metadata: authors: 0: author: Balcazar Salazar,Carlos Felipe
count: Latin America
docna: 0: docna: Long-run effects of democracy on income
            inequality : evidence from repeated cross-sections
docty: Policy Research Working Paper
subtopic: Labor & Employment Law,Educational Sciences,Poverty Reduction Strategies,Gender and Development,Armed Conflict
teratopic: Private Sector Development
abstracts: cdata!: This paper assesses the link between
            democracy and inequality. Inequality is measured at the
            cohort level with pseudo-panel data built from nine Latin
            American countries' household surveys (1995-2009,
            biannual). Democracy is measured as a stock during long
            periods of time both before and after each cohort's
            year of birth. The paper presents evidence that long-run
            historical patterns in the degree of democracy relate to
            income inequality. However, this relationship is
            non-monotonic: inequality ?rst increases with the stock of
            democracy before falling. The paper also presents evidence
            that education may be a mechanism explaining this result.
repnme: repnme: Long-run effects of democracy on income
            inequality : evidence from repeated cross-sections
display_title: Long-run effects of democracy on
            income inequality : evidence from repeated cross-sections
listing_relative_url: /research/2015/01/23172053/long-run-effects-democracy-income-inequality-evidence-repeated-cross-sections
topicv3: Education m1327048 470,Political Violence and War m1327310 2462,Gender m1327621 615,Income Inequality m1503891 2668,Household Surveys m1504071 2761
publishtoextweb_dt: 2015-01-01T00:00:00Z
historic_topic: Private Sector Development
no_of_pages: 31
author_keys: 0: author_key: 1271568
model_extractions: WPS7153 Policy Research Working Paper 7153 Long-Run Effects of Democracy on Income Inequality Evidence from Repeated Cross-Sections _Carlos Felipe Balcázar_ Poverty Global Practice Group January 2015
Policy Research Working Paper 7153

# **Abstract**

This paper assesses the link between democracy and inequality. Inequality is measured at the cohort level with pseudo-panel data built from nine Latin American countries’ household surveys (1995–2009, biannual). Democracy is measured as a stock during long periods of time both before and after each cohort’s year of birth. The paper presents evidence that long-run historical patterns in the degree of democracy relate to income inequality. However, this relationship is non-monotonic: inequality first increases with the stock of democracy before falling. The paper also presents evidence that education may be a mechanism explaining this result.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbalcazarsalazar@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Long-Run Effects of Democracy on Income Inequality: Evidence from Repeated Cross-Sections

Carlos Felipe Balc ́azar<sup>_∗_</sup> Keywords: Inequality, democracy, Latin America, pseudo panel JEL Classification: C23, D31, N36

> _∗_ I thank Jose Cuesta, Leopoldo Fergusson, Camilo Garcia-Jimeno, Ana Mar ́ıa Ib ́a ̃nez, Hugo  ̃Nopo, Pablo Querub ́ın, Martha Schmitz, Jaime Ram ́ırez and Javier Rojas for their comments and suggestions.
# **1 Introduction**

This paper assesses the relationship between democracy and income inequality. It is reasonable to think that democracies reduce income inequality by allowing citizens to vote for political parties that privilege redistributive platforms. Thus, societies with a more egalitarian distribution of political power may have lower income inequality. Nonetheless, Asian countries, for example, display low income inequality and low-rating democratic institutions while Latin America, a region with higher-rating democratic institutions, displays extreme income inequality (Savoia et al., 2009).

The lack of a clear relationship between democracy and income inequality has two popular explanations. The first one is the political Kuznets curve. This hypothesis states that democratization first produces an increase in inequality before it produces an improvement in the distribution of income. However, Palma (2011) states that inequality does not decline after democratization in countries with historically high levels of inequality. Acemoglu and Robinson (2002) clarify that there are exceptions to the political Kuznets curve, for example, inequality might fluctuate if countries switch between less and more democratic regimes (e.g., Latin America). A second explanation suggests that democracies that begin with high inequality may develop poor economic and political institutions that perpetuate the unequal distribution of income. Indeed, recent research attributes high and persistent inequality to the presence of “bad” economic and political institutions, which are in turn a result of high initial inequality (Savoia et al., 2009).<sup>1</sup> Gerring et al. (2005; 2012) argue that that the best way to think about the relationship between democracy and social outcomes is as a time-dependent, historical phenomenon. Most recent empirical studies assess the link between democracy and inequality using cross-country panel data sets, traditionally: the Deninger and Squire data base; UNU-WIDER’s World Income Inequality data base; and the World Bank’s World Development Indicators.<sup>2</sup> In general they conceptualize this link as the effect of regime type on inequality at time _t_ plus some specified period. However, regimes are historically informed phenomena rather than contemporary variables. Gerring et al. find, for example, that a country’s level of democracy in a given year is not likely to affect its level of human development, but its stock of democracy over a very long-period of time does.

On top of the previous problem “traditional” cross-country studies have other considerable problems: i) Some surveys report data on income and some on expenditure, making cross-country comparisons difficult, ii) The degree of accuracy is still a problem; e.g., some surveys undertaken in the midst of civil wars in sub-Saharan Africa have “national” coverage, but polities were fragmented

> 1See Acemoglu et al. (2013) for further discussion.

> 2For example: Li et al. (1998), Gradstein et al. (2001), Reuveny and Li (2003), Chong (2004), Hamanaka (2008) and Timmons (2010), to name a few.

2
during those events, iii) None of the data sets provide relevant distributional information given that they are composed of country-level aggregates, iv) Democracy is endogenous –it might be a result of many socioeconomic phenomena (omitted variables bias) and may vary between groups within countries (measurement error), v) Although many studies resort to instrumental variables to solve the problem of endogeneity, the variables related to the quality of democratic institutions relate to either a prior or subsequent redistribution of income, violating the exclusion restriction.<sup>3</sup> I assess the link between democracy and inequality using pseudo-panel data built from nine harmonized Latin American countries’ household surveys (1995-2009, biannual), conceiving withincohort inequality as a function of the accumulated level of democracy (or its stock) over longperiods of time around the cohort’s year of birth. My approach has some advantages over crosscountry studies, in that: i) I explore a relatively homogenous region, ii) surveys report the same sources of income, making surveys comparable between countries and over time, iii) I do not rely upon country-level measures of income inequality but upon cohort-level inequality (somewhat accounting for heterogeneity between groups within countries), iv) I measure democracy over long periods of time; thus, I explore the accumulated effect of historical legacies on the contemporary level of inequality.

The biggest empirical challenge to overcome in this study is endogeneity in the proxy for democracy. To address endogeneity I include a set of fixed-effects that capture gender differences, longrun and short-run country-level economic differences, long-lasting effects of colonial institutions, and changes in survey lifting. Therefore, my identification strategy, although it does not completely solve the problem of endogeneity, attenuates it considerably. Furthermore, to reassure the reader about the validity of my results I perform various robustness checks.

A number of messages emerge from this paper. First, cohorts that “experienced” long periods of quality democratic institutions have lower inequality. I find that 20 years of high quality democratic institutions could reduce the dispersion in labor income (on average) by around 17%. Second, I find suggestive evidence that this relationship is non-monotonic: The level of inequality increases with the democracy stock and then starts decreasing with long periods of quality democratic institutions. Third, cohorts that “experienced” long periods of quality democratic institutions show lower dispersion in educational attainment, and higher educational attainment. I find that 20 years of high quality democratic institutions could reduce the dispersion in the years of education (on average) by around 9%, while average educational attainment increases (on average) by 5%. These relationships also appear to be non-monotonic. These results give credence to the hypothesis that

> 3See Atkinson and Brandolini (1999), Timmons (2010), Palma (2011), Niemeyer et al. (2008), Wejnert (2005), Savoia et al. (2009), Bardhan (2005), Nikoloski (2009, 2010), Engermann and Sokoloff (1997, 2000) and Acemoglu et al. (2013) for further discussion on each of these problems.

3
education maps democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality.

The rest of the paper proceeds as follows. The next section briefly presents a review of four mechanisms that link democracy and inequality, their limitations, and presents an alternative approach. Section three describes the data. Section four describes the econometric approach and addresses the methodological challenges. Section five presents the main results and shows suggestive evidence that education may be one mechanism explaining the link between democracy and inequality. The last section concludes.

# **2 Do democracies breed more egalitarian societies?**

Meltzer and Richard (1981), Alesina and Rodrik (1994), and Persson and Tabellini (1994) propose that utility-maximizing individuals redistribute based on rational choices; if the median income lies below the mean income, the median voter chooses redistribution and higher taxation for rich people. This model predicts that democracies have lower levels of inequality than non-democracies (Acemoglu and Robinson, 1998).

A second mechanism alleges that democracy lowers the costs of political participation of organized labor, allowing labor unions to obtain a privileged position in the policy process (Schumpeter, 1942; Rodrik, 1999). As a result, democracy encourages unionization, centralized wage bargaining, and minimum wages, which reduce wage dispersion (Katz and Autor, 1999; Rodrik, 1999).

Democracies should also guarantee broad access to property rights. With well-defined property rights and broad access to them, the poor have the possibility to gain access over improved or produced assets by facilitating the development of efficient market-based economies and opening up markets and institutions, which also prevent the elite from erecting entry barriers and enjoying markets with monopoly power (Gerring et al., 2005; Acemoglu, 2008).

A fourth mechanism suggests that democracy increases competition among politicians for citizen support. This causes governments to invest more in public services, such as education (SaintPaul and Verdier, 1993). Education, in turn, acts as a redistribution channel reducing the dispersion of human capital and increasing a generation’s human capital relative to the previous generation.

I find these four mechanisms the most relevant.<sup>4</sup> However, recent empirical findings reveal caveats in most of these mechanisms. In a well regarded paper, Milanovic (2000), using microlevel data from the Luxembourg Income Studies, finds weak evidence for redistribution through

> 4See Thorbecke and Charumilind (2002) and Savoia et al. (2009) for further discussion.

4
the median voter channel. Indeed, he finds that the middle classes are not net beneficiaries from redistributive transfers. Timmons (2010), using the University of Texas Inequality Project and the United Nations Industrial Development Organization (UTIP-UNIDO) data set, finds no evidence for the existence of the second channel. He shows that although democracies may pay higher average wages in manufacturing, democracy does not dampen wage dispersion between industries. Amendola et al. (2013), using the UNU-WIDER Income Inequality data set, find evidence that democracy is not a sufficient condition to reduce income inequality in the presence of strong property rights. They find that in presence of weak democratic institutions, strong property rights actually lead to an increase in the level of inequality.

Education presents an interesting alternative. The historical record offers many cases where the birth of democracies opened up new possibilities for the scaling up of public education (Sachs, 2012). Engerman et al., (2000), Baum and Lake (2001), Lindert (2004) and Glaeser et al. (2004) suggest that democracy increases the education output, mapping democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality. However, if the quality of education is low for the poor or the social payoff from education diminishes as a consequence of deeply ingrained patterns of social exclusion and discrimination, educational attainment may not reduce inequality (Perry et al., 2003).

A considerable issue that democracy faces to achieve redistribution is inequality itself: If democracies display high initial levels of inequality, it is difficult for the poor to hold the rich and powerful accountable. Such a state of affairs affects the social norms about the legitimacy of rules and institutions. On the one hand, the wealthy minority may be inclined and able to establish a legal framework to ensure themselves a disproportionate share of political power, and use it to establish rules, laws, and government policies to give themselves broader access to economic opportunities. On the other hand, citizens may feel discouraged to press for redistributive changes that may not ultimately benefit them. Hence, property rights, legal systems, and fiscal and economic institutions may perpetuate the unequal distribution of income. (Engerman and Sokoloff, 2000; You and Kagram, 2004; Savoia et al., 2009; Acemoglu and Dell, 2010.) In other words, the effects of democracy on inequality may be endogenous to the initial levels of inequality.

# **2.1 Conceptualizing democracy over long periods of time**

The quality of democratic institutions might have long-lasting impacts on social outcomes. On the one hand, democracies are more likely to increase the allocation of resources to social programs. These social programs might benefit people during their early childhood: Kudmatsu (2011), for example, shows that democratization was associated with an increase in the use of some health 5
inputs on children in Sub-Sharan Africa. Or during their school years: For example, Brown and Hunter (2004), Ansell (2008), Huber and Stephens (2012), and Harding and Stasavage (2014), show that more democratic regimes are more likely to spend more on school education. On the other hand, democratic institutions might also benefit people during their work years: Workers might enjoy more bargaining power as a result of the political power of unions (Rodrik, 1999), or they might have access to job opportunities that were previously available only to the elite.

Institutional effects unfold over time, sometimes a great deal of time, and are cumulative. Think for example of health and education. If our parent’s generation benefited from high public spending on health during their early childhood, they are more likely to have grown healthier and have acquired competitive cognitive and social skills (Heckman, 2011). Add onto that high public spending on education, which may had provided them also with good educational resources to develop their cognitive skills. Now, our generation would have benefited as well from the benefits our parents enjoyed, as it is well known (for example) that more educated parents are more likely to invest more time and money in the education of their children. Furthermore, we may also benefit from high public spending as our parent’s did, or from the capacity that was built previously. It is precise to explore the long-term effects of these legacies; democracy is likely to have intergenerational impacts on social outcomes.

# **3 Data**

The data come from several harmonized household surveys of nine Latin American countries: Argentina, Brazil, Bolivia, Colombia, Honduras, Peru, Panama, Paraguay and Uruguay. To maximize the number of countries and periods I use surveys between 1995 and 2009, biannual, and restrict myself to urban areas in Bolivia due to the lack of rural data for the 1995 and 2009 surveys. Peru and Honduras surveys for 1995 are not harmonizable, but the countries represent sources of variation too important to exclude them; therefore, my pseudo-panel is unbalanced. Table A1, in the appendix, reports the data sources.

I restrict myself to individuals born between 1936 and 1977. However, I drop individuals between 18 and 24 years of age in 1995 and between 21 and 24 years of age in 1997 to consider that by 2009 life expectancy in the region was about 74 years –to keep a representative sample of “old” individuals- and that a person’s educational attainment is likely to remain unchanged after age 25 –to avoid truncation in the distribution of educational attainment.

The source of income for every observation is monthly monetary labor income (constant prices of 2005, US$, adjusted by purchasing power parity), which is the sum of earnings from wages, 6
tips (as applicable), over-time payments, commissions and bonuses. I drop monthly monetary labor income outliers.<sup>5</sup> The previous procedure comprises 2% of the sample with positive labor income.<sup>6</sup> I measure educational attainment as the number of years of education attained in the year previous to the survey. Missing values for the variable of educational attainment comprise an additional 3% of the sample.

# **The pseudo panel**

When using repeated cross-sections ordinary least squares estimators are likely to be biased, however we can use cohort aggregates to obtain consistent estimators (Deaton, 1985). In other words, we can use a pseudo-panel. A pseudo-panel consists of cohorts (synthetic individuals) that we are able to follow over time. However, to obtain consistent estimators from a pseudo-panel, grouping variables –those that aggregate individuals into cohorts- must i) not have missing values for any individual in the sample, ii) not vary over time, and iii) be exogenous and relevant (Verbeek, 2008). It is also important that the number of cohorts is large enough to avoid small sample size problems, that cohort-sizes are large enough to avoid measurement error problems, and that cohorts somehow minimize within-cohort heterogeneity and maximize heterogeneity between cohorts (Verbeek and Nijman, 1993; McKenzie, 2000).

I use gender, country and birth year in three-year spans as grouping variables –which should be exogenous and relevant (Verbeek, 2008). I group individuals in three-year-span birth-cohorts to reduce within cohort heterogeneity and obtain high heterogeneity between cohorts. This also provides me a relatively large number of cohorts to mitigate sample size problems and cohorts that, due to their large sizes, should not present considerable measurement error problems.<sup>7</sup> Table A2, in the Appendix, shows the distribution of individuals and of cohorts.

Income inequality estimates consist of Gini indices at the cohort level using survey weights. However, one potential problem of using labor income to compute inequality is that of self-selection into the labor markets, which leads to self-selection bias. However, if grouping variables are exogenous and relevant, they should act as instruments for self-selection into the labor markets (Moffitt, 1983; Moscarini and Vella, 2003).<sup>8</sup> Therefore self-selection is not a concern. I proxy educational

> 5I detect outliers using the blocked adaptive computationally efficient outlier nominators algorithm (Billor et al.,

> 2000), using the first percentile of the chi-squared distribution as a threshold to separate outliers from non-outliers.

> 6The sample with positive labor income comprises 59% of the individuals born between 1936 and 1977.

> 7I check the robustness of my results using also five-year-span birth-cohorts and seven-year-span birth-cohorts, with the concomitant of smaller samples.

> 8The identification assumption is that selection into labor has to do exclusively with cohort membership, while

7
attainment as the average number of years of education attained and its dispersion as the standard deviation of the number of years of education attained.

# **Measuring democracy**

I measure democracy as _stock_ rather than a level variable. This implies conceptualizing democracy as an asset. It implies that democracy is a fungible resource that may accumulate over time, promising increased returns in the future. Along these lines, if a democratic regime endures it is likely to yield some return, for example: social-welfare-augmenting economic policies, market-augmenting economic policies, political stability, rule of law, and efficient public bureaucracies (Gerring et al., 2005). But, why should we expect better results from a long-term democracy?

Paraphrasing Gerrin et al. (2005): In democratic regimes the policy-making process, by definition, involves a considerable number of players. Policy-making is a continual back and forth between different interest groups (e.g., legislators, citizens, economic organizations, etc.). Each of these players must learn to anticipate the goals, interests, and special sensitivities of the other players. Hence, the process of defining a “good policy” is likely to take considerable time. Not only must governing politicians learn what constitutes good policy; voters must also learn to recognize good policies. In authoritarian regimes, in contrast, this process is generally monopolized by a small number of elite actors and has few mechanisms of accountability. Naturally, the longer these elites are in power, the greater their opportunities for gaining experience in the diverse tasks of governance. But, since the political environment is highly constrained, the development of legal-bureaucratic authority is virtually impossible. Therefore, it is less likely that “good” policies institutionalize.

To measure democracy I make use of the Polity2 democracy index, which I draw from the Polity IV data set. This variable measures the extent to which “authority patterns are institutionalized” in a given country. It takes into account how the executive is selected, the degree of checks on executive power, and the form of political competition (Marshall et al., 2010). This indicator employs a twenty-one-point scale (-10 to 10) from more autocratic to more democratic, and offers good historical coverage (1800 onwards).

To create a stock measurement of democracy I first add up each person’s Polity2 score from 30 years before their year of birth to 20 years after, and between their year of birth and 20 years after. I assume that the stock of democracy depreciates at a rate of _r_ per year, with _r ∈_ [0 _,_ 1]. This captures the possibility that the democratic stock does not accumulate linearly over time. Now, given that for every individual born in any given year ( _t_ ) belonging to any given country ( _p_ ) the value of the cohort-time-varying components play little to no role.

8
stock would be the same, I compute the simple average of the proxy for the three birth years that compose the cohort ( _c_ ) to obtain the cohort average.

To estimate _r_ it is precise to specify how a particular historical path in country _p_ up to year _t_ maps into a value of the stock of democracy _Sp,t_ . The simplest way to do this might be to assume that democratic capital accumulates and is discounted geometrically at the rate (1 _− r_ ): where _Dp,t_ denotes the value of Polity2 for the year _t_ and country _p_ .

We can solve backwards to obtain where _t_ 0 denotes the initial period. Thus, democratic experience is more valuable the closer to the present it is. I use several values of _r ∈_ [0 _._ 01 _,_ 0 _._ 1] for my main exercises.<sup>9</sup> Summarizing, my main variables of interest are the depreciated democracy stock of country _p_ in the birth-year _t_ , averaged for cohort _c_ , defined by: Figure 1 illustrates the distribution of these variables for different levels of _r_ . There are a some particularities worth highlighting: i) Younger cohorts have higher stocks of democracy, ii) The lower _r_ , the lower the variation of the stock of democracy in the entire sample; however, variation between cohorts remains much more stable, iii) The longer the time span, the greater the variation of the stock of democracy.

It is important to note that I define the upper limit to be at most 20 years after the year of birth to consider all the years of individual formation (childhood and teenage years), and to avoid dropping the youngest cohorts from the sample. Defining the lower limit of the span “30 years before the year of birth” allows me to avoid considering the _one thousand days war_ , when Panama was not 9 Person and Tabellini (2009) find that (globally) _r_ lies between 0.01 and 0.06, thus the values of _r_ I use are not unreasonable.

9
yet an independent country,<sup>10</sup> and therefore allows me avoid loosing my oldest cohorts. These time spans also permit me to explore whether democracy has intergenerational effects or not; that is, the variable defined by Equation (a) is statistically significant and it is statistically bigger than that defined by Equation (b) –and as such, historical legacies are important contemporaneously.

Figure 1: Stock of democracy by birth cohort <!-- Start of picture text --> (a) (b)<br>Birth cohort Birth cohort<br>r=0.01 r=0.03 r=0.01 r=0.03<br>r=0.06 r=0.10 r=0.06 r=0.10<br>95% confidence intervals 95% confidence intervals<br>150 150<br>100 100<br>50 50<br>0 0<br>Average stock of democracy<br>Average stock of democracy, 30years before birth to 20 years after −50 birth year to 20 years after birth −50<br>1936−38 1939−41 1942−44 1945−47 1948−50 1951−53 1954−56 1957−59 1960−62 1963−65 1966−68 1969−71 1972−74 1975−77 1936−38 1939−41 1942−44 1945−47 1948−50 1951−53 1954−56 1957−59 1960−62 1963−65 1966−68 1969−71 1972−74 1975−77<br><!-- End of picture text --> Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

# **4 Approach**

Given the panel settings of my data (panel of successive cross-sections), we can model the relationship between democracy and inequality as follows: where _Yc,g,p,e_ denotes the Gini index of birth-cohort _c_ , of gender _g_ , of country _p_ and survey _e_ ; _Dc,p_ is a proxy for the accumulated quality of democratic institutions; and _<u>ε</u> c,g,p,e_ denotes the idiosyncratic error. Given that my interest lies on the historically determined component of institutions, this regression does not (cannot) control for a full set of cohort dummies. Since I have more than one observation per cohort, but my key regressor only varies by cohort, I cluster the standard errors by birth-cohort and country. This equation, however, is misspecified.

> 10The one thousand days war was a civil armed conflict between the liberal and conservative parties in the newly created Republic of Colombia. After the peace treaty Panama separated from Colombia.

10
The relationship between democracy and inequality is likely to be non-monotonic (Figure 2) –it shows the form of an inverted U. Acemoglu and Robinson (2002) suggest that, “economic development tends to increase inequality, but this inequality contains the seeds of its own destruction, because it induces a change in the political regime towards a more redistributive system.” In this setup, where I measure contemporary inequality, this is a plausible mechanism if it acts through an intergenerational channel –a channel that can be influenced by long-periods of democracy, for example education (Saint-Paul and Verdier, 1993). Indeed, the historical evidence suggests that democratization led to a surge in redistribution: The increased supply of educated workers that came to be as a result, and the direct impacts of these redistributive efforts, are key factors in the non-monotonic pattern of inequality (Acemoglu and Robinson, 2002).

Figure 2: Stock of democracy vs. labor income Gini <!-- Start of picture text --> (a) (b)<br>R2=0.18 R2=0.11<br>−300 −250 −200 −150 −100 −50 0 50 100 150 200 −300 −250 −200 −150 −100 −50 0 50 100 150 200<br>Democracy stock between 30 years before birth to 20 years after Democracy stock between birth year to 20 years after birth<br>70 70<br>60 60<br>50 50<br>40 40<br>Standard deviation of years of education Standard deviation of years of education<br>30 30<br><!-- End of picture text --> Note: The figures above assume _r_ = 0 _._ 01, however the U-shape also appears for values higher than 0.01 (figures available upon request to the author). Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

**D** is endogenous. Indeed, some researchers attribute the persistently high level of inequality in Latin America to the long lasting effects of colonial institutions. The encomienda (which gave Spanish conquistadors the right to Amerindian labor), the mita (a system of forced labor used in the mines), the repartimiento (the forced sale of goods to Indians, typically at highly inflated prices), the resguardo (which organized the Amerindian labor to be provided in the _Haciendas_ and mines) and slavery, established important sources of economic and political inequality that endured after the region gained independence from their colonial masters and despite significant social, economic and political changes during the 20th century (Perry et al., 2003). This would likely lead to omitted variable bias, _E_ ( _Dc,p,_ _<u>ε</u> c,g,p,e_ ) = 0.

11
To address the potential misspecification problems I rewrite Equation (1) as follows: where _η_ , _φ_ , _φ_ and _ψ_ denote fixed effects by gender, country, survey, and an interaction between survey and country fixed effects respectively. The gender fixed effects capture the differences in socioeconomic conditions between men and women, for example gender roles and other gender differences which are prevalent in the region ( Nopo, 2012).<sup> ̃</sup> The country fixed effects, survey fixed effects, and their interaction allow to control for long-run and short-run country-level economic changes, such as GDP growth, social public spending, inflation, etc., changes in survey lifting, and other possible long-lasting effects of colonial institutions. Also, note that I multiply both sides of the equation by _wc,g,p,e_ , which is equal to<sup>�</sup> _Nc,g,p,e_ , with _N_ the cohort size. The reason for this is to correct for underlying heteroscedasticity due to variation between periods in cohort sizes. Finally, _D_<sup>~~2~~</sup> _c,p_<sup>captures the non-monotonic relationship between democracy and inequality.</sup> All in all, I exploit the variation coming from differences in the stock of democracy between gender-specific-cohorts, within-country, in a given survey period.

Given that I have a number of time invariant variables I use ordinary least squares (OLS) to estimate my main regression. On this regard, if grouping variables are exogenous and relevant and cohort sizes are large enough (as they indeed are) I can estimate Equation (2) using OLS (Moffitt, 1983; Verbeek and Vella, 2005).<sup>11</sup>

# **5 Results**

Table 1 shows the estimates of democracy on inequality for several specifications of the pseudopanel model. Each duplet of columns corresponds to computing Equation (2) using 0.01, 0.03, 0.06 and 0.10 as values for _r_ respectively. The first column of each duplet shows the results obtained after estimating Equation (2) not including gender, country, survey and survey-country fixed effects; the second column adds the complete set of fixed effects.

In general columns (1), (3), (5) and (7) suggest a non-linear relationship between democracy and inequality ( _δ <_ 0 and statistically significant), but our coefficient of interest ( _β_ ) is not statistically significant. Nonetheless, by controlling for the set of fixed effects I obtain a lower value for _β_ , and statistically significant. Note also that coefficients in panel A are larger than those in panel

> 11Girma (2000) and Collado (1997) provide other methods to estimate pseudo-panel models that are useful in the presence of time-invariant variables. However, these do not offer any gain in terms of consistency, and provide less efficient estimators (Verbeek and Vella, 2005). Thus, I do not consider these approaches.

12
B and that the higher _r_ , the bigger _β_ . These results suggest that the “older” the institutions, the smaller their effect on contemporary outcomes ( _β_ increases with _r_ ), however legacies are still very important –democracy has intergenerational impacts ( _β_ is bigger for a longer time span). These results also provide a sense about the direction of the bias due to non-observable covariates: _β_ is likely to be biased upwards. It is likely that the region’s democratic institutions are failing as a commitment device that prevents ruling minorities from erecting barriers to the excluded, likely due to the effects of long-lasting legacies; e.g., high initial inequality. (I test for the robustness of all these specifications in section 5.2.) Table 1: Estimates of the pseudo-panel model, income inequality

|**A. Assuming**<br>_Dc,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈_</sup><br>Equation:_Yc,g,p,ewc,g,p,e_=|<sup>_c_</sup><br>�<br>∑<sup>_t_+20</sup><br>_s_=_t−_30<sup>(1</sup><br> [_α_+_β_<br>_Dc,p_+|<sup>_−r_)</sup><sup>_t−sDp,s_</sup><br>�<br>_δ_<br>_D_<sup>2</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup><sup>_φp_</sup>|<sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup> <sup>+</sup><br>_ε_|_c,g,p,e_]_wc,g,p,e_|||||
|---|---|---|---|---|---|---|---|---|
||||**Dependent var**|**iable: Income**|**inequality (Gin**|**i, 0-100 scale)**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From 30 years before<br>birth to 20years after it|0.0022<br>(0.005)|-0.0367***<br>(0.009)|0.0025<br>(0.008)|-0.0425***<br>(0.012)|-0.0045<br>(0.012)|-0.0495***<br>(0.014)|-0.0205<br>(0.017)|-0.0591***<br>(0.016)|
|Squared value|-0.0002***<br>(0.000)|-0.0000<br>(0.000)|-0.0003***<br>(0.000)|-0.0001**<br>(0.000)|-0.0005***<br>(0.000)|-0.0004***<br>(0.000)|-0.0008**<br>(0.000)|-0.0010***<br>(0.000)|
|Constant|50.9524***<br>(0.844)|41.3353***<br>(1.665)|50.8682***<br>(0.892)|46.2987***<br>(1.615)|50.8253***<br>(0.933)|47.0358***<br>(1.540)|50.9335***<br>(0.952)|43.5713***<br>(1.522)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.178|0.684|0.163|0.676|0.146|0.671|0.141|0.670|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1850|1850|1850|1850|1850|1850|1850|1850|
|**B. Assuming**<br>_Dc,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈_</sup><br>Equation:_Yc,g,p,ewc,g,p,e_=|<sup>_c_(∑</sup><sup>_t_+20</sup><br>_s_=_t_ <sup>(1</sup><sup>_−r_</sup><br> [_α_+_β_<br>_Dc,p_+|<sup>)</sup><sup>_t−sDp,s_)</sup><br>_δ_<br>_D_<sup>2</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup><sup>_φp_</sup>|<sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup> <sup>+</sup><br>_ε_|_c,g,p,e_]_wc,g,p,e_|||||
||||**Dependent va**|**riable: Income**|**inequality (Gi**|**ni, 0-100 scale)**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From the year of birth to|-0.0069|-0.0221*|-0.0103|-0.0288**|-0.0170|-0.0393***|-0.0281*|-0.0526***|
|20years after it|(0.009)|(0.011)|(0.011)|(0.012)|(0.013)|(0.014)|(0.017)|(0.015)|
|Squared value|-0.0002**<br>(0.000)|-0.0002***<br>(0.000)|-0.0003**<br>(0.000)|-0.0003***<br>(0.000)|-0.0004**<br>(0.000)|-0.0006***<br>(0.000)|-0.0008*<br>(0.000)|-0.0011***<br>(0.000)|
|Constant|50.5659***<br>(1.002)|48.0631***<br>(1.437)|50.6058***<br>(0.995)|47.9851***<br>(1.462)|50.7030***<br>(0.989)|47.9669***<br>(1.483)|50.8865***<br>(0.991)|43.9699***<br>(1.588)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.112|0.625|0.117|0.633|0.124|0.644|0.134|0.655|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1850|1850|1850|1850|1850|1850|1850|1850|

_Note_ : Robust standard errors clustered by country and birth-cohort in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Specifications (1), (3), (5) and (7) do not include fixed effects. Specifications (2), (4), (6) and (8) include the following fixed effects: gender ( _η_ ), country ( _φ_ ), survey ( _φ_ ), and an interaction between survey and country fixed effects ( _ψ_ ). Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

Now I focus on columns (2), (4), (6) and (8). Within the parameters of these specifications, a 13
cohort with no existing stock of democratic capital experiences the following democratic inequality effect: for each full decade of high-quality democracy (Polity2 = 10), the democracy stock increases by approximately 100. To estimate the predicted effect of this change on inequality, I simply multiply this change by the coefficient on democracy stock, for example (in panel A) -0.0367 –column (2)- or -0.0591 –column (8). Focusing on panel A, the predicted impact on inequality of a decade of high-quality democracy is then approximately between -3.7 and -5.9. On panel B, in contrast, is approximately between -2.2 and -5.2. Considering that the average labor income Gini is around 48, this means that by having 20 years of high quality democratic institutions we could reduce (on average) labor income inequality by 17% –a sizeable improvement considering the high and persistent level of inequality in the region (Perry et al., 2003; Lopez-Calva and Lustig, 2010).

# **5.1 The role of education**

It is plausible to regard democracy as an important institutional factor in the development of education. Political elites in a democracy have electoral incentives to improve the quality of life for the least advantaged; these incentives are present to a much smaller degree in authoritarian systems. Indeed, studies have shown that democratic rule translates into improvements on education (Baum and Lake, 2003; Brown and Hunter, 2004; Ansell, 2008; Huber and Stephens, 2012; Harding and Stasavage, 2014).

Because of the long-lived nature of human capital, the longer a democracy is in place, the more pronounced we can expect its impact on human capital to be (Gerring et al., 2012). Democracy might change the education output, mapping democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality. Education, therefore, might be a suitable mechanism to explain the link between democracy and income inequality. This is indeed plausible, the level and the dispersion of educational attainment and income inequality are highly correlated (Figure 3).

The pseudo-panel models for the level and dispersion of educational attainment follow that of Equation (2). Although for these equations **D** is also endogenous,<sup>12</sup> the set of fixed effects should deal with endogeneity. It is also important to note the number of years of education has serial correlation. Although I restrict myself to individuals ages 24 years and older to avoid truncation in the distribution of educational attainment, individuals might still attain more education with time

> 12Governments in Latin America, particularly democratic governments, conducted substantial efforts to expand primary education during the 20th century, however the elite was able to procure schooling services for their own children and resist subsidizing services for others (Bethell, 1997; Perry et al., 2003; Schiefelbein, 2007). Indeed, governments did not provide adequate access to education to most people until the second half of the 20th century. Furthermore, the differences in the quality of education between the wealthy and the poor of the population prevailed. This would likely lead to omitted variables bias.

14
(e.g., graduate studies). Therefore, I also introduce a lag of the dependent variable as a regressor to control for serial correlation when analyzing the level of educational attainment.<sup>13</sup> Figure 3: Educational attainment vs. labor income Gini <!-- Start of picture text --> (a) (b)<br>R2=0.30 R2=0.34<br>2 3 4 5 6 7 0 5 10 15<br>Standard deviation of years of education Average years of education<br>70 70<br>60 60<br>50 50<br>40 40<br>Labor income Gini (0−100 scale) Labor income Gini (0−100 scale)<br>30 30<br><!-- End of picture text --> Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

Similar to Table 1, in Table 2 each duplet of columns corresponds to computing Equation (2) using 0.01, 0.03, 0.06 and 0.10 as values for _r_ respectively. Our results are consistent with our prior: democracy is related to the level of educational attainment. (Note that _β_ is biased downwards.) Furthermore, in columns (2), (4), (6) and (8), democracy and educational attainment show a slight non-monotonic relationship.

Now, let us consider the results of columns (2), (4), (6) and (8). A cohort with no existing stock of democratic capital experiences the following democratic educational effect: for each full decade of high-quality democracy (Polity2 = 10), democracy stock increases by approximately 100 points. Considering panels A and B, I find that the predicted impact on educational attainment of a decade of high-quality democracy is approximately between 0.09 and 0.19 years of education. Considering that the average educational attainment is 5.6 years, having 20 years of high quality democratic institutions would increase educational attainment (on average) by 5%.

> 13Verbeek and Vella (2005) show that if grouping variables are exogenous and relevant and cohorts sizes are large enough, it is possible to obtain and appropriate measure of _Y c,g,p,e−_ 1, equivalent to the lagged value of _Y c,g,p,e_ .

15
Table 2: Estimates of the pseudo-panel model, level of educational attainment

|**A. Assuming**<br>_Dc,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈_</sup>|<sup>_c_</sup><br>�<br>∑<sup>_t_+20</sup><br>_s_=_t−_30<sup>(1</sup>|<sup>_−r_)</sup><sup>_t−sDp,s_</sup><br>�|||||||
|---|---|---|---|---|---|---|---|---|
|Equation:_Yc,g,p,ewc,g,p,e_=|[_α_+_θYc,g,p,e−_|1+_β_<br>_Dc,p_+_δ_<br>|_D_<sup>~~2~~</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup><sup>_φp_</sup>|<sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup> <sup>+</sup><br>|_εc,g,p,e_]_wc,g,p,e_||||
||||**Dependent**|**variable: Ave**|**rage years of e**|**ducation**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From 30 years before<br>birth to 20years after it|-0.0008***<br>(0.000)|0.0017***<br>(0.001)|-0.0011***<br>(0.000)|0.0018***<br>(0.001)|-0.0015***<br>(0.000)|0.0019***<br>(0.001)|-0.0016***<br>(0.000)|0.0019**<br>(0.001)|
|Squared value|0.0000<br>(0.000)|0.0000***<br>(0.000)|0.0000<br>(0.000)|0.0000***<br>(0.000)|0.0000<br>(0.000)|0.0000***<br>(0.000)|-0.0000<br>(0.000)|0.0001***<br>(0.000)|
|Constant|0.1418<br>(0.122)|2.1783***<br>(0.316)|0.1474<br>(0.119)|2.0949***<br>(0.312)|0.1365<br>(0.118)|1.9990***<br>(0.314)|0.1273<br>(0.123)|1.8085***<br>(0.314)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.918|0.955|0.918|0.955|0.918|0.954|0.918|0.954|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1598|1598|1598|1598|1598|1598|1598|1598|
|**B. Assuming**<br>_Dc,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈_</sup>|<sup>_c_</sup><br>�<br>∑<sup>_t_+20</sup><br>_s_=_t_ <sup>(1</sup><sup>_−r_</sup>|<sup>)</sup><sup>_t−sDp,s_</sup><br>�|||||||
|Equation:_Yc,g,p,ewc,g,p,e_=|[_α_+_θYc,g,p,e−_|1+_β_<br>_Dc,p_+_δ_<br>|_D_<sup>~~2~~</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup><sup>_φp_</sup>|<sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup> <sup>+</sup><br>|_εc,g,p,e_]_wc,g,p,e_||||
||||**Dependen**|**t variable: Av**|**erage years of**|**education**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From the year of birth to|-0.0010***|0.0009**|-0.0012***|0.0011**|-0.0013***|0.0013**|-0.0015***|0.0014**|
|20years after it|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|
|Squared value|0.0000|0.0000***|0.0000|0.0000***|-0.0000|0.0001***|-0.0000|0.0001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|0.1327|0.4144|0.1291|0.4379|0.1258|0.4314|0.1260|1.5764***|
||(0.122)|(0.283)|(0.124)|(0.300)|(0.126)|(0.320)|(0.130)|(0.294)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.918|0.953|0.918|0.953|0.918|0.953|0.917|0.953|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1598|1598|1598|1598|1598|1598|1598|1598|

_Note_ : Robust standard errors clustered by country and birth-cohort in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Specifications (1), (3), (5) and (7) do not include fixed effects. Specifications (2), (4), (6) and (8) include the following fixed effects: gender ( _η_ ), country ( _φ_ ), survey ( _φ_ ), and an interaction between survey and country fixed effects ( _ψ_ ). Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

Let us consider now dispersion in educational attainment. Focusing on coulmns (2), (4), (6) and (8), and both panel A and panel B, I find that the predicted impact on educational attainment of a decade of high-quality democracy is approximately between 0.02 and 0.4 years of education. Considering that the average dispersion is almost 4.7 years, this means that by having 20 years of high quality democratic institutions we could reduce the dispersion in the years of education (on 16
average) by 9%.

Table 3: Estimates of the pseudo-panel model, dispersion in educational attainment

|**A. Equation: Assuming**<br>_D_|_c,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈c_</sup>|�<br>∑<sup>_t_+20</sup><br>_s_=_t−_30<sup>(1</sup><sup>_−r_</sup>|<sup>)</sup><sup>_t−sDp,s_</sup><br>�||||||
|---|---|---|---|---|---|---|---|---|
|Equation:_Yc,g,p,ewc,g,p,e_=|[_α_+_β_<br>_Dc,p_|+_δ_<br>_D_<sup>2</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup>|<sup>_φp_</sup> <sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup>|<sup>+</sup><br>_εc,g,p,e_]_wc,g,_|_p,e_||||
||**De**|**pendent variab**|**le: Standard**|**deviation of y**|**ears of educat**|**ion**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From 30 years before|-0.0010**|-0.0014*|-0.0014**|-0.0019**|-0.0022***|-0.0029***|-0.0034***|-0.0043***|
|birth to 20years after it|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Squared value|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|0.0000<br>(0.000)|
|Constant|4.3260***<br>(0.071)|4.1864***<br>(0.149)|4.3202***<br>(0.069)|4.1644***<br>(0.153)|4.3120***<br>(0.067)|4.1361***<br>(0.157)|4.3026***<br>(0.066)|4.3012***<br>(0.120)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.039|0.240|0.051|0.255|0.071|0.278|0.097|0.304|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1850|1850|1850|1850|1850|1850|1850|1850|
|**B. Assuming**<br>_Dc,p_= <sup>1</sup><br>3 <sup>∑</sup><sup>_t∈_</sup>|<sup>_c_</sup><br>�<br>∑<sup>_t_+20</sup><br>_s_=_t_ <sup>(1</sup>|<sup>_−r_)</sup><sup>_t−sDp,s_</sup><br>�|||||||
|Equation:_Yc,g,p,ewc,g,p,e_=|[_α_+_β_<br>_Dc,p_|+_δ_<br>_D_<sup>2</sup><br>_c,p_ <sup>+</sup><sup>_ηg_</sup> <sup>+</sup>|<sup>_φp_</sup> <sup>+</sup><sup>_φe_</sup> <sup>+</sup><sup>_ψp,e_</sup>|<sup>+</sup><br>_εc,g,p,e_]_wc,g,_|_p,e_||||
||**D**|**ependent varia**|**ble: Standar**|**d deviation of**|**years of educa**|**tion**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Stock of democracy**|||||||||
|From the year of birth to|0.0001|-0.0002*|0.0001|-0.0003**|0.0001|-0.0004**|-0.0000|-0.0005***|
|20years after it|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Squared value|-0.0000*<br>(0.000)|-0.0000***<br>(0.000)|-0.0000*<br>(0.000)|-0.0000***<br>(0.000)|-0.0000<br>(0.000)|-0.0000***<br>(0.000)|-0.0000<br>(0.000)|-0.0000***<br>(0.000)|
|Constant|0.2991***<br>(0.014)|0.2049***<br>(0.010)|0.2986***<br>(0.014)|0.2052***<br>(0.010)|0.2980***<br>(0.014)|0.2064***<br>(0.011)|0.2974***<br>(0.015)|0.2290***<br>(0.014)|
|**Full set of fxed effects**|No|Yes|No|Yes|No|Yes|No|Yes|
|**_R2_**|0.050|0.616|0.043|0.624|0.036|0.632|0.031|0.632|
|**_Clusters_**|126|126|126|126|126|126|126|126|
|**Observations**|1850|1850|1850|1850|1850|1850|1850|1850|

_Note_ : Robust standard errors clustered by country and birth-cohort in parentheses. * Significant at ten percent; ** significant at five percent; *** significant at one percent. Specifications (1), (3), (5) and (7) do not include fixed effects. Specifications (2), (4), (6) and (8) include the following fixed effects: gender ( _η_ ), country ( _φ_ ), survey ( _φ_ ), and an interaction between survey and country fixed effects ( _ψ_ ). Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

# **5.2 Robustness**

In this section I verify if my results survive statistical scrutiny. As a first robustness check I test if my results hold when using five-year-span birth-cohorts and seven-year-span birth-cohorts, with the concomitant of smaller samples. I include the results of these regressions in an online appendix in 17
order to save space.<sup>14</sup> I find that the signs and statistical significance of my coefficients of interest ( _β_ and _δ_ ) hold for these specifications. Although the coefficients are larger, we have to keep in mind that the related pseudo-panel samples are smaller; hence, they have to be considered with a grain of salt.

I also check whether my findings hold after controlling for periods of wars and economics crises. Both wars and economic crises can lead to regime transitions and affect socioeconomic outcomes. They can destroy or deplete capital assets, reducing top capital incomes (Piketty and Saez, 2003). They can also raise the income of the poor, for example, by employing them in the arms industry (as in the case of the United States during the Second World War) or reduce it, by leaving poor people jobless.

These phenomena can also have effects on human capital. For example, UNESCO (2010) reported that conflict often results in smaller shares of the population with formal schooling, fewer average years of education, and decreased literacy rates, which persist over time and might affect particularly marginalized groups. Economic crises might also affect human capital. In Latin America, for example, the economic crisis and structural adjustment during the 1980s economic crisis led to declines in family income, which may have resulted in growing inequality of educational opportunity (Fernandez and Lopez-Calva, 2010; Torche, 2010).

Wars and economic crises can also hasten regime transitions by destabilizing political regimes. In Latin America, in many opportunities regime changes were result of a war that replaced the incumbent head of government with another. In Brazil, for example, after the end of World War II, Get ́ulio Vargas regime became unsustainable because during the period of the war the threat of a German attack did not materialize, leading him to be swiftly overthrown in a military coup that “restated” democracy. In the Argentine _Revoluci ́on Libertadora_ (1955), the Peron regime was deposed for a military dictatorship in a coup d’ ́etat by military forces.

Economic crisis also preceded regime transitions. The economic depression of the 1930s weakened the legitimacy of emerging democratic institutions, which led to social and political unrest between liberal movements and oligarchs and eventually to a military dictatorship in most countries backed up by the right-wing (which sought to protect the _status quo_ ). The economic crisis of the 1980s brought important economic problems for dictatorships to deal with: high unemployment, inflation and growth stagnation, which had deligitimazing effects. Eventually the economic crisis of the 1980s led non-democratic regimes to an end and to the birth of representative democracies in the region. (Bethell, 1997; Hagopian and Mainwaring, 2005.) The effects of wars and economic crises can be far reaching, and although we can consider

> 14See: https://sites.google.com/site/cfbalcazars/misc.

18
them as shocks, they might have long-lasting consequences. Given that these two phenomena can precede regime transitions, I test for the stability of my proxy of democracy by including proxies that account for these events in my main regressions. However, given their transitory nature it would not be wise to measure wars and economic crisis as stock variables. Instead, I compute several proxies as level variables at different periods around a person’s year of birth. For both wars and economic crisis I compute the percentage of years of war or crisis that an individual born in year _t_ experienced between _t −_ 30 and _t −_ 1; _t −_ 20 and _t −_ 1; _t −_ 10 and _t −_ 1; _t_ and _t_ + 10; _t_ and _t_ + 20 ; _t −_ 30 and _t_ + 20 ; _t −_ 20 and _t_ + 20, and _t −_ 10 and _t_ + 10. These comprise a comprehensive set of proxies. Table A3, in the appendix, presents the periods of wars and economic crisis that took place in the countries of interest.

Given that for every individual born in any given year ( _t_ ) belonging to any given country ( _p_ ), and for a given time interval, the percentage of years of war or crisis would be the same, I compute the simple average of the proxy for the three birth years that compose the cohort to obtain the cohort average. That is where _Ht,c,p_ denotes the percentage of years of war or economic crisis for the birth year _t_ belonging to cohort _c_ and country _p_ , and for a given time interval.

I run 648 different regressions, which come from including each proxy in my main regressions and first set of robustness tests with replacement. These regressions can be also found in the online appendix. Overall, my main results hold for the relationship between inequality in labor income and educational attainment and democracy; that is, _β <_ 0 and statistically significant. Furthermore, I also find _β >_ 0 in most specifications when analyzing the relationship between democracy and the level of educational attainment.

# **6 Conclusions**

Despite many research documents assessing the link between democracy and inequality, empirical research is still not conclusive on whether and how democracy and inequality relate. This relationship is a complex and multidimensional process that involves many socioeconomic phenomena. Empirical research usually resorts to cross-country panel data sets, but these impose several measurement error and endogeneity problems that are hard to solve. This paper tries to overcome many of these difficulties using pseudo-panel data built from several household surveys of nine Latin American countries, exploiting panel data techniques to control for endogeneity due to unobserved factors, and proposing a renewed approach which seeks to determine if long-run patterns in the 19
degree of democracy relate to contemporary income inequality.

My findings show that democracy appears to be non-monotonically related to income inequality, and that it also appears to be non-monotonically related to the level of and dispersion in educational attainment. In particular, long periods of high-quality democratic institutions can lead to substantial decreases in inequality on income and education. My interpretation of these results is: It seems plausible to regard democracy as an important institutional factor in the development of education. Political elites in a democracy have electoral incentives to improve the quality of life for the least advantaged; incentives that are present to a much smaller degree in authoritarian systems. By changing the education output, political regimes map democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality.

One of the concrete implications of these results is that if democratic institutions contribute to secular-historical changes in policy outcomes, then it is inappropriate to judge the results of institutional reforms on the basis of immediate policy gains. The immediate effects of institutional change are often negative since such change introduces uncertainties and information costs in the short run. Positive changes are likely to take longer to materialize, since they depend upon the establishment of a new equilibrium. (Gerring et al., 2005.) It is unrealistic to expect such reforms to show instantaneous results.

Many important points remain to be considered, I focus on a few important ones. First, what would happen if we consider younger cohorts by having access to recent household survey data –those born after the third democratic wave (Mainwaring and Hagopian, 2005). These cohorts should show larger democracy stocks. Using such data would allow us to further test the validity of the results herein. Second, I did not spend much time analyzing the non-monotonic relationship between the stock of democracy and inequality from the theoretical perspective. It is precise to rethink Acemoglu and Robinson’s theory of the political Kuznets curve from the inter-generational perspective, introducing the role of political change (Person and Tabellini, 2009). However, this is a task that I defer to future work.

# **References**

> Acemoglu, D. (2008). Oligarchic vs. democratic societies. _Journal of the European Economic Association_ , 6(1):1–44.

20
- Acemoglu, D. and Dell, M. (2010). Productivity differences between and within countries. _American Economic Journal: Macroeconomics_ , 2(1):169–88.

- Acemoglu, D., Naidu, S., Restrepo, P., and Robinson, J. (2013). Democracy, redistribution and inequality. National Bureau of Economic Research, Working Paper 19746.

- Acemoglu, D. and Robinson, J. (1998). Why did the west extend the franchise? democracy, inequality, and growth in historical perspective. _Quarterly Journal of Economics_ , 115(4):1167– 69.

- Acemoglu, D. and Robinson, J. (2002). The political economy of the kuznets curve. _Review of Development Economics_ , 6(2):183–203.

- Alesina, A. and Rodrik, D. Distributive politics and economic growth. _The Quarterly Journal of Economics_ , 109(2):465–490.

- Amendola, A., Easaw, J., and Savoia, A. (2013). Inequality in developing economies: the role of institutional development. _Public Choice_ , 155(1):43–60.

- Ansell, B. (2008). Traders, teachers, and tyrants: Democracy, globalization, and public investment in education. _International Organization_ , 62(2):289–322.

- Atkinson, A. and Brandolini, A. (2001). Promise and pitfalls in the use of ’secondary’ data-sets: Income inequality in oecd countries as a case study. _Journal of Economic Literature_ , 39(3):771– 799.

- Bardhan, P. (2005). _Scarcity, conflict and cooperation: Essays in political and institutional economics of development_ , chapter History, Institutions, and Underdevelopment, pages 1–27. Massachusetts: MIT Press.

- Baum, M. and Lake, D. (2001). The invisible hand of democracy: Political control and the provision of public services. _Comparative Political Studies_ , 34(6):587–621.

- Baum, M. and Lake, D. (2003). The political economy of growth: Democracy an human capital. _American Journal of Political Science_ , 47:333–334.

- Bethell, L. (1997). _Historia de Am ́erica Latina: Pol ́ıtica y Sociedad desde 1930_ . Barcelona: Cambridge University Press.

- Billor, N., Hadi, A., and Velleman, P. (2000). Bacon: Blocked adaptive computationally efficient outlier nominators. _Computational Statistics and Data Analysis_ , 34(3):279–298.

- Brown, D. and Hunter, W. (2004). Democracy and human capital formation education spending in latin america, 1980 to 1997. _Comparative Political Studies_ , 37(7):842–864.

- Chong, A. (2004). Inequality, democracy, and persistence: Is there a political kuznets curve?

21
_Economics and Politics_ , 16(2):189–212.

- Collado, M. D. (1997). Estimating dynamic models from time series of independent cross-sections. _Journal of Econometrics_ , 82(1):37–62.

- Deaton, A. (1985). Panel data from time series of cross-sections. _Journal of Econometrics_ , 30:109– 126.

- Engerman, S., Mariscal, E., and Sokoloff, K. (2000). _Political Institutions and Economic Growth in Latin America_ , chapter Schooling, Suffrage, and the Persistence of Inequality in the Americas, 1800-1945, pages 159–217. Stanford: Hoover Institution Press.

- Engerman, S. and Sokoloff, K. (1997). _How Latin America Fell Behind_ , chapter Factor endowments, institutions, and differential paths of growth among new world economies: a view from economic historians of the United States. Stanford: Stanford University Press.

- Engerman, S. and Sokoloff, K. (2000). History lessons: Institutions, factor endowments, and paths of development in the new world. _Journal of Economic Perspectives_ , 14(3):217–232.

- Fernandez, A. and Lopez-Calva, L. (2010). Transitory shocks, permanent effects: impact of the economic crisis on the well-being of households in latin america and the caribbean. _Estudios Economicos_ , 1(49):3–35.

- Gerring, J., Bond, P., Barndt, W., and Moreno, C. (2005). Democracy and economic growth: A historical perspective. _World Politics_ , 57:323–336.

- Girma, S. (2000). A quasi-differencing approach to dynamic modelling from a time series of independent cross-sections. _Journal of Econometrics_ , 98(2):365–383.

- Glaeser, E., Porta, R. L., de Silanes, F. L., and Schleifer, A. (2004). Do institutions cause growth? _Journal of Economic Growth_ , 9(3):271–303.

- Gradstein, M., Milanovic, B., and Ying, Y. (2001). Democracy and income inequality: an empirical analysis. CESifo Working Paper No. 411.

- Harding, R. and Stasavage, D. (2014). hat democracy does (and doesn’t do) for basic services: School fees, school inputs, and african elections. _The Journal of Politics_ , 76(1):229–245.

- Heckman, J. (2007). The economics, technology and neuroscience of human capability formation. National Bureau of Economic Research Working Papers 13195.

- Huber, E. and Stephen, J. (2012). _Democracy and the left: social policy and inequality in Latin America_ . University of Chicago Press.

- Katz, L. and Autor, D. (1999). _Handbook of Labor Economics_ , volume 3, chapter Changes in the wage structure and earnings inequality, pages 1463–1555. San Diego, CA: North-Holland.

22
- Li, H., Squire, L., and fu Zou, H. (1998). Explaining international and intertemporal variations in income inequality. _The Economic Journal_ , 108:26–43.

- Lindert, P. (2004). _Growing Public: Social Spending and Economic Growth since the Eighteenth Century_ . Cambridge: Cambridge University Press.

- Lopez-Calva, L. and Lustig, N., editors (2010). _Declining inequality in Latin America: a decade of progress?_ Brookings Institution Press.

- Mainwaring, S. and Hagopian, F., editors (2005). _The Third Wave of Democratization in Latin America: Advances and Setbacks_ . Cambridge: Cambridge University Press.

- Marshall, M., Jaggers, K., and Gurr, T. (2010). Political regime characteristics and transitions, 1800-2010: Dataset users’ manual. Technical report, Center for Systemic Peace.

- McKenzie, D. (2004). Asymptotic theory for heterogeneous dynamic pseudopanels. _Journal of Econometrics_ , 120(2):235–262.

- Meltzer, A. and Richard, S. (1981). A rational theory of the size of government. _Journal of Political Economy_ , 89(5):914–927.

- Milanovic, B. (2000). The median-voter hypothesis, income inequality, and income redistribution: an empirical test with the required data. _European Journal of Political Economy_ , 16(3):367–410.

- Moffit, R. (1993). Identification and estimation of dynamic models with a time series of repeated cross-sections. _Journal of Econometrics_ , 59:99–123.

- Nikoloski, Z. (2009). Economic and political determinants of income inequality. Master’s thesis, University College London.

- Nikoloski, Z. (2010). Democracy and income inequality: Revisiting the long- and short-term relationship. University of College London.

-  ̃Nopo, H. (2012). _New Century, Old Disparities: Gender and Ethnic Earnings Gaps in Latin America and the Caribbean_ . New York: Inter-American Development Bank and The World Bank.

- Palma, J. (2011). Homogeneous middles vs. heterogeneous tails, and the end of the ’inverted-u’: It’s all about the share of the rich. _Development and Change_ , 42(1):87–153.

- Perry, G., Ferreira, F., and Walton, M. (2003). _Inequality in Latin America and the Caribbean, Breaking with history_ . The World Bank.

- Persson, T. and Tabellini, G. (1994). Is inequality harmful for growth. _The American Economic Review_ , 84(3):600–621.

- Persson, T. and Tabellini, G. (2009). Democratic capital: The nexus of political and economic

23
change. _American Economic Journal: Macroeconomics_ , 1(2):88–126.

- Piketty, T. and Saez, E. (2003). Income inequality in the united states , 1993-1998. _The Quarterly Journal of Economics_ , 518 (1):1–39.

- Reuveny, R. and Li, Q. (2003). Economic openness, democracy and income inequality: An empirical analysis. _Comparative Political Studies_ , 36(5):575–601.

- Rodrik, D. (1999). Democracies pay higher wages. _Quarterly Journal of Economics_ , 114(3):707– 738.

- Sachs, J. (2012). Reply to acemoglu and robinson’s response to my book review. Retrieved from: http://jeffsachs.org/2012/12/reply-to-acemoglu-and-robinsons-response-to-my-book-review/.

- Saint-Paul, G. and Verdier, T. (1993). Education, democracy and growth. _Journal of Development Economics_ , 42(2):399–407.

- Sarkees, M. and Wayman, F. (2010). Resort to war: 1816 - 2007. _CQ Press._

- Savoia, A., Easaw, J., and Mckay, A. (2010). Inequality, democracy, and institutions: A critical review of recent research. _World Development_ , 38(2):142–154.

- Scheina, R. (2003). _Latin America’s Wars Volume II: The Age of the Professional Soldier, 19002001_ . Dulles: Potomac Books, Inc.

- Schiefelbein, E. (2007). _Universalization of Primary Education in the Historical and Developmental Perspective_ , chapter Universalization of Primary Education in Latin America: The Poor Results and Their causes, pages 141–177. IDE-JETRO.

- Schumpeter, J. (1942). _Capitalism, Socialism and Democracy_ . New York: Harper Brothers.

- Stinchcombe, A. (1965). _Handbook of Organizations_ , chapter Social Structure and Organizations. Chicago: Rand McNally.

- Thorbecke, E. and Charumilind, C. (2002). Economic inequality and its socioeconomic impact. _World Development_ , 30:1477–1495.

- Timmons, J. (2010). Does democracy reduce economic inequality? _British Journal of Political Science_ , 40(4):741–757.

- Torche, F. (2010). Economic crisis and inequality of educational opportunity in latin america. _Sociology of Education_ , 83 (2):85–110.

- UNESCO (2010). The quantitative impact of conflict on education. Think piece prepared for the Education for All Global Monitoring Report 2011.

- Verbeek, M. (2008). _The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice_ , chapter Pseudo panels and repeated cross-sections, pages 369–384. Berlin:

24
Springer.

- Verbeek, M. and Nijman, T. (1993). Minimum mse estimation of a regression model with fixed effects from a series of cross-sections. _Journal of Econometrics_ , 59(ss):125–136.

- Verbeek, M. and Vella, F. (2005). Estimating dynamic models from repeated cross-sections. _Journal of Econometrics_ , 127:83–102.

- You, J.-S. and Khagram, S. (2004). Inequality and corruption. Harvard University, Kennedy School of Government, RWP04-001.

25
|**Coverage**|National|National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>National|National|National<br>National<br>National<br>National|National<br>National<br>National<br>National<br>National<br>National<br>National|
|---|---|---|---|---|---|
|**Year**|1995|1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>a)<br>1995<br>1997<br>1999|2001|2003<br>2005<br>2007<br>2009|1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009|
|**Name of survey**|Encuesta de Hogares|Encuesta de Hogares (Mano de Obr<br>Encuesta Integrada de Hogares<br>Encuesta Permanente de Hogares|Encuesta Integrada de Hogares|Encuesta Permanente de Hogares|Encuesta Nacional de Hogares|
|**Country**|**Panama**|**Paraguay**|||**Peru**|
|**Coverage**|Urban|Urban<br>Urban<br>Urban<br>Urban<br>Urban<br>Urban<br>Urban<br>Urban<br>National<br>National|National|National<br>National<br>National<br>Urban|National<br>National<br>National<br>National<br>National<br>National<br>National<br>National|
|**Year**|1995|1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>1995<br>1997<br>1999|2003|2001<br>2005<br>2007<br>2009|1995<br>1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009|
|**Name of survey**|Encuesta Permanente de Hogares|Encuesta Permanente de<br>Hogares-Continua<br>Encuesta Integrada de Hogares<br>Encuesta Nacional de Empleo<br>Encuesta Continua de Hogares||Encuesta de Empleo|Pesquisa Nacional por Amostra de<br>Domicilios|
|**Country**|**Argentina**|**Bolivia**|||**Brazil**|

26
|**Coverage**<br>Urban|Urban|Urban|Urban|Urban<br>Urban<br>Urban<br>Urban|
|---|---|---|---|---|
|**Country**<br>**Name of survey**<br>**Year**<br>**Uruguay**<br>Encuesta Continua de Hogares<br>1995|1997|1999|2001|2003<br>2005<br>2007<br>2009<br>hold surveys, 1995-2009, biannual.|
|**Coverage**<br>National|National|National|National|National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>National<br>merican house|
|**Year**<br>1995|1997|1999|2001|2003<br>2005<br>2007<br>2009<br>1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>Latin A|
|**Name of survey**<br>Encuesta Nacional de Hogares-Fuerza|de Trabajo||Encuesta Continua de Hogares|Gran Encuesta Integrada de Hogares<br>Encuesta Permanente de Hogares de<br>Prop ́ositos M ́ultiples<br>Author’s compilations based on data from|
|**Country**<br>**Colombia**||||**Honduras**<br>Source:|

27
Table A2: Sample Size

|**Birth cohort**|||**Numb**|**er of co**|**horts b**|**y year**|||**Number of**|**individuals**|
|---|---|---|---|---|---|---|---|---|---|---|
|**(years)**|**1995**|**1997**|**1999**|**2001**|**2003**|**2005**|**2007**|**2009**|**Unweighted**|**Weighted**|
|1936-38|14|18|18|18|18|18|18|18|29237|10034004|
|1939-41|14|18|18|18|18|18|18|18|42596|14354638|
|1942-44|14|18|18|18|18|18|18|18|54768|18299629|
|1945-47|14|18|18|18|18|18|18|18|75225|25036860|
|1948-50|14|18|18|18|18|18|18|18|95509|32201858|
|1951-53|14|18|18|18|18|18|18|18|117188|39522084|
|1954-56|14|18|18|18|18|18|18|18|139507|47440640|
|1957-59|14|18|18|18|18|18|18|18|159606|54468349|
|1960-62|14|18|18|18|18|18|18|18|175293|60275779|
|1963-65|14|18|18|18|18|18|18|18|191295|66839428|
|1966-68|14|18|18|18|18|18|18|18|188610|66279462|
|1969-71|14|18|18|18|18|18|18|18|198280|68779300|
|1972-74|0|18|18|18|18|18|18|18|178815|62720401|
|1975-77|0|0|18|18|18|18|18|18|165060|57798437|
|Total|168|216|216|216|216|216|216|216|1467114|503532031|

Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual.

28
Table A3: Periods of Economic Crisis and Wars (1905-2013)

|**a. Periods of war**||
|---|---|
|**Country**<br>**Years**|**Type of war**|
|1955|Intra-state|
|Argentina<br>1975-1977<br>1982|Intra-state<br>Inter-state|
|Bolivia<br>1932-1935|Inter-state|
|1912-1916|Intra-state|
|Brazil<br>1932|Intra-state|
|1944-1945|Inter-state|
|1948-1958|Intra-state|
|Colombia<br>1951-1953|Inter-state|
|1989-2013|Intra-state|
|1906|Inter-state|
|Hd<br>1907|Inter-state|
|onuras<br>1924|Intra-state|
|1969|Inter-state|
|Panama<br>n.a.|n.a.|
|1911-1912|Intra-state|
|Paraguay<br>1932-1935|Inter-state|
|1947|Intra-state|
|1932|Intra-state|
|Peru<br>1982-1992|Intra-state|
|1995|Inter-state|
|Uruguay<br>n.a.|n.a.|
|**b. Periods of economic instabil**|**ity**|
|**Name of the crisis**|**Years**|
|Economic recession|1928-1939|
|Latin American debt crisis|1982-1992|
|Latin American fnancial crisis|1998-1999|
|Global fnancial crisis|2008-2013|

_Note_ : Intra-state wars encompass wars that predominantly take place within the recognized territory of a state. Inter-state wars encompass wars that take place between or among the recognized states.

Source: Author’s compilations and Correlates of War.

29