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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.
2
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
5
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
6
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
7
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.
8
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
9
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).
10
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
11
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
12
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.
13
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.
References: Abbink, K., & Harris, D. (2019). In-group favouritism and out-group discrimination in naturally
occurring groups. PloS one, 14(9), e0221616. Andersson, H. (2020). Ethnic Enclaves, Self-employment, and the Economic Performance of
Refugees: Evidence from a Swedish Dispersal Policy. International Migration Review, 0197918320912195.
Andersson, H., Berg, H., & Dahlberg, M. (2021). Migrating natives and foreign immigration: Is there a
preference for ethnic residential homogeneity?. Journal of Urban Economics, 121, 103296. Alesina, A., & La Ferrara, E. (2002). Who trusts others?. Journal of public economics, 85(2), 207-234. Alesina, A., Miano, A., & Stantcheva, S. (2018). Immigration and redistribution (No. w24733).
National Bureau of Economic Research. Åslund, O., Edin, P. A., & Fredriksson, P. (2001). DP2730 Settlement Policies and the Economic
Success of Immigrants. Åslund, O., Edin, P. A., Fredriksson, P., & Grönqvist, H. (2011). Peers, neighborhoods, and immigrant
student achievement: Evidence from a placement policy. American Economic Journal: Applied Economics, 3(2), 67-95.
Banerjee, A., Duflo, E., & Pulipaka, S. (2019). Good Economics for Hard Times. Editorial Board, 10(2),
79-80. Barmen, V. (2019). Does Refugee Migration Make Right-wing Populists More Popular? Evidence from
a Swedish Refugee Dispersal Program. Barone, G., D'Ignazio, A., de Blasio, G., & Naticchioni, P. (2016). Mr. Rossi, Mr. Hu and politics. The
role of immigration in shaping natives' voting behavior. Journal of Public Economics, 136, 1-13.
Barrera, O., Guriev, S., Henry, E., & Zhuravskaya, E. (2020). Facts, alternative facts, and fact checking
in times of post-truth politics. Journal of Public Economics, 182, 104123. Bengtsson, M. (2002). Stat och kommun i makt (o) balans: En studie av flyktingmottagandet (No.
124). Lund University.
Blumer, H. (1958). Race prejudice as a sense of group position. Pacific sociological review, 1(1), 3-7. Borjas, G. J. (1999). The economic analysis of immigration. Handbook of labor economics, 3, 1697-
1760. Card, D. (2005) Is the new immigration really so bad?. The economic journal, 115(507),
F300-F323
38
Chanley, V. A., Rudolph, T. J., & Rahn, W. M. (2000). The origins and consequences of public trust in government: A time series analysis. Public opinion quarterly, 64(3), 239-256.
Clarke, H. D., Stewart, M. C., & Whiteley, P. F. (1998). New models for new labour: The political
economy of labour party support, January 1992-April 1997. American Political Science Review, 559-575.
Colussi, T., Isphording, I. E., & Pestel, N. Minority Salience and Political Extremism Online Appendix. Dahlberg, M., & Edmark, K. (2008). Is there a “race-to-the-bottom” in the setting of welfare benefit
levels? Evidence from a policy intervention. Journal of Public Economics, 92(5-6), 1193-1209. Dahlberg, M., Edmark, K., & Lundqvist, H. (2012). Ethnic diversity and preferences for
redistribution. Journal of Political Economy, 120(1), 41-76. Dahlberg, M., Edmark, K., & Berg, H. (2017). Revisiting the relationship between ethnic diversity and
preferences for redistribution: Reply. The Scandinavian Journal of Economics, 119(2), 288-294.
Dustmann, C., Vasiljeva, K., & Piil Damm, A. (2019). Refugee migration and electoral outcomes. The
Review of Economic Studies, 86(5), 2035-2091. Edin, P. A., Fredriksson, P., & Åslund, O. (2003). Ethnic enclaves and the economic success of
immigrants—Evidence from a natural experiment. The quarterly journal of economics, 118(1), 329-357.
Evans, G., & Whitefield, S. (1995). The politics and economics of democratic commitment: Support
for democracy in transition societies. British Journal of Political Science, 485-514. Hainmueller, J., & Hiscox, M. J. (2007). Educated preferences: Explaining attitudes toward
immigration in Europe. International organization, 399-442. Halla, M., Wagner, A. F., & Zweimüller, J. (2017). Immigration and voting for the far right. Journal of
the European Economic Association, 15(6), 1341-1385. Heléne Lundqvist, Karin Edmark, Matz Dahlberg. Uppsala University, Department of Economics
(2012). Ethnic Diversity and Preferences for Redistribution - Graphic Data. Swedish National Data Service. Version 1.0.
Hooghe, M., Marien, S., & Pauwels, T. (2011). Where do distrusting voters turn if there is no viable exit or voice option? The impact of political trust on electoral behaviour in the Belgian regional elections of June 2009 1. Government and opposition, 46(2), 245-273.
Hudson, J. (2006). Institutional trust and subjective well‐being across the EU. Kyklos, 59(1), 43-62. Inglehart, R. (1997). Modernization and postmodernization in 43 societies (pp. 67-107). Princeton
university press. Jackson, J. W. (1993). Realistic group conflict theory: A review and evaluation of the theoretical and
empirical literature. The Psychological Record, 43(3), 395.
39
Keele, L. (2007). Social capital and the dynamics of trust in government. American Journal of Political Science, 51(2), 241-254.
Kessler, A. (2001). Immigration, economic insecurity, and the" ambivalent" American public. Klingemann, H. D., & Fuchs, D. (Eds.). (1995). Citizens and the State (Vol. 1). OUP Oxford. Lancee, B., & Dronkers, J. (2008, May). Ethnic diversity in neighborhoods and individual trust of
immigrants and natives: A replication of Putnam (2007) in a Western European country. In International Conference on Theoretical Perspectives on Social Cohesion and Social Capital, Royal Flemish Academy of Belgium for Science and the Arts, Brussels .
Lanoue, D. J., & Headrick, B. (1994). Prime Ministers, parties, and the public: the dynamics of
Government popularity in great Britain. Public Opinion Quarterly, 58(2), 191-209. Lau, R. R. (1982). Negativity in political perception. Political behavior, 4(4), 353-377. Leigh, A. (2006). Trust, inequality and ethnic heterogeneity. Economic Record, 82(258), 268-280. Mayda, A. M. (2006). Who is against immigration? A cross-country investigation of individual
attitudes toward immigrants. The review of Economics and Statistics, 88(3), 510-530. McLeish, K. N., & Oxoby, R. J. (2011). Social interactions and the salience of social identity. Journal of
Economic Psychology, 32(1), 172-178. Mehic, A. (2019). Immigration and Right-Wing Populism: Evidence from a Natural Experiment.
Department of Economics, School of Economics and Management, Lund University. Mehic, A. (2020). The Electoral Consequences of Nuclear Fallout: Evidence from Chernobyl (No. 2020:
23). Nekby, L., & Pettersson‐Lidbom, P. (2017). Revisiting the relationship between ethnic diversity and
preferences for redistribution: Comment. The Scandinavian Journal of Economics, 119(2), 268-287.
Norén, Y. (2000). Explaining variation in political trust in Sweden. In Copenhagen: European
Consortium for Political Research Joint Session of Workshops.
OECD (2019). Retrieved March 12, 2021, from http://www.oecd.org/gov/trust-in- government.html
Oreopoulos, P. (2003). The long-run consequences of living in a poor neighborhood. The quarterly journal of economics, 118(4), 1533-1575.
Otto, A. H., & Steinhardt, M. F. (2014). Immigration and election outcomes—Evidence from city
districts in Hamburg. Regional Science and Urban Economics, 45, 67-79. Paluck, E. L., Green, S. A., & Green, D. P. (2019). The contact hypothesis re-evaluated. Behavioural
Public Policy, 3(2), 129-158. Pettigrew, T. F. (1998). Intergroup contact theory. Annual review of psychology, 49(1), 65-85.
40
Putnam, R. (1993). The prosperous community: Social capital and public life. The american prospect, 13(Spring), Vol. 4. Available online: http://www. prospect. org/print/vol/13 (accessed 7 April 2003).
Putnam, R. D. (2007). E pluribus unum: Diversity and community in the twenty‐first century the 2006
Johan Skytte Prize Lecture. Scandinavian political studies, 30(2), 137-174.
Ryabichenko, A., & Shenderyuk, M. (2013). The transformation of the Swedish political party system in
the late 20th/early 21st century. Baltic Region, (3), 107-116. Scheepers, P., Gijsberts, M., & Coenders, M. (2002). Ethnic exclusionism in European countries.
Public opposition to civil rights for legal migrants as a response to perceived ethnic threat. European sociological review, 18(1), 17-34.
Scheve, K. F., & Slaughter, M. J. (2001). Labor market competition and individual preferences over
immigration policy. Review of Economics and Statistics, 83(1), 133-145.
Schoon, I., & Cheng, H. (2011). Determinants of political trust: A lifetime learning model. Developmental psychology, 47(3), 619.
Soininen, M. (1992). The municipal refugee reception: Implementation and organization . Center for Research on International Migration and Ethnic Relations (CEIFO).
Steinmayr, A. (2016). Exposure to refugees and voting for the far-right:(Unexpected) results from
Austria.
Stevenson, B., & Wolfers, J. (2011). Trust in public institutions over the business cycle. American Economic Review, 101(3), 281-87.
Stets, J. E., & Burke, P. J. (2000). Identity theory and social identity theory. Social psychology
quarterly, 224-237. Stryker, S. (2001). Traditional symbolic interactionism, role theory, and structural symbolic
interactionism: The road to identity theory. In Handbook of sociological theory (pp. 211-231). Springer, Boston, MA.
Svensk valundersökning 1988 (SNES) | Svensk Nationell Datatjänst. (2021). Retrieved 24 July 2021, from https://snd.gu.se/sv/catalogue/study/snd0227
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