NBER WORKING PAPER SERIESIMMIGRATION AND REDISTRIBUTION
Alberto Alesina
Armando Miano
Stefanie Stantcheva
Working Paper 24733
http://www.nber.org/papers/w24733

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
June 2018

For comments we are indebted to Nicola Gennaioli, Larry Katz, Thomas Piketty, Andrei Shleifer,
Guido Tabellini, Diego Ubfal, Matthew Weinzierl, and seminar participants at Bocconi, Boston University,
Brown, UCLA, and Yale. We thank Pierfrancesco Mei, Davide Taglialatela, Raphael Raux and especially
Leonardo D'Amico for outstanding research assistance. Alesina and Stantcheva are grateful to thePershing Square Fund for Research on the Foundations of Human Behavior and the Wiener Centerfor generous support. The views expressed herein are those of the author and do not necessarily reflect
the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official
NBER publications.
© 2018 by Alberto Alesina, Armando Miano, and Stefanie Stantcheva. All rights reserved. Short sectionsof text, not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including © notice, is given to the source.

Immigration and Redistribution
Alberto Alesina, Armando Miano, and Stefanie Stantcheva
NBER Working Paper No. 24733
June 2018
JEL No. D71,D72,H2
ABSTRACTWe design and conduct large-scale surveys and experiments in six countries to investigate hownatives' perceptions of immigrants influence their preferences for redistribution. We findstrikingly large biases in natives' perceptions of the number and characteristics of immigrants: inall countries, respondents greatly overestimate the total number of immigrants, think immigrants
are culturally and religiously more distant from them, and are economically weaker – lesseducated, more unemployed, poorer, and more reliant on government transfers – than is the case.
While all respondents have misperceptions, those with the largest ones are systematically theright-wing, the non-college educated, and the low-skilled working in immigration-intensive
sectors. Support for redistribution is strongly correlated with the perceived composition of
immigrants – their origin and economic contribution – rather than with the perceived share of
immigrants per se. Given the very negative baseline views that respondents have of immigrants,
simply making them think about immigration in a randomized manner makes them support lessredistribution, including actual donations to charities. We also experimentally show respondents
information about the true i) number, ii) origin, and iii) “hard work” of immigrants in theircountry. On its own, information on the “hard work” of immigrants generates more support forredistribution. However, if people are also prompted to think in detail about immigrants'characteristics, then none of these favorable information treatments manages to counteract their
negative priors that generate lower support for redistribution.
Alberto Alesina
Department of Economics
Harvard University
Littauer Center 210
Cambridge, MA 02138
and IGIER
and also NBER
aalesina@harvard.edu

Stefanie Stantcheva
Department of Economics
Littauer Center 232
Harvard University
Cambridge, MA 02138
and NBER
sstantcheva@fas.harvard.edu

Armando Miano
Department of Economics
Harvard University
Littauer Center
Cambridge, MA 02138
amiano@g.harvard.edu
An online appendix is available at http://www.nber.org/data-appendix/w24733

1Introduction

The current vitriolic debate about immigration may appear light-years away from the poem
“Give me your tired, your poor,
Your huddled masses yearning to breathe free”
on the Statue of Liberty. The Economist recently called immigration “perhaps the defining issueof the 2016 election” in the U.S.,1 and it has also been an incandescent campaign topic in many
recent elections in Europe. At the same time, despite a sharp increase in inequality, sustaining agenerous level of redistribution in light of stagnating growth and aging populations is becoming
increasingly difficult. The conflicts about scarce resources become even more intense when they areintertwined with national, ethnic, and religious fragmentation.
We examine native citizens’ perceptions of and attitudes towards immigration, and how these
relate to support for redistribution. In what ways do people (mis)perceive the number and thecharacteristics of immigrants? Does a surge in real or perceived immigration flows reduce support
for the welfare state? Are people worried about the number of immigrants or rather about theircomposition – in terms of origin, religion, or economic circumstances? We uncover large misperceptions about the quantity, origin, and characteristics of immigrants and these misperceptions are
related to lower support for redistribution among natives.
We design and run large-scale international surveys on a representative sample of around 22,500
respondents from six countries (France, Germany, Italy, Sweden, the U.K., and the U.S.).2 These
countries are very different economically and socially, but, in many ways, have had the immigration
issue at the center of their political arenas. We elicit the respondents’ perceptions of immigrants,
such as the number, origin, or economic circumstances of the latter; we then explore natives’
attitudes towards immigrants, and their views on immigration and redistribution policies. To
investigate the causal link between immigration perceptions and redistribution, we also randomly
treat respondents with three “information treatments,” which provide different sets of information
about the true share, the origin, and the work ethic of immigrants.
In the survey, we define an “immigrant” as somebody legally living in the country of the
respondent, but born abroad; we repeat this definition very clearly several times in the survey.3 The
surveys – which are restricted to natives – begin with detailed background information questions
about respondents’ income, sector of work, family status, zip code, whether he has immigrantparents, political orientation, and voting. We then ask respondents about their perceptions ofimmigrants along many dimensions, which is one of our key contributions. Some perceptions canbe verified using actual statistics and data: the number, the origin, the education, the employment,
1

The Economist, “The state of the opposition: Democrats have plenty of anger, but few good ideas.” 05/17/2018.
The surveys are run through commercial survey companies between January and March 2018, with an additional
wave in the U.S. in June 2018.
3
This is the definition officially used by the OECD (OECD, 2015). See Section 2 for a more detailed justificationof this definition.
22

the poverty of immigrants, and the transfers they receive. Others are personal attitudes about how
hard immigrants work or whether they free-ride on the system. We then ask respondents about
their views on their country’s immigration policies.4 The perception of immigration and attitudes
towards immigration questions are referred to as the “immigration block.”
The next set of questions ask about respondents’ views on policies, with a focus on redistributive
policies, such as how to allocate the government’s budget or how much of the total tax burden
people with different incomes should bear. To get at the question of private (non-government
based) redistribution, as well as to test for a real effect of the treatments, we also tell respondents
that they are enrolled in a lottery to win $1000, but that before knowing whether they have won,they have to commit a share (zero or positive) of their gain to one or two charities that help
low-income people. This set of questions is called the “redistribution block.”
Natives have overall striking misperceptions about the number and composition of immigrants.
In all the countries, the average and median respondents vastly overestimate the number of immigrants. For instance, in the U.S., the actual number of legal immigrants as defined above is 10%,
but the average perception is 36%; In Italy, the true share of immigrants is 10%, but the perceived
share is 26%. Respondents also systematically misperceive the composition of immigrants. They
believe immigrants are more likely to come from more culturally distant regions (which are often
branded as “problematic” in the public debate) and that they are economically much weaker andless able to contribute to their host country than is the case. For instance, respondents starkly
overestimate the share of Muslim immigrants, immigrants from the Middle East and North Africa,and strongly underestimate the share of Christian immigrants. They believe that immigrants areless educated, poorer, more likely to be unemployed, and more likely to receive government transfers
than they are in reality.
What is perhaps most striking is that these stark misperceptions hold across all groups of respondents, whether we split them by income, age, gender, education, political affiliation, or sectorof work. While there is substantial heterogeneity and while some respondent groups are moreaccurate than others, they are still substantially wrong. Respondents who have the largest misperceptions along most dimensions we ask about are the low-skilled who work in sectors more exposed
to immigrants, the non college educated, women, and right-wing respondents. While left and rightwing respondents misperceive the share of immigrants to the same extent, they have very different
views about the composition of immigrants; right-wing respondents in all countries systematicallyconsider immigrants to have “less desirable” in their views characteristics. Respondents who personally know an immigrant have more accurate perceptions of immigrations. Those who live in acommuting zone in the U.S. with a high share of immigrants have larger misperceptions.5
The perceived share of immigrants alone is not a key driver of the support for either immigration
4

These include: how much immigration there should be, whether the government should care equally about
immigrants and natives, when immigrants should be eligible for benefits, when they should be able to get citizenshipand vote, when they would be considered to be truly part of the country, etc.
5
We do not currently have as detailed local data for the European countries.

3

or redistribution policies, but the perceived characteristics of immigrants are. Controlling forthe full array of individual respondent characteristics including political affiliation, we see that
support for immigration and redistribution are strongly positively predicted by the perceived work
ethic of immigrants, and the share of immigrants that are highly educated, as well as by knowing
an immigrant personally. They are significantly negatively predicted by the perceived share of
immigrants who are free-riding, low educated, unemployed, or Muslim.
We then turn to our experimental part and our informational treatments. We begin with
our “order of the questions” treatment, whereby half of the respondents are randomly shown theimmigration block before the redistribution block and vice versa. This allows us to study the effects of purely making respondents think about immigration and the characteristics of immigrants
on their answers to redistribution policy related questions. We find significantly negative effectsof simply prompting respondents to think about immigrants and their composition: respondents
who are asked first about their perceptions of immigration (without receiving any information on
immigrants) and only then about redistributive policies show a significantly larger aversion to redistribution – including actual donations to charity – than those who are asked about redistribution
first and immigration second. This is to be interpreted in light of our aforementioned findings ofvery negative views that respondents hold about immigrants, their difference to them, and theireconomic contributions to their host country. Consistent with this, it is those respondents withthe worst baseline priors about immigrants who react most negatively to being prompted to think
about immigrants.
Respondents are also randomized into one of three informational treatment groups. The first
informational treatment informs respondent about the true number of immigrants in their country;
the second treatment informs them about which regions immigrants in their country come from; thethird one shows them an anecdotal day in the life of a low-income, very hard-working immigrant.6
Our three informational treatments have strong first-stage effects: treated respondents’ perceptions
on the number, origin, or hard work of immigrants are significantly different from those of the control
group in the way that was expected. We also conducted a follow-up survey in the U.S. to showthat the effects on perceptions of the informational treatments persist after one to three weeks.
The “hard working immigrant” treatment on its own has strong effects on support for redistribution: treated subjects become significantly more favorable to redistribution when remindedthat at least some immigrants are hard-working. However, when respondents are shown the immigration block first, and are thus asked to go through detailed questions about the number andcharacteristics of immigrants, their negative priors dominate; none of the favorable informational
treatments is able to overcome the negative effects on redistribution of prompting people to thinkat length about immigrants’ characteristics.
Since all groups of respondents have negative and biased baseline views of immigrants, all of
6

In addition, our main surveys and experiments are complemented by a series of smaller pilots, in which we
tested some other interesting randomizations, which we report in the text, such as randomizing the name given to
immigrants in the examples.

4

them react negatively to being made to think about immigrants. Groups which have more negative
baseline views (the non-college educated, the low-skilled in immigration intensive sectors, the rightwing) react more strongly to the order treatment and are less inclined to change their views after
viewing the favorable “hard work treatment.”
Our results imply that people’s attitudes on immigration and redistribution are formed in anenvironment of misinformation – perhaps even disinformation. Rather than being corrected as we
attempt to do here, these misperceptions may be strategically manipulated or even fostered byparties or interest groups averse to immigration or redistribution.
Our paper is related to the abundant literature on the relationship between general culturaland social fragmentation (not just immigration) and the welfare state. It is impossible to do justiceto the full literature, which spans fields, from economics to sociology and political science. Many
papers, mostly in economics, are reviewed in Alesina and Giuliano (2011) as well as in Stichnothand Van der Straeten (2013). A common result is that generosity (both public and private) travels
less well across racial, ethnic, religious, and nationality groups than it does within such groups.
Luttmer (2001) shows that “interpersonal preferences” defined as preferences that depend on
the characteristics of others are key in understanding support for welfare benefits in the U.S., inaddition to financial self-interest. Racial group loyalty means that individuals show stronger support
for welfare spending if their own racial group is more strongly represented among its recipients.
This can help explain why states with more racial fragmentation exhibit less welfare spending.
Similar findings appear in Lee and Roemer (2006), Roemer, Lee, and Van der Straeten (2007), andGilens (1995). Luttmer and Singhal (2011) show that immigrants bring with them and preserve
the preferences for redistribution that apply in their country of origin.7
Emmenegger and Klemmensen (2013) argue that the link between preferences for redistribution
and attitudes towards immigration may not be simple, depending on whether a voter is reciprocal,
self-interested, egalitarian, or humanist. Reeskens and Van Oorschot (2012) address the sociology
concept of the “New Liberal Dilemma” namely the difficulty of generating widespread support
for welfare programs, which were put in place in times of cultural homogeneity. The authors use
the European Social Survey (ESS) to show that preferences for redistribution and opinions about
immigrants’ access to welfare are not related in the same way in different countries, dependingon whether they are more or less culturally heterogenous.8 Alesina and Angeletos (2005) provide
a model in which the perception of effort put in by the beneficiaries of redistribution is a crucial
determinant of citizens’ views about the welfare state. This consideration applies to perceived effort
by immigrants as well, as we will document empirically in this paper.
On the theory side, in recent work, Bisin and Verdier (2017) provide a model of public good
7
Finseraas (2008) uses data from the ESS and demonstrates that support for redistribution among the rich islower in regions where the proportion of ethnic minorities among the poor is high. Using the same data, Mau and
Burkhardt (2009) show that there is only a weak negative effect of ethnic diversity on public social expenditure levels,
but a significant negative effect on support for the welfare state and immigrants.
8
Reeskens and Van Oorschot (2015) show that immigrants do not have significantly different preferences forredistribution than natives, after controlling for income.

5

provision as a function of the fragmentation versus integration of minorities. Hansen (2003) develops
a model where the median voter is affected by the welfare burden of transfers to immigrants and has
cultural preferences. Spolaore and Wacziarg (2017) also develop a theory of cultural heterogeneityand inter-group conflict that leads to different predictions depending on whether the focus is on
public good provision (in which case heterogeneous groups face more conflict) or a rival good (inwhich case more homogeneous groups do).
Importantly, Bordalo, Coffman, Gennaioli, and Shleifer (2016) provide a model of “stereotypes,” building on Gennaioli and Shleifer (2010), which could explain many of our findings. In
the Stereotypes model, when agents (in our setting, natives) form judgments about a target group(here, immigrants) they overweight its representative characteristics, i.e., those features that occur
relatively more frequently in that group than in a reference group (in our case, natives). These
stereotypes thus have a “kernel of truth” (in our case, it is true that immigrants are somewhat
more unemployed, poorer, less educated, more likely to be Muslim, etc. than natives) but theytend to focus excessively on these differences and exaggerate them: this is in line with one of ourmain results about the very widespread and stark misperceptions about immigrants. We can alsointerpret the effects of our “hard work” treatment, which shows positive information about a hardworking immigrant in this model: the treatment shifts the perceived frequency of hard working
types in the target group and leads to an update in the beliefs about how hard immigrants work.
Several papers have studied the views on immigrants using existing surveys, such as the ESS,
and how these views are correlated with views on immigration policies. The ESS has much less detailed variables than we have in our survey and does not have any experimental (causal) component.
In this context, a long-standing debate focuses on whether anti-immigration sentiments arise purely
from economic considerations or rather from worries about cultural dilution. Economic considerations can be socially-minded (e.g., the worry that an inflow of immigrants will reduce the wagesof some workers or tilt public good provision in a less favorable direction for some natives) or selfinterested (e.g., the worry that one’s own wages may decrease). Hainmueller and Hopkins (2010)
argue that economic self-interest alone does not explain anti-immigration sentiments.9 Hanson,
Scheve, and Slaughter (2007), however, show that fiscal pressure reduces support for immigration.
Hainmueller and Hopkins (2015) provide an expansive conjoint analysis of what characteristics ofimmigrants U.S. natives deem “desirable.”10 Mayda (2006) studies which people are against immigration. On balance, it seems that there is support in the literature for both economic and culturalissues mattering.
Senik, Stichnoth, and Van der Straeten (2009) use the ESS to argue that there is only a weaknegative link between the perceived presence of immigrants and natives’ support for the welfare
state. Consistent with our findings, the authors argue that it is especially natives who dislike
immigrants and who are worried about their economic impacts who react more negatively to ahigher share of immigrants. Alesina, Murard, and Rapaport (2018) also use the ESS to examine
910

Hainmueller and Hopkins (2015) also emphasize the importance of “sociotropic” rather than economic concerns.
Bansak, Hainmueller, and Hangartner (2016) repeat this conjoint analysis on asylum seekers in Europe.

6the correlation between preferences for redistribution and immigration in a sample of European
countries. Their results are consistent with ours, despite the aforementioned shortcomings of the
ESS. Dahlberg, Edmark, and Lundqvist (2012) identify a negative impact of refugees – a very
specific type of immigrants – on reduced redistribution support in Swedish localities. Chevalier
et al. (2017) look at the inflow of poor immigrants with voting rights in West Germany post WWII
and their effects on redistribution. Tabellini (2018) shows that there has been political backlash
against immigrants, even if the latter economically benefit the host community, by exploiting
exogenous variation in European immigration to U.S. cities in the first half of the 20th century.
Card, Dustmann, and Preston (2012) show that compositional concerns about local amenities
and public goods is important in explaining support for immigration. In an important recent
paper, Damm, Dustmann, and Vasiljeva (2016) estimate the causal impact of refugee migration
on electoral outcomes in Denmark, exploiting a policy that assigned refugees quasi-randomly todifferent municipalities.11
Methodologically, we are contributing to a growing literature that runs surveys and implements
online information experiments. The most recent and closest work is by Kuziemko et al. (2015),
Kuziemko et al. (2014), Charité, Fisman, and Kuziemko (2015), Karadja, Mollerstrom, and Seim
(2017), Cruces, Perez-Truglia, and Tetaz (2013), Alesina, Teso, and Stantcheva (2018), Weinzierl(2017), and Fisman, Kuziemko, and Vannutelli (2017). In addition, two very recent papers study
how providing the correct information about immigrants changes people’s views about immigration(not redistribution) policies, a focus different from ours. Grigorieff, Roth, and Ubfal (2018) consider
giving correct information about immigrants in a survey in the U.S. Facchini, Margalit, and Nakata(2016) study the effects on support for immigration of an informational campaign in Japan about
the economic contribution of immigrants in that country.
Finally, our estimates help assess the factors that people use when determining the “generalizedsocial welfare weights” they place on others, as proposed by Saez and Stantcheva (2016) in the public
economics literature on social preferences. Various principles for preferences for redistribution
have recently been explored by Lockwood and Weinzierl (2015), Lockwood and Weinzierl (2016),
Weinzierl (2014a), and Weinzierl (2014b), which our analysis can help inform.
We contribute the following four aspects to the earlier literature. First, we provide new detailed
cross-country standardized surveys that combine a series of questions on immigration and different
policies. Second we investigate more detailed perceptions than is usually the case, about not only
the number of immigrants, but also their origins, religion, education, work effort, unemployment,
and transfer receipts. Importantly, we check these perceptions against reality. Third, we not only
11

Our work is also related to the large literature studying the impacts of immigration on the wages of locals (Card(1990, 2009), Borjas, Freeman, and Katz (1992, 1996), Borjas et al. (1997), Borjas (2006)). Dustmann, Schönberg,
and Stuhler (2016) analyze and try to reconcile the differences in the estimated impacts of immigration on locals in
different studies. This could be one of the mechanisms explaining the opposition to immigration, which we find tobe particularly stark among those employed in low-skilled jobs in sectors exposed to immigration (but not those inhigh-skilled in sectors exposed to immigration). Mayda and Facchini (2009) study whether support for immigration
could be shaped by the welfare state and Mayda and Facchini (2012) focus specifically on skilled migration.

7

focus on the relation between perceptions and immigration policies, but also between perceptions
and redistribution policies. Fourth and finally, our informational experiments allow us to show
causality in these relations.
The rest of the paper is organized as follows. Our data collection, survey construction, andexperimental design are explained in detail in Section 2. The full survey text is in the Appendix.
Section 3 describes the perceptions about immigrants, across countries and respondent characteristics. Section 4 investigates the correlation between perceptions of immigration and preferences
for redistribution. The findings from the experimental part of our study and the informational
treatments are discussed in Section 5. The last section concludes.

2

The Survey, the Experiments, and Actual Data

2.1

Data Collection and Sample

We conducted large-scale surveys in six countries: Germany, France, Italy, Sweden, the U.K., andthe U.S. The sample sizes are 4000 for the U.S., 4001 for the U.K., 4001 for Germany, 4000 for
France, 4000 for Italy, and 2004 for Sweden, for a total of 22006 respondents. The surveys wereconducted in January and February 2018 in the U.S. and in February and March in the European
countries. In the U.S., a follow-up survey was implemented for each respondent, one week after hetook the initial one. This allows us to test for the persistence of the treatment effects. Only natives(non-immigrants) between 18 and 70 years of age were allowed to take the survey in each country.
We design the surveys using an online platform; the survey links are then diffused by commercial survey companies in each country. For the U.S., the respondents were reached through C&T
Marketing (http://www.ctmarketinggroup.com). The European countries were centrally managed by Respondi (https://www.respondi.com/EN/). These companies are in touch with panelsof respondents to whom they send out survey links per email. Respondents who click on the linkare first channelled through some screening questions that ensure that the final sample is nationally representative along gender, age, and income dimensions. Respondents are paid if they fullycomplete the survey. The pay per survey completed was $2.75 in the U.S., and $2.5 in Europe. The
average time for completion of the survey was 27 minutes and the median time was 21 minutes.12
The final sample is close to representative in each country. Table 1 shows the characteristics of
our sample relative to the population in each country. Population statistics come from the Census
Bureau and the Current Population Survey for the U.S. and from Eurostat and various national
statistical offices for European Countries, as described in the table notes. By construction, weare almost perfectly representative along the dimensions of age, gender, income (binned into fourbrackets, the way that the quotas are imposed during the survey). In addition, our sample is also
representative on non-targeted dimensions such as the share of respondents who are married. Our
12

The full distribution of survey durations is provided in Online Appendix OA.2.

8

sample is slightly less likely to be employed, but not more likely to be unemployed (except to asmall extent in the U.S.).13 In some countries, such as the U.S., France, and Italy, respondents inour sample are more likely to be college-educated than the general population. In Online Appendix
OA.6, we show that our results are robust to re-weighting the sample so that it is representative
along the employment and education dimensions as well.

2.2

The Survey Structure

The full survey in English is available in Appendix A-5. The questionnaires in German, Italian,
French, and Swedish can be seen by following the links in the Appendix, which lead to the webinterface of the survey. We enrolled the help of several native speakers for each language to ensurethat the translation was suited to the local culture and understanding.14 Below, text in italic
represents actual survey text. Italic text in square brackets represents the answer options provided
to the respondents, if any. We provide the text as it is in the U.S. survey and refer to the hostcountry as the U.S.
We give the following definition of an immigrant:“In what follows, we refer to immigrants as people who were not born in the U.S. and legally
moved here at a certain point of their life. We are NOT considering illegal immigrants.”
In general, there could be two definitions of legal immigrants: i) by citizenship, (i.e., all people
legally living in the country who do not have citizenship), and ii) by country of birth (i.e., allpeople who legally live in the country but were born in another country). Our definition is thesecond one, which is also the one most frequently used by the OECD (OECD, 2015) because it ismore comparable across countries, i.e., is not affected by countries’ citizenship policies, which arevery heterogeneous.
The survey is structured a follows:
1) Background socio-economic questions about the respondent:
Employment status, family situation, highest education level achieved, household income, politicalorientation, sector of employment, immigrant parents, zip code, etc.
2) Information Treatments:
We show one of three information treatments to randomly chosen subsamples. Before proceeding,we provide the above definition of an immigrant. The first treatment provides the correct information about the number of immigrants in the respondent’s country; the second provides the correct
information about the country of origin of the immigrants in the respondent’s country; the thirdshows an example of a “day in the life” of a hard-working immigrant.
We then have two blocs of questions, the order of which is randomized, in addition to the randomization of the information treatments.
13
14

Our definition of employed includes full-time and part-time employment.
The three authors are fluent in four of the five languages.

93) Immigration Block:
The first block includes questions about the perception of immigrants, namely, their number, origin,religion, economic circumstances, transfers received, and work ethic. It also contains questions
about support for various immigration policies, such as how much immigration there should be,when immigrants should get citizenship, or when they should be eligible for benefits.
4) Redistribution Block:
The other set of questions is about redistributive policies, including the progressivity of the tax system, and the allocation of government spending. We also investigate the willingness of respondents
to donate to charities and ask about attitudes towards government.
We now provide more details on each of these survey blocks.
Background socio-economic questions.
We collect information on respondents’ gender, age, income, highest level of education achieved,
sector of occupation, employment status, marital status, number of children, place of residence,and political orientation. The latter is investigated in two ways. First, we ask respondents toclassify themselves in terms of their views on economic policy, along a spectrum ranging from “veryconservative” to “very liberal.”15 Second, we ask them for which party or candidate they voted orwould have voted (in case they did not vote) in the last presidential (or chancellor) election.16 If anelection is impending (as was the case for Italy and Sweden), we also ask which party or candidate
they planned to vote for.
We also ask the respondent whether one or both of his parents were immigrants (i.e., not born in
his current country of residence). We collect information on the respondent’s sector of employment(and, if he is currently unemployed, on the sector in which he last worked). We are thus able
to classify respondents into “high immigration sectors,” which we define as sectors in which theshare of immigrants is above the national average. The full sector classification is summarized inAppendix A-3, and described in greater detail and in the original language in Online Appendix
OA.4.17
15“On economic policy matters, where do you see yourself on the liberal/conservative spectrum?” With options[Very liberal, Liberal, Center, Conservative, Very Conservative] in the U.S. and the U.K., and [Far left, Left, Center,
Right, Far right] in the other countries.
16
More precisely, we first ask respondents whether they voted in the last elections or not. If they did, we ask themto select the candidate or party they voted for; if they did not, we ask them to select the candidate or party theywould have most likely supported if they had voted. In some countries, the electoral system is such that people votefor parties. In others, they vote for candidates. In the U.S. and in France we provide a list of all the presidential
candidates. In the other countries we list all the major parties that together attract more than 95% of the voteand also add an empty field for “Other” where respondents can write the party that they voted for. Afterwards weclassify candidates and parties into Far left, Left, Center, Right and Far right.
17
For instance, in the U.S., immigration intensive sectors are: Farming, fishing, and forestry; Building and grounds
cleaning and maintenance; Construction and extraction; Computer and mathematical occupations; Production occupations; Life, physical, and social science; Food preparation and serving related occupations; Occupations related totransportation and material moving; Occupations related to personal care, childcare and leisure; Healthcare support
occupations.

10

The information treatments
The randomly chosen treated respondents see one of three information videos, which are available
on YouTube.18 We provide some screenshots below to give an idea of the design of each treatment.

Figure 1: Treatment “Share of Immigants”

The first treatment informs respondents about the actual share of immigrants in their country, and compares this to the shares in the OECD countries with the lowest share of immigrants(Finland, with 6.1%) and the highest share (Switzerland, with 29.1%). This second piece of information is destined to give respondents an accurate view of how their own country ranks among
other developed economies in terms of immigration. We refer to this treatment as the “Share of
immigrants” treatment (see Figure 1).19

The second treatment informs respondents about the origins of the immigrants in their country.
The world is broken down in nine broad areas (North America, Latin America, Eastern Europe,
Western Europe, Subsaharan Africa, the Middle East, North Africa, Australia and New Zealand,and Asia). Respondents are shown a map, with each region sequentially appearing in a different
color (so that there is no doubt about which region any given country is part of) and a number of
18

The links are:https://youtu.be/2bVzfv0a-fE; https://youtu.be/-603kdm_GkA; https://youtu.be/_1SoLYX8OyE.
19
For the U.S., we run two versions of this treatment: one version shows respondents the share of total immigrants(13.5%) and one shows them the share of legal immigrants (10%). Each version specifies in the text displayed whether
the number relates to total or legal immigrants. As we show in Section 5, neither of these two treatments manages
to increase support for redistribution, which is not driven by the perceived share of immigrants per se, but rather by
their perceived characteristics.

11

“stick men” proportional to the number of immigrants from that region appearing and moving to
the bottom of the screen, where they remain until the end of the video. This is referred to as the“Origin of immigrants treatment.” It is illustrated in Figure 2.

Figure 2: Treatment “Origin of Immigrants”

The third treatment shows a “day in the life” of a very hard-working immigrant woman, basedon true cases.20 She works long hours, puts in a lot of effort to also study at night in order to20
There are many articles in the media providing examples of very hard-working immigrants. We have combined
several sources and changed the names. Two examples are: The Washington post “They said I was going to work
like a donkey. I was grateful” July 11, 2017 available at https://www.washingtonpost.com/news/wonk/wp/2017/07/11/they-said-i-was-going-to-work-like-a-donkey-i-was-grateful and Forbes “6 Immigrant Stories That
Will Make You Believe In The American Dream Again” Oct 4, 2016 available at https://www.forbes.com/sites/monteburke/2016/10/04/6-immigrant-stories-that-will-make-you-believe-in-the-american-dream-again.

12

improve her modest living conditions and that of her children, and hopes to start her own small
business in the future. The video (see the screenshots in Figure 3) walks respondents through thehours of the day, as indicated by a clock at the top of the screen.

Figure 3: Treatment “Hard Work of Immigrants”

Immigration block
This block begins with the key questions about perceptions of immigrants. First, the respondentis asked about what share of the population are immigrants using a slider and a pie chart as
illustrated in Figure 4. When the respondent lands on this page, the pie chart appears fully grey
and the slider is at zero. If anything, this initialization should bias respondents towards providing
a small number, the exact opposite of our findings. As respondents move the slider, the pie chartinteractively appears in two colors, one representing the share of U.S. born people, the other theshare of foreign born ones. The slider and pie chart design serves two purposes: first, it is muchless tempting to enter round numbers: indeed, as the histograms in Figure A-4 show, there are
relatively few round numbers reported. Second, the interactive and colored display, that reacts inreal-time to a respondent’s movements captures his attention.
We then ask respondents about what share of the total immigrants in their country comefrom each of the nine regions of origin (described above in the context of treatment “Origin of13

immigrants.”) Again, we use a slider plus pie chart display shown in Figure 5. There is one slider
per region of origin and the pie chart adapts in real-time with different colors for each region. A
sticky map at the top shows the boundaries of each region, with matching colors.

Figure 4: Eliciting Perceptions on the Share of Immigrants

The next questions ask about the share of immigrants of each of the major religions. We thenturn to questions about the economic conditions of immigrants, namely, their unemployment levels,
the share with high (at least college) and low education (who have not completed high school), theshare living below the poverty line in the country, and the average transfer they get relative to theaverage native. We always ask first about natives and then about immigrants, in order to have acomparison point, as well as to make respondents specifically think about the difference between
natives and immigrants.
To give an example, for poverty the question reads:“Out of every 100 people born in the U.S., how many live below the poverty line? The poverty lineis the estimated minimum level of income needed to secure the necessities of life.”“Let’s compare this to poverty among legal immigrants. Out of every 100 legal immigrants in the
U.S. today, how many do you think live below the poverty line?”
The questions about unemployment and education are analogous. We also ask about the transfers
received by immigrants. The question for the U.S. reads as follows (and is adapted to each country
to reflect the transfer programs in place there):“U.S. .born residents receive government transfers in the form of public assistance, Medicaid, child14

Figure 5: Eliciting Perceptions on the Origin of Immigrants

credits, unemployment benefits, free school lunches, food stamps or housing subsidies when needed.
How much do you think each legal immigrant receives on average from such government transfers?
An average immigrant receives:” [No transfers; ... ; More than ten times as much as a US born
resident].
We then ask about perceptions of the work effort of immigrants:“Which has more to do with why an immigrant living in the U.S. is poor?” [Lack of effort on his
15

or her own part; Circumstances beyond his or her control.]“Which has more to do with why an immigrant living in the U.S. is rich?” [Because she or heworked harder than others; Because she or he had more advantages than others.]
Our next question describes two people, John and Mohammad, who are identical along all
dimensions, except that Mohammad is a legal immigrant. The exact names used are adapted toeach country to feature one native-sounding and one Muslim-sounding name. Respondents are
asked whether Mohammad pays more or less taxes than John and whether he receives more or lesstransfers. This complements the question above on unconditional transfers, by holding everything
relevant fixed – thus, if respondents respond anything other than “the same” they are expressing
some bias in favor or against the immigrant.
The next set of questions asks about views on immigration policy and covers four areas: 1) the
number of immigrants the respondent believes should be allowed to enter the country and whether
or not the current number is problematic; 2) when immigrants should be eligible for transfers suchas welfare; 3) when immigrants should be allowed to apply for citizenship and vote in U.S. elections;4) when the respondent would consider an immigrant to be “truly’ American.”At the very end of the block, we also asked whether a respondent has a close acquaintance orfriend who is an immigrant.

Redistribution block
This block of questions is about general redistribution towards low income individuals. It nevermake any reference to immigrants. The questions also refer to the “government” in general, notspecifically to the incumbent government. For the U.S. and Germany (the two federal countries inour sample), we explicitly state that we refer to total spending and taxes at the “federal, state, andlocal levels.”
It is important in our view to separate respondents’ views on 1) the total size of the government(how much involvement and spending is optimal), 2) how to raise the funds needed for government
policies and 3) how to spend a given level of funds. Our questions are designed to address each ofthese three aspects in isolation, holding the two other aspects fixed. We first explain to respondents
that we will ask them separately about how to raise a given tax burden (aspect 2) and then how toallocate it to the different major spending categories (aspect 3): “For the purpose of these questions,
suppose that the level of government spending is fixed at its current level and cannot be changed.”
We also ask about support for various policies targeted to the poor (public housing assistance,
support for schools for low-income children, and income support programs). Here, we allow the
respondent to express a wish to either increase or decrease the total size of government (aspect 1),
but we still emphasize that this may come at a cost: “Keep in mind that, in order to finance an
expansion of any of these, other types of spending (like spending on infrastructure and defense, forexample) would have to be scaled down or taxes would have to be raised.”16

Taxes: To provide more detail about aspect 2), respondents are asked to select average income tax
rates for four income groups using sliders: the top 1%, the next 9%, the next 40% and the bottom
50%. The taxes they select are constrained to raise the current level of revenue in their country.
This is illustrated in Figure A-2.21
Spending: On aspect 3), we ask respondents to allocate 100% of the budget to seven spending
categories: 1) Defense and National Security, 2) Public Infrastructure, 3) Spending on Schooling andHigher Education, 4) Social Security, Medicare, Disability Insurance, and Supplementary Security
Income, 5) Social Insurance and Income Support Programs, 6) Public Spending on Health, and 7)
Affordable Housing (see Appendix Figure A-3). Some of these spending categories are redistributive(in particular, 3), 4), 5), 6), and 7)) while others are not (i.e., 1) and 2)).
Views of government: We also ask respondents four questions about their views on the role andscope of government.22 These include whether “income differences between rich and poor people”
are a problem or not; whether to “reduce income differences between rich and poor people thegovernment (at the local, state, or federal level) has the ability and the tools to do” something or
not; whether they generally trust the government to do what is right; and to rate how strongly
the government should “concern itself with income inequality” on a scale of 1 (“Not at all”) to 7(“Everything in its power to reduce income inequality”).
Donation to charity.
To end the redistribution block (and to check whether respondents put their money where theirmouth is), we tell respondents that they have been automatically enrolled in a lottery to win $1000.
Before they know whether they have won or not, they need to commit to donating none of it, partof it, or all of it to one or two charities. We selected two charities in each country to be 1) targeted
towards low income adults or children in general and not concerned with immigrants particularly;2) popular and well-perceived in each country.23
Layers of Randomization
The order in which the “redistribution block” and “immigration block” are shown to respondents
is randomized. Therefore, there are two randomizations in place, which create eight treatment
or control groups, summarized in Table OA1 in the Online Appendix: 1) the three information
treatments (“Share of immigrants”, “Origin of immigrants” and “Hard work of immigrants”); 2)
21

We already used an identically designed tax question and budget allocation question (described below) in Alesina
et al. (2018). While respondents select tax rates on each of the four groups, a fifth slider at the bottom moves anddepicts the fraction of the revenue target that has been raised. When the revenue target has been met, the slider
turns green and a message alerts the respondent.
22
In Alesina et al. (2018), we found that the level of trust in government was quite low especially in France and
Italy and this may lead to negative views about government intervention in general.
23
We also tried to pick charities without a religious connotation whenever possible, although this was sometimes
difficult, e.g., in Italy.

17the order of the redistribution and immigration blocks. Table 2 shows that each randomization isbalanced along observable characteristics.
Based on these many and detailed survey questions, we define several variables and indices usedin our analysis. We define them as we go and refer to them throughout the text and in the tables
and figures. The reader can refer to their detailed definitions collected in Appendix A-1.

2.3

Data on Immigration Across Countries

Many of our perception questions can be checked against actual data. Appendix Section A-2
provides the detailed data sources. To summarize, for the number of immigrants and their origins,
we used “Trends in International Migrant Stocks” from the U.N. (for the U.K., Italy, France,
Germany), the OECD’s “International Migration Database” (for Sweden), and data from the Pew
Research Center (for the U.S.). For the religion of immigrants, we used data from the Pew Research
center, which was checked against other sources, such as The New Immigrant Survey for the US,
or Dossier Statistico Immigrazione (2017) for Italy. Unemployment data comes from the OECD’s“International Migration Outlook”; poverty and education levels are from the Migration Policy
Institute (for the U.S.) and Eurostat (for the U.K., Italy, France, Germany, and Sweden).

2.4

Ensuring High Quality Answers

We implemented several methods to ensure the highest possible quality of answers. In the survey’s
landing page – the consent page, depicted in Appendix Figure A-1 – we warn respondents that
“responding without adequate effort may result in [their] responses being flagged for low quality andthat their pay for the survey may be withheld. We also attempt to make them feel involved andsocially responsible by emphasizing that we are non-partisan researchers seeking to advance social
studies. We highlight that it is “it is very important for the success of our research that you answer
honestly and read the questions very carefully before answering.”
Questions are designed so as to prevent careless answers: for instance, percentages are constrained to add up to 100% and respondents cannot move to the next page before this is the case.
Rather than using data entry boxes, we let the respondents move sliders, the values of which are
shown in real-time on the pie charts. This fulfills two goals: first, respondents face an interactive
screen, which captures their attention, and, second, there is much less risk of selecting round numbers. Questions are initialized in a neutral way, with sliders at zero and the pie charts fully grey
(i.e., not showing any of the answer categories).
We also keep track of and check the time spent by the respondent on the survey as a whole,as well as on individual pages. Thus, we can flag respondents who spend too little time on either
the full survey or on one of the questions about immigrant perceptions. For instance, only 1%of our respondents completed the survey in less than 6 minutes or spent less than 11 seconds on
the question about immigrants origins. We also have the number of clicks on each page. For
18

the benchmark sample, we drop respondents in the top 2% and bottom 2% of the survey time
distribution. We checked that none of our results are affected by trimming these outliers.24
Just before the questions on immigrant perceptions, we strategically place an attention checkquestion. We ask respondents whether they have paid careful attention to the preceding questions
and whether they honestly believe that we should count their responses in our analysis. Almost
all respondents (99.5%) answer that yes. This type of questions is used to prompt the respondents
to pay attention to the subsequent questions of the survey. Its purpose is fulfilled regardless ofwhether the respondents answer honestly (Meade and Craig, 2012).
Finally, we ask respondents whether they thought that our survey was biased towards either
left-wing or right-wing opinions. Only 16.8% of respondents say they felt some bias, out of which
10.6% thought it was left-wing biased and 6.2% thought it was right-wing biased.

3

Perceptions of Immigration

We now describe all the perceptions about immigrants, focusing on some key results in the Figures
below. For a more comprehensive overview, Table 3 reports average perceived values and actual
values for each country and for all perception-related variables, as well as the corresponding medians
and interquartile ranges. Table 4 provides the same information, but by respondent groups. Online
Appendix Tables OA5 to OA9 repeat Table 4, but splitting the sample in an even finer way, bycountry and by respondent characteristics.25 All these descriptive statistics are based on the control
group, namely the respondents who did not see any of the informational treatments.

3.1

The Share of Immigrants

Perceptions by country: Panel A of Figure 6 shows the average perception of respondents in eachcountry about the share of immigrants (red squares), as well as the actual share (blue diamonds).
The shaded areas represent the 95% confidence intervals around the average perceptions.
The discrepancy between perceptions and reality is striking. With the exception of Sweden, theaverage respondent in all countries thinks the share of immigrants is twice higher than in reality.
In the U.S., respondents believe that there are on average 36.1% immigrants, when the actual shareof immigrants is 10%. In Italy, the share of immigrants is 10%, but respondents believe it is 26.4%.
Swedish respondents are the most accurate, but still substantially inaccurate: the actual numberof immigrants is 17.6%, but the average perception is 27%.26 As shown in Table 3, the median
respondent perceives a lower share than the average respondent, indicating some right-skewness inthe distribution of perceptions. However, even the median respondent starkly overestimates the24
These results are available on demand. The distribution of survey durations is depicted in Online Appendix
OA.2.
25
Online Appendix OA.6 re-weights the sample to make it fully representative also along the non-targeted dimensions of employment and education.
26
Sweden is the country with the highest share of immigrants.

19

Figure 6: Perceived vs. Actual Share of Immigrants

H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
10

20
30
40
Share of Immigrants
Mean

Actual1520
25
Misperception
Yes

No

Notes: The left panel shows the average perceived share of immigrants (red squares) and the actual share (bluediamonds) in each country. The right panel shows the average misperception (perceived minus actual share) of theshare of immigrants by groups. Groups are defined by the indicator variables listed to the left: the mean when theindicator is equal to 1 (respectively, to 0) is represented by the orange squares (respectively, by the red diamonds).
The shaded areas are 95% confidence intervals around the mean.

share of immigrants. In fact, it is only the 25th percentile respondent’s perception that is somewhat
closer to reality.
To further see the dispersion in responses, Figure A-4 shows the distribution of misperceptionsof the share of immigrants. Misperceptions are defined as the perceived value minus the actual
value. There is a share of respondents who believe the share of immigrants is very high. However,even if we exclude respondents whose misperception is in the top 20%, we still get very substantial
misperceptions, as was already clear by looking at the median respondent: the average perceived
share of immigrants excluding the top 20% is 27% in the U.S., 23% in the U.K., 22% in France,
19% in Italy, 22% in Germany, and 20% in Sweden. We also get very substantial misperceptions
even if we exclude respondents who spent too little time on the this question.27
Perceptions by respondent characteristics: Panel B of Figure 6 shows the average misperceptions (distance between the perception and the actual value) of respondents grouped according
to several personal characteristics (all countries pooled), listed on the y axis. The orange diamond
27
The average perceived share of immigrants excluding those who spent less than 12 seconds on this question is
35% in the U.S., 30% in the U.K., 28% in France, 24% in Italy, 30% in Germany, and 26% in Sweden.

20

shows the average perception among respondents who fall into this category (“Yes”); the red squareshows the average perception among respondents who do not (“No”). The shaded areas represent
the 95% confidence intervals around the average perceptions.
We distinguish between respondents who are highly educated in high immigration sectors, low
educated in high immigration sectors, college-educated vs. not, rich vs. poor, those who have an
immigrant parent, the young vs. old, male vs. female, and left-wing vs. right-wing. While most ofthese characteristics are self-explanatory, we provide more detail on two of them. First, as explained
above, we classify respondents into high immigration sectors based on whether their current sectorof employment (or their last sector, if they are currently unemployed), has an immigrant sharehigher than the national average. Within low and high immigration sectors, we distinguish between
respondents with college education and those without. Left wing and right-wing respondents areclassified based on their voting in the last election. Our classification of all parties into left, right andcenter is shown in Appendix A-1.; “center” respondents are not shown in the graphs. The results
are robust to classifying respondents based on their views on economic policy issues (ranging fromvery conservative to very liberal).
There are three key findings. First, respondents in all groups think there are substantiallymore immigrants than there actually are – in no group is the average misperception lower than 15
percentage points. Second, some groups of respondents have substantially higher misperceptions
than others. These are respondents who are low educated in high immigration sectors, the noncollege educated, those with an immigrant parent, the young, and women. Third, there is nodifference in the average perception of the share of immigrants across the political spectrum.
Why are the misperceptions of the number of immigrants so large? One possibility is that
respondents mistakenly include in their estimates illegal immigrants. It is hard to imagine this being
the main reason for the large misperception, unless respondents are inflating the number of illegal
immigrants to improbably large proportions: In the U.S., the actual share of illegal immigrants is
3.5% and in the European countries, it is generally less than 0.5% of the population. Alternatively,
it may be that, because of media coverage and the general political rhetoric in these countries, theissue of immigration is highly salient. Finally, it could be that people confuse immigrants withminorities which have been in the country for several (sometimes many) generations. This may
signal genuine lack of knowledge, or, alternatively an attitude that all minorities are “foreigners”(despite having been in the country for many generations).28

3.2

Origin and Religion of Immigrants

Respondents misperceive not only the total share of immigrants in their country, but also their
origins and religion.29 These main perceptions on religion by country and respondent characteristics
28

We provide suggestive evidence for this in Section 3.5.
Recall that the complete set of perceptions about the fraction of immigrations that come from each possible origin
region and their religion are shown in detail in Table 3 (by country) and Table 4 (by respondent characteristics).
Appendix Tables OA5, to OA9 report all results by country.
29

21

are summarized in Figure 7.
Respondents in all countries think that immigrants come disproportionately from non-occidental
countries – often branded as “problematic” in the recent public debate – such as the Middle East,
Subsaharan Africa, or North Africa. They underestimate the share of occidental immigrants.
There are some variations across countries: U.S. respondents very sharply overestimate North
African and Middle Eastern immigrants, as do Italian, U.K, and Swedish respondents (the latterdo so to a lesser extent). France overestimates Middle Eastern immigrants by a factor of two,but slightly underestimates North African immigrants (of which there are substantially more thanin all other countries in our sample and who are in part linked to France’s colonial past). In
Germany, respondents overestimate the share of North African immigrants by a factor of eight,
but are exceptionally accurate on the share of Middle Eastern immigrants, perhaps because they
are very aware that the large Turkish minority, to which they are accustomed, are not immigrants.
Accordingly, in all countries except France, respondents also very significantly overestimate theshare of Muslim immigrants. The largest misperceptions along this dimension are in the U.S. –where respondents believe that the share of immigrants who are Muslim is 23%, while the reality iscloser to 10% – and in Sweden — where the perceived share of muslims is 45%, while the reality is
27%. The U.K., Italy, and Germany overestimate the share of Muslim immigrants by between 10
and 14 percentage points. In all countries, without exception, respondents strongly underestimate
the share of Christian immigrants (the religion of the majority of people living in our sample
countries), by at least 20 percentage points and often by much more. In the U.S. respondents
believe 40% of immigrants are Christian, when the true number is 50% higher (at 61%); in theU.K, the perception is 30%, while there are in reality again almost twice as many at 58%. The
same holds for all other countries.
These misperceptions are systematic: there is no group of respondents that does not overestimate the share of Muslim immigrants and underestimate the share of Christian immigrants. Those
who have the largest misperceptions are the non college-educated, especially the non-educatedworking in a high immigration sector, and the older, the female, and the right-wing respondents.
In this case, the gap between left and right-wing respondents is very large and significant.

3.3

Economic Circumstances of Immigrants

Figure 8 shows that in every country except Sweden the respondents believe that immigrants
are much poorer than they actually are, especially in France and the U.S. In addition, in all
countries, respondents overestimate the share of immigrants that is unemployed by an enormous
amount: In Germany, the gap is 30 percentage points; in Italy it is 35 percentage points; inthe U.S. it is around 25 percentage points. One conjecture is that respondents do not properly
understand the distinction between unemployed and out of the labor force, although we do state it22

clearly.30 Indeed, respondents tend to overestimate the unemployment rate amongst natives as well.
However, in all countries, and across all groups of respondents, perceptions and, most crucially, themisperceptions on immigrants’ unemployment are systematically and significantly larger than for
natives’ unemployment (see Appendix Figure A-5). In addition, left and right-wing respondents
do not significantly differ in their views on natives’ unemployment, but do differ in terms of theirviews on immigrants’ unemployment.
In all countries with the exception of Italy, respondents think that there are more immigrants
who have not completed high school than is the case, as shown in Figure 9. Respondents also
think that fewer respondents have college education than is true. However, the misperception gaps
about education at the country level are not large. Nevertheless, there are clear heterogeneities by
respondent characteristics, and some respondents have large misperceptions along the education
dimension as well. The non college educated, those without education and working in high immigration sectors, right-wing respondents, older respondents, and female respondents have muchlarger misperceptions.
An important consideration respondents may have is whether those who benefit from redistribution are immigrants. Figure 10 addresses this question in two ways. First, the top left panelof Figure 10 plots the perceived share of immigrants among poor people in the country; the top
right panel plots the perceived share of immigrants among the low-educated in the country.31 The
perceived representation of immigrants among these economically fragile groups is starkly inflated
relative to reality. Thus, respondents may be overestimating the extent to which redistribution
and, thus, their own taxpayer dollars go towards helping immigrants rather than natives.
Second, and more directly, Figure 10 shows the perceived transfers to an average immigrant
relative to the transfers to an average native, as well as the actual relative transfers, including
or excluding pensions. Since immigrants in these six countries are on average younger than thegeneral population, the relative transfers to immigrants including pensions are much lower thanthose excluding pensions. In most countries, the average perceived relative benefit is close to thetrue one excluding pensions, but significantly above the true one including pensions – three times
as high in Italy, twice as high in France. Perhaps even more revealing, in all countries, a significant
proportion of respondents (around 20% in the U.S., Italy, and Sweden, close to 25% in France)
believe that immigrants receive more than twice as many benefits as natives (see Figure A-6); thisview is especially common among right wing respondents. Once again, those who think immigrants
benefit more from government transfers are very consistently the non college educated, women,
lower income respondents, and left-wing respondents.

30“By unemployed we mean people who are currently not working, but searching for a job (and maybe unable tofind one)”31
We do not ask respondents about these numbers directly (see the Questionnaire in A-5), but infer them basedon their perceived share of poor (respectively, low educated) immigrants and natives, as well as their perceived shareof immigrants.

23

3.4

Perceived Effort and Contribution of Immigrants

Panel A of Figure 11 plots the share of respondents who say that immigrants are poor becauseof a lack of effort rather than due to circumstances beyond their control in each country (thered squares). This is compared to the share of respondents who say this about the general, nonimmigrant population taken from our earlier work (Alesina, Teso, and Stantcheva, 2018) (the blue
diamonds). In France and Italy people have a more negative attitude towards poor immigrants thanthey do towards poor people in general. In the U.K. and Sweden, there is no difference in views
about immigrants and natives. In the U.S., views are slightly more positive towards immigrants.
Even for natives, U.S. respondents put much more weight on individual responsibility in shaping
outcomes and, perhaps because of that, assess better the hurdles stacked against immigrants when
they have to make it out of poverty. We did not have data for Germany in our earlier survey.32
Right-wing respondents are much more likely to believe that immigrants are poor because oflack of effort. These patterns fit well with earlier findings (see our literature review above) that
U.S. respondents are much more likely to associate poverty with lack of effort than do European
respondents (especially those from France and Italy) and that right-wing respondents are more
inclined to blame poor people for their own fate. Despite these variations in views on the meritsof poor immigrants, views on the merits of rich immigrants are strikingly consistent across countries.
Panel B of Figure 11 show the perceptions related to immigrants and natives for being richbecause of one’s own effort (as opposed to exogenous advantages) by country and by core respondent
characteristic. In all countries, respondents agree that, if an immigrant is rich, it must be mostly
because of their own effort. Respondents also agree that this is more true for immigrants than it isfor natives. In Italy the gap is particularly large: while Italians believe that only 17% of the richpeople are rich because of their own effort, they think that 70% of rich immigrants are rich dueto their own merit. The U.K. and France have less extreme, but still similar patterns. This may
reflect the beliefs of Italians especially, but also French and English respondents, that the system
is penetrated by family connections and social advantages, which maintain rich dynasties at the
top even though they are not the ones who worked hardest. Consequently, because immigrants byconstruction lack these inherited advantages and sticky social classes, the (possibly very few) richimmigrants must have put in a lot of effort to become rich.
Figure 12 plots the share of respondents, by country and by core characteristics who say that
“Mohammad” gets more transfers or pays more taxes than “John.” This is to check whether thereis implicit bias, i.e., whether respondents believe that Mohammad gets more than John not because
he is poorer, but because he is an immigrant (John and Mohammad are described to be exactly
the same except for the fact that one of them is an immigrant). In all countries except Sweden, asubstantial share of respondents say this is the case, especially in France, Italy, and the U.S. The
32

To give a sense of attitudes in Germany for comparison, the Online Appendix describes German respondents’ answers to a question from the German General Social Survey (ALLBUS/GGSS, 2014), inquiring about the importanceof several factors, including luck and hard work, for one’s success.

24

right panel shows that, again, it is low-educated respondents in high immigration sectors, those
without college education, those who do not have immigrant parents, the old, and especially rightwing respondents that are significantly more likely to say Mohammad gets more on net from thegovernment.
In a smaller pilot, we randomized the name of the immigrant that was given in this question
between a name that sounded i) North American (“Jack”) in the U.S. and Western European
for the European countries; ii) Hispanic in the U.S. (“Miguel”) and Eastern European in Europe;iii) Muslim (“Mohammad” or “Ibrahim”).33 Appendix Figure A-8 tabulates the answers to thequestions of whether that immigrant receives (respectively, pays) more, less or the same transfers(respectively, taxes) as “John.” There are no strong patterns. It thus seems that the answers to thisquestion reflect a general anti-immigrant bias and a perception that immigrants may be free-riders,rather than a specifically anti-Muslim immigrant bias.

3.5

Exposure to Immigrants and Perceptions

Does exposure to immigrants shape respondent’s views on immigration? We explore this questionin two ways. First, we make use of our survey question asking about direct contact with immigrants(“Do you have friends of close acquaintances which are immigrants?”). Second, we consider thecorrelation between perceptions of immigrants and local data on the share of immigrants, together
with many other local characteristics. This analysis is currently done for the U.S. only at thecommuting zone (CZ) level due to lack of data for European countries. These tests provide only
simple correlations because where a native lives and whether he knows an immigration could beendogenous; we do control for the full array of personal characteristics in all the analyses below,
but we do not claim causality.
We start with Figure 13, which plots the coefficients on a dummy for “Knowing an immigrant”
in regressions of the form:
Yi = α + βKnowing an Immigrant + γXi + εi
where Yi is each variables listed on the y axis and X are all individual level controls (income,education, political affiliation, etc.) including country fixed effects. The regression is performed on
the full sample and all variables Yi are turned into z-scores so as to make the coefficients directlycomparable.
Knowing an immigrant is significantly and strongly correlated with lower misperceptions acrossall dimensions, even after controlling for the full set of individual characteristics. Respondents
who know an immigrant overestimate by less the total share of immigrants, the share of Muslim
immigrants, the share of low-educated, unemployed, and poor immigrants. They underestimate byless the share of Christian immigrants and the share of highly educated immigrants. They are much
33
The exact names used were as follows. UK: William, Andrei, Mohammad; France: Paul, José, Mohammad; Italy:
Francesco, Andrei, Mohammad; Germany: Michael, Vladimir, Ibrahim.

25less likely to think that immigrants are poor because of lack of effort and much more likely to thinkthat they are rich because of their own effort. Finally, they are significantly less likely to believethat the average immigrant receives more transfers than the average native and that Mohammad
gets more transfers (than John) on net.
Figure 14 shows immigration perceptions across U.S. states. Panel A represents the actual
shares of legal immigrants in each state; Panel B shows the average perception of the national
share of legal immigrants across respondents in each state.34 As the colors move from darker green
to darker red, the share of immigrants (actual or perceived) becomes higher. The misperceptions
are very large in all states, as can be seen by the complete lack of overlap of colors between thetwo panels. Some states with relatively more immigrants, such as California, Texas, or Florida,appear to have larger absolute misperceptions about the share of immigrants. But there are other
Midwestern and Southern states which also have high misperceptions, despite the fact that theirshare of immigrants is very low relative to the national average.35
However, looking at the state-level may obscure heterogeneity at finer geographical levels. We
thus turn to the commuting zone level. Figure 15 shows the coefficients β from a regression at the
respondent level
Perceived share of Immigrantsi = α + βAi + γX + εi
where the left-hand side is the respondent’s perceived share of immigrants, Ai is the full set ofvariables listed vertically (each of them standardized into a z-score), and X is, as before, the fullset of respondent characteristics. Respondents who live in CZs with a larger share of immigrants
perceive a larger (and, hence, less accurate) share of immigrants. The same goes for respondents
who live in areas with high crime rates. Conditional on the share of immigrants, a recent inflow
of immigrants into the CZ (since 2010) is not significantly associated with the perceived shareof immigrants. On the other hand, respondents who perceive a lower share of immigrants andare thus more accurate tend to live in areas with larger shares of Hispanics or African-Americans– this could indicate that they are much more aware about the distinction between immigrants
and minorities, and are used to seeing Americans of different ethnicities. Consistent with thishypothesis, respondents from CZs with higher racial segregation tend to perceive a higher share of
immigrants (but these effects are just marginally insignificant at the 10% level). Living in an areawith a higher share of college-educated people is also correlated with a lower perceived share of
immigrants (even conditional on a respondent’s own education).

34

Recall that the actual average is 10% for legal immigrants.
The overall correlation between perceptions and reality is -0.05, but is not significant. Appendix Figure ?? shows
the share of total immigrants (legal and illegal) at the state level.
35

26

4

Immigration Perceptions and Support for Redistribution

We now turn to the relation between perceptions of immigration and support redistribution. We
start with some descriptive facts about support for redistribution and immigration across countries
and respondents. These results are again based on the control group only.

4.1

Support for Immigration and Redistribution Policies

We start by describing results on support for immigration across countries and respondent characteristics. The complete set of results is in Tables A-1 and A-2, where we also tabulate the full setof descriptive statistics on redistribution.
Figure 16 tabulates the attitudes towards immigration by country (Panel A) and by core characteristics (Panel B). Each plot shows the share of respondents by country or group who answer
yes to the following questions (from bottom to top): i) Immigration is not a problem; ii) Immigrants should be eligible for benefits at most three years after arrival; iii) Immigrants should beallowed to apply for citizenship at most five years after arrival; iv) The respondent would consider
an immigrant to be truly “American” as soon as they get citizenship; v) The government shouldcare equally about everyone living in the country whether native or not.
There are varied patterns of attitudes towards immigration in different countries, highlighting
the need to ask several questions about different aspects, as we do. In the U.S., people believe
strongly that immigrants should be considered “truly American” as soon as they become citizens,and that they should get citizenship relatively soon. They are also most likely to say immigration is
not a problem and relatively likely to say that the government should care equally about everyonein the country. However, and consistent with their general aversion to benefits, they are theleast likely to say that immigrants should be eligible for benefits soon. In contrast, in European
countries, most starkly in France, Italy, Germany, and, to a lesser extent in the U.K., respondents
are less likely to say the government should care equally about everyone, that immigrants shouldbe allowed to apply for citizenship soon, or that they will be considered as truly part of the country
upon citizenship. Very few respondents (around a fifth) say that immigration is not a problem intheir country. Overall, the U.S. is the country that is most supportive of immigration and France,
Italy, and Germany are the least supportive ones.
Turning to the attitudes towards immigration by respondent characteristics, we see that for
all variables, the most favorable attitude for any respondent group is always that of left wing
respondents, and the least favorable view is that of right-wing respondents. In between, the noncollege-educated are consistently less supportive than the college-educated, across all dimensions.
Those who are not highly-educated in immigration-intensive sectors are more averse to immigration
than either people in high immigration sectors in general, or the non college-educated in general. On
the other hand, those who are high-skilled in high immigration sectors are weakly more supportive
than those with college. This large heterogeneity even within immigration intensive sectors is clear
in the public debate too; recall in the U.S. the outcry in Silicon Valley (a perfect example of a high27

skill, immigrant intensive area) in light of the Trump immigration bans.

4.2

Perceptions, Redistribution and Immigration policies

To summarize views of immigration and redistribution, we construct two standardized indices,
called the “Immigration Support Index,” and the “Redistribution Index” following the methodology
in Kling et al. (2007). Each index consists of an equally weighted average of the z-scores of thepolicy outcomes variables related to immigration support (respectively, support for redistribution)with signs oriented so that more support for those policies means a higher corresponding index.
The full set of variables used in the construction of these indices is tabulated across countries andrespondent characteristics in Table A-1 and in columns (1) to (12) of Table A-2.
The immigration support index components are variables i) through v) from Figure 16, which
we just described.36 For the redistribution support index, they are a set of dummies each equal
to one if i) the respondent supports more spending on education, ii) public housing, iii) income
support programs for low income people, and iv) if they believe that inequality is a big problem,v) a variable ranging from 1 to 7 where 1 means that the respondent thinks that the government
should not care at all about income inequality and 7 means they think that the government should
do everything in its power to reduce inequality, vi) their preferred tax rate on the top 1%, vii)
minus their preferred tax rate on the bottom 50%, viii) the share of the budget they would like toallocate to health, ix), education, x) safety net policies, xi) pensions, and xii) affordable housing.37
Figure 17 highlights two key patterns related to support for immigration and redistribution.
The top panel plots the redistribution index against the immigration support index by bins, controlling for many individual characteristics and country fixed effects. Support for redistribution
and immigration are closely correlated. The bottom panel plots the redistribution index against
the perceived representation of immigrants among the poor. This graph shows more directly that,conditional on a set of individual-level controls, respondents who believe that immigrants are more
represented among the beneficiaries of redistribution and to receive transfers also support lessredistribution.
Figure 18 plots the vector of coefficients β from the regression:
Immigration Support Indexi = α + βAi + country fixed effects + εi36
Variable v) enters the index in levels from 1 to 7, as it is asked in the survey, where 1 means the respondent
thinks that the government should only care about natives and 7 means he thinks that the government should careequally about everyone living in the country.
37
Appendix Figures A-9 shows the results from a regression of the immigration support index (left panel) and theredistribution support index (right panel) on all respondent core characteristics and on the country fixed effects (allincluded jointly), i.e., the coefficients β and γ from:

Index = α + βXi + γcountry FE + ε
The single strongest factor is again political affiliation, with left-wing respondent being much more supportive of both
immigration and redistribution, but especially of more redistribution.

28

where the left-hand side is the respondent’s Immigration Support index and Ai is the full set ofvariables listed vertically (each of them standardized into a z-score), which includes perceptions ofimmigrants, as well as personal characteristics. The perceived share of immigrants has no effectat all on support for immigration. On the contrary, the perceived share of Muslim immigrants,of North African and Middle Eastern immigrants, the perceived share of unemployed immigrants
and immigrants with low education is negatively correlated with support for immigration. Those
respondents who believe that immigrants are poor because of lack of effort, that immigrants who
are rich have no merit, that there are few highly educated immigrants, that immigrants get moretransfers than natives, and that “Mohammad” gets more transfers on net support much less immigration. Thus, even conditional on political affiliation, which is itself very highly correlated withsupport for immigration, a favorable attitude towards immigration is very strongly correlated witha respondents’ beliefs about the economic contribution of immigrants to the host country and their
hard work.

Figure 19 repeats this analysis, but the dependent variable is now support for redistribution,and the set A is expanded to also include views on immigration policy. Consistent with the findings
on support for immigration, the perceived share of immigrants per se does not negatively impact
support for redistribution (if anything, the correlation is mildly positive after including this detailed
set of controls). Again, respondents’ beliefs on whether immigrants can contribute economically tothe host country are strongly correlated with views on redistribution, especially beliefs on the hard
work of immigrants and, importantly, the share of poor who are immigrants, as discussed above.
A lot of the effects are absorbed by the proximate variables entering the immigration support
index. These proximate variables summarize a respondent’s overall attitude towards immigrants,
itself shaped by their underlying perceptions about the characteristics of immigrants: respondents
who support more redistribution are those who think that immigration is not a problem, that the
government should care about everyone equally, that immigrants should be entitled to benefits andallowed to apply for citizenship soon after arrival. Finally, despite controlling for both proximate
and more fundamental perceptions, the left-wing dummy is still very strongly correlated withsupport for redistribution.

Figure 20 plots the coefficients on a dummy for “Knowing an immigrant” in regressions of theform:
Yi = α + βKnowing an Immigrant + γXi + εi
where Yi is each variables listed on the y axis and X are all individual level controls (income, education, political affiliation, etc.) including country fixed effects. Again, the regression is performedon the control group only and all variables are z-scores so as to make the coefficients directly comparable. Knowing an immigrant is significantly correlated with support for immigration overall, as
well as according to each separate dimension.
29

5

Experimental Evidence on Immigration and Redistribution

So far, we have focused on correlations between support for redistribution and immigration, conditional on personal respondent characteristics. We now turn to the experimental evidence to provide
a causal link.

5.1

The Order of Questions Treatment

Our first experimental treatment is simply to randomize the order in which respondents see the“Redistribution block” and the “Immigration block.” The effects of this treatment are shown in
the first line of Table 5. Those who were shown the immigration questions first are more averse
to redistribution, believe inequality is less of a serious problem, and donate less to charity. The
magnitudes are economically significant; Being prompted to think about immigrants reduces theredistribution index by 0.02; this effect is equivalent to 7% of the gap in the redistribution index
between left and right wing respondents.38 The share of respondents who say inequality is a serious
problem declines by 3%, which represents around 5% of the control group mean and 13% of thegap between left and right-wing respondents.39 Thus, when natives are prompted to think about
immigration, all the negative views on immigration which we documented in Section 3 resurface and,as we showed in Section 4, these negative views are correlated with lower support for redistribution.
The other lines of the table focuses on the effects of the three informational treatments, to which
we now turn.

5.2

First Stage Effects of the Informational Treatments

We randomly show respondents one of the three informational treatments described in Section
2. The control group sees no such information. The first-stage effects on the key perceptionsof immigration of these treatments are shown in Table 6 and work very well. Each treatment
significantly affects perceptions along the dimension it was designed to do and generally does notshift perceptions along the other dimensions. In Appendix Table A-3, we show the treatment
first-stage effects on the full range of perception variables.
The treatment “Share of immigrants” significantly reduces respondents’ misperception of theshare of immigrants by 5 percentage points (column 1). Given how far perceptions were from reality
to start with, this represents about one third of the average misperception in the control group.
This is to be expected: some respondents may not have believed the info provided, especially if
it clashes with their prior, or they may not have paid sufficient attention to the exact number.
Appendix Figure A-4 shows the full histograms of responses in the control and treatment groups
for each country. The “Share of immigrants” treatment significantly compresses all responses inthe treatment group towards zero or low misperceptions. Some respondents – especially those38

The average redistribution index is 0.126 for left-wing respondents and -0.15 for right-wing respondents.
The answers to all questions about redistribution and all components of the redistribution index are tabulated
by country and respondent characteristics in Table A-2.
39

30

with extreme initial responses maintain their extreme opinions. Column 2 shows the effects of thetreatment on a dummy equal to 1 if the respondent’s misperception is zero.40 While only 4.3% of
respondents are correct in the control group, this share increases to 27% among respondents treatedwith information on the number of immigrants. In fact, the share of respondents who are accurate
within 2 percentage points is 34% in the treatment group, as opposed to 10% in the control group;
the share of those who are accurate within 5 percentage points is 49% in the treatment groupand 25% in the control group. This treatment does not significantly affect the perceived origin of
immigrants, nor their perceived work ethic, which is as intended.
The treatment “Origin of immigrants” on the other hand significantly reduces the misperception
on the origins of immigrants. It decreases the misperception of the share of immigrants from
the Middle East and North Africa by 42% relative to the control group (column 3), as well as
Muslim immigrants overall by 17% (column 5). It decreases also the misperceptions (equivalent toincreasing the perceived shares) of immigrants from North America, Eastern and Western Europe
by 35% (column 4) and Christian immigrants by 9% (column 6). It does not shift the perceived
work effort of immigrants (column 7). It does, however, increase the perceived share of immigrants
overall. This may be due to the fact that the animation makes respondents think about all theregions that immigrants may come from and this not only makes the topic more salient, but also
can lead them to perceive that there are more immigrants.
The third treatment, “Hard work of immigrants,” is designed to influence the perception of thework effort of immigrants. Indeed, treated respondents are 5 percentage points less likely to saythat lack of effort is the reason why poor immigrants are poor, which represents a 14% reduction
relative to the control group. There is no effect on the perceptions why rich immigrants are rich
– which is consistent with the fact that the experiment only focused on a poor, hard-working
immigrant, and not on wealth and effort (see Appendix Table A-3). In addition, there is very small
and barely significant effect on the perceived total share of immigrants, which could again be dueto the treatment prompting people to think about immigrants overall.41

5.3

Persistence of the Effects

We also ran a follow-up survey to check how persistent the effects on perceptions were. We limit
ourselves to the U.S. for this exercise and send out follow-up survey invitations to respondents one
week after they take the first survey. 25% of the originally surveyed respondents end up taking thefollow up between one and three weeks after the original survey. There is no strong selection on
who took the follow-up; groups which in general have lower response rates, namely male, rich, andyoung respondents are less likely to take the follow-up (see Appendix Table OA11.)
40
Technically, we allow for an error of less than 1 in absolute value, so that the respondent can round the true
number up or down to the nearest integer.
41
Note that this treatment also slightly reduces the perceived share of Muslim immigrants. This could be becauseof the non Muslim name “Emma.” However, as described in Section 5.6, we randomized the name in a smaller pilot
and found no difference in the effect of the treatment on views about redistribution.

31

Table 7 shows the results. The treatment “hard work of immigrants” displays very strong
persistence. The treatment effect on respondents who took the first and follow-up survey is almost
identical in the first and follow-up surveys. The treatment “Origins of immigrants” also persistently
affects the perceived share of Middle Eastern and North African immigrants (negatively) and theperceived share of Latin American immigrants (positively). The treatment “Share of immigrants”
does not show persistent effects.
Perhaps the reason is that the “Share of immigrants” treatment is difficult to remember because
it shows a precise number. On the other hand, the “Hard work” treatment may be the easiest for
respondents to remember, as it does not require the memorization of any number, but simply
conveys a message that is easy to hold on to. Treatment “Origins of immigrants” also does not
require the memorization of an exact number, but rather conveys an impression about the share of
immigrants from each region – respondents seem to easily remember this information as well.

5.4

Second Stage Effects of the Informational Treatments

The rest of Table 5 shows a selection of the main results of the three informational treatmentson respondents’ views about immigration and redistributive policies, as well as the interactions ofeach informational treatment with having seen the Immigration block first. We show the effects ofall the treatment combinations on the overall immigration support index, on whether respondents
think immigration is not a problem, on the redistribution index, on whether respondents think that
inequality is a serious problem, and on donations to charity.42
Let us first consider the effects of the informational treatments on respondents who were notmade to think about immigration first, i.e., on those respondents who saw the Redistribution block
first.
All treatments affect support for immigration positively, and treatments “Share of immigrants”
and “Hard Work of Immigrants” significantly so. Showing respondents information about the true
number of immigrants increases support for immigration by 0.03, equivalent to 6.5% of the gap
between left and right-wing respondents and the share of respondents who say that immigration is
not a problem by 10% relative to the control group. Showing them an example of a hard-working
immigrant increases support for immigration by even more, namely 9.3% of the left-right-wing gap.
Thus, when respondents are informed about the correct number of immigrants and prompted to
re-evaluate positively the work effort of immigrants, they seem to feel that immigration is less of aproblem and become more supportive of pro-immigration policies.
Only the “hard work” treatment has a very significant positive effect on the redistribution
index: treated respondents have a higher redistribution index by an amount equal to 11% of thegap between left and right-wing respondents. The other two treatments have essentially zero effecton support for redistribution.43 These patterns are in line with our findings from Section 4 about
42

In Appendix Tables A-4 and A-5, we show the outcomes on all the components of the redistribution and immigration support indices.
43
Appendix Figure A-10 provides the results from a permutation test, which confirms that treatment “Hard work32

the correlation of support for redistribution with perceptions, which was strongest for perceptions
related to the economic contributions and hard work ethic of immigrants. To the contrary, theperceived share of immigrants per se was not negatively related to views on redistribution.
Next, consider the interaction effects of the informational treatments with having seen the immigration block first. None of the informational treatments manage to overcome the very negative
effects on support for redistribution of making respondents think in detail about various characteristics of immigrants before answering redistribution questions. This is true even for the strong“hard work treatment,” for which the negative priors cancel almost entirely the positive effect of
the favorable information.
To sum up, asking respondents about a series of characteristics of and attitudes towards immigrants before asking them about redistribution makes them significantly less favorable to redistributive policies. Presumably, this is due to the very negative baseline views they hold about
immigrants, which are made salient as they go through our detailed questions. Providing somepiece of reassuring information about immigrants on top of this – whether their actual number,their actual origins, or their hard work – does not manage to counteract the negative effect on
support for redistribution.

5.5

Heterogeneity in Treatment Effects

Table 8 shows the heterogeneity in treatment effects according to three key respondent characteristics, which we highlighted in Section 3: left and right-wing respondents (Panel A); college andnon-college educated (Panel B); low skill workers in immigration intensive sectors and the rest
(Panel C). For brevity, we focus here on the effects of the “Order” treatment and the “Hard work”
treatment (on those respondents who see the redistribution questions first), which are the twotreatments with the strongest effects in the overall sample.
The groups which react most negatively to seeing the immigration block first are those with themost negative priors about immigrants, namely the right-wing, the non-college educated, and thelow-skill in high-immigration sectors: these groups want to reduce government-based redistribution
and private charity donations by more. Note that right-wing respondents react to this treatment
by strongly reducing the charity donations only. This can be seen as a more “right-wing” way ofredistributing income without relying on the government.
Second, almost all groups respond positively to the “hard work of immigrants” treatment by
increasing their support for redistribution, as was the case for the full sample. However, thosesame groups which have the more negative baseline views of immigrants also seem harder to convince to support more redistribution: they react less in the positive direction on the redistribution
margin, after seeing the favorable information on the hard work of immigrants. The low-skilled in
immigration-intensive sectors for instance – who hold especially negative views of immigrants – areof immigrants” has significantly positive effects on all components of the Immigration and Redistribution support
indices.

33

not moved at all to support more redistribution by the favorable “hard work” treatment.

5.6

Summary of Robustness Checks

To conclude, we summarize in one place the robustness checks we do on all the results in Sections
3 - 5, which were alluded to before. We check that dropping respondents who (i) spend too little
time on the survey, or ii) felt that the survey was biased (based on their response to the feedbackquestions at the end of the survey), or (iii) give extreme answers to the perception questions does
not significantly change our results.44 The full set of results for these alternative respondent samples
are available on demand.
In a smaller pilot study, we randomize the names given as examples of immigrants in thequestion about whether immigrants receive more transfers on net (see Section 3.4 and Appendix
Figure A-8). We also randomized the name of the immigrant whose story is told in the hard work
treatment between i) a native-sounding name (“Emma”); ii) a Hispanic sounding name (“Isabella”)
for the U.S. and an Eastern European name for European countries; and iii) a Muslim-sounding
name (“Fatima”). The effects of the “Hard work” treatment were not significantly different acrossthe three name groups, but the samples were small.
Finally, we re-weight the sample to make it representative also along the two non-targeted
dimensions of education and employment. As a result, the sample is representative along allimportant dimensions. These results are in Online Appendix OA.6.

6

Conclusion

According to our surveys, natives from six developed countries have strongly biased views about
immigrants. They think that there are many more immigrants than there actually are. They also
have incorrect views about the origins of immigrants: they overestimate the share of immigrants
from the Middle East, North Africa, and the share of Muslim immigrants, and they sharply underestimate the share of Christian immigrants. Natives also believe that immigrants are poorer,more reliant on the host country’s welfare state, more unemployed, and less educated than they
actually are. All these misperceptions contribute to making natives more averse to redistribution,
as they perceive that immigrants are culturally and religiously more distinct from them and thatthey benefit disproportionately from the generosity of the welfare state. Respondents who know
an immigrant personally have more accurate perceptions; the opposite holds for respondents wholive in high immigration areas, although both of these margins are endogenous. Misperceptions
about immigrants, and the subsequent lack of support for immigration and redistribution, arestarkest among three groups of respondents: the non-college educated, the low-skilled working in
immigration intensive sectors, and right-wing respondents.
44
Dropping respondents who felt the survey was biased strengthens the significance of the treatment effects, perhaps
because the remaining respondents are more receptive to what they perceive to be non-biased information.

34

Given the very negative priors that people have of immigrants, our randomized order treatmentthat prompts respondents to think about immigration and immigrants’ characteristics generates asignificant negative effect on support for redistribution. Respondents who are prompted to thinkthat at least some immigrants are very hard-working become significantly more favorable to redistribution. However, if respondents are also first prompted to think in detail about immigrants’
number and composition, then none of the favorable information treatments is able to compensate
for the negative priors that resurface and that lower support for redistribution.
These results suggest that much of the political debate about immigration takes place in aworld of misinformation. Citizens and voters have distorted views about the number, the origin,and the characteristics of immigrants. The amount and nature of information that citizens receiveis endogenous. Anti-immigration parties have an incentive to maintain and even foster the extent
of misinformation. Because information is endogenous, a vicious cycle of disinformation may arise.
The more natives are misinformed, the more they become averse to immigrants and redistribution,and the more they may look for confirmation of their views in the media. As a result, the media has an incentive to offer information supporting these views. For instance, immigrants who
commit crimes or who free-ride on the welfare system may receive more media coverage than a
non-immigrant doing the same. In addition, anti-redistribution parties, even those not averse to
immigration per se, can play the immigration card to generate backlash against redistribution.

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38

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39

FIGURES AND TABLES
Figure 7: Perceived Religion of Immigrants(a) Perceived share of Muslim immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
10

20
30
40
50
Muslim Immigrants
Mean

8

Actual10 12 14 16
Misperception
YesNo(b) Perceived share of Christian immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
20

30
40
50
60
Christian Immigrants
Mean-28 -26 -24 -22 -20
Misperception

ActualYes

No

Notes: Panel A shows the perceived share of Muslim immigrants; panel B shows the perceived share of Christianimmigrants. See the notes to Figure 6.

40

Figure 8: Perceived Economic Circumstances of Immigrants(a) Perceived Share of Unemployed Immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
0
10 20 30 40
Unemployed Immigrants
Mean

20
25
30
Misperception

ActualYesNo(b) Perceived Share of Poor Immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
10

20
30
40
Poor Immigrants
Mean

50

Actual810 12 14 16
Misperception
Yes

No

Notes: Panel A shows the perceived immigrants’ unemployment rate: panel B shows the perceived share of immigrants
who live in poverty. See the notes to Figure 6.

41

Figure 9: Perceived Education of Immigrants(a) Perceived Share of Low Educated Immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
20 30 40 50 60
Low Educ. Immigrants
Mean

2

4 6 8 10Misperception

ActualYesNo(b) Perceived Share of High Educated Immigrants
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
10
20
30
40
50
High Educ. Immigrants
Mean

-8

-6 -4 -2 0
Misperception

ActualYes

No

Notes: Panel A shows the perceived share of immigrants who have not completed high-school; panel B shows theperceived share of immigrants with a college degree. See the notes to Figure 6.

42Figure 10: Are Immigrants the Beneficiaries of Redistribution?
(a) Perceived Representation of Immigrants among the Poor and the Low-Educated
US

US

UK

UK

FranceFrance

Italy

Italy

Germany

Germany

SwedenSweden10
20
30
40% of Poor who are Imm
Mean

10
20
30
40
50
% of Low Educ. who are Imm.

ActualMeanActual(b) Perceived Reliance on Government Transfers of Immigrants Relative to Natives
H.Sect.&L.Ed

US

H.Sect.&H.Ed

UK

College
FranceRich
Italy

Imm. Parent

Germany

Young
Male

SwedenLeft-Wing
0
.5
1
1.5
2
Relative Transfers Received
Mean

Act. no Pens.

-.2

0 .2 .4 .6
Misperception
Yes

Act. w Pens.

No

Notes: Panel A shows the perceived share of poor people who are immigrants, on the left, and the perceived share of
low educated people who are immigrants, on the right; Panel B shows the perceived government transfers received
by an average immigrant relative to the average native. Actual government transfers are represented by diamonds(excluding pension benefits) or circles (including pension benefits). See the notes to Figure 6.

43Figure 11: Views on Immigrants’ Work Effort(a) % of Respondents who think Immigrants (or Natives) are Poor due to Lack of Effort
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
.1

.2
.3
.4
.5
Share of Respondents

.2
.3
.4
.5
Share of Respondents
Q on Imm.

Yes

Alesina et al. (2018)

No(b) % of Respondents who think Immigrants (or Natives) are Rich Because of Own Effort
H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing.6 .65 .7 .75 .8
Share of Respondents.2 .3 .4 .5 .6 .7
Share of Respondents
Q on Imm.

Yes

Alesina et al. (2018)

No

Notes: Panel A shows the share of respondents who think that immigrants who are poor are in that situation becauseof lack of effort, by country (left panel) and by groups (right panel). Panel B shows the share of respondents who
think that immigrants who are rich owe this to their own effort. Blue diamonds report the share of respondents who
say the same about the general, non-immigrant population, with numbers coming from Alesina et al. (2018). In theright panel, groups are defined by the indicator variables listed to the left: the share when the indicator is equal to 1
(respectively, to 0) is shown in orange (respectively, in red). The shaded areas are 95% confidence intervals aroundthe average perception.

44

Figure 12: Mohammad Receives More on Net

H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
Germany
Male
SwedenLeft-Wing
0

.1
.2
.3
.4
Share of Respondents

0.1 .2 .3 .4
Share of Respondents

Mean

Yes

No

Notes: The figure shows the share of respondents who think that Mohammad receives more benefits on net (i.e.,either receives more gross benefits or pays less taxes). See notes to Figure 11.

45

Figure 13: Effect of Knowing an Immigrant on Perceptions of Immigrants
Effort Is Reason for Being Rich
Lack of Effort Is Reason for Being Poor
Mohammad Gets More (Net)
Perc. Relative Transfers to Imm.
Perc. % of Poor Immigrants
Perc. % of Low Educated Immigrants
Perc. % of High Educated Immigrants
Perc. % of Unemployed Immigrants
Perc. % of Christian Immigrants
Perc. % of Muslim Immigrants
Perc. % of Immigrants from Asia
Perc % of Immigrants from L. America
Perc. % of Immigrants from E. Europe
Perc. % of Imm. from W. Europe & N. America
Perc. % of Imm. from N. Africa & M. East
Perc. % of Immigrants
-.2

-.1

0

.1

.2

Partial Corr. Knowing an Imm.

Notes: The figure shows the coefficients β from Yi = α + βKnowing an Immigrant + γXi + εi where Yi is thez-score of each variable listed on the y axis and X are all individual level controls (income, education, politicalaffiliation, etc.) including country fixed effects. Variables Yi are standardized z-scores and include the following
misperceptions (from bottom to the top): share of immigrants, share of immigrants from North Africa and Middle
East, share of immigrants from Western Europe and North America, share of immigrants from Eastern Europe, shareof immigrants from Latin America, share of immigrants from Asia, share of Muslim immigrants, share of Christianimmigrants, share of unemployed immigrants, share of high educated immigrants, share of low educated immigrants,
share of poor immigrants, average transfers received by immigrants relative to natives. Mohammad Gets More (Net),
Lack of Effort is Reason for Being Poor, Effort is Reason for Being Rich are dummies equal to 1, respectively, if the
respondent believes that Mohammad gets more transfers on net than John, immigrants are poor because of lack ofeffort, immigrants are rich thanks to their own effort. Shaded areas are 90% confidence intervals.

46

Figure 14: Misperceptions Across U.S. States:(A) Actual Share of Legal Immigrants(B) Average Perception of the National Share of

by State

Legal Immigrants by State

Notes: Panel A shows the actual share of legal immigrants in each state in 2014 (Source: Pew Research Center).
Panel B shows, for each state, the average perception of the national share of legal immigrants for respondents inthat state. The actual national share of legal immigrants is 10%.

47

Figure 15: Perceived % of Immigrants and CZ Level Characteristics
Unemployment Rate (2017)
Crime rate
Share Living in Rural Area
Share in Manufacturing
Share living in poverty
Racial Segregation
Share of Hispanic People
Share of Black People
Share College EducatedImmigrants Inflow since 2010
Share of Immigrants
-.2

0

.2

.4

Partial correlation

Notes: The figure shows the coefficients β from the regression: Perceived share of Immigrantsi = α + βAi + γX + εi
where the left-hand side is the respondent’s perceived share of immigrants, Ai is the full set of z-scores of the variables
listed vertically, and X are all individual level controls (income, education, political affiliation, etc.). The shaded areas
are 90% confidence intervals.

Figure 16: Support for Immigration
A: By Country
Govt. should care about everyone

American upon citiz. or before

Imm. allowed to get citiz. soon

Imm. should get benefits soon

Imm. not a problem

.2

.4
.6
Share Answering Yes

.8

US

UK

France

Italy

Germany

Sweden

B: By Core Characteristics
Govt. should care about everyone
American upon citiz. or before
Imm. allowed to get citiz. soon
Imm. should get benefits soon
Imm. not a problem

.1

.3
.5
Share Answering Yes.7

Left-Wing

Right-Wing

College

No College

H Imm, No college

H Imm, College

No H Imm

Notes: The figure shows the share of respondents answering “Yes” to the questions listed on the vertical axis, bycountry (Panel A) and respondent groups (Panel B). Govt. should care about everyone is a dummy equal to 1 if the
respondent thinks that the government should care about all the people living in the country (6 and 7 in a scale from
1 to 7). American upon citiz. or before is a dummy equal to 1 if the respondent would consider an immigrant truly“American” at the latest when he gets citizenship. Imm. allowed to get citiz. soon, Imm. should get benefits soon,
and Imm. not a problem, are dummies equal to 1 if the respondent thinks that immigrants should be allowed toapply for citizenship at most five years after arriving, immigrants should be eligible for benefits at most three years
after arriving, and immigration is not a problem.

Figure 17: Support for Immigration and Support for Redistribution
Panel A: Correlation between support for immigration and redistribution

Redistribution Index
0.05

Panel B: Perceived share of poor who are immigrants and support for redistribution-.05-0.0011***(0.0004)

-20

0
20
40
Perceived Share of the Poor who are Immigrants

60

Notes: Binscatter of the Redistribution Support index against the Immigration Support index (top panel) andthe perceived share of poor people in the country who are immigrants (bottom panel). Indices are constructedfollowing the methodology in Kling et al. (2007), as explained in detail in Section 4. Each dot is the average
residual in each bin from regressing respondents’ redistribution and immigration indices on X, i.e., all individual level
controls (income, education, political affiliation, etc.), including country fixed effects; in the bottom panel we also
add as controls all variables from Figure 19. The fitted line and reported coefficient β come from the regression:
Support for Redistributioni = α + βSupport for Immigrationi + γX + εi . Standard error in parenthesis. *** p < 0.01.

Figure 18: What Correlates with Support for Immigration?
Effort Is Reason for Being Rich
Lack of Effort Is Reason for Being Poor
Mohammad Gets More (Net)
Perc. Relative Transfers to Imm.
Perc. % of Poor Immigrants
Perc. % of Low Educated Immigrants
Perc. % of High Educated Immigrants
Perc. % of Unemployed Immigrants
Perc. % of Christian Immigrants
Perc. % of Muslim Immigrants
Perc. % of Immigrants from Asia
Perc % of Immigrants from L. America
Perc. % of Immigrants from E. Europe
Perc. % of Imm. from W. Europe & N. America
Perc. % of Imm. from N. Africa & M. East
Perc. % of Immigrants
High Immigration Sector, Low EducatedHigh Immigration Sector, High EducatedCollegeRich
Immigrant Parent
Young
Male
Left-Wing
-.2

-.1

0

.1

.2

Partial Correlation

Notes:

The figure shows the coefficients β from the regression Immigration Support indexi = α + βAi +country fixed effects + εi where the left-hand side is the respondent’s Immigration support index and Ai is thefull set of z-scores of the variables listed vertically, which includes perceptions of immigrants, as well as personal
characteristics. Shaded areas are 90% confidence intervals. See notes to Figure 13.

Figure 19: What Correlates with Support for Redistribution?
Immigration Not a Problem
Govt. Should Care about Everyone
American upon Citiz. or Before
Imm. Allowed to Get Citiz. Soon
Imm. Should Get Benefits Soon
Effort is Reason for Being Rich
Lack of Effort Is Reason for Being Poor
Mohammad Gets More (Net)
Perc. Relative Transfers to Imm.
Perc. % of Poor who are Immigrants
Perc. % of Low Educated who are Immigrants
Perc. % of High Educated Immigrants
Perc. % of Unemployed Immigrants
Perc. % of Christian Immigrants
Perc. % of Muslim Immigrants
Perc % of Immigrants from Asia
Perc % of Immigrants from L. America
Perc. % of Immigrants from E. Europe
Perc. % of Imm. from W. Europe & N. America
Perc. % of Imm. from N. Africa & M. East
Perc. % Immigrants
High Immigration Sector, High EducatedHigh Immigration Sector, Low EducatedCollegeRich
Immigrant Parent
Young
Male
Left-Wing

-.2

-.1

0

.1

.2

.3

Partial Correlation

Notes:

The figure shows the coefficients β from the regression Redistribution Support Indexi = α + βAi +country fixed effects + εi where the left-hand side is the respondent’s Redistribution support index and Ai is thefull set of z-scores of the variables listed vertically, which includes perceptions of immigrants, as listed in the notes to
Figure 18, and personal characteristics. Ai also includes views on immigration policy (see notes to Figure 16). Govt.
Should Care About Everyone ranges from 1 to 7, where 1 means that the respondent thinks the government should
only care about natives and 7 means that he thinks the government should care about all the people living in the
country. Shaded areas represent 90% confidence intervals.

Figure 20: Effect of Knowing an Immigrant on Support for Immigration
Immigration Support Index
Immigration Not a Problem
Govt. Should Care About Everyone
American upon Citiz. or Before
Imm. Allowed to Get Citiz. Soon
Imm. Should Get Benefits Soon

0

.05

.1

.15

Partial Corr. Knowing an Imm.

Notes: See notes to Figure 13. Variables on the y-axis are as defined in the notes to Figure 16 and 19.

Table 1: Sample Characteristics
US
Pop
Sample(1)(2)
Male
18-29 y.o.
30-39 y.o.
40-49 y.o.
50-59 y.o.
60-69 y.o.
Income Bracket 1
Income Bracket 2
Income Bracket 3
Income Bracket 4
MarriedEmployed
Unemployed
College

0.48
0.24
0.19
0.19
0.20
0.17
0.16
0.19
0.22
0.43
0.51
0.60
0.08
0.51

0.49
0.24
0.20
0.19
0.20
0.17
0.16
0.19
0.22
0.43
0.49
0.70
0.05
0.41

UK
Sample
Pop
(3)
(4)
0.48
0.25
0.19
0.22
0.18
0.16
0.30
0.37
0.12
0.23
0.52
0.68
0.04
0.37

0.48
0.26
0.19
0.21
0.18
0.16
0.31
0.35
0.11
0.23
0.41
0.74
0.05
0.36

FranceSample
Pop
(5)
(6)
0.49
0.23
0.20
0.21
0.20
0.15
0.31
0.33
0.14
0.24
0.42
0.64
0.10
0.51

0.49
0.23
0.20
0.21
0.20
0.15
0.32
0.30
0.14
0.24
0.46
0.65
0.09
0.31

Italy
Sample
Pop
(7)
(8)
0.50
0.20
0.22
0.24
0.18
0.16
0.28
0.31
0.20
0.24
0.58
0.66
0.11
0.36

0.50
0.19
0.22
0.23
0.19
0.17
0.27
0.28
0.19
0.26
0.46
0.57
0.11
0.16

Germany
Sample
Pop
(9)
(10)
0.49
0.23
0.18
0.20
0.23
0.15
0.25
0.30
0.23
0.22
0.48
0.66
0.04
0.27

0.49
0.22
0.18
0.20
0.23
0.17
0.26
0.29
0.23
0.22
0.46
0.75
0.04
0.25

SwedenSample
Pop
(11)
(12)
0.50
0.24
0.19
0.20
0.19
0.18
0.33
0.25
0.28
0.17
0.34
0.72
0.04
0.43

0.50
0.24
0.19
0.21
0.18
0.18
0.33
0.29
0.22
0.17
0.33
0.77
0.05
0.36

Notes: This table displays summary statistics from our surveys (in odd columns) alongside nationally representative
statistics (in even columns). Detailed sources for each variable and country are: 1) For the U.S.: The Census Bureau
and Current Population Survey. Income brackets (annual gross household income) are defined as less than $20,000;$20,000-$40,000; $40,000-$70,000; more than $70,000. 2) For the U.K.: Eurostat Census Data and Office of National
Statistics. Income brackets (monthly net household income) are: less than £1,500; £1,500-£2,500; £2,500-£3,000;
more than £3,000, 3) For France: Eurostat Census Data and INSEE. Income brackets (monthly net householdincome, in Euros) are: less than 1,500; 1,500-2,500; 2,500-2,000; more than 3,000; 4) For Italy: Eurostat Census
Data, Bank of Italy and ISTAT. Income brackets (monthly net household income, in Euros) are: less than 1,500;
1,500-,2450; 2,450-3,350; more than 3,350; 5) For Germany: Eurostat Census Data and GfK Demographics. Income
brackets (monthly net household income, in Euros) are: less than 1,500; 1,500-2,600; 2,600-4,000; more than 4,000;
6) For Sweden: Eurostat Census Data and Statistics Sweden. Income brackets (monthly gross household income, inSEK) are: less than 33,000; 33,000-42,000; 42,000-58,000; more than 58,000. We count as employed both full-timeand part-time employees. Out of the labor force = 1 - (employed + unemployed).

Table 2: Ability of Covariates to Predict Treatment Status
Imm Q First
Coefficient P-value
Voted right
Voted left
Male
Young
Immigrant parent
College degree
Rich
High immigration sector-0.0060.002
0.009-0.001
0.009-0.001-0.006-0.005

0.436
0.820
0.168
0.918
0.479
0.887
0.516
0.485

Share of Immigrants
Coefficient
P-value-0.001-0.003-0.003
0.009
0.003
0.011
0.001
0.004

0.888
0.668
0.588
0.130
0.738
0.088
0.857
0.518

Origins of Immigrants
Coefficient
P-value
0.004-0.0020.002-0.0030.002
0.001-0.003-0.004

0.548
0.758
0.721
0.671
0.875
0.934
0.732
0.537

Hard Work of Immigrants
Coefficient
P-value-0.0060.009-0.000-0.011-0.013-0.011-0.005-0.002

0.325
0.150
0.999
0.063
0.195
0.087
0.558
0.769

Notes: The table shows the coefficients and p-values from a series of regressions of the form yic = α + βCovariatei +γc + ic , where Covariatei is the variable listed in the row and γc are country fixed effects. In the column “Imm Q
First”, yic is a dummy equal to one if the respondent was shown the Immigration block before the Redistribution
block. In columns “Share of Immigrants”, “Origins of Immigrants”, and “Hard Work of Immigrants” yic is a dummy
equal to one if the respondent saw the corresponding informational treatment.

Table 3: Perceptions by Country
Actual(1)

U.S.
Perceived
Mean
Median(Stand. Error) [Interq. Range](2)(3)

Actual(4)

U.K.
Perceived
Mean
Median(Stand. Error) [Interq. Range](5)(6)

Actual(7)

FrancePerceived
Mean
Median(Stand. Error) [Interq. Range](8)(9)

Panel A: Perceptions
Share of Immigrants

10.00

Share Immigrants from North Africa

1.00

Share of Immigrants from Middle East

4.00

Share of Immigrants from Western Europe

7.00

Share of Immigrants from Easter Europe

6.00

Share of Immigrants from North America

2.00

Share of Immigrants from Latin America

42.00

Share of Muslim Immigrants

10.00

Share of Christian Immigrants

61.00

Share of Unemployed Immigrants

3.30

Share of Poor Immigrants

11.30

Share of Low-Educated Immigrants

22.70

Share of High-Educated Immigrants

34.90

Relative Transfers Received

1.23

36.08
(0.73)
8.43(0.23)
12.20(0.32)
10.88(0.27)
9.88(0.23)
9.69(0.33)
24.42(0.55)
22.69(0.50)
39.17(0.72)
26.39(0.77)
34.66(0.76)
28.96(0.79)
34.86(0.77)
1.17(0.06)

31.00[20.00, 48.00]
7.00[4.00, 11.00]
10.00[5.00, 16.00]
10.00[5.00, 15.00]
10.00[5.00, 13.00]
7.00[4.00, 11.00]
20.00[12.00, 32.00]
20.00[10.00, 30.00]
40.00[20.00, 50.00]
20.00[8.00, 40.00]
30.00[16.00, 50.00]
20.00[10.00, 40.00]
30.00[15.00, 50.00]
1.00[0.33, 1.00]

13.40
1.00
5.00
19.00
20.00
3.00
4.00
23.00
58.00
5.70
19.00
16.60
48.80
1.42

31.39
(0.64)
9.88(0.27)
10.84(0.34)
16.22(0.43)
23.51(0.47)
6.10(0.22)
5.61(0.19)
33.89(0.68)
29.45(0.65)
27.00(0.78)
29.05(0.72)
25.58(0.76)
25.33(0.69)
1.02(0.04)

30.00[15.00, 42.00]
10.00[5.00, 14.00]
9.00[5.00, 15.00]
13.00[7.00, 21.00]
20.00[14.00, 30.00]
5.00[2.00, 9.00]
5.00[2.00, 8.00]
30.00[20.00, 45.00]
25.00[15.00, 40.00]
20.00[8.00, 40.00]
22.00[10.00, 40.00]
20.00[8.00, 40.00]
20.00[10.00, 40.00]
1.00[0.50, 1.00]

12.20
35.00
6.00
29.00
5.00
1.00
3.00
48.00
43.00
16.60
23.80
39.10
28.80
1.39

28.81
(0.61)
27.21(0.50)
10.98(0.34)
10.94(0.33)
14.53(0.34)
5.97(0.31)
5.69(0.20)
50.23(0.72)
24.30(0.53)
38.79(0.85)
41.57(0.82)
51.62(0.84)
27.36(0.61)
1.77(0.08)

Panel B: Attitudes
Immigrants Poor due to Lack of Effort
Immigrants Rich because of Effort
Mohammad Gets MoreObservations

0.41(0.02)
0.67(0.02)
0.26(0.01)
960

0.36(0.02)
0.70(0.01)
0.18(0.01)
973

0.31(0.01)
0.62(0.02)
0.34(0.02)
980

25.00[14.00, 40.00]
25.00[18.00, 35.00]
9.00[4.00, 15.00]
10.00[4.00, 15.00]
13.00[8.00, 20.00]
3.00[1.00, 7.00]
4.00[2.00, 8.00]
50.00[30.00, 65.00]
20.00[10.00, 31.00]
30.00[15.00, 60.00]
40.00[20.00, 60.00]
50.00[30.00, 70.00]
24.50[10.00, 40.00]
1.00[1.00, 1.10]

Table 3: Perceptions by Country (cont.)

Actual(10)

Italy
Perceived
Mean
Median(Stand. Error) [Interq. Range]
(11)
(12)

Actual(13)

Germany
Perceived
Mean
Median(Stand. Error) [Interq. Range]
(14)
(15)

Actual(16)

SwedenPerceived
Mean
Median(Stand. Error) [Interq. Range]
(17)
(18)

Panel A: Perceptions
Share of Immigrants

10.00

Share Immigrants from North Africa

10.00

Share of Immigrants from Middle East

3.00

Share of Immigrants from Western Europe

14.00

Share of Immigrants from Easter Europe

38.00

Share of Immigrants from North America

1.00

Share of Immigrants from Latin America

9.00

Share of Muslim Immigrants

33.00

Share of Christian Immigrants

57.00

Share of Unemployed Immigrants

14.70

Share of Poor Immigrants

34.90

Share of Low-Educated Immigrants

49.10

Share of High-Educated Immigrants

11.70

Relative Transfers Received

1.29

26.41
(0.65)
24.90(0.49)
8.95(0.25)
6.02(0.24)
18.63(0.38)
4.55(0.26)
9.59(0.26)
46.95(0.73)
26.82(0.63)
41.80(0.87)
42.86(0.82)
43.56(0.84)
18.75(0.59)
1.34(0.06)

20.00[10.00, 35.00]
23.00[15.00, 31.00]
8.00[3.00, 13.00]
4.00[1.00, 9.00]
18.00[10.00, 25.00]
2.00[0.00, 5.00]
9.00[4.00, 13.00]
45.00[30.00, 60.00]
20.00[10.00, 40.00]
40.00[20.00, 60.00]
40.00[20.00, 60.00]
40.00[20.00, 60.00]
10.00[5.00, 30.00]
1.00[0.50, 1.10]

14.80
2.00
17.00
15.00
43.00
1.00
3.00
30.00
51.00
6.90
20.50
35.10
22.30
0.72

30.26
(0.68)
16.02(0.37)
16.84(0.45)
13.43(0.42)
23.45(0.41)
4.92(0.20)
5.42(0.16)
43.89(0.68)
31.66(0.61)
39.20(0.93)
33.53(0.81)
37.23(0.80)
21.88(0.58)
1.13(0.04)

25.00[15.00, 40.00]
15.00[8.00, 21.00]
14.00[7.00, 23.00]
10.00[4.00, 20.00]
22.00[15.00, 30.00]
4.00[1.00, 6.00]
5.00[2.00, 8.00]
40.00[30.00, 60.00]
30.00[20.00, 45.00]
30.00[12.00, 60.00]
30.00[10.00, 50.00]
30.00[16.00, 50.00]
20.00[10.00, 30.00]
1.00[1.00, 1.00]

17.60
1.00
24.00
24.00
22.00
1.00
6.00
27.00
61.00
16.10
29.80
33.70
37.90
1.44

27.00
(0.81)
12.09(0.37)
25.09(0.72)
15.00(0.71)
13.80(0.41)
4.74(0.41)
7.93(0.32)
44.77(1.01)
32.67(0.97)
37.16(1.14)
25.26(1.00)
40.88(1.21)
36.39(1.01)
1.28(0.06)

21.00[15.00, 33.00]
10.00[7.00, 17.00]
22.00[15.00, 34.00]
10.00[4.00, 20.00]
13.00[8.00, 20.00]
3.00[1.00, 5.00]
6.00[3.00, 10.00]
40.00[30.00, 60.00]
30.00[16.00, 48.00]
30.00[15.00, 55.00]
20.00[10.00, 35.00]
38.00[20.00, 60.00]
35.00[20.00, 50.00]
1.00[1.00, 1.10]

Panel B: Attitudes
Immigrants Poor due to Lack of Effort
Immigrants Rich because of Effort
Mohammad Gets MoreObservations

0.31(0.01)
0.69(0.01)
0.33(0.02)
971

0.40(0.02)
0.59(0.02)
0.20(0.01)
973

0.32(0.02)
0.69(0.02)
0.01(0.01)
481

Notes: Panel A reports mean and median perceptions for each country. The standard errors of the means are in parentheses and theinterquartile ranges (25th and 75th percentiles) are in square brackets. The actual value of the statistic for each country is reported incolumns (1), (4), (7), (10), (13) and (16). Panel B reports the mean of each attitude variable for each country and its standard error (inparentheses).

Table 4: Misperceptions By Respondent Groups
MuslimImmigrants

Yes
College Educated
No
Yes
Rich
No
Yes
Young
No
Yes
Male
No
Yes
Left-Wing
No
Yes
Immigrant Parent
No
Yes
Immigrant AcquaintancesNo
Yes
Low Educ in High-Imm Sector
No
YesHigh Educ in High-Imm Sector
No

Christian

Unemployed

Poor

Low Educ

High Educ

Transfers

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

15.34

10.00

8.95

7.00

-21.66-23.00

19.98

13.10

10.35

6.20

3.47

-0.10

-1.83

-4.90

-0.10

-0.39

(0.45) [0.0;25.0] (0.44) [-3.0;20.0] (0.43) [-37.0;-11.0] (0.53) [1.7;34.3] (0.51) [-8.8;25.1] (0.52) [-14.1;18.4] (0.48) [-17.9;11.2] (0.03) [-0.8;-0.2]
19.23

15.80

12.95

10.00

-25.78-28.00

28.08

23.10

14.31

10.10

6.20

2.30

-4.59

(0.37) [3.0;30.0] (0.39) [-3.0;27.0] (0.36) [-41.0;-12.0] (0.48) [4.3;46.7] (0.47) [-6.0;31.2] (0.48) [-14.1;24.9] (0.41)
16.90

11.50

10.33

7.00

-22.63-26.00

21.15

13.90

10.30

5.10

4.47

0.90

-3.69

(0.74) [0.1;26.5] (0.69) [-3.0;22.0] (0.70) [-37.0;-11.0] (0.86) [0.3;35.3] (0.82) [-9.0;25.2] (0.87) [-14.1;20.9] (0.73)
17.82

14.20

11.54

10.00

-24.42-27.00

25.53

18.10

13.18

9.50

5.22

0.90

-3.44

-6.70

0.16

-0.29

[-18.8;8.3](0.03)[-0.4;0.3]

-0.13

-0.39

-6.70
[-18.1;8.3]-6.70(0.05) [-0.8;-0.1]
0.09(0.31) [1.6;27.8] (0.32) [-3.0;25.0] (0.30) [-41.0;-11.0] (0.40) [3.4;43.9] (0.38) [-7.8;29.5] (0.39) [-14.1;22.3] (0.34) [-18.8;11.2] (0.03)
19.77

16.60

10.30

7.00

-24.05-27.00

25.42

18.40

14.10

9.50

3.97

0.90

-0.83

-2.30

0.01

(0.39) [3.6;30.0] (0.41) [-3.0;22.0] (0.38) [-38.0;-11.0] (0.49) [3.4;43.9] (0.49) [-5.5;31.0] (0.48) [-14.7;20.9] (0.44) [-17.3;12.7] (0.03)
15.32

10.00

12.52

10.00

-24.22-27.00

24.14

15.30

11.16

6.20

6.36

0.90

-6.45

(0.41) [0.0;25.0] (0.42) [-2.0;27.0] (0.40) [-41.0;-11.0] (0.53) [3.1;43.1] (0.50) [-9.0;26.2] (0.52) [-13.7;24.9] (0.44)
15.52

10.00

11.12

9.50

-22.88-26.00

23.01

14.30

11.36

6.20

5.55

0.90

-5.88

(0.42) [0.0;25.0] (0.42) [-3.0;25.0] (0.41) [-38.0;-11.0] (0.52) [1.7;41.7] (0.50) [-9.0;26.2] (0.52) [-14.1;23.4] (0.43)
19.73

16.80

11.56

10.00

-25.33-28.00

26.55

21.70

14.01

9.50

4.66

0.90

-1.17

14.00

9.40

7.00

-22.94-26.00

23.64

15.30

13.49

9.50

2.37

-1.60

-0.83

-0.34
[-0.8;0.3]

-8.30

0.10

-0.29

[-19.3;5.1](0.03)[-0.4;0.3]

-7.90

-0.03

-0.29

[-18.8;7.1](0.03)[-0.7;0.3]

-2.30

0.14

(0.39) [4.2;30.0] (0.41) [-3.0;23.0] (0.37) [-41.0;-12.0] (0.50) [4.3;44.3] (0.48) [-5.0;30.1] (0.49) [-14.1;20.9] (0.44) [-17.8;12.7] (0.04)
18.01

-0.29
[-0.4;0.3]

-2.30

-0.21

-0.29
[-0.4;0.3]-0.34(0.43) [1.2;27.8] (0.41) [-3.0;20.0] (0.41) [-37.0;-11.0] (0.52) [3.1;41.7] (0.50) [-4.9;29.5] (0.50) [-15.1;17.4] (0.46) [-14.9;12.1] (0.03) [-0.8;-0.2]
18.45

15.00

15.08

(0.44) [2.4;29.0] (0.47)
23.46

20.00

9.79

12.00
[0.0;30.0]
7.00-26.50-31.00

27.84

21.70

12.36

8.70

8.47

3.40

-7.31

(0.45) [-41.0;-13.0] (0.59) [4.3;46.7] (0.57) [-9.0;30.1] (0.59) [-12.7;27.3] (0.48)-21.66-23.00

22.48

14.30

11.85

8.70

5.82

-1.10

-1.55

-8.80

0.38

-0.23

[-23.8;5.1](0.05)[-0.4;0.4]

-2.30

-0.10

-0.39

(0.99) [5.2;36.6] (0.95) [-3.0;22.0] (0.89) [-36.0;-11.0] (1.14) [3.1;39.3] (1.10) [-8.8;28.7] (1.12) [-12.7;21.3] (1.05) [-18.8;12.7] (0.06)
17.05

12.80

11.51

10.00

-24.39-27.00

25.07

17.70

12.79

8.70

5.02

0.90

-3.68

(0.30) [1.0;26.9] (0.31) [-3.0;25.0] (0.29) [-40.0;-11.0] (0.38) [3.4;43.4] (0.37) [-8.5;29.5] (0.37) [-14.6;21.3] (0.33)
16.37

11.60

10.33

7.00

-22.47-23.00

21.84

14.30

10.83

6.20

3.99

-0.10

-1.88

-6.70

0.07

-0.29

[-18.8;8.3](0.03)[-0.4;0.3]

-3.80

-0.02

(0.35) [0.4;26.2] (0.35) [-3.0;22.0] (0.33) [-37.0;-11.0] (0.42) [2.3;36.7] (0.42) [-9.0;26.2] (0.42) [-14.1;20.9] (0.38) [-17.3;11.2] (0.03)
20.27

16.60

13.37

10.00

-27.44-31.00

30.78

25.30

16.49

13.70

7.32

3.40

-6.68

(0.50) [4.0;31.6] (0.54) [-3.0;27.0] (0.49) [-42.0;-13.0] (0.66) [5.8;53.1] (0.61) [-4.0;35.1] (0.66) [-13.6;27.3] (0.54)
20.56

17.20

12.62

10.00

-26.13-28.00

28.77

23.40

15.47

11.00

7.00

3.40

-6.82

(0.51) [4.2;30.8] (0.54) [-3.0;27.0] (0.50) [-41.0;-13.0] (0.66) [4.3;46.7] (0.64) [-4.9;35.1] (0.66) [-14.1;26.3] (0.57)
16.14

11.20

11.66

10.00

-23.70-26.00

23.44

15.30

11.58

6.70

4.11

0.90

-1.53

10.80

8.40

7.00

-21.84-23.00

21.44

13.40

10.91

6.20

4.17

-1.60

-2.69

-0.32
[-0.4;0.3]

-8.80

0.21

-0.29

[-20.8;7.7](0.05)[-0.7;0.3]

-8.80

0.17

-0.29

[-23.4;7.7](0.05)[-0.4;0.3]

-3.80

0.05

(0.34) [0.2;26.0] (0.35) [-3.0;23.0] (0.33) [-38.0;-11.0] (0.43) [3.1;42.3] (0.42) [-8.8;28.7] (0.42) [-15.1;20.9] (0.36) [-15.3;11.2] (0.03)
16.49[-0.7;0.3]

-4.90

-0.13

-0.29
[-0.4;0.3]-0.42(0.67) [0.2;26.6] (0.64) [-5.0;20.0] (0.64) [-37.0;-11.0] (0.80) [1.7;35.3] (0.73) [-8.8;26.0] (0.76) [-12.7;17.3] (0.72) [-18.8;12.1] (0.05) [-0.9;-0.2]
16.14

11.20

11.66

10.00

-23.70-26.00

23.44

15.30

11.58

6.70

4.11

0.90

-1.53

-3.80

0.05

(0.31) [0.2;26.0] (0.32) [-3.0;23.0] (0.31) [-38.0;-11.0] (0.39) [3.1;42.3] (0.39) [-8.8;28.7] (0.39) [-15.1;20.9] (0.33) [-15.3;11.2] (0.02)-0.29[-0.4;0.3]

Obs.
(17)
2155
3185
872
4470
2826
2516
2615
2727
2452
2146
505
4836
3560
1782
1721
3593
1025
4289

Notes: The table shows the mean (in odd columns) and median (in even columns) misperceptions – computed as perceived minus real – by groups. Groups are defined by the indicatorvariables listed to the left: the mean and median when the indicator is equal to 1 (respectively, to 0) are shown in the “Yes” row (respectively, in the “No” row). The standard errors of themeans are in parentheses and the interquartile ranges (25th and 75th percentiles) are in square brackets.

Table 5: Treatment Effects on Support for Immigration and Redistribution
Imm Support

Imm Not

Redistribution

Inequality

Donation

Index

A Problem

Index

Serious Problem

Above Median

(1)

(2)

(3)

(4)

(5)

-0.0184*-0.0280**-0.0479***(0.0102)(0.0132)(0.0138)

Imm Questions First

Share of Immigrants

Origins of Immigrants

Hard Work of Immigrants

0.0200*0.0233***-0.00211-0.00569-0.0159(0.0119)(0.00826)(0.0103)(0.0133)(0.0139)0.006140.004560.001830.00505

0.00136(0.0119)(0.00826)(0.0103)(0.0133)(0.0139)0.0465***0.0255***0.0310***0.01620.00828(0.0119)(0.00826)(0.0102)(0.0133)(0.0139)

Share of Immigrants X Imm. Q. First

Origins of Immigrants X Imm. Q. First

Hard Work of Immigrants X Imm. Q. First-0.00311

0.01340.0179(0.0145)(0.0188)(0.0196)-0.0120-0.0184-0.0116(0.0145)(0.0188)(0.0196)-0.0290**-0.00718

0.00183(0.0145)(0.0188)(0.0196)

Observations

20049

20011

20049

20049

20049

Control mean

0.000

0.248

0.000

0.572

0.446

Notes: The table reports the effect of the Order treatment and the three information treatments, as well as theirinteractions on the variables in the columns. Outcome variables are described in Appendix A-1. Controls included
in all regressions are: indicator variables for gender, age less than 45, having children, being in the top quartileof the income distribution, having a college degree, political affiliation, having at least one parent not born in the
country, working in a high immigration sector, and country fixed effects. Standard errors in parentheses. * p < 0.1,** p < 0.05, *** p < 0.01.

Table 6: First Stage Treatment Effects on Perceptions
All
Immigrants(misp.)(1)

Accurate Perception
All Immigrants

N. America, W. andE. Europe(misp.)(4)

MuslimChristian(2)

M. East and
N. Africa(misp.)(3)

Share of Immigrants

Lack of Effort
Reason Poor

(misp.)(5)(misp.)(6)(7)-4.716***(0.421)0.224***(0.00559)-0.255(0.304)0.184(0.355)0.00816(0.407)0.146(0.395)0.000963(0.00907)

Origins of Immigrants

2.314***(0.422)0.00285(0.00560)-4.762***(0.304)

1.785***(0.355)-1.825***(0.407)2.471***(0.395)-0.000169(0.00908)

Hard Work of Immigrants

0.752*(0.422)-0.00409(0.00560)-0.431(0.304)0.433(0.355)-0.854**(0.407)0.732*(0.395)-0.0529***(0.00907)

Observations
Control mean

20018
17.021

20018
0.043

20031
12.44820011-5.39720045
11.29520041-23.985

20049
0.356

Notes: The table reports first-stage effects on (mis)perceptions of immigration. Misperceptions are computed asperception minus actual statistic. Accurate Perception All Immigrants is a dummy equal to 1 if the absolute valueof the respondent’s misperception of the share of immigrants is less than 1. Appendix A-1 defines all variables.
All regressions include the same controls as Table 5. Standard errors in parentheses. * p < 0.1, ** p < 0.05, ***p < 0.01.

Table 7: First Stage Effects: Persistence in Follow-Up (US only)All
Accurate Perception
immigrants
All immigrants(misp.)(1)(2)
Panel A: First survey who took the follow-up

M. East and
N. Africa(misp.)(3)

L. America

MuslimChristianLack of Effort
reason poor

(misp.)(4)(misp.)(5)(misp.)(6)(7)

Share of Immigrants-7.045***(2.051)0.230***(0.0217)1.515(1.032)-1.016(1.574)0.578(1.302)3.745*(2.048)0.0109(0.0405)

Origins of Immigrants

1.671(2.107)-0.0214(0.0223)-7.220***(1.060)15.12***(1.617)-3.436**(1.338)5.457***(2.105)-0.0470(0.0417)

Hard Work of Immigrants

1.035(2.030)21.2930.00854(0.0215)0.023

1.889*(1.020)14.2560.278(1.556)-16.550

1.008(1.287)12.0800.336(2.025)-22.660-0.0888**(0.0401)
0.450

Control mean

Panel B: Follow-up respondents
Share of Immigrants-1.369(1.851)0.0201(0.0161)0.853(1.023)-1.303(1.420)0.539(1.229)3.411*(1.947)-0.0125(0.0401)

Origins of Immigrants-1.301(1.902)-0.0177(0.0165)-2.808***(1.051)

7.234***(1.459)-0.566(1.263)2.148(2.001)-0.0424(0.0412)

Hard Work of Immigrants-1.246(1.832)-0.00130(0.0159)

1.057(1.012)0.640(1.403)

1.102(1.215)-1.584(1.925)-0.0821**(0.0397)

Observations
Control mean

1032
21.082

1032
0.034

1033
15.3551034-18.3091034
11.0501034-21.851

1034
0.469

Notes: Panel A reports estimates of the first-stage effects in the first-round survey, on the subsample of respondents
who also took the follow up survey. Panel B shows the persistence of treatment effects on that subsample in thefollow up survey. See notes to Table 6. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 8: Heterogeneous Treatment Effects
Imm Support
Index
(1)
Panel A: Right-Wing vs. Left-Wing

Imm Not
A Problem(2)

Redistribution
Index
(3)

Inequality
Serious Problem(4)

DonationAbove Median(5)

Imm. Q First X Right-0.0102(0.0158)

0.00772(0.0204)-0.0526**(0.0212)

Imm. Q First X Left

-0.0291**(0.0148)-0.0575***(0.0191)-0.0480**(0.0199)

0.385-0.000
5064

0.020
0.586
5064

0.875
0.470
5064

p-value diff.
Control mean
Observations
Hard Work of Imm. X Right

0.0699***(0.0259)

0.0236(0.0179)

0.0310*(0.0159)

0.0195(0.0204)-0.00992(0.0215)

Hard Work of Imm. X Left

0.0164(0.0240)0.00299(0.0165)

0.0288**(0.0147)0.0204(0.0188)

0.0221(0.0199)

p-value diff.
Control mean
Observations

0.130
0.000
9964

0.397
0.238
9942

0.920
-0.000
9964

0.974
0.586
9964

0.274
0.470
9964

Imm. Q First X College-0.000886(0.0161)-0.0161(0.0208)-0.0575***(0.0217)

Imm. Q First X No College-0.0296**(0.0133)-0.0356**(0.0172)-0.0406**(0.0179)

0.170-0.000
5064

0.470
0.586
5064

0.547
0.470
5064

Panel B: College-Educated vs. No College

p-value diff.
Control mean
Observations
Hard Work of Imm. X College

0.0525**(0.0264)0.0137(0.0181)0.0409**(0.0161)

0.0260(0.0207)

0.0364*(0.0219)

Hard Work of Imm. X No College

0.0375*(0.0217)0.0131(0.0150)

0.0260*(0.0133)

0.00872(0.0171)-0.00937(0.0180)p-value diff.
Control mean
Observations

0.661
0.000
9964

0.978
0.238
9942

0.476
-0.000
9964

0.519
0.586
9964

0.107
0.470
9964

Imm. Q First x H imm-0.0428**(0.0180)-0.0308(0.0233)-0.0814***(0.0242)

Imm. Q First x Not H imm-0.00633(0.0125)-0.0266*(0.0161)-0.0316*(0.0168)p-value diff.
Control mean
Observations

0.097-0.000
5064

0.884
0.586
5064

0.091
0.470
5064

Panel C: High Immigration sector/No college vs. Not

Hard Work of Imm. X H Imm.

0.0603**(0.0293)0.0352*(0.0202)0.0171(0.0179)

0.0157(0.0230)-0.0164(0.0243)

Hard Work of Imm. X Not H Imm.

0.0345*(0.0205)

0.00239(0.0141)0.0392***(0.0125)0.0157(0.0161)

0.0215(0.0170)p-value diff.
Control mean
Observations

0.471
0.000
9964

0.182
0.238
9942

0.312
-0.000
9964

1.000
0.586
9964

0.201
0.470
9964

Notes: The Table reports the effects of the Order and the Hard Work of Immigrants treatments. The effects of the Order treatment
are estimated only on the respondents who have not seen any informational treatment. The effects of the “Hard work” treatment
are estimated only on respondents who see the redistribution block first. Panel A reports heterogeneous effects on Left-wing andon Right-wing respondent. The regressions also include a “Treatment x Center” interaction, not reported. Panel B reports theeffects on respondents with a college degree and respondents without. Panel C reports the effects on respondents working in a highimmigration sector who do not have a college degree, and on all the other respondents. “p-value diff.” is the p-value of the testof equality of treatment effects on the pairs of groups. All regressions include the same controls as Table 5. Standard errors inparentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

APPENDIX
A-1

Variable Definitions

Core Respondents’ Characteristics:
Male: respondent is male.
Young: respondent’s age is below 45 years.
Rich: respondent’s household income is in the top quartile of the household income distribution in
the country.
College: respondent has at least a college degree.
Left-wing: respondent has voted or is planning to vote (in Italy and Sweden) for a party or presidential candidate classifiable as Left or Far-Left.45
Right-wing: respondent has voted or is planning to vote (in Italy and Sweden) for a party or presidential candidate classifiable as Right or Far-Right.46
Immigrant parent: dummy equal to one if at least one of the respondent’s parents is not born inthe country.
High Immigration & Low Educated: dummy equal to one if respondent works in an immigrationintensive sector and does not have a college degree. See Appendix A-3 for details on the sector
classification.
High Immigration & High Educated: dummy equal to one if respondent works in an immigrationintensive sector and has a college degree. See Appendix A-3 for details on the sector classification.
Children: respondent has one or more children.
Perceptions of Immigration
Note: For all cross-country analyses we transform these variables into misperceptions, that is, we
subtract the actual value of the variable in the data from the respondent’s perception; a positive
value represents an overestimation of the actual value, and a negative value represents an underestimation. See Section A-2 for a description of the data sources.
All Immigrants: perceived share of immigrants (according to the OECD definition of “foreignborn”) in the country.
Share of Immigrants from ...: perceived share of immigrants born in, respectively, North Africa,
Middle East, Western Europe, Eastern Europe, North America, Latin America, Asia, Sub-SaharanAfrica, Oceania.
45
The candidates or parties that we classify as Left or Far-Left are: in the U.S., Clinton and Stein; in the U.K.,
Labour Party, Scottish National Party, Sinn Fein, Green Party and Party of Wales; in France, Arthaud, Hamon,Mélenchon and Poutou; in Italy, Democratic Party (PD), +Europa, Civica Popolare, Five Star Movement, Liberi e
Uguali, Potere al Popolo; in Germany, SPD, Bundnis 90, Die Linke; in Sweden, Socialdemokraterna, Miljöpartiet,Vänsterpartiet, and Feministiskt Initiativ.
46
The candidates or parties that we classify as Right or Far-Right are: in the U.S., Trump and Johnson; in the
U.K., Conservative Party, Democratic Unionist Party, Ukip; in France, Dupont-Aignan, Fillon, Le Pen; in Italy, Forza
Italia, Fratelli d’Italia, The League; in Germany, CDU, AfD, ODP; in Sweden, Sverigedemokraterna, Liberalerna,
Moderaterna, and Kristdemokraterna.

Share of Muslim/Christian immigrants: perceived share of immigrants of Muslim or Christian religion.
Share of Low-Educated immigrants: perceived share of immigrants without a high school diploma(in the U.S.) or equivalent in other countries.
Share of Low-Educated who are immigrants: perceived share of low educated people who are immigrants. This perception is computed by combining the perceived share of low-educated immigrants,
the perceived share of low-educated natives, and the perceived share of immigrants in the country.
Share of High-Educated immigrants: perceived share of immigrants with at least a two-year bachelor degree in the U.S. or equivalent in other countries.
Share of Unemployed immigrants: perceived share of unemployed immigrants.
Share of Poor immigrants: perceived share of immigrants who live below the poverty line.
Share of Poor who are immigrants: perceived share of poor people who are immigrants. This
perception is computed by combining the perceived share of poor immigrants, the perceived shareof poor natives, and the perceived share of immigrants in the country.
Relative Transfers Received: perceived social benefits paid to immigrants relative to natives. Thisvariable aggregates numerically the answers to the question “An average immigrant receives... No
transfers; One third as much as a U.S. born resident; Half [...]; As much [...]; Slightly more [...];
Twice [...]; Three times [...]; More than ten times [...].”
Attitudes towards Immigration
Immigrants Poor due to Lack of Effort: dummy equal to 1 if the respondent thinks that an immigrant living in the country is poor because of lack of effort.
Immigrants Rich because of effort: dummy equal to 1 if the respondent thinks an immigrant is richbecause he has worked harder than others.
Mohammad Gets More: dummy equal to 1 if the respondent thinks that Mohammad receives on
net more than John – either receives more social benefits but pays weakly less taxes, or receives
weakly more social benefits but pays less taxes.
Immigration Support
Imm. Not A problem: dummy equal to 1 if the respondent thinks that immigration is not a problem
or not a problem at all.
Imm. Benefits Soon: dummy equal to 1 if the respondent thinks that immigrants should get social
benefits on the same basis as natives at most three years after they arrive in the country.
Imm. Citizenship Soon: dummy equal to 1 if the respondent thinks that immigrants should beallowed to apply for citizenship at most five years after they arrive in the country.
American Upon Citizenship/Before: dummy equal to 1 if the respondent would consider an immigrant to be “truly American” as the latest when the latter gets citizenship.
Govt. Should Care about Everyone: variable ranging from 1 to 7 where 1 means that the respondent
thinks the government should only care about natives in the country and 7 means that he thinks
the government should care equally about all the people living in the country.
Support for Redistribution

Inequality Serious Problem: dummy equal to 1 if the respondent thinks that income inequality isa serious or very serious problem.
Govt. Should Care about Inequality: variable ranging from 1 to 7 where 1 means that the respondent thinks the government should not care at all about income inequality and 7 means that hethinks the government should do everything in its power to reduce inequality.
Schooling Favor: dummy equal to 1 if the respondent favors or strongly favors spending moremoney on schools in poor neighborhoods.
Housing Favor: dummy equal to 1 if the respondent favors or strongly favors spending more money
to provide decent housing for those who cannot afford it.
Income Support Favor: dummy equal to 1 if the respondent favors or strongly favors spending moremoney on income support programs for the poor.
Tax Top1: respondent’s preferred tax rate on the top 1% of the income distribution in his country.
Tax Bottom50: respondent’s preferred tax rate on the bottom 50% of the income distribution in
his country.
Budget Education: share of the government budget that the respondent would allocate to “Schooling and Higher Education.”
Budget Health: share of the government budget that the respondent would allocate to public spending on Health.
Budget Safety Net: share of the government budget that the respondent would allocate to social
insurance and income support programs.
Budget Pensions: share of the government budget that the respondent would allocate to Social Security, Medicare, Disability Insurance and Supplemental Security Income in the U.S. or equivalent
spending items in the other countries.
Budget Housing: share of the government budget that the respondent would allocate to affordable
housing programs.
Donation
Donation above Median: dummy equal to 1 if the respondent’s donation amount is above the median in his country.
Total % donation: total amount the respondent wishes to donate to the charities, as a percentageof the potential prize ($ 1000 in the U.S., 1000 pound in the U.K., 1000 euro in France, Italy andGermany, 10000 SEK in Sweden).
Commuting-zone-level variables
To construct commuting-zone-level variables we take data at the county-level from different sources
and we aggregate them using the county-to-commuting zone crosswalk by David Dorn.
Unemp rate (2017): unemployment rate at the CZ level. Source: own calculations using the BLS
Labor Force Data by County (2017).
Crime rate: Number of crimes reported in 2014 over total population. Source: own calculations
on county-level data from the ICPSR Uniform Crime Reporting Program Data 2014.
Share Living in Rural area: share of people living in an area that is defined as “Rural”. Source:own calculations on county-level data from the 2010 Census.
Share in Manufacturing: Fraction of employed persons 16 and older working in manufacturing.
Source: own calculations on county-level data from the 2016 5-year ACS.
Share living in poverty: Fraction of population below the poverty line. Source: own calculations
on county-level data from the 2016 5-year ACS.
Share of Hispanic People: Share of the population that is Hispanic. Source: own calculations on
county-level data from the 2016 5-year ACS.
Share of Black People: Share of the population that is Black. Source: own calculations on countylevel data from the 2016 5-year ACS.
Share of college-educated: Share of residents older than 25 and who have at least a Bachelor degree.
Source: own calculations on county-level data from the 2016 5-year ACS.
Immigrants Inflow since 2010: Share of the population not born in the U.S. who has moved tothe commuting zone in 2010 or after. Source: own calculations on county-level data from the 20165-year ACS.
Share of immigrants: Share of the population not born in the U.S.. Source: own calculations on
county-level data from the 2016 5-year ACS.
The following variable is taken from Chetty et al. (2016). Racial Segregation: Multi-group Theil
Index calculated at the census-tract level over four groups: White alone, Black alone, Hispanic,
and Other. Source: 2000 Census.
Immigration Support and Redistribution Support Indices
Following the methodology in Kling, Liebman, and Katz (2007), each index consists of an equallyweighted average of the z-scores of the policy outcomes variables related to immigration support(respectively, support for redistribution) with signs oriented so that more support for those policies
means a higher corresponding index. Variables are transformed into z-scores by subtracting thecontrol group mean and dividing by the control group standard deviation, so that each z-score hasmean 0 and standard deviation 1 for the control group. The Immigration support index includes
the z-scores of the 5 variables listed under Immigration Support. The Redistribution support index
includes the z-scores of the 12 variables listed under Support for Redistribution.

A-2

Statistics about Immigrants: Data Sources and Definitions

We report here a brief summary of the definitions and of the sources we used. Further details arereported in the Online Appendix Section OA.3.
Number, Origins and Religion of Immigrants
Share of immigrants: share of foreign-born in the country. Sources: Pew Research Center for the
U.S.; UN (Trends in International Migrant Stock: the 2017 Revision) for European countries. Shareof immigrants in U.S. states is from the Pew Research Center (2014).
Origin of immigrants: share of the foreign-born residents in the country born in, respectively,
North America, Latin America, Western Europe, Eastern Europe, North Africa, Middle East,
Asia. Sources: Pew Research Center for the U.S.; OECD (International Migration Database) forSweden; UN (Trends in International Migrant Stock: the 2017 Revision) for the U.K., France, Italy,
and Germany.
Religion of immigrants: share of foreign-born residents in the country who are of, respectively,
Muslim and Christian religion. Source: Pew Research Center (Global Religious Futures 2017).
Economic Circumstances of Immigrants
Share of Low Educated Immigrants: share of foreign-born population holding a qualification corresponding to ISCED 2011 levels 0-2 (in European countries) or having no high-school diploma in
the U.S.. Sources: Migration Policy Institute (2016) for the U.S.; Eurostat (2016) for European
countries.
Share of High Educated Immigrants: share of foreign-born population holding a qualification corresponding to ISCED 2011 levels 5-8 (in European countries) or having at least an associate degree(two year bachelor degree in the U.S.). Sources: Migration Policy Institute (2016), Eurostat (2016)
for European countries.
Unemployment: Unemployment rate among the foreign-born in the country. Source: Migration
Policy Institute (2016) for the U.S.; OECD (International Migration Outlook 2017) for the othercountries.
Poverty: U.S.: share of foreign-born population having income below the official Poverty Threshold.47 European countries: share of foreign-born population with an adult-equivalent disposable
income below the at-risk-of-poverty threshold, (60% of the national median disposable income).
Sources: Migration Policy Institute (2016) for the U.S.; Eurostat (2016) for European countries.
Relative Transfers - No Pensions: Average amount of benefits paid per immigrant household
divided by average amount of benefits paid per native household (2007-09). Benefits include social
assistance (e.g., social exclusion allowance in E.U., public assistance and Medicaid in the U.S.),
unemployment benefits, family allowances (e.g., family/child allowances in E.U., school lunch andfood stamp benefits in the U.S.), housing benefits (e.g., housing allowance in E.U., housing subsidy
in the U.S.). Source: Liebig and Mo (2013).
Relative Transfers - With Pensions: as in the previous variable, but including pension benefits(e.g., old-age benefits in the EU, social security payments, supplementary security income andMedicare in the U.S.). Source: Liebig and Mo (2013).

A-3

High Immigration Sectors

We define a sector as High Immigration if the share of immigrants working in that sector is higher
than the average share of immigrants employed in the country. The sectors that we classify as High47

See https://www.census.gov/cps/data/povthresholds.html

Immigration are listed here for each country.
• U.S.: Farming, fishing, and forestry, Building and grounds cleaning and maintenance, Construction and extraction, Computer and mathematical occupations, Production occupations,
Life, physical, and social science, Food preparation and serving related occupations, Occupations related to transportation and material moving, Occupations related to personal care,childcare and leisure, and Healthcare support occupations. Source: CPS 2016.
• U.K.: Domestic personnel; Accommodation and food services; Transport and storage; Information and communication; Administrative and support service activities; Manufacturing;
Professional, scientific and technical activities; Health and social work; Financial and insurance activities. Source: Annual Population Survey, April 2016 - March 2017 (https://discover.ukdataservice.ac.uk/catalogue?sn=8197). Sector breakdown criteria: SIC
2007.
• France: Non qualified artisanal workers; Domestic personnel; Merchants and retailer workers;
Qualified artisanal workers; Craftsmen; Agricultural workers; Non qualified industrial workers; Police ad military; Information, arts and entertainment; Drivers; Teachers and scientificoccupations; Industrial workers. Source: INSEE (Enquete Emploi en continu 2016). Sector
breakdown criteria: CSE two digits sectors.
• Italy: Street and related sales and service workers; Personal care workers; Cleaners andhelpers; Food preparation assistants; Agricultural, forestry and fishery laborers; Laborers
in mining, construction, manufacturing and transport; Building and related trades workers,
excluding electricians; Refuse workers and other elementary workers; Personal service workers;
Food processing, wood working, garment and other craft and related trades workers; Marketoriented skilled forestry, fishery and hunting workers; Stationary plant and machine operators;
Metal, machinery and related trades workers; Assemblers; Drivers and mobile plant operators.
Source: RCFL Survey, January 2016 - December 2016. Sector breakdown criteria: ISCO2008.
• Germany: Transport, logistics, protection and security; Commodity production and manufacturing; Commercial services, trade, sales, hotels and tourism; Construction, architecture,
surveying and mapping, and facility technology. Source: Destatis (Mikrozensus 2015).
• Sweden: Hotel and restaurant; Transport; Healthcare and care; Education; Business andfinancial operations. Source: Statistics Sweden (Sysselsatta efter näringsgren 2006-2015,
Table 3).

A-4

Links to Surveys• Survey U.S.: https://harvard.az1.qualtrics.com/jfe/form/SV_eKEjDcjYFz33eHr• Survey U.K.: https://harvard.az1.qualtrics.com/jfe/form/SV_0ILUH3So1ChjhPv• Survey France: https://harvard.az1.qualtrics.com/jfe/form/SV_77K4hoafSeGsuWN• Survey Italy: https://harvard.az1.qualtrics.com/jfe/form/SV_004wAyEt6lDcE6N• Survey Germany: https://harvard.az1.qualtrics.com/jfe/form/SV_1GgElOhY9ef75Pf• Survey Sweden: https://harvard.az1.qualtrics.com/jfe/form/SV_cUvZMTYuYPRiAw5

A-5

Full U.S. Questionnaire in English
Figure A-1: First page of the survey (English version)

Answer options are in italic, separated by a semicolon.

1. See Figure A-1

Figure A-2: Question on preferred income tax rates for various income
groupsYes, I would like to take part in this study, and confirm that I WAS BORN IN THE U.S.
and I am 18 or older ; No, I would not like to participate
2. Were you born in the United States?
Yes; No
3. What is your gender?

Figure A-3: Question on preferred allocation of government budget

Male; Female

4. What is your age?
5. What was your TOTAL household income, before taxes, last year? $0-$9999 ; $10000-$14999 ;$15000-$19999 ; $20000-$29999 ; $30000-$39999 ; $40000-$49999 ; $50000-$69999 ; $70000$89999 ; $90000-$109999 ; $110000-%149999 ; $150000-$199999 : $200000+
6. Please indicate your marital status.
Single; Married ; Legally separated or divorced ; Widowed
7. How many children do you have?
I do not have children: 1; 2; 3; 4; 5 or more
8. How would you describe your ethnicity/race?
European American/White; African American/Black; Hispanic/Latino; Asian/Asian American; Other
9. Were both of your parents born in the United States?
Yes; No
10. [If Yes to Q9] Where was your father born?
[dropdown menu with list of countries]
11. [If Yes to Q9] Where was your mother born?
[dropdown menu with list of countries]
12. What is your ZIP code?
13. Which category best describes your highest level of education?
Eighth Grade or less; Some High School; High School degree / GED; Some College; 2-year
College Degree; 4-year College Degree; Master’s Degree; Doctoral Degree; Professional Degree(JD, MD, MBA)
14. What is your current employment status?
Full-time employee; Part-time employee; Self-employed or small business owner; Unemployed
and looking for work; Student; Not currently working and not looking for work; Retiree
15. [If Full-time employee; Part-time employee; Self-employed or small business owner] Are you
employed in one of the following sectors? Check the one that applies. If you have multiple
jobs, check the one that describes your main occupation.
[See Appendix A-1]
16. [If Unemployed and looking for work; Not currently working and not looking for work; Retiree]
Even if you are not currently working, what sector did your latest occupation fall under?
Check the one that applies. If you have had multiple jobs, check the one that describes yourmain occupation.
[See Appendix A-1]

17. On economic policy matters, where do you see yourself on the liberal/conservative spectrum?
Very liberal; Liberal; Moderate; Conservative; Very conservative
18. In politics, as of today, do you consider yourself a Republican, a Democrat or an independent?
Republican; Democrat; Independent
19. Did you vote in the last presidential election?
Yes; No
20. [If Yes to Q19] In the last presidential election, you supported:
Hillary Clinton; Donald Trump; Jill Stein; Gary Johnson
21. [If No to Q19] Even if you did NOT vote, please indicate the candidate that you were most
likely to have voted for or who represents your views most closely
Hillary Clinton; Donald Trump; Jill Stein; Gary Johnson
22. Before proceeding to the next set of questions, we want to ask for your feedback about the
responses you provided so far. It is vital to our study that we only include responses frompeople who devoted their full attention to this study. This will not affect in any way thepayment you will receive for taking this survey. In your honest opinion, should we use yourresponses, or should we discard your responses since you did not devote your full attention
to the questions so far?
Yes, I have devoted full attention to the questions so far and I think you should use my
responses for your study; No, I have not devoted full attention to the questions so far and I
think you should not use my responses for your study
In the next two questions, we ask you to think about the total level of funds that the government raises and spends today on various policies. For the purpose of these questions, supposethat the level of government spending is fixed at its current level and cannot be changed. We
will ask about your views on two aspects: a. First, on the fair split of the tax burden to raisethese funds; b. Second, on how you think the government should spend these funds.
23. See Figure A-2
24. See Figure A-3
25. Do you think income differences between rich and poor people are:Not a problem at all; A small problem; A problem; A serious problem; A very serious problem
26. To reduce income differences between rich and poor people, the government (at the local,
state, or federal level) has the ability and the tools to do:
Nothing at all; Not much; Some; A lot

27. Some people think that the government (at the local, state, or federal level) should not careabout income differences between rich and poor people. Others think that the government
should do everything in its power to reduce income inequality. Rate on a scale of 1 to 7 on
how you feel about this issue, with 1 being the government should not concern itself withincome inequality and 7 being the government should do everything in its power to reduceincome inequality.
Here are several things that the local, state, or federal government might do to reduce income
differences between rich and poor people. Please indicate if you favor or oppose them. Keepin mind that, in order to finance an expansion of any of these, other types of spending (likespending on infrastructure and defense, for example) would have to be scaled down or taxes
would have to be raised.
28. Would you say that you strongly favor, favor, neither favor nor oppose, oppose or strongly
oppose spending more money on schools in poor neighborhoods?
Strongly favor; favor; neither favor nor oppose; oppose; strongly oppose
29. Would you say that you strongly favor, favor, neither favor nor oppose, oppose strongly oppose
spending more money to provide decent housing for those who cannot afford it?
Strongly favor; favor; neither favor nor oppose; oppose; strongly oppose
30. Would you say that you strongly favor, favor, neither favor nor oppose, oppose or strongly
oppose increasing income support programs for the poor?
Strongly favor; favor; neither favor nor oppose; oppose; strongly oppose
31. How much of the time do you think you can trust our federal government to do what is right?
Almost always; A lot of the time; Not very often; Almost never
32. By taking this survey, you are automatically enrolled in a lottery to win $1000. In a few days
you will know whether you won the $1000. The payment will be made to you in the same way
as your regular survey pay, so no further action is required on your part. In case you won,
would you be willing to donate part or all of your $1000 gain for a good cause? Below you
will find 2 charities which help people in the U.S. deal with the hurdles of everyday life. You
can enter how many dollars out of your $1000 gain you would like to donate to each of them.
If you are one of the lottery winners, you will be paid, in addition to your regular survey pay,
$1000 minus the amount you donated to charity. We will directly pay your desired donation
amount to the charity or charities of your choosing. Enter how much of your $1000 gain you’d
like to donate to each charity:
Feeding America: ... ; The Salvation Army: ...
In what follows, we refer to immigrants as people who were not born in the U.S. and legally
moved here at a certain point of their life. We are NOT considering illegal immigrants.
33. See Figure 4

34. The map here shows you the main regions of the world. The pie chart below represents
all legal IMMIGRANTS currently living in the U.S.. Where do you think these immigrants
come from? Move the sliders to indicate how many out of every 100 immigrants come fromeach region, in your opinion. As you move the sliders, the pie chart will adjust to show yourresponses, reflecting the colors of the various regions, as in the map. (Your responses must
add up to 100)[See Figure 5]
35. Think again about all of the legal immigrants currently living in the U.S.. What do you thinkis their religion? Fill in the boxes below to indicate how many out of every 100 immigrants
you think practice each religion.
Christianity ...; Islam ...; Buddhism ...; Hinduism ...; Other Religions/Atheist ...
36. Out of every 100 people born in the U.S. how many are currently unemployed? By unemployed
we mean people who are currently not working but searching for a job (and maybe unable tofind one).
Now let’s compare this to the number of unemployed among legal immigrants. Out of every
100 legal immigrants how many do you think are currently unemployed?
37. Out of every 100 people born in the U.S., how many have at least a two-year college degree?
Now let’s compare this to the number of college-educated legal immigrants. Out of every 100
immigrants in the U.S. today how many do you think have at least a two-year college degree?
38. Out of every 100 people born in the U.S., how many have not completed high school?
Out of every 100 legal immigrants in the U.S. today how many do you think have not completed high school?
39. Out of every 100 people born in the U.S., how many live below the poverty line? The poverty
line is the estimated minimum level of income needed to secure the necessities of life.
Let’s compare this to poverty among legal immigrants. Out of every 100 legal immigrants in
the U.S. today, how many do you think live below the poverty line?
40. Some people think that the government (at the local, state, or federal level) should only
support people who were born in the U.S.. Others think that the government should careequally about all the people living in the country, regardless of their country of origin andregardless of whether they are born in the U.S.. Rate on a scale of 1 to 7 on how you feel
about this issue, with 1 being the government should focus on supporting people born in the
U.S. and 7 being the government should care equally about everyone.
41. What is your view on the number of legal immigrants from foreign countries who are permitted
to come to the United States to live? Pick the answer that best reflects your view.
The excessive number of legal immigrants today is a very big problem. We should ask many
legal immigrants to leave the country and we should stop accepting new immigrants; The
number of legal immigrants today is a big problem and we should decrease by a lot the number

permitted to come to the U.S. in the future; The number of legal immigrants today is somewhat
of a problem and we should try and decrease a bit the number permitted to come in; The
number of legal immigrants today is not a problem. We should keep letting in the same
number of immigrants each year as until now; The number of legal immigrants today is not a
problem at all. We should let even more legal immigrants come live in the U.S. and increase
the number that is permitted to come every year
42. In your view, how soon after arriving should immigrants be entitled to government assistancesuch as Medicaid, food stamps, or welfare on the same basis as citizens?
Immediately, as soon as they arrive; 1 year after; 3 years after; 5 or more years after; only
after they receive citizenship; never
43. As you may know, once immigrants who come into the country receive U.S. citizenship, they
are allowed to vote in all local, state, and federal elections. In your view, when should
immigrants who come into the country legally be allowed to apply for U.S. citizenship?
2 years after arriving; 5 years after arriving; 10 years after arriving; 20 years after arriving;
They should never be allowed to apply for citizenship44. Suppose someone is not born in the United States but now lives here. At what point would
you consider this person to be “American”?
Immediately, as soon as he arrives; After he has spent 5 years in the U.S.; After he has spent
10 years in the U.S.; It depends on where he comes from; As soon as he gets citizenship;
I would never consider him to be American, but if his kids were born in the U.S. I would
consider them truly American; I would not consider him or his kids to ever be truly American
45. Which has more to do with why an immigrant living in the U.S. is poor?
Lack of effort on his or her own part; Circumstances beyond his or her control
46. Which has more to do with why an immigrant living in the U.S. is rich?
Because she or he worked harder than others; Because she or he had more advantages thanothers
47. U.S. born residents receive government transfers in the form of public assistance, Medicaid,child credits, unemployment benefits, free school lunches, food stamps or housing subsidies
when needed. How much do you think each legal immigrant receives on average from suchgovernment transfers? An average immigrant receives...
No transfers; One third as much as a U.S. born resident; Half as much as a U.S. born resident;
As much as a U.S. born resident; Slightly more than a U.S. born resident; Twice as much asa U.S. born resident; Three times as much as a U.S. born resident; More than ten times asmuch as a U.S. born resident
48. Imagine two people, John and Mohammad, currently living in the U.S. with their families.
John is born in the U.S., while Mohammad legally moved to the U.S. five years ago. They areboth 35, have three children, and earn the same low income from their jobs. In your opinion,
does Mohammad pay more, the same, or less in income taxes than John?
A lot more; More; same; less; a lot less
49. In your opinion does Mohammad, who is an immigrant, receive more, the same, or lessgovernment transfers (such as e.g., public assistance, Medicaid, child credits, unemployment
benefits during unemployment spells, free school lunches, food stamps or housing subsidies)than John?
A lot more; More; same; less; a lot less
50. Do you have any friends or acquaintances who were born outside the U.S.?
Yes; No
51. [If Yes to Q50] Where do they come from? (check all that apply)
Canada; Latin America; Western Europe; Eastern Europe; North Africa; Sub-Saharian Africa;
Middle-East; Asia; Australia/New ZealandA-6

Additional Figures and Tables

Figure A-4: Misperception of the Share of Immigrants: Control vs. Shareof Immigrants Treatment groups(a) U.S.

45
40
35
Percent
25
30
20
15
10
5
0

0

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

50

Control Group

-20

0
20
40
60
80
Misperception share of immigrants

-20

0
20
40
60
80
Misperception share of immigrants(b) U.K.

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

0

0

5

10

15

20

Percent
25
30

35

40

45

50

Control Group

-20

0
20
40
60
80
Misperception share of immigrants

-20

0
20
40
60
80
Misperception share of immigrants

Figure A-4: Misperception of the Share of Immigrants: (Cont.)(c) France

45
40
35
Percent
25
30
20
15
10
5
0

0

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

50

Control Group

-20

0
20
40
60
80
Misperception share of immigrants

-20

0
20
40
60
80
Misperception share of immigrants(d) Italy

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

0

0

5

10

15

20

Percent
25
30

35

40

45

50

Control Group

0
20
40
60
80
Misperception share of immigrants

0
20
40
60
80
Misperception share of immigrants

Figure A-4: Misperception of the Share of Immigrants: (Cont.)(e) Germany

45
40
35
Percent
25
30
20
15
10
5
0

0

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

50

Control Group

-20

0
20
40
60
80
Misperception share of immigrants

-20

0
20
40
60
80
Misperception share of immigrants(f) Sweden

45
40
35
Percent
25
30
20
15
10
5
0

0

5

10

15

20

Percent
25
30

35

40

45

50

Share of Immigrants Treatment Group

50

Control Group

-20

0
20
40
60
Misperception share of immigrants

80

-20

0
20
40
60
Misperception share of immigrants

80

Notes: The figure shows the distribution of the misperception of the share of immigrants of respondents in the control
group (left panel) and in the “Share of Immigrants” treatment group (right panel), by country.

Figure A-5: Misperception of Immigrants’ and Natives’ Unemployment

H.Sect.&L.Ed

US

H.Sect.&H.Ed
UK
College
FranceRich
Imm. Parent

Italy

Young
GermanyMale
Left-Wing

Sweden10
10 15 20 25 30 35
Unemployment
Imm.

Natives

15 20 25 30
Misperception
Yes(Imm)

No(Imm)Yes(Nat)

No(Nat)Notes: The left panel shows the average misperception by country of the unemployment rate of immigrants (redsquares) and natives (blue diamonds). The right panel shows the average misperception by group. Groups aredefined by the indicator variables listed to the left: the average perceived unemployment of immigrants by the groupdefined by the indicator being equal to 1 (respectively, to 0) is represented by the orange squares (respectively,
red diamonds). The average perceived unemployment of natives by the group defined by the indicator being equal
to 1 (respectively, to 0) is represented by green squares (respectively, blue diamonds). The shaded areas are 95%
confidence intervals around the mean.

Figure A-6: Share of Respondents who Believe Immigrants Receive at Least
Twice as Many Government Transfers as Natives

H.Sect.&L.Ed

US

H.Sect.&H.Ed

UK

College
FranceRich
Imm. Parent

Italy

Young

Germany

Male
SwedenLeft-Wing.05 .1 .15 .2 .25
Share of Respondents.05 .1 .15 .2 .25
Share of Respondents

Mean

Yes

No

Notes: The figure shows the share of respondents who think that an average immigrant receives at least twice as
many government transfers as an average native. See the notes to Figure 11.

Figure A-7: Misperceptions Across U.S. States:(A) Actual Share of Total (Legal & Illegal)
Immigrants by State(B) Average Perception of the National Share of
Legal Immigrants by State

Notes: Panel A shows the actual share of total (legal and illegal) immigrants in each state in 2014 (Source: Pew
Research Center). Panel B shows, for each state, the average perception of the national share of legal immigrants
for respondents in that state. The actual national share of legal immigrants is 10%; the share of total immigrants is
13.5%.

Figure A-8: Name Randomization in the Pilot: Do Immigrants Pay More
Taxes or Receive More Transfers?

.6
.4
.2
0

Share of Respondents.8(a) Does an Immigrant Pay More or Less Taxes?

A lot less

Less
Jack

Same

More

MiguelMohammadA lot more

.6
.4
.2
0

Share of Respondents.8(b) Does an Immigrant Receive More or Less Benefits?

A lot less

Less
Jack

Same

More

MiguelMohammadA lot more

Notes: Panel A shows the share of respondents in the pilot survey who think that, conditional on the same income
and family situation, an immigrant pays a lot less, less, the same, more, or a lot more taxes than a native. Panel B
shows the share of respondents who think that immigrants receive more or less transfers (responses are to Q47 andQ48 in Appendix A-5). Respondents in the pilot where randomized into three groups and each group was shown aversion of the questions with a different name for the immigrant: Jack, in the U.S., William in the U.K., Francesco, in
Italy, and Paul in France (blue bars); Miguel, in the U.S., Andrei in the U.K. and Italy, José in France, and Vladimirin Germany (brown circles); Mohammad, in the U.S., the U.K., France and Italy, and Ibrahim in Germany (orange
diamonds). The pilot was administered in the U.S. in October 2017 and in the U.K., France, Italy and Germany inNovember 2017. The sample size was 400 for the U.S. and 200 for each European country.

Figure A-9: Support for Immigration and Redistribution: Influence of Core
Characteristics
H.Sec & H.Ed

H.Sec & H.Ed

H.Sec & L.Ed

H.Sec & L.Ed

CollegeCollege

Rich

Rich

Imm Parent

Imm Parent

Young

Young

Male

Male

Left-Wing

Left-Wing

DE

DE

SE

SE

FR

FR

IT

IT

UK

UK

US

US
-.1

0

.1

.2

.3

Support for Immigration

-.2

0

.2

.4

Support for Redistribution

Notes: The left panel shows the coefficients β from the regression Support for immigrationi = α + βAi + εi where theleft-hand side is the respondent’s Immigration support index and Ai is the set of z-scores of the variables listed vertically and country fixed effects. The right panel shows the coefficients from a similar regression for the Redistribution
support index. See Section 4 for a definition of the indices. Shaded areas are 90% confidence intervals.

Figure A-10: Permutation Test on the Variables Entering the Immigration
Support and Redistribution Indices

.8
.6
CDF
.4
.2

.2

.4

CDF

.6

.8

1

(b) T2: Origins of Immigrants

1(a) T1: Share of Immigrants

p-value = 0.488
00p-value = 0.651

0

.2

.4
.6
Share of Positive Coefficients for T1

.8

1

0

.2

.4
.6
Share of Positive Coefficients for T2

.8

1

.2

.4

CDF

.6

.8

1

(c) T3: Hard Work of Immigrants

0

p-value = 0.003

0

.2

.4
.6
Share of Positive Coefficients for T3.81

Notes: Results from a permutation test for each of our information treatments (“Share of immigrants”, “Origin of
immigrants” and “Hard work of immigrants”). Respondents are randomly assigned to the control group and each of
the treatment groups. We then estimate the following set of regressions, on the corresponding control and treatmentgroup: yi = α + β T̃ + Ai + εi where T̃ is the randomly assigned treatment status and Ai is the set of respondent
characteristics and country fixed effects. yi ’s are the 17 variables entering the immigration support index and theredistribution support index, reported in Tables A-4 and A-5 (oriented such that a higher value means more support
for immigration or redistribution). For each repetition we count how many of the 17 β’s are positive and computethe statistic “share of positive coefficients”. We repeat this procedure 1000 times for each treatment and we obtain
the distribution of the statistic reported in the graphs. The red vertical line indicates the true value of the statistic,that is, the share of coefficients that are positive when T̃ = true T. “p-value” is P (simulated share ≥ real share).

Table A-1: Views on Immigration Policies

U.S.
U.K.
France
Italy
Germany
SwedenLeft-wing
Right-wing
College
No College
Young
Old
Imm. Parent
Rich
H Imm. Sec., Low Ed.
H Imm. Sec., High Ed.
No H Imm. Sec

Imm. Not
A Problem(1)
0.37(0.02)
0.19(0.01)
0.26(0.01)
0.19(0.01)
0.23(0.01)
0.25(0.02)
0.35(0.01)
0.12(0.01)
0.33(0.01)
0.19(0.01)
0.28(0.01)
0.22(0.01)
0.30(0.02)
0.28(0.02)
0.16(0.01)
0.31(0.01)
0.28(0.01)

Imm. Benefits
Soon
(2)
0.39
(0.02)
0.45(0.02)
0.46(0.02)
0.51(0.02)
0.56(0.02)
0.63(0.02)
0.59(0.01)
0.35(0.01)
0.53(0.01)
0.46(0.01)
0.51(0.01)
0.46(0.01)
0.55(0.02)
0.51(0.02)
0.45(0.01)
0.56(0.02)
0.49(0.01)

Imm. Citizenship
Soon
(3)
0.53
(0.02)
0.35(0.02)
0.41(0.02)
0.32(0.02)
0.41(0.02)
0.53(0.02)
0.49(0.01)
0.32(0.01)
0.44(0.01)
0.40(0.01)
0.42(0.01)
0.41(0.01)
0.44(0.02)
0.40(0.02)
0.37(0.01)
0.45(0.02)
0.43(0.01)

American Upon
Citizenship/Before(4)
0.80(0.01)
0.52(0.02)
0.64(0.02)
0.57(0.02)
0.55(0.02)
0.61(0.02)
0.68(0.01)
0.53(0.01)
0.68(0.01)
0.57(0.01)
0.61(0.01)
0.62(0.01)
0.61(0.02)
0.64(0.02)
0.55(0.01)
0.68(0.01)
0.63(0.01)

Govt. Should care
About Everyone(5)4.68(0.06)
4.43(0.06)
4.48(0.06)
4.35(0.06)
4.58(0.06)
4.85(0.09)
5.07(0.04)
3.85(0.04)
4.74(0.04)
4.39(0.03)
4.65(0.03)
4.40(0.04)
5.00(0.08)
4.68(0.06)
4.23(0.05)
4.80(0.06)
4.63(0.04)

Notes: The table reports the mean of the variables capturing views on immigration policies for each group of
respondents or country. See Appendix A-1 for the variable definitions. Standard errors in parentheses.

Table A-2: Views on Redistributive Policies
Inequality
Govt. Should Care Schooling Housing Income Support
Serious Problem about Inequality
Favor
Favor
Favor
(1)
(2)
(3)
(4)
(5)
U.S.
0.49
4.60
0.63
0.60
0.53
(0.02)(0.06)(0.02)(0.02)(0.02)
U.K.
0.52
5.00
0.65
0.68
0.53
(0.02)(0.05)(0.02)(0.02)(0.02)
France
0.60
5.19
0.57
0.74
0.69
(0.02)(0.05)(0.02)(0.01)(0.01)
Italy
0.59
5.29
0.68
0.72
0.67
(0.02)(0.05)(0.02)(0.01)(0.02)
Germany
0.74
5.31
0.74
0.66
0.50
(0.01)(0.05)(0.01)(0.02)(0.02)
Sweden
0.43
4.88
0.63
0.57
0.27
(0.02)(0.07)(0.02)(0.02)(0.02)
Left-wing
0.68
5.51
0.76
0.76
0.66
(0.01)(0.03)(0.01)(0.01)(0.01)
Right-wing
0.46
4.57
0.52
0.56
0.43
(0.01)(0.04)(0.01)(0.01)(0.01)
College
0.53
4.87
0.65
0.65
0.52
(0.01)(0.04)(0.01)(0.01)(0.01)No College
0.60
5.19
0.65
0.69
0.58
(0.01)(0.03)(0.01)(0.01)(0.01)
Young
0.57
5.05
0.67
0.67
0.57
(0.01)(0.03)(0.01)(0.01)(0.01)
Old
0.58
5.07
0.64
0.67
0.53
(0.01)(0.03)(0.01)(0.01)(0.01)
Imm. Parent
0.58
5.15
0.72
0.71
0.58
(0.02)(0.07)(0.02)(0.02)(0.02)
Rich
0.48
4.72
0.64
0.63
0.50
(0.02)(0.06)(0.02)(0.02)(0.02)
H Imm. Sec., Low Ed.
0.60
5.22
0.65
0.70
0.61
(0.01)(0.04)(0.01)(0.01)(0.01)
H Imm. Sec., High Ed.
0.56
4.95
0.64
0.66
0.54
(0.02)(0.05)(0.01)(0.01)(0.02)No H Imm. Sec
0.57
5.00
0.66
0.66
0.53
(0.01)(0.03)(0.01)(0.01)(0.01)Tax
Tax
BudgetBudgetBudgetBudget Budget Total %Top1 Bottom50 Education Health Safety Net Pensions Housing Donation
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
26.31
9.61
15.11
12.25
11.07
17.69
10.51
34.11
(0.39)(0.32)(0.25)(0.23)(0.20)(0.29)(0.27)(1.16)
33.82
8.11
15.22
20.35
11.83
14.82
12.09
27.54
(0.43)(0.26)(0.24)(0.33)(0.26)(0.26)(0.32)(1.06)
40.76
9.24
17.67
17.19
13.75
13.37
11.01
29.67
(0.54)(0.30)(0.23)(0.24)(0.20)(0.19)(0.21)(1.07)
33.15
11.29
14.66
19.13
14.41
16.84
8.61
25.37
(0.54)(0.33)(0.25)(0.30)(0.30)(0.29)(0.23)(0.95)
42.00
13.14
18.25
14.52
13.00
17.26
10.46
29.78
(0.52)(0.36)(0.24)(0.21)(0.26)(0.23)(0.20)(0.95)
46.60
23.69
18.04
21.77
13.06
18.19
6.22
29.82
(0.60)(0.46)(0.36)(0.34)(0.35)(0.33)(0.25)(1.63)
37.03
10.85
16.75
17.54
13.45
16.28
10.69
31.15
(0.33)(0.21)(0.15)(0.18)(0.16)(0.17)(0.16)(0.66)
34.66
12.22
15.59
16.59
11.87
16.44
9.46
27.08
(0.35)(0.24)(0.17)(0.19)(0.17)(0.18)(0.16)(0.71)
35.90
10.97
17.10
17.40
12.40
15.63
9.53
30.75
(0.34)(0.22)(0.17)(0.19)(0.16)(0.17)(0.14)(0.73)
36.53
11.84
15.84
16.99
13.13
16.57
10.57
28.35
(0.30)(0.20)(0.14)(0.16)(0.14)(0.15)(0.15)(0.57)
35.43
12.16
17.04
16.75
12.68
14.81
10.74
31.41
(0.31)(0.20)(0.16)(0.17)(0.16)(0.14)(0.16)(0.64)
37.20
10.72
15.58
17.61
13.02
17.74
9.49
27.00
(0.32)(0.21)(0.14)(0.17)(0.14)(0.17)(0.14)(0.63)
36.49
12.24
16.86
16.79
12.88
15.46
11.36
30.12
(0.72)(0.52)(0.36)(0.39)(0.32)(0.32)(0.41)(1.46)
33.83
11.56
16.96
16.80
12.37
15.64
9.73
32.34
(0.54)(0.35)(0.26)(0.31)(0.28)(0.27)(0.27)(1.18)
34.93
11.74
15.46
17.21
13.30
16.22
10.75
27.80
(0.41)(0.27)(0.19)(0.22)(0.20)(0.19)(0.20)(0.77)
35.32
10.79
16.98
17.71
12.20
15.16
9.84
31.56
(0.50)(0.32)(0.25)(0.29)(0.24)(0.25)(0.22)(1.09)
37.48
11.61
16.69
16.89
12.79
16.59
9.86
29.49
(0.31)(0.21)(0.15)(0.16)(0.15)(0.16)(0.15)(0.64)

Notes: The table reports the mean of the variables capturing views on redistribution for each group of respondents or country. See Appendix A-1 for the variable definitions. Standard
errors in parentheses.

Table A-3: First Stage Effects on Perceptions – Full Set of VariablesAll
immigrants(misp.)(1)

North
Africa(misp.)(2)

MiddleEast(misp.)(3)

WesternEurope(misp.)(4)

EasternEurope(misp.)(5)

North
America(misp.)(6)

Latin
America(misp.)(7)

MuslimChristian(misp.)(8)(misp.)(9)

Share of Immigrants-4.716***(0.421)-0.187(0.228)-0.0631(0.220)-0.207(0.217)-0.0735(0.239)0.513***(0.179)0.264(0.204)0.00816(0.407)0.146(0.395)

Origins of Immigrants

2.314***(0.422)-3.107***(0.228)-1.646***(0.220)0.344(0.217)2.156***(0.239)-0.723***(0.179)2.577***(0.204)-1.825***(0.407)2.471***(0.395)Hard Work of Immigrants

0.752*(0.422)-0.339(0.228)-0.0921(0.220)-0.0738(0.216)

0.409*(0.239)0.107(0.179)0.348*(0.204)-0.854**(0.407)0.732*(0.395)Observations
Control mean

20018
17.021

20040
7.842

20040
4.612

20031
-5.51520043-4.463

20027
4.576

20042
-1.85820045
11.29520041-23.985

Unemployment

Poverty

Relative
Transfers(misp.)(14)

EffortRich

Mohammad Gets
More(misp.)(11)High
Educated(misp.)(13)

Lack of Effort
Poor(misp.)(10)

Low
Educated(misp.)
(12)

(15)

(16)

(17)

Share of Immigrants-1.461***(0.505)-0.559(0.481)-0.685(0.500)0.0411(0.402)0.0403(0.0337)0.000963(0.00907)-0.0110(0.00940)

0.000173(0.00806)

Origins of Immigrants-0.107(0.505)0.677(0.481)

0.425(0.501)-0.309(0.403)-0.00236(0.0337)-0.000169(0.00908)-0.00872(0.00940)-0.00766(0.00807)

Hard Work of Immigrants-2.155***(0.504)

3.117***(0.481)-0.786(0.500)-0.858**(0.403)-0.0267(0.0337)-0.0529***(0.00907)-0.00369(0.00940)-0.0125(0.00806)

Observations
Control mean

20013
24.853

20020
12.839

20003
5.214

20010
-3.676

20029
0.046

20049
0.356

20049
0.657

20036
0.239

Notes: The table reports first-stage effects on an extended set of (mis)perceptions of immigration. Misperceptions
are computed as the perception minus the actual value. See Appendix A-1 for variable definitions. All regressions
include the same controls as those in Table 5. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table A-4: Treatment Effects on Immigration Support – Full Set of Variables
Imm. Not
A Problem(1)

Imm. Benefits
Soon
(2)

Imm. Citizenship
Soon
(3)

AmericanUpon Citizenship/Before(4)

Govt. Should care
About Everyone(5)

Share of Immigrants

0.0233***(0.00826)

0.0109(0.00954)

0.0101(0.00956)

0.00478(0.00931)-0.0114(0.0356)

Origins of Immigrants

0.00456(0.00826)

0.00322(0.00954)

0.00385(0.00956)

0.00325(0.00932)-0.00128(0.0357)

Hard Work of Immigrants

0.0255***(0.00826)

0.0215**(0.00954)

0.0111(0.00955)

0.0186**(0.00931)

0.134***(0.0356)

20011
0.248

20033
0.488

20029
0.417

20026
0.619

20038
4.534

Observations
Control mean

Notes: The table reports the effect of the three informational treatments on the variables entering the Immigration
support index. Outcome variables are defined in Appendix A-1. All regressions include the same controls as those in
Table 5. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table A-5: Treatment effects on Redistribution Support – Full Set of Variables
Inequality
Serious Problem(1)

Govt. Should Careabout Inequality(2)

Schooling
Favor
(3)

Housing
Favor(4)

Income Support
Favor
(5)

Tax
Top1
(6)

Imm Questions First-0.0280**(0.0132)

0.0311(0.0428)

0.00752(0.0129)-0.0181(0.0128)0.00976(0.0132)-1.948***(0.421)

Share of Immigrants-0.00569(0.0133)

0.0110(0.0431)0.0316**(0.0129)-0.0113(0.0129)-0.00163(0.0133)-0.671(0.424)

Origins of Immigrants

0.00505(0.0133)

0.0218(0.0431)0.0196(0.0129)-0.00289(0.0129)-0.00818(0.0133)-0.0833(0.424)

Hard Work of Immigrants

0.0162(0.0133)

0.115***(0.0431)0.0350***(0.0129)-0.00345(0.0129)

0.0364***(0.0133)

0.0664(0.424)

Share of Immigrants X Imm. Q. First

0.0134(0.0188)0.0352(0.0608)-0.0404**(0.0182)-0.00337(0.0182)

0.00715(0.0187)

0.624(0.598)

Origins of Immigrants X Imm. Q. First-0.0184(0.0188)

0.00329(0.0608)-0.0285(0.0182)0.0000299(0.0182)-0.00657(0.0188)-0.0799(0.598)Hard Work of Immigrants X Imm. Q. First-0.00718(0.0188)-0.108*(0.0608)-0.0273(0.0182)0.000576(0.0182)-0.0529***(0.0187)

0.166(0.598)Observations
Control mean

20049
0.572

20045
5.056

20049
0.653

20049
0.671

20049
0.556

20049
36.136

Tax
Bottom50(7)

BudgetEducation(8)

Budget
Health(9)

BudgetSafety Net(10)

BudgetPensions(11)

BudgetHousing(12)

Imm Questions First

0.914***(0.277)0.523**(0.212)-0.0949(0.231)-0.249(0.210)-0.131(0.227)0.121(0.209)

Share of Immigrants

0.0989(0.279)0.218(0.213)-0.220(0.232)-0.446**(0.211)0.0594(0.229)0.288(0.210)

Origins of Immigrants

0.0470(0.279)0.257(0.214)0.00679(0.232)-0.419**(0.212)0.0281(0.229)0.0373(0.211)

Hard Work of Immigrants-0.197(0.279)0.348(0.213)0.0241(0.232)-0.132(0.211)0.0304(0.228)0.561***(0.210)Share of Immigrants X Imm. Q. First

0.0426(0.393)-0.584*(0.301)

0.151(0.327)0.546*(0.298)0.332(0.322)-0.736**(0.296)

Origins of Immigrants X Imm. Q. First-0.193(0.393)-0.539*(0.301)-0.129(0.327)0.392(0.298)0.207(0.322)-0.303(0.297)

Hard Work of Immigrants X Imm. Q. First

0.0644(0.393)-0.640**(0.301)0.0119(0.327)0.408(0.298)0.442(0.322)-1.047***(0.296)

Observations
Control mean

20049
11.412

20045
16.35720040
17.09020037
12.783

20045
16.18920036
10.157

Notes: The table reports the effect of the order treatment and the three informational treatments on the variables
entering the Redistribution support index. Outcome variables are defined in Appendix A-1. All regressions include
the same controls as those in Table 5. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.