Redistributive Preferences in Truncated Welfare States

Alisha C. Holland*

April 23, 2016

The relationship between income and support for redistribution is weak and variable in Latin America, despite the region’s extreme income inequality. This paper shows that these facts are consistent with a straightforward adjustment of material interest models to the structure of Latin American welfare states. Tax and transfer policies in Latin America historically were truncated, meaning that they concentrated benefits on the middle class and failed to redistribute resources to the majority of workers in the informal sector. Legacies of welfare state truncation persist, even as some governments have extended social policies to cover those previously excluded from the welfare state. Therefore, it is not always in the material interests of the poor to support social policy expansions in Latin America, in contrast to the usual assumption in advanced industrial democracies. Using public opinion data from across Latin America and original survey data from Colombia, I demonstrate that income is less predictive of attitudes in countries and social policy areas where the poor gain less from social expenditures. Thus, important differences in the structure of public opinion can be explained by paying closer attention to what individuals get and expect from the welfare state.

* Harvard University, Society of Fellows, aholland@fas.harvard.edu. I thank Jorge Domínguez, Fran Hagopian, Steven Levistky, and Torben Iversen for their guidance on this project. Melina Altamirano, Andy Baker, Charlotte Cavaillé, Emily Clough, Kyle Jaros, Herbert Kitschelt, Glen Weyl, Erik Wibbels, and the participants of a 2012 APSA panel and the workshop on Clientelism and Social Policy at the University of Cologne provided helpful comments on previous versions of this paper.

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I. INTRODUCTION

The most basic assumption of redistributive politics is that the poor prefer social welfare

spending and the rich resist it. It follows that income predicts support for redistribution, and the

poor vote for politicians who champion it. Yet this theory of redistributive demands flops precisely

in one of the most unequal regions of the world where it should operate most seamlessly, Latin

America. Public opinion surveys show that the poor in Latin America are no more likely than the

nonpoor to support government efforts to reduce inequality.1 While inequalities in political power

may explain why the poor are unable to enact welfare state expansions, such distortions cannot

explain the underlying puzzle about social demands: Why don’t the poor want to “soak” the rich?

My argument is that it is only in the economic interests of the poor to support social

expenditures in contexts where policies redistribute resources or risks in their favor. This condition

is almost always fulfilled (to some degree) in advanced industrial democracies. It is not in much of

the developing world. In Latin America, social expenditures historically have done little to aid the

poor. Although governments have made important progress to provide a basic social safety net for

their citizens, legacies of what scholars have called truncated welfare states persist.2 Spending in many

countries and policy areas remains limited to those with steady formal sector employment.

Important subsidies primarily benefit middle-income groups. And the rich pay little in income tax.3

My argument is that these differences in the distribution of costs and benefits affect what citizens

want from the welfare state: in particular, poor voters have little reason to support expansions in

spending if they do not expect to benefit. The rich express limited opposition if they too receive

benefits, or at least do not foot the bill. Empirically, then, I expect that the relationship between

1 Blofield and Luna 2011; Dion and Birchfield 2010; Kaufman 2009. 2 DeFerranti, Perry, and Ferreira 2004; Lindert, Skoufias, and Shapiro 2006. 3 Income tax collection remains well below “expected” levels for Latin America’s wealth, accounting for just 1.4 percent of GDP compared to 8.4 percent in developed countries. See, Corbacho, Cibils, and Lora 2013, 115-16.

2

income and support for redistribution is contingent on how benefits are structured in a country and

policy area. Countries and policies that do more to aid the poor have sharp income divisions in

social policy preferences. The poor support benefit expansions and tax policies that the rich

oppose. In contrast, truncated welfare regimes lead poor voters to receive little, expect little, and

thus demand little from social expenditures.

While an intuitive argument, the logic that welfare state truncation alters social preferences

contrasts with alternatives that emphasize that the poor are confused about their interests, or that

the rich are enlightened about theirs in Latin America. On the one hand, scholars have portrayed

the poor, and especially those who work in the informal sector, as uninformed or distracted from

their material interests. Without the mobilizing power of unions and strong Left parties, the

informal sector poor may struggle to understand their class interests.4 More generally, the poor in

unequal societies may be naturalized to accept high levels of inequality and deferential to propertied

classes.5 Or, the poor may hold religious beliefs that dampen the need for state welfare.6 If any of

these explanations hold, then poor voters voice limited support for social expenditures of all types.

Yet, as this paper demonstrates, the preferences of the poor are more nuanced. They vary by social

policy area. Poor voters voice strong support for social policies, such as cash transfers and

progressive income taxes, which work in their favor.

On the other hand, scholars have proposed that the rich have instrumental reasons to

support redistribution, especially in unequal societies. The upper class may want to minimize

societal conflict or crime, leading them to pay for redistributive policies.7 From this perspective, the

elevated support of the rich explains the weak relationship between income and redistributive

preferences in many developing countries. The challenge for such a theory is that it implies that the 4 Huber and Stephens 2012; Roberts 2002. 5 Aalberg 2003; Trump Forthcoming; Ansell and Samuels 2015, 41. 6 De La O and Rodden 2008; Scheve and Stasavage 2006. 7 Dion and Birchfield 2010; Morgan and Kelly 2010; Rueda and Stegmueller 2016.

3

rich should be willing to pay income taxes and fund programs that reduce income differentials. Elite

resistance to income taxation, as seen in studies of tax debates as well as the public opinion data in

this paper, suggests sharp limits on enlightened self-interest.8 I offer a simpler explanation for the

convergence of preferences between the rich and the poor in Latin America based on the structure

of welfare states. If upper and middle-class groups capture benefits from the welfare state, then

there is little reason to expect them to oppose expenditures or the poor to rally around them.

It is important to underscore that I do not make a causal claim: in a democracy, citizens both

pressure to change social policy in the direction of their preferences, and their preferences are

shaped by the social policy context. The contribution of this paper is to show that welfare

truncation and the limited polarization of redistributive attitudes go together as political

complements, not that one causes the other. This corrective is important because it gives a very

different perspective on the workings of the welfare state. Canonical material interest models

assume that redistribution and popular demands are substitutes. In other words, countries with

inadequate welfare states also have sharp differences in the preferences of the poor and rich. The

empirical implication is that inequality is associated with more polarized preferences and more

support for redistribution among poor masses. Yet as Figure 1 shows, there is almost no

relationship between inequality (measured by the Gini coefficient), and the polarization of

preferences (measured by the coefficient from regressing income on preferences for redistribution)

or the average level of support for redistribution across societies.

Using public opinion data from across Latin America, this paper instead identifies an

insightful correlation between the structure of welfare benefits and redistributive preferences.

Figure 2 previews the central results. In countries that do less to include the poor in the welfare

state (measured by the coverage of basic social policies like health, pensions, and cash assistance),

8 Corbacho, Cibils, and Lora 2013; Fairfield 2015.

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the attitudes between poor and rich are more similar (i.e. the coefficient on income approaches

zero). The level of support for redistribution also is lower. In other words, precisely in contexts

where redistribution does the least to help the poor, the public provides the weakest pressure to

change the situation. Again, I do not disentangle cause and effect. Truncated social policies can

make the poor skeptical that redistribution will serve their interests (the welfare state structure

causing preferences), and tepid demand for redistribution can reinforce the exclusionary nature of

social policy (preferences causing the welfare state structure).

Figure 1. The Weak Explanatory Power of Inequality for Redistributive Preferences Source: LAPOP 2010, World Bank 2010.

Figure 2. The Importance of Welfare State Coverage for Redistributive Preferences Source: LAPOP (2010), SEDLAC (2011) and World Bank ASPIRE Database (2011).

Argentina

Bolivia

Brazil

Chile

ColombiaCosta Rica

Dom. Rep.

Ecuador

El Salvador Guatemala

Honduras

Mexico Nicaragua

Panama

Paraguay

Peru

Uruguay

Venezuela

-.1-.0

50

.05

Coe

ffici

ent o

n In

com

e

.4 .45 .5 .55 .6Gini Coefficient

Argentina

Bolivia

Brazil

Chile

Colombia

Costa Rica

Dom. Rep.Ecuador

El SalvadorGuatemala

Mexico

NicaraguaPanama

Paraguay

Peru

Uruguay

Venezuela

.5.6

.7.8

.9Fr

actio

n Su

ppor

tive

of R

edis

tribu

tion

.4 .45 .5 .55 .6Gini Coefficient

Figure 1: Class Polarization and Support for Redistribution in Latin AmericaSource: AmericasBarometer (2010)

but more support in places like Paraguay and Ecuador, despite similar levels of inequality?

Third, the materialist model predicts that a larger fraction of the population supports redistribution in

unequal societies. While some surveys show higher levels of support for redistribution in Latin America than

in Europe (Dion and Birchfield, 2010; Gaviria, 2008), the results depend on question framing and do not

hold within Latin America. The right panel of Figure 1 shows almost no correlation between the fraction of

the population that supports government inequality reduction and the Gini coe�cient.

Based on this trio of anomalies, many scholars have rejected the materialist model of preferences and

searched for alternative explanations for the weak link between class and redistributive demands outside

Europe. My argument is that the materialist model has been applied without attention to a key assumption

that underpins it: that welfare spending redistributes resources to the poor. While a trivial premise for

advanced industrial economies, the next section shows that fiscal progressivity varies widely in Latin America,

generating divergent material incentives.

Truncated Welfare States

Social policy coverage is a key variable to understand preferences that largely has been overlooked by

the literature. Truncated welfare state are defined by low levels of welfare coverage for the poor and, in

contrast to minimalist welfare states, by important benefits for nonpoor groups. Accordingly, the hallmark

of truncated welfare states is expenditures that do little to redistribute risks or resources in favor of the poor.

Benefits often do not reach the poor in truncated welfare states because they are linked to formal employ-

ment relationships or designed so that the poor make little use of them in practice. Major social programs,

such as pensions, health care, family allowances, and unemployment benefits, that require formal labor con-

tracts to access exclude workers in the informal economy, who are mostly poor and constitute half of Latin

5

ArgentinaBrazil

Chile

Colombia

Costa Rica

Dom. Rep.

Ecuador

El SalvadorGuatemala

MexicoNicaragua Panama

Paraguay

Peru

Uruguay

Venezuela

-.1-.0

50

.05

Coe

ffici

ent o

n In

com

e

.2 .3 .4 .5 .6Coverage

Argentina

Bolivia

Brazil

Chile

Colombia

Costa Rica

Dom. Rep.

Ecuador

El SalvadorGuatemala

Mexico

NicaraguaPanama

Honduras

Peru

Uruguay

Venezuela

.5.6

.7.8

Frac

tion

Supp

ortiv

e of

Red

istri

butio

n

.2 .3 .4 .5 .6Coverage

Figure 6: Class Polarization and Support for RedistributionSource: AmericasBarometer (2010)

reasonable.

To build up the hierarchical model, I first present bivariate plots from a two-stage regression, which lend

themselves to greater transparency at the national level.8 Figure 6 plots the income coe�cients from a logistic

regression, and the fraction of the population supportive of redistribution, against welfare coverage. Contrast

these plots with the paper’s opening figure: while inequality did little to explain these same dependent

variables, social policy coverage provides substantial leverage to understand cross-national di�erences. The

fit between class and redistributive preferences is tighter and support for redistribution is higher in countries

that include the poor in social expenditures.

I estimate a multilevel logit analysis with country fixed e�ects, and then with random e�ects. All

estimators use heteroscedasticity-robust standard errors clustered by country. The models presume that

survey answers reflect an underlying support for redistribution, which is the unobserved variable y

ú. The

equation for the fixed e�ects model, where i indexes individuals and j indexes countries, is:

y

úij

= –0 + —1nij

+ —2zj

n

ij

+ Wijb + u

j

+ ‘

ij

The equation has an intercept –0, the income variable for each individual nij

and its coe�cient —1, the

interaction of income with national-level coverage z

j

and its coe�cient —2, the matrix of remaining controls

and their coe�cients Wijb, a country fixed e�ect u

j

and an individual error term ‘

ij

. The fixed e�ects

model controls for cross-country heterogeneity and has the advantage that the estimates on the interaction8 These analyses are conducted using the mlt2stage and mltl2scatter commands in Stata, which are designed to estimate

and plot two-stage (or slopes as outcomes) multilevel models where the dependent variable in the second stage, being estimated

coe�cients, is measured with error. I use a controls for age, urban, and female in the first stage.

23

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Empirically, one of the challenges is that many factors used to explain attitudes cluster

together at the national-level. Inclusive social policy, Left party rule, unionization, state strength,

ethnic homogeneity, and so on move together. For this reason, I designed an original public survey

on tax and social policy attitudes in a truncated welfare state, Colombia. By looking across social

policies within a single country, I can isolate how expectations of benefits translate into preferences

while holding constant broad features of the political and economic environment. Consistent with

my theory, I demonstrate that the poor do not expect to benefit from social expenditures writ large.

But income strongly predicts attitudes when respondents are asked about tax and spending policies

that primarily help the poor. Most of the change in support comes in the poor’s preferences; the

rich’s preferences are largely stable, and they are least supportive of the tax and spend policies that

do the most to reduce inequality. Thus, even unequal democracies have the “expected” income-

based attitudinal patterns where social expenditures actually redistribute resources to the poor. The

snag is that taxes and social policy often have not favored Latin America’s poorest.

II. THE PUZZLE

The materialist model of preferences, as formulated by Meltzer and Richard, assumes that

the gap between the median voter’s preferences and mean income determines individual support for

redistribution.9 Scholars extend this intuition to the cross-national level to predict a larger welfare

state in unequal democracies,10 and to greater redistributive conflict in unequal societies.11

Synthesizing, the model has three predictions: (1) that the poor support more welfare spending than

the nonpoor, (2) that preferences are more polarized by income in unequal societies, and (3) that

unequal societies support more redistribution on average than equal ones. These predictions should

9 Meltzer and Richard 1981; also see, Romer 1975. 10 Iversen and Soskice 2009; Lindert 2004; Perotti 1996. 11 Acemoglu and Robinson 2006; Boix 2003.

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find clear support in Latin America, given that the income distribution is among the most unequal in

the world. They do not.

First, the poor do not support more redistribution than the nonpoor in Latin America. The

usual expectation is that regressing redistributive attitudes on income (plus a small set of controls

like gender, education, and age) results in a negative coefficient (less support for redistribution at

higher income levels). For the rest of the paper, I will call this the income coefficient (other scholars

refer to it as the income slope). While some scholars find the expected negative income coefficients

(i.e. higher demand for redistribution among poor individuals),12 several others find no relationship

between income and preferences.13 The fragility of these results contrasts with advanced

democracies where a robust negative relationship between income and support for redistribution has

been found.14 Moreover, regardless of statistical significant, the substantive effects usually are tiny.

Blofield and Luna conclude that, while income may predict attitudes in some models and datasets on

Latin America, its “significance is less consistent across countries and over time, and the predictive

power of the models is weaker overall” than in advanced industrial democracies.15

Second, the Meltzer-Richard model predicts that the gap between the preferences of the

poor and nonpoor should be larger in unequal societies. Empirically, the income coefficient should

be more negative in countries with high levels of inequality. The left panel of Figure 1 plots the

level of inequality, measured by the Gini coefficient, on the horizontal axis. On the vertical axis, it

shows the coefficient of income for a standard measure of redistributive attitudes (whether an

individual agrees or strongly agrees that the government should take action to reduce inequality).

12 Gaviria 2008; Haggard, Kaufman, and Long 2013; Morgan and Kelly 2010. 13 Cramer and Kaufman 2011; Dion and Birchfield 2010; Kaufman 2009. 14 For a review, see Bean and Papadakis 1998; Dion and Birchfield 2010; Beramendi and Rehm 2016. 15 Blofield and Luna 2011, 167.

7

Against standard expectations, there is no relationship between inequality and the coefficient on

income across countries in Latin America.16

Third, the standard material interest model predicts that a larger fraction of the population

supports redistribution in unequal societies. As Moene and Wallerstein sum up the intuition,

“Welfare policy is expected to ‘lean against the wind’ in the sense that the greater inequality of pre-

tax and transfer income, the greater the electoral support for government policies that redistribute

from rich to poor.”17 Some studies have found higher average levels of support for redistribution in

Latin America than in Europe.18 However, the results do not hold within Latin America, and

depend on the question specification cross-regionally. The right panel of Figure 1 shows that, if

anything (excluding Venezuela), there actually is a negative correlation between inequality and the

fraction of the population that supports redistribution.19 Some of the most equal countries, such as

Argentina and Uruguay, have the highest levels of support for redistribution, while the most unequal

societies, such as Guatemala and Bolivia, have the lowest.

These three stylized facts have led many scholars to jettison material interest models and

focus on other factors, such as the limited class identity of informal sector workers, religious

cleavages, and other-oriented concerns. In this paper, I return to a very simple point: the Meltzer

Richard model assumes that tax and spending policies are redistributive. Indeed, most political

economy models think of redistribution as an income tax that results in lump sum transfers to the

poor. Yet, as I turn to now, most Latin American countries failed to live up to this assumption for

16 I show the results from the 2010 wave of LAPOP’s Americas Barometer given that it included a more extensive set of control variables. But for more recent waves (2012 and 2014), the relationship between inequality and the coefficient on income is even slightly positive. In other words, income is a worse predictor of social policy attitudes precisely in the most unequal societies where the theory would expect the material interests of the poor and rich to diverge. 17 Moene and Wallerstein 2003, 486. 18 Dion and Birchfield 2010; Gaviria 2008. 19 Given the top coding of questions on inequality reduction, I code an individual as supportive if they answer with a 6 or above on a 7-point scale where “7” means “strongly agree.”

8

most of the twentieth century and still do much less to redistribute resources and risks today than

their counterparts in advanced economies. Modifying the material interest model to allow for

differences in the redistributive impact of taxes and social spending leads to a very different set of

implications for redistributive preferences.

III. THEORY

Political economists make what Iversen and Soskice label a “nonregressivity assumption”

that the rich always lose from welfare policies, whereas the middle class does less well than the poor

but better than the rich.20 Indeed, advanced democracies substantially favor the poor in their tax

and transfer polices.21 This logic holds even for insurance spending. Moene and Wallerstein

emphasize that social-insurance programs draw stronger support from the middle class because they

stand to lose more income in the event of a job loss.22 However, nonregressivity holds even when

considering social-insurance expenditures in advanced capitalist societies because low-income

households face greater risk and pay less in taxes.23

The nonregressivity assumption is more tenuous in Latin America. Figure 3 plots a standard

measure of the redistributive impact of welfare spending, the change in the Gini coefficient from

market income to net income after taxes and transfers, against the Gini coefficient.24 Measures of

redistributive impact reflect both the size of the welfare state and the progressivity of spending.

What stands out is that Latin American welfare states do very little to reduce the gap between rich

20 Iversen and Soskice 2006, 167. 21 Mahler 2010; Milanovic 2000. 22 Moene and Wallerstein 2001. 23 Rehm, Hacker, and Schlesinger 2012. 24 Data come from the Standardized World Income Inequality Database (2014). I use absolute redistribution measure, which looks at the difference in market-income inequality minus net-income inequality. The Gini coefficient is on a log scale so other measures like relative redistribution exaggerate the comparison because it is harder to reduce inequality at higher levels of the Gini coefficient. Measures of absolute redistribution reflect both the size of the welfare state and the progressivity of spending. Although Chile is part of the OECD, I include it in the Latin American regional category.

9

and poor. On average, taxes and transfers reduce the Gini coefficient by 18 points in Europe and

North America, but only by 3 points in Latin America. The regional averages hide substantial

variation in the redistributive nature of spending within Latin America. Government spending

leaves inequality basically unchanged in Colombia, Guatemala, and Peru but it improves the income

distribution by almost 10 points in Uruguay.25

Figure 3. Redistributive Impact Compared across Latin America and the OECD Source: Standardized World Income Inequality Database (SWIID, 2014).

A related second assumption of material interest models is that welfare states begin with a

commitment to the absolute poorest and differ in how they extend benefits up the income ladder.

Pontusson captures the idea: “Means-tested social assistance constitutes the minimalist core of the

modern welfare state—even the least welfare-oriented societies must somehow take care of the

indigent—and it is the extent to which they have gone beyond the minimalist core that distinguishes

25 These aggregate trends mirror findings broken down by specific spending areas: Rudra (2004) shows that only spending on education in less developed countries results in a more favorable distribution of income, while all types of expenditures do so in advanced economies.

ArgentinaArgentinaArgentinaArgentinaArgentinaArgentinaArgentina

BrazilBrazilBrazilBrazilBrazilBrazil

ChileChileChileChileChile

ColombiaColombiaColombiaColombiaColombiaColombiaPeruPeruPeruPeruPeruPeru

UruguayUruguayUruguayUruguayUruguayUruguay

FranceFranceFranceFranceFranceFrance

GermanyGermanyGermanyGermanyGermanyGermany

SwedenSwedenSwedenSwedenSwedenSweden

United KingdomUnited KingdomUnited KingdomUnited KingdomUnited KingdomUnited KingdomUnited Kingdom

United StatesUnited StatesUnited StatesUnited StatesUnited StatesUnited States

05

1015

2025

Abso

lute

Red

istrib

utio

n

25 30 35 40 45 50Gini Coefficient

Latin America OECD

10

the social market economies from the liberal market economies.”26 Put otherwise, the “minimalist

core assumption” is that welfare states cover the poor and differ in their inclusion of the middle

class, or what often is thought of in the European context as universalism.

Latin American welfare states did not originate with a minimalist core. Quite the opposite,

most welfare states began in the early or mid-twentieth century with occupation-based social-

insurance programs. The origins of social spending in protective schemes for public sector and

organized workers are not unique to Latin America. As Esping-Andersen underscores, several

European welfare states (most notably, Otto von Bismarck’s Germany) began as conservative

welfare regimes that linked welfare benefits to labor market status, and thus created stratified

benefits.27 But the small size of the industrial working class in Latin America meant that tying

benefits to labor contracts left most of the population in the informal and rural sector without social

protection.28 In contrast to other parts of the developing world, however, Latin American

governments provided substantial entitlements and labor protections to the industrial working class

as they endeavored to promote domestic industry. Welfare states were “deep but not wide.”29

Historically, then, Latin American governments invested in social policies that provided benefits to

those above a minimum level of income and risk, rather than below it.

Many Latin American countries still struggle to provide the most basic income and

protection against risks to the poor. Countries differ in how far down the income ladder they have

moved to extend benefits and social-insurance programs. Contrast the description of Latin

America’s welfare states by Díaz-Cayeros and Magaloni with the minimalist core assumption made

about Europe: “Social policy in Latin America has traditionally failed to benefit the poor. . . Latin

America’s biggest social policy-challenge is to extend benefits to those who are now excluded from 26 Pontusson 2005, 148. 27 Esping-Andersen 1990. 28 Huber and Stephens 2012, 22-24. 29 Haggard and Kaufman 2008, 17.

11

social-insurance programs—or in other words, to reach the poor.”30

Particularly since the 1990s, Latin American countries have reformed and expanded welfare

programs to cover poor majorities. Almost every government in the region now runs some form of

means-tested income transfer program. Some governments also have moved to delink social

insurance benefits from labor status and extended coverage through noncontributory pensions and

healthcare.31 Nevertheless, some countries and some areas of social policy changed far less than

others. Consider the case of non-contributory pensions: while Brazil now covers a third of elderly

households with monthly transfers of $328, the comparable program in Peru includes just 1.5

percent of the elderly and makes monthly transfers of only $46.32

There are two basic reasons that social policies in Latin America still fail to redistribute

resources. First, the lion’s share of the budget goes to contributory social-insurance programs. The

fraction of social insurance spending has remained stable over time, even through Latin America’s

economic liberalization, state cuts, and full democratization.33 Comparatively inexpensive programs

transfer income or broaden service access to the poor often were layered on top of (and in some

cases, even led to expansions in) generous contributory programs.34 Second, in many countries,

transfers that nominally go to all households exclude those below a minimum income level in

practice. For instance, subsidies for energy prices, higher education, and home mortgages still

constitute important parts of social assistance budgets. Because richer households spend more in

absolute value on these goods, these subsidies disproportionately benefit better-off households.35

Combined, contributory programs and universal transfers account for substantial portions of the

30 Díaz-Cayeros and Magaloni 2009, 36-7. 31 For discussions of these reforms and their variation, see De La O 2015; Garay Forthcoming; Huber and Stephens 2012; Levy and Schady 2013; Pribble 2010, 2013. 32 Levy and Schady 2013, 201. 33 Wibbels 2013. 34 Hunter and Sugiyama 2009. 35 Levy and Schady 2013, 210.

12

budget. In Argentina, for instance, contributory pensions cost the government roughly 8 percent of

GDP annually over the past three decades.36 Subsidies for urban transport, gas, and electricity

accounted for 10 percent of the total Argentine budget in 2010.37 Meanwhile cash transfers cost the

government just 0.5 percent of GDP, or roughly 2 percent of the annual budget.38

The politics of welfare state expansion is the topic of a growing body of scholarship. My

point is simply that the structure of spending also has implications for redistributive attitudes in the

region.39 Latin America’s poor face variable—and sometimes weak—incentives to support

redistribution, depending on the structure of expenditures where they live. Extending the model to

a context in which not all individuals receive benefits leads to four central hypotheses. First, quite

simply, individuals who receive welfare benefits should be more supportive of redistribution than those who do not.

Studies of social insurance spending find uneven support for this proposition, but I extend the

intuition to redistributive attitudes.

Second, in empirical tests of the Meltzer-Richard model, the quantity of interest tends to be

the coefficient on income, which captures how preferences differ by income. Higher income should

predict less support for redistribution, leading to a negative coefficient on income. In contrast,

consider the case of a truncated welfare state in which most benefits go to individuals above a

minimum income threshold (at which point they enter the formal labor market or access subsidized

goods). Intuitively, the poor should voice weaker support for welfare expenditures because they get

less out of the system. The nonpoor also should be more supportive given that they stand to

36 Rofman, Fajnzylber, and Herrera 2008, 15 37 Bril-Mascarenhas and Post 2014. 38 Levy and Schady 2013, 201. 39 Recent work on Latin America has focused on preferences over social-insurance provision, namely whether individuals support that the government or the private sector provides pensions and health insurance. Carnes and Mares (2015) find that even labor market “insiders” may fear falling outside the protected sectors and support insurance spending by the government. But Berens (2015) finds that workers in the informal sector (who tend to be excluded from social insurance expenditures) are more likely to withdraw their support from government insurance provision.

13

benefit. Thus, the slope on income should be flatter when benefits are proportional to income

(rather than the lump sum transfer assumed by the Meltzer-Richard model). Therefore, my

hypothesis is that income is more strongly associated with preferences (the coefficient on income is more negative) in

welfare states where social expenditures do more for the poor.

Third, recognizing the distinct trajectory of welfare state formation leads to distinct

predictions about the level of support for redistribution across countries. In advanced democracies,

investments in means-tested social policies, which tend to be the most economically progressive, are

thought to erode support for the welfare state because few citizens ever use these benefits.

Meanwhile, universal social policies are attractive to middle-class workers facing economic risks, and

not just low incomes.40 The common wisdom is that welfare state progressivity and societal support

will be inversely related. Latin America reverses this logic due to the inverse trajectory of welfare

state development. Moving toward a more fiscally progressive welfare state leads the poor to gain

inclusion and thus increase their support for welfare expenditures. As such, I expect that mean

support for redistribution is higher in Latin American welfare states where social expenditures are more inclusive and

downward redistributive.

Figure 4 visually summarizes these predictions about the functional form of the relationship

between income and preferences. My macro-comparative claim is that most of variation in Latin

America comes in the extent to which countries have moved from (a) truncated to (b) more

universal welfare states (or at least those that layer targeted benefits to the poor on a contributory

system). As such, I expect the income coefficient and mean support to be positively related to the

coverage and progressivity of the welfare state across countries. This theory contrasts with work

focused on advanced economies that underscores the differences between (b) more universal and (c)

40 Korpi and Palme 1998; Moene and Wallerstein 2001, 2003.

14

targeted welfare states.41 In these cases, more progressive expenditures may be associated with more

polarized preferences, but should be negatively related to support for the welfare state.

Figure 4. The Shape of Preferences in Welfare State Ideal Types

A final way to test if the structure of welfare spending affects attitudes comes from looking

at different types of policy expenditures. Even within the same country, social policies vary

dramatically in their targeting. Some policies like cash transfer programs are sharply targeted by

income, while others like housing benefits primarily benefit the middle class. Therefore, a fourth

implication of my theory is that income is more strongly associated with preferences (the coefficient on income is

more negative) in social policy areas where benefits concentrate more heavily on the poor.

In suggesting the importance of the targeting of benefits, I depart from a literature that

emphasizes the difference between insurance and redistributive expenditures. Moene and

Wallerstein emphasize that insurance spending generates less polarized preferences because the

middle class are risk averse and benefit from the protection against risks.42 Part of the reason that

expenditures in Latin America are less progressive is a heavy reliance on contributory social

insurance. Therefore, the insurance-nature of spending could explain higher levels of middle and

upper-class support.43 However, many transfers, such as housing, electricity, or higher education

subsidies also disproportionately favor better-off households in Latin America. I therefore look

41 Beramendi and Rehm 2016. 42 Moene and Wallerstein 2001, 2003. 43 Wibbels and Ahlquist 2011; Wibbels 2013.

15

across both insurance and transfer programs with different beneficiary profiles. My expectation is

that income divisions in public preferences vary with the targeting of expenditures, not the insurance or redistributive

nature.

PATTERNS OF REDISTRIBUTIVE PREFERENCES ACROSS LATIN AMERICA

My first empirical test comes from individual-level models of redistributive preferences. I

then turn to the cross-level implications. To measure redistributive preferences, I draw on data

from the Latin American Public Opinion Project’s (LAPOP) AmericasBarometer.44 This survey

includes the most common operationalization of redistributive demand used in cross-national survey

research, namely whether an individual agrees or disagrees that the government should take actions

to reduce inequality. Responses are measured on a Likert scale that ranges from “strongly disagree”

(1) to “strongly agree” (7). A drawback of this question is that it does not ask about trade-offs

between taxation and expenditures, which can create a ceiling effect where most respondents agree

to some extent with redistribution. Therefore, most scholars recode this question to differentiate

between respondents who strongly support redistribution (“6” or above), and all other respondents.

I follow this convention and use a binary dependent variable (Redistribution), and show the results are

robust to using the full scoring in the Appendix. I present the results using the 2010 wave, which

includes crucial questions on benefit access. The Appendix confirms that the results are robust to

the use of other waves and surveys from Latinobarometer.45

Given that questions about inequality reduction are framed in abstract terms, it may seem

puzzling to argue that social policy inclusion shapes responses. Wouldn’t individuals express their

44 The survey has full regional coverage (18 countries), and each country survey uses national probability samples of adults. All survey data are publicly available through LAPOP. 45 An alternative approach is to use the World Values Survey, which asks a question that forces respondents to trade-off between greater inequality and incentives for private enterprise. Beyond the fact that this question makes no mention of government social policy in these value judgments, WVS uses very crude measures of income, making it extremely challenging to test theories about the relationship between income and preferences with the necessary precision.

16

preferences about the policies that they would like to see enacted? I follow Zaller and Feldman in

suggesting that the way that the public responds to survey questions—and even quite abstract

ones—is shaped by their existing environment.46 More concretely, respondents think about whether

inequality reduction involves policies that cost them money or benefit them when they respond.

Current policy inclusion maters, but so do their expectations about what would happen in the

future. Thus, my theory predicts that, if the poor do not see themselves as benefitting from

inequality reduction, they will voice less support. Conversely, if they rich do not expect to pay for

inequality reduction, they will offer stronger support.

Testing my first hypothesis requires measuring income and benefit access at the individual

level. LAPOP has respondents self-report their household income range, divided into ten or sixteen

categories depending on the survey wave. To standardize across waves, I take the logged mid-point

of each income bracket and divide the sample into income deciles (Income). The first implication of

my theory is that respondents who receive social policy benefits should have higher levels of support

for redistribution than those who do not, even controlling for income level. LAPOP included three

questions on benefit receipt on the 2010 wave: whether the respondent has health insurance (Health),

has a pension (Pension), and receives a cash transfer (CCT). I expect that receipt of these benefits

will be associated with stronger support for redistribution.

I include several basic socio-economic controls. First, years of education (Education) provide

an additional measure of class and a proxy for skill specificity (there are no occupational questions).

Second, I control for gender (Female), given that some studies find that women are more supportive

of inequality reduction than men.47 Third, older respondents (Age) are more likely to benefit from

pension and health insurance policies, and therefore should be more supportive of redistribution.

46 Zaller and Feldman 1992. For a great empirical illustration of this point, see Gingrich and Ansell 2012. 47 Alesina and Giuliano 2011; Linos and West 2001.

17

Fourth, urban residents (Urban) may access higher quality social benefits and therefore support more

redistribution.48

Another group of independent variables test for more specific alternative explanations.

First, economic insecurity has been hypothesized to increase redistributive demands so I include an

indicator variable for whether an individual has lost a job in the past two years (Unemployed) or seen

their economic situation deteriorate (Shock).49 Second, to examine the possible anti-redistribution

effect of religiosity, I use a measure of attendance at religious meetings (Religion).50 I recode this

measure from 0 for individuals who never attend religious meetings to 1 for individual who attend

weekly religious meetings (monthly or yearly attendance are intermediate values). Third, individuals

who identify as politically conservative may be less supportive of all forms of government welfare

provision. The survey asks respondents to place themselves on a political scale that ranges from the

far left to the far right, rescaled from 0 to 1 (Right). Fourth, clientelism can provide basic goods and

insurance to the poor, and therefore may decrease redistributive demands. I include an indicator

variable that takes on a value of “1” if an individual reports being approached with a clientelistic

offer (Client). Lastly, I include a measure of crime fears, which takes on a value of “1” if the

respondent names crime and violence as one of the problems that “regularly worries” him or her

(Crime). The intuition is that respondents who fear crime will be more supportive of inequality

reduction as a way to improve security.

My main finding is that benefit receipt, and especially access to a pension and receipt of cash

transfers, is associated with more support for redistribution. The Appendix presents the coefficients

from logit models using a binary dependent variable, and shows that ordinary least-square 48 For a development of this idea, see Haggard, Kaufman, and Long (2013). 49 Carnes and Mares (2015) hypothesize that economic shocks lead even protected and wealthier individuals to support more government spending. Workers cycle in and out of formal sector employment in Latin America, and therefore I do not expect (and the data confirm) that current employment status has much predictive power. 50 Scheve and Stasavage 2006.

18

regressions using the full variation in responses return the same results. To give concrete meaning

to the results, I estimate the predicted probability of strongly supporting inequality reduction. For a

typical woman from the bottom 20 percent of the income distribution who receives no social

benefits, the predicted probability of supporting inequality reduction is 72 percent. The same

respondent who has access to both a pension and cash transfer programs has an 84 percent

probability of supporting inequality reduction. Perhaps what is most telling is that benefit receipt

has a larger effect on attitudes toward redistribution than income. Table 1 makes this point. There

is little predicted difference in attitudes between the bottom and top quantiles of the income

distribution (looking across the rows), but access to benefits is associated with substantial predicted

differences (looking down the columns).

Table 1. Predicted Probability of Supporting Redistribution by Income and Benefit Access

Bottom Income Quintile Top Income Quintile No Benefits 72% 68% All Benefits 84% 81%

In terms of the other variables, unemployment and identifying with the political Right are

associated with less support for redistribution, as expected. Exposure to vote buying has no

significant relationship with redistributive attitudes. Fear of crime has the opposite effect theorized

in the literature (it is associated with less support for redistribution).

I now turn to my second hypothesis, which looks at the relationship between the structure

of preferences and who gets what from the welfare state. My key national-level explanatory variable

is the extent of welfare state “truncation.” How should it be measured? An ideal approach would

look at the incidence of all taxes and benefits to calculate a measure of benefit concentration. The

problem is that these statistics are not available for all Latin American countries. A more tractable

approach measures the coverage of major social programs. As discussed above, the poor historically

19

were excluded from the welfare state. Efforts to extend health care, pensions, and cash transfers all

indicate greater effort to include the poor in the welfare state. I therefore use statistics from the

Socio-Economic Database for Latin America on health insurance and pension coverage, and the

World Bank on the percent of the population that receives cash transfers. I average these statistics

to create a single measure Coverage that considers the fraction of the population covered by health,

pension, and cash transfer programs. Another possible approach is to look at the redistributive

effect of taxes and spending as measured by the reduction in the Gini coefficient through taxes and

transfers. I also use a measure of the redistributive impact of spending from the SWIID database

(Progressivity). The downside of progressivity measures is that they are not available for all Latin

American countries.

Figure 5. The Predictive Power of Income by Welfare State Coverage Source: LAPOP 2010, income coefficients estimated with socioeconomic controls.

At the cross-national level, I expect that the coefficient on income will be more negative, and

the average level of support for redistribution will be higher, in countries that do more to include the

poor in major social welfare programs. The logical approach to test these hypotheses is a

hierarchical model (or as preferred in economics, a cross-level interaction or two-stage regression

ArgentinaBrazil

ChileColombiaCosta Rica

Dominican Republic

Ecuador

El SalvadorGuatemala

MexicoNicaraguaPanama

Paraguay

Peru

Uruguay

Venezuela

-.1-.0

50

.05

.1C

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Inco

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.2 .3 .4 .5 .6Coverage

20

model). But there are only eighteen countries in Latin America, which is right at the recommended

threshold for the number of units necessary to run multi-level models. Therefore, it is particularly

important to first verify that the basic hypothesized patterns are present in the raw data before

turning to more complicated statistical models. The basic intuition for these relationships was

plotted in Figure 2. However, this figure masks the fact that income coefficients are measured with

error from regression models. Figure 5 plots the estimated coefficients with controls (e.g. education,

age, urban, female), as well as the 95% confidence intervals. The basic pattern remains clear—

income is more predictive of redistributive preferences in countries with more inclusive welfare

states.

Moving beyond the plots, I estimate a cross-level regression with fixed effects and then with

random effects. The models presume that survey answers reflect an underlying support for

redistribution, the unobserved variable y*. The equation for the fixed effects model, where i indexes

individuals and j indexes countries, is:

!!"∗ = !! + !!!!" + !!!!!!!" +!!"!+ !!! + !!"

The equation has an intercept !!!, the income variable for each individual by country !!" ,

the interaction of income with national-level coverage !! , the matrix of remaining controls Wij, a

country fixed effect !!!, and an individual error term !!" . This model allows me to examine my

hypothesis that the coefficients on income vary depending on social policy coverage. However, it

cannot be used to understand how social policy inclusion affects the level of support for

redistribution in a country because it controls for cross-country heterogeneity. A cross-level random

effects model, which directly estimates the effect of national variables !! allows me to examine this

final hypothesis as follows:

!!"∗ = !!! + !!!!" + !!!!!!!" + !!!!! +!!"!+ !! + !!"

21

Notably, the random effects model makes a stronger distributional assumption of a country-specific

error term !! drawn from a normal distribution. Table 2 presents the results from both types of

models.51

Table 2. Preferences for Redistribution and National-Level Social Policy Coverage

51 For a similar modeling approach to understanding the impact of national-level policy design, see Gingrich and Ansell 2012.

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

Income 0.0201⇤ 0.0203⇤ -0.00313 -0.00300 0.0410⇤ 0.0413⇤ -0.00606 -0.00585(2.40) (2.42) (-0.59) (-0.56) (3.70) (3.72) (-0.54) (-0.53)

Income*Coverage -0.104⇤ -0.104⇤ -0.0919⇤ -0.0918⇤ -0.0731⇤ -0.0730⇤

(-4.29) (-4.29) (-3.75) (-3.74) (-2.86) (-2.85)

Coverage 1.699⇤ 1.827⇤ 1.235⇤

(3.74) (3.84) (2.73)

Income*Prog. -0.00332⇤ -0.00332⇤

(-3.47) (-3.46)

Progressivity 0.0504⇤

(4.18)

Income*Union -0.153⇤ -0.154⇤

(-3.50) (-3.51)

Union 0.386(0.48)

Income*Crime 0.000874⇤ 0.000874⇤

(3.58) (3.58)

Crime -0.0120⇤

(-2.96)

Education 0.00199 0.00180 0.00398 0.00392 0.00324 0.00301 0.00168 0.00140(0.79) (0.71) (1.48) (1.46) (1.25) (1.17) (0.67) (0.56)

Urban 0.00257 0.00150 -0.0123 -0.0118 0.00968 0.00905 0.00371 0.00314(0.12) (0.07) (-0.53) (-0.51) (0.43) (0.40) (0.17) (0.14)

Female -0.0418⇤ -0.0416⇤ -0.0354 -0.0347 -0.0445⇤ -0.0442⇤ -0.0417⇤ -0.0415⇤

(-2.31) (-2.29) (-1.82) (-1.78) (-2.39) (-2.38) (-2.30) (-2.29)

Age 0.0629 0.0622 0.112⇤ 0.117⇤ 0.0824 0.0815 0.0595 0.0586(1.45) (1.44) (2.42) (2.52) (1.86) (1.85) (1.37) (1.35)

Observations 25490 25490 21556 21556 24342 24342 25490 25490Countries 17 17 14 14 16 16 17 17Model FE RE FE RE FE RE FE RER2 (between) 0.321 0.279 0.233 0.235 0.186 0.380 0.447 0.419

Source: LAPOP 2010, support for inequality reduction on a 7-point scale.

t statistics in parentheses, ⇤ p < 0.05.

5

22

Model 1 and 2 use social policy coverage as a higher-level variable. In both the fixed and

random effects models, the interaction between coverage and income is negative, which means that

income more strongly predicts redistributive support in countries that do more to assist the poor. In

the random effects model, the coefficient on coverage is positive. In other words, welfare states that

do more for the poor have a higher average level of redistributive support across all citizens.

To give these coefficients substantive meaning, consider how an individual’s support is

predicted to change with income in two very different welfare regimes. In a country like Uruguay in

which the majority of citizens have access to pensions, health coverage, and if below a certain

income, cash transfers, moving from the lowest to the highest income quantile reduces the predicted

probability of supporting redistribution from 85 to 77 percent. The level of support for

redistribution is extremely high, and the difference across class groups is meaningful. In contrast, in

a country that does much less to assist the poor like Guatemala, the predicted probability of

supporting redistribution only decreases from 63 percent among the poor to 62 percent among the

wealthy. Preferences become less distinguishable across income groups—and the level of support

for redistribution drops— in welfare states that do little to assist the poor.

Models 3 and 4 confirm that the progressivity of the welfare state impacts the structure of

preferences in much the same way as coverage does. These results are expected in Latin America,

given that extensions in welfare state coverage tend to increase progressivity. Again, income is more

predictive of redistributive preferences in countries with more progressive welfare states (the

coefficient on the interaction term is negative), and more progressive welfare states have higher

levels of support for redistribution (the coefficient on the national-level progressivity variable is

positive). An important caution is that progressivity measures only are available for fourteen

countries, which is why I put greater emphasis on the coverage measures.

As mentioned earlier, many things cluster together at the national level and therefore it is

23

important to control for possible additional and alternative explanations. What I want to rule out

empirically is that the correlation between social policy structure and preferences is driven by a

lurking variable. Some of the most plausible omitted variables that affect both societal demands and

welfare state inclusion are dense union organizations, strong leftist political parties, and more

programmatic party systems.52 Unions, in particular, are thought to socialize workers to understand

their class interests. Political parties that favor redistribution and compete on programmatic

dimensions similarly may help make issues of redistribution salient to different class groups. At the

same time, unions and programmatic Left parties may improve the generosity of welfare benefits

through their activities. Thus, a threat to inference is that differences in benefit structure have no

independent relationship with preferences once we account for the density of unions and features of

the party system. I test for potential omitted variables using the proportion of the workforce

organized into unions (Union Density), and the fraction of years that the executive has been from the

left from 1945-2008 (Left Rule) from Huber et al.’s Latin America and the Caribbean Social Policy

and Political Datasets.53 I use a measure of programmatic party structuration (Program) from the

Democratic Accountability and Linkages Project, where higher values indicate that parties are more

programmatic (and less likely to resort to vote buying and other clientelistic linkage strategies).54

Models 5 and 6 include both the coverage and union density measures. Overall unionization

rates indeed predict greater polarization, but social policy coverage remains a significant predictor of

preferences too. Union density also does not explain variation in the level of support for

redistribution across countries. The Appendix shows that years of Left rule have no significant

effects on the structure of preferences, and programmatic party systems actually are associated with

52 Huber and Stephens 2012; Kitschelt et al. 2010; Pribble 2013; Segura-Ubiergo 2007. 53 Data on the proportion of the workforce organized in unions is limited and only available for the 1990s in Latin America. Huber et al. supplement data compiled by Roberts and Wibbels (1999) with U.S. State and Labor Department reports. 54 In the original dataset, this is variable cosalpo_4nwe.

24

less polarized preferences (and have no impact on the level of support for redistribution).

Another potential omitted variable is crime. High rates of crime are thought to make the rich

more supportive of social policies as a means to reduce crime and violence. If this is the case, then

high crime rates should attenuate the coefficient on income and boost the overall level of support

for redistribution. To examine this possibility cross-nationally, Models 7 and 8 include the crime

rate, measured through the Economic Commission on Latin America and the Caribbean’s estimates

of the homicide rate per 100,000 inhabitants (Crime). The results indicate that high crime is

associated with less polarized preferences, as expected. However, in contrast to findings in the

European literature and theoretical expectations, high crime predicts less overall support for

redistribution, just as crime fears did at the individual level. 55 Reassuringly, social policy coverage

remains a statistically and substantively important predictor of preferences.

One interpretation of these results could emphasize state strength. Certainly eyeballing the

data in Figure 5, countries with more inclusive welfare states also tend to be “strong states” on other

dimensions. Arguments about state capacity follow a similar logic to my emphasis on welfare state

inclusion: in weak states, the poor may be less likely to expect to receive meaningful benefits from

government efforts to reduce inequality and therefore will express less support. Economic elites will

not fear effective redistribution where state capacity is weak, and thus they may express greater

support.56 State weakness therefore may be an additional factor leading to different expectations of

what redistribution entails. I use the World Bank’s government effectiveness measure to proxy for

state strength (Effectiveness). The Appendix shows that government effectiveness is associated with

more polarized preferences across income levels, and a higher level of support for redistribution, as

expected. But social policy coverage measures remain significant predictors of attitudes.

Lastly, measuring household income is a major challenge in developing countries because 55 Rueda and Stegmueller 2016. 56 Soifer 2013 provides a similar argument in the context of democratic transitions.

25

recall of volatile income flows can be inaccurate. A threat to inference is that income is measured

with error, and that error may be larger in countries with less progressive welfare states, leading the

estimates asymptotically to approach zero. In low-income countries, wealth indicators are often

more accurate (with less missing data) than self-reported income and also better reflect the fact that

households smooth consumption patterns. The Appendix therefore shows that the results are

robust to alternative consumption-based measures of income, socioeconomic categories, and

imputations of missing data.57 These findings increase my confidence that measurement error does

not drive the findings.

TESTING THE CROSS-POLICY IMPLICATIONS

The individual-level and cross-national results presented are consistent with my theory of

how the structure of the welfare state relates to redistributive preferences. But many macro-level

variables cluster together. Although I tested for omitted variables, the small number of countries

and imprecise measures makes it difficult to rule out other explanations. In the remainder of the

paper, I therefore look within a single country to probe the relationship between benefit structure

and preferences. This approach allows me to hold constant other features of the political

environment better than any regression specification allows.

No existing survey provides suitably fine-grained measures of attitudes toward a range of

social policies. I therefore ran an original public opinion survey of 900 voters in Bogotá, Colombia

in 2014. Colombia exemplifies a truncated welfare state. While Colombia spends 14 percent of

GDP on social expenditures, or roughly the Latin American average, it improves the income

distribution by less than one percent through tax and spending policies (LIS 2011). Rather than

conduct a nationally representative sample, I focused on Bogotá to further maximize the

57 Handlin (2013) suggests the use of socioeconomic categories based on a combination of education, income or wealth, and household size to operationalize material interests, rather than income alone.

26

comparability of responses. Colombia decentralized governance in the early 1990s, and social

welfare programs vary in quantity and coverage by region.

Another reason that I chose to run a survey in Colombia concerns the operationalization of

income. The Colombian government divides the population into six socioeconomic strata (where

“1” is the lowest and “6” is the highest, which I rescaled from 0 to 1) based on household features.

Because strata are used to determine eligibility for a host of benefits, tax brackets, and service prices,

households are very aware of their class strata. Citizens refer to themselves as “Strata 1”, “Strata 2,”

and so on. Given problems measuring income in developing countries, government class

stratifications provide a much better proxy for material interest and also directly tap into eligibility

for benefit receipt. Bogotá also is an ideal case to study due to significant variation in the targeting of

benefits across different programs.

With the luxury to design my own survey, I consider three separate types of questions. First,

do the poor expect to benefit from redistribution? Second, do individuals who receive more from

the welfare state at present have different social policy preferences from those who are excluded

from expenditures? And, third, how do preferences over specific social policies vary with the

progressivity of taxes and expenditures?

Figure 6 addresses the first question. I asked respondents whether they believe that

government welfare expenditures taken as a whole benefit “people like them.” Individuals’

estimates of whether redistributive policies will benefit them should reflect their perceived chance of

getting assistance, regardless of whether this perception is accurate. The specific question wording

is:

“People often speak about social expenditures, such as health, pension, cash transfers, housing, unemployment benefits, and family benefits. Taken together, do you think state social expenditures tend to help people like you?”

The solid line shows the fraction of respondents who expect welfare expenditures to benefit

27

similarly situated individuals plotted by socioeconomic strata (strata are rescaled from 0 to 1, with 1

being the highest). Overall, the relationship between strata and expectations of benefits is flat. This

finding goes against the expectation from advanced industrial countries that social expenditures are

downwardly progressive. I also asked the question in terms of whether individuals believed that

benefits went to the “middle classes” or to the “lower classes.” The blue dotted line shows that

most respondents, and especially less well-off ones, believe that the middle class receives most

benefits.

Figure 6. Perceived Beneficiaries of Social Welfare Expenditures by Strata.

A drawback of subjective assessments of benefits is that they could reflect underlying

support for the welfare state or political views, rather than actual material benefits. Individuals may

claim to benefit from the welfare state because it suits their political agenda. Rather than focus on

expectations of benefits, I therefore analyze if the receipt of concrete benefits in the present makes

individuals more supportive of expenditures. I focus on benefits that a large number of poor

respondents receive to test whether their weak redistributive preferences can be explained by their

exclusion from expenditures. In particular, my key explanatory variable is whether an individual

receives subsidized health insurance. Subsidized health insurance is likely to predict higher

0.1

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28

redistributive support because it is one the most widespread and progressive social expenditures in

urban Colombia.58 In my survey, roughly half of poor respondents receive subsidized health

insurance.

My dependent variables are binary measures of whether an individual supports or strongly

supports that the government increases spending on a given social policy, even if it means raising

taxes. The advantage of these spending questions is that they invoke a real tension between

additional spending and higher taxes. Such spending questions are tricky in the cross-national

setting because they ask about priorities relative to different national baselines, but this is not a

concern within a single country. I also included the stock question on support for inequality

reduction, as well as a question on taxation that asks whether the rich should pay higher taxes to

fund social expenditures on the poor. As above, I control for basic sociodemographic

characteristics thought to impact social policy preferences, including strata, age, gender, and labor

market status.

Welfare benefits, of course, are not randomly assigned in the population. Despite controls,

it is plausible that beneficiaries differ in unobservable ways beyond their program participation from

nonbeneficiaries with similar demographic profiles. Given my theoretical framework, the key

comparison is the role of income in shaping preferences with and without the measures of benefit

receipt. Controlling for the receipt of benefits represents a counterfactual in which Colombia does

not have targeted spending on the poor, and thus the income coefficient should be less negative

under my theory. Under traditional material interest models, the income coefficient would be more

negative because it would lead the poor to exert more pressure for such a program. Even with the 58 Other progressive benefits like cash transfers are less widespread in Bogotá. Colombia introduced its subsidized health program (SISBEN) in the early 1990s to provide benefits to informal sector workers and insurance coverage of all types increased from under a third to more than 90 percent of the population. The subsidized program costs the government 1.2 percent of GDP and constitutes as large fraction of the social assistance budget, see Camacho, Conover, and Hoyos 2013.

29

relatively barebones control for just one types of targeted social spending, I find that the income

coefficient on redistributive support is reduced by a third (from -0.24 to -0.16) with the addition of

the control for receipt of health subsidies. In other words, if Colombia did not run its subsidized

health program, the predictive power of income would be a third lower.

A final test of my theory is that class should be more predictive of attitudes for types of

expenditures that target the poor. To test this cross-policy hypothesis, I return to questions about

whether funding should be altered for types of social policies that range from clearly progressive to

less clearly progressive. I ask about two types of targeted social programs—cash transfers and

subsidized health insurance. I also ask about two types of truncated programs—housing subsidies

and unemployment insurance. These four programs include both transfer-based expenditures—

cash and housing—and insurance—health and unemployment.

Figure 7 plots support for spending on each social policy by class. The left panel shows the

relationship between class strata and support for targeted social policy tools. There are relatively

sharp differences across class groups with roughly 80 percent of the bottom two class strata in

support of increased expenditures on programs like noncontributory health care, compared to 55

percent of the top strata. Preferences over the tax code are the most polarized by class, with the rich

strongly opposed to “paying more taxes to fund social expenditures on the poor.” The right panel

compares the structure of preferences for less clearly progressive policy areas, namely

unemployment insurance, housing benefits, and the standard question about support for inequality

reduction. Consistent with my theory, the slope on preferences is much flatter.59 The Appendix

shows that these results hold controlling for a host of factors thought to affect an individual’s social

policy beliefs.

59 The inequality reduction item is plotted as strong agreement for inequality reduction only, given that nearly all respondents voice some degree of agreement for inequality reduction when it does not invoke concrete funding proposals.

30

Figure 7. Support for Social Policy Across Domains by Strata.

The beneficiaries of expenditures seem to be more important than whether a policy insures

against risks or transfers resources. For example, compare the solid lines in Figure 7, which

represent expenditure preferences for two standard insurance programs, health and unemployment

insurance. Moene and Wallerstein emphasize that insurance is a normal good and thus demand

should increase with income.60 Neither policy has a positive income gradient, as insurance models

would expect. Rather, consistent with my theory, the slopes differ depending on the progressivity of

the type of insurance. Unemployment programs only cover formal sector workers in Colombia—

perhaps the most surprising part is that low-income groups voice so much support for spending on

them, when they do not benefit. Subsidized health insurance is widespread and progressive. Levels

of support are much higher, and the difference across class groups is sharper, consistent with my

theory. For instance, the income coefficient for support for spending on health insurance is almost

double that for employment (-0.17 compared to -0.08, respectively).

Similar dynamics emerge across types of transfer policies. Means-tested cash transfers

clearly target those below a certain income level. As expected, the poor are the primary supporters

of such policies from almost 70 percent in favor in the lowest class strata to 42 percent in favor in

60 Moene and Wallerstein 2003.

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Chile

ColombiaCosta Rica

Dom. Rep.Ecuador

El Salvador

Guatemala

Mexico

NicaraguaPanama

Paraguay

Peru

Uruguay

-.05

0.0

5C

oeffi

cien

t on

Inco

me

0 5 10Percent Reduction in Gini Coefficient

Argentina

Bolivia

BrazilChileColombia

Costa Rica

Dom. Rep.

Ecuador

El Salvador

Guatemala

Mexico

Nicaragua

PanamaParaguay

Peru

Uruguay.5

5.6

.65

.7.7

5.8

Frac

tion

Supp

ortiv

e of

Red

istri

butio

n

0 5 10Percent Reduction in Gini Coefficient

2

31

the upper-most class strata. Meanwhile, housing subsidies benefit lower-middle and middle-class

groups in Bogotá. The government provides housing vouchers that must then be complemented

with private savings and a bank mortgage to purchase a house, which is out of reach to most poor

families. Thus, lower-income groups voice weak support for these expenditures. As expected, the

income coefficient on conditional cash transfers is much more negative (-0.20) compared to housing

subsidies (-0.06). These results seem to confirm that income matters for social policy preferences

when expenditures actually redistribute resources from rich to poor.

Lastly, my theory predicts that the people whose preferences should vary the most are the

poor, given that they have been excluded from certain policy areas historically. Inspecting Figure 7

gives a sense for the fact that most variation comes in the opinions of the poor. The poor’s support

peaks at almost 80 percent for taxation on the rich and health spending, while it drops to just 50

percent for employment and housing benefits. The nonpoor’s attitudes vary little by type of

expenditure, clustering around 50 percent supportive of additional spending regardless of type.

These results are hardly dispositive, but they are consistent with the idea that the historic exclusion

of the poor from welfare programs helps to explain the weak salience of income for redistributive

preferences. It also is possible that nonpoor respondents are more sensitive to the tax aspect of

spending questions, and that they respond simply based on whether they are willing to pay taxes,

rather than elements of program design.

The Colombia survey data thus bear out each of my hypotheses: the poor do not expect to

benefit from redistribution more than the nonpoor. But, when poor groups do benefit more and

respondents are asked specifically about social policies that do more for them, the “expected”

income-based patterns of support emerge. Just as support for redistribution is weaker and less

polarized in countries in which spending is less progressive, support for different social policies in

Colombia is weaker and less polarized in domains in which the poor are less likely to benefit.

32

CONCLUSIONS

As Esping-Andersen most famously noted, the amount of social spending is epiphenomenal

to its content.61 In some nations and policy domains, tax and spending policies do little to aid the

poor. In other nations and policy domains, the welfare state is much more progressive, both in

terms of who pays and who benefits from state expenditures. These distinctions apply both to

social insurance spending and redistributive transfers. These alternative scenarios, I contend, have

very different implications for the structure of public opinion because they shape what each income

group expects to receive from the welfare state. When social expenditures actually redistribute

resources, the preferences of income groups diverge more strongly. Income-based preferences, as

well as overall higher levels of support for redistribution, become more likely.

I find several types of empirical support for my argument. First, I found that individual

beneficiaries of social policy programs are more supportive of redistribution, both across Latin

America and within Colombia. Second, I documented a cross-national pattern in which income is

more predictive of redistributive preferences, and average levels of support are higher, in welfare

states with greater coverage and progressivity. Third, marshaling original evidence from Colombia, I

show that the poor do not expect to benefit more than the nonpoor from welfare expenditures.

But, poor voters are more supportive than nonpoor voters of fiscally progressive policies. In

drawing on multiple forms of evidence from across as well as within countries, I strengthen the

plausibility of my theory. Combined, my results suggest the continued usefulness of material

interests to understand redistributive demands, once we incorporate the fact that “redistribution”

does not uniformly favor the poor in much of the developing world.

While this paper focuses on the inclusion of the poor, the results help adjudicate between

competing perspectives on the behavior of the rich. On the one hand, scholars who observe the

61 Esping-Andersen 1990.

33

small size and truncation of Latin American welfare states emphasize that the rich use their de facto

influence to stymie the redistributive potential of democracy.62 On the other, others have proposed

that elites affected by rising crime see targeted welfare spending as a way to improve their personal

security or prevent social conflict, attenuating the effects of income on redistributive attitudes in

unequal societies. My results are more consistent with accounts of elite power in blocking taxation

and costly redistributive measures.63 I find little evidence that crime has the hypothesized effects on

boosting support for redistribution, at least at the individual and country level in Latin America. In

Colombia, support for progressive tax policies declines sharply across income groups, although

perhaps it is encouraging that half of upper-income respondents state willingness to pay taxes to

fund spending on the poor. Nevertheless, the evidence is much clearer that the poor turn away

from social policies that do not favor their interests, which are more prevalent truncated welfare

regimes. Rather than the case of an “enlightened elite” that realizes redistribution serves their

interests, I find evidence of “enlightened poor” voters that recognize that their interests often get

lost in debates over the design and administration of social policy programs. This framework also is

consistent with evidence that shows that poor voters have been highly responsive to the roll out of

cash transfer programs, rewarding politicians who introduce or expand CCTs with their votes.64

Uncovering how the structure of public opinion varies across contexts has important

implications for rethinking political coalition formation and change. The nonregressivity assumption

implies that under no circumstances can political coalitions form between the poor and the rich.65 In

contrast, truncated welfare states permit ends-coalitions to cut out the middle class. The poor and

62 Acemoglu and Robinson 2006; Lindert 2004; Weyland 1996a. 63 For instance, on elite resistance to taxation, see Fairfield 2015. It is true that Latin American elites have been willing to increase taxes when spending is earmarked for funding the police, but in contrast to most theories, the mechanism does not work through increased welfare spending (Flores Macías 2014). 64 De La O Torres 2013; 2015; Zucco 2013. 65 Iversen and Soskice 2006.

34

the rich have shared interests in more targeted forms of redistribution, and reductions in social

policies like contributory pensions and health insurance that primarily service formal sector workers.

In this light, the prevalence of populism in developing countries—and particularly “neoliberal”

populists66 who unite the informal sector and economic liberals in opposition to organized labor—

does not merely reflect the irrational embrace of charismatic leaders. Populist leaders may result

organically from a truncated welfare structure in which the poor and rich have far more similar

preferences than would be the case in more “standard” liberal or social democratic welfare states.

More generally, these preference patterns help suggest why the classic Left (pro-state, pro-lower

class) versus Right (anti-state, pro-upper class) political spectrum is an inaccurate description of the

politics of many developing countries. If the state is not consistently perceived as redistributing

resources in the favor of the poor, there is little reason why poor voters should rally behind

expansions in the welfare state and the political Left.

This paper also provides a bridge to recent work on social policy preferences in Europe. In

particular, Beramendi and Rehm have called attention to the role that the design of social policy

plays in explaining why income is a better predictor of attitudes toward redistribution in some

countries in Europe than others.67 But given the greater development and progressivity of welfare

states in advanced economies, they focus primarily on the difference between relatively lower and

higher benefit concentrations, and find that the coefficient on income is more negative in countries

in welfare states in which lower-income groups benefit more from tax and spending policies.68 I

extend the intuition that the distribution of benefits matters to a context in which welfare state

design shows even greater variation. Figure 8 provides a preliminary view of this variation, using a

combination of data from the ISSP (which includes a small subset of Latin American and Asian

66 Roberts 1995; Weyland 1996b. 67 Beramendi and Rehm 2016. 68 Beramendi and Rehm 2016.

35

countries) and the Luxembourg Income Study (on spending progressivity). The relationship

between progressivity and income polarization is stronger and clearer when the set of cases is

expanded to Latin America. There is even greater, and more meaningful, variation in the extent of

inequality reduction when looking beyond advanced industrial economies. Future work may extend

these findings in an even broader set of developing countries, with the proviso that this type of

empirical work rests on the accurate measurement of income, which is notoriously difficult in much

of the developing world.

Figure 8. Income Coefficients, Support for Redistribution, and Progressivity in Europe and Select Latin American and Asian Countries Source: ISSP (2006) and LIS Fiscal Redistribution Database (2011)

Empirically, this paper treated the relationship between public opinion and policy design as

an endogenous one in which inclusive social policies both reflect and shape popular demands.

Future work may probe these causal arrows. If changes in the structure of social spending alter

public opinion, then a logical next step is to examine how attitudes evolve over time. Unfortunately,

AustraliaCanada

Czech R.

Denmark

FinlandFranceGermany

Hungary

Ireland

Netherlands

Norway

Poland

Slovenia

Spain

Sweden

Switzerland

UK

United States

Korea

Taiwan

Japan

Brazil

Colombia

Guatemala

MexicoPeru

Uruguay

Argentina

BoliviaVenezuela

-.25

-.2-.1

5-.1

-.05

0C

oeffi

cien

t on

Inco

me

0 10 20 30 40 50Percent Gini Reduction

36

questions on redistribution have not been asked in Latin America for a sufficient period of time or

frequency to construct such a test. But as regional public opinion polls continue to probe

redistributive attitudes, it is possible to imagine future work examining whether public opinion leads

or follows policy changes. During the 2000s, many Latin American countries moved to include

labor market outsiders in social policies. If these policies expand and solidify—or if the contract

with an economic slowdown in the wake of a commodities boom—it will provide an important

testing ground for how public opinion reacts to changes in the structure and generosity of social

expenditures. Another important area for future behavioral research is to explore how opinions

about specific policy areas carry over into abstract demand for redistribution. The cross-policy

results from Colombia suggest that improvements in some areas, such as conditional cash transfers

and noncontributory health plans, do not automatically improve assessments and demands for other

social policies. Understanding how “sticky” expectations of benefits are and what leads them to

shift remains an important area for future research on redistributive attitudes.

Lastly, the optimistic interpretation of my findings is that Latin America does not face a

tension between the targeting and level of redistribution (yet): when starting from truncated welfare

states, more spending on the poor seems to be associated with higher levels of popular support and

a tighter linkage between income and redistributive preferences. Targeted social policy programs

give the informal sector poor a stake in benefits. So rather than being seen as a death knell for social

democratic coalitions, as in advanced democracies, more targeted spending in Latin America may

reorient politics around redistribution. Tying income more tightly to redistributive preferences may

pave the way for stronger class-based coalitions and parties to reduce the region’s longstanding

inequalities.

37

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Appendix

Table 1 presents the results from logit models using strong support for inequality reduction as abinary dependent variable. Model 1 presents the most basic specification: access to health Healthhas no association with redistributive attitudes, possibly because the question asks about bothprivate and state provision. Pension access falls short of statistical significance in some modelspecifications, but is properly signed across models. Receipt of cash transfers is highly significant,although unfortunately only available for a subset of countries in 2010. Table 2 repeats the samemodels using consumption-based wealth measures instead of income. The results are substantivelysimilar. Table 3 repeats the same models using OLS models and the full 7-point response to supportfor inequality reduction as a dependent variable.

Table 4 replicates the individual-level results using Latinobarometer 2008. Latinobarometerdoes not ask about household income, so I measure income through a series of questions aboutconsumer durables (Wealth). Inclusion, measured only by health Health coverage and formal sectoremployment, due to the lack of questions on pensions, is significant across model specifications.

Table 5 presents additional results from cross-level models, including alternative national-levelvariables, such as government e↵ectiveness, left party rule, programmatic party structuration, andinequality. Table 6 repeats the cross-level models using the LAPOP 2012 AmericasBarometer. Theresults are basically unchanged.

To check that di↵erences in measurement error across country do not drive my results, I usea variety of alternative income measures. AmericasBarometer includes questions about ownershipof consumer durables and access to basic services, which I weight through a PCA and use toinstrument for income (Wealth). Using Handlin (2013)’s recommendations, I also construct analternative measure of socioeconomic class. The additive index gives equal weight to income (orwealth) and educational components. Income (or wealth) is condensed into four categories from1 to 4 of roughly equal size. Then, education is divided into four categories based on meaningfulcut points (the groups capture those with no education or incomplete primary education; completeprimary education but incomplete secondary; complete high school degrees but no higher education;and higher education). These scales are combined to create an index that ranges from 1 to 7; Ishow the wealth-based index Wealth 2 below, although the results are very similar using the incomeindex.

Another version of this scale constructs the same scale using income (or wealth) data weightedby household size, dividing the total household income by the number of adult equivalences (inwhich children are given a lesser weight of 0.65, and economies of scale are adjusted for at a rateof 0.5). Again, I show the wealth based index below, Wealth 3. The weighted wealth indices havethe expected e↵ects on the income slope; the level e↵ects point in the expected direction but losestatistical significance. Table 7 presents the results.

Table 8 runs the same multi-level models using alternative and disaggregated measures of socialpolicy inclusion, including the World Bank’s aggregate measure of social policy coverage, andSEDLAC’s measures for health coverage and pension coverage. I also include the size of the laborforce that works in the informal sector from the ILO. Countries with larger informal sectors actuallyappear to have more polarized preferences, which goes against my theoretical expectations. Theoverall support for redistribution is lower. The coverage variables remain significant even wheninformality is included as a control variable.

Table 9 presents summary statistics for the 2010 LAPOP Americas Barometer. Table 10presents summary statistics for my original survey in Bogota, Colombia.

1

Table 1: Strong Support for Redistribution and Benefit Access, Income Measures

(1) (2) (3) (4) (5)Income -0.0222⇤ -0.0256⇤ -0.0199⇤ -0.0207⇤ -0.0207⇤

(-4.07) (-3.76) (-3.24) (-3.67) (-3.71)

Health 0.0122 0.0368 0.0197 0.0255 0.0209(0.35) (0.87) (0.51) (0.72) (0.59)

Pension 0.0742 0.124⇤ 0.0892⇤ 0.0698 0.0711(1.90) (2.61) (2.09) (1.76) (1.81)

Education -0.00198 -0.00644 0.000854 -0.00354 -0.00351(-0.51) (-1.29) (0.19) (-0.89) (-0.89)

Female -0.0447 -0.0345 -0.0651⇤ -0.0322 -0.0393(-1.61) (-0.99) (-2.08) (-1.12) (-1.38)

Urban -0.0225 -0.0603 0.00586 -0.0300 -0.0287(-0.67) (-1.36) (0.16) (-0.88) (-0.85)

Age 0.110 0.0971 0.158⇤ 0.106 0.123(1.62) (1.14) (2.06) (1.51) (1.77)

CCT 0.152⇤

(3.60)

Unemployed 0.141⇤ 0.0828 0.0867(2.34) (1.48) (1.57)

Shock 0.0204 -0.000570 -0.00861(0.57) (-0.02) (-0.26)

Right -0.205⇤

(-3.51)

Client 0.0142(0.22)

Crime -0.0125⇤

(-6.75)Observations 25896 16621 20503 25116 25462

Source: LAPOP 2010, logit models, country fixed-e↵ects not shown.

t statistics in parentheses, ⇤ p < 0.05.

2

Table 2: Strong Support for Redistribution and Benefit Access, Wealth Measures

(1) (2) (3) (4) (5)Wealth -0.0304⇤ -0.0237⇤ -0.0332⇤ -0.0310⇤ -0.0308⇤

(-5.48) (-3.45) (-5.29) (-5.43) (-5.44)

Health 0.0271 0.0514 0.0291 0.0306 0.0263(0.82) (1.28) (0.79) (0.90) (0.78)

Pension 0.0761⇤ 0.124⇤ 0.0844⇤ 0.0717 0.0751⇤

(2.05) (2.74) (2.06) (1.89) (2.00)

Education -0.000695 -0.00713 0.00470 -0.00104 -0.00143(-0.18) (-1.48) (1.10) (-0.27) (-0.37)

Female -0.0201 -0.00751 -0.0420 -0.0104 -0.0172(-0.76) (-0.23) (-1.40) (-0.38) (-0.64)

Urban 0.00523 -0.0397 0.0467 -0.00115 -0.00110(0.16) (-0.91) (1.25) (-0.03) (-0.03)

Age 0.112 0.0663 0.166⇤ 0.120 0.133⇤

(1.75) (0.83) (2.27) (1.81) (2.03)

CCT 0.156⇤

(3.85)

Unemployed 0.142⇤ 0.0792 0.0833(2.45) (1.48) (1.57)

Shock 0.0119 -0.00123 -0.00882(0.35) (-0.04) (-0.28)

Right -0.205⇤

(-3.65)

Client -0.00120(-0.02)

Crime -0.0119⇤

(-6.95)Observations 28814 18365 22299 27626 28041

Source: LAPOP 2010, logit models, country fixed-e↵ects not shown.

t statistics in parentheses, ⇤ p < 0.05.

3

Table 3: Support for Redistribution and Benefit Access, OLS Model, Income Measures

(1) (2) (3) (4) (5)Income -0.0134⇤ -0.0155⇤ -0.0117⇤ -0.0133⇤ -0.0134⇤

(-3.72) (-3.53) (-2.88) (-3.63) (-3.67)

Health 0.00643 0.0393 0.0115 0.0132 0.0103(0.28) (1.44) (0.45) (0.57) (0.45)

Pension 0.0420 0.0551 0.0447 0.0342 0.0365(1.67) (1.84) (1.61) (1.34) (1.44)

Education 0.000884 -0.00120 0.000674 -0.0000388 0.0000525(0.34) (-0.37) (0.23) (-0.01) (0.02)

Female -0.0402⇤ -0.0277 -0.0469⇤ -0.0314 -0.0361(-2.19) (-1.23) (-2.25) (-1.68) (-1.94)

Urban 0.0119 0.000661 0.0316 0.00628 0.00785(0.54) (0.02) (1.26) (0.28) (0.35)

Age 0.0386 0.0525 0.0559 0.0398 0.0500(0.87) (0.96) (1.10) (0.87) (1.10)

CCT 0.120⇤

(4.47)

Unemployed 0.0864⇤ 0.0490 0.0489(2.21) (1.37) (1.37)

Shock -0.0123 -0.0181 -0.0231(-0.51) (-0.84) (-1.08)

Right -0.212⇤

(-5.50)

Client 0.0150(0.36)

Crime -0.0123⇤

(-9.80)Observations 25281 16232 20215 24569 24884

Source: LAPOP 2010, OLS models, country fixed-e↵ects not shown.

t statistics in parentheses, ⇤ p < 0.05.

4

Table 4: Redistributive Preferences, Latinobarometer2008

Education Pension Health(1) (2) (1) (2) (1) (2)

Wealth -0.040⇤ 0.067 -0.029⇤ 0.104⇤ -0.048⇤ 0.0417(-3.08) (1.29) (-2.54) (2.25) (-3.91) (0.86)

Inclusion 0.187⇤ 0.180⇤ 0.268⇤ 0.259⇤ 0.272⇤ 0.268⇤

(2.60) (2.50) (4.15) (4.01) (4.07) (3.99)

Education -0.002 -0.001 -0.016 -0.015 -0.013 -0.012(-0.21) (-0.09) (-1.95) (-1.78) (-1.48) (-1.38)

Unemployed -0.227 -0.228 -0.235⇤ -0.239⇤ -0.122 -0.123(-1.85) (-1.87) (-2.15) (-2.18) (-1.06) (-1.07)

Shock -0.258 -0.254 -0.051 -0.048 -0.072 -0.071(-1.84) (-1.81) (-0.38) (-0.36) (-0.51) (-0.50)

Female -0.054 -0.059 0.081 0.073 0.053 0.049(-0.82) (-0.90) (1.38) (1.25) (0.88) (0.80)

Urban -0.243⇤ -0.244⇤ -0.252⇤ -0.255⇤ -0.108 -0.110(-3.18) (-3.19) (-3.71) (-3.73) (-1.55) (-1.57)

Age 0.009⇤ 0.009⇤ 0.009⇤ 0.009⇤ 0.009⇤ 0.009⇤

(3.93) (3.93) (4.31) (4.34) (4.39) (4.39)

Right -0.018 -0.018 -0.023⇤ -0.023 -0.030⇤ -0.030⇤

(-1.36) (-1.36) (-1.96) (-1.94) (-2.43) (-2.41)

Religion -0.036 -0.034 0.027 0.030 -0.085 -0.084(-0.54) (-0.51) (0.46) (0.50) (-1.37) (-1.35)

Observations 10692 10692 10652 10652 10748 10748

t statistics in parentheses, country-fixed e↵ects, ⇤ p < 0.05

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Table 5: Preferences for Redistribution and Alternative National-Level Variables(1) (2) (3) (4) (5) (6) (7) (8)

Income 0.0361⇤ 0.0363⇤ 0.0211⇤ 0.0212⇤ 0.00221 0.00238 0.194⇤ 0.194⇤

(3.66) (3.67) (2.41) (2.43) (0.19) (0.20) (4.27) (4.29)

Income*Coverage -0.0689⇤ -0.0690⇤ -0.102⇤ -0.102⇤ -0.112⇤ -0.112⇤ -0.142⇤ -0.142⇤

(-2.59) (-2.59) (-4.17) (-4.17) (-4.58) (-4.58) (-5.45) (-5.46)

Income*E↵ectiveness -0.0569⇤ -0.0570⇤

(-3.08) (-3.08)

Coverage 1.010⇤ 1.845⇤ 1.672⇤ 2.126⇤

(2.32) (3.70) (3.27) (4.16)

E↵ectiveness 1.233⇤

(4.26)

Income*Left -0.00901 -0.00900(-0.39) (-0.39)

Left Rule -0.631(-1.43)

Income*Program 0.0891⇤ 0.0891⇤

(2.17) (2.18)

Programmatic 0.253(0.29)

Income*Inequality -0.317⇤ -0.318⇤

(-3.90) (-3.91)

Inequality 3.354⇤

(2.18)

Education 0.00209 0.00204 0.00197 0.00180 0.00186 0.00171 0.00195 0.00177(0.83) (0.81) (0.78) (0.72) (0.74) (0.68) (0.77) (0.70)

Urban 0.00285 0.00232 0.00233 0.00162 0.00210 0.00135 0.00897 0.00852(0.13) (0.11) (0.11) (0.07) (0.10) (0.06) (0.41) (0.39)

Female -0.0413⇤ -0.0412⇤ -0.0419⇤ -0.0417⇤ -0.0417⇤ -0.0415⇤ -0.0417⇤ -0.0416⇤

(-2.28) (-2.28) (-2.31) (-2.30) (-2.30) (-2.29) (-2.30) (-2.29)

Age 0.0634 0.0633 0.0630 0.0628 0.0632 0.0625 0.0607 0.0601(1.46) (1.46) (1.45) (1.45) (1.46) (1.44) (1.40) (1.39)

Observations 25490 25490 25490 25490 25490 25490 25490 25490Countries 17 17 17 17 17 17 17 17Model FE RE FE RE FE RE FE RE

t statistics in parentheses⇤ p < 0.05, ⇤ p < 0.01, ⇤ p < 0.001

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Table 6: Preferences for Redistribution and National-Level Variables, LAPOP 2012

(1) (2) (3) (4) (5) (6) (7) (8)Income*Progressivity -0.020⇤ -0.018⇤ -0.022⇤ -0.021⇤ -0.010⇤ -0.009⇤

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Progressivity 0.180⇤ 0.204⇤ 0.094⇤

(0.04) (0.05) (0.04)Income*Coverage -0.012⇤ -0.012⇤

(0.00) (0.00)Coverage 0.080⇤

(0.03)Income*Union Density -0.509⇤ -0.523⇤

(0.17) (0.17)Union Density 3.394

(2.03)Income*Crime 0.004⇤ 0.004⇤

(0.00) (0.00)Crime -0.029⇤

(0.01)Income 0.078⇤ 0.073⇤ 0.202⇤ 0.186⇤ 0.177⇤ 0.177⇤ -0.028 -0.018

(0.01) (0.01) (0.03) (0.03) (0.03) (0.03) (0.03) (0.02)Age 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Education -0.032⇤ -0.031⇤ -0.030⇤ -0.032⇤ -0.032⇤ -0.032⇤ -0.031⇤ -0.031⇤

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Urban -0.049 -0.047 -0.061⇤ -0.044 -0.051 -0.050 -0.049 -0.047

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)Female -0.025 -0.025 -0.023 -0.025 -0.017 -0.017 -0.027 -0.025

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)Religion 0.044⇤ 0.043⇤ 0.043⇤ 0.043⇤ 0.042⇤ 0.041⇤ 0.042⇤ 0.042⇤

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Observations 12189 12189 12189 12189 11629 11629 12189 12189Countries 16 16 16 16 15 15 16 16Model FE RE FE RE FE RE FE RE

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Table 7: Redistributive Preferences and Alternative Wealth Measures, LAPOP 2012

(1) (2) (3) (4) (5) (6) (7) (8)Income*Left Rule 0.044 0.063

(0.08) (0.07)Income*Progressivity -0.021⇤ -0.019⇤

(0.00) (0.00)Left Rule -1.177

(0.90)Progressivity 0.180⇤ 0.064⇤ 0.031 0.031

(0.04) (0.02) (0.02) (0.02)Wealth*Progressivity -0.006⇤ -0.006⇤

(0.00) (0.00)Wealth2*Progressivity -0.004⇤ -0.004⇤

(0.00) (0.00)Wealth3*Progressivity -0.004⇤ -0.004⇤

(0.00) (0.00)Income 0.076⇤ 0.067⇤

(0.02) (0.02)Wealth -0.008 -0.007

(0.01) (0.01)Wealth2 -0.002 -0.002

(0.01) (0.01)Wealth3 -0.002 -0.002

(0.01) (0.01)Age 0.001 0.001 0.002 0.002 0.001 0.001 0.001 0.001

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Education -0.032⇤ -0.031⇤ -0.019⇤ -0.019⇤ -0.027⇤ -0.027⇤ -0.027⇤ -0.027⇤

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Urban -0.051 -0.048 -0.013 -0.013 -0.092⇤ -0.092⇤ -0.092⇤ -0.092⇤

(0.03) (0.03) (0.03) (0.03) (0.02) (0.02) (0.02) (0.02)Female -0.010 -0.009 -0.010 -0.010 -0.008 -0.007 -0.008 -0.007

(0.03) (0.03) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)N 12189 12189 14009 14009 20562 20562 20562 20562Countries 16 16 16 16 16 16 16 16Model FE RE FE RE FE RE FE RE

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Table 8: Alternative Measures of Social Policy Inclusion and Informality, LAPOP 2012

(1) (2) (3) (4) (5) (6) (7) (8)Income*Social Inclusion -0.051⇤ -0.023⇤

(0.01) (0.01)Social Inclusion 0.076

(0.12)Income*Health Coverage -0.234⇤ -0.236⇤

(0.04) (0.04)Health Coverage 2.875⇤

(0.49)Income*Pension Coverage -0.113⇤ -0.092⇤

(0.02) (0.02)Pension Coverage 0.958

(0.73)Income*Informality 0.004⇤ 0.004⇤

(0.00) (0.00)Informality -0.047⇤

(0.01)Income -0.024 0.012 0.122⇤ 0.121⇤ 0.074⇤ 0.058⇤ -0.143⇤ -0.147⇤

(0.01) (0.01) (0.02) (0.02) (0.01) (0.01) (0.05) (0.05)Age 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Education -0.029⇤ -0.030⇤ -0.028⇤ -0.028⇤ -0.023⇤ -0.026⇤ -0.030⇤ -0.030⇤

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Urban -0.063⇤ -0.057 -0.059 -0.058 -0.044 -0.026 -0.065⇤ -0.063⇤

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)Female -0.021 -0.017 -0.023 -0.024 -0.029 -0.032 -0.020 -0.020

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)Religion 0.043⇤ 0.043⇤ 0.043⇤ 0.043⇤ 0.044⇤ 0.044⇤ 0.043⇤ 0.043⇤

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)N 13215 13215 13215 13215 13215 13215 12725 12725Countries 18 18 18 18 18 18 17 17Model FE RE FE RE FE RE FE RE

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Table 9: Summary Statistics, LAPOP 2010

Individual-Level Variable Mean Std. Dev. Min. Max. N

Redistribution (Binary) 0.682 0.466 0 1 30873Redistribution 5.819 1.478 1 7 30023Income 4.774 2.923 1 10 26182Health 0.395 0.489 0 1 30746Pension 0.231 0.422 0 1 30641CCT 0.275 0.447 0 1 18704Education 0.526 0.247 0 1 30780Age 0.306 0.226 0 1 30873Female 0.516 0.5 0 1 30873Right 0.518 0.267 0 1 24597Religion 0.441 0.426 0 1 30710Client 0.05 0.217 0 1 28741Crime Fear 0.035 0.185 0 1 30873Unemployed 0.073 0.259 0 1 30873Shock 0.268 0.443 0 1 30159National-Level Variables Mean Std. Dev. Min. Max. N

Coverage 0.313 0.125 0.166 0.602 17Progressivity 3.721 3.414 0.019 11.095 14Unions 0.174 0.072 0.08 0.363 16Crime 22.561 20.453 3.1 90.400 17E↵ectiveness 0.459 0.183 0.133 0.839 17Left Rule 0.163 0.137 0 0.437 17Programmatic 0.23 0.076 0.089 0.332 17Gini 0.512 0.042 0.43 0.570 17

Table 10: Summary Statistics, Social Policy Attitudes in Bogota, Colombia

Variable Mean Std. Dev. Min. Max. N

Benefits Me 0.319 0.361 0 1 893Benefits Middle Class 0.613 0.487 0 1 852Receives Health Subsidy 0.254 0.436 0 1 893Tax 0.701 0.361 0 1 896Housing 0.467 0.404 0 1 896Health 0.597 0.387 0 1 896Inequality 0.822 0.291 0 1 895Unemployment 0.44 0.405 0 1 899Strata 3.167 1.537 1 6 900Education 0.637 0.304 0 1 900Female 0.492 0.5 0 1 891Age 0.538 0.314 0 1 900

10