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https://doi.org/10.1177/01925121241309929
International Political Science Review
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DOI: 10.1177/01925121241309929
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Moving in parallel? Economic
inequality and public demand for
redistribution in unequal societies
Cristian Márquez Romo
Institute of Sociology, Goethe University Frankfurt, Germany
Abstract
Although extensive research indicates that economic inequality drives public demand for redistribution,
longitudinal evidence of this association in unequal contexts remains scarce. Using pooled cross-sections of
surveys from over 140,000 individuals consistently observed between 2008 and 2019, this study tests the
inequality-redistribution nexus in Latin America. I examine both the general association between inequality
and public demand for redistribution as well as the conditional effect of individual-level income. Main results
suggest that public preferences over redistribution systematically react to rising inequality. Findings further
indicate that this effect is consistent across income groups. In line with a growing body of work, public
demand for state-led redistribution increases as inequality grows, holding household income constant,
suggesting that individuals tend to update their redistributive preferences in parallel and the gap in support
for redistribution among income groups is small given the region’s sharp levels of economic inequality.
Keywords
Inequality, redistribution, public opinion, political economy, Latin America
Introduction
Extant research has emphasised the importance of examining the existence of a self-regulating
mechanism that prevents income inequality from rising to too high a level in a society. From a self-
interest framework, the Romer–Meltzer–Richard (RMR) model (Meltzer and Richard, 1981;
Romer, 1975) expects that citizens demand more redistribution as inequality increases. Although
some studies have offered evidence consistent with this hypothesis (e.g., Finseraas, 2009; Franko,
2016; Kenworthy and Pontusson, 2005), scholarship yields conflicting results. Some scholars
argue that since citizens are unlikely to know where they fall in the income distribution, people’s
understanding of the level of inequality tends to be biased (e.g., Becker, 2024; Bobzien, 2020;
Cruces et al., 2013; Gimpelson and Treisman, 2018). Others show, however, that exposure to une-
qual contexts can increase the awareness of objective levels of inequality (e.g., Minkoff and Lyons,
Corresponding author:
Cristian Márquez Romo, Institute of Sociology, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, Frankfurt am
Main, Hesse 60323, Germany.
Email: marquezromo@em.uni-frankfurt.de
1309929IPS0010.1177/01925121241309929International Political Science ReviewMárquez Romo
review-article2025
Review
2 International Political Science Review 00(0)
2019; Sprong et al., 2019) and that people can react to macroeconomic changes by updating their
opinion and signalling their relative preference towards a desired policy (e.g., Enns and Kellstedt,
2008; Wlezien, 1995, 2004; Wlezien and Soroka 2011, 2012).
Given that recent evidence suggests that income inequality is on the rise in most countries
(Piketty and Sáez, 2014; Zucman, 2019) and that regions including Latin America have compara-
tively high levels of income inequality (Alvaredo and Gasparini, 2015; Amarante et al., 2016;
Sánchez-Ancochea, 2020), testing the proposition underlying the RMR model remains crucial.
Yet, to do so, most studies employ either non-random samples or samples from countries with
comparatively low levels of inequality (e.g., Breznau and Hommerich, 2019; Hillen and Steiner,
2024; Schmidt-Catran, 2016), or test their expectations by relying on cross-sectional or aggregate
longitudinal data (e.g., Dallinger, 2010; Finseraas, 2009; Lübker, 2007), which makes it harder to
validate whether this relationship is spurious or not (Fairbrother, 2014).
To contribute to filling this gap, this article explores the inequality-redistribution nexus in Latin
America, using pooled cross-sections of surveys from over 140,000 individuals consistently
observed in 18 countries during a period spanning over a decade (2008–2019). In order to test the
general relationship between income inequality and public demand for redistribution, I first fit
fixed effects (FE) and random effects within and between (REWB) models. To further assess the
extent to which this relationship is conditioned by individuals’ positions in the income distribution,
I test the interaction effect between country-level inequality and individual-level income.
Main results indicate a strong positive and sizeable longitudinal effect of income inequality on
public demand for redistribution. Firstly, echoing Wlezien’s (1995, 2004) thermostatic model, find-
ings suggest that public preferences regarding redistribution systematically react to rising levels of
income inequality. In line with a growing scholarship (e.g., Andersen et al., 2021; Hillen and Steiner,
2024; Schmidt-Catran, 2016), this result suggests that when inequality is comparatively high in a
country, public demand for redistribution tends to increase. Secondly, the conditional relationship
suggests that this positive within effect is consistent across income groups. That is, people tend to
demand more state-led economic redistribution as inequality increases, regardless of where they fall
within the income distribution. These results provide evidence that in highly unequal contexts, the
gap in support for redistribution among income groups tends to be small. In line with previous
research indicating that income groups often change their redistributive preferences in parallel (e.g.,
Enns and Kellstedt, 2008; Gonthier, 2017; Soroka and Wlezien, 2009), findings suggest that all
income groups update their preferences towards more redistribution as inequality rises, with a
slightly weaker effect among the well-off. Implications and limitations of these findings are further
discussed in the conclusions section.
Income inequality and public demand for redistribution: The
Romer–Meltzer–Richard model and beyond
An abundant body of research analyses the existence of a self-regulating mechanism that prevents
income inequality from rising too high in a society. The RMR model, a pioneering theory in the
study of redistributive preferences (originally proposed by Romer (1975) and developed by Meltzer
and Richard (1981)), expects public demand for redistribution to be stronger in countries with
higher levels of economic inequality. Under a basic tax assumption, this theory expects the median
voter to demand more redistribution as long as that voter’s income is smaller than the average
income. From a self-interest approach, preferences over redistribution will depend on the effect of
redistribution on an individual’s net income. As inequality increases, the median voter will have
more incentives to benefit from, and thus support, redistribution (Franko, 2016; Kevins et al.,
2018; Meltzer and Richard, 1981).
Márquez Romo 3
Scholars have offered some evidence in support of the RMR model. Using cross-sectional indi-
vidual-level data from the European Social Survey (ESS) and the International Social Survey
Program (ISSP), Dallinger (2010) and Finseraas (2009) find a positive effect of economic inequal-
ity on public demand for redistribution. Using panel data, Jæger (2013) finds that economic growth
generates a lower demand for redistribution, but the opposite is true for income inequality.
Analysing both differences across countries and over time, Schmidt-Catran (2016) finds a strong
positive longitudinal effect of inequality on public demand for redistribution in 27 European
countries.
Despite its importance as a pioneering theory in the study of redistributive preferences, an
important body of research has questioned the RMR model, yielding conflicting results. Analysing
the USA, Bénabou (2000) initially found that income inequality could actually depress support for
redistribution across all income groups, showing that the relationship can be negative depending
largely on welfare-enhancing benefits. This finding has been supported by most American scholar-
ship (e.g., Erikson et al., 2002; Kelly, 2009; see Romero Vidal, 2021, for a detailed explanation),
suggesting that increasing inequality can trigger conservative preferences among both the rich and
the poor (Luttig, 2013).
Several cross-national studies also claim that public demand for redistribution can largely
depend on persistent differences in values and cultural understandings or beliefs about inequality
(e.g., Breznau and Hommerich, 2019; Gimpelson and Treisman, 2018). For example, support for
redistribution can depend on which is social group is considered the main beneficiary of these
redistributive policies (i.e., redistribution is conceived of as either ‘taking’ from the rich or ‘giving’
to the poor; see Cavaillé and Trump, 2015), or simply whether people are more or less averse to
inequality (Fehr and Schmidt, 1999). Furthermore, against the expectations of the RMR model,
some studies find a positive interaction effect between individual- and country-level income ine-
quality (e.g., Dion and Birchfield, 2010; Finseraas, 2009), indicating that the negative effect of
individual-level income can become weaker in contexts of sharp inequality. Thus, although demand
for redistribution is expected to decline for individuals located at the highest income deciles, this
effect can be highly dependent on the macrolevel of inequality. Under certain circumstances (e.g.,
when they acknowledge the negative externalities of inequality, such as violent crime; see Rueda
and Stegmueller, 2016), even affluent individuals can be prone to supporting redistribution. Hence,
the structure of inequality (i.e., the relative distance between the rich and the poor and thus the
level of stratification of a society) can be a more important predictor of demand for redistribution
than the absolute level of inequality (Lupu and Pontusson, 2011).
One of the main shortcomings of the RMR model is that it assumes individuals are aware of
their exact positions relative to the median income. Yet, knowing where one falls within the income
distribution requires both access to information and abilities to process it (i.e., to compare their
current situation with that of someone earning the median income). Since this information can be
costly to acquire—and the advantages of doing so not always evident—people tend to develop
biased perceptions of the overall income distribution (Cruces et al., 2013). Recent research shows
that, since people are not aware of their exact position in the income distribution, this makes them
more prone to underestimating the objective levels of inequality (e.g., Becker, 2024; Bobzien,
2020; Cruces et al., 2013; Gimpelson and Treisman, 2018).
Going beyond the expectations of the RMR model, an important body of work suggests that
demand for redistribution can depend not only on the net tax benefits or disadvantages of redistri-
bution, but also on the extent to which people are uncertain about their future income (Drazen,
2000; Rehm, 2009). This approach echoes Rawls’ (1971) ‘veil of ignorance’, suggesting that so
long as people are uncertain about their societal position in the future, they will have incentives to
support policies in favour of the most disadvantaged. Given that people can be risk averse, demand
4 International Political Science Review 00(0)
for redistribution can largely depend on peoples’ risk exposure, that is, the extent to which they
think that they will need redistributive support given the possibility of being poor in the future
(Moene and Wallerstein, 2001). The expectation here is straightforward: The higher the risk expo-
sure, the more individuals will be in favour of redistribution (Rehm, 2009).
The underlying expectation of the risk aversion framework is that both rich and poor citizens
can support redistribution given that it ‘smooths the income stream of individuals and shares the
risk of income shocks across society’ (Rehm, 2009: 858). In this vein, an alternative salient expla-
nation of why macroeconomic changes can be a driving force of changes in people’s support of
redistribution builds upon Wlezien and Soroka’s (2011, 2012) ‘thermostatic feedback’. The ther-
mostatic feedback model illustrates the evolution of public preferences by measuring how policy
and public preferences adjust to each other. For instance, people can support government interven-
tion when unemployment is rising, update their preferences towards a desired level of taxation or
react to incumbents by shifting ideologically (Bartle et al., 2011, 2020; Weiss, 2012). In a nutshell,
the ‘thermostat’ effect measures how members of the public signal their position towards a desired
policy direction in respect to current policy (Romero-Vidal, 2020). Thus, while people might not
be aware of their exact position in the income distribution, nor of the exact amount of governmen-
tal spending required, they can react and signal their relative preferences about the extent to which
they believe government should implement policies to reduce the gap between the rich and the
poor. Echoing the long-standing argument that claims macroeconomic fundamentals affect indi-
vidual preferences and behaviour, I expect economic inequality to be a driving force of changes in
mass-policy attitudes and, more specifically, that public preferences react to rising levels of income
inequality.
To examine the association between income inequality and support for redistribution, scholar-
ship relies either on cross-sectional or aggregate-level longitudinal data (e.g., Dallinger, 2010;
Dion and Birchfield, 2010; Finseraas, 2009; Jæger, 2013; Kenworthy and Pontusson, 2005; Lübker,
2007) or tests expectations using non-random samples or samples from economically developed
countries (e.g., Breznau and Hommerich, 2019; Hillen and Steiner, 2024; Schmidt-Catran, 2016)
with comparatively low levels of income inequality (Theyson and Heller, 2015). Yet, static survey
data makes it more difficult to validate whether a relationship is spurious or note and survey data
considered in aggregated form are exposed to the risk of committing an ecological fallacy
(Fairbrother, 2014). Furthermore, considering that demand for redistribution can depend largely on
the macrolevel of inequality and thus the degree of stratification in a society, income differences
can be less relevant in explaining support for redistribution in high-inequality contexts (Dion and
Birchfield, 2010; Rueda and Stegmueller, 2016). An exception to this research agenda is Franetovic
and Castillo (2022). The authors assess the longitudinal effect of inequality on support for redistri-
bution in 17 Latin American countries.1 However, the authors do not find statistically significant
associations between income inequality and economic redistribution, concluding that ‘in contrast
to the evidence from studies conducted in other regions, the results reveal that in Latin America it
is not possible to detect a clear association between income and redistributive preferences at spe-
cific times’ (Franetovic and Castillo, 2022: 1). As I will show below, the results presented here
differ substantively from those presented in their study.
Building on this literature, I establish three hypotheses. Firstly, I expect a positive cross-sec-
tional relationship between income inequality and public demand for economic redistribution
(H1). Secondly, since not only persistent levels but also changes in macrolevels of inequality affect
redistributive preferences, I also expect a positive longitudinal relationship between income ine-
quality and public demand for economic redistribution (H2). Acknowledging that, particularly in
highly unequal contexts, exposure to inequality can affect individuals from different income groups
(Dimick et al., 2018; Minkoff and Lyons, 2019; Rueda and Stegmueller, 2016), I expect the
Márquez Romo 5
positive association between income inequality and public demand for economic redistribution to
be consistent across income levels. In other words, the redistributive preferences gap among
income groups should be smaller when and where inequality is comparatively higher (H3).
Data
This study uses data from six survey waves of the Americas Barometer from the Latin American
Public Opinion Project (LAPOP Lab, 2021),2 which has gathered data on the policy preferences of
citizens within the region every two years since 2004. Each survey wave typically includes between
25,000 and 30,000 respondents from all Latin American countries. These data are gathered in face-
to-face interviews and the final sample is representative at the national level.3
LAPOP data allows us to study the evolution of citizens’ public demand for economic redistri-
bution, using pooled cross-sections of surveys (non-repeated observations on a large random sam-
ple of micro-level units, nested in a repeated set of observations from a non-random sample of
macro-level units; see Fairbrother, 2014), consistently collected every two years for over a decade
(2008–2019). Combining data from six survey waves strongly increases the number of cases, mak-
ing results less dependent on specific survey-wave peculiarities (Duijndam and van Beukering,
2021). After listwise deletion of missing values, pooling six survey waves in which citizens were
asked about their preferences towards economic redistribution results in a sample of 140,001
respondents, 101 country-years and 18 countries.4
The dependent variable in my analysis is measured with a question asking citizens the extent to
which they believe the government should implement policies to reduce income inequality on a
7-point Likert scale (1—strongly disagree, 7—strongly agree).5 This survey item has been vali-
dated and used in previous studies that measure public demand for economic redistribution (e.g.,
Finseraas, 2009; Luttmer and Singhal, 2011; Schmidt-Catran, 2016). For descriptive statistics of
the dependent variable, see the online appendix (Table A2, supplementary materials).
The independent variables of my analysis are country-level economic inequality and individual-
level income. To measure country-level economic inequality I rely on the Gini index, based on
disposable (post-tax, post-transfer) household income distributions, from the Standardized World
Income Inequality Database (SWIID) (Solt, 2020). To account for potential confounding, consider-
ing that disposable income partially measures how much the government is currently redistributing
via policies and outlays, I also present additional models, including the pre-tax Gini measure (see
Table A5, supplementary materials).
Individual-level income is measured using the item from the Americas Barometer: ‘And into
which of the following ranges does the total monthly income of this household fit, including remit-
tances from abroad and the income of all the working adults and children?’. Given that the LAPOP’s
income measure was introduced with a scale that ranges between 0 and 10 in waves 2008 and 2010,
yet between 0 and 16 afterwards, I recoded the 17-point scale into an 11-point scale, following
Franetovic and Castillo (2022: 5).
At the country level, previous research suggests that it is necessary to control for a country’s
level of economic prosperity to ensure that the effect of the Gini index is not spurious (e.g.,
Finseraas, 2009; Heston et al., 2002; Schmidt-Catran, 2016). Although the relationship might not
always be linear and it may be more pervasive in some countries than others, economic prosperity
can be a potential confounder when growth produces more inequality. Although some studies have
found this association to be positive (e.g., Forbes, 2000; Li and Fu Zou, 1998) and others negative
(e.g., Alesina and Rodrik, 1994; Persson and Tabellini, 1994), an important body of work has
offered evidence of this relationship (see Van der Weide and Milanovic, 2018, for a discussion). To
account for this, I include the logged annual national real gross domestic product (GDP) per capita,
6 International Political Science Review 00(0)
drawn from the Penn World Table (PWT) (Feenstra et al., 2015). Finally, at the individual level, I
control for a set of standard socio-demographic variables: gender (1 = female), age (a continuous
variable with a mean of 39), years of schooling (19-point scale, from 0—none to 18—university or
more), location (1 = urban) and employment (1 = employed). For descriptive statistics of all vari-
ables, see the online appendix (Table A3, supplementary materials).
Method
To examine whether public demand for redistribution is affected by a country’s level of economic
inequality, I fit FE and REWB models. Firstly, since economic inequality is a property of the con-
text in which individuals are socially embedded, not accounting statistically for this dependency
between observations would violate the independent errors assumption (Bell and Jones, 2015;
Moulton, 1986). Secondly, given that including the Gini index in a random effects framework
without decomposing it into within and between components would provide an uninterpreted
weighted average of both (see Schmidt-Catran and Fairbrother, 2016), I calculate and separately
introduce the group-mean of economic inequality for each country, pooling across all available
years and then subtracting each overall average from each country-year. The latter procedure, also
known as ‘demeaning’, has the important advantage of allowing researchers to relax the assump-
tion of omitted variable bias caused by any time-invariant, unit-specific differences (Jordan and
Philips, 2023). This is the standard procedure used in FE or within-group models. Thus, the so-
called ‘within transformation’ should make the resulting coefficient and standard error similar in
the within portion of both the REWB and the FE models (Bell et al., 2019). The model can be
specified as follows:
yxtrendZZZ
ue
itk itk tk WE tk kBEk ktkitk
0112
The model treats respondents ( i) as nested in country-years (
t
), which are in turn nested in coun-
tries ( k). Predictors can thus be included at any of these levels. While the coefficient vector
γ
WE
provides the within effects and the coefficient vector
γ
BE the between effects, the
ZZ
tk k
term
is equivalent to the FE transformation. This estimation will not be identical if the transformation is
not applied to all time-varying predictors and the panel is unbalanced, but the corresponding coef-
ficients should be similar (Andreß et al., 2013). Finally, to ensure that the relationship is not spuri-
ous given the fact that both the outcome and predictor variables are trending due to unrelated
reasons, I control for time by including a time trend. I believe that the most appropriate functional
form for time given the data generating process is linear, considering that the evolution of the out-
come variable appears to be trending linearly (see Figure A1, supplementary materials). The next
section presents the results.
Results: Descriptive
At the individual level, the grand-mean of citizens’ support for redistribution is 5.57, indicating
that Latin Americans tend to endorse the implementation of policies to reduce income inequality
between the rich and the poor.6 However, demand for redistribution in the region has declined dur-
ing the last decade (see Figure A1, supplementary materials). As Figure 1 shows, this pattern is
consistent overall within countries.
Márquez Romo 7
At the contextual level, preliminary results indicate a positive association between citizens’
demand for redistribution and the level of income inequality in their countries. Yet, the correlation
between the cross-sectional portion of the Gini index and the average levels of public demand for
redistribution across all years is only 0.09 (p < 0.001) (Figure 2(a)). Instead, the longitudinal com-
ponent suggests a stronger association between income inequality and public demand for redistri-
bution within countries, with a correlation of 0.54 (p < 0.001) (Figure 2(b)). To assess whether this
this association holds when both individual- and country-level controls are included, the next sec-
tion presents the multivariate results.
Results: Multivariate
Table 1 presents the results. Models 1–3 introduce the REWB specification, and Models 4 and 5
the FE specification. Model 1 includes the Gini index, without decomposing into its cross-sectional
and longitudinal components. Model 2 introduces separate cross-sectional and longitudinal effects
of economic inequality on public demand for redistribution. Models 3 and 5 include interaction
terms between inequality and individual-level income.
Model 1 suggests that the Gini index is positively and significantly associated with public
demand for redistribution, indicating that citizens’ support for redistribution is stronger in coun-
tries with higher inequality. Nevertheless, Model 2 shows that when decomposing into the within
and between portions of inequality, the association only reaches statistical significance within
countries. This clear positive association is statistically significant both in the main (
β
= 0.099,
p < 0.001) (Model 2) and the interactive model (
β
= 0.140, p < 0.001) (Model 3). As Models 2
and 4 show, the coefficient is similar in the REWB and the FE specifications.
Figure 1. Public demand for redistribution in Latin America.
Source: Author’s own elaboration based on Latin American Public Opinion Project data (2008–2019). Note: The figure
shows individuals’ mean levels of support for redistribution (black line) and level of income inequality (red line) for each
country-year. Both variables have been rescaled for substantive interpretation. Colour online only.
8 International Political Science Review 00(0)
These results suggest that, opposite to what was expected in H1, there is no evidence of a cross-
sectional association between economic inequality and public demand for redistribution. Instead,
as hypothesised in H2, these results indicate a clear positive longitudinal relationship between
income inequality and public demand for redistribution. Thus, while we cannot reject the null
hypothesis that countries with greater inequality tend to have higher levels of support for redistri-
bution, results are consistent with the hypothesis that, as inequality grows within a country, public
preferences regarding redistribution tend to increase. The coefficient indicates than a 1-unit
increase on the Gini index produces an average increase of 0.10 points on the 7-point public sup-
port for redistribution scale. In other words, support for redistribution should increase (or decrease)
by 1 percent for each 1-unit change in the Gini index. For example, in Argentina, where inequality
declined from 41.6 to 37.9 between 2008 and 2019, the model predicts a decrease in support for
redistribution of about 4 percentage points. In Bolivia, where inequality declined from 48.8 to 40.5
during the same period, the model predicts a decrease in demand for redistribution of about 8 per-
centage points.
In order to test H3, Figure 3 presents the association between economic inequality and demand
for redistribution over time, conditional on individual-level income differences. The figure shows
an overall positive relationship between income inequality and public demand for redistribution,
which holds across income groups. As hypothesised in H3, individuals tend to support income
redistribution as inequality increases, regardless of where they are located within the income dis-
tribution. The slope, however, is less steep for individuals located at the highest income levels,
indicating that although there are no significant differences between income groups (i.e., the asso-
ciation remains positive for both more and less advantaged individuals), the relationship between
inequality and demand for redistribution becomes slightly weaker as household income increases.
In other words, these results suggest that all income groups tend to update their preferences towards
more redistribution with rising inequality, with a marginally weaker effect among the well-off. The
positive association, however, is consistent across income levels overall, within yet not between
countries (see also Figure A2, supplementary materials). This result suggests that the redistributive
preferences gap between income groups is consistently low when but not where income inequality
is comparatively higher.
Figure 2. (a) Income inequality and public demand for redistribution in Latin America. (b) Income
inequality and public demand for redistribution in Latin America.
Source: Author’s own elaboration based on Latin American Public Opinion Project data (2008–2019). Note: The figures
show the mean level of demand for redistribution versus income inequality (a) or time (b), with a linear regression line.
BE: between; WE: within.
Márquez Romo 9
Table 1. The effect of income inequality on Latin Americans’ redistributive preferences.
Predictors REWB specification FE specification
(0) (1) (2) (3) (4) (5)
Income 0.001 (0.002) 0.001 (0.002) –0.180* (0.072) –0.002 (0.002) –0.065** (0.021)
Economic inequality 0.064*** (0.016) 0.097*** (0.003) 0.091*** (0.004)
GDP/capita (logged) 0.057 (0.109) 0.004 (0.026) –0.004 (0.027)
Economic inequality (BE) 0.008 (0.018) –0.008 (0.018)
Economic inequality (WE) 0.099*** (0.020) 0.140*** (0.021)
GDP/capita (logged) (BE) 0.259* (0.124) 0.285* (0.123)
GDP/capita (logged) (WE) 0.000 (0.141) –0.022 (0.141)
Cross-level interactions
Economic inequality (BE)*Income 0.004* (0.002)
Economic inequality (WE)*Income –0.010*** (0.001)
Economic inequality*Income 0.001** (0.000)
Constant 5.580*** (0.073) 2.792** (0.869) 4.781*** (0.918) 5.467*** (0.913) 0.868*** (0.173) 1.187*** (0.206)
Var (countries) 0.073 0.105 0.049 0.047
Var (country-years) 0.124 0.091 0.087 0.088
Var (individuals) 2.619 2.570 2.570 2.564
Countries Yes (18) Yes (18)
Source: Author’s calculations, Latin American Public Opinion Project 2008–2019.
REWB: random effects within and between; FE: fixed effects; GDP: gross domestic product; BE: between; WE: within.
Notes: ***p < 0.001, **p < 0.01, *p < 0.05. Standard errors in parentheses. All models are based on 18 countries, 101 country-years and 140,001 individual observations, and
control for gender, age, level of education, location and labour market status. Model 3 includes both random intercept and random slope for the income variable. FE models
include robust standard errors, and Bolivia, the country with the widest sample of interviewees (12,789), is the reference category. Full results in Table A4.
10 International Political Science Review 00(0)
To further assess the consistency of these results, I conducted a series of additional tests. Firstly,
considering the Gini index of disposable incomes makes theoretical sense but can also introduce
potential confounding, I re-estimated the model, including the market Gini index. Introducing both
measures yields similar results (see Table A5, supplementary materials). Secondly, to assess the
extent to which this result is driven by some countries more than others—and hence to confirm
whether this association is robust to potential outlier cases—I conducted jackknife estimations,
re-estimating the model while excluding each country at a time. All models yield positive and sta-
tistically significant coefficients (p < 0.001), which range from 0.066 when excluding Venezuela
to 0.109 when excluding Honduras (see full results in Table A6, supplementary materials). Thirdly,
these jackknife estimations shed light on the absence of a relationship across countries. As Table
A6 shows, except when Venezuela is excluded from the sample, the coefficient of the cross-sec-
tional portion of the Gini index is always positive, yet insignificant (with a modest effect size,
ranging from 0.003 when excluding Paraguay to 0.020 when excluding Uruguay). While this result
yields further support for H2 and against H1, it also shows how both coefficients consistently point
in the same direction. Substantively, this suggests a relationship both across countries and over
time, which can be potentially explained due to constraints in the available sample. These limita-
tions and their implications are further discussed in the next section.
Discussion and conclusion
This article has reassessed the association between income inequality and public demand for redis-
tribution, taking into account the direct effect higher macrolevels of inequality as well as changes
in the conditional effect of individual-level income. Building on the political economy scholarship
that explains the relationship between inequality and public demand for redistribution across time
and space, I fitted FE and REWB models to a sample of over 140,000 individuals in 18 Latin
American countries, surveyed every two years between 2008 and 2019.
Figure 3. Public demand for redistribution, conditional on levels of income inequality and individual-level
income (95% confidence interval).
Source: Author’s own elaboration. Note: results are based on Model 3, including individual and contextual controls.
Márquez Romo 11
Two main findings emerge from the analysis. Firstly, both using disposable and pre-tax Gini
measures, results indicate a clear longitudinal association between economic inequality and public
demand for redistribution. That is, while there is no evidence to substantiate that countries with
higher levels of income inequality tend to have higher levels of support for redistribution, results
show that Latin Americans demand more redistribution as inequality grows. This finding stands in
contrast with comparative studies that suggest demand for redistribution does not change as a func-
tion of levels of inequality (e.g., Breznau and Hommerich, 2019; Gimpelson and Treisman, 2018).
It also points in the opposite direction of the work of Franetovic and Castillo (2022: 12), who
conclude that, ‘unlike what has tended to be stated in other contexts, such as Europe, in Latin
America it is possible to observe an absence of a relationship between people’s income and their
agreement with the application of public policies to reduce inequalities’.
Instead, the results are in line with comparative studies that provide evidence of a longitudinal
effect of income inequality on public demand for redistribution (e.g., Andersen et al., 2021; Hillen
and Steiner, 2024; Jæger, 2013; Schmidt-Catran, 2016). A longitudinal effect—net of countries’
levels of economic prosperity and controlling for compositional effects at the individual level—is
more robust evidence to ensure this relationship is not spurious. Still, it is theoretically reasonable
to expect cross-sectional and longitudinal effects to converge over the long run (i.e., that increases
in demand for redistribution within countries translate into countries with higher levels of demand
for redistribution). In this vein, as Table A6 shows, the coefficient of the cross-sectional portion of
the Gini index is very consistent with the longitudinal portion. This consistently positive coeffi-
cient, although insignificant, suggests a potential effect across countries. Therefore, these results
should be taken carefully, considering that this study is constrained by limitations given the avail-
able sample of Latin American countries. While the study benefits from including representative
samples from all Latin American countries, reducing case selection bias or due to non-random
samples of countries (Beck, 2001; Schmidt-Catran et al., 2019), it is also constrained by limited
statistical power (see Britt and Weisburd, 2010) given the available number of units (N = 18) and
time points (T = 6). With these caveats in mind, there is also no contradiction if within and between
effects point in substantively different directions. In turn, ‘it is a big leap to interpret differences
between countries as a potential effect of a change within a country’ (Gelman, 2005: 461).
Secondly, the longitudinal effect of income inequality on public demand for redistribution
appears robust when accounting for individual-level income differences. That is, Latin Americans
tend to demand more redistribution when inequality is comparatively higher, regardless of where
they are located within their country’s mean income. This finding stands in line with scholarship
that argues household income differences are less relevant in explaining redistributive preferences
in contexts of sharp inequality (e.g., Dimick et al., 2018; Dion and Birchfield, 2010; Romero-
Vidal, 2021; Rueda and Stegmueller, 2016). Notably, however, the results show that the slope is
less steep for higher income individuals. This suggests that this positive association becomes
weaker as an individual’s household income increases and, more specifically, that the effect is
marginally weaker for the well-off (as the RMR model would expect). Nevertheless, a clear posi-
tive association across different income groups suggests that even individuals not located at the
lowest income levels tend to support economic redistribution in the long run (also in line with the
risk exposure framework; Drazen, 2000; Moene and Wallerstein, 2001; Rehm, 2009). All in all,
these findings provide empirical evidence in line with the scholarship arguing that individuals tend
to update their levels of support for redistribution with rising inequality, often experiencing parallel
shifts in their redistributive preferences (e.g., Enns and Kellstedt, 2008; Gonthier, 2017; Soroka
and Wlezien 2009).
This study contributes to a growing body of work that explores the association between inequal-
ity and redistribution across countries and over time. Delving into the implications of these results,
12 International Political Science Review 00(0)
further studies should go beyond the first proposition of the RMR model (i.e., that public demand
for redistribution should increase as inequality rises) in order to assess the extent to which demand
for redistribution is in fact being expressed in votes (proposition 2) and supplied by incumbent
parties that implement redistributive policies (proposition 3). Differently put, in contexts where
increases in income inequality do seem to translate into higher demand for redistributive policies,
researchers should start focusing less on the demand and more on the supply side of redistribution;
that is, the extent to which public demands are in fact being translated into public policy (see Hillen
and Steiner, 2024). Following propositions 2 and 3 from the RMR model, the often-puzzling
absence of redistributive policies may be more about political parties and elites being reluctant to
implement them than about people not reacting and demanding more redistribution. In fact, recent
studies have shown how policy-makers can become less supportive of redistribution with growing
inequality (e.g., Márquez Romo and Marcos-Marne, 2023), and that higher inequality tends to
produce wider gaps and more disparity in redistributive preferences between political elites and the
public (e.g., Weihua and Maoliang, 2017). These considerations should be analysed taking into
account both levels and changes in inequality on a particular set of cases, or comparative studies
from countries with historical similarities. Despite its being well known that Latin America is one
of the regions with one of the highest levels of inequality in the world (Alvaredo and Gasparini,
2015; CEPAL, 2016; Sánchez-Ancochea, 2020), history evidences important periods of decrease
in income inequality under specific circumstances (Kapiszewski et al., 2021; Lustig, 2011).
Therefore, offering robust empirical evidence to examine the extent to which public preferences
for redistribution lead to governmental action to tackle inequality is key to understanding both the
structural levels of inequality in the region as well as specific trends towards increasing or decreas-
ing inequality over the last decades.
Acknowledgements
The author is indebted to two anonymous reviewers of this journal for their helpful comments and sugges-
tions. All remaining errors are the author’s own.
Data availability statement
The necessary materials to replicate the analyses in the study are available in the online supplementary
materials.
Declaration of conflicting interests
The author declares no conflict of interest with respect to the research, authorship and/or publication of this
article.
Funding
The author disclosed receipt of the following financial support for the research, authorship and/or publication
of this article: This study is part of the project POLAR (‘Polarization and its discontents: does rising economic
inequality undermine the foundations of liberal societies?’) that has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme
(Grant agreement n° 833196). Neither the European Research Council nor the primary data collectors or the
providers of the data used in this research bear any responsibility for the analysis or the conclusions of this
paper.
Ethical considerations
Ethical approval is not applicable to this article.
Márquez Romo 13
ORCID iD
Cristian Márquez Romo https://orcid.org/0000-0003-3272-6802
Supplemental material
Supplemental material for this article is available online.
Notes
1. In contrast with Franetovic and Castillo (2022), I include the complete sample of 18 Latin American
countries, taking advantage of the available information for all sources of interest (see Tables A1–A3 in
the online appendix for a display of the sample and descriptive statistics of the main variables included
in the analysis).
2. I am grateful to the LAPOP and its main donors for making this data available.
3. More detailed information is available at: https://www.vanderbilt.edu/lapop/methods-practices.php
4. Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras,
Nicaragua, Mexico, Panama, Paraguay, Peru, Dominican Republic, Uruguay and Venezuela. Only four
countries have been surveyed on less than six occasions: Chile, Venezuela, Guatemala and Nicaragua.
Chile and Venezuela have been observed on five, and Guatemala and Nicaragua on four occasions. See
Table A1 in the supplementary materials for a display of the specific years of fieldwork and total number
of interviewees.
5. ‘The (Country) government should implement strong policies to reduce income inequality between the
rich and the poor. To what extent do you agree or disagree with this statement?’.
6. This figure is similar in regions such as Europe, with a grand-mean of 3.80 using a 5-point scale (cf.
Schmidt-Catran, 2016).
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Author biography
Cristian Márquez Romo is a postdoctoral fellow at the Institute of Sociology at Goethe University Frankfurt.
He is currently a researcher in the European Research Council project POLAR (‘Polarization and its discon-
tents: does rising economic inequality undermine the foundations of liberal societies?’) and the chair for
Social Stratification and Social Policy. His research, comparative in nature, lies in the intersection between
social stratification and political behaviour. (marquezromo@em.uni-frankfurt.de)
