Blaise Gnimassoun
email: blaise.gnimassoun@univ-lorraine.fr

Joseph Keneck Massil
email: joseph.keneckmassil@u-paris10.fr

Determinants of corruption: Can we put all countries in the same basket?

Keywords: JEL classification: D73, P16, P35, C11, C31 Corruption, Political Economy, Public Economics, Bayesian Model Averaging, Cross-Sectional Models

This paper aims to study the determinants of corruption by examining specificities relating to the region and the level of economic development. Starting from a cross-sectional study on 130 countries, we rely on the Bayesian Model Averaging (BMA) approach to address the issue of model uncertainty and identify the key determinants of corruption according to the level of development and the region. Our results highlight the need for specific remedies in the fight against corruption given the regional, sociocultural, economic and institutional specificities. Indeed, the key determinants of corruption in sub-Saharan Africa are not the most relevant in the East Asia and Pacific region. Similarly, the most important determinants in developed countries are not the most worrying in developing countries.

INTRODUCTION

The literature abounds with arguments and empirical evidence on the negative effects of corruption. 1 Corruption is considered among others as a factor inhibiting domestic and foreign investment [START_REF] Mauro | Corruption and growth[END_REF][START_REF] Tanzi | Corruption, public investment, and growth[END_REF][START_REF] Campos | The impact of corruption on investment: Predictability matters[END_REF][START_REF] Wei | How taxing is corruption on international investors?[END_REF][START_REF] Habib | Corruption and foreign direct investment[END_REF][START_REF] Méon | Does corruption grease or sand the wheels of growth?[END_REF][START_REF] Beekman | Corruption, investments and contributions to public goods: Experimental evidence from rural Liberia[END_REF], restricting economic growth [START_REF] Mauro | Corruption and growth[END_REF][START_REF] Tanzi | Corruption, public investment, and growth[END_REF][START_REF] Mo | Corruption and economic growth[END_REF][START_REF] Gyimah-Brempong | Corruption, economic growth, and income inequality in Africa[END_REF][START_REF] Méon | Does corruption grease or sand the wheels of growth?[END_REF][START_REF] Aidt | Governance regimes, corruption and growth: Theory and evidence[END_REF] and worsening fiscal deficits, inequality and poverty (Gupta et al., 2002;[START_REF] Gyimah-Brempong | Corruption, economic growth, and income inequality in Africa[END_REF][START_REF] Jong-Sung | A comparative study of inequality and corruption[END_REF][START_REF] Alesina | Corruption, inequality, and fairness[END_REF][START_REF] Glaeser | Corruption in america[END_REF][START_REF] Apergis | The relationship between corruption and income inequality in US states: evidence from a panel cointegration and error correction model[END_REF][START_REF] Oto-Peralías | Does fiscal decentralization mitigate the adverse effects of corruption on public deficits?[END_REF]. All governments, whether of developed or developing countries, hold up the fight against corruption as a priority objective of their economic policy. For developing countries, this commitment is sometimes a condition for receiving financial support from development partners. Despite these pledges, the level of corruption remains high and heterogeneous between countries.

As treatment and healing are dependent on the diagnosis, several studies have striven to study the determinants of corruption by focusing on the historical, socio-cultural, institutional and economic factors2 that could explain it [START_REF] Shleifer | Corruption[END_REF][START_REF] Porta | The quality of government[END_REF][START_REF] Leite | Does mother nature corrupt? Natural resources, corruption, and economic growth[END_REF][START_REF] Ades | Rents, competition, and corruption[END_REF][START_REF] Adsera | Are You Being Served?: Political Accountability and Quality of Government[END_REF][START_REF] Treisman | The causes of corruption: a cross-national study[END_REF][START_REF] Van Rijckeghem | Bureaucratic corruption and the rate of temptation: do wages in the civil service affect corruption, and by how much[END_REF][START_REF] Persson | Electoral rules and corruption[END_REF][START_REF] Acemoglu | The choice between market failures and corruption[END_REF][START_REF] Fisman | Decentralization and corruption: evidence across countries[END_REF][START_REF] Paldam | The cross-country pattern of corruption: economics, culture and the seesaw dynamics[END_REF][START_REF] Adsera | Are you being served? Political accountability and quality of government[END_REF][START_REF] Brunetti | A free press is bad news for corruption[END_REF][START_REF] Knack | Trade intensity, country size and corruption[END_REF][START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF][START_REF] Fan | Political decentralization and corruption: Evidence from around the world[END_REF][START_REF] Henderson | Corruption and local democratization in Indonesia: The role of Islamic parties[END_REF]. This paper is no different, but has the particularity of making a specific diagnosis according to the level of development and the region. Indeed, while it can be accepted that some common factors may explain corruption regardless of the level of economic development or the region, it is also plausible that regional specificities in terms of natural resource endowments and socio-cultural and historical conditions add special features to the determinants of corruption. Despite growing interest among studies on the determinants of corruption, this view has not been explored. However, there are several reasons to think that the determinants of corruption depend on the level of economic development of countries3 and their geographical location (their region).

First, all cross-country analyses on the determinants of corruption show that corruption decreases with the level of economic development. This is the only point of consensus among authors on the determinants of corruption [START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF]. Likewise, in his contribution on the causes of corruption, Trieisman (2000) raised the question of regional specificity, which he analyzes through dummy variables for each region. According to his results, Africa, Eastern Europe, Asia, Latin America and the Middle East are perceived as more corrupt than Western Europe and North America. He also shows that Latin America and Asia are perceived as significantly more corrupt than the average of all other continents. More interestingly, he found that even after controlling for economic development, Latin America and Eastern Europe are significantly more corrupt than Western Europe and North America. These interesting results raise the question of the determinants of corruption according to the level of economic development and the region. In other words, are the poor results of developing countries in terms of reducing corruption not partly due to their specificity? Can we really consider the same causes of corruption across all countries as is generally presented in crosscountry empirical works? If not, what are the particularities or the most relevant determinants depending on economic development and region? And finally what are the traits common to all countries? The answers to these questions bring a new lease of life to the literature on the determinants of corruption and have important implications for targeted anti-corruption policy.

Second, by allowing, as it is often the case in the literature, that corruption is decreasing with the level of economic development, it could be thought that the determinants of corruption also vary with the level of economic development of countries. [START_REF] Billger | Do existing corruption levels matter in controlling corruption?: Cross-country quantile regression estimates[END_REF] show from a quantile regression that certain determinants of corruption, such as democracy and economic freedom, are more or less important depending on the level of corruption. They show that for the most corrupt countries, a greater presence of government and greater economic freedom do not seem to reduce corruption, and this is not the case for the least corrupt countries.

Third, disagreements between authors about the influence of some of the determinants of corruption, such as the legal origin of the law, could be explained by the heterogeneity of the samples considered. For example, while some studies show that a socialist legal origin is associated with greater corruption and that an Anglo-Saxon legal origin would favor lower corruption [START_REF] Porta | The quality of government[END_REF][START_REF] Glaeser | Legal origins[END_REF][START_REF] Gerring | Do neoliberal policies deter political corruption?[END_REF][START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF], other economists have their reservations or disagree with the results [START_REF] Adsera | Are You Being Served?: Political Accountability and Quality of Government[END_REF][START_REF] Brunetti | A free press is bad news for corruption[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF].

This paper provides answers to these questions starting out from the broad literature on the potential determinants of corruption. To this end, it relies on variable selection techniques to address the issue of uncertainty regarding the model specification. These techniques are much better known in economics for addressing the issue of uncertainty in cross-country determinants of economic growth [START_REF] Levine | A sensitivity analysis of cross-country growth regressions[END_REF][START_REF] Sala-I-Martin | I just ran two million regressions[END_REF][START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF][START_REF] Masanjala | Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging[END_REF]. Specifically, we use the Bayesian Model Averaging approach (BMA), which, based on the observed data, identifies the most relevant determinants of corruption, without a priori concerning the corruption model specification. Applied to countries according to their level of development or their region, this method serves to isolate the most robust determinants specific to each country grouping.

Our paper contributes to the literature in several ways. Firstly, as data have become more available and accurate, particularly for developing countries, studies on the determinants of corruption are more likely to be more accurate and informative. However, as it is impossible to take into account all the potential determinants of corruption through a single theoretical model, an empirical approach starting with the set of determinants and then identifying the most relevant of them seems to be the most optimal solution. Next, going further than previous papers on the determinants of corruption, we identify the determinants of corruption specific to the level of economic development and to the region. Finally, by providing a specific analysis framework to better understand the determinants of corruption according to economic development level and geographical location, this article allows governments as well as international organizations to better target the root causes of corruption in order to fight effectively against this scourge.

Based on a set of 130 developed and developing countries, our results show that while some determinants of corruption are common to all countries, others are specific to the level of economic development and to the region. Indeed, for developed countries, corruption is mainly determined by the "willingness to delegate authority,"4 while for developing countries in general, factors such as the quality of education and GDP per capita determine corruption. Focusing on the specificity of sub-Saharan Africa, our results show that freedom of the press, the burden of regulation, linguistic fragmentation, political system, religion and political stability are the determinants of corruption peculiar to this region. In the region of East Asia and the Pacific, corruption is mainly explained by legal origin, religion, political stability and education. In Europe and Central Asia, we find as determinants of corruption freedom of information, the burden of government regulations, legal origin, religion, political stability and education. Regarding Latin America and the Caribbean, freedom of information and education were identified as determinants of corruption. Finally, in the Middle East and North Africa region, determinants of corruption are the political regime, political stability and education.

The rest of the article is organized as follows: Section 2 discusses the potential determinants of corruption. Section 3 presents the empirical approach. Section 4 is devoted to the presentation of results and comments. Section 5 provides a robustness analysis of the results. Finally, Section 6 concludes the paper.

POTENTIAL DETERMINANTS OF CORRUPTION

The determinants of corruption have been extensively addressed in the literature5 since the early 1990s. These determinants can be broadly grouped into three main blocks: historical and cultural factors, economic factors and institutional factors.

Historical and sociocultural factors

In this block of factors, we can group the legal system and the colonial origin, religious culture, ethno-linguistic fragmentation and education.

The legal system. This is generally regarded as one of the leading determinants of corruption. Indeed, the cost of corruption in terms of probability of arrest, exposure and punishment depends on the efficiency of the legal system. Several authors highlight the fact that the common law system that characterizes Britain and its former colonies is more dissuasive than the civil law system which is present in continental Europe and its former colonies [START_REF] Porta | The quality of government[END_REF][START_REF] Treisman | The causes of corruption: a cross-national study[END_REF][START_REF] Glaeser | Legal origins[END_REF][START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF]. For the proponents of this theory based on the historical roots of corruption, the former British colonies have a better code of public service because of the influence of the British bureaucracy. In this system, the functioning of the bureaucracy is focused on the procedural aspects of the law, which improves the ability of subordinates and judges to challenge the hierarchies in order to enforce the law, thereby reducing corruption [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF]. However, the theory of the effectiveness of the British legal system is contested by several authors, such as [START_REF] Adsera | Are You Being Served?: Political Accountability and Quality of Government[END_REF], [START_REF] Brunetti | A free press is bad news for corruption[END_REF], [START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF]. Moreover, like [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF], [START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF] shows that the colonial heritage of a country is an important determinant in explaining contemporary corruption. But colonial heritage is strongly linked with legal origin.

Religious culture. Since the work of [START_REF] Weber | The Religion of India[END_REF] and [START_REF] Putnam | Making Democracy Work: Civic Traditions in Modern Italy[END_REF] on the importance of culture in the quality of institutions, studies have shown that religious culture may explain a significant share of corruption [START_REF] Shleifer | Corruption[END_REF][START_REF] Porta | The quality of government[END_REF]). The explanation is that religious traditions to some extent determine the relationships of individuals with social hierarchy, with the state and the family. For some authors, hierarchical religions such as Catholicism, Islam and Eastern Orthodoxy would be less stringent vis-à-vis the hierarchy. Similarly, the familialist traditions of some religions promote nepotism. From this point of view, Protestantism is perceived as more egalitarian and individualistic and less tolerant of abuses by public authorities. Thus, countries with a high proportion of Protestant worshipers would have a lower level of corruption [START_REF] Porta | The quality of government[END_REF][START_REF] Treisman | The causes of corruption: a cross-national study[END_REF][START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF]. However, like [START_REF] Adsera | Are You Being Served?: Political Accountability and Quality of Government[END_REF], [START_REF] Melgar | The perception of corruption[END_REF] find no influence of religious affiliation on corruption.

Ethnic and linguistic fragmentation. In a pioneering study, [START_REF] Mauro | Corruption and growth[END_REF] shows that countries with a strong ethno-linguistic fragmentation tend to be more corrupt. Similarly, some authors find the same effect with regard to ethnic fragmentation. In countries with high ethnic fragmentation, government officials are more likely to favor members of their ethnic group [START_REF] Shleifer | Corruption[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF]. This effect is however not confirmed in [START_REF] Brunetti | A free press is bad news for corruption[END_REF].

Education. The shaping of the behavior of individuals, civic learning and the exemplary are favored by the school. Thus the quality and level of education are determining factors in the fight against corruption. [START_REF] Melgar | The perception of corruption[END_REF] found that people who have completed at least secondary education are more likely to perceive the level of corruption than people with a primary education level. Similarly, [START_REF] Van Rijckeghem | Bureaucratic corruption and the rate of temptation: do wages in the civil service affect corruption, and by how much[END_REF] believe that good citizenship at school contributes to the reduction of corruption.

Economic factors

Among the economic factors whose influences on corruption are discussed in the literature, the level of development, government wage, natural resource rents and economic openness can all be put forward.

Economic development. This is the variable among the determinants of corruption on which there is a consensus in the literature. Corruption tends to be reduced with economic development [START_REF] Paldam | The cross-country pattern of corruption: economics, culture and the seesaw dynamics[END_REF][START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF][START_REF] Goel | Causes of corruption: History, geography and government[END_REF][START_REF] Melgar | The perception of corruption[END_REF]. For example, [START_REF] Paldam | The cross-country pattern of corruption: economics, culture and the seesaw dynamics[END_REF] shows that the level of corruption decreases when the country moves from being a poor country to a rich country. Similarly, according [START_REF] Melgar | The perception of corruption[END_REF], the level of corruption tends to be reduced with the country's economic performance. For [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF] there is a causal relationship between the improvement in income level and the decline in corruption, from the level of economic development to corruption.

Government wage. The influence of wage on corruption has been theorized by [START_REF] Becker | Crime and punishment: an economic approach[END_REF]. [START_REF] Van Rijckeghem | Bureaucratic corruption and the rate of temptation: do wages in the civil service affect corruption, and by how much[END_REF] show that the low level of salaries of civil servants in developing countries would be a serious explanation for their very high level of corruption, insofar as in the public service, it attracts dishonest officials who seek to compensate for foregone wages by corruption. Higher wages involve higher costs when a position in the public service is lost due to corruption. A cost-benefit analysis suggests that higher wages provide an incentive to refrain from corruption [START_REF] Becker | Crime and punishment: an economic approach[END_REF]). However, the wage effect can be ambiguous because politicians and the most corrupt parliamentarians can award themselves the largest remuneration (La [START_REF] Porta | The quality of government[END_REF][START_REF] Treisman | The causes of corruption: a cross-national study[END_REF].

Natural resource rents. According to the "rent seeking" theory, rent seeking explains a large part of corruption. When a state is highly centralized and public officials may have additional resources, the level of corruption is high (Rose-Ackerman, 1999). Similarly, when rents are high in a country because of its abundance of natural resources, the level of corruption is high [START_REF] Porta | The quality of government[END_REF][START_REF] Leite | Does mother nature corrupt? Natural resources, corruption, and economic growth[END_REF][START_REF] Ades | Rents, competition, and corruption[END_REF][START_REF] Acemoglu | The choice between market failures and corruption[END_REF].

Trade openness. Several studies empirically support the thesis that government corruption is lower in countries where the intensity of international trade is stronger [START_REF] Ades | The new economics of corruption: a survey and some new results[END_REF], 1999[START_REF] Leite | Does mother nature corrupt? Natural resources, corruption, and economic growth[END_REF][START_REF] Graeff | The impact of economic freedom on corruption: different patterns for rich and poor countries[END_REF]. However, the literature is very ambiguous on this issue. Azfar and [START_REF] Knack | Trade intensity, country size and corruption[END_REF] show that the empirical link between corruption and trade intensity is the result of selection bias. This relationship is weakened or disappears when the data used on corruption are more recent and cover a large sample of countries [START_REF] Knack | Trade intensity, country size and corruption[END_REF][START_REF] Graeff | The impact of economic freedom on corruption: different patterns for rich and poor countries[END_REF]. [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF] and [START_REF] Brunetti | A free press is bad news for corruption[END_REF] do not find any positive effect of trade intensity on corruption.

Institutional factors

In this group of determinants of corruption, we distinguish the factors related to political institutions, and those related to economic institutions.

Political institutions.

In the literature, the most cited political variables are democracy, political stability, political regime, proportion of women in parliament and freedom of the press. For some authors, greater democracy promotes the reduction of corruption (Shleifer andVishny, 1993 La Porta et al., 1999;Paldan 2000). For others, it is the duration of the democratic regime resulting in political stability that reduces corruption [START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF][START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF][START_REF] Montinola | Sources of corruption: a cross-country study[END_REF]. So if democracy reduces corruption, political instability favors corruption [START_REF] Melgar | The perception of corruption[END_REF]. Concerning the functioning of political systems, Rose-Ackerman and [START_REF] Kunicova | Electoral rules as constraints on corruption: the risks of closed-list proportional representation[END_REF] indicate that a competitive electoral process helps reduce corruption. [START_REF] Persson | Electoral rules and corruption[END_REF] argue along similar lines and indicate that political competition within the government may lead to a decrease in corruption. According to [START_REF] Gerring | Political institutions and corruption: The role of unitarism and parliamentarism[END_REF], parliamentary political systems and unitary states (as opposed to the federal state) lead to a lower level of corruption. These results are also present in [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF], who shows that there is a negative correlation between corruption and federalism in a State. Concerning gender, some authors believe that a higher proportion of women in senior positions in the administration and parliament would help reduce corruption [START_REF] Swamy | Gender and corruption[END_REF][START_REF] Dollar | Are women really the "fairer" sex? Corruption and women in government[END_REF]. As regards the influence of information on corruption, [START_REF] Adsera | Are You Being Served?: Political Accountability and Quality of Government[END_REF] see in newspaper circulation a major determinant of the fight against corruption. Indeed, when citizens are sufficiently informed and the media are free from all political powers, freedom of information contributes to the fight against corruption [START_REF] Brunetti | A free press is bad news for corruption[END_REF].

Economic institutions.

In this category we include the decentralization of decision-making and the influence of the State, generally captured through government regulation. The structure of government in terms of degree of decentralization plays an important role in reducing corruption [START_REF] Brunetti | A free press is bad news for corruption[END_REF][START_REF] Graeff | The impact of economic freedom on corruption: different patterns for rich and poor countries[END_REF][START_REF] Rose-Ackerman | Corruption and government: causes, consequences, and reform[END_REF]. When public officials have more, and more highly concentrated power, the level of corruption tends to increase [START_REF] Leite | Does mother nature corrupt? Natural resources, corruption, and economic growth[END_REF]. Like [START_REF] Shleifer | Corruption[END_REF], Fishman and Gatti (2002) show that corruption decreases as the level of decentralization increases. [START_REF] Arikan | Fiscal decentralization: A remedy for corruption?[END_REF] addresses this issue from a tax perspective and shows that fiscal decentralization leads to a lower level of corruption. Regarding government regulation, several authors point to the fact that an increase in the burden of government regulation causes more temptation for bribes, exacerbating corruption. Indeed, in order to limit costs associated with the proliferation of administrative procedures (e.g. custom procedures, number of procedures to start a business), the agents involved (companies in general) tend to bribe the officials in charge of the proper execution these procedures.

EMPIRICAL STRATEGY

While the previous section outlines the potential determinants of corruption, this section is first and foremost intended to address the issue of uncertainty in the most relevant determinants. Second, it presents the data considered in this study.

Bayesian model averaging (BMA) methodology

No theoretical model is capable of taking into account all the determinants of corruption highlighted in the previous section. The empirical approaches are often criticized for their a priori choice of some determinants at the risk of missing the most relevant determinants. To shed light on the key determinants of corruption while considering the uncertainty associated with model specification given the relatively large number of potential determinants, we rely on the BMA method. 6 The interesting aspect of this approach is that it addresses two major issues that typically arise in empirical studies with a relatively large number of explanatory variables and limited data and for which classical regression models do not provide an effective response, namely: (i) which variables should be included in the model and (ii) their respective importance. We apply this technique to several subsamples of countries given their level of economic development and their region in order to identify the key determinants that are specific to them.

Let us consider the following cross-country empirical corruption model:

y , ~ 0, (1)
where is the level of corruption, is the matrix of potential explanatory variables, is the constant, denotes the coefficients, is the error term and is an index for a specific model.

BMA addresses the problem of uncertainty in relation to model specification by estimating models for all possible combinations of and constructing a weighted average. Assuming that contains potential explanatory variables, this means estimating 2 variable combinations and thus 2 models, each with a certain probability of being the "true" model. Technically, this can be cumbersome to estimate when is large but BMA allows this through Monte Carlo Markov Chain (MCMC) simulations. If is the quantity of interest, such as coefficients , the associated posterior distribution given data is:

| ∑ , (2)
Thus, the posterior distribution of is an average of the posterior distribution under each of the models considered, weighted by their posterior model probability. For a model , the latter are obtained using Bayes' theorem:

∑ | (3)
where is the integrated likelihood of model . Like [START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF], we choose a uniform prior probability which means a common prior model probability, i.e.

2 . This is a popular choice to represent the lack of prior knowledge. This implies that the prior probability of including a regressor is 1/2 independently of the other regressors included in the model. On this basis, we consider that a potential determinant of corruption can be seen as a relevant determinant if its posterior inclusion probability is greater than or equal to 50%.

Data

The data used cover 130 developed and developing countries. Regarding our dependent variable, we use the corruption index provided by the World Bank (Corrup_WGI) ranging between -2.5 (high corruption) to 2.5 (low corruption) and which is one of the most widely used indices in the literature. To avoid effects from a particular year and to obtain a large sample of countries with more reliable data, we consider an average of the index over the recent period from 2006 to 2013 for all countries in the study. We proceed in the same way for all variables except dummy variables.

Concerning the historical and sociocultural variables, we rely on data constructed by La [START_REF] Porta | The quality of government[END_REF] on legal systems and religious culture. Legal systems are dummy variables that take the value 1 for a particular legal system and 0 otherwise. We consider the British system of common law (legor_uk), the French system of civil law (legor_fr), the system of socialist law (legor_so), the system of German law (legor_ge) and the legal system of Scandinavian law (legor_sc). Regarding religious culture, three religious denominations were considered, namely Catholic (catho80), Islam (muslim80) and Protestant (protmg80). The weight of each religious culture is measured by the proportion of the population adhering to this religion. In the same category of historical and sociocultural variables, we consider ethnic fractionalization (ethnic) and linguistic fragmentation (language) as constructed by [START_REF] Alesina | Fractionalization[END_REF]. Finally, we add to this category quality of education (qual_educ) as built by the World Economic Forum (WEF).

The economic variables that we consider are trade openness (openness) measured by the sum of exports and imports as a ratio of GDP, mining rents (mineral_rent) and oil (oil_rents), obtained from the WDI database of the World Bank. These rents, which capture the influence of natural resources, are the difference between the value of production resources (mining and oil) at world prices and total costs of production. GDP per capita in purchasing power parity, expressed in logarithm (lgdppc_ppp) is also obtained from the WDI database.

In the category of institutional variables, we rely on several variables in line with the literature. We take the Freedom House democracy index (polity_right). The political stability index (ps_wgi) is provided by the World Bank through the Worldwide Governance Indicators. The type of political system (parliamentary or presidential) is captured by a dummy variable that takes the value 1 for a given plan and 0 elsewhere. It is obtained from the updated database of [START_REF] Beck | New tools in comparative political economy: The Database of Political Institutions[END_REF] on political institutions (Database of Political Institutions 2013). The effect of gender in the political sphere is taken into account by the ratio of women parliamentarians to men (Rwpseats) obtained from the Human Development Report. Freedom of information (Free_info) indicating the degree of freedom of the press, radio and television is constructed by Freedom House. Willingness to delegate authority (Wildg_aut), which is a measure of decentralization of power, is provided by WEF. The presence of government in the economy (gov_reg) is understood as the burden of government regulation and is also obtained through the WEF. All the data used and their sources are summarized in Table A.2 in the Appendix.

EMPIRICAL RESULTS

To begin with, we present the results from our regressions for the whole sample. Then the results are presented by country sub-groups to show the relevant determinants depending on the level of development and region.

Broader determinants of corruption

Table 1 shows the results of the BMA obtained from the whole sample consisting of 130 countries. These results are based on 22 potential determinants and thus the results of several million regressions (exactly 2 4,194,304 regressions). 7 The dummy variable "Developing" we then added captures the specificity of developing countries compared to developed countries. It takes the value 1 for developing countries and 0 for developed countries. The relevance of each variable in explaining corruption in all regressions ( 0| ) is given by the "PIP" column, which represents the posteriori inclusion probability (PIP), that is, the sum of the posterior probabilities of the different regressions in which the variable is included. A variable is considered to be relevant in explaining corruption if its PIP is greater than or equal to 50%. In other words, this variable has at least a 50% chance of being included in the explanatory model of corruption. Columns "Post Mean" and "Post SD" represent the posterior mean and the posterior standard deviation of the parameter for each variable.

Based on these elements, several lessons can be drawn from the results of Table 1. These findings identify eight key determinants of corruption for the whole sample, namely freedom of the press, level of economic development,8 regulatory burden, judicial system, political system, political stability and willingness to delegate authority. The quality of education and the oil rent are also distinguished, but with lower probabilities. These results are fairly standard in the literature. Indeed, the negative sign associated with the freedom of the press shows that more press freedom allows for a lower level of corruption, as has been demonstrated by several authors [START_REF] Brunetti | A free press is bad news for corruption[END_REF]Weder 2003 Pellegrini and[START_REF] Pellegrini | Causes of corruption: A survey of cross-country analyses and extended Results[END_REF]. The role of the level of economic development is a matter of consensus in the literature (see Sierra, 2006) and the positive sign associated with it shows that corruption decreases with improving economic development level. The burden of regulation is also identified as a main cause of corruption, particularly because of the bribery phenomena it generates [START_REF] Shleifer | Corruption[END_REF]. Our results also show that the socialist legal system is more permeable to corruption, as shown by La [START_REF] Porta | The quality of government[END_REF]. However, an improvement in the quality of education would reduce corruption, which is consistent with the results of [START_REF] Melgar | The perception of corruption[END_REF]. Similarly, a parliamentary political system compared to a presidential system would be associated with lower corruption. Like [START_REF] Serra | Empirical determinants of corruption: A sensitivity analysis[END_REF], we find that political stability enables a reduction in corruption. Finally, the favorable influence of decentralization captured by the willingness to delegate authority is consistent since we find that a greater proportion of power to delegate is associated with lower corruption.

While these results are interesting and consistent with previous studies, they are very general and in fact not very informative because they fail to grasp the specificities linked to economic development level or those related to the region that are essential for conducting specific diagnostics. For example, these results clearly show what is often referred to as the "tragedy of developing countries" because of their level of corruption that is higher than that of developed countries. However, without a specific study, no one is able to state that the determinants of corruption that are relevant to developed countries are also valid for developing countries. The following analyses are intended to address these limitations.

Determinants of corruption by level of economic development

The results presented in Table 2 distinguish the determinants of corruption that are specific to developed countries from those specific to developing countries. Some determinants are common to both types of countries such the legal system and political stability. However, other determinants such as religion, oil rents and quality of education are more relevant to developing countries. Indeed, the results show that an increase in oil rent is conducive to corruption. Moreover, corruption appears higher in countries with a higher proportion of Catholics faithful and a socialist legal system. For developed countries, the special feature is willingness to delegate authority, that is, decentralization. In other words, the developed countries with a greater level of decentralization appeared less corrupt than the others. Moreover, the level of economic development does not explain the differences in the perception of corruption in developed countries; this shows some homogeneity in their structural characteristics. The analysis of developing countries can be deepened since, within these countries, the level of wealth per capita significantly explains the differences in corruption. We therefore proceed to a distinction between sub-Saharan African countries and the other developing countries.

The results obtained on this basis are presented in Table 3. These results show that the most relevant determinants of corruption for sub-Saharan Africa are freedom of the press, burden of regulation, linguistic fragmentation, political system, religion and political stability. Specifically, the presidential political regime appears favorable to corruption. Moreover, corruption appears lower in countries with a higher proportion of Protestant faithful.

This distinction also provides valuable additional information. As Sub-Saharan Africa (SSA) is without a socialist legal system, conclusions drawn previously on this variable for developing countries do not apply to it. The influence of the quality of education on corruption is much more important in the other developing countries outside sub-Saharan Africa. Moreover, the level of economic development does not appear to be a main determinant of corruption within SSA, indicating some homogeneity of the characteristics of member countries. Despite its relatively important influence in the other developing countries, we cannot go further in the disaggregation by distinguishing other developing regions such as sub-Saharan Africa because the number of countries is relatively limited in the other developing regions such as Latin America or Eastern Europe. Although this distinction cannot be made on the basis of all the potential determinants, we propose a way to go further in analyzing the specifics later in the document.

Determinants of corruption by geographic location

To further the analysis on the determinants of corruption, we follow the classification of the World Bank and we distinguish several regions. Given the relatively high number of potential determinants, we cannot perform regressions for the regions for which the number of countries, in our sample, is less than the number of explanatory variables. We therefore limit the number of explanatory variables to avoid being faced with a problem of insolvency of the model.

On the basis of results achieved so far, several variables appeared to be more fundamental in explaining the differences in perception of corruption. Irrespective of the specificities, the key determinants highlighted are the level of economic development, education, the burden of regulation, the political system, freedom of the press, decentralization, legal origin, oil rents and, to a certain extent, religion. Considering only these variables, we study regional characteristics in order to measure the weight of each of these variables by region. The division performed by the World Bank groups all the countries into seven (07) geographical regions, namely East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and Caribbean (LAC), Middle East and North Africa (MENA), North America (NA), South Asia (SA) and Sub-Saharan Africa (SSA). Although the case of SSA has previously been studied, it is not redundant to review it here as the number of variables is now greatly reduced. Rather, it allows us to see if we get the same determinants for this region on the basis of our new variables sample and thus serves as a control of previous results. Given the small number of countries in North America (two countries) and South Asia (five countries), these regions are not considered. However, the perception of corruption in all regions compared to Europe and Central Asia (ECA) is presented in Table A.3 in the Appendix. In this table, the "tragedy" of certain areas where the perception of corruption is greater clearly appears. Specifically, compared to the ECA region, corruption is higher in SSA, in LAC, in MENA and in SA. However, corruption is not significantly more pronounced in EAP and NA where it actually seems relatively lower.

Returning now to the regional characteristics presented in Table 4, we can draw several conclusions. Concerning EAP, the main determinants of corruption are economic development, religion, and political stability. Specifically, corruption is lower in countries with a high proportion of Protestant faithful, greater political stability and a higher level of economic development. The results for the ECA region show that corruption is mainly determined by freedom of the press, burden of regulation, legal system and willingness to delegate authority. Regarding LAC, corruption is mainly explained by freedom of the press, economic development and quality of education. In this region, corruption is lower in countries where there is more freedom of the press and where the quality of education is higher. For the MENA region, the differences in the perception of corruption are explained by linguistic fragmentation, political system, political stability, quality of education and willingness to delegate authority. Specifically, corruption appears to be lower in countries with a parliamentary political system, greater political stability, higher quality of education and greater decentralization of authority. However, contrary to expectations, linguistic fragmentation seems to reduce corruption in the region. This could possibly be explained by the fact that linguistic fragmentation in this region does not necessarily create the dominant groups and that the groups in power are controlled by others. Finally, the results for Sub-Saharan Africa also support those in Table 3. Indeed, the differences in the perception of corruption in this region are explained by freedom of the press, burden of regulation, linguistic fragmentation, political system, religion and political stability. The least corrupt countries in this region are those in which the freedom of the press is higher, the burden of regulation is lower, linguistic fragmentation is lower, the proportion of Protestants is higher and political stability is greater. In contrast, countries with a presidential political system are those with the highest level of corruption. The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a uniform model prior and the birth-death MCMC sampler. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%.

In summary, our results clearly show that there are regional particularities as well as specificities related to levels of economic development that should be considered when conducting an analysis on the determinants of corruption. Attempts in some studies to consider them through dummy variables only serve to capture the perception of corruption by region, but are not sufficient to identify the particularities that may be decisive in specific diagnoses.

ROBUSTNESS ANALYSIS

To test the robustness of our results, several robustness analyses were performed. First, this Section looks at the question of endogeneity between corruption and level of development that is often noted but very little explored in previous studies. Second, we change the indicator of the perception of corruption by considering not the Kaufmann indicator, but the perception of corruption indicator of Transparency International (TI).9 Third, we consider an alternative method for dealing with model uncertainty and identifying the most relevant determinants of corruption. Finally, due to the unavailability of data for all the countries in our sample, we study the influence of wages on corruption in the case of 89 countries, in line with the previous literature. While the latter analysis is not really a proof of robustness, given the purpose of our paper, it has the advantage of testing the sensitivity of the overall results (without specificity of income level and of region) with respect to the introduction of the variable representing the remuneration of officials (government wage).

Issue of potential reverse causality between corruption and economic development

Like all cross-national studies on the determinants of corruption, the BMA method is based on the OLS principle. It therefore does not directly consider the problem of endogeneity. But although it is often not considered in previous studies, the risk of endogeneity is present, especially as regards the relationship between the level of economic development and the level of corruption, because of the potential simultaneity bias between these two variables [START_REF] Treisman | The causes of corruption: a cross-national study[END_REF]. Specifically, if an improvement in the level of economic development can reduce corruption as shown by all the studies on the determinants of corruption, several studies also show that an increase in corruption hinders economic development (inter alia [START_REF] Mauro | Corruption and growth[END_REF]Gray and Kaufmann, 1998;[START_REF] Wei | Corruption in economic development: Beneficial grease, minor annoyance, or major obstacle?[END_REF]. Because of this potential reverse causality, the OLS procedure provided biased and inconsistent results, because the explanatory variable (the level of economic development in our case) is correlated with the error term. To avoid reproducing this bias in our estimates, we use the method of instrumental variables. Indeed, instead of using the level of economic development as an explanatory variable in the equation of corruption, we rather use its estimated value from the exogenous variables that can simultaneously affect the level of economic development and the level of corruption but are not affected by the level of corruption.

The literature tells us that the level of development and the level of corruption can be influenced by common exogenous factors including geography and natural resource endowment. On geographical factors, it is now well known that tropical countries tend to have poor harvests, more disease and a suboptimal use of production technologies developed in more temperate zones; this negatively affects their level of economic development. Furthermore, because of its geographical position, a landlocked country is de facto limited in its economic development because it has limited access to large economic markets [START_REF] Sachs | Fundamental sources of long-run growth[END_REF]. Regarding natural resource endowment, this is considered by [START_REF] Sachs | Fundamental sources of long-run growth[END_REF] as a factor that can influence the level of development of countries. Ultimately, tropical location, landlocked location and dependence on natural resource are considered factors that adversely affect the level of development (Sachs andWarner, 1997, Bloom and[START_REF] Bloom | Geography, Demography, and Economic Growth in Africa[END_REF]. Geographical factors are also considered by institutionalists as factors that explain the level of corruption through the establishment of sustainable institutions. For example, [START_REF] Hall | Why do some countries produce so much more output per worker than others?[END_REF] and [START_REF] Easterly | It's Not Factor Accumulation: Stylized Facts and Growth Models[END_REF], among others, argue that when the geographical position of countries allows them to have good crop yields, it helps establish sustainable and effective institutions to protect landowners. These institutions are also effective in the fight against corruption.

Based on these arguments, we estimate the level of economic development from geographical factors. We do not consider natural resource endowment in the estimation of economic development because the variables related to it, namely oil and mining rents, are directly included in the regression of corruption. The relationship between the level of economic development and geographical factors is presented in Table A.1 in Appendix of the document.

The value estimated on this basis is then used in the regression of corruption and to address the problem of endogeneity. The results, which are presented in Tables B.1 to B.3 in Appendix B, are very similar to those obtained previously in each sub-sample.

Alternative measure of corruption perception

To examine whether the choice of the measure of corruption influences our results, we consider the Transparency International Index (Corrup_CPI) as an alternative measure of corruption perception. This indicator whose value varies between 0 (highly corrupt) to 10 (low corruption) is a composite index based on surveys indicating the extent to which public power is used for personal gain. The results obtained from this new measure are very similar to previous results obtained with the Kaufmann indicator. 10 This similarity between results is not surprising since both indicators of corruption are highly correlated (with a correlation coefficient of about 0.99). The differences lie in the posterior means, which are almost twice as high with the TI indicator. This difference comes from the coding differences between the two institutions. It should be noted that only the PIP really matters because it is what determines the importance of each potential determinant in explaining corruption. Regarding the determinants of corruption according to the level of development, the results also support the results in Table 2. The same determinants of corruption perception among the countries are identified, underlining the strength of our results.

Alternative to BMA: General-to-Specific (GETS) approach 11

As a robustness analysis, we use the automated General-to-Specific (GETS) approach as an alternative to BMA to deal with model uncertainty. GETS, just like BMA, is one of the most influential econometric and statistical approaches for handling uncertainty modelling (see [START_REF] Ding | Why has China Grown So Fast? The Role of Physical and Human Capital Formation[END_REF]. Roughly speaking, while BMA addresses model uncertainty by estimating models for all possible combinations of explanatory variables leading to thousands (or millions) of regressions, GETS addresses the same problem only relying on a single model, namely the general unrestricted model (GUM). The latter, which contains all the potential explanatory variables, is subjected to a series of step-wise statistical tests (see [START_REF] Hendry | We Ran One Regression[END_REF], leading to the removal of empirically unimportant variables to arrive at the proposed specific or final model. The validity of a selected model is mainly subject to the suitability of GUM to the data generation process (DGP). Thus it is important to rely on economic theory and previous empirical findings to determine the "prior general model" as we did in Section 2.

Implementing this approach leads to the specific (or final) models presented in tables B.4 and B.5 in Appendix B. These results are similar to previous results (Tables 1 and2). Indeed, the determinants retained in the final models of the GETS approach are exactly those whose PIP are greater than or equal to 50% in the BMA approach both for the whole sample and for subsamples of developed and developing countries. All this confirms previous findings and shows that our results are robust to the variable selection method.

Influence of government wage

Although its influence is controversial, government wage is often cited among the main determinants of corruption. Our previous analyses have not included this variable because it is not available for many of the countries in our sample (41 out of 130 countries). However, to study the influence of this variable, we apply the BMA to the 89 countries for which this variable is available. The results of this procedure clearly show that the government wage has no influence on the level of corruption across countries. These results are not very surprising because although it is generally believed that low wages relative to living standards can increase corruption temptations, it is also true that the most corrupt politicians are also generally those who share the highest salaries, as stressed by Triesman (2000). Thus an increase in wages does not necessarily lead towards a reduction in corruption. Furthermore, determinants whose PIP is more than 50% in this reduced sample are the same as those of the total sample, confirming once again the strength of our results.

CONCLUSION AND DISCUSSION

If corruption is a scourge that plagues all societies, its root causes have been the subject of quantitative studies over recent years as data have become available, particularly on institutional variables, including corruption itself. Indeed, several authors have focused on identifying the causes of corruption through cross-country empirical studies. These studies have enriched the literature by proposing discussions on economic, institutional, sociocultural and historical determinants of corruption. However, the question of specific groups or regions is generally ignored, suggesting that one could "put all countries in the same basket." This paper proposes to enrich the literature on the determinants of corruption by addressing the issue of specificities relating to the level of economic development and the region. More concretely, we start from the premise that the causes of corruption can evolve with the level of development or may vary from region to region. Is it likely for example that the determinants of corruption in Sub-Saharan Africa are the same as the determinants of corruption in Europe?

To investigate this issue and consider the potential differences that there might be in the explanation of corruption, we study the determinants of corruption according to level of development and region. To this end, we rely on the existing literature to obtain a relatively large number of potential determinants without discriminating between these variables. On this basis, and considering the problem of model uncertainty, we use a BMA approach to identify the most relevant determinants according to economic development level and region.

The results clearly show that differences exist according to the level of economic development and the region and that we cannot apply the same treatment to all countries. Corruption in SSA is not explained by the same factors as in Europe. Our paper contributes to the literature on the determinants of corruption not only by analyzing these specificities, but also by addressing the issue of model uncertainty due to the relatively large number of potential determinants. It is thus helpful to have a model according to development level and region. By identifying these peculiarities, our study also provides the possibility of a specific treatment of this common scourge. Our results are also robust to several tests, including tests on the dependent variable and the method for the treatment of model uncertainty.

Appendices

Appendix A: Data

Table 1 : Broader determinants of corruption (whole sample)

 1 Note: The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a uniform model prior and the birth-death MCMC sampler. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%.

	Determinants	PIP	Post Mean	Post SD	PIP	Post Mean	Post SD
	Catho80	0.149 -0.001	0.002	0.261	-0.001	0.002
	Developing	---	---	---	0.998	-1.087	0.262
	Ethnic	0.164 -0.095	0.255	0.110	-0.052	0.183
	Free_info	0.931 -0.023	0.011	0.708	-0.014	0.011
	GDP_pc	0.999 0.441	0.116	0.992	0.381	0.116
	Gov_reg	0.862 0.418	0.220	0.911	0.468	0.206
	Language	0.041 -0.002	0.065	0.041	-0.005	0.063
	Legor_fr	0.049 -0.006	0.058	0.040	-0.004	0.043
	Legor_ge	0.041 0.008	0.088	0.033	0.002	0.068
	Legor_so	0.966 -0.869	0.304	0.997	-1.008	0.269
	Legor_uk	0.037 0.000	0.040	0.035	0.000	0.033
	Mineral_rents	0.038 0.000	0.003	0.035	0.000	0.003
	Muslim80	0.041 0.000	0.001	0.037	0.000	0.001
	Oil_rents	0.400 -0.009	0.012	0.461	-0.009	0.012
	Openness	0.040 0.000	0.000	0.047	0.000	0.000
	Parl_system	0.724 0.410	0.303	0.222	0.084	0.182
	Polity_right	0.386 0.079	0.115	0.266	0.048	0.093
	Pr_system	0.149 -0.052	0.166	0.144	-0.045	0.131
	Protmg80	0.052 0.000	0.001	0.093	0.001	0.002
	PS_wgi	1.000 0.641	0.142	1.000	0.604	0.137
	Qual_educ	0.453 0.134	0.168	0.336	0.088	0.142
	Rwpseats	0.048 0.017	0.131	0.038	0.009	0.102
	Wildlg_aut	0.999 0.701	0.162	0.989	0.610	0.161

Table 2 : Determinants of corruption according to level of development

 2

		Developing countries	Developed countries
			Post			Post
	Determinants	PIP	Mean Post S.D.	PIP	Mean Post S.D.
	Catho80	0.638	-0.002	0.002	0.195	0.001	0.002
	Ethnic	0.055	-0.007	0.053	0.049	-0.003	0.074
	Free_info	0.186	-0.001	0.002	0.056	0.000	0.002
	GDP_pc	1.000	0.262	0.054	0.057	0.011	0.085
	Gov_reg	0.242	0.038	0.078	0.637	0.166	0.155
	Language	0.047	-0.002	0.034	0.061	0.012	0.090
	Legor_ge	---	---	---	0.048	-0.004	0.036
	Legor_sc	---	---	---	0.049	-0.004	0.045
	Legor_so	0.999	-0.657	0.140	0.945	-0.573	0.209
	Legor_uk	0.067	0.006	0.033	0.095	0.015	0.062
	Mineral_rents	0.050	0.000	0.002	0.118	0.008	0.026
	Muslim80	0.070	0.000	0.000	0.082	0.001	0.006
	Oil_rents	0.980	-0.016	0.005	0.044	0.000	0.006
	Openness	0.045	0.000	0.000	0.107	0.000	0.000
	Parl_system	0.199	0.038	0.089	---	---	---
	Polity_right	0.075	-0.002	0.012	0.059	0.001	0.031
	Pr_system	0.105	-0.014	0.054	0.072	-0.012	0.060
	Protmg80	0.049	0.000	0.001	0.051	0.000	0.001
	PS_wgi	1.000	0.409	0.060	0.852	0.309	0.165
	Qual_educ	0.491	0.071	0.083	0.045	-0.002	0.031
	Rwpseats	0.081	-0.025	0.119	0.049	0.010	0.096
	Wildlg_aut	0.051	0.002	0.024	1.000	0.543	0.098
	Note: The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a
	uniform model prior and the birth-death MCMC sampler. Statistics in bold are those for which the
	posterior inclusion probability is greater than or equal to 50%.

Table 3 : Determinants of corruption in Sub-Saharan Africa

 3

		Sub-Saharan African countries	Others developing countries
			Post	Post		Post	Post
	Determinants	PIP	Mean	S.D.	PIP	Mean	S.D.
	Catho80	0.098	0.000	0.001	0.294	-0.001	0.002
	Ethnic	0.052	-0.006	0.077	0.042	-0.006	0.058
	Free_info	0.937	-0.011	0.004	0.152	-0.001	0.004
	GDP_pc	0.265	0.034	0.067	0.614	0.209	0.192
	Gov_reg	0.819	0.237	0.139	0.108	0.018	0.066
	Language	0.898	-0.548	0.241	0.044	0.006	0.054
	Legor_so	---	---	---	0.980	-0.649	0.201
	Legor_uk	0.073	0.001	0.033	0.316	0.095	0.161
	Mineral_rents	0.157	-0.001	0.003	0.062	0.001	0.005
	Muslim80	0.061	0.000	0.000	0.079	0.000	0.001
	Oil_rents	0.290	-0.003	0.005	0.587	-0.011	0.011
	Openness	0.092	0.000	0.000	0.160	0.000	0.001
	Parl_system	0.201	0.080	0.188	0.076	0.012	0.058
	Polity_right	0.062	0.000	0.014	0.066	0.002	0.024
	Pr_system	0.838	-0.427	0.215	0.071	-0.010	0.053
	Protmg80	0.621	0.004	0.004	0.110	-0.001	0.004
	PS_wgi	0.972	0.217	0.078	0.999	0.517	0.095
	Qual_educ	0.233	0.021	0.045	0.685	0.197	0.160
	Rwpseats	0.048	0.001	0.058	0.138	-0.096	0.291
	Wildlg_aut	0.105	0.013	0.052	0.041	0.002	0.030
	Note: The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a
	uniform model prior and the birth-death MCMC sampler. Statistics in bold are those for which the
	posterior inclusion probability is greater than or equal to 50%.

Table 4 : The main determinants of corruption by region

 4

		EAP	ECA	LAC	MENA	SSA
	Determinants		Post		Post		Post		Post		Post
		PIP	Mean	PIP	Mean	PIP	Mean	PIP	Mean	PIP	Mean
	Catho80	0.299 0.002	0.375 0.001	0.214 -0.004	0.195 0.001	0.125 0.000
	Free_info	0.194 -0.001	0.864 -0.016	0.746 -0.023	0.409 -0.006	0.964 -0.011
	GDP_pc	0.950 0.812	0.210 0.068	0.509 0.266	0.402 0.191	0.318 0.037
	Gov_reg	0.093 -0.005	0.613 0.152	0.440 0.199	0.337 -0.017	0.868 0.241
	Language	0.164 -0.086	0.088 -0.010	0.076 0.006	0.723 0.699	0.935 -0.556
	Legor_so	0.208 -0.105	0.950 -0.514	0.145 -0.006			---	----
	Muslim80	0.358 -0.004	0.079 0.000			0.363 0.003	0.091 0.000
	Oil_rents	0.126 -0.003	0.197 -0.004	0.088 -0.001	0.336 -0.004	0.339 -0.003
	Parl_system	0.106 0.014	0.084 -0.006	0.214 0.288	0.626 0.694	0.246 0.092
	Pr_system	0.139 0.032			0.253 0.293	0.398 0.344	0.822 -0.413
	Protmg80	0.831 0.021	0.271 0.001	0.296 -0.012	0.362 0.005	0.694 0.005
	PS_wgi	0.857 0.380	0.246 0.075	0.323 0.193	0.588 0.133	0.973 0.213
	Qual_educ	0.144 0.028	0.091 -0.007	0.665 0.386	0.918 0.319	0.292 0.026
	Wildlg_aut	0.192 -0.065	0.996 0.454	0.187 0.102	0.690 0.325	0.146 0.015
	Note:

Table A .1: Economic development and geography

 A

		(1)		(2)
	VARIABLES	ln	_	ln	_
		-0.826***	-0.846***
		(0.191)	(0.235)
		4.171***	0.578***
		(0.422)	(0.125)
		8.103***	10.23***
		(0.190)	(0.179)
	Observations	130	129
	R-squared	0.445	0.266
	In the results in (1), the latitudinal distance from the equator is not included as a
	logarithm. In (2), this variable is expressed as a logarithm.
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1
	Figure 1:

Correlation between the observed economic development and the estimated economic developmentTable A .2: Data description and sources

 A

	Variables	Definition	N	mean	S.D.
		Corruption indicators
	Corrup_wgi	Corruption indicator of Kaufmann et al. (2004) (World Governance	130	0.00195 1.063
		Indicators). The indicator ranges from -2.5 (most corrupt) to 2.5
		(least corrupt) over the period 2006-2013.
	Corrup_cpi	Indicator of corruption of Transparency International, ranging from	130	4.281	2.168
		1 (most corrupt) to 10 (least corrupt) over the period 2006-2013.
		Historical and sociocultural variables
	Catho80	The percentage of the population belonging to the Roman Catholic	130	33.13	36.34
		religion in 1980. The values are in percent (scale from 0 to 100).
		Source: La Porta et al. (1999))
	Muslim80	The percentage of the population belonging to the Muslim religion	130	21.59	33.79
		in 1980. The values are in percent (scale from 0 to 100). Source: La
		Porta et al. (1999))
	Legor_uk	The dummy variable for the origin of the legal system Common	130	0.300	0.460
		Law (Laporta et al., 1999)
	Legor_fr	The dummy variable for the origin of the legal system French Civil	130	0.508	0.502
		Law (Laporta et al., 1999)
	Legor_so	The dummy variable for the origin of the legal system Socialist Law	130	0.123	0.330
		(Laporta et al., 1999)
	Legor_ge	The dummy variable for the origin of the legal system German Civil	130	0.0308 0.173
		Law (Laporta et al., 1999)
	Legor_sc	The dummy variable for the origin of the legal system Scandinavian	130	0.0385 0.193
		Law (Laporta et al., 1999)
	Protmg80	The percentage of the population of each Country belonging to the	130	12.26	20.79
		Protestant religion in 1980. The values are in percent (scale from 0
		to 100). Source: La Porta et al. (1999).
	Ethnic	The index of ethnic fractionalization that captures the probability	130	0.452	0.268
		that two randomly selected persons from a given country will not
		belong to the same ethnic group (Alesina et al. 2003)
	Language	The index of linguistic fractionalization that captures the probability	126	0.400	0.300
		that two randomly selected persons from a given country will not
		speak the same language (Alesina et al. 2003)
	Qual_educ	Quality of education, World Economic Forum (WEF)	122	4.434	0.802
		Economic variables
	Gov_reg	Burden of government regulation, 1-7 (best), World Economic	122	3.331	0.635
		Forum (WEF)
	Openness	The proxy for the degree of country openness to international	130	94.09	58.00
		competition. It is the sum of merchandise exports and imports
		measured in current U.S. dollars divided by the value of GDP
		converted to international dollars using purchasing power parity
		(PPP) rates. Source: World Development Indicator
	Mineral_rent	Mineral rents are the difference between the value of production for	130	1.919	4.963
		a stock of minerals at world prices and their total costs of
		production, 2006-2013. Source: World Development Indicators
		(CD-ROM 2015)

Table A .2: Continued. Economic variables (Continued)

 A Polity_rightIndex of political rights. Higher ratings indicate countries that come closer to the ideals suggested by the checklist questions of: (1) free and fair elections;(2) those elected rule;(3) there are competitive parties or other competitive political groupings; (4) the opposition has an important role and power; and (5) the entities have selfdetermination or an extremely high degree of autonomy. Source:Ps_wgiThe proxy for the possibility to have wrenching changes in Government. It ranges from around -2.5 to around 2.5 (higher values correspond to less political instability). Source: Kaufmann et al.LatitudeAbsolute value of the latitude of the country (i.e., a measure of distance from the equator), scaled to take values between 0 and 1, where 0 is the equator. Source: La[START_REF] Porta | The quality of government[END_REF]. The results are based on 500,000 draws and 100,000 burn-ins. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%. ECA is considered as a reference since we cannot introduce all regions due to perfect collinearity.

		Tables A.3: Perception of corruption by region
	Oil_rents	Oil rents are the difference between the value of crude oil production PIP Post Mean at world prices and total costs of production 2006-2013. Source: World Development Indicators (CD-ROM 2015) SSA 1.000 -1.329	130 Post SD 0.223	5.535	12.79
	Lgdppc_ppp	The logarithm of real GDP per capita in constant dollars 2006-2013. Source: The World Bank's World Development Indicators (CD-LAC 0.994 -1.023 MENA 0.911 -0.787 ROM 2015) SA 0.836 -1.036	130 0.272 0.363 0.603	9.111	1.286
		EAP	Institutional variables 0.260	-0.127	0.263
		NA.	0.210	0.200	128 0.491	3.400	2.055
		ECA	…	…	…
		Note:
		Freedom House.
					130	-0.123 0.918
		(1999)
	Wildlg_aut	Willingness to delegate authority, 1-7 (best). Source: World	122	3.809	0.794
		Economic Forum (WEF)
	Pr_system	Dummy Presidential system of governance (Data Base of Political	130	0.577	0.496
		Institution, 2015)
	Parl_system	Dummy Parliamentary system of governance (Data Base of Political	130	0.354	0.480
		Institution, 2015)
	Rwpseats	Parliamentary seats, female to male ratio: Percentage of	129	0.255	0.176
		parliamentary seats held by women expressed as a ratio of those held
		by men. Source: Human Development Report, UNDP
	Free_info	The index of freedom of information provided by Freedom House on	129	48.00	22.16
		the basis of the following criteria: (1) laws and regulations that
		influence media content (2) political influence over media content;
		(3) economic influence over media content (4) repressive actions
		which constitute violations of press freedom. Values range from 0
		(total freedom) to 100 (total repression). Source: Freedom House
		(2015)
					129	-1.604 0.989
	Landlocked	Equal to 1 if the country is landlocked and 0 otherwise.	130	0.200	0.401

Table B .3: The main determinants of corruption by region

 B The results are based on 500,000 draws and 100,000 burn-ins. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%.

			EAP		ECA		LAC	MENA		SSA
	Determinants		Post		Post		Post		Post		Post
		PIP	Mean	PIP	Mean	PIP	Mean	PIP	Mean	PIP	Mean
	Catho80	0.095 0.000	0.406 0.002	0.267 -0.006	0.192 0.001	0.132 0.000
	Free_info	0.405 -0.008	0.973 -0.018	0.675 -0.022	0.346 -0.005	0.994 -0.012
	GDP_pc_bar	0.464 -0.281	0.108 -0.008	0.170 0.051	0.245 -0.132	0.090 0.003
	Gov_reg	0.132 0.013	0.608 0.149	0.219 0.070	0.373 -0.040	0.943 0.277
	Language	0.139 -0.040	0.091 -0.008	0.093 0.034	0.875 0.896	0.994 -0.630
	Legor_so	0.719 -0.839	0.991 -0.542	---	---	---	---	---	---
	Muslim80	0.901 -0.021	0.084 0.000	0.099 -0.002	0.316 0.003	0.095 0.000
	Parl_system	0.143 0.017	0.094 -0.006	0.325 0.567	0.638 0.701	0.155 0.044
	Pr_system	0.469 -0.402	---	---	0.267 0.392	0.324 0.211	0.919 -0.477
	Protmg80	0.823 0.027	0.284 0.001	0.370 -0.020	0.327 0.145	0.743 0.005
	PS_wgi	0.871 0.577	0.208 0.055	0.428 0.267	0.757 0.180	0.987 0.217
	Qual_educ	0.852 0.802	0.098 -0.009	0.644 0.399	0.964 0.365	0.388 0.037
	Wildlg_aut	0.104 0.003	0.995 0.456	0.267 0.163	0.867 0.420	0.176 0.021
	Note:

Table B .4: Specific model of corruption (all sample, GETS approach)

 B

	VARIABLES	GETS2	GETS2
	Ps_wgi	0.342***	0.316***
		(0.0567)	(0.0547)
	Legor_so	-0.479***	-0.500***
		(0.108)	(0.104)
	Free_info	-0.00834***	-0.00713***
		(0.00258)	(0.00251)
	Wildlg__aut	0.283***	0.251***
		(0.0642)	(0.0626)
	Parl_system	0.246***
		(0.0853)
	Oil_rents	-0.0115***	-0.0109***
		(0.00369)	(0.00356)
	Gov_reg	0.252***	0.264***
		(0.0637)	(0.0614)
	GDP_pc	0.236***	0.201***
		(0.0429)	(0.0428)
	Developing		-0.476***
			(0.113)
	Constant	-3.577***	-2.805***
		(0.395)	(0.430)
	Observations	121	121
	R-squared	0.899	0.906
		Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table B .5: Specific model of corruption by level of economic development (GETS approach)

 B

		(Developing	(Developed
		countries)	countries)
	VARIABLES	GETS1	GETS2
	Ps_wgi	0.381***	0.334***
		(0.0551)	(0.0921)
	Legor_so	-0.557***	-0.621***
		(0.119)	(0.136)
	Oil_rents	-0.0183***
		(0.00335)
	Gov_reg	0.214***	0.211***
		(0.0687)	(0.0684)
	GDP_pc	0.277***
		(0.0408)
	Wildlg__aut		0.504***
			(0.0699)
	Constant	-3.207***	-1.820***
		(0.444)	(0.324)
	Observations	88	34
	R-squared	0.716	0.928
		Standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

We adopt the definition by Transparency International whereby corruption reflects the perception of the extent to which public power is used for personal gain.

The factors discussed in the literature are developed in more detail in Section 2.

The idea here is to distinguish between countries according to their income level according to the World Bank classification.

This term refers to the extent to which senior managers (public and private) delegate decision-making to their subordinates. It could thus be approximated by decentralization of decision-making.

Refer also to[START_REF] Lambsdorff | Causes and consequences of corruption: what do we know from a cross-section of countries[END_REF] for more detailed explanations on the causes of corruption.

This technique is briefly presented in this paper. For more technical details, we refer the reader to some key references, such as[START_REF] Hoeting | Bayesian model averaging for linear regression models[END_REF][START_REF] Hoeting | Bayesian model averaging: a tutorial[END_REF] and[START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF] and also[START_REF] Gnimassoun | The importance of the exchange rate regime in limiting current account imbalances in sub-Saharan African countries[END_REF].

We do not include all the determinants, especially dummy variables for perfect colinearity reasons. This is the case for example of the variables on the legal system in which the Scandinavian legal system (legor_sc) is not included in the regression.

[START_REF] Treisman | The causes of corruption: a cross-national study[END_REF], we used alternatively to GDP per capita, the latitudinal distance from the equator to account for the endogeneity problem between corruption and economic development to some extent. We get similar results that are available upon request from the authors. See also Section 5 for more details on the issue of endogeneity.

It should be noted that other measures of corruption exist such as the ICRG (International Country Risk Guide) measurement. But because of their low coverage in terms of number of countries, we prefer, for the robustness analysis, the Transparency International indicator for which we have complete coverage of our sample of countries.

To save space, these results are not reported here, but are available upon request from the authors.

This approach is briefly discussed here. For details, see among others Krolzig and[START_REF] Hendry | We Ran One Regression[END_REF],[START_REF] Hoover | Truth and Robustness in Cross-country Growth Regressions[END_REF], and, for a practical implementation, see[START_REF] Owen | General-to-specific modelling using PcGets[END_REF] for OxMetrics and[START_REF] Clarke | General to Specific Modelling in Stata[END_REF] for Stata.

Appendix B: Robustness checks

The results are based on 500,000 draws and 100,000 burn-ins. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%.