% GDP)			-0.0169	0.0338			-0.00306	0.0792
				(0.0244)	(0.0626)			(0.0500)	(0.0669)
	Debt (% GDP)			0.00674*	0.0404***			-0.00510	0.0121
				(0.00352)	(0.0151)			(0.00578) (0.0156)
	Government stability				0.275				0.348**
					(0.181)				(0.159)
	Corruption				-0.297				-0.353
					(0.463)				(0.384)
	Law and Order				0.173				0.403
					(0.328)				(0.350)
	Tax on Goods and Services				0.577***				0.309*
					(0.143)				(0.168)
	Tax on Income				0.571***				0.309*
					(0.166)				(0.166)
	Tax on Trade				1.135***				0.690***
					(0.217)				(0.186)
	Constant	-8.602*** -15.32*** -12.04***	-24.46**	26.21***	25.12***	23.07***	14.33***
		(2.993)	(2.636)	(2.829)	(11.11)	(0.660)	(0.860)	(1.340)	(2.159)
	Observations	954	926	595	120	1,046	1,022	677	118
	R-squared	0.678	0.685	0.773	0.801	0.606	0.594	0.602	0.860
	Number of countries	93	89	62	20	85	83	59	19

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors are reported in brackets.

Appendix 2.

12. Replication results of Gupta (2007) -Common Correlation Coefficients (Robustness)

	Dependent variable: Tax ratio
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GDP per capita (log)	2.330***	2.409***	1.993***	0.0881*
		(0.207)	(0.206)	(0.197)	(0.0485)
	Agriculture (% GDP)					-0.169*** -0.162***	-0.103***	-0.00172
						(0.0117)	(0.0120)	(0.0122)	(0.00275)
	Import (% GDP)		0.0166***	0.0537***	0.00266**		0.0280***	0.0555***	0.00315**
			(0.00453)	(0.00691)	(0.00122)		(0.00430)	(0.00670)	(0.00132)
	Aid (% GDP)			-0.0206**	-0.00231			-0.0218**	-0.00264
				(0.00829)	(0.00159)			(0.00973)	(0.00179)
	Debt (% GDP)			-0.00464***	6.78e-05			-0.00450**	1.76e-05
				(0.00143)	(0.000302)			(0.00194) (0.000411)
	Government stability				0.00737				0.00693
					(0.0124)				(0.0116)
	Corruption				-0.0110				-0.0248
					(0.0348)				(0.0344)
	Law and Order				-0.0305				-0.0313
					(0.0356)				(0.0331)
	Tax on Goods and Services				1.045***				1.039***
					(0.0156)				(0.0159)
	Tax on Income				0.969***				0.983***
					(0.0175)				(0.0166)
	Tax on Trade				1.015***				1.015***
					(0.0174)				(0.0169)
	Constant	-3.919** -5.465***	-3.817**	-0.226	19.15***	17.67***	14.32***	0.515***
		(1.735)	(1.710)	(1.576)	(0.338)	(0.374)	(0.405)	(0.431)	(0.189)
	Observations	3,874	3,729	2,317	1,256	4,195	4,073	2,552	1,321
	R-squared	0.321	0.337	0.348	0.953	0.307	0.324	0.341	0.956
	Number of countries	187	183	116	73	181	177	112	71

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Chapter 2: Tax Effort in Sub-Saharan African Countries: Evidence from a New Dataset 57 Appendix 2.

13. Replication results of Gupta (2007) -Panel Specific Correlation Coefficients (Robustness)

	Dependent variable: Tax ratio
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	GDP per capita (log)	2.027***	2.097***	1.984***	0.00538
		(0.169)	(0.159)	(0.197)	(0.0509)
	Agriculture (% GDP)					-0.154*** -0.145***	-0.106***	-0.00234
						(0.0117)	(0.0126)	(0.0130)	(0.00262)
	Import (% GDP)		0.0202***	0.0612***	0.00529***		0.0326***	0.0622***	0.00510***
			(0.00423)	(0.00712)	(0.00116)		(0.00486)	(0.00730)	(0.00128)
	Aid (% GDP)			-0.0198**	-0.00344**			-0.0237**	-0.00340*
	Appendix 2.			(0.00912)	(0.00168)			(0.0103)	(0.00190)
	Debt (% GDP)			-0.00383*** -0.000193			-0.00518***	-8.41e-07
				(0.00133)	(0.000346)			(0.00191)	(0.000430)
	Government stability				0.00524				0.00243
					(0.0110)				(0.0102)
	Corruption				-0.0272				-0.0332
					(0.0328)				(0.0322)
	Law and Order				-0.0471				-0.0378
					(0.0354)				(0.0332)
	Tax on Goods and Services				1.056***				1.042***
					(0.0132)				(0.0146)
	Tax on Income				0.985***				0.999***
					(0.0186)				(0.0176)
	Tax on Trade				1.009***				1.020***
					(0.0197)				(0.0182)
	Constant	-1.206	-2.878**	-3.451**	0.352	18.69***	16.87***	14.40***	0.431***
		(1.382)	(1.289)	(1.504)	(0.373)	(0.352)	(0.447)	(0.488)	(0.154)
	Observations	3,874	3,729	2,317	1,256	4,195	4,073	2,552	1,321
	R-squared	0.613	0.620	0.633	0.984	0.559	0.541	0.555	0.984
	Number of countries	187	183	116	73	181	177	112	71

14. Parameter of the SF tax function for Non-resource dependent countries

		Verification		Robustness
		BC-HN	BC-TN	BC-HN	BC-TN	BC-HN	BC-TN
	GDP per capita (log) Appendix 2.	0.523** (0.203)	0.534*** (0.206)	-7.745* (4.680)	10.66** (5.161)	5.883*** (0.450)	2.494*** (0.490)
	Agriculture (% GDP)	-0.005***	-0.005***	-0.03	-0.03	0.017	-0.057
		(0.002)	(0.002)	(0.04)	(0.04)	(0.036)	(0.038)
	PEE (% GDP)	0.031***	0.031***	1.268***	0.809***	1.184***	0.888***
		(0.006)	(0.006)	(0.136)	(0.142)	(0.134)	(0.134)
	Trade (% GDP)	0.0006**	0.0006**	0.04***	0.005	0.037***	0.008
		(0.0002)	(0.0002)	(0.007)	(0.007)	(0.007)	(0.006)
	GINI index	-0.008***	-0.008***	-0.140***	-0.125*** -0.159*** -0.128***
		(0.001)	(0.001)	(0.025)	(0.028)	(0.026)	(0.028)
	GDP_pc (log)_Squared	-0.022**	-0.023**	0.772***	-0.463
		(0.011)	(0.011)	(0.264)	(0.290)
	Constant	0.966	0.925	32.35	-16.60	-24.63***	23.90
		(0.961)	(0.968)	(20.23)	(23.33)	(5.035)	(0)
	Sigma	-1.880***	-2.066***	4.273***	2.983***	4.575***	2.868***
		(0.185)	(0.455)	(0.220)	(0.180)	(0.205)	(0.151)
	Gamma	3.558***	3.367***	3.196***	1.840***	3.514***	1.678***
		(0.204)	(0.475)	(0.243)	(0.226)	(0.224)	(0.192)
	Mu	(omitted)	0.0892	(omitted)	20.25*** (omitted)	25.09***
			(0.184)		(5.366)		(5.152)
	Eta	-0.004*	-0.004**	-0.024***	-0.002	-0.019***	-0.002
		(0.002)	(0.002)	(0.003)	(0.001)	(0.002)	(0.0008)
	Observations	533	533	561	561	561	561
	Number of countries	68	68	70	70	70	70
	sigma2	0.153	0.127	71.73	19.75	97.01	17.60
	gamma	0.972	0.967	0.961	0.863	0.971	0.843
	sigma_u	0.385	0.350	8.301	4.129	9.706	3.851
	sigma_v	0.065	0.065	1.679	1.645	1.675	1.664

15. Parameter of the SF Tax Function for Non-resource Dependent and Resources Dependent Countries

		Verification		Robustness
		BC-HN	BC-TN	BC-HN	BC-TN	BC-HN	BC-TN
	GDP per capita (log) Chapter 3.	0.599*** (0.193)	0.615*** (0.197)	-9.203* (4.740)	7.646 (4.732)	5.454*** (0.441)	2.188*** (0.493)
	Agriculture (% GDP)	-0.005***	-0.005***	-0.071**	-0.0758**	-0.029	-0.093***
		(0.002)	(0.002)	(0.034)	(0.036)	(0.032)	(0.033)
	PEE (% GDP)	0.035***	0.034***	1.310***	0.838***	1.215***	0.857***
		(0.006)	(0.006)	(0.137)	(0.132)	(0.134)	(0.130)
	Trade (% GDP)	0.0007***	0.0007***	0.028***	0.002	0.023***	0.002
		(0.0002)	(0.0003)	(0.007)	(0.006)	(0.007)	(0.006)
	GINI index	-0.008***	-0.008***	-0.130***	-0.096*** -0.144***	-0.097***
		(0.001)	(0.001)	(0.024)	(0.026)	(0.026)	(0.026)
	GDP_pc (log)_Squared	-0.026***	-0.027***	0.829***	-0.306
		(0.010)	(0.011)	(0.267)	(0.266)
	Constant	0.592	0.536	41.10**	-3.334	-20.41***	20.64***
		(0.909)	(0.923)	(20.47)	(20.87)	(4.878)	(6.686)
	Sigma	-1.888***	-2.171***	4.332***	3.161***	4.647***	3.127***
		(0.181)	(0.417)	(0.205)	(0.146)	(0.187)	(0.136)
	Gamma	3.487***	3.196***	2.938***	1.723***	3.279***	1.677***
		(0.200)	(0.437)	(0.232)	(0.188)	(0.209)	(0.174)
	Mu	(omitted)	0.132	(omitted)	20.66***	(omitted)	20.52***
			(0.158)		(2.212)		(3.569)
	Eta	-0.003	-0.003	-0.018***	-0.0007	-0.014***	-0009
		(0.002)	(0.002)	(0.003)	(0.001)	(0.002)	(0.001)
	Observations	566	566	681	681	681	681
	Number of countries	73	73	95	95	95	95
	sigma2	0.151	0.114	76.13	23.59	104.3	22.82
	gamma	0.970	0.961	0.950	0.849	0.964	0.842
	sigma_u	0.383	0.331	8.503	4.474	10.03	4.384
	sigma_v	0.067	0.067	1.957	1.890	1.946	1.896

Fiscal Resilience Building: Insights from a New Tax Revenue Diversification Index*

  A version of this chapter, co-authored with R. Ouedraogo, N. Mousse Sow, and R. Tapsoba, was published in IMF Working Paper Series and is currently under review in Journal of Public Economics.

*

Table 3 .2. Descriptive Statistics of Tax Revenues (Percent of GDP)

 3

Source: GFS, and authors' calculations

Table 3 .

 3 3 provides an RDI-based country ranking over the period 2000-2015. The top 5 countries with the most diversified structure of tax sources belong to the AEs and EMEs, while the bottom 5 countries are either commodity-dependent or fragile/small countries. Over the most recent period(2010)(2011)(2012)(2013)(2014)(2015), Japan emerges as the top performer in terms of tax revenue diversification (RDI of 0.06), followed by France and the United Kingdom (RDI of 0.15 and 0.17, respectively). Bolivia, Kuwait, and Anguilla display the least diversified structure of tax sources (RDI of1.34, 1.34 and 1.32, respectively). These least diversified economies tend to rely mostly on taxes on goods and services, and international trade.

Table 3 .3. An excerpt of RDI-based Country Ranking 55

 3

  structure of tax sources. Overall, tax revenue diversification appears positively correlated with countries' level of development, as confirmed by Figure3.3, which shows that the concentration of tax revenue decreases as per capita GDP increa ses.

		2000-2004			2005-2009			2010-2015
		Country	RDI	Rank	Country	RDI	Rank	Country	RDI
	1	Japan	0.053	1	Japan	0.055	1	Japan	0.057
	2	France	0.160	2	United Kingdom	0.140	2	France	0.153
	3	United Kingdom 0.167	3	France	0.149	3	United Kingdom	0.172
	4	United States	0.188	4	United States	0.156	4	United States	0.176
	5	South Africa	0.192	5	Switzerland	0.182	5	South Africa	0.178
	…	…	…	…	…	…	…	…	…
	95	Bolivia	1.040	113	Maldives	1.141	120	Bahrain, Kingdom of	1.169
	96	Bahamas, The	1.203	114	Bahamas, The	1.162	121	United Arab Emirates 1.218
	97	Maldives	1.213	115	Qatar	1.173	122	Anguilla	1.317
	98	Qatar	1.215	116	Anguilla	1.350	213	Kuwait	1.336
	99	Anguilla	1.340	117	Burkina Faso	1.350	124	Bolivia	1.336

concentrated

Table 3 .5. Macroeconomic and Structural drivers of RDI, by Region and Income Level

 3 , **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Note: Same as inTable 3.4.

	Dependent variable: Revenue diversification index (RDI)
		Baseline	Advanced vs EME/LIDC	Resource rich
		(1)	(2)	(3)
	RDI (-1)	0.611***	0.784***	0.597***
		(0.011)	(0.077)	(0.019)
	RDI × Dummy ADV (-1)		-0.172*
			(0.096)
	Dummy ADV (-1)		0.014
			(0.091)
	Real GDP_pc (-1)	-0.617***	-0.534	-0.407***
		(0.080)	(0.342)	(0.146)
	Real GDP_pc_squared (-1)	0.038***	0.032	0.026***
		(0.005)	(0.021)	(0.009)
	Financial development (-1)	-0.396***	0.003	-0.357**
		(0.108)	(0.238)	(0.149)
	Trade openness (-1)	-0.032*	-0.034	-0.012
		(0.019)	(0.042)	(0.027)
	Export concentration index (-1)	0.025***	0.043***	-0.004
		(0.007)	(0.015)	(0.021)
	Dummy_EAP	0.027
		(0.041)
	Dummy_LAC	0.081***
		(0.031)
	Dummy_MENA	0.240***
		(0.047)
	Dummy_SA	0.140***
		(0.039)
	Dummy_SSA	-0.002
		(0.033)
	Resource rich dummy			0.095**
				(0.043)
	Constant	2.631***	2.195	1.822***
		(0.346)	(1.360)	(0.601)
	Nb. of observations	1141	1141	1141
	Countries	97	97	97
	AR(1)	0.08	0.07	0.09
	AR(2) p-value	0.30	0.31	0.29
	Hansen OID (p-value)	0.07	0.71	0.17
	Nb. of instruments	70	35	58
	Year FE	Yes	Yes	Yes
	Region FE	Yes	No	Yes

*

Table 3 .6. Political and Institutional Drivers of RDI, 2000-2015

 3

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Note: Same as in

Table 3.4.

Table 3 .8. Effects of RDI on Tax Revenue Volatility

 3 suggest that a one-point improvement in tax revenue diversification is associated with a reduction in tax revenue volatility of up to 0.5-2.8 points. , **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Note: Same as in Table7.

	Dependent variable: Tax revenue (in % GDP)
		Baseline and additional controls			Additional controls			Political and institutional controls
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Tax revenue (-1)	0.866*** 0.933*** 0.925*** 0.929*** 0.928***	0.966*** 0.955*** 0.912*** 0.917*** 0.929***	0.938***	0.922***
		(0.013)	(0.010)	(0.010)	(0.014)	(0.011)	(0.013)	(0.010)	(0.013)	(0.010)	(0.015)	(0.012)	(0.008)
	RDI (-1)	-0.036*** -0.025*** -0.028*** -0.016*** -0.021***	-0.017* -0.021*** -0.028*** -0.018** -0.029***	-0.015*	-0.012**
		(0.010)	(0.008)	(0.006)	(0.006)	(0.008)	(0.009)	(0.008)	(0.008)	(0.007)	(0.009)	(0.009)	(0.005)
	Real GDP_pc (-1)	0.177***	0.073 0.142*** 0.151***	0.089*	-0.170*** -0.137*** 0.173*** 0.234***	0.039	-0.021	0.118***
		(0.051)	(0.050)	(0.051)	(0.044)	(0.054)	(0.042)	(0.034)	(0.052)	(0.061)	(0.049)	(0.049)	(0.043)
	Real GDP_pc_squared (-1)	-0.010***	-0.005* -0.009*** -0.010*** -0.006*	0.008*** 0.006*** -0.011*** -0.014***	-0.003	-0.001	-0.008***
		(0.003)	(0.003)	(0.003)	(0.002)	(0.003)	(0.002)	(0.002)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
	Trade openness (-1)		0.002	-0.001	-0.001	0.001	0.002	0.002	0.016*	0.001	0.003	0.000	-0.001
			(0.005)	(0.005)	(0.005)	(0.006)	(0.003)	(0.003)	(0.009)	(0.006)	(0.004)	(0.004)	(0.009)
	Informality (-1)			-0.038*
				(0.023)
	Agricultural VA (-1)				-0.018**
					(0.008)
	Natural ress. rents (-1)					-0.102***
						(0.024)
	Internal conflicts (-1)						0.004*
							(0.002)
	Political risks (-1)							0.001***
								0.000
	Official ER (-1)								0.015***
									(0.005)
	Public Debt/GDP (-1)								0.028***
										(0.006)
	FDI net inflows (-1)										0.003*
											(0.001)
	Quality of bureaucracy (-1)											0.040***
												(0.007)
	Democracy (-1)												0.002**
													(0.001)
	Constant	-0.318	-0.050	-0.136	-0.264	-0.093	0.924*** 0.785*** -0.457** -0.769***	0.059	0.401**	-0.168
		(0.208)	(0.199)	(0.178)	(0.168)	(0.212)	(0.180)	(0.164)	(0.200)	(0.258)	(0.184)	(0.200)	(0.173)
	Nb. of observations	1223	1191	958	1139	1191	930	930	989	1174	1109	930	991
	Countries	104	102	84	101	102	75	75	90	100	100	75	83
	AR(1)	0.00	0.00	0.00	0.00	0.00	0	0	0	0	0	0.00	0.00
	AR(2) p-value	0.43	0.49	0.11	0.78	0.48	0.08	0.08	0.48	0.46	0.97	0.08	0.29
	Hansen OID (p-value)	0.25	0.16	0.10	0.13	0.17	0.86	0.79	0.41	0.33	0.27	0.64	0.22
	Nb. of instruments	80	81	87	96	82	84	84	84	84	84	82	83
	Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Region FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

results

*countries over the period 2000-15. To the best of our knowledge, this is the first study to create such an index at the national level. Existing tax revenue diversification indexes were only

Appendix 3.2. Data Sources and Descriptions

	Appendix 3.7	Appendix 3.4. Full RDI-based Country Ranking Appendix 3.4. Full RDI-based Country Ranking (Cont'd) Appendix 3.6. Full HHI RDI-based Country Ranking
	Variables Real GDP_pc Real GDP_pc_squared Financial development Trade openness Exports concentration index Natural resource rents Net ODA received_pc Informal share Inflation rate / Informality De jure globalization index GDP growth Human capital index IMF program dummy Democracy Political polarization Government fractionalization Political/Government stability Largest gov. party orient. Quality of bureaucracy Rule of law Government effectiveness Voice and accountability Control of corruption Agriculture VA Growth volatility Regulatory quality Polity 2 Political risk Internal conflicts Fiscal rules Foreign direct investment (FDI), net inflows Public debt (% GDP) Overall fiscal balance Rank Country Real GDP per capita Square of real GDP per capita Index of financial development Sum of imports and exports over GDP Description Theil index of exports concentration Natural resource rents in percentage of GDP Net Official Development Assistance received per capita Share of the informal sector in the economy (percentage) Consumer price index growth rate (in percentage) It measures the extent of investment restrictions, capital account oppenness and international investment agreements. Rate of real GDP growth Human capital index, based on years of schooling and returns to education Penn World Tables 9.1 IMF's World Economic Outlook (WEO) database Data sources Authors' calculations Svirydzenka (2016) World Bank's World Development Indicators IMF datasets World Bank's World Development Indicators World Bank's World Development Indicators Medina, Jonelis and Cangul (2017) IMF's World Economic Outlook (WEO) database Gygli et al. (2019) IMF's World Economic Outlook (WEO) database Binary variable taking the value of 1 if the country has an IMF program and 0 otherwise IMF datasets Degree of democracy. The polity 2 score ranges from -10 to +10, with higher value representing more democracy. Marshall and Gurr (2018) It measures the maximum polarization between the executive party and the four principle parties of the legislature. Database of Political Institutions It measures the probability that two deputies picked at random from among the government parties will be of different parties. Database of Political Institutions It measures the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means. World Bank's Worldwide Governance Indicators It measures the largest party orientation with respect to economic policy Database of Political Institutions It measures the institutional strength and quality of the bureaucracy International Country Risk Guide (ICRG) It measures the extent to which agents have confidence in and abide by the rules of society World Bank's Worldwide Governance Indicators It measures the quality of public services, the quality of the civil service and the degree of its independence from political pressures and the quality of policy formulation World Bank's Worldwide Governance Indicators It measures the extent to which a country's citizens are able to participate in selecting their government, and freedom of expression, association and a free media World Bank's Worldwide Governance Indicators It represents the extent to which public power is exercised for private gain, including petty and grand forms of corruption. World Bank's Worldwide Governance Indicators Agriculture valued added (in percentage of GDP) World Bank's World Development Indicators Standard deviation of GDP growth (using rolling window method) Authors' calculations It measures the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. World Bank's Worldwide Governance Indicators Measure of the degree of democracy/ autocracy ranging from +10 (strongly democratic) to !10 (strongly autocratic) Marshall and Gurr (2018) Assessment of countries's political stability Assessment of political violence in the country and its actual or potential impact on governance Dummy: 1 if numerical fiscal rule in place, 0 otherwise IMF Fiscal Rules Dataset Direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. World Bank's Worldwide Governance Indicators General government total debt, percent of fiscal year GDP Overall fiscal balance percentage of GDP IMF's World Economic Outlook (WEO) database International Country Risk Guide (ICRG) RDI Rank Country RDI Rank Country 1 Japan 0.053 1 Japan 0.055 1 Japan 2 France 0.160 2 United Kingdom 0.140 2 France 3 United Kingdom 0.167 3 France 0.149 3 United Kingdom 4 United States 0.188 4 United States 0.156 4 United States 5 South Africa 0.192 5 Switzerland 0.182 5 South Africa 6 Switzerland 0.193 6 South Africa 0.187 6 Switzerland 7 Norway 0.216 7 Israel 0.192 7 Singapore 8 Australia 0.229 8 Australia 0.193 8 Australia 9 Israel 0.230 9 Norway 0.204 9 Norway 10 Spain 0.243 10 Spain 0.209 10 Korea, Republic of 11 Luxembourg 0.245 11 Belgium 0.235 11 Israel 12 Belgium 0.252 12 Cyprus 0.254 12 Belgium 13 Netherlands 0.266 13 Luxembourg 0.255 13 Spain 14 Ukraine 0.285 14 Korea, Republic of 0.266 14 Luxembourg 15 Ireland 0.289 15 Netherlands 0.287 15 Ireland 16 Cyprus 0.300 16 Ireland 0.297 16 Cyprus 17 Oman 0.301 17 Barbados 0.310 17 Iceland 18 Finland 0.305 18 Germany 0.323 18 Netherlands 19 Italy 0.318 19 Poland 0.330 19 Italy 20 Greece 0.334 20 Italy 0.335 20 Malta 21 Yemen, Republic of 0.337 21 Finland 0.335 21 Germany 22 Latvia 0.339 22 Ukraine 0.338 22 Georgia 23 Poland 0.341 23 Oman 0.340 23 Greece 24 Barbados 0.349 24 Greece 0.344 24 Portugal 25 Czech Republic 0.352 25 Iceland 0.344 25 Oman 26 Bhutan 0.352 26 Indonesia 0.347 26 India 27 Germany 0.359 27 Latvia 0.350 27 Poland 28 Denmark 0.378 28 Malta 0.355 28 Kenya 29 Iceland 0.378 29 Czech Republic 0.358 29 Finland 30 Malaysia 0.385 30 India 0.363 30 Azerbaijan, Republic of 31 Portugal 0.386 31 Denmark 0.374 31 Barbados 32 Malta 0.405 32 Azerbaijan, Republic of 0.375 32 Indonesia 33 Montserrat 0.406 33 Yemen, Republic of 0.382 33 Slovak Republic 34 Romania 0.408 34 Portugal 0.383 34 Malawi 35 Austria 0.409 35 Lithuania 0.385 35 Philippines 36 Philippines 0.427 36 Jamaica 0.387 36 Latvia 37 Morocco 0.427 37 Montserrat 0.389 37 Denmark 38 Egypt 0.431 38 Slovenia 0.391 38 Egypt 39 Slovak Republic 0.434 39 Thailand 0.402 39 Montserrat 40 Canada 0.436 40 Egypt 0.407 40 Malaysia 41 Namibia 0.444 41 Romania 0.409 41 Tunisia 42 Georgia 0.447 42 Austria 0.414 42 Ukraine 43 India 0.448 43 Slovak Republic 0.415 43 Peru 44 Slovenia 0.454 44 Philippines 0.419 44 Honduras 45 Jamaica 0.455 45 Canada 0.427 45 Czech Republic 46 Sweden 0.462 46 Malaysia 0.428 46 Jamaica 47 Hungary 0.462 47 Malawi 0.428 47 Brazil 48 São Tomé and Príncipe 0.465 48 Georgia 0.432 48 Thailand 49 Thailand 0.468 49 Tunisia 0.433 49 Austria 50 Lithuania 0.473 50 Kenya 0.434 50 Namibia 51 Bulgaria 0.476 51 Morocco 0.443 51 Romania 52 Honduras 0.480 52 Hungary 0.443 52 Yemen, Republic of 53 Estonia 0.484 53 Peru 0.444 53 Costa Rica 54 Bahrain, Kingdom of 0.495 54 Honduras 0.445 54 Canada 55 San Marino 0.501 55 Sweden 0.452 55 Congo, Republic of 56 St. Kitts and Nevis 0.523 56 El Salvador 0.454 56 Morocco 57 Tunisia 0.534 57 Brazil 0.465 57 Sweden 58 St. Vincent and the Grenadines 0.537 58 Namibia 0.481 58 Solomon Islands 59 Peru 0.547 59 Estonia 0.484 59 Sierra Leone 60 El Salvador 0.549 60 Congo, Republic of 0.491 60 Micronesia, Federated States of 0.470 RDI 0.057 0.153 0.172 0.176 0.178 0.181 0.186 0.193 0.207 0.217 0.220 0.230 0.238 0.252 0.265 0.277 0.297 0.302 0.330 0.330 0.332 0.338 0.339 0.341 0.342 0.345 0.350 0.364 0.367 0.368 0.370 0.379 0.389 0.391 0.391 0.392 0.396 0.398 0.403 0.403 0.404 0.404 0.404 0.405 0.406 0.415 0.415 0.424 0.424 0.427 0.431 0.435 0.435 0.440 0.442 0.443 0.449 0.454 0.464 61 Swaziland 0.574 61 Turkey 0.518 61 Mozambique 0.473 62 Dominica 0.578 62 Costa Rica 0.533 62 China, P.R.: Mainland 0.474 63 Vietnam 0.585 63 Bhutan 0.542 63 Bhutan 2000-2004 2005-2009 2010-2015 65 Sierra Leone 0.595 65 China, P.R.: Mainland 0.558 65 Kiribati 0.491 66 Benin 0.607 66 Cabo Verde 0.568 66 El Salvador 0.496 67 Moldova 0.614 67 São Tomé and Príncipe 0.575 67 Estonia Rank Country RDI Rank Country RDI Rank Country Appendix 3.6. Full HHI RDI-based Country Ranking (Cont'd) RDI 0.497 68 St. Lucia 0.618 68 Ghana 0.589 68 Slovenia 0.498 69 Grenada 0.649 69 Vietnam 0.590 69 São Tomé and Príncipe 0.516 70 Côte d'Ivoire 0.649 70 Armenia, Republic of 0.593 70 Angola 0.521 71 Pakistan 0.656 71 Côte d'Ivoire 0.594 71 Algeria 0.522 72 Armenia, Republic of 0.656 72 Grenada 0.603 72 Swaziland 0.523 73 Colombia 0.665 73 St. Kitts and Nevis 0.608 73 Lithuania 0.524 74 Lesotho 0.684 74 St. Vincent and the Grenadines 0.609 74 Vietnam 0.526 75 Congo, Democratic Republic of 0.688 75 San Marino 0.610 75 Seychelles 0.531 76 Angola 0.693 76 Sierra Leone 0.615 76 Armenia, Republic of 0.534 77 Syrian Arab Republic 0.700 77 Colombia 0.621 77 Turkey 0.544 78 Ethiopia 0.700 78 Congo, Democratic Republic of 0.623 78 Bulgaria 0.550 79 Dominican Republic 0.702 79 Marshall Islands, Republic of 0.629 79 Hungary 0.553 80 Kuwait 0.714 80 St. Lucia 0.632 80 Congo, Democratic Republic of 0.563 81 Croatia 0.720 81 Dominica 0.644 81 Burundi 0.566 82 Congo, Republic of 0.732 82 Croatia 0.647 82 Cabo Verde 0.568 83 Togo 0.751 83 Serbia, Republic of 0.652 83 Bangladesh 0.587 84 Nepal 0.774 84 Benin 0.671 84 Uganda 0.604 85 Mauritius 0.783 85 Swaziland 0.684 85 Lesotho 0.607 86 Kenya 0.799 86 Mauritius 0.688 86 St. Vincent and the Grenadines 0.611 87 Uganda 0.814 87 Uganda 0.702 87 St. Kitts and Nevis 0.611 88 Jordan 0.816 88 Guatemala 0.706 88 Marshall Islands, Republic of 0.628 89 Algeria 0.818 89 Algeria 0.707 89 Serbia, Republic of 0.631 90 Sri Lanka 0.822 90 Moldova 0.716 90 Grenada 0.633 91 Bangladesh 0.824 91 Bangladesh 0.721 91 Samoa 0.635 92 Guatemala 0.875 92 Angola 0.729 92 St. Lucia 0.636 93 Antigua and Barbuda 0.884 93 Dominican Republic 0.736 93 San Marino 0.640 94 Cambodia 1.010 94 Togo 0.738 94 Côte d'Ivoire 0.647 95 Bolivia 1.040 95 Antigua and Barbuda 0.742 95 Guatemala 0.653 96 Bahamas, The 1.203 96 Seychelles 0.749 96 Dominican Republic 0.654 97 Maldives 1.213 97 Jordan 0.749 97 Mauritius 0.659 98 Qatar 1.215 98 Micronesia, Federated States of 0.752 98 Croatia 0.659 99 Anguilla 1.340 99 Kuwait 0.765 99 Dominica 0.669 100 Azerbaijan, Republic of … 100 Ethiopia 0.773 100 Ethiopia 0.710 101 Botswana … 101 Syrian Arab Republic 0.779 101 Pakistan 0.722 102 Brazil … 102 Lesotho 0.790 102 Nepal 0.740 103 Burkina Faso … 103 Paraguay 0.805 103 Benin 0.741 104 Burundi … 104 Nepal 0.824 104 Moldova 0.742 105 Cabo Verde … 105 Equatorial Guinea 0.838 105 Colombia 0.742 106 China, P.R.: Mainland … 106 Botswana 0.860 106 Antigua and Barbuda 0.750 107 Costa Rica … 107 Sri Lanka 0.875 107 Jordan 0.755 108 Equatorial Guinea … 108 Kyrgyz Republic 0.876 108 Paraguay 0.772 109 Indonesia … 109 Cambodia 0.906 109 Kyrgyz Republic 0.781 110 Kiribati … 110 Bolivia 0.966 110 Cambodia 0.790 111 Korea, Republic of … 111 Bahrain, Kingdom of 1.029 111 Kosovo, Republic of 0.833 112 Kosovo, Republic of … 112 West Bank and Gaza 1.087 112 Botswana 0.842 113 Kyrgyz Republic … 113 Maldives 1.141 113 Togo 0.857 114 Lebanon … 114 Bahamas, The 1.162 114 Sri Lanka 0.898 115 Malawi … 115 Qatar 1.173 115 Lebanon 0.942 116 Marshall Islands, Republic of … 116 Anguilla 1.350 116 Maldives 1.017 117 Micronesia, Federated States of … 117 Burkina Faso 1.350 117 West Bank and Gaza 1.041 118 Mozambique … 118 Burundi … 118 Bahamas, The 1.117 119 Paraguay … 119 Kiribati … 119 Qatar 1.159 120 Samoa … 120 Kosovo, Republic of … 120 Bahrain, Kingdom of Appendix 3.5. Alternative Estimates Baseline (1) (2) (3) (4) RDI (t-1) 0.611*** 0.478*** 0.459*** 0.453*** (0.011) (0.024) (0.022) (0.023) Log real GDP_pc (t-1) -0.6170*** -0.8073*** -0.7172*** -0.7804*** (0.080) (0.123) (0.128) (0.135) Log real GDP_pc_squared (t-1) 0.038*** 0.051*** 0.0442*** 0.0487*** (0.005) (0.008) (0.008) (0.009) Financial development (t-1) -0.3958*** -0.6439*** -0.5385*** -0.6052*** (0.108) (0.133) (0.138) (0.139) Trade openness (t-1) -0.0318* -0.0212 -0.0257 -0.0116 (0.019) (0.026) (0.026) (0.028) Export concentration index (t-1) 0.0252*** 0.0294*** 0.0339*** 0.0302*** (0.007) (0.009) (0.009) (0.009) VA Services / VA Agri. -0.0001*** -0.0001*** 0.000 0.000 VA Services / VA Manuf. 0.0023** 0.0020** (0.001) (0.001) Constant 2.631*** 3.486*** 3.137*** 3.361*** (0.346) (0.518) (0.540) (0.564) Nb. of observations 1141 1089 1074 1074 Countries 97 96 95 95 AR(1) 0.08 0.04 0.05 0.06 AR(2) p-value 0.3 0.14 0.25 0.25 Hansen OID (p-value) 0.07 0.11 0.2 0.22 Nb. of instruments 70 71 71 72 Year FE Yes Yes Yes Yes Region FE Yes Yes Yes Dependent variable: Revenue diversification index (RDI) Controling for VA share of services 1 Japan 0.276 1 Japan 0.276 1 Japan 0.277 2 France 0.326 2 United Kingdom 0.316 2 France 65 Ghana 0.553 65 Brazil 0.518 65 Marshall Islands, Republic of 0.490 0.322 3 Norway 0.328 3 France 0.321 3 South Africa 66 Sierra Leone 0.554 66 Turkey 0.519 66 Brazil 0.493 0.325 4 United Kingdom 0.329 4 Norway 0.322 4 Norway 67 Dominica 0.561 67 Cabo Verde 0.534 67 Estonia 0.495 0.326 5 South Africa 0.329 5 South Africa 0.323 5 United Kingdom 68 Pakistan 0.573 68 Costa Rica 0.539 68 Seychelles 0.496 0.332 6 Switzerland 0.341 6 Australia 0.331 6 Switzerland 69 Benin 0.587 69 São Tomé and Príncipe 0.541 69 Costa Rica 0.500 0.335 7 Oman 0.342 7 United States 0.334 7 Australia 70 Angola 0.588 70 Ghana 0.550 70 San Marino 0.502 0.335 8 Australia 0.35 8 Switzerland 0.335 8 Singapore 71 St. Lucia 0.59 71 Bulgaria 0.558 71 Angola 0.503 0.342 9 Luxembourg 0.351 9 Israel 0.343 9 United States 72 Moldova 0.599 72 China, P.R.: Mainland 0.561 72 Swaziland 0.503 0.346 10 United States 0.351 10 Spain 0.347 10 Belgium 73 Lesotho 0.608 73 Côte d'Ivoire 0.563 73 Slovenia 0.505 0.355 11 Israel 0.356 11 Belgium 0.356 11 Luxembourg 74 Côte d'Ivoire 0.611 74 Sierra Leone 0.566 74 El Salvador 0.509 0.356 12 Yemen, Republic of 0.364 12 Luxembourg 0.357 12 Spain 75 Ethiopia 0.613 75 Micronesia, Federated States of 0.575 75 China, P.R.: Mainland 0.520 0.362 13 Belgium 0.364 13 Oman 0.368 13 Israel 76 Congo, Democratic Republic of 0.618 76 Congo, Democratic Republic of 0.576 76 Lithuania 0.523 0.363 14 Spain 0.366 14 Ireland 0.379 14 Korea, Republic of 77 Kuwait 0.619 77 St. Kitts and Nevis 0.580 77 São Tomé and Príncipe 0.527 0.364 15 Ukraine 0.369 15 Cyprus 0.381 15 Ireland 78 Grenada 0.623 78 St. Vincent and the Grenadines 0.581 78 Congo, Democratic Republic of 0.531 0.368 16 Ireland 0.373 16 Netherlands 0.384 16 Oman 79 Kenya 0.633 79 Armenia, Republic of 0.582 79 Burundi 0.532 0.371 17 Finland 0.377 17 India 0.385 17 India 80 Armenia, Republic of 0.633 80 Syrian Arab Republic 0.583 80 Cabo Verde 0.534 0.372 18 Bhutan 0.379 18 Germany 0.389 18 Iceland 81 Congo, Republic of 0.634 81 Algeria 0.585 81 Pakistan 0.537 0.382 19 Netherlands 0.379 19 Korea, Republic of 0.390 19 Malta 82 Dominican Republic 0.637 82 Guatemala 0.589 82 Hungary 0.537 0.383 20 Italy 0.392 20 Finland 0.391 20 Cyprus 83 Croatia 0.64 83 Croatia 0.593 83 Turkey 0.540 0.385 21 Malaysia 0.395 21 Yemen, Republic of 0.394 21 Kenya 84 Togo 0.656 84 St. Lucia 0.596 84 Bangladesh 0.542 0.389 22 Cyprus 0.401 22 Italy 0.396 22 Netherlands 85 Uganda 0.662 85 Serbia, Republic of 0.599 85 Guatemala 0.547 0.390 23 Germany 0.404 23 Iceland 0.403 23 Germany 86 Nepal 0.669 86 Grenada 0.605 86 Armenia, Republic of 0.548 0.393 24 Czech Republic 0.405 24 Indonesia 0.403 24 Italy 87 Algeria 0.7 87 Swaziland 0.615 87 Uganda 0.553 0.396 25 Iceland 0.414 25 Malta 0.406 25 Malawi 88 Mauritius 0.701 88 Kuwait 0.620 88 Bulgaria 0.563 0.407 26 Austria 0.416 26 Czech Republic 0.406 26 Finland 89 Jordan 0.706 89 Angola 0.620 89 St. Kitts and Nevis 0.580 0.408 27 Latvia 0.419 27 Ukraine 0.408 27 Malaysia 90 Bangladesh 0.714 90 Benin 0.622 90 St. Vincent and the Grenadines 0.580 0.409 28 Denmark 0.425 28 Jamaica 0.414 28 Philippines 91 Guatemala 0.717 91 Uganda 0.624 91 Lesotho 0.582 0.410 29 Greece 0.429 29 Austria 0.417 29 Portugal 92 Antigua and Barbuda 0.727 92 Dominica 0.626 92 Serbia, Republic of 0.592 0.416 30 Malta 0.43 30 Barbados 0.418 30 Georgia 93 Sri Lanka 0.728 93 Seychelles 0.631 93 Samoa 0.596 0.417 31 Montserrat 0.431 31 Latvia 0.419 31 Tunisia 94 Bolivia 0.806 94 Mauritius 0.631 94 St. Lucia 0.600 0.419 32 Philippines 0.438 32 Malaysia 0.420 32 Indonesia 95 Cambodia 0.819 95 Bangladesh 0.640 95 Côte d'Ivoire 0.604 0.419 33 Poland 0.438 33 Egypt 0.420 33 Egypt 96 Bahamas, The 0.914 96 Togo 0.653 96 Colombia 0.610 0.419 34 Barbados 0.438 34 Azerbaijan, Republic of 0.421 34 Austria 97 Maldives 0.92 97 Jordan 0.659 97 Croatia 0.613 0.422 35 Namibia 0.441 35 Denmark 0.423 35 Azerbaijan, Republic of 98 Qatar 0.921 98 Moldova 0.664 98 Dominican Republic 0.615 0.423 36 Portugal 0.442 36 Lithuania 0.425 36 Jamaica 99 Anguilla 0.984 99 Botswana 0.665 99 Ethiopia 0.615 0.428 37 Jamaica 0.448 37 Montserrat 0.425 37 Yemen, Republic of 100 Azerbaijan, Republic of … 100 Dominican Republic 0.669 100 Mauritius 0.615 0.430 38 Egypt 0.449 38 Poland 0.427 38 Montserrat 101 Botswana … 101 Paraguay 0.669 101 Grenada 0.625 0.431 39 India 0.45 39 Philippines 0.429 39 Greece 102 Brazil … 102 Ethiopia 0.672 102 Paraguay 0.640 0.432 40 San Marino 0.455 40 Thailand 0.432 40 Peru 103 Burkina Faso … 103 Antigua and Barbuda 0.684 103 Dominica 0.641 0.435 41 São Tomé and Príncipe 0.46 41 Slovenia 0.433 41 Poland 104 Burundi … 104 Nepal 0.688 104 Nepal 0.645 0.439 42 Lithuania 0.46 42 Malawi 0.434 42 Denmark 105 Cabo Verde … 105 Lesotho 0.692 105 Botswana 0.650 0.439 43 Slovenia 0.462 43 Greece 0.434 43 Bhutan 106 China, P.R.: Mainland … 106 Equatorial Guinea 0.721 106 Jordan 0.655 0.439 44 Bahrain, Kingdom of 0.466 44 Kenya 0.439 44 Latvia 107 Costa Rica … 107 Sri Lanka 0.737 107 Kyrgyz Republic 0.661 0.442 45 Hungary 0.467 45 Tunisia 0.441 45 Thailand 108 Equatorial Guinea … 108 Kyrgyz Republic 0.740 108 Benin 0.664 0.444 46 Romania 0.469 46 Portugal 0.443 46 Sierra Leone 109 Indonesia … 109 Bolivia 0.749 109 Cambodia 0.676 0.445 47 Canada 0.469 47 Morocco 0.457 47 Namibia 110 Kiribati … 110 Cambodia 0.750 110 Moldova 0.676 0.447 48 Estonia 0.471 48 Hungary 0.459 48 Czech Republic 111 Korea, Republic of … 111 Bahrain, Kingdom of 0.796 111 Antigua and Barbuda 0.698 0.449 49 Sweden 0.475 49 Peru 0.459 49 Ukraine 112 Kosovo, Republic of … 112 West Bank and Gaza 0.871 112 Lebanon 0.728 0.452 50 Morocco 0.476 50 Bhutan 0.459 50 Slovak Republic 113 Kyrgyz Republic … 113 Maldives 0.882 113 Togo 0.736 0.452 51 Slovak Republic 0.482 51 Canada 0.462 51 114 Lao People's Democratic Republic … 114 Bahamas, The 0.889 114 Kosovo, Republic of 0.737 Micronesia, Federated States of 0.457 52 Thailand 115 Lebanon … 115 Qatar 0.896 115 Sri Lanka 0.746 0.485 52 116 Malawi … 116 Anguilla 0.988 116 Maldives 0.787 Yes 117 Marshall Islands, Republic of … 117 Burkina Faso 0.896 117 West Bank and Gaza 0.846 1.169 121 Serbia, Republic of … 121 Lebanon … 121 United Arab Emirates *, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported 118 Micronesia, Federated States of … 118 Burundi 0.988 118 Bahamas, The 0.859 1.218 122 Seychelles … 122 Mozambique … 122 Anguilla in brackets. 119 Mozambique … 119 Kiribati … 119 Bahrain, Kingdom of 0.868 1.317 123 Singapore … 123 Pakistan … 123 Kuwait 120 Nicaragua … 120 Kosovo, Republic of … 120 Qatar 0.887 1.336 124 Solomon Islands … 124 Samoa … 124 Bolivia 121 Nigeria … 121 Lebanon … 121 United Arab Emirates 0.920 1.336 125 Turkey … 125 Singapore … 125 Burkina Faso 122 Palau … 122 Mozambique … 122 Anguilla 0.974 … 126 United Arab Emirates … 126 Solomon Islands … 126 Equatorial Guinea 123 Paraguay … 123 Pakistan … 123 Kuwait 0.977 … 127 West Bank and Gaza … 127 United Arab Emirates … 127 Syrian Arab Republic 124 Samoa … 124 Samoa … 124 Bolivia … … 125 Serbia, Republic of … 125 Singapore … 125 Burkina Faso … Sources: Authors' calculations 126 Seychelles … 126 Solomon Islands … 126 Equatorial Guinea … 0.483 64 Ghana 0.595 64 Bulgaria 0.543 64 Ghana Sources: Authors' calculations 0.487 127 Singapore … 127 United Arab Emirates … 127 Syrian Arab Republic …
	Exchange rate	Official exchange rate (LCU per US$, period average)	World Bank's Worldwide Governance Indicators

. Adjusted (accounting for non-tax revenue) RDI-based Country Ranking

	Appendix 3.7. Adjusted (accounting for non-tax revenue) RDI-based Country Ranking
		2000-2004			2005-2009 (Cont'd)		2010-2015
	Rank 1 65 2 66 3 67 4 68 5 69 6 70 7 71 8 72 9 73 10 74 11 75 12 76 13 77 14 78 15 79 16 80 17 81 18 82 19 83 20 84 21 85 22 86 23 87 24 88 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 127 126 125 124 123 122 121 120 119 118 117 116 115 114 113 112 111 110 109 108 107 106 105 104 103 102 101 100 99 98 97 96 95 94 93 92 89 90 91 Chapter 4. Country Japan Maldives Switzerland Bahrain, Kingdom of Israel Qatar United Kingdom Oman Netherlands Anguilla Luxembourg Kuwait Ukraine Bahamas, The Finland Australia Spain Azerbaijan, Republic of Belgium Benin Iceland Bolivia Malta Botswana Morocco Brazil India Bulgaria Czech Republic Burkina Faso Austria Burundi Jamaica Cabo Verde Philippines Canada Honduras China, P.R.: Mainland San Marino Costa Rica Hungary Cyprus Egypt Côte d'Ivoire Thailand Denmark Estonia Equatorial Guinea Sweden Barbados Montserrat Bhutan São Tomé and Príncipe St. Kitts and Nevis Georgia Peru Moldova St. Vincent and the Grenadines Tunisia El Salvador Angola Dominica Namibia St. Lucia Armenia, Republic of Swaziland Grenada Croatia Jordan Bangladesh Colombia Mauritius Nepal Sierra Leone Lesotho Dominican Republic Congo, Democratic Republic of Algeria Sri Lanka Ghana Togo Antigua and Barbuda Syrian Arab Republic Guatemala Congo, Republic of Cambodia Kenya Yemen, Republic of West Bank and Gaza Vietnam United States United Arab Emirates Turkey South Africa Solomon Islands Slovenia Slovak Republic Singapore Seychelles Serbia, Republic of Samoa Romania Portugal Poland Paraguay Pakistan Norway Mozambique Micronesia, Federated States of Marshall Islands, Republic of Malaysia Malawi Lithuania Lebanon Latvia Kyrgyz Republic Kosovo, Republic of Korea, Republic of Kiribati Italy Ireland Indonesia Greece Ethiopia France Germany	RDI 0.044 1 Rank 1.335 65 0.151 2 1.340 66 0.195 3 1.379 67 0.197 4 1.686 68 0.210 5 1.779 69 0.238 6 1.846 70 0.241 7 1.888 71 0.244 8 … 72 0.258 9 … 73 0.264 10 … 74 0.323 11 … 75 0.333 12 … 76 0.339 13 … 77 0.346 14 … 78 0.347 15 … 79 0.352 16 … 80 0.364 17 … 81 0.379 18 … 82 0.382 19 … 83 0.387 20 … 84 0.398 21 … 85 0.399 22 … 86 0.405 23 … 87 0.407 24 … 88 0.426 25 0.426 26 0.435 27 0.435 28 0.448 29 0.451 30 0.454 31 0.464 32 0.471 33 0.495 34 0.506 35 0.517 36 0.520 37 0.527 38 0.560 39 0.598 40 0.617 41 0.629 42 0.629 43 0.637 44 0.642 45 0.650 46 0.651 47 0.658 48 0.697 49 0.747 50 0.749 51 0.750 52 0.757 53 0.786 54 0.787 55 0.808 56 0.837 57 0.902 58 0.911 59 0.950 60 0.960 61 1.021 62 1.142 63 … 127 … 126 … 125 … 124 … 123 … 122 … 121 … 120 … 119 … 118 … 117 … 116 … 115 … 114 … 113 … 112 … 111 … 110 … 109 … 108 … 107 … 106 … 105 … 104 … 103 … 102 … 101 … 100 … 99 … 98 … 97 … 96 … 95 … 94 … 93 … 92 … 89 … 90 … 91	Country Japan Botswana Switzerland Antigua and Barbuda United Kingdom Lesotho Israel Nepal Korea, Republic of Sierra Leone Netherlands Guatemala Spain Togo Belgium Equatorial Guinea Luxembourg Kyrgyz Republic Finland West Bank and Gaza Ukraine Micronesia, Federated States of 0.946 75 RDI Rank 0.049 1 0.784 65 0.143 2 0.786 66 0.153 3 0.792 67 0.169 4 0.812 68 0.209 5 0.814 69 0.225 6 0.823 70 0.230 7 0.888 71 0.243 8 0.897 72 0.250 9 0.901 73 0.260 10 0.930 74 0.264 11 Indonesia 0.273 12 Cambodia 0.975 76 Iceland 0.289 13 Congo, Republic of 0.993 77 Malta 0.306 14 Maldives 1.003 78 India 0.313 15 Uganda 1.013 79 Jamaica 0.335 16 Syrian Arab Republic 1.022 80 Austria 0.336 17 Sri Lanka 1.031 81 Czech Republic 0.338 18 Qatar 1.358 82 Thailand 0.356 19 Oman 1.603 83 Honduras 0.359 20 Bahamas, The 1.765 84 Peru 0.366 21 Anguilla 1.784 85 Egypt 0.368 22 Kuwait 2.013 86 Barbados 0.372 23 Bahrain, Kingdom of 2.324 87 Hungary 0.374 24 Australia … 88 El Salvador 0.375 25 Morocco 0.386 26 Philippines 0.387 27 Montserrat 0.388 28 Georgia 0.403 29 Tunisia 0.404 30 Estonia 0.404 31 Sweden 0.411 32 China, P.R.: Mainland 0.444 33 Costa Rica 0.472 34 Kenya 0.507 35 Armenia, Republic of 0.524 36 Croatia 0.551 37 Colombia 0.556 38 Angola 0.560 39 Serbia, Republic of 0.568 40 San Marino 0.572 41 Congo, Democratic Republic of 0.573 42 Namibia 0.582 43 Azerbaijan, Republic of 0.589 44 Bangladesh 0.600 45 Moldova 0.605 46 St. Kitts and Nevis 0.614 47 St. Vincent and the Grenadines 0.616 48 São Tomé and Príncipe 0.617 49 Mauritius 0.625 50 Jordan 0.635 51 Dominica 0.635 52 Marshall Islands, Republic of 0.635 53 Malawi 0.648 54 Grenada 0.649 55 St. Lucia 0.658 56 Ghana 0.697 57 Bhutan 0.708 58 Cabo Verde 0.731 59 Paraguay 0.734 60 Seychelles 0.739 61 Algeria 0.740 62 Swaziland 0.757 63 Yemen, Republic of … 127 Vietnam … 126 United States … 125 United Arab Emirates … 124 Turkey … 123 South Africa … 122 Solomon Islands … 121 Slovenia … 120 Slovak Republic … 119 Singapore … 118 Samoa … 117 Romania … 116 Portugal … 115 Poland … 114 Pakistan … 113 Norway … 112 Mozambique … 111 Malaysia … 110 Lithuania … 109 Lebanon … 108 Latvia … 107 Kosovo, Republic of … 106 Kiribati … 105 Italy … 104 Ireland … 103 Greece … 102 Germany … 101 France … 100 Ethiopia … 99 Denmark … 98 Côte d'Ivoire … 97 Cyprus … 96 Canada … 95 Burundi … 94 Burkina Faso … 93 Bulgaria … 92 Benin … 89 Bolivia … 90 Brazil … 91	Country Japan Marshall Islands, Republic of Switzerland St. Lucia Singapore Jordan United Kingdom Grenada Korea, Republic of Nepal Israel Moldova Belgium Cabo Verde Luxembourg Dominican Republic Netherlands Burundi Spain Antigua and Barbuda Iceland Kyrgyz Republic Malta Botswana Finland Guatemala Indonesia Togo India Micronesia, Federated States of 0.839 RDI 0.633 0.054 0.661 0.142 0.679 0.147 0.693 0.176 0.694 0.187 0.694 0.196 0.694 0.225 0.709 0.231 0.732 0.237 0.732 0.243 0.738 0.254 0.766 0.282 0.779 0.283 0.819 0.293 0.309 Ukraine Cambodia 0.894 0.315 Honduras Congo, Republic of 0.898 0.330 Peru Maldives 0.910 0.335 Egypt Uganda 0.925 0.339 Austria Sri Lanka 1.047 0.346 Georgia West Bank and Gaza 1.077 0.365 Czech Republic Kiribati 1.086 0.371 Montserrat Lebanon 1.150 0.372 Thailand Qatar 1.382 0.374 Morocco 0.376 Jamaica 0.376 Tunisia Costa Rica Philippines China, P.R.: Mainland Sweden Barbados El Salvador Estonia Solomon Islands Lithuania Kenya Hungary Bhutan Namibia Armenia, Republic of Ghana Sierra Leone São Tomé and Príncipe Angola Algeria Serbia, Republic of Malawi Seychelles Lesotho Croatia St. Kitts and Nevis Samoa Dominica Mauritius Bangladesh Paraguay Azerbaijan, Republic of Swaziland Mozambique Colombia 0.612 Yemen, Republic of … 0.610 Vietnam … 0.610 United States … 0.607 Turkey … 0.603 Syrian Arab Republic … 0.603 South Africa … 0.602 Slovenia … 0.597 Slovak Republic … 0.594 Romania … St. Vincent and the Grenadines 0.578 Portugal … 0.578 Poland … 0.569 Pakistan … 0.563 Norway … Congo, Democratic Republic of 0.561 Malaysia … 0.551 Latvia … 0.544 Kosovo, Republic of … 0.544 Italy … 0.541 Ireland … 0.530 Greece … 0.526 Germany … 0.523 France … 0.481 Ethiopia … 0.477 Equatorial Guinea … 0.470 Denmark … 0.464 Côte d'Ivoire … 0.460 Cyprus … 0.455 Canada … 0.438 Burkina Faso … 0.429 Bulgaria … 0.426 Brazil … 0.418 Bolivia … 0.416 Benin … 0.409 Australia … 0.395 Kuwait 3.383 0.395 Bahrain, Kingdom of 2.927 0.391 Bahamas, The 1.691 0.390 United Arab Emirates 1.465 Anguilla 1.648 Oman 1.682
	64	Uganda	1.196 64	Dominican Republic Sources: Authors' calculations	0.784 64	San Marino	0.614

Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries*

* A version of this chapter is under advanced review in The Quarterly Review of Economics and Finance.

Table 4 .3. Effects of Transmission Channel Variables on Tax Revenue Variable

 4

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Robust standar d errors are reported in brackets. Note: fixed effects are included

Table 4 .4. Robustness Check: Adding more Control Variables

 4 , **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors are reported in brackets. Note:: Constant terms, as well as vector X variables are included but not reported for space purpose.

*

The Impact of Conflicts and Political Instability on Banking Crises in Developing Countries*

  , **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard errors are reported in brackets.

	Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing
									Countries
	Appendix 4.2. Descriptive Statistics
	Variables			Obs. Mean Std. Dev. Min	Max
	Real GDP_pc			739 4,220			3,529	284.4 14,688
	GDP growth (annual %)			739 4.908			4.247	-36.7	34.5
	Total natural resources rents (% of GDP)			738 7.327			7.84	0.071	44.56
	Population, total			741 3.92E+07 5.44E+07 613,353 2.58E+08
	Trade (% of GDP) Appendix 4.5. Robustness Check: Using Alternative Tax Data Source 738 78.54 34.05 21.45	210.4
	Number of credit unions and credit cooperatives Depositors with commercial banks Dependent variable: Total non-resource tax-to-GDP ratio Dependent variable: Non-resource tax-to-GDP ratio	(1) (1)	(2) (2)	373 4,193 455 1.75E+07 2.73E+07 37,746 1.32E+08 18,916 1 111,797 (3) (4) (5) (6) (7) (3) (4) (5) (6)
	Loan accounts with commercial banks Dep. variable (-1)	450 1.46E+07 4.91E+07 1,400 4.67E+08 0.98195*** 0.91934*** 0.99999*** 0.97207*** 0.96158*** 0.94884*** 0.93131*** Tot. Tax Indirect Direct Income Gds & Serv. VAT
	Outstanding deposits with commercial banks Dep. variable (-1)	836 1.61E+08 8.53E+08 170.7 1.10E+10 (0.065) (0.039) (0.035) (0.047) (0.039) 0.93419*** 0.94664*** 0.72407*** 0.55052*** 0.83982*** 0.84138*** (0.026) (0.034)
	Number of commercial bank branches/ 100,000 adults Bank branches /100,000 adults (Log) 0.39697* (0.054)	(0.019)	822 13.68 (0.048)	11.66 (0.073)	0.289 (0.043)	71.21 (0.026)
	Number of depositors with commercial banks/ 1,000 adults (0.216) ATMs /100,000 adults (Log) 0.57344*** 0.03778 0.09862*** 0.17310** 0.03613 0.09707*** 455 568.5 531.8 7.513 3,380
	Number of loan accounts with commercial banks/ 1,000 adults Loan accounts with CBs / 1,000 adults 0.00085** (0.221) (0.038)	450 323.9 (0.035)	359.3 (0.084)	0.529 (0.069)	2,909 (0.038)
	Automated teller machines (ATMs) /100,000 adults Real GDP_pc 0.00051** 0.00041*** 0.00004 -0.00007 0.00025*** 0.00030*** 785 30.95 29.05 0 185.3 (0.000)
	GINI index Agriculture value added / GDP Depositors with CBs/ 1,000 adults (Log) Squared real GDP_pc (Log) Chapter 5:	473 41.33 852 13.3 0.39437*** (0.130) (0.000) -0.22008** -0.09573*** -0.01931 0.01345 -0.06755** -0.09786*** 8.939 24 64.8 8.996 2.089 43.4 (0.000) (0.000) (0.000) (0.000) (0.000)
	Tax-to-GDP ratio Direct taxes Taxes on income Taxes on property Indirect taxes Total taxes on goods and services Value-added tax (VAT) Credit unions and credit cooperatives (Log) Outstanding deposits with CBs (Log) Total natural resources rents (Log) Depositors with CBs (Log) Trade openness Loan accounts with CBs (Log) Agriculture value added Taxes on trade Control variables Polity2 index	781 15.76 680 5.208 731 5.403 601 0.307 749 10.57 761 8.319 675 5.326 0.17861* (0.035) (0.048) 5.674 2.717 0.0182 17.44 3.133 36.33 2.8 0 18.01 0.399 0 1.847 4.233 1.619 26.54 3.77 0.422 18.91 2.98 0 (0.033) (0.017) (0.094) 0.24967 -0.08976* 0.32007*** 0.41351*** -0.18594** 0.04066 (0.096) (0.022) 0.22248** (0.155) (0.047) (0.073) (0.117) (0.079) (0.038) (0.104) 2.61191*** -0.01759 1.55332*** 1.70134*** -0.01693 0.98626*** 0.26622** (0.993) (0.279) (0.511) (0.383) (0.375) (0.274) (0.130) -0.15356* -0.01422 -0.06269** -0.05246 -0.03955 -0.05176*** 0.00000** 14.68 748 1.749 1.759 0.169 (0.082) (0.014) (0.029) (0.051) (0.034) (0.018) (0.000) 13.13 YES YES YES YES YES YES YES 0.05684** 0.01377 0.02256 0.00114 0.01302 0.03541***
	Debt-to-GDP ratio Nb. of observations	585 (0.029)	320 (0.009)	711 41.36 341 282 (0.022)	22.25 591 (0.021)	3.89 341 (0.014)	160.5 320 (0.013)
	Polity2 index Countries Constant	739 4.453 37 31 1.90469 5.33194*** -3.96212 -5.64911 5.34860** 1.76028 5.202 -10 10 61 38 61 37 38
	Inflation AR(1) p-value	0.000 (7.966)	0.003 (1.773)	742 7.314 0.000 0.000 (3.056) (3.996) 7.802 0.000	-3.109 121.7 0.000 0.005 (2.613) (1.610)
	Gross fixed capital formation / GDP AR(2) p-value Nb. of observations	0.938 431	0.277 321	858 25.12 0.933 0.345 320	8.071 0.726 431	6.812 0.971 421	69.53 0.324 411
	Net ODA received_pc Hansen OID (p-value ) Countries	0.145 52	0.276 37	852 45.88 0.308 0.257 37	52	48.88 0.113	-49.54 304.6 0.334 0.350 52 51
	Human capital index Nb. of instruments AR(1) p-value	34 0.000	37 0.003	616 2.359 27 30 0.006		0.55 33 0.009	1.137 32 0.000	3.357 27 0.023
	Financial markets efficiency Year FE AR(2) p-value	YES 0.234	YES 0.213	671 0.188 YES YES 0.858		0.283 YES 0.241	0 YES 0.370	1 YES 0.581
	Private consumption_pc Hansen OID (p-value )	0.121	0.290	430 2,434 0.249		1,709 0.766	311.5 0.176	8,406 0.377
	Remittances/ GDP Nb. of instruments	27	32	840	26	5.93		27	7.208 0.0106 44.13 27 36
	Unemployment Year FE	YES	YES	476 8.049 YES		YES	6.181	0.914 YES	46.03 YES
	Tax revenue diversification index *, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard error s are reported 381 0.622 0.276 0.155 1.539
	Private credit bureau in brackets.			374		17.631	17.063	0	54
		121

**This chapter is co-authored with M. Mlachila, R. Ouedraogo, and W. Sandrine Sourouema. A version was published in IMF Working Paper Series and is currently under advanced review in The World Economy.

Table 5 .1. Number of Countries in Conflict and Experiencing Banking Crises Source

 5 : Uppsala Conflict Data Program,[START_REF] Laeven | Systemic Banking Crises Revisited[END_REF] and authors' calculations. *, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively.

Table 5 .2. Banking and Fiscal Crises in Years of Conflict and Political Instability Source

 5 : Uppsala Conflict Data Program,[START_REF] Laeven | Systemic Banking Crises Revisited[END_REF] and authors' calculations. *, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively.

	1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
			All	No. conflict or political instability	Conflict or political instability	Difference ((5)-(4))	T-test p-value	Pearson Chi2
		Observations	3026	2364	662
	Conflict	Number of banking crises	191	125	66			19.17***
		Probability of a banking crisis	6.3	5.3	10.0	4.68	0.00
		Observations	2975	1768	1207
	Cabinet change	Number of banking crises	191	88	103			17***
		Probability of a banking crisis	6.4	5.0	8.5	3.56	0.00
	Change in effective executive	Observations Number of banking crises Probability of a banking crisis	2975 191 6.4	2532 151 6.0	443 40 9.0	3.07	0.02	8.08*
	Anti-government demonstrations	Observations Number of banking crises Probability of a banking crisis	2994 191 6.4	2160 115 5.3	834 76 9.1	3.79	0.00	54.61**
		Observations	2994	2679	315
	Government crises	Number of banking crises	191	158	33			16.03***
		Probability of a banking crisis	6.4	5.9	10.5	4.58	0.00
		Observations	2994	2689	305
	General strikes	Number of banking crises	191	153	38			27.69***
		Probability of a banking crisis	6.4	5.7	12.5	6.77	0.00
		Observations	2994	2636	358
	Assassinations	Number of banking crises	191	149	42			46.19***
		Probability of a banking crisis	6.4	5.7	11.7	6.08	0.00

Table 5 .

 5

	2 presents the statistics about the occurrence of banking and fiscal crises following a
	conflict or political instability. In column (3), we report the conditional probability of a banking
	crisis following a conflict or political instability, which is closely similar to what we reported in
	column (5) of Table

  provides a method by which it is possible to incorporate both the timeinvariant variables with the demeaned coefficients from the fixed effects model and at the same time use the framework of a random effects model (hence a hybrid model). This method, called the correlated random effects, assumes that the unobserved heterogeneity is a function of the country-level time averages of 𝑋 𝑖𝑡 , which we denote as 𝑋 𝑖 ̅ . That is, 𝐶 𝑖 = 𝜔 + 𝑋 𝑖 ̅ 𝛿 + 𝑎 𝑖 , where 𝑋 𝑖 ̅ is an average of 𝑋 𝑖𝑡 over time for country 𝑖 (hence time invariant); 𝑎 𝑖 is assumed uncorrelated

	with 𝑋 𝑖 ̅ and normally distributed. Therefore, the random effects-Mundlak (1978) model allows
	for modeling the distribution of the omitted variable conditional on the means of the strictly
	exogenous variables, instead of treating the omitted variable as a parameter to estimate. The
	probability that 𝑌 𝑖𝑡 = 1 can now be written as:
	𝑃𝑟(𝑌 𝑖𝑡 = 1 | 𝑋 𝑖𝑡 , 𝐶 𝑖 ) = 𝑃𝑟(𝑌 𝑖𝑡 = 1 | 𝑋 𝑖𝑡 , 𝑋 𝑖 ̅ , 𝐶 𝑖 ) = 𝐹(𝑋 𝑖𝑡 𝛽 + 𝜔 + 𝑋 𝑖 ̅ 𝛿 + 𝑎 𝑖 ) = 𝑋 𝑖𝑡 𝛽 + 𝑋 𝑖 ̅ 𝛿 +
	𝑎 𝑖 + 𝜀 𝑖𝑡

  Figure 5.3 shows clearly that the higher the likelihood or intensity of conflict and political instability, higher the likelihood of experiencing a banking crisis.

	Dependent variable: Banking crisis Variable in column (X) (-1) Exchange rate (-1) M2/reserves (-1) Inflation (-1) Credit growth (-1) External debt (-1) GDP per capita (-1) Economic growth (-1) Terms of trade (-1) Degree of democracy (-1) Nb. of observations 5.3.a. Probability of Conflict (1) (2) Conflict (binary) Conflict (intensity) 0.9097*** 0.4483*** (0.245) (0.167) -0.0903** -0.0892** (0.039) (0.039) 0.5660*** 0.5531*** (0.124) (0.124) 1.0096*** 1.0140*** (0.172) (0.173) 0.4867* 0.3878 (0.293) (0.286) 0.7992*** 0.7981*** (0.144) (0.145) -0.3966 -0.3913 (0.315) (0.317) -0.0324* -0.0312 (0.019) (0.019) 0.0286 0.0910 (1.206) (1.207) -0.0460** -0.0469** (0.019) (0.019) 3,026 3,026 3.c. Number of Cabinet Changes .05 .1 .15 Probability of banking crisis 0 .2 .4 .6 .8 Probability of conflict .25 .3 crisis 2,972 intervals. Figure 5.3. Predicted Probability of a Banking Crisis (3) (4) (5) (6) Cabinet changes Changes in Effective Executive Anti-Government Demonstrations Government Crises 0.3620*** 0.3277** 0.0295* 0.2894** (0.126) (0.150) (0.017) (0.134) -0.0931** -0.0959** -0.0910** -0.1024** (0.039) (0.039) (0.039) (0.040) 0.5605*** 0.5658*** 0.5691*** 0.5675*** (0.123) (0.123) (0.123) (0.123) 1.0163*** 1.0224*** 1.0509*** 1.0638*** (0.169) (0.172) (0.170) (0.172) 0.3468 0.2923 0.3007 0.3571 (0.281) (0.281) (0.280) (0.283) 0.8284*** 0.8410*** 0.8267*** 0.8388*** (0.144) (0.144) (0.144) (0.144) -0.3530 -0.3983 -0.4916 -0.3989 (0.318) (0.316) (0.317) (0.316) -0.0257 -0.0277 -0.0324* -0.0289 (0.019) (0.019) (0.019) (0.020) -0.1673 -0.0626 0.0235 -0.0540 (1.218) (1.218) (1.206) (1.210) -0.0524*** -0.0540*** -0.0466** -0.0514*** (0.019) (0.019) (0.019) (0.019) 5.3.b. Intensity of Conflict (7) General Strikes 0.2211** (0.094) -0.0987** (0.039) 0.5666*** (0.123) 1.0106*** (0.172) 0.2995 (0.280) 0.8137*** (0.144) -0.4452 (0.313) -0.0331* (0.019) 0.0422 (1.204) -0.0518*** (0.019) 5.3.d. Number of Changes in Effective Executive (8) Assassinations 0.0788* (0.042) -0.0899** (0.039) 0.5675*** (0.123) 1.0148*** (0.172) 0.3088 (0.281) 0.8282*** (0.144) -0.4245 (0.317) -0.0340* (0.019) -0.0172 (1.210) -0.0483*** (0.019) 1 .05 .1 .15 .2 Probability of banking crisis 0 1 2 Intensity of conflict crisis .25 .3 5.3.g. Number of General Strikes 5.3.h. Number of Assassinations
	banking	.2					banking	.2
	Probability of	.1 .15					Probability of	.1 .15
		.05						.05
		0	1	2	3		4	0	1				2		3	4
				Number of cabinet changes				Number	of	changes	in	effective	executive
	5.3.e. Number of Anti-government Demonstrations		5.3.f. Number of Government Crises
		.12						.6
	crisis	.1					crisis	.4
	of banking	.08					of banking
	Probability	.06					Probability	.2
		.04						0
		0	2	4	6	8	10	0	1	2		3		4	5	6	7
			Number of anti-government demonstrations					Number of government crises

Source: Authors' calculations

Table 5 .

 5

		.2					.2
	banking crisis	.15				banking crisis	.15
	of					of
	Probability	.1				Probability	.1
		.05					.05
		0	1	2	3	4	0	2	4	6	8	10
				Number of general strikes					Assassinations

4

reports the results when we split the sample into two subsamples: emerging markets and low-income countries, following the IMF classification of countries. The results show that

Table 5 .6. Duration of Conflict and Political Instability

 5

	Dependent variable: Banking crisis	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Conflict (binary)	Conflict (intensity)	Cabinet changes	Changes in Effective Executive	Anti-Government Demonstrations	Government Crises	General Strikes	Assassinations
	Variable in column (X) (-1)	0.0402	-0.0610	0.0215	0.1698	0.0277	0.0368	0.1251	0.0772
		(0.380)	(0.349)	(0.158)	(0.173)	(0.087)	(0.185)	(0.191)	(0.273)
	Variable in column (X) (-1)	1.1330*** 0.4942*** 0.4795*** 0.5990**	0.0291*	0.4784***	0.2162**	0.0902**
		(0.286)	(0.172)	(0.142)	(0.238)	(0.017)	(0.157)	(0.099)	(0.042)
	Variable in column (X), t-1	1.2874*** 0.5474*** 0.3223*	0.6230*	0.0260	0.6454**	0.2094**	0.0934**
		(0.298)	(0.174)	(0.176)	(0.337)	(0.018)	(0.272)	(0.104)	(0.042)
	Variable in column (X) (-1)	1.1588*** 0.4514**	-0.0168	0.6500	0.0258	1.2352***	0.1421	0.1883***
		(0.302)	(0.183)	(0.238)	(0.595)	(0.019)	(0.432)	(0.125)	(0.060)
	Variable in column (X) (-1)	1.0238*** 0.3308*	-0.0762	1.1809*	0.0263	1.4419***	-0.0577	0.4256***
		(0.306)	(0.191)	(0.317)	(0.699)	(0.020)	(0.470)	(0.213)	(0.106)
	Variable in column (X) (-1)	1.3082*** 0.4562**	-0.1093	1.3174*	0.0188	1.6203**	-0.2888	0.5313***
		(0.311)	(0.191)	(0.384)	(0.743)	(0.025)	(0.782)	(0.462)	(0.156)
	Variable in column (X) (-1)	1.4018*** 0.5400***	0.0460	1.4833*	0.0070	0.8735	0.0235	0.6380***
		(0.324)	(0.197)	(0.426)	(0.810)	(0.035)	(1.129)	(0.394)	(0.202)
	Variable in column (X) (-1)	1.5458*** 0.6565***	0.3641	1.4833*	-0.0107	0.9207		1.5646***
		(0.337)	(0.211)	(0.397)	(0.810)	(0.051)	(1.112)		(0.572)
	Variable in column (X) (-1)	1.6883*** 0.6740*** 0.9237**	1.4833*	-0.0490	-	-	1.1000*
		(0.350)	(0.214)	(0.451)	(0.810)	(0.079)	-	-	(0.573)
	Variable in column (X) (-1)	1.6462*** 0.6257*** 1.0215**	1.4833*	-0.0764	-	-	0.5283
		(0.375)	(0.223)	(0.448)	(0.810)	(0.094)	-	-	(0.402)
	Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

*, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent level, respectively. Standard error s are reported in brackets.

  In Appendix 5.2, we use the indicators of country risk from the International Country Risk Guide (ICRG) as in[START_REF] Neumayer | The Impact of Political Violence on Tourism: Dynamic Cross-National Estimation[END_REF]. These variables include the risks of internal conflict (civil war, civil disorder and terrorism), external conflict (cross border conflict, interstate war and foreign pressures) and political risk which is an aggregate index combining both internal and external conflict. The results reported in Appendix 5.2 are in line with our baseline findings as the coefficients associated with the different indicators are positive and highly significant. Appendix 5.3 presents the results obtained using various indicators of conflict and political instability from multiple sources. In column[START_REF]General Introduction and Overview[END_REF](2)(3)(4) we use some data from the Marshall (2017)'s Major Episodes of Political Violence (MEPV) dataset as in Quereshi (2013) and IMF

	Dependent variable: Banking crisis Variable in column (X) (-1) Fiscal crisis (-1) Variable in column (X) x Fiscal crisis (-1) Exchange rate (-1) M2/reserves (-1) Inflation (-1) Credit growth (-1) External debt (-1) GDP per capita (-1) Economic growth (-1) Terms of trade (-1) Degree of democracy (-1) (0.019) and political instability. (1) (2) (3) (4) (5) (6) (7) (8) Conflict (binary) Conflict (intensity) Cabinet changes Changes in Effective Executive Anti-Government Demonstrations Government Crises General Strikes Assassinations 0.5134 0.3031 0.3006 -0.0032 -0.0903 -0.5423 -0.0607 0.0495 (0.340) (0.256) (0.190) (0.296) (0.086) (0.429) (0.254) (0.060) 0.3734** 0.4307** 0.4307** 0.4179** 0.3953** 0.3967** 0.4514** 0.4510** (0.190) (0.188) (0.192) (0.185) (0.185) (0.183) (0.183) (0.183) 0.5597* 0.1554 0.0733 0.4469 0.1528* 1.1221** 0.3469 0.0589 (0.338) (0.255) (0.212) (0.326) (0.087) (0.449) (0.256) (0.080) -0.0787** -0.0783** -0.0815** -0.0827** -0.0794** -0.0988** -0.0890** -0.0771** (0.038) (0.038) (0.039) (0.039) (0.039) (0.041) (0.038) (0.038) 0.5483*** 0.5362*** 0.5416*** 0.5502*** 0.5565*** 0.5679*** 0.5493*** 0.5493*** (0.125) (0.124) (0.123) (0.123) (0.123) (0.124) (0.123) (0.123) 0.9128*** 0.9217*** 0.9223*** 0.9156*** 0.9529*** 0.9344*** 0.9043*** 0.9048*** (0.169) (0.170) (0.167) (0.169) (0.168) (0.172) (0.171) (0.171) 0.5211* 0.4208 0.3800 0.3317 0.3045 0.4215 0.3154 0.3570 (0.295) (0.287) (0.282) (0.281) (0.280) (0.284) (0.281) (0.282) 0.6656*** 0.6769*** 0.7102*** 0.7141*** 0.7036*** 0.6954*** 0.6952*** 0.7031*** (0.148) (0.148) (0.147) (0.147) (0.146) (0.147) (0.147) (0.147) -0.3676 -0.3679 -0.3278 -0.3554 -0.3619 -0.4238 -0.3938 -0.3983 (0.323) (0.322) (0.323) (0.321) (0.326) (0.323) (0.320) (0.322) -0.0321* -0.0321* -0.0267 -0.0251 -0.0298 -0.0229 -0.0336* -0.0355* (0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019) -0.0412 0.0720 -0.1641 -0.0686 0.0289 0.0669 0.0202 -0.0491 (1.204) (1.208) (1.219) (1.221) (1.218) (1.224) (1.211) (1.214) -0.0441** -0.0452** -0.0498*** -0.0515*** -0.0416** -0.0520*** -0.0492*** -0.0465** (0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)

3, several indicators of conflicts and political instability have been used in the literature. In this robustness exercise, we use multiple indicators in an attempt to test the different indicators used so far in the literature to capture the occurrence of conflict

It is worth noting that domestic revenues sources also include non-tax revenues such as royalties and resource rents from extractive industries and, to some extent, user fees for public services, delivered by local governments(Junquera-Varela et al.,

2017).2 See Domestic Revenue Mobilization: Mapping Existing Research, Initiatives, and People for a summary on the increasing number of domestic tax revenue mobilization initiatives over the recent past years.Chapter 1: General Introduction and Overview

Sahel includes Burkina Faso, Chad, Mali, Mauritania, and Niger.

Although indirectly, this Chapter might pretend to show how conflicts and political instabilities may compromise tax revenue collection through their consequences on the financial sector.

The OECD statistics do not report resource revenues unless they are accounted for as corporate taxes. This may not be an issue in OECD countries, where oil revenue is derived primarily through the tax system. However, in SSA countries, production sharing agreements and turnover-based royalties are prominent.

For these authors, a direct optimization of the log-likelihood of the model appears complex.

Note that Senegal comes one place higher.

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors are reported in brackets Chapter 2: Tax Effort in Sub-Saharan African Countries: Evidence from a New Dataset

*, **, and *** denote statistical significance at 10 percent, 5 percent and 1 percent level, respectively. Standard errors ar e reported in brackets. Note: HN: Half Normal, BC: Battese and Coelli, TN: Truncated Normal, PEE: Public Expenditure in Education

Chapter 3: Fiscal Resilience Building: Insights from a New Tax Revenue Diversification Index

The regressions passed the standard diagnostic tests for the validity of instruments -the AR (2) test for the absence of second-order autocorrelation of the error term and Hansen's overidentification test.Chapter 3: Fiscal Resilience Building: Insights from a New Tax Revenue Diversification Index

The average per capita GDP threshold level is $ 4222, corresponding broadly to the current levels recorded by countries such as Georgia, and Tunisia.

Volatility of tax revenue is measured as the standard deviation over a 3-year rolling window.

The regressions passed the standard diagnostic tests for the validity of instruments -the AR (2) test for the absence of second-order autocorrelation of the error term and Hansen's overidentification test.Chapter 3: Fiscal Resilience Building: Insights from a New Tax Revenue Diversification Index

Income groups: HIC: High Income Country; UMIC: Upper Middle Income Country; LMIC: Lower Middle Income. Country; LIC: Low Income Country. Regions: ECA: Europe and Central Asia; EAP: East Asia and Pacific; SA: South Asia; LAC: Latin America; SSA: Sub-Saharan Africa; NA: North America; MENA: Middle East and North Africa

Chapter 4: Access-for-all to Financial Services: Non-resources Tax Revenue-harnessing Opportunities in Developing Countries

List of acronyms

AEs

Advanced Economies

Dealing with Missing Data

We fill missing observations in the GFS using available data from the IMF's Worldwide Revenue Database. We take great care at ensuring consistency between these data and our baseline dataset (GFS). To this end, we first check whether the historical data available in both databases match. Then, we make sure that filling the missing data does not lead to inconsistencies in the resulting database. Particularly, we refrain from filling a gap when this is likely to result in a substantial discrepancy between the total tax figure and the sum of the sub -components. Appendix 3.3 provides an overview of the missing observations that were filled with data from the IMF's Worldwide Revenue Database. conflict and political instability are a predictor of banking crises in both emerging economies and low-income economies. However, the results differ slightly between the two groups of countries. For emerging markets, the coefficients associated with conflict and political instability variables are all positive and significant in all columns, except for government crises and political assassinations. For low-income countries, all coefficients are positive and significant, except those associated with effective changes in the executive, anti -government demonstrations, and general strikes. Considering the case of conflict, its effect on banking crises is higher in low-income countries than in emerging markets. The probability of experiencing a banking crisis increases to 14.12 percent when an emerging market is hit by a conflict, while that probability jumps to 17.15 percent in a low-income country.

. Definition of conflict and political instability variables used in robustness checks

• International Country Risk Guide (ICRG). This database contains two important variables that provide an assessment of the risk of instability: internal conflict and external conflict. The index of internal conflict is an assessment of political violence in the country and its actual or potential impact on governance, and comprises three subcomponents: civil war, terrorism, and civil disorder. The index of external conflict is an assessment both of the risk to the incumbent government from foreign action, ranging from non-violent external pressure to violent external pressure. It encompasses three subcomponents: war, cross-border conflict, and foreign pressures. In the paper, we use not only the indices of internal and external conflicts, but also the different subindices. We also compute an aggregate index of conflict, which is the simple average of the internal and external conflict indices.

• Correlates of Wars (COW). In this database, civil war is defined as an armed conflict between an internationally recognized state and (mainly) domestic challengers, able to mount an organized military opposition to the state. A war must have caused more than 1,000 battle-related deaths in total and within at least a three-year period. We define a binary variable taking the value of 1 if the condition is met, and 0 otherwise.

• Political Terror Scale of Amnesty International. This database provides a measure of political terror defined as violations of physical integrity rights carried out by states or their agents. It refers to state-sanctioned killings, torture, disappearances, and political imprisonment. The data are ranged from 1 to 5, with higher values representing widespread and systemic violations of civil and political rights where murders, disappearances, and torture are a common part of life.

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