The distributive effects of institutional quality when government stability is endogenous

European Journal of Political Economy 25 (2009) 409–421

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European Journal of Political Economy j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e j p e

The distributive effects of institutional quality when government stability is endogenous Fabrizio Carmignani ⁎ School of Economics, The University of Queensland, QLD 4072, Australia

a r t i c l e

i n f o

Article history: Received 28 September 2006 Received in revised form 23 March 2009 Accepted 23 March 2009 Available online 5 April 2009 JEL classification: D31 D72 C33

a b s t r a c t Redistribution is the strategic response of the incumbent to a decrease in its survival probability resulting from weak institutions and growing income inequalities. The purpose of the paper is to test empirically the validity of this conjecture. System and single equation estimations provide a consistent picture: (i) bad institutions increase income inequality, while more redistribution reduces income inequality; (ii) greater inequality increases the probability of government termination; and (iii) a higher probability of termination increases the extent of redistribution. Overall, there is strong evidence in support of the proposed conjecture. © 2009 Elsevier B.V. All rights reserved.

Keywords: Institutional quality Income distribution Political stability Panel data models

1. Introduction What triggers (or halts) redistribution? Meltzer and Richard (1981) apply the median voter theorem to show that redistribution is the outcome of electoral competition on a uni-dimensional policy space. Most of the subsequent literature moves away from the median voter framework and focuses on limited voter turnout (Franzese, 2002), electoral competition on a multidimensional issue space (Roemer, 1998), interest groups lobbying (Harms and Zink, 2003), coalition bargaining in legislatures (Austen Smith, 2000), and voters' preference for “middle-of-the-road” policies as opposed to extreme policies (Gruner, 2008). This paper investigates the empirical relevance of a different trigger mechanism: the deterioration of institutional quality when government stability is endogenous to income inequality. More specifically, the conjectured mechanism goes as follows: lower institutional quality increases income inequality, which in turn weakens political support for the incumbent. The government then redistributes as a means to chase away popular dissatisfaction and thus extend its survival in office. While the effect of institutions on income distribution has received some attention in recent theoretical and empirical literature, the feedback effect that institutional quality has on redistribution through income inequality and government instability is, to the best of my knowledge, still unexplored. This paper proposes an econometric test of this effect through the estimation of a system of three structural equations, where income inequality, government stability, and the size of redistribution are the dependent variables. In this system, institutional quality figures as an explanatory variable. However, its potential endogeneity with the dependent variables is accounted for through the use of instruments. The system is estimated on a large sample of countries and on various sub-samples, using panel data techniques. To check the robustness of results, system estimates are complemented by single equation estimates. Results strongly support the conjectured

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mechanism: (i) a decrease in the quality of institutions increases income inequality, (ii) greater inequality increases government turnover and therefore reduces the expected survival in office of the incumbent, and (iii) a higher government turnover increases the extent of redistribution. Since redistribution is found to reduce income inequality after controlling for the quality of institutions, (i), (ii), and (iii) together imply that redistribution is an effective response the government can play to limit the adverse political consequences of bad institutions and growing inequalities. The rest of the paper is organized as follows. Section 2 provides the motivation of the paper by reviewing a few stylized facts and some simple correlations between the variables of interest. Section 3 presents the econometric model, the estimation methodology, and the dataset. Section 4 discusses the results, and Section 5 contains the conclusion. A description of the variables, data sources, and the list of countries in the sample can be found in Appendix A. 2. Motivation of the paper The motivation of this paper lays in a few stylized facts and results uncovered by the existing literature. These facts and results are reviewed below. 2.1. Bad institutions increase income inequality First of all, a large body of evidence suggests that bad institutions increase income inequality. Chong and Calderon (2000), Gupta et al. (2002), Dincer and Gunalp (2005), and Chong and Gradstein (2007) use different proxies of institutional quality and different measures of income inequality, but they all come to the conclusion that lower institutional quality makes income distribution less equal. Theoretical rationalizations of this result can be found in Tanzi (1995) and Chong and Gradstein (2007) and build on the logic that higher-income groups better cope with institutional inefficiencies than lower-income groups. Li et al. (2000) argue that the relationship between institutions and inequality is potentially non-linear – very inefficient institutions drive all individuals away from the productive sector into the rent-seeking sector so that revenues are equalized across income groups. However, in their regression of the Gini coefficient on an indicator of corruption and a number of controls, the square term on corruption (which should capture the non-linear effect) is only occasionally statistically significant. In the sample that will be used to estimate the econometric model (see Section 3), the bilateral correlation coefficient between the Gini index and the index of institutional quality of Economic Freedom of the World is −0.21. In a simple OLS regression of the Gini index on a constant and institutional quality, the coefficient of institutional quality is equal to −0.04, significant at the 1% level. Broadly speaking, one can therefore take as a stylized fact that bad institutions increase income inequality. 2.2. Redistribution reduces income inequality Most empirical studies of the determinants of inequality do not include redistribution as a regressor. The few that do, however, generally find a negative and significant effect. Milanovic (1994) argues that social choice variables, which include social transfers and state employment, significantly reduce the Gini index. Bulíř (2001) reports a similar result for the share of cash and in-kind social transfers in GDP, albeit the statistical significance of the estimated coefficient varies, depending on the estimator used. Li et al. (2000) include government consumption, which is a component of broad redistribution (see Section 3), in their regression of the Gini index. The estimated coefficient is again negative, but not always significant. While the evidence from the existing literature is not particularly voluminous, the negative association between redistribution and inequality is very clear in the panel dataset of this paper. Here redistribution is measured by the share of subsidies and transfers, and its simple bilateral correlation with the index of institutional quality is − 0.56. The OLS regression of the Gini index on this measure of redistribution (and a constant) gives a point estimate of − 0.05, significant at 1% confidence level. 2.3. Bad institutions do not necessarily cause government instability The political science literature has recently detected an interesting stylized fact: bad institutions worsen citizens' perceptions and attitudes towards the government and the parliament. Anderson and Tverdova (2003) find that people living in countries where corruption is higher tend to express more negative evaluations of the performance of the political system. Rohrschneider (2005) argues that citizens are more likely to believe that parliaments and governments account for their interests when national administrative and judicial institutions work well. The coverage of these studies is limited to advanced and newly established democracies. However, they hint that institutional deterioration can shorten the expected life of the incumbent. In fact, the empirical association between institutional quality and government duration is extremely ambiguous. On the one hand, institutional theories model government's survival as a function of the specific institutional features in place (i.e. whether or not two chambers share the right to terminate the government, what rules discipline the formation and termination of the government, etc.), without addressing the issue of the quality of institutions (see, for instance, Diermeier et al., 2007). As a consequence, previous empirical work on the duration of governments does not normally control for indicators of institutional quality. On the other hand, casual observation suggests that governments in weak institutional settings are not necessarily shorterlived than governments in countries where institutions are good (Manzetti and Wilson, 2007). As a matter of fact, in the panel dataset, the bilateral correlation between the frequency of government terminations over a five-year period and the average level

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of institutional quality is practically 0 (− 0.02). The estimated coefficient of institutional quality in the OLS regression of government terminations is 0.05, but the associated p-value is well above 0.5. 2.4. Government survival is endogenous to economic outcomes The importance of macroeconomic conditions on voting in elections is well established in both theoretical and empirical literature: poor economic performance determines a fall in popular support for the incumbent (see, inter alia, Eulau and LewisBeck, 1985). If constitutional arrangements allow for early government terminations and/or anticipated elections, then a government's survival in office is, at any point in time, a function of underlying economic conditions. Warwick (1994), Merlo (1998), and Carmignani (2002) provide some empirical evidence that worse economic conditions reduce government duration. In those papers, economic performance is captured by the inflation rate and/or the growth rate of some “real” variable (industrial production, employment, and output). No explicit test of the effect of income inequality on government durability is available in the literature, to the best of my knowledge. Nevertheless, if government duration responds to macroeconomic conditions in general, it seems plausible to conjecture that it might also respond to changes in income distribution. Indeed, in the panel dataset there is a positive, albeit marginally insignificant correlation between the Gini index and the frequency of government terminations. 2.5. Public expenditure is used strategically A large amount of theoretical and empirical work predicts that governments use fiscal policy strategically to gain votes, satisfy supporting constituencies, and restrict the policy options of their potential successors. Alesina and Tabellini (1990) show that, when facing a positive probability of being soon replaced in office, the incumbent will tend to overspend and achieve excessively high debt levels to tie the hands of future governments. Drazen (2000) surveys models of electoral competition where the incumbent strategically uses subsidies and transfers to obtain more votes. Persson and Tabellini (2002) present several cases of special-interest politics leading to overspending and large fiscal deficits. Within this context, one possible strategy for unstable governments is to gather consensus through fiscal expenditure. Annet (2001) and Darby et al. (2004), inter alia, report a positive effect of political instability on government consumption expenditure. While government consumption is not the same as narrow redistribution, the logic underlying the strategic use of fiscal policy is consistent with the idea that the government will redistribute to increase its chances of survival in office. Again, data do not seem to be at odds with this hypothesis. The correlation between the frequency of government terminations and the extent of redistribution in the panel dataset is 0.11; the coefficient of government terminations in the OLS bivariate regression of redistribution is 1.36, with a p-value of 0.019. 2.6. Taking stocks of the facts, theories, and existing empirical results The facts, theories, and empirical results surveyed so far motivate the following story. A decline in the quality of institutions makes income distribution more unequal. Even though government stability might not be directly affected by institutional deterioration, growing inequalities bring about some popular discontent; and this in turn puts the survival of the incumbent at risk. To stay in office, the government can use fiscal policy strategically and eliminate the direct cause that is threatening its survival. Since survival is threatened by growing inequalities and redistribution is effective in reducing inequalities, then the strategic response of the government is to increase redistribution. In a nutshell, bad institutions increase inequality, greater inequality causes greater government instability, and the government responds to increased instability by redistributing income. This story is a conjecture that relies on three structural relations: (i) bad institutions increase income inequality, while redistribution reduces income inequality; (ii) income inequality increases government instability; and (iii) government instability increases redistribution The purpose of the next two sections is to provide a systematic econometric test of these three structural relations. 3. Econometric model, specification, and data 3.1. Model and estimators Let y be a measure of income inequality, s a measure of government instability, r the inverse of redistribution, and x an indicator of institutional quality. Then, the econometric representation of the three structural relations under investigations is: yct = A1 Π ct + α 2 xct + α 3 rct + ect

ð1Þ

sct = C1 Z ct + u2 yct + ϑct

ð2Þ

rct = B1 W ct + β2 sct + υct

ð3Þ

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where Π, W and Z are vectors of control variables; Α, Β, Γ, α, β, and φ are coefficients to be estimated; ε, υ, and ϑ are random disturbances; c denotes a generic country, and t denotes time. The story conjectured in Section 2.6 implies the following hypothesis on the sign of the estimated coefficients: α2 b 0, α3 N 0, φ2 N 0, and β2 b 0 (recalling that r is an inverse measure of redistribution). Eqs. (1)–(3) can be estimated separately as unrelated regressions or jointly as a system. System estimation allows for correlation in the residuals across equations, and hence it is generally more efficient than single equation estimation. However, in case of misspecification of one of the equations, system estimation implies that the poor estimates of the misspecified equation contaminate estimates for the other equations. To be pragmatic, this paper will make use of both approaches. The system estimator is based on the Generalized Method of Moments (GMM), with White's heteroskedasticity consistent covariance matrix (see Wooldridge, 2002). The system-GMM results will be then complemented by standard panel estimates with instrumental variables (see Baltagi, 2000).1 3.2. Specification 3.2.1. Dependent variables The three dependent variables are as follows. Income inequality y is measured by the Gini index (gini). Government instability s is measured by the annual number of government changes occurring over a five-year period (gov_termin).2 The inverse of redistribution r is defined as (Vmax − Vc) / (Vmax − Vmin), where V is the sum of transfers and subsidies over GDP and Vmax and Vmin respectively denote the maximum and minimum of V in the sample in the base year 1990 (redist_inverse).3 3.2.2. Index of institutional quality Institutional quality (inst_quality) is measured by the aggregate index of economic freedom of the Fraser Institute (Gwarteny and Lawson, 2007). The index, which draws on survey data from the Global Competitiveness Report and the International Country Risk Guide, measures the extent to which institutions and policies in a country provide a legal structure and a law-enforcement system that protect property rights, assure fair regulation of credit and labor, allow free exchange with foreigners, and lift restriction on entry into occupations and business activities. Several other indicators of institutional quality are available in the literature. Henisz (2000) constructs an index that identifies institutional quality with the effectiveness and strength of checks and balances in policy formation. Kaufmann et al. (2003) present, for a large cross-section of countries, indicators of government effectiveness, extent of corruption, rule of law, regulatory burden, political instability, and voice and accountability. The main limitation of their dataset is its very short time-series dimension – the series start in 1995 and therefore can hardly be used in panel estimation. Clague et al. (1999) measure institutional quality by contract intensive money. This is empirically defined as the part of M2 that is not currency in circulation outside banks. The bilateral cross-correlations between the various indicators of institutional quality are all positive and very high, albeit lower than one. The index of economic freedom of the world is chosen for the breadth of its coverage and the multidimensionality of its definition. However, to check the robustness of results, two other measures are used. One is a single component of the aggregate index of economic freedom that explicitly focuses on the quality of the legal system. The other is the contract intensive money of Clague et al. (1999). This latter is indeed the indicator that shows the lowest correlation coefficient with the other institutional variables (ranging between 0.5 and 0.6) and therefore might generate somewhat different results. 3.2.3. Control variables The specification of the set of controls for each equation draws on previous relevant empirical work. For the equation with gini as the dependent variable (Eq. (1)), the vector of controls includes: the share of population completing higher education (education) as a proxy for the inequality of distribution of human capital (see Lundberg and Squire, 2003); an indicator of democracy (polity) to account for the impact of democratization on distribution (see Gradstein and Milanovic, 2004); the lagged value of per-capita income (pc_income) to proxy for the impact of economic and financial development on distribution; and the exports plus imports ratio to GDP (tradeopen) to account for the role of globalization and international trade openness (see Milanovic, 2005 and references therein). The choice of controls for the equation that has government instability as the dependent variable (Eq. (2)) is subtler. Most of the literature in this field analyzes the case of advanced parliamentary democracies within the statistical framework of duration analysis and employs a large set of regressors or covariates (see Grofman and Van Roozendaal, 1997; Carmignani, 2002). However, when working with a large cross-section of mostly developing countries and within the framework of systems of equations, parsimony becomes necessary. The rich specifications found in the duration literature are meant to provide a detailed representation of a few structural determinants of government survival: the constitutional features that shape the interaction between the executive, the parliament, and the voters; the economic conditions of the country; the degree of political fragmentation and polarization of the system; critical social events; and some characteristics of the ruling coalition (such as its 1 Estimation results are obtained from the System Estimation and Basic Regression commands in E-Views 5.1 (see Quantitative Micro Software, 2005). The first stage diagnostics reported in Section 4.1 are instead generated from the ivreg2 command in Stata 10 (see Baum et al. 2003, 2007). 2 For the purpose of this paper, a government termination is identified with a change in the head of the executive. The variable gov_termin is constructed from the variable yrsoffc in the Database of Political Institutions (Beck et al. 2001). 3 The index refers to a narrow definition of redistribution. A broad definition would include government consumption in addition to transfers and subsidies. However, given that government consumption is weakly targeted to the poor, indicators of broad redistribution are likely to overestimate the actual size of redistribution. In econometric terms, all results reported in the next section do not qualitatively change if an indicator of inverse broad redistribution replaces the indicator of inverse narrow redistribution. The only difference is that the coefficients of broader redistribution tend to be statistically less strong in general than the coefficients of narrow redistribution.

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ideological orientation and composition). The choice of controls for Eq. (2) is meant to capture these dimensions while preserving a minimum of parsimony. In the baseline specification, constitutional arrangements are represented by the variables system and rule. System separates parliamentary systems (value 1) from presidential systems (value 0) and assembly-elected systems (value 0.5). Rule takes value 1 if the electoral rule in the country is majoritarian. Economic conditions are represented by the average annual growth rate of per-capita GDP (growth). In the sensitivity analysis, the baseline specification will be extended to include the Herfindhal index of parliamentary composition (concentration) as a proxy for political fragmentation. The sensitivity analysis will also look at the effect on instability of a measure of social unrest and at how estimated coefficients change, depending on the ideological orientation of the incumbent. In this way, all crucial structural determinants of government duration should be given appropriate empirical consideration. The specification of the equation that has inverse redistribution as the dependent variable (Eq. (3)) follows the political economy literature on the determinants of fiscal policy outcomes (i.e., Persson and Tabellini, 2003). Then the set of controls is as follows. The share of population aged above 65 (oldshare) reflects the impact of population ageing; polity captures the effect of democracy; tradeopen is entered to test the argument that government redistribution serves as a form of risk-insurance in economies that are more exposed to terms of trade volatility; and lagged pc_income accounts for Wagner's law of spending. In fact, Persson and Tabellini (2003) document the responsiveness of fiscal spending outcomes to constitutional features such as the type of political system (presidential vs. parliamentary) and the type of electoral rule (proportional vs. majoritarian). Their argument is that, together with polity, these features affect the relationship between executive and legislature and hence the scope for collusion in fiscal-policy making. The variables representing constitutional features (system and rule) already appear as controls in Eq. (2). Therefore, they exert their impact on redistribution through their effect on government instability. 3.2.4. Instruments In addition to the three dependent variables, which are jointly endogenous, some of the controls in each equation are potentially correlated with the error terms. This is the case of institutional quality, education, and growth.4 Based on the results of La Porta et al. (1999), institutional quality in Eq. (1) can be instrumented by dummy variables that capture a country's legal origins. The findings of Carmignani (2008) and Henisz (2000) suggest that constitutional arrangements may determine social outcomes through their impact on per-capita income. Since the constitutional variables system and rule are included as regressors only in Eq. (2), then they can be used as instruments for education in Eq. (1). Finally, the findings of Barro (1996) suggest that the indicator of democracy polity could serve as an instrument for growth in Eq. (2). Overall, the list of instruments used for system estimation includes the exogenous variables tradeopen, pc_income, polity, system, rule, oldshare, and the legal origin dummies plus a set of time dummies. The following diagnostic tests are then performed to assess the validity of this choice of instruments. First, a test of overidentifying restrictions (Newey and West, 1987) is run for each set of estimates, and results are reported at the bottom of the tables in the next section. The purpose of this test is to ascertain the independence of instruments from the error process. Second, various indicators of fitness of the first stage regression are computed to examine whether or not instruments are correlated with the endogenous variables. These first stage diagnostics therefore allow assessing the relevance of instruments. 3.3. Data The panel includes 120 countries and covers the period 1970–2000 (see Appendix A for country list, detailed variables description, and data sources). To focus on structural relationships and reduce short-term fluctuations, data are averaged over periods of five years. This is a standard approach in the literature (see Deininger and Squire, 1997; Li et al., 1998; Li et al., 2000; Chong and Gradstein, 2007). Moreover, the five-year period is the frequency over which the institutional indicators are normally available in the database of the Fraser Institute. Even though institutions evolve slowly over time so that annual data would probably make little economic sense, the index of economic freedom does show a significant variability over periods of five years. The Gini index is often missing in more than one year of each of the five-year periods for several countries. In this case, the fiveyear average is computed based on non-missing observations. Again, this is a standard procedure in the literature (see Li et al., 1998, 2000) and should not represent a problem given that Gini series are relatively stable over time. 4. Results 4.1. Base models Estimated coefficients for Eqs. (1)–(3) are reported in Table 1. The first five columns report estimates obtained from the system estimator; the last two columns report single equation panel estimates. The indicator of institutional inequality is the index of economic freedom of the world (efw) in all columns, with the exception of columns 2 and 3. The statistic of the test of overidentifying restrictions is reported at the bottom of the table, together with the associated p-value. As it can be seen, the null

4

The variable pc_income is always entered with a one period lag. Therefore, it is pre-determined and treated as exogenous.

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Table 1 Base model: baseline system and single equation estimates. 2

3

4

5

6

7

Eq. (1): dependent variable is gini Constant 3.008⁎⁎⁎ − 0.104⁎⁎ inst_quality a redist_inverse 0.086⁎⁎⁎ education 2.307⁎⁎⁎ pc_income 0.047 polity − 0.001 tradeopen 0.248⁎⁎

1

2.681⁎⁎⁎ − 0.058⁎⁎⁎ 0.075⁎⁎⁎ 1.724⁎⁎ 0.073 − 0.004 0.098

3.269⁎⁎⁎ − 2.133⁎⁎ 0.111⁎⁎⁎ 1.695⁎⁎ 0.137⁎ 0.007 0.165

3.290⁎⁎⁎ − 0.101⁎⁎ 0.079⁎⁎⁎ 2.463⁎⁎⁎ 0.018 − 0.000 0.223

2.904⁎⁎⁎ − 0.112⁎⁎ 0.089⁎⁎⁎ 2.183⁎⁎ 0.064 − 0.003 0.246⁎⁎

2.907⁎⁎⁎ − 0.118⁎⁎ 0.089⁎⁎⁎ 2.177⁎⁎ 0.067 − 0.003 0.275⁎⁎

2.906⁎⁎⁎ − 0.142⁎⁎ 0.090⁎⁎⁎ 2.237⁎⁎ 0.082 − 0.003 0.305⁎⁎

Eq. (2): dependent variable is gov_termin Constant − 0.409 gini 0.165⁎⁎ system 0.080⁎⁎⁎ rule − 0.051⁎⁎⁎ growth − 3.032⁎⁎⁎

− 0.460 0.178⁎⁎ 0.082⁎⁎⁎ − 0.053⁎⁎⁎ − 2.953⁎⁎⁎

− 0.420 0.167⁎⁎ 0.082⁎⁎⁎ − 0.052⁎⁎⁎ − 3.052⁎⁎⁎

− 0.451 0.177⁎⁎ 0.082⁎⁎⁎ − 0.053⁎⁎ − 3.153⁎⁎⁎

− 0.344 0.150⁎ 0.083⁎⁎⁎ − 0.063⁎⁎⁎ − 3.193⁎⁎⁎

− 0.342 0.149⁎ 0.084⁎⁎⁎ − 0.060⁎⁎⁎ − 3.425⁎⁎⁎

− 0.335 0.148⁎ 0.089⁎⁎⁎ − 0.061⁎⁎⁎ − 4.070⁎⁎

Eq. (3): dependent variable is redist_inverse Constant 15.657⁎⁎⁎ gov_termin − 6.366⁎⁎⁎ pc_income − 0.458⁎⁎⁎ oldshare − 0.336⁎⁎⁎ tradeopen − 3.073⁎⁎⁎ polity 0.081⁎⁎⁎

15.723⁎⁎⁎ − 6.280⁎⁎⁎ − 0.475⁎⁎⁎ − 0.332⁎⁎⁎ − 2.983⁎⁎⁎ 0.082⁎⁎⁎

15.713⁎⁎⁎ − 6.208⁎⁎⁎ − 0.476⁎⁎⁎ − 0.332⁎⁎⁎ − 2.964⁎⁎⁎ 0.081⁎⁎⁎

15.400⁎⁎⁎ − 4.501⁎⁎ − 0.486⁎⁎⁎ − 0.335⁎⁎⁎ − 2.336⁎⁎⁎ 0.063⁎⁎

15.464⁎⁎⁎ − 6.585⁎⁎ − 0.426⁎⁎⁎ − 0.349⁎⁎⁎ − 3.048⁎⁎⁎ 0.090⁎⁎⁎

15.677⁎⁎⁎ − 6.467⁎⁎ − 0.442⁎⁎⁎ − 0.345⁎⁎⁎ − 3.471⁎⁎⁎ 0.095⁎⁎⁎

16.269⁎⁎⁎ − 8.632⁎⁎ − 0.453⁎⁎ − 0.340⁎⁎⁎ − 4.108⁎⁎⁎ 0.113⁎⁎⁎

N. obs Overid restrictions (p-value)

802 27.886 0.17

795 27.887 0.17

806 24.83 0.25

806 26.19 0.21

806 n.a c n.a c.

n.a b. n.a c n.a c.

806 29.786 0.17

Notes: Estimation is by System GMM in columns 1, 2, 3, and 4, three stages least squares in column 5, two-stages least squares 6, panel fixed effect in column 7 (see text for details). Instruments are: the exogenous variables (tradeopen, PC_income, oldshare, polity, system, rule), dummies for legal origins and time dummies. PC_income is always entered with a one period lag. ⁎,⁎⁎,⁎⁎⁎ denote statistical significance at the usual confidence level of 10%, 5%, and 1% respectively. a Institutional quality (inst_quality) measures are as follows: efw in columns 1, 4, 5, 6, and 7, leg in column 2, and cim in column 3. b Number of observations by equation: 199 (Gini equation), 274 (Gov_termin equation), 333 (Redist_inverse equation). c Test of overidentifying restrictions is applied equation-by-equation. p-values are as follows: (i) column 6: 0.25 (Gini equation), 0.28 (Gov_termin equation), 0.33 (Redist_inverse equation); (ii) column 7: 0.18 (Gini equation), 0.21 (Gov_termin equation), and 0.26 (Redist_inverse equation).

hypothesis is always rejected at usual confidence levels. This means that instruments are effectively exogenous. First stage diagnostics are computed from the single equation estimates and hence they are discussed later. The estimates in column 1 support the conjecture that the government will use redistribution to strengthen its survival in office when inefficient institutions make income distribution less equal. The coefficients on inst_quality and redist_inverse in Eq. (1), on gini in Eq. (2), and on gov_termin in Eq. (3) all display the expected sign, and they are all statistically significant. Worse institutions increase income inequality, greater inequality increases government instability, higher government instability increases redistribution, and more redistribution reduces inequality. Turning to the other control variables, the coefficient on education indicates a perverse effect of higher education on income inequality. Further research on this point is necessary in the future. A possible explanation is that, especially in developing countries, an increase in the share of people in higher education corresponds to a decrease in the share of people in middle education, so that overall the polarization of human capital grows. Trade openness also seems to increase inequality, but this result is not robust to changes in the empirical definition of institutional quality (see below). A weaker economic performance makes the government at a greater risk of termination. Constitutional features also affect government instability to a significant extent: parliamentary regimes and/or proportional electoral rules increase government turnover. This is probably due to the fact that parliamentary systems and proportional rules generate more fragmented and fragile ruling coalitions. There is also evidence that population ageing increases redistribution. As the share of older people grows, political power shifts in favor of the retirees. This in turn leads to the reallocation of spending towards transfers and subsidies to support the income of the elderly population.5 The size of redistribution also increases with economic development, suggesting that equality becomes more of a concern as countries grow progressively richer. Finally, more open economies seem to redistribute more. This is consistent with the previous finding that trade openness causes greater inequality and is in line with the seminal argument of Rodrik (1998). Columns 2 and 3 use different empirical definitions of institutional quality. In column 2, institutional quality is measured by the legal component of the efw index (leg). In column 3, contract intensive money (cim) replaces efw. The results concerning the relations linking institutional quality, income inequality, government instability, and redistribution are not significantly affected. The only noteworthy change is the loss of significance of tradeopen in Eq. (1). 5 The income of the retirees (old generation) is generally lower than the income of the workers (new generation). From a political economy perspective, population ageing reallocates political power to the old generation, determining stronger pressures for intergenerational redistribution. Gonzalez-Eiras and Niepelt (2007) formalize this type of intergenerational conflict within the context of an overlapping generations model.

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Table 2 First stage diagnostics: statistics of the fit of the first stage regression. Endogenous regressors

Column 1 Partial R

Eq. (1) inst_quality redist_inverse education

Eq. (2) gini growth

Eq. (3) Govter

2

Column 2 Shea partial R2

0.1831 (0.000) 0.4563 (0.000) 0.2867 (0.000)

0.1563

0.2953 (0.000) 0.1495 (0.000)

0.2739

0.048 (0.042)

0.4291 0.2250

0.1325

0.048

Notes: p-values of the F-test of joint significance of the excluded instruments in the first stage regression are reported in brackets in column I.

Columns 4 and 5 report estimated coefficients obtained from different versions of the system estimator. In column 4, the estimator is still based on GMM, but now the weighting matrix of the criterion function is obtained from a heteroskedasticity and autocorrelation consistent covariance matrix of hortogonality conditions (Wooldridge, 2002). All results are qualitatively unchanged. In column 5, estimated coefficients are derived from a three-stage least squares (3SLS) estimator that uses a different set of orthogonality conditions than the GMM.6 While the coefficient on gini in Eq. (2) becomes insignificant at 5% confidence level, it does remain significant at 10% level. Everything else is qualitatively the same as in the first column. The next two columns present the results from single equation panel estimation. For each equation, the set of instruments includes all of the instruments and exogenous variables used for system estimation. In Column 6 the estimator is a standard twostage least squares (2SLS). Estimated coefficients are numerically very similar to those obtained from the 3SLS and not substantially different from those reported in the rest of the table. Column 7 shows the coefficients obtained from a panel estimator with fixed effects (random effects estimates are available upon request). Again, there are no major changes, and estimated coefficients still support the conjecture outlined in Section 2.6. In addition to serving as a test of the robustness of system estimates, single equation estimates are also useful for conducting diagnostic tests on the relevance of instruments. This can be done by computing measures of the goodness of fit of the first stage regression. However, the standard R2 and F-test of the joint significance of all instruments are not particularly useful in this context: high values of the R2 and F-statistic in the first stage regression do not necessarily imply that instruments are relevant (see Nelson and Startz, 1990). Two more informative statistics are the partial R2 (Bound et al., 1995) and Shea's partial R2 (Shea, 1997), which are computed by partialling-out the “included” instruments (that is, the instruments that are also included as exogenous explanatory variables in the original regression). The interpretation of these two statistics is however limited by two factors: (i) the partial R2 is reliable if there is only one endogenous variable per equation (and this is the case only in Eq. (3)); (ii) Shea's partial R2 is designed to account for multiple endogenous regressors, but its distribution has not been derived, and hence no formal test of significance can be conducted. Baum et al. (2003, 2007), however, propose a helpful rule of thumb: if the partial R2 is high and the Shea's partial R2 is low, then instruments probably lack sufficient relevance. Table 2 reports the partial R2 (column 1) and Shea's partial R2 (column 2) of the first stage regression of the model shown in column 6 of Table 1. The p-value in brackets in column 1 of Table 2 refers to the F-test of the joint significance of the “excluded” instruments (that is, the instruments that are not included as explanatory variables in the original regression). It can be seen from the table that the partial R2 and Shea's partial R2 are indeed very similar. Moreover, the null hypothesis of the F-test of joint significance of the excluded instruments in the first stage regression can always be rejected at usual confidence levels. Taken together these two pieces of evidence suggest that instruments are indeed relevant.7

6 See Wooldridge (2002, pages 197–199) for a discussion. If any variable exogenous in one equation is assumed to be exogenous in all equations, then the two estimators are algebraically the same. In general, the GMM estimator is asymptotically never worse than the 3SLS. However, in the absence of heteroskedasticity, the 3SLS might have better finite sample properties than the GMM. 7 Note that in Eq. (3) there is only one endogenous regressor, govter. The partial R2 and Shea's partial R2 are therefore identical by definition. While they are not very high in absolute terms, the null hypothesis of no significance of the excluded instruments is still rejected at the 5% confidence level. As a further check, the eigenvalue test of Stock and Yogo (2002) was applied to Eq. (3). Results have to be interpreted with caution since the test is based on the assumption that errors are iid . Nevertheless, the null hypothesis that the instruments of govter are weak can be rejected for a 0.2 size (and above) of a 5% Wald test.

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Table 3 Sensitivity analysis and robustness checks of baseline estimates. 2

3

4

5

6

7

8d

Eq. (1): Dependent variable is gini Constant 3.465⁎⁎⁎ − 0.329⁎⁎⁎ inst_quality a redist_inverse 0.090⁎⁎ education − 2.189 pc_income 0.135 polity − 0.000 tradeopen 0.424 Financial_depth

7.665⁎⁎⁎ − 0.098⁎ − 0.000 7.106 − 0.523⁎⁎⁎ − 0.023⁎⁎ 0.512

3.079⁎⁎⁎ − 0.122⁎⁎⁎ 0.085⁎⁎⁎ 2.490⁎⁎⁎ 0.049 − 0.001 0.264⁎⁎

3.035⁎⁎⁎ − 0.084⁎ 0.089⁎⁎⁎ 2.158⁎⁎ 0.035 − 0.000 0.120

3.062⁎⁎⁎ − 0.155⁎⁎⁎ 0.079⁎⁎⁎ 2.958⁎⁎⁎ 0.091⁎⁎ − 0.010⁎ − 0.041

3.42⁎⁎⁎ − 0.136⁎⁎⁎ 0.109⁎⁎⁎ 1.136 − 0.001 0.020⁎⁎⁎ 0.304⁎⁎⁎

3.178⁎⁎⁎ − 0.151⁎⁎⁎ 0.081⁎⁎⁎ 2.830⁎⁎⁎ 0.064 0.001 0.424⁎⁎⁎ − 0.055

2.980⁎⁎⁎ − 0.115⁎⁎ 0.084⁎⁎⁎ 3.283⁎⁎⁎ 0.052 − 0.005 0.315⁎

Eq. (2): Dependent variable is gov_termin Constant − 0.839⁎⁎ gini 0.247⁎⁎ system 0.163⁎⁎⁎ rule 0.016 growth − 2.961⁎⁎ Concentration

2.325⁎⁎ − 0.600⁎⁎ 0.067 0.067 − 2.937

− 0.501⁎ 0.191⁎⁎ 0.082⁎⁎⁎ − 0.045⁎⁎ − 2.622⁎⁎⁎

− 0.282 0.132⁎ 0.075⁎⁎⁎ − 0.061⁎⁎⁎ − 3.243⁎⁎⁎

− 0.505 0.197⁎ 0.101⁎⁎⁎ − 0.133⁎⁎⁎ − 2.126

− 0.856⁎⁎ 0.286⁎⁎⁎ 0.042⁎⁎ − 0.001 − 2.483⁎⁎

− 0.226 0.145⁎⁎ 0.053⁎⁎⁎ − 0.007 − 2.755⁎⁎⁎ − 0.252⁎⁎⁎

− 0.705⁎⁎ 0.238⁎⁎⁎ 0.051⁎⁎ 0.039 − 3.304⁎⁎

15.837⁎⁎⁎ − 5.627⁎⁎⁎ − 0.492⁎⁎⁎ − 0.327⁎⁎⁎ − 3.288⁎⁎⁎ 0.073⁎⁎⁎

16.062⁎⁎⁎ − 6.077⁎⁎ − 0.522⁎⁎⁎ − 0.331⁎⁎⁎ − 2.977⁎⁎⁎ 0.085⁎⁎⁎

14.596⁎⁎⁎ − 6.195⁎⁎⁎ − 0.362⁎⁎ − 0.322⁎⁎⁎ − 3.119⁎⁎⁎ 0.079⁎⁎⁎

17.952⁎⁎⁎ − 5.654⁎⁎⁎ − 0.698⁎⁎ − 0.351⁎⁎⁎ − 2.793⁎⁎⁎ 0.048

16.155⁎⁎⁎ − 5.081⁎⁎⁎ − 0.778⁎⁎⁎ − 0.365⁎⁎⁎ − 3.729⁎⁎⁎ 0.073⁎⁎⁎ 0.476⁎⁎ − 0.061

15.429⁎⁎⁎

1

Eq. (3): Dependent variable is redist_inverse Constant 12.670⁎⁎⁎ 13.138⁎⁎⁎ gov_termin − 7.713⁎⁎ − 2.077⁎⁎⁎ pc_income − 0.051 − 0.112 oldshare − 0.468⁎⁎ − 0.623⁎⁎⁎ tradeopen − 3.828⁎⁎⁎ − 2.545⁎⁎⁎ polity 0.009 0.012 inst_quality gini gov_termin _F N. Obs Overid Restrictions (p-value)

164 28.00 0.14

100 29.44 0.12

− 0.550⁎⁎⁎ − 0.361⁎⁎⁎ − 1.518⁎⁎ 0.067⁎⁎⁎

− 2.674⁎⁎⁎ 749 25.95 0.20

771 26.64 0.18

348 24.51 0.26

240 24.15 0.28

683 43.68 0.15

n.a.b n.ac n.ac

Notes: Sample is as follows: 1 countries with bad polity (polity b− 2), 2 low-income countries (per-capita income b 1500 dollars) 3 small countries (population b 1 million) excluded, 4 large countries (population N150 millions) excluded, 5 countries with left-wing or center-wing governments, 6 countries with right-wing governments, 7–8 all countries. Estimation is by System GMM in columns 1 to 7, equation-by-equation panel in column 8 (see text for details). a The institutional quality measure is always efw. b Number of observations by equation: 122 (Gini equation), 81 (Gov_termin equation) 225 (Redist_inverse equation). c Test of overidentifying restrictions is applied equation-by-equation. p-values are as follows: 0.27 (Gini equation), 0.31 (Gov_termin equation), 0.30 (Redist_inverse equation). ⁎,⁎⁎,⁎⁎⁎ denote statistical significance at the usual confidence level of 10%, 5%, and 1% respectively. d Dependent variable in the Gini equation is post-tax Gini. In the gov_termin equation, Gini is measured gross of taxes. In the Redist_inverse equation, gov_termin_F is the forecasted rate of government termination.

4.2. Sensitivity analysis Various sensitivity checks are reported in Table 3. Estimation is by system-GMM in all columns but the last one.

4.2.1. Democratic vs. non-democratic countries The story conjectured in Section 2.6 implicitly makes the assumption that the government needs to maintain some degree of popular support to stay in office. While it would be difficult to argue the opposite in a democracy, the assumption might not apply to non-democratic countries. In this sense, it is important to check whether the conjectured mechanism holds independently from the level of democracy and/or political development of the country. To address this concern, the model has been re-estimated on the sub-sample of bad polity countries only.8 Results are displayed in column 1 of Table 3. The coefficients of inst_quality and redist_inverse in Eq. (1), gini in Eq. (2) and gov_termin in Eq. (3) all remain significant and with the expected sign, while some minor changes are observed for other control variables. It can therefore be concluded that the conjecture holds for bad polity countries.

8 The indicator polity is indeed a measure of democratization of countries. Based on its distribution in the sample (mean 0.5, median – 1.6, maximum 10, minimum –10, skewness 0.13), the value – 2 of this indicator is identified as the threshold to define bad polity countries. The results reported in column 1 of Table 2 do not change substantially if the threshold for bad polities is set to – 1 or to –3.

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Column 2 provides additional evidence that the conjecture holds not only for developed democracies, but for less advanced countries as well. Per-capita income is taken as a rough proxy for politico-economic development, and column 2 shows the coefficients estimated on the sub-sample of low-income countries. The pattern of coefficients of the variables of interests holds, with the exception of redist_inverse in Eq. (1), which is now non-significant. Thus, weak institutions increase inequality, greater inequality threatens government stability, and the government responds by redistributing more. However, in this sub-sample, redistribution does not seem to be an efficient response since it does not significantly reduce inequality and therefore does not contribute to restoring political support for the incumbent. Certainly, the role of redistribution in poorer countries is an interesting avenue of future research. 4.2.2. Small and large countries The sample used to obtain the estimates of Table 1 includes some very small countries. The existence of fixed costs and economies of scale linked to partial or complete non-rivalry in the supply of public goods might imply a systematic relationship between country size and redistributive expenditure (see Alesina and Wacziarg, 1998). It is therefore necessary to check the robustness of econometric results to the exclusion of extremely small and extremely large countries from the sample. This is done in columns 3 and 4 of Table 3. Results are once again substantially unchanged relative to the base models of Table 1. 4.2.3. Ideological orientation of the incumbent The seminal work of Hibbs (1977) and the subsequent literature on the partisan business cycles highlight the importance of accounting for ideological (partisan) preferences over economic policy. Furthermore, there is evidence that the ideological orientation of the incumbent significantly affects its survival in office (Carmignani, 2002). Taken together, those two observations might imply that the way a government reacts to increasing institutional inefficiency and income inequality will depend on its ideological orientation. In columns 5 and 6, the sample is split according to the ideological orientation of the executive.9 Column 5 shows the coefficients estimated for the group of center-left governments; column 6 shows the estimates for the group of right-wing governments. In general, all results on the variables of interest are confirmed. An interesting aspect is the stronger effect that income inequality has on government stability in right-wing regimes. The estimates suggest that, when income inequalities increase, then a left-wing government is less at risk of termination than a right-wing one. A possible explanation for this finding is that people are confident that a left-wing government will sooner or later take care of growing inequalities. Therefore, they keep on supporting the left-wing incumbent. A right-wing incumbent instead is believed to be less interested in fighting inequality. Thus, when inequality grows, people might have a stronger incentive to vote it out of office. 4.2.4. Model specification Column 7 reports estimates obtained from an extended specification of the three structural equations. Eq. (1) now includes an indicator of financial depth to capture the possible effect of credit market underdevelopment on inequality. Eq. (2) includes an indicator of concentration of the political system (Herfindhal Index of parties in the legislature, concentration) to account for the effect political fragmentation on government stability (Warwick, 1994). Finally, inst_quality and gini are added to the regressors of Eq. (3). In this way, institutional quality and income inequality are allowed to affect redistribution directly.10 Once again the four coefficients of interest on inst_quality, redist_inverse, gini, and gov_termin continue to be statistically significant and with the expected sign. Therefore, the estimates obtained from the extended model specification support the conjecture of Section 2.6. Turning to the newly added variables, financial depth appears to have a statistically negligible effect on income inequality. Instead, a less fragmented political system significantly reduces government instability.11 Finally, there is evidence in Eq. (3) that better institutions directly reduce redistribution after controlling for the indirect effect through income inequality and government stability. The two effects (direct and indirect) go in the same direction; better institutions are associated with less redistribution. On the contrary, the residual effect of income inequality after controlling for government instability and institutional quality is statistically insignificant. A further possible extension of the system specification concerns the effect of institutional quality on government stability. The baseline specification only allows for an indirect effect via income inequality. Even though the stylized facts in Section 2 suggest that governments in worse institutional environment are not necessarily shorter-lived, it could still be argued that institutional quality directly affects government stability. To take this possibility into account, Eq. (2) should include inst_quality among its regressors. One problem in this respect is that gini and efw appears to be collinear, and their coefficients in Eq. (2) are rather imprecisely estimated. The collinearity problem is less strong when cim replaces efw as the measure of institutional quality in Eq. (2). In this case, gini retains its positive and statistically significant coefficient, while the coefficient of cim is marginally 9

Information on ideological orientation of the chief executive is taken from the variable govrlc in the Database of Political Institutions (Beck et al., 2001). It turns out that the original set of instruments needs to be reinforced once the model specification is extended. Drawing on results of La Porta et al. (1999) on the determinants of institutional quality, dummy variables that capture the religious orientation of countries (Orthodox, Protestant, Catholic, Buddhist, Hindu) are added to the original group of instruments. 11 The specification of Eq. (3) was also extended by adding an indicator of social unrest obtained as the first principal component of (i) political assassinations, (ii) general strikes, (iii) guerrilla warfare, and (iv) riots and revolutions. The index is taken from Carmignani et al. (2008). However, the estimated coefficient is not significant. Probably, the index is collinear with growth, which also turns out to be insignificant in the regression with social unrest. To check whether social unrest is an omitted variable in the specification that includes both concentration and growth, a Likelihood Ratio test for omitted variables was run. The null hypothesis that social unrest does not belong to the model specification was never rejected at usual confidence levels. 10

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insignificant. All of the other results are unchanged.12 In conclusion, the indirect effect of institutional quality on government stability via income inequality is still there even when the specification allows institutional quality to affect government termination directly. This clearly strengthens the evidence in support of the conjecture put forward in Section 2.

4.2.5. Expected government instability and pre-tax vs. post-tax inequalities The base models of Table 1 do not separate between pre-tax and post-tax inequality. In fact, the difference might be relevant: pre-tax inequality is likely to determine government instability, but it is post-tax inequality (and not pre-tax inequality) that is determined by institutional quality. Similarly, in measuring government instability, it might be important to distinguish between actual government terminations and the likelihood of government terminations. What pre-tax inequality determines is the actual number of government terminations, while what drives redistribution is the expectation of a termination.13 Whether differences between pre- and post-tax inequality and between actual and expected government terminations really matter in terms of the conjecture under examination is an empirical issue. The estimates in column 8 of Table 3 specifically try to address this issue. The dependent variable of Eq. (1) is now gini net of taxes, while gini in Eq. (2) is gross of taxes. The actual frequency of government terminations is used as the dependent variable in Eq. (2), while expected government termination (gov_termin_F) is entered as regressors in Eq. (3). Following Alesina et al. (1996), this expectation is computed by fitting the government instability equation estimated in column 7 of Table 1 with actual values of the regressors. Because the dependent variable of Eq. (1) is no longer a regressor in Eq. (2), and the dependent variable of Eq. (2) is no longer a regressor in Eq. (3), the model is estimated with the same single equation panel estimator used in column 6 of Table 1. Overall, the conjectured story holds well. The contraction in total number of available observations for Eqs. (1) and (2) is explained with the fact that in some cases it was not possible to establish whether the Gini index was net or gross of taxes. However, all coefficients of interest are precisely estimated, with signs that accord with the conjecture of Section 2. The role of gini in determining actual instability is particularly evident, as well as the fact that expected government terminations increase redistribution.

5. Conclusions Drawing on some stylized facts and existing theoretical and empirical results, this paper elaborates a conjecture on what triggers redistribution. The conjecture is as follows. A decrease in the quality of institutions makes income distribution less equal. In turn, greater income inequality reduces popular support for the incumbent and hence threatens its survival in office. The incumbent then has an incentive to redistribute to remain in office. This conjecture involves three structural relations on the determinants of (i) income inequality, (ii) government instability, and (iii) redistribution. The paper provides an econometric representation of these three structural relations. The estimates strongly support the proposed conjecture. Results are robust to changes in the estimation method, sample definition, and model specification. The contribution of the paper can be seen from both a policy and a research perspective. From a policy perspective, the paper suggests that bad institutions lead to income inequality, but significantly less so if the government has the option to adopt redistributive policies. Two complementary implications then follow. One is that redistribution can explain the persistence of inefficient institutions. The other is that institutional reforms can be undertaken while keeping the level of redistribution unchanged. An interesting question that remains open for future work is to estimate the maximum level of institutional inefficiency that the government can compensate for through redistribution and to establish which factors contribute to determining this maximum inefficiency level. From a research perspective, the paper indicates that a complete characterization of the effects of institutions on inequality and redistribution must take into account the endogeneity of government stability. In particular, this endogeneity implies that institutional inefficiency has both a direct and an indirect effect on inequality. The indirect effect works through the increase in the likelihood of government termination and therefore the increase in redistribution needed to maintain popular support. The existing empirical works surveyed in Section 2 do not account for this indirect effect, which instead this paper's estimates show to be statistically important. The endogenous response of government stability to changing income distribution is therefore a feature that ought to be incorporated into future empirical and theoretical work.

Acknowledgements I would like to thank Emilio Colombo, Ralph Heinrich, Robert Nowak, Patrizio Tirelli, two anonymous referees, and the seminar participants at the United Nations Economic Commission for Africa in Addis Ababa (Ethiopia), the University of Queensland in Brisbane (Australia), and the University of Sidney (Australia) for helpful comments and suggestions. Dorthy Schepps and Elsevier staff provided precious editorial assistance. All remaining errors are solely mine. 12 13

These additional results are available from the author upon request. I thank an anonymous referee for stressing this point.

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Appendix A. List of countries, variables description, and data sources

List of countries included in the econometric analysis. Argentina Armenia Australia Austria Azerbaijan Burundi Belgium Burkina Fasu Bangladesh Bulgaria Bahamas Belarus Bolivia Brazil Barbados Botswana Central African Republic Canada Switzerland Chile China Ivory Coast Cameroon Colombia Costa Rica Czech Republic Germany Zambia Denmark Dominica Algeria Ecuador Egypt Spain Estonia Ethiopia Finland Fiji France Gabon a

UK Georgia Ghana Guinea Gambia Greece Guyana Hong Kong Honduras Hungary Indonesia Israel Italy Jamaica Jordan Japan Kazakhstan Kenya Kyrgyzstan Cambodia South Korea Lao Lebanon Zimbabwe Sri Lanka Lesotho Lithuania Luxembourg Latvia Morocco Moldova Madagascar Mexico Macedonia Mali Mozambique Mauritania Mauritius Malawi Malaysia

Niger Nigeria Nicaragua Netherlands Norway New Zealand Pakistan Panama Peru Philippines Papua New Guinea Poland Portugal Paraguay Romania Russia Rwanda Sudan Senegal Singapore El Salvador Slovakia Slovenia Sweden Seychelles Chad Thailand Trinidad and Tobago Tunisia Turkey Taiwan Tanzania Uganda Ukraine United States Uruguay Venezuela Vietnam Yugoslavia a South Africa

After 1990 Serbia and Montenegro.

Variables description and data sources. Variable

Description and source

Legal

Index of legal structure and security of property rights (judicial independence, impartiality of courts, protection of intellectual property, military interference in rule of law and the political process, integrity of the legal system). It is one of the five components of Efw. Sources: Fraser Institute (Gwarteny and Lawson, 2007). Index of Economic Freedom of the World. Includes the following components: size of government, legal structure and property rights, access to soundmoney,freedomtoexchangewithforeigners,regulationofcredit,labor,andbusiness.Source:FraserInstitute(GwartenyandLawson,2007). Contract intensive money. It is defined as the ratio of M2 minus currency in circulation outside banks to total M2. The original definition is from Clague et al. (1999). Source: computed from raw data in International Financial Statistics. Gini coefficient. Sources: UN-WIDER dataset, Deininger and Squire (1996). Average per-capita GDP in real terms. Sources: Penn World Tables. Average per-capita growth rate of income. Source: same as income. Share of population completing higher education. Source: Barro and Lee (2000). Inverse extent of redistribution. The index is defined as (Vmax − Vc) / (Vmax − Vmin), where V is the sum of transfers and subsidies over GDP and Vmax and Vmin respectively denote the maximum and minimum of V in the sample in a base year (1990). Source: International Financial Statistics, International Government Statistics and Fraser Institute. Index of financial depth. It is computed as the ratio of M2 to GDP. Source: International Financial Statistics. Total international trade (exports plus imports) in percent of GDP. Source: Penn World Tables. Index of quality of democracy. Source Polity Database IV. Variable taking value 0 for presidential systems, 0.5 for assembly-elected systems, and 1 for parliamentary systems. Source: Database of Political Institutions (Beck et al., 2001).

Efw Cim gini pc_income growth education redist_inverse

Financial_depth tradeopen polity system

(continued on next page)

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Appendix A (continued) (continued ) Variable

Description and source

rule oldshare gov_termin

Dummy variable taking value 1 if the electoral rule is non-proportional. Source: Database of Political Institutions (Beck et al., 2001). Share of population aged above 65. Source: World Development Indicators. Average annual number of changes in government and/or regime over the five-year spell. The variable is constructed from the information coded in the variable yrsoffc of the Database of Political Institutions (Beck et al., 2001). Concentration Total Herfindhal Index of parliament: sum of the squared seat shares of all parties in the parliament. Source: Database of Political Institutions (Beck et al., 2001). Social unrest First principal component of (i) political assassinations, (ii) general strikes, (iii) guerrilla warfare, and (iv) riots and revolutions. Source: Carmignani et al. (2008). Catholic Dummy variable taking value 1 if Catholicism is the dominant religion in the country. Source: Gradstein et al. (2001). Buddhist_Hindu Dummy variable taking value 1 if Buddhism or Hinduism is the dominant religion in the country. Source: Gradstein et al. (2001). Protestant Dummy variable taking value 1 if Protestantism is the dominant religion in the country. Source: Gradstein et al. (2001) Orthodox Dummy variable taking value 1 if Orthodoxy is the dominant religion in the country. Source: Gradstein et al. (2001). Legoruk Dummy variable taking value 1 if country has British legal origins. Source: La Porta et al. (1999). Legorsc Dummy variable taking value 1 if country has Scandinavian legal origins. Source: La Porta et al. (1999). Legorfr Dummy variable taking value 1 if country has French legal origins. Source: La Porta et al. (1999).

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