# Are Fairness Perceptions Shaped by Income Inequality? Evidence from Latin
America††thanks: Gasparini: Centro de Estudios Distributivos, Laborales y
Sociales (CEDLAS) - IIE-FCE, Universidad Nacional de La Plata and CONICET
(e-mail: lgasparini@cedlas.org). Reyes: Cornell University (e-mail
gjr66@cornell.edu). For helpful comments, we thank Carolina García Domench,
Giselle Del Carmen, Rebecca Deranian, Luis Laguinge, participants of the 2021
Annual Bank Conference on Development Economics, and two anonymous referees. A
previous version of this article circulated as “Perceptions of Distributive
Justice in Latin America during a Period of Falling Inequality.” Errors and
omissions are our own.

Leonardo Gasparini  Germán Reyes

( February 2022)

###### Abstract

A common assumption in the literature is that the level of income inequality
shapes individuals’ beliefs about whether the income distribution is fair
(“fairness views,” for short). However, individuals do not directly observe
income inequality (which often leads to large misperceptions), nor do they
consider all inequities to be unfair. In this paper, we empirically assess the
link between objective measures of income inequality and fairness views in a
context of high but decreasing income inequality. We combine opinion poll data
with harmonized data from household surveys of 18 Latin American countries
from 1997–2015. We report three main findings. First, we find a strong and
statistically significant relationship between income inequality and
unfairness views across countries and over time. Unfairness views evolved in
the same direction as income inequality for 17 out of the 18 countries in our
sample. Second, individuals who are older, unemployed, and left-wing are, on
average, more likely to perceive the income distribution as very unfair.
Third, fairness views and income inequality have predictive power for
individuals’ self-reported propensity to mobilize and protest independent of
each other, suggesting that these two variables capture different channels
through which changes in the income distribution can affect social unrest.

##  1 Introduction

Several theoretical and empirical papers study how objective measures of
income inequality affect individual-level behavior.111Researchers have studied
how income inequality affects cooperation (Cozzolino, 2011), demand for
redistribution (Meltzer and Richard, 1981; Finseraas, 2009), dishonesty
(Neville, 2012), social cohesion (Alesina and Perotti, 1996), subjective
wellbeing (Oishi et al., 2011), and trust (Gustavsson and Jordahl, 2008).
Implicit in much of this literature is the assumption that inequality shapes
individuals’ beliefs about whether the income distribution is fair (“fairness
views,” for short).222For example, one important reason why social cohesion
might be related to income inequality is that, as inequality increases, more
individuals perceive the income distribution as unfair, making them more prone
to mobilize. Similarly, we would not expect inequality to be negatively linked
to subjective wellbeing if increases in income disparities were perceived as
fair. However, two pieces of evidence suggest that the link between inequality
and fairness views is not straightforward. First, fairness views are not
informed by objective measures of inequality—since these are not directly
observable by individuals—but instead by perceived inequality. Research about
how accurately people perceive income inequality shows large gaps between
individuals’ perceptions and the actual levels of inequality (Norton and
Ariely, 2011; Kuziemko et al., 2015; Gimpelson and Treisman, 2018; Choi,
2019). Second, even absent any misperceptions, individuals do not consider all
inequities to be unfair. Specifically, individuals largely accept income
disparities derived from personal choices and effort, but deem inequities
driven by luck or chance as unfair (Cappelen et al., 2007; Alesina and
Giuliano, 2011; Cappelen et al., 2013; Almås et al., 2020). Thus, the extent
to which fairness perceptions are shaped by income inequality remains an
important empirical question.

In this paper, we empirically study the link between fairness views and income
inequality in a particular scenario: a region of highly unequal
countries—Latin America—but during a period in which inequality pronouncedly
declined. First, we assess the extent to which fairness views are linked to
income inequality, both across countries and over time. Then, we analyze how
individual-level factors such as education, political ideology, and religious
views relate to fairness views. Finally, we investigate the predictive power
of fairness views for individuals’ propensity to protest and mobilize.

In Section 2, we describe the institutional context and our data. Our setting
is Latin America, one of the most unequal regions in the world (Alvaredo and
Gasparini, 2015). We focus on the 2000s, an unusual period in that income
inequality saw a widespread decrease across countries in the region (Gasparini
et al., 2011). To relate income inequality to fairness views, we combine data
from two harmonization projects. Our source for income inequality data is
SEDLAC, a project that increases cross-country comparability of household
surveys. These data enable us to compare the evolution of income inequality
across countries and over time. The data on fairness perceptions come from
public opinion polls conducted by Latinobarómetro in 18 Latin American
countries since the 1990s.

In Section 3, we document a series of stylized facts about fairness views in
the region. A strikingly high, albeit decreasing, share of the population
believes that the income distribution in their country is unfair. In 2002,
almost nine out of ten individuals perceived the income distribution as unfair
(86.6%). By 2015, this figure declined to 75.1%. This decline is particularly
striking considering that previous research highlights people’s tendency to
report perceptions of stable or increasing inequality, regardless of its
actual evolution (Gimpelson and Treisman, 2018).

Next, we link fairness views to income inequality. We study how the Gini
coefficient—our main measure of inequality—correlates with fairness views
across countries and over time. We find a strong linear correlation between
the Gini coefficient and the percent of the population that perceives
inequality as unfair across country-years. Fairness views evolved in the same
direction as the Gini in 17 out of the 18 countries in our sample during the
2000s.

The decline in income inequality during the 2000s—although remarkable by
historical standards—was not enough to substantially modify the view of Latin
Americans with respect to distributive fairness in their societies. Three out
of four citizens of the region still believe that the income distribution is
unfair. A one percentage point decrease in the Gini is associated with a 1.4
percentage point decrease in the share of the population perceiving the income
distribution as unfair. Holding constant this elasticity and the pace of
inequality reduction of the 2000s, reducing the population that perceives
inequality as unfair to 50% would take roughly more than a decade.

Next, we investigate whether inequality measures other than the Gini
coefficient are stronger predictors of unfairness beliefs. This question is of
interest in its own right, given the ongoing debate on whether income
inequality should be measured with relative or absolute indicators (Ravallion,
2003; Atkinson and Brandolini, 2010). We take an agnostic approach and
correlate a large number of relative and absolute measures of inequality with
unfairness views. We find that relative indicators are strongly positively
correlated with people’s perceptions of unfairness. In contrast, absolute
indicators tend to be negatively correlated with unfairness views. This is
because absolute income gaps became wider in Latin America in the booming
years under analysis, yet perceptions about unfairness went down.

In Section 4, we assess whether the correlation between income inequality and
fairness views is robust to controlling for observable variables and
investigate which individual-level characteristics are predictive of fairness
views. The relationship between unfairness views and the Gini coefficient is
positive and statistically significant even after controlling for country
fixed effects, year fixed effects, and a large set of individual-level
characteristics. We find that older, unemployed, and left-wing individuals are
more likely to perceive income distribution as very unfair. A decomposition
exercise shows that the decline in unfairness perceptions during the 2000s is
better accounted for by income inequality trends rather than changes in the
composition of the population.

In Section 5, we analyze the link between fairness perceptions and
individuals’ self-reported propensity to protest. A vast literature relates
income inequality to social cohesion, conflict, and activism. One might expect
this link to be partly mediated by fairness views. Hence, we study whether
fairness views have predictive power for social unrest, conditional on income
inequality. To do this, we measure individuals’ likelihood of participating in
different political activities, such as mobilizing in a demonstration, signing
a petition, refusing to pay taxes, or complaining on social media.
Participation in these activities is self-reported, so the results should be
interpreted with caution. With this caveat in mind, we find that both fairness
views and inequality have predictive power independent of each other for some
political activities, such as complaining on social media. Other behaviors are
exclusively predicted by fairness views (e.g., signing a petition) or by
income inequality (e.g., refusing to pay taxes). This suggests that both
fairness views and income inequality are important determinants of the
propensity to engage in political activism.

This paper contributes to the literature that links objective measures of the
income distribution to individuals’ perceptions of such measures. Previous
papers have shown that individuals tend to misperceive their relative incomes
(Cruces et al., 2013; Karadja et al., 2017; Hvidberg et al., 2020; Fehr et
al., 2021) and other relevant features of the income distribution, such as the
level of inequality, poverty, and mobility (Kuziemko et al., 2015; Page and
Goldstein, 2016; Alesina et al., 2018; Fehr et al., 2020).333Mismatches
between beliefs and reality are important because there is mounting evidence
that perceptions of facts, more than facts themselves, affect individual
behavior. For example, demand for redistribution is affected more by
perceptions of the income distribution than by the actual distribution
(Gimpelson and Treisman, 2018; Choi, 2019). Thus, understanding what people
believe about the income distribution is important from a policy perspective.
If there are mismatches between perceptions and reality, interventions that
make information less costly or more salient might be desirable. Evidence on
the relationship between fairness perceptions and income inequality,
particularly in Latin America, is rather scarce. To our knowledge, the only
other paper that studies the link between inequality and fairness views in the
region is Zmerli and Castillo (2015), although the focus of this paper is on
political trust. Using the same data that we use, the authors find a positive
association between unfairness views and the Gini. However, the authors only
use data from one year. Hence, they cannot study the joint evolution of both
variables over time or control for unobserved heterogeneity at the country or
year level, which, as we argue below, could generate a spurious correlation
between income inequality and fairness views. We contribute to this literature
by providing novel empirical evidence linking fairness views to income
inequality in a highly unequal region, but during a period of falling
inequality.444A related literature exploits opinion surveys to study
distributive issues in Latin America. CEPAL (2010) documents patterns of
perceptions of distributive inequity during 1997–2007. Using data from
Argentina, Rodríguez (2014) shows that people who consider their income to be
fair tend to perceive lower levels of inequality. Martínez Correa et al.
(2020) explore the effect of immigration on preferences for redistribution in
Latin America.

This paper also contributes to the literature on inequality measurement. This
literature makes a crucial distinction between two types of inequality
indicators: the relative ones (such as the Gini coefficient) and absolute ones
(such as the variance). Relative and absolute indicators often provide
different answers to important issues such as the distributive effects of
globalization or trade openness (Ravallion, 2003; Atkinson and Brandolini,
2010). Hence, it is important to understand whether people think about
distributive fairness through the lens of relative or absolute indicators. We
show that relative indicators have a much stronger correlation with fairness
views than absolute indicators.

Finally, we make a small contribution to the growing literature that relates
income inequality—and more recently, measures of polarization—to conflict and
political activism (Esteban and Ray, 2011; Esteban et al., 2012). Previous
papers have shown that income inequality is predictive of conflict and social
unrest. We contribute to this literature by showing that fairness views have
predictive power for social unrest above and beyond income inequality (and
vice-versa).

##  2 Institutional Context, Data, and Descriptive Statistics

This section provides institutional context on Latin America, describes our
data, and provides summary statistics on our sample.

###  2.1 Context

Latin America has long been characterized as a region with high levels of
income inequality. Together with South-Saharan Africa, Latin America is one of
the two most unequal regions in the world (Alvaredo and Gasparini, 2015; World
Bank, 2016). After a period of increasing inequality during the 1980s and
1990s, the region experienced a “turning point” in the 2000s, when income
inequality saw a widespread decrease (Gasparini et al., 2011; Gasparini and
Lustig, 2011; Lustig et al., 2013).555The widespread decline in inequality
contrasts to what happened in other developing regions in the world, where
inequality modestly decreased (e.g., such as in the Middle East and North
Africa), or even increased (such as in East Asia and Pacific, Alvaredo and
Gasparini, 2015, c.f.). In developed countries, inequality tended to increase
(Atkinson et al., 2011).

###  2.2 Data

We use data on fairness views and income inequality from 18 Latin American
countries from 1997–2015. The data comes from two harmonization projects,
known as Latinobarómetro and SEDLAC (Socio-Economic Database for Latin America
and the Caribbean).

We use public opinion polls conducted by Latinobarómetro to measure fairness
perceptions. Latinobarómetro conducts opinion surveys in Latin American
countries, interviewing about 1,200 individuals per country. The survey is
designed to be representative of the voting-age population at the national
level (in most Latin American countries, individuals aged over 18).666In
Appendix Table B1 we show the percentage of the voting-age population
represented by the opinion polls in our sample for the years in which fairness
data is available. The main variable for our empirical analysis is
individuals’ fairness views, which we measure using the following question:
“How fair do you think the income distribution is in [country]? Very fair,
fair, unfair or very unfair?” We construct binary variables indicating whether
individuals believe that the income distribution is unfair or very
unfair.777Latinobarómetro does not ask respondent why they believe that the
income distribution is unfair. It is possible that some people view the
distribution as unfair because existing disparities are not sufficiently
large. We think that this is unlikely and interpret unfairness views as
reflective of too much inequality.

Income inequality data comes from SEDLAC, a joint project between CEDLAS-UNLP
and The World Bank, which increases cross-country comparability from official
household surveys. We measure inequality in household income per capita
(measured in 2005 USD at purchasing power parity). Whenever possible, we use
comparable annual household surveys to calculate inequality indicators.
However, some countries do not conduct surveys every year, and some of the
household surveys available in a given country are not comparable over time
(usually, due to important methodological changes). In Appendix B we describe
the partial fixes we implement to maximize the sample size. In two countries
of our sample (Argentina and Uruguay), the household survey is representative
of urban areas only.

###  2.3 Sample and Summary Statistics

For our regression analysis, we use individual-level data. Our sample includes
all individuals in the 11 different waves of Latinobarómetro surveys over
1997–2015.

Appendix Tables A1 and A2 show descriptive statistics on our sample. The
average respondent is 39.7 years old. Roughly half of the respondents are men
(49%), over half (56.3%) are married or in a civil union, and 68% are
Catholics. The majority of respondents (76%) completed at least elementary
school, while a third of them (33.6%) had completed high school or more.
Almost two-thirds of the sample (64%) were part of the labor force, and 9.9%
of the economically active individuals were unemployed.888In Appendix B.2, we
show that Latinobarómetro’s sample is similar to the sample in SEDLAC.

##  3 The Relationship between Fairness Views and Inequality

This section first provides descriptive evidence on the evolution of fairness
views in Latin America from 1997 to 2015. Then, we link fairness perceptions
to income inequality both across countries and over time. Finally, we
investigate whether absolute or relative inequality indicators have a stronger
predictive power for fairness views.

###  3.1 The Evolution of Fairness Views in Latin America during the 2000s

Figure 1 shows how fairness views evolved over time (Panel A) and across
countries (Panel B). Panel A plots the fraction of individuals who believe
that the income distribution of their country is very unfair, unfair, fair, or
very fair over 1997–2015, pooling across all countries in our sample. Panel B
shows the fraction of individuals who believe that the income distribution is
either unfair or very unfair in each country of our sample during 2002 and
2015.

Figure 1, Panel A shows that a strikingly high, albeit decreasing, share of
the population believes that the distribution of income is unfair. In 2002,
almost nine out of ten individuals perceived the income distribution as unfair
(86.6%). By 2015, this figure declined to 75.1%. The decrease in unfairness
perceptions was driven mainly by individuals with strong beliefs about
unfairness (i.e., individuals who believe that the income distribution was
very unfair). While in 2002, 33.1% of the population perceived the income
distribution as very unfair, this figure declined to 25.8% by 2015. In
contrast, weak beliefs about unfairness (i.e., individuals who believe that
the income distribution was merely unfair) behaved more erratically,
increasing at the beginning of our sample and decreasing by the end of the
period. Overall, the share of individuals with weak beliefs about unfairness
slightly declined during the 2000s, from 53.5% in 2002 to 49.2% in 2015. On
the other hand, the share of the population that believe that the income
distribution is fair doubled from 11.3% in 2001 to 22.6% in 2015.

Figure 1, Panel B shows that, while most individuals perceive the income
distribution as unfair, fairness perceptions improved in most countries during
2002–2013. A substantial share of the population in all countries perceived
the income distribution as unfair in both 2002 and 2013. For example, in 2002,
the share of the population that perceived the distribution as unfair ranged
from 74.5% in Venezuela to 97.7% in Argentina (which, at the time, was in the
midst of a severe economic crisis). Throughout the following decade, there was
a widespread decrease in the share of the population that perceived income
inequality as unfair or very unfair. Compared to 2002, in 2013, a lower
fraction of the population perceived the income distribution as unfair in 16
out of the 18 countries in our sample. The change in fairness perceptions
ranged from modest decreases, like in Chile, where the decline was of less
than one percentage point, to remarkable reductions, like in Ecuador, where
perceptions about unfairness declined from 87.5% to 38.6%.

Appendix Figure A1 shows that the decline in unfairness perceptions was
widespread across heterogeneous groups of the population. To show this, we
study how fairness views evolved for different subgroups of the population,
according to individuals’ age, gender, education, and employment. This
analysis reveals that young individuals are less likely to perceive the income
distribution as unfair (Panel A), while females are more likely to do so,
although with some heterogeneity across time (Panel B). Similarly, individuals
with a higher educational achievement (Panel C) and the unemployed (Panel D)
are more likely to believe that the income distribution is unfair.
Importantly, the perception of unfairness consistently fell across all these
subpopulations during the 2000s.

Next, we explore the extent to which these changes in fairness views were
accompanied by changes in the actual distribution of income.

###  3.2 Fairness Perceptions and Income Inequality: Some Stylized Facts

Figure 2 shows how fairness views evolved vis-à-vis changes in income
inequality. Panel A shows a binned scatterplot of the Gini coefficients and
unfairness views for all country-years in our sample. Panel B plots the
percentage point change in unfairness perceptions between 2002 and 2013 on the
$y$-axis against the change in the Gini over the same period on the $x$-axis.

Panel A shows that unfairness perceptions and income inequality are strongly
correlated across country-years. The linear correlation between the Gini and
unfairness perceptions across country-years is $0.93$ ($p<0.01$). The share of
the population who perceive income as unfair or very unfair ranges from 63% in
country-years with a Gini in the 0.40 bin (roughly, the average level of
inequality in Venezuela during the 2000s), to 88% in country-years with a Gini
in the 0.60 bin (roughly, the level of inequality in Honduras during the early
2000s).999An OLS regression of unfairness views on the Gini estimated on the
plotted points yields an intercept of 28.2. This implies that, even in a
society where all incomes are equalized, about 28% of the population would
still perceive the income distribution as unfair. This exercise relies on the
strong assumption that the relationship between fairness views and income
inequality is linear. While such a relationship indeed appears to be linear in
Panel A, our data only covers a very narrow range of Gini coefficients
(between 0.40 and 0.60). It is likely that the relationship is non-linear for
Gini coefficients close to zero or one. The correlation between unfairness
views and the Gini is driven by individuals who perceive inequality as very
unfair. The correlation between perceptions of a very unfair distribution and
the Gini is sizable and statistically significant. In contrast, the
correlation between perceptions of a merely unfair distribution and the Gini
is small and indistinguishable from zero.

Panel B shows that the evolution of fairness views tends to mirror the
evolution of income inequality at country level. Fairness views moved in the
same direction as the Gini in 17 out of the 18 countries in our sample. The
one exception is Honduras, where, despite falling inequality, the population
perceived the distribution as more unjust. Most countries lie in the third
quadrant, where both the Gini and unfairness perceptions decreased. The only
country where inequality increased (Costa Rica), also saw an increase in
unfairness beliefs. In Appendix Figure A2 we show that the correlation between
the Gini and unfairness views over time is also strong when pooling across
countries. In this case, the linear correlation is equal to 0.80 ($p<0.01$).
During 2002–2013, a one percentage point decrease in the Gini was associated
with a 1.4 percentage point decrease in the share of the population perceiving
the distribution as unfair. To put this figure in context, this means that, at
the pace of inequality reduction of the 2000s, it would roughly take Latin
America more than a decade to reduce the population that perceives income
inequality as unfair to 50%.

###  3.3 Is Fairness Absolute or Relative?

The literature on inequality measurement makes a crucial distinction between
two types of indicators: relative ones (such as the Gini) and absolute ones
(such as the variance). Relative indicators fulfill the scale-invariant axiom,
while the absolute indicators meet the translation-invariant axiom.101010These
two axioms yield different implications for how inequality responds to a
proportional change in the income of the entire population. A proportional
income increase does not generate changes in income inequality as measured by
relative indicators, but can provoke a large increase in inequality as
measured by absolute indicators. The question of which indicator should be
used in practice has led to a heated debate in the literature (Milanovic,
2016). This is because relative and absolute indicators often provide
different answers to important issues such as the distributive effects of
globalization or trade openness.111111As measured by absolute indicators,
globalization has deteriorated the income distribution since the absolute
income difference between the rich and the poor has increased. However, under
the lens of relative measurement, globalization reduced income inequality
since the poor’s income has grown proportionally more than the income of the
rich.

To shed some light on this debate, we assess whether people think about
distributive fairness through the lens of relative or absolute indicators. To
do this, we take a data-driven approach. We calculate 13 different measures of
income inequality for all the countries in our sample and correlate each
inequality indicator with the share of the population that believes income
distribution is unfair over time.121212The indicators are the Gini
coefficient, the ratio between the 75th percentile and the 25th percentile of
the income distribution, the ratio between the 90th and 10th percentile, the
Atkinson index with an inequality aversion parameter equal to 0.5 and 1, the
mean log deviation, the Theil index, the Generalized entropy index, the
coefficient of variation, the absolute Gini, the Kolm index with an inequity
aversion parameter equal to one, and the variance of the per capita household
income (in 2005 PPP). These last three indices correspond to the absolute
inequality measures, while the other ten indicators are relative inequality
measures.

We calculate the correlation between unfairness perceptions and each
inequality indicator at the regional level using three alternative aggregation
methods. First, we calculate each correlation using the individual-level data
and pooling all countries and years in our sample (columns 1-3). Second, we
calculate the average unfairness views in each country-year and then calculate
the correlation between each inequality indicator and the average fairness
views in the corresponding country-year (columns 4-6). Third, we calculate the
correlation between each inequality indicator and fairness views over time for
each country separately (using the individual-level data) and then average the
correlations across countries (columns 7-9). Table 1 shows the results.

Fairness views tend to be positively correlated with relative indicators and
negatively correlated with absolute ones. In Table 1, column 1 shows that the
Gini is the indicator with the highest linear correlation. On the other hand,
the absolute indicators of inequality tend to be negatively correlated with
unfairness perceptions, and the magnitude of such correlations tends to be
small. The high correlation between unfairness perceptions and income
inequality seems to be driven by the population that perceives inequality as
very unfair (columns 2, 5, and 8), rather than just unfair (columns 3, 6, and
9).131313It is interesting to note that indicators sometimes mentioned in the
mass media, such as the ratio between the richest 90% and the poorest 10%,
exhibit low explanatory power. This may be due to mismeasurement of top
incomes in household surveys.

These results are consistent with experimental evidence from Amiel and Cowell
(1992, 1999), who show that support for the scale-invariance axiom is greater
than for translation invariance, reflecting greater support for relative
inequality indicators.

##  4 Individual-level Fairness Determinants

In this section, we study the individual-level correlates of fairness views.
The purpose of this section is twofold. First, to assess whether the
association between fairness views and income inequality is robust to
including controls. Second, to investigate which individual-level
characteristics are systematically related to fairness views.

###  4.1 Empirical Design

To assess the relationship between individuals’ characteristics and fairness
perceptions, we estimate two-way fixed effects Logit models. This design
controls for two important sources of heterogeneity that could drive the
positive association between inequality and fairness perceptions documented in
the previous section. First, it controls for country-level heterogeneity. This
could matter if, for example, countries with historically extractive
institutions have both higher levels of income inequality and more negative
fairness views as a legacy of such institutions. Insofar as institutions are
stable over time, the country fixed effects deal with this potential bias.
Second, the design controls for year-level heterogeneity. This is important
if, for example, in some particular years, macroeconomic events such as a
financial crisis or a corruption scandal increase income disparities and
worsen fairness views, again generating a spurious correlation between
inequality and fairness perceptions. Including year fixed effects helps to
alleviate such concerns.

Given that changes in fairness views over the last decade were driven by the
share of the population that perceived the income distribution as very unfair
(Figure 1), in our baseline specification, we focus on explaining the
determinants of this variable, although we also show the results for a broader
definition of unfairness. We assume that unfairness perceptions can be
characterized according to the following equation:

$\displaystyle\text{VeryUnfair}_{ict}=F(\lambda_{c}+\lambda_{t}+\gamma\text{Gini}_{ct}+\beta
x_{ict}),$ (1)

where the dependent variable, $\text{VeryUnfair}_{ict}$ is equal to one if
individual $i$ believes that the income distribution of country $c$ during
year $t$ is very unfair and zero otherwise. Equation (1) includes country
fixed effects, $\lambda_{c}$; year fixed effects, $\lambda_{t}$; the country’s
Gini coefficient in year $t$, $\text{Gini}_{ct}$; and a vector of individual
characteristics, $x_{ict}$, that contains age, age squared, sex, marital
status, education, employment status, an assets index, political ideology, and
religious views.141414The assets index takes the value one if individual $i$
has access to running water and sewerage, owns a computer, a washing machine,
a telephone, and a car. In household surveys, these variables tend to be
correlated with household income, although the correlation is usually small.
Unfortunately, we do not observe household income in the Latinobarómetro data.
To measure political views, we rely on the question “In politics, people
normally speak of “left” and “right.” On a scale where 0 is left and 10 is
right, where would you place yourself?” We interpret values closer to zero
(ten) as closer to a liberal (conservative) worldview. We measure religious
views using a dummy that is equal to one if individual $i$ is a Catholic —the
predominant religion in the region—coding other religions (and lack of
religion) as zero. In our baseline specification, $F(\cdot)$ is the logistic
function. We cluster the standard errors at the country-by-year level.

We are interested in $\frac{\partial\text{VeryUnfair}_{ict}}{\partial
x_{ict}}=\beta f(\cdot)$ and
$\frac{\partial\text{VeryUnfair}_{ict}}{\partial\text{Gini}_{ict}}=\gamma
f(\cdot)$. The first of these partial derivatives captures the relationship
between an individual characteristic and unfairness perceptions, controlling
for the rest of the characteristics, the Gini, and the fixed effects.
Similarly, $\gamma f(\cdot)$ measures the relationship between the Gini and
perceived fairness after controlling for individual-level traits and fixed
effects. The magnitude of the partial derivatives depends on the value at
which covariates are evaluated. We compute the marginal effects by evaluating
all covariates at their average value.

###  4.2 Regression Results

Table Tables and Figures shows the estimated marginal effects under different
specifications. Column 1 presents the results controlling only for the fixed
effects and the Gini coefficient. Column 2 includes basic demographic
indicators (age, gender, and marital status). Column 3 includes dummies for
maximum educational attainment (the omitted category is completing up to
elementary school). Column 4 includes dummies for labor force participation
and unemployment. Column 5 includes an index for access to basic services and
asset ownership. Column 6 includes political and religious views.

Consistent with the evidence shown in Section 3, the Gini is positively and
statistically significantly related to unfairness perceptions. In a country
with average characteristics, a one point decrease in the Gini (from 0.49 to
0.48) decreases the share of the population that believes that the income
distribution is very unfair by about half a percentage point ($p<0.01$). This
magnitude does not vary much across specifications (columns 1–6).151515It is
important to stress that the interpretation is not necessarily causal. The
relationship between income inequality and unfairness perceptions can go both
ways. On the one hand, higher inequality can increase the share of the
population that believes the distribution is very unfair. But as more people
perceive inequality as very unfair, the income distribution can change through
several channels (e.g., more corruption or social unrest).

Several individual-level characteristics predict fairness views. Older people
are more likely to perceive the income distribution as very unfair, although
the relationship between age and unfairness perception is non-linear. On
average, males are just as likely as females to perceive the income
distribution as very unfair, while married individuals are slightly less
likely to do so. Completing high school is negatively associated with
perceptions of unfairness, although the magnitude of the coefficient is small.
Being economically active does not seem to be correlated with unfairness
views, but being unemployed does. On average, unemployed individuals are about
two percentage points more likely to perceive the income distribution as
unfair than the employed population. The coefficient on the assets index is
negative, suggesting that relatively better-off individuals are less likely to
view the income distribution as very unfair, although the coefficient is not
statistically different from zero. Ideologically conservative people are
statistically less likely to perceive the income distribution as very unfair,
although the effect size is small (below half a percentage point). Finally,
Catholics are less likely to perceive the income distribution as very unfair.

###  4.3 Robustness

We conduct three robustness checks. First, we use a broader measure of
unfairness that equals one if an individual perceives the income distribution
as unfair or very unfair as the dependent variable in equation (1). Appendix
Table LABEL:unfair_logit shows the results. The magnitude of the coefficient
on the Gini is smaller relative to our baseline specification. Given the
relatively large standard errors, the 95% confidence interval on the Gini
includes zero; but the interval also contains the estimated marginal effects
in our baseline set of regressions (Table Tables and Figures). In other words,
when we use the broader measure of unfairness, our estimates become less
informative. This is because strong unfairness views are more strongly
correlated with income inequality than weak unfairness views (Table 1 and
Figure 2).161616The coefficients of some individual-level characteristics are
also different when using the broader definition of unfairness views. The
effect of completing college on perceptions of unfairness becomes strong and
statistically significant. Civil status stops being statistically significant,
while the male dummy becomes negative and statistically significant (in both
cases consistently so across specifications). The coefficient on the assets
index becomes larger and statistically different from zero. Finally, the
effect of political ideology and religious views becomes statistically
indistinguishable from zero. These results suggest that the population that
perceives the income distribution as very unfair tends to be different in
observable variables than the population that believes that the income
distribution is merely unfair.

As a second robustness check, we estimate an analogous specification to the
one in column 6 of Table Tables and Figures, but controlling for inequality
indicators other than the Gini coefficient. Appendix Table
LABEL:unfair_ineq_logit shows the results. We find a positive correlation
between income inequality and unfairness perceptions across all relative
measures of inequality (columns 1–4). The Gini calculated without households
with zero income, the Atkinson index, the Theil index, and the Generalized
Entropy indicator are consistently correlated with unfairness, and all the
coefficients are statistically different from zero at the usual levels. In
contrast, the absolute Gini (the only absolute inequality indicator in the
table) is negatively correlated with unfairness perceptions, although the
coefficient is statistically indistinguishable from zero.

As a final robustness check, we estimate equation (1) using a linear
probability model (LPM) instead of a Logit. The choice of a LPM is consistent
with the visual evidence shown in Figure 2, Panel A, where fairness views seem
to be linearly related to the Gini coefficient. Appendix Table
LABEL:very_unfair_lpm shows the results. The estimates are quite similar
across specifications. For example, in the specification with the larger set
of controls (column 6), the marginal effect of the Gini coefficient is 0.68 in
the Logit model and 0.63 in the LPM.

###  4.4 Decomposing Changes in Fairness Views Over Time

Both income inequality and individual-level characteristics are associated
with fairness perceptions. Next, we ask which of these two factors mainly
explain (in an accounting sense) the reduction in unfairness beliefs over the
2000s. To do this, we perform a Oaxaca-Blinder decomposition, taking 2002 and
2013 as the two groups to be compared (see Appendix C for details on the
Oaxaca-Blinder decomposition). In the decomposition, we use the broad
definition of unfairness perceptions as the dependent variable and include
controls for demographics, educational attainment, employment status, assets,
political views, and religion. Figure 3 summarizes the results.

During 2002–2013, the share of the population perceiving the distribution as
unfair decreased 14 percentage points, from 87% to 73%. The decomposition
suggests that about 28% of this change (4 percentage points) is accounted for
by changes in the elasticity of fairness views to each covariable (i.e.,
changes in the values of the coefficients in the regression), while the other
72% can be explained by changes in the covariables’ values. Among the
covariables included in the decomposition, the one that mainly explains the
decline in unfairness perceptions is the change in the Gini, which accounts
for 88.9% of the explained component. In contrast, changes in the composition
of the population only account for 11.1% of the explained component. This
result suggests that the decline in unfairness views during the 2000s in Latin
America was mainly driven by changes in income inequality and not by changes
in the composition of the population.

##  5 The Predictive Power of Fairness Views for Social Unrest

There is a vast literature that relates economic inequality—and more recently,
measures of polarization—to social cohesion, conflict, and activism.171717For
instance, Gasparini et al. (2008) find a strong empirical correlation between
inequality and conflict in Latin America. Most previous studies linking
inequality and conflict are based on cross-country regressions, and therefore
have a notably smaller sample size than our paper. Arguably, the relationship
between income inequality and conflict is partly mediated by fairness views.
That is, many individuals mobilize in part because they believe existing
inequities are unfair. However, a given level of income inequality might not
be seen as unfair by some individuals due to, for example, misperceptions of
the actual level of inequality (Gimpelson and Treisman, 2018) or a perception
that income gaps are mainly driven by differences in effort (Alesina et al.,
2001). For these reasons, a regression that links social unrest to income
inequality can contain substantial measurement error. We sidetrack these
issues by directly measuring the link between social unrest and fairness
views.

We measure propensity to engage in social unrest using the opinion polls data.
For several political activities, Latinobarómetro asks respondents whether
they (i) have ever done the activity; (ii) would do the activity; or (iii)
would never do the activity. We investigate eight different types of
demonstrations: making a complaint on social media, making a complaint to the
media, signing a petition, protesting with authorization, protesting without
authorization, refusing to pay taxes, participating in riots, and occupying
land, factories or buildings. We also create a composite index of political
participation which takes the value one if the individual engaged in tax
evasion, an illegal protest, signed a petition, or complained to the media,
and zero otherwise.181818We use those four measures to construct the index
because we do not have data on the other political activities during 2015.
Other years have data on fewer political activities. Participation in these
activities is self-reported. Given that respondents had no financial
incentives for truth-telling, our results should be taken with caution.

For each activity (and the index), we consider two measures of social unrest.
First, we use an indicator that takes the value one if an individual says she
has done the activity in the past and zero otherwise. Second, we use an
indicator that takes the value one if the individual did the activity in the
past or says that she is willing to do the activity. We use these measures as
dependent variables in Logit regressions. The regressions control for
unfairness perceptions, the Gini, and individual-level covariates.
Unfortunately, participation in political activities is available only in a
few years, so our sample size for these regressions is substantially smaller.

Table LABEL:very_unfair_unrest_past, Panel A shows that unfairness perceptions
correlate with participating in political activities in the past. We find
positive and statistically significant effects for complaining on social media
(column 1) and signing a petition (column 3). Conditional on income
inequality, individuals who perceive the income distribution as very unfair
are 1.6 percentage points more likely to have complained through social media
in the past (from a baseline of 7.8%) and 1.3 percentage points more likely to
have signed a petition in the past (from a baseline of 18.6%). The rest of the
effects tend to be positive, although not statistically different from zero.
Conditional on fairness views, we find a statistically significant correlation
between the Gini and complaining on social media (column 1), taking part in an
authorized demonstration (column 5), and the composite index (column 9). The
effect of income inequality on the rest of the activities is statistically
indistinguishable from zero.

Table LABEL:very_unfair_unrest_past, Panel B shows the results when the
dependent variable also includes the willingness to participate in the
political activities. The set of political activities predicted by fairness
views are somewhat different than in Panel A. In Panel B, we find positive and
statistically significant effects for complaining through media (either social
media or traditional media, columns 1 and 2, respectively) and refusing to pay
taxes (column 6). The magnitude of the coefficients that are statistically
significant tends to be larger than in the baseline specification. For
example, the effect of unfairness views on the propensity to complain on
social media is twice as large in Panel B than in Panel A (3.3 vs. 1.6
percentage points, correspondingly). Finally, we find that—holding fairness
views constant—income inequality is predictive of refusing to pay taxes
(column 4). The effect of income inequality on the rest of the political
activities is not statistically different from zero.

Taken together, these results show that there are political activities for
which fairness views and income inequality have predictive power independent
of each other (like complaining through social media). However, there are also
activities that are exclusively predicted by income inequality (like
participating in an unauthorized protest) or fairness views (like signing a
petition). This suggests that both fairness views and income inequality
capture different channels through which changes in the income distribution
can affect social unrest.

##  6 Conclusions

In this paper, we analyze perceptions of distributive justice in a context of
falling income inequality. We show that fairness beliefs moved in line with
the evolution of objective inequality indicators: both unfairness perceptions
and income inequality declined across countries and over time in our sample.
Some individual-level characteristics, such as employment status and political
ideology, are systematically correlated with fairness views. Fairness views
have predictive power for self-reported propensity to mobilize above and
beyond income inequality (and vice-versa).

Our findings are relevant for both researchers and policymakers. For
researchers, our results suggest that, in some contexts, one can proxy
fairness views using relative measures of income inequality, such as the
widely used Gini coefficient. This is reassuring since inequality measures are
much more widely available than fairness views in standard datasets.

For policymakers, our findings warn about concerning levels of dissatisfaction
with existing income disparities. Three in four Latin Americans believe that
the income distribution is unfair, and such perceptions have proved to be
relatively inelastic to a large compression of the income distribution by
historical standards. If fairness perceptions are interpreted as preferences
for some leveling of income, our results indicate that a striking majority is
in favor of reducing the existing disparities between the rich and the poor,
while relatively few people believe that income disparities should remain the
same. A second actionable insight for policymakers is that fairness views act
as a thermometer of individuals’ latent propensity to engage in political
activities. Thus, if policymakers want to prevent social unrest, they ought to
pay attention to the evolution of fairness views and take preventive measures
before the majority of people perceive the income distribution as unfair.

A caveat with our results is that we cannot tell whether most individuals
believe that the income distribution is unfair because (i) they have
inaccurate views about the level of income inequality (perhaps, believing that
income is more unequally distributed than it objectively is); or (ii)
individuals accurately assess the level of inequality and believe that
existing inequities are unfair (perhaps, because the process that generates
income differences is not fair or because existing inequities are too large).
Disentangling the contribution of these and other channels is a challenge for
future research.

## Tables and Figures

Table 1: Correlation between inequality indicators and fairness views,
1997-2015

|  |  |  |  | Averaging correlations
---|---|---|---|---|---
| Individual-level data |  | Country-by-year level data |  | across countries
| U./V.U. | V.U. | U. |  | U./V.U. | V.U. | U. |  | U./V.U. | V.U. | U.
| (1) | (2) | (3) |  | (4) | (5) | (6) |  | (7) | (8) | (9)
Gini coefficient | 0.39 | 0.36 | 0.10 |  | 0.84 | 0.82 | 0.25 |  | 0.39 | 0.28 | 0.15
| (0.07) | (0.07) | (0.09) |  | (0.10) | (0.16) | (0.35) |  | (0.07) | (0.07) | (0.09)
Theil index | 0.39 | 0.38 | 0.05 |  | 0.85 | 0.82 | 0.27 |  | 0.33 | 0.20 | 0.18
| (0.07) | (0.07) | (0.09) |  | (0.09) | (0.16) | (0.35) |  | (0.07) | (0.07) | (0.09)
Atkinson, A(0.5) | 0.38 | 0.35 | 0.10 |  | 0.84 | 0.82 | 0.25 |  | 0.37 | 0.26 | 0.15
| (0.07) | (0.07) | (0.09) |  | (0.10) | (0.16) | (0.35) |  | (0.07) | (0.07) | (0.09)
Atkinson, A(1) | 0.36 | 0.30 | 0.13 |  | 0.84 | 0.82 | 0.25 |  | 0.38 | 0.28 | 0.13
| (0.07) | (0.08) | (0.09) |  | (0.10) | (0.16) | (0.34) |  | (0.07) | (0.08) | (0.09)
Mean log deviation | 0.35 | 0.29 | 0.14 |  | 0.84 | 0.82 | 0.25 |  | 0.38 | 0.28 | 0.13
| (0.07) | (0.08) | (0.09) |  | (0.10) | (0.16) | (0.34) |  | (0.07) | (0.08) | (0.09)
Coefficient Variation | 0.33 | 0.36 | 0.00 |  | 0.78 | 0.78 | 0.19 |  | 0.18 | 0.20 | 0.10
| (0.08) | (0.08) | (0.09) |  | (0.13) | (0.16) | (0.36) |  | (0.08) | (0.08) | (0.09)
Ratio 75/25 | 0.29 | 0.15 | 0.24 |  | 0.80 | 0.78 | 0.26 |  | 0.36 | 0.29 | 0.12
| (0.07) | (0.09) | (0.08) |  | (0.11) | (0.17) | (0.33) |  | (0.07) | (0.09) | (0.08)
Generalized entropy | 0.29 | 0.35 | -0.04 |  | 0.80 | 0.72 | 0.35 |  | 0.18 | 0.09 | 0.19
| (0.05) | (0.08) | (0.08) |  | (0.11) | (0.18) | (0.34) |  | (0.05) | (0.08) | (0.08)
Ratio 90/10 | 0.23 | 0.10 | 0.21 |  | 0.81 | 0.79 | 0.25 |  | 0.30 | 0.30 | 0.07
| (0.07) | (0.08) | (0.08) |  | (0.11) | (0.17) | (0.32) |  | (0.07) | (0.08) | (0.08)
Variance | -0.08 | -0.01 | -0.12 |  | -0.25 | 0.10 | -0.71 |  | -0.06 | 0.04 | -0.12
| (0.07) | (0.08) | (0.08) |  | (0.39) | (0.43) | (0.14) |  | (0.07) | (0.08) | (0.08)
Absolute Gini | -0.21 | -0.10 | -0.18 |  | -0.71 | -0.46 | -0.64 |  | -0.18 | -0.10 | -0.22
| (0.09) | (0.10) | (0.08) |  | (0.23) | (0.26) | (0.31) |  | (0.09) | (0.10) | (0.08)
Kolm, K(1) | -0.31 | -0.18 | -0.22 |  | -0.80 | -0.64 | -0.50 |  | -0.22 | -0.16 | -0.22
| (0.09) | (0.10) | (0.08) |  | (0.12) | (0.18) | (0.37) |  | (0.09) | (0.10) | (0.08)

Notes: This table presents correlations between fairness views and income
inequality. In columns 1–3, we calculate the correlations at the individual-
level pooling all countries and years in our sample. In columns 4–6, we
calculate the average unfairness views in each country-year and then calculate
the correlation between each inequality indicator in the corresponding
country-year and the average fairness views. In columns 7–9, we calculate the
correlation between each inequality indicator and fairness views over time for
each country separately (using the individual-level data) and then average the
correlations across countries. U./V.U. stands for “Unfair or Very Unfair”;
V.U. stands for “Very Unfair”; and U. stands for “Unfair.” Boostrapped
standard errors are in parenthesis.

Table 2: Logit regressions of unfairness perceptions (very unfair) and individuals’ characteristics |  | Dependent Variable: Believes income distribution is very unfair
---|---|---
|  |  (1) |  |  (2) |  |  (3) |  |  (4) |  |  (5) |  |  (6) |
Gini |  |  0.679 |  ∗∗∗ |  0.674 |  ∗∗∗ |  0.678 |  ∗∗∗ |  0.680 |  ∗∗∗ |  0.678 |  ∗∗∗ |  0.684 |  ∗∗∗
|  |  (0.227 |  ) |  (0.225 |  ) |  (0.225 |  ) |  (0.225 |  ) |  (0.225 |  ) |  (0.223 |  )
Age |  |  |  |  0.004 |  ∗∗∗ |  0.004 |  ∗∗∗ |  0.004 |  ∗∗∗ |  0.004 |  ∗∗∗ |  0.004 |  ∗∗∗
|  |  |  |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  )
Age squared |  |  |  |  $-$0.000 |  ∗∗∗ |  $-$0.000 |  ∗∗∗ |  $-$0.000 |  ∗∗∗ |  $-$0.000 |  ∗∗∗ |  $-$0.000 |  ∗∗∗
|  |  |  |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  ) |  (0.000 |  )
Male |  |  |  |  $-$0.002 |  |  $-$0.002 |  |  $-$0.001 |  |  $-$0.001 |  |  0.001 |
|  |  |  |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  )
Married |  |  |  |  $-$0.006 |  ∗ |  $-$0.006 |  ∗∗ |  $-$0.006 |  ∗ |  $-$0.006 |  ∗ |  $-$0.005 |
|  |  |  |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  ) |  (0.003 |  )
Finished HS |  |  |  |  |  |  $-$0.010 |  ∗ |  $-$0.009 |  ∗ |  $-$0.008 |  ∗ |  $-$0.006 |
|  |  |  |  |  |  (0.005 |  ) |  (0.005 |  ) |  (0.005 |  ) |  (0.005 |  )
Finished coll. |  |  |  |  |  |  $-$0.001 |  |  0.000 |  |  0.002 |  |  0.006 |
|  |  |  |  |  |  (0.007 |  ) |  (0.007 |  ) |  (0.006 |  ) |  (0.006 |  )
In labor force |  |  |  |  |  |  |  |  $-$0.003 |  |  $-$0.003 |  |  $-$0.002 |
|  |  |  |  |  |  |  |  (0.004 |  ) |  (0.004 |  ) |  (0.003 |  )
Unemployed |  |  |  |  |  |  |  |  0.020 |  ∗∗∗ |  0.019 |  ∗∗∗ |  0.019 |  ∗∗∗
|  |  |  |  |  |  |  |  (0.007 |  ) |  (0.007 |  ) |  (0.007 |  )
Assets index |  |  |  |  |  |  |  |  |  |  $-$0.009 |  |  $-$0.007 |
|  |  |  |  |  |  |  |  |  |  (0.006 |  ) |  (0.006 |  )
Conservative |  |  |  |  |  |  |  |  |  |  |  |  $-$0.003 |  ∗
|  |  |  |  |  |  |  |  |  |  |  |  (0.002 |  )
Catholic |  |  |  |  |  |  |  |  |  |  |  |  $-$0.011 |  ∗∗∗
|  |  |  |  |  |  |  |  |  |  |  |  (0.004 |  )
N |  |  167,436 |  |  166,105 |  |  164,662 |  |  164,436 |  |  164,436 |  |  164,436 |

Notes: This table shows estimates of the relationship between an indicator
that equals one for individuals who believe that the income distribution is
very unfair and the Gini coefficient controlling for individuals’
characteristics. Coefficients are estimated through Logit regressions and
represent the marginal effects evaluated at the mean values of the rest of the
variables. Observations are weighted by the individual’s probability of being
interviewed. All specifications include country and year fixed effects. ∗∗∗,
∗∗ and ∗ denote significance at 10%, 5% and 1% levels, respectively.
Heteroskedasticity-robust standard errors clustered at the country-by-year
level in parentheses.

Very unfair0.033∗∗0.031∗$-$0.0120.0250.0160.024∗∗$-$0.003$-$0.0010.011
(0.015)(0.017)(0.008)(0.025)(0.014)(0.010)(0.006)(0.008)(0.015)
Gini0.6270.603$-$0.5970.1620.4520.629∗0.0390.0140.954
(0.786)(0.842)(0.479)(0.661)(0.446)(0.328)(0.107)(0.147)(0.695) Mean Dep.
Var.0.4130.4770.5270.2960.2100.1870.0630.0840.688

N18,60518,82752,70417,47518,84618,55316,82316,77918,155

Notes: This table shows estimates of the determinants of participating in the
political activity listed in the column header. Coefficients are estimated
through Logit regressions and represent the marginal effects evaluated at the
mean values of the rest of the variables. Observations are weighted by the
individual’s probability of being interviewed. All specifications control for
age, age squared, gender, marital status, maximum educational attainment,
labor force participation, unemployment status, assets index, political
ideology, and religion. The regression in column 3 also controls for country
and year fixed effects. Column 9 shows a composite index which takes the value
one if the individual reports having engaged in tax evasion, an illegal
protest, signed a petition, or complained in the media, and zero otherwise.
The rest of the political activities are available in only one year and thus
we cannot include fixed effects. Participation in political activities is
self-reported. ∗∗∗, ∗∗ and ∗ denote significance at 10%, 5% and 1% levels,
respectively. Heteroskedasticity-robust standard errors clustered at the
country-by-year level in parentheses.

Figure 1: Fairness views in Latin America over time and across countries

Panel A. Fairness views over time
(1997–2015)

Panel B. Fairness views across countries
(2003 vs. 2013)

Notes: Panel A shows the percentage of individuals who perceive the income
distribution as very unfair, unfair, fair, and very fair in our sample. We
calculate the shares as the unweighted average of fairness views across the 18
countries in our sample. Panel B presents the percentage of the population who
perceives the income distribution as either unfair or very unfair in 2002 and
2013 in each country of our sample.

Figure 2: Fairness views and income inequality in Latin America

Panel A. Correlation between unfairness views and Gini

Panel B. Change in fairness and Gini across countries

Notes: Panel A shows a binned scatterplot of fairness views and the Gini
coefficient. To construct this figure, we group the Ginis of all country-years
in bins of width equal to 0.02 Gini points and calculate the average fairness
perceptions in each bin.

Panel B plots the percentage point change between 2002 and 2013 in the share
of the population that believes that the income distribution is either unfair
or very unfair ($y$-axis) against the change in the Gini coefficient between
2002 and 2013 ($x$-axis) for countries in our sample. Due to a break in data
comparability or household data unavailability, for some countries, we use
inequality data from adjacent years. In 2002, we use: Argentina 2004, Chile
2003, Costa Rica 2010, Ecuador 2003, Guatemala 2006, Nicaragua 2001, Panama
2008, and Peru 2004. In 2013 we use: Guatemala 2014, Mexico 2014, Nicaragua
2014, and Venezuela 2012. See Appendix B for more details.

Figure 3: Oaxaca-Blinder decomposition of unfairness perceptions, 2002-2013

Notes: This figure presents estimates of the Oaxaca-Blinder decomposition (see
Appendix C). The dependent variable is an indicator that equals one for
individuals who believe that the income distribution is unfair or very unfair.
The regressors in the decomposition include the Gini coefficient, age, age
squared, and dummy variables for marital status, gender, educational
attainment, labor force participation, unemployment status, an assets index,
political ideology, and religious views. The “explained” part of the results
refers to changes in the value of the covariables, while the “unexplained”
refers to changes in the coefficients and the interaction terms.

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Appendix

##  Appendix A Additional Figures and Tables

Figure A1: Perceptions of unfairness and individual characteristics, 1997–2015

Panel A. By age

Panel B. By sex

Panel C. By educational attainment

Panel D. By employment status

Notes: This figure shows the share of individuals who perceive the income
distribution as unfair or very unfair according to their age, gender, maximum
educational attainment, and employment status.

Figure A2: The evolution of fairness views and income inequality in Latin
America

Notes: This figure shows the evolution of the average Gini coefficient across
countries in our sample (right-hand-side variable) and the fraction of the
population who perceive the income distribution as unfair or very unfair
(left-hand-side variable) over 1997–2015. To have a balanced panel of
countries over time, we linearly extrapolated the Gini coefficient in years in
which income microdata is not available (see Appendix B).

Table A1: Descriptive statistics of our sample

| Mean | Standard Dev. | Observations
---|---|---|---
| (1) | (2) | (3)
Panel A. Sociodemographic |  |  |
Age | 39.75 | 16.23 | 225,551
Male (%) | 48.97 | 0.50 | 225,567
Married or civil union (%) | 56.27 | 0.50 | 224,081
Catholic religion (%) | 68.01 | 0.47 | 222,790
Ideology (10 = right-wing) | 5.48 | 2.64 | 131,980
Panel B. Education and Labor market |  |  |
Literate (%) | 90.31 | 0.30 | 224,056
Secondary education or more (%) | 33.65 | 0.47 | 224,056
Parents with secondary education (%) | 17.43 | 0.38 | 184,884
Economically active (%) | 64.14 | 0.48 | 225,222
Unemployed (% Labor Force) | 9.89 | 0.30 | 225,222
Panel C. Access to services |  |  |
Access to a sewerage (%) | 69.59 | 0.46 | 222,530
Access to running water (%) | 88.83 | 0.31 | 204,340
Panel D. Asset ownership |  |  |
Car (%) | 28.21 | 0.45 | 222,338
Computer (%) | 33.79 | 0.47 | 222,645
Fridge (%) | 79.22 | 0.41 | 146,686
Homeowner (%) | 73.92 | 0.44 | 223,603
Mobile (%) | 80.61 | 0.40 | 172,253
Washing machine (%) | 54.71 | 0.50 | 223,122
Landline (%) | 42.28 | 0.49 | 222,968

Note: This table shows summary statistics on our sample pooling data from all
countries in our sample over 1997–2015.

Table A2: Fairness views by population group

| % of individuals who believe income distribution is:
---|---
|  Very unfair |  Unfair |  Fair |  Very fair
|  (1) |  (2) |  (3) |  (4)
All |  28.2 |  51.6 |  17.3 |  2.9
Panel A. Gender |  |  |  |
Female |  28.3 |  52.2 |  16.7 |  2.8
Male |  28.0 |  51.1 |  17.9 |  3.0
Panel B. Age group |  |  |  |
15-24 |  25.2 |  52.0 |  19.7 |  3.1
25-40 |  28.5 |  51.3 |  17.2 |  3.0
41-64 |  29.5 |  51.7 |  16.0 |  2.8
65+ |  29.2 |  51.6 |  16.6 |  2.5
Panel C. Civil status |  |  |  |
Married |  28.3 |  51.9 |  17.0 |  2.8
Not married |  27.9 |  51.4 |  17.7 |  3.1
Panel D. Religion |  |  |  |
Catholic |  28.2 |  51.7 |  17.2 |  2.9
Not catholic |  28.0 |  51.5 |  17.5 |  3.0
Panel E. Education level |  |  |  |
Less than Primary |  27.7 |  51.6 |  17.7 |  3.0
Complete Primary |  27.9 |  52.2 |  17.4 |  2.6
Complete Secondary |  29.1 |  53.2 |  15.0 |  2.7
Complete Tertiary |  29.0 |  50.8 |  17.1 |  3.1
Panel F. Type of employment |  |  |  |
Employee |  28.3 |  51.5 |  17.2 |  2.9
Employer |  24.3 |  53.9 |  19.0 |  2.8
Self-employed |  28.0 |  51.4 |  17.5 |  3.1
Unemployed |  30.3 |  51.6 |  15.1 |  3.0
Panel E. Country |  |  |  |
Argentina |  38.17 |  50.74 |  10.26 |  0.83
Bolivia |  18.01 |  56.13 |  23.39 |  2.48
Brazil |  31.95 |  53.71 |  12.85 |  1.49
Chile |  40.20 |  49.93 |  8.42 |  1.45
Colombia |  35.15 |  51.20 |  11.40 |  2.26
Costa Rica |  23.20 |  53.55 |  20.13 |  3.12
Dominican Rep. |  32.31 |  46.52 |  17.61 |  3.56
Ecuador |  21.45 |  47.46 |  27.58 |  3.51
El Salvador |  22.73 |  53.16 |  20.45 |  3.65
Guatemala |  28.29 |  51.34 |  16.70 |  3.66
Honduras |  28.87 |  53.42 |  14.33 |  3.38
Mexico |  32.15 |  49.75 |  15.32 |  2.78
Nicaragua |  18.69 |  51.88 |  24.33 |  5.11
Panama |  27.39 |  48.01 |  20.25 |  4.34
Paraguay |  38.31 |  48.80 |  10.95 |  1.93
Peru |  25.03 |  61.89 |  11.70 |  1.38
Uruguay |  18.22 |  57.51 |  22.64 |  1.64
Venezuela |  23.51 |  42.96 |  26.62 |  6.92

Note: This table shows the fraction of individuals in our sample who perceive
the income distribution as very unfair, unfair, fair, or very fair.

Notes: This table shows estimates of the relationship between an indicator
that equals one for individuals who believe that the income distribution is
unfair or very unfair and the Gini coefficient controlling for individuals’
characteristics. Coefficients are estimated through Logit regressions and
represent the marginal effects evaluated at the mean values of the rest of the
variables. Observations are weighted by the individual’s probability of being
interviewed. All specifications include country and year fixed effects. ∗∗∗,
∗∗ and ∗ denote significance at 10%, 5% and 1% levels, respectively.
Heteroskedasticity-robust standard errors clustered at the country-by-year
level in parentheses.

Notes: This table shows estimates of the relationship between an indicator
that equals one for individuals who believe income distribution is unfair or
very unfair and several inequality indicators controlling for individuals’
characteristics. Coefficients are estimated through Logit regressions and
represent the marginal effects evaluated at the mean values of the rest of the
variables. Observations are weighted by the individual’s probability of being
interviewed. All specifications include country and year fixed effects. ∗∗∗,
∗∗ and ∗ denote significance at 10%, 5% and 1% levels, respectively.
Heteroskedasticity-robust standard errors clustered at the country-by-year
level in parentheses.

Notes: This table presents estimates of the correlation between a dummy
variable that indicates if the individual believes income distribution is
unfair or very unfair and the Gini coefficient controlling for individuals’
characteristics. Coefficients are estimated through a linear probability
model. Observations are weighted by the individual’s probability of being
interviewed. All specifications include country and year fixed effects. ∗∗∗,
∗∗ and ∗ denote significance at 10%, 5% and 1% levels, respectively.
Heteroskedasticity-robust standard errors clustered at the country-by-year
level in parentheses.

##  Appendix B Data Appendix

The figures presented in this paper are based on two harmonization projects,
known as Latinobarómetro and SEDLAC (Socio-Economic Database for Latin America
and the Caribbean). In this Appendix, we describe how we make both sources
compatible.

Our perceptions data come from Latinobarómetro, which has conducted opinion
surveys in 18 LA countries since the 1990s, interviewing about 1,200
individuals per country about individuals’ socioeconomic background, and
preferences towards political and social issues. Unfortunately, not all years
contain questions about individuals’ fairness perceptions. The survey was
designed to be representative of the voting-age population at the national
level (in most LA countries, individuals aged over 18). In Table B1 we show
what percentage of the voting-age population is represented by the survey in
each country for all the years in which the fairness question is available.

Table B1: Coverage of each country’s population in Latinobarómetro overtime (in %) | 1997 | 2001 | 2002 | 2007 | 2009 | 2010 | 2011 | 2013 | 2015
---|---|---|---|---|---|---|---|---|---
Argentina | 68 | 75 | 75 | 100 | 100 | 100 | 100 | 100 | 100
Bolivia | 32 | 52 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Brazil | 12 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Chile | 70 | 70 | 70 | 100 | 100 | 100 | 100 | 100 | 100
Colombia | 25 | 71 | 51 | 100 | 100 | 100 | 100 | 100 | 100
Costa Rica | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Dominican Republic | N/A | N/A | N/A | 100 | 100 | 100 | 100 | 100 | 100
Ecuador | 97 | 97 | 100 | 100 | 100 | 100 | 100 | 100 | 100
El Salvador | 65 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Guatemala | 100 | 100 | 100 | 97 | 100 | 100 | 100 | 100 | 100
Honduras | 100 | 100 | 100 | 98 | 100 | 99 | 99 | 99 | 99
Mexico | 93 | 88 | 95 | 100 | 100 | 100 | 100 | 100 | 100
Nicaragua | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Panama | 100 | 100 | 100 | 99 | 99 | 99 | 99 | 99 | 99
Paraguay | 46 | 46 | 46 | 100 | 100 | 100 | 100 | 100 | 100
Peru | 52 | 52 | 100 | 100 | 100 | 100 | 100 | 100 | 100
Uruguay | 80 | 80 | 80 | 100 | 100 | 100 | 100 | 100 | 100
Venezuela | 100 | 100 | 100 | 100 | 93 | 100 | 100 | 100 | 100
Weighted average | 68 | 86 | 91 | 100 | 100 | 100 | 100 | 100 | 100

Since our goal is to analyze how unfairness perceptions evolved vis-à-vis
changes in income inequality, we put a lot of effort into getting income
inequality data for each data point for which we have perceptions data
available. We made two partial fixes to increase the number of observations
available (without pushing the data too much). First, we filled the data gaps
using household surveys of relatively close years in which previously unused
data were available (see Appendix Table B2). For instance, Chile conducts
household surveys on average every two years. In 1997, there is perceptions
data available, but no data on income inequality. Therefore, we use the
inequality data from an adjacent year (1998). As noted previously, we only use
data from close years if the data from the adjacent year correspond to a year
in which the perceptions question was not asked (and therefore, inequality
data are not needed in that year).

Table B2: Circa years used to fill data gaps

Country | Year without household data | Data point used instead
---|---|---
Chile | 1997 | 1998
Chile | 2001 | 2000
Chile | 2002 | 2003
Chile | 2007 | 2006
Colombia | 2007 | 2008
Ecuador | 2002 | 2003
El Salvador | 1997 | 1998
Guatemala | 2001 | 2000
Guatemala | 2015 | 2014
Mexico | 1997 | 1998
Mexico | 2001 | 2000
Mexico | 2007 | 2006
Mexico | 2009 | 2008
Mexico | 2011 | 2012
Mexico | 2015 | 2014
Nicaragua | 1997 | 1998
Nicaragua | 2007 | 2005
Nicaragua | 2015 | 2014
Venezuela | 2013 | 2012

Our second partial fix involves interpolating inequality indicators for some
years. For some countries, a few years had perceptions data available but no
comparable household survey over time and no close year available. In this
case, and to analyze the same set of countries every year, interpolation was
applied to the inequality indicators (see Appendix Table B3).

Table B3: Years in which inequality indicators were calculated with a linear
interpolation

Country | Years interpolated
---|---
Argentina | 1997, 2001, and 2002
Bolivia | 2010
Brazil | 2010
Chile | 2010
Colombia | 1997
Costa Rica | 1997, 2001, 2002, 2007, and 2009
Ecuador | 1997, 2001
Guatemala | 1997, 2002, 2009, 2010, and 2013
Mexico | 2013
Nicaragua | 2002, 2010, 2011, and 2013
Panama | 1997, 2001, 2002, and 2007
Peru | 1997, 2001, 2002
Venezuela | 2015

Overall, the years in which income inequality was calculated using linear
interpolations represent a relatively small share of the total data points
(17% of total). The majority of our inequality data points (69%) are
calculated using a household survey from the same year in which the
perceptions polls were conducted, while the remaining 14% of our inequality
indicators are calculated using household surveys from adjacent years. Table
B4 summarizes the data sources used in years perceptions data are available.

Table B4: Summary of the data used in every country-year

| 1997 | 2001 | 2002 | 2007 | 2009 | 2010 | 2011 | 2013 | 2015
---|---|---|---|---|---|---|---|---|---
Argentina | 3 | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 2
Bolivia | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1
Brazil | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1
Chile | 2 | 2 | 2 | 2 | 1 | 3 | 1 | 1 | 1
Colombia | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1
Costa Rica | 3 | 3 | 3 | 3 | 3 | 1 | 1 | 1 | 1
Dominican Rep. | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Ecuador | 3 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1
El Salvador | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Guatemala | 2 | 2 | 3 | 2 | 3 | 3 | 1 | 3 | 2
Honduras | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Mexico | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 3 | 2
Nicaragua | 2 | 1 | 3 | 2 | 1 | 3 | 3 | 3 | 2
Panama | 3 | 3 | 3 | 3 | 1 | 1 | 1 | 1 | 1
Paraguay | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Peru | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Uruguay | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
Venezuela | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 3
|  |  |  |  |  |  |  |  |
|  | Both perceptions and inequality data available
|  | Inequality was calculated with a close survey
|  | Inequality was calculated with a linear interpolation
|  | Latinobarómetro did not conduct survey in this year

###  B.1 Imputation of Missing Values for the Regression Analysis

Two of our individual-level variables (political ideology and religion) have
many missing values in some country-years. To deal with this in our
regressions, we imputed the average value of each variable to individuals with
a missing value. In those cases, we included in the regression a dummy that
takes the value one if the value of the variable was imputed and zero
otherwise. The results are similar if we do not impute the values, but the
sample size of the regressions is smaller.

###  B.2 Comparison between Latinobarómetro’s and SEDLAC’s samples

To assess whether there are systematic differences between Latinobarómetro’s
sample and the household surveys’ sample, in Appendix Table B5 we compare a
set of variables available in both datasets during 2013. To ensure
comparability across databases, we restrict the calculations to individuals
over age 18 and countries with data available in both harmonization projects.

In general, the samples are similar in observable characteristics. For
instance, the average age in Latinobarómetro’s 2013 sample is 40.6 years,
while in SEDLAC it is 42.7 years. Similarly, the percentage of males is 48.9%
in Latinobarómetro and 47.6% in SEDLAC. The main difference arises from
educational attainment. On average, the SEDLAC sample is more educated: 46.1%
of the population has secondary education or more, while this figure is 38.8%
in Latinobarómetro.

Table B5: Descriptive statistics in Latinobarómetro and SEDLAC, 2013 (selected
countries)

| Mean |  | Standard Dev. |  | Observations
---|---|---|---|---|---
| Latinob. | SEDLAC |  | Latinob. | SEDLAC |  | Latinob. | SEDLAC
| (1) | (2) |  | (3) | (4) |  | (5) | (6)
Panel A. Sociodemographic |  |  |  |  |  |  |  |
Age | 40.59 | 42.68 |  | 16.43 | 17.25 |  | 14,855 | 1,004,894
Male (%) | 48.97 | 47.63 |  | 0.50 | 0.50 |  | 14,855 | 1,004,894
Married or civil union (%) | 56.77 | 36.41 |  | 0.50 | 0.48 |  | 14,804 | 915,117
Panel B. Education and Labor market |  |  |  |  |  |
Literate (%) | 91.18 | 92.17 |  | 0.28 | 0.27 |  | 14,855 | 1,004,744
Secondary education or more (%) | 38.83 | 46.11 |  | 0.49 | 0.50 |  | 14,855 | 1,001,672
Economically active (%) | 65.14 | 68.66 |  | 0.48 | 0.46 |  | 14,855 | 1,004,718
Unemployed (%) | 5.78 | 4.08 |  | 0.23 | 0.20 |  | 14,855 | 1,004,718
Panel C. Assets and Services |  |  |  |  |  |  |  |
Access to a sewerage (%) | 68.76 | 63.41 |  | 0.46 | 0.48 |  | 13,799 | 975,726
Car (%) | 26.37 | 21.09 |  | 0.44 | 0.41 |  | 11,612 | 643,350
Computer (%) | 46.55 | 47.82 |  | 0.50 | 0.50 |  | 12,747 | 894,003
Fridge (%) | 82.76 | 88.89 |  | 0.38 | 0.31 |  | 12,763 | 894,003
Homeowner (%) | 74.09 | 69.64 |  | 0.44 | 0.46 |  | 14,761 | 1,003,306
Mobile (%) | 86.91 | 91.78 |  | 0.34 | 0.27 |  | 12,754 | 896,079
Washing machine (%) | 60.49 | 56.88 |  | 0.49 | 0.50 |  | 11,816 | 848,350
Landline (%) | 40.22 | 39.47 |  | 0.49 | 0.49 |  | 12,736 | 896,425

Note: This table compares the observable characteristics of individuals in
Latinobarómetro and SEDLAC. Summary statistics were calculated on a restricted
sample (individuals aged over 18) to ensure comparability between both
datasets, pooling data from 14 countries in 2013: Argentina, Bolivia, Brazil,
Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador,
Honduras, Panama, Peru, Paraguay, and Uruguay.

##  Appendix C The Oaxaca-Blinder Decomposition

The starting point to decompose changes in unfairness perceptions between 2002
and 2013 is the following equation:

$\displaystyle\text{Unfair}_{ict}=\beta_{t}X_{ict}+\gamma_{t}\text{Gini}_{ct}+\varepsilon_{ict}\quad\text{for}\quad
t\in\\{2002,2013\\},$ (C1)

where $t$ indicates the year in which perceptions are elicited and $X_{ict}$
is a vector that contains individual-level controls. The fraction of
individuals who perceive the income distribution as unfair in year $t$ can be
calculated as

$\displaystyle\overline{\text{Unfair}}_{t}=\hat{\beta}_{t}\bar{X}_{t}+\hat{\gamma}_{t}\overline{\text{Gini}}_{t}\quad\text{for}\quad
t\in\\{2002,2013\\},$ (C2)

where $\bar{X}_{t}$ is a vector of the average values of the explanatory
variables in year $t$, and $\hat{\beta}$ the vector of OLS-estimated
coefficients. The change in unfairness beliefs between 2013 and 2002 is given
by

$\displaystyle\underbrace{\overline{\text{Unfair}}_{2013}-\overline{\text{Unfair}}_{2013}}_{\equiv\Delta\text{Unfair}}=(\hat{\beta}_{2013}\bar{X}_{2013}+\hat{\gamma}_{2013}\overline{\text{Gini}}_{2013})-(\hat{\beta}_{2002}\bar{X}_{2002}+\hat{\gamma}_{2002}\overline{\text{Gini}}_{2002})$
(C3)

Adding and subtracting
$\hat{\beta}_{2002}\bar{X}_{2013}+\hat{\gamma}_{2002}\overline{\text{Gini}}_{2013}$
to equation (C3) yields

$\displaystyle\Delta\text{Unfair}$
$\displaystyle=\underbrace{\hat{\beta}_{2002}(\bar{X}_{2013}-\bar{X}_{2002})}_{\equiv\Delta\text{Demog.}}+\underbrace{\hat{\gamma}_{2002}(\overline{\text{Gini}}_{2013}-\overline{\text{Gini}}_{2002})}_{\equiv\Delta\text{Gini}}$
$\displaystyle+\underbrace{\bar{X}_{2013}(\hat{\beta}_{2013}-\hat{\beta}_{2002})+\overline{\text{Gini}}_{2013}(\hat{\gamma}_{2013}-\hat{\gamma}_{2002})}_{\text{Residual}}$
(C4)

The first two terms of equation (C) are usually known as the “composition
effect.” These effects capture the difference between the average perceptions
in 2002 and the counterfactual perceptions 2013 had the $\hat{\beta}$’s and
$\hat{\gamma}$—i.e., the elasticity of perceptions to the different
covariables—remained constant during the 2002–13 period. The first term
captures differences in individual-level demographic variables that determine
unfairness perceptions in the model (such as educational attainment, age, and
employment status). The second term captures changes in aggregate trends in
income inequality.

The third term of (C) reflects the difference between the average fairness
views in 2013 and the counterfactual fairness views in 2002 with the
observable attributes of 2013. Thus, this component reflects changes in
fairness views due to changes in the elasticity of the different covariables
between both years. Since we cannot explain why the coefficients attached to
each variable changed, this term is usually viewed as the “unexplained” part
of the decomposition and treated as the residual of the decomposition.