Acting in the Interest of the Represented A Field Experiment on Ethnic Discrimination in the Contact Between Citizens

and Local Swedish Politicians

Master Thesis Fall 2018

Author: Angelica Kristiansson Åström

Supervisor: Per Adman

Department of Government

Uppsala University

Word Count: 19 510

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Abstract This thesis presents a field experiment on the topic of ethnic discrimination in the contact

between citizens and local Swedish politicians. A correspondence technique was used to

investigate whether ethnicity affects how responsive Municipal Commissioners are to questions

regarding school politics. By sending an email to each Municipal Commissioner, randomising

whether a putative Arabic or Swedish alias was used, it aimed to find potential patterns in how

different groups of citizens are treated by their local political representatives. In total, eight

fictitious individuals were created which each contained a unique mixture of ethnicity, gender

and socioeconomic status. In general, the result shows no statistically significant signs of

discrimination of individuals with Arabic-sounding names. Evidence of gender disparities

among Arabic individuals was neither found. However, Arabic females are significantly less

likely to receive a reply than Swedish females. Furthermore, the result indicates that

socioeconomic status affects political responsiveness toward individuals with Arabic

background. This should be considered problematic and noteworthy in the case of Sweden,

given its reputation of being a highly egalitarian and well-functioning democracy. The findings

further stress the importance to acknowledge that different categories of social identities may

interact with ethnic discrimination. This consequently calls upon appropriate approaches to

study it.

Key Words: ethnic discrimination, responsiveness, Municipal Commissioners, Sweden, field

experiment, correspondence testing

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Table of Contents INTRODUCTION ................................................................................................................................................4

WHY RESPONSIVENESS? .....................................................................................................................................4 PURPOSE AND RESEARCH QUESTION ......................................................................................................................5 DISPOSITION ....................................................................................................................................................7

THEORY AND PREVIOUS RESEARCH ..................................................................................................................7 WHAT IS ETHNIC DISCRIMINATION? .......................................................................................................................7 SOCIAL DOMINANCE THEORY AND ETHNIC HIERARCHIES .............................................................................................8

Interaction of Gender and Class .............................................................................................................. 10 PREVIOUS RESEARCH ........................................................................................................................................ 11

Previous Research on Discrimination in Sweden ...................................................................................... 11 Field Experiments on Ethnic Discrimination Amongst Public Officials ....................................................... 13 Conclusion .............................................................................................................................................. 15

METHODS ....................................................................................................................................................... 16 MEASURING ETHNIC DISCRIMINATION .................................................................................................................. 16 FIELD EXPERIMENTS – WHAT ARE THEY AND WHY ARE THEY USEFUL?......................................................................... 17

Correspondence Testing ......................................................................................................................... 18 ETHICAL CONSIDERATIONS ................................................................................................................................. 18 RESEARCH DESIGN ........................................................................................................................................... 20

Setting and Population ........................................................................................................................... 20 Treatment .............................................................................................................................................. 22 Ethnically Distinctive Names ................................................................................................................... 23 Experimental Execution .......................................................................................................................... 24

Why Emails? ..................................................................................................................................................... 24 Choosing Names ............................................................................................................................................... 25 Randomisation Strategy .................................................................................................................................... 26 Collecting the Data ............................................................................................................................................ 28 Formulating the Email ....................................................................................................................................... 29 Sending the Emails ............................................................................................................................................ 31 Operationalisation............................................................................................................................................. 32

CONTROL VARIABLES ........................................................................................................................................ 33 VALIDITY AND RELIABILITY DISCUSSION ................................................................................................................. 34

RESULTS .......................................................................................................................................................... 35 EXCLUDED OBSERVATIONS ................................................................................................................................. 35 OVERVIEW OF VARIABLES .................................................................................................................................. 36 ANALYSIS ...................................................................................................................................................... 38

Effect of Identity on Reply ....................................................................................................................... 39 Effect of Ethnicity on Reply ..................................................................................................................... 40 Effect of Gender on Reply ....................................................................................................................... 40 Effect of Socioeconomic Status on Reply ................................................................................................. 41 Extended Analysis - Interaction Effects .................................................................................................... 42 Summary................................................................................................................................................ 43

DISCUSSION .................................................................................................................................................... 44 THE RESULTS .................................................................................................................................................. 44 IDEAS FOR FUTURE RESEARCH............................................................................................................................. 50

CONCLUSION .................................................................................................................................................. 51 REFERENCE LIST .............................................................................................................................................. 53 APPENDIX ....................................................................................................................................................... 58

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Introduction The existence of political equality is a fundamental premise of democracy. If one considers

democracy as a goal or ideal, implicitly one must view political equality as a goal or ideal. Yet,

as the prominent democracy scholar Robert Dahl points out: “even in democratic countries, as

any citizen who carefully observes political realities can conclude, the gap between the goal of

political equality and its actual achievement is huge” (Dahl, 2006, p. 1f). To which extent

modern governments and bureaucracies succeed in treating citizens equally is a question of

debate. The latter half of the century has shown considerable progress in the treatment of ethnic

minorities with regard to political equality. This has led to some researchers and policymakers

to doubt that ethnic discrimination remains a concern in contemporary democracies. Some

researchers argue that ethnicity is no longer a dominant factor in the political sphere and that

the trampling of minority rights is now rare (for review, see Hajnal, 2009, p. 39). Others,

however, have produced convincing evidence that ethnic minorities continue to be

disadvantaged in a wide range of social domains (for an overview, see Pager and Shepard,

2008). Yet, it is also pointed out that contemporary forms of discrimination are often subtle and

covert, which makes it problematic to conceptualise and measure. Empirical research on ethnic

discrimination that occurs between citizens and different kind of authorities has mainly been

conducted through in-depth interviews, surveys and statistical analyses (Ibid). While

recognising the value of using such methods to get a deeper understanding about experiences

of discrimination, these kinds of approaches will not necessarily reveal the extent of

discrimination in action. Hence, in order to isolate the casual link between ethnicity and

behaviour, field experiments are becoming increasingly used within political science. The

research field is, however, relatively new and marked by research gaps as well as weaknesses.

Most previous research has been focusing on bureaucrats and tend to ignore what could be

important interacting factors in relation to ethnic discrimination. Inspired by this, this thesis

aims to develop the literature by investigating political responsiveness while taking valid

criticism towards the method in consideration.

Why Responsiveness? In her seminal work, Hanna Pitkin defines political representation as “acting in the interest of

the represented, in a manner responsive to them” (Pitkin, 1967, p. 209) I find investigating

political responsiveness important based on mainly four reasons. First, as Butler & Broockman

(2011) argues, how responsive politicians are to their constituents is important. How legislators

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vote does not necessarily tell us much about their priorities or preferences – but the time and

effort they spend on being of service to their citizens do. On an aggregated level, it is possible

to see if there are any patterns in how people are prioritised. Second, given that Sweden is a

representative democracy, the importance of equal treatment is self-evident. If those who are

supposed to represent our interests engage in discrimination, it is a problem that goes against

the very corner stone of a democratic system – political equality and representation. This relates

to my third point, which is that discrimination appear to decrease trust in the political system

as well as interest to take part in politics. Evidence suggests that perceived discrimination has

a clear negative correlation with trust for politicians and governmental institutions and further

contributes to disintegration (Lange, 2000; De los Reyes & Wingborg, 2002; The Equality

Ombudsman, 2010). Furthermore, research has shown that when minorities view their

representatives as more responsive, they participate in politics at higher rates (Griffin & Keane,

2006). Finally, measuring responsiveness is advantageous from a methodological perspective.

The senders of the email have a very straight forward interest: they want a response. As Butler

& Broockman (2011) states, it is therefore clear when the politician act in the interest of the

citizen – a clarity that is otherwise difficult to achieve from political action where often complex

policy interests are involved.

Purpose and Research Question The aim of this study is to contribute to the field by investigating whether Swedish Municipality

Commissioners tend to discriminate citizens on the basis of ethnicity. Municipality

Commissioners are political representatives working either part-time or full-time with their

responsibilities and thus have the highest level of insight and influence in respective

municipality. Using a correspondence design, fictitious individuals with either putative

Swedish or Arabic origin send emails to Municipal Commissioners with questions related to

school politics. For greater generalisability and more reliable results, potential interaction

effects of gender and socioeconomic status are accounted for. Ethnic discrimination is then

measured by comparing the extent to which the different identities receive answers to their

email. This is a rewarding method, since field experiments “can reliably document

discrimination in a fashion that is difficult to debate” as other potential factors are controlled

for via random assignment of treatment (Quillian, 2006, p. 304).

Ignoring for a moment the obvious connection between the author of this thesis and Sweden,

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there are also comparative reasons why Sweden is an adequate case in relation to ethnic

discrimination. First, to my knowledge, this is the first attempt to perform this kind of study in

Scandinavia or even EU. Similar experiments conducted in the US and South Africa provide

evidence of political representatives engaging in ethnic discrimination, being less responsive

towards minority groups in their constituency. Second, Sweden is deemed particularly

interesting given its reputation as a highly democratic and egalitarian country (Eger, 2010, p.

204). Sweden is further ranked first among 31 developed countries regarding integration

policies and immigrants’ opportunities to participate in society (Migration Policy Group, 2014).

Lastly, Sweden has an increasingly ethnically, linguistically and culturally diverse population.

This allows one to assume that ethnic discrimination is less likely to be found here. However,

as we will see below, evidence suggests that Sweden does suffer from ethnic disparities in a

wide range of social domains. Sweden is further facing increased urban segregation, social

exclusion, as well as urban unrest and extremist populism (Schierup & Ålund, 2011). Thus, due

to these contrasts, choosing Sweden as a case in finding evidence of ethnic discrimination in

relation to political representation is deemed of high socio-political relevance.

The main research question of this thesis is: Do Municipal Commissioners treat citizens

differently on the basis of ethnicity? Grounded in assumptions from social dominance theory

and the theory of ethnic hierarchies as well as empirical evidence of discrimination, three

hypotheses are tested.

First, previous research suggests that people from Arabic background are particularly

vulnerable to discrimination. This leads to the hypothesis that:

H1 = Ethnic Swedes will receive better treatment than people from Arabic origin in their

contact with Municipal Commissioners.

Part from suggesting that ethnic Swedes receive better treatment than ethnic Arabs, previous

research also indicates that discrimination is more intense towards minority males than to

minority females. To test this, a second hypothesis with a gender aspect is added:

H2 = Among the Arabic-sounding names, females will receive better treatment than

males.

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Lastly, despite scarce research on the interaction effect of socioeconomic status on ethnic

discrimination, it is reason to think that it may have an impact on the level of discrimination

minorities sustain. To investigate this, a final hypothesis is added:

H3 = Among the Arabic-sounding names, those who signal high socioeconomic status

will receive better treatment those who signal no such status.

Disposition The thesis begins with presenting theories and previous research relevant for this study. Then

the method section follows. It accounts for the method in general as well as narrating the design

of this particular field experiment in detail, accompanied by an account of its strengths and

weaknesses. Next the results are presented, followed by a discussion of the findings and

meaningful directions for future research. Lastly, there is a concluding remark.

Theory and Previous Research Following chapter will begin with defining ethnic discrimination. Next, the social dominance

theory and the theory of ethnic hierarchies are accounted for due to their relevance in relation

to discrimination in general and the design choices in this thesis in particular. Lastly, an

overview of previous research is presented.

What Is Ethnic Discrimination? A key feature of any definition of discrimination is its focus on behaviour. Unlike prejudice

(attitudes), sterotypes (beleifs), and racism (ideologies) which are existing in people’s head,

discrimination is present in action. Discrimination may be motivated by prejudice, sterotypes

and racism, but the definition of discrimination does not presume any specific underlying cause

(Pager & Shepherd, 2008, p. 182). However, while defintions of ethnic discrimination all

emphasise unequal treatment among ethnical groups, they differ quite a lot in scope – some

claiming that all inequality among ethnic groups is due to discrimination, others restricting

discrimination only to acts that are intended to harm the target group (Quillian, 2006, p. 300).

An intermediate definition is presented in a report by the National Research Council. They

define it from a social science perspective which includes not only legal definitions of

discrimination but also aspects that goes beyond legal concepts. Their definition of ethnic

discrimination includes two components: differential treatment and differential effect. The first

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refers to when a member of one ethnic group is treated less favourbly than a similarly situated

member of another ethnic group. This type of discrimination is often unlawful. The other

compontent includes instances in which behaviour or treatment are not caused by ethnicity but

have adverse ethnic consequences. Individuals may be treated equally according to a given set

of rules but the rules are constructed in ways that favour members of one group over another

(National Research Council, 2004, p. 39f). In relation to this study, ethnic discrimination as

differential treatment is of concern, i.e. when an individual is treated less favourably because

of his or her ethnicity.

Social Dominance Theory and Ethnic Hierarchies Despite progress in civil and human rights during the last century, the problems of intergroup

discrimination, bigotry and oppression are still painfully with us. For some reason, human

societies tend to organise as group-based social hierarchies where members of dominant social

groups tend to enjoy a disproportionate share of social status and power. In their book about

group-based social dominance, Sidanius & Pratto (1999) developed the social dominance

theory (SDT) in an attempt to understand this phenomenon. They argue that all societies that

produce stable economic surplus contain three domains of group-based hierarchy; (1) the

domain of age, in which adults have more power than children (2) the domain of gender, in

which men have more power than women; and (3) an arbitrary-set domain in which groups are

constructed on arbitrary bases. The latter refers to socially constructed groups based on factors

such as citizenship, ethnicity, class or religion, which can be used as social distinctions related

to power and status. The “arbitrariness” refers to that these groups are highly flexible and

situationally contingent. In relation to this study, it is the arbitrary-set domain that links

discrimination with ethnicity that is of main concern.

This brings us to the concept of ethnicity and the theory of ethnic hierarchies. In essence, an

ethnicity is a named category of people who belong to a social group that has a common national

or cultural tradition (Peoples & Bailey, 2010, p. 389). Hence, there are a substantial number of

ethnicities in the world, and individuals can identify with more than one. Members of the ethnic

group see themselves as sharing the same culture and history that distinguish them from other

groups. Hence, ethnic group identity has a strong psychological or emotional component that

divides the people of the world into opposing categories of “us” and “them” (Ibid).

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When studying discrimination, it is important to acknowledge this complex nature of ethnicity

and that it is not simply about belonging to the majority or minority. Research has shown that

some ethnicities are more prone to experience discrimination than others. The theory of ethnic

hierarchies suggests that ingroup values are used for intergroup differentiation and evaluation,

where ingroup preference and stereotyping plays an important role to anchor social

representations in an ethnic ranking of group positions (Hagendoorn, 1993). It is suggested that

people’s ethnocentrism (i.e. beliefs in their cultural superiority) and stereotypes may lead to a

ranking closer or further away from the majority population. Hence, the theory links

discrimination to the social distance between the majority and different minority groups and

the prestige this brings. The greater the social distance, i.e. the more different the majority

population experience a group, the lower prestige is attributed to the group and the latter feels

less accepted in society (Snellman & Ekehammar, 2005, p. 84).

Previous research has shown that multi-ethnic societies tend to form hierarchies of their ethnic

groups in e.g. the Netherlands (Hraba, et al., 1989), former Soviet (Hagendoorn, et al., 1998)

as well as in Sweden (Snellman & Ekehammar, 2005; Lange, 2000). A common pattern seems

to be that North Europeans are ranked at the top, whereas people from Africa and the Middle

East are found at the bottom of the scale.

Since some groups are perceived as more socially distant than others and therefore placed

further down the hierarchy, they are more vulnerable to discrimination. Research on

discrimination in Sweden confirms this. It is suggested that ethnic categories are the best

predictor (in a statistical sense) of perceived discrimination and that immigrants originating

from Africa and the Middle East are the most likely to have experienced discrimination (Lange,

2000, p. 72f).

In sum, the SDT claims that societies form group-based social hierarchies and the theory of

ethnic hierarches suggests that some ethnic groups are more vulnerable to discrimination than

others. By looking at the most vulnerable group in Sweden, namely immigrants with an

Arabic/Middle Eastern origin, it should maximise the possibility to detect discrimination in the

contact between citizens and their political representatives. It could thus serve as a benchmark

in future research on other ethnic groups.

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Interaction of Gender and Class

The SDT points out that social inequalities can be based on a number of factors such as

ethnicity, gender and class. These characteristics may further overlap and interact with each

other, leading to different experiences of discrimination depending on different mixtures of

subordinate group identities.

Research on discrimination has shown that rates of discrimination differ between men and

women. There is substantial evidence that men rather than women are the most ill-treated

targets of arbitrary-set discrimination across domains such as the labour market, housing market

and the criminal justice system (see Sidanius & Pratto, 1999). Derived from the SDT, the

“subordinate male target hypothesis” is proposed as an explanation for this phenomenon. In

short, it suggests that arbitrary-set discrimination is a largely “male-on-male” project - primarly

excecuted by males and primarly targeted against out-group males rather than out-group

females due to intrasexual competition. In essence, they argue that arbitrary-set discrimination

is, in part, grounded in evolution and a result of gender differences in reproductive strategies

(where males tend to have a predilication for quantity and females for quality). Thus, dominant

males will tend to view all women, both from the minority and majority group, as a reproductive

resource while outgroup males are viewed as mating competition that need to be debilitated

(Ibid, p. 294ff). Research on social stereotypes can be related to this male-on-male focus as

well. Studies within social psychology suggests that differences in discrimination between

minority men and minority women can be that negative national stereotypes are more strongly

tied to males than to females. It is found that stereotypes of men tend to resamble stereotypes

of their nationalities while women - regardless of their nationality – to a greater degree are

stereotyped according to general female stereotypes (Eagly & Kite, 1987).

Swedish research on both subjective and objective discrimination goes in line with this

hypothesis. Lange (2000) shows that minority men in general report experiences of

discrimination more frequently than minority women1. Similarly, experimental studies on the

labour market shows that putative Arabic males are discriminated against to a greater extent

than putative Arabic females (e.g. Arai, et al., 2008; Bursell, 2014). Similar results can be found

in other parts of the world, e.g. in Canada (Veenstra, 2013) and Germany (Grohs, et al., 2016).

1 With a few exceptions, within a few minority groups the situation was turned. See Lange (2000, p. 70).

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Part from ethnicity and gender, class is another basis for stratification or “ranking”. Ethnicity,

gender and class are overlapping systems of stratification that people experience

simultaneously. Class may therefore also interact with ethnicity when it comes to discrimination

and influence the effect of ethnicity on behaviour.

In sum, since ethnicity, gender and class (class is henceforth referred to as socioeconomic

status) all are domains connected to social dominance, I find it adequate to investigate these

dynamics in relation to ethnic discrimination. As research points to gender influencing the level

of ethnic discrimination, including both men and women into the analysis makes the results

better equipped to be generalised. Furthermore, while a few correspondence experiments have

taken perceived socioeconomic status under consideration in their designs (see e.g. Hemker &

Rink, 2017; Jilke, et al., 2018; Ahmed, et al., 2010), I have failed to find research that explicitly

investigate the interacting effect of socioeconomic status on ethnic discrimination. Theoretical

approaches to social inequality points to how overlapping systems of power impact those most

vulnerable and marginalised in society (part from SDT, see e.g. the analytical framework of

intersectionality). It is claimed that social identities do not exist separately but are interwoven

together. Thus, I find it appropriate to investigate whether both gender and socioeconomic

status may influence how minority groups are treated in their contact with political

representatives.

Previous Research To get a grasp on relevant research in relation to this study the following section will be two-

fold. It will cover (1) previous research on discrimination in Sweden and (2) previous research

using correspondence testing to investigate discrimination amongst public officials. The first

shows that Sweden is no exempt when it comes to the occurrence of ethnic discrimination. The

latter contributes to put this experiment in an international context. The chapter ends with a

conclusion and identification of current research gap.

Previous Research on Discrimination in Sweden

Literature on discrimination in Sweden shows that ethnic minorities often are

disadvantageously treated in a wide range of social domains. The evidence takes form of both

experimental research as well as subjective experiences of discrimination.

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When investigating ethnic discrimination in the Swedish market place, several studies draw on

field experiments. The results are as clear as gloomy – by virtue of their ethnicity, foreign-

named individuals do not enjoy the same opportunities as their Swedish-named counterparts.

Bursell (2007) shows that in hiring, applicants of African/Arabic origin are evaulated more

negatively than applications from native Swedes. Using a correspondence design, pairs of

equally merited applications with different names were sent to employers advertising vacant

positions. Discrimination were then measured in employer call-backs. The occopations were

chosen to cover all segments of the Swedish labour market, including both public and private

sector and different education level requirements. The result showed that Swedish-named

applicants were called back twise as often as Arabic-named applicants with equal merits.

Altough the results differed substantially between occopational categories, significant

differences was found in 14 of 16 occupations.

Using similar correspondence designs, further indications of discrimination on the Swedish

labour market are found by Carlsson & Rooth (2007) and Arai, et al. (2008). The former finds

that Swedish-sounding names receive fifty percent more callbacks for an interview than Middle

Eastern-sounding names. The latter takes the study of labour market discrimination even furhter

by showing that the call-back gap for Arabic males remain despite positive adjustment in terms

of extra experience. The gap decreased and lost statistical significance for the Arabic females

when doing the same. This indicates that discrimination against Arabic males is more intense

than that against Arabic females, which is also shown in Bursell (2014).

Other, non exprimental studies also shows that large differences in labour market outcomes

remain between native Swedes and migrants when controlling for human capital factors

(Nordin & Rooth, 2009; Rydgren, 2004).

Similar evidence is found on the housing market. Experimental research has shown that Arabic

named individuals that apply for vacant rental apartments face far fewer callbacks, enquiries

and showings than Swedish named ones and that the ethnic gap remains when information

indicating high socioeconomic status is provided (Ahmed & Hammarstedt, 2008; Ahmed, et

al., 2010). It is, however, also shown that the degree of discrimination varies substantially with

landlord/apartment and regional characteristics (Carlsson & Eriksson, 2014).

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Evidence from the labour and housing market point in essentially the same direction: people

from Arabic/Middle Eastern background receive fewer responses from employers and landlords

than do applicants from Swedish background. A recent study by Adman & Jansson (2017)

furhter suggests that individuals with Arabic-sounding names are being treated less favourably

than ethnic Swedes when in contact with municipality bureaucrats.

The experimental research above goes in line with studies on perceived discrimination among

immigrants. Commissioned by the now closed Swedish Integration Board, Lange (2000)

conducted extensive surveys on how immigrants from different ethnic groups view themselves

in relation to native Swedes, other ethnic groups, and also their experiences with discrimination.

He showed that ethnicity appears to be strongly correlated with anxiety about losing ones job,

perceived correlation between unemployment and discrimination and perceved experiences of

discrimination in general. The study included a wide range of areas such as the labour and

housing market, governmental institutions, banking, medical care, restaurant visits and the

police. The results indicate that people with Arabic/Middle Eastern origin appear to be the most

vulnerable of perceived discrimination. For example, about 40 percent of the Persians in the

study believe they are worse treated than native Swedes by the social services, 49 percent by

the bank and 60 percent by the Swedish Public Employment Service (p. 61). Lange further

showed that perceived discrimination strongly correlates with decreased trust for politicians

and public institutions, which is of course highly interesting in relation to political science.

Other reports conducted by De los Reyes & Wingborg (2002) and the Equality Ombudsman

(2010) points to the same conclusions.

Field Experiments on Ethnic Discrimination Amongst Public Officials

Internationally, ethnic discrimination within the market place has been investigated through

various forms of field experiments for almost 50 years (for an excellent review, see Riach &

Rich, 2002). However, while field experiments are common to investigate discrimination

within the labour and housing market, the body of research using experimental techniques

within political science is relatively rare. In his seminal book Making Democracy Work, Putnam

undertook the first research design of this kind when investigating discrimination amongst

street-level bureaucrats in Italy (1993, p. 73). Almost twenty years later, Butler & Broockman

(2011) built on his approach and were the first to investigate ethnic discrimination amongst

legislatives. They used an experimental correspondence technique to investigate whether

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ethnicity affects how responsive state legislators are to requests for help to register to vote. By

sending an email to each legislator, randomising whether a putative black or white alias was

used, they find that putatively black requests receive fewer replies. Since then, similar research

has followed in a great pace. Following, I will give a brief overview of these studies.

With much of the same research design, other researchers have shown that in-group favouritism

goes both ways. In both the US and South Africa, white politicians tend to better respond to requests

from whites, whereas black politicians tend to favour blacks’ interests (Broockman, 2013;

McClendon, 2016). Another field experiment has further shown that responsiveness bias amongst

US legislatives is associated with legislator policy preferences (Mendez & Grose, 2018).

Similar research has been conducted in China by Distelhorst & Hou (2014). They argued that the

earlier correspondence experiments may be marked by selection bias due to historical and

institutional conditions (referring to being conducted in the US and South Africa which carries

legacies of institutionalised racism, and furthermore focus on elected representatives which may

have stronger norms of reciprocity among members of the same ethnic group as potential voters).

Hence, they focused their experiment on unelected local officials in a radically different political,

historical and ethnic environment - namely China. They did, however, find strong evidence for in-

group bias. The results show that local officials were 33% less likely to aid citizens with ethnic

Muslim names than to ethnically unmarked peers, demonstrating that neither legacies of

institutionalised racism nor elected representatives are necessary to produce large in-group biases

in official behaviour. Since then, most field experiments within political science seem to have

shifted their focus from political representatives to bureaucrats. With this shift, more informal forms

of discrimination seem to be most prevalent.

Einstein & Glick (2016) investigated ethnic discrimination in US bureaucracies that provide access

to valuable government programs. Using a randomised field experiment they explored

responsiveness to putative white, black and Hispanic requests for aid in the housing application

process. In contrast to prior findings, they found that public housing officials respond at equal rates

to black and white requests, and limited evidence on discrimination toward Hispanics. Similarly,

Grohs, et al., (2016) examined the unequal treatment of citizens by gender and ethnicity using a

survey-based field experiment in German local governments. Randomising assignment of names,

responses to fake citizen requests were analysed for speed, quality and service orientation. They

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found very limited evidence of general ethnic discrimination, but an indication of discrimination

conditioned by gender.

Another field experiment conducted in the US is by White, Nathan and Faller (2015). They explored

whether street-level bureaucrats discriminate in services they provide to citizens. By contacting

election administrators responsible for providing information to voters and implementing voter ID-

laws, they found that officials provide different information to potential voters of different

ethnicities. Latino-aliases were significantly less likely to receive any response from local election

officials than white aliases, and also received responses of lower quality. Similar to Grohs, et al.,

(2016), White, Nathan and Faller expand on using responsiveness as a measure of discrimination to

include the dimension of quality. This seems to be common in more recent research on bureaucrats.

Hemker and Rink (2017) conducted a field experiment including multiple dimensions of

discrimination to assess whether German welfare offices treat requests from German and non-

German applicants equally. They found that applicants with foreign-sounding aliases are no less

likely to receive responses in general. However, putative foreigners receive replies with

significantly lower quality. Finally, a similar study was recently conducted in Sweden. Adman and

Jansson (2017) explored ethnic discrimination in contacts between local public officials and

members of Swedish society. They too used a correspondence experiment which included quality

of response when contacting municipalities asking for access to preschools. Similar to Hemker and

Rink (2015), they find no statistically significant signs of discrimination in terms of formal aspects

of the emails, that is if the questions were answered at all. However, Arabic-sounding names got

disadvantageous treatment in terms of more informal aspects, such as being responded to in a less

friendly and welcoming way.

Conclusion

Previous research on discrimination points to that despite great progress in the extension of

civil and human rights, ethnic inequality is still a problem in a wide range of domains and

settings. This can be connected to the social dominance theory and the theory of ethnic

hierarchies. However, by looking closer at previous research most related to this study – field

experiments on public officials - one finds that they yield quite different results as well as uses

different research designs. They differ in targets (politician or bureaucrat), setting, and how the

correspondence design is conducted. No previous studies have attempted to investigate

informal and hence more ambiguous dimensions of discrimination on politicians. In addition,

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no study outside the US or South Africa studies politicians as subjects at all but have instead

focused on bureaucrats. Thus, there seems to be a gap in the literature regarding different

contexts and design traits. This has encouraged me to contribute to the research field by

investigating discrimination amongst politicians in a Scandinavian context.

Previous research also suggests what kind of results we can expect to find. Research of

discrimination in Sweden presents a rather clear picture: despite being a front runner for

equality, discrimination occurs even here and does so in most parts of society. It may thus be

reason to suspect that discrimination also occurs among political representatives. Furthermore,

previous research indicates that gender may interact with ethnicity and influence the level of

discrimination. It is also reason to believe that socioeconomic status may do the same.

The hypotheses presented in the introductory section are based on these expectations. They

express that if a difference is found, it will be to the disadvantage of the ethnic minority. It is

also expected that the minority female will receive better treatment than her male counterpart,

and that the minorities will receive better treatment if they signal high socioeconomic status.

Methods The following section discusses the method and design of this study. It begins with discussing

how discrimination is problematic to measure due to its ambiguous nature and how field

experiments are particularly useful in this regard. However, the method requires ethical

considerations which are then accounted for. Thereafter, a detailed presentation of the research

design follows, as well as a discussion of its strengths and weaknesses.

Measuring Ethnic Discrimination The persistent ethnic disparity in a wide range of social domains attract scholars’ interest in the

possible role of discrimination. Unlike in the pre-civil rights era, when prejudice and

discrimination were overt and widespread, discrimination today is way subtler and less readily

identifiable. Consequently, this poses problems for conceptualisation and measurement (Pager

& Shepherd, 2008, p. 181). How can we measure discrimination when it is an often illegal and

hidden practice? Investigating discrimination in social science has mainly involved personal

reports by discriminators and discriminated and statistical analyses on ethnic inequalities.

While understanding persistent prejudices and stereotypes among discriminators is valuable to

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grasp what drives biased behaviour, it will not necessarily reveal the extent of discrimination

in action. Reports from discriminators furthermore suffer from the reasonable suspicion that

given that discrimination in many contexts are illegal, those guilty of it will not report their

actions accurately. Furthermore, reports about experienced discrimination are highly important

in their own right, not the least given that evidence demonstrates that perceived discrimination

is connected to negative outcomes in health and other domains of life (see e.g. Pascoe &

Richman, 2009; Socialstyrelsen, 2000). However, as argued by Pager & Shepherd (2008), these

methods may sometimes capture perceptions rather than reality. People may feel that they are

discriminated when they in reality are not. Likewise, it could be the case that people do not

perceive themselves as discriminated when they in fact are so. Lastly, discrimination in

statistical models are often measured as the residual difference in outcomes that remains after

controlling for all other ethnical-related influences. The major problem with statistical analyses

is that it is nearly impossible to know that all other important factors have been controlled for.

This leaves a risk of omitted variable bias that may both exaggerate or underestimate the effect

of ethnicity on behaviour. Similar argumentation is presented by e.g. Quillian (2006) and Pager

& Shepherd (2008). Hence, in order to isolate the causal link between ethnicity and behaviour

and so measure ethnic discrimination in action, field experiments are being increasingly used

within social and political science.

Field Experiments – What Are They and Why Are They Useful? Experiments provide a powerful means for isolating causal mechanisms. The procedure of

assigning treatment at random ensures that there is no systematic tendency for either group to

have an advantage. However, a common weakness of traditional, laboratory experiments in

social sciences is their lack of external validity, i.e. the results may not generalise to contexts

outside the particular experiment. The term field experiment refers to any fully randomised

research design in which research subjects found in a natural setting are assigned to treatment

and control conditions. This type of method thus blends experimental methods with field-based

research. While it relaxes certain controls over environmental influences it better simulates real-

world interactions. Because of random assignment, differences between the groups provide

some evidence of an effect of the manipulation since it ensures that on average, the two groups

are similar except for the treatment (Gerber & Green, 2012, p. 8ff). When it comes to measure

ethnic discrimination, field experiments are attractive because if they are done well they excel

in those areas in which statistical analyses falter: providing a clean estimate of the impact of

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ethnicity, yet have the advantage of occuring in a realistic setting and thus strenghten

generalisability. Furthermore, for measuring discrimination, they reflect the most simple

definition of what discrimination is – the different treatment of two (nearly) identical groups.

Due to these traits, field experiments are widely viewed as providing the most convincing

evidence on discrimination (see e.g. Pager, 2007; Riach & Rich, 2002; National Research

Council, 2004; Quillian, 2006; Pager & Shepherd, 2008).

Correspondence Testing

Correspondence testing is a field-experimental technique that has been widely used to

investigate discrimination in the labour and housing market. Different types of discrimination

within these areas have been investigated such as sexual, age and ethnic discrimination (see

Riach & Rich, 2002). In this type of research, two carefully matched, fictitious applicants are

forwarded in response to advertised jobs or housing opportunities. The group characteristic of

interest of the fictitious individual are then the only distinguishing feature of the two

applications which are randomly assigned to the research subjects. This way, the influence of

ethnicity (or other characteristic) on selection for job interview or renting opportunity is

isolated.

The experiments within political science that are presented in the previous research section uses

much of the same approach. However, instead of applications of different kind they send

fictional requests for information or help to officials by email. Each official receives one email

and is randomly assigned to receive a request from either a putative majority or minority citizen.

These emails are identical apart from the treatment and control variables which are signalled

by ethnically distinctive names. Several treatments can further be used, signalling e.g.

partisanship or socioeconomic status to test for different theories of discrimination. The

researcher then uses the variance in officials’ replies as a measure of ethnical bias.

Ethical Considerations Experiments like this comes with ethical aspects that need consideration. I discuss here which

ethical considerations I took into account before conducting my experiment. Inspired by Butler

& Broockman (2011) I considered three ethical issues: (1) the use of deception (2) minimising

harm and (3) minimising burden.

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First, field experiments on discrimination involves using bogus requests. Clearly deception is

involved, as subjects are approached by individuals who do not genuinely need their service.

Furthermore, research ethics normally dictate that the people studied need to give their consent

to participate. Here, those subject to the research have not had that opportunity. However,

deception is unfortunately a necessary trait in experiments like this as the ability to assign

desired treatments to individuals is only available in a field experiment with fictitious

individuals. Furthermore, a central part of the design is that the research subjects are unaware

that they are participating in an experiment. If the politicians know that they are being evaluated

regarding their responses, they are more likely to adjust their behaviour and the estimated effect

of ethnicity on responsiveness would probably be biased.

Second, I considered how I could minimise any harm that my experiment might cause. I have

taken steps to maintain the anonymity of the politician’s responses. All results are reported on

an aggregated group level and it is impossible to identify any individual. If a data set is compiled

and published for replication purposes, it will not contain any information that can identify

individuals. Email-addresses, names and parties will be deleted, and the order of observations

randomised. Furthermore, since the politicians only receive one email from one of the identities

we do not observe all potential outcomes (how they respond to each identity) from individual

politicians. In other words, we do not know how they would have responded to the other

treatments and hence whether individual politicians are biased. All we know is the average

comparisons across groups.

Third, since the emails are sent from fictitious individuals, the politicians are in a sense “wasting

their time” answering it. Thus, it is important to minimise the burden in terms of time and effort

for answering the inquiry. Effort was made to achieve minimal inconvenience but enough

burden to stimulate variation in replies. I tried to choose questions that allowed for more or less

elaborated answers but would be fairly easy to respond to. This way, I aimed to not prevent the

politicians from doing their everyday duties of serving actual citizens. Based on the responses

I received, my judgement is that I found an acceptable middle way between these two values.

Of the replies that I received the median length was 706 characters long, which is roughly this

far in this paragraph. In sum, I think that the way I conducted my experiment did not cause any

significant harm to neither the politicians nor any citizen who may have been seeking their

attention at the time.

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Lastly, in Sweden, research conducted on humans must be approved beforehand by the

Regional Ethics Board (Etikprövningsnämnden). Students however, such as I, are exempt from

these regulations. Nevertheless, to ensure that the experiment is up to ethical standards, I

applied and got my research proposal approved.

In sum, I would assert that any limited costs involved in the experiment are outweighed by the

precise information provided on discrimination, which cannot be obtained by any alternative

procedure. I tried to achieve the standard set by Putnam when he described his own experiment

as “slightly deceptive, but innocuous and highly informative” (1993, p. 73). However, the

ethical aspects are highly important and must be considered in experiments like this. As with

all research, there need to be an assessment of the value of the study in relation to the ethical

drawbacks. My conclusion is that this study is justified in that regard.

Research Design The purpose of this thesis is to conduct a field experiment for studying responsiveness bias in

the contact between citizens and Swedish local politicians. The study involves a correspondence

design, where emails are sent out from fictitious individuals with either distinctive Arabic- or

Swedish sounding names to Municipal Commissioners. Whether a commissioner receives an

email from a citizen of supposed Swedish versus Arabic origin is randomized. All Municipality

Commissioners in Sweden’s 290 municipalities2 are included, which makes this a population

study including a total of 812 observations.

Setting and Population

The present study focuses on Swedish politicians. There are mainly three features that render

this interest. First, contemporary Sweden has a reputation of being a highly democratic and

egalitarian state and social norms surrounding tolerance are by many accounts very strong here.

Second, the Swedish population is increasingly ethnically diverse. Today, roughly 24 percent

of the population are of foreign background3 (Statistics Sweden, 2018). Thirdly, research

suggests that despite the above features, ethnic disparities and social exclusion nevertheless

2 Except a few observations which were excluded from the analysis, see commentary about this below. 3 With foreign background means either born abroad or born in Sweden with two immigrant parents.

21

exist in Sweden. Thus, ethnic discrimination in relation to political representation should be of

high socio-political relevance.

As described earlier, ethnic inequality and discrimination can be measured in a number of

different ways and on a variety of subjects. The purpose of this study is to investigate ethnic

discrimination in the contact between local politicians and the citizens they represent. So why

municipality politicians and not representatives on the national level? I reasoned as follows: the

municipality is the lowest level of government in Sweden and a substantial amount of political

power are decentralised to the municipalities. The municipalities must follow frameworks and

regulations decided on the highest level of government, the parliament, but the municipal

autonomy gives them the right to take independent decisions as well as tax their citizens. This

gives local authorities responsibility over many services that are of relevance for peoples every-

day lives. Amongst the most important functions are pre-schools, schools, social services and

elder care. Furthermore, the municipalities are governed by politicians chosen directly by its

citizens. This means that citizens have great opportunities to influence and control how their

representatives perform their tasks. This should not only make contacting them as a citizen

seem realistic, it should also be an incentive for the politician to be of service.

Next step was to decide the exact subject to study. To get as broad a picture as possible I wanted

to include all 290 municipalities. This decision generated more selection considerations to be

made. Swedish municipalities are governed by political assemblies chosen directly by its

citizens, whereas the city council (Kommunfullmäktige) is the highest decision-making body.

In addition, there are political assignments in the municipal executive board

(Kommunstyrelsen) as well as several different committees with different areas of

responsibilities. There are more than 36,000 elected representatives in Sweden. The majority,

96 percent, are politicians on their spare time, i.e. managing their duties in addition to work or

studies (Statistics Sweden, 2016). In this study, only individuals that are either full-time or part-

time politicians are included. Furthermore, they need to have the title of Municipality

Commissioner or Commissioner of the Opposition (Kommunal/Oppositionsråd). Both are

henceforth referred to as MCs). This decision is based on several reasons. From an ethical

perspective, I found it appropriate to only include politicians that get paid for their efforts and

in fact have it as their profession to be of service to their citizens. I chose to further restrict my

focus to representatives that have the title of MC out of both convenience and consistency.

Convenience because the contact information to MCs often are easily found. Consistency

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because only including this title would reasonably mean that the politicians have roughly the

same working time and level of insight, influence as well as incentive to be of service.

The design resulted in 812 observations. There are no exact limits for what is to be seen as a

sufficiently large n for doing this kind of study, but 812 observations should offer a substantial

size. Furthermore, it has the benefit of being a population study as it includes all MCs in

Sweden.

This study focuses on the responsiveness of politicians towards different parts of their

constituency. As mentioned in the previous research section, previous field experiments in

political science within Europe have focused on bureaucrats as research subjects. I recognise

the importance of investigating discrimination within the bureaucracy, especially given that

modern state bureaucracies are ascribed a central role for the equal treatment of citizens

(Rothstein & Teorell, 2008). Still, previous research suggests that biased bureaucrats exist in

Sweden and elsewhere within Europe. Therefore, I deem it necessary to investigate whether

our politicians tend to discriminate their constituents on the basis of ethnicity. If they do, it is a

democracy problem from a representative perspective, not the least given that one quarter of

the population in Sweden have foreign background. Furthermore, Sweden is not only facing

increasing heterogeneity but also growing distrust for politicians and the political system

(Novus, 2017). Therefore, responsiveness amongst politicians may have important social and

political implications.

Treatment

As mentioned in the theory section, categories of different social identities may interact with

one another in relation to discrimination. To study the effect of ethnicity on politicians’

responsiveness, a total of three different characteristics of the fictitious individuals were

included: the sender’s ethnicity, gender and socioeconomic status. This gives us a 2 x 2 x 2

experimental design including three factors with two values each and a total of eight different

identities. The treatments are summarised in table I. Treatment A, ethnicity, is of key interest

and varies between the groups of Swedes and Arabs. The second treatment is B, which varies

the sender’s gender. The third and final treatment is C, which varies the sender’s socioeconomic

status. Since the names that are used in this study are assumed to signal low socioeconomic

status (more about this below), I chose to signal the sender’s socioeconomic status by varying

whether the identities have a signature including their profession. I chose a profession that is

23

relatively gender-balanced and common among immigrants, namely dentists (Socialstyrelsen,

2018; Gärdkvist, 2006). Furthermore, becoming a dentist require extensive education and a

certificate to practice and should thus signal high socioeconomic status. Since many dentists

run private practices it should further not be considered odd that the individual highlights his

or her profession.

Ethnically Distinctive Names

This study relies on a correspondence design where the name that signs the email function as a

proxy for ethnicity. Hence, it is crucial that the names that are used have clear connotations

with the desired treatment. In this study, ethnicity is defined as “a category of people who

belong to a social group that has a common national or cultural tradition. (Peoples & Bailey,

2010, p. 389). Of course, these factors cannot always be related to certain names. Yet, name

traditions do differ between countries and regions. Comparing Swedish-sounding and Arabic-

sounding names has the advantage that they are clearly distinguishable from each other and

hence carry greater associations between name and ethnicity.

However, an important note is that researchers who wish to study discrimination by using

ethnically distinctive names are facing some challenges. The ability of these experiments to

inform us about ethnic discrimination depends in large part on the excludability assumption,

i.e. that subjects’ response to the name is driven solely by the signal that the name provides

about ethnicity and not some other relevant information. In relation to political science, this

challenge is important because ethnically distinctive names can also signal politically relevant

information. It has been argued that ethnically distinctive names have come to be increasingly

associated with socioeconomic status (Fryer & Levitt, 2004). Consequently, correspondence

tests may be confounding the effect of ethnicity and socioeconomic status and thus support a

potential socioeconomic-related discrimination rather than ethnical. In a respond to this

criticism, Butler & Homola (2017) empirically test the excludability assumption, providing

Table I. Treatments

Treatment 1 Treatment 2

A: Ethnicity Arabic Swedish

B: Gender Female Male

C: Socioeconomic status Low SES (no signature) High SES (Signature)

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empirical support for the use of ethnically distinctive names for investigating ethnic

discrimination. However, I find Fryer and Levitt’s criticism valid and chose to use a strategy

that attempts to meet this criticism. To choose names that signal different ethnicities but similar

socioeconomic status, I have identified Swedish names that are associated with lower

socioeconomic status. Given the often underprivileged position of minorities in societies,

minority names more generally signal low socioeconomic status (Fryer & Levitt, 2004). By

consciously choosing a majority name that also signal low socioeconomic status, the names

better correspond in this regard. A similar approach can be found in Jilke, et al., (2018).

Furthermore, more or less correct language and grammar may signal socioeconomic status.

Since the language is identical (and correct) across the identities, the risk that the Arabic-

sounding names are perceived to be of lower socioeconomic status than the Swedish-sounding

names is minimised. In sum, it should be acknowledged that previous correspondence tests may

overestimate the occurrence of ethnic discrimination due to insufficient consideration of

perceived socioeconomic status. Hence, to separate ethnic discrimination from socioeconomic

ditto, my research design attempts to account for this problem.

Experimental Execution

Why Emails?

Citizens may contact their representatives in many ways, both formal and informal. Besides

more formal events organised by the municipalities for dialogue between citizens and

politicians, one can simply call on the phone, send an email or visit in person. Some

municipalities organise so called “Politicians cafés”, a venue for politicians and citizens to get

together and discuss local politics. The opportunity to chat online with your local

representatives is also becoming increasingly common. I have chosen to use a correspondence

design using email as means of contact. There are mainly four reasons for this. First, this

experimental technique is relatively new but has grown considerably within political science in

the last decade which makes meaningful comparisons possible. Second, from a practical

perspective - especially given the time- and resource constraints of this project - email is the

easiest way of contacting a large number of politicians and thus improve the generalisability of

the study. I further think that it is safe to say that email is a common means of contact in

contemporary Sweden. Third, sending emails has the benefit of minimising the influence of

factors that are otherwise difficult to control for. The alternative of using “live” testers that visit

the subjects in person or contact them through phone fails to ensure that people from different

25

groups appear identical. As argued by Heckman (1998), factors such as appearance, voice and

dialect may affect the behaviour of the research subjects, possibly generating biased evidence

of discrimination. Lastly, using email allows for minimal interaction between researcher and

research subject, which means that the risk of experimenter effects can be minimised. Based on

these considerations, contacting the politicians via email is deemed the best alternative.

Choosing Names

When choosing which ethnic group to investigate, I did consider the fact that similar

experiments in other contexts in Sweden have already demonstrated discrimination against

immigrants with an Arabic background while less research has been conducted on other

minorities. It would therefore be meaningful to investigate discrimination against other ethnic

groups as well. However, given that this study is the first of its kind in investigating

discrimination amongst political representatives in Sweden and, to my knowledge, even within

the EU, the use of Arabic names is still relevant in this context. Furthermore, since breaking

new ground, I found it motivated to maximise the possibility to observe discriminating

behaviour. Thus, I chose names from a group that based on previous research are expected to

be the most vulnerable to discrimination in Sweden. At the same time, roughly 24 percent of

the population in Sweden are of foreign background in which a considerable amount originates

from the Arabic/Middle Eastern part of the world. In 2017, 35 percent of all new Swedish

citizens originated from Syria, Somalia, Iraq, Afghanistan and Iran (Statistics Sweden, 2017).

Hence, this group should be of substantial size.

The Arabic-sounding names, Abdelhakim and Fatima, are taken from previous correspondence

studies. This means that these names already have been tested and proved to work in this

context. While Fatima is a common Arabic name in Sweden, Abdelhakim is quite uncommon.

However, it was chosen in an attempt to avoid stereotypical male Arabic names that might to a

greater extent be associated with low socioeconomic status. And, as mentioned, the name was

successfully utilised in both Arai, et al., (2008) and Adman & Jansson (2017). I further chose

to use Hassan as their surname. Hassan is a common Arabic surname, and on place 81 of all

surnames in Sweden (Statistics Sweden, 2017).

For the Swedish identities, as discussed in the section about ethnically distinctive names, I

chose names with the intent to signal low socioeconomic status. Research shows that strong

26

relationships persist between social background and choice of first name (Elchardus &

Siongers, 2011). For males, Kevin is shown to be associated with low socioeconomic status to

a similar degree as the ethnically minority name Mohammed (Malm & Zetterström, 2007;

Aldrin, 2017). For females, Marita is associated with low education level (Mattfolk, 2012).

When choosing the Swedish surname, I simply chose the most common one, a tactic that is also

used by Carlsson & Rooth (2007). While some Swedish surnames might be associated with

socioeconomic status (such as noble surnames) I think that Andersson is socioeconmically

neutral.

The aliases used are the following:

• Arabic male: Abdelhakim Hassan

• Arabic female: Fatima Hassan

• Swedish male: Kevin Andersson

• Swedish female: Marita Andersson

Since this study also investigates how gender interact with ethnicity in how different groups are

treated, it is crucial that the names clearly signal the gender of the sender. It is of course

impossible to be absolutely certain that no misinterpretations are made about the gender

specificity of the the name, perhaps especially regarding foreign names. After the emails were

sent out, I did realise that in fact a few females are named Abdelhakim in Sweden. However,

no females have it as their first name and the majority of people named Abdelhakim are men

(Statistics Sweden, 2017). Still, I cannot be completely sure that no confusion about the gender

specificity of the names takes place and how that potentially might affect the results. That said,

I do believe that the gender of someone named Abdelhakim and Fatima, as well as Kevin and

Marita, should be fairly clear.

Randomisation Strategy

As previously mentioned, correspondence testing is a field experimental technique that has

mainly been used to investigate discrimination in the labour and housing market. These often

involve the more traditional way of conducting experiments, i.e. with a clearly defined

“treatment” and “control” group. The researchers present carefully matched and equally

qualified fictitious applicants that are forwarded in response to advertised jobs or housing

27

opportunities. Hence, in these studies, the design allows the same research subject to be

contacted two times – once by the putative minority applicant and once by the putative majority.

It would arguably have been interesting to use a comparative design by sending each MC an

email from all identities, allowing direct comparison between the replies for the different

groups. However, this study takes a slightly different approach. Instead of a clearly defined

“treatment” and “control” group and sending each MC an email from each identity, I have

chosen to randomise the treatments across the municipalities. This means that after creating the

eight different identities, each containing a mix of treatment specific for each group, I randomly

assigned which identity is to contact each municipality. In other words, every MC only receives

one email, and every municipality, regardless of how many MCs it contains, only receive emails

from the same identity. This decision is based on several considerations. The most important

reason is to avoid suspicion. Although questions from people living in the municipality might

be common, there is a risk that several emails containing similar questions but from different

people would raise suspicion. In job and housing application processes it is common to get a

large amount of answers and the fictitious applications sent by researchers are less likely to

stand out. However, I reason that MCs do not get a great amount of constituent questions. Only

sending one email to each MC minimizes the risk of exposure. Sending the request from the

same identity to all MCs in the same municipality is based on the same argument. If MCs in a

municipality would talk to each other and realise they have received identical emails, it

minimizes suspicion if they are sent by the same identity. This argument is based on the belief

that it is neither unusual nor suspicious that a citizen concerned about local issues contacts their

politicians, both in the majority and the opposition, to inform themselves before an election.

Furthermore, as I am using eight different identities varied to beside ethnicity test the interaction

effects of gender and socioeconomic status, a full comparison would entail each MC to receive

eight emails. To avoid suspicion that would entail great effort to formulate different emails that

are varied enough to not stand out as fishy, but similar enough to be comparable and give

legitimate results. I found that alternative to be untenable of both practical and ethical reasons.

Only sending one email to each MC minimizes the time that the politician needs to spend on

answering, which has ethical advantages. Hence, the option to randomise the treatment on

municipality rather than MC is preferable.

This randomisation approach also comes with some drawbacks. As mentioned, it hinders the

possibility of full comparison between the groups. I cannot investigate differences between

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groups by the same MC or within the same municipality. Another downside is that the eight

different identities do not send the same amount of emails. Since the identities are randomly

and equally allocated to the different municipalities, each identity sending to 36 or 37

municipalities, the amount of emails the identity sends varies depending on how many MCs the

municipality has. The amount of emails sent by the different identities range between 80-133.

However, based on the arguments raised above, I deem these drawbacks worth the advantage

of minimising the risk of suspicion. The strategy proved to be necessary, as it became evident

that MCs within the same party talked to each other about the email, referring to each other etc. Minimising suspicion is not only important for ensuring the credibility of this study, but also

for not jeopardising the future prospects of similar research. Furthermore, the difference in

number of emails sent does not create any major issues. All groups are in this context of

significant size and response rate is measured as an average, why the quantity of replies is not

as important for comparison.

Collecting the Data

The means of contact between the identities and the MCs is email. All emails are collected from

the official website of the municipalities. While municipalities by The Administrative Law

(1986:223) are legally bound to offer email as a way of communicating with citizens, elected

representatives are not obliged to display any other information than name and party. This

means that not all MCs offer contact information. As most email addresses to municipality

employees are constructed as “firstname.surname@municipality.se” it would have been quite

easy to guess the email address to those who do not offer it online. However, based on an ethical

perspective I chose not to. Given that the subjects do not have the opportunity to give their

consent to participate in the study, I have chosen to only include those who encourage contact

by displaying the opportunity to email. This means that not all MCs are represented. If the

opportunity to email the MC was not offered, he or she was not contacted. This approach

resulted in thirteen MCs being excluded from the study. In the end, 812 emails were sent. The

small number of excluded MCs (equivalent to 1,6 percent of the population) is not considered

problematic for the results.

29

Formulating the Email

What questions to ask required careful consideration. The questions needed to be general

enough to be sent to all municipalities, but specific enough to generate meaningful answers.

The reason for sending identical emails that works in every municipality is twofold. First, given

the time constraints, learning about local issues to provide unique and adequate questions for

each municipality is not tenable. Second, sending identical emails to all subjects enables reliable

comparisons and minimises the risk of unintended interacting factors.

Several issues regarding the emails were considered. When formulating the emails, I used

following guideline: (1) The email must be realistic. Both in order to see the way that general

citizens are treated by their representatives and also to avoid suspicion, (2) the questions must

be relatively simple to answer in order to avoid wasting time of the MCs and (3) the request

must induce meaningful variation in responses4.

Regarding the first point, a critical issue is the risk that unusual names, especially foreign ones,

may stand out and cause suspicion in small municipalities with few immigrants5. To minimise

the risk of suspicion, I chose to formulate the email as if sent by someone that has just moved

to the municipality. This way, unusual names did hopefully not cause suspicion by a MC from

a municipality where “everyone knows everyone”. I thought about formulating it as someone

that was only thinking about moving to the municipality based on the same reason. However,

since I used the 2018 national election as a cause for the fictitious citizen to approach the MC

(more about this below), I deemed it better that the sender just had moved there and so is eligible

to vote in the municipality election.

Furthermore, effort was made to find a political topic that was credible to be of interest of a

new citizen as well as general enough to be asked to any municipality. I reasoned that beside

local issues, school related inquiries should be “universal” given that the school is one of the

most important responsibilities of municipalities. School politics was also generally regarded

4 Initially, I wanted to make it possible to investigate not only response rate, but also the quality of replies. Thus, the questions were formulated to generate answers suitable for that purpose. However, due to time constraints and the large data material, it proved untenable and I ended up only analysing formal discrimination (response rate) and not informal discrimination (quality of replies). 5 The lowest share of people with foreign background in a municipality is about 7% (Statistics Sweden, 2017)

30

as a “hot topic” before the 2018 national election. Therefore, I found school related questions

to be suitable for the study and created an inquiry about local school politics.

Related to the second point is consideration about the complexity of the questions asked. I did

not want to use too standard or easily answered questions. If doing so, the cost in time and effort

for the MC to answer might be so small that no meaningful differences would be seen. If the

request is somewhat “costly” to answer, subconscious (or conscious for that matter) bias is more

likely to be displayed. As Butler and Broockman argues in their work: “seeing how legislators

choose to expend time and effort is the best way to learn about their priorities” (2011, p. 468)

However, a too time-consuming request may decrease the response rate in general as well as

being ethically questionable. Hence, I have tried to balance these considerations to find a middle

way. I have included two questions, one broader about local school politics (how they want to

improve school in the municipality) and one more specific question (whether the MC thinks

that school resources should be distributed in regard to need, or to be equally distributed)6. I

found these questions to also be of accordance with the third point of my guideline, that is to

be of a nature that allow meaningful variation in replies7.

The email was furthermore formulated so that it refers to the election as motive for the contact.

The reason for this was that the upcoming election may serve as an incentive for the MC to be

of service. The sender could have been a new, potential voter in the municipality election –

both for the party but also by giving a personal vote for the MC. The complete email can be

found in the appendix.

Lastly, in projects of this kind it is common to run a pilot where the questions are tested on a

smaller scale. That would have been useful even here but was deemed difficult given the time

constraints. Since correspondence testing is a novel design within political science in Sweden,

help from previous research is limited. Adman & Jansson (2017) is to my knowledge the only

published research using a correspondence test on public officials in a Swedish context. They

too used school issues (practical questions about pre-schools) as topic in their design, why using

6 This question is based on a recent recommendation from Swedish Association of Local Authorities and Regions (SKL) about socioeconomic resource allocation in municipalities that have diverse socioeconomic composition of the student groups. 7 The questions seemed to have the desired effect. The broader issue generated a wide variance of replies, from highly developed answers to short referrals to the party program. The more specific question was successful in generating more replies, as while the broader question sometimes got referred elsewhere or skipped entirely by the MC, the more specific one was often answered directly.

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a similar subject in my study seemed adequate. However, as they investigated bureaucrats I

needed to shift the questions to be more political. In sum, given the circumstances there is a

risk that the email is not optimally formulated. Nevertheless, I find the results to be a

contribution to the field.

Sending the Emails

For each identity an email account was created. All followed the same form:

name.surnameXX(XX)@gmail.com, where the “X” is a number. The numbers are needed since

some of the aliases were more common. To make the addresses as similar as possible, numbers

were added to all of them. Gmail was chosen since it allows for creating several accounts and

sending several emails to different recipients at the same time. I also consider it to be a serious

and common enough email domain to avoid raising suspicion or getting stuck in spam filters.

For a list of the email addresses that was used, please see appendix.

Another advantage with using Gmail is that I could download a program which allowed me to

send all emails from the same identity at the same time. I could further personalise the emails

to greet by name and mention respective municipality. Thanks to this, all emails were sent

within two hours and thus gave the MCs roughly the same amount of time to answer. The

personalised email is beneficial since it sounds friendlier and more realistic.

The emails were sent out on Sunday the 26th of August. Sending the emails on a Sunday aimed

to make sure that most MCs read the email roughly at the same time, i.e. when arriving to work

on Monday morning. I furthermore had to set an end date for when an observation would be

considered a “no reply”. This of course involved the risk that some answers, if they were

received too late, did not count. To know the optimal time between send out and deadline is

difficult. Previous research differs when it comes to response time. Adman & Jansson (2017)

which I deem the most comparative study given its Swedish context had a response time of two

months. They state though that most answers were received within one week (p. 51). I chose to

use the Swedish election as deadline for replies. This gave the MCs a duration of two weeks

maximum to process the request. The time-frame appear adequate, as most replies came within

the first two days. Only one answer arrived after deadline, and thus got coded as a “no reply”.

It is, however, of course difficult to know if a longer time frame would in fact have generated

more replies.

32

I used the 2018 national election as deadline based on two reasons. First, the upcoming election

may have served as an incentive for the MC to reply, as the sender could have been a potential

voter in the municipality election both for the party and for the MC itself. Second however,

while serving as an incentive, the election simultaneously served as a stress factor for the MCs.

I wanted to send the emails during the time when the MCs were busy with campaign season, so

that the extra level of activity could be an excuse for ignoring the email. I reasoned that the

increased pressure before the election could potentially lead to more biased behaviour -

conscious or unconscious - when the MCs must prioritise their time and effort to a higher level.

It has been replies in which the MC directs a question to the identity, such as if the reply is

satisfactory or which school the children will attend. In these cases, no reply from the identity

was sent. I thought about the option to briefly answer in some cases. I considered that not

answering could be seen as impolite, which could affect how the MC perceive the identity.

However, to minimise the inconvenience of the MC who would have to read another email and

perhaps answer once again, I deemed it best to avoid taking up more time than absolutely

necessary and gave no further reply on these occasions. Furthermore, given the probably high

level of activity during election campaigning, I do not think that an absent reply would cause

much reflection.

Operationalisation

The design allows for a very straight forward operationalisation: do the response rate differ

between the groups? If the Arabic sounding identities are consistently being answered to a

lesser extent than the Swedish ones, it indicates a problem of discrimination when politicians

are contacted by citizens of different ethnic origin. Comparing the response rate between the

eight different groups makes it possible to detect responsiveness bias and also whether gender

and socioeconomic status may be interacting with the effect of ethnicity. In sum, the advantage

of using response rate as a measure of discrimination is that it is highly objective and

transparent: did the MC reply or not?

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Control Variables As mentioned, a great advantage with experiments is that randomising treatment ensures that

specific factors among the municipalities that could potentially influence the results are

automatically controlled for. However, it is wise to see if the randomisation is robust and that

there are no accidental systematic differences between the groups. A number of control

variables that potentially could affect the MCs responsiveness were therefore added to the data.

The included control variables are municipality population size, average income, economic

results (result per capita for 2017), population growth (calculated as the percentage change

between the beginning and the end of 2017), population density (inhabitants per square

kilometre), percentage of people voting for the Swedish Democrats (in the 2014 national

election), percentage of people with foreign background, refugee population growth and region

dummies. All the data are taken from Statistics Sweden and the Swedish Migration Agency.

The size of the municipality may affect the results in several ways. It is likely to affect the

municipal administration and the resources that MCs have at hand. It is also possible to imagine

that MCs within larger municipalities are busier. Together, this could lead to more delegation

on the MCs part and the emails being forwarded to a larger extent in larger municipalities than

in smaller ones. Another important factor is that larger municipalities tend to have more MCs.

Since the email are sent to all MCs, there is a risk that MCs within the same municipality talk

to each other about the email which may affect the results. MCs within the same party could

for example decide to let one of them answer for all of them, or MCs from opposing parties

could be driven to answer based on competitive reasons. In other words, the design allows for

“spillover effects” due to interpersonal communication among the research subjects. This could

produce a biased assessment of the treatment’s causal effect. Hence, municipality size is

deemed an important variable to control for in this context. Furthermore, region dummies are

included to control for spillover effects, that is, if municipalities belong to the same region

which could enhance the risk of research subjects affecting one another.

The economy of the municipality could also affect the administration and resources at hand,

why economic results and average income are included. Both population growth and population

density are considered factors that could potentially affect how positively the MCs view new

citizens (not the least highly skilled ones). Furthermore, the SD vote share, population share of

34

people with foreign background and refugee population growth are considered to potentially

affect how ethnic minorities are treated.

Validity and Reliability Discussion Field experiments are considered as “fair tests” in terms of both validity and reliability (Gerber

& Green, 2012, p. 8f). As mentioned, a common weakness of laboratory experiments is their

lack of external validity and thus lack of generalisability. People probably act differently when

they know they take part in an experiment, which means that the results might not hold in the

“real world”. Field experiments, like this study, have the advantage of being conducted in a

natural setting in which the subjects do not know that they are part of an experiment. This

greatly increases the external validity of the results.

Internal validity is also to be considered and refers to how well an experiment is done. Here,

avoiding confounding variables are essential, as well as reliable coding of the dataset. Although

I believe that my design has its clear advantages, as in all empirical investigations there are

potential problems. In this study, one factor that potentially could be confounded with ethnicity

is religion. The names that are used in this study are chosen because they are Arabic. However,

since Arabic names are common in Muslim countries, it is possible that the research subjects

do not tell the difference. If the MCs are biased against Muslims in particular but not Arabs in

general, this may affect the results. Another potential confounder in relation to the names, in

particular the Swedish ones, is that beside the desired treatments it is possible that they signal

age. The average age for people named Kevin in Sweden is 16 years old while the average age

for Marita is 60 (Statistics Sweden, 2017). However, asking about school somewhat controls

for the identities being in the same, but still a wide range of ages that reasonably could have

children in the age between 6-18. Still, if age is a ground for discrimination this could affect

the results.

A strength of this study is that it accounts for possible interaction effects of both gender and

socioeconomic status, which otherwise may carry misleading results about the effect of

ethnicity on discrimination. Furthermore, only including response rate facilitates objective

coding since no quality aspects are involved which would entail subjective judgements.

However, not all replies are passed as “real” replies, which means that some judgements have

been made even here. Examples of judgements will be provided below. To ensure that all emails

35

are equally judged, all replies were copied into an anonymised document where all information

regarding name, party and gender of the sender were excluded. Occasional cases were extra

difficult to judge. These were looked over an extra time and also judged by a second person.

This way, I aimed to make sure that the coding was both correct and coherent throughout the

dataset. This further relates to the reliability of the study, i.e. that it involves transparent,

reproducible procedures and thus should produce similar results under consistent conditions.

While containing some weaknesses, given the overall design and measures taken to ensure

consistent coding, this study should provide both valid and reliable results.

Results Following chapter will account for the results from the correspondence test. First, I account for

some observations that were deemed best to exclude from the analysis. Next, I present

descriptive statistics of the dependent and independent variables along with a report on how the

replies are coded, i.e. what is considered an answer. Lastly, the findings related to my

hypotheses are presented.

Excluded Observations Before moving on to the analysis, a note regarding excluded observations should be accounted

for. When going through the answers, I came across a few problems that needed consideration.

First, it appeared evident that a small group of MCs realised that the email was fake. The MCs

in question were all from the same party and came from small municipalities in the same region.

I realised they had talked to each other about the email since they sent identical answers to the

different identities soon after the emails were sent out. My guess is therefore that I unluckily

sent the emails during some kind of get together for party members from small municipalities

in the region, and that they saw them at the same time and realised they had got identical emails

from different persons. Since the identical answers I received from these MCs obviously were

contaminated, I decided to exclude them from the analysis. Six observations got excluded based

on this. I realise that more than these six MCs might have been aware that the email was fake,

but simply chose not to answer. This means that some observations might have been coded as

a “no reply” when the reason for the absent reply is not the treatment but knowing that the

request is fake. However, since the treatment is randomly assigned, the probability of this

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problem should be equal for all identities. Including the region control variable also helps this

problem. Hence, differences in response rate between the identities should still be valid.

Second, I chose to exclude MCs from Stockholm from the analysis. The reason for this is that

many replies were sent from political secretaries. This is not very surprising, Stockholm is a

special case due to its size and thus assumingly works more as a “professional” organisation

with more resources, delegation etc. Since replies from political secretaries don’t count, it felt

better to exclude Stockholm entirely since it otherwise would give misleading results on

discrimination. This applies to 13 observations.

Lastly, there were a few bounce backs when sending the emails which were also excluded from

the analysis. So, from the originally 812 emails sent, 788 observations were ultimately analysed.

Overview of Variables In table II we find descriptive statistics of the dependent and independent variables (response

rate and identity). The mean, standard deviation and number of emails sent by each identity are

displayed. Two different response rates are shown in this table. The first, which I refer to as the

“meaningful” response rate, includes replies in which the MC has provided an adequate answer

to the email. A meaningful reply needs to answer one or both of the questions in some regard.

How elaborate that answer is, however, does not matter. In the “meaningful” response rate,

answers that only inform that the email has been forwarded to another person, referrals to call

on the phone or meet in person etc., are not considered an adequate answer and are therefore

coded as a no reply. If a party program is included in the email, it is considered an answer since

it provides direct access to the first question. If the MC only refer to the party program but does

not provide it, however, it is not considered a reply since the identity get no valuable

information directly in the email. To give a sense of the distinction between what is considered

a meaningful reply, I show two different answers that are both short but where I code one of

them as a reply and the other not.

“Hello X, thank you for your question. You are going to get an answer from one of my

party members”.

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This answer is coded as a no reply in the meaningful response rate. The MC itself does not

answer any of the questions and only inform the identity that the email is forwarded to someone

else. Any potential answers from other party members, political secretaries etc. do not count,

as they are not my research subject.

“Everyone should have the same”.

The above answer is considered a meaningful reply. Even though minimal, the MC has in fact

provided an adequate answer to one of the questions (about resources).

In the column “generous” response rate all replies are included, even those which did not

provide any meaningful answers. The first example above is thus included in the generous

response rate. Automatic replies (usually informing that the email has been received) are not

included at all.

As we can see, the Swedish female got the highest meaningful response rate. Marita got equally

high response rate regardless of signalling high socioeconomic status or not. Abdelhakim got

the lowest response rate when not signalling high socioeconomic status but second highest

when doing so. Both Kevin and Fatima got roughly the same meaningful response rate

regardless of socioeconomic status. We can further see that the meaningful and generous

response rate differ quite a lot, with the biggest difference for Kevin signalling high

socioeconomic status which means that he to the highest degree got referred elsewhere in

different ways.

Table II. Descriptive statistics of dependent variable

Meaningful response rate Generous response rate Identity Mean Std.Dev. Mean Std.Dev. Freq.

Kevin, dent. 0.52 0.5 0.67 0.47 113 Marita, dent. 0.68 0.47 0.77 0.42 79 Kevin 0.51 0.5 0.62 0.49 103 Marita 0.68 0.47 0.78 0.42 69 Abdelhakim, dent. 0.65 0.48 0.78 0.42 94 Fatima, dent. 0.53 0.5 0.63 0.48 117 Abdelhakim 0.51 0.5 0.58 0.5 132 Fatima 0.54 0.5 0.68 0.47 81

Total 0.57 0.5 0.68 0.47 788

“dent.” refers to the email including the signature “licensed dentist”

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In the analyses that follows, the “meaningful” response rate serves as dependent variable since

meaningful replies are considered the most valuable to analyse. However, results for the

generous response rate can be found in the appendix.

To better understand the potential impact of the control variables, table III presents an overview

of their descriptive statistics. For example, municipality population size differs between as little

as 2 448 citizens to almost a million, the average municipality having about 75 000 inhabitants.

The average SD vote share is 14 percent, ranging between about 5 to 30 percent. People with

foreign background range between about 7 to 59 percent with a mean on 21 percent. The lowest

refugee population growth is 0.3 percent and the highest about 4 percent. The average

municipality have a refugee population growth on 0.8 percent of its population. In the

regressions that follows, the majority of the control variables are transformed into logarithm

variables. The reason for this is to help reduce skewness due to outliers. All variables part from

SD vote share and population growth are logged.

Analysis In the following analysis, linear regression models are used. When analysing binary dependent

variables such as response rate (i.e. reply or no reply), logistic regressions are often advocated

with reference to more appropriate significance tests. However, logistic regressions are difficult

to understand and can be counterintuitive from a substantial point of view. Linear regression

models provide estimates that are more intuitive, as they may be interpreted as the difference

in probability for having a certain value on the dependent variable for units with different values

on the independent variable. They further have better applicability in causal analysis (Hellevik,

2009). Hence, using linear regression is deemed preferable over logistic regression in this study.

Table III. Descriptive statistics of control variables

Mean Std. Dev. Min Max

Population size 74769 140165 2448 947380 Average income 274 45 208 705 Economic result 2155 5554 -10225 108598 SD vote share 14 4 5 30 Population growth 1 1 -3 4 Population density 336 927 .2 5689 Foreign background 21 9 7 59 Refugee population growth .8 .3 .3 4

Observations 812

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For those who are interested, a table with a logistic regression for table IV can be found in the

Appendix.

As the following tables will show, including the control variables affect the results to varying

degrees. The results in the second model, i.e. the coefficients which includes all of the

mentioned control variables, are the ones that will be discussed.

Effect of Identity on Reply

As we can see, all coefficients are positive which means that all the other identities are more

likely to get a reply than Abdelhakim who does not signal high socioeconomic status. Marita is

the identity most likely to get a reply. When signalling high socioeconomic status, Marita is 15

percentage points more likely to get an answer to her questions than Abdelhakim. That

difference is statistically significant on the 5% level.

We can further see that signalling high socioeconomic status seems to have an effect on

Abdelhakim’s chances to get a response to his inquiry. On the 10% significance level,

Abdelhakim is about 13 percentage points more likely to get a reply when he signals high

Table IV. Regression on effect of identity on reply

Ref.cat Abdelhakim Model 1 Model 2 Kevin, dent. 0.015 0.011 (0.063) (0.067) Marita, dent. 0.176** 0.150** (0.070) (0.074) Kevin 0.007 0.023 (0.065) (0.070) Marita 0.174** 0.101 (0.073) (0.078) Abdelhakim, dent. 0.141** 0.129* (0.067) (0.073) Fatima, dent. 0.022 0.044 (0.063) (0.067) Fatima 0.036 0.002 (0.070) (0.073) Including controls No Yes _cons 0.508*** 0.420 (0.043) (1.602) Adj R2 0.011 0.037 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

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socioeconomic status. The difference towards Kevin and Fatima is rather small, between 0 to 4

percentage points and not statistically significant. The above table indicates what may be found

in the following analyses which takes a closer look at the three hypotheses.

Effect of Ethnicity on Reply

The main question of this study is whether ethnic discrimination occurs in the contact between

citizens and their political representatives. It is assumed that if a difference is found, it is to the

disadvantage of the minority group.

Table V shows the effect of ethnicity on reply. Here, the identities are divided into two different

groups, one with the Arabic-sounding identities which are coded 1 and one with the Swedish-

sounding identities which are coded 0. This means that ethnic Swedes are the reference

category. The table shows that Arabic-sounding names are about 2 percentage points less likely

to get a reply than Swedish-sounding names. The difference is, however, not statistically

significant. This suggests that we may reject the first hypothesis: in general, it seems to be no

difference in how responsive MCs are to Swedish-sounding and Arabic-sounding names.

Effect of Gender on Reply

Part from suggesting that ethnic Swedes receive better treatment than ethnic Arabs, previous

research also indicates that discrimination is more intense towards minority males than to

minority females. The assumption of the second hypothesis is therefore that the Arabic female

will receive more replies than the Arabic male.

Table V. Regression on effect of ethnicity on reply

Model 1 Model 2 Ethnicity -0.033 -0.023 (0.035) (0.037) Including controls No Yes _cons 0.585*** 0.736 (0.026) (1.565) Adj R2 -0.000 0.035 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

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In table VI, only the Arabic-sounding names are included to compare the effect of gender on

reply within the minority group. The Arabic female is coded as 1 and the Arabic male as 0

which makes Abdelhakim the reference category. As we can see, there is no gender difference

in response rate. Opposite to expected, Fatima in fact receives fewer replies than Abdelhakim.

The result thus shows that the second hypothesis can be rejected, and that Arabic females are

not favoured above Arabic males in their contact with MCs.

Effect of Socioeconomic Status on Reply

Lastly, it has been argued necessary to investigate if socioeconomic status may have an impact

on the level of discrimination minorities sustain. The expectation from the third hypothesis is

that among people with Arabic-sounding names, those that signal high socioeconomic status

will receive better treatment than those who do not signal such status.

Table VII shows the effect of socioeconomic status within the minority group, that is, only the

Arabic-sounding identities are included. The emails from the Arabic-sounding names that

includes the signature “licenced dentist” are coded as 1 and the emails that did not are coded as

0. As we can see, with 10% significance level, the Arabic-sounding identities that signal high

Table VI. Regression on effect of gender on reply within minority group

Model 1 Model 2 Arabicfemale -0.031 -0.005 (0.049) (0.054) Including controls No Yes _cons 0.566*** -0.240 (0.033) (2.349) Adj R2 -0.001 0.041 N 424 424 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Table VII. Regression on effect of socioeconomic status on reply within minority group

Model 1 Model 2 ArabicSES 0.062 0.091* (0.048) (0.055) Including controls No Yes _cons 0.521*** -0.470 (0.034) (2.341) Adj R2 0.002 0.048 N 424 424 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

42

socioeconomic status are about 9 percentage points more likely to get a meaningful reply than

their counterparts that do not signal such status. Thus, with 90 percent certainty, the third

hypothesis can be accepted. This suggests that socioeconomic status play a role in how

minorities are treated in their contact with MCs.

Extended Analysis - Interaction Effects

The results point to that gender has no effect on reply for the Arabic-sounding names while

socioeconomic status does. To broaden the picture and get a better understanding on how these

factors interact with ethnicity, it is further interesting to see if the effects differ between the

ethnic groups.

First, as table II on descriptive statistics shows, both of Marita’s identities got around 15 percent

higher response rate than Kevin’s identities. So, it looks like females from the majority group

are better treated in this regard than majority males. To see if the effect of gender significantly

varies between the ethnic groups, a regression including the interaction effect is provided.

Table VIII displays the interaction effect of gender in regard to receiving a meaningful reply.

An interaction effect can be interpreted as a variable modifying the effect of another variable.

In other words, we will investigate whether gender have an effect on the effect of ethnicity on

reply, and if that effect differs between Swedish and Arabic-sounding names. By looking at the

variable “Ethnicity_x_gender” we can see that the interaction effect is statistically significant

on the 10% level. Hence, there seems to be a difference between the ethnic groups on gender’s

effect on receiving a meaningful reply. While gender has no effect for the Arabic-sounding

Table VIII. Regression on the interaction effect of gender

Model 1 Model 2 Ethnicity 0.048 0.035 (0.047) (0.050) Gender 0.164*** 0.113** (0.053) (0.058) Ethnicity_x_gender -0.195*** -0.138* (0.071) (0.078) Including controls No Yes _cons 0.519*** 0.744 (0.034) (1.564) Adj R2 0.010 0.037 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

43

names, gender matters significantly for receiving a meaningful reply for the Swedish-sounding

names8. Furthermore, ethnicity has no effect on the meaningful response rate among males,

while an Arabic background has a significantly negative effect among females. Fatima is about

15 percentage points less likely to receive a reply than Marita, a difference that is significant

on the 5% level. For a regression table on the effect of ethnicity among females, please see

Appendix.

As with gender, it is also interesting to see if the effect of socioeconomic status differs between

the Arabic and Swedish-sounding names. Table IX shows the interaction effect of

socioeconomic status on meaningful reply.

As we can see, the interaction coefficient “Ethnicity_x_SES” is not statistically significant.

Thus, while signalling high socioeconomic status seems to increase the probability of receiving

a meaningful reply among Arabic-sounding names, the effect does not significantly differ

between the ethnic groups.

Summary

The results point to that one hypothesis can be rejected, one confirmed and one neither. H1

expected that identities with Arabic-sounding names would receive fewer replies than their

Swedish counterparts. In general, although the minority group were less likely to receive a

reply, we cannot establish that there actually is a difference between the groups. However, by

8 With 5% significance level, the Swedish female is roughly 16 percentage points more likely to get a meaningful reply than the Swedish male. For a regression table on this result, please see Appendix.

Table IX. Regression on the interaction effect of socioeconomic status

Model 1 Model 2 Ethnicity -0.060 -0.052 (0.051) (0.054) SES 0.007 0.017 (0.052) (0.054) Ethnicity_x_SES 0.055 0.068 (0.071) (0.076) Including controls No Yes _cons 0.581*** 0.551 (0.038) (1.577) Adj R2 -0.001 0.036 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

44

digging deeper and separating the genders, it was found that on the 5% significance level

ethnicity does have a negative effect on reply among females. Thus, my judgement is that H1

cannot be completely rejected nor confirmed.

H2 assumed that females from the minority group would receive more replies than males from

the minority group. That proved false and the result instead hinted the opposite, as Fatima was

in fact slightly less likely to receive a reply than Abdelhakim. That difference was, however,

not statistically significant. The interaction analysis further showed that there is a significant

difference between the ethnic groups on gender’s effect on receiving a meaningful reply.

Swedish females are favoured over Swedish males, while the same effect is not found among

the Arabic-sounding identities.

Lastly, H3 assumed that within the minority group, individuals that signal high socioeconomic

status would be better treated than those who do not signal such status. With 10% significance

level, this is confirmed. The interaction analysis, however, showed no significant difference in

the effect of socioeconomic status between the ethnic groups.

Discussion The following section will present an analytical discussion of the thesis and subsequent ideas

for future research.

The Results In contrast to previous research on ethnic discrimination in Sweden, no evidence of general

ethnic discrimination is found in this context. This further stands in contrast to similar field

experiments on political representatives in other parts of the world. Furthermore, also unlike

previous research, the findings show no evidence of female premium among the Arabic-

sounding names. A female premium was, however, found for the Swedish-sounding names. We

could see that the effect of gender significantly differs between the ethnic groups, suggesting

that the value of being female is higher among native Swedes than among individuals of Arabic

background. It was also demonstrated that among females, ethnicity did in fact have a negative

effect on reply. The fictitious Arabic female, Fatima, got significantly less replies than her

Swedish counterpart, Marita. Lastly, the findings support the notion that perceived

socioeconomic status should be considered in relation to ethnic discrimination. On the 10%

45

significance level, signalling high socioeconomic status increased the probability of the Arabic-

sounding names to receive a meaningful reply with 9 percentage points. For Abdelhakim only,

the effect was about 13 percentage points. In fact, Abdelhakim of (perceived) low

socioeconomic status received the lowest amount of replies of all identities, while Abelhakim

that signalled high socioeconomic status received roughly the same response rate as the

Swedish female who went out on top.

At first glance, these findings are good news. Responsiveness bias on the basis of ethnicity only

does not seem to occur in the contact between citizens and MCs. Yet, we can see that the results

tend to vary between groups and that different social identities may interact with the level of

discrimination. It is for example important to note that local politicians are significantly less

responsive towards Arabic females than towards Swedish females. Furthermore, perceived

socioeconomic status seem to affect the level of responsiveness towards the minority group.

However, the effect of socioeconomic status differed between the genders as it mattered

significantly for Abdelhakim but not for Fatima. This goes against what is expected from the

SDT’s subordinate male target hypothesis as well as much previous research on ethnic

discrimination. As males and females with foreign background vote at roughly the same rates9,

political strategy should not be the explanation here. If high socioeconomic status and skill

reward foreign males more than foreign females in relation to political representation, it could

have both social and political implications. These findings are particularly concerning in the

case of Sweden, considering its reputation as immigration friendly, egalitarian and

democratically well-functioning. In general, the somewhat diffuse results and that

discrimination seems to vary depending on different mixtures of ethnicity, gender and

socioeconomic status is important. It stresses the need to acknowledge that ethnic

discrimination is a complex issue where different social identities overlap and interact with

each other. It so encourages the literature to develop and get a more profound understanding of

ethnic discrimination, in contemporary Sweden as well as elsewhere.

First, including interacting effects of other relevant social identities is deemed necessary. While

gender is more common, the interacting effect of socioeconomic status seems to be rather

neglected in previous discrimination research. If the likelihood of ethnic discrimination depends

9 Of those with foreign background, 76 percent of the females and 74 percent of the males voted in the 2014 national election (Statistics Sweden).

46

on (perceived) socioeconomic status and gender, it is highly motivated to be further researched.

I argue that the results here support this notion.

Second, this may also be related to different theories of discrimination in important ways. To

isolate the mechanisms behind ethnic discrimination is not the focus of this study. Quantitative

methods are in general limited when it comes to reach deeper insights about the world. For this,

qualitative methods are better equipped. Here, I only investigate if ethnic discrimination occurs

in the contact between citizens and MCs but do not test why ethnical bias exist. This does,

however, not prevent us from ponder upon what may explain biased behaviour. In the field of

economics, the two leading theories of ethnic discrimination are statistical and taste-based

discrimination (see e.g. Phelps, 1972 and Becker, 1971). The first refers to when individuals

engage in discrimination because of strategic and “rational” considerations which are based on

overall statistical trends. Decisions are then based on the beliefs about typical characteristics of

the group the individual belongs to or is believed to belong to. In the context of political science,

it is for example possible to imagine that politicians that belong to a party which usually do not

attract votes from people with foreign background are less inclined to be of service towards that

group. Not because they dislike them, but because they do not think that they will gain from it

politically and therefore prioritise other constituents. In other words, politicians may for

example be less responsive to a request from someone named Abdelhakim than someone named

Kevin due to strategic reasons. Similar reasoning could be applied on socioeconomic status,

which further points to its importance in the context of discrimination. In this study,

socioeconomic status had an effect on the minority group’s likeliness to get a meaningful reply.

This could be an example of statistical discrimination, as the politicians potentially base their

treatment on perceived correlation between the identity’s ethnicity and socioeconomic status

which could in turn be related to political strategy. Such beliefs may not necessarily be

incorrect. For example; immigrants, as a group, have a lower employment rate than natives.

However, even if they are accurate, those kinds of perceptions may lead to actions that are

discriminatory and unlawful. Statistical discrimination stands in contrast to what is called taste-

based discrimination, which instead are grounded in preferences that are not readily explicable

by rationality. One taste-based explanation in this context could thus be that politicians

discriminate because of their own personal prejudices and dislike against people with Arabic-

sounding names. It is, however, difficult to convincingly distinguish between statistical and

taste-based discrimination in quantitative research as they often overlap. Fryer & Levitt (2004)

highlights this challenge, what appears to be taste-based bias may often be another form of

47

statistical ditto. It is further particularly difficult to pin down taste-based motivations among

politicians since there are few observable environments where politicians are likely to believe

that their actions have no political consequences (Broockman, 2013). However, this should not

prevent us from trying and some attempts have been made in previous research (see e.g. Butler

and Brookman, 2011 and Broockman, 2013). So why may it be considered important to

differentiate between different mechanisms of discrimination? As Butler and Brookman (2011)

states, it hardly matters from the perspective of the receiving end. Discrimination is unfair

regardless of its source and violates the democratic principle of equality. Simply proving that

ethnic discrimination still exists in contemporary democracies is indeed important in itself.

However, the decisions politicians make when their political incentives are weak still matter a

great deal for people. As previously mentioned, how politicians act, regardless of political

incentives, may have effect on issues such as trust for the political system and political

participation. And not the least, deeper insights about what drives discriminatory acts is

necessary to inform us about the range of possible solutions to overcome it.

Lastly, this study only includes response rate as dependent variable. Using only response rate

as a measurement of discrimination is very transparent and takes little effort. Since a non-

response is a well-defined data, it makes it a simple and easily implementable measure of how

willing the MCs are to help citizens of different ethnicity. However, it is reasonable to believe

that the helpfulness of replies varies. As Hemker and Rink (2017) points out, this variation in

response quality matters for the substantive question whether some requesters receive better

treatment than others. As they exemplify, suppose that officials are obliged to respond to every

incoming request. This would lead to response rates on 100% for everyone, but these may not

necessarily be equally helpful across different groups. For example, individual officials that are

prejudiced against minorities would in this scenario answer all requests but could treat them

differently in terms of helpfulness or encouragement. Hence, focusing only on response rates

in correspondence tests may be problematic due to measurement imperfection or even

misleading results (Ibid, p. 788). Measuring more than response rate is increasingly used in

correspondence tests among bureaucrats and would indeed have been interesting here. If

politicians treat citizens differently in terms of the quality of the information they provide, how

friendly and encouraging they are, and so forth, it would too be discriminatory. However, while

quality bias amongst Swedish politicians would be interesting to investigate, in contrast to many

types of bureaucrats, politicians are not obliged to answer all requests. Thus, Hemker and

Rink’s apprehension are potentially not as important in relation to politicians since their bias

48

should to a larger extent be able to manifest in response rate, that is, if they bother to answer at

all.

When conducting this experiment, I identified elements that worked more or less as intended.

Following, I will discuss the instances that I deem important to note as they may have an effect

on the result. The design is, as mentioned, first of its kind in this context and the lessons learned

here may help guide future experiments.

First, it should be considered that MCs that did not run for re-election might be less induced to

be of service. This undermine the incentive to answer and could potentially be a reason for

absent replies. In retrospect, it may therefore have been wise to only include MCs that run for

re-election in the study. This notion got consolidated as some MCs did refer to stepping down

as a reason to forward the email to another representative. It is also possible to imagine that

some simply did not answer at all based on the same reason. Thus, a no reply could be due to

the MC planning not to continue his or her political career rather than an effect of the treatment. However, it could be argued that regardless of the future plans of the MC, they should be of

service and represent their constituents for as long as they hold office. It could, potentially, also

be the case that such a situation and lack of incentive to answer would enhance the MCs bias

as he or she only bother to answer requests in line with their preferences. This is, however,

difficult to know but worth keeping in mind. Related to this is also that the point in time for the

study, i.e. during election campaigning, potentially makes the study less representative for other

points in time. Although using the national election as deadline had its advantages and

arguments, it is possible that the result would be different if one would contact the

representatives at other times of the mandate period.

Worth mentioning is also that this study treats the MCs email addresses and not necessarily the

MCs themselves. Responses (or lack thereof) may come from someone else than the MC, such

as a political secretary or other staff- or party members. However, since I use the MCs official

email addresses I would argue that the person answering the email did so in an official capacity

on the behalf of the MC. In addition, all responses that are included in the results are signed by

the MCs themselves. I find no reason to suspect that someone else is the actual writer or that it

threatens the validity of the results.

49

Another consideration that could be valuable to think upon if conducting a similar experiment

is to match the research subject with the inquiry, i.e. include the politicians particularly involved

in the questions asked. I recommend this due to that it was quite common that the MC forwarded

the email to the party member that was responsible for school issues. Since the MCs were the

research subject in this study and not whoever was forwarded, some answers needed to be coded

as a “no-reply” even though the identity was in fact answered - but by the wrong person. This

is of course preferable to avoid, as a real citizen likely would be equally or even more content

with an answer from the most conversant representative. Directing the emails toward politicians

specifically responsible for the particular inquiry would likely generate a higher response rate

that could be analysed.

Furthermore, a general method problem is the so called “spillover effect”, that is that the

subjects may talk to and influence each other. This, of course, may affect the result.

Unfortunately, it is also difficult to know to which extent it happens. As previously mentioned,

it became evident that a few MCs realised that the emails were fake and those got excluded

from the analysis. Similarly, it became clear that some MCs that were party members in the

same municipality talked to each other about the email and let one of them send a joint answer.

Hence, there is a risk that absent replies that got coded as such were due to spillover effects and

not the treatment. This also shows the importance to take measures to avoid suspicion.

Occasionally, the subjects evidently discuss incoming emails, why using the same identity to

all MCs in the same municipality proved to be wise. This is noteworthy for this kind of

experiments - for credible results for one self but also for not jeopardising the ability to perform

credible correspondence tests in the future.

In general, one should keep in mind that responsiveness may depend on other things than bias.

As discussed above, other, non-observable factors may interfere and affect the results. Also,

using names as proxy for ethnicity suffers from the risk that names carry other connotations

such as religion or age. I cannot say for sure whether differential treatment is based on ethnicity

or other characteristics associated with the names that are used. In this study I interpret

responsiveness bias as having been caused by ethnicity in combination with gender and

socioeconomic status. However, it should be kept in mind that it may also have been caused by

other factors or by a combination of factors. Still, compared to other methods, the experimental

design is particularly applicable to provide reliable estimates of discrimination. The random

distribution of treatment help preventing non-observed factors from being systematic and

50

thereby producing measurement errors. So, while it is important to note that other factors than

the treatment may impact responsiveness, one can assume that the probability for such factors

interfering is equally large for all identities. Thus, since the eight groups on average are similar

except for the treatment, the estimated differences should be a reliable measurement of

discrimination. As we could see however, the control variables did have some impact which

suggests that the randomisation was not completely robust. Hence, the results that include them

have been discussed here.

Ideas for Future Research In sum, the above discussion leads to mainly four appeals: First, this study acknowledges the

need to assure that we do not confound ethnic discrimination with socioeconomic ditto. It is

problematic if research on ethnic discrimination produce misleading results due to neglecting

the potential impact of perceived socioeconomic status. The results here indicate that

socioeconomic status affects how minorities are treated. This is furthermore related to my

second appeal: beside providing evidence of discrimination in action, it is meaningful to try to

understand the underlying causes of that behaviour to better inform us how to correct it. Do

people discriminate minorities simply because they dislike them, or because they use ethnicity

cues to infer other information? For example: if politicians tend to be less responsive towards

individuals of foreign origin because they generally vote at lower rates (which would be an

example of statistical discrimination), a solution could be to increase the voter turnout among

that group. If, instead, responsiveness bias is due to taste-based discrimination, different tactics

would be needed. In other words, knowing the reasons for discrimination helps identify the

most effective methods for overcoming it. This is a limitation in this study which would be

meaningful to investigate in future research. The third appeal is grounded in another limitation

here, i.e. the narrow focus on response rate. In contrast to previous field experiments on political

responsiveness in the US and South Africa, no evidence of general discrimination is found.

This is positive. However, it is pointed out that discrimination may occur in more obscure ways,

why it could be meaningful to investigate also the quality of replies in future correspondence

tests on politicians. Previous research has in fact shown that these more informal aspects of

discrimination rather than simply response rate are most prevalent within EU. As mentioned in

footnote four in the methods section, the original plan was to include multiple dimensions of

discrimination in this study. Due to time constraints it proved untenable and response rate had

to suffice. Lastly, lessons from this study shows that in future correspondence tests, adjustments

51

of the design should be made in consideration to the purpose and population, and not the least

to avoid spillover effects and the risk of exposure.

Other interesting approaches for future studies could be to investigate if the level of

discrimination differ among male and female politicians. Both the SDT and previous research

on the labour market suggest that discrimination is primarily executed by majority males (see

e.g. Carlsson & Rooth, 2007). It would further be valuable to do similar studies involving other

ethnic minorities, such as Finnish or Latin American. Another interesting factor to investigate

in this context is potential differences between political parties.

Conclusion Ethnic discrimination is disadvantageous treatment on the basis of ethnicity. Discrimination

does not necessarily imply to be ill-treated but treated less favourably than a similar situated

individual from another ethnic group. This study aimed to capture this particularly subtle form

of discrimination by presenting a field experiment on the topic of ethnic discrimination in the

contact between politicians and their citizens. A correspondence technique was used to

investigate whether ethnicity affects how responsive Municipal Commissioners are to questions

regarding school politics. Eight fictitious individuals were created, consisting mixtures of

treatment that beside ethnicity tested the effect of gender and socioeconomic status.

Previous research on discrimination in Sweden has found that people with Arabic/Middle

Eastern background are treated less favourably than native Swedes in most part of society. It

has also been suggested that discrimination against foreign males is more intense than that

against foreign females. However, according to these findings, the same do not apply to MCs.

The result of this inquiry is somewhat ambiguous. The hypotheses presented in the introduction

expressed an expectation to find (H1) differentiated treatment to the disadvantage of the Arabic-

sounding names, (H2) that the Arabic male would be more disadvantageously treated than the

Arabic female and (H3) that among the Arabic-sounding names, those who signal high

socioeconomic status would be more advantageously treated than those who do not. While the

Arabic-sounding names indeed got less replies than the Swedish-sounding names, it cannot be

confirmed that ethnic discrimination occurs in general. However, an effect was found

conditioned by gender. Thus, the first hypothesis cannot be completely accepted nor rejected.

The second hypothesis can be rejected however, as the findings show that there is no gender

52

premium for the female within the minority group. It is also demonstrated that there is a gender

premium for females with Swedish names and that the effect of gender differs significantly

between the ethnic groups. When it comes to the effect of socioeconomic status, the hypothesis

was confirmed. The identities within the minority group that signalled high socioeconomic

status were more likely to receive a reply. The results further demonstrate that socioeconomic

status has a bigger effect for the Arabic male than the Arabic female. Considering Sweden’s

reputation as a highly egalitarian and democratic country, these findings are sufficient cause for

concern.

As demonstrated, the issue of ethnic discrimination within the context of political

responsiveness is rather complex. The findings are not straight forward. On the contrary, they

vary depending on different mixtures of treatment and also contrasts previous research

conducted in other social domains. This is interesting in itself and stresses the importance to

get a better understanding of ethnic discrimination in contemporary societies. These findings

urge future research to consider how other factors, especially other social identities, may

interact with ethnicity and affect levels of discrimination. It is also deemed necessary, even

though difficult, to try to identify the underlying causes of ethnic disparities. Thus, testing

different theories of ethnic discrimination would be meaningful in future experiments. While

identifying discrimination in action is an important first step – one must know that a problem

exist to approach it – understanding what drives biased behaviour is necessary to guide effective

measures to overcome it. There is a number of reasons for politicians and policymakers to

prevent discrimination. Because discrimination is negatively correlated with health, trust for

the political system, interest to take part in politics and integration, to name a few. But also, as

the very first sentence of this thesis states, because the existence of political equality is a

fundamental premise of democracy.

Since this study is the first of its kind in a Swedish context, the design is not perfect and has its

limitations. It is challenging to break new ground and I have in retrospect found things that

could have been done differently. However, irrespectively of potential flaws in execution, I

consider the results valuable and a contribution to the research field. In addition to increased

knowledge about the occurrence of ethnic discrimination, the thesis also makes a

methodological contribution by adapting and developing the correspondence technique to better

inform us about ethnic discrimination in a Swedish political setting.

53

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Appendix Email Formulation

Below is the email that was sent. The {{ }} indicates where the recipient’s name and

municipality were inserted. The [ ] contains the signature that was included in four of the

identities.

“Hej {{Förnamn}},

Jag och min familj har just flyttat till {{Kommun}}.

Då vi har barn i skolan är just skolfrågor extra

intressant inför valet. Vi undrar hur ni skulle vilja

förbättra skolan i kommunen? Tycker du att det är rätt

att ge vissa skolor mer resurser eller ska alla ha lika?

Tacksam för svar via mail.

Med vänlig hälsning

{{Avsändare}}

[Leg. Tandläkare]”

“Hi {{First name}},

Me and my family have just moved to

{{Municipality}}. Since we have children in school,

school issues are extra interesting before the election.

We wonder how you would like to improve school in

the municipality? Do you think it’s right to give some

schools more resources or should all have the same?

Grateful for reply via email.

Sincerely

{{Sender}}

[Licenced dentist]”

Email Addresses The email addresses that was used were the following:

Identity Signature Email address

Kevin Andersson Yes kevin.andersson43@gmail.com

Marita Andersson Yes marita.andersson495@gmail.com

Kevin Andersson kevin.andersson200@gmail.com

Marita Andersson marita.andersson572@gmail.com

Abdelhakim Hassan Yes abdelhakim.hassan123@gmail.com

Fatima Hassan Yes fatima.hassan1346@gmail.com

Abdelhakim Hassan abdelhakim.hassan2457@gmail.com

Fatima Hassan fatima.hassan5476@gmail.com

59

Coding Scheme The different variables were coded as follows:

Reply Ethnicity Gender Soc.eco status

Yes = 1 Arabic = 1 Female = 1 High SES = 1

No = 0 Swedish = 0 Male = 0 Low SES = 0

Logistic Regression Table I Appendix. Logistic regression on effect of identity on generous reply

Ref.cat Abdelhakim Model 1 Model 2 Kevin, sign. 1.060 1.039 (0.272) (0.293) Marita, sign. 2.096** 1.95** (0.625) (0.637) Kevin 1.028 1.12 (0.270) (0.340) Marita 2.073** 1.54 (0.646) (0.528) Abdelhakim, sign. 1.793** 1.79* (0.498) (0.574) Fatima, sign. 1.094 1.21 (0.278) (0.347) Fatima 1.154 1.04 (0.326) (0.328) Including controls No Yes _cons 1.031 2.057 (0.179) (15.57) Pseudo R2 0.01 0.06 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Regression on Effect of Gender on Reply for Swedish-sounding Names

Table II Appendix. Regression on effect of gender within the majority group

Model 1 Model 2 Swedishfemale 0.164*** 0.156** (0.052) 0.063) Including controls No Yes _cons 0.519*** 2.087 (0.033) (2.517) Adj R2 0.02 0.02 N 364 364 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

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Regression on Effect of Ethnicity Among Females Table III Appendix. Regression on effect of ethnicity among females

Model 1 Model 2 Ethnicity -0.147*** - 0.145** (0.053) 0.064) Including controls No Yes _cons 0.682*** 0.991 (0.040) (2.406) Adj R2 0.02 0.00 N 346 346 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Regressions on Generous Response Rate Effect of Identity on Reply Table IV Appendix. Regression on effect of identity on reply.

Ref.cat Abdelhakim Model 1 Model 2 Kevin, sign. 0.097 0.079 (0.060) (0.063) Marita, sign. 0.196*** 0.144** (0.066) (0.070) Kevin 0.046 0.017 (0.061) (0.066) Marita 0.207*** 0.127* (0.069) (0.073) Abdelhakim, sign. 0.201*** 0.178** (0.063) (0.069) Fatima, sign. 0.057 0.066 (0.059) (0.064) Fatima 0.103 0.039 (0.066) (0.069) Including controls No Yes _cons 0.576*** 1.611 (0.040) (1.513) Adj R2 0.02 0.04 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

61

Effect of Ethnicity on Reply

Table V Appendix. Regression on effect of ethnicity on reply

Model 1 Model 2 Ethnicity -0.045 -0.023 (0.033) (0.035) Including controls No Yes _cons 0.701*** 1.812 (0.025) (1.481) Adj R2 0.00 0.03 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Effect of Gender on Reply

Table VI Appendix. Regression on effect of gender on reply within minority group

Model 1 Model 2 Arabicgender -0.008 -0.013 (0.046) (0.052) including controls No Yes _cons 0.659*** 0.837 (0.032) (2.258) Adj R2 -0.00 0.03 N 424 424 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Table VII Appendix. Regression on the interaction effect of gender on reply

Model 1 Model 2 Ethnicity 0.011 0.023 (0.044) (0.048) Gender 0.129*** 0.092* (0.050) (0.055) Ethnicity_x_gender -0.137** -0.108 (0.067) (0.073) Including control No Yes _cons 0.648*** 1.815 (0.032) (1.480) R2 0.01 0.03 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

62

Effect of Socioeconomic Status on Reply Table VIII Appendix. Regression on effect of socioeconomic status on reply within minority group Model 1 Model 2 ArabicSES 0.082* 0.121** (0.046) (0.052) Including controls No Yes _cons 0.615*** 0.578 (0.033) (2.243) Adj R2 0.01 0.04 N 424 424 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01

Table IX Appendix. Regression on the interaction effect of socioeconomic status on reply

Model 1 Model 2 Ethnicity -0.071 -0.043 (0.048) (0.051) SES 0.028 0.050 (0.049) (0.051) Ethnicity_x_SES 0.054 0.055 (0.067) (0.072) Including controls No Yes _cons 0.686*** 1.486 (0.036) (1.489) Adj R2 0.00 0.04 N 788 788 Standard errors in parentheses * p<0.10 ** p<0.05 *** p<0.01