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ERASMUS UNIVERSITY ROTTERDAM Erasmus School of Economics

Bachelor Thesis

International Bachelor of Economics and Business Economics

The impact of refugee arrival on political trust – Quasi-experimental evidence from Sweden

Abstract

Immigration policies and its effect on the host economy remain a central question in political

debates today. Therefore, several studies have investigated the relationship between ethnic

diversity and the local population’s political attitudes. This study takes a step aside from studying

political preferences, and instead uses the individual fixed effects methodology to investigate the

causal impact of refugee inflow on changing levels of trust in politicians. For this, I exploit the

exogenous variation of refugee placement to municipalities in Sweden, during a refugee dispersal

program carried out in the 1985-1994 time period. The baseline estimates indicate a statistically

insignificant relationship between refugee inflow and changing levels of political trust, mainly driven

by the effect sizes being indistinguishable from zero. However, a moderately small negative effect

between refugee inflow and varying political trust is seen during the time period 1986-1991, which

is arguably the time frame where refugee inflow was more exogenous to changing political trust

levels. Nevertheless, it remains unknown whether the statistically significant estimate obtained

translates to a change in citizens’ support for policies. Furthermore, no statistically significant

relationship is seen between political trust levels and refugee inflows amongst the following

demographic groups: (1) left-wing supporters, (2) right-wing supporters, (3) highly educated

individuals and (4) low educated individuals. Future studies may consider studying the difference

between short and long term impact of refugee arrival on political trust of the local population.

Naina Kumar 494629

Supervisor: Professor Robert Dur

Second assessor: Professor Dina Sisak

Date final version: 26 July 2021

The views stated in this thesis are those of the author, and not necessarily those of the supervisor,

second assessor, Erasmus School of Economics and Erasmus University Rotterdam.

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Table of Contents

Section 1: Introduction ................................................................................................................................................ 3

Section 2: Literature Framework and Hypotheses ......................................................................................................... 6

2.1 Studies based on political trust ......................................................................................................................... 6

2.2 Studies based on immigration and political extremism ............................................................................................ 8

2.3 Potential Mechanisms ...................................................................................................................................... 9 2.3.1 Group position theory ............................................................................................................................................. 9 2.3.2 Salience and size of minority community............................................................................................................. 10 2.3.3: The ethnic competition theory ............................................................................................................................ 10 2.3.4 Contact theory ....................................................................................................................................................... 11

2.4 Hypothesis ..................................................................................................................................................... 11

2.5 Studies supporting heterogeneity analysis ....................................................................................................... 12 2.5.1 Political trust and left-right self-placement ......................................................................................................... 12 2.5.2 Political Trust and Education ................................................................................................................................ 13 2.5.3 Political Trust and Age ........................................................................................................................................... 13

Section 3: Institutional context .................................................................................................................................. 13

3.1 The refugee dispersal program ........................................................................................................................ 13

3.2 Exogeneity of the program .............................................................................................................................. 15

3.2.1 Threat to identification: internal migration of refugees after their initial placement........................................ 16

3.2.2 Threat to identification: municipalities negotiating with the state to allocate fewer refugees to their region.... 17

3.2.3 Threat to identification: preference allocation for some refugees to bigger municipalities ............................... 18

Section 4: Data ......................................................................................................................................................... 18

Section 4.1 Dataset 1: SNES .................................................................................................................................. 18

Section 4.2 Dataset 2: Heléne et al., (2011)............................................................................................................ 19

Section 4.3 descriptive statistics ........................................................................................................................... 20

Section 5 Methodology ............................................................................................................................................. 22

5.1 Main analysis ................................................................................................................................................. 22 5.1.1 Motivation for chosen methodology: ................................................................................................................... 22 5.1.1 Regression equation: ............................................................................................................................................. 22 5.1.2. Assumptions of methodology .............................................................................................................................. 23

5.2 Heterogeneity analysis ................................................................................................................................... 23

5.3 Robustness Checks ......................................................................................................................................... 24

Section 6 Results ....................................................................................................................................................... 24

6.1 Main analysis ................................................................................................................................................. 24

6.2 Heterogeneity analysis across demographic groups .......................................................................................... 26

6.3 Robustness checks .......................................................................................................................................... 28

Section 7 Discussion: ............................................................................................................................................ 30 Section 7.1 Discussion of results .................................................................................................................................... 30 Section 7.2 Limitations of the study .............................................................................................................................. 33 Section 7.3 Robustness checks for future research....................................................................................................... 34 Section 7.4 External Validity .......................................................................................................................................... 35

Section 8 Conclusion: ................................................................................................................................................ 36

References: .............................................................................................................................................................. 37

Appendix .................................................................................................................................................................. 40

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Section 1: Introduction

As a response to the dwindling political trust levels worldwide, the area of government and

political trust has been widely studied since the 1980s (Soon & Cheng, 2011). From the literature in

the trust field, it is known that government performance may impact one's political perceptions,

thereby influencing one's trust towards the government and politicians (Keele, 2007). Therefore, it is

worthwhile to investigate whether specific policy changes that have altered political perceptions

today also impact political trust.

One of the critical factors dividing political opinion and increasing polarization remains the

debate surrounding immigration policies (Banerjee & Duflo, 2019). With increased immigration, the

population of the host economy are concerned over labour market opportunities and overall

economic progress, even if the consensus in economics literature points towards minimal changes in

the labour market driven by immigration (Card, 2005). Thus, using evidence from Sweden, my

research answers the following question: To what extent does refugee inflow impact variations in

individuals' trust towards politicians? This question is essential to be answered because political and

government trust is closely linked, and the population's support is instrumental for a functioning

economy and the implementation of new policies (OECD, 2019). Therefore, this investigation

examines whether increasing refugee inflows cause a change in trust levels of the Swedish public

towards politicians, who are the elected officials representing the public's views and sentiments within

the government. This investigation is relevant as it studies the populations' trust in politicians as a

consequence of one of the most crucial political debates today, thereby providing insight to political

actors on how their policies can impact trust levels.

The main contribution of this paper is to investigate whether a causal relationship exists between

the refugee inflow per municipality and changing political trust amongst the population. For my

research, political trust is defined as one's trust in politicians. To measure "political trust", the

following question is asked to respondents: "Generally speaking, how high is your trust in Swedish

politicians? Is it very high, rather high, rather low, or very low?". For this, quasi-experimental evidence

from a refugee dispersal program conducted between 1985-1994 is used to carry out the analysis.

This program was formed due to a surge in refugee inflows to larger municipalities resulting in a

heavier concentration of refugee population. Therefore, as a reaction to the growing population in

larger cities, the program aimed to achieve an equal distribution of refugees throughout the country

by calling upon smaller municipalities to participate in the program (Dahlberg, Edmark & Lundqvist,

2012). Upon its inception in 1985, only 60 municipalities were involved in the refugee intake. However,

by 1990, almost all 288 municipalities took part in the program (Åslund, Edin, Frediksson & Grönqvist,

2011).

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The refugee dispersal program had created substantial variation in the share of refugees within

and between municipalities over the investigated time period. Moreover, it has also provided

exogenous variation of the allocation of refugees to municipalities by eliminating refugee self-

selection. Previous literature exploits this quasi-random allocation of refugees to municipalities to

investigate several relationships, such as the impact of: ethnic enclaves on migrants' economic success

(Edin, Frederiksson & Åslund, 2003; Andersson, 2020), neighbourhoods on education attainment

(Åslund, Edin, Fredriksson & Grönqvist, 2011) and ethnic diversity on redistribution preferences of

population (Dahlberg et al., 2012), to name a few. The identifying assumption in all these papers vary

significantly. In my study, the main identifying assumption is that the refugee inflow and allocation to

municipalities is exogenous to the changing levels of political trust experienced by the Swedish citizens

living in those municipalities.

I used individual-level data from the Swedish National Election Studies Program (SNES) to obtain

individual levels of political trust. SNES surveys political subjects every national election year. This

survey is in the form of a rotating panel, whereby each individual is interviewed twice, and half of the

sample changes in each wave. Since each individual is interviewed more than once, time-invariant

variables affecting political trust levels can be effectively controlled using the individual fixed

methodology. Furthermore, using Helené et al., 2011's dataset, I matched the individual-level political

trust responses to the corresponding municipality-level variables. These include the explanatory

variable refugee inflow (which I transform into refugee shares) into each municipality and other

municipality characteristics used as controls. In my analysis, I employ the individual fixed effects

method to eliminate the impact of any time-invariant omitted variables while controlling for

municipality and year fixed effects. Further, I perform a heterogeneity analysis to see whether certain

demographic groups drive a substantial effect across education, age, and political preferences.

The contribution of this paper is threefold. Firstly, the article contributes to the general body of

literature investigating variation in political or government trust levels. The consensus in this realm

indicates that people's trust in politicians and the government vary as a consequence of the behaviour

of these stakeholders, especially if it contrasts with their views (Chanley, Rudolph & Rahn, 2000;

Clarke, Stewart & Whiteley, 1998; Lanoue & Headrick, 1994). However, my investigation adds

explicitly to the sub-set of literature that explores the impact of public policies on political trust levels.

I do this by focusing on a specific socio-economic policy, which is less frequently carried out in the

fields of public administration.

Secondly, it contributes to the vast literature investigating the consequences of migration on

host countries. To elaborate, it adds to the sub-domain of literature that studies the population's

political attitudes towards immigration and diversity. Several studies carried out in European

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countries point towards increasing far-right party support as a response to rising immigration levels

(Halla, Wagner & Zweimüller, 2017; Dustmann, Vasiljeva, Damm 2019; Mehic, 2019; 2020; Barone,

Ignazio, Blasio, Naticchioni; 2016; Otto & Steinhardt; 2014). Similarly, preferences for redistribution

have also decreased upon greater ethnic diversity within a community (Dahlberg et al., 2012). While

there may be several theories that could explain this relationship, such as ethnic competition

(Scheepers, Gijsberts & Coenders, 2002), group identity (Stets & Burke, 2000) and salience (Stryker,

1980), among others; it remains challenging to determine which of these effects are more prominent.

While most empirical studies indicate a direct relationship between the increasing size of the migrant

community and anti-immigrant sentiments, bearing in mind the contact hypothesis, I postulate that

increasing refugee inflow may cause either a deteriorated or improved impact on political trust.

My methodology also differs from most studies, wherein I exploit the exogenous variation in

refugee allocation using individual fixed effects. In contrast, most studies use the refugee allocation

as an instrument to explain another variable, thus opting to use the instrumental variables

methodology. Therefore, unlike several studies in this field, this investigation uses the influx of

refugees as an explanatory variable rather than the more heavily investigated topic of immigration or

ethnic diversity, in general.

Finally, this study also bridges the gap between the literature on political trust and refugee-

intake by investigating the impact of refugee inflow on variation in political trust. To my knowledge,

this topic has not yet been causally concluded.

Despite my initially proposed hypothesis, the baseline results of this study point towards a

statistically insignificant relationship between refugee shares and political trust. The insignificant

relationship realized may stem from the coefficients obtained, which are nearly indistinguishable from

zero. However, the robustness checks reveal that a statistically significant relationship arises between

refugee shares and political trust in the time period: 1986-1991, where refugee inflows was likely to

be more exogenous to changing political trust levels. In this case, after controlling for municipality-

covariates, an increase in share of refugees by 1% point causes a 0.263 points reduction in political

trust levels along the 4-point Likert scale, on average. Therefore, the robustness checks reveal that

the estimates are sensitive to the time period considered and sample size used. These estimates

indicate that there remains a trade-off when answering the question whether refugee inflow impacts

changes in political trust levels.

On the one hand, a larger sample size involving all time periods indicates an insignificant

relationship whereby the point estimates nearly reach 0. However, the restricted time period which

arguably allocated refugees to municipalities more randomly, indicates a statistically negative

relationship with a moderately small negative point estimate. Thus, I fail to reject the null hypothesis

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that refugee inflow causes no effect on individual political trust levels for the overall sample. However,

I reject the null hypothesis for the 1986-1991 time period.

Similarly, the heterogeneity analysis also reflects a statistically insignificant relationship for those

who are (1) left-wing supporters, (2) right-wing supporters, (3) highly educated individuals and (4) low

educated individuals across age groups, stemming from the very small effect sizes obtained in the

regression analysis. These results are consistent with literature which show that groups minimally

impacted by policies do not experience significant fluctuations in political trust (Klingemann & Fuchs,

1995).

The structure of the paper is as follows: Section 2 provides an overview of the literature that

has been carried out so far, along with a discussion on potential mechanisms that can cause a

relationship between the main variables investigated. Section 3 describes the refugee dispersal

program and discusses the extent to which refugee inflow can be considered exogenous to political

trust levels. Section 4 describes the data, followed by section 5, which outlines the methodology. The

results of the main analysis and robustness checks are reported in Section 6. Finally, section 7

discusses these results obtained, and section 8 concludes the research.

Section 2: Literature Framework and Hypotheses

The literature framework is split into the following sections. Section 2.1 provides an overview

of the literature based on political trust which are closed linked to this paper and section 2.2 explains

the link between immigration and political preferences. Section 2.3 provides several mechanisms that

may allow for a relationship between refugee inflow and changing political trust. Section 2.4 uses

these mechanisms to hypothesize the relationship between the two main variables. Lastly, section 2.5

outlines some studies that indicate why changes in political trust may vary between demographic

groups, and accordingly hypotheses are derived for sub-groups of the population.

2.1 Studies based on political trust

Political trust refers to the confidence an individual places in its institutions and governments

(Soon & Cheng, 2011). Realizing a dip in political trust levels since the 1980s, it has been of value and

interest to study what factors cause variations in political trust levels (Soon & Cheng, 2011). While

political trust is multi-faceted, I specifically focus on an individual’s trust for his politicians; thereby

defining political trust as such in my paper. I use this definition as politicians are instrumental to the

functioning of both political institutions and the government. Thus one’s political trust is likely to be

heavily impacted by their perceptions of politicians.

Political scientists have developed several theories and reasons for varying political and

government trust levels while usually investigating when changes in political trust occur, what reasons

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cause individuals to experience such changes, and which groups are most likely to experience them.

The following paragraphs elaborates on each of these components respectively.

There remain two main theories exploring political trust levels experienced over time, namely

institutional and cultural theories. While the determinants of political trust variations overlap

between the two approaches, the critical difference remains when an individual experiences a change

in political trust. Cultural theories indicate that values generating political trust usually occur at a

young age (Ingelhart, 1997; Putnam, 1993; Soon & Cheng, 2011). Those advocating for cultural

theories generally believe that direct interactions with institutions matter less as an adult. Therefore,

according to this stream of thought, political trust is likely to vary less after reaching adulthood and

may only fluctuate due to defining experiences.

On the other hand, institutional theories postulate that political trust depends on one’s

experience with institutions in the form of knowledge acquired during such interactions (Hudson,

2006; Evans & Whitefield, 1995; Schoon & Cheng, 2011). Therefore, institutional theories do not

necessarily restrict variations in levels of trust to early-life experiences and focus more on rational

perceptions formed due to these interactions with institutions. There seems to be no real consensus

in the political trust literature pointing towards more support of one theory over the other.

Some reasons for varying political trust levels point to the social capital theory and economic

fluctuations. According to Putnam (1993), social capital refers to the social trust, norms and networks

in a community, the absence of which can cause a decline in government or political trust levels (Keele,

2007). For example, using a multivariate time series analysis from the U.S. for the years 1980 to 1997,

Chanley, Rudolph and Rahn, (2000) show how increasing crime rate may result in decreased

government trust levels, in turn, significantly impacting citizens’ support towards the government.

Furthermore, economic reasons also seem to affect government trust levels. By also exploring time-

series correlations from the U.S., Stevenson and Wolfers, (2011) show how perceptions towards

institutions such as banks and governments closely align with the fluctuations of the country’s

business cycle, where trust is seen to be pro-cyclical. While other factors may also cause a change in

trust levels, variations in government performance or political actors’ behaviour remain key to forming

political perceptions, thereby translating to variation in trust (Keele, 2007).

According to Klingemann & Fuchs, (1995), political trust does not vary much across groups.

The slight variation that occurs happens due to public policies that positively or adversely impact these

demographic groups. Moreover, Lau (1982) demonstrates how negative information concerning

political matters carries a heavier weight than positive, leading to a sharper decrease in confidence

levels. Perhaps this indicates that groups who are adversely affected by government policies may

experience greater dissatisfaction, resulting in comparably lower political trust levels overall.

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Following the reasoning of the trust theories mentioned, it can be seen that groups that are

adversely affected by specific policies are more likely to experience deteriorating trust levels. For

example, groups that believe social capital or the economy’s position is worsening will also realize a

decrease in political trust levels. Furthermore, within these groups that are adversely affected, the

weight of an unfavourable policy is likely to overshadow perceptions of favourable policies. In this

case, those favouring the refugee-intake policy are more likely to realize higher political trust.

However, groups that feel as if the refugee intake negatively impacts them may have a higher marginal

decrease in trust levels. Moreover, according to cultural theories, respondents may realize limited

variations in political trust upon reaching adulthood. As young adults are more likely to experience

changes in trust levels than the overall sample, it may be worthwhile to carry the same analysis for

the overall sample and the sub-sample of young adults. This paper adds to the political trust literature

by adding onto the sub-set of literature testing cultural theories and investigating how some political

and demographic groups are impacted.

2.2 Studies based on immigration and political extremism

Closely related to the central question of this paper, a sub-field of migration literature

explores the relationship between ethnic diversity and the shift in political perceptions/preferences

amongst the local population, usually with a focus on electoral outcomes. This section outlines

explicitly the research carried out in Sweden using the same or similar quasi-experimental designs.

One such study is the impact of ethnic diversity on redistribution (Dahlberg et al., 2012)

whereby the authors use the quasi-random allocation of refugees as an instrumental variable and thus

find a negative causal relationship between immigration (ethnic diversity) and redistribution. Their

paper argues that the allocation of refugees is exogenous with respect to the individual level changes

in preferences for redistribution. While unemployment and housing vacancy rate per municipality are

used as the main control variables, they also use other time-varying municipality characteristics, which

they argue may also have impacted the refugee inflow and the individual redistribution preferences.

I take inspiration from this thought and also use the same control variables. With this in mind, it is also

likely that my identification assumption is also fulfilled; wherein the refugee inflow into municipalities

is exogenous to the changing levels of individual political trust for the population living in those

respective municipalities.

Andersson, Berg and Dahlberg, (2021) use the same methodology and exogenous variation to

explain the relationship between foreign immigration in Swedish citizens' residential choice, whereby

no flight behaviours amongst citizens were realized. Moreover, Mehic, (2019) also uses the same

design to estimate the causal relationship between immigration and right-wing populism using the

2015 refugee intake while conditioning on municipality-related covariates. His estimates show a

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significantly positive relationship between refugee inflow and political extremism. Finally, Mehic,

(2020) analyses how right-wing populist voting after the 2015 refugee crisis was influenced by regional

characteristics of municipalities from the 1990s. His results show that municipalities with more anti-

immigrant sentiments in the 1990s and higher crime rates experienced a stronger relationship

between refugee inflow and right-wing populism. These studies indicate that the Swedish refugee

dispersal program of the 1980s and 2015, had caused a shift in political preferences amongst the local

population. Using the quasi-experimental evidence after the refugee crisis of 2015, Barmen, (2019)

also investigates whether refugee-inflows into Sweden result in higher right-wing populist support.

Moreover, he evaluates the extent to which the ethnic competition theory and welfare chauvinistic

preferences come to play. However, no statistically significant results are realized in his analysis.

To add to the set of literature investigating the impact of immigration in Sweden, this paper

takes a step aside from evaluating political preferences and instead studies the change in trust

towards politicians. Moreover, to estimate the impact of immigration flow on the respective

dependent variable, previously stated literature use refugee inflow (as an instrument) to explain

immigration inflows. However, it is essential to note that refugees are forced to relocate to other

countries, whereas migrants move by choice. Therefore, refugees may be different to most economic

migrants regarding their observable and unobservable characteristics. Hence, using an individual

fixed-effects model (as opposed to using an instrumental variable), this research only studies the

impact of refugee inflow on variation in political trust, rather than investigating the effects of

immigration on political trust changes. Moreover, as empirical evidence indicates a moderately strong

relationship between political extremism and lack of political trust (Hooghe, Marien & Pauwels, 2011),

similar results between the studies mentioned above and my paper are likely to be found.

2.3 Potential Mechanisms

2.3.1 Group position theory

Blumer, (1958) discusses the group position theory as defining one's group's position vis-à-vis

another group. It is likely that in this context, those from Sweden (in-group) characterised themselves

vis-à-vis the refugees (out-group) according to race and nationality. Blumer, (1958) further postulates

that four feelings or beliefs are most apparent amongst the in-group when individuals suffer from

racial prejudice: (1) superiority, (2) the belief that the out-group is different (3) the belief that the in-

group deserves better access to proprietary and (4) a fear that the out-group poses a threat. The

remaining theories in this section therefore, stem from the group-position view. As a consequence of

the in-group's beliefs, they may display in-group favouritism, defined as preferring one's group over

others. In-groups may also practise out-group discrimination when the in-group treat those from the

out-group considerably worse than other groups and themselves (Abbink, Klaus & Harris, 2019).

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2.3.2 Salience and size of minority community

Salience has become a growing topic in explaining the link between group identity and

electoral outcomes. For example, Colussi, Isphording and Pestel (2020) exploit the exogenous

variation in the period between the election dates and Ramadan period to study how the Muslim

community’s salience causally leads to higher political extremism and violent attacks carried against

Muslims in Germany. One of the mechanisms that may be at play come from identity salience, which

refers to the act of specific identities becoming more prominent in particular situations (Stets & Burke,

2000; McLeish & 2011).

Closely linked to the studies on salience is the strand of literature investigating the size of

immigrant groups and its impact on communal harmony. In Alesina and Ferrara, (2002) U.S.

neighbourhoods realize lower amounts of interpersonal trust with increased ethnic diversity. Further,

it is visible that those who display lesser social trust in such neighbourhoods are especially the

individuals who do not favour racial integration. Putnam, (2007) goes a step further and tries to

explain this relationship over time, using various immigration waves to the U.S. In the short run, social

capital, trust, altruism, and community cooperation decrease in ethnically diverse neighbourhoods,

although most areas successfully integrate and maintain high trust levels in the long run. Replications

have been carried out in other geographical regions, such as in Europe and Australia, with similar

results to be found (Lancee & Dronkers, 2008; Leigh, 2006).

From these perspectives, one may postulate that a larger refugee population size may thus

be more ‘noticeable’ or salient, leading to further in-group favouritism or out-group discrimination.

Moreover, a massive influx of refugees in the short run is likely to have caused the Swedish citizens to

experience lower social trust levels, which may have spilt over to their trust towards politicians. This

paper contributes to the existing literature on ethnic diversity in neighbourhoods while focusing on

refugees rather than on migrants in general. Finally, due to the nature of the quasi-experiment (see

section 3), this paper shifts its focus from neighbourhoods to municipalities.

2.3.3: The ethnic competition theory

The ethnic competition theory supposes that negative feelings towards the out-group

(Jackson, 1993) and strengthening of in-group favouritism arise due to limited resources (Scheepers,

Gijsberts & Coenders, 2002). For example, Scheepers, Gijsberts and Coenders, (2002) saw that

European citizens were more likely to display hostile behaviour towards non-EU migrants in

competitive environments. As a mechanism, it is explained that the in-group's behaviour of excluding

the out-group may stem from perceiving the latter as a threat. Thus, the ethnic competition theory

postulates that a higher share of refugees may negatively affect the general population's feelings

towards the out-group, especially if it is believed that resources are scarce. While Swedish citizens and

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economic migrants are more likely to work in the same labour market than refugees, Swedish citizens

may still perceive refugees negatively if the population believes they are in direct competition for

public resources with them.

Literature suggests that the fears of the general population towards immigration is greatly

exaggerated and misinformed. So even if the general population and refugees are not in direct

competition for public resources (Card, 2005), it may still be likely for the local population to believe

that they are (Kessler, 2001; Mayda, 2006). Moreover, there appears to be heavy misinformation on

the extent to which migrants are dependent on the welfare system (Alesina, Miano & Stantcheva,

2018). However, perfect information is still unlikely to alter the opinions of individuals. In a

randomized experiment conducted amongst French voters before and after elections, fact-checking

on crucial immigrant-related statements helped respondents update their knowledge. However,

despite the change in information, their policy recommendations remained the same (Barrera, Guriev,

Henry & Zhuravskaya, 2020).

2.3.4 Contact theory

The contact theory suggests that while more harmonious relationships between the in-group

and out-group can stem from a better sense of familiarity, other conditions also need to be fulfilled.

According to Pettigrew, (1998), the groups may share better relationships if they regard each other

equally, pursue common goals and have enough scope for intergroup cooperation. This is supported

by a meta-analysis of 27 studies using random assignment, where intergroup contact was seen as an

effective measure to reduce prejudice. Although, it is noteworthy that interventions examining

ethnical and racial differences between groups, weaker effects were observed (Paluck, Green &

Green, 2018). Closely linked to this paper, Steinmayr, (2016) uses pre-existing accommodations for

refugees in upper Austrian communities as an instrumental variable to test for a relationship between

refugee exposure and its impact on voting for the extreme right. For the communities that hosted

refugees, far-right support had substantially decreased, lending support to the contact hypothesis.

While little is known on how the Swedish population interacted with refugees during the 1980s, it is

likely that compared to economic migrants, refugees and Swedish citizens had little opportunities for

inter-group interactions and also embodied different goals.

2.4 Hypothesis

With the vast literature stemming from the fields of political trust, psychology and economics,

one can postulate that higher refugee inflow into a municipality causes individual political trust to

either increase or decrease, on average. If the Swedes at the time believed there was more

competition for public resources (Scheepers, Gijsberts & Coenders, 2002); or thought that the refugee

inflow caused an obliteration of social capital (Alesina & Ferrara, 2002; Putnam, 2007), a prominent

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anti-refugee feeling may be observed amongst the Swedish population. These views of in-groupism

may translate to reduced satisfaction towards politicians, as they view them in charge of such policies.

In fact, most evidence shows how minority groups’ salience tends to alter political perceptions in

various contexts (Colussi et al., 2020; Dahlberg et al., 2012; Mehic, 2019;2020), thus allowing more

support towards a hypothesis wherein political trust is negatively impacted by increasing refugee

inflows.

On the other hand, the contact theory (Pettigrew, 1998) suggests that the more exposure one

has with refugees (i.e. under aligned objectives, equal group status), the more likely it is for the

Swedish population to develop greater social trust within the community. This may also result in a

spill-over effect whereby individuals’ trust towards other groups; including politicians, is also raised.

Furthermore, greater exposure to refugees may also lead to less racial prejudice, thus resulting in

more favourable perceptions of the policy, and consequently leading to higher political trust.

It is necessary to note that while several mechanisms can explain the potential relationship

between refugee influx and political trust, it is beyond the scope of this research to tease out or to

disentangle the mechanisms that are most fitting in the relationship observed.

2.5 Studies supporting heterogeneity analysis

2.5.1 Political trust and left-right self-placement

Noren, (2000) uses evidence from Sweden to investigate whether an individual’s initial

electoral vote impacts trust levels towards the government after elections. Consistent with the home-

team hypothesis, it is seen that those who have different political preferences than the ruling

government will generally also display lower government trust levels. As the ruling party during the

time period investigated was the Social Democratic party, which aligns with centre-left values

(Ryabichenko & Shenderyuk, 2013), it is likely that those with left-wing views are more likely to

support the refugee dispersal policy.

Moreover, as left-wing individuals also often hold pro-immigration views, it is more likely for

them to experience an increase in their political trust levels as a result of the dispersal program. This

is supported by Mehic, (2020) who realizes that historical anti-immigration attitudes in Swedish

municipalities cause Swedish citizens to vote for the far-right. I extend this analysis by investigating

whether individual-level political ideology leads to varied political trust upon refugee arrival.

With this in mind, I hypothesize that left-wing voters have an increase in individual level

political trust as a result of higher refugee inflows in their municipality on average. Conversely, higher

municipality refugee inflows causes lower individual political trust amongst right-wing supporters, on

average.

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2.5.2 Political Trust and Education

Hainmueller and Hiscox, (2007) use data from Europe to show that high-skilled and educated

individuals are more likely to favour immigration regardless of how skilled the immigrants are. This is

because educated individuals are more likely to value cultural diversity and have less racist attitudes

towards immigrants. In general, they are also more likely to believe that immigration provides more

benefits to the host economy as well. Further, research also indicates that low-skilled and less

educated individuals are more likely to have anti-immigration sentiments as they feel immigrants are

in direct competition with them in the labour market (Kessler, 2001; Mayda, 2006; Scheve and

Slaughter, 2001).

Political trust in this situation, is likely to vary depending on one’s preferences to immigration

attitudes. With the above literature in mind, I hypothesize that within the sub-sample of educated and

skilled individuals’, higher refugee inflows into an individual’s municipality causes higher individual

level political trust. Moreover, the sub-sample of lower educated individuals experience lower political

trust levels as a result of higher refugee inflows on average.

2.5.3 Political Trust and Age

According to the supporters of cultural theories (see section 2.1), political and social trust

mainly fluctuates during an individual’s formative years and minimally varies after reaching adulthood

(Ingelhart, 1997; Putnam, 1993; Soon & Cheng, 2011). This makes it imperative to also test whether

the Swedish youth experienced changes in political trust as a consequence of the refugee dispersal

program, as they may be one of the key demographic groups experiencing a potential change in trust.

The hypotheses remains the same for young individuals as the one formulated for the main analysis

and heterogeneity analysis (2.4, 2.5.1 and 2.5.2).

Section 3: Institutional context

This section describes the refugee dispersal program in 3.1. Section 3.2 discusses the

implications of the program for the identification strategy. Followed by section 3.3 which explores

potential threats to the internal validity.

3.1 The refugee dispersal program

A surge in refugees since 1985, resulted in larger concentration of refugees in the bigger cities

of Sweden, causing an uneven distribution of economic resources across municipalities (Dahlberg et

al., 2012). Therefore, a new refugee dispersal policy was implemented wherein the Swedish

integration board was to assign refugees to municipalities based on housing availability, thus

disallowing self-selection of the refugees into their preferred choice of location (Åslund et al, 2011).

In 1985, sixty municipalities decided to take part in the program to even out the distribution

of refugees (Åslund et al., 2011). The original idea was to allocate refugees to localities based on

14

available housing and decent labour market opportunities (Dahlberg et al., 2012). However, due to

the large refugee influx and high demand for housing towards the end of the decade, almost all

municipalities (277 out of 284) accepted refugees whenever there was a housing availability (Åslund

et al, 2011). Moreover, in 1988, the state had formally asked all municipalities to participate in the

program (Dahlberg et al., 2012).

Initially the refugees were allocated to asylum centres until they received a residence permit

by the Swedish Integration Board (Edin, Fredriksson & Åslund, 2003). During the initial years of the

dispersal program, refugees were allowed to indicate their preference of municipalities. Most

preferred living in the bigger cities such as Malmö, Stockholm or Göteborg, where economic

conditions were favourable (Edin et al., 2003). However, the housing market in bigger cities were

particularly constrained, therefore the municipalities’ housing availability was the only deciding factor

in the allocation of refugees. Thus, most refugees’ preferences were not abundantly realized (Edin et

al., 2003). As the number of applicants for the bigger cities rose, and a huge shortage in housing

availability followed, refugees fulfilling a certain criteria were picked to live in these areas (Edin et al.,

2003).

The selection criteria to bigger cities could have been based on three main attributes:

educational qualifications, the language they spoke, and family size. To elaborate, the higher the

educational qualification, the more likely it was for the refugee to realize their preferred choice of

location. Secondly, if they spoke a language that the rest of the migrant stock spoke as well, they were

more likely to be placed in the same area (Edin et al., 2003).

There is very little reason to believe that other (un)observable factors were taken into account

when assigning a refugee to a municipality. This is because there was no direct contact between the

refugees and the decision makers (Åslund et al., 2011). However, it was still possible for the refugees

to move after the initial placement to the municipality. Essentially, the refugees had little costs to

move from their initial location, other than the direct moving costs and a delay in their language

courses’ enrolment (Åslund et al., 2011).

Due to the growing participation of municipalities for the program in the late 1980s,

municipalities felt a sense of collectivism and responsibility towards the state, leading to less refugee

rejections (Dahlberg et al., 2012). However, it is possible that some municipalities started refusing the

intake of refugees from 1991 onwards (Bengtsson, 2002; Dahlberg et al., 2012). This is because

municipalities felt strained in terms of their available resources, especially after the collapse of

Yugoslavia which had resulted in a larger refugee wave in 1992. Moreover, the refugee policy was

heavily debated across municipalities due to the rise of anti-immigrant parties and anti-immigrant

15

sentiments. Additionally, the country faced a rise in unemployment rates and decline in economic

growth, which led to further critique on the acceptance of refugees (Bengtsson, 2002).

From figure 1, it is observed that there was substantial variation within and between the share

of refugees per municipality, providing more scope for the identification strategy. Furthermore, we

are able to see the implications of the refugee program as we see a reversing trend from 1985

onwards; wherein bigger municipalities faced lower refugee inflow, and smaller municipalities

experienced higher.

Figure 1: annual refugee inflow as a share of total population according to size of municipalities before and during the

program period (Dahlberg et al., 2012.)

Note. Size of municipality is determined by population wherein small-sized, medium-sized and large-sized municipalities

have a population of below 50,000, between 50,000 and 200,000, and above 200,000 respectively.

3.2 Exogeneity of the program

In most countries, including Sweden, it is seen that migrants settle in cities where individuals

of their own ethnicity and nationality are located (Edin, Fredriksson & Åslund, 2001; Borjas, 1999).

Moreover, educational prospects, job opportunities and favourable welfare benefits may also be

some reasons for migrants to locate to these areas. However when there is heavy self-selection of

migrants into these cities, it is difficult to estimate any causal relationship when using the size of the

migrant community as an explanatory variable. Here too, without a random allocation of refugees to

cities, there is a possibility of the relationship between the change in Swedish citizens’ trust in

politicians and refugee inflow, to be confounded; making it difficult to draw any causal conclusions.

However, to determine the causal relationship between these variables, I exploit the quasi-

random allocation of refugees to municipalities in the previously mentioned refugee placement policy.

Since the refugees had little choice on where they were allocated to, it can be assumed that the

allocation of refugees to municipalities was random after controlling for the availability of housing and

the employment rate. During the earlier years of the program, economic opportunities were also

considered when allocating refugees to municipalities. However, after 1988, one of the key

determinants of a refugee being assigned to a particular municipality was based on the availability of

16

housing (Dahlberg et al., 2012). For refugees to avail their preferences, it was necessary for them to

receive a residence permit and a vacant house in their preferred location of choice; the joint

probability of which was extremely low (Åslund et al., 2011; Oreopolos, 2003). Moreover, Edin et al.,

(2003) show that the pre-policy immigrant sorting into Swedish municipalities is significantly different

from the allocation of refugees to municipalities during the policy. This can also be taken as evidence

that individuals could not realize their preferred choice of location during the program period.

Fortunately, the data set of Heléne et al., (2012) allows me to control for the municipality’s

unemployment rate and availability of housing, thus the random allocation of refugees to

municipalities can still be achieved conditional on these covariates. The identifying assumption here

is that the placement of refugees (and refugee inflow) to municipalities was exogenous to the

changing levels of political trust observed amongst the Swedish population of the corresponding

municipalities. As described in section 2.2, I use the reasoning in Dahlberg et al., (2012) to include

certain control variables. These are used to make my identifying assumption even more plausible.

Nevertheless, there may still be factors that could threaten the identification strategy by

introducing bias into the equation. Here, I consider the following reasons as they may pose the

greatest threats to the identifying assumption: 1) the possibility of refugees relocating after their

initial placement to another municipality 2) the possibility of municipalities negotiating with the state

to allocate fewer refugees to their region 3) the selection of refugees to bigger municipalities by the

administrative officers based on the following covariates: language spoken, educational qualifications

and family size. The extent to which these may threaten the identification strategy, and the potential

sign of bias is further analysed in the subsequent sections.

3.2.1 Threat to identification: internal migration of refugees after their initial placement

Given that costs to remigrate were considerably low, and welfare benefits available to the

refugees were not conditional to their residence of municipality, it is possible that a proportion of

refugees relocated to bigger cities (Åslund et al., 2011). Remigration estimates carried out by Dahlberg

and Edmark, (2008) indicate that around 60% of the refugees remained in their initially assigned

municipality. Moreover, most of who relocated either moved to or moved within some of the bigger

counties in Sweden (Stockholm, Malmö and Göteburg). Fortunately, majority of the individuals stayed

in their initial municipality of residence even after four years of their arrival. However, there may still

be a bias stemming from factors influencing internal migration of refugees which simultaneously

impact political trust. Some potential factors are now elaborated upon.

Firstly, the estimates could be biased if the refugees’ internal migration is also related to

reasons that cause a variation in political trust amongst Swedish citizens. For example, refugees may

have moved from smaller municipalities to bigger regions because of higher economic growth.

17

Moreover, political trust also responds positively to higher economic growth (Stevenson and Wolfers,

2011). Therefore, it is likely that an overestimation of this effect is realized in bigger cities. Hence, a

wide range of economic factors such as tax base, welfare spending and unemployment are controlled

for.

Secondly, refugees who re-migrated to the bigger cities from smaller municipalities are likely

to have (un)observable characteristics that may impact citizens’ impressions of refugees, resulting in

a change in their stance towards the refugee policy. As a consequence, a change in political trust level

may be realized. To elaborate, the sign of bias would depend on the kind of refugee that decides to

migrate to bigger cities. If more educated/skilled refugees decided to remigrate to bigger

municipalities, then it is likely for the population in these municipalities to view them highly in

comparison to the average refugee. This would lead to an overestimation of the true effect in bigger

municipalities. Since most refugees migrated from small to big regions, a model excluding the three

main counties is also analysed as part of a robustness check.

3.2.2 Threat to identification: municipalities negotiating with the state to allocate fewer refugees to

their region

As briefly explained in the earlier section, almost all municipalities participated in the refugee

dispersal policy initiative. This meant that whenever a housing availability arose in any of the

municipalities, a refugee would be accordingly designated to an accommodation there. The

municipalities had the power to object the allocation of refugees if they preferred. However, as a

budding number of municipalities had already signed up for the initiative, there was a sense of

collectiveness to help the state (Bengtsson, 2002; Dahlberg et al., 2012). Further, the refugee influx

had reached peculiarly high amounts so municipalities felt like they had to share a sense of

responsibility. Finally, the few municipalities that did object to the dispersal policy were faced with

negative publicity (Dahlberg et al., 2012). Thus, very few objections were realized during the 1987-

1991 time period.

However, after 1991, municipalities questioned the state on these policies and there was a

growing rise of anti-immigrant sentiments (Bengtsson, 2002). These factors may have resulted in

dwindling support for the program, leading to a possible rejection of refugee inflow into some

municipalities. For my identification strategy, it would be essential that no variables that are

correlated with these municipality acceptance/refusals, are linked to the individual-level changes in

political trust. While this is less likely to hold for after 1991, my analysis also carries out a robustness

check where only the years 1986-1991 is used, since the identifying assumption is more likely to be

exogenous in this time period. Lastly, following the example of Dahlberg et al., (2012), I also control

for other municipality-related characteristics related to the political atmosphere, which may impact

18

refugee inflow and the political trust levels. I do this even if housing availability and labour market

conditions were the two main components that were said to impact refugee inflow into a municipality

(Dahlberg et al., 2012; Edin et al., 2003).

3.2.3 Threat to identification: preference allocation for some refugees to bigger municipalities

Most refugees could not realize their preferred choice of location. Yet when the number of

refugees who desired to locate in a big city (Malmö, Stocholm, Göteburg) exceeded the number of

housing vacancies in the region, administrative officers had to pick the ‘best’ refugees according to

the following observed criteria: education, family size and language spoken (Edin et al., 2003; Åslund

et al., 2011). Unfortunately, there is no data available on the number of refugees that were allocated

to the bigger cities based on these covariates, and so these cannot be controlled for. However, if

higher numbers of educated refugees were allocated to bigger cities on average; It is likely that an

overestimation of this relationship in larger municipalities is seen. As previously stated, an analysis

excluding the bigger cities is conducted. In this scenario, it should provide a lower bound for the

estimated political trust coefficient. This is because it is likely that the less educated individuals were

sorted into smaller municipalities, and as a consequence, more negative perceptions towards refugees

in smaller municipalities may arise. Hence, lower satisfaction of the refugee intake policy may be seen.

Section 4: Data

To answer whether refugee inflow impacts the changes in the political trust levels of the

Swedish population, two datasets are used: (1) Swedish National Election surveys (SNES) and (2)

Helené et al., (2011). Section 4.1 describes the data from the SNES, followed by section 4.2 which

discusses Helené et al., (2011). Finally, section 4.3 discusses the descriptive statistics.

Section 4.1 Dataset 1: SNES

The individual-level data comes from the SNES. Their first survey was taken in 1956 and it has

since been used for all referendums, parliamentary and national elections in Sweden. From the 1970s

onwards, the surveys have been carried out in a two-stage rolling process whereby each individual is

interviewed twice and half the sample changes in each wave. As each individual is interviewed twice,

time-omitted variables can be controlled for using individual fixed effects. The surveys include a wide

range of questions on various subjects related to politics; such as, political opinion, confidence and

voting behaviour. A random sample of around 4000 individuals are interviewed each year, with a

response rate of about 75.2% (SNES, 2021). Since surveys are carried out every election year (a wave),

the following years of political trust is observed: 1988, 1991 and 1994.

The main dependent variable which is individual level of political trust, is measured with the

following survey question: “Generally speaking, how high is your trust in Swedish politicians? Is it very

19

high, rather high, rather low, or very low?”. The levels of political trust is therefore coded as 4 for “very

high”, 3 for “rather high”, 2 for “rather low” and 1 for “very low”.

For the heterogeneity analysis, the following demographic variables are used: education, age

and right-left self-placement. Individuals are asked to determine where they fit in the following

education categories: low educated, high educated, or neither low nor high educated. Further,

individuals also report their own self-placement on the political spectrum. Values from 0 to 4 are

considered left, 6 to 10 are considered right, while 5 remains neither left nor right. Unfortunately the

survey only includes adults, thus the cultural theories mentioned in the theoretical framework cannot

be appropriately made use of. Instead, the following age categories are available: 18-30, 31-60 and

61-80. Motivated by the thought that younger individuals display higher fluctuations in political trust

levels, a sub-sample of the youngest individuals in the dataset who fall in the age category between

18 and 30 years are used. More details on how these variables were coded are included in the

appendix (A.1).

Section 4.2 Dataset 2: Heléne et al., (2011)

Heléne et al., (2011) consists of a relevant set of municipality-level variables for around 270

Swedish municipalities. They created panel periods wherein the cumulative amount of each

municipality characteristic is taken for the following years: 1986 to 1988, 1988 to 1991 and 1991 to

1994. The municipality-level variables for each of the panel periods is matched with the individual

levels of political trust for the years 1988, 1991 and 1994 respectively.

However, the data provided in Heléne et al., (2011) only includes the refugee inflow (the

change in the share of refugees per panel period within a municipality) to each municipality for the

corresponding panel period, but does not include the share of refugees per municipality. It is worth

noting that using refugee inflow as an explanatory variable while carrying out individual fixed effects

may cause for little variation between time periods.

To estimate whether refugee inflow impacts changing political trust using individual fixed

effects, I create my own explanatory variable and call it “share of refugees”. The refugee inflow

variable provided in the dataset, indicates the cumulative number of refugees allocated to each

municipality as a share of the average population of the municipality expressed for the respective

panel period (Dahlberg et al., 2012). I thus add all refugee inflows for each panel period to calculate

the “share of refugees” variable. Therefore, I assume there was no refugee inflow before 1986, and

consider 1986 as the base year. In line with this definition, the first difference of the “share of

refugees” variable using the individual fixed effects method would obtain refugee inflow. Further

details of the steps involved in calculating the “share of refugees” can be found in the appendix (A.2).

20

Besides this, the average unemployment rate and vacant housing rate per municipality for

each panel period are also controlled for. This is because these were argued to be the driving factors

determining to which municipality a refugee was assigned. Further, a set of control factors are also

included in case they impacted refugee inflow and influenced the change in political trust levels. These

set of continuous variables include the average welfare spending, population, and tax base for each

of the municipalities’ panel periods. The leading political party of the municipality at the time is also

included in the analysis as a control variable; these include the Socialist Majority party, Green party

(both being left-wing parties) and New Democrats. (right-wing party) The same set of control variables

were also used in the regressions carried out by Dahlberg et al., (2012) when investigating the

relationship between refugee inflow and redistribution preferences amongst the Swedish population.

I have used the same set of controls as it is likely that similar factors influence changes in redistribution

preferences and political trust.

Section 4.3 descriptive statistics

This section provides the descriptive statistics on the two main variables: political trust and

share of refugees of municipalities. The descriptive statistics for the rest of the variables are

provided in table A.3 of the appendix.

Figure 2: Graph to show the change in average political trust levels (dependent variable) for the following groups over time: (1) overall sample (2) highly-educated (3) low educated (4) left-wing supporters (5) right-wing supporters (6) sample of youth. Note. Political trust, measured on the y-axis, is expressed between 1-4, wherein 1 stands for the lowest trust level and 4 for the highest trust level.

As indicated in table A.3 of the appendix, the average level of political trust for the overall

sample stands at 2.30 over all panel periods. This means that political trust is below the mid-way point

of 2.5, indicating generally lower political trust among the Swedish population. The variation in

average trust levels are also shown in figure 2. All groups fluctuate minimally within the trust range of

2.2 and 2.6 over the years, wherein a general decline in political trust is seen for most groups; the

exception being highly educated individuals and right-wing supporters in the year 1991. For the overall

2.2

2.25

2.3

2.35

2.4

2.45

2.5

2.55

2.6

2.65

Po

litic

al t

rust

(av

erga

e)

Years

Overall sample

Left-wing supporters

Right-wing supporters

Highly educated individuals

Low educated individuals

Sample of youth

21

sample, a decrease in average trust levels from 2.6 to nearly 2.36 is seen. Despite what cultural trust

theories postulate, the average levels of political trust among the youth is minimal, wherein just a 0.05

points decline is seen over the years.

Figure 3: shows the average share of refugees (average cumulative refugee inflow) across municipalities after the policy placement began. Note. The X axis indicates the year (and panel period), and the Y-axis is the share of refugees as a percentage of the overall population of the municipality per panel period. Notes: the share of refugees indicated on the graph are calculated by adding the cumulative refugee inflow after the placement policy began. Therefore, the 1986-1988 panel period is considered as a base year and assumes that there were no refugees before this time period. The three datapoints include the refugee shares for baseline period: 1986-1988 (depicted by year 1988), 1988-1991 (depicted by year 1991), and 1991-1994 (depicted by year 1994) respectively. Cities are classified as big if they were in the same province as “Malmö”, “Stockholm” and “Göteburg”, which is different from the definition used in figure 1.

Furthermore, the average share of refugees across all municipalities and panel periods (since

the policy began), is 1.47% (see A.3). This means that the cumulative refugee inflow expressed as a

percentage of the average population, over all panel periods and municipalities is 1.47%. There were

twenty two municipalities in the analysis that had no refugee inflows during some point in time, thus

the minimum level of refugee shares per municipality reported is 0. Moreover, the maximum refugee

share reported is 10.47% (see A.3). Even though the municipality refugee share is comparatively

higher than what is observed in other municipalities, this value is not excluded from the analysis as

the observation is still valid.

As can be seen from figure 2, the larger cities had higher refugee inflows during the 1986-

1988 panel period by approximately 0.03%. To compensate for the initially high share of refugees in

larger municipalities before the placement policy began (not seen on this graph, but refer to figure 1),

a reversal in the refugee inflow and refugee shares is seen. Both big and small cities realize an increase

of about 0.7% of refugee shares between 1988 and 1991, although the smaller cities observe slightly

larger refugee inflows. In the next panel period, an increase of about 1.2% points in refugee shares is

seen. To see the direct trend of refugee inflows across size of municipalities over time and the

corresponding explanation, refer to A.4 (see appendix).

22

Section 5 Methodology

5.1 Main analysis

5.1.1 Motivation for chosen methodology: To estimate the causal relationship between the changing share of refugees (refugee inflow)

and variation in political trust levels, I exploit the quasi-random variation of the placement of refugees

to municipalities, primarily controlling for the municipality’s unemployment and vacant housing rate.

To carry out this analysis, I perform an individual fixed effects regression as opposed to ordinary least

squares (OLS) which regresses levels, since it is more plausible for the changes in refugee shares of

municipalities to be exogenous with respect to the changes in individual level of political trust, rather

than to the individual levels of political trust itself. Moreover, the individual fixed effects methodology

controls for time-invariant omitted variables which may impact the share of refugees per municipality

along with individual preferences, thus being superior to regressing levels on OLS. Moreover, I choose

to use this methodology over instrumental variables as I would like to directly investigate the

relationship between changing refugee shares and variation in political trust, unlike most papers

which use refugee inflow to instrument for immigration or ethnic diversity.

5.1.1 Regression equation:

The following equation is used for all regressions:

𝑦𝑖𝑚𝑡 = 𝑎𝑖 + 𝛽1 ∗ 𝑠ℎ𝑎𝑟𝑒𝑜𝑓𝑟𝑒𝑓𝑢𝑔𝑒𝑒𝑠𝑖𝑚𝑧 + 𝛽2 ∗ 𝑣𝑎𝑐𝑎𝑛𝑡ℎ𝑜𝑢𝑠𝑖𝑛𝑔𝑟𝑎𝑡𝑒𝑖𝑚𝑧+ 𝛽3

∗ 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑟𝑎𝑡𝑒𝑖𝑚𝑧 + 𝛽4 ∗ 𝑋𝑖𝑚𝑧 + Υ𝑧 + 𝛿𝑚 + 𝑒𝑖𝑚𝑡

Where: 𝑖 refers to individual 𝑖, 𝑚 refers to municipality 𝑚, 𝑡 denotes the specific time period an

individual is interviewed in: 1988, 1991 or 1994 and 𝑧 refers to the panel periods: 1986-88, 1988-91,

1991-94.

𝑦𝑖𝑚𝑡 = the individual’s political trust level

𝑎𝑖= individual fixed effect

Υ𝑧= year fixed effect

𝛿𝑚= municipality fixed effect

𝑒𝑖𝑚𝑡 = error term

𝑋𝑖𝑚𝑧= municipality-level control variables

To elaborate on the specifications of the model, 𝑦𝑖𝑚𝑡 is the dependent variable of political

trust of an individual 𝑖 from a particular municipality 𝑚 at a specific time period 𝑡 where 𝑡 is either

1988 and 1991 or 1991 and 1994. This is regressed on the “share of refugees” variable which indicates

the cumulative inflow of refugees as a percentage of the average size of the population for

municipality (𝑚) for each panel period (𝑧), the first difference of which is the refugee inflow (see A.2

for explanation). The right hand side of the equation includes municipality-level covariates matched

23

to the municipality (𝑚) from which individual (𝑖) comes, corresponding to the time period (𝑡) the

respondent was interviewed in, where (𝑧) refers to the following panel periods: 1986-1988, 1988-

1991 or 1991-1994. Therefore, the responses of an individual in 1988 (1991) is matched to the

individual’s respective municipality variables of 1986-88 (1988-91), and so on. As an individual

responds only twice, the individual fixed effects method takes the first difference of the equation.

Following this, it is seen that I am essentially investigating the causal impact of changing share of

refugees on variation on levels of political trust. Therefore, 𝛽1 is the main parameter of interest.

Importantly, 𝑎𝑖 is the individual fixed effect, which controls for all time-invariant omitted

variables affecting the individual. Υ𝑧 is the time dummy, thereby controlling for year fixed effects. 𝛿𝑚

is the municipality fixed effects which controls for time-invariant variables attributed to the

municipality (i.e. culture/history).

5.1.2. Assumptions of methodology

An important assumption of the individual fixed effects design is that there should be no time-

varying omitted variables influencing the independent and dependent variables. Therefore, one of the

crucial factors being controlled for is the “vacant housing rate” which measures the share of vacant

public housing in the individual’s municipality, averaged over the panel period. Further, I also control

for “unemployment rate” which is the share of individuals unemployed in an individual’s municipality

over the panel period. Not controlling for unemployment rate may lead to an overestimation of the

relationship as unemployment negatively impacts political trust and refugee inflow. Further, not

controlling for the vacant housing rate would also lead to an upward bias as housing vacancies could

positively impact political trust and refugee inflow. Moreover, 𝑋𝑖𝑚𝑧 controls for several municipality-

level variables that could impact political trust and refugee inflow. These include the population, tax

base and welfare spending realized on the municipality level, averaged over the respective panel

period.

Finally, standard levels are clustered around the municipality level as the treatment is at the

level of the municipality.

5.2 Heterogeneity analysis

The sample is split to see whether the results obtained in the main analysis vary within groups

with certain demographic characteristics; namely self-reported education attainment, age and

political preference.

Therefore, four main groups of individuals are analysed. These include individuals who align with (i)

right wing preferences and (ii) left wing preferences respectively. Further, individuals who are (i) high

and (ii) low educated are also analysed.

I first execute this analysis for the overall sample, and then for young individuals specifically between

the ages 18 and 30.

24

5.3 Robustness Checks

Due to the threats identified in section 3.2, 3 main robustness checks are carried out. These

include the following regression analysis:

1) The exclusion of years after 1991: The time period of 1987-1991 is argued to be the most

exogenous period since individuals had little room to choose which municipality they were allocated

to, as there were very little housing vacancies. Furthermore, it was more likely that municipalities

before 1991 would accept the refugee inflow imposed by the state, as opposed to after (Åslund, 2011;

Dahlberg et al., 2012). Therefore, the 2nd panel period (1991-1994) is excluded in one of the

regressions, to investigate whether similar results are obtained in the time period that is described as

more exogenous. This robustness check is motivated by the reasoning and methodology in Dahlberg

et al., 2012 and Åslund, 2011.

2) The exclusion of larger municipalities: The ‘best’ refugees according to the administrative boards

(i.e. higher education, larger family size, and more languages spoken) could have been sorted into the

bigger municipalities (Åslund, 2011). An upwardly-biased relationship may be realized if the selection

criteria is correlated to changing levels of individual political trust.

Moreover, refugees from small municipalities relocated to bigger municipalities, which could also

confound the estimated relationship. Therefore, in one of the regressions, the bigger municipalities

and surrounding areas (Stockholm, Malmö and Göteburg) are excluded from the analysis. This

regression is supported by the robustness check carried out in Dahlberg et al., (2012).

3) Both robustness checks (1&2) are combined into one regression analysis.

Section 6 Results

6.1 Main analysis

Table 1. Estimation of the impact of share of refugees on political trust

(1) (2) (3)

Political Trust Political Trust Political Trust

Share of refugees -0.050 (0.062)

-0.058 (0.063)

-0.069 (0.069)

Vacant housing rate

Unemployment rate

Welfare spending

Tax base

Population

0.001

(0.010)

0.028 (0.032)

0.006

(0.011)

0.017 (0.037)

0.005

(0.011)

-0.001 (0.001)

0.002

(0.006)

25

Socialist majority

Green party

Constant

Number of observations

Individual FE

Municipality FE

Year dummy

2.410*** (0.087)

3116

YES

YES

YES

2.332*** (0.113)

3116

YES

YES

YES

0.015 (0.059)

0.001

(0.063)

2.760* (1.447)

3116

YES

YES

YES

Note. Individual fixed effects regressions estimates of the impact of refugee share on political trust. Column 1 shows the simple regression of political trust on share of refugees. Column 2 adds in the controls for vacant housing and unemployment rate. Column 3 is the full model including all control variables. All three regressions control for individual, municipality and year fixed effects. Standard errors, which are indicated within parentheses, are clustered around the municipality level. The significance levels are denoted as the following: * p<0.1, ** p<0.05 *** p<0.01

Without adding any controls, column 1 shows the results of the simple regression where the

individual political trust levels is regressed on share of refugees. A 1 percentage point increase in the

share of refugees (refugee inflow) results in a 0.050 points decrease in political trust levels along the

4-point Likert scale, on average. However, the relationship is insignificant on the 10% significance

level, which may mainly be driven by the point estimate being indistinguishable from zero. After the

inclusion of the two main controls in model 2 namely; unemployment and vacant housing rates of

municipalities, I regress the individual political trust-levels on share of refugees again. However, little

change in the point estimate and no change in the statistical significance is seen between models 1

and 2.

Finally, the third model controls for all municipality covariates proposed in Dahlberg et al.,

(2012). Here too, little changes in the point estimates and statistical significance is realized. Following

this, it is seen that with a 1 percentage point increase in the share of refugees in an individual’s

municipality, political trust level decreases on average by 0.069 on the 4-point scale.

All three models show that when political trust is regressed on the share of refugees, no

statistically significant relationship is observed. The statistical insignificance may mainly stem from the

point estimates being indistinguishable from zero. If, for example, the effect size obtained was 0.14

(twice the size of the standard errors obtained), the estimate would be statistically significant on the

5% significance level. However, a move of 0.140 points on a 4-point Likert scale could still be

considered as a relatively small effect. Therefore, given that the point estimate of 0.075 points is an

even smaller effect, the estimated coefficient is statistically indistinguishable from zero.

26

6.2 Heterogeneity analysis across demographic groups

To see whether the insignificant results obtained in the main analysis varies between groups;

the sample is split across the following categories: (1) political preferences – right or left wing (2)

education – high and low educated. I also carry this analysis for the overall sample, and then for young

individuals between the ages 18 and 30. According to the hypotheses derived in the literature

framework, regardless of age, I expect a positive relationship between share of refugees and political

trust amongst highly educated and left-wing individuals. Further, I expect a negative relationship

between share of refugees and political trust among low-educated and left-wing individuals (see

section 2.5 for details).

Table 2. Estimation of the impact of share of refugees on political trust across demographic groups

(1) High

educated

individuals

(2) Low

Educated

individuals

(3) Left-wing

individuals

(4) Right-wing

individuals

(5) Young

individuals

(Whole sample)

(6) Young

Individuals

(Left)

(7) Young

Individuals

(Right)

(8) Young

individuals

(High Educate

d)

(9) Young

Individuals

(Low Educatio

n)

Share of refugees

--0.076 (0.176)

-0.012 (0.107)

-0.017 (0.120)

-0.022 (0.145)

-0.032 (0.198)

0.250 (0.367)

0.187 (0.322)

0.117 (0.294)

0.280 (0.752)

Vacant

housing rate

Unemployment rate

Welfare spending

Tax base

Population

Socialist majority

Green party

Constant

Number of observations

Individual FE

Municipality

FE

Year dummy

0.006 (0.026)

-0.001 (0.073)

-0.001 (0.022)

-0.001 (0.002)

0.005

(0.009)

0.055 (0.146)

0.026

(0.105)

2.775 (3.025)

1014

YES

YES

YES

0.016 (0.019)

0.088

(0.083)

0.003 (0.020)

0.000

(0.002)

-0.002 (0.011)

-0.058 (0.127)

0.080

(0.104)

1.744 (2.183)

1030

YES

YES

YES

0.019 (0.027)

0.012

(0.072)

0.011 (0.022)

-0.002 (0.002)

0.006

(0.009)

-0.107 (0.124)

-0.070 (0.127)

3.541

(2.385)

582

YES

YES

YES

0.001 (0.037)

0.062

(0.085)

-0.022 (0.018)

-0.002 (0.001)

0.003

(0.012)

0.176 (0.142)

0.033

(0.129)

3.619 (2.650)

900

YES

YES

YES

-0.016 (0.023)

-0.004 (0.090)

0.003

(0.020)

0.002 (0.002)

0.004

(0.013)

0.139 (0.179)

0.068

(0.140)

3.885 (3.725)

836

YES

YES

YES

0.018 (0.06)

0.021

(0.213)

0.031 (0.080)

-0.005 (0.004)

0.030

(0.056)

0.229 (0.246)

0.024

(0.287)

3.526 (0.863)

104

YES

YES

YES

0.043 (0.087)

-0.067 (0.185)

-0.033 (0.036)

-0.002 (0.003)

0.032

(0.019)

-0.033 (0.036)

-0.047 (0.253)

-1.406 (5.480)

264

YES

YES

YES

-0.004 (0.032)

-0.123 (0.153)

-0.020 (0.030)

-0.005 (0.004)

0.011

(0.016)

0.067 (0.240)

0.020

(0.175)

6.042 (5.744)

368

YES

YES

YES

0.058 (0.146)

0.185

(0.232)

-0.011 (0.077)

-0.004 (0.009)

-0.008 0.030)

0.628

(0.700)

0.116 (0.506)

5.813

(11.076)

88

YES

YES

YES

27

Note. Regressions results reporting the impact of refugee share on political trust using individual fixed effects. Column 1 displays the results of a regression of only highly educated individuals. Column 2 reports the results of the regression of only low educated individuals. Column 3 indicates the results for individuals with left wing preferences. Column 4 reports the results for right-wing supporters. Column 5 shows the regression analysis estimates for only young individuals (18 to 30). Column 6 reports estimates for young individuals with left wing preferences. Column 7 indicates estimates for young individuals with right wing preferences. Column 8 reports estimates for young individuals with high education. Column 9 shows estimates for young individuals with low educated. All regressions control for individual, municipality and year fixed effects. Standard errors, which are indicated within parentheses, are clustered around the municipality level. The significance levels are denoted as the following: * p<0.1, ** p<0.05 *** p<0.01

The results obtained when regressing political trust on share of refugees for the sub-sample

of highly educated individuals is consistent with the point estimates obtained for the overall sample.

In fact, a 1% point increase in the share of refugees also results in a decrease of political trust by 0.076

points along the Likert scale, on average. Both low-educated and high educated individuals display a

negative relationship. However, the sample of low-educated individuals have a point estimate which

is 6-fold larger than those observed for high educated individuals, therefore being even closer to zero.

The result of higher educated individuals realizing more negative point estimates than the lower

educated sample may be surprising. However, the relationship should be analysed with caution as the

standard errors for both regressions are comparatively large, so it is possible that the relationship is

estimated imprecisely. Finally, both regressions across education categories are statistically

insignificant, which may largely stem from the point estimates being statistically indistinguishable

from zero.

When analysing the results obtained from the regressions carried out only amongst the

individuals with left-wing and right-wing preferences, around a four-fold increase in the estimated

coefficients are realized for both groups as compared to those found in the main analysis. Right-wing

individuals have a slightly more negative point estimate than left-wing individuals when regressing

share of refugees on political trust, which is consistent with literature. Even if the point estimates for

both regressions are negative, they are also mostly indistinguishable from zero, thus causing the

relationship observed to be statistically insignificant.

A similar analysis is carried out among the sub-sample of young individuals, who according to

cultural theories, are meant to experience higher fluctuations in trust levels as compared to the main

sample. The point estimates obtained are almost twice the size as those derived in the main analysis.

Therefore, a 1% point increase in share of refugees results in a 3.2 points decrease in average political

trust levels along the 4-point Likert scale. However, it is noteworthy that once again, there is no

statistically significant relationship between the share of refugees and political trust for the sub-

sample of young individuals either, which may be due to the point estimate being indistinguishable

from zero.

Further analysis of the sub-set of young individuals across different levels of education

attainment and left-right self-placement reveals a moderately small positive coefficient upon

28

regressing political trust levels on refugee shares. Young individuals who are left-wing supporters see

an increase in political trust levels by 0.250 points along the Likert scale as a result of a 1% point

increase in share of refugees, on average. Yet, young individuals identifying with right-wing views, also

realize a positive coefficient of 0.187. However, such analysis should be interpreted with discretion as

these results cannot be compared to each other due to statistical insignificance of the relationship,

which may have been partly driven by the large standard errors.

Among all groups, it is surprising that young individuals with low self-reported education

attainment have the highest point estimate, wherein a 1% point increase in share of refugees results

in a 0.280 increase in average political trust levels, along the Likert scale. However, these estimates

cannot be directly compared to other groups of young individuals nor the overall sample, given the

vastly different sample sizes analysed across regressions. The statistical insignificance resulting from

the regressions carried out between refugee shares and political trust amongst the young group of

individuals may mainly stem from the comparatively large standard errors, due to the sample size

being particularly small.

6.3 Robustness checks

Three robustness checks are carried out to test whether individual political trust levels and

municipality-level share of refugees still display a statistically insignificant relationship after re-

estimating the models seen in the main analysis.

Table 3. Robustness checks to test the impact of share of refugees on political trust

(1) 1986-1991

(2) Small municipalities

(3) 1&2 combined

Political Trust Political Trust Political Trust

Share of refugees -0.263** (0.124)

-0.119 (0.078)

-0.338** (0.145)

Vacant housing rate

Unemployment rate

Welfare spending

Tax base

Population

Socialist majority

Green party

Constant

-0.020 (0.038)

-0.014 (0.103)

-0.001 (0.034)

-0.001 (0.001)

-0.009 (0.017)

-0.050 (0.100)

0.020

(0.087)

5.210

0.009 (0.013)

0.074

(0.047)

0.027 (0.017)

-0.001 (0.002)

-0.017 (0.008)

0.067

(0.083)

-0.021 (0.078)

4.006

-0.031 (0.040)

0.100

(0.120)

0.070 (0.039)

-0.002 (0.002)

-0.009 (0.023)

0.332*** (0.097)

-0.015 (0.094)

4.516

29

Number of observations

Individual FE

Municipality FE

Year dummy

(3.249)

1660

YES

YES

YES

(2.349)

1826

YES

YES

YES

(2.748)

964

YES

YES

YES

Note. Regressions results reporting robustness checks carried out on main analysis results. Column 1 displays the results of a regression carried out only for the years 1988-1991. Column 2 reports the results of a regression where only individuals from small municipalities remained. Column 3 reports regressions combining the robustness checks for column 2 and 3. All three regressions control for individual, municipality and year fixed effects. Standard errors, which are indicated within parentheses, are clustered around the municipality level. The significance levels are denoted as the following: * p<0.1, ** p<0.05 *** p<0.01

The first regression of table 3 excludes responses from the years 1991-1994 and re-estimates

the baseline results. This is motivated by earlier studies which claim there was little possibility for

individuals to self-select into municipalities during the 1987-1991 time period (Dahlberg et al., 2012;

Åslund et al., 2011). Further, it is argued that municipalities were more likely to refuse the refugee

inflow dictated by the state during the 1991-1994 time period, thus disrupting the quasi-random

allocation of refugees to municipalities (Dahlberg et al., 2012; Åslund et al., 2011). While it is difficult

to estimate whether the inclusion of the years 1991-1994 would lead to an upward or downward bias,

it is likely that the refugee inflow was more exogenous to varying levels of political trust during the

1986-1991 time period. Upon re-estimating the model by excluding the years 1991-1994, it is seen

that a 1% point increase in refugee share decreases political trust by 0.263 points on the 4-point Likert

scale (as opposed to the 0.069 points decrease realized in the main analysis) on average, while

remaining statistically significant at the 5% significance level. Despite the statistical significance, a

0.263 point decrease in political trust along a 4 point-Likert scale may not necessarily translate to an

economic significanct relationship as it is unlikely for a moderately small decline in political trust to

affect the population’s support for policies.

To provide further motivation for the second robustness check which excludes individuals

living in the bigger provinces; it is likely that refugees relocated from smaller to bigger municipalities

within four years after their arrival, thus disrupting the quasi-random nature of the allocation of

refugees to municipalities. Furthermore, there was a possibility that refugees with certain

characteristics (i.e. higher education) were allocated to bigger municipalities, which remain

uncontrolled for in the analysis. Therefore column 2 of table 3 re-estimates the model used in the

main analysis, after having excluded individuals living in the bigger provinces; namely, Malmö,

Stockholm and Göteburg. In the main analysis which includes bigger municipalities, an over-estimation

of the effect is likely to be seen. This is because the refugees taken into the bigger municipalities may

have been more educated than the average refugee. Thus after having excluded individuals who lived

30

in the bigger municipalities, a lower-bound of the point estimate is expected. Upon re-analysing the

initial model, a slight decrease of about 0.05 points is realized. Nevertheless, an insignificant

relationship between the share of refugees and political trust is seen yet again.

Lastly, model 3 evaluates whether the point estimates and insignificant results change when

both robustness checks 1 and 2 are combined. Therefore, if the sample size were not to be reduced,

the analysis should provide a more accurate estimate of the impact of refugee shares on political trust

levels. With this smaller sample, the coefficient estimated in column 3 is close to what is found in

column 1. As the point estimates are statistically significant at the 5% significance level, the robustness

check tends to suggest that a 1% increase in refugee shares decreases political trust by 0.338 points

on the full-Likert scale, or similarly; 8.45% points, on average.

Altogether, only the second robustness check supports the results found in the main analysis

by pointing towards a statistically insignificant relationship between the share of refugees within a

municipality and varying individual political trust levels. However, the comparatively larger coefficient

(in absolute value) obtained in the first and third robustness check, points towards a statistically

significant negative relationship between share of refugees and political trust. It is noteworthy that

the point estimates obtained are nearly four times larger than those realized in the main analysis.

Robustness checks for the sub-groups are not performed as it would greatly restrict the

sample size.

Section 7 Discussion:

Section 7.1 Discussion of results

My initial hypothesis was based on several theories in literature (i.e. ethical competition,

group position, salience), which pointed towards a negative relationship between refugee inflow and

political trust. However, studies supporting the contact hypothesis would suggest that increasing

refugee inflow would lead to higher political trust. Therefore, the hypothesis was built to reflect the

possibility that increasing refugee shares could cause political trust to decrease or increase.

The findings of the main analysis indicate no statistically significant relationship between the

share of refugees in municipalities and variation in individual political trust, which may mainly be

driven by the point estimates being statistically indistinguishable from zero. However, the robustness

checks reveal that the share of refugees and variation in individual political trust levels are sensitive

to the time period and sample size analysed. More specifically, the first robustness check involving

just the 1986-1991 time period indicates that an increase in share of refugees by 1% point is matched

with a 0.263 points reduction in trust levels along the 4-point Likert scale, on average. Furthermore,

the third robustness check excludes individuals from the provinces Malmö, Gothenburg and

Stockholm, and restricts the time period investigated to just 1986-1991. Similar to the first robustness

31

check, here too, a statistically significant result at the 5% significance level is obtained; whereby an

increase in refugee shares by 1% point decreases political trust by 0.338 points along the full Likert

scale on average.

As previously explained, due to the population’s growing worries over immigrants and the

economy, along with deteriorating trust between the municipalities and state, it is possible that

municipalities may have entirely refused or restricted refugee inflows during the 1991-1994 time

period. Moreover, it is argued that the refugees had little possibility to self-select into the preferred

cities during the 1987-1991 time period. With these two arguments, it is likely that the refugee inflow

was more exogenous to changing levels of political trust between 1987 and 1991. Therefore, one may

argue that the true effect of changing refugee shares on political trust is observed after excluding for

the years 1991-1994. If this is the case, these results provide support to the wide range of literature

demonstrating a change in voting behaviour upon increased immigration (Halla, Wagner &

Zweimüller, 2017; Dustmann et al., 2019; Mehic, 2019; 2020; Barone, Ignazio, Blasio, Naticchioni;

2016; Otto &Steinhardt; 2014) while providing more insight on how political trust levels, too, can be

negatively impacted.

However, the sample excluding for the years 1991-94 is limited to just 1660 observations, thus

the effect is still uncertain. One may consider using only the years 1986-1991 as a trade-off wherein

using the limited sample provides for a smaller sample size, yet provides scope for more interval

validity; while using the full time period allows for a larger sample size but compromises on

exogeneity. Hence, the negative relationship between changing share of refugees on the municipality

level and individual level political trust is sensitive to changes in time period and sample size.

In the case that the full time period provides a closer approximation to the true effect

compared to the model only including responses from 1988-1991, I also discuss reasons that cause

the analysis to be sensitive to different time periods, other than those stemming from the trade-off

described above.

Firstly, the surveys were carried out in a two-step rotating procedure, wherein half the sample

changes each wave. This may be a cause for concern as the individuals interviewed during the 1988-

1991 period may have unobserved characteristics that are different from the individuals surveyed in

the 1991-1994 period, making the two samples incomparable. If this is the case, it is possible that the

unobservable characteristics may reduce the randomness of the sample and essentially drive the

results to be different for the two time periods.

Secondly, it may also be possible that effects of in-groupism and out-group discrimination

caused by the refugee inflows fades over time. Putnam, (2007) uses several waves of immigration to

the U.S. to explain how social trust initially declines upon increased ethnic diversity, but returns to

32

normal or even higher levels in the long run. While social trust differs from trust in politicians, it is

possible that a spill-over effect is seen, whereby trust towards several groups in society rises. It could

also be that in-group bias reduced over the years, lending support to the contact hypothesis. This

could be further substantiated by a regression analysis carried out for the years 1991-1994 only,

whereby the statistical insignificance may stem from the coefficient being indistinguishable from zero

(see A.5)

The results derived from the main analysis which uses all time periods are also in line with

Nekby and Pettersson, (2017) findings who replicated the results of Dahlberg et al., (2012). As

mentioned earlier, Dahlberg et al., (2012) investigated the impact of ethnic diversity on re-

distributional preferences using the same quasi-experimental design and therefore, investigated the

same time periods as me. More specifically, Nekby and Pettersson, (2017) also used the refugee inflow

to instrument for ethnic diversity while investigating whether immigration impacted individuals'

responses towards the following proposals: (1) "accepting fewer refugees" and (2) "increasing

economic support to immigrants so they can maintain their own culture". Neither of these two survey

questions were found to be statistically significant in response to increasing refugee inflows for the

1986-1995 time period (full sample). Therefore, the lack of relationship between the share of refugees

and political trust may stem from the population's indifference towards the refugee policy in general,

as the policy may not threaten or extensively support an individuals' values or lifestyle. These findings

point towards the refugee inflow perhaps not being as salient as described in previous literature. Thus,

I fail to reject the null hypothesis that refugee inflows causes no effect on individual political trust

levels for the full time period. However, I reject the null hypothesis for the 1986-1991 time period as

refugee inflow causes political trust to decline.

When regressing the political trust levels on the share of refugees across the following sub-

groups: (1) political ideology and (2) education attainment for the whole sample and sub-samples of

young individuals, a statistically insignificant relationship is also observed. The effects obtained for

these groups are also small, which may indicate that none of the groups extensively support nor refute

the refugee dispersal policy, which may be why the point estimates are nearly statistically

indistinguishable from zero. These results may align with what Lau, (1982) theorizes, wherein groups

adversely affected by policies are more likely to experience fluctuations in political trust than groups

in favour of the program. However, the results obtained for right-wing groups and low educated

individuals are in contrast to most literature (Mehic, 2020; Kessler, 2001; Mayda, 2006; Scheve and

Slaughter, 2001). Nevertheless, this relationship may still be plausible given that refugees of the time

were not well-integrated into the Swedish labour market (Dahlberg et al., 2017), and therefore

individuals from these groups may not have seen them as a threat.

33

Since I only observe statistically insignificant relationships between the share of refugees and

individual level political trust for all sub-sample groups studied, I fail to reject the null hypothesis which

states that refugee inflow impacts political trust levels for these sub-groups. Therefore, it is crucial to

acknowledge that the estimates obtained are also economically insignificant.

Section 7.2 Limitations of the study

There are several limitations to the existing research that I will now elaborate upon. Firstly,

Nekby and Petterson, (2017) comment on Dahlberg et al., (2012), who use the same quasi-

experimental design and dataset as me. Thus their comments on Dahlberg et al., (2012) are also

questions to consider for the results obtained in my paper. A matter of concern using the data in

Dahlberg et al., (2012) is the possibility of mismeasuring the explanatory variable: share of refugees.

Nekby and Petterson, (2017) believe that the data of Dahlberg et al., (2012) contains measurement

error as they indirectly trace the refugee inflow. The indirect method consists of evaluating the

payments provided by the Swedish Integration Board (SIV), which were used as means to compensate

municipalities for the increased social expenditure upon refugee arrival.

However, Nekby and Petterson, (2017) argue that the SIV payments do not distinguish the

payments for immigrants partaking in the program with those not participating. They also state that

the municipalities received grants after a significant time lag. Therefore, the number of refugees

actually placed into each municipality may be substantially different from the numbers indirectly

calculated through the annual compensations. If this is the case, my study may have issues in

accurately depicting the relationship between the share of refugees and political trust. As Dahlberg et

al., (2012) may have overestimated the number of refugees (asylum seekers and tied-stayers) in

comparison to the numbers predicted by Nekby and Petterson, (2017); the estimated coefficient of

political trust is likely biased downwards. A way to further improve upon the existing paper is by

carrying out the same analysis with Nekby and Petterson, (2017)’s estimates of refugee inflow to see

whether similar estimates are obtained.

Furthermore, Nekby and Petterson, (2017) criticize the body of literature using the Swedish

refugee dispersal policy as a relevant quasi-experimental setting. The argument here is that the SIV

did not quasi-randomly allocate refugees into municipalities as stated in Åslund et al., 2011 and

Dahlberg et al., 2012. In fact, municipalities were allowed to decide the amount of refugee inflow ex-

ante. Therefore, Nekby and Petterson, (2017) contradict Åslund et al., 2011 and Dahlberg et al., 2012

who state that the SIV decided the amount of refugee inflow for municipalities solely based on housing

vacancies. In the view of Nekby and Petterson, (2017), the SIV did not have the upper hand and could

not dictate refugee inception on municipalities. Although most of these studies, along with mine,

include variables controlling for the political situation, it may be that some municipality characteristics

34

are still not controlled. In this case, the refugee allocation to cities is still not random after conditioning

for this set of covariates. Once again, this would bias the estimator depending on how the time-varying

omitted variables influence refugee inflow and political trust.

According to Soinienen, (1992), municipalities also considered the prevailing crime rate,

quality of housing, and magnitude of social problems in neighbourhoods, when deciding upon the

refugee intake. Indeed, such variables are not controlled for in my analysis and could bias the

estimator. If, for example, most municipalities face a high crime rate, then municipalities may choose

to decrease the intake of refugees. Moreover, a higher crime rate and other such social issues are

likely to impact individual levels of political trust negatively. Likewise, the lack of such social problems

and abundance of quality housing may result in some municipalities accepting more refugees. These

factors may also contribute towards a rise in political trust levels. Therefore, an overestimation of the

current effect is likely to be seen in my analysis. For further improvement, variables like crime rate

and other social factors could also be controlled for.

Another cause for concern may be if changing political trust amongst individuals caused

municipalities to accept/reject more refugees. If reverse causality threatens the analysis, decreasing

political trust levels would likely have led to a lesser acceptance of refugees, resulting in an upward

bias. A further reason for endogeneity may stem from attrition, whereby individuals drop out from

the survey. Suppose their reasons for dropping out from the survey was due to a variable influencing

the treatment (refugee inflow), while also impacting political trust levels; in this case, the estimator is

likely to contain selection bias. However, if attrition is random, no such bias exists. It is challenging to

assume what reasons there may be for the resulting attrition. Thus no conclusions can be made on

the extent to which this threatens the internal validity of the design.

Moreover, this paper also attempted to examine the influence of refugee inflow on changing

levels of political trust using cultural theories, which stated that the younger demographic is more

likely to experience fluctuations in political trust. However, I was only able to use a sample of young

individuals between 18-30 years of age. For future research, it would be more beneficial to investigate

whether early-life experiences (during childhood or young adulthood) of observing refugee inflows

affects political trust more significantly. Finally, it would greatly benefit the study if there were more

observations for the groups used for the heterogeneity analysis. Currently, the standard errors are

considerably large, indicating that the relationship may be imprecisely estimated. Moreover, the

sample may lack representativeness.

Section 7.3 Robustness checks for future research

It is noteworthy that the statistical significance and point estimates alter very little after

adding municipality-related controls in the main analysis. These provide support to what is known

35

about the Swedish refugee dispersal program; whereby, refugee inflow was quasi-randomly allocated

to municipalities after controlling for unemployment and vacant housing rates. It also provides some

strength to the argument that the refugee inflow to municipalities is exogenous to changing levels of

political trust amongst individuals living in those cities. Nevertheless, it is essential to discuss what

robustness checks have been already carried out by different authors using the same quasi-

experimental setting and what could have been added to this pape, had there been sufficient data.

To investigate whether refugee inflow to municipalities is exogenous to changes in

redistribution preferences, Dahlberg et al., (2012) use two placebo analyses: (1) a placebo in

treatment and (2) placebo in outcome. Unfortunately, a placebo in treatment is impossible for my

paper, as the political trust variable is reported only from 1988 onwards. This means that pre-policy

levels of political trust are not available and cannot be regressed on refugee shares after the policy

begins. Therefore, it is difficult to establish whether refugee inflows after 1985 are exogenous to pre-

program pollical trust levels. An insignificant relationship upon regressing pre-policy political trust

levels on refugee shares, would support the claim that refugee inflow to municipalities is exogenous

to inhabitants’ political trust levels.

Dahlberg et al., (2012) also analysed whether changes in nuclear power and private healthcare

preferences result from the refugee dispersal policy. However, no statistically significant relationship

was obtained, providing further support to their claim that refugee inflow is exogenous to changing

levels of re-distributional preferences. For my paper's purpose, it would be beneficial to see whether

a significant relationship is realized when regressing trust on a group that is unlikely to be impacted

by the refugee inflow (i.e. trust in primary school teachers), on the share of refugees. This would

indicate that general trust levels or trust levels towards other occupational groups remain unaffected,

thus reducing the possibility of a spurious relationship.

To investigate whether refugee inflow was quasi-randomly allocated to municipalities, it

would also be beneficial to regress the refugee inflow on pre-policy municipality covariates, (i.e.

unemployment, vacant housing rates, policy stance). Such robustness checks have been carried out in

other research designs such as Dustmann et al., (2019) which also aimed to answer similar research

questions. To my knowledge, this has not yet been carried out for the Swedish refugee dispersal

program. If it were to be, and no statistically significant results are seen, then it is likely that the

refugee inflow is exogenous with respect to pre-policy municipality covariates.

Section 7.4 External Validity

In terms of external validity, the high attrition rate only allows me to determine the effect for

the individuals whose answers were recorded in two consecutive waves. If, for example, individuals

responding in both waves are high-income earners, this analysis may likely be more representative of

36

the high-income than low-income groups of Sweden during the 1980s. It is also likely that the situation

prevailing in Sweden four decades ago cannot be extrapolated to the current time. For example, the

1980s was a time period when refugee migrants were first introduced to the Swedish system and

therefore, labour market opportunities for refugees were limited (Dahlberg et al., 2017). Perhaps the

effects of the ethnic competition theory would be stronger in this scenario. Moreover, digital

communication did not exist. Thus, the media's role in influencing political perceptions was limited as

compared to today. Future research may want to consider studying this link in today’s context.

Section 8 Conclusion:

This paper investigates the impact of refugee inflow on changing political trust levels amongst

individuals. For this, the Swedish quasi-experimental setting of the 1980s is used where I exploit the

exogenous variation of refugee inflow into cities. This reasoning is motivated by previous literature

such as Åslund et al., 2011 and Dahlberg et al., 2012 who suppose that the refugee inflow is quasi-

random in nature after controlling for some municipality-level covariates.

While using the individual fixed effects methodology to regress the individual political trust

levels on refugee shares (the first difference of which is refugee inflow), no statistically significant

relationship is observed in the baseline estimates. However, the robustness checks are sensitive to

the sample size and time period used. Using just the years 1986-1991 where refugee allocation was

more likely to be exogenous to political trust levels, a statistically significant point estimate at the 5%

significance level is realized; wherein a 1% point increase in refugee shares causes a 0.263 points

decrease in political trust levels along the Likert scale, on average. Thus, I fail to reject the null

hypothesis that municipality-level refugee inflow causes no effect on individual political trust levels

for the overall sample, on average. However, I reject the null hypothesis for the 1986-1991 time

period.

Furthermore, the heterogeneity analysis reveals no statistically significant relationship

between refugee shares and political trust among individuals across 1) education 2) political

preferences for both, the overall sample and the sub-samples of young individuals. I fail to reject the

null hypothesis which states that municipality-level refugee inflow causes no change in individual

political trust levels for these sub-groups, on average.

This research bridges the gap between the vastly studied fields of (1) political trust and (2) the

citizens of the host economy’s attitudes towards ethnic diversity. Future studies may want to tease

out the mechanisms behind the inconclusive results obtained in the main analysis, while also

investigating which groups drove the statistically significant effect in the 1986-1991 time period.

Finally, it would be relevant to study the impact of refugee inflow on variations in political trust over

37

time, as my paper only considered a ten year time span, with responses from just 3 waves. The current

analysis hints at a weakening of in-group bias over time, however, the results remain inconclusive.

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Appendix

A.1: Coding of variables

Variables included in

the SNES dataset

Variable of interest in my

paper

Type of variable coded

into

Interpretation

41

Self-reported

Education:

Low educated, High

educated, Medium

Educated

• Self-reported

high educated

individuals

• Self-reported low

educated

individuals

Binary variable

• Coded as 1 if

individual is High

educated and 0 if

not

• Coded as 1 if

individual is low

educated and 0 if

not.

Self-reported age:

18-30 years , 31-60 and

61-80

18-30 years Binary variable

Coded as 1 if individual

falls under 18-30 years age

bracket, and 0 otherwise.

Self-reported:

Right-left self

placement:

1-4 (left), 6-10 (right), 5

(middle)

• Left

• Right

Binary variable • Takes value 1 if

individual self-

reports being left.

• Takes value 1 if

individual self-

reports being a

right-wing

supporter

Table A.1: indicates how the variables in the SNES dataset were transformed for the purpose of my paper.

Column 1 shows the variables in the SNES were received. Column 2 indicates the variable of interest in my paper.

Column 3 shows the type of variable I generated and column 4 shows the interpretation.

A.2 Explanation of the calculation of the “share of refugees” variable

The Dahlberg et al.’s dataset only consists of the refugee inflows as the share of average population for that

particular panel period. As previously explained, there are three panel periods: 1986 to 1988, 1988 to 1991,

and 1991 to 1994 in his (and my) dataset. As an example, the data looks like the following on Dahlberg et al.,’s

dataset:

Year Municipality code Refugee inflow (Dahlberg’s X

variable)

1986 to 1988 180 (Stockholm) .74267036

1988 to 1991 180 .46297279

1991 to 1994 180 1.0552762

Table A.2.1

To provide an interpretation of the data in table 1 indicates, the first row indicates that the municipality

Stockholm experienced a refugee inflow of 0.74% (expressed as a percentage of the average population during

the panel period) between the years 1986 to 1988.

42

To calculate the “share of refugees” as an average of the population, I added the refugee inflows per panel

period. However, I do not have the initial share of refugees before or after the program begins. My data looks

like the following whereby I calculate the cumulative amount of refugee inflows for each of the panel periods

to derive “share of refugees”:

Year Municipality code Cumulative Refugee inflow/ Share

of refugees

1986 to 1988 180 (Stockholm) .74267036

1988 to 1991 180 .46297279+.74267036

1991 to 1994 180 1.0552762+46297279+.74267036

Table A.2.2

As seen above, the column of table A.2.1 and A.2.2 have the same refugee inflow estimate in the 1986-1988

panel period. Therefore, it is assumed that before 1985 to 1988 there are no existing refugee shares, and it is

thus used as a “base year”.

In my analysis, each respondent is only interviewed twice. Therefore, the municipality characteristics in my

dataset also only appears twice (the third year’s data for that particular individual and respective municipality

they come from is therefore, missing). Here is an example of an individual who is interviewed twice in the years

1985-1988 and 1988-1991:

Year Municipality Refugee share/ cumulative refugee

inflow

(X variable)

ID number Trust

(Y variable)

1985 to 1988 180 (Stockholm) 0.743 200 1

1988 to 1991 180 1.205 200 0

1991 to

1994

. . 200 .

Table A.2.3

Since there are only two time periods by which a respondent is interviewed, the cumulated refugee inflow is

differenced from the previous year once, and thus I only obtain the refugee inflow amounts when using

individual fixed effects.

I realize that a possible limitation of this estimation is that I do not have the initial share of refugees in any of

the municipalities before the time period 1985-1988, nor do I have the shares for after. I only have the refugee

inflows per panel periods mentioned above. Therefore, the term “share of refugees” is inaccurate. However, I

use this term for my thesis since I could not find a more convenient variable name without confusing the reader.

43

Nevertheless, the estimates should not be impacted and I should still be able to investigate the effect of refugee

inflows on changing levels of political trust using this method.

A.3 Descriptive statistics

Mean Standard

deviation

Minimum Maximum

Trust in politicians

(Individual)

2.30 0.70 1 4

Left-wing preferences

(individual)

0.12 0.32 0 1

Right-wing preferences

(individual)

0.14 0.34 0 1

High-educated

(individual)

0.14 0.35 0 1

Low educated

(individual)

0.17 0.37 0 1

Share of refugees

(Municipality)

1.47 0.99 0 10.47

Welfare spending

(Municipality)

9.38 5.68 0 29.26

Housing vacancy rate

(Municipality)

1.66 2.43 0 18.97

Unemployment rates

(Municipality)

3.65 2.73 0.19 11.7

Tax base

(Municipality)

991.63 131.82 717.55 1738.67

Population

(Municipality)

145.31 201.17 2.94 698.29

Socialist majority

(Left wing)

(Municipality)

0.41 0.49 0 1

Green party

(Municipality)

0.78 0.42 0 1

New Democrats

(Right wing)

(Municipality)

0.46 0.50 0 1

Note. The variable “trust in politicians”, “stance towards refugees”, “left wing”, “right wing”, “centrist”, “high

education”, “low education” and “medium education” are individual-level variables. The rest of the variables’

statistics reported are on the municipality-level. Population is measured in thousands. Tax base and welfare

44

spending are measured in Swedish Kronor (SEK 100) per capita deflated as per inflation levels in 1994. Binary

variables include the individual-level “trust in politicians” and municipality-level “socialist majority”, “green

party” and “new democrats”. Around 3106 observations exist for the variable “Trust in politicians”; this is

equivalent to around 1553 individuals responding to this question twice.

A.4: refugee inflow across small and big municipalities over time

Figure 3: shows the average refugee inflow into municipalities as a share of average population size per municipality, per panel period. Therefore, the three datapoints include the refugee inflow for 1986-1988 (depicted by year 1988), 1988-1991 (depicted by year 1991), and 1991-1994 (depicted by year 1994) respectively. Cities are classified as big if they are in the same province as the municipalities “Malmö”, “Stockholm” and “Göteburg”. Due to the initially high refugee inflow into larger municipalities (refer to figure 1), refugees were largely

dispersed towards smaller municipalities in order to reduce the concentration of refugees in larger cities. The

1988-1991 (second data point) and 1991-1994 (third data point), show that smaller municipalities had higher

amounts of refugee inflow compared to larger municipalities. Thereby, reversing the trend. The refugee inflow

as a share of the average municipality population was 0.61% and 0.64% between 1986 and 1988 for small and

big municipalities respectively. Between 1988 and 1991, smaller cities had higher refugee inflows of 0.85%

whereas bigger cities had 0.65% of refugee inflows. Consequently, between 1991 and 1994, smaller and bigger

cities had refugee inflows of 1.32% and 1.12% respectively.

A.5: Regression of political trust on share of refugees for the years 1991-1994

(1) 1991-1994

Political Trust

45

Share of refugees -0.030 (0.081)

Vacant housing rate

Unemployment rate

Welfare spending

Tax base

Population

Socialist majority

Green party

Constant

Number of observations

Individual FE

Municipality FE

Year dummy

0.014 (0.013) 0.018

(0.046) 0.001

(0.016) 0.000

(0.003) 0.013

(0.008) 0.015

(0.077) -0.126 (0.100) 0.210

(2.792) 1444

YES

YES

YES

Table A.5: Regressions results reporting the impact of refugee share on political trust for the panel period 1991-

1994 using all controls, along individual, municipality and year fixed effects. Standard errors, which are indicated

within parentheses, are clustered around the municipality level. The significance levels are denoted as the

following: * p<0.1, ** p<0.05 *** p<0.01