cen02-2021
Constitutional
Economics
Network
Working Paper
Series
ISSN No. 2193-7214
No. 02-2021
Racism and Trust in Europe
Matthew Bonick*
* Götz Werner Chair of Economic Policy and Constitutional Economic Theory,
University of Freiburg, Germany.
E-Mail: matthew.bonick@vwl.uni-freiburg.de
University of Freiburg
Institute for Economic Research
Götz Werner Chair of Economic Policy and
Constitutional Economic Theory (GWP)
Rempartstraße 16 D-79098 Freiburg
www.wipo.uni-freiburg.de
Freiburg Institute for Basic Income Studies (FRIBIS)
Abstract
I study the impact of racism on trust in Europe. To operationalize trust and racism, I use inpidual level
responses from the European Social and World Value Surveys. The results of the multivariate analysis
indicate, inpiduals who possess a self-reported racist attitude are less likely to be trusting. To address the
issue of causality, I examine second generation immigrants. When analyzing immigrants and using the level
of racism of their origin country as a proxy for inpidual racial attitudes, I find, racism continues to predict
lower levels of trust. These results provide evidence racism has a negative, significant, and causal impact on
generalized trust. Additionally, the paper supports the notion that racism could have negative economic
consequences via the erosion of social capital.
Keywords: Racism; Trust; Culture
JEL codes: O1; Z1
1 Introduction
The intersection between race and trust is well documented (Fershtman and Gneezy 2001; Alesina and La Ferrara
2002; Smith 2010; Burns 2012; Bonick and Farfán-Vallespın 2016). For example, Burns (2012), in a trust game
experiment in South Africa, finds a systematic distrust of black players by white participants. Given the extensive
literature on the impact of generalized trust on a number of economic outcomes, it is important to understand the
nature of the relationship between racism and trust (Knack and Keefer 1997; Guiso, Sapienza, and Zingales 2008,
2009, 2016; Tabellini 2010; Bjørnskov and Méon 2013).
My definition of racism is taken from Bonick and Farfán-Vallespın (2018). The authors stipulate, it is a cultural
heuristic that uses race as the cue for interpreting and guiding social interactions, often leading to discriminatory
behavior, which is transmitted across generations. Generalized trust entails the trust a person has toward a generic
and unknown member of a broader community (Tabellini 2010). I hypothesize, a racist inpidual will not trust
other races which will reduce their level of generalized trust through narrowing the pool of inpiduals they can
trust in their broader society. Therefore, I argue, possessing a racist attitude should have a causal and negative
effect on generalized trust.
To test this hypothesis, using responses from the World Value Survey (WVS) and European Social Survey (ESS),
I first show there is a robust correlation between racist attitudes and lower generalized trust. Second, to address
the issues of causality, I examine second generation immigrants in Europe using the level of racism of their
country of origin as proxy for inpidual racial attitudes. The outcome supports that racism has a robust and
negative impact on inpidual’s level of generalized trust.
2 Racism and trust within country analysis
The first step in my analysis it to establish a correlation between self-professed racist attitudes and generalized
trust at the inpidual level.
2.1 Data
My study is done using responses to survey questions from the WVS and ESS within Europe. I use all waves up
to 2014 for the WVS and waves 1-7 for the ESS.
To measures racism in the WVS, I use responses to the question: "On this list are various groups of people. Could
you please mention any that you would not like to have as neighbors?". For racism, the answer is coded 1 if the
inpiduals mention people of a different race in their response. For robustness, I also use a second question within
this survey. The question prompts the participant to express their views on ethnic persity. They can choose
between 1-10 with 10 indicating ethnic persity enriches my life and 1, ethnic persity erodes the countries
unity.
For the ESS, I use responses to five questions. The first measure is the reply to the question, “To what extent do
you think your country should allow people of a different race or ethnic group from most people?". The answers
are on a scale from 1 to 4 with 1 corresponding to allowing many into the country and 4 indicating allowing none,
I refer to this variable as, race immigration. The next two variables taken from the response to the question
“Thinking of people who have come to live in your country from another country who are of a different race or
ethnic group from most people. How much would you mind or not mind if someone like this was appointed as
your boss and if someone like this married a close relative of yours”. The variables are on scale from 0 to 10 with
0 signifying not minding at all and 10 indicating minding a lot. For the final two variables, I use the response to
two questions, “Do you think some races or ethnic groups are born less intelligent than others?” and “Do you
think some races or ethnic groups are born harder working than others”, both answers are coded 1 if the answer
is yes and 0 if no.
The variable trust is derived from the question "Generally speaking, would you say that most people can be trusted
or that you need to be very careful in dealing with people?". For the WVS, the answer is coded 1 if people can be
trusted and 0 if you cannot be too careful. The ESS measure is coded on a scale from 0 to 10, with 10 representing
most people can be trusted and 0, you cannot be too careful.
2.2 Specification
For the empirical analysis, I run a series of regressions of the specification:
𝑇 𝛽 𝛽 𝑅 𝑋 𝛾 𝛿 𝜖
where the left-hand side variable 𝑇 is the measure for trust of inpidual i in country c at time t. I run probit
regressions if 𝑇 is binary and OLS if it is not. 𝑅 is our variable of interest for racism and 𝑋 are the controls
which include dummies for income or views on income status, education, age, age squared, gender and for the
ESS additionally, a dummy for if the inpidual is a minority. The controls used are standard in literature on
culture and racial attitudes (Alesina and Giuliano 2010; Bobo 2012; Alesina, Giuliano, and Nunn 2013; Bonick
and Farfán-Vallespın 2018). All the regressions contain country fixed effects 𝛾 and time fixed effects 𝛿 .
Standard errors are clustered at the country level.
2.3 Results
Table 1 reports the regression results for the relationship between different measures of racism and trust. Overall,
across all columns, the outcomes show a negative and statistically significant association between racism and
generalized trust independent of the measure used for racism. For robustness, I run regressions which, separately
add to the baseline, controls for religious denomination, political scale and for the ESS additionally, if the
inpidual has other ethnic groups as their neighbor. The robustness checks produce consistent results compared
to Table 1. Given the lack of space, the findings are not shown here.
Table 1 : Trust and Tolerance
(1) (2) (3) (4) (5) (6) (7)
Variables of Interest
WVS WVS ESS ESS ESS ESS ESS
Race Ethnic Race Race Race Less Less Hard
Dep.Variable Neighbor Diversity Immigrant Boss Marrage Intelligent Working
Trust -0.110*** 0.087*** -0.368*** -0.056** -0.054*** -0.376*** -0.247***
(0.035) (0.008) (0.023) (0.008) (0.006) (0.064) (0.025)
Observations 63,838 14,288 292,617 67,569 67,879 34,924 35,119
R-squared 0.188 0.189 0.189 0.178 0.177
Robust standard errors are in parenthes. *** p<0.01, ** p<0.05, * p<0.1. Standard errors are
clustered by country. All regressions have inpidual controls, country and time fixed effects.
3 Racism and trust for second generation immigrants ESS
While I have taken steps to address issues of omitted variable bias, there is still the possible problem of reverse
causality. To tackle this concern, I adopt the strategy of Alesina and Giuliano (2010) and examine second
generation immigrants in Europe. Racism, in this context, is defined as the average level of racism from an
inpidual’s country of origin1.
3.1 Origin Data
I construct the country of origin variable for racism and trust by taking the average country measure of race
neighbor and trust questions for all available countries from the WVS. I join these two country level measures,
along with variables for origin country education (Barro and Lee 2010) and economic conditions2 to an
inpidual’s father and mother’s country of origin in the ESS.
3.2 Specification
For this empirical analysis, I run a series of OLS regressions on exclusively ESS responses of the specification:
𝑇 𝛽 𝛽 𝑅 𝑋 𝛾 𝛿 𝜖
where the left-hand side variable 𝑇 is the measure for trust of inpidual i in country c at time t. 𝑅 is the
measure for racism which varies by immigrant’s country of origin and 𝑋 are the controls. I use the same
inpidual controls as the baseline from Table 1 plus dummies for if the inpidual was born in the country of
destination and a minority. All the regressions contain country fixed effects 𝛾 and wave fixed effects 𝛿 . Standard
errors are clustered at the country of origin.
3.3 Results
Table 2 reports the OLS results for the relationship between racism of an inpidual’s origin country and trust.
Column 1 shows racism has a causal and significant effect on generalized trust independent of using racism from
the mother’s or father’s origin country. The coefficient is significant at the 1% level. To account for other origin
country omitted variables, I control for country of origin trust, average schooling, and economic conditions in
columns 2-4. The coefficients of interest remain significant but do drop in size and in some cases, level of
significance. In columns 5 and 6, I account for factors that could influence the transmission of racism across
generations, specifically the origin parent’s level of education and the inpidual’s religious denomination. In
both cases, the coefficients remain consistent in size and level of significance.
1
This is a well-established methodology for addressing reverse causality within research on culture. Since I define racism as a cultural
heuristic within the research following Boyd and Richerson (1985), the empirical approach is appropriate. For a more detailed
discussion of the validity of the empirical strategy and the definition of second generation, see Alesina and Giuliano (2010).
2
Economic data is taken from World Bank Development Indicators.
Table 2 : Trust and Racism Second Generation Immigrants
(1) (2) (3) (4) (5) (6)
Dependent Varable : Trust
Panel A ESS : Father's Origin Country
Origin Racism -1.247*** -0.721** -1.127** -0.792* -1.259*** -1.235***
(0.420) (0.305) (0.485) (0.399) (0.477) (0.375)
Origin Trust 1.180***
(0.182)
Origin ln GDP per_cap 1995 0.022
(0.037)
Origin Avg Schooling (1985-1995) 0.026**
(0.012)
Schooling, Father -0.002
(0.004)
Dummies religion X
Observations 24,407 24,407 22,999 21,959 21,594 23,853
R-squared 0.110 0.112 0.109 0.110 0.114 0.112
Panel B ESS : Mother's Origin Country
Origin Racism -1.467*** -0.834*** -1.224** -0.949** -1.436*** -1.501***
(0.457) (0.302) (0.518) (0.439) (0.495) (0.419)
Origin Trust 1.376***
(0.173)
Origin ln GDP per_cap 1995 0.027
(0.039)
Origin Avg Schooling (1985-1995) 0.032**
(0.014)
Schooling, Mother 0.002
(0.004)
Dummies religion X
Observations 23,942 23,942 22,611 21,607 22,284 23,380
R-squared 0.105 0.108 0.103 0.104 0.107 0.108
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at
the country of origin. All regrssions contain baseline inpidual controls, country and time fixed effects.
4 Conclusion
The results of my analysis support the hypothesis that racism has negative and causal impact on generalized trust.
While this paper is preliminary and limited in scope, it contributes to the literature on culture and economics by
empirically identifying that, within a sample of European countries, racism could have broader detrimental
economic consequences via the erosion of generalized trust. Additionally, the results support the findings of
Bonick and Farfán-Vallespın (2018) that racism can persist across generations.
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