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Kenneth Foster Amponsah , Christian-Albrechts- University of Kiel, 2015
Citation preview
Immigration Effects on Satisfaction
Master’s thesis
for the Master’s degree programme
Quantitative Finance
in the Faculty of Business, Economics and Social Sciences
at the Christian-Albrechts-Universitat zu Kiel
submitted by
Kenneth Foster Amponsah
First assessor: Prof. Dr. Uwe Jensen
Second assessor: Prof. Dr. Kai Carstensen
Kiel, November 2015
Acknowledgment
I am extremely grateful to Almighty God, for His grace and guidance
from the beginning to the completion of this thesis. I would like to ex-
press my very great appreciation to my supervisor Prof. Dr. Uwe Jensen
for his valuable and constructive suggestions during the planning and
development of this research work. His willingness to give his time so
generously has been very much appreciated. I would like to also thank
my friend Victoria Sam Abaidoo for her insightful inputs and useful sug-
gestions.
I am also ever grateful to my parents and siblings, for their
prayers, encouragement, support and love in challenging moments.
i
Abstract
This paper explores how the share of immigrants in Germany affect the
life satisfaction of German residents. In particular, immigrants are also
categorized into five different immigrant subgroups- European Economic
Area (EEA), Turkey, Other Europeans, Asia and the rest of the world.
The number of immigrants from these regions are also independently ex-
plored to know their effect on the satisfaction levels of German residents.
Three models are used for the analysis, namely the Ordered probit model,
Ordaniry Least Squares (OLS) regression and the Fixed effect model.
The results indicate a positive significant effect of the total immigration
share on satisfaction when the Ordered probit and OLS modelS are used,
but a negative non-significant effect when the Fixed effect model is used.
The Ordered probit model is therefore used as our benchmark model
with which the various immigrant subgroup effects are also analysed.
Immigrants from EEA, Turkey and the Other European countries had a
significant positive effect on the satisfaction of German residents, while
those from Asia had a negative non-significant effect and those from the
rest of the world also had a positive but non-significant effect on the
satisfaction of German residents. The results therefore indicate that in
general German residents are more happy when more immigrants come
to Germany, especially immigrants from Europe; therefore there should
not be much concern in Germany with regards to extending the EEA to
include other European countries.
ii
Contents
Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . v
1 Introduction 1
2 Evolution of Immigration Policy in Germany 4
3 Literature Review 10
4 Data and Methods 19
4.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Data Description and Summary Statistics . . . . . . . . 21
4.3 Econometric specifications . . . . . . . . . . . . . . . . . 26
4.3.1 Panel Data Estimation . . . . . . . . . . . . . . . 26
4.3.2 Ordered Probit Model . . . . . . . . . . . . . . . 31
4.3.3 The Model . . . . . . . . . . . . . . . . . . . . . . 32
iii
5 ESTIMATION RESULTS 35
5.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . 35
5.2 Effect of Total Immigrant Share on Skilled Groups . . . . 51
5.3 Endogeneity and Heteroskedasticity . . . . . . . . . . . . 52
6 Conclusion 55
Appendix I
Bibliography VIII
Affirmation . . . . . . . . . . . . . . . . . . . . . . . . . . XVI
iv
List of Acronyms
OECD Organisation for Economic Co-operation and
Development
Totimmshare Total immigrant share in the federal state
Totimmsh hat Predicted Total immigrant share in the federal
state
immshareEE∼t Predicted Immigrant share from EEA in the
federal state
immEurOth ∼t Predicted Immigrant share from Other European
countries in the federal state
YrsofEdu Number of years of formal education
Age2 Age squared
Empdum Dummy for employed persons
NotinLabFor Dummy for those not in the labour force
NWinEduTra Dummy for those not working because they are
still in education or training
MatLeave Dummy for women not working because they are
on maternity leave
NWUnem Dummy for those not working because they are
unemployed
EasGer Dummy for residents in East Germany
logNumPrsHH Natural log of number of persons living in the
household
WrkHrs Number of worked hours in the previous year
WrkHrs2 Worked hours squared
logHhInc Natural log of annual household income in the
previous year
v
logNumDocVis Natural log of number of visits to the doctor in
the previous year
logUnempExp Natural log of length of time in years that one has
gone unemployed
ThreeChild Individual with 3 or more children
loggdp Natural log of Gross Domestic Product of federal
state in a given year
unemprate Unemployment rate in a federal state in a given
year
vi
Chapter 1
Introduction
The impact of immigration on the welfare of host countries has been a
topic of grave concern for both policy makers and economists over the
years now; with a large number of people migrating to foreign countries
for one reason or the other. The United Nations (UN) and the Organi-
sation for Economic Co-operation and Development (OECD) in a joint
report stated that the number of international immigrants increased from
154.2 million in 1990 to 231.5 million people in 2013, which is about a 50
% increase (UNDESA, 2013). This has led to quite a number of studies
and academic literature on immigration, addressing a broad variety of
topics.
Generally, researchers and policy makers would want an answer
to the question of whether immigrants have a positive or negative im-
pact on the welfare of the residents in the host country. In answering
this question, economists and researchers have over the years tradition-
ally employed the use of ’objective measures’ of welfare such as wages
and employment ( Card (1990), Card (1997), Dustmann et al. (2005),
Butcher and Card (1991), Borjas (1994), Borjas (2003), Ottaviano and
1
Peri (2012)). Another part of the migration literature also focuses on
the effect of migration on public expenditure, fiscal effects and prices
(Brucker et al. (2002), Dustmann et al. (2010), Dustmann et al. (2013)).
However very recent studies have started to explore the relationship be-
tween immigration and the subjective welfare of the residents in the host
country. To give some examples, Burton and Phipps (2010) identified
that immigrant parents and their children have a relatively lower self-
reported life satisfaction as compared to native-born Canadians. Fur-
thermore, they find that immigrants are less probable to have a sense of
belonging to the society (Burton and Phipps (2010)). Ding (2013) also
found that immigrants have a negative effect on natives’ subjective well-
being in Canada. Akay et al. (2012), however finds a positive impact of
immigrants on the life satisfaction of German natives.
Germany is one of the leading countries receiving immigrants.
According to a UN report in 2013, Germany had the third largest number
of immigrants in the world and number one in Europe, with an immi-
grant share constituting 11.9 % of its population. Statistics by Eurostat
also show that Germany received the highest number of immigrants in
2013 with a total of about 693,000 immigrants entering Germany that
year. Part of Germany’s reasons for welcoming hundreds of thousands
of migrants lies in demographics. Germany has one of the world’s most
swiftly ageing and declining populations. Also reported to have one of
the world’s lowest birthrates, Germany relies on immigration to fill a
growing workforce gap. Therefore due to this high influx of immigrants,
it is unsurprising to say that the immigrants affect the objective and
subjective welfare of German residents.
Over the years, most of the studies investigating immigration’s
impact on welfare have done so using the traditional objective measures
approach. But in recent years there have been the motivation to consider
2
subjective well-being measures. Kahneman and Sugden (2005) argue
that, on a broader level, objective measures are only able to partially
capture most of the determinants of one’s welfare. In the UN report on
happiness, De Neve et al., 2013, argued that there are actually objective
benefits of subjective well-being.
In Germany, there is only one known literature, as at now, that
measure the impact of immigration on the subjective well-being of Ger-
man natives (by Akay et al. (2012)). In this paper, I investigate whether
the spatial concentration of immigrants within a German federal state
will have an effect on residents’ subjective well-being. I group the immi-
grants into five categories - EEA, Turkey, Other Europeans, Asia and the
rest of the world - to check their independent impact on residents’ subjec-
tive well-being. Overall, the results indicate that German residents are
more satisfied with their lives when more immigrants move to Germany.
The remainder of this thesis is organized as follows: Chapter 2
takes a look at the evolution of immigration policy in Germany. Chapter
3 encapsulates findings from previous literature which focus on immigra-
tion and subjective well-being, for that matter, life satisfaction. Chapter
4 is in two parts, with the first part describing the data and providing de-
scriptive statistics, and the second giving the econometric specifications
and the models used. In Chapter 5, the estimation results and findings
are given and discussed. I conclude in Chapter 6 as well as highlighting
some policy implications of the results.
3
Chapter 2
Evolution of Immigration
Policy in Germany
In this chapter, I will succinctly review the historical evolution of immi-
gration policy in Germany and address the essential findings from the
studies which are closely associated with this policy evolution.
According to an OECD ranking in 2012, Germany was the sec-
ond largest recipient of migrants in the world, after the United States,
and the number one in Europe. Migration to and from Germany has
a long history. The reasons for such migration have primarily been the
same over the years: to seek greener pastures; flight from ethnic, polit-
ical, or religious persecution; forced expulsion. The history of German
immigration policies can be mainly dated back to the post World War II
era right through to the Immigration Act in 2005.
The first group of immigrants in Germany are those called the
’expellees’ or the ethnic Germans. These were people who had a German
background but had been living outside of Germany before the World
War II. These group of people settled in Eastern and some parts of Cen-
4
tral Europe, mainly present day Poland and Czech Republic. They were
expelled as a result of the Nazi German invasion on the Eastern bloc.
According to German Ministry of Interior (2014), between mid 1944 and
the end of 1949, roughly 7.7 million German expellees had been admit-
ted into the Federal Republic of Germany. Between that time and by
the end of 1981, 1.8 million more ethnic Germans and expellees were
admitted and brought this figure to 9.5 million. Between the years 1982
and 2013, another 3.5 million ethnic German re-settlers and their families
from Eastern Europe and the former Soviet Union came into the Fed-
eral Republic. This has resulted in a total of 12 million ethnic Germans
admitted into Germany over these years.
The second influx of foreign population comprised of foreign
workers, also known as ’guest workers’, who were admitted into the coun-
try from 1955 to 1973. During the 1950s, the West Germany’s ’economic
miracle’ led to an increasing demand for both semi-skilled and unskilled
labour. The labour supply from the local Germans was not sufficient in
meeting this demand, so the government signed recruitment agreements
with Italy (1955), Spain and Greece (1960), Turkey (1961), Morocco
(1963), Portugal (1964), Tunisia (1965) and Yugoslavia (1968) (Ministry
of Interior’s publication on ’Migration and Integration’ (2014)) . Also af-
ter 1961, East Germany’s decision to build the Berlin Wall and close its
borders to the West, cut off the supply of workers from East Germany,
thereby contributing to increased shortage of labour. The number of
Germans in the labour force fell by 2.3 million from 1960 to 1972, which
led to an increment in the recruitment of foreign workers. In 1960, only
1.3% of those in employment were foreigners and by 1973 this number
had risen to 11.9%. Reportedly, most of these foreign workers were em-
ployed in the states of North Rhine-Westphalia, Baden-Wuerttemberg ,
Bavaria and Hesse. (German Ministry of Interior, 2014)
5
Between the years 1973 and 1979, the proportion of foreigners
in Germany was quite stable. Although the proportion of foreigners
leaving Germany was more than those moving in, an increasing birth
rate significantly made up the difference. As a matter of fact, from 1973
to 1988 the number of immigrants increased quite slowly from 4 million
to 4.8 million. But starting in 1986, the proportion of immigrants moving
to Germany increased tremendously, exceeding the proportion of those
leaving. Within just eleven years (1986 to 1996), the number of foreigners
in Germany rose from 2.8 million to 7.3 million. This surge was only
partly as result of family members rejoining those living in Germany
and to the birth of about 1 million children from foreign parents during
this period. Most of the surge was as a result of the rising numbers of
asylum seekers commencing around 1980 and increasing stronger from
1985 onwards. (German Ministry of Interior, 2014)
Apart from the immigration of foreign workers and their family
relatives, Germany has admitted asylum seekers since the 1950s. However
during the late 1970s, the number of asylum seekers was relatively low
at around 10,000 immigrants a year, most of them from Eastern Bloc
countries. The number of asylum seekers saw a momentary increase in
1979 and 1980. Of the 107,000 people who applied for asylum in 1980,
more than half were Turkish citizens. The increase was also due to the
fact that recruitment of foreign labour had been banned in 1973, as it
is evident from the much lower rate of applications for asylum being
accepted in comparison to the previous years. The number of asylum
seekers again fell below 20,000 in 1983, but steadily increased from 1984
until 1992, when it peaked at roughly 440,000. In 1993 the asylum law
was reformed, which reduced the influx of asylum seekers steadily; with
nearly 19,200 applications in 2007, it was back to a comparable rate as
in 1983 (German Ministry of Interior, 2014). Since 2007, Germany has
6
again seen a rise in the number of asylum seekers. According to Eurostat,
in 2014 alone Germany had 202,645 asylum seekers, and that figure is
expected to be more than double in 2015.
The last group of immigrants I look at are those who were ad-
mitted into East German as foreign workers in the mid-1960s. This was
done within the framework of labour cooperation in the Council for Mu-
tual Economic Assistance (Comecon). Similar to West Germany, foreign
workers were normally employed in areas that Germans found not so at-
tractive. But East Germany strictly enforced its principle of rotation,
therefore ensuring that there was no subsequent immigration of family
members. Workers’ residence permits were also strictly linked to their
place of work, which made it basically impossible for them to become in-
tegrated. According to German Democratic Republic sources, foreigners
made up just about 1% of the labour force though (German Ministry of
Interior, 2014).
All these immigrants and their families over the years make up
the population of residents with immigrants background. But most of
these immigrants have gained German citizenship over the years leaving
the actual foreign population in Germany to be about 8.2 million out of
a total population of about 81 million in 2014; constituting about the
10% of the population (’Statistisches Bundesamt’, 2015).
There have been conflicting ideas among policy-makers over the
years, on how immigration is to be viewed. There had not been any clear
immigration policy until the late 1990s, when Germany began to see
itself as an immigration country. The Schroder government during the
early 2000s, then saw the need to pass a new immigration bill. Two
main changes were ushered into action. The first was a new citizenship
act, in effect since 2000, which recognizes both the ’law of blood’ and
7
the ’law of soil’; meaning children who were born in Germany with at
least one parent being a German citizen, under some circumstances also
became a citizen. The second was the ’green card’ regulation, which was
intended to attract more highly skilled foreign professionals to Germany.
The latter was primarily focused on IT-specialists (Constant and Tien
(2011)). However, the program could not be as successful as expected
failing to attract highly skilled migrants, as evidenced by the sharp drop
in visa applications. This caused the government to officially abandon
this scheme in 2005 (Constant et al., 2010b). This is believed to be as
a result of the fact that the German government spent a lot of time
debating whether high-skilled immigrants should be admitted, and how
to ensure that they leave after expiration of their contracts.
After about four years of political controversies and deliber-
ations, as well as a couple of rejections of the immigration bill, the
Federal Government finally passed an Immigration Act which took ef-
fect from January 1, 2005. Through this Act, Germany’s policy-makers
built the foundation for immigration policy and the social integration
of migrants, thereby finally recognising that Germany is an immigration
country. These reforms were brought about mainly in response to the
problem of high unemployment; mainly due to mismatches of demand
and supply as well as labour shortages for high skilled immigrants (Con-
stant and Tien (2011)). Chaloff and Lemaitre (2009), made an insightful
assertion about the motives behind immigration policy reforms made in
recent years by countries like Germany. They explained that the policy
has been to regulate immigration, while still leaving an allowance for em-
ployers to employ high-skilled workers. It is worth noting that despite the
Immigration Act upholding the ban on the recruitment of foreign labour,
especially for unskilled and low-skilled workers, Section 18 Subsection 1
of the Residence Act required that the recruitment of foreign workers
8
must be ’geared to the requirements of the German economy, according
due consideration to the situation on the labour market and the need to
combat unemployment effectively.’ (Constant and Tien (2011)).
Since this Immigration Act was effected into place there have
been just some minor modifications over the years, but generally this Act
is what is still being used and what the immigration policy of Germany
is based on.
9
Chapter 3
Literature Review
In this thesis, I explore the effect of the main immigrant subgroups’ share
on the subjective well-being of German residents. As indicated earlier,
Germany is a typical immigration country and therefore policy makers
are concerned about the impact of international migration on the welfare
of residents, for that matter natives of the country. There has been a
surge in the number of studies examining the use of the broader sub-
jective well-being measures rather than the traditional objective welfare
measurements. It is therefore essential to apply this area of research to
explore the direct impact of the different immigrants subgroups on the
subjective well-being of the resident population.
In this chapter, I will briefly present the findings of existing lit-
erature in Germany and the world as whole, that examines the impact of
immigrants. I will also review contemporary economic studies in terms of
happiness and life satisfaction. By so doing, I will compare and contrast
the two types of well-being measures with supporting literature . I then
argue why it is important to measure the impact of immigrants on a host
country, subjectively too, but not just objectively.
10
For decades, researchers and economists have been examining
the impact of migration on the host countries. A large part of these
studies target the impact of immigrants on the labour market of the host
country, considering both natives and immigrants. Another part of these
studies also explores the effect of migration on the second generation
of immigrants. In recent years, there have also been studies that basi-
cally estimate the effect of immigration on public expenditure and fiscal
policies.
With respect to labour market outcomes, researchers mainly
examine the impact of immigration on natives’ wages and employment,
which are objective measures of welfare. The normal approach has been
to correlate these measures with the immigrants’ share in the host coun-
tries’ labour markets (Akay et al. (2012)). In general, the findings in the
labour market are mixed, depending on the host country. For instance,
just recently, Ottaviano and Peri (2012) discovered a significant positive
effect of immigration on the wages of high-skilled United States(US) na-
tives, and a negative non-significant effect on low-skilled natives. But
prior to this, Borjas (2003) had found a negative impact of immigration
on the wages of US natives, while others conclude that the impact of
immigration is insignificant (Card (1990), Card (1997)). A panel data
study in the UK (Dustmann et al. (2005),Dustmann et al. (2008)), finds a
slight impact of immigration on unemployment, participation and wages
both economically and statistically . There is also evidence that since im-
migrants and Uk natives are imperfect substitutes in the labour market,
there is no adverse wage effect on the latter (Manacorda et al. (2012)).
However, the authors also finds that immigration has basically reduced
immigrants’ wages; especially that of university educated immigrants,
but has only a slight noticeable effect on the wages of native-born work-
ers. In Canada, Islam (2007) finds no evidence of a significant impact of
11
immigration on unemployment in the long run. Tu (2010) also finds that
the wage growth rate of Canadian-born workers is not affected by rising
immigrant inflows. In Germany, Pischke and Velling (1997) also conclude
that immigration does not negatively affect natives’ employment. Quite
recently, D’Amuri et al. (2010) investigate the wage and employment ef-
fects of immigration in West Germany, and conclude that immigrants
have no substantial effect on natives’ labour market outcomes, but have
a negative effect on that of previous immigrants.
Another branch of the literature investigates the impact of mi-
gration on children’s educational attainment and the results are quite
mixed across countries. Hunt (2012), finds that overall, immigration has
a slight positive impact on United States’ children completing 12 years of
education. However, Van Ours and Ohinata (2011) investigates how the
share of immigrant children in the classroom affects the educational at-
tainment of native Dutch children and do not find any tangible evidence
that immigrants’ children affect the academic performance of natives in
the Netherlands.
There is also a part of the literature that examines the relation
between immigration and public finance and expenditure. For instance,
Dustmann et al. (2010) examine whether the inclusion of the Eastern
European countries into the EU have an impact on UK public finances.
They find that immigrants from the accession countries had a positive
contribution to public finances, since they were found relatively more
probable to be in work than as compared to natives, and therefore less
probable to depend on social benefits. In Europe, Barrett et al. (2013)
also indicate that immigrants have a higher likelihood to be poor because
of their comparatively lower level of welfare receipt as compared to na-
tives. They therefore question the efficacy of the current welfare system
in protecting the interest of immigrants as well. However in Canada,
12
Grubel and Grady (2011) find that recent immigration to Canada has
created a fiscal burden to the country’s economics. Now turning to Ger-
many, Sinn and Werding (2001) concluded that as at 1997, immigration
had a net fiscal burden on Germany’s public finances. But they noted
that in the long-term, immigrants who stayed over 25 years produced
a net surplus. Bonin et al. (2000) and Bonin (2001) also argued that
immigration however produces a slight net benefit for the public sector
over the entire lifespans of the immigrants owing to their young average
arrival age and the manner through which the German pension system
is tied to one’s earned income.
Recent studies also examine the relationship between immigra-
tion and the attitudes of natives. For example, Card (2005) analyse
European Social Survey data and find that even though attitudes to-
wards immigrants are partly forged by economic factors, other aspects
such as culture, and natives’ social status are essential in affecting per-
ception about immigration. Also, Boeri (2010) argues that the business
cycle affect natives’ attitudes towards immigrants. Other studies also
examine the determinants of attitudes toward immigrants (Facchini and
Mayda (2009), Mayda (2006), Rustenbach (2010), Senik et al. (2009),
Bauer et al. (2000)).
While welfare and other traditional economic measurements are
essential in exploring the impact of immigration, our understanding can
be deepened using the relatively new method of subjective well-being.
There is a burgeoning consent among governments and international in-
stitutions on two points: first, that GDP is a very limited and imper-
fect measure, and second, that measures of subjective well-being have a
paramount role to play in defining welfare(O’Donnell (2013)). O’Donnell
(2013) explains how countries are using well-being data to improve pol-
icy making, and concludes that this approach leads to better policies and
13
a better policy process, since at the end of the day, happiness is what
matters most to most citizens. He give examples of countries which have
started to measure their progress with regards to the happiness of their
citizens. He indicates that Bhutan is the best known example where the
government has adopted the objective of maximizing its Gross National
Happiness (GNH) Index; but others, like the UK, US, Canada and New
Zealand are also now systematically collecting data on happiness and
life satisfaction. For example, British Prime Minister David Cameron
has set up a system demanding the Office for National Statistics (ONS)
to measure well-being frequently (O’Donnell (2013)). O’Donnell (2013)
also noted that the OECD is leading the way in establishing fair stan-
dards so that cross-country comparisons can be made; and also some of
these measurements use survey evidence to measure how happy people
feel at the moment, while others ask about overall satisfaction with life.
De Neve et al. (2013) also argues that it is essential to balance economic
measures of societal development with subjective well-being measures,to
ensure that economic growth leads to broad development across all life
domains, not just greater economic capacity.
In recent years, the amount of literature that evaluate subjec-
tive well-being has increased extensively. Quite a number of studies in
this area aim to investigate ’the determinants of subjective well-being’
(e.g., Dolan et al. (2008), Clark et al. (2008); Deaton (2010); DeVoe and
Pfeffer (2009); Blanchflower and Oswald (2011)). Most of these studies
tend to find out the factors that make citizens of a country happy, and
they do this mostly through correlations and just recently some going
further with test of hypothesis. Most researchers use a “happy equation”
to measure “happiness”. Normally, economists tend to use a cardinal ver-
sion of ’happiness’ or ’life satisfaction’ as a dependent variable in their
econometric analysis. With respect to the independent variables, many
14
researchers report employment status, health status, age, sex, marital
status, educational attainment, income, personal characteristics, and ge-
ographical characteristics as having significant effect on one’s satisfaction
with life (e.g., Dolan et al. (2008), Clark et al. (2008); Deaton (2010);
DeVoe and Pfeffer (2009); Blanchflower and Oswald (2011)). For exam-
ple, Blanchflower and Oswald (2011) conclude from an interdisciplinary
literature on subjective well-being that: ”happy people are dispropor-
tionately the young and old (not middle-aged), rich, educated, married,
in work, healthy, exercise-takers, with high fruit-and-vegetable diets, and
slim.”
There have also been cross-country comparison, with respect to
happiness in general, in other parts of the literature. This have basically
been done by using sources such as the World Value Survey, European
Quality of Life Survey and the OECD. For instance, Blanchflower and
Oswald (2011), using data from the 2007 European Quality of Life Sur-
vey, find that on the average Western Europeans have a higher life sat-
isfaction than Eastern Europeans. Okulicz-Kozaryn (2011) explores the
relationship between working hours and happiness among Americans and
Europeans, by using data from World Value Survey and other sources.
His findings suggests that Americans feel happier to work more as com-
pared to Europeans, because unlike the Europeans, Americans believe
that hard work is associated with success. However, Blanchflower and
Oswald (2011) caution that in the multi-country studies the diversity
of language and culture could influence the understanding of the ques-
tionnaire and hence the veracity of the data. They recommend that
researchers should therefore be wary about the validity of their results.
Nonetheless, the strand of literature which links happiness to
migration is quite new and not so common. One aspect of this literature
explores the differences between the subjective well-being of natives and
15
that of immigrants. For example, Bartram (2011) using data from the
World Values Survey, concludes that the relationship between income and
happiness is much weaker for USA natives than for immigrants; however,
he noted that even for immigrants that relationship is still relatively
weak. He also notes that migration is most likely a journey to better
one’s economic welfare, but an increase in income does not necessarily
yield better happiness. Gokdemir and Dumludag (2012) also examine the
role of several socio-economic and non-economic factors explaining the
differences of happiness levels of Turkish and Moroccan Immigrants in
the Netherlands; being the two largest non-EU immigrant communities
in the Netherlands. They find that Moroccans, despite having lower
income levels and higher unemployment rates than Turkish immigrants,
their happiness level is higher than the Turkish immigrants. They explain
that this is because there is insignificant effect of absolute income for
Turkish immigrants, however, the effect of relative income, which largely
explains the lower life satisfaction, matters for Turkish immigrants.
Normally it is presumed that people migrate in search of bet-
ter income, from rural areas towards urban areas, or from developing
countries to well-to-do countries, and therefore it is most probable that
the less happy ones choose to migrate. Therefore, another aspect of the
literature tends to address the change in happiness for immigrants af-
ter they have migrated. For example, Nowok et al. (2011) investigate
the question of ’Does migration make you happy?’ by exploring if per-
sons who migrate within the UK become happier than they were before
and whether the effect is permanent or temporary. They find that the
internal migrants within the UK on average experience a significant de-
cline in subjective well-being (SWB), in the period just before the time
of migration; however there is a boost associated with migration which
tends to bring people back to their initial level of happiness. Knight and
16
Gunatilaka (2012) also explore the change in life satisfaction for inter-
nal migrants who move from rural to urban China, since they constitute
18% of the total population; and they find that generally this internal
migration leads to a drop in happiness.
To focus on the literature for this paper, there is a relatively new
strand of economic study which examines the effect of migration on na-
tives’ subjective well-being. Akay et al. (2012) is the first research paper
that attempts to address this situation by combining information from
the German Socio-Economic Panel and regional data from INKAR at the
’RaumOrdnungsRegionen’ level; between 1997 to 2007. They investigate
the impact of immigration on the subjective well-being of natives and
immigrants and conclude that immigration positively affects the SWB
of native Germans; after conducting robustness checks and addressing
endogeneity issues. Another example is Betz and Simpson (2013), who
use the European Social Survey to examine the impact of aggregate im-
migration inflows on the subjective well-being of native-born European
populations in a panel of 26 countries in the time period between 2002
and 2010. Their main conclusion is that, and I quote: ”recent immigrant
flows have a non-linear, yet overall positive impact on the well-being of
natives. Specifically, we find that immigrant flows from two years prior
have larger positive effects on natives’ well-being than immigrant inflows
from one year prior. Our findings are very small in magnitude and in
practical application; only large immigrant flows would affect native well-
being significantly (Betz and Simpson (2013)).” In Canada, Ding (2013)
also examines the effect of immigration on both natives and immigrants’
subjective well-being but finds that there is generally a negative impact.
Nonetheless, there is no such similar study that examines the direct ef-
fect of migration from different immigrant sub-groups on the subjective
well-being of residents. To the best of my knowledge, this thesis is the
17
first study that will examine this question.
18
Chapter 4
Data and Methods
4.1 Data Sources
The sources for the empirical analysis used in this paper are mainly
from two distinct sources. I combine a dataset extracted from the Ger-
man Socio-Economic Panel (GSOEP) and rich regional data at the state
level(’Bundeslander’) from official statistics of Germany. The GSOEP
has been widely used in the SWB literature (Winkelmann and Winkel-
mann (1998), Ferrer-i Carbonell and Frijters (2004), Van Praag et al.
(2003)). The GSOEP is a wide-ranging representative longitudinal study
of about 11,000 private households consisting of about 30000 residents;
this includes both natives and immigrants. This annual panel survey
was first executed in the Federal Republic of Germany in 1984, collect-
ing data on German and immigrant households who are reinterviewed
every year. The sample has been enlarged and refreshed over the years,
particularly with the inclusion of about 2,000 East German households
in 1990 and a sample of Eastern European immigrants who migrated to
Germany after the collapse of the Soviet Block. The GSOEP surveys
19
are conducted by the German Institute for Economic Research, and con-
tains data with respect to topics like household composition, occupation,
employment, earnings, health and life satisfaction. I extract a rich set
of socio-economic variables at the individual level; particularly, I retrieve
information to formulate the SWB variable. The SWB variable is derived
from the question ”How satisfied are you at present with your life as a
whole?”, which grants responses on an ordinal scale from 0 to 10, where 0
stands for ’completely dissatisfied’ and 10 for ’completely satisfied’. Since
my focus is to examine the impact of the various immigrant subgroups
on the SWB of the entire population, I do not separate the immigrant
sample from the natives, but rather treat them as one- residents of Ger-
many. The definition of immigration employed is based on citizenship,
with respect to the law of blood and the law of soil in Germany.
The second data source is the Federal Office of Statistics (’Statis-
tisches Bundesamt’), from which I extract statistics for the 16 federal
states of Germany. Since the GSOEP contains information on the fed-
eral states of the sampled individuals, it is possible to match the micro-
data with the regional statistics. The advantages of using the federal
states level data are manifold. First, federal states are well-defined re-
gions which are somehow distinct in their culture, economic policies and
labour market characteristics. This precise geographical level allows for
the adequate capturing of the heterogeneity of German local labour mar-
kets. Moreover, my main interest of statistics from immigrant subgroups
is readily available only at the federal states level, even though it would
have been more efficient to get these statistics at the ROR level; but then
also at the ROR level, the immigration subgroups would constitute small
percentages . The key variable of interest, the immigrant share in the
federal states, is defined as the ratio between the number of immigrants
(or immigrant subgroup) and the total resident population (for the sake
20
of convenience, this ratio is multiplied by 100). In line with the GSOEP
data, the Federal Office of Statistics immigration definition is based on
citizenship. In addition, I extract data on regional unemployment rates
and GDP.
The required statistics from the Federal Office of Statistics are
available for the period 2005 to 2014 and that from the GSOEP is from
1984 to 2012; since I had the version 29 available. The analysis is there-
fore restricted to time period from 2005 to 2012. Furthermore, I focus
my analysis on individuals between ages 16 and 65 years. The final sam-
ple obtained by merging the GSOEP and the Federal Office of Statistics
data consists of 125,494 individual-year observations.
4.2 Data Description and Summary Statis-
tics
As stated earlier, the SWB variable is derived from the question ”How
satisfied are you at present with your life as a whole?”. Originally the
responses are on an ordinal scale from 0 to 10, where 0 stands for ’com-
pletely dissatisfied’ and 10 for ’completely satisfied’. Table 1 shows the
summary of responses for this question and as we can see, only 8.36% of
residents who rate their life satisfaction use a number less than ’5’ and
just 3.58% pick a rating of 10. It is evident that too few observations
in many cells, so I decided to recode the responses into fewer groups by
recoding ’0-5’ to 0, ’6-7’ to 1, ’8’ to 2 and ’9-10’ to 3. This is done to
reduce the variation in the responses in order to get more efficient es-
timates. The summary of responses for the recoded variable, SWB, is
presented in Table 2. But I must say that using the original variable in
my analysis did not yield significantly different results from the that of
21
the recoded variable.
Table 1
CurrLifeSatis Freq. Percent
0 412 0.331 535 0.432 1,601 1.283 3,366 2.684 4,580 3.655 13,650 10.886 13,675 10.907 28,666 22.848 39,114 31.179 15,398 12.2710 4,497 3.58
Total 125,494 100.00
Table 2
SWB Freq. Percent Cum.
0 24,144 19.24 19.241 42,341 33.74 52.982 39,114 31.17 84.153 19,895 15.85 100.00
Total 125,494 100.00
The second key variable of interest is the immigrants’ share in
the various federal states of Germany. Table 3 shows the number of
immigrants in Germany and in the sixteen federal states between the
years 2005 and 2012. As we can see North-Rhine-Westfalia has the most
number of immigrants of around 1.8 million people over the years, while
Mecklenburg-Vorpommern recorded the lowest number of immigrants of
around 30000 people over the years. Table 4 also shows the immigrants’
share; as a percentage of the population in each federal state, from the
various immigration subgroups,just for the year 2012. This gives us an
overview of the percentage of the various immigrant subgroups over the
years, since this does not really change much from year to year. I took
these classification of subgroups since most of these groups had pecu-
22
liar immigration policies; for example immigrants from the EEA can just
come to Germany and secure jobs without requiring a visa as well as
easier integration. I singled out Turkey because it is by far the most
populous immigrants’ country, even representing a greater proportion
than America and Africa combined. The subgroup ’Others’ comprises
of mainly immigrants from Africa, America and other undeclared na-
tionalities. And from Table 4, it is evident that the largest number of
immigrants come from EEA constituting about 3.58% of the entire Ger-
man population; with the lowest number coming from ’Others’ at a mea-
gre 0.72% of the population. It can also be seen that the East German
states- Brandenburg, Mecklenburg-Vorpommern, Saxony, Saxony-Anhalt
and Thuringia, have the lowest share of immigrants in comparison with
the West German states.
Table 5 shows all the variables I use in my analysis which were
partly extracted from the GSOEP responses on individual characteristics
such as age, gender, marital status, employment status, household size,
health status (number of doctor visits), Education, number of children,
amount of work hours and household income. I also extracted the re-
gion and federal states of individuals, to address regional heterogeneity,
in addition to labour market characteristics with respect to the various
federal states; here I consider only GDP and unemployment rates. But
it must be noted that I decomposed most of these variables and used
them as dummies, leaving out the ones with the highest ratios out. The
abbreviated variables are expressed fully in the ’list of Acronyms’ section.
I extracted the individual characteristics variables from the GSOEP
’version 29 (long)’ dataset; specifically, from the ’pl’, ’pequiv’ and ’pgen’
datasets. However, I did some re-coding, notably by excluding all indi-
viduals who did not have data on ’life satisfaction’, for any given time
period, and also by adding a ’one’ to all metric variables before finding
23
Table 3:
24
Table 4: Immigrants Background in Germany (2012)
25
the logarithms in order to avoid having a lot of missing data.
4.3 Econometric specifications
In this section I will give an insight into the econometrics methods I will
use and choose one as my benchmark model. I start by briefly explaining
panel or longitudinal data estimation and then I highlight on the reason
for the choice of models I use. I then present the econometric model and
explain its parameters.
4.3.1 Panel Data Estimation
A longitudinal, or panel, data set is one that contains data on a given
sample of individuals over time, and therefore provides multiple obser-
vations on each individual in the sample. In other words it is a cross-
sectional data collected over a number of years using the same sam-
ple.(Hsiao (2014), chapter 1). My working dataset is an unbalanced
micro-panel dataset. It is a micro-panel dataset due to the fact that the
individual dimension, N, is far bigger than the time dimension, T. And it
is also unbalanced because I do not have equal time periods, t = 1, ..,T,
for each cross section observation. But we must note that the mechan-
ics of the unbalanced case are similar to the balanced case (Wooldridge
(2002), chapter 10).
There are quite a number of advantages that comes with the
use of panel data. First of all panel data usually give the researcher
a large number of data points (N*T), thereby increasing the degrees of
freedom and decreasing the collinearity among the independent variables
and hence bettering the efficiency of econometric estimates. Moreover,
26
Table 5 : Summary statistics
Variable Mean Std. Dev. Min. Max. NSWB 1.436 0.974 0 3 125494Totimmshare 7.79 3.698 1.391 14.039 125494Age 42.766 13.333 17 65 125494Age2 2006.728 1120.059 289 4225 125494maledummy 0.476 0.499 0 1 125494YrsofEdu 12.467 2.696 7 18 117613Empdum 0.736 0.441 0 1 125494NotinLabFor 0.123 0.328 0 1 125494NWinEduTra 0.044 0.205 0 1 125494MatLeave 0.019 0.135 0 1 125494NWUnem 0.075 0.263 0 1 125494Married 0.578 0.494 0 1 125494Separated 0.02 0.14 0 1 125494Divorced 0.085 0.279 0 1 125494Single 0.297 0.457 0 1 125494Widowed 0.02 0.141 0 1 125494EasGer 0.237 0.425 0 1 125494logNumPrsHH 1.296 0.333 0.693 2.708 125494WrkHrs 1380.781 1081.473 0 7007 125494WrkHrs2 3076129.253 3254431.368 0 49098048 125494logHhInc 9.768 2.961 0 14.772 125494logNumDocVis 1.564 1.256 0 5.984 124986logUnempExp 0.377 0.638 0 3.638 124306NoChild 0.643 0.479 0 1 125494OneChild 0.185 0.388 0 1 125494TwoChild 0.132 0.338 0 1 125494ThreeChild 0.04 0.196 0 1 125494Schleswig-Holstein 0.029 0.167 0 1 125494Hamburg 0.015 0.12 0 1 125494LowerSaxony 0.088 0.284 0 1 125494Bremen 0.007 0.083 0 1 125494North-RhineWestfalia 0.204 0.403 0 1 125494Hessen 0.071 0.257 0 1 125494RheinlandPfalz 0.046 0.209 0 1 125494Baden-Wuerttemberg 0.123 0.328 0 1 125494Bavaria 0.148 0.356 0 1 125494Saarland 0.012 0.108 0 1 125494Berlin 0.038 0.191 0 1 125494Brandenburg 0.04 0.196 0 1 125494Mecklenburg-Vorpommern 0.025 0.155 0 1 125494Saxony 0.072 0.258 0 1 125494SaxonyAnhalt 0.041 0.199 0 1 125494Thuringia 0.043 0.202 0 1 125494loggdp 10.252 0.21 9.793 10.88 125494unemprate 9.075 3.857 3.7 20.3 12549427
longitudinal data allows researchers to address a number of essential eco-
nomic questions that cannot be addressed using cross-sectional or time-
series datasets in empirical studies. For instance, panel data helps in
clarifying the often heard assertion that the real reason one finds (or
does not find) certain effects is the presence of omitted (mismeasured
or unobserved) variables that are correlated with the independent vari-
ables (Hsiao (2014), chapter 1). Panel data therefore allows to control
for omitted (unobserved or mismeasured) variables. One practical exam-
ple is that, the least-squares regression coefficients of yit on xit are well
known to be biased; but under panel data estimation, when we take the
first difference of individual observations over time, Least squares regres-
sion now provides unbiased and consistent estimates of β (Hsiao (2014),
chapter 1).
After talking about the importance of panel data estimation,
I now look at some dynamics with some panel data estimation models.
There is a part of linear panel data models where the error in each time
period is assumed to be uncorrelated with the independent variables in
the same time period. This assumption is too strong for most panel data
applications. As stated earlier, a primary motivation for using panel data
is to solve the omitted variables problem.
With regards to this thesis I study population models that ex-
plicitly contain a time-constant, unobserved effect. This is in line with
modern econometrics in the sense that unobserved effects are treated as
random variables, drawn from the population along with the observed
explained and independent variables, as opposed to parameters to be
estimated. In this regard, the paramount issue is whether the unob-
served effect is uncorrelated with the independent variables. (Wooldridge
(2002), chapter 10)
28
Let y and x ≡ (x1, x2, ..., xk) be observable random variables,
and let α be an unobservable random variable (but not a parameter to
be estimated); the vector (y, x1, x2, ..., xk, α) represents the population of
interest. As is normally the case, econometricians are interested in the
partial effects of the observable independent variables xj in the popula-
tion regression function:
E(y | x1, x2, ..., xk, α) = β0 + xβ + α
On one hand, if α has no correlation with any xj , then α is just
another unobserved factor affecting y that is not systematically related to
the observable independent variables whose effects are of interest. On the
other hand, if Cov(xj, α) = 0 for some j, adding α to the error term can
create a lot of problems. Without additional information it is impossible
to consistently estimate β, and furthermore we would not be able to
determine whether there is a problem.(Wooldridge (2002), chapter 10)
Under additional assumptions there are ways to address the
problem Cov(x, α) = 0 : (1) it is possible to find a suitable proxy variable
for α, such that, we can estimate an equation by Ordinary Least Squares
(OLS) where the proxy is plugged in for α; (2) it may be possible to
find instruments for the elements of x that are correlated with α and
use an instrumental variables method, such as 2SLS; or (3) it may be
possible to find indicators of α that can then be used in multiple indicator
instrumental variables procedure.(Wooldridge (2002), chapter 10)
The basic unobserved effects model (UEM) can be written, for
a randomly drawn cross section observation i, as
yit = xitβ + αi + uit
29
t = 1, 2, ..., T
If i serves as an index for individuals, then αi could be called
an individual effect or individual heterogeneity; it follows analogously for
families, firms, cities, states and other cross-sectional units.
In quite a number of methodological literature, and also in ap-
plications, there is often the debate about whether αi will be treated as a
random effect or a fixed effect. Traditionally, in longitudinal data models,
αi is called a ’random effect’ when it is treated as a random variable and
a ’fixed effect’ when it is seen as a parameter to be estimated across each
cross section observation i. Considering a large number of random draws
from the cross section, it usually makes sense to treat the unobserved
effects, αi, as random draws from the population, along with yit and
xit. This approach is absolutely convenient from an omitted variables
or neglected heterogeneity perspective. However, in modern econometric
argot, ’random effect’ is used when there is no correlation between the
observed independent variables and the unobserved effect; and ’fixed ef-
fect’ does not necessarily mean that αi is being treated as non-random
but rather, arbitrary correlation between the unobserved effect αi and
the observed independent variables xit is allowed. So, if αi is called an
’individual fixed effect’ or a ’federal state fixed effect,’ then, for practical
reasons, this terminology means that αi is allowed to be correlated with
xit. These lead us to two different estimation methods random effects
estimation and fixed effects estimation. (Wooldridge (2002), chapter 10).
In this thesis, I stick to the modern econometric definitions.
In fixed effects analysis, one can consistently estimate partial
effects in the presence of time-constant omitted variables that can be ar-
bitrarily correlated with the explanatory variables xit. In this sense, the
fixed effects estimation is more robust than random effects estimation.
30
However, this robustness comes at a price: without further assumptions,
we cannot include time-constant factors in xit. This is because if αi can
be arbitrarily correlated with each element of xit, then it is impossible
to differentiate between the effects of time-constant observables and the
time-constant unobservable αi (Wooldridge (2002), chapter 10). The fact
that xit cannot include time-constant independent variables is a short-
coming in certain applications; for example when analysing individuals,
factors such as gender or race cannot be included in xit. And also in
this approach, (N − 1) individual dummies are employed, which implies
(N − 1) additional parameters; thereby leading to loss of degrees of free-
dom and multicollinearity. However one can employ some standard trick
in dealing with this situation. The idea is that the information on lost
βj is in αi; therefore we can run an Auxiliary OLS regression:
αi = µ+m∑l=1
δlwil + εi
to recover βj via δl .
Where wi’s are the time-invariant exogenous variables including the de-
composed dummies (e.g. gender, federal state, etc.). But I must say that
another shortfall with this arises when the time period T is small. This
makes αi inconsistent, which implies inconsistent δl and βj
4.3.2 Ordered Probit Model
Now I switch the focus to the ordered probit model which is a suitable
candidate for my analysis due to ordinal nature of my dependent variable.
If y is an ordered response, then the values we assign to each outcome
are no more arbitrary. For example, in this thesis, y is the SWB variable
on a scale from zero to three, with y = 3 representing the highest life
31
satisfaction and y = 0 the lowest rating. The fact that three is a higher
life satisfaction rating than 2 carries useful information, even though the
life satisfaction ratings itself only has ordinal meaning. For example, we
cannot say that the difference between three and one is somehow twice
as important as the difference between one and zero.(Wooldridge (2002),
chapter 15)
Assuming y is an ordered response which takes on the values
0, 1, 2, ..., J for some known integer J . The ordered probit model for y
(conditional on independent variables x) can be derived from a latent
variable model. I further assume that a latent variable y∗ is determined
by:
y∗ = xβ + ε, ε | x ∼ Normal(0,1)
where β is K x 1 and, x does not contain a constant.
Let a1 < a2 < · · · < aJ be unknown cut points (or threshold parameters),
and define
y = 0 if y∗ ≤ a1
y = 1 if a1 < y∗ ≤ a2
...
y = J if y∗ > aJ
The parameters a and β can be estimated by maximum likeli-
hood. This log-likelihood function is well behaved, and many statistical
packages like what I used, Stata, routinely estimate ordered probit mod-
els.(Wooldridge (2002), chapter 15)
4.3.3 The Model
Since my dependent variable is measured on an ordinal scale from zero
to three, it would seem most appropriate to use an ordered probit econo-
32
metric model in which well-being is considered to be latent:
SWBit = βIMrt + η′Xit + λ′Zrt + εit (4.1)
εit = αi + γr + εit (4.2)
where,
SWBit - captures the latent well-being of an individual i at time t.
IMrt - immigrant share in federal state of residence r at time t.
Xit - comprises individual socio-demographic and economic characteris-
tics such as age, marital status and income.
Zrt - includes time-varying labour market characteristics, such as unem-
ployment rate and GDP per capita in each federal state of residence r at
a given time t.
The error term εit and its components are represented in equa-
tion (4.2): α captures individual unobservable heterogeneity as well as
federal-state specific time-invariant attributes; and γ represents period-
specific effects captured in the regression by time dummies; whereas ε is
an error term that follows a standard normal distribution due to identi-
fication in the ordered probit specifications.
Although the ordered nature of the dependent variable favour
an ordered probit model, for simpler and better interpretation of re-
sults, I will run some linear regressions (OLS and Fixed Effects model)
as well. The merits of using a linear specification are that it makes the
interpretation of the parameter estimates easier, and enables controlling
for individual unobservable characteristics in a simpler way (Akay et al.
(2012)). Moreover, Ferrer-i Carbonell and Frijters (2004) demonstrate
that using the cardinal model or the ordinal model in SWB analysis,
33
do not yield any significant difference in the estimation results. There-
fore, the ordered probit will be my main model but I will also present
the linear regression models only for comparison purposes. There have
been some studies that have sought to use an ordered probit model or
a linear regression model (Boes and Winkelmann (2010); Ding (2013);
Akay et al. (2012)). Akay et al. (2012Akay et al. (2012) use both a
random-effect ordered probit model and a fixed-effect model to examine
the impact of migration on the life satisfaction of German natives and
immigrants. They find that random effects ordered probit and fixed ef-
fects have similar results. In this study, I will use the ordered probit,
OLS and fixed-effect model for the analysis of only the total immigrants
share; for comparison purposes. But with the rest of the results, that is,
from the immigrant subgroups, I will use only the ordered probit model.
There are quite a number of shortcomings in the analysis using
the ordered probit model. First of all, the role of unobserved individual
characteristics, such as personality traits, as well as unobserved federal
state characteristics, is highly essential when analysing subjective well-
being (Boyce (2010)). If these factors are in anyway correlated with the
explanatory variables, then a specification controlling for time-invariant
individual characteristics is preferred; but that is not the case with the
ordered probit model.(Akay et al. (2012)). Furthermore, there could
also be some multicollinearity between the immigrants’ share and the
federal state labour characteristics. I therefore use instrumental variable
approach in getting an estimate for the immigrants’ share before running
the main analysis.
Futhermore, in view of accounting for serial correlation in the
error term, I cluster the standard errors at the individual level.
34
Chapter 5
ESTIMATION RESULTS
5.1 Main Results
In this section, I report the estimation results of equation (4.1), described
in the previous chapter. All of these results were obtained using the
statistical analysis package, Stata; and the results are just presented
directly from the output. In Table 6, I instrument the total immigrant
share with the region of residence of the individual, as well as the labour
market characteristics of the federal state of the individual; this is done in
view of curbing multicollinearity as mentioned in the previous chapter. In
Table 7 and 8, I regress the impact of the instrumented total immigrant
share on the subjective well-being of German residents- both immigrants
and natives, with ordered probit(OP) and OLS respectively. Tables 9 and
10 gives the β estimates using the fixed effects (FE) model. I employed
the fixed effects estimation trick I outlined in the previous chapter, by
first running the fixed effect regression without the dummy variables, and
then running the acquired residuals on the dummy variables via OLS. In
Tables A1 to A5 in the Appendix, I instrument the immigrant share from
35
Table 6: OLS Instrumenting of Total Immigrant Share
the various immigrant subgroups like I did for the total in Table 6. I then
present the results of the instrumented immigrant subgroups population
share on the SWB of residents, using the ordered probit model in Tables
11 to 15. I will interpret the results from table to table, and then I
will make comparisons within these interpretations with respect to the
various models.
One thing to note is that I decomposed some variables into
dummy variables, thereby leaving out the reference groups in the analysis.
Reference groups for having children is No children; for marital status is
Married; for employment status is Employed; for gender is Female; for
region is West Germany; for West German federal states is North-Rhine
Westfalia; and for East German federal states is Saxony. For consistency,
the largest groups in the undecomposed variables were chosen as the
reference groups. Another is that I control for federal state characteristics
in all the models by including federal state indicators.
Now, before discussing the main results of interest, it is essential
to discuss how the estimates of my model compare with those from ex-
36
Table 7: Impact of Total Immigrant Share on SWB (OP model)
37
isting literature. With respect to the OP model, the full estimates of the
socio-economic characteristics listed in Table 5, are reported in Table 7.
As one can see from a quick preview of this table, my results are consis-
tent with previous literature regarding the subject of SWB in Germany
(Ferrer-i Carbonell (2005), Winkelmann and Winkelmann (1998), Akay
et al. (2012)). For example, the pattern of SWB over a person’s life cycle
exhibits the ’classic’ U-shaped behaviour, suggesting that well-being is
high when one is young, and then decreases to a lowest level around the
age of 40, and then increases again (though it is not so clear in Figure
1) (see Frey and Stutzer (2002), Dolan et al. (2008)). Being married
has a positive effect on one’s life satisfaction. Moreover, an increase
in the educational attainment will lead to a higher level of well-being.
Bad health is negatively correlated with life satisfaction as evident in
the sign of ’Number of Doctor Visits’. Higher income also leads to a
higher life satisfaction as seen in Figure 2. As unsurprisingly entrenched
in the SWB literature, being unemployed is negatively associated with
life satisfaction (Wilson and Walker (1993), Clark and Oswald (1994),
Frey and Stutzer (2002) and Dolan et al. (2008)). However I find an
interesting result in the amount of worked hours; in the estimates there
appears to be a concave relationship between a SWB and worked hours.
This suggests that those with lower working hours are less satisfied with
their lives; and this satisfaction increases and is maximum around the
average hours worked, and then finally decreases to a lower satisfaction
again as the worked hours exceeds this average. The first part does not
make much sense and the reason could be that the lower working hours
are associated with those not working- either not in the labour force or
unemployed. Another reason could be that since this group of people
work less, they therefore earn less, hence less satisfied.
Regarding my key variable of interest, it is evident from Table 7
38
Figure 1: Scatter Plot of SWB and Age
Figure 2: Scatter Plot of SWB and Household Income
39
that a higher immigrant share in the federal state leads to a positive and
significant increase in the SWB of German residents; thus immigration
has a positive impact on the residents’ SWB. This is in line with Akay
et al. (2012). These results were not significantly different when I used
the original 0-10 satisfaction scale (Table A6)
In Table 8, the coefficients are estimated using the OLS model
and the interpretation is direct and easier. The results are not too dif-
ferent from that of the OP model. Generally the signs of the coefficients
are the same, with the exception of the coefficient for ’Bremen’ which
is positive however non-significant for the OP model, but negative and
significant for the OLS model. Also it can be seen that the coefficients of
the OLS model are generally smaller in magnitude than that of the OP
model. For example with regards to my main variable of interest, while
the OP model gave a coefficient of 0.0332 to the total immigrant share,
the OLS model gave a coefficient of 0.0276. With regards to the total
immigrant share, the results presented in the Table 8 imply that there is
0.0276 life satisfaction increase in the 4-scale life satisfaction of German
residents associated with a 1% increase in the immigrant share in each
federal state. Also, in terms of standardized coefficients, it can be seen
that: an increase of one standard deviation in the immigrant share in
each federal state is estimated to increase residents’ life satisfaction by
0.0945 standard deviation units (Table A7 in Appendix). This seems to
be a rather large effect, considering that the standardized coefficient for
an individual being unemployed is -0.0539 and for household income is
0.0532.
Tables 9 and 10 gives the β estimates using the fixed effects
model. Table 9 contains individual and federal state specific characteris-
tics which cannot be decomposed, whereas Table 10 contains th auxiliary
OLS of the residuals (’res’) from Table 9 on the decomposed variables
40
Table 8: Impact of Total Immigrant Share on SWB (OLS model)
41
Table 9: Impact of Total Immigrant Share on SWB (FE model)
42
Table 10: Impact of Total Immigrant Share on SWB (FE model:Auxiliary OLS)
43
(dummies). These estimates are somewhat different from that of the OP
and OLS models, in terms of most of the signs and even the coefficient
magnitudes. Specifically, the coefficient of the total immigrant share is
negative however insignificant; that of educational attainment is signifi-
cantly negative, which is not in line with literature. Having children also
has a significant negative effect on life satisfaction, however the result in
literature is quite mixed (eg. Clark et al. (2008), Myrskyla and Margo-
lis (2014)). Inconsistent with literature, unemployment experience has a
positive significant impact on SWB. There are also some differences in
the signs of some federal state dummies, in comparison with the OP and
OLS models; in particular, Rheinland-Pfalz, Bavaria and Berlin. These
inconsistencies in the Fixed-effects model could be because the within-
cluster variation in the data is minimal or most of variables are slow
changing over time.
These inconsistencies in the FE model makes me stick with the
OP model in the remainder of the results. Tables 11 to 15 present the
estimation results of the immigrant share from the various immigrant
subgroups, using the OP model. I will compare these results to each
other and also to that of the total immigrant share.
In Table 11, I estimate the impact of immigrants from the EEA
on the SWB of German residents. These estimates are almost exactly as
that of the total immigrant share. Specifically the signs are the same with
the exception of the indicator for Baden-Wuerttemberg which changes
sign from positive to negative; however in both cases it is insignificant.
Another remarkable difference is the magnitude of the Immigrant share
here which is almost triple that of the total immigrant share; 0.1094
against 0.03317. This suggests that immigrants from EEA have a more
positive effect than the average effect of immigration. This effect could
possibly be as a result of having mostly skilled labour as immigrants
44
coming from the EEA.
Table 12, presents the estimates in examining the impact of im-
migrants from Turkey on the SWB of German residents. These estimates
are also similar to that of the total immigrant share; with the coefficient
of the immigrant share being 0.0404 which is not too different from that
of the total immigrant share of 0.03317. However there’s a significant
change in the coefficient of the indicator ’Hamburg’. In Table 7 this co-
efficient has a negative and insignificant effect on the SWB of residents,
however, here it has a significant positive and quite large coefficient of
0.1537. This could possibly mean that immigrants from Turkey have a
higher and positive impact on the residents of Hamburg than the aver-
age immigrant. A possible reason for this impact could be as a result
of the numerous retail and grocery shops with Turk owners in Hamburg,
thereby employing a number of people and contributing a lot of tax as
well.
Table 13 presents the estimates with respect to other Euro-
peans other than those in Tables 11 and 12. These estimates are also
very similar to that of the total immigrant share and those from EEA.
The remarkable difference here is the magnitude of the coefficient of the
immigrant share. This is the highest coefficient (0.2244) among all the
immigrant subgroups and is about seven times bigger than the coeffi-
cient of the total immigrant share. This suggests that immigrants from
this subgroup has the highest impact on the life satisfaction of German
residents. A possible reason could be that most of these immigrants are
skilled workers, and even the non-skilled workers do not compete with
German natives for their work since they take on lowly jobs which natives
would not take.
Tables 14 represents the estimates from the immigrants from
45
Table 11: Impact Immigrant Share from EEA on SWB (OP model)
46
Table 12: Impact of Immigrant Share from Turkey on SWB (OP model)
47
Table 13: Impact of Immigrant Share from ’Other Europeancountries’ on SWB (OP model)
48
Table 14: Impact of Immigrant Share from Asia on SWB (OP model)
49
Table 15: Impact of Immigrant Share from ’Other parts of the world’on SWB (OP model)
50
Asia and the coefficient of the immigrant share here is different from
that of the total and those from Europe. There is a negative however
insignificant effect of immigrants from Asia on the SWB of German resi-
dents. This insignificant effect may be as a result of the relatively smaller
amount of Asian immigrants in the various federal states.
In Table 15, I estimate the impact of immigrants from ’Other
parts of the world’ on the SWB of German residents. Generally these
estimates are not far from that of the total immigrant share; with the
coefficient of immigrant share from ’Other parts of the world’ being also
positive, however insignificant. Nonetheless in both Tables 14 and 15,
there are changes of sign of some of the coefficients of the federal state
indicators, in comparison with that of the total population. Specifically,
the coefficients of Hamburg and Hessen which were insignificantly nega-
tive under the total immigrant share, become significantly positive; and
that of Mecklenburg-Vorpommern changes from insignificantly positive
to significantly negative. This could possibly mean that immigrants from
Asia and ’Other parts of the world’ have a higher positive impact on
Hamburg and Hessen than those from EEA, ’Other European countries’
and the total immigrant share. However, these same subgroups in Ta-
bles 14 and 15 could possibly have a higher negative impact on the SWB
of residents in Mecklenburg-Vorpommern, as compared with the other
subgroups.
5.2 Effect of Total Immigrant Share on Skilled
Groups
In Tables A8 to A10 in the Appendix, I examine the impact of the total
immigrant share on the various skilled groups- Low, Medium and High
51
skilled; using the OP model. I use the ’Erikson and Goldthorpe Class
Categorie’ in the GSOEP data and recode those in high service, self-
employed with employees and Skilled manual jobs as ’High skilled’; low
service, routine service-sales, routine non-manual and self-employed with
no employees as ’Middle skilled’ jobs. And then finally semi-skilled and
unskilled manual, farm labour and self-employed farm as ’Low skilled’
jobs. From the results of the estimation, the immigrant share has a pos-
itive impact across the three skilled groups. And the coefficients are not
far from each other, with just a slight increase in that of the immigrant
share as we move from ’High skilled’ to ’Low skilled’. This suggest that
none of the labour force participants should feel threatened by the influx
of immigrants.
5.3 Endogeneity and Heteroskedasticity
On studies which tend to employ spatial correlation; in this case the
variation of the immigrant share in the various federal states, the identi-
fication of the effect of immigrants on natives welfare is usually challenged
by causality issues (Borjas (1999)). Normally, immigrants are not spread
evenly across the various labour markets, for example, as seen in Tables
3 and 4; and this begs a serious question of causality. Intuitively, most
immigrants choose their preferred location of settlement in function of
the characteristics of the local labour market of preferred location or to
places where many fellow compatriots already live (Bartel (1989)). In
the case that these characteristics are significantly correlated with the
immigrant share, and cannot be controlled for in the analysis, then the
problem of omitted variable or simultaneity bias could be present. When
there is no exogenous variation (eg. in Card (1990)), analysis of the effect
of immigration on labour market outcomes such as earnings and employ-
52
ment are liable to the problem of causal interpretation. Traditionally,
most studies use a standard approach in addressing the causality issues;
which is, finding an instrumental variable which only has an effect on
the outcome of interest through the immigration variable. For instance,
Hatton and Tani (2005) use lagged immigration as an instrument for
current immigration. Another way is to instrument by a variable that is,
first, correlated with the change in the immigrant share relative to the
total resident labour force and, also, uncorrelated with changes in the
dependent variable other than through the immigration channel (Basten
and Siegenthaler (2013)).
My analysis might also contain causality issues, since, like the
studies above, I employ a spatial correlation approach. However, the
panel structure of my data allows me to control for federal-state fixed ef-
fects. This implies that any unobservable time-invariant factor, possibly
correlated with immigration and life satisfaction is already absorbed by
the federal-state indicators. Moreover, I use the federal-state labour mar-
ket characteristics such as GDP and unemployment rate, as instruments
for the immigrant share. Through this, I reduce the role of unobservable
factors which can influence both immigration and SWB, which therefore
to an extent solves the causality issue. Moreover Akay et al. (2012),
dedicate a section to thoroughly explore potential threats to a causal
interpretation of their results, and they conclude that issues of selection
and reverse causality are not likely to be a problem.
Also there seem to be some heteroskedasticity between the re-
gression of some of the explanatory variables and the dependent variable.
For example in Figure 2 we see evidence of heteroskedasticity in the plot
of life satisfaction and household income. I could not find any test for
heteroskedasticity after using an OP model, so I went on to do a formal
test using the ’Breusch-Pagan / Cook-Weisberg test for heteroskedastic-
53
ity ’ in Stata, after running an OLS regression, and the null hypothesis
of homoskedasticity was rejected. And so some of the methods I use in
minimizing this effect of heteroskedasticity is by taking the natural logs
and in some cases the squares of the metric variables. Also in my OP
analysis I employ robust standard errors and also cluster these standard
error at the individual level.
54
Chapter 6
Conclusion
The main objective of this study is to analyse whether the geographic
concentration of various immigrant subgroups in Germany has an im-
pact on the residents’ subjective well-being. I merge panel data from
Germany with official statistics on immigrants and local labour markets
from the period 2005 to 2012, and then I estimate three regression mod-
els in which I correlate residents’ subjective well-being variable with the
immigrant share in the federal state, as well as a wealth of other control
variables. The main findings of this paper indicate that, in general, an
increasing number of the total immigrant share in each federal state lead
to an increase in the subjective well-being of residents in Germany. In
other words, German residents’ life satisfaction increases as the number
of immigrants increases. Also I find out that immigrants from the EEA,
Turkey and the rest of the European countries have a significant posi-
tive impact on the life satisfaction of Germans, with those from ’Other
Europeans’, which includes EU candidate countries, having the highest
effect. In addition, those from Asia have a negative, however insignificant
effect and the last group which I term as the ’Other parts of the world’
also had a positive but insignificant effect on the SWB of residents. In
55
summary, immigrants, especially Europeans have a positive impact on
the SWB of German residents. To the best of my knowledge, this is the
first paper to examine the direct effect of immigrant share from different
immigrant background on the subjective well-being of German residents.
Furthermore, this research confirms the findings in the existing happiness
literature: personal characteristics such as marriage, health, income and
education have significant effects on the SWB of individuals.
The findings presented in this paper seem to reveal that the
current immigrant selection policy in Germany favours the welfare of
German residents, at least with regards to subjective well-being. This
increase in happiness also serves as a plus to the assertion by policy
makers that immigrant inflows could relieve labour market shortages and
boost the population growth rate.
There have also been concerns about extending the EEA to-
wards the EU candidate countries, mainly in Germany, since it has the
best economy in Europe. There have been fears that immigrants from
these countries if admitted into the EEA, would troop to Germany and
weaken the welfare of the citizens. If my findings are credible, there seem
to be no cause for alarm in this regard, since immigrants from ’Other
European countries’ had the maximum positive impact on the SWB of
residents in Germany.
There has been the advocacy by some researchers in recent years
for policy makers to consider the impact of immigration beyond the tradi-
tional labour market outcomes and include a more broader welfare mea-
sure in the form of happiness. This has led to the UN even publishing a
yearly ’World Report on Happiness’, and therefore, my results concur to
the fact that policy makers and public interventions aiming at altering
the effects of immigration should take this key aspect of happiness into
56
consideration as well.
57
Appendix
Table A1: OLS Instrumenting of Immigrant Share from EEA
Table A2: OLS Instrumenting of Immigrant Share from Turkey
I
Table A3:OLS Instrumenting of Immigrant Share from ’Other European Countries’
Table A4: OLS Instrumenting of Immigrant Share from Asia
Table A5: OLS Instrumenting of Immigrant Share from ’Other partsof the world’
II
Table A6: Impact of Total Immigrant Share on SWB (OP modelusing 0-10 scale)
III
Table A7: Impact of To-tal Immigrant Share on SWB (OLS model with standardized coefficients)
IV
Table A8: Impact of Total Immigrant Share on High Skilled (OP model )
V
Table A9: Impact of Total Immigrant Share on Middle Skilled (OPmodel )
VI
Table A10: Impact of Total Immigrant Share on Low Skilled (OPmodel )
VII
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Affirmation
I hereby declare that I have composed my Master’s thesis “Immigration
Effects on Satisfaction” independently using only those resources men-
tioned, and that I have as such identified all passages which I have taken
from publications verbatim or in substance. Neither this thesis, nor any
extract of it, has been previously submitted to an examining authority,
in this or a similar form.
I have ensured that the written version of this thesis is identical
to the version saved on the enclosed storage medium.
Kiel, November 3, 2015 .....................................
XVI