DETERMINANTS OF ATTITUDES TOWARDS
IMMIGRANTS IN DENMARK
Thomas Hafner
Advised by Professor Matthew Incantalupo
Senior Thesis in Economics
Haverford College
April 2016
Hafner 2
TABLE OF CONTENTS
I.
ABSTRACT………………………………………………………………....3
II.
INTRODUCTION…………………………………………………………..4
III.
LITERATURE REVIEW…………………………………………………..7
IV.
DATA……………………………………………………………………….12
V.
METHODOLOGY………………………………………………………...17
VI.
RESULTS…………………………………………………………………..18
VII.
ANALYSIS…………………………………………………………………32
VIII. CONCLUSION…………………………………………………………….36
IX.
REFERENCES…………………………………………………………….37
Hafner 3
I. ABSTRACT
This paper explores how attitudes towards immigrants are shaped in Denmark.
While other studies have looked at this issue in other countries, to my knowledge there
has not been a study on this issue in Denmark. With a changing population and contested
views on immigration, Denmark is an ideal country for looking at the effects of
immigration. Using data from the European Social Survey (ESS), I attempt to examine
what factors contribute the most to the formation of attitudes towards immigration in
Denmark, and why these factors are important. The results show that education has a
significant effect in shaping attitudes, but it is difficult to determine whether this is due to
economic or cultural reasons. Other factors such as income, religion and gender also
appear to affect attitudes towards immigration, but the results for these variables are not
as conclusive as education.
Hafner 4
II. INTRODUCTION
For much of its history, Denmark has been a small country with a fairly
homogeneous population. While the country is indeed relatively small, with a population
of around 5.5 million, its population has become increasingly diverse, with more and
more immigrants seeking citizenship in Denmark. Denmark is an attractive destination
for immigrants for a number of reasons, including its welfare system and integration
programs that seek to assimilate immigrants into Denmark as easily as possible. As of
2014, roughly 600,000 immigrants and their descendants live in Denmark, almost 11% of
the total population (Denmark.dk). The number of immigrants in Denmark has increased
since 2008, where there were close to 380,000 immigrants, compared to around 530,000
in 2015, as seen in Figure 1.
" This high proportion of immigrants has led to some controversy among native-born
citizens, with many arguing that immigrants take away jobs from native citizens and
receive benefits that should be going towards the native population (Hedetoft 2006).
Hafner 5
More specifically, since Denmark depends heavily on a welfare system, some native
citizens worry that immigrants who are unable to assimilate well into the Danish system
will place more of a burden on the average Danish taxpayer. Another worry is that
immigrants will take away jobs from native Danes. With immigration showing no signs
of lessening, especially in light of recent refugee crises, it is critical to understand how
native Danes view and treat immigrants. This can help with assimilation, not only for the
immigrants, but for natives as well. How can we look at these attitudes towards
immigrants, and how can we explain how these attitudes are shaped? By learning more
about these attitudes, we can better understand both the ideological and socioeconomic
factors that affect native Danes and potentially provide more answers to why integration
is sometimes difficult.
In this paper, I look at what factors contribute to shaping attitudes towards
immigrants. While there are likely a number of factors at play, skill level should have a
significant impact on attitudes towards immigrants. The reasoning behind this is that if,
for example, a large number of low-skilled immigrants are coming into Denmark, lowskilled native workers will likely have more negative attitudes towards immigrants, since
these immigrants are taking more jobs away from native Danes. Another possibility is
that higher-skilled workers may have a more negative view of immigrants, since they
have a higher unemployment rate and therefore more of the higher-skilled workers’ tax
money goes towards welfare for the immigrants. While these are two potential
explanations for how attitudes are shaped towards immigration, there could potentially be
other effects. One possibility is that people feel that immigrants are undermining their
country’s culture, and therefore hold negative attitudes towards immigration. Another
Hafner 6
possibility is that people have racial motivations, and may hold more negative
discriminatory attitudes towards immigrants. There also might be an income effect;
people who have higher incomes may feel more economically secure because immigrants
generally tend to have lower incomes. While some factors may have a bigger
contribution in shaping attitudes towards immigration, there is likely a mix of factors at
play. In this paper, I will use data from the European Social Survey to explore what
factors help shape attitudes towards immigrants.
Hafner 7
III. LITERATURE REVIEW
There is a sizable amount of previous research that deals with the issue of
attitudes towards immigrants. However, it is first useful to understand how Denmark’s
welfare system operates. Christiansen (1996) provides a basic overview of the Danish
welfare system and lists some of its benefits and drawbacks. While this article does not
go into too much detail, it gives readers a clear understanding of how the system works.
Christiansen writes about how Denmark is a universal welfare state, meaning that the
general public is responsible for paying taxes, which fund most welfare assistance. The
main ideology behind the Danish welfare state is to ensure that all citizens have a
minimum standard of living, and people who are unemployed or unable to earn a living
will be provided for by the state via social security and unemployment benefits. In terms
of how the welfare system actually operates, most of the burden falls on the general
public; the average worker is taxed 47% of his or her income. Some of the money from
this taxation goes towards unemployment benefits, with 78% of the labor force covered
by these benefits. Relating this to the subject of immigrant unemployment, it seems that
if immigrants struggle in finding jobs, some of the burden will fall on native Danish
taxpayers, as immigrants can also qualify for unemployment benefits. It is clearly
important for Denmark to find a way to lessen immigrant unemployment, not only for
economic reasons, but also for social and political stability.
Brochmann and Hagelund (2011) provide an overview of immigration in the
Scandinavian countries and discuss some of the challenges that Scandinavian countries
face with immigration. This includes the difficulties many immigrants encounter in
finding a job and the controversy created when native citizens have to pay for the welfare
Hafner 8
of the immigrants. The paper also talks about the different types of immigrants migrating
to Scandinavia, including immigrants looking for labor and immigrants that are fleeing
their native country due to a humanitarian crisis. Most of the immigrants were low-skilled
workers. Immigration did not affect the Scandinavian countries much until the early
1970’s, at which point the countries began imposing restrictions on immigration. These
were mainly because of the social issues immigration presented, including the creation of
a lower class due to an inability to integrate into the new society. In a later section,
Brochmann and Hagelund focus specifically on Denmark’s attitude and policies towards
immigrants, including the Integration Act in 1998, which gave the government more
control over the integration of immigrants. More specifically, the Act gave Danish
municipalities power to disperse immigrants throughout Denmark, partly to deter ghettos
from being formed. The act also brought forward introductory programs (Doobay and
Jørgensen say that these include language and Danish society courses) designed to
prepare immigrants for future educational or employment opportunities. For participation
in these programs, immigrants were awarded an “introduction allowance” which,
interestingly enough, was lower than unemployment benefits for native Danes.
How has immigration affected the political system in Denmark? Harmon (2014)
explores this by looking at how political outcomes are affected by the addition of new
ethnic groups to the countries involved. More specifically, he examines how immigration
affects Danish political outcomes between 1981-2001. Harmon asks whether increased
immigrant presence contributes to the success of anti-immigrant, nationalist parties.
Harmon’s main result confirms this above statement; namely that “immigration-driven
increases in ethnic diversity have a causal impact on political outcomes.” It is clear that
Hafner 9
the presence of immigrants may cause a negative backlash by native citizens. With this
in mind, steps to ensure the proper integration of immigrants are necessary to avoid
conflict between native citizens and immigrants.
There are several papers that focus on how skill level of native workers affects
their attitudes towards immigrants. These papers tend to use a “factor proportions”
analysis model. Hainmueller and Hiscox (2010) write that the model “…derives the
distributional effects in the native economy from the impact that immigration has on the
relative supplies of factors of production.” This means that if there are lots of low-skilled
immigrants coming into a country, low-skilled labor in that country will be affected not
only through a loss of their jobs, but potential wage decreases as more low-skilled labor
enters the country. Scheve and Slaughter (2001) use this model to explore the issue.
They find that, in the United States, low-skilled workers have more negative attitudes
towards immigration. They do not find any evidence that there is stronger antiimmigration sentiment as a result of skill level in areas with high immigration. To
measure the skill level of workers, the authors use years of education completed as a
proxy for skill level. This could potentially be problematic, however, as different levels
of education may correspond to different attitudes towards immigration for reasons not
related to labor market issues. For example, Hainmueller and Hiscox (2010) explain that
a higher level of education may lead people to have a higher tolerance for different ethnic
and racial groups other than their own, or may want to have more diversity in their
country, thereby having more positive attitudes towards immigrants. Another issue is
that papers such as Scheve and Slaughter’s (2001) ask respondents about their attitudes
towards immigrants in general, not about their attitudes towards high or low-skilled
Hafner 10
immigrants. This is problematic because people may have differing views about the skill
level of immigrants coming into the country. Hainmueller and Hiscox (2007) and
Hainmueller and Hiscox (2010) address this by looking at how people view high and
low-skilled immigrants. In their 2007 paper, they use the country of origin of the
immigrants and determine whether each country is a “rich” or “poor” country, and then
ask respondents about their attitudes towards these countries. They find that highly
educated people are more likely to favor immigrants. They also do not see any difference
in the relationship between education and attitudes towards immigrants for respondents in
the labor market and out of the labor market. This raises questions as to whether skill
level of workers is a determining factor in anti-immigrant attitudes. In Hainmueller and
Hiscox (2010), they conduct a survey where they ask respondents their attitudes towards
high and low-skilled immigrants. They find that both high-skilled and low-skilled
workers prefer high-skilled immigrants, for native workers both in the labor market and
out of it. This result implies that labor-market concerns may not be a determining factor
in people’s attitudes towards immigrants. The authors also investigate how income level
affects an individual’s attitudes towards immigrants; the reasoning behind this is that
richer natives will likely have more negative attitudes than poorer natives towards lowskilled immigrants, as they will have to spend more money paying for their public
services. Hainmueller and Hiscox instead find that both high-skilled and low-skilled
workers prefer high-skilled immigrants. This shows that traditional thinking about
attitudes towards immigration may not be entirely accurate.
Finally, knowledge about how immigrants affect the labor market in Denmark can
prove useful in analyzing attitudes towards immigrants. Foged and Peri (2015) examine
Hafner 11
the effect of immigrant workers on native workers’ wages. More specifically, they
compare labor market outcomes of low-skilled natives and immigrants. Interestingly,
they found that immigrants had positive effects on native workers’ wages; immigrants led
native workers to seek more skill-intensive jobs, while the immigrant workers were more
likely to take the manually intensive jobs. They did not find any evidence to imply that
immigration increased the probability of unemployment for unskilled natives. This result
suggests that, in Denmark, low-skilled workers should not have to worry about losing
their jobs as a result of immigration.
Hafner 12
IV. DATA
The data in this study was obtained from the European Social Survey (ESS). This
organization has conducted a survey of various European countries every two years since
2001. In the survey, they ask a number of questions relating to attitudes and patterns of
behavior, and also various other questions about respondents’ backgrounds. To collect its
data, the ESS randomly selects individuals ages 15 and over within private households in
the country being surveyed. The minimum sample size each country aims for is 1500
respondents. In the 2014 survey, there were over 500 variables included. For this paper,
I will be focusing on variables that look at the attitudes of Danish citizens towards
immigrants. There is no variable in the data set that explicitly states the skill level of the
individual surveyed, so to account for that, I will use the highest level of education
reached as a substitute for skill level. This has been done in papers such as Scheve and
Slaughter (2001) and intuitively makes sense, as the higher level of education someone
reaches, the more likely they are to get a better job.
Table 1 provides an overview of the variables included in the dataset, as well as
what ESS round each variable is included in.
Hafner 13
TABLE 1. List of variables
Variable
age
attitude_culture
Description
Age of respondent
Country’s cultural life enriched or undermined by immigrants (0 if cultural
life undermined, 10 if enriched)
Rounds
1-7
1-7
attitude_economy
Immigration good or bad for Denmark’s economy? (0 if bad, 10 if good)
1-7
attitude_job
Immigrants create new jobs or take away existing jobs (0 if take jobs away,
10 if create new jobs)
7
attitude_living
Immigrants make country a better or worse place to live (0 if worse, 10 if
better)
1-7
attitude_poor
Allow few/many immigrants for poorer countries (1 if none, 4 if manay)
7
attitude_white
Should immigrants be white? (0 if yes, 10 if no)
7
discriminated
Member of a group discriminated against in Denmark (0 if not discriminated
against, 1 if discriminated against)
1-7
education
Highest level of education the respondent has reached (1 for less than lower
secondary, 5 for a tertiary education)
1-7
equal
Important that people be treated equally and have equal opportunities (1 if
the respondent agrees, 6 if not at all)
7
feel_close
Feel close to country (1 if very close, 4 if not close at all)
7
gender
Gender of respondent (0 if female, 1 if male)
1-7
health
income
native
Subjective general health (1 if very good, 5 if bad)
Income level of respondent (shows income decile of respondent)
Whether or not the respondent was born in Denmark (1 if yes, 2 if no)
1-7
1-7
1-7
religious
How religious the respondent is (0 if not at all, 10 if very)
1-7
work
Whether the respondent has done paid work in the last 7 days (0 if yes, 1 if
no)
1-7
There are several variables included which measure attitudes towards
immigration. attitude_culture shows whether the respondent think immigrants enrich or
undermine Denmark’s culture, attitude_economy lists how the respondent feels
immigration impacts the economy, attititude_job describes whether people think
immigrants take away or create new jobs, attitude_living shows whether respondents
think immigrants make the country a worse or better place to live, attitude_poor asks
whether more or fewer immigrants should be allowed from poorer countries, and
attitude_white lists whether respondents think immigrants should be white. equal is a
Hafner 14
variable which shows how people think others should be treated. Other variables include
discriminated and feel_close; discriminated shows if the respondent is part of a group
that has been discriminated against, while feel_close shows how close the respondent
feels to their country. For a variable that shows the skill level of the respondent, I am
using the variable education, which shows the highest level of education completed for
each respondent. The education levels are divided into five parts: less than secondary
education, lower secondary education, upper secondary education, advanced vocational
education, and tertiary education. Lower secondary education is generally completed by
the time the respondent is 16-17, while upper secondary education is usually finished by
the time the individual is 19. Advanced vocational education is designed to prepare
students for employment and make their transition into the labor market easier. Tertiary
education means that the respondent has at least completed a university degree.
Table 2 shows the descriptive statistics for this data set. The mean of
attitude_economy is 5.083 (on a 1-10 scale) indicating that respondents have mixed
opinions on whether immigration is good or bad for Denmark’s economy. The results for
attitude_job also show that people disagree on whether immigrants take away or create
new jobs, with the mean indicating that slightly more people think immigrants create new
jobs. attitude_living also appears to show that people have mixed opinions on whether
immigrants make the country a better or worse place to live.
Hafner 15
TABLE 2. Descriptive Statistics
VARIABLE
age
attitude_culture
attitude_economy
attitude_job
attitude_living
attitude_poor
attitude_white
discriminated
education
equal
feel_close
gender
health
income
native
religious
work
OBSERVATIONS
10800
10565
10447
1461
10583
1477
1490
10762
10769
10622
1496
10832
10810
9226
10816
10774
10836
MEAN
48.233
5.988
5.083
5.424
5.988
2.624
8.600
0.040
3.379
2.245
1.360
0.498
1.916
6.591
1.062
4.181
0.587
STD. DEV.
18.253
2.401
2.300
1.950
2.401
0.788
2.307
0.197
1.221
1.181
0.562
0.500
0.909
2.688
0.242
2.623
0.492
MIN
15
0
0
0
0
1
0
0
1
1
1
0
1
1
1
0
0
MAX
102
10
10
10
10
4
10
1
5
6
4
1
5
10
2
10
1
Other variables not related to immigration include feel_close, discriminated,
income, and work. The results for feel_close indicate that most people tend to feel close
to Denmark, meaning that they are likely more patriotic. This could potentially result in
this group wanting the population to remain relatively homogenous, meaning this group
is more averse to immigration. discriminated is another interesting variable, as it shows
whether the respondent is part of a group that has been discriminated against in Denmark.
The results show that most people have not been discriminated against, but the people
who have been discriminated against may empathize with immigrants, or may even be
immigrants themselves. The variable for income shows the income level of the
respondent; it is possible that people with higher incomes may have to pay more of their
taxes to welfare, which could potentially be going towards immigrants unable to
assimilate into the economy. This may result in more negative attitudes towards
immigration for higher earners. Another variable, work, shows whether the respondent
has done paid work in the last seven days. Individuals who have done paid work may
Hafner 16
have more positive attitudes towards immigrants than people who have not done paid
work, as the people who have not worked may feel that immigrants are taking jobs away
from them. Finally, education is included in all seven rounds, and has a mean of 3.379,
which shows that the average respondent has reached an upper secondary education.
Hafner 17
V. METHODOLOGY
The models used in this paper take an Ordinary Least Squares (OLS) form. The
dependent variable is one of the variables measuring attitudes towards immigrants. For
each attitude variable used, I run two regressions: the first with no interaction term, and
the second with an interaction term between education and income included. The first
regression takes the following form:
Y = β0 + β1 education + β2 income + β3 pdwrk + β4 native + β5 discriminated + β6 female +
β7 age + β8 religion + β9 equal + β10 essround + ε
In this regression, Y is one of the attitudes variables (i.e. attitude_living, attitude_culture,
etc.). ε is the error term. I also include a variable for the ESS round to control for
potential changes over time.
The second regression for each attitude variable includes an interaction term
between education and income. This is because education likely has an effect on the
amount of income people earn. The regression takes the following form:
Y = β0 + β1 education + β2 income + β3 education * income + β4 pdwrk + β5 native + β6
discriminated + β7 female + β8 age + β9 religion + β10 equal + β11 essround + ε
Again, Y is one of the attitudes variables, while ε is the error term. This second
regression should better account for any potential interactions between variables.
Hafner 18
VI. RESULTS
Variables for all 7 Rounds
I first look at regressions with attitudes variables that are included in all seven
ESS rounds. The first regression uses attitude_living as the dependent variable, as shown
in Table 3.
Table 3.
income
education
income * education
worked
native
discriminated
female
age
religion
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
TABLE 3 (Attitude_living)
Coefficients without
Coefficients with
interaction
interaction
.0521 ***
.0295
(.0097)
(.0241)
.4569 ***
.4132 ***
(.0190)
(.0467)
n/a
.0068
(.0066)
-.0802
-.0790
(.0526)
(.0526)
-.4830 ***
-.4850 ***
(.0917)
(.0917)
-.3842 ***
-.3846 ***
(.1084)
(.1084)
.1517 ***
.1510 ***
(.0432)
(.0432)
-.0139 ***
-.0141 ***
(.0013)
(.0014)
.0218 ***
.0219 ***
(.0084)
(.0084)
.3826 ***
.3824 ***
(.0181)
(.0181)
0.1625
0.1625
8945
8945
As seen earlier, attitude_living is a variable that shows whether or not the
respondent thinks immigrants make Denmark a better or worse place to live. There are
several significant results that appear. First, there is a slight but significant income effect;
Hafner 19
as people reach a higher income bracket, their attitudes towards immigrants improve by
.0521. The education effect here is large, with almost a half a point increase in
attitude_living when reaching a higher education level. People who have worked during
the last seven days tend to have a negative attitude toward immigrants, but this result is
not significant. The coefficients for native, discriminated, and female are all significant;
native is the largest, with a .4830 decrease in attitudes towards immigrants for people
born in Denmark. Interestingly, members of a group who had been discriminated against
were more likely to have a negative view of immigrants. Females have a slightly more
positive view toward immigrants than males. Looking at the last three variables
included, as age increases, the value of attitude_living decreases, while people who are
more religious and want more equality for all tend to have more positive attitudes
towards immigrants.
Next, I run the same regression with an interaction term included between
education and income to better account for any potential effects between the two
variables. The results are comparable to the first regression; the same variables were all
significant, with similar values for the coefficients. A graphical representation of how
education and income affect attitude_living is shown in Figure 2.
Hafner 20
Figure 2. 4
5
attitude_living
6
7
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
As seen in the figure, reaching a higher level of education has a significant effect on
attitude_living. There is also an income effect, but this is relatively slight. This income
effect also does not appear to change much when the respondent is at a higher level of
education.
The next regression uses attitude_economy as the dependent variable, which looks
at how people think immigration affects the Danish economy. The results are shown in
Table 4.
income
education
income * education
worked
native
discriminated
female
age
religion
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
Hafner 21
TABLE 4 (Attitude_economy)
Coefficients without
Coefficients with
interaction
interaction
.0607 ***
-.0186
(.0105)
(.0261)
.4718 ***
.3189 ***
(.0206)
(.0505)
n/a
.0236 ***
(.0071)
.0184
-.0228
(.0569)
(.0568)
-.5910 ***
-.5990 ***
(.0994)
(.0994)
-.1977
-.1984
(.1177)
(.1176)
-.3949 ***
.3968 ***
(.0467)
(.0467)
-.0062 ***
-.0070 ***
(.0015)
(.0015)
.0223 **
.0226 **
(.0091)
(.0091)
.3482 ***
.3477 ***
(.0195)
(.0195)
0.1337
0.1347
8845
8845
Again, there are similar results; the coefficients for income, education, native, female,
age, religion, and equal were all significant. Income and education both have a positive
effect on attitudes, with education playing a much larger role (.4718 increase in attitudes
for reaching a higher level of education). There is again a big decrease in attitudes if the
respondent is a native. One interesting result is that females tend to have more negative
attitudes about how immigrants affect the economy than males do; this is different from
the previous example, where women had a more positive view about how immigrants
Hafner 22
affect Denmark’s culture. Age again has a very slight negative affect, while the
coefficients for religion and equal are positive.
When the regression is run including the interaction between income and
education, the results are comparable; all the same coefficients are significant and have
similar values. However, the interaction term is also significant, with a value of .0236. I
again graphed income and education with attitude_economy to see how these affected
attitude_economy. This is shown in Figure 3.
Figure 3. 3
4
attitude_economy
5
6
7
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
There is a clear change in attitude_economy for reaching higher levels of education.
There does not appear to be much of an income effect at low levels of education, but as
Hafner 23
respondents reach higher levels of education, having more income will lead to a higher
value for attitude_economy.
Next, I run the regression using the dependent variable attitude_culture, and the
results are displayed in Table 5.
income
education
income * education
worked
native
discriminated
female
age
religion
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
TABLE 5 (Attitude_culture)
Coefficients without
interaction
.0461 ***
(.0106)
.5637 ***
(.0209)
n/a
.0287
(.0578)
-.4166 ***
(.1006)
-.1688
(.1193)
.2197 ***
(.0475)
-.0113 ***
(.0015)
-.0035
(.0092)
.4181 ***
(.0199)
0.1634
8942
Coefficients with
interaction
-.0097
(.0266)
.4562 ***
(.0514)
.0166 **
(.0073)
-.0258
(.0578)
-.4217 ***
(.1006)
-.1687
(.1193)
.2182 ***
(.0475)
-.0118 ***
(.0015)
-.0033
(.0092)
.4177 ***
(.0199)
0.1638
8942
We again see a slight income effect and a much larger education effect; reaching a
higher level of education translates into over a half point change in attitudes about how
immigrants impact Denmark’s culture. The coefficients for native, female, age, and
equal are also significant; being a native or being an older respondent translates to more
Hafner 24
negative attitudes, while being a female or believing in more equality for all translates to
better attitudes.
After including the interaction term, we again see comparable results. The
coefficient on income, however, is negative, but this result is not significant. The graph
showing how education and income affect cultural attitudes towards immigrants can be
seen in Figure 4.
Figure 4. 4
5
attitude_culture
6
7
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
This graph shows fairly similar results to the last figure; education clearly has a
significant effect on the value of attitude_culture, while the income effect is negligible at
lower levels of education but increasing with education.
Hafner 25
Variables for only Round 7
I next look at variables only included in round 7 of the ESS dataset. The first
variable I use is attitude_job, whether people think immigrants create jobs or take jobs
away. The results are shown in Table 6.
income
education
income * education
worked
native
discriminated
female
age
religion
feel_close
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
Table 6 (Attitude_job)
Coefficients without
interaction
.0347
(.0210)
.3346 ***
(.0449)
n/a
.1244
(.1265)
-.2950
(.1985)
-.4871 **
(.2343)
.0811
(.1044)
-.0041
(.0032)
-.0528 ***
(.0199)
-.1269
(.0977)
.2729 ***
(.0444)
0.1018
1281
Coefficients with
interaction
-.0149
(.0556)
.3035 ***
(.0922)
.0056
(.0146)
-.1258
(.1266)
-.2975
(.1987)
-.4856 **
(.2344)
.0800
(.1045)
-.0039
(.0032)
-.0525 ***
(.0199)
-.1279
(.0977)
.2725 ***
(.0444)
0.1012
1281
Using attitude_job as the dependent variable, and without including the
interaction term, there are several significant results. As seen in previous tables,
education has large and significant positive effect on attitude_job. The coefficient on
Hafner 26
discriminated is significant at the 5% significance level, but in the negative direction.
There is also a negative but slight effect for religion. equal, on the other hand, has a
significant and positive effect.
A similar outcome occurs when the interaction term is included. The income
effect switches from positive to negative, but this is not significant. education and
discriminated have the biggest effects, with education in the positive direction and
discriminated in the negative direction. A graphical representation of how income and
education affect attitude_job is shown in Figure 5.
Figure 5. 4
4.5
attitude_job
5
5.5
6
6.5
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
There is again a clear education effect; there is over a one-point difference in the value
for attitude_job when comparing the lowest level of education to the highest. There
Hafner 27
appears to be a slight income effect, but this is most pronounced at higher levels of
education.
The next dependent variable used is attitude_poor, and the results are shown in
Table 7.
income
education
income * education
worked
native
discriminated
female
age
religion
feel_close
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
TABLE 7 (Attitude_poor)
Coefficients without
interaction
.0123
(.0083)
.1022 ***
(.0178)
n/a
.0884
(.0503)
-.0070
(.0782)
-.0359
(.0929)
-.0172
(.0415)
-.0044 ***
(.0013)
.0091
(.0079)
-.1633 ***
(.0381)
.1871 ***
(.0177)
0.1451
1295
Coefficients with
interaction
-.0088
(.0220)
.0967 ***
(.0363)
.0010
(.0058)
-.0887
(.0503)
.0066
(.0783)
-.0357
(.0930)
-.0174
(.0416)
-.0044 ***
(.0013)
-.0090
(.0079)
-.1635 ***
(.0381)
.1871 ***
(.0177)
0.1444
1295
There are four variables with significant coefficients: education, age, religion,
and feel_close. Education has a positive, but slightly smaller effect than in previous
Hafner 28
tables. Age has a very slight negative effect. Since this regression includes variables that
were only included in ESS round 7 data, the variable feel_close, how close the respondent
feels to Denmark, is also included. The coefficient on feel_close is significant and
negative, showing that people who feel closer to Denmark have a more negative view of
immigrants. The last significant coefficient is on equal, and has a positive value.
When the regression is run with the interaction term, the results are again very
similar. The values for the coefficients change very slightly, and there are no other
coefficients that become significant when the interaction is included. Figure 6 shows a
graphical representation of the effects of education and income on attitude_poor. Figure 6. 2.2
2.4
attitude_poor
2.6
2.8
3
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
Hafner 29
As seen before, education has a clear effect on the values of attitude_poor, with higher
education translating to more positive attitudes towards immigrants. There is also a slight
income effect, but this does not seem to change much with education.
Table 8 shows the results for the regression with the variable attitude_white
included. This variable shows whether respondents think Denmark should allow
immigrants who are not white into the country.
income
education
income * education
worked
native
discriminated
female
age
religion
feel_close
equal
R2
Sample Size
** = 5% significance level
*** = 1% significance level
TABLE 8 (Attitude_white)
Coefficients without
interaction
.0592 **
(.0236)
.2278 ***
(.0504)
n/a
-.0869
(.1426)
.2399
(.2254)
.0052
(.2673)
.3562 ***
(.1175)
-.0287 ***
(.0036)
-.0857 ***
(.0222)
-.2493 **
(.1078)
.3543 ***
(.0500)
0.1701
1304
Coefficients with
interaction
-.1074
(.0621)
.3031 ***
(.1030)
-.0137
(.0163)
-.0909
(.1427)
.2459
(.2256)
-.0021
(.2673)
.3591 ***
(.1176)
-.0283 ***
(.0036)
-.0850 ***
(.0223)
-.2468 **
(.1078)
.3550 ***
(.0500)
0.1699
1304
Hafner 30
There are several variables that have significant coefficients. income has a significant
and positive effect, but at the 5% significance level. At the 1% significance level,
education has a positive effect, as do female and equal. On the other hand, age, religion,
and feel_close have negative effects. When the regression is run with the interaction term
included, the coefficient for income takes on a negative value but loses its significance.
The coefficients for the other variables retain similar values. Figure 7 graphically shows
how education and income affect attitude_white.
Figure 7. 7
7.5
attitude_white
8
8.5
9
9.5
Predictive Margins with 95% CIs
1
10
income decile
Less than Secondary Education
Lower Secondary Education
Upper Secondary Education
Advanced Vocational Education
Tertiary Education
As expected, education has a positive effect on attitude_white. Interestingly, when
people have a high income, education appears to have less of an effect. Another
Hafner 31
observation to note is that the income effect is highest at low levels of education but
flattens out at higher levels of education.
Hafner 32
VII. ANALYSIS
These results show that education is large determinant in shaping attitudes
towards immigrants. Education has a significant impact on all the attitude variables; it
has a positive effect on economic, cultural, and racial attitudes towards immigrants.
However, the channel through which education affects attitudes is unclear. People who
are highly educated have a more positive view towards various aspects of immigration,
but why is this the case? Take the variable attitude_economy, for example, which
measures whether people think immigration is good or bad for a country’s economy. As
respondents reach a higher level of education, the value for attitude_economy increases
by almost half a point. There are a few reasons for why this could be occurring.
Achieving a higher education level may make respondents more knowledgeable about
economic outcomes, and as a result may not feel as threatened by immigrants. Another
potential reason is that highly educated people should also be more highly skilled, so they
might not worry that an influx of low-skilled immigrants will negatively affect them by
taking their jobs away. There also could be a cultural awareness factor; as people are
more educated, they learn more about different cultures and gain tolerance for cultural
groups other than their own. The exact mechanism at play is unclear; higher education
results in better attitudes for both variables that measure economic attitudes towards
immigrants and variables that measure cultural attitudes towards immigrants. There is
likely a mix of both economic and cultural awareness towards immigration that results
from increased education.
What about the effect of income on attitudes towards immigrants? Based on the
results, income does not appear to have much of an impact. There are a few occurrences
Hafner 33
when income is significant, but the actual value of the income effect is slight. When the
interaction term between education and income is included in the regressions, there are
no cases where income alone has a significant effect. There are two cases where the
interaction term (education times income) is significant: for the regression with
attitude_economy as the dependent variable, the interaction term was significant at a 1%
significance level, and for the regression that uses attitude_culture as the dependent
variable, the interaction term was significant at the 5% significance level. However, the
magnitude of these effects was very small.
Another variable that had an impact on attitudes was native, whether or not the
respondent was born in Denmark. For the regressions with attitudes variables that were
included in all seven rounds, the coefficient on native was significant and negative. In
the regression with attitude_living, being a native decreased the value of attitude_living
by almost half a point. While it makes sense that natives will have more negative
attitudes towards immigration than immigrants themselves will, the magnitude of this
effect is likely due to the fact that there are not a lot of immigrants in the sample.
The variable discriminated, which shows whether the respondent has been
discriminated against, is significant in some of the regressions. In the regression that
includes attitude_living, the coefficient on discriminated is significant at a 1%
significance level, while for the regression that includes attitude_job, the coefficient is
significant at a 5% significance level. The coefficients on discriminated are negative,
indicating that people who have been discriminated against generally have more negative
attitudes towards immigrants. This seems counterintuitive; it would make more sense
that people who have been discriminated against would empathize with immigrants, a
Hafner 34
group of people who are also often discriminated against. It is possible that there may be
a cultural group in Denmark that experiences discrimination, but does not come from
immigrant origins, and therefore the group members may have negative views towards
immigrants.
Gender of the respondent also affects attitudes. In all the regressions except the
one including attitude_economy, the coefficient on female takes on positive values. For
the regression including attitude_economy, being a female has a significant negative
impact on the dependent variable. The reason for this occurrence is unclear; one reason
might be that women worry about losing their jobs to immigrants. On the other hand, for
the regression including attitude_job as the dependent variable, the coefficient on female
is positive, indicating that females don’t think immigrants take jobs away. However, this
coefficient is not significant, so it remains unclear why women have more negative
attitudes towards the economic impact of immigration.
The results for age, religion, and equal are relatively straightforward. As
respondents get older, they tend to have a more negative view of the cultural and
economic impacts of immigration, although this effect is slight. People who are more
religious appear to have mixed view on immigration; in some regressions, the coefficient
on religion is significant and positive, while in others, it is significant and negative. For
all the regressions, the magnitude of the coefficient is small. This makes sense
intuitively, as some religions may be more welcoming to immigrants of different faiths,
while others may have a more negative view of religions other than their own. The
variable equal, which shows whether the respondent believes that people should be
treated equally and given equal opportunities, has a positive and significant coefficient in
Hafner 35
every regression. Again, it makes sense that people who believe in equality would also
have a positive view towards immigration.
Finally, the variable feel_close is included in the regressions with ESS round 7
variables. This variable shows how close to Denmark the respondent feels, and the
coefficient takes on negative values in each regression. For the regressions that include
attitude_poor and attitude_white, the coefficient on feel_close is negative and statistically
significant. This shows that people who feel closer to their country are also more likely
to have negative views towards immigration. In terms of why this is the case, people may
be more supportive of their country’s economic policies, and may think that immigration
might undermine these policies, or they may feel that immigration may undermine the
culture of the country. The exact mechanism through which this occurs is unclear.
Hafner 36
VIII. CONCLUSION
The goal of this paper was to determine the channels through which attitudes
towards immigration are shaped. Education was expected to have a significant effect,
and the results confirm that education is a key factor in shaping attitudes. However, the
mechanism through which education shapes attitudes remains unclear. Based on the ESS
dataset, it is difficult to determine whether education corresponds to more proimmigration attitudes because of increased cultural or economic awareness, or because
increased skill levels as a result of education make the respondent feel more
economically safe from immigration.
While there were several interesting results from this research, having access to
more data would be helpful. In later research, it could be useful to ask specific questions
about high or low skilled workers instead of including immigrants in one category. This
is a technique used in Hainmueller and Hiscox (2010) which could better help explain the
way in which education affects attitudes towards immigration.
Hafner 37
IX. REFERENCES
Brochmann, G. & Hagelund, A. (2011). “Migrants in the Scandinavian Welfare State,”
Nordic Journal of Migration Research, 1(1), pp. 13-24. Retrieved 28 Sep. 2015, from
doi: 10.2478/v10202-011-0003-3
Christiansen, Hans. “Denmark: The costs of the welfare state,” Organisation for
Economic Cooperation and Development. The OECD Observer, Apr/May 1996, 199,
ProQuest Research Library, pp. 35-36
Doobay, Chad, and Jørgensen, Morten. "Pushing the Limits of Acculturation: An
Examination of the Danish Policy on Integration." Humanity In Action.
European Social Survey. http://www.europeansocialsurvey.org/
"Facts and Statistics." -The Official Website of Denmark.Wed. 23 Nov. 2015.
http://denmark.dk/en/quick-facts/facts/
Foged, M. & Peri, G. “Immigrants’ Effect on Native Workers: New Analysis on
Longitudinal Data,” Institute for the Study of Labor, March 2015.
http://ftp.iza.org/dp8961.pdf
Hainmueller, Jens and Hiscox, Michael J. “Educated Preferences: Explaining Attitudes
Toward Immigration in Europe,” International Organization, 61, pp 399-442, 2007.
http://journals.cambridge.org/abstract_S0020818307070142
Hainmueller, Jens and Hiscox, Michael J. Attitudes toward Highly Skilled and Lowskilled Immigration: Evidence from a Survey Experiment.” American Political
Science Review, 104.01 (2010): 61-84.
http://hdl.handle.net/1721.1/59821
Harmon, Nikolaj A. “Immigration, Ethnic Diversity and Political Outcomes: Evidence
from Denmark”. University of Copenhagen, April 2014.
http://www.econ.ku.dk/nharmon/docs/harmon2013immigration.pdf
Hedetoft, Ulf. “Denmark: Integrating Immigrants into a Homogeneous Welfare State.”
Nov. 2006.
http://www.migrationpolicy.org/article/denmark-integrating-immigrantshomogeneous-welfare-state
Scheve, Kenneth F. and Slaughter, Matthew J. “Labor Market Competition and
Individual Preferences over Immigration Policy.” The Review of Economics and
Statistics, Vol. 83, Feb. 2001.
http://www.jstor.org/stable/2646696