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

Bibliography

Akay, A., Constant, A. F., and Gulietti, C. (2012). The impact of im-

migration on the well-being of natives, iza dp no. 6630, bonn: Iza.

Online: http://ftp. iza. org/dp6630. pdf (10.09. 2013).

Basten, C. C. and Siegenthaler, M. (2013). Do immigrants take or create

residents’ jobs? Quasi-experimental evidence from Switzerland KOF

Working Papers.

Barrett, A., Kahanec, M., Zimmermann, K. F., Barrett, A., and Maıtre,

B. (2013). Immigrant welfare receipt across europe. International

Journal of Manpower, 34(1):8–23.

Bartel, A P (1989). Where do the new US immigrants live? Journal of

Labor Economics, pages 371–391.

Bartram, D. (2011). Economic migration and happiness: Comparing im-

migrants’ and natives’ happiness gains from income. Social Indicators

Research, 103(1):57–76.

Bauer, T. K., Zimmermann, K. F., and Lofstrom, M. (2000). Immigra-

tion policy, assimilation of immigrants and natives’ sentiments towards

immigrants: evidence from 12 oecd-countries.

Betz, W. and Simpson, N. B. (2013). The effects of international migra-

tion on the well-being of native populations in europe. IZA Journal of

Migration, 2(1):1–21.

VIII

Blanchflower, D. G. and Oswald, A. J. (2011). International happiness:

A new view on the measure of performance. The Academy of Manage-

ment Perspectives, 25(1):6–22.

Boeri, T. (2010). Immigration to the land of redistribution. Economica,

77(308):651–687.

Boes, S. and Winkelmann, R. (2010). The effect of income on general life

satisfaction and dissatisfaction. Social Indicators Research, 95(1):111–

128.

Bonin, H. (2001). Fiskalische effekte der zuwanderung nach deutschland:

Eine generationenbilanz. Technical report, IZA Discussion paper series.

Bonin, H., Raffelhuschen, B., and Walliser, J. (2000). Can immigration

alleviate the demographic burden? FinanzArchiv: Public Finance

Analysis, 57(1):1–21.

Borjas, G. J. (1994). The economics of immigration. Journal of economic

literature, pages 1667–1717.

Borjas, George J (1999). The economic analysis of immigration Handbook

of labor economics, 3:1697–1760.

Borjas, G. J. (2003). The labor demand curve is downward sloping:

Reexamining the impact of immigration on the labor market. Technical

report, National Bureau of Economic Research.

Boyce, C. J. (2010). Understanding fixed effects in human well-being.

Journal of Economic Psychology, 31(1):1–16.

Brucker, H., Epstein, G. S., McCormick, B., Saint-Paul, G., Venturini,

A., and Zimmermann, K. F. (2002). Managing migration in the euro-

pean welfare state. Immigration Policy and the Welfare System, Oxford

University Press, Oxford, pages 1–168.

IX

Burton, P. and Phipps, S. (2010). The well-being of immigrant children

and parents in canada. In 31st general conference of the International

Association for Research in Income and Wealth, St. Gallen, Switzer-

land.

Butcher, K. F. and Card, D. (1991). Immigration and wages: Evidence

from the 1980’s. The American Economic Review, pages 292–296.

Card, D. (1990). The impact of the mariel boatlift on the miami labor

market. Industrial & Labor Relations Review, 43(2):245–257.

Card, D. (1997). Immigrant inflows, native outflows, and the local la-

bor market impacts of higher immigration. Technical report, National

Bureau of Economic Research.

Card, D. (2005). Is the new immigration really so bad?*. The Economic

Journal, 115(507):F300–F323.

Chaloff, J. and Lemaitre, G. (2009). Managing highly-skilled labour

migration.

Clark, A. E., Frijters, P., and Shields, M. A. (2008). Relative income,

happiness, and utility: An explanation for the easterlin paradox and

other puzzles. Journal of Economic literature, pages 95–144.

Clark, A. E. and Oswald, A. J. (1994). Unhappiness and unemployment.

The Economic Journal, pages 648–659.

Constant, A. F. and Tien, B. N. (2011). Germany’s immigration policy

and labor shortages. IZA, DP, 41.

Constant, A. F., Tien, B. N., and Xidious, A. (2010b). Germany.

in:migration, employment and labour market integration policies in the

european union – part 2: Labour markets integration policies in the

X

european union (2000-2009). International Migration Journal, pages

99–106.

D’Amuri, F., Ottaviano, G. I., and Peri, G. (2010). The labor market

impact of immigration in western germany in the 1990s. European

Economic Review, 54(4):550–570.

De Neve, J.-E., Diener, E., Tay, L., and Xuereb, C. (2013). The objective

benefits of subjective well-being. World happiness report.

Deaton, A. (2010). Income, aging, health, and well-being around the

world. Research Findings in the Economics of Aging, page 235.

DeVoe, S. E. and Pfeffer, J. (2009). When is happiness about how much

you earn? the effect of hourly payment on the money–happiness con-

nection. Personality and Social Psychology Bulletin.

Ding, W. H. (2013). What is the effect of immigration on the changes of

subjective well-being for native-born canadians?

Dolan, P., Peasgood, T., and White, M. (2008). Do we really know what

makes us happy? a review of the economic literature on the factors

associated with subjective well-being. Journal of economic psychology,

29(1):94–122.

Dustmann, C., Fabbri, F., and Preston, I. (2005). The impact of im-

migration on the british labour market*. The Economic Journal,

115(507):F324–F341.

Dustmann, C., Frattini, T., Halls, C., et al. (2010). Assessing the fiscal

costs and benefits of a8 migration to the uk. Fiscal Studies, 31(1):1–41.

Dustmann, C., Frattini, T., and Preston, I. P. (2013). The effect of

immigration along the distribution of wages. The Review of Economic

Studies, 80(1):145–173.

XI

Dustmann, C., Glitz, A., and Frattini, T. (2008). The labour market

impact of immigration. Oxford Review of Economic Policy, 24(3):477–

494.

Facchini, G. and Mayda, A. M. (2009). Does the welfare state affect

individual attitudes toward immigrants? evidence across countries.

The review of economics and statistics, 91(2):295–314.

Ferrer-i Carbonell, A. (2005). Income and well-being: an empirical anal-

ysis of the comparison income effect. Journal of Public Economics,

89(5):997–1019.

Ferrer-i Carbonell, A. and Frijters, P. (2004). How important is method-

ology for the estimates of the determinants of happiness?*. The Eco-

nomic Journal, 114(497):641–659.

Frey, B. S. and Stutzer, A. (2002). What can economists learn from

happiness research? Journal of Economic literature, pages 402–435.

Gokdemir, O. and Dumludag, D. (2012). Life satisfaction among turkish

and moroccan immigrants in the netherlands: The role of absolute and

relative income. Social indicators research, 106(3):407–417.

Grubel, H. and Grady, P. (2011). Immigration and the canadian welfare

state 2011. H. Grubel & P. Grady, Studies In Immigration and Refugee

Policy, Fraser Institute.

Hatton, T. J. and Tani, M. (2005). Immigration and Inter-Regional Mo-

bility in the UK, 1982–2000*. The Economic Journal, 115(507):F342–

F358.

Hsiao, C. (2014). Analysis of panel data. Cambridge university press, 54.

XII

Hunt, J. (2012). The impact of immigration on the educational attain-

ment of natives. Technical report, National Bureau of Economic Re-

search.

Islam, A. (2007). Immigration unemployment relationship: The evidence

from canada*. Australian Economic Papers, 46(1):52–66.

Kahneman, D. and Sugden, R. (2005). Experienced utility as a stan-

dard of policy evaluation. Environmental and resource economics,

32(1):161–181.

Knight, J. and Gunatilaka, R. (2012). Income, aspirations and the he-

donic treadmill in a poor society. Journal of Economic Behavior &

Organization, 82(1):67–81.

Manacorda, M., Manning, A., and Wadsworth, J. (2012). The impact

of immigration on the structure of wages: Theory and evidence from

britain. Journal of the European Economic Association, 10(1):120–151.

Mayda, A. M. (2006). Who is against immigration? a cross-country

investigation of individual attitudes toward immigrants. The Review

of Economics and Statistics, 88(3):510–530.

Ministry of Interior, (2014). Residence law and policy on migration and

integration in germany. German Ministry of Interior’s Publication on

’Migration and Integration’, pages 10–46.

Myrskyla, Mikko and Margolis, Rachel (2014). Happiness: Before and

after the kids. Demography, 51(5):1843–1866.

Nowok, B., Van Ham, M., Findlay, A., and Gayle, V. (2011). Does

migration make you happy? a longitudinal study of internal migration

and subjective well-being (discussion paper). IZA Discussion Paper

6140.

XIII

O’Donnell, G. (2013). Using well-being as a guide to policy. United

Nations’ World Happiness Report, pages 99–108.

Okulicz-Kozaryn, A. (2011). Europeans work to live and americans live to

work (who is happy to work more: Americans or europeans?). Journal

of Happiness Studies, 12(2):225–243.

Ottaviano, G. I. and Peri, G. (2012). Rethinking the effect of immigration

on wages. Journal of the European Economic Association, 10(1):152–

197.

Pischke, J.-S. and Velling, J. (1997). Employment effects of immigra-

tion to germany: an analysis based on local labor markets. Review of

Economics and Statistics, 79(4):594–604.

Rustenbach, E. (2010). Sources of negative attitudes toward immigrants

in europe: A multi-level analysis1. International Migration Review,

44(1):53–77.

Senik, C., Stichnoth, H., and Van der Straeten, K. (2009). Immigration

and natives’ attitudes towards the welfare state: evidence from the

european social survey. Social indicators research, 91(3):345–370.

Sinn, H.-W. and Werding, M. (2001). Immigration following eu eastern

enlargement. In CESifo Forum, volume 2, pages 40–47. Ifo Institute

for Economic Research at the University of Munich.

Tu, J. (2010). The impact of immigration on the labour market outcomes

of native-born canadians.

United Nations Department of Economic and Social Affairs (2013). In-

ternational Migration Report 2013. United Nations, Department of

Economic and Social Affairs, Population Division (2013).

XIV

Van Ours, J. C. and Ohinata, A. (2011). How immigrant children affect

the academic achievement of native dutch children.

Van Praag, B. M., Frijters, P., and Ferrer-i Carbonell, A. (2003). The

anatomy of subjective well-being. Journal of Economic Behavior &

Organization, 51(1):29–49.

Wilson, S. H. and Walker, G. (1993). Unemployment and health: a

review. Public health, 107(3):153–162.

Winkelmann, L. and Winkelmann, R. (1998). Why are the unemployed

so unhappy? evidence from panel data. Economica, 65(257):1–15.

Wooldridge, J. M. (2002). Econometric analysis of cross section and panel

data. MIT press.

XV

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