Julieta Peveri
email: julieta.peveri@univ-amu.fr.

The Wise, the Politician and the Strongman: National Leaders' Type and Quality of Governance

Keywords: national leaders, politicians' quality, leaders' characteristics JEL: H70, N10, J45

There is strong evidence that national leaders matter for the performance of their nations, but little is known about what drives the direction of their effects. I assess how national leaders' quality of governance, measured by six indicators, varies with their career and education. Using a sample of 1,000 rulers between 1931 and 2010, I identify three types of leaders: military leaders, academics, and politicians. I find that military leaders are associated with an overall negative performance, while politicians who have held important offices before taking power tend to perform well. Academics have on average non-significant effects. These results are partially driven by differences in policy decisions and in leadership styles.

Military leaders (politicians) spend less (more) in health and education, are more (less) likely to establish a personalistic regime, to disrespect the constitution, and to move towards a non-electoral regime. Additionally, this paper highlights the weakness of using educational attainment as a proxy for politicians' quality, and of growth as a measure of national leaders' performance.

Introduction

The debate on who should be at the head of a state goes back to at least the beginning of the Athenian democracy when most of the public offices were randomly selected by lottery. For Aristotle, it was a fair feature of democracy: everyone gets a say regardless of their background.1 Among critics of this practice, Socrates pointed out the incompetence factor: "No one would choose a pilot or builder or flutist by lot, nor any other craftsman for work in which mistakes are far less disastrous than mistakes in statecraft" (Xenophon, Memorabilia Book I, 2.9). Individual backgrounds affect beliefs, skills, preferences, and degree of risk-aversion; features that are likely to be relevant to leaders' policy decisions and ultimately to the overall performance of the state they run. 2This paper uses economic tools and a worldwide sample of a thousand national leaders who were in office between 1931 and 2010 to assess whether some categories of national leaders perform better than others. I first estimate their effects on their quality of governance, measured by economic growth, democracy, corruption, rule of law, property rights, and conflict. I use a recent method proposed by Easterly and Pennings (2020) to isolate national leaders' effects from their countries and their regions' effects in a more precise way than by using fixed effects. In a second step, I analyze how those estimates vary according to the leaders' backgrounds. For this purpose, I identify three groups of leaders based on their academic track record and previous career: academics, politicians, and military leaders. Each of these contains two or more sub-categories allowing for a more granular analysis.

I find robust evidence that leaders' backgrounds are significantly correlated with their quality of governance. Leaders with a former military career tend to be associated with an overall negative effect. Conversely, politicians who have held important offices before becoming national leaders, such as former prime ministers or vice-presidents, are associated with improvements in national outcomes. Using an aggregated outcome, that goes from -4.3 to 3 and summarizes the six indicators used for performance, the effect of military leaders is around -0.6, while politicians have a nearly symmetric effect of 0.5.

I investigate two potential channels that can account for the previous findings. First, education, experience, and institutional quality might correlate with the constructed leaders' categories and be at play. I find that the positive effect associated with politicians is partially triggered by their longer experience in politics before entering office. When it comes to education, having a university degree in law, economics, or political science is not systematically associated with a positive or negative effect on the quality of governance. Together with the non-significant effect of academics, this weakens the support for the idea that more educated leaders are more desirable. Additionally, the overall negative performance of military rulers is explained, at least to some extent, by the lower constraints on the executive of the nations they run. Yet, constraints on leaders, education, and experience are not enough to explain the effects of leaders' backgrounds on national performance. Instead, I provide suggestive evidence that rulers with different backgrounds undertake different policy decisions and have different leadership styles. Military rulers (politicians) invest less (more) in education and health, are more (less) likely to switch to a non-electoral regime, to disrespect constitution, and to establish a regime based on personal authority, which pervades formal institutions. All those variables show in turn a strong correlation with the leaders' effects previously estimated. This second channel is consistent with life experiences affecting behaviors, which is a basic premise of sociology and psychology [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF]. More precisely, leaders' backgrounds shape preferences, skills, the assesment of costs and benefits of each action, leadership styles and policy decisions.

It is also relevant to consider how leaders' backgrounds interact with institutions. I find that the positive effect of politicians vanishes when initial institutional constraints are strong. This is in line with [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] who find that leaders' abilities matter only under unconstrained regimes, and with [START_REF] Besley | Do Educated Leaders Matter?[END_REF] who show that educated leaders improve growth only in autocracies. Constraints can also compensate for the negative effect of military leaders, compatibly with the idea that institutions can discipline politicians. Therefore, institutional constraints can avoid the negative effect of bad leaders but also hide the positive impact of good rulers. Nonetheless, the aforementioned differences in the extent to which they respect constitution, manipulate electoral regimes and impose authority based on personal patronage rather than on law or ideology suggest that leaders have room to evade institutional constraints. Altogether, both leaders' backgrounds and institutions matter for national outcomes, and they interact with each other.

One of the major threats to identification is the endogeneity in the timing of political transitions, implying that the probability of observing a leader in a certain country, at a certain time, depends on his current and past performance. To assess this issue, I follow [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF], who focus on transitions where the leader died while in power by natural causes, considering these as unpredictable events and therefore independent of economic conditions. The results are robust with this sample restriction. Moreover, the methodology used in this paper provides estimators that have been shown to be accurate in the presence of endogeneity in transitions (Easterly and Pennings, 2020). A second concern is that leaders' backgrounds are endogenous to the initial quality of governance, in particular to the level of democracy. Indeed, it is more common to see former military staff in autocracies, where there are fewer academics, former mayors, governors, and civil servants. The results hold when controlling for the initial level of democracy and the initial property rights score. However, the last controls do not fully solve the endogeneity in leaders' backgrounds. Besides, it is not possible to tackle endogeneity in timing together with endogeneity in leaders' backgrounds. Hence, the estimates presented in this paper cannot be fully interpreted as causal. They remain nonetheless informative and support the recent evidence that individual characteristics of politicians matter for aggregated outcomes. Finally, despite leaders' categories being data-driven, there is an unavoidable degree of arbitrariness when creating leaders' groups. I show the robustness of these choices by using a LASSO estimation to select under an unsupervised approach the best set of predictors among a large pool of leaders' characteristics.

This paper refers to the broad literature on the impact of individual leaders in shaping aggregated outcomes. From history to economics, the debate is polarized. Defenders of the Great Man theory such as Thomas Carlyle or Sidney Hook argue that history can be largely explained by decisions and actions impulsed by certain leaders. 3 Conversely, many philosophers consider historical events and leaders as determined by the society itself. In economics, we can find a resembling rift in the literature of the origins and persistence of the great divergence between the poorest and richest nations. Factors such as geography, culture, history, and institutions have been highlighted to a great extent. [START_REF] Besley | Political selection[END_REF] instead points out that the major economic changes over the past two centuries can be attributed to individual political leaders. Beyond observational evidence, [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF] provide causal evidence that national leaders shape the growth path of a country.

If national leaders matter, it is important to explore how their characteristics map to their effects. This paper mainly contributes to the literature that studies how national leaders' characteristics affect national outcomes. A closely related paper is the one by [START_REF] Besley | Do Educated Leaders Matter?[END_REF], who tested whether the impact of leaders on their nation's economic growth varies according to their educational attainment, finding a positive association. Leaders with an economic background are also associated with higher growth rates in democracies [START_REF] Brown | Economic leadership and growth[END_REF] and with higher foreign direct investment in autocracies [START_REF] François | Educated dictators attract more foreign direct investment[END_REF]. National leaders' backgrounds also affect the implementation of market-liberalization reforms [START_REF] Dreher | The impact of political leaders' profession and education on reforms[END_REF], the level of democracy in developing countries [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF], corruption in Africa [START_REF] Efobi | Politicians' attributes and institutional quality in Africa: A focus on corruption[END_REF] and militarized conflicts [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF].

Closely related is the literature on politicians' quality and valence. While many theoretical models have disentangled "good" politicians from "bad" ones, it is empirically challenging to detect them and to measure their quality. 4 Certainly, the quality of leaders is unobserved and difficult to quantify. Thus, it is common to use the level of education or previous experience as a proxy for politicians' valence. 5 Moreover, candidates themselves tend to strengthen their education or previous careers in political campaigns, suggesting that this would impact voters' perception of their abilities. Yet, as argued by Volden et al. (2020), the mapping between personal traits and subsequent politicians' performance is often opaque. 6 Some novel works measure politicians' ability by exploiting unusual data, such as [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] who focus on monarchs' intellectual ability scores instrumented by their degree of breeding, or Dal [START_REF] Bó | Who becomes a politician?[END_REF] who rely on politicians' IQ scores.

My empirical frame differs from previous works in three ways. First, I focus on multiple outcomes to measure national leaders' quality of governance in a broad sense. 7 Second, I use data-driven categories that allow me to exploit detailed information on national leaders' past experience and to reduce prior judgment on the selection of attributes that affect leaders' performance. Third, the cited studies use fixed effects or the average outcome to measure the ruler's impact. Here, I use the leaders' value-added estimators of Easterly and Pennings (2020), which better isolates rulers' effects from shocks and countries' effects, and detects significant leaders in relative terms both with respect to other rulers in their region at the same time and to leaders in the same country at different times.

With these new features, my results contribute to the existing literature by showing first that the positive effect of university education is positive for growth, consistent with [START_REF] Besley | Do Educated Leaders Matter?[END_REF], but negative or non-significant on all other indicators, in line with [START_REF] Carnes | What good is a college degree? Education and leader quality reconsidered[END_REF]. Leaders' effects on growth are uncorrelated with leaders' effects on all other outcomes. Together with the notices of Easterly and Pennings (2020) regarding the difficulty of using growth rate to identify leaders' effects given its noise and volatility, as well as the high variation across alternative data sources, this paper emphasizes that the most widely used proxies for national leaders' performance (growth) and leaders' quality (graduate education) have to be taken with precaution. 8 Second, I show that leaders' previous experience in politics, both in the type of office and the number of years of experience, is informative of a positive overall performance and rulers' military career is strongly associated with negative effects. Third, I provide suggestive evidence that leaders from different backgrounds choose different policies: politicians are associated with higher levels of social spending, and the reverse applies to military rulers. Fourth, I show that leaders' effects interact with institutions. I support previous 6 Volden et al. (2020) also show that the role of education on effectiveness is not straightforward, as it depends on ideology and networks. [START_REF] Carnes | What good is a college degree? Education and leader quality reconsidered[END_REF] conclude that overall graduate leaders are non-different from others in terms of corruption, the number of bills that they pass, polls' performance, and do not have a positive effect in national outcomes.

7 There are few exceptions: [START_REF] Papaioannou | The dictator effect: how long years in office affect economic development[END_REF] use economic growth, inflation and two indexes for the quality of governance, [START_REF] Carnes | What good is a college degree? Education and leader quality reconsidered[END_REF] use GDP, inequality, protests, unemployment, inflation, and militarized disputes at the national level, and [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] use as state performance a score provided by historian Woods that accounts for several dimensions.

8 For the inconsistency of growth across data sources also see [START_REF] Johnson | Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates[END_REF] and [START_REF] Ponomareva | Does the version of the Penn World Tables matter? An analysis of the relationship between growth and volatility[END_REF].

findings by [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF], [START_REF] Besley | Do Educated Leaders Matter?[END_REF], and [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] that leaders' effects are stronger under unconstrained regimes, and I also show that leaders of different types differ on the extent to which they respect formal institutions. Altogether, these results contribute to the mapping between leaders' attributes and their performance.

The rest of this paper is organized as follows. Section 2 reviews the existing related literature. Section 3 describes the data and sources used in this work. Section 4 provides the estimations for leaders' effects. Section 5 analyzes how leaders' backgrounds affect the quality of governance. Section 6 presents the results of robustness checks, and Section 7 concludes.

2 Related literature

The broad debate

The long-term interest in political leaders is grounded in the belief that leaders shape historical outcomes. Proponents of this claim, known as the "Great Man" view, argue that history can be explained by the idiosyncratic influence of certain individuals such as Mahatma Gandhi, Kamal Atatürk, Abraham Lincoln, Vladimir Lenin, Joseph Stalin, Adolph Hitler, Mao Tse Tung, and Winston Churchill. 9 At the other extreme, opponents of this premise claim that historical events and leaders can be determined by the society itself. 10 In the middle-ground, [START_REF] Weber | The theory of social and economic organization[END_REF] argues that "charismatic" leaders can have substantial effects, but only when bureaucracy or social norms do not interfere. For him, historical forces, institutions, and leaders all matter, and they interact in an important way. 11 Acknowledging that political leadership matters opens the debate on who is better suited to run a nation. In ancient Greece, political officials were selected by sortition, that is, randomly among the whole pool of citizens. Aristotle saw this as the channel through which to achieve equality of law and political rights, as opposed to an election system that would lead to an oligarchical system where only a few would rule [START_REF] Susemihl | The politics of Aristotle: books IV: a revised text[END_REF]. The most striking limitation of the random allocation of political offices is the lack of qualifications for some individuals. Epistocrats would argue for discrimination based on knowledge. John Stuart Mill proposed a voting system where the number of votes of each individual would be based on their educational attainment and occupational background 9 See for instance [START_REF] Carlyle | On heroes, hero-worship, and the heroic in history[END_REF][START_REF] Carlyle | On heroes, hero-worship, and the heroic in history[END_REF]; [START_REF] Hook | The hero in history: A study in limitation and possibility[END_REF]. 10 For instance, Spencer (1892) states: "You must admit that the genesis of a great man depends on the long series of complex influences which has produced the race in which he appears, and the social state into which that race has slowly grown. ... Before he can remake his society, his society must make him."

11 See also [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF] for a more detailed summary of this debate. (Estlund, 2003). Yet, epistocracy is not likely to come after democracy. As stated by Weber, once universal suffrage has been granted, the backlash effect of taking it away would be too terrible [START_REF] Runciman | How democracy ends[END_REF]. It is, nonetheless, acceptable to promote such discrimination in the case of politicians rather than voters. For instance, if educational attainment is a measure of knowledge, this may be translated into the desire to have a national leader with a higher level of education and/or degrees in several fields. This vision of knowledge measured by the level of education is called scholocracy (Estlund, 2003).12 Criticisms of epistocracy come in many forms, one being demographic representation. The most educated group of a country may share some common demographic features in terms of gender, age and social class that make them unrepresentative of the entire society (Estlund, 2003). Nowadays, when educational attainment in the political elite is high, the core of the debate has been turned to which field of education is better suited for politicians. Supporters of technocracy would promote being governed by experts. But technocracy may not always be the best option. For instance, as argued by [START_REF] Runciman | How democracy ends[END_REF]: "Technocrats are the people who understand what's best for the machine. But keeping the machine running might be the worst thing we could do. Technocrats won't help with that question."

Besides education, previous political experience may give rulers a comparative advantage by acquiring specific skills, understanding the political arena, and making political connections. The type of people who are called to the profession of politics share some characteristics. Weber argued that politics may be a man's avocation or his vocation. While we are all "occasional" politicians, Weber defines "professional" politicians as those who live "for" and/or "off" politics. Rulers who have made a career in politics instead of having had other former occupations may have an inherent vocation and stronger motivations than others. Yet, Weber found vanity to be a common characteristic among politicians, meaning that politicians are tempted to make decisions based on emotional attachments to followers, and not on the rational reasoning needed to govern justly and effectively [START_REF] Weber | The vocation lectures[END_REF].

History is also marked by military leaders. Napoléon Bonaparte, John F. Kennedy, Nikita Khrushchev, and De Gaule are examples of the leaders who fought in armed conflicts before reaching power. [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF] claim that former military experience might have a powerful and systematic effect on leaders' behavior. More broadly, the observation that life experiences shape an individual's behavior is a basic premise in psychology and sociology.

The economic literature

In the political economy literature, the interest in and evidence on the importance of political leaders' identity are more recent, as traditionally economic outcomes have been mostly attributed to geography, history, macroeconomic cycles, or institutions. Instead, [START_REF] Besley | Political selection[END_REF] observes that every major episode of economic change over the past 200 years can be associated with a key political personality, conveying the Great Man theory to this field. 13Going beyond anecdotal evidence and providing causal evidence that national leaders shape economic outcomes has proved to be difficult, given the endogeneity in leaders' emergence, and the difficulty to disentangle between leaders' effects, contemporaneous shocks, and their constituencies' fixed-effects. [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF] overcome those limitations by exploiting transitions in which the leader in power died by natural death while in office, considering those as unpredictable events, and therefore exogenous to the current politic-economic context. They find that national leaders matter to the extent of shaping the growth path of their country.

Consistent with the scholocracy argument, [START_REF] Besley | Do Educated Leaders Matter?[END_REF] show that more educated leaders are associated with higher economic growth rates. In the spectrum of technocracy, [START_REF] Brown | Economic leadership and growth[END_REF] found that national leaders with a degree in economics are associated with higher rates of economic growth, and [START_REF] François | Educated dictators attract more foreign direct investment[END_REF] concluded that they also trigger higher levels of foreign investment. The economic performance of a nation is also affected negatively by national leaders' age (Jong-A-Pin and Mierau, 2011; [START_REF] Atella | Is gerontocracy harmful for growth? A comparative study of seven European countries[END_REF] and by their tenures in autocracies [START_REF] Papaioannou | The dictator effect: how long years in office affect economic development[END_REF]. More broadly, [START_REF] Dreher | The impact of political leaders' profession and education on reforms[END_REF] studied how the profession and education of heads of government are important for implementing market-liberalizing reforms, [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF] examined how national leaders' characteristics affect military decisions, [START_REF] Jha | Does combat experience foster organizational skill? Evidence from ethnic cleansing during the partition of South Asia[END_REF] document that exposure to war helps to develop organization skills and to take collective actions, and Dube and Harish (2020) show that, contrary to popular beliefs, female monarchs were more likely to undertake policies that lead to war than male ones. In developing countries, [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF] analyzed the effects of national leaders' migration experience on the level of democracy, and [START_REF] Efobi | Politicians' attributes and institutional quality in Africa: A focus on corruption[END_REF] found that the attributes of African national leaders are significant in determining the level of corruption.

At the local level, there is a larger body of compelling evidence that links politicians' characteristics and their constituencies' outcomes, as it is easier to design an empirical strategy to identify a causal effect. Educated leaders have been associated to better educational outcomes [START_REF] Martinez-Bravo | The local political economy effects of school construction in Indonesia[END_REF]Diaz-Serrano and Pérez, 2013), and public investment (Mitra, 2020). Leaders' gender has become an active research topic at the local level. [START_REF] Chattopadhyay | Women as policy makers: Evidence from a randomized policy experiment in India[END_REF] use data from India, where some council head positions were randomly allocated to women. They found that female leaders spend more on infrastructure that is directly relevant to the needs and complaints of women citizens. [START_REF] Brollo | What happens when a woman wins an election? Evidence from close races in Brazil[END_REF] used a discontinuity approach based on close elections and found that in Brazil, female mayors are less likely to engage in corruption, hire fewer temporary public employees during the electoral year and attract fewer campaign contributions when running for reelection. Profeta and Accettura (2022) also find genderdifferences in political cycles by showing that in Italian municipalities female mayors engage in less strategic spending at pre-electoral and electoral years. In the same line, Alesina et al. (2015) show that strategic responses to electoral incentives also depend on politicians' age. The evidence on the role of individual characteristics in politics also extends to other political offices, such as central bankers [START_REF] Göhlmann | The educational and occupational background of central bankers and its effect on inflation: An empirical analysis[END_REF], finance ministers [START_REF] Jochimsen | The perfect finance minister: Whom to appoint as finance minister to balance the budget[END_REF][START_REF] Moessinger | Do the personal characteristics of finance ministers affect changes in public debt?[END_REF] and legislators [START_REF] Volden | Legislative effectiveness in the united states senate[END_REF][START_REF] Lippmann | Gender and lawmaking in times of quotas[END_REF][START_REF] Jain | Do Educated Leaders Affect Economic Development? Evidence from India[END_REF].

Steering away from political rulers, leaders have been proved to be relevant in the most diverse areas, going from firms to religion. For instance, [START_REF] Bertrand | Managing with style: The effect of managers on firm policies[END_REF] and [START_REF] Bandiera | Ceo behavior and firm performance[END_REF] stress that differences in firms' performance depend on the specific characteristics of the firm's CEO, [START_REF] Dippel | Leadership in Social Movements: Evidence from the" Forty-Eighters" in the Civil War[END_REF] shows how the "Forty-Eighters" influenced enlistment decisions in the US civil war, [START_REF] Wang | Media, pulpit, and populist persuasion: Evidence from Father Coughlin[END_REF] finds that Father Charles Coughlin shaped votes preference, and [START_REF] Bassi | Persuasion: A case study of papal influences on fertility-related beliefs and behavior[END_REF] show that the Pope's persuasive messages had an effect on fertility.

Unsettled conclusions on three aspects are worth mentioning. Most of the cited studies focus on assessing the effect of one characteristic on one outcome. If diverse outcomes are uncorrelated or negatively correlated between them, a positive effect in one dimension does not allow us to conclude about the broad performance of leaders. Some exceptions are [START_REF] Papaioannou | The dictator effect: how long years in office affect economic development[END_REF], [START_REF] Carnes | What good is a college degree? Education and leader quality reconsidered[END_REF] and [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF]. Moreover, the use of fixed-effects models to measure leaders' effect can be problematic (see Section 4.1). This paper differs from previous work by using a multi-dimensional measure of leaders' performance and by estimating leaders' effects through a method inspired by the teacher-value added literature.

Similarly, politicians' quality is relatively easy to define and implement in theoretical models. For instance, [START_REF] Besley | Political selection[END_REF] and [START_REF] Caselli | Bad politicians[END_REF] defined it according to honesty and competence.14 Moving to the empirical analysis is difficult as quality is unobserved and we know little about how politicians' attributes map to abilities (Volden et al., 2020). In a novel attempt to measure quality, [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] proxy monarchs' ability by historian Woods's intelligence scores instrumented by rulers' degree of inbreeding and Dal [START_REF] Bó | Who becomes a politician?[END_REF] rely on unusual data on politicians' IQ scores. Backed by the labor economics literature, some authors have used as a measure of quality the level of education and previous experience in politics [START_REF] Ferraz | Motivating politicians: The impacts of monetary incentives on quality and performance[END_REF]Galasso and Nannicini, 2011). However, there is mixed evidence on the effect that certain politicians' characteristics have, in particular the positive effect of formal education. 15 Carnes and Lupu (2016) conclude that graduate politicians do not run more prosperous nations, and at the local level they are no less likely to be corrupt, they do not do better at polls, and they do not pass more bills. 16 Lahoti and Sahoo (2020) find heterogenous effects of leaders' education on educational outcomes according to the state's level of development. Volden et al. (2020) show that the impact of education on legislators' effectiveness is not straightforward. 17 I reinforce this skepticism by showing that national leaders' education is positively correlated with growth, consistent with [START_REF] Besley | Political selection[END_REF], but it has no effect on democracy, corruption, property rights, rule of law or conflict. Moreover, I show that leaders' previous experience in politics, with respect to both the type of office and the years of experience, is informative of a positive overall performance and that rulers' military career is strongly associated with negative effects.

Finally, the evidence of how rulers interact with institutions is limited in the leaders' literature. 18 Jones and Olken (2005), [START_REF] Besley | Do Educated Leaders Matter?[END_REF][START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF][START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] underline that politicians matter less when institutions are stronger. I provide further evidence in favor of this finding, and I add to the literature by highlighting that leaders' respect to formal institutions varies with their background. Military leaders (politicians) are more (less) likely to infringe the constitution; to move towards a nonelectoral system; and to establish a personalistic regime.

In sum, previous literature suggests that leaders from different backgrounds have different skills, preferences, leadership styles, and risk behaviors, which affect policy decisions and consequently the performance of the constituency that they run. Leaders' effects interact with institutional constraints, as those can prevent political leaders from implementing their preferred policy [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF]. Figure 1 presents this theoretical relationship, which is an adaptation from Horowitz and Stam (2014). 19 15 See also [START_REF] Selb | Examining a most likely case for strong campaign effects: Hitler's speeches and the rise of the nazi party, 1927-1933[END_REF] who question that charismatic leaders are relevant for the electoral success of populist parties suggested by [START_REF] Eatwell | The rebirth of the'extreme right'in Western Europe?[END_REF]; [START_REF] Kitschelt | The radical right in Western Europe: A comparative analysis[END_REF]; [START_REF] Mény | The constitutive ambiguity of populism[END_REF], among others. 16 Their empirical strategy at the national level rely on a fixed-effects model with an indicator for countries that have experienced random transitions (defined as in [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF]; [START_REF] Besley | Do Educated Leaders Matter?[END_REF]) in the last five years and that had college-educated leaders and an indicator for countries that have experienced these transitions and that had leaders without college degrees.

17 [START_REF] Jain | Do Educated Leaders Affect Economic Development? Evidence from India[END_REF] find a positive effect of legislators' education on economic development, but they find other traits such as gender, or criminality to have a greater effect.

18 Instead, the political science literature has largely studied the ability of "veto players" (such as constitutions, or opposing political parties) to constrain politicians [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF]. See for instance [START_REF] Tsebelis | Veto players[END_REF]. 19 The original representation by [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF] is Figure E1 from Appendix E. This paper uses the Cursus Honorum database [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] as the primary data source for national leaders' characteristics and the Leader Experience and Attribute Descriptions [START_REF] Ellis | Introducing the LEAD data set[END_REF] as a supplementary source. Both build on [START_REF] Goemans | Introducing Archigos: A Dataset of Political Leaders[END_REF] which identify the effective leaders of each independent state: those who de facto exercise power.

In most cases, the effective leader corresponds to the president in presidential systems, the prime minister in parliamentary systems, and the dictator in autocracies. There are few exceptions, such as the case of Rafael Trujillo in the Dominican Republic, who did not formally occupy the presidency between 16 August 1938 and 18 May 1942, but remained the de facto ruler of the country [START_REF] Goemans | Introducing Archigos: A Dataset of Political Leaders[END_REF]. Another example is the brief presidency of Cámpora in Argentina in 1973 who ran under the shadow of Perón, as his campaign slogan explicitly claimed: "Cámpora to the government, Perón to power." 20 I use data from 1931 to 2010 from 148 countries and I focus on leaders who stayed three or more years in power. 21 This gives a total of 1, 002 national leaders that are distributed all across the world as illustrated by Figure 2. 22 Table 1 displays descriptive statistics. The median national leader stays in office for 6 years and is a graduate 53 years-old man counting with 11 years of experience in formal politics. 23 To define leaders' background, I rely on the detail of their previous career and their academic field, provided by the Cursus Honorum database [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF]. First, I analyze the most frequent leaders' careers and degrees by restricting the analysis to those occupations and educational fields with more than 30 repetitions in the sample. I list 20 The original slogan is "Cámpora al gobierno, Perón al poder". 21 Thus, I do not include 691 leaders who stayed two or fewer years in power and 87 who stayed three or more, but for whom I do not have sufficient data to estimate leaders' effects in the next section.

22 In Section 5 I further restrict the sample to those leaders without missing information for their background and the outcome analyzed. Descriptive statistics of these sub-samples are provided in Table A3 of Appendix A.

23 Since men represent a high percentage (97%) of the dataset, it is not possible to assess the effect of gender in the present work.

those in Table 2. Second, I consider the interdependence among and across careers and degrees. Figure 3 depicts the associated correlations. The thicker the line that links two words, the higher the correlation coefficient between them. On the top left, there is a triangle of correlations: leaders with a law degree are likely to have been members of parliament and/or party leaders, and many party leaders also served as members of parliaments. On the bottom, we can identify a cluster of words related to leaders with a military background, such as military career, military education, commander, chief, army and officer. A high proportion of these leaders are unlikely to have a university degree. Finally, the last cluster at the right relates to academics, either with an economic background or a humanity degree. Economist rulers are also likely to have served previously as finance ministers.

The previous analysis suggests that (i) controlling separately for academic degrees and occupation is likely to lead to bias the results, (ii) there are some occupations (degrees) which are not systematically associated with a specific academic degree (occupation), those are the words listed in Table 2 and which do not appear in Figure 3, and (iii) some careers, particularly the ones related to the legislative arena (senators, legislators, members of parliament) involve similar responsibilities but are named differently in different countries.

Taking all these points into account, I create three main groups of leaders, each one with two or more sub-categories. The first encompasses military leaders, either because (i) they had a career in the military force or (ii) a military education. Then, there are the academic leaders. Within this group, we can distinguish between economists and other academics. Finally, the third group is composed of politicians: which includes (i) The map shows the distribution of national leaders who stayed in power three or more years from 1931 to 2010 and for whom I have data on at least one of the following macroeconomic variables: economic growth, corruption, democracy, property right score, rule of law and/or conflict. There is a total of 148 countries in the sample. politicians with a legal background such as former members of parliament, senators and legislators; (ii) mayors and governors; (iii) prime ministers, vice-presidents and presidents;

General statistics

Number

(iv) party leaders, (v) ministers and (vi) civil servants or diplomats. The number of leaders in each category is displayed in Table 1. The frequency between sub-categories as well as further details of the criteria used to construct each category are found in Appendix A.

Even though previous categories are data-driven, and I use the sub-categories throughout this paper, allowing for a more granular analysis, there is an unavoidable degree of arbitrariness in categories' aggregation. In Section 6.3, I show the robustness of the results when using a LASSO regression to select among a large pool of covariates (which includes a larger number of occupations, degrees, experience, constraints on the executive, as well as all relevant interaction among these variables) the best sub-set of covariates that better predict leaders' effects.

Quality of governance

Economic growth has been the primary indicator of national leaders' effects in the economic literature.24 However, it is difficult to properly estimate leaders' effects on economic growth, given the high volatility in growth data and its inconsistency when using alternative sources.25 Moreover, growth is only one aspect when evaluating a leader's performance, as high growth rates may not always be desirable if they come at the cost of adverse environmental effects, or if it do not lead to a more equal society. In this regard, Alvaredo et al. (2018) show that the global top 1 % earners has captured twice as much total growth than the bottom half of the distribution between 1980 and 2016. Therefore, I use several other indicators of the quality of governance linked to national performance justified below.

Following [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF], [START_REF] Papaioannou | The dictator effect: how long years in office affect economic development[END_REF] and [START_REF] Marx | Electoral Turnovers[END_REF] The table shows the words with more than 30 repetitions in the leaders' background variable for the leaders included in the sample. The leaders' background variable is the concatenation of a variable that describes the leader's career prior to taking office and the one that describes his/her university field(s), taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] dataset. I grouped the following words into single expressions: "member of the parliament", "economics degree", "humanities degree", "political science", "party leader", "no education", "pre-independence leader", "military career", "military education" and "civil servant". Ungraduate refers to "no education" in [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF]. There is a total of 188 distinct expressions in the background variable. The figure shows the correlation of words in the leaders' background variable, which is the concatenation of a variable that describes the leader's career prior to taking office and the one that describes his/her university field(s), taken from Baturo (2016) dataset. I restrict to words with a correlation coefficient higher than 0.10 and to words repeated more than 30 times in the dataset. I grouped the following words into single expressions: "member of the parliament", "economics degree", "party leader", "law degree", "commander in chief", "military education" and "humanities degree". Humanities degree refers to a degree in philosophy, theology, sociology or psychology. [START_REF] Kaufmann | The worldwide governance indicators: Methodology and analytical issues1[END_REF]. 26 As [START_REF] Marx | Electoral Turnovers[END_REF] and Dube and Harish (2020), I consider conflict in the analysis of national performance. Finally, I include property rights as it relates to economic development [START_REF] Besley | Property rights and economic development[END_REF] but it does not suffer from the volatility or noise observed for growth. Individually, each of these variables may not properly capture the quality of governance. For instance, some of them, such as corruption, rule of law and property rights, are perception-based and may not truly reflect what the variable aims to measure. [START_REF] Rodrik | Institutions and economic performance-getting institutions right[END_REF] argued that investors' ratings of the rule of law may capture how well the rules regarding property rights are perceived to operate, rather than what those rules are. However, he also argued that performance is superior when the score is higher, even though the results do not allow us to determine what makes investors feel that way. Similarly, [START_REF] Glaeser | Do institutions cause growth?[END_REF] claimed that the Polity variables are highly volatile and do not reflect durable institutions, but the political choices made by the leader. This latter point is an advantage when focusing on leaders' effects. Further, there is still a unidi-rectional relationship between the variable and the evaluation of a leader's performance. Taken together, if most of the outcomes improve and the remainder does not worsen, it is reasonable to claim that there is an increase in the quality of governance. To measure global performance, I use two synthetic indexes that combine leaders' effects in all the 6 selected variables.

Another potential concern is the extent to which individual leaders can affect the chosen indicators. Anecdotal and empirical evidence supports that leaders influence the previous outcomes. [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF] argue that national leaders shape economic growth, particularly through monetary policies, while [START_REF] Besley | Do Educated Leaders Matter?[END_REF] and [START_REF] Brown | Economic leadership and growth[END_REF] show that leaders' effect on growth varies with education. The role of national leaders in initiating conflict has been pointed out by [START_REF] Horowitz | How Prior Military Experience Influences the Future Militarized Behavior of Leaders[END_REF], and Dube and Harish (2020) study the role of gender in militarized disputes. [START_REF] Besley | Property rights and economic development[END_REF] give examples of how thorough expropriations, governments, and specifically some national leaders shaped property rights. 27 When it comes to democracy, while regime turnovers are often triggered by social movements and revolutions rather than by a sole national leader, the definition of the new system and the new institutions (such as implementing a new constitution) are within the national leader's reach. Moreover, [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF] shows that national leaders' migration experience is associated with changes in democracy and [START_REF] Marx | Electoral Turnovers[END_REF] also use it as an indicator for national performance. Concerning corruption, I restrict to the level of corruption on the executive, which by construction is likely to depend on the individual with the most executive power (whom I refer to as the national leader). Following the definition provided in Table A4, this index can be affected by favors in exchange for bribes, kickbacks, or other material inducements, and by how often leaders steal, embezzle, or misappropriate public funds. To further address this concern, in Section 4.2.1 I give real-life examples of the means through which national leaders included in this paper shaped, for the best and for the worst, each of the considered outcomes; in Section 5.2 I explore heterogeneity among leaders depending on whether or not they are subject to strong constraints, which may affect the extent to which leaders can shape outcomes; and in Section 6.3 I show correlations between the indicator of overall performance used in this paper with external indexes of quality of governance.

Data for economic and social outputs used in the present paper is drawn from the Maddison Project (Bolt et al., 2018), the Polity IV Project (Marshall and Gurr, 2018) and the V-Dem (Varieties of Democracy) dataset (Coppedge et al., 2019). Each variable definition, as well as its source, can be found in Appendix A.

Measuring leaders' effects 4.1 Methodology

Identifying the effects of political leaders at the national level is a hard task, due to endogeneity issues, the availability of data to perform cross-country comparisons and noise in the data. Recently, Easterly and Pennings (2020) proposed a new methodology inspired by the teacher value-added literature to estimate the growth contribution of every individual head of state since 1950. They started with a simple model in which growth, under leader i's term in country c in year t, follows:

g * ict = λ i + µ c + ϵ ict (1)
where g * ict is the mean-zero residual of g ict after removing region×year fixed effects in a first regression; λ i ∼ N (0, σ 2 i ) is the leader's effect on growth, which is fixed over all of their tenure; µ c ∼ N (0, σ 2 c ) is the country effect, which captures the growth trend that is due to time-invariant variables at the country level beyond the leader's control, such as institutions, culture and geography; and ϵ ict ∼ N (0, σ 2 ϵc ) is the random noise of growth with a country specific variance. 28 The goal of Easterly and Pennings (2020) was to propose a good estimator of λ i . The first intuitive alternative is to use leaders' fixed effects, which is simply the average growth under leader i's tenure. However, using Monte Carlo simulations, they found that this estimator performs very poorly, as the root mean squared error of the estimator is very large. 29 , 30

They proposed instead a least-squares leader estimator (λ LS i ) given by equation (2).

λLS i = ψ i (g i -γ i g -ic ) (2)
where γ i intuitively reflects how g -ic , the average growth under other leaders from the same country, is a good measure of the country effect, and similarly ψ i , the shrinkage factor, measures how the adjusted leader output average reflects the true leader effect. If ψ i is low, this means that the adjusted average will be a poor measure of the leader's effect, which is the case when year-to-year growth is very noisy; the leader has a short 28 The six regions are: 1) Europe, the United States, Canada, Australia and New Zealand; 2) Communist Bloc countries close to the Soviet Union (Albania, Bulgaria, Hungary, Mongolia, Poland and Romania); 3) Sub-Saharan Africa; 4) the Middle East and North Africa; 5) Latin America and the Caribbean; 6) Asia.

29 Easterly and Pennings (2020) show that the root squared error of this estimator for economic growth is more than twice as large as when assuming λi = 0.

30 The error of this naive estimator is 1 Ti Ti t=1 ϵ ict + µ c , where T i is the total tenure of leader i. The first term of the sum is the country fixed effect, which could be removed in a first regression, as done with region×year dummies, but by doing so, we would also be removing part of each leader's effect, especially the ones with long tenure. Even if controlling for µ c , the estimator would perform poorly as empirically the most important part of the error is ϵ ict .

tenure or country effects vary greatly, making it difficult to distinguish between country and leader effects.

Easterly and Pennings (2020) concluded by estimating confidence intervals at the 95% confidence level for leaders' effects. Using this methodology, a leader will be significant for a certain outcome if the change in this outcome during their tenure is significantly higher than that of other leaders in the same region at the same time and than that of other rulers from the same country in different years. I also condition significance on the nominal variation in the outcome being different from zero, meaning that the significance does not come only from the relative difference with other heads of government but also from a fluctuation in the outcome during the leader's term. A more detailed review of the methodology and the formulas used to calculate γ i and ψ i are presented in Appendix B.

Estimating leaders' effects

I estimate the effects of national leaders on six outcomes: growth, democracy, corruption, rule of law, property rights and conflict. Table 3 shows, for each of the outcomes considered, the estimated country and leader variance (σ c and σ λ , respectively) as well as γ i and ψ i , defined above. It also shows the number of significant leaders who have stayed in power for three or more years. For corruption and conflict I multiply leaders' effects by -1, so leaders with a positive effect are the ones associated with lower corruption and lower conflict. 31Easterly and Pennings (2020) found that only 6% of national leaders in their sample had a significant impact on economic growth. I find the same proportion for growth in my sample of national leaders, as shown by column 1 in Table 3. As Easterly and Pennings (2020) explained, this is not necessarily because most rulers do not shape growth, but because the noise in the growth data makes it difficult to identify their effects. It could also be because growth is endogenous to political cycles. 32 Further, the sample of significant leaders is not robust when changing the data source for growth. When using alternative indicators for leaders' performance, a greater proportion of leaders becomes significant. It may seem counter-intuitive, given that these variables are much less volatile than growth rates. Indeed, it is precisely because they are more likely to change across leaders' terms, rather than within, that these variations are easily attributed to political leaders rather than to common regional shocks or luck. Thus, contrary to growth where variations are common from one year to another within a ruler's term, it becomes easier to identify the leaders' effects.

Rankings

Obtaining these leaders' effects allows us to rank national leaders according to their performance in each area. In Appendix C, I provide the rankings of the best and worse fifty heads of government for each variable studied. Below, I discuss some examples.

The ranking of leaders' contributions on growth (Table C1) is led by Leopold Figl from Austria being appointed as Chancellor in the first free elections since 1930, in the aftermaths of World War II. Austria showed a greater recovery than its neighbor nations, partially explained by its higher financial help from the USA's Marshall Plan. He is followed in the ranking by Franklin Roosevelt, who was elected for four consecutive terms. At the time he won the first election, the U.S. was undergoing at the worst depression in its history. He responded with the New Deal program which rapidly boosted the economy. It is also worth noting that his last years were marked by World War II, and the US is coded in the same region as Europe for computing leaders' effects. Thus, part of his place in the ranking may be explained by the heavier economic costs in European countries. As it is noticeable, those two leaders were in office in times of a deep deterioration of the economic situation, where there is arguable more room for economic reforms. The third national leader in growth contributions is Seretse Khama, the first President of Botswana, who stayed in office from 1966 to 1980. In 1966, Botswana was the world's third poorest country. Once in office, Khama, unlike other African leaders, adopted market-friendly policies such as low and stable taxes, liberalized trade, increased personal freedoms and he fought against corruption. He set out on an economic program intended to transform σϵ, σc and σ λ are the estimates of the error, the country and the leader variance components respectively. ψ refers to the shrinkage coefficient and γ to the country effect's weight from equation ( 4). The detailed formulas for each component can be found in Appendix B. Leaders' effects are estimated for those with tenure equal to or longer than 3 years. I exclude countries with less than 30 observations and in the first column those observations where the absolute value for growth was greater than 40%.

(1) (2) (3) (4) (5) (6)
the nation into an export-based economy, which was fostered by the discovery of Orapa's diamond deposits in 1967 [START_REF] Haskins | African heroes[END_REF]Tupy, 2008).

When it comes to property rights, the ranking is led by two Mongolian leaders: Bagabandi and Enkhbayar. During this period, the government implemented free-market reforms, among which there was an ambitious plan for the privatization of Mongolia's largest state enterprises. The IMF also recognized Bagabandi for improving Mongolia's overall economic situation, by increasing revenues and better controlling government finances [START_REF] Finch | Mongolia in 2001: Political consolidation and continued economic reform[END_REF]. Fidel Castro, who banned property sales when he came to power in 1959, is the worst of this ranking.

On the list of the most corrupt leaders, Hugo Chavez from Venezuela is first. According to the national Parliament, he made the country a paradise for corruption (Table C2).33 He is closely followed by Rajapaksa, head of state of Sri Lanka. 34 Rajapaksa was charged with corruption involving the illegal transfer of state-owned weapons after his time in office. He also faced accusations of human rights abuses and his son and his brother were arrested for money laundering. 35 . Leaders at the bottom of the corruption ranking also appear to be at the bottom of rule of law. Chavez is indeed the worst effective ruler in both indicators; Pinochet is classed as the 4 th most corrupt and also 3 rd in negative effects on rule of law; Franco is 5 th and 6 th in the respective rankings; and Fujimori from Peru occupied the 6 th and 5 th bottom positions. Rajapaksa is moreover listed in the negative ranks of leaders' effects on property rights and rule of law.

When it comes to democracy, it is not surprising that most of them followed or preceded regimes changes (Table C3). Jawara (in 1 st position) was the first president of Gambia who already dominated the pre-independence political life and instituted a new constitution, Mara (in 2 nd ) position served as Chief Minister from 1967 to 1970 when Fiji gained its independence from the United Kingdom, and afterwards as head of state in 1987, while Balewa (in 3 rd position) was the first Prime Minister of Nigeria after its independence. Among the worst leaders are renowned dictators such as Marcelo Caetano from Portugal (who lead a costly war against Portuguese colonies' independence), Schuschnigg (Hitler's Austrian ally), Franco, Mussolini, Pinochet, among others.

Similarly, leaders who have the worse effects on conflict lead to violent periods. The worst in the conflict ranking is Tojo, Prime Minister of Japan during World War II, known as the man who ordered Pearl Harbor. He was afterwards arrested, convicted, and sentenced to death for his war crimes, crimes against humanity and peace. He is followed by Macias Nguema, who despite being formally the first president of Equatorial Guinea, he is remembered as one of the most brutal dictators in history. He established a cult of personality, declared himself president for life and the "Grand Master of Science, Education and Culture". He was a bloody dictator. He launched a purge against intellectuals, killed or exiled nearly all the educated people in the country and banned the use of the word "intellectual". Macias also ordered to kill Nigerian immigrants workers, political preindependence leaders, political opponents, and priests.36 On the opposite, the most peaceful head of state is Ariel Sharon, an influential and controversial Israelian figure. Indeed, before entering office he was a soldier and an officer in the Palestinian war. However, in the 2001 electoral campaign, he reinvented himself as a peace-seeker. When he entered office, he recognized Palestinians the right to establish their state, engaged in dialogue with Palestinian leaders, and ordered Israelian settlers to withdraw from the Gaza strip. 37In most of the above examples leaders' effects on multiple outcomes tend to go in the same direction. This is confirmed in Table 4 where I show that correlations of leaders' effects in different outcomes are all positive. It is interesting to notice that the correlation between leaders' effects on growth with the alternative outcomes are relatively low, reinforcing the importance of not judging a political leader by merely relying on economic indicators.

Overall, the reading of the rankings suggests that leaders who matter more, for the best or for the worse, were at the head of important economic or political reforms and led to drastic changes in society. Thus, individual politicians have the power to shape each of the outcomes considered through economic and social reforms, war or peace declarations, acts of corruption, among others.

Aggregated indexes

Assessing leaders' effects on multiple outcomes raises the issue of multiple testing. Therefore, besides using leaders' effects on the above-mentioned outcomes, I aggregate leaders' effects on the different dimensions to reduce the risk of over-rejection of the null hypothesis and to gain statistical power, similarly to [START_REF] Kling | Experimental analysis of neighborhood effects[END_REF] and [START_REF] Marx | Electoral Turnovers[END_REF]. To do so, I first standardize leaders' effects in order to all of them have a mean of 0 and a standard deviation of 1. Then, I build two indexes: an unweighted sum of leaders' effects in all six outcomes and a linear combination of the outcomes in such a way that we retain most of the information. The last is obtained through a principal component analysis (see Appendix F).38 5 Leaders' backgrounds and the quality of governance

Benchmark results

In this section, I analyze how leaders' effects previously estimated vary according to national leaders' backgrounds. To do so, I estimate the following equation:

LS yi = α + βX i + ϵ (3)
where LS yi is leader i's effect on outcome y as estimated in the previous section, X is a vector of leaders' characteristics, which includes age of entry, tenure and background categories discussed in Section 3 and detailed in Appendix A. To take into account the two-stage procedure, I estimate equation 3 using a weighted least square (WLS) where the weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation.39 By doing so, coefficients that are more (less) precisely estimated in the first stage will have a higher (lower) weight. Results do not hinge on weighting, as estimations using OLS with bootstrapped standard errors yield similar results (see Section 6.1). Standard errors are clustered at the country level. Panel A of Figure 4 shows the effects of leaders' broad categories on the two aggregated indexes. Military leaders are associated with a negative effect on the overall performance of their nation, while a positive one is attributed to politicians. On the other hand, academics do not have a significant impact. Panel B reiterates the analysis for subcategories, bringing into light that leaders with a military career trigger the negative effect of military leaders, while politicians who perform better are former prime ministers, vice-presidents or presidents.

Table D1 and The graphs plot the coefficient for each leaders' category on two aggregated indexes of the estimated leaders' effects obtained in Section 4. Those are post-estimations of weighted least-squares estimations where the weights are the ratio between the absolute value of the leader effect and the posterior standard deviation. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level. In each regression, I control for all categories together, tenure, entry age and the square of these two variables. The criteria used to build the background categories are detailed in Appendix A. I restrict the sample to leaders with tenure equal to or longer than three years, and to those where there is no missing value in any of the leader's effects. Each regression contains 693 leaders from 123 different countries. The coefficients and standard errors for the aggregated index as well as for each individual outcome are displayed in Table D1 for the main categories and in Table D2 for sub-categories.

driven by one specific variable. Military leaders have a negative effect in four out of six outcomes, even though they are associated with a positive (although insignificant) effect on growth and conflict. Politicians have a beneficial effect on five out of six outcomes, three of them being statistically significant from zero. Moreover, academics other than economists are associated with negative effects in corruption, rule of law, and conflict (see Table D2). However, the low precision of these estimates, as well as their positive coefficients regarding economic outcomes (growth and property rights) lead to a non-significant effect on the overall performance as measured by the aggregated index. Similarly, tenure and entry age have negative but non-significant effects on overall performance. Yet, the longer in office the leaders are, the more likely they are to be corrupt. This is consistent with the positive association between political longevity and corruption documented in the literature. 40 Leaders' age also negatively correlates with corruption and with rule of law. 41,42

Mechanisms

The above-documented effects can be triggered by (i) differences in observables and context-specific characteristics correlated with the constructed leaders' categories, and (ii) leaders with different backgrounds undertaking different policy choices, as they have different leadership styles, preferences and objectives. These two set of channels are explored below.

Education, experience and institutions

In this section, I test three potential explanations to account for the positive effect of politicians and the negative one associated with military rulers. First, politicians may be more likely to come from an academic field that provides relevant knowledge for policymaking, such as a degree in law, political science or economics, and the negative effect of military leaders can be due to their lower educational attainment. Second, previous experience in politics may give politicians an advantage through learning-by-doing skills. Third, the effects can be driven by institutions, as military leaders (politicians) may be more likely to take power in those nations where institutions' quality is low (high).

To test these hypotheses, I begin by estimating equation 3 with a new vector X that includes leaders' educational outcomes (I control for having a degree in law, economics and political science, for having a military education, and for other graduate degrees), 40 See for instance [START_REF] Besley | Handcuffs for the grabbing hand? Media capture and government accountability[END_REF]; Coviello and Gagliarducci (2017); Tsur (2022). 41 These results are also in line with (Jong-A-Pin and Mierau, 2011; [START_REF] Atella | Is gerontocracy harmful for growth? A comparative study of seven European countries[END_REF] who show that national leaders' age affects negatively the economic performance of a nation and with [START_REF] Papaioannou | The dictator effect: how long years in office affect economic development[END_REF] who find that years in office negatively correlate with economic development.

42 Table D3 displays the estimates with interactions between sub-categories.

(1)

( WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. Definition of independent variables are detailed in Appendix A. Other degree equals one if university degree is coded one, and economic, law, political science and military education is coded 0. Years of experience is taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and includes years in formal politics that accounts for years in official politics, such as being a member of parliament, governor or city mayor, among others, and political activity such as party membership or civil service offices. Constraint equals one if the Polity IV Executive Constraints is higher or equal than 4 the year before the leader enters office. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

previous years of experience in politics (from the Baturo (2016) dataset), a dummy that equals one if there are substantial constraints on the executive, tenure and entry age. 43 Table 5 suggests that the positive effect of politicians on the overall performance does not come from the level of education or leaders' academic specialization. This is an important result as empirical works on politicians' valence often use educational attainment as a proxy for politicians' quality. 44 More importantly, laws settling minimum educational attainment to run for political offices are becoming increasingly popular in India, and could potentially be followed by other developing countries. Yet, it is interesting to notice that when controlling only for graduate education in Table D4 the coefficient associated with it is positively significant for growth; positive but non-significant for the 43 See Appendix A for the detailed definition of each variable. 44 See for instance Galasso and Nannicini (2011); [START_REF] Ferraz | Motivating politicians: The impacts of monetary incentives on quality and performance[END_REF].

other economic indicator (property rights), and negative for the remaining outcomes. 45For the PCA indicator that summarizes the overall performance, the negative coefficient is at the borderline of statistical significance at conventional levels (p= 0.106). This strengthens the warning of using growth as an indicator of the overall performance and educational attainment as a proxy for leaders' quality.

In contrast, former years of experience in politics are associated with a positive effect on all outcomes. Together with the positive effect of politicians who have held important offices (prime ministers, vice-presidents, or former presidents), it suggests that learningby-doing skills can foster good performance. 46 Institutions also matter as constraints on the executive are correlated with strong and positive effects. This last result is consistent with constraints disciplining national leaders. 47Next, I explore heterogeneity effects by interacting the level of education, experience, and constraints with leaders' backgrounds. Table 6 shows the results for the additive index and Table D7 for the PCA index. 48 When accounting either for educational variables and/or for experience in politics, being a politician is no longer significant. This suggests that their positive effect is triggered by their field of specialization and their experience. Among politicians, only graduate ones have a positive effect on the additive index, but they represent a large majority of 84% among this category. A more precise distinction is made in Figure E2 that creates exclusive dummies for each leader's sub-categories depending on whether or not they are graduates. Once controlling for leaders' backgrounds, education does not alter leaders' effects within each sub-category.

Regarding previous years of experience in politics, it also mitigates the negative effect of military leaders and academics. This is confirmed in Figure 5 which plots the marginal effects of years of experience on the overall performance by leaders' background. Panel A uses the additive index and shows that military (academic) leaders with more than 7 (3) previous years of experience in formal politics have a neutral effect, instead of a negative one. Symmetrically, the positive effect of politicians is only significant for those with more than 7 years of experience. Panel B rises similar results when using the PCA indicator.

Once controlling for the initial constraints on the executive in column (3) of Table 5, the negative effect of military leaders on the overall performance is heavily reduced WLS estimators. The dependent variable is the additive index which is an unweighted sum of the standardized leaders' effects on all the outcomes estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years.

The criteria used to build the background's categories is detailed in Appendix A.Years of experience is taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and includes years in formal politics that accounts for years in official politics, and political activity such as party membership or civil service offices. Standard errors are clustered at the country level.

and no longer significant. 49 However, in the most complete specification (column (4)) that combines education, experience and constraints, as well as their interactions with backgrounds, military leaders are associated with strong negative effects, and graduate 49 Initial constraints is defined with a dummy that equals one if the Polity IV Executive Constraints is higher or equal than 4 the year before the leader enters office.

politicians with a positive coefficient. It is also remarkable that institutional constraints shrink the positive effect of politicians, as suggested by the negative coefficient of the interaction between politicians and constraints. This is in line with [START_REF] Ottinger | History's Masters: The Effect of European Monarchs on State Performance[END_REF] and [START_REF] Besley | Do Educated Leaders Matter?[END_REF] who found that leaders' abilities or education matter only when rulers are not under constraints. Altogether, bad leaders with no constraints may be undisciplined and act in their own interest, but good leaders subordinated to institutions may not be able to shape outcomes. Therefore, constraints can avoid the negative effect of bad leaders but also hide the positive effect of good rulers as they don't have room to shape outcomes.

For the same level of experience, educational attainment, and constraints, leaders of distinct types have different effects, suggesting that these variables might explain only partially the differential of leaders' performance across categories. 6 and Table D7. Years of experience is taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and includes years in formal politics that accounts for years in official politics, and political activity such as party membership or civil service offices. Standard errors are clustered at the country level.

Policy choices and leadership styles

As illustrated in Figure 1, rulers' backgrounds affect preferences, risk attitudes and leadership styles, which in turn affect policy decisions and ultimately national outcomes. I test those channels by studying whether the leaders' categories previously constructed are correlated with three proxies of ruling styles and with five measures of policy choices.

To capture leadership styles, I rely on (i) an index that measures leaders' respect for the constitution, (ii) leaders' effects on the probability that the chief executive is selected through elections, and (iii) a neopatrimonial rule index, which assesses the extent to which rule is based on personal authority.50 I estimate rulers' effects on those three outcomes using the methodology detailed in Section 4.1.

To study policy choices, I focus on the level of (i) total government expenditure, (ii) tax revenue, (iii) expenditure in education, (iv) expenditure in health, and (v) military expenses (all measured in percentage points of the GDP). 51, 52 Except for total government expenditure, the estimation of leaders' effects on outcomes (ii)-(v) cannot be done with Easterly and Pennings (2020) method given the limited number of observations, and the restriction of excluding countries with 30 or fewer observations. Hence, I focus on the average level of these variables during a leader's term.

From the estimates displayed in Table 7, it appears that military leaders (politicians) are more (less) likely to move towards a non-electoral regime, they are associated with a significantly lower (higher) level of respect for constitution, and with a higher (lower) score of neopatrimonialism. Regarding public finances, military rulers are associated with lower levels of total expenditure, education and health expenditure, and with higher levels of military expenditure. On the contrary, politicians are associated with higher levels of taxes, expenditure in health and in education. Academics have significantly lower levels of education expenditure, and higher scores of neopatrimonialism, but are neutral for the remaining outcomes.

Figure 6 links the previous variables and the estimated leaders' effects to the overall performance analyzed in the previous section. Leaders associated with a switch towards an electoral regime, and/or with a higher levels of respect for the constitution tend to perform better, while the reverse holds for leaders with a higher score of neopatrimonialism. The total level of taxes or expenditures are not significant for predicting the overall leaders' performance. However, the composition of the budget is relevant. The average expenditure in education and health positively correlates with both aggregated indexes.

( Columns 1-4 are WLS estimates where the dependent variables are leaders' effects on each outcome estimated using the methodology of Section 4.1, and weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. Columns 5-8 are OLS estimates where the dependent variables are the mean of each outcome during a leader's term expressed in % of GDP. Standard errors are clustered at the country level. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories and the source of each dependent variable are detailed in Appendix A.

On the contrary, there is a negative (but insignificant) association between military expenditure and leaders' performance. Yet, as policy outcomes from columns 5-8 of Table 7 are the simple average over leaders' terms, the coefficients regarding leaders' type might be explained by the endogeneity of leaders' backgrounds with respect to the initial policy level rather than by leaders' choices. While I will discuss endogeneity concerns in more detail in the following section, Figure E3 of Appendix E shows that the initial level of taxes and of expenditures in the analyzed dimensions does not explain leaders' effects on the overall performance.

Even though former military leaders are sometimes acclaimed because they worry less about dissenters' views and can therefore take unpopular but necessary policy decisions, I show that on average they damage the performance of a nation. This can be, at least partially, explained by the lack of institutional constraints on them, but also by the fact that they tend to establish a personalistic regime, disrespect constitution, and therefore pervade formal institutions in place. Moreover, their lower levels of social spending can be at play. When it comes to politicians, their positive effect is to some extent triggered by their experience, by their respect to institutions, and by their higher level of investment in health and education. Previous experience can promote learning-by-doing skills to choose the optimal policy choices. Also, there can be a selection effect in electoral systems, since the fact that they previously held visible offices can send voters a signal of their quality. In other words, among those politicians, those who performed well would have higher chances to become national leaders. As stated by [START_REF] Besley | Political selection[END_REF], previous experience is an opportunity to reveal political competence. Dal [START_REF] Bó | Who becomes a politician?[END_REF] also show that politicians are positively selected, as they are on average smarter and with better leadership potential than the population they represent. Finally, the non-significant effect for academics is not necessarily driven by their incompetence or because higher education does not matter. It could indeed be driven because they prefer not to intervene [START_REF] Ellis | Introducing the LEAD data set[END_REF]; or because there is a high heterogeneity of skills within this category.

6 Robustness tests

Endogeneity concerns

One important threat to identification is that selection of rulers from a specific background might be driven by the initial quality of governance, giving rise to potential endogeneity. In Table 8, I use a conditional logit to test whether the dependent variables used in this paper predict the background of a leader. The level of democracy in the year previous to a leader's transition is highly significant in explaining the ruler's background and so does the level of property rights. Even though it is not possible to fully account for this source of endogeneity, I control for the initial level of these two variables when the leader enters office. Table D9 shows the robustness of the baseline results: rulers with a military career are associated with a strong and negative effect, while former vice presidents, prime ministers, or presidents are instead correlated with positive national outcomes.

Another difficulty of empirically analyzing the impact of politicians is that leaders' transitions depend on economic and social conditions. Coups are more likely to occur in recessions, and heads of democracies are more likely to be reelected during economic booms [START_REF] Londregan | Poverty, the coup trap, and the seizure of executive power[END_REF]. This endogeneity in the timing of transitions is confirmed in Part A of Table 9, which shows that growth rate, democracy, corruption, conflict and rule of law scores are significant for predicting a transition the following year. I test whether the results hold using a restricted sample of leaders' transitions considered exogenous. I follow [START_REF] Jones | Do leaders matter? National leadership and growth since World War II[END_REF], who used the political transitions of leaders who died in office by natural causes, as these are unpredictable events and independent of economic conditions. With the same principle, I add to these types (1)

(2) (3) (4) (5) (6) of transitions 14 cases where the leader resigned due to health issues. 53 As shown in Part B of Table 9, none of these variables remains significant for predicting a leader's transition, except for the second order lagged value of conflict. I then replicate the baseline results with leaders belonging to this selected set of transitions in Table D10 of Appendix D, and the main conclusions hold: military leaders tend to decrease the quality of governance, despite their positive and significant effect on economic growth, while politicians are positively associated with better governance. Notice that when controlling only for graduate education in Table D11, the positive effect of holding a university degree is still significantly positive only for growth, in line with [START_REF] Besley | Do Educated Leaders Matter?[END_REF] who also focus on this kind of exogenous transitions. Yet, it is non-significant for all other outcomes. Despite being no exogenous variation that can be exploited for every individual leader, Easterly and Pennings (2020) found, using Monte Carlo simulations, that even if tenure is as endogenous as it is in the data, it does not affect the accuracy of forecast bias of the least-squares estimates proposed by them for their analysis of growth data. It is important to mention that even though the timing of those transitions can be considered exogenous, it does not solve the endogeneity of leaders' backgrounds. Therefore, endogeneity both in timing and in the leaders' background cannot fully be tackled together, and the presented estimates in this paper cannot be claimed as causal. As stated by [START_REF] Ahlquist | Leadership: What it means, what it does, and what we want to know about it[END_REF] one of the important remaining challenges of the leaders-related literature is to incorporate the endogenous emergence of leaders.

X = Growth X = Democracy X = Corruption X = Conflict X = Property rights X = Rule of law A-Full sample X t

Sample period and estimation methods

Another concern may arise from the fact that the sample includes the inter-war period as well as that of World War II, which were marked by intense conflicts, severe external constraints for specific countries and a higher weight on national leaders' decisions, which may trigger the results. Indeed, even though Baturo (2016) dataset considers leaders who were in office from 1960 to 2010, some of those entered office as from 1931. Information from the remaining leaders (i.e those who stayed in power between 1931 and 1960) was complemented using the [START_REF] Ellis | Introducing the LEAD data set[END_REF]. To test for the robustness of this choice, I restrict the sample to the period 1960-2010, and the main results hold, as shown in Appendix D (Tables D12 andD13 when controlling for the main and sub categories respectively).

Given the two-stage procedure employed to estimate leaders' effects, it is important to test the robustness of the results for the chosen method. In the previous section, I estimate a WLS using as weights the ratio between the absolute value of leaders' effects and the post-estimated standard deviation. This approach gives more weight to those leaders whose effects are more accurately estimated. Figure 8 presents the robustness using an OLS with bootstrapped standard errors, giving the same weight to all the observations. 54 Alternatively, I estimate the probability of having a positive and significant effect on the quality of governance with respect to national leaders' categories. I define, for each outcome y considered, a categorical variable that I denote Good yi with three levels: 1 if the leader i's effect on output y is significant and positive; 0 if the leader's effect is not significant and -1 if the leader's effect is significant and negative. I then estimate an ordered probit on the probability that a leader i has either a negative, a non-significant, or a positive effect. Figure 9 displays the odds ratios of the marginal effects of having a significant and positive effect (Good yi = 1) for the aggregated indexes. 55 Both the bootstrap and the ordered probit approaches suggest that the main findings are not driven by the bias of the weights given in the baseline estimations, as the positive (negative) effect of former prime ministers, vice-presidents or presidents (military career) remains robust.

Finally, I test the results without using Easterly and Pennings (2020) method nor restricting the sample to those leaders with more than three years of tenure, as this was a substantial sample restriction. 56 Similarly to [START_REF] Marx | Electoral Turnovers[END_REF] and [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF], I use as dependent variables the variation of each outcome, measured as the mean variable during a leader's term minus the initial value of the variable. On the right side of the 54 Tables D14 and D15 display the associated regression tables. 55 Tables D16 and D17 display the associated regresstion tables. 56 There is an inherent trade-off between precision in leaders' estimates and sample restriction. The graphs plot the coefficient for each leaders' category on the estimated leaders' effects obtained in Section 4. Those are post-estimations of OLS with bootsrapped standard errors with 1, 000 repetitions. In each regression I control for all categories together, tenure, entry age. The criteria used to build the background categories is detailed in Appendix A. I restrict the sample to leaders with tenure equal to or longer than three years. The number of leaders included in each regression is 693.

(1)

( OLS estimators. Dependent variables are the average variation of the outcome during a leader's term minus the initial value. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories is detailed in Appendix A.

equation, I include leaders' characteristics, country fixed-effects, and the initial level of the outcome, as in [START_REF] Mercier | The return of the prodigy son: Do return migrants make better leaders?[END_REF]. Indeed, improvements or deteriorations in an outcome might be more or less likely to occur depending on the initial level of the variable. Table 10 displays the estimates for the sub-categories. With a significant larger number of leaders, from 1, 493 to 1, 641 depending on the outcome considered, we still observe a general negative effect of leaders with a military career, and a positive one triggered by politicians who held visible offices before coming to power (party leaders, prime minister, vice presidents, former presidents or ministers).

Robustness of leaders' categories and national outcomes

Considering the correlation between leaders' careers and educational backgrounds illustrated in Figure 3, in the main section I constructed categories based on the most frequent leaders' occupation and degrees, and accounted for the observed correlations. These choices, as well as the aggregation of backgrounds into three groups, may suffer from arbitrariness. In this section, I rely on a LASSO model to choose the relevant variables.

First, I include as a set of potential covariates all the disaggregated categories of Table 11: Leaders' effects and leaders' selected covariates using a LASSO approach.

(1) (2) (3) (4) (
LASSO estimators. Dependent variables are the leaders' effects estimated in Section 4. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Informative weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. Standard errors are clustered at the country level.

(1)

( careers, academic degrees, educational level, as well as those interactions between and across careers and degrees with over 30 observations. Thus, I consider new variables such as holding a degree in science, engineering, the interaction between having a degree in law with ministers and party leaders, among others. Panel A of Table 11 displays the results under different model choices. The relevant variables triggering the results that are robust for all model specifications are a military career (with a negative coefficient), former prime ministers, vice-presidents and presidents (with a positive coefficient) and tenure. Further, in half of the specifications, having been a party leader or holding a bachelor's degree in economics is associated with a positive effect, while holding a degree in science with a negative coefficient stressing the heterogeneity behind holding a university degree.

When adding constraints on the executive, former years of experience in formal politics, as well as all the interactions of these variables with careers and degrees, Panel B reveals that the effect of former prime ministers, vice presidents and presidents is still robust as well as the negative effect for tenure. Further, as previously found, constraints have a positive effect on leaders' performance and military career is only robust under two specifications. These results confirm that the negative effect of military rulers can be explained by the initial weakness of institutions in where they tend to come to power. Notice, however, that the interaction between military career and military education remains significant for the PCA index. Yet, completely disentangling the effect of leaders' backgrounds from the institutional context is a hard task that is out of the scope of this paper.

Next, I present robustness on the aggregated indexes. First, I drop one by one the outcomes used to construct the aggregated indicators. Results are displayed in Table 12. In all cases, rulers with military careers are associated with negative and strong coefficients, while prime ministers, vice-presidents and former presidents with positive effects. This suggests that results presented in the main section are not fully driven by one specific indicator. Finally, I show the correlation between the aggregated indexes and alternative indicators of quality of governance: the World Bank government effectiveness measure, and the ICRG Quality of governance index. The first combines a measure of the quality of public service, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressures, and the credibility of the government's commitment to policies. The ICRG indicator corresponds to the mean value of the ICRG variables "Corruption", "Law and Order" and "Bureaucracy Quality", scaled from 0 to 1 (Teorell et al. (2018) based on ICRG (2017)). Both indicators are positively and strongly correlated with the additive index (see Table 13 ) and with the PCA index (Table D18).

Conclusion

This paper estimates national leaders' effects on quality of governance using a methodology inspired by the teacher-value added literature proposed by Easterly and Pennings (2020). I compare three type of leaders: politicians, wisemen, and strongmen.

I find robust evidence that leaders with a former military background are associated with a worse quality of governance, while rulers who have been prime ministers or have a long career trajectory in politics show an overall positive effect on institutional and economic outcomes. Leaders' educational attainment is not relevant for explaining the direction of leaders' effects, casting doubt on the epistocracy and technocracy argument for being ruled by the wise, the scientists, or the experts. It also questions the implementation of laws that impose minimum education requirements for candidates. 57 Indeed, the average effect for graduate leaders is only positive when not controlling for leaders' backgrounds, and for leaders' effects on growth which are weakly correlated with leaders' effects on alternative outcomes. These results forewarn on the use of growth for assessing national leaders' performance and of graduate education for assessing leaders' quality.

The effect of leaders' backgrounds has to be considered in their given institutional context. The positive effects of politicians are stronger under unconstrained regimes, where they have more room to shape outcomes. Yet, constraints compensate for the negative effect of military leaders by disciplining them. Imposing strong institutional constraints can avoid the negative effect of bad leaders but also hide the positive impact of good rulers. Nonetheless, the bond between leaders and institutions appears to be more complex. Military leaders are more likely to establish a regime based on personal authority, with lower levels of respect for the law, and with a higher likelihood of removing elections as the mechanism to select the chief executive. Therefore, leaders from different backgrounds differ in the extent to which they respect formal institutions. Overall, as [START_REF] Weber | The theory of social and economic organization[END_REF] argued, both leaders and institutions matter, and they both interact in an important way. Whether leaders prime over institutions, or whether the contrary holds remains an open question for future research. Leaders' backgrounds are also relevant for policy decisions. Consistently, I find that military leaders invest less in education and health, while the opposite holds for politicians.

Even though the results of this paper cannot be fully claimed as causal, they are strongly informative and contribute to the mapping between national leaders' characteristics and their overall performance. 57 Those requirements have been recently applied in certain Indian states. Table A1: Comparison between career politician variables across datasets Thus, I built new categories starting from the detailed previous career and education provided by [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF]. For a precise analysis, we would need to interact each career with each academic degree, as well as with any other career which is correlated. As the number of observations does not enable this, I consider the three clusters displayed in Figure 3. Then, I associate to those groups careers with similar duties, as detailed below. 60Military leader encompasses leaders with a military career and/or a military education defined as follows:

1. Military career is coded one if the variable careerdescribe contains at least one of the following expressions "army", "mil junta", "military", "armed forces", "commander", "chief", "armed forces" or "defence", or if the variable militarycareer from [START_REF] Ellis | Introducing the LEAD data set[END_REF] equals one.

2. Military education is coded one if the variable militaryedu from [START_REF] Ellis | Introducing the LEAD data set[END_REF] equals one, or if the variable education alldetail from Baturo (2016) contains the word "military".

Academic is coded one if the variable Ph.D. from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] equals one, or the variable careerdescribe contains the word "academic". This category includes:

1. Economists: is coded one if academic equals one and the leader holds a degree in economics. Degree in economics is defined as a dummy that equals one if the variable education alldetail from Baturo (2016) includes the word "economics".

2. Other academic: is coded one if academic equals one and economist is coded zero.

Politician includes the following sub-categories:

1. Legislative career is coded one if the variable careerdescribe contains at least one of the following expressions: "mp" (member of parliament), "parliament", "senator", "legislator", "senate", "speaker", "supreme court judge" or "legislative".

2. Prime minister, vice-president or former president is coded one if the variable careerdescribe contains at least one of the following expressions: "vice president", "preindependence leader", "pm", "ex president", "pres of mali", or "president" without including "son".

3. Party leader is coded one if the variable careerdescribe contains at least one of the following expressions: "party leader", "party chairman", "1st secretary" 4. Minister is coded one if the variable careerdescribe contains at least one of the following expressions: "min" and the above-defined dummy prime minister differs from one.

5. Mayor or governor is coded one if the variable careerdescribe contains at least one of the following words: "mayor" or "governor".

I do not restrict to exclusive categories, both because defining one sole career per individual can be subject to interpretation and because the secondary career might matter as well for policy choices. Table A2 shows common frequency between different subcategories.

A.2 Descriptive statistics by sample restriction

Leaders' effects on each individual outcome are estimated using the sample of leaders without missing information on each outcome. Further, Section 5 restricts to leaders without missing information on background categories. Table A3 shows that this doesn't affect the representativeness of the sample, as leaders in each sample restriction are similar in their age, experience, and backgrounds.

A.3 Outcomes data

Table A3: Leaders' characteristics by sample restriction

National leaders' sample is restricted to those with tenure equal or longer than 3 years who were in power from 1931 to 2010. Each column provides data description of the leaders without missing information for each outcome. Data is taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and [START_REF] Ellis | Introducing the LEAD data set[END_REF]. Standard deviations are reported in parenthesis. Years in formal politics (from Baturo (2016)) account for years in official politics, such as being a member of parliament, governor or city mayor, among others, while the general indicator additionally accounts for any political activity such as party membership or civil service offices. Leaders with substantial constraints are those from whom the Polity IV Executive Constraints is higher or equal than 4 the year before the leader enters office. The criteria used to build the background categories is detailed in Appendix A.

Variable Definition Source

Growth ln(rGDPpct) -ln(rGDPpc t-1 ) where rGDPpct is the real GDP per capita in year t based on multiple benchmark comparisons of prices and incomes across countries. Bolt et al. (2018) Democracy Polity IV score. There are threespecial values -66, -77 and -88 which correspond to anocracies, and I recode them as 0.

Marshall and Gurr (2018)

Corruption

Executive corruption index. Answer to the question: "How routinely do members of the executive, or their agents grant favors in exchange for bribes, kickbacks, or other material inducements, and how often do they steal, embezzle, or misappropriate public funds or other state resources for personal or family use?"

Military expenditures

Military expenditures in % of GDP using the NATO definition, which includes all current and capital expenditures on the armed forces, including peace-keeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities General government expenditure on education (current, capital, and transfers) in % of GDP. General government usually refers to local, regional and central governments.

Teorell et al. (2018) based on ICRG (2017)

Government effectivness

Combines into a single index responses on the quality of public service, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressures, and the credibility of the government's commitment to policies.

Teorell et al. (2018) based on World Bank (2016) ICRG Indicator of quality of governance

The mean value of the ICRG variables "Corruption", "Law and Order" and "Bureaucracy Quality",scaled from 0 to 1, with higher values corresponding higher quality of government.

B Easterly and Pennings' (2020) methodology

Easterly and Pennings (2020) propose a least-squares leader estimator (λ LS i ) using the average growth for leader i, g i , and the average growth under other leaders from the same country, g -ic , which is considered a better proxy for the country effect. The idea is to give a weight to those two variables according to their signal-to-noise ratio. More precisely, they estimate:

min ψ,γ E[λ i -λLS i ] 2 where λLS i = ψ i (g i -γ i g -ic ) (4)
where the optimal weights γ i and ψ i are given respectively by equations ( 5) and ( 6).

γ i = cov(g i g -ic ) var(g -ic ) = σ 2 c σ 2 c + σ 2 e N c-T i + σ 2 λ L -ict (5)
ψ i = cov(λ i , g i -γg -ic ) var(g i -γg -ic ) = σ 2 λ σ 2 λ + σ 2 c (1 -γ) + σ 2 e T i (6)
Before estimating the weights it is necessary to estimate the variance components which are given by equations ( 7), ( 8) and ( 9).

σ2 ϵ,c = 1 T -N L N L i=1 T i t=1 (g ict -g ic ) 2 (7)
where T i is the tenure of leader i, T is de total number of observations for country c and N L is the total number of leaders of country c.

σ2 c = c i̸ =j,t̸ =s g ict g jcs i̸ =j,t̸ =s 1 ict 1 jcs (8) σ2 λ = c t̸ =s g ict g ics t̸ =s 1 ict 1 ics -σ2 c (9)
Once estimating λLS i , their final step is to check whether it is statistically different from zero, by calculating confidence intervals at 95% confidence assuming that λLS follows a normal distribution with mean 0 and variance σλ 2 , given by the following equation:

95%CI = [ λLS i -1.96 σ λ 1 -ψ i ; λLS i + 1.96 σ λ 1 -ψ i ] where σ λ √ 1 -ψ i is the post-standard deviation of leaders' effects.
C Leaders' rankings WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A.

The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

(1) WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

(2) (3) (4) (5) (6) (7) (8)
(1)

( D4: Leaders' effects and graduate education WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

( Table D7: Heterogenous effects using the PCA index.

WLS estimators. The dependent variable is the PCA (Principal Component Analysis) index which is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories is detailed in Appendix A.Years of experience is taken from [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and includes years in formal politics that accounts for years in official politics, and political activity such as party membership or civil service offices. Standard errors are clustered at the country level.

(1)

( Table D11: Leaders' effects and education using exogenous transitions.

WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. The sample is restricted to leaders who died in office by natural death, or resigned due to health issues, and their successors conditional on having had a tenure equal to or longer than three years. The criteria used to build the background's categories is detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

( WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

) (2) (3) (4) (5) (6) (7) 1
(1)

( OLS estimates with bootsrapped standard errors calculated with 1,000 repetitions. Dependent variables are the leaders' effects estimated in Section 4. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F).

( OLS estimates with bootsrapped standard errors calculated with 1,000 repetitions. Dependent variables are the leaders' effects estimated in Section 4. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F).

(1)

( Table D16: Leaders' background and the probability of being a "good" and significant leader Marginal effects on the probability of a leader having a positive and significant effect. Those are post-estimations of an ordered probit with three outcomes: having a negative and significant effect; having a non-significant effect and having a significant and positive one. I restrict the sample to leaders with tenure equal or longer than three years. The criteria used to build the background's categories is detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F).

( Table D17: Leaders' sub-categories and the probability of being a "good" and significant leader Marginal effects on the probability of a leader having a positive and significant effect. Those are post-estimations of an ordered probit with three outcomes: having a negative and significant effect; having a non-significant effect and having a significant and positive one. I restrict the sample to leaders with tenure equal or longer than three years. The criteria used to build the background's categories is detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). The graphs plot the coefficient for the inital value of the labeled outcome during a leader's term on the two aggregated indexes of leaders' effects when controllinf for decade fixed-effects. Each coefficient is obtained in a different OLS regression. Standard errors are clustered at the country level. The regression for tax revenue includes 237 observations, for education expenditure includes 224 observations, for military expenditure 412 observations, for health expenditure 121 observations. The criteria used to build the background's categories and the source and definition of each dependent variable are detailed in Appendix A.

F Dimensionality-reduction

The PCA index used in this paper is obtained through a principal component analysis detailed in this section. I first restricted the sample to those leaders for whom I have data for all outcomes.As before, I multiply leaders' effects on corruption and conflict by -1 so a higher value is associated to a better performance.

Each dimension of the principal component analysis is a linear combination of variables in such a way that we retain most of the information. Figure E4 shows how much of the variance is explained by each principal component. Thus, with the two first dimensions we account for more than half of leaders' performance in all the considered outcomes. Table F1 shows the contributions for each variable in the three first dimensions. Dimension one mainly summarizes the effects of rule of law, democracy, and corruption, which are indeed the outcomes for which there are more significant leaders. Besides, a positive value in this dimension summarizes an overall positive performance, as it is positively associated with all economic and social outcomes and with lower corruption. More precisely, the first dimension is obtained as follows:

Dim 1 = 0.08 LS Growth + 23.64 LS Democracy + 25.04 LS Corruption + 38.25 LS Rule of law + 11.76 LS Property rights + 1.21 LS Conflict A positive value in the second dimension can be interpreted as the leader having an overall positive effect on growth and peace, which, as said before are the two outcomes mote uncorrelated with the other variables. In part (a) of Figure E5 it is possible to visualize the contribution of each variable for each of the two first dimensions.

The next step consists in clustering leaders' according to their coordinates in the axis using a k-means clustering approach with k=2. It is possible to see in part (b) of Figure E5 that clusters are mainly constructed based on the first dimension, which is the one that best summarizes the general performance. Thus, it is the value of this first dimension that I use in previous tables as the synthetic outcome.

Figure E4: Variance explained by dimension

The figure shows the variance explained by each one of the dimensions of the principal component analysis using the leaders' effects for growth, democracy, corruption, rule of law, property rights and conflict obtained in Section 4.

  Figure 1: Theoretical relationship between leader's backgrounds and policy outcomes

Figure 2 :

 2 Figure 2: Number of national leaders per country

Figure 3 :

 3 Figure 3: Words' network in the leaders' background variable

  D2 of Appendix D show the effects of leaders' backgrounds for each outcome that encompasses the aggregated indexes. Reassuringly, previous results are not Panel A: Main categories (a) Additive index (b) PCA index Panel B: Sub-categories (a) Additive index (b) PCA index

Figure 4 :

 4 Figure 4: Leaders' performance and leaders' background.

Figure 5 :

 5 Figure 5: Marginal effects of years of experience in politics on overall performance by leaders' background.

Figure 6 :

 6 Figure 6: Leaders' performance, leadership styles and policy choices.

Figure 7 :

 7 Figure 7: Leaders' performance and leaders' background using a restricted sample of exogenous transitions.

Figure 8 :

 8 Figure 8: Leaders' performance and leaders' background with bootstrapped standard errors.

  in % of GDP. Includes recurrent and capital spending from government (central and local) budgets, external borrowings and grants. Teorell et al. (2018) based on ICRG (2017) Education expenditures

Figure E3 :

 E3 Figure E3: Leaders' performance and initial policy levels.

Table 1 :

 1 Leaders' characteristics

	of leaders	1,002
	Mean age	52.3 years-old [s.d: 10.8]	Median age	53 years-old
	Mean tenure	8.5 years [s.d: 7.4]	Median tenure	6 years
	Men	824 leaders (97%)
	Leaders with substantial constraints	462 leaders (54.93%)
	Leaders with a university degree	783 (78.14%)
	Experience
	Mean years of experience in formal politics (CH)	12 years [s.d: 10.3]	Median years of experience in formal politics	11 years
	Mean years of experience in general politics (CH)	15.6 years [s.d: 11.3]	Median years of experience in general politics	15 years
	Mean years of experience in politics (LEAD)	15.03 years [s.d: 12.2]	Median years of experience in general politics	14 years
	Leaders' categories
	Number of leaders with career information	858
	Military leaders		280
	Military career		240
	Military education		215
	Academics		107
	Economist		38
	Other academic		69
	Politicians		604
	Legislative career		121
	PM, VP or president		214
	Party leader		183
	Minister		164
	Mayor or governor		51
	Civil servant/diplomat		59
	National leaders' sample is restricted to those with tenure equal or longer than 3 years who were in power from 1931 to 2010
	and for whom I have data on at least one of the following variables: economic growth, corruption, democracy, property
	right score and/or rule of law. Data is taken from Baturo (2016) and Ellis et al. (2015). Standard deviations are reported
	in brackets. Years in formal politics (from Baturo (2016)) account for years in official politics, such as being a member
	of parliament, governor or city mayor, among others, while the general indicator additionally accounts for any political
	activity such as party membership or civil service offices. Leaders with substantial constraints are those from whom the
	Polity IV Executive Constraints is higher or equal than 4 the year before the leader enters office. The criteria used to build
	the background categories is detailed in Appendix A.

  , I include in the analysis the Polity IV score as a measure of democracy. I add corruption and rule of law, as those are considered two dimensions of governance by

	Word	Repetition
	Law	242
	Ungraduate	219
	Party leader	172
	Minister	165
	Prime minister	99
	Military education	99
	Member of the Parliament	93
	Economics degree	88
	Humanities degree	72
	Military (in career description)	69
	Engineering	49
	Chief	49
	Pre-independence leader	48
	Army	46
	Science	39
	Academic	38
	Finance	36
	President	35
	Vice president	32
	Commander	31

Table 2 :

 2 Word repetition in leaders' background variable

Table 3 :

 3 Variance components and coefficients for leaders' effects estimation

Table 4 :

 4 Correlation coefficients between leaders' effectsThis table presents the correlation coefficients between leaders' effects estimated usingEasterly and Pennings (2020) methodology.

Table 5 :

 5 Education, constraints or experience?

			2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth	Democracy	Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Degree in law	0.375	-0.209	-0.364	-0.168	0.496***	0.161	0.045	-0.097
		(0.300)	(0.187)	(0.301)	(0.291)	(0.172)	(0.335)	(0.193)	(0.247)
	Degree in economics	-0.113	-0.159	0.242	0.405	-0.011	0.663*	0.257	0.264
		(0.454)	(0.194)	(0.362)	(0.287)	(0.362)	(0.340)	(0.239)	(0.243)
	Degree in pol. science	-0.505	-0.071	-0.782*	-0.286	0.367	0.452	-0.087	-0.275
		(0.364)	(0.248)	(0.436)	(0.270)	(0.415)	(0.543)	(0.276)	(0.279)
	Other degree	-0.057	0.034	-0.110	0.013	0.671**	-0.119	0.035	-0.023
		(0.348)	(0.218)	(0.372)	(0.294)	(0.274)	(0.449)	(0.214)	(0.255)
	Military education	0.488	-0.532***	-0.475	-0.362	0.340	1.134**	-0.077	-0.460
		(0.298)	(0.179)	(0.379)	(0.327)	(0.297)	(0.510)	(0.245)	(0.282)
	Years of experience	0.044**	0.017	0.067**	0.057*	0.000	0.097***	0.053***	0.052*
		(0.021)	(0.019)	(0.030)	(0.032)	(0.015)	(0.034)	(0.020)	(0.027)
	Years of experience 2	-0.001	-0.000	-0.001*	-0.001	-0.000	-0.003**	-0.001**	-0.001
		(0.001)	(0.000)	(0.001)	(0.001)	(0.000)	(0.001)	(0.000)	(0.001)
	Constraint	-0.219	1.252***	0.116	0.810**	0.625**	0.271	0.638***	0.773***
		(0.236)	(0.244)	(0.274)	(0.354)	(0.296)	(0.259)	(0.240)	(0.291)
	Tenure (x 10 years)	0.023	0.057	-1.178**	-0.504	0.663**	0.351	-0.035	-0.317
		(0.381)	(0.220)	(0.464)	(0.451)	(0.306)	(0.456)	(0.313)	(0.377)
	Tenure 2	-0.008	-0.086	0.216*	0.086	-0.261***	0.011	-0.053	-0.005
		(0.089)	(0.052)	(0.121)	(0.112)	(0.073)	(0.112)	(0.074)	(0.093)
	Entry age (x 10 years)	0.160	0.106	-1.607***	-1.057	-0.172	-1.185	-0.697	-0.727
		(0.489)	(0.389)	(0.594)	(0.715)	(0.436)	(0.925)	(0.456)	(0.559)
	Entry age 2	0.003	-0.015	0.144**	0.085	0.025	0.130	0.069	0.060
		(0.052)	(0.039)	(0.059)	(0.071)	(0.043)	(0.091)	(0.043)	(0.055)
	Observations	626	652	659	673	665	631	585	585
	Adjusted R-squared	0.057	0.416	0.143	0.243	0.222	0.098	0.193	0.281
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 6 :

 6 Heterogenous effects using the additive index.

		(1)	(2)	(3)	(4)
		Education	Experience	Constraints	All
	Military leader	-0.582**	-1.046***	-0.034	-0.937**
		(0.248)	(0.321)	(0.188)	(0.453)
	Academic	-0.141	-0.330	-0.136	-0.667
		(0.239)	(0.433)	(0.218)	(0.419)
	Politician	0.076	0.087	0.467*	-0.327
		(0.276)	(0.335)	(0.240)	(0.424)
	Graduate	-0.739**			-0.834***
		(0.303)			(0.306)
	Military leader*Graduate	0.247			0.417
		(0.363)			(0.357)
	Politician*Graduate	0.546*			0.577*
		(0.300)			(0.333)
	Years of exper.		-0.046		-0.062
			(0.039)		(0.041)
	Years of exper. 2		0.002		0.002*
			(0.001)		(0.001)
	Military leader*Years of exper.		0.072**		0.078*
			(0.034)		(0.041)
	Military leader**Years of exper. 2		-0.001		-0.001
			(0.001)		(0.001)
	Academic*Years of exper.		0.043		0.086*
			(0.052)		(0.051)
	Academic*Years of exper. 2		-0.001		-0.002*
			(0.001)		(0.001)
	Politician*Years of exper.		0.065		0.084*
			(0.040)		(0.045)
	Politician*Years of exper. 2		-0.002*		-0.003**
			(0.001)		(0.001)
	Constraint			0.793*	1.308***
				(0.463)	(0.375)
	Military leader*Constraints			-0.377	-0.455
				(0.492)	(0.597)
	Politician*Constraints			-0.247	-0.794**
				(0.448)	(0.340)
	Tenure (x 10 years)	-0.084	-0.010	-0.109	-0.189
		(0.247)	(0.273)	(0.298)	(0.312)
	Tenure 2	-0.055	-0.066	-0.042	-0.019
		(0.061)	(0.069)	(0.070)	(0.070)
	Entry age (x 10 years)	-0.490	-0.564	-0.583	-0.613
		(0.422)	(0.461)	(0.429)	(0.457)
	Entry age 2	0.049	0.052	0.057	0.060
		(0.040)	(0.044)	(0.039)	(0.043)
	Observations	693	637	635	585
	Adjusted R-squared	0.122	0.167	0.150	0.232
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

Table 7 :

 7 Policy choices, leadership styles and leaders' background.

		1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Electoral	Respect for	Neopatri-	Government	Tax	Education	Military	Health
		regime	constitution	monialism	expenditure	revenue	exp.	exp.	exp.
	Military leader	-0.480*	-0.749***	0.633**	-0.441**	-0.202	-0.434***	0.204*	-0.357***
		(0.259)	(0.272)	(0.250)	(0.212)	(0.124)	(0.109)	(0.121)	(0.116)
	Politician	0.613**	0.701**	-0.550**	0.258	0.323***	0.277**	-0.144	0.250*
		(0.259)	(0.306)	(0.263)	(0.218)	(0.122)	(0.113)	(0.127)	(0.129)
	Academic	0.095	-0.365	0.524*	-0.274	-0.088	-0.201*	-0.088	0.038
		(0.220)	(0.297)	(0.297)	(0.206)	(0.114)	(0.121)	(0.100)	(0.129)
	Tenure (x 10y)	0.889***	0.087	0.549*	0.295	0.070	-0.090	0.067	-0.306
		(0.306)	(0.330)	(0.324)	(0.443)	(0.207)	(0.228)	(0.195)	(0.203)
	Tenure 2	-0.222***	-0.056	-0.021	0.012	0.047	0.037	0.043	0.005
		(0.078)	(0.085)	(0.090)	(0.126)	(0.057)	(0.066)	(0.051)	(0.055)
	Entry age (x 10y)	-0.400	-0.084	0.780*	-0.095	0.334	-0.040	-0.252	-0.630**
		(0.655)	(0.510)	(0.464)	(0.601)	(0.266)	(0.326)	(0.336)	(0.309)
	Entry age 2	0.043	0.007	-0.063	0.012	-0.024	0.000	0.032	0.046
		(0.058)	(0.048)	(0.043)	(0.052)	(0.026)	(0.031)	(0.035)	(0.028)
	Constant	0.016	-0.019	-2.620**	-0.061	0.594	2.044**	1.357	4.580***
		(1.852)	(1.490)	(1.279)	(1.643)	(0.670)	(0.852)	(0.840)	(0.850)
	Observations	847	849	849	722	496	508	677	369
	Adjusted R-squared	0.107	0.171	0.179	0.044	0.045	0.050	0.052	0.079
			Robust standard errors in parentheses
				*** p<0.01, ** p<0.05, * p<0.1

Table 8 :

 8 Endogeneity of leaders' background

		(1)	(2)	(3)
		Military leader	Academic	Politician
	Growth t-1	0.174	-0.104	0.211
		(0.175)	(0.156)	(0.206)
	Normalized Polity IV t-1	-0.485***	0.209***	0.368***
		(0.068)	(0.061)	(0.067)
	Corruption t-1	0.016	0.102	0.048
		(0.148)	(0.112)	(0.133)
	Conflict t-1	0.007	-0.013	-0.025
		(0.041)	(0.035)	(0.041)
	Property Right t-1	-0.191*	0.132*	0.150*
		(0.098)	(0.075)	(0.077)
	Rule of law t-1	-0.029	-0.057	0.156
		(0.189)	(0.130)	(0.180)
	Observations	773	773	773
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

Pooled probit with errors clustered at the country level. Table reports the average marginal effects of each variable on the probability of a national leader from a specific category, denoted as column names, taking power in year t. The criteria used to build the background categories is detailed in Appendix A.

Table 9 :

 9 Endogeneity of transitions' timingConditional fixed-effects logistic regression model with standard errors clustered at the country level. Part A reports the average marginal effects of each variable on the probability of occurring a national leader transition in year t. Part B reports the average marginal effects of each variable on the probability of occurring a national leader transition that follows a leader's death by natural causes or his resignation due to health issues in year t.

	-1	-0.479***	0.313***	-0.572***	0.067**	0.266	0.495***
		(0.122)	(0.064)	(0.174)	(0.031)	(0.172)	(0.151)
	X t-2	0.045	-0.055	0.485***	0.008	0.040	-0.187
		(0.125)	(0.066)	(0.177)	(0.032)	(0.174)	(0.155)
	Observations	8,171	9,295	9,193		8,518	9,193
				B-Restricted sample
	X t-1	-0.268	0.245	-0.342	0.121	-0.054	-0.456
		(0.336)	(0.203)	(0.539)	(0.089)	(0.575)	(0.572)
	X t-2	0.274	-0.237	0.382	-0.243**	-0.024	0.481
		(0.344)	(0.205)	(0.540)	(0.096)	(0.583)	(0.574)
	Observations	5,248	6,034	6,041	5,058	6,041	6,041
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 10 :

 10 Outcomes variation and leaders' background.

			2)	(3)	(4)	(5)	(6)
		∆ Growth	∆ Democracy	∆ Corruption	∆ Rule of law	∆ Property rights	∆ Conflict
	Military leader
	Military career	0.032	-0.433***	-0.258**	-0.465***	-0.168	-0.126
		(0.137)	(0.106)	(0.122)	(0.136)	(0.112)	(0.084)
	Military education	-0.130	-0.069	0.061	0.033	0.282***	0.025
		(0.123)	(0.097)	(0.128)	(0.117)	(0.106)	(0.092)
	Academic
	Economist	0.131	0.056	0.123	0.285**	0.091	0.088
		(0.110)	(0.113)	(0.099)	(0.113)	(0.092)	(0.064)
	Other academic	0.144	0.169*	0.006	0.008	0.138	-0.051
		(0.114)	(0.101)	(0.100)	(0.083)	(0.088)	(0.080)
	Politician
	Legislative career	0.094	0.168**	-0.116	-0.043	0.202**	0.014
		(0.063)	(0.080)	(0.071)	(0.062)	(0.083)	(0.057)
	PM, VP or president	0.131	0.213***	-0.003	0.102	0.198**	0.020
		(0.096)	(0.071)	(0.086)	(0.080)	(0.078)	(0.075)
	Mayor or governor	0.034	0.378**	-0.195	0.144	0.425**	0.036
		(0.193)	(0.154)	(0.144)	(0.171)	(0.186)	(0.124)
	Party leader	0.196**	0.295***	0.188*	0.412***	0.137*	0.050
		(0.081)	(0.078)	(0.097)	(0.117)	(0.083)	(0.078)
	Minister	0.275***	0.253***	0.068	0.199***	0.226**	0.062
		(0.070)	(0.071)	(0.060)	(0.075)	(0.100)	(0.073)
	Civil servant or diplomat	0.030	0.087	0.019	0.002	0.182	-0.086
		(0.091)	(0.110)	(0.085)	(0.123)	(0.149)	(0.109)
	Tenure (x 10 years)	0.940***	-0.325**	-0.470**	-0.333***	0.374**	0.465***
		(0.150)	(0.129)	(0.201)	(0.124)	(0.187)	(0.131)
	Tenure 2	-0.116***	-0.011	0.107	0.027	-0.157**	-0.075*
		(0.041)	(0.040)	(0.081)	(0.037)	(0.078)	(0.038)
	Entry age (x 10 years)	0.105	-0.007	-0.115	-0.180	-0.005	0.087
		(0.269)	(0.182)	(0.183)	(0.208)	(0.178)	(0.185)
	Entry age 2	-0.014	0.004	0.011	0.015	0.003	-0.005
		(0.024)	(0.016)	(0.016)	(0.019)	(0.016)	(0.017)
	Initial growth	-0.308***
		(0.071)
	Initial democracy		-2.537***
			(0.196)
	Initial corruption			3.907***
				(0.462)
	Initial rule of law				-4.045***
					(0.472)
	Initial property right					-2.359***
						(0.392)
	Initial conflict						1.807***
							(0.121)
	Country FE	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	1,493	1,635	1,641	1,641	1,641	1,533
	Adjusted R-squared	0.264	0.239	0.226	0.212	0.125	0.370
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 12 :

 12 Leaders' effects on the additive index dropping one outcome at time.

			2)	(3)	(4)	(5)	(6)
		Without	Without	Without	Without	Without	Without
		growth	democracy	corruption	rule of law	property rights	conflict
	Military leader
	Military career	-1.823***	-1.528**	-1.778***	-1.448***	-1.820***	-1.685***
		(0.534)	(0.589)	(0.544)	(0.423)	(0.545)	(0.584)
	Military education	-0.229	0.445	0.214	0.199	-0.081	-0.327
		(0.616)	(0.775)	(0.594)	(0.529)	(0.651)	(0.599)
	Academic
	Economist	-0.120	0.565	-0.081	-0.139	0.470	0.111
		(0.697)	(0.589)	(0.660)	(0.507)	(0.502)	(0.642)
	Other academic	-1.211	-0.859	-0.336	-0.335	-1.316	-0.544
		(0.892)	(0.707)	(0.782)	(0.669)	(0.819)	(0.773)
	Politician
	Legislative career	0.668	0.545	0.735	0.475	0.243	0.868
		(0.614)	(0.546)	(0.507)	(0.442)	(0.554)	(0.557)
	PM, VP or president	1.186**	1.065**	1.166***	0.960**	1.362***	1.294***
		(0.499)	(0.518)	(0.389)	(0.372)	(0.506)	(0.463)
	Mayor or governor	-0.402	-0.270	0.090	-0.200	0.287	0.516
		(0.892)	(0.787)	(0.755)	(0.681)	(0.866)	(0.844)
	Party leader	0.409	0.101	0.388	0.336	0.831	0.418
		(0.663)	(0.725)	(0.507)	(0.468)	(0.611)	(0.709)
	Minister	-0.161	0.149	0.180	0.137	0.437	0.275
		(0.627)	(0.632)	(0.590)	(0.508)	(0.558)	(0.468)
	Civil servant or diplomat	-0.219	0.085	0.578	0.485	-0.077	0.466
		(0.648)	(0.650)	(0.706)	(0.662)	(0.756)	(0.698)
	Tenure (x 10 years)	-0.607	-0.037	0.973	0.324	-0.759	-0.328
		(0.809)	(0.813)	(0.686)	(0.578)	(0.766)	(0.835)
	Tenure 2	-0.110	-0.125	-0.405**	-0.239*	0.042	-0.205
		(0.216)	(0.199)	(0.182)	(0.141)	(0.191)	(0.219)
	Entry age (x 10 years)	-1.066	-1.700	-0.592	-0.690	-1.421	-0.840
		(1.153)	(1.311)	(1.236)	(0.998)	(1.251)	(1.062)
	Entry age 2	0.096	0.159	0.071	0.074	0.129	0.074
		(0.107)	(0.122)	(0.116)	(0.093)	(0.113)	(0.098)
	Observations	752	707	693	693	693	749
	Adjusted R-squared	0.134	0.060	0.132	0.102	0.130	0.163
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

WLS estimators. Dependent variables are an additive index on the leaders' effects estimated in Section 4 when dropping one outcome in each column. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. Standard errors are clustered at the country level.

Table 13 :

 13 Correlation between alternative performance indicators and the additive index. WLS estimators. The dependent variable is the additive index which is an unweighted sum of the standardized leaders' effects on all the outcomes. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories is detailed in Appendix A. Standard errors are clustered at the country level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	ICRG Quality of governance		1.265***		1.967***		1.822***		1.958***
	Government effectiveness	0.430**		0.801***		0.746***		0.734***
		(0.165)		(0.209)		(0.214)		(0.213)
	Decade FE	Yes	Yes	No	No	Yes	Yes	Yes	Yes
	Region FE	No	No	Yes	Yes	Yes	Yes	Yes	Yes
	Region*Decade FE	No	No	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	218	338	218	338	218	338	218	338
	Adjusted R-squared	0.127	0.074	0.220	0.065	0.232	0.087	0.310	0.140
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table A2 :

 A2 TableA4provides the definition and the source of each variable used to measure leaders' performance, as well as for mechanisms and heterogeneity in Section 5.2. Frequency of leaders' subcategoriesThe table displays the frequency of leaders who belong simultaneously to the subcategory listed in the row and the one listed in the column.

						Military leaders	Growth sample	Academics Democracy sample		Corruption sample	Rule of law sample	Property rights Politicians sample	Conflict sample	Aggregated indexes sample
	Number of leaders Mean entry age		Military career	Military education 771 52.6	Economists 816 52.2	Other academics	839 52.1	Legislative career	PM, VP or president 848 52.2	Party leader 857 52.2	Mayor or governor 780 51.9	Minister	Civil servant 693 or diplomat 52.4
	Mean previous years of experience in politics			15.9	15.6				15.7	157.0	15.6	15.4	156.3
	Military Mean previous years of experience in formal politics Military career 240		12.2	12.0				12.1	121.4	12.1	12.0	120.3
	leaders Leaders category Military education	175	215
	Military leaders
	Military career Academics Military education	Economists Other academics	0 6		0 218 (28.3%) 2 193 (25.0%)	38 235 (28.8%) 0 212 (26.0%)	69	235 (28.0%) 212 (25.3%)	240 (28.0%) 215 (25.1%)	236 (27.8%) 213 (25.1%)	229 (29.4%) 210 (26.9%)	209 (30.2%) 189 (27.3%)
	Academics
	Economist			Legislative career	3		3 28 (3.6%)	4 30 (3.7%)	17	30 (3.6%)	121	31 (3.6%)	31 (3.7%)	27 (3.5%)	25 (3.6%)
	Other	PM, VP or president	36		35 70 (9.1%)	8 69 (8.5%)	18	71 (8.5%)	4	214 75 (8.8%)	74 (8.7%)	63 (8.1%)	56 (8.1%)
	Politicians Politicians Legislative career	Party leader Mayor or governor	6 1		13 27 116 (15.0%)	8 1 110 (13.5%)	19 5	118 (14.1%)	69 2	31 6 121 (14.1%)	183 3 120 (14.2%)	51 110 (14.1%)	98 (14.1%)
	PM, VP or president	Minister	34		18 187 (24.3%)	10 201 (24.6%)	17	211 (25.1%)	9	0 214 (25.0%)	21 212 (25.0%)	2 190 (24.4%) 164	162 (23.4%)
	Party leader	Civil servantor diplomat	2		1 175 (22.7%)	5 171 (21.0%)	3	181 (21.6%)	4	17 182 (21.2%)	8 182 (21.5%)	5 164 (21.0%)	8	59 147 (21.2%)
	Minister							151 (16.6%)	159 (19.5%)		162 (19.3%)	164 (19.1%)	163 (19.2%)	152 (19.5%)	140 (20.2%)
	Mayor or governor					47 (6.1%)	47 (5.8%)		49 (5.8%)	51 (6.0%)	51 (6.0%)	43 (5.5%)	38 (5.5%)
	Civil servant/diplomat				50 (6.5%)	55 (6.7%)		57 (6.8%)	59 (6.9%)	58 (6.8%)	54 (6.9%)	46 (6.6%)

  Do citizens enjoy the right to private property? Clarification: Private property includes the right to acquire, possess, inherit, and sell private property, including land. Limits on property rights may come from the state which may legally limit rights or fail to enforce them; customary laws and practices; or religious or social norms. This question concerns the right to private property, not actual ownership of property." To what extent are laws transparently, independently, predictably, impartially, and equally enforced, and to what extent do the actions of government officials comply with the law?"Coppedge et al. (2019)ConflictDummy coded one if the country participated in an international or national armed conflict; or if there is a civil war (defined as at least one intra-state war with at least 1,000 battle deaths).

			Coppedge et al. (2019)
	Property rights	Answer to the question: "Coppedge et al. (2019)
	Rule of law	Answer to the question: "Coppedge et al. (2019)
	Constraints	Dummy coded one if Constraint on Chief Executive (XCONST) is greater or equal than 4 (which corresponds to substantial constraints or executive parity or subordination).	Marshall and Gurr (2018)
	Electoral regime index	Dummy coded one if regularly scheduled national elections are on course, as stipulated by election law or well-established precedent.	Coppedge et al. (2019)
	Respect for
	constitution
			Coppedge et al. (2019)
	Total expenditures	Share of government consumption at current PPPs.	Feenstra et al. (2015)
	Total government revenue	Total government revenue including taxes, non-tax revenue, grants and social contributions in % of GDP.	ICTD/UNU-WIDER (2016)

Original name: Executive respects constitution. Answer to the question: "Do members of the executive (the head of state, the head of government, and cabinet ministers) respect the constitution?"

Coppedge et al. (

2019

)

Neopatrimonialism index

Answer to the question: "To what extent is rule based on personal authority?" Clarification: Following

Clapham (1985)

, neopatrimonial rule reflects the idea that personalistic forms of authority pervade formal regime institutions.

Table A4 :

 A4 Variables' sources and definition

Table C1 :

 C1 Ranking of significant leaders' effects on growth and property rights

	D Additional Tables
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	(1) Growth Growth	Democracy	Corruption	Rule of law	Property rights	(2) Property rights Additive Conflict index	PCA index
	Military leader	0.128	-0.797***	-0.322	-0.518*	-0.164	0.394	-0.409**	-0.611**
		(0.207)	(0.140)	(0.285)	(0.265)	(0.214)	(0.381)	(0.189)	(0.237)
	Academic	0.088	-0.340	-0.561*	-0.403	0.063	-0.149	-0.202	-0.360
		(0.214)	(0.291)	(0.328)	(0.308)	(0.189)	(0.273)	(0.240)	(0.299)
	Politician	0.463*	0.642***	0.206	0.651**	-0.031	0.346	0.467**	0.525**
		(0.239)	(0.180)	(0.330)	(0.273)	(0.249)	(0.370)	(0.222)	(0.264)
	Tenure (x 10 years)	0.511	-0.089	-1.080***	-0.554	0.417	0.693	0.009	-0.397
		(0.364)	(0.215)	(0.404)	(0.379)	(0.272)	(0.426)	(0.253)	(0.329)
	Tenure 2	-0.104	-0.082	0.193*	0.071	-0.231***	-0.075	-0.074	-0.007
		(0.093)	(0.055)	(0.106)	(0.101)	(0.070)	(0.107)	(0.065)	(0.087)
	Entry age (x 10 years)	-0.171	0.198	-1.012**	-0.952*	0.083	-0.827	-0.606	-0.678
		(0.467)	(0.317)	(0.468)	(0.528)	(0.498)	(1.188)	(0.429)	(0.480)
	Entry age 2	0.037	-0.018	0.084*	0.081	-0.008	0.077	0.058	0.058
		(0.045)	(0.031)	(0.042)	(0.049)	(0.048)	(0.123)	(0.040)	(0.044)
	Observations	771	815	838	856	847	779	693	693
	Adjusted R-squared	0.043	0.277	0.075	0.133	0.120	0.039	0.112	0.164
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D1 :

 D1 Leaders' effects and leaders' background.

Table D2 :

 D2 Leaders' effects and leaders' sub-categories.

		Growth	Democracy	Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader
	Military career	-0.001	-0.886***	-0.376	-0.701**	-0.231	-0.244	-0.640***	-0.685***
		(0.285)	(0.195)	(0.256)	(0.306)	(0.226)	(0.374)	(0.192)	(0.235)
	Military education	0.159	-0.284	-0.188	-0.116	0.078	0.366	0.016	-0.213
		(0.299)	(0.179)	(0.281)	(0.262)	(0.231)	(0.473)	(0.232)	(0.241)
	Academic
	Economist	-0.034	-0.549**	0.061	0.303	-0.478	0.642**	0.055	0.081
		(0.258)	(0.275)	(0.363)	(0.271)	(0.482)	(0.284)	(0.216)	(0.255)
	Other academic	0.156	-0.229	-0.779**	-0.575*	0.219	-0.464*	-0.290	-0.506
		(0.253)	(0.354)	(0.368)	(0.328)	(0.251)	(0.275)	(0.285)	(0.336)
	Politician
	Legislative career	-0.024	0.025	-0.168	0.415*	0.485	-0.094	0.222	0.269
		(0.404)	(0.270)	(0.286)	(0.248)	(0.339)	(0.329)	(0.193)	(0.244)
	PM, VP or president	0.405*	0.587***	0.164	0.525**	0.035	-0.193	0.460***	0.551***
		(0.217)	(0.149)	(0.282)	(0.227)	(0.218)	(0.288)	(0.165)	(0.203)
	Mayor or governor	-0.253	0.045	-0.031	0.616*	-0.502*	-0.406	0.010	0.132
		(0.336)	(0.354)	(0.416)	(0.356)	(0.276)	(0.382)	(0.289)	(0.332)
	Party leader	0.311	0.305**	0.078	0.164	-0.312	0.090	0.162	0.221
		(0.267)	(0.135)	(0.344)	(0.296)	(0.229)	(0.565)	(0.226)	(0.289)
	Minister	0.334	0.002	-0.033	0.100	-0.237	0.010	0.065	0.016
		(0.300)	(0.188)	(0.254)	(0.249)	(0.244)	(0.471)	(0.202)	(0.211)
	Civil servant or diplomat	0.631	0.242	-0.250	-0.003	0.346	-0.471	0.082	-0.064
		(0.434)	(0.259)	(0.274)	(0.260)	(0.287)	(0.412)	(0.246)	(0.247)
	Tenure (x 10 years)	0.575	-0.130	-1.111***	-0.626	0.443	0.772*	-0.023	-0.453
		(0.355)	(0.201)	(0.418)	(0.407)	(0.276)	(0.447)	(0.271)	(0.354)
	Tenure 2	-0.123	-0.076	0.198*	0.088	-0.233***	-0.099	-0.067	0.005
		(0.089)	(0.050)	(0.108)	(0.109)	(0.071)	(0.110)	(0.069)	(0.092)
	Entry age (x 10 years)	-0.109	0.604**	-0.854*	-0.746	0.183	-0.607	-0.399	-0.431
		(0.475)	(0.285)	(0.473)	(0.515)	(0.470)	(1.071)	(0.428)	(0.467)
	Entry age 2	0.031	-0.055*	0.068	0.061	-0.014	0.059	0.038	0.034
		(0.046)	(0.028)	(0.042)	(0.048)	(0.045)	(0.109)	(0.039)	(0.042)
	Observations	771	815	838	856	847	779	693	693
	Adjusted R-squared	0.051	0.310	0.085	0.164	0.154	0.045	0.128	0.193
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D3 :

 D3 Leaders' effects and leaders' sub-categories with interactions.WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

					2)		(3)	(4)		(5)			(6)	(7)	(8)
			Growth Democracy Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military career		-0.461	-0.819***	-0.282	-0.492		-0.342		0.056	-0.594**	-0.595*
			(0.334)	(0.227)		(0.404)	(0.406)		(0.287)		(0.535)	(0.282)	(0.344)
	Military education		-0.096		-0.160		0.144	0.475		-0.048		0.623	0.301	0.167
			(0.510)	(0.287)		(0.495)	(0.361)		(0.315)		(0.459)	(0.272)	(0.343)
	Economist		0.002		-0.477		0.073	0.397		-0.442		0.525*	0.059	0.125
			(0.280)	(0.304)		(0.311)	(0.288)		(0.492)		(0.290)	(0.229)	(0.280)
	Other academic		0.155		-0.217		-0.795**	-0.582*		0.228			-0.409	-0.296	-0.516
			(0.258)	(0.326)		(0.358)	(0.323)		(0.251)		(0.268)	(0.279)	(0.332)
	Legislative career		-0.354		0.419		-0.176	0.486		0.488			-0.664	0.185	0.362
			(0.585)	(0.389)		(0.324)	(0.323)		(0.433)		(0.463)	(0.266)	(0.303)
	PM, VP or president		0.129	0.608***	0.212	0.429		-0.068		0.400	0.451**	0.460
			(0.317)	(0.206)		(0.412)	(0.308)		(0.260)		(0.339)	(0.216)	(0.288)
	Party leader		0.036	0.576***	0.300	0.334		-0.439		0.793	0.299	0.353
			(0.340)	(0.196)		(0.495)	(0.448)		(0.325)		(0.598)	(0.323)	(0.426)
	Mayor or governor		-0.442		0.053		-0.015	0.555	-0.531*		-0.351	-0.025	0.077
			(0.360)	(0.362)		(0.445)	(0.390)		(0.284)		(0.414)	(0.317)	(0.373)
	Civil servant or diplomat		0.627		0.254		-0.295	0.020		0.351			-0.559	0.105	-0.043
		(1)	(0.440) (2)	(0.259)	(3)	(0.282) (4)	(0.279) (5)		(0.291) (6)		(0.415)	(7)	(0.250)	(8)	(0.259)
	Tenure (x 10 years)	Growth	0.542 Democracy	-0.106 Corruption -1.195*** Rule of law	-0.725* Property rights	0.501* Conflict	0.465 Additive -0.092 index	-0.495 PCA index
	Tenure 2 Graduate	0.672*	(0.349) -0.111 -0.438	(0.207) -0.079 -0.355	(0.410) 0.214** -0.465	(0.393) 0.103 0.296	(0.273) -0.245*** -0.606		(0.450) -0.036 -0.456	(0.272) -0.054 -0.649 γ (0.344) 0.012
		(0.375)	(0.089) (0.334)	(0.051) (0.376)	(0.106) (0.495)	(0.105) (0.381)	(0.071) (0.501)	(0.111) (0.329) (0.069) (0.395) (0.089)
	Entry age (x 10 years) Tenure (x 10 years)	0.507	-0.114 0.232	0.661** -0.010	-0.752 0.148	-0.671 0.592		0.165 -0.564		-0.804 0.270	-0.366	-0.351 0.326
	Entry age 2 Tenure 2	(0.833) -0.124	(0.451) (0.748) 0.032 -0.166	(0.263) (0.810) -0.061** -0.029	(0.480) (0.741) 0.057 -0.043	(0.538) (0.591) 0.053 -0.215	(0.448) (0.498) -0.012 0.147	(0.897) (0.534) (0.431) (0.675) (0.484) 0.079 0.035 0.026 -0.115 -0.134
		(0.191)	(0.044) (0.186)	(0.026) (0.239)	(0.043) (0.222)	(0.050) (0.151)	(0.042) (0.119)	(0.088) (0.151) (0.040) (0.203) (0.044)
	Interactions Entry age (x 10 years)	0.416	0.333	0.157	0.025	-0.769		-0.941		-0.219	-0.089
	Military career * Military education (0.844) Entry age 2 -0.026	0.327 (0.473) (0.522) -0.035	-0.274 (0.539) (0.358) -0.013	-0.509 (0.719) (0.540) -0.003	-1.076** (0.879) (0.452) 0.071	0.146 (1.244) (0.390) 0.123	-0.482 (0.544) (0.513) (0.348) -0.478 (0.646) -0.666 (0.419) 0.026 0.009
	Military career * PM, VP or president (0.088)	0.618 (0.052)	-0.158 (0.050)	0.608 (0.068)	0.592 (0.082)	-0.174 (0.143)	0.348 (0.055)	0.369 (0.061) 0.446
	Constant	-1.918	(0.415) -1.066	(0.382) -0.214	(0.508) -0.294	(0.530)		(0.447) 2.420		(0.641) 0.238	(0.338) -0.059 (0.445)
	Military career * Minister	(2.253)	1.054** (1.290)	0.839** (1.700)	-0.054 (1.944)	0.992* (2.396)	0.524 (2.642)	-1.032* (1.407)	0.477 (1.821) 0.659
			(0.482)	(0.353)		(0.626)	(0.558)		(0.375)		(0.578)	(0.389)	(0.472)
	Party leader * PM, VP or president Observations 127	0.282 136	-0.159	144	-0.950 147	-0.536 145		0.604	141	-3.272** 117	-0.694	117	-0.394
	Adjusted R-squared	0.031	(0.558) 0.074	(0.369) -0.009	(0.804) -0.013	(0.648) 0.041		(0.491) 0.084		(1.342) 0.015	(0.531) 0.029 (0.660)
	Party leader * Legislative career	0.591	-0.957*** Robust standard errors in parentheses -0.103 -0.371		-0.028		0.798	-0.109	-0.403
			(0.643) *** p<0.01, ** p<0.05, * p<0.1, γ p = 0.106 (0.348) (0.437) (0.429)		(0.462)		(0.842)	(0.390)	(0.395)
	Observations		771		815		838	856		847			779	693	693
	Adjusted R-squared	Table	0.064	0.328 Robust standard errors in parentheses 0.100 0.191		0.160			0.159	0.137	0.203
				*** p<0.01, ** p<0.05, * p<0.1

Table D5 :

 D5 Education, constraints or experience? -Alternative measure of experience WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. Definition of independent variables are detailed in Appendix A. Other degree equals one if university degree is coded one, and economic, law, political sciance and military education is coded 0. Years of experience is taken from[START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and accounts for years in official politics, such as being a member of parliament, governor or city mayor, among others. Constraint equals one if the Polity IV Executive Constraints is higher or equal than 4 the year before the leader enters office. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

		1)	(2) Full sample	(3)		(4)		(5)		(6) Restricted sample	(7)	(8)
		Growth Previous years of experience Democracy Corruption (1) Constraints Rule of law (2)	Property rights Previous years of experience (3) Conflict	Additive (4) index Constraints PCA index
	Degree in law Military leader	0.416	-0.216 -2.650***	-0.347	-0.127 -0.270***		0.447*** -2.553*** 0.100	0.022 -0.301*** -0.099
		(0.301)	(0.185) (0.839)	(0.317)	(0.298) (0.046)		(0.165)	(0.332) (0.950)	(0.197) (0.049)	(0.256)
	Degree in economics Politician		-0.213 5.588***		0.208	0.365 0.282***		-0.045 6.388*** 0.592	0.197 0.262***	0.212
		(0.469)	(0.204) (0.898)	(0.362)	(0.301) (0.043)		(0.337)	(0.368) (0.981)	(0.234) (0.046)	(0.247)
	Degree in pol. science Academic	-0.462	-0.033 -2.813***	-0.784*	-0.183 0.090*		0.359	0.536 -2.794**	-0.027 0.071	-0.204
		(0.374)	(0.242) (0.881)	(0.453)	(0.276) (0.048)		(0.427)	(0.542) (1.128)	(0.265) (0.055)	(0.267)
	Other degree Tenure (x 10 years)	-0.044	-0.021 0.423	-0.128	-0.010 -0.312***		0.643**	0.903	-0.094	-0.009 -0.296*** -0.064
		(0.349)	(0.226) (1.096)	(0.386)	(0.294) (0.071)		(0.246)	(0.425) (1.243)	(0.210) (0.081)	(0.258)
	Military education Tenure 2	0.448	-0.595*** -0.005	-0.599	-0.413 0.047**		0.332		1.094** -0.216	-0.139 0.045**	-0.527*
		(0.311)	(0.185) (0.263)	(0.377)	(0.313) (0.019)		(0.270)	(0.511) (0.283)	(0.244) (0.022)	(0.280)
	Years of experience Entry age (x 10 years)	0.013	0.018 0.828		0.039		0.063** -0.022		0.009		0.131*** -1.589	0.061*** 0.014	0.060**
		(0.027)	(0.020) (2.307)	(0.031)	(0.030) (0.086)		(0.023)	(0.033) (2.361)	(0.023) (0.108)	(0.027)
	Years of experience 2 Entry age 2	-0.000	-0.000 0.325	-0.001		-0.001* 0.006		-0.001	-0.004*** 0.544**	-0.002*** 0.005	-0.001**
		(0.001)	(0.001) (0.237)	(0.001)	(0.001) (0.008)		(0.001)	(0.001) (0.243)	(0.001) (0.010)	(0.001)
	Constraint Observations	-0.176	1.280*** 847		0.139		0.823** 849		0.619**	637	0.196	0.640*** 642	0.783***
	Adjusted R-squared	(0.245)	(0.243) 0.330	(0.257)	(0.357) 0.374		(0.297)	0.354	(0.255)	(0.232) 0.379	(0.284)
	Tenure (x 10 years)	0.063	0.099 Robust standard errors in parentheses -1.153** -0.484 0.617**	0.192	-0.034	-0.296
		(0.359)	(0.229) *** p<0.01, ** p<0.05, * p<0.1 (0.475) (0.459)	(0.305)	(0.436)	(0.311)	(0.382)
	Tenure 2	-0.013	-0.092*	0.215*	0.085		-0.252***	0.056	-0.047	-0.004
		(0.083)	(0.055)	(0.124)	(0.112)		(0.073)	(0.108)	(0.073)	(0.094)
	Entry age (x 10 years)	0.478	0.038	-1.321**	-1.186*		-0.152	-1.298	-0.719*	-0.838
		(0.455)	(0.381)	(0.531)	(0.601)		(0.437)	(0.814)	(0.432)	(0.508)
	Entry age 2	-0.026	-0.002	0.119**	0.104*		0.022		0.138*	0.075*	0.077
		(0.048)	(0.038)	(0.051)	(0.057)		(0.045)	(0.079)	(0.041)	(0.048)
	Observations	626	652		659		673		665		631	585	585
	Adjusted R-squared	0.047	0.410		0.127		0.231		0.226		0.136	0.198	0.275
			Robust standard errors in parentheses
				*** p<0.01, ** p<0.05, * p<0.1

Table D6 :

 D6 Experience, constraints and leaders' type.OLS estimators. Columns (1) and (2) use the full sample of leaders without missing information, while columns (3) and (4) restricts to the sample of leaders with tenure equal to or longer than three years. Years of experience is taken from[START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and includes years in formal politics that accounts for years in official politics, and political activity such as party membership or civil service offices. Constraints is coded one if the index Polity IV Executive Constraints from Polity IV is higher or equal than 4. Standard errors are clustered at the country level.

		(1)	(2)	(3)	(4)
		Education	Experience	Constraints	All
	Military leader	-0.657**	-1.260***	-0.215	-0.936*
		(0.297)	(0.385)	(0.236)	(0.516)
	Academic	-0.286	-0.474	-0.291	-0.825
		(0.291)	(0.527)	(0.270)	(0.518)
	Politician	0.136	0.210	0.418	-0.305
		(0.356)	(0.454)	(0.274)	(0.560)
	Graduate	-0.798**			-0.962**
		(0.399)			(0.380)
	Military leader*Graduate	0.078			0.194
		(0.420)			(0.411)
	Politician*Graduate	0.541			0.644
		(0.403)			(0.393)
	Years of exper.		-0.049		-0.056
			(0.044)		(0.043)
	Years of exper. 2		0.002*		0.002**
			(0.001)		(0.001)
	Military leader*Years of exper.		0.062		0.066
			(0.039)		(0.045)
	Military leader**Years of exper. 2		-0.001		-0.001
			(0.001)		(0.001)
	Academic*Years of exper.		0.048		0.097*
			(0.060)		(0.058)
	Academic*Years of exper. 2		-0.002		-0.002*
			(0.001)		(0.001)
	Politician*Years of exper.		0.067		0.073
			(0.048)		(0.053)
	Politician*Years of exper. 2		-0.002**		-0.002**
			(0.001)		(0.001)
	Constraint			0.881	1.319***
				(0.563)	(0.474)
	Military leader*Constraints			-0.333	-0.387
				(0.642)	(0.780)
	Politician*Constraints			-0.156	-0.628
				(0.512)	(0.423)
	Tenure (x 10 years)	-0.495	-0.289	-0.515	-0.484
		(0.318)	(0.332)	(0.381)	(0.375)
	Tenure 2	0.011	-0.020	0.027	0.029
		(0.080)	(0.087)	(0.095)	(0.088)
	Entry age (x 10 years)	-0.503	-0.547	-0.765	-0.606
		(0.454)	(0.534)	(0.471)	(0.529)
	Entry age 2	0.044	0.038	0.067	0.048
		(0.042)	(0.052)	(0.042)	(0.052)
	Observations	693	637	635	585
	Adjusted R-squared	0.180	0.235	0.211	0.315
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

Table D8 :

 D8 Leaders' effects and leaders' background when controlling for initial democracy and property rights.WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

			2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth Democracy Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader	0.354*	-0.549***	-0.297	-0.217	0.040	0.600	-0.222	-0.373
		(0.209)	(0.148)	(0.296)	(0.310)	(0.223)	(0.407)	(0.215)	(0.282)
	Politician	0.554**	0.494***	0.167	0.623**	-0.105	0.446	0.433*	0.462
		(0.265) (1)	(0.184) (2)	(0.353) (3)	(0.297) (4)	(0.233) (5)	(0.377) (6)	(0.238) (7)	(0.289) (8)
	Academic	0.237 (0.195) Growth	-0.295 (0.245) Democracy Corruption -0.398 (0.321)	-0.294 (0.289) Rule of law	-0.003 (0.193) Property rights	-0.069 (0.293) Conflict	-0.127 (0.216) Additive index	-0.258 (0.266) PCA index
	Tenure (x 10 years)	0.299	0.055	-1.400***	-0.774*	0.349	0.656	-0.099	-0.516
	Military leader	(0.349)	(0.219)	(0.468)	(0.402)	(0.315)	(0.401)	(0.293)	(0.365)
	Tenure 2 Military career	-0.046 -0.039	-0.111* -0.531**	0.245** -0.366	0.121 -0.544	-0.213*** -0.007	-0.046 -0.275	-0.052 -0.535**	0.017 -0.507*
		(0.084) (0.315)	(0.061) (0.209)	(0.122) (0.302)	(0.104) (0.344)	(0.080) (0.218)	(0.101) (0.394)	(0.069) (0.235)	(0.092) (0.279)
	Entry age (x 10 years) Military education	0.022 0.309	-0.199 -0.281	-1.364** -0.139	-1.172** 0.041	-0.008 0.035	-0.342 0.512	-0.631 0.082	-0.821 -0.151
		(0.466) (0.313)	(0.347) (0.218)	(0.540) (0.320)	(0.558) (0.320)	(0.443) (0.215)	(1.261) (0.507)	(0.453) (0.253)	(0.514) (0.273)
	Entry age 2 Academic	0.016	0.017	0.121**	0.104**	-0.001	0.034	0.061	0.073
	Economist	(0.045) 0.043	(0.032) -0.581**	(0.049) 0.035	(0.051) 0.314	(0.045) -0.537	(0.132) 0.697**	(0.042) 0.044	(0.046) 0.061
	Initial level of democracy	-0.629 (0.241)	1.593*** (0.272)	0.256 (0.358)	1.100* (0.285)	0.032 (0.381)	-0.442 (0.293)	0.410 (0.206)	0.952** (0.253)
	Other academic	(0.382) 0.291	(0.322) -0.162	(0.486) -0.551	(0.567) -0.444	(0.362) 0.110	(0.450) -0.411	(0.371) -0.205	(0.479) -0.384
	Initial property rights	1.243** (0.227)	-1.157*** (0.296)	-0.656 (0.362)	-0.871* (0.306)	1.609*** (0.254)	1.022** (0.303)	0.201 (0.261)	-0.526 (0.306)
	Politician	(0.480)	(0.256)	(0.413)	(0.475)	(0.288)	(0.461)	(0.301)	(0.388)
	Constant Legislative career	-1.634 -0.090	0.229 -0.074	4.839*** -0.104	3.122** 0.457	-0.853 0.301	-1.065 -0.147	1.002 0.170	2.104 0.247
		(1.134) (0.425)	(0.864) (0.267)	(1.486) (0.296)	(1.490) (0.285)	(1.079) (0.338)	(3.045) (0.314)	(1.247) (0.199)	(1.402) (0.265)
	PM, VP or president	0.586***	0.463***	0.040	0.486*	0.052	-0.268	0.476*** 0.528**
	Observations	692 (0.190)	737 (0.135)	737 (0.301)	755 (0.252)	746 (0.220)	688 (0.334)	638 (0.175)	638 (0.215)
	Adjusted R-squared Mayor or governor	0.073 -0.165	0.381 0.107	0.100 0.056	0.180 0.731**	0.238 -0.573**	0.049 -0.522	0.139 0.012	0.208 0.161
		(0.280)	Robust standard errors in parentheses (0.297) (0.396) (0.354) (0.285)	(0.456)	(0.285)	(0.332)
	Party leader	0.126	*** p<0.01, ** p<0.05, * p<0.1 0.171 0.149 0.137	-0.583**	0.152	0.063	0.132
		(0.254)	(0.133)	(0.353)	(0.299)	(0.246)	(0.599)	(0.234)	(0.292)
	Minister	0.438	0.039	0.020	0.196	-0.215	0.025	0.086	0.060
		(0.326)	(0.171)	(0.259)	(0.231)	(0.238)	(0.451)	(0.195)	(0.199)
	Civil servant or diplomat	0.664	0.251	-0.396	-0.094	0.446*	-0.420	0.036	-0.161
		(0.453)	(0.234)	(0.288)	(0.283)	(0.265)	(0.405)	(0.258)	(0.245)
	Tenure (x 10 years)	0.375	0.086	-1.416***	-0.836*	0.434	0.794*	-0.107	-0.554
		(0.344)	(0.215)	(0.470)	(0.426)	(0.305)	(0.457)	(0.304)	(0.377)
	Tenure 2	-0.063	-0.118**	0.246**	0.134	-0.228***	-0.091	-0.052	0.024
		(0.081)	(0.057)	(0.121)	(0.110)	(0.076)	(0.111)	(0.072)	(0.094)
	Entry age (x 10 years)	0.021	0.046	-1.192**	-1.004*	-0.051	-0.109	-0.483	-0.616
		(0.470)	(0.333)	(0.546)	(0.536)	(0.422)	(1.082)	(0.450)	(0.501)
	Entry age 2	0.015	-0.006	0.104**	0.089*	0.005	0.015	0.046	0.053
		(0.046)	(0.031)	(0.049)	(0.049)	(0.043)	(0.112)	(0.041)	(0.044)
	Initial level of democracy	-0.493	1.563***	0.147	0.903*	0.171	-0.442	0.354	0.801*
		(0.404)	(0.318)	(0.509)	(0.545)	(0.365)	(0.505)	(0.376)	(0.476)
	Initial property rights	1.153**	-0.990***	-0.598	-0.848*	1.658***	0.891**	0.253	-0.449
		(0.494)	(0.237)	(0.409)	(0.471)	(0.256)	(0.428)	(0.299)	(0.382)
	Constant	-1.506	-0.257	4.615***	3.048**	-0.891	-1.138	0.871	1.879
		(1.128)	(0.842)	(1.481)	(1.472)	(1.071)	(2.549)	(1.255)	(1.400)
	Observations	692	737	737	755	746	688	638	638
	Adjusted R-squared	0.093	0.391	0.104	0.195	0.278	0.053	0.148	0.221
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D9 :

 D9 Leaders' effects and leaders' background when controlling for initial democracy and property rights.WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth	Democracy	Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader	0.453**	-0.672***	-0.193	-0.369*	-0.389**	0.355	-0.243	-0.559**
		(0.175)	(0.202)	(0.234)	(0.208)	(0.176)	(0.303)	(0.215)	(0.241)
	Academic	0.544**	0.219	0.432*	0.475	0.212	0.089	0.457	0.492
		(0.224)	(0.521)	(0.227)	(0.349)	(0.418)	(0.186)	(0.576)	(0.573)
	Politician	0.247	0.175	0.253	0.390*	0.101	0.550**	0.507**	0.326
		(0.257)	(0.222)	(0.222)	(0.211)	(0.186)	(0.234)	(0.251)	(0.268)
	Tenure (x 10 years)	0.161	-0.411	-0.327	-0.206	-0.228	-0.022	-0.244	-0.361
		(0.300)	(0.287)	(0.324)	(0.245)	(0.205)	(0.191)	(0.267)	(0.319)
	Tenure 2	-0.033	0.060	0.044	0.031	0.023	-0.003	0.007	0.040
		(0.065)	(0.072)	(0.087)	(0.066)	(0.051)	(0.045)	(0.069)	(0.089)
	Entry age (x 10 years)	0.039	0.403	0.041	-0.274	-0.485	-0.581	-0.550	-0.216
		(0.565)	(0.458)	(0.318)	(0.341)	(0.377)	(0.383)	(0.539)	(0.706)
	Entry age 2	0.012	-0.041	-0.014	0.017	0.039	0.058	0.044	0.009
		(0.056)	(0.042)	(0.027)	(0.031)	(0.034)	(0.042)	(0.049)	(0.061)
	Constant	-1.047	-0.499	0.421	0.945	1.559	1.012	1.511	0.981
		(1.397)	(1.263)	(0.905)	(0.928)	(0.970)	(0.744)	(1.430)	(1.937)
	Observations	124	131	138	137	138	133	120	116
	Adjusted R-squared	0.040	0.139	0.047	0.095	0.051	0.024	0.087	0.109
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D10 :

 D10 Leaders' effects and leaders' background using exogenous transitions.WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. The sample is restricted to leaders who died in office by natural death, or resigned due to health issues, and their successors conditional on having had a tenure equal to or longer than three years. The criteria used to build the background's categories is detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth	Democracy	Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Graduate	0.382*	-0.090	-0.140	-0.075	0.153	-0.038	0.037	-0.148
		(0.210)	(0.197)	(0.189)	(0.196)	(0.187)	(0.171)	(0.229)	(0.228)
	Tenure (x 10 years)	0.118	-0.208	0.037	0.064	0.284	-0.075	-0.054	0.043
		(0.344)	(0.318)	(0.290)	(0.263)	(0.265)	(0.179)	(0.305)	(0.314)
	Tenure 2	-0.031	0.018	-0.027	-0.021	-0.094	0.026	-0.033	-0.042
		(0.076)	(0.083)	(0.084)	(0.077)	(0.065)	(0.039)	(0.077)	(0.089)
	Entry age (x 10 years)	-0.084	0.511	0.065	0.036	-0.224	-0.272	-0.104	0.121
		(0.451)	(0.354)	(0.236)	(0.301)	(0.375)	(0.370)	(0.454)	(0.482)
	Entry age 2	0.019	-0.046	-0.005	-0.002	0.019	0.036	0.009	-0.015
		(0.047)	(0.035)	(0.023)	(0.029)	(0.035)	(0.042)	(0.044)	(0.044)
	Constant	-0.315	-1.274	-0.078	-0.181	0.304	0.598	0.327	-0.307
		(1.134)	(0.953)	(0.696)	(0.845)	(1.009)	(0.742)	(1.202)	(1.338)
	Observations	127	136	144	147	145	141	117	117
	R-squared	0.043	0.012	0.029	-0.033	0.023	0.018	0.032	0.019
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D12 :

 D12 Leaders' effects and leaders' background after 1960. WLS estimators. Dependent variables are the leaders' effects estimated in Section 4. Weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. I restrict the sample to leaders with tenure equal to or longer than three years. The criteria used to build the background's categories are detailed in Appendix A. The additive index is an unweighted sum of the standardized leaders' effects on all the outcomes. The PCA (Principal Component Analysis) index is a linear combination of the six outcomes in such a way that we retain most of the information (see Appendix F). Standard errors are clustered at the country level.

		(1)	(2)		(3)		(4)			(5)	(6)	(7)	(8)
		Growth	Democracy	Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader
	Military career	0.135	-0.832***		-0.610		-1.083***		-0.547*	-0.350	-0.860***	-0.923***
		(0.406)	(0.258)		(0.378)		(0.331)		(0.297)	(0.499)	(0.264)	(0.302)
	Military education	0.082	-0.397		-0.183		0.029			0.263	0.765	0.159	-0.151
		(0.406)	(0.269)		(0.386)		(0.317)		(0.350)	(0.656)	(0.289)	(0.291)
	Academic
	Economist	0.020	-0.533*		0.082		0.351			-0.496	0.696**	0.116	0.142
		(0.274)	(0.281)		(0.359)		(0.273)		(0.507)	(0.293)	(0.212)	(0.243)
	Other academic	0.125	-0.093		-0.548		-0.342			0.305	-0.330	-0.063	-0.251
		(0.258)	(0.314)		(0.370)		(0.356)		(0.267)	(0.285)	(0.284)	(0.357)
	Politician
	Legislative career	-0.173	-0.016		0.040		0.581**			0.527	-0.221	0.293	0.418*
		(0.420)	(0.271)		(0.295)		(0.255)		(0.368)	(0.358)	(0.193)	(0.251)
	PM, VP or president	0.315	0.597***		0.213		0.492**			-0.006	-0.128	0.502***	0.588***
		(0.243)	(0.155)		(0.302)		(0.226)		(0.253)	(0.339)	(0.181)	(0.217)
	Mayor or governor	-0.458	-0.051		0.005		0.694*			-0.475	-0.139	0.036	0.181
	Party leader	Growth Democracy Corruption (0.365) (0.399) (0.429) 0.250 0.344** -0.068 (0.273) (0.140) (0.368)	Rule of law (0.397) Property rights (0.308) Conflict 0.106 -0.377 (0.326) (0.246)	Additive index (0.414) 0.081 (0.678)	(0.320) PCA 0.107 index (0.237)	(0.375) 0.180 (0.320)
	Military leader Minister Politician Civil servant or diplomat	0.121 0.144 (0.235) (0.319) 0.188 0.552 (0.258) (0.375)	-0.859*** 0.066 (0.155) (0.171) 0.747*** 0.282 (0.177) (0.271)		-0.488 (0.311) 0.116 (0.267) -0.784*** 0.176 (0.270) (0.230) -0.348 (0.268) 0.353 0.730** 0.030 -0.130 0.076 (0.343) (0.288) (0.328) (0.298) (0.276)	0.571 -0.053 (0.415) (0.314) 0.324 0.197 (0.355) (0.321)	-0.513** -0.230 (0.198) (0.488) 0.525** -0.744* (0.234) (0.435)	0.113 -0.776*** (0.199) (0.252) 0.035 0.672** (0.281) (0.254)	0.141 (0.215) -0.009 (0.270)
	Academic Tenure (x 10 years) Tenure (x 10 years) Tenure 2 Tenure 2 Entry age (x 10 years) Entry age (x 10 years) Entry age 2 Entry age 2 Constant	0.082 (0.219) 0.848* 0.774 (0.457) (0.471) -0.269** -0.240* (0.124) (0.125) 0.382 0.350 (0.481) (0.473) -0.021 -0.016 (0.047) (0.046) -1.951 (1.193)	-0.220 (0.243) -0.302 -0.349 (0.339) (0.373) 0.047 0.073 (0.103) (0.112) 0.919*** 0.512 (0.347) (0.373) -0.087** -0.050 (0.034) (0.037) -1.940** (0.914)	-0.372 (0.315) -0.850* -0.803 (0.488) (0.496) 0.132 0.123 (0.135) (0.139) -1.129** -1.254** -1.203** -0.206 (0.317) -0.779* 0.120 (0.198) -0.697 -0.484 (0.469) (0.498) (0.356) 0.174 0.158 0.127 (0.128) (0.145) (0.107) -1.085* 0.598 (0.566) (0.554) (0.543) (0.546) (0.454) 0.100* 0.097* 0.111** 0.108** -0.051 (0.052) (0.053) (0.050) (0.053) (0.044) 3.764** 3.239** (1.588) (1.572)	-0.013 (0.295) -0.438 0.051 (0.337) (0.620) 0.111 0.113 (0.099) (0.183) 0.648 -0.199 (0.468) (1.409) -0.051 0.025 (0.046) (0.147) -1.572 (1.227)	-0.010 (0.221) 0.069 -0.166 (0.650) (0.371) 0.101 0.009 (0.190) (0.103) 0.092 -0.320 (1.251) (0.432) -0.002 0.036 (0.129) (0.041) -1.034 (2.899)	-0.127 -0.262 (0.291) (0.366) -0.473 0.030 (0.469) (0.099) 0.061 -0.163 (0.135) (0.449) -0.557 0.022 (0.457) (0.042) 0.051 0.279 (0.043) (1.257)	-0.581 (0.464) 0.091 (0.130) -0.319 (0.469) 0.030 (0.044) 1.106 (1.360)
	Observations Adjusted R-squared Observations Adjusted R-squared	634 0.025 634 0.042	661 0.219 0.241 661 Robust standard errors in parentheses 682 698 690 0.087 0.188 0.027 682 698 0.095 0.225 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 *** p<0.01, ** p<0.05, * p<0.1	622 0.026 0.072 690	557 0.121 622 0.042	557 557 0.193 0.137	557 0.216

Table D13 :

 D13 Leaders' effects and leaders' sub-categories after 1960.

Table D14 :

 D14 Leaders' effects and leaders' background with bootstrap standard errors.

			2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth Democracy Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader	0.106	-0.370***	-0.088	-0.205**	-0.020	0.083	-0.206**	-0.304***
		(0.082)	(0.077)	(0.083)	(0.083)	(0.088)	(0.105)	(0.104)	(0.097)
	Politician	0.181**	0.324***	0.125	0.268***	0.016	0.073	0.313***	0.310***
		(0.087)	(0.084)	(0.091)	(0.085)	(0.097)	(0.107)	(0.107)	(0.108)
	Academic	0.056	-0.016	-0.122	-0.113	0.047	-0.126	-0.093	-0.130
		(0.088)	(0.102)	(0.105)	(0.099)	(0.101)	(0.105)	(0.119)	(0.128)
	Tenure (x 10 years)	0.259*	-0.288**	-0.238*	-0.160	0.253	0.170	0.031	-0.124
		(0.139)	(0.143)	(0.136)	(0.141)	(0.191)	(0.134)	(0.162)	(0.166)
	Tenure 2	-0.057*	0.016	0.030	0.007	-0.104*	-0.009	-0.049	-0.027
		(0.033)	(0.044)	(0.041)	(0.044)	(0.062)	(0.033)	(0.046)	(0.050)
	Entry age (x 10 years)	-0.043	0.041	-0.261	-0.314	0.178	-0.270	-0.258	-0.266
		(0.217)	(0.191)	(0.203)	(0.204)	(0.206)	(0.251)	(0.282)	(0.291)
	Entry age 2	0.011	-0.001	0.024	0.029	-0.016	0.030	0.029	0.026
		(0.020)	(0.018)	(0.018)	(0.019)	(0.019)	(0.024)	(0.026)	(0.026)
	Constant	-0.415	0.115	0.896	0.897	-0.515	0.266	0.426	0.698
		(0.585)	(0.509)	(0.548)	(0.562)	(0.549)	(0.670)	(0.790)	(0.810)
	Observations	771	815	838	856	847	779	693	693
	Adjusted R-squared	0.010	0.148	0.014	0.055	0.019	0.008	0.063	0.089
			Standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table D15 :

 D15 Leaders' effects and leaders' sub-categories -bootsrapped standard errors.

		1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
		Growth Democracy Corruption	Rule of law	Property rights	Conflict	Additive index	PCA index
	Military leader
	Military career	0.024	-0.417***	-0.159	-0.368***	-0.071	-0.004	-0.382*** -0.438***
		(0.115)	(0.100)	(0.104)	(0.097)	(0.110)	(0.123)	(0.132)	(0.118)
	Military education	0.097	-0.135	-0.048	0.001	0.036	0.083	0.033	-0.056
		(0.110)	(0.101)	(0.106)	(0.097)	(0.108)	(0.123)	(0.129)	(0.117)
	Academic
	Economist	-0.013	-0.187	0.125	0.121	-0.172	0.115	0.004	-0.005
		(0.128)	(0.142)	(0.142)	(0.113)	(0.177)	(0.121)	(0.143)	(0.151)
	Other academic	0.086	0.059	-0.236*	-0.221*	0.132	-0.225*	-0.133	-0.190
		(0.099)	(0.130)	(0.132)	(0.127)	(0.110)	(0.136)	(0.153)	(0.166)
	Politician
	Legislative career	0.072	0.043	-0.049	0.061	0.176*	-0.077	0.051	0.050
		(0.111)	(0.098)	(0.087)	(0.084)	(0.096)	(0.119)	(0.104)	(0.106)
	PM, VP or president	0.123	0.237***	0.110	0.167**	-0.005	0.041	0.248**	0.251**
		(0.089)	(0.075)	(0.088)	(0.077)	(0.085)	(0.112)	(0.102)	(0.098)
	Mayor or governor	0.037	0.074	0.046	0.180	-0.250*	-0.100	0.048	0.061
		(0.139)	(0.134)	(0.135)	(0.114)	(0.144)	(0.219)	(0.164)	(0.151)
	Party leader	0.060	0.174**	0.070	0.101	-0.103	0.138	0.174*	0.188*
		(0.091)	(0.078)	(0.081)	(0.076)	(0.079)	(0.106)	(0.101)	(0.100)
	Minister	0.188*	0.025	-0.030	0.016	-0.102	0.038	0.033	-0.007
		(0.097)	(0.087)	(0.079)	(0.079)	(0.086)	(0.106)	(0.101)	(0.098)
	Civil servant or diplomat	0.216	0.115	-0.106	-0.075	0.092	-0.211	0.014	-0.048
		(0.136)	(0.154)	(0.112)	(0.117)	(0.116)	(0.169)	(0.159)	(0.147)
	Tenure (x 10 years)	0.271**	-0.304**	-0.257*	-0.177	0.257	0.175	0.016	-0.143
		(0.135)	(0.145)	(0.136)	(0.142)	(0.188)	(0.140)	(0.171)	(0.168)
	Tenure 2	-0.059*	0.017	0.033	0.010	-0.105*	-0.010	-0.048	-0.025
		(0.033)	(0.045)	(0.040)	(0.043)	(0.060)	(0.034)	(0.049)	(0.051)
	Entry age (x 10 years)	-0.038	0.196	-0.198	-0.192	0.221	-0.206	-0.131	-0.132
		(0.209)	(0.203)	(0.207)	(0.217)	(0.222)	(0.254)	(0.290)	(0.278)
	Entry age 2	0.011	-0.016	0.017	0.018	-0.019	0.024	0.016	0.012
		(0.020)	(0.019)	(0.019)	(0.020)	(0.021)	(0.024)	(0.026)	(0.025)
	Observations	771	815	838	856	847	779	693	693
	Adjusted R-squared	0.005	0.156	0.017	0.063	0.026	0.008	0.064	0.100
			Robust standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

At least, everyone who was considered to be a citizen, which at the time excluded women, foreigners, children, slaves, and paupers. See[START_REF] Susemihl | The politics of Aristotle: books IV: a revised text[END_REF].

See Horowitz and Stam (2014).

See for instance[START_REF] Carlyle | On heroes, hero-worship, and the heroic in history[END_REF][START_REF] Carlyle | On heroes, hero-worship, and the heroic in history[END_REF];[START_REF] Hook | The hero in history: A study in limitation and possibility[END_REF].

For the theoretical literature see[START_REF] Besley | Political selection[END_REF];[START_REF] Caselli | Bad politicians[END_REF];[START_REF] Coate | On the form of transfers to special interests[END_REF].

See Ferraz and Finan (2009);Galasso and Nannicini (2011).

It has been recently applied in some Indian states by setting minimum education requirements for contesting elections.

As examples,[START_REF] Besley | Political selection[END_REF] mentions Otto von Bismarck as the promoter of a welfare state in Germany, the role of Theodore Roosevelt in the economic reforms of the Progressive Era, Franklin Roosevelt's association to the New Deal; Mao's role in China's Great Leap; and Margaret Thatcher's economic legacy of privatization and deregulation attributed to her single-minded pursuit of these goals.

See also[START_REF] Besley | Political selection[END_REF];Galasso and Nannicini (2011);[START_REF] Coate | On the form of transfers to special interests[END_REF].

See Jones and Olken (2005);[START_REF] Besley | Do Educated Leaders Matter?[END_REF]; Brown (2020); Jong-A-Pin and Mierau (2011);[START_REF] Atella | Is gerontocracy harmful for growth? A comparative study of seven European countries[END_REF].

See Easterly and Pennings (2020);[START_REF] Johnson | Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates[END_REF];[START_REF] Ponomareva | Does the version of the Penn World Tables matter? An analysis of the relationship between growth and volatility[END_REF].

A corruption index is also included in the ICRG indicator of Quality of Government(Teorell et al. (2018) based on ICRG (2017)) together with "Bureaucracy Quality" and "Law and Order" which relates to rule of law.[START_REF] Efobi | Politicians' attributes and institutional quality in Africa: A focus on corruption[END_REF] has also studied national leaders' effects on corruption.

Some examples are the expropriation of the Knights Templar by Philip IV of France in 1307; the dissolution of monasteries by Henry VIII in England in 1536; the expropriation by President Cardenas of Mexico of petroleum assets in the 1930s; and more recently the Zimbabwean program of forceful land redistribution since 2000.

As inEasterly and Pennings (2020), I exclude countries with less than 30 years of observations and years for which the absolute value of economic growth was greater than 40%. To simplify, I do not estimate country and leaders' variance depending on the level of democracy, but results remain robust if doing so.

See for instance[START_REF] Cukierman | Political economy, growth, and business cycles[END_REF].

See https://www.efe.com/efe/america/politica/el-chavismo-hizo-de-venezuela-un-paraiso-para-lacorrupcion-segun-parlamento/20000035-3826238

 34 He was president from 2005 to 2015, although the estimated leaders' effects are computed until

2010. 35 See https://www.bbc.com/news/world-asia-37234654

See https://web.archive.org/web/20141104035212/http://www.afroarticles.com/articledashboard/Article/Macias-Nguema-Ruthless-and-bloody-dictator/117291 or https://en.wikipedia.org/wiki/Francisco Macías Nguema

 37 See https://www.theguardian.com/world/2014/jan/11/ariel-sharon-israel-warrior-death.

The aggregated indexes are estimated for the restricted sample of leaders without missing values in any of the components.

See Appendix B for the estimation of the posterior standard deviation.

This result is consistent with[START_REF] Carnes | What good is a college degree? Education and leader quality reconsidered[END_REF] who show that graduate politicians do not perform better than non-graduate ones.

As discussed in Appendix A, counting years of experience in politics is subject to a certain degree of subjectivity. I use as an alternative variable in TableD5, a more restricted one that only includes official politics, excluding years of civil service, revolutionary movements, and party membership. The positive effect of experience is robust.

In TableD6, I regress experience and constraints with respect to previous leaders' categories. Experience is positively associated merely with politicians and the absence of substantial constraints on the executive with military leaders.

 48 I exclude those interactions with less than 30 observations, such as the interaction between academics and constraints.

Clapham (1985) define a neopatrimonialism regime the one where the chief executive maintains authority through personal patronage, rather than through ideology or law, pervading formal institutions. See Appendix A for a detailed definition and sources of the three indexes.

The analysis of leaders' effects on policy choices is limited by data availability for cross-country comparisons over a long period of time. The last three expenditure categories are the only ones available in the World Bank (2016) dataset.

For the source and definition of those variables see Appendix A.

I provide the names of the leaders belonging to this restricted sample in Appendix G.

The graphs plot the coefficient for each leaders' category on the estimated leaders' effects obtained in Section 4. Those are post-estimations of weighted least-squares estimations where the weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. The criteria used to build the background categories is detailed in Appendix A. I restrict the sample to leaders with tenure equal to or longer than three years. The number of leaders included in each regression is: .

Alternatively, I use a LASSO approach to include all potential interactions between careers and degrees, as well as within careers, with more than 30 observations and detect the relevant variables in Section 6.3.

Acknowledgments

I thank my advisors Nicolas Berman and Marc Sangnier. I also thank Vincenzo Galasso, Marion Mercier, Pierre Michel, Avner Seror, four anonymus referees and seminar participants at AMSE, Konstanz University and the CESifo 13th Workshop on Political Economy, and four anonymous referees for valuable comments and feedback. The project leading to this publication has received funding from the french government under the "France 2030" investment plan managed by the French National Research Agency (reference :ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University -A*MIDEX.

The graphs plot the coefficient for each leaders' category on the estimated leaders' effects obtained in Section 4. Those are post-estimations of weighted least-squares estimations where the weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. In each regression I control for all categories together, tenure, entry age. The criteria used to build the background categories is detailed in Appendix A. I restrict the sample to leaders with tenure equal to or longer than three years. There are 693 observations in each regression.

Appendix A Data source

A.1 Leaders data

The principal source of leaders' data is [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] which provides detailed information regarding leaders' careers and academic fields. It identifies the effective leaders of each state, the ones with de facto executive power, who have been in power from 1960 to 2010. However, among those, some rulers took power as from 1931. To have the full sample of leaders who were in power from 1931 to 1960 I complement the data with [START_REF] Ellis | Introducing the LEAD data set[END_REF] database. Having a larger set of leaders allows for a better estimation of leaders' effects in section 4.1.

As both databases were built based on [START_REF] Goemans | Introducing Archigos: A Dataset of Political Leaders[END_REF], in the period and the countries in which both overlap, 1, 232 out of 1, 331 leaders are present in both dataset Among the ones who don't, 70 are present only in the [START_REF] Ellis | Introducing the LEAD data set[END_REF] and 19 only in the Baturo (2016) data set. Notice, that among the total of discrepancies, only 23 had a tenure larger or equal than three years and are relevant for the main analysis. Where those mismatches occur, I retained the head of government pointed by the [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] dataset. In the period and/or countries not covered by this data set, and whenever there is more than one leader per country in a given year (mainly in transitional years) I design as national leader the one who stayed more months in office.

Both databases use multiple data sources such as leaders' biographies, reference works, government websites, newspaper archives, book references, as well as web references or historical dictionaries. 58 . The variables related to leaders' backgrounds vary in their reliability. However, as the authors of [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] claim, career or prior political posts are generally reliable because most bibliographic sources usually report such facts and those are not subject to interpretation. While other variables, such as the number of years in politics, can involve a certain degree of subjectivity. [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] includes years in formal politics that accounts for years in official politics, such as being a member of parliament, governor or city mayor, among others, and general years of experience that additionally accounts for any political activity such as party membership or civil service offices.

A.1.1 Leaders' categories

Despite listing each leader's previous career and education is roughly an objective task, the aggregation of those into categories implies an unavoidable grade of discretion. For instance, both datasets have a variable to identify "career politicians". This variable is labeled career pol in [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] and careerpolitician in [START_REF] Ellis | Introducing the LEAD data set[END_REF]. The following table highlights great discordance among this dimension: 462 leaders out of the 1, 232 who overlap are classified differently. 59 58 Examples for book references in [START_REF] Baturo | Cursus Honorum: Background, Careers and Experience of Political Leaders in Democracy and Dictatorship -New Data and Analyses[END_REF] are [START_REF] Bienen | Of time and power: leadership duration in the modern world[END_REF] or [START_REF] Lentz | Heads of States and Governments-A Worldwide Encyclopedia of Over 2,300 Leaders, 1945 through 1992[END_REF]; examples of web references are www.rulers.org, www.worldstatesmen.com and of historical dictionaries is http://www.hls-dhs-dss.ch/ 59 Baturo (2016) code Career politician as 1 if "leader is primarily career politician, as ascertained from years in formal politics and prior career categories", while [START_REF] Ellis | Introducing the LEAD data set[END_REF] do not provide information of the criteria used.

(

E Additional Figures

Life experiences Leader The graphs plot the coefficient for each leaders' category interacted with the constraint dummy on the aggregated leaders' effects obtained in Section 4. Those are post-estimations of weighted least-squares estimations where the weights are the ratio between the absolute value of the leader's effect and the posterior standard deviation. The criteria used to build the background categories and the definition of constraints is detailed in Appendix A. I restrict the sample to leaders with tenure equal to or longer than three years. The number of leaders included in each regression is 693. I also control for tenure, entry age, the square of these variables, and their interactions with constraints.