CESifo Working Paper no. 6251


 

Experienced Inequality and Preferences 
for Redistribution 

 
 
 

Christopher Roth 
Johannes Wohlfart 

 
 
 

CESIFO WORKING PAPER NO. 6251 
CATEGORY 2: PUBLIC CHOICE 

ORIGINAL VERSION: DECEMBER 2016 
THIS VERSION: SEPTEMBER 2018 

 
 

 
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CESifo Working Paper No. 6251 
 
 
 

Experienced Inequality and Preferences 
for Redistribution 

 
 

Abstract 
 
We examine whether individuals’ experienced levels of income inequality affect their 
preferences for redistribution. We use several large nationally representative datasets to show 
that people who have experienced higher inequality during their lives are less in favor of 
redistribution, after controlling for income, demographics, unemployment experiences and 
current macroeconomic conditions. They are also less likely to support left-wing parties and to 
consider the prevailing distribution of incomes to be unfair. We provide evidence that these 
findings do not operate through extrapolation from own circumstances, perceived relative 
income or trust in the political system, but seem to operate through the respondents’ fairness 
views. 

JEL-Codes: P160, E600, Z130. 

Keywords: inequality, redistribution, macroeconomic experiences, fairness. 
 
 
 

Christopher Roth 
Department of Economics 

University of Oxford & CSAE 
Manor Road Building, Manor Road, 
United Kingdom - Oxford OX1 3UQ 
christopher.roth@economics.ox.ac 

Johannes Wohlfart 
Department of Economics 

Goethe University Frankfurt 
Theodor-W.-Adorno-Platz 3, PF H32 
Germany - 60323 Frankfurt am Main 

wohlfart@econ.uni-frankfurt.de 
 

  
  

 
September 17, 2018 
We are grateful to the editor, Monica Singhal, and two anonymous referees for providing thoughtful 
comments that improved the paper considerably. We also would like to thank Alberto Alesina, Heike 
Auerswald, Peter Bent, Enzo Cerletti, Anujit Chakraborty, Leonhard Czerny, Franziska Deutschmann, 
Ester Faia, Eliana La Ferrara, Nicola Fuchs-Schündeln, Alexis Grigorieff, Ingar Haaland, Michalis 
Haliassos, Emma Harrington, Lukas Hensel, Michael Kosfeld, Ulrike Malmendier, Salvatore Nunnari, 
Matthew Rabin, Sonja Settele, Uwe Sunde, Guido Tabellini, Bertil Tungodden, Paul Vertier, Ferdinand 
von Siemens and seminar and conference participants at Frankfurt, ZEW Mannheim, the CESifo 
Workshop on Political Economy (Dresden), the Econometric Society (Edinburgh) and the Spring 
Meeting of Young Economists (Halle) for helpful comments. We also thank Ulrike Malmendier and 
Stefan Nagel for sharing code. Johannes Wohlfart thanks for support through the DFG project 
“Implications of Financial Market Imperfections for Wealth and Debt Accumulation in the Household 
Sector”. 



1 Introduction

Over the last decades, many industrialized countries have seen dramatic increases in income in-

equality (Piketty, 2014). This is reflected in substantial variation across cohorts in the level of

inequality individuals were exposed to during their lives. Macroeconomic experiences play a key

role in shaping people’s preferences, beliefs and economic choices in various contexts, such as in-

vestment behavior (Malmendier and Nagel, 2011), inflation expectations (Malmendier and Nagel,

2016) and political attitudes (Fuchs-Schuendeln and Schuendeln, 2015; Giuliano and Spilimbergo,

2014). In this paper, we use large observational datasets to explore whether experienced levels

of income inequality affect the level of inequality people find acceptable and their demand for

redistribution.

People have an aversion to inequality (Fehr and Schmidt, 1999) and their views about what is

an acceptable level of inequality are an important determinant of their demand for redistribution

(Alm̊as et al., 2011; Cappelen et al., 2007, 2013a,b, 2017). Experiencing a high level of inequality

during one’s lifetime could either increase or decrease people’s acceptance of inequality. On the

one hand, people could get accustomed to high levels of inequality and demand less redistribution.

This is related to the idea that individuals evaluate the current state against a reference point

that is influenced by the state in past periods (Abel, 1990; Coppock and Green, 2017). On the

other hand, people may develop an even stronger distaste for inequality if they have first-hand

experience of high inequality, resulting in greater demand for redistribution.1

The direction of the effect of inequality experiences has important implications for the long-

run evolution of inequality and redistribution in Western societies. If experiences of high in-

equality make people more accepting of inequality, younger generations, who are used to an

unequal distribution of incomes, will be less likely to vote for policies that reduce inequality.

Consequently, support for such policies could become weaker as these cohorts make up a larger

share of the electorate. If, by contrast, living through times of high inequality makes people

more averse to unequal distributions of income, this could translate into increasing support for

redistributive policies, and could thereby curb inequality.

We present evidence on the effect of inequality experiences on people’s demand for redistribu-

tion using several large nationally representative datasets on political attitudes: the US General

1This hypothesis is related to a literature from psychology on the differential effects of description versus
experience on belief formation and decision making (Hertwig et al., 2004; Nisbett and Ross, 1980; Simonsohn et
al., 2008; Weber et al., 1993). Accordingly, if inequality is a “bad”, then experiencing this “bad” directly will have
a stronger influence on people’s preferences and beliefs than simply reading about inequality.

1



Social Survey, the German General Social Survey as well as the European Social Survey.2 We

examine inequality experiences defined in several ways, with a particular focus on our respon-

dents’ experiences of income inequality while growing up, which we measure by calculating the

average level of income inequality that prevailed in their country while they were between 18 and

25 years old. This period of life, which is sometimes referred to as “impressionable years”, has

been identified as particularly important for the formation of political attitudes and beliefs (Giu-

liano and Spilimbergo, 2014; Krosnick and Alwin, 1989; Mannheim, 1970). For each birth-cohort

in our datasets we compute the share of total income held by the top five percent of earners

while this cohort was in their impressionable years.3 We find very similar results using measures

of life-time income inequality experiences following the methodology in Malmendier and Nagel

(2011). In some of our specifications we also exploit variation in inequality experiences according

to the region in which the respondent has grown up.

In all of our main specifications we control for age fixed effects and year fixed effects, i.e. we

identify our key coefficient of interest using between-cohort differences in inequality experiences

within age groups and years. By including age fixed effects, we rule out that our findings result

from changes in preferences over people’s lifetime, for example, by people becoming more conser-

vative as they get older. The inclusion of year fixed effects ensures that our results are not driven

by common shocks that affect everyone in a given year. In addition, we control for cohort-group

fixed effects (cohort group brackets of 25 years) which mitigates the concern that our findings

are driven by differences in political attitudes across cohorts associated with longer-term changes

in zeitgeist.4 Throughout, we control for income and a number of socioeconomic characteristics

as well as the national unemployment rate people experienced during their impressionable years

which could be correlated with inequality experiences and could itself affect people’s demand for

redistribution (Giuliano and Spilimbergo, 2014).

Across datasets we provide evidence that individuals who have experienced higher levels of

income inequality are less in favor of redistributive policies and are less likely to vote for left-

wing parties. We also find that people who have lived through times of high inequality hold

more positive beliefs about inequality and are less likely to consider the prevailing distribution

2We also replicate our main findings using data from the International Social Survey Program.
3Our results are robust to using alternative measures of income inequality, namely the share of total income

held by the top ten percent of earners, the share of total income held by the top one percent of earners, as well
as the Gini coefficient of equivalized household incomes.

4Since we control for both age and year fixed effects, we cannot also include dummies for every individual
cohort (Campbell, 2001). In addition, inequality experiences vary at the cohort-level, which prohibits separate
identification of unrestricted cohort effects.

2



of incomes to be unfair. One plausible interpretation of these findings is that growing up under

an unequal income distribution alters people’s perception of what is a fair division of resources,

and thereby reduces their demand for redistribution.

We also examine alternative channels through which experiencing inequality could affect our

respondents’ demand for redistribution. First, people could form their redistributive preferences

based on the effect inequality had on them personally. In this case only people who personally

benefited from high inequality while growing up should demand less redistribution. However,

the effects are not significantly different for individuals with better starting conditions or more

success in life, suggesting that this channel is an unlikely driver of our findings. Second, we

show that the effects are unlikely to operate through people’s perceived relative income. Third,

higher inequality experiences during impressionable years are not correlated with lower trust in

the political system later in life.

To provide evidence against the possibility that our effects are driven by cohort-level changes

in zeitgeist accompanied with changes in general political preferences, we conduct a series of

placebo tests. We provide evidence that inequality experiences do not affect how conservative

individuals are in matters unrelated to redistribution and inequality, such as nationalism, at-

titudes towards democracy, attitudes towards guns or attitudes towards immigrants. This is

consistent with the interpretation that inequality experiences are driving the changes in redis-

tributive preferences, rather than picking up more general differences in political attitudes across

cohorts.

Moreover, we examine the sensitivity of our results to controlling for other experiences during

people’s impressionable years, such as the experienced growth rate of real per capita GDP, the

experienced political ideology of the leading party as well as the experienced size of the govern-

ment. Our results barely change after controlling for these other experiences, indicating that

omitted variable bias from other experiences during our respondents’ lives is less likely. Finally,

our results are robust to controlling for proxies of parental values. This mitigates the concern

that our findings reflect policy preferences of the respondents’ parent generation which are trans-

mitted to the respondents and at the same time affect the level of inequality the respondents

experienced during their formative years.

3



1.1 Related Literature

We contribute to a growing literature on the determinants of redistributive preferences (Alesina

and La Ferrara, 2005; Alesina and Giuliano, 2011; Alesina et al., 2013; Durante et al., 2014),

beliefs about inequality (Piketty, 1995) and fairness concerns (Alm̊as et al., 2011; Cappelen et

al., 2007, 2013a).5 This literature has established that redistributive preferences are affected

by culture (Alesina and Giuliano, 2011; Luttmer and Singhal, 2011), institutions (Alesina and

Fuchs-Schündeln, 2007; Pan and Xu, 2015), relative income (Cruces et al., 2013; Karadja et al.,

2017), reference points (Charité et al., 2015), beliefs about government debt (Roth and Wohlfart,

2018), and historical experiences (Chen et al., 2016; Roland and Yang, 2016).6

Our paper is most closely related to Giuliano and Spilimbergo (2014) who show that indi-

viduals who have experienced a recession during their formative years believe that success in life

depends more on luck than effort, support more government redistribution, and tend to vote

for left-wing parties. Carreri and Teso (2017) find an effect in the opposite direction on the

preferences for redistribution of U.S. Members of Congress as measured by their voting records.

Our paper shows that people’s experiences of unequal distributions of incomes matter on top of

the effects of experienced macroeconomic conditions.

More generally, we complement the growing literature on the effects of life-time experiences

on belief formation and economic behavior (Hertwig et al., 2004; Nisbett and Ross, 1980; Weber

et al., 1993). For instance, Malmendier and Nagel (2011) provide evidence that having expe-

rienced negative macroeconomic shocks makes people less likely to invest in stocks. Moreover,

Malmendier and Nagel (2016) show that people’s experienced inflation rates predict their con-

temporaneous inflation expectations. Fuchs-Schuendeln and Schuendeln (2015) provide evidence

that people’s experience of living in a democracy increases their support for democratic regimes.

Our paper contributes to this literature by highlighting that experiences of income inequality

are associated with lower preferences for redistribution. Moreover, we conduct a series of ro-

bustness checks that have not been commonly carried out in the previous experience literature.

Specifically, we use various different experience measures following the methodologies in Giuliano

and Spilimbergo (2014) and Malmendier and Nagel (2011), we control for other macroeconomic

5More generally, our paper is related to the literature on the malleability of (social) preferences (Becker et al.,
2016; Kosse et al., 2016; Nunn and Wantchekon, 2011; Rao, 2018; Schildberg-Hörisch et al., 2014).

6For excellent reviews, see Alesina and Giuliano (2011) and Nunn (2012).

4



experiences, we conduct placebo exercises, and we provide a consistent set of results using two

datasets reliant on within-country variation as well as two cross-country datasets.

Our findings also relate to a large theoretical and empirical literature studying the effects

of inequality on demand for redistribution (Alesina and Rodrik, 1994; Benabou and Ok, 2001;

Persson and Tabellini, 1994). According to the seminal theoretical contribution by Meltzer and

Richard (1981), an increase in income inequality in an economy should be reflected in a higher

level of redistribution. Intuitively, as the mean income in the economy increases relative to the

income of the median voter, it becomes rational for the median voter to demand more redistribu-

tion. Empirically, inequality and the average demand for redistribution are negatively correlated

across countries, but this pattern vanishes when looking at within-country movements of inequal-

ity (Kenworthy and McCall, 2008). Kerr (2014) finds that short-run increases in inequality within

countries and within U.S. regions are associated with greater acceptance for wage differentials,

but also with higher support for redistributive policies. Moreover, our paper also relates to a

growing literature studying people’s perceptions of inequality and their redistributive preferences

(Kuziemko et al., 2015; Norton and Ariely, 2011). For instance, Kuziemko et al. (2015) show that

people’s demand for redistribution is fairly inelastic to information about the degree of inequal-

ity, while Alesina et al. (2018) find some effects of beliefs about intergenerational mobility on

people’s demand for government redistribution. While the existing empirical literature studies

the relationship between (perceptions of) current inequality and the demand for redistribution,

we ask whether there is a persistent effect of experienced past levels of inequality, controlling for

the influence of current inequality by including year fixed effects.

The paper proceeds as follows: Section 2 describes the data. In section 3, we present the main

results of our analysis. Section 4 conducts a series of robustness checks. We discuss potential

alternative mechanisms in section 5. Finally, the paper concludes.

2 Data

2.1 General Social Survey (US)

We leverage rich data on political preferences and beliefs from the General Social Survey (GSS).

This dataset consists of repeated cross-sections from 1972 to 2014 that are representative of the

US and has been widely used in previous research in economics (Alesina and La Ferrara, 2000;

Giuliano and Spilimbergo, 2014). Following Giuliano and Spilimbergo (2014) we focus on out-

5



come measures of preferences as well as beliefs related to inequality and redistribution, which may

both be shaped by people’s experiences while growing up. We also examine effects on beliefs be-

cause the previous literature emphasizes that people’s beliefs about the sources and consequences

of economic inequality are an important determinant of their support for redistribution (Alesina

and Angeletos, 2005; Alesina et al., 2001; Benabou and Tirole, 2006; Fong, 2001). For instance,

individuals who believe that success in life is due to effort rather than luck will demand less

redistribution (Piketty, 1995). Part of the effects of inequality experiences on redistributive pref-

erences could therefore operate through changes in beliefs. Alternatively, inequality experiences

could directly affect people’s preferences, and people may adjust their beliefs independently of

their preferences. We are not able to distinguish between these alternatives with the available

cross-sectional data.7 Specifically, we focus on the following survey measures:

• Help Poor: The respondents’ view on whether the government in Washington should do

everything to improve the standard of living of all poor Americans or whether it is not the

government’s responsibility, and that each person should take care of herself or himself.

• Pro-welfare: Individuals’ opinion on whether the government is not spending enough

money on assistance to the poor.

• Success due to luck: People’s belief on whether success is mostly due to luck or owing

to hard work.

We also consider people’s self-placement on a conservative-liberal scale, their party affiliation,

and their self-reported past voting behavior. We examine whether people identify more as Demo-

crat or Republican and whether they report having voted for Democrats or Republicans. All

variables are coded such that high values mean that they are more in favor of redistribution and

more likely to vote for Democrats. We also use questions on people’s self-assessed social and

economic position in society that allow us to shed light on the mechanisms behind our findings

(see Appendix D for details). Table A.14 displays the summary statistics for our sample from

the General Social Survey.

7Since inequality experiences could affect people’s beliefs, controlling for people’s beliefs about inequality in
regressions of redistributive preferences on inequality experiences would result in a bad control problem (Angrist
and Pischke, 2008).

6



2.2 German General Social Survey

The German General Social Survey (Allbus) collects data on political attitudes and behavior,

as well as a large set of demographics in Germany. Every two years since 1980 a representative

cross-section of the population was surveyed. We use data from the waves from 1980 to 2014.

We focus on unique variables on beliefs about the sources and consequences of inequality and on

views on the fairness of the prevailing income distribution in the German General Social Survey:

• Inequality is Unfair: People’s opinion on whether the social inequalities prevailing in

Germany are unfair.

• Inequality does not increase motivation: Individuals’ beliefs about the effect of in-

equality on people’s motivation.

• Inequality reflects luck: Respondents’ disagreement to the statement that differences

in rank between people are acceptable as they essentially reflect how people used their

opportunities.

We code the variables such that high values stand for less favorable beliefs and attitudes regarding

inequality. In addition, we focus on outcomes that are similar to the outcomes we use in the

General Social Survey. We look at political behavior as measured through voting intentions, self-

reported past voting behavior and people’s self-assessment on a political scale. These variables

are described in detail in Appendix D. Due to lacking inequality data we drop all respondents who

have grown up in the German Democratic Republic and focus only on West German Respondents.

In Table A.15 we show summary statistics for our sample from the Allbus.

2.3 European Social Survey

The European Social Survey (ESS) is a dataset containing rich information about political at-

titudes and beliefs of the various populations in Europe. It also contains data on a rich set of

demographic variables. The ESS has been widely used to study redistributive preferences, see

for example Luttmer and Singhal (2011). We make use of all available waves from the ESS

(2002-2014).8

8Most of our sample from the ESS comes from three countries: France, Germany and the United Kingdom,
each of which makes up for around 20 percent of the sample. Denmark, Finland, Italy, the Netherlands, Norway,
Portugal, Spain, Sweden and Switzerland all together constitute about 40 percent of the overall sample. We drop
all East German respondents from the sample.

7



Our key outcome variables of interest are a measure capturing whether people are in favor

of redistribution, people’s self-reported voting behavior, and their self-placement on a political

scale. As in the other datasets higher values represent more left-wing views. We also use data on

people’s trust in the political system to shed light on mechanisms. All outcomes are described

in more detail in Appendix D. In Table A.16 we provide summary statistics for our sample from

the ESS.

2.4 Normalizations, Controls and Missings

The outcome variables we use in our analysis are mostly self-placements between left and right

or between agreement and disagreement to a particular statement on 4-point, 5-point or 10-

point scales. We normalize all outcome variables and our measures of experienced inequality

using the mean and the standard deviation of the respective variables in our samples of interest.

These normalizations enable us to compare effect sizes across outcomes and across datasets. We

construct a consistent set of controls for key demographics, such as income, gender, marital

status, education, religious affiliation and employment status for all of the datasets of interest.

In Appendix E we describe the exact controls we include for each of the different datasets.9

2.5 Inequality and Unemployment Data

We use data on top income shares from the “World Wealth and Income Database” (WID) (Al-

varedo et al., 2011), which is the most complete source of internationally comparable data on

income inequality. The database contains very rich data on the share of overall national income

earned by people at the top of the distribution. We focus on the share of total gross income earned

by the top one, the top five and the top ten percent of earners respectively. We also use data on

the Gini coefficient of equivalized disposable household incomes taken from the “Chartbook of

Economic Inequality” (Atkinson and Morelli, 2014), which also captures variation in inequality

that is not reflected in top income shares. For most countries data on the Gini coefficient are

available only from a much later point in time than data on top income shares. Therefore, we

focus on the experienced share of total income earned by the top five percent of earners in our

main analysis.

Our data on top income shares refer to total earnings before taxes and transfers, while our data

on the Gini coefficient are based on disposable household income after taxes, due to differences

9To deal with missing values and to keep the sample as large as possible, for each of the above categories of
controls we code missings as zero and include a dummy variable indicating missing values in that category.

8



in data availability between the two measures. Similarly, while the data on top income shares

from different countries in the WID are the most internationally comparable data on inequality

available, there are still some differences in the construction of these measures. In all our cross-

country estimations we include country fixed effects to make sure that our findings are not driven

by these differences.10

In our analysis we focus on those countries for which we could obtain historical inequality

data from the World Wealth and Income Database. We use linear interpolation to impute data

for years in which inequality data are missing. We impute inequality data if the gap between any

two points in time for which inequality data are available, is at most six years.11 In addition, we

use historical data on national unemployment rates from Global Financial Data (GFD) and use

the same rule to impute missing values.

2.6 Construction of Experience Variable

The literature on experience effects has identified the age period between 18 and 25 (“impres-

sionable years”) as particularly important for the formation of political attitudes and beliefs.

During this age period most individuals begin to participate in political life and enter the labor

market. Krosnick and Alwin (1989) provide evidence that individuals’ susceptibility to attitude

change is high during the impressionable years and drops considerably thereafter. Giuliano and

Spilimbergo (2014) find that experiencing a recession while aged between 18 and 25 significantly

affects political preferences later in life, while similar experiences in other age ranges do not

seem to matter. Following this literature, we calculate, for each birth-cohort in our datasets,

the average share of total income held by the top five percent of earners while this birth cohort

was in their impressionable years. While we focus on experiences during impressionable years in

our main specifications, it could also be the case that inequality experiences in other periods of

life affect individual preferences. We therefore examine the robustness of our findings to using

a measure of inequality experiences along the lines of Malmendier and Nagel (2011) that allows

experiences over the entire lives of our respondents to affect their preferences. The construction

of this alternative measure of inequality experiences is described in Appendix G.

In our main specifications we focus on the national-level inequality that our respondents

experienced during their impressionable years in their country of residence, IEit. In an alternative

10In Appendix F, we provide a detailed overview on the inequality data that are available for each country and
the respective cohorts we are able to use in our analysis.

11This allows us to use much larger samples of individuals in our analyses. We have made sure that our results
are robust to using different maximum gaps for the imputation of the inequality data.

9



specification we use region-specific inequality experiences, IEirt. The GSS provides data on the

census division in which the respondents lived at age 16, and we compute someone’s experienced

inequality during his or her impressionable years using historical data on top income shares in

this census division.12

As our datasets do not contain any direct measures of the level of inequality people perceived

while growing up, we measure experienced inequality using the actual level of inequality that

prevailed during our respondents’ formative years. One concern is that people’s beliefs about

inequality are biased (Norton and Ariely, 2011) and do not co-move with actual levels of inequal-

ity. We address this concern using data from the International Social Survey Program on Social

Inequality (ISSP) to show that people’s perceived levels of inequality closely co-move with actual

inequality in their country of residence. Specifically, we show that people believe that they live

in a more unequal society when inequality is higher. Similarly, people report higher estimates of

pay gaps between CEOs, cabinet ministers and doctors on the one hand, and unskilled workers

on the other hand, when inequality is high. These results are robust to including country and

time fixed effects as well as demographic controls (Tables A.21 and A.22).

While these findings indicate that our measure of inequality experiences is valid, the extent to

which individuals “experience inequality” depends on individual-level characteristics like people’s

media consumption, their place of residence or their work place during their formative years.

This means that our measure of “inequality experience” is measured with noise. However, this

measurement error does not constitute a threat to the internal validity of our findings and, if

anything, will bias our estimates towards zero.

Figure 1 plots the average income share of the top five percent experienced over impressionable

years against cohort for the largest countries in our sample. In the US and in the UK cohorts

born from around 1960 onward experienced higher levels of inequality during their impressionable

years relative to earlier cohorts. The pattern is reversed for France. In the case of Germany,

experienced inequality is the lowest for people born around 1960 and higher for those born before

that or after. Figure 2 shows experienced inequality for cohorts growing up in the different US

census divisions. The large differences across census divisions provide an additional source of

variation that we exploit in our estimations.13

12We provide evidence that our results are robust to excluding movers (defined as people living in a different
census division when they are interviewed than the census division they lived in at age 16).

13In Figures 3 and 4 in Appendix B we display the evolution of top income shares over time for these countries
and for the different US census divisions.

10



[Insert Figures 1 and 2]

To account for other macroeconomic experiences that might affect redistributive preferences

(Giuliano and Spilimbergo, 2014), we calculate the average national unemployment rate that

prevailed during our respondents’ impressionable years, UEit. As our main experience variables

are reliant on having lived through the impressionable years (age 18 to 25) we restrict our

attention to people of age 26 and older.

3 Empirical Strategy and Results

3.1 Empirical Specification: GSS and Allbus

We estimate the effect of inequality experiences, IEit, on people’s redistributive preferences or

beliefs about inequality, yirt. We control for people’s national-level unemployment experiences,

UEit, and a vector of individual characteristics, Xit.
14 In addition, we account for age fixed

effects, δit, regional fixed effects
15, ρr, cohort group fixed effects, πi

16, and year fixed effects, βt.

Specifically, we estimate the following equation:

yirt = α1IEit + α2UEit + Π
T Xit + δit + ρr + βt + πi + εirt (1)

The inclusion of age fixed effects ensures that our findings are not driven by changes in political

preferences that occur over people’s lifetime, such as people becoming more conservative as

they get older. The year fixed effects control for current macroeconomic conditions that affect

everyone in a given year, such as adverse economic shocks or the contemporaneous level of

inequality. Finally, by including fixed effects for different groups of cohorts it becomes less likely

that our findings are driven by longer-term shifts in political attitudes across cohorts that are

unrelated to inequality experiences.17

We also use region-specific inequality experiences, IEirt, for the GSS. In these estimations we

control for fixed effects for the census division our respondent lived in at age 16, ρ16i, interacted

14This vector includes household income, household size, the respondent’s marital status, religion, educational
level and employment status.

15In the US this corresponds to census division and in Germany to the federal state.
16We include dummy variables for the cohorts born between 1876 and 1900, between 1901 and 1925, between

1926 and 1950, between 1951 and 1975, and 1976 or later, respectively. We are not powered to include too
fine-grained cohort group fixed effects.

17We obtain very similar results if we do not include cohort group fixed effects.

11



with age fixed effects, δit, cohort group fixed effects, πi, as well as year fixed effects, βt. This

in turn allows us to non-parametrically control for age-specific trends at the regional level, dif-

ferences across cohort groups at the regional level, as well as shocks that are correlated within

groups of people who have grown up in the same census division. The specification is given as

follows:

yirt = α1IEirt + α2UEit + Π
T Xit + ρ16it ×δit + ρ16it ×βt + ρ16it ×πi + ρr + εirt (2)

3.2 Empirical Specification: ESS

The empirical specification for the European Social Survey is very similar to the specification

that uses region-specific variation in inequality experiences in the US. We estimate the effect of

country-specific inequality experiences during impressionable years, IEict, on people’s redistribu-

tive preferences, yict. We control for national-level unemployment experiences during impression-

able years, UEict, and a vector of individual controls, Xit. In addition, we account for country

fixed effects, ρc, interacted with both time fixed effects, βt, and cohort group fixed effects, πi, as

well as country-specific age trends, ageit ×ρc.18

yict = α1IEict + α2UEict + Π
T Xit + ρc ×ageit + ρc ×βt + ρc ×πi + εict (3)

For all of the previous three empirical specifications, we report standard errors that are two-way

clustered by the respondents’ age and cohort as we might expect large intra-cluster correlations

in these non-nested clusters (Cameron and Miller, 2015). Our results are robust to clustering

standard errors just by cohort or age.19 Since we test for a large set of outcome variables,

we account for multiple hypothesis testing by adjusting the p-values using the “sharpened q-

value approach” (Anderson, 2008; Benjamini et al., 2006). Within each family of outcomes,

we control for a false discovery rate of 5 percent (Anderson, 2008). We employ false discovery

rate adjustment of p-values as it is the most power-preserving way of adjusting for multiple-

18Since each country is part of the ESS only in a few waves (sometimes only one) and since the time dimension
of the ESS is short (2000-2015), we do not have enough variation of inequality experiences within country-age
groups to include an interaction of age fixed effects and country fixed effects. Our independent variable varies at
the country-cohort level, so in the extreme case of observing observations from a particular country only in one
year, all the variation in the independent variable would be absorbed by the interaction of age fixed effects and
country fixed effects.

19For all of these datasets we make use of population weights. This makes sure that we can make statements
about a sample that is representative of the general population.

12



hypothesis testing within families of outcomes. These adjusted p-values are displayed in the

tables as FDR-adjusted p-values.

3.3 Results

Table 1 presents the results from the General Social Survey. Panel A reports the results on

national-level inequality experiences during impressionable years, while Panel B shows the results

using regional inequality experiences. Individuals with higher inequality experiences are less

likely to be in favor of helping the poor and less in favor of welfare (Columns 1 and 2). People

who experienced higher inequality are marginally significantly more likely to attribute success

in life to effort rather than luck (Column 3). Moreover, people with higher levels of inequality

experience are less likely to be liberal and less likely to vote for Democrats (Columns 4 to 6).

Across specifications we find similar effects for national and regional experiences in terms of both

significance and magnitude.

In Table 2 we show the results from the German General Social Survey (ALLBUS). Individuals

who experienced higher inequality while growing up are less likely to consider the prevailing level

of inequality as unfair (Column 1), consistent with the idea that inequality experiences affect

views on what is a fair division of resources. Moreover, they are more likely to believe that

inequality is important for motivation (Column 2) and to attribute differences in income to

effort rather than luck (Column 3). In line with the previous findings, experiences of higher

inequality are correlated with lower support for left-wing parties (Columns 4 to 6).

Table 3 displays the results from the European Social Survey. As can be seen in Column

1, people with high inequality experiences are less likely to agree to the statement that “the

government should take measures to reduce differences in income levels”. In addition, people

with more inequality experiences place themselves less on the left on a political scale and are

less likely to have voted for a left-wing party in the last election. All of our results are robust to

taking into account multiple-hypothesis testing.20,21

[Insert Tables 1 - 3]

20The FDR adjusted p-values are sometimes lower than the näıve p-values. This is the case as a lot of the
tests reject or almost reject the null. Therefore, the algorithm assumes that actually most null hypotheses are
false, and computes a very low false discovery rate associated with each hypothesis.

21In Tables A.17 - A.19 in Appendix A, we present our main results displaying all relevant controls. The
controls predict preferences for redistribution in line with the previous literature (Alesina and Giuliano, 2011,
2015). For instance, individuals with higher incomes and more education are more against redistribution, while
females are more in favor of redistribution.

13



To illustrate the magnitude of the effects, we compare our estimated effect sizes on our

respondents’ self-placement on a political scale to the effects of other determinants of preferences

for redistribution. According to our estimates using national-level inequality experiences and the

General Social Survey (US), a one standard deviation increase in inequality experiences leads to

a decrease of 3.8 percent of a standard deviation in people’s tendency to consider themselves as

left-wing. Moving from the inequality experiences of the cohort born in 1950 (very low inequality

experiences) to the cohort born in 1980 (high inequality experiences) implies a 10.3 percent of a

standard deviation decrease in the dependent variable. For comparison, being female is associated

with an increase by 12.5 percent of a standard deviation, while a high school degree is associated

with a decrease by around 9.5 percent of a standard deviation.

We obtain larger effect sizes in the sample from the German General Social Survey. Here, a

one standard deviation increase in inequality experiences leads to a decrease of people’s tendency

to consider themselves left-wing by around 9.6 percent of a standard deviation. Moving from the

low inequality experiences of people born in 1950 to high inequality experiences of the cohort of

1980 implies a decrease in the dependent variable by 21.2 percent of a standard deviation. For

comparison, being female increases the self-assessment as left-wing by around 7.7 percent of a

standard deviation. Moving from the lowest to the highest quintile in the income distribution

leads to a decrease in the dependent variable by 19.2 percent of a standard deviation.

According to our estimations on the cross-country sample from the ESS a one standard

deviation increase in inequality experiences leads to a decrease in people’s self-classification as

left-wing by around 11.7 percent of a standard deviation. For the cohort born in 1980, moving

from the country where this cohort has the lowest inequality experience (Denmark) to the country

where this cohort has the highest inequality experience (UK) implies a decrease in the tendency

of people to consider themselves left-wing by almost 50 percent of a standard deviation.

We also replicate our key results using data on voting behavior and support for redistribution

from the International Social Survey Program Module on Social Inequality. Our estimates are

fairly similar in terms of magnitude and significance, which provides us with additional confidence

in our results (Appendix C).

Our findings are not contradictory to Kerr (2014) who finds that short-run increases in in-

equality within countries or U.S. regions are associated with greater demand for redistribution.

He identifies contemporaneous effects of short-term changes in inequality that operate uniformly

across cohorts. These effects are absorbed by year fixed effects in our analysis. By contrast, we

14



identify the persistent effect of the level of inequality people experienced during their formative

years on top of the effects of contemporaneous inequality. While all cohorts may exhibit a dis-

taste for inequality, our findings are consistent with the idea that the strength of this concern

depends on people’s experiences during their lives. Our results are also in line with Alesina and

Fuchs-Schündeln (2007) who provide evidence that people who have grown up and lived in East

Germany under the communist regime are more in favor of redistribution than people from West

Germany. Our finding of a negative effect of inequality experiences on demand for redistribution

provides an additional explanation for higher demand for redistribution in formerly communist

countries, where income inequality was often low.

4 Robustness

4.1 Other Measures of Inequality Experiences

In our main specifications we have focused on inequality experiences during our respondents’ im-

pressionable years, i.e. between the age of 18 and 25 (Giuliano and Spilimbergo, 2014; Krosnick

and Alwin, 1989; Mannheim, 1970). In this section, we examine the influence of experiences dur-

ing other periods of life on people’s preferences for redistribution and demonstrate the robustness

of our results to using the methodology in Malmendier and Nagel (2011) to measure inequality

experiences.

First, we use the Allbus and the GSS to examine the effect of inequality experiences during

different eight year age intervals (2–9, 10–17, 26–33, 34–41, and 42–49) in our respondents’ lives.

While we still find significantly negative effects of experiences in life periods surrounding the

impressionable years (10-17 and 26-33, respectively), the effects are weaker or vanish completely

for other age ranges (see Tables A.1 - A.3).22

Second, we find very similar effects of inequality experiences when we use the methodology

developed by Malmendier and Nagel (2011). While in our main estimations we look at the

effect of inequality experiences during people’s formative years, this alternative measure is based

on a weighted average of top income shares experienced over a respondent’s lifetime until the

time of the interview. Thus, in contrast to our previous measures, we now allow more recent

22Given the nature of the dataset, it is difficult to compare the importance of experiences during impressionable
years versus experiences during other periods of life. Since in each estimation we only focus on individuals who
have lived through the relevant life period, we cannot hold constant the sample size and sample composition in
the different specifications.

15



experiences to still have some effect. In line with the above findings that earlier experiences

matter more than later experiences, we use a weighting factor that gives more weight to early

experiences and in which experiences only matter beginning from age 18.23 We re-estimate

our main specifications using the same set of controls but employing these alternative measures

of experienced inequality and experienced unemployment. We obtain very similar results in

terms of effect size and statistical significance when we use this alternative measure of inequality

experiences (Panels G of Tables A.1 and A.3). All in all, this evidence corroborates our finding

that inequality experiences are reflected in people’s beliefs, values and political preferences, and

experiences during people’s formative years seem to be particularly important.

4.2 Placebo Outcomes

It could be the case that our estimates merely pick up across-cohort differences in political

preferences and in particular in how left-wing people in different cohorts generally are. The

inclusion of 25-year cohort-group fixed effects in our main specifications ensures that our results

are not driven by longer-term general shifts in preferences across cohorts. To further address

this concern, we show that other political attitudes that differ between the left and the right of

the political spectrum, but that are not directly related to inequality and redistribution, are not

affected by our measures of inequality experiences. Inequality experiences do not significantly

affect nationalism, attitudes towards guns, attitudes towards immigrants24, attitudes towards

democracy, attitudes towards the unification of the EU and people’s belief in god (Tables A.4 -

A.6).25

4.3 Other Experiences during Impressionable Years

We also examine whether our results are sensitive to controlling for other macroeconomic expe-

riences during impressionable years. First, we control for the experienced size of the government

by including the average ratio of total tax revenue relative to GDP experienced during the im-

pressionable years. Second, we include a proxy for experienced political ideology, namely the

fraction of someone’s impressionable years in which a Republican president (US) or conservative

chancellor (Germany) was in office. In addition, we examine whether our estimates are sensitive

23For details regarding the construction of this alternative measure see Appendix G.
24In the Allbus we focus only on attitudes towards immigrants that are not related to economic concerns.

The respective variables in the GSS and the ESS mainly refer to whether the number of immigrants should be
increased or decreased.

25In Appendix D, we provide detailed information on the placebo variables used in our analysis.

16



to the inclusion of the average growth rate of real GDP per capita during the impressionable

years. When we control for these other experiences our main results barely change (see Tables

A.7 and A.8).26 This indicates that our results are less likely to reflect other experiences people

made while growing up which are correlated with inequality experiences.

4.4 Parental Values

One alternative interpretation of our findings is that the experienced inequality of 18-25-year-olds

is an outcome of policy demands made by their parents’ cohort, and that these parents transmit

values to the respondents (Dohmen et al., 2011). We examine how sensitive our estimated effects

of inequality experiences are to controlling for a large set of proxies for parental values available

in the General Social Survey, such as parental education, whether a respondent’s mother worked

while the respondent grew up and whether the respondent lived in an urban area at age 16.27 To

the extent that these variables vary across cohorts, they should capture differences in parental

values that could be transmitted to respondents and also affect the inequality experiences of our

respondents through their parents’ policy demands.

Panel A of Table A.9 shows the coefficient estimates of national inequality experiences from

our main specification. Panel B displays the coefficient estimates of national inequality experi-

ences conditional on the additional set of controls proxying for parental values. Our estimated

effects do not change much after including additional control variables, suggesting that it is less

likely that our results are driven by parental values that affect the level of inequality through the

parents’ policy demands and at the same time are transmitted to the respondents.

4.5 Other Robustness Checks

In Tables A.10-A.13 we examine how sensitive our results are to a variety of robustness checks.

Our results are robust to using different definitions of income inequality based on (i) the share of

income earned by the top ten percent, (ii) the share of income earned by the top one percent as

well as (iii) the Gini coefficient of equivalized disposable household incomes which is available for

26We demonstrate robustness of our main specification by including these other experiences one at a time. Since
all experience variables vary at the cohort level, and since macroeconomic variables tend to be highly correlated,
including all these other experiences at once would lead to problems of multicollinearity.

27Specifically, we control for whether the respondent’s father completed at most high school, whether the
respondent’s father completed college, whether the respondent’s mother completed at most high school, whether
the respondent’s mother completed college, whether the father was mostly self-employed during the respondent’s
childhood, whether the mother worked when the respondent was young, whether the respondent lived in an urban
area, whether the family’s income was above the median income when the respondent was aged 16 and for census
division fixed effects of the respondents’ residence at age 16.

17



a much smaller sample of respondents.28 The Gini coefficient also captures variation in inequality

that is not reflected in top income shares. In contrast to top income shares which are based on

before-tax incomes, the Gini coefficient measures after-tax income inequality. We obtain very

similar results when we use these alternative inequality measures. If anything, we find larger

effect sizes when we use the Gini coefficient instead of top income shares.

In addition, we show that our results remain unchanged when we exclude all individuals with

missing values in any of the controls. Our findings are also robust to not controlling for people’s

national unemployment experiences which alleviates concerns that inequality experiences operate

through unobservable long-run effects of unemployment experiences. Moreover, the results are

unaffected when we control for age trends rather than age fixed effects or when we exclude

movers from our estimations on the GSS which rely on regional variation in income inequality

experiences.29 As a final robustness check we exclude the 25-year cohort group fixed effects from

our specifications and obtain very similar results.

5 Alternative Mechanisms

One plausible interpretation of our findings is that an experienced distribution of incomes can

affect the level of inequality people find acceptable and thereby influence people’s demand for

redistribution.30 Above we presented evidence that people are less likely to perceive the prevailing

distribution of incomes as unfair if they have higher inequality experiences. Since everyone in a

given year faces the same aggregate level of inequality, this is consistent with people interpreting

the fairness of the prevailing distribution in light of their inequality experiences. In this section

we address three alternative mechanisms that could be driving our findings.

5.1 Extrapolation from Own Circumstances

The negative effect of experiencing inequality on preferences for redistribution could be driven

by individuals who benefited personally from high levels of inequality while they were young. If

this was the case, we would expect the effect to be stronger for those who had better starting

28While a lot of historical data on the Gini coefficient exist in the US, much less data on the Gini coefficient
are available for most European countries in our sample. This implies that the samples we can use in our analysis
for the ESS are much smaller than for the measures of top income inequality.

29We define movers as people who live in a different census division when they are interviewed than when they
were aged 16.

30Previous literature has established that people’s views about what is an acceptable level of inequality are
an important determinant of their demand for redistribution (Almås et al., 2011; Cappelen et al., 2007, 2013a,b,
2017; Herz and Taubinsky, 2017).

18



conditions in life and for those who were more successful in life. To shed light on this mechanism,

we examine heterogeneous effects by a variety of proxies for starting conditions in life and for

economic status. For each of our main outcomes, we estimate the following specification:

yirt = α1IEit + α2IEit × interactit + α3interactit + α4UEit + ΠT Xit + δit + ρr + βt + πi + εirt
(4)

where interactit is the interaction variable of interest. We then calculate the estimated

average effect sizes (AES) for the coefficients α1 and α2 across the six specifications we estimate

in the GSS or the Allbus, respectively (Giuliano and Spilimbergo, 2014; Kling et al., 2005).31

Using the AES instead of individual coefficients increases our effective statistical power. This is

particularly important for the heterogeneity analysis for which we have lower statistical power.

In Table 4 we show that there is no significant heterogeneity by relative family income at age

16 and by father’s education in our sample from the GSS.32,33 This suggests that the effect is not

driven by those who had better starting conditions in life. Moreover, the effect is not significantly

different for those with high current relative income or those with high education. In Table 5 we

show that also in the Allbus sample the effects are fairly uniform across groups. Taken together,

these homogeneous results suggest that extrapolation from own circumstances is an unlikely

explanation for the association of inequality experiences with redistributive preferences.34

[Insert Tables 4 and 5]

5.2 Perceived Relative Income

Experiences of inequality could also change people’s beliefs about their economic status. Specif-

ically, people who have grown up in times of high income inequality, and who are therefore used

to more inequality, could be less likely to perceive their current relative income as low. People’s

31The AES is defined as the average of all coefficient estimates across a family of estimations, where each
coefficient is divided by the standard deviation of the respective outcome. All our outcomes are normalized to
have standard deviation one, so the AES is the simple average of the estimated coefficients. We calculate p-values
for the AES based on simultaneous estimation of the six regressions.

32These variables are coded as one if the respondent considered the income of his family at age 16 to be at
least average and if the respondent’s father had at least high school education, respectively.

33We also do not find heterogeneity according to education of the mother.
34We also examined heterogeneity according to age, but do not report the results for brevity. We found that

the effect is fairly uniform across age groups, suggesting that the effects persist over the lives of the respondents.
In addition, we checked whether the effects vary by the degree to which someone’s perceived relative income
increased or decreased between his or her youth and the survey year. We found no evidence for heterogeneous
effects along this dimension.

19



beliefs about their position in the income distribution have been shown to change people’s de-

mand for redistribution (Cruces et al., 2013; Karadja et al., 2017). We therefore test whether

experiences of high inequality lower people’s perceived relative income and perceived social class.

We find no significant effect of inequality experiences on perceived relative income and social

class using the GSS and the Allbus (Columns 1 to 3 of Table 6).

[Insert Table 6]

5.3 Trust in the Political System

Kuziemko et al. (2015) document that providing people with information about the high level

of income inequality in the US lowers their trust in the government to do what is right. They

interpret this as an explanation why their information treatment does not shift respondents’

demand for redistribution. Similarly, experiencing high levels of income inequality could lower

people’s trust in the government, which in turn could reduce their demand for government

redistribution. Our datasets do not contain questions on trust in the government. However,

respondents in the ESS are asked whether they trust their national parliament, politicians and

political parties. We regress these measures of trust in the political system on our respondents’

inequality experiences conditional on the same set of controls as in the main specification. As

can be seen in columns 4 to 6 of Table 6, we do not find an effect of inequality experiences on

our respondents’ trust in the national parliament, on their trust in politicians or on their trust

in political parties.

Taken together, we find no evidence that the effects work through extrapolation from own

circumstances, perceived relative income or trust in the political system. Our preferred interpre-

tation of the findings is therefore that respondents evaluate the fairness of the prevailing level of

inequality in light of the level of inequality they have experienced during their lives.

6 Conclusion

We use several large nationally representative datasets to highlight that people who have lived

through times of higher inequality are less left-wing as measured by their redistributive prefer-

ences as well as their party affiliation and voting behavior. We also show that individuals with

higher inequality experiences hold more positive beliefs about inequality and are less likely to

consider the prevailing level of inequality as unfair.

20



One plausible interpretation of our findings is that people evaluate current levels of inequality

in light of their experiences, and that preferences for redistribution are shaped by the level of

inequality people experienced while growing up. Accordingly, increases in inequality over the last

decades are reflected in lower preferences for redistribution among younger generations relative

to older generations. While fairness concerns may have led to an increasing demand for redis-

tribution across cohorts (Kerr, 2014), these concerns seem to be weaker for younger generations

who are more used to high levels of inequality. Going forward, the longer high levels of inequal-

ity prevail, the higher will be the average level of experienced inequality among voters. Under

our preferred interpretation of the data, the forces pushing society back towards lower levels of

inequality may become weaker the longer high levels of inequality prevail.

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Roth, Christopher and Johannes Wohlfart, “Public Debt and the Demand for Government

Spending and Taxation,” Working Paper, 2018.

Schildberg-Hörisch, Hannah, Thomas Deckers, Armin Falk, and Fabian Kosse, “How

Does Socio-Economic Status Shape a Child’s Personality?,” Working Paper, 2014.

Simonsohn, Uri, Niklas Karlsson, George Loewenstein, and Dan Ariely, “The Tree

of Experience in the Forest of Information: Overweighing Experienced Relative to Observed

Information,” Games and Economic Behavior, 2008, 62 (1), 263–286.

Weber, Elke U, Ulf Böckenholt, Denis J Hilton, and Brian Wallace, “Determinants

of Diagnostic Hypothesis Generation: Effects of Information, Base Rates, and Experience,”

Journal of Experimental Psychology: Learning, Memory, and Cognition, 1993, 19 (5), 1151.

26



Main Tables

Table 1: Main Results: General Social Survey (US)

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

FDR-adjusted p-values [.009]*** [.027]** [.067]* [.004]*** [.001]*** [.004]***

Observations 23,199 26,135 29,083 40,136 46,327 32,907
R-squared 0.108 0.128 0.024 0.078 0.146 0.200

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Panel B

Inequality Experiences -0.0415** -0.0268* -0.0142 -0.0598*** -0.0522*** -0.0377***
(Regional) (0.0179) (0.0138) (0.0111) (0.0134) (0.0123) (0.0141)
FDR-adjusted p-values [.022]** [.045]** [.075]* [.002]*** [.001]** [.002]**

Observations 22,987 25,831 28,670 39,632 45,703 32,597
R-squared 0.139 0.159 0.054 0.099 0.170 0.226

Census div 16 FE x Age FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Year FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. The p-values adjusted for a false discovery rate of five percent
are presented in brackets. Inequality experiences in Panel A are based on the experienced national-level share of total income earned by the top 5
percent during the impressionable years. Inequality experiences in Panel B are based on the experienced regional-level share of total income earned
by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced national unemployment rate during
the impressionable years. All specifications in Panel A control for age fixed effects, year fixed effects, cohort group fixed effects, as well as region fixed
effects. In Panel B, we control for age fixed effects, year fixed effects and cohort group fixed effects interacted with census division at age 16 fixed
effects and we also control for current census division fixed effects. All specifications control for a large set of controls: household income, marital
status, education, employment status, household size, religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

27



Table 2: Main Results: German General Social Survey (Allbus)

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

Inequality: Inequality does not Inequality Left-wing Intention to Vote: Voted: Left
Unfair increase motivation reflects luck Left

Inequality Experiences -0.0543* -0.0428 -0.0684* -0.0957*** -0.0836*** -0.0961**
(0.0307) (0.0296) (0.0349) (0.0196) (0.0298) (0.0457)

FDR-adjusted p-values [.065]* [.08]* [.053]* [.001]*** [.013]** [.049]***

Observations 10,401 10,357 10,309 18,979 14,691 9,533
R-squared 0.071 0.044 0.068 0.080 0.109 0.111

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Region FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. The p-values adjusted for a false discovery rate of
five percent are presented in brackets. Inequality experiences are based on the experienced share of total income earned by the top 5
percent during the impressionable years. Unemployment experiences are based on the experienced national unemployment rate during
the impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed
effects . All specifications control for a large set of controls: household income, marital status, education, employment status, household
size, religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

28



Table 3: Main Results: European Social Survey

(1) (2) (3)

Pro-redistribution Left-wing Voted: Left

Inequality Experiences -0.0390* -0.117*** -0.121***
(0.0234) (0.0200) (0.0389)

FDR-adjusted p-values [.096]* [.001]*** [.002]***

Observations 85,529 81,167 25,462
R-squared 0.143 0.079 0.153

Country FE x Age trends Yes Yes Yes
Country FE x Year FE Yes Yes Yes
Country FE x Cohort group FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. The p-values
adjusted for a false discovery rate of five percent are presented in brackets. Inequality experiences
are based on the experienced share of total national income earned by the top 5 percent during
the impressionable years. Unemployment experiences are based on the experienced unemployment
rate during the impressionable years. All specifications control for age trends, year fixed effects and
cohort group fixed effects, each interacted with country fixed effects. All specifications control for
a large set of controls: household income, marital status, education, employment status, household
size, religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

29



Table 4: Heterogeneous Effects: General Social Survey (GSS)

(1) (2) (3) (4)

AES AES AES AES

Inequality Experiences -.0213*** -.0435*** -.0278*** -0.0291**
[0.001] [0.000] [0.000] [0.010]

Inequality Experiences × High relative income at 16 -.0104
[0.128]

Inequality Experiences × High father’s education .008
[0.336]

Inequality Experiences × High relative income -.0114
[0.120]

Inequality Experiences × High education -.006
[0.401]

Observations 25,078 24,818 30,271 31,919

Age FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes
Region FE Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes

P-values from simultaneous estimation clustered by cohort are displayed in parentheses. The
number of observations refers to the average number used for the estimation of a given AES.
Inequality experiences are based on the experienced share of total income earned by the top 5
percent during the impressionable years. Unemployment experiences are based on the experienced
national unemployment rate during the impressionable years. All specifications control for age fixed
effects, year fixed effects, cohort group fixed effects as well as region fixed effects . All specifications
control for a large set of controls: household income, marital status, education, employment status,
household size, religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05,
*** p < 0.01.

30



Table 5: Heterogeneous Effects: German General Social Survey (Allbus)

(1) (2) (3)

AES AES AES

Inequality Experiences -.0744*** -.0691*** -.0681***
[0.000] [0.005] [0.000]

Inequality Experiences × High father’s education .0059
[0.769]

Inequality Experiences × High relative income -.0177
[0.353]

Inequality Experiences × High education -.0204
[0.375]

Observations 14,052 10,308 14,122

Age FE Yes Yes Yes
Year FE Yes Yes Yes
Cohort group FE Yes Yes Yes
Region FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

P-values from simultaneous estimation clustered by cohort are displayed in parentheses.
The number of observations refers to the average number used for the estimation of a
given AES (average effect size). Inequality experiences are based on the experienced share
of total income earned by the top 5 percent during the impressionable years. Unemploy-
ment experiences are based on the experienced national unemployment rate during the
impressionable years. All specifications control for age fixed effects, year fixed effects,
cohort group fixed effects as well as region fixed effects. All specifications control for a
large set of controls: household income, marital status, education, employment status,
household size, religion, and gender. All outcome measures are z-scored. * p < 0.10, **
p < 0.05, *** p < 0.01.

31



Table 6: Other outcomes: GSS, Allbus, and ESS

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

GSS (national inequ. exper.) Allbus ESS

Low relative Low social Low social Trust Trust Trust
income position position parliament politicians parties

Inequality Experiences 0.00340 0.00467 -0.0368 0.0168 0.0169 0.0283
(0.0121) (0.0118) (0.0234) (0.0191) (0.0255) (0.0242)

Observations 43,234 44,402 15,025 81,933 82,270 69,406
R-squared 0.257 0.205 0.204 0.163 0.193 0.208

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Region FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. Inequality experiences are based on the
experienced share of total income earned by the top 5 percent during the impressionable years. Unemployment experiences
are based on the experienced national unemployment rate during the impressionable years. The estimations on the GSS
and the Allbus control for age fixed effects, year fixed effects, cohort group fixed effects as well as regions fixed effects. The
estimations on the ESS control for age trends, year fixed effects and cohort group fixed effects, each interacted with country
fixed effects. All specifications control for a large set of controls: household income, marital status, education, employment
status, household size, religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

32



Main Figures
20

25
30

35
40

E
xp

er
ie

nc
ed

 to
p 

5 
%

 in
co

m
e 

sh
ar

e 
(a

ge
 1

8-
25

)

1860 1880 1900 1920 1940 1960 1980 2000
Cohort

DE FR
UK US

Figure 1: Experienced top 5 percent income share (age 18-25) against cohort across countries.
Source: World Wealth and Income Database (Alvaredo et al., 2011).

33



25
30

35
40

45
E

xp
er

ie
nc

ed
 to

p 
5 

%
 in

co
m

e 
sh

ar
e 

(a
ge

 1
8-

25
)

1880 1900 1920 1940 1960 1980 2000
Cohort

New England
Middle Atlantic
East North Central
West North Central
South Atlantic

20
25

30
35

40
E

xp
er

ie
nc

ed
 to

p 
5 

%
 in

co
m

e 
sh

ar
e 

(a
ge

 1
8-

25
)

1880 1900 1920 1940 1960 1980 2000
Cohort

East South Central
West South Central
Mountain
Pacific

Figure 2: Experienced top 5 percent income share (age 18-25) against cohort across US census
divisions. Source: World Wealth and Income Database (Alvaredo et al., 2011).

34



Online Appendix: Experienced Inequality and Preferences
for Redistribution

Christopher Roth and Johannes Wohlfart

A Additional Tables

Table A.1: GSS: National Inequality Experiences during Other Periods of Life

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: 2 to 9

Inequality Experiences -0.0269 -0.0313 0.0358 -0.106*** -0.0309 0.00160
(0.0332) (0.0253) (0.0362) (0.0327) (0.0369) (0.0361)

Observations 21,998 23,703 26,806 37,492 42,790 30,219

Panel B: 10 to 17

Inequality Experiences -0.0588** -0.0361* -0.00608 -0.0533** -0.0346 -0.0502**
(0.0232) (0.0207) (0.0227) (0.0212) (0.0215) (0.0215)

Observations 22,881 25,299 28,331 39,313 45,158 32,054

Panel C: 18 to 25 (main)

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel D: 26 to 33

Inequality Experiences -0.0403*** -0.0279** -0.00735 -0.0422*** -0.0664*** -0.0422***
(0.0144) (0.0119) (0.0111) (0.00920) (0.00972) (0.0131)

Observations 18,663 20,942 23,487 32,320 37,467 27,738

Panel E: 34 to 41

Inequality Experiences -0.00458 -0.0206 -0.0183 0.0235 -0.0831*** -0.0626***
(0.0254) (0.0183) (0.0186) (0.0174) (0.0189) (0.0203)

Observations 14,181 16,070 18,015 24,608 28,798 21,873

Panel F: 42 to 49

Inequality Experiences 0.0369 -0.0174 -0.0278 0.0473*** -0.0544*** -0.0230
(0.0331) (0.0229) (0.0186) (0.0182) (0.0178) (0.0178)

Observations 10,325 11,821 13,253 17,976 21,124 16,363

Panel G: Life-time experiences
λ = −1
Inequality Experiences -0.0463*** -0.0162 -0.00953 -0.0224 -0.0359*** -0.0256**

(0.0140) (0.0163) (0.0133) (0.0149) (0.0116) (0.0130)
Observations 23,199 26,135 29,083 40,136 46,327 32,907

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. Inequality experiences are based on the national level experienced
share of total income earned by the top 5 percent during different life periods. Unemployment experiences are based on the experienced national
unemployment rate during the different life periods. We control for age fixed effects, year fixed effects, cohort group fixed effects as well as region
fixed effects. All specifications control for a large set of controls: household income, marital status, education, employment status, household size,
religion, and gender. All outcome measures are z-scored. In Panel G we display the results on the effect of life-time inequality experiences based on
the methodology developed by Malmendier and Nagel (2011). We use a weighting factor of λ = -1. * p < 0.10, ** p < 0.05, *** p < 0.01.

1



Table A.2: GSS: Regional Inequality Experiences during Other Periods of Life

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: 2 to 9

Inequality Experiences -0.0398 -0.0478* 0.0201 -0.0903*** -0.0534** -0.00692
(Regional) (0.0338) (0.0252) (0.0243) (0.0213) (0.0247) (0.0271)
Observations 21,829 23,460 26,458 37,063 42,270 29,970

Panel B: 10 to 17

Inequality Experiences -0.0287 -0.00502 -0.00782 -0.0381** -0.00525 -0.0333*
(Regional) (0.0188) (0.0210) (0.0215) (0.0168) (0.0146) (0.0174)
Observations 22,704 25,037 27,963 38,865 44,603 31,784

Panel C: 18 to 25 (main)

Inequality Experiences -0.0415** -0.0268* -0.0142 -0.0598*** -0.0522*** -0.0377***
(Regional) (0.0179) (0.0138) (0.0111) (0.0134) (0.0123) (0.0141)
Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel D: 26 to 33

Inequality Experiences -0.0384** -0.0406*** -0.00866 -0.0509*** -0.0696*** -0.0377***
(Regional) (0.0165) (0.0150) (0.0135) (0.0115) (0.0105) (0.0142)
Observations 18,500 20,699 23,162 31,916 36,956 27,455

Panel E: 34 to 41

Inequality Experiences 0.00763 -0.0125 -0.0244 0.00213 -0.0581*** -0.0527***
(Regional) (0.0249) (0.0174) (0.0181) (0.0161) (0.0177) (0.0189)
Observations 14,065 15,880 17,767 24,303 28,403 21,631

Panel F: 42 to 49

Inequality Experiences 0.0550 -0.0183 -0.0282 0.0485*** -0.0470** -0.00858
(Regional) (0.0425) (0.0271) (0.0242) (0.0186) (0.0232) (0.0237)
Observations 10,236 11,674 13,067 17,745 20,826 16,165

Census div 16 FE x Age FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Year FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. Inequality experiences are based on the regional experienced share
of total income earned by the top 5 percent during the different periods of life. Unemployment experiences are based on the experienced national
unemployment rate during the different periods of life. We control for age fixed effects, year fixed effects and cohort group fixed effects, each interacted
with census division at age 16 fixed effects and we also control for current census division fixed effects. All specifications control for a large set of
controls: household income, marital status, education, employment status, household size, religion, and gender. All outcome measures are z-scored. *
p < 0.10, ** p < 0.05, *** p < 0.01.

2



Table A.3: Allbus: Inequality Experiences during Other Periods of Life

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

Inequality: Inequality does not Inequality Left-wing Intention to Vote: Voted: Left
Unfair increase motivation reflects luck Left

Panel A: 2 to 9

Inequality Experiences 0.0502 -0.0671 0.00994 0.0205 0.0672** 0.0455
(0.0898) (0.0677) (0.0571) (0.0541) (0.0329) (0.0528)

Observations 7,506 7,427 7,408 14,072 10,940 6,855

Panel B: 10 to 17

Inequality Experiences -0.137*** -0.0555 -0.112*** -0.0358 -0.120*** -0.148***
(0.0483) (0.0454) (0.0331) (0.0287) (0.0232) (0.0408)

Observations 8,942 8,885 8,852 16,248 12,524 8,023

Panel C: 18 to 25 (main)

Inequality Experiences -0.0543* -0.0428 -0.0684* -0.0957*** -0.0836*** -0.0961**
(0.0307) (0.0296) (0.0349) (0.0196) (0.0298) (0.0457)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel D: 26 to 33

Inequality Experiences -0.00389 0.00454 -0.00287 -0.0196* -0.00724 -0.00974
(0.0171) (0.0203) (0.0183) (0.0101) (0.0145) (0.0157)

Observations 9,740 9,669 9,628 17,462 13,758 9,123

Panel E: 34 to 41

Inequality Experiences 0.0315** 0.0222 0.0505*** -0.0152 0.0140 0.0432**
(0.0141) (0.0302) (0.0152) (0.0116) (0.0126) (0.0171)

Observations 8,411 8,329 8,300 14,933 11,916 7,989

Panel F: 42 to 49

Inequality Experiences -0.00839 0.0190 0.0107 -0.0275 0.00227 -0.0296
(0.0297) (0.0298) (0.0163) (0.0233) (0.0230) (0.0202)

Observations 6,826 6,726 6,710 12,355 9,993 6,685

Panel G: Life-time experiences
λ = −1
Inequality Experiences -0.0430** -0.0532*** -0.0698*** -0.0610*** -0.0845*** -0.112***

(0.0192) (0.0188) (0.0232) (0.00827) (0.0181) (0.0219)
Observations 7,556 7,532 7,499 14,163 10,963 7,249

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Region FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. Inequality experiences are based on the experienced share of total
income earned by the top 5 percent during the different periods of life. Unemployment experiences are based on the experienced national unemployment
rate during the different periods of life. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed
effects. All specifications control for a large set of controls: household income, marital status, education, employment status, household size, religion,
and gender. In Panel G we display the results on the effect of life-time inequality experiences based on the methodology developed by Malmendier and
Nagel (2011). We use a weighting factor of λ = -1. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

3



Table A.4: GSS: Placebos

(1) (2) (3)

Pro-immigration Pro-guns God exists

Inequality Experiences 0.0329 -0.000870 0.00392
(0.0343) (0.0104) (0.0112)

Observations 8,266 30,527 15,322
R-squared 0.098 0.084 0.301

Age FE Yes Yes Yes
Year FE Yes Yes Yes
Cohort group FE Yes Yes Yes
Census div FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experi-
ences are based on the experienced share of total income earned by the top
5 percent during the impressionable years. Unemployment experiences are
based on the experienced national unemployment rate during the impression-
able years. All specifications control for age fixed effects, year fixed effects,
cohort group fixed effects as well as region fixed effects. All specifications
control for a large set of controls: household income, marital status, education,
employment status, household size, religion, and gender. All outcome measures
are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

4



Table A.5: Allbus: Placebos

(1) (2) (3)

Pro-immigration Nationalism Nature determines life

Inequality Experiences -0.0330 -0.0298 -0.0712
(0.0272) (0.0344) (0.0623)

Observations 11,057 5,666 4,178
R-squared 0.235 0.122 0.090

Age FE Yes Yes Yes
Year FE Yes Yes Yes
Cohort group FE Yes Yes Yes
Region FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the
experienced share of total income earned by the top 5 percent during the impressionable years.
Unemployment experiences are based on the experienced national unemployment rate during the
impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group
fixed effects as well as region fixed effects. All specifications control for a large set of controls:
household income, marital status, education, employment status, household size, religion, and
gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

Table A.6: ESS: Placebos

(1) (2) (3)

Pro-immigration Pro-EU unification Pro-democratic

Inequality Experiences -0.00592 -0.0210 0.0419
(0.0213) (0.0217) (0.0273)

Observations 81,136 55,907 48,045
R-squared 0.209 0.109 0.070

Country FE x Age trends Yes Yes Yes
Country FE x Year FE Yes Yes Yes
Country FE x Cohort group FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the
experienced share of total income earned by the top 5 percent during the impressionable years.
Unemployment experiences are based on the experienced national unemployment rate during the
impressionable years. All specifications control for age trends, year fixed effects and cohort group
fixed effects, each interacted with country fixed effects. All specifications control for a large set of
controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

5



Table A.7: GSS (national): Other Experiences during the Impressionable Years

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: Unemployment (main)

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

Unemployment Experiences 0.00868 0.000302 0.00397 0.0177** -0.00213 0.00609
(0.0158) (0.00841) (0.00823) (0.00823) (0.00757) (0.00627)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel B: GDP Growth

Inequality Experiences -0.0363** -0.0225** -0.00855 -0.0306** -0.0455*** -0.0317***
(0.0153) (0.0110) (0.00959) (0.0127) (0.0103) (0.0109)

Experienced GDP growth -0.00925 0.00219 0.0129 0.00119 0.00910* 0.0203***
(0.0124) (0.00620) (0.00873) (0.00761) (0.00539) (0.00707)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel C: Tax Revenue

Inequality Experiences -0.0430** -0.0123 -0.0378** -0.0668*** -0.0425*** -0.0501**
(0.0204) (0.0162) (0.0147) (0.0179) (0.0151) (0.0199)

Experienced Tax Revenue rel to GDP -0.0435 0.0433 -0.175*** -0.159** -0.0247 -0.108*
(0.0957) (0.0562) (0.0536) (0.0632) (0.0471) (0.0650)

Observations 22,411 24,409 27,498 38,335 43,856 31,063

Panel D: Political Ideology

Inequality Experiences -0.0351** -0.0226** -0.0113 -0.0317*** -0.0465*** -0.0370***
(0.0139) (0.0108) (0.00918) (0.0121) (0.0101) (0.0123)

Experienced Republican President 0.0163** -0.00559 -0.0122** 0.00726 -0.0163** -0.0107
(0.00763) (0.00553) (0.00598) (0.00768) (0.00767) (0.00723)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the national level experienced share of total income
earned by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced unemployment rate during the
impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed effects. All
specifications control for a large set of controls: household income, marital status, education, employment status, household size, religion, and gender.
All outcome measures are z-scored. In Panel A we show the main results. In Panel B, we show the results controlling for experienced growth of real
GDP per capita. In Panel C, we control for the experienced size of the government as the experienced ratio of total tax revenue to GDP. In Panel D,
we control for the fraction of the impressionable years in which a Republican president was in office. * p < 0.10, ** p < 0.05, *** p < 0.01.

6



Table A.8: Allbus: Other Experiences during the Impressionable Years

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

Inequality: Inequality does not Inequality Left-wing Intention to Vote: Voted: Left
Unfair increase motivation reflects luck Left

Panel A: Unemployment (main)

Inequality Experiences -0.0544* -0.0428 -0.0684** -0.0956*** -0.0837*** -0.0962**
(0.0307) (0.0296) (0.0349) (0.0196) (0.0298) (0.0456)

Unemployment Experiences 0.108* 0.0293 0.0994* 0.00953 0.0254 0.0949
(0.0614) (0.0465) (0.0539) (0.0337) (0.0499) (0.0734)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel B: GDP Growth

Inequality Experiences -0.0891*** -0.0518** -0.105*** -0.102*** -0.0952*** -0.127***
(0.0215) (0.0248) (0.0276) (0.0219) (0.0277) (0.0446)

Experienced GDP Growth 0.00369 0.00314 -0.0253 -0.0119 -0.0131 -0.0186
(0.0341) (0.0244) (0.0305) (0.0139) (0.00937) (0.0231)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel C: Tax Revenue

Inequality Experiences -0.0902*** -0.0725*** -0.0968*** -0.118*** -0.111*** -0.130***
(0.0211) (0.0246) (0.0251) (0.0209) (0.0292) (0.0498)

Experienced Tax Revenue rel to GDP 0.111 0.144 -0.00542 -0.0157 -0.0111 -0.0637
(0.0853) (0.0989) (0.0490) (0.0573) (0.0824) (0.112)

Panel D: Political Ideology

Inequality Experiences -0.0835*** -0.0620** -0.0939*** -0.121*** -0.0993*** -0.129**
(0.0204) (0.0251) (0.0277) (0.0224) (0.0288) (0.0518)

Experienced Conservative Chancellor 0.0139 0.0126 -0.0104 0.00211 -0.0254* -0.0158
(0.0164) (0.0134) (0.0129) (0.00987) (0.0145) (0.0241)

Observations 9,974 9,955 9,902 17,740 13,602 9,046

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the national level experienced share of total income
earned by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced unemployment rate during the
impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed effects . All
specifications control for a large set of controls: household income, marital status, education, employment status, household size, religion, and gender.
All outcome measures are z-scored. In Panel A we show the main results. In Panel B, we show the results controlling for experienced growth of real
GDP per capita. In Panel C, we control for the experienced size of the government as the experienced ratio of total tax revenue to GDP. In Panel D,
we control for the fraction of the impressionable years in which a conservative chancellor was in office. * p < 0.10, ** p < 0.05, *** p < 0.01.

7



Table A.9: GSS (national): Controlling for Proxies for Parental Values

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: Main

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

Observations 23,199 26,135 29,083 40,136 46,327 32,907
R-squared 0.108 0.128 0.024 0.078 0.146 0.200

Panel B: Additional Controls

Inequality Experiences -0.0376** -0.0235* -0.0146 -0.0371*** -0.0485*** -0.0433***
(0.0146) (0.0124) (0.0110) (0.0122) (0.0119) (0.0126)

Observations 23,199 26,135 29,083 40,136 46,327 32,907
R-squared 0.112 0.131 0.026 0.081 0.155 0.204

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors two-way clustered by age and cohort are displayed in parentheses. Inequality experiences are based on the experienced national-level
share of total income earned by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced national
unemployment rate during the impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects, as well
as region fixed effects. All specifications control for a large set of controls: household income, marital status, education, employment status, household
size, religion, and gender. All outcome measures are z-scored. In Panel B we additionally include the following controls: whether the respondent’s
father completed at most high school, whether the respondent’s father completed college, whether the respondent’s mother completed at most high
school, whether the respondent’s mother completed college, whether the father was self-employed while the respondent grew up, whether the mother
worked when the respondent was young, whether the respondent lived in an urban area, whether the family’s income was above the median income
when the respondent was aged 16 and for census division fixed effects of the respondents’ residence at age 16. * p < 0.10, ** p < 0.05, *** p < 0.01.

8



Table A.10: GSS: Robustness (National Inequality Experiences)

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: Main

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel B: Top 1 percent

Inequality Experiences -0.0398*** -0.0215* -0.0125 -0.0451*** -0.0469*** -0.0411***
(0.0141) (0.0126) (0.0112) (0.0124) (0.0123) (0.0129)

Observations 23,238 26,325 29,233 40,302 46,587 33,097

Panel C: Top 10 percent

Inequality Experiences -0.0361** -0.0258** -0.0163 -0.0347*** -0.0515*** -0.0426***
(0.0158) (0.0129) (0.0113) (0.0127) (0.0128) (0.0131)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel D: No missings

Inequality Experiences -0.0458*** -0.0373*** -0.0221* -0.0467*** -0.0510*** -0.0437***
(0.0155) (0.0126) (0.0128) (0.0139) (0.0124) (0.0141)

Observations 20,935 23,817 26,311 36,458 40,736 29,298

Panel E: No unemployment
experience controls

Inequality Experiences -0.0337** -0.0233** -0.0129 -0.0310** -0.0485*** -0.0383***
(0.0143) (0.0105) (0.00891) (0.0122) (0.0107) (0.0121)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel F: Age trend

Inequality Experiences -0.0441*** -0.0284** -0.0183* -0.0303*** -0.0547*** -0.0428***
(0.0142) (0.0131) (0.0102) (0.0114) (0.0117) (0.0113)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Panel G: Gini coefficient

Inequality Experiences -0.0494*** -0.0157 -0.00360 -0.0750*** -0.0481*** -0.0517***
(0.0122) (0.0127) (0.00907) (0.0113) (0.00914) (0.0130)

Observations 19,626 20,246 23,272 33,004 37,253 25,906

Panel H: No cohort group FE

Inequality Experiences -0.0254** -0.0298*** -0.0133 -0.0294*** -0.0211** -0.0240***
(0.0113) (0.00863) (0.00825) (0.00902) (0.0107) (0.00833)

Observations 23,199 26,135 29,083 40,136 46,327 32,907

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the national level experienced share of total income earned
by the top 5 percent during the impressionable years, unless otherwise stated. Unemployment experiences are based on the experienced unemployment
rate during the impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed
effects. All specifications control for a large set of controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. In Panel A, we show the main results. In Panel B, we show the results using the top 1 percent income
share as our measure of inequality. In Panel C, we show the results using the top 10 percent income share as our measure of inequality. In Panel D, we
only use observations for which we do not have missings in any of the controls. In Panel E, we do not make use of unemployment experience controls.
In Panel F, we use an age trend rather than age fixed effects. In Panel G, we show the results using the Gini coefficient as our measure of income
inequality. In Panel H, we do not make use of cohort group fixed effects. * p < 0.10, ** p < 0.05, *** p < 0.01.

9



Table A.11: GSS: Robustness (Regional Inequality Experiences)

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Panel A: Main

Inequality Experiences -0.0415** -0.0268* -0.0142 -0.0598*** -0.0522*** -0.0377***
(0.0179) (0.0138) (0.0111) (0.0134) (0.0123) (0.0141)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel B: Top 1 percent

Inequality Experiences -0.0398*** -0.0215* -0.0125 -0.0451*** -0.0469*** -0.0411***
(0.0141) (0.0126) (0.0112) (0.0124) (0.0123) (0.0129)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel C: Top 10 percent

Inequality Experiences -0.0419** -0.0241* -0.0168 -0.0564*** -0.0498*** -0.0351**
(0.0179) (0.0143) (0.0119) (0.0137) (0.0125) (0.0144)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel D: No missings

Inequality Experiences -0.0523*** -0.0455*** -0.0180 -0.0687*** -0.0591*** -0.0417***
(0.0185) (0.0146) (0.0130) (0.0147) (0.0123) (0.0154)

Observations 20,748 23,545 25,957 36,017 40,234 29,033

Panel E: No unemployment
experience controls

Inequality Experiences -0.0370** -0.0276** -0.0129 -0.0526*** -0.0529*** -0.0358***
(0.0176) (0.0123) (0.00923) (0.0135) (0.0109) (0.0138)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel F: Age trend

Inequality Experiences -0.0479*** -0.0272* -0.0174* -0.0494*** -0.0575*** -0.0355***
(0.0176) (0.0140) (0.00966) (0.0125) (0.0111) (0.0130)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Panel G: No movers

Inequality Experiences -0.0409** -0.0239* -0.0135 -0.0499*** -0.0584*** -0.0376***
(0.0203) (0.0128) (0.0141) (0.0153) (0.0128) (0.0146)

Observations 17,653 19,914 22,108 30,481 35,252 24,833

Panel H: No cohort group FE

Inequality Experiences -0.0274** -0.0310*** -0.0122* -0.0355*** -0.0184* -0.0168**
(0.0109) (0.0100) (0.00731) (0.00943) (0.00981) (0.00733)

Observations 22,987 25,831 28,670 39,632 45,703 32,597

Census div 16 FE x Age FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Year FE Yes Yes Yes Yes Yes Yes
Census div 16 FE x Cohort group FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the national level experienced share of total income earned
by the top 5 percent during the impressionable years, unless otherwise stated. Unemployment experiences are based on the experienced unemployment
rate during the impressionable years. All specifications control for age fixed effects, year fixed effects and cohort group fixed effects, each interacted
with census division at 16 fixed effects. All specifications control for a large set of controls: household income, marital status, education, employment
status, household size, religion, and gender. All outcome measures are z-scored. In Panel A, we show the main results. In Panel B, we show the results
using the top 1 percent income share as our measure of inequality. In Panel C, we show the results using the top 10 percent income share as our
measure of inequality. In Panel D, we only use observations for which we do not have missings in any of the controls. In Panel E, we do not make use
of unemployment experience controls. In Panel F, we use an age trend rather than age fixed effects. In Panel G, we show the results excluding those
who moved to a different census division between age 16 and the time of the interview. In Panel H, we do not make use of cohort group fixed effects.
* p < 0.10, ** p < 0.05, *** p < 0.01.

10



Table A.12: Allbus: Robustness

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

Inequality: Inequality does not Inequality Left-wing Intention to Vote: Voted: Left
Unfair increase motivation reflects luck Left

Panel A: Main

Inequality Experiences -0.0544* -0.0428 -0.0684** -0.0956*** -0.0837*** -0.0962**
(0.0307) (0.0296) (0.0349) (0.0196) (0.0298) (0.0456)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel B: Top 1 percent

Inequality Experiences -0.109*** -0.0650* -0.123*** -0.125*** -0.138*** -0.141***
(0.0333) (0.0352) (0.0286) (0.0229) (0.0287) (0.0430)

Observations 13,635 13,537 13,478 25,555 20,236 12,930

Panel C: Top 10 percent

Inequality Experiences -0.0678 -0.0697 -0.0579 -0.127*** -0.0742* -0.0824
(0.0466) (0.0436) (0.0525) (0.0312) (0.0394) (0.0729)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel D: No missings

Inequality Experiences -0.0382 -0.0610* -0.0626 -0.102*** -0.0987*** -0.0833
(0.0365) (0.0339) (0.0411) (0.0254) (0.0352) (0.0562)

Observations 7,719 7,686 7,669 13,609 10,997 6,831

Panel E: No unemployment
experience controls

Inequality Experiences -0.0896*** -0.0523** -0.101*** -0.0983*** -0.0904*** -0.124***
(0.0212) (0.0242) (0.0271) (0.0219) (0.0266) (0.0430)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel F: Age trend

Inequality Experiences -0.0548** -0.0498** -0.0864*** -0.0982*** -0.105*** -0.0987**
(0.0236) (0.0213) (0.0239) (0.0186) (0.0295) (0.0478)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Panel G: Gini coefficient

Inequality Experiences -0.0188 -0.0616*** -0.0458** -0.0530*** -0.0695*** -0.0539**
(0.0216) (0.0172) (0.0226) (0.00863) (0.0123) (0.0221)

Observations 9,783 9,766 9,716 17,318 13,242 8,827

Panel H: No cohort group FE

Inequality Experiences -0.0347* -0.0398** -0.0512** -0.0492*** -0.0674*** -0.0979***
(0.0200) (0.0202) (0.0239) (0.0132) (0.0178) (0.0294)

Observations 10,401 10,357 10,309 18,979 14,691 9,533

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Cohort group FE Yes Yes Yes Yes Yes Yes
Region FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the national level experienced share of total income earned
by the top 5 percent during the impressionable years, unless otherwise stated. Unemployment experiences are based on the experienced unemployment
rate during the impressionable years. All specifications control for age fixed effects, year fixed effects, cohort group fixed effects as well as region fixed
effects. All specifications control for a large set of controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. In Panel A, we show the main results. In Panel B, we show the results using the top 1 percent income
share as our measure of inequality. In Panel C, we show the results using the top 10 percent income share as our measure of inequality. In Panel D, we
only use observations for which we do not have missings in any of the controls. In Panel E, we do not make use of unemployment experience controls.
In Panel F, we use an age trend rather than age fixed effects. In Panel G, we show the results using the Gini coefficient as our measure of income
inequality. In Panel H, we do not make use of cohort group fixed effects. * p < 0.10, ** p < 0.05, *** p < 0.01.

11



Table A.13: ESS: Robustness

(1) (2) (3)

Pro-redistribution Left-wing Voted: Left

Panel A: Main

Inequality Experiences -0.0390* -0.117*** -0.121***
(0.0234) (0.0200) (0.0389)

Observations 85,529 81,167 25,462

Panel B: Top 1 percent

Inequality Experiences -0.0509* -0.124*** -0.127***
(0.0279) (0.0193) (0.0371)

Observations 92,831 87,731 28,591

Panel C: Top 10 percent

Inequality Experiences -0.0412* -0.108*** -0.114***
(0.0223) (0.0203) (0.0404)

Observations 84,485 79,676 25,438

Panel D: No missings

Inequality Experiences -0.0486** -0.124*** -0.118***
(0.0244) (0.0251) (0.0453)

Observations 68,937 66,498 21,899

Panel E: No unemployment
experience controls

Inequality Experiences -0.0435* -0.118*** -0.118***
(0.0241) (0.0193) (0.0367)

Observations 85,529 81,167 25,462

Panel F: Gini coefficient

Inequality Experiences -0.0850** -0.155** -0.183**
(0.0344) (0.0613) (0.0795)

Observations 44,670 42,077 15,852

Panel G: No cohort group FE

Inequality Experiences -0.0491*** -0.106*** -0.139***
(0.0144) (0.00980) (0.0245)

Observations 85,529 81,167 25,462

Country FE x Age trend Yes Yes Yes
Country FE x Year FE Yes Yes Yes
Country FE x Cohort group FE Yes Yes Yes
Unemployment Experiences Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on
the experienced share of total income earned by the top 5 percent during the impressionable
years unless otherwise stated. Unemployment experiences are based on the experienced
national unemployment rate during the impressionable years. All specifications control for
age trends, year fixed effects as well as cohort group fixed effects, each interacted with
country fixed effects. All specifications control for a large set of controls: household income,
marital status, education, employment status, household size, religion, and gender. All
outcome measures are z-scored. In Panel A, we show the main results. In Panel B, we show
the results using the top 1 percent income share as our measure of inequality. In Panel C,
we show the results using the top 10 percent income share as our measure of inequality. In
Panel D, we only use observations for which we do not have missings in any of the controls.
In Panel E, we do not make use of unemployment experience controls. In Panel F, we show
the results using the Gini coefficient as our measure of income inequality. In Panel G, we
do not include cohort group fixed effects. * p < 0.10, ** p < 0.05, *** p < 0.01.

12



Table A.14: GSS: Summary Stats

Variable Mean Std. Dev. Min. Max. N

Vote: Democrat 0.466 0.499 0 1 32907
Share top 10 during impr years (national) 34.879 4.16 31.779 46.294 47207
Share top 5 during impr years (national) 23.587 3.785 20.679 33.926 47207
Share top 1 during impr years (national) 10.375 2.882 7.875 17.805 47207
Gini during impr year (national) 38.566 2.035 36.838 45.225 36080
Unemployment during impr years 6.545 2.913 3.875 17.988 47207
Share top 10 during impr years (regional) 35.411 3.898 28.246 50.609 46544
Share top 5 during impr years (regional) 24.458 3.514 18.935 39.275 46544
Share top 1 during impr years (regional) 11.429 2.705 7.565 23.098 46544
Full-time employed 0.517 0.5 0 1 47207
Part-time employed 0.096 0.294 0 1 47207
Temporarily not working 0.024 0.152 0 1 47207
Unemployed 0.03 0.17 0 1 47207
Retired 0.135 0.342 0 1 47207
In School 0.011 0.103 0 1 47207
Keeping house 0.168 0.374 0 1 47207
Other labor force 0.021 0.142 0 1 47207
Married 0.675 0.468 0 1 47207
Widowed 0.072 0.258 0 1 47207
Divorced 0.107 0.31 0 1 47207
Separated 0.028 0.164 0 1 47207
Single 0.118 0.323 0 1 47207
Age 48.249 14.975 26 88 47207
Less than high school 0.207 0.405 0 1 47207
High school 0.570 0.495 0 1 47207
College 0.221 0.415 0 1 47207
Female 0.547 0.498 0 1 47207
White 0.839 0.367 0 1 47207
Black 0.133 0.339 0 1 47207
Other race 0.028 0.165 0 1 47207
Born in US 0.84 0.367 0 1 47207
Household Size 2.853 1.28 1 5 47203
Urban 0.466 0.499 0 1 47207
Protestant 0.614 0.487 0 1 47207
Catholic 0.234 0.423 0 1 47207
Jewish 0.019 0.137 0 1 47207
No religion 0.097 0.297 0 1 47207
Other religion 0.032 0.175 0 1 47207

13



Table A.15: Allbus: Summary Stats

Variable Mean Std. Dev. Min. Max. N

Share top 10 during impr years 31.814 1.253 30.997 39.479 20712
Share top 5 during impr years 22.068 0.965 21.171 31.464 20712
Share top 1 during impr years 10.54 0.864 9.631 19.825 20712
Gini during impr years 25.27 0.812 24.576 28.075 18626
Unemployment during impr years 5.699 4.186 1.188 22.212 20712
Age 42.72 12.798 26 102 20712
Female 0.517 0.5 0 1 20712
Education: No schooling 0.012 0.107 0 1 20712
Education: “Hauptschule” 0.422 0.494 0 1 20712
Education: Middle school 0.284 0.451 0 1 20712
Education: A-levels 0.277 0.448 0 1 20712
Married 0.686 0.464 0 1 20712
Separated 0.016 0.126 0 1 20712
Widowed 0.04 0.195 0 1 20712
Divorced 0.071 0.256 0 1 20712
Single 0.186 0.389 0 1 20712
Full-time employed 0.544 0.498 0 1 20712
Part-time employed 0.118 0.322 0 1 20712
Out of labor force 0.314 0.464 0 1 20712
Unemployed 0.007 0.081 0 1 20712
Retired 0.014 0.117 0 1 20712
Student 0.002 0.049 0 1 20712
Other employment 0 0.02 0 1 20712
Protestant 0.412 0.492 0 1 20712
Catholic 0.412 0.492 0 1 20712
Other religion 0.015 0.123 0 1 20712
No Religion 0.157 0.363 0 1 20712
Household Size 2.851 1.228 1 5 20665

14



Table A.16: ESS: Summary Stats

Variable Mean Std. Dev. Min. Max. N

Share top 10 during impr years 31.468 3.126 21.839 43.379 79640
Share top 5 during impr years 20.898 2.622 13.343 32.606 86303
Share top 1 during impr years 8.48 1.918 4.024 17.25 86303
Gini during impr years 28.411 3.717 20.569 37.15 43918
Unemployment during impr years 6.854 4.284 0.01 35.287 86303
Male 0.479 0.5 0 1 86303
Age 47.236 12.989 26 102 86303
Below high school 0.264 0.441 0 1 86303
High school 0.451 0.498 0 1 86303
College 0.278 0.448 0 1 86303
Married 0.601 0.49 0 1 86303
Separated 0.014 0.116 0 1 86303
Divorced 0.088 0.283 0 1 86303
Widowed 0.036 0.185 0 1 86303
Never married 0.207 0.405 0 1 86303
Employed 0.789 0.408 0 1 86303
Self-employed 0.13 0.336 0 1 86303
Not in paid work 0.055 0.229 0 1 86303
Religion: Catholic 0.265 0.441 0 1 86303
Religion: Protestant 0.197 0.398 0 1 86303
Religion: Eastern Orthodox 0.001 0.023 0 1 86303
Religion: other christian 0.013 0.114 0 1 86303
Religion: Jewish 0.001 0.034 0 1 86303
Religion: Islamic 0.006 0.078 0 1 86303
Religion: Other 0.005 0.074 0 1 86303
Religion: None 0.403 0.49 0 1 86303
Household Size 2.778 1.218 1 5 86270
Income bracket (waves 1-3) 7.29 2.169 1 12 31690
Income bracket (waves 4-7) 5.905 2.748 1 10 43220
Denmark 0.021 0.145 0 1 86303
Finland 0.019 0.137 0 1 86303
France 0.189 0.391 0 1 86303
Germany 0.238 0.426 0 1 86303
Great Britain 0.21 0.408 0 1 86303
Italy 0.044 0.205 0 1 86303
Netherlands 0.078 0.268 0 1 86303
Norway 0.02 0.14 0 1 86303
Portugal 0.019 0.138 0 1 86303
Spain 0.09 0.286 0 1 86303
Sweden 0.041 0.199 0 1 86303
Switzerland 0.03 0.171 0 1 86303

15



Table A.17: GSS (national inequality experiences): Main Results showing Key Controls

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

Help poor Pro welfare Success due to luck Liberal Party: Democrat Voted: Democrat

Inequality Experiences -0.0370** -0.0234* -0.0147 -0.0383*** -0.0476*** -0.0414***
(0.0147) (0.0126) (0.0112) (0.0123) (0.0126) (0.0129)

Unemployment Experiences 0.00868 0.000302 0.00397 0.0177** -0.00213 0.00609
(0.0158) (0.00841) (0.00823) (0.00823) (0.00757) (0.00627)

Female 0.137*** 0.0130 -0.101*** 0.125*** 0.126*** 0.117***
(0.0171) (0.0165) (0.0156) (0.0124) (0.0141) (0.0146)

Part-time employed 0.0176 0.0452 0.0540** 0.0204 -0.0384** 0.00530
(0.0259) (0.0278) (0.0233) (0.0132) (0.0159) (0.0184)

Temporarily not working 0.0397 0.0739 -0.0147 0.109*** 0.0420 0.0478
(0.0416) (0.0486) (0.0442) (0.0384) (0.0308) (0.0359)

Unemployed 0.157*** 0.226*** 0.110** 0.0481* 0.0806*** 0.0913***
(0.0377) (0.0458) (0.0480) (0.0267) (0.0305) (0.0296)

Retired 0.0986*** 0.120*** -0.000732 0.0152 0.0677*** 0.0836***
(0.0373) (0.0192) (0.0282) (0.0231) (0.0182) (0.0255)

In school 0.0151 0.300*** 0.142*** 0.0821 0.0827 0.0421
(0.0570) (0.0506) (0.0512) (0.0569) (0.0504) (0.0640)

Keeping the house 0.0631*** 0.148*** -0.0248 -0.0797*** -0.0509*** -0.0259
(0.0215) (0.0179) (0.0201) (0.0203) (0.0157) (0.0216)

Other labor force 0.250*** 0.326*** -0.0295 0.0619* 0.0385 0.0635*
(0.0534) (0.0503) (0.0497) (0.0372) (0.0379) (0.0351)

Married -0.121*** -0.151*** -0.0789*** -0.186*** -0.115*** -0.134***
(0.0207) (0.0287) (0.0227) (0.0212) (0.0149) (0.0216)

Widowed -0.0928** -0.0928*** -0.0883*** -0.0997*** -0.0287 -0.0933***
(0.0414) (0.0339) (0.0310) (0.0280) (0.0182) (0.0301)

Divorced -0.0329 -0.0285 -0.00705 -0.0210 -0.0331* -0.0613**
(0.0216) (0.0269) (0.0306) (0.0245) (0.0194) (0.0247)

Separated 0.00189 -0.0145 -0.0570 -0.00536 -0.0853*** -0.0858***
(0.0447) (0.0489) (0.0383) (0.0329) (0.0276) (0.0308)

High-school -0.244*** -0.123*** 0.0624*** -0.0958*** -0.176*** -0.190***
(0.0232) (0.0162) (0.0196) (0.0147) (0.0198) (0.0206)

College -0.319*** 0.0310 0.100*** -0.0137 -0.246*** -0.137***
(0.0260) (0.0223) (0.0261) (0.0176) (0.0269) (0.0237)

Black 0.526*** 0.604*** 0.180*** 0.317*** 0.888*** 1.027***
(0.0199) (0.0282) (0.0202) (0.0195) (0.0179) (0.0143)

Other race 0.160*** 0.234*** 0.0784* 0.157*** 0.339*** 0.441***
(0.0349) (0.0546) (0.0459) (0.0395) (0.0307) (0.0519)

Income bracket 2 0.103 0.0769 -0.0732 -0.116 0.0377 0.0604
(0.0979) (0.0756) (0.0869) (0.0851) (0.0484) (0.0752)

Income bracket 3 0.0830 0.0170 -0.00125 -0.0922 0.105** 0.171**
(0.112) (0.0694) (0.0867) (0.0979) (0.0535) (0.0853)

Income bracket 4 0.131 -0.107 -0.107 -0.0490 0.159*** 0.164**
(0.105) (0.0820) (0.0796) (0.0955) (0.0498) (0.0804)

Income bracket 5 0.119 -0.109 -0.0625 -0.111 0.174*** 0.141*
(0.103) (0.0731) (0.0704) (0.0944) (0.0485) (0.0751)

Income bracket 6 0.111 -0.171** -0.145** -0.0804 0.115** 0.119
(0.116) (0.0741) (0.0649) (0.0841) (0.0472) (0.0764)

Income bracket 7 0.00347 -0.233*** -0.0755 -0.0977 0.185*** 0.108
(0.101) (0.0785) (0.0774) (0.0896) (0.0464) (0.0825)

Income bracket 8 -0.0282 -0.243*** -0.0536 -0.0447 0.183*** 0.140*
(0.102) (0.0780) (0.0604) (0.0833) (0.0482) (0.0829)

Income bracket 9 -0.0445 -0.345*** -0.0811 -0.0533 0.181*** 0.103
(0.0955) (0.0742) (0.0608) (0.0799) (0.0440) (0.0735)

Income bracket 10 -0.0354 -0.417*** -0.152** -0.0854 0.149*** 0.0780
(0.0851) (0.0679) (0.0638) (0.0780) (0.0443) (0.0768)

Income bracket 11 -0.141* -0.409*** -0.137* -0.115 0.112** 0.0423
(0.0842) (0.0646) (0.0727) (0.0802) (0.0470) (0.0724)

Income bracket 12 -0.234*** -0.498*** -0.138** -0.134* 0.0209 -0.0655
(0.0878) (0.0684) (0.0625) (0.0781) (0.0415) (0.0702)

Protestant -0.160*** -0.144*** -0.158*** -0.535*** -0.348*** -0.535***
(0.0188) (0.0231) (0.0244) (0.0235) (0.0188) (0.0241)

Catholic -0.0755*** -0.107*** -0.114*** -0.401*** -0.0534*** -0.317***
(0.0247) (0.0233) (0.0260) (0.0247) (0.0191) (0.0232)

Jewish 0.0504 0.200*** 0.0814 0.0642 0.378*** 0.196***
(0.0523) (0.0505) (0.0555) (0.0461) (0.0443) (0.0486)

Other religion -0.0429 -0.112*** -0.0184 -0.327*** -0.176*** -0.240***
(0.0355) (0.0396) (0.0455) (0.0486) (0.0337) (0.0430)

Cohort: 1876 - 1900 -0.464 -0.191** 0.0247 0.0176 -0.132 0.0393
(0.303) (0.0757) (0.106) (0.135) (0.136) (0.0841)

Cohort: 1901 - 1925 -0.0569 -0.0102 0.0636 0.0858 -0.0332 -0.00948
(0.0601) (0.0623) (0.0523) (0.0875) (0.0778) (0.0726)

Cohort: 1926 -1950 -0.0832* 0.0153 0.0477 0.0487 -0.0792 -0.0349
(0.0473) (0.0550) (0.0417) (0.0719) (0.0574) (0.0563)

Cohort: 1951 - 1975 -0.0662* 0.00629 0.00460 -0.0184 -0.151*** -0.106***
(0.0399) (0.0478) (0.0333) (0.0534) (0.0418) (0.0408)

Observations 23,199 26,135 29,083 40,136 46,327 32,907
R-squared 0.108 0.128 0.024 0.078 0.146 0.200

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Census div FE Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the experienced share of total income
earned by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced unemployment
rate during the impressionable years. All specifications control for age fixed effects, region fixed effects as well as year fixed effects . All
specifications include a large set of controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

16



Table A.18: Allbus: Main Results showing Key Controls

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

Inequality: Inequality does not Inequality Left-wing Intention to Vote: Voted: Left
Unfair increase motivation reflects luck Left

Inequality Experiences -0.0543* -0.0428 -0.0684* -0.0957*** -0.0836*** -0.0961**
(0.0307) (0.0296) (0.0349) (0.0196) (0.0298) (0.0457)

Unemployment Experiences 0.104* 0.0279 0.0950* 0.00877 0.0243 0.0909
(0.0586) (0.0443) (0.0515) (0.0321) (0.0477) (0.0701)

Female 0.0884*** 0.126*** 0.0969*** 0.0771*** 0.0547*** 0.0425
(0.0231) (0.0240) (0.0232) (0.0132) (0.0200) (0.0322)

Education: “Hauptschule” -0.185** -0.0912 -0.0306 -0.0140 -0.0189 0.0780
(0.0944) (0.117) (0.101) (0.0594) (0.0796) (0.0689)

Education: “Middle School” -0.239** 0.0269 0.122 0.0226 -0.0402 0.0414
(0.0974) (0.122) (0.104) (0.0573) (0.0755) (0.0561)

Education: “A-level” -0.171* 0.196* 0.370*** 0.249*** 0.119 0.244***
(0.0906) (0.116) (0.0897) (0.0593) (0.0739) (0.0653)

Married -0.0541 -0.0294 -0.0389 -0.137*** -0.131*** -0.183***
(0.0341) (0.0284) (0.0289) (0.0248) (0.0214) (0.0351)

Separated 0.0162 -0.0822* -0.00468 -0.119* 0.00804 -0.121
(0.0633) (0.0495) (0.0697) (0.0640) (0.0635) (0.0808)

Widowed -0.0283 0.0562 0.0585 -0.163*** -0.0767** -0.127**
(0.0710) (0.0573) (0.0909) (0.0436) (0.0361) (0.0552)

Divorced 0.103** 0.0298 0.0797** -0.0806** 0.0261 -0.0569
(0.0521) (0.0500) (0.0360) (0.0379) (0.0381) (0.0387)

Part-time employed 0.0453 0.00123 0.0389 0.0757*** 0.0958*** 0.125***
(0.0318) (0.0454) (0.0362) (0.0176) (0.0289) (0.0317)

Out of the labor force 0.0837*** 0.0231 0.0536* 0.0422* 0.0748*** 0.0804***
(0.0278) (0.0285) (0.0277) (0.0229) (0.0209) (0.0224)

Unemployed 0.379*** 0.164 0.378*** 0.317*** 0.325*** 0.336***
(0.0956) (0.114) (0.100) (0.123) (0.0292) (0.0995)

Retired 0.0112 0.0109 0.0520 0.0405 0.207*** 0.162
(0.110) (0.136) (0.167) (0.0523) (0.0741) (0.125)

Student 0.445*** 0.675*** 0.186 0.473*** 0.203* 0.289**
(0.0871) (0.105) (0.163) (0.147) (0.119) (0.117)

Other employment 0.573 -0.643 -0.316 0.206 0.485 0.611***
(0.502) (0.426) (0.576) (0.224) (0.453) (0.127)

Protestant -0.0492** -0.0692** -0.0562* -0.177*** -0.230*** -0.204***
(0.0250) (0.0319) (0.0317) (0.0196) (0.0159) (0.0176)

Catholic -0.0793*** -0.0634* -0.0505 -0.323*** -0.490*** -0.506***
(0.0242) (0.0372) (0.0351) (0.0245) (0.0224) (0.0322)

Other religion -0.137* 0.0117 -0.0476 -0.112* -0.0247 -0.113
(0.0823) (0.0926) (0.0837) (0.0585) (0.0762) (0.107)

Income quintile: 2 -0.0793** 0.0145 -0.000689 -0.0736* -0.130*** -0.130***
(0.0328) (0.0501) (0.0371) (0.0386) (0.0346) (0.0484)

Income quintile: 3 -0.108*** -0.0784* -0.0760** -0.0946*** -0.143*** -0.135***
(0.0364) (0.0461) (0.0319) (0.0342) (0.0330) (0.0398)

Income quintile: 4 -0.176*** -0.0870* -0.0970** -0.0985*** -0.150*** -0.173***
(0.0378) (0.0466) (0.0453) (0.0339) (0.0345) (0.0389)

Income quintile: 5 -0.296*** -0.153*** -0.186*** -0.192*** -0.298*** -0.295***
(0.0332) (0.0465) (0.0377) (0.0389) (0.0307) (0.0438)

Cohort: 1876 - 1900 0.692** -0.165 0.441 0.720*** 0.945** 0.825**
(0.345) (0.342) (0.404) (0.265) (0.449) (0.400)

Cohort: 1901 - 1925 0.130 0.328
(0.266) (0.422)

Cohort: 1926 - 1950 -0.117** -0.0695 -0.101 -0.106* -0.0842 -0.0754
(0.0546) (0.0819) (0.0635) (0.0637) (0.0691) (0.125)

Cohort: 1951 - 1975 -0.0975* -0.0474 -0.0866 -0.123** -0.0149 -0.0318
(0.0583) (0.0739) (0.0541) (0.0574) (0.0676) (0.121)

Observations 10,401 10,357 10,309 18,979 14,691 9,533
R-squared 0.071 0.044 0.068 0.080 0.109 0.111

Age FE Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Region FE Yes Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the experienced share of total income earned
by the top 5 percent during the impressionable years. Unemployment experiences are based on the experienced national unemployment
rate during the impressionable years. All specifications control for age fixed effects, region fixed effects as well as year fixed effects. All
specifications include a large set of controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

17



Table A.19: ESS: Main Results showing Key Controls

(1) (2) (3)

Pro-redistribution Voted: Left Left-wing

Inequality Experiences -0.0390* -0.117*** -0.121***
(0.0234) (0.0200) (0.0389)

Unemployment Experiences -0.0328** -0.00740 0.0164
(0.0163) (0.0140) (0.0285)

Male -0.119*** -0.0870*** -0.101***
(0.00963) (0.0118) (0.0163)

High school -0.0756*** -0.0298** -0.0682**
(0.0138) (0.0121) (0.0291)

College -0.204*** 0.140*** 0.141***
(0.0114) (0.0142) (0.0273)

Married -0.0751*** -0.118*** -0.0747***
(0.0130) (0.0128) (0.0230)

Separated -0.0626 -0.219*** -0.120*
(0.0405) (0.0537) (0.0654)

Divorced -0.0259 -0.0397* -0.0404
(0.0178) (0.0211) (0.0385)

Widowed -0.0550** -0.0882*** -0.0813*
(0.0225) (0.0318) (0.0485)

Self-Employed -0.183*** -0.232*** -0.228***
(0.0123) (0.0194) (0.0170)

Not in paid work -0.0387 -0.0548*** 0.00122
(0.0255) (0.0202) (0.0381)

Religion: Catholic -0.127*** -0.353*** -0.421***
(0.0158) (0.0179) (0.0232)

Religion: Protestant -0.113*** -0.269*** -0.247***
(0.0119) (0.0146) (0.0227)

Religion: Eastern Orthodox -0.170 0.117 -0.142
(0.192) (0.128) (0.272)

Religion: Other Christian 0.0235 -0.226*** -0.0571
(0.0485) (0.0384) (0.0822)

Religion: Jewish -0.502*** -0.216* -0.318*
(0.158) (0.123) (0.189)

Religion: Islamic 0.170*** 0.158** 0.445***
(0.0638) (0.0789) (0.0904)

Religion: Other 0.0714 0.109* 0.135
(0.0630) (0.0634) (0.0931)

Income bracket: 1 -0.0118 -0.141 -0.0517
(0.246) (0.193) (0.246)

Income bracket: 2 0.0142 -0.202 -0.128
(0.227) (0.195) (0.240)

Income bracket: 3 -0.0558 -0.155 -0.0891
(0.239) (0.192) (0.223)

Income bracket: 4 -0.0943 -0.275 -0.200
(0.240) (0.196) (0.233)

Income bracket: 5 -0.115 -0.257 -0.170
(0.241) (0.185) (0.238)

Income bracket: 6 -0.162 -0.272 -0.190
(0.233) (0.195) (0.227)

Income bracket: 7 -0.209 -0.235 -0.185
(0.231) (0.193) (0.234)

Income bracket: 8 -0.275 -0.281 -0.243
(0.240) (0.189) (0.244)

Income bracket: 9 -0.398* -0.298 -0.206
(0.222) (0.187) (0.223)

Income bracket: 10 -0.669*** -0.443** -0.415*
(0.243) (0.195) (0.232)

Observations 85,529 81,167 25,462
R-squared 0.143 0.079 0.153

Country FE x Age trends Yes Yes Yes
Country FE x Year FE Yes Yes Yes
Country FE x Cohort group FE Yes Yes Yes
Demographic controls Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on
the experienced share of total income earned by the top 5 percent during the impressionable
years. Unemployment experiences are based on the experienced unemployment rate during the
impressionable years. All specifications control for age trends, year fixed effects as well as cohort
group fixed effects, each interacted with country fixed effects. All specifications include a large
set of controls: household income, marital status, education, employment status, household size,
religion, and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

18



B Additional Figures
15

20
25

30
35

T
op

 5
 %

 in
co

m
e 

sh
ar

e

1880 1900 1920 1940 1960 1980 2000 2020
Year

DE FR
UK US

Figure A.1: Top 5 percent share in total income over time and countries. Source: World Wealth
and Income Database (Alvaredo et al., 2011).

19



20
25

30
35

40
T

op
 5

 %
 in

co
m

e 
sh

ar
e

1900 1920 1940 1960 1980 2000 2020
Year

New England
Middle Atlantic
East North Central
West North Central
South Atlantic

15
20

25
30

35
40

T
op

 5
 %

 in
co

m
e 

sh
ar

e
1900 1920 1940 1960 1980 2000 2020

Year

East South Central
West South Central
Mountain
Pacific

Figure A.2: Top 5 percent share in total income over time and US census divisions. Source:
World Wealth and Income Database (Alvaredo et al., 2011).

20



C Additional Results from the ISSP

C.1 Description of the ISSP

We also make use of a unique dataset containing rich data on perceptions about inequality, the

International Social Survey Program (ISSP) module on Social Inequality. The ISSP has been

widely used to study perceptions of social inequality, see for example Kiatpongsan and Norton

(2014) or Norton and Ariely (2011). There are in total four waves of the social inequality module:

one in 1987, one in 1992, one in 1999 and the last available one in 2009. On the one hand, the

ISSP allows us to examine whether perceived and actual income inequality co-move. On the

other hand, we provide an additional robustness check by replicating our main results on the

ISSP.

In Table A.20 we report summary statistics for the sample from the ISSP that we use to

replicate our main findings.1 Most of our sample from the ISSP comes from six countries:

Australia, France, Germany2, Norway, the United Kingdom and the US, each of which makes up

for around ten percent of the sample. Canada, Denmark, Finland, Italy, Japan, the Netherlands,

New Zealand, Portugal, Spain, Sweden and Switzerland together constitute about 40 percent of

the overall sample.

C.2 Co-movement between Actual and Perceived Inequality

C.2.1 Outcome Variables: Perceptions of Inequality

First, we create a variable capturing people’s beliefs about how much inequality there is in their

countries based on their response to the following question: “These five diagrams show different

types of society. Please read the descriptions and look at the diagrams and decide which you

think best describes [ COUNTRY ]:

• Type A: A small elite at the top, very few people in the middle and the great mass of

people at the bottom.

• Type B: A society like a pyramid with a small elite at the top, more people in the middle,

and most at the bottom.

1We can use a slightly larger sample to examine the correlation between actual inequality and perceived
inequality because we can also use respondents who are younger than 26.

2Due to lacking inequality data we drop all respondents currently living in Eastern Germany and focus only
on Western German Respondents.

21



Table A.20: Summary Stats: ISSP

Variable Mean Std. Dev. Min. Max. N

Share top 10 during impr years 30.994 3.936 21.839 44.81 38239
Share top 5 during impr years 20.36 3.329 13.343 38.555 38974
Share top 1 during impr years 8.147 2.346 4.024 18.709 40508
Gini during impr years 31.137 5.857 20.569 44.712 18246
Unemployment during impr years 5.293 3.962 0.01 35.287 40663
Age 49.962 15.334 26 98 44918
Female 0.524 0.499 0 1 44918
Below secondary 0.45 0.498 0 1 44918
Secondary 0.227 0.419 0 1 44918
Above secondary 0.309 0.462 0 1 44918
Married 0.667 0.471 0 1 44918
Widowed 0.076 0.265 0 1 44918
Divorced 0.076 0.265 0 1 44918
Separated 0.02 0.139 0 1 44918
Single 0.154 0.361 0 1 44918
Full-time employed 0.402 0.49 0 1 44918
Part-time employed 0.082 0.275 0 1 44918
Unemployed 0.031 0.174 0 1 44918
Student 0.012 0.109 0 1 44918
Retired 0.18 0.384 0 1 44918
Other employment 0.118 0.323 0 1 44918
Catholic 0.273 0.445 0 1 44918
Church of England 0.086 0.28 0 1 44918
Protestant 0.104 0.306 0 1 44918
No religion 0.21 0.407 0 1 44918
Other religion 0.261 0.439 0 1 44918
Household Size 2.774 1.294 1 5 43151
Australia 0.138 0.345 0 1 44918
Canada 0.035 0.183 0 1 44918
Denmark 0.025 0.157 0 1 44918
Finland 0.015 0.122 0 1 44918
France 0.094 0.292 0 1 44918
Germany 0.111 0.314 0 1 44918
Great Britain 0.072 0.259 0 1 44918
Italy 0.021 0.143 0 1 44918
Japan 0.051 0.22 0 1 44918
Netherlands 0.03 0.171 0 1 44918
Norway 0.082 0.274 0 1 44918
NZL 0.059 0.235 0 1 44918
Portugal 0.051 0.22 0 1 17269
Spain 0.046 0.209 0 1 44918
Sweden 0.063 0.242 0 1 44918
Switzerland 0.025 0.157 0 1 44918
US 0.114 0.318 0 1 44918

• Type C: A pyramid except that just a few people are at the bottom.

• Type D: A society with most people in the middle.

• Type E: Many people near the top, and only a few near the bottom.

22



What type of society is [ COUNTRY ] today – which diagram comes closest?” We code this

variable such that high values mean that people think that the country they live in today is more

unequal, ranking perceived society progressively as more equal moving from type A to type E.

Second, we use unique data on people’s beliefs about earnings in different occupations to

construct measures of beliefs about the pay gaps between CEOs and unskilled workers; Cabinet

ministers and unskilled workers; and doctors and unskilled workers. For example, the respondents

are asked: “How much do you think an unskilled worker in a factory earns before taxes?”; or they

are asked: “How much do you think a chairman of a large national company earns before taxes?”

We calculate pay gaps as the ratios between the estimates for the higher-earning professions and

the estimate for unskilled workers. To account for outliers we winsorize the estimated pay gaps

at the 99th percentile.

C.2.2 Results: Perceptions of Inequality

In Tables A.21 and A.22 we show the results from regressing beliefs about inequality on actual

top income shares. In some specifications we add country and year fixed effects and a set of

demographic controls. Across specifications, we find that actual inequality strongly predicts

people’s perceived level of inequality. These findings suggest that the actual level of inequality

that prevailed during our respondents’ formative years is a good proxy for the level of inequality

our respondents experienced.

Table A.21: ISSP: Perceptions of Inequality

(1) (2) (3)

Belief: Belief: Belief:
High inequality High inequality High inequality

Current Income Share of Top 5 % 0.0371*** 0.0490*** 0.0488***
(0.00142) (0.00520) (0.00524)

Observations 33,052 33,052 33,052
R-squared 0.025 0.126 0.157

Year FE No Yes Yes
Country FE No Yes Yes
Demographic controls No No Yes

Robust standard errors are displayed in parentheses. Specification (3) includes a large set of
controls: household income, marital status, education, employment status, household size, religion,
and gender. All outcome measures are z-scored. * p < 0.10, ** p < 0.05, *** p < 0.01.

23



Table A.22: ISSP: Estimated Pay Gaps

(1) (2) (3)

Estimated Estimated Estimated
CEO worker cabinet minister worker doctor worker

pay gap pay gap pay gap

Current Income Share of Top 5 % 1.786*** 0.227*** 0.153***
(0.200) (0.0368) (0.0198)

Observations 43,841 43,809 44,191
R-squared 0.182 0.129 0.128

Year FE Yes Yes Yes
Country FE Yes Yes Yes
Demographic controls Yes Yes Yes

Robust standard errors are displayed in parentheses. All specifications include a large set of controls: house-
hold income, marital status, education, employment status, household size, religion, and gender. * p < 0.10,
** p < 0.05, *** p < 0.01.

C.3 Replication of Main Results on the ISSP

C.3.1 Outcomes Variables: Experienced Inequality

Our main outcome variables of interest on preferences for redistribution focus on the role the

government should play and are given as follows:3

• Too much inequality: “Differences in income in [ COUNTRY ] are too large.” We code

this variable such that high values in this question correspond to more agreement to this

statement.

• Tax the rich more: Moreover, we use a question on people’s desired tax levels for people

with different income levels: “Do you think people with high incomes should pay a larger

share of their income in taxes than those with low incomes, the same share, or a smaller

share?” High values mean that individuals want higher shares of taxes for richer people.

• Do not reduce benefits to the poor: “The government should spend less on benefits

for the poor”. We code this variable such that high values indicate disagreement with this

statement.

• Party affiliation: Left: We also use data on people’s party affiliation and their voting

intention. In particular, individuals are asked for which party they intend to vote in the

3These questions are answered on a 5-point scale where 1 means “strongly agree” and 5 means “strongly
disagree”.

24



next election. The data provided by the ISSP then classifies people’s voting behavior on a

scale from (1) far right to (5) far left.4

• Voted: Left: Moreover, individuals are asked about their voting behavior in the last

election. As before we use the derived data from the ISSP that classifies the voting intention

on a scale ranging from (1) far right to (5) far left.

C.3.2 Results: Experienced Inequality

We show the results from the replication of our main findings on the ISSP sample in A.23. We

find that high inequality experiences are associated with less agreement that there is too much

inequality in the respondent’s country (Column 1). We find no significant effect on agreement

to the statement that the rich should be taxed more than the poor, even though the sign of the

coefficient goes into the expected direction (Column 2). However, people who have experienced

high inequality are significantly more likely to be in favor of reducing the benefits to the poor

(Column 3) and are significantly less likely to be affiliated to a left-wing party or to vote for a

left-wing party (Colums 4 and 5).

Taken together, the results from the ISSP strongly replicate our earlier findings on the samples

from the GSS, Allbus and ESS. This provides us with additional confidence in the robustness of

our results.

4We set this variable to missing for all individuals who either do not intend to vote, or intend to vote for
another party not part of this left-right spectrum.

25



Table A.23: ISSP: Replication of main findings

(1) (2) (3) (4) (5)

Too much Tax the Do not reduce Party affiliation Voted:
inequality rich more benefits to the poor Left Left

Inequality Experiences -0.0535** -0.0101 -0.0612** -0.121** -0.0529**
(0.0242) (0.0215) (0.0280) (0.0560) (0.0218)

Observations 34,439 33,445 19,100 7,761 26,048
R-squared 0.142 0.075 0.103 0.058 0.075

Country FE x Age trends Yes Yes Yes Yes Yes
Country FE x Year FE Yes Yes Yes Yes Yes
Country FE x Cohort group FE Yes Yes Yes Yes Yes
Unemployment Experiences Yes Yes Yes Yes Yes
Demographic controls Yes Yes Yes Yes Yes

Standard errors are two-way clustered by age and cohort. Inequality experiences are based on the experienced share
of total income earned by the top 5 percent during impressionable years. Unemployment experiences are based on the
experienced national unemployment during impressionable years. All specification control for age trends, year fixed
effects and cohort group fixed effects, each interacted with country fixed effects. All specifications control for a large
set of controls: household income, marital status, education, employment status, household size, religion, and gender.
All outcome measures are z-scored.* p < 0.10, ** p < 0.05, *** p < 0.01.

26



D Description of Outcomes

D.1 General Social Survey

D.1.1 Main Outcomes

• Help Poor: “Some people think that the government in Washington should do everything

to improve the standard of living of all poor Americans (they are at point 5 on this card).

Other people think it is not the government’s responsibility, and that each person should

take care of himself (they are at point 1). Where are you placing yourself in this scale?”

• Pro-welfare: “We are faced with many problems in this country, none of which can be

solved easily or inexpensively. I am going to name some of these problems, and for each one

I would like you to tell me whether you think we are spending too much money on it, too

little money or about the right amount.” We focus on people’s answer to that question on

the issue of “assistance to the poor.” We code this variable such that higher values indicate

too little assistance to the poor.

• Success due to luck: “Some people say that people get ahead by their own hard work;

others say that lucky breaks or help from other people are more important. Which do you

think is most important?” The answer can take a value from 1 to 3: hard work is most

important (1), hard work and luck are equally important (2), luck is most important (3).

• Liberal: “We hear a lot of talk these days about liberals and conservatives. I am going

to show you a seven-point scale on which the political views that people might hold are

arranged from extremely liberal to extremely conservative. Where would you place yourself

in this scale?” We coded the question such that high values mean that the respondent is

liberal.

• Party: Democrat: “Generally speaking, do you usually think of yourself as a Republican,

Democrat, Independent, or what?” We coded this variable such that higher values corre-

spond to support of the Democratic party and lower values to the support of Republicans.

We set observations to missing if respondents identify with another party.

• Voted: Democrat: We also look at people’s past voting behavior. Specifically, this

variable takes value one if the respondent voted for the Democratic candidate in the last

presidential election and takes value zero if the respondent voted for the Republican can-

27



didate. We set this measure to missing if the respondent did not vote in the presidential

election or if the respondent voted for an independent candidate.

D.1.2 Alternative Mechanisms

• Low relative income: People’s self-assessed position in the income distribution on a

five-point scale reaching from “Far below average” to “Far above average”. We code this

variable such that high values correspond to perceived low relative income.

• Low social position: People’s self-assessed position in society on a four-point scale,

reaching from “Lower class” to “Upper class” We code this variable such that high values

indicate a perceived low social position.

D.1.3 Placebo Outcomes

• Pro-immigration: People’s view on whether the number of immigrants should be in-

creased or decreased on a five-point scale from (1) decrease a lot to (5) increase a lot.

• Pro-guns: This variable takes value one for people opposing a law which would require a

person to obtain a police permit before he or she could buy a gun.

• God exists: People’s belief in god on a six-point scale from (1) people not believing in

God to (6) people stating that they know God really exists and that they have no doubts

about it.

D.2 Allbus

D.2.1 Main Outcomes

• Inequality: Unfair: Disagreement on 4-point scale to the statement: “I think the so-

cial inequalities in this country are fair.” We coded this variable such that higher values

correspond to more distaste of inequality.

• Inequality does not increase motivation: This variable captures people’s beliefs about

the effect of inequality on motivation. High values mean that people think that inequality

does not increase motivation.

28



• Inequality reflects luck: Disagreement on 4-point scale to the statement: “Differences

in rank between people are acceptable as they essentially reflect how people used their

opportunities.” High values mean that people disagree with this statement.

• Left-wing: People’s self-assessment of their political views on a 10-point scale. We coded

this variable such that high values indicate a more left-wing self-assessment.

• Intention to vote: Left: We classified each party based on the classification of parties

on the left-right spectrum from Huber and Inglehart (1995). Higher values correspond to

intentions to vote for more left-wing parties.

• Voted: Left: As above we create an index for each party that our respondent voted

for using the classification of parties on the left-right spectrum from Huber and Inglehart

(1995). Higher values of this variable mean that people voted for more left-wing parties.

D.2.2 Alternative Mechanisms

• Low social position: “In our society there are people who are at the top and people

who are at the bottom. Where would you place yourself on such a scale?” This is coded

such that high values mean that people think that they are closer to the bottom of the

distribution.

D.2.3 Placebo Outcomes

• Pro-immigration: We construct an index of attitudes towards immigrants by looking at

the following questions on a scale from (1) strongly disagree to (7) strongly agree.

– Immigrants should adapt to German customs.

– Immigrants should not have any right to participate politically.

– Immigrants should not be allowed to marry Germans.

The index is coded such that more disagreement to these statements receives higher values.

• Nationalism: People’s nationalism is measured on a four point scale ranging from (1)

very proud to be German to (4) not very proud to be German. We code this variable such

that high values indicate high nationalism.

29



• Nature determines life: People’s agreement to the statement “in the final analysis, our

life is determined by the laws of nature.” on a scale from (1) strongly agree to (5) strongly

disagree. We code this variable such that high values indicate agreement to this statement.

D.3 ESS

D.3.1 Main Outcomes

• Pro-redistribution: “The government should take measures to reduce differences in in-

come levels.” We code this variable such that high values correspond to agreement to this

statement.

• Left-wing: “In politics people sometimes talk of ‘left’ and ‘right’. Where would you place

yourself on this scale, where 0 means the left and 10 means the right?” We recode this

variable such that high values refer to people placing themselves on the left.

• Voted: Left: People’s voting behavior in the last election. In particular, we coded up

this voting behavior on a right-left scale, taking higher values for left-wing parties and

lower values for right-wing parties. As in Giuliano and Spilimbergo (2014), we used the

classification of parties on the left-right spectrum from Huber and Inglehart (1995). If

the party was not part of Huber’s classification or if a person did not vote, we coded the

observation as missing.

D.3.2 Alternative Mechanisms

• Trust parliament / politicians / political parties: “Please tell me on a score of 0-10

how much you personally trust each of the institutions I read out. 0 means you do not

trust an institution at all, and 10 means you have complete trust.

– . . . [COUNTRY]’s parliament?”

– . . . politicians?”

– . . . political parties?”

D.3.3 Placebo Outcomes

• Pro-immigration: We construct an index of attitudes towards immigrants by looking at

the following questions with scales from (1) to (4) and (0) to (10).

30



– “Allow many immigrants of same race/ethnic group” (4) vs. “Allow no immigrants of

the same race/ethnic group” (1).

– “Allow many immigrants of different race/ethnic group” (4) vs. “Allow no immigrants

of different race/ethnic group” (1).

– “Allow many immigrants from poorer countries to Europe” (4) vs. “Allow no immi-

grants from poorer countries to Europe” (1).

– “Immigration is good for the economy” (10) vs. “Immigration is bad for the economy”

(0).

– “Immigration is good for cultural life” (10) vs, “Immigration is bad for cultural life”

(0).

– “Immigration makes this country a better place to live” (10) vs. “Immigration makes

this country a worse place to live” (0).

We code the index such that high values indicate more positive attitudes towards immi-

grants.

• Pro-EU unification: “European unification should go further” (10) or “European unifi-

cation has gone too far” (0).

• Pro-democratic: People’s agreement on a 5-point scale to the statement “Political parties

that wish to overthrow democracy should be banned.”

31



E Data Description: Control Variables

E.1 General Social Survey

We control for our respondents’ employment status by including dummy variables on whether

the respondent is employed part-time, temporarily not working, unemployed, retired, in school,

keeping the house or in other employment (the base category is full-time employment). To

account for the respondent’s marital status, we include the following dummies: whether the

respondent is married, widowed, divorced or separated (the omitted category is “never married”).

We include the following set of indicator variables to capture our respondent’s educational

attainment: an indicator for whether our respondent completed at most high school as well

as a dummy for whether our respondent completed college (“below high school” is the omitted

category). We also include a dummy for whether our respondent is black. Following Giuliano

and Spilimbergo (2014) we include dummies for each of the 12 income brackets available in the

GSS to control for absolute household income. In addition, we include a set of dummies for our

respondents’ household size. Finally, we also control for our respondent’s religion by including

dummies for whether they are Protestant, Catholic, Jewish or whether they have another religion.

Finally, we include a dummy indicating the gender of the respondent.

E.2 German General Social Survey (Allbus)

We control for key demographics, such as income, gender, marital status, education, religious

affiliation and employment status. In particular, we control for education by including dummy

variables for the type of schooling our respondent completed.5 We control for marital status

by including dummy variables for whether our respondent is married, widowed, divorced or

separated (single is the omitted category).

We account for people’s employment status by dummies for whether our respondent is part-

time employed, unemployed, out of the labor force, student, retired, or in other employment (the

omitted category is full-time employment). We also control for people’s position in the income

distribution in a given year by including dummies for quintiles of self-reported monthly household

income.

We also control for our respondent’s religion by including dummy variables for whether our

5In particular, we include dummies for “Hauptschule”, “Realschule” and “Abitur/FH”. “below Hauptschule” is
the omitted category.

32



respondent is Catholic, Protestant or member of another religion (the omitted category is “no

religion”). Finally, we also include a dummy variable indicating the gender of the respondent.

E.3 European Social Survey

We control for education by including dummy variables for whether our respondent completed at

most high-school or holds a college degree (no completion of high school is the omitted category).

We control for marital status by including dummy variables for whether our respondent is mar-

ried, widowed, divorced or separated (single is the omitted category). We account for people’s

employment status by including dummy variables for whether they are self-employed or not in

paid work (the omitted category is that they are employed).

We also control for people’s income level. For waves one to three we make use of the only

available income variable which measures absolute household income levels categorized into 12

brackets. For waves four to seven we use a variable on the country-specific income decile that

our respondent’s household belongs to. We also control for household size with a set of dummies

indicating whether there is one person in the household, two, three, four or more than five.

We also control for our respondent’s religion by including dummies for whether our respondent

is Catholic, Protestant, affiliated to another Christian religion, Islamic, Jewish or affiliated to

another religion (the omitted category is “no religion”). Finally, we also include a dummy variable

indicating the gender of our respondent.

33



F Inequality Data

We now provide an overview of the inequality data we use in our analysis. We linearly interpolate

missing inequality data up to gaps of six years. In our analysis we make use of those cohorts for

which this method gives inequality data for their full “impressionable years” (age 18-25). Table

A.24 shows the years for which inequality data are available for the different countries in our

sample.

Table A.24: Availability of Inequality Data

Country Share top 10 percent Share top 5 percent Share top 1 percent Gini coefficient

Australia 1941-2010 1939-2010 1921-2010 1981-2010

Canada 1941-2010 1920-2010 1920-2010 1976-2011

Denmark 1903-2010 1903-2010 1903-2010 -

Finland 1920-2009 1920-2009 1920-2009 1966-2011

France 1919-2012 1915-2012 1915-2012 1956-2011

Germany 1891-1936; 1961-2008 1891-1938; 1961-2008 1891-1938; 1957-2008 1962-2010

Italy 1974-2009 1974-2009 1974-2009 1967-2010

Ireland 1975-2009 - 1975-2009 -

Japan 1947-2010 1907-1924; 1947-2010 1886-2010 1962-2001

Netherlands 1914-2012 1914-2012 1914-2012 1977-2008

New Zealand 1924-2012 1921-2012 1921-2012 1982-2009

Norway 1948-2011 1948-2011 1948-2011 1986-2011

Portugal 1976-2005 1976-2005 1976-2005 1993-2011

Spain 1981-2012 1981-2012 1981-2012 1990-2011

Sweden 1903-1920; 1930-2013 1903-1920; 1930-2013 1903-1920; 1930-2013 1975-2011

Switzerland 1933-2010 1933-2010 1933-2010 -

United Kingdom 1949-2012 1949-2012 1949-2012 1961-2011

United States (national) 1917-2014 1917-2014 1913-2014 1944-2012

United States (state-level) 1917-2015 1917-2015 1917-2015 -

In this table we provide an overview of the available inequality data for the countries in our sample. These data are taken
from “The World Wealth and Income Database” (Alvaredo et al., 2011) and from the “Chartbook of Economic Inequality”
(Atkinson and Morelli, 2014).

34



G Construction of Life-time Experiences

As in Malmendier and Nagel (2011) and Malmendier and Nagel (2016), we construct a weighted

average of past national-level income shares of the top five percent6 for each individual i in

country c and in year t, using a specification of weights that introduces merely one additional

parameter to measure past experiences (Malmendier and Shen, 2016):

IEict(λ) =

ageit−1∑
k=1

wit(k,λ)Ic,t−k (5)

where

wit(k,λ) =
(ageit −k)λ∑ageit−1

k=1 (ageit −k)λ
(6)

where Ic,t−k is the share of total income held by the top five percent of earners in year t-k. Given

that the empirical literature on the role of experiences in the formation of political attitudes posits

a big importance of early experiences and in particular experiences during the impressionable

years (Giuliano and Spilimbergo, 2014; Krosnick and Alwin, 1989), we assume that experiences

before age 18 do not matter. In other words, we construct the experience measures as the

weighted average of experiences from age 18 onwards.

The weights wit(k,λ) are a function of k, i.e. how distant the inequality was experienced

relative to the individual’s age at time t, and of the weighting parameter λ. The value of λ

determines the relative importance of distant experiences compared to more recent experiences.

In our estimations we use a weight of λ = −1 which gives rise to a weight that increases linearly

when one moves further into the past from the survey year.7 This weighting scheme gives more

importance to people’s early experiences, while still allowing for some impact of more recent

experiences in life.8

6We used the exact same methodology to look at alternative measures of inequality. The results looked very
similar and are omitted for brevity.

7We obtain very similar results when we use weights of λ = −0.5 or λ = −2 instead.
8If λ > 0, the weights are decreasing in lag k, i.e. income inequality experienced closer to current age at time

t receives higher weight.

35


	CESifo Working Paper No. 6251
	Category 2: Public Choice
	Original Version: December 2016
	Abstract
	Wohlfart experienced inequality.pdf
	Introduction
	Related Literature

	Data
	General Social Survey (US)
	German General Social Survey
	European Social Survey
	Normalizations, Controls and Missings
	Inequality and Unemployment Data
	Construction of Experience Variable

	Empirical Strategy and Results
	Empirical Specification: GSS and Allbus
	Empirical Specification: ESS
	Results

	Robustness
	Other Measures of Inequality Experiences
	Placebo Outcomes
	Other Experiences during Impressionable Years
	Parental Values
	Other Robustness Checks

	Alternative Mechanisms
	Extrapolation from Own Circumstances
	Perceived Relative Income
	Trust in the Political System

	Conclusion
	Additional Tables
	Additional Figures
	Additional Results from the ISSP
	Description of the ISSP
	Co-movement between Actual and Perceived Inequality
	Outcome Variables: Perceptions of Inequality
	Results: Perceptions of Inequality

	Replication of Main Results on the ISSP
	Outcomes Variables: Experienced Inequality
	Results: Experienced Inequality


	Description of Outcomes
	General Social Survey
	Main Outcomes
	Alternative Mechanisms
	Placebo Outcomes

	Allbus
	Main Outcomes
	Alternative Mechanisms
	Placebo Outcomes

	ESS
	Main Outcomes
	Alternative Mechanisms
	Placebo Outcomes


	Data Description: Control Variables
	General Social Survey
	German General Social Survey (Allbus)
	European Social Survey

	Inequality Data
	Construction of Life-time Experiences