Internal Migration and Labour Market Outcomes Among Refugees in Sweden

Internal migration and labour market outcomes among refugees in Sweden

Maria Mikkonen

To My Grandfather

Sven Perfors

Preface

First I would like to thank my advisors. Mats Hammarstedt, my supervisor for the last year, has contributed to this thesis in so many ways. Your friendly attitude and encouragement have been invaluable, so have your suggestions and comments on several versions of this manuscript. Thank you so much for your patience and your conﬁdence in me. Jan Ekberg introduced me to the idea of this thesis. Thank you for your valuable advice and support. I have gained much insight in this topic from our discussions.

Olof Åslund was the discussant for the ﬁnal seminar for this thesis. Your suggestions strongly contributed to my work. I am also grateful for comments by and discussions with Dominique Anxo, Lennart Delander, Håkan Locking,

Jonas Månsson, Harald Niklasson, and Ghazi Shukur.

My colleagues and friends at the department have made working hours much more enjoyable, thank you all. A special thanks to my fellow PhD students and Viktoria.

Brian Fenn helped me correct the English language. Thanks for your excellent suggestions. Financial support from the Swedish Association of

Local Authorities is also gratefully acknowledged.

i I want to thank my friends, and especially Elisabeth, Maria, Regina,

Therése A. and Therése A.L. for genuine friendship.

My parents, Inger and Esa have always meant a great deal to me. Thank you for your love and support. Finally, I would like to thank Alexander,

Gun, and Totte for the same reasons.

Växjö, May 2006

Maria Mikkonen

ii Contents

Introduction 1

1 Immigrants in Sweden 5

1.1 Immigration and the immigrant population ...... 5

1.2 Immigration and refugee policy ...... 10

1.3 Integration policies ...... 14

1.4 Thelabourmarketpositions...... 16

2 Internal migration among refugees 21

2.1 Introduction...... 21

2.2 Theoreticalframework ...... 24

2.3 Dataanddescriptivestatistics...... 29

2.4 Empirical speciﬁcation ...... 37

2.4.1 Factors aﬀecting the internal migration decision and

labourmarketstatus ...... 37

2.4.2 Estimating internal migration propensities ...... 40

2.4.3 Estimating labour market outcomes ...... 41

2.5 Empiricalresults ...... 43

iii 2.5.1 Internal migration propensities ...... 43

2.5.2 Labourmarketoutcomes...... 47

2.6 Conclusions ...... 55

Bibliography ...... 57

Appendix 2.A Local labour market regions (LLM) ...... 69

Appendix 2.B Classiﬁcation of municipalities ...... 69

iv List of Tables

1.1 Index for employment rates among foreign-born individuals

(16–64yearsofage)...... 18

2.1 Variable deﬁnitions...... 31

2.2 Mean values or proportions of individuals’ characteristics in

period t0...... 32

2.3 Movers and stayers per period t ...... 34

2.4 Internal migration ﬂows among Bosnian refugees in Sweden,

period t0 to period t9...... 35

2.5 Diﬀerences in employment and log annual earnings between

movers and stayers during their ﬁrst years in Sweden. The em-

ployment numbers show the fraction of the group with positive

earnings...... 36

2.6 Number of refugees with Swedish citizenship ...... 36

2.7 Marginal eﬀects from logit estimations of the probability of

beingamoverindiﬀerentperiods,men ...... 45

2.8 Marginal eﬀects from logit estimations of the probability of

beingamoverindiﬀerentperiods,women ...... 46

v 2.9 Marginal eﬀects from logit estimations of the probability of

being employed in t given that the individual was unemployed

int-1(includingMOVERt-1),men ...... 49

2.10 Marginal eﬀects from logit estimations of the probability of

being employed in t given that the individual was unemployed

int-1(includingMOVERt-1),women ...... 50

2.11 Income estimations of women and men in t9, 34–64 years of

age. Income from work in SEK (logaritic form)...... 54

vi Introduction

In the year 2006, more than one million of the individuals living in Sweden, or about 12 per cent of the total population, had been born abroad. The large share of foreign-born individuals implies that immigrants have become increasingly important on the Swedish labour market.

The labour market positions among immigrants in Sweden are relatively well documented.1 Diﬀerent studies have shown that labour force migrants who immigrated during the 1950s and 1960s were doing well on the Swedish labour market. It is also a fact that refugees who arrived from Eastern

Europe during the same period of time did quite well on the labour market.

However, as immigration changed from labour force migration to refugee migration in the 1970s, the labour market position of immigrants started to deteriorate markedly. The deterioration continued during the 1980s and the situation became even worse during the recession of the 1990s.2

Despite the fact that immigrants’ labour market position in Sweden is relatively well documented and despite the fact that refugees has been hit

1See Wadensjö (1972); Ekberg (1983); Aguilar and Gustafsson (1991); Ekberg (1994); Rooth (1999); Aslund˙ (2000); Hammarstedt (2001); Österberg (2000). 2See Edin and Aslund˙ (2001) and Gustafsson, Hammarstedt and Zheng (2004).

1 the hardest by unemployment and low earnings during recent years, only a few studies have focused on the determinants behind their labour market positions. Ekberg and Ohlson (2000) found large regional diﬀerences in the labour market position among refugees in Sweden. The results in

Hammarstedt (2002, 2003a) pointed in the same direction. In a more recent study, Rooth and Aslund˙ (forthcoming) have shown that the point of time for immigration as well as the place of residence is of great importance for the labour market outcome for refugees.

Even less attention has been paid to how refugees can improve their labour market positions. One way for refugees to do so is by internal migration. Little research exists on the location choice and internal migration pattern among immigrants and refugees in Sweden. Aslund˙ (2005) found that a substantial representation from the individual’s birth country at the relocation site is of importance for immigrants’ decisions to relocate and for their location choices. Boman (2006) showed that immigrants and refugees are more mobile than native Swedes, but that settlement in ethnic enclaves reduces geographical mobility for non-Nordic immigrants. These ﬁndings are consistent with the results from US studies. Bartel (1989) found that the presence of co-ethnics is an important determinant of immigrants’ location choice. Jaeger (2000) showed that the presence of immigrants is important

(i.e. that immigrants prefer "international neighbours") without regard to their country of origin. Other studies, for example Belanger and Rogers

(1992), show that internal migration goes in the direction of regions with populations that are already large.3

3However, Bartel (1989) ﬁnds that highly educated groups tend to move to less

2 This thesis contributes to research regarding immigrants on the Swedish labour market since it focuses on internal migration decisions and labour market outcomes for refugees from Bosnia-Herzegovina living in Sweden.

The thesis consists of two parts. The ﬁrst part contains an overview of immigration to Sweden and the immigrant population. Furthermore, it also deals with immigration, refugee, and integration policies. Finally, an overview of the labour market position among immigrants is presented in the ﬁrst part.

The second part contains an empirical investigation. This section of the thesis focuses on refugees from Bosnia-Herzegovina. Internal migration decisions and labour market outcomes as regards Bosnian refugees are studied.

Several interesting results emerge from the study. We ﬁnd, for example, that refugees during their ﬁrst nine years in Sweden migrate towards big cities.

We also discover that migration within Sweden is more common among immigrant and refugee residents of smaller cities and especially rural areas.

Furthermore, migration is negatively related to marriage and children, positively related to unemployment and education, and is most common in the

ﬁrst years after arrival. We also conclude that internal migration alone does not improve the labour market situation for refugees in terms of income, though the chances for male refugees to obtain a job might increase. Inte- gration signals, such as citizenship, seem to be of great importance for the integration of refugees into the Swedish labour market.

ethnically-concentrated areas compared to where they resided initially.

Chapter 1

Immigrants in Sweden

1.1 Immigration and the immigrant population

Sweden has received a substantial number of immigrants since the Second

World War.1 In 1940, only one per cent of the Swedish population was born abroad. During the following decade, almost 200,000 individuals immigrated to Sweden. Most of the immigration during the 1940s took place after the war, and the immigration was primarily made up of refugees from the Baltic countries and Poland.

The inﬂow continued in the 1950s, with 260,000 immigrants arriving.

Since Sweden had not been involved in the world war and had an intact industry, the demand for Swedish industrial products increased from abroad.

This led to a labour shortage, which in turn changed the character of immigration towards labour force immigration.

The labour force immigration was made possible by three institutional

1For a survey of immigration to Sweden, see Lundh and Ohlsson (1994).

5 changes: ﬁrst, the agreement about a common Nordic labour market in 1954, removing the need for residence and work permits for immigrants from the

Nordic countries; second, search for labour by joint actions with recruitment agencies and ﬁrms across Europe instituted by the Swedish Labour Market

Board in co-operation with local trade unions and companies; and, third, the approval of the 1953 Work Regulation of the OEEC and the Alien Act of 1954. This act made it possible for non-Nordic immigrants to enter Swe- den individually and then apply for a work permit here. The labour force migration during the 1950s consisted mostly of immigrants from the Nordic countries and Western and Southern Europe. Besides this, there was also refugee immigration from Hungary during the 1950s.

The number of immigrants increased during the following years, and more than 400,000 individuals immigrated during the 1960s. At this time, labour migration still dominated the inﬂow of immigrants to Sweden. The great majority of the immigrants were lowly educated individuals from the

Nordic countries, and countries in the Southern Europe, such as Yugoslavia,

Italy and Greece. Furthermore, there was also labour force migration from countries in Western Europe, such as Germany, Austria and the Netherlands, as well as refugee migration from Czechoslovakia and Poland during the

1960s. The inﬂow reached a peak in 1970, when almost 80,000 people arrived, most of them from Finland.

From the late 1940s to the mid 1970s, there was a relationship between immigration and the labour market situation in Sweden. In addition, the immigrant population consisted mostly of European immigrants up until the mid 1970s. Immigration increased in times of high demand for labour

6 Figure 1.1: Migration ﬂows, 1940-2005

Figure 1.1 illustrates the migration flows to and from Sweden between 1940 and 2005. The net immigration flow is the difference between the inflow and the outflow of individuals. Source: Statistics from the Swedish Migration Board (2006).

7 and vice versa. However, during the 1970s, labour force migration tapered oﬀ. This was a result of a more restrictive immigration policy, as well as a recession in the Swedish economy. All the same, as labour force migration decreased, refugee migration started to increase. During the 1970s, large groups came as refugees and tied movers from Chile and other countries in

Latin America. Furthermore, there was also refugee migration from Asia.

Ultimately, almost 400,000 individuals immigrated to Sweden during the

1970s.

In the 1980s, an even larger number of individuals reached Sweden. The majority of these were classiﬁed as refugees and tied movers. The greater part was made up of refugees from the Middle East and Africa. Refugee immigration from the Middle East was a result of the revolution in Iran and the Iran–Iraq war. In addition, large refugee immigration from Lebanon took place. Refugee immigration from Africa, from Ethiopia in particular, was due to war and political instability.

Refugee immigration continued during the 1990s. Almost half a million individuals immigrated to Sweden during this decade. The majority during this period was made up of refugees from former Yugoslavia.

Refugee migration has continued during the 2000s as well. It now mostly consists of refugees from the Middle East. During the years 2000–2005, about

370,000 individuals immigrated to Sweden.

To sum up, immigration to Sweden has been substantial during the last decades. As shown by Figure 1.1, immigration has exceeded emigration, with the exception of a few years.

The changed character of the immigration has also led to a change in

8 Figure 1.2: Region of origin in the Swedish population stock

Figure 1.2 shows the numbers of individuals living in Sweden who were born in another part of the world. Source: Statistics Sweden, Statistical Yearbook, diﬀerent volumes.

9 the composition of the immigrant population (see Figure 1.2). In 1970, only about ﬁve per cent of the foreign born population were born outside Eu- rope. About 60 per cent were born in the Nordic countries and about 35 per cent were born in other European countries. In 2006, approximately 35 per cent were born outside Europe. About 30 per cent were born in the Nordic countries, while about 35 per cent were born in other European countries.

Moreover, there is a growing group of second-generation immigrants (that is, children born in Sweden with at least one parent born abroad). This group amounts to more than 800,000 individuals today. Thus, the total number of

ﬁrst and second-generation immigrants amounts to about 1.8 million individuals. This is about 20 per cent of the total population in Sweden.

1.2 Immigration and refugee policy

Economic expansion led to labour shortage and paved the way for a liberalisation of the Swedish immigration policy. The aforementioned increased migration during the 1950s and 1960s can be explained both by demand for labour and by some institutional changes.

During the inter-war period, negotiations concerning a common and free labour market for the Nordic citizens had begun. However, it was not until

1954 that an agreement was signed. The signing removed the need for work and residence permits.

The Swedish Labour Market Board in co-operation with local unions also applied a new system for organised collective labour immigration, companies

10 being in need of labour and governments in the countries concerned.2

Furthermore, the approval of the 1953 Work Regulation of the OEEC and the new Alien Act of 1954 made it possible for non-Nordic citizens to enter Sweden individually. Furthermore, when they were oﬀered a job they could now apply for a work permit.

During the 1960s, the liberalisation of the immigration policy began to worry the Swedish trade unions. The unions were troubled about immigration having a negative eﬀect on the labour market. In 1968, a more restrictive migration policy was introduced, with the aim of regulating immigration from non-Nordic countries. To a large extent, this was a result of the economic recession of 1966 and 1967 that took place after a few years of high immigration rates. In contrast to the 1950s and the ﬁrst half of the

1960s, immigrants had large difficulties then finding employment and housing. The effect of the new policy was that non-Nordic immigrants had to have housing, work and a permit for work settled before they came to Sweden.

However, these restrictions did not reduce the total labour force immigration. The policy became even more restrictive after 1972 when the Swedish

Trade Union Confederation recommended that the trade unions reject all applications for work permits by non-Nordic citizens. It was, however, not until the economic recession in the mid-1970s that labour immigration to

Sweden decreased. The more restrictive policy temporarily reduced immigration in the early 1970s. However, the increase in the number of civil wars and military coups in diﬀerent parts of the world resulted in an increase in refugee immigrants seeking asylum in Sweden.

2Through such arrangements, about 15,000 workers were recruited in the 1950s.

11 But refugee immigration to Sweden was not a new phenomenon. When

Sweden became a signatory of the Geneva Convention, approved by the

United Nations in 1951, and inserted the Geneva Rights into the Swedish

Asylum Act of 1954, certain groups of refugee immigrants were given legal protection and security.

The Geneva Convention regulates diﬀerent categories of refugees, but the

Asylum Act of 1954 only takes convention refugees into account. Those are deﬁned as persons who risk persecution in the country of their nationality on the grounds of race, religious or political beliefs, etc. This group of refugees applied for residence permit and asylum after arrival in Sweden. Quota refugees, another category, were instead transferred to Sweden and granted asylum by a section in the Convention, even though Sweden does not have any international obligations towards quota refugees. Until the end of the

1960s, almost 70 per cent of all refugee immigrants belonged to the quota refugee category.

In 1967, when refugee immigration increased strongly, the Sweden Aliens

Act of 1954 was interpreted in a wider sense and enabled more categories of refugees to be granted a permanent visa. Conscientious objectors and aliens who left their home country due to political conditions and who could refer to circumstances that gave support for their request for asylum, i.e. de facto refugees, were granted permanent residence permits. Individuals also had the possibility to claim humanitarian reasons for being granted a permanent residence permit, based on their need for asylum. In contrast to convention and quota refugees, the other categories’ legal rights to asylum can be rejected. During the 1970s and 1980s the integration policies became

12 more important as the Swedish authorities started to focus on the immigrants already resided in Sweden.

In the end of the 1980s, the waiting times for a residence permit increased due to large cohorts of immigrants. Therefore, the Swedish Immigration

Board decided to restrict immigration and only allow convention refugees to be granted asylum (with some exceptions). However, in 1991, this restriction was again abolished. In the summer of 1993, the waiting times for a decision were again so long that the Swedish Government was forced to grant permanent visas to a large amount of immigrants on a general decision based on humanitarian reasons.3

The increased number of refugees in 1993–1994, mainly caused by the conﬂict in former Yugoslavia, led to new restrictions for migrants from this area. At the same time, Sweden was opened up for labour migrants from

EU-countries by the EES agreement. In 1994, the Asylum Act was rewritten.

Nonetheless, the practical result was in principle the same.

In addition to the increasing numbers of refugees, the number of tied movers also increased from the mid-1970s. This category of immigrants qualiﬁes for a visa by being a close relative of a Swedish resident. The deﬁ- nition of a close relative is: a spouse, children, parents or some other relative that is dependent on a person living in Sweden. As shown in Figure 1.3, the number of tied movers immigrating to Sweden has increased substan-

3When refugee immigrants seek asylum or a residence permit an investigation is started, first by the police authorities and then by the Immigration Board, to see if the individual has the legal rights to obtain asylum. During the waiting time for the decision, the applicant is temporarily transferred to a residence centre. Another possibility is staying with relatives, but that has to be approved both by the municipality in question and by the Immigration Board if the refugee is going to receive any financial benefits. It is not until the applicant has got a positive decision that permanent housing can be supplied.

13 Figure 1.3: Refugees and tied movers, 1980–2005

Figure 1.3 shows how refugee migration has decreased whereas the number of tied movers has increased in recent years. Source: Statistics from the Swedish Migration Board (2006). tially during recent decades. In contrast to other immigrant categories, tied movers to Sweden have not encountered any restrictions.

1.3 Integration policies

The issue concerning the economic integration of immigrants has grown increasingly important as the number of immigrants in Sweden has increased and as their labour market situation has worsened. Labour market policies, education policies, and criteria for participation in the public transfer system all play diﬀerent roles for the immigrants’ labour market integration and self-support.

Until the mid-1970s, Sweden lacked a coherent immigrant policy. The

14 current Swedish immigrant policy, established in 1975 and conﬁrmed anew in 1996, comprises three goals formulated by the Swedish Parliament: the principle of equality, the freedom of choice, and the goal of positive integration.4 These goals have recurred in several documents from the Swedish

Labour Market Board and from the Swedish Integration Board.5

The principle of equality implies that immigrants shall have the same rights and obligations as Swedish citizens. The freedom of choice protects individuals’ rights to their own cultural heritage. Finally, the goal of positive integration implies that there should be co-operation between immigrants and natives as regards issues of common interest. To sum up, the superior goal of the Swedish immigrant policy is that all individuals shall have equal rights and opportunities regardless of ethnic or cultural background.

An important organisational change in the Swedish immigrant policy took place in 1985. In that year, the responsibility for the reception of immigrants was transferred from the Ministry of Labour to the Swedish Immigra- tion Board. Prior to 1985, the integration of refugee immigrants had a direct connection to labour market policy, since refugee issues were the responsibility of the Labour Market Board. The main focus was on employment, and a majority of refugee immigrants went directly to a municipality, applied for asylum and took the ﬁrst steps toward employment. The Migration

Board, however, put a greater emphasis on the social integration of refugees in Sweden. In the mid-1980s, the so-called "Whole of Sweden strategy" was introduced. Before 1985, a large number of immigrants ended up in the

4See Prop (1968:142) and Prop (1975:26). 5See for example SOU (1996:55).

15 largest cities. The idea with the new settlement policy was to avoid concentration and thereby improve the immigrants’ chances to integrate by placing them in municipalities that provided good job and educational opportunities.

The Immigration Board made an agreement with the municipalities, making them responsible for the integration of new immigrants in Sweden. In practice, the supply of housing became the deciding factor for the resettlement of immigrants, with less attention being paid to the state of the local economic conditions and the local labour market. A general conclusion regarding this strategy is that the implementation of the policy increased the dispersion of immigrants in Sweden.6

1.4 The labour market positions

Research on the integration into the Swedish labour market shows high employment rates for immigrants in the 1950s and 1960s.7 The labour-force migrants did well on the labour market, and the employment rate was often higher among immigrants than among natives. This is shown in Table 1.1.

The occupational mobility among these early immigrants was also the same as that among natives.8 But, as is also shown by the table, during the period

1950–2002, the employment rate for immigrants with foreign citizenship fell from about 20 percent higher than the employment rate among the total population to about 30 per cent lower. Thus, as the refugee migration from

6For studies about the "Whole of Sweden strategy," see for example Andersson (1993, 1996, 1998); Borgeg˙ard, H˙akansson and Müller (1998); Rooth (1999); Aslund˙ (2000). 7See for example Wadensjö (1972); Ekberg (1983); Scott (1999); Bevelander (2000); Ekberg and Hammarstedt (2002). 8See Ekberg (1996).

16 Latin America and other non-European countries started to reach signiﬁ- cant proportions at the end of 1970s, the labour market integration began to deteriorate. Since then, this tendency has become even stronger, with increasing unemployment rates as a consequence. It should be noted that these immigrants have about the same educational level (the same number of years in school) as the native population and are better educated than former immigrants. Despite that, a great number of refugees who arrived during the 1980s never entered the labour market.

The employment situation for naturalized citizens is somewhat better. In the 1990s, the employment rates among this group of immigrants were almost

90 per cent of the employment rate of the total population. One explanation of this is the fact that obtaining a Swedish citizenship is positively correlated with time of residence in Sweden.

There might be diﬀerent explanations for why naturalized immigrants are doing better on the Swedish labour market than foreign citizens. Natu- ralized immigrants intend to stay in Sweden and are therefore more inclined to invest time in factors such as language training, etc., which has become increasingly important on the Swedish labour market. Besides this, natu- ralization also signals stability to Swedish employers. This might lead to a Swedish employer being more willing to promote naturalized immigrants than foreign citizens.

However, in contrast to foreign citizens, the naturalized citizens relative employment rate continued to decrease during the late 1990s and the beginning of the 2000s. One reason might be that many immigrants from non-European countries have acquired Swedish citizenship during the 1990s.

17 Table 1.1: Index for employment rates among foreign-born individuals (16– 64 years of age).

Non-Swedish Year citizens Naturalised Total 1950 120 – – 1960 105 102 104 1967 110 – – 1975 99 102 100 1978 94 102 98 1987 83 96 90 1992 74 95 84 1994 61 91 75 1999 69 87 76 2000 70 87 77 2002 71 86 77 Note: The individuals are standardized with respect to age and gender. The interpretation is as follows: In 1960, the index for non-Swedish citizens is 105, which means that non- Swedish citizens with the same age and gender as natives in the age of 16–64 have five per cent higher employment rate than natives. If the index is 70, foreign-born citizens have a 30 per cent lower employment rate. Though the index is relative, it can be affected by fluctuations in the natives’ employment rates. The decrease in employment rates in the 1970s among foreign-born is due to a combination of lower absolute employment rates among immigrants and higher employment rates among native women (in absolute terms). In the 1980s, the lower index is definitely a result of the decrease in immigrants’ employment rates. Source: Gustafsson, Hammarstedt and Zheng (2004).

18 Differences in the labour market situation between immigrants from different regions have also been reported in different studies.9 There are differences in the labour market situation between Europeans and non-Europeans.

There are also diﬀerences between non-Europeans with diﬀerent regions of origin. For example, immigrants from Latin America seem to have had a better labour market position than immigrants from Africa and Asia.

To sum up, the immigrant labour market situation in Sweden has worsened since the beginning of the 1980s. This has occurred despite the boom in the Swedish economy in the 1980s, despite the goal of the Swedish integration policy to integrate immigrants (as well as refugees) to about the same extent as natives in the labour market, and despite the good educational level among the immigrants who arrived after 1980.

During the deep recession in the beginning of the 1990s, the employment situation deteriorated even further. The crucial situation had been stabilized in the mid-1990s and, in some senses, improved from the end of the 1990s.

Yet, those immigrants who came during the 1980s still stand outside the labour market.10 Also, second-generation immigrants with non-European background have a high unemployment rate and lower incomes.11 There have been no further improvements in recent years. We are still in a situation with a very low employment rate and a very high unemployment rate, especially for immigrants born outside Europe. Furthermore, the existing literature also provides evidence that the relative earnings of immigrants compared to

9For a survey of diﬀerent immigrant groups in the Swedish labour market, see, for example, SOU (2004:21). 10See for example Edin and Aslund˙ (2001). 11See Österberg (2000); Rooth and Ekberg (2003); Hammarstedt and Palme (2006).

19 natives have declined.12

12See for example Aguilar and Gustafsson (1991) and Ekberg (1994).

20 Chapter 2

Internal migration among refugees

2.1 Introduction

While several studies have shown that immigrants in Sweden had relatively high earnings and employment rates up to the mid-1970s, it is well documented that immigrants in Sweden today are generally less successful compared to natives as regards employment and earnings.1 Furthermore, there is strong evidence that the lowest earnings and highest unemployment rates are found among refugees having a recent year of arrival.2

Against this background, the aim of this paper is to study and describe the internal migration behaviour among refugees in Sweden and examine whether migration within the country aﬀects their labour market outcomes.

1See Wadensjö (1972); Ekberg (1983); Österberg (2000); Ekberg and Hammarstedt (2002); Hammarstedt (2003a). 2See Edin and Aslund˙ (2001) and Hammarstedt and Shukur (forthcoming).

21 The analysis involves two steps: ﬁrst, we analyse the characteristics of a mover compared to a non-mover in order to ﬁnd out who migrates internally; and, second, in order to analyse the extent to which the decision to move changes the labour market outcome for the individual, we compare the employment rates and the incomes of movers with those of non-movers.

The issue of the economic integration of immigrants has grown increasingly important as the number of immigrants in Sweden has increased. Sev- eral possible explanations have been put forward for the situation that has arisen. One view is that immigrants are systematically discriminated against on the labour market because of their race and ethnicity.3 Another explanation is that immigrants suffer from bad labour market outcomes as a result of the fact that their human capital is not fully transferable to the labour market in Sweden. This is reflected in, for example, a lack of knowledge about the institutional structure and poor language skills.4 This characteristic has become increasingly important over the years, since the significance of factors regarding the institutional structure has increased.5 A third explanation is found in the Swedish immigrant policy. In 1985, the responsibility for the reception of immigrants was transferred from the Ministry of Labour to the

Swedish Immigration Board. The Immigration Board made an agreement with the municipalities making them responsible for the integration of new immigrants in Sweden. The idea was to put people in municipalities that

3See le Grand, Szulkin and Ekberg (2004); Ahmed (2005). 4See Larsson (1999) and Delander et al. (2005). For studies in other countries, see for example Chiswick and Miller (1995, 2002). 5According to Scott (1999), changes in the Swedish labour market (such as the major transformation in work organisation toward more co-operative modes of working) have increased the importance of knowledge in language and other host-country speciﬁc human capital.

22 provided good job and educational opportunities. However, in reality, the supply of housing became the deciding factor, with less attention being paid to the state of local economic conditions and the local labour market. This strategy is referred to as the "Whole of Sweden strategy." 6 Diﬀerent studies have shown that immigrants out-placed in municipalities on the Swedish countryside often suﬀered from high unemployment rates.7

Independent of which explanation is used, it is important for the immigrants themselves, as well as for society, that immigrants (labour market immigrants as well as refugees) can improve their labour market situation.

One way for them to do so is through internal migration. We can expect immigrants to move internally in order to ﬁnd a better location to match their skills.

This study is unique in diﬀerent ways. Firstly, the data consist of all individuals who were registered as refugees born in Bosnia-Herzegovina and arrived during the period 1993–1994. This means that there will be no differences according to their reason for immigration, conditions at arrival, and home-country speciﬁc human and social capital. 8 Secondly, since we have been able to track the same individuals during the period 1993–2003, we will utilise longitudinal data. This means that we are able to follow individuals from the time of their immigration and identify their initial location. By doing so, we are able to study longitudinal changes in their location and labour

6The strict application of the assignment policy was between 1987 and 1991. During this time, the placement rate was close to 90 percent. This gradually eroded during the period 1992-94, but in practice the strategy was still in use. 7See Ekberg and Ohlson (2000); Aslund˙ (2000); Hammarstedt (2002, 2003b). 8Conditions at arrival have been found important for immigrants’ possibilities in the labour market according to, for example, Rooth and Aslund˙ (forthcoming).

23 market assimilation. Thirdly, since the "Whole of Sweden strategy" was still in use, the municipal placement generated an initial geographic distribution and the refugees were out-placed among 276 municipalities.9

The outline of the paper is as follows: section 2 presents the theoretical framework for this study; data and descriptive statistics are found in Section

3; section 4 provides the empirical speciﬁcation; ﬁnally, we turn to the empirical results in Section 5, while conclusions and a summary are presented in Section 6.

2.2 Theoretical framework

Early work by Sjaastad (1962) used the concept of human capital to explain internal migration as well as international migration. In Sjaastad’s model, an individual migrates in order to maximise the present value of lifetime earnings and utility. The migration decision is based upon a comparison of earnings across diﬀerent settlements. The human capital model is a dom- inant paradigm underlying much work on micro-level migration research.

Migration is viewed as an investment expected to pay oﬀ in the form of increased earnings or other kinds of returns.

The probability of individual migration then becomes a function of personal characteristics and market variables. Personal characteristics inﬂu- ence the migration decision mainly through the eﬀect on potential earnings and through the cost of movement. A number of personal lifecycle

9Edin, Fredriksson and Aslund˙ (2003) argue that the authorities distributed refugee immigrants independent of individual unobserved characteristics. This implies that studying those who migrate internally will not generate biased results due to the selection of movers. For further methodological discussions, see Aslund˙ (2005).

24 considerations—such as marriage, divorce, education, birth, aging, and retirement—are critical in an individual’s or a family’s decision to migrate. More- over, diﬀerent characteristics of regions can provide a potential incentive for moving: other conditions in land and housing markets may be important, and local taxes and the associated availability of public goods may be critical for certain potential migrants.10

The theory of internal migration that could be applied to immigrants

(and also to refugees) is very much like the theory of general migration behaviour. There exist, however, some significant differences. First, there is a self-selection in the immigrant group, as they have already demonstrated a propensity to move. Second, there is reason to believe that internal migration costs of recently arrived immigrants are relatively low since the immigrants have not yet accumulated location specific human capital. The probability of moving is increasing with the number of movements made and the costs are decreasing. Studies of internal migration within the United States indicate that recent migrants have a high probability of moving on to other locations or returning to their country of origin.11

Different types of self-selection might affect the size and direction of the internal migration flow. Moreover, self-selection is also important for the economic outcome resulting from migration. Chiswick (1978, 1980) explained that individuals who migrate are positively selected since they are ’more able and motivated’ than those who do not migrate. The idea of positive

10Various motives for migration besides the maximisation of actual or expected economic return have been pointed out in migration literature. The literature of sociologists emphasises social mobility and social status attainment as a motivation for migration and mobility. See for example, Sabagh, Arsdol and Butler (1969). 11See for example DaVanzo (1983) and Fields (1979).

25 selection among individuals who migrate was later called into question by

Borjas (1985, 1987, 1989). Borjas argued that negative selection might also occur, and that it is the earnings and labour market opportunities at the place of origin and at the new settlement that determines what type of selection occurs.12

As regards onward migration, Borjas and Bratsberg (1996) suggest that such migration can also be characterised by both positive and negative selection. Furthermore, onward migration may result from ’mistakes’ in the initial migration decision. If so, migration among immigrants occurs because immigrants based their initial migration decision on erroneous information about economic opportunities in the host country or in the initial location area.

However, the internal migration decision for immigrants might also be aﬀected by other factors. Studies concerning immigrants’ location choices

ﬁnd the presence of fellow countrymen to be an important determinant (i.e. living in ethnic enclaves).13 Diﬀerent theories have been suggested to explain this. First, according to the ethnic network hypothesis, the presence of other individuals from the native country facilitates the adjustment to the new society.14 Second, Chiswick and Miller (2005) proposed the ethnic goods

12Chiswick’s and Borjas’ ideas of positive and negative selection were applied to international migrants but could be applied to internal migration as well. 13According to Aslund˙ (2000), substantial representation from one’s own country is an important factor for both the relocation decision and the choice of destination. This result is equivalent to that found by Rephann and Vencatasawmy (2000), who ﬁnd that immigrants tend to migrate to regions with a large overall foreign-born population. Belanger and Rogers (1992) and Bartel (1989) present similar evidence as regards secondary migration of immigrants in the US. For other studies about ethnic enclaves, see LaLonde and Topel (1992); Cutler and Glaeser (1997); Borjas (1998); Edin, Fredriksson and Aslund˙ (2003). 14See Piore (1979).

26 theory, which emphasises that living in ethnic enclaves reduces the costs of so-called ethnic goods, i.e. home-country specific goods. A third theory is the herd effect theory suggested by Epstein and Gang (2004). Herd effects in location choices may exist if migrants observe previous migrants’ relocation decisions but are imperfectly informed about the signals that drove the individuals already living in the enclave.

There are also diﬀerent competing hypothesis about the eﬀects of living in enclaves. One hypothesis is that residing in an enclave is associated with a lower rate of host country skills, e.g. language.15 Besides that, the enclave in turn may be distant from employment opportunities. In this view, it is not the enclaves that lead to a failure in the labour market; rather, it is that the enclave is distant from employment opportunities. Another hypothesis is that enclaves represent a network that increases the opportunities for gainful trade in the labour market, e.g. information on job opportunities. Granovet- ter (1974) argued that networks and other social structures are important for

finding jobs. He showed that information acquired from personal contacts is of higher quality than other available information. The conclusion was that the existence and structure of social networks significantly affects the chances of job mobility. Korpi (2001) examined the impact of network size on the

Swedish labour market. He found a positive correlation between network size and job ﬁnding. Olli-Segendorf (2005) studied job search strategies among immigrants in Sweden. The empirical evidence showed that immigrants are likely to use networks in job search. It also demonstrated that networks are

15Borjas (1998) deﬁned an ethnic neighbourhood as a neighbourhood where the share of the ethnic group was at least twice as large as the share of the ethnic group in the entire population. A similar deﬁnition was used by Edin, Fredriksson and Aslund˙ (2003).

27 more eﬀective than employment agencies for job search among immigrants.

Thus, networks are important for immigrants in terms of job search. But the study also shows that the efficiency of networks in job search is more effective for natives than for immigrants. One plausible explanation is that the quality of the networks differs across the groups. Overall, we can conclude that ethnic enclaves might improve as well as worsen the immigrants’ labour market opportunities.16

Against the theoretical background regarding internal migration among immigrants, we can expect that refugees are more likely to migrate internally than natives. But we can also expect that the probability of migrating decreases as the time spent in Sweden increases. Furthermore, it is possible that refugees who have high educational skills and are employed are more likely to migrate internally than others, since they are positively selected.

But it is also possible that it is the refugees who are unemployed or have the lowest earnings that are most likely to move internally, since they want to improve their labour market situation. Thus, it turns into an empirical question if it is employed refugees with high earnings or unemployed refugees and refugees with low earnings that are most likely to migrate internally.

Finally, it is also possible that refugees move towards settlements with a high concentration of individuals possessing the same ethnic origin as they do themselves. However, we cannot tell whether living in an enclave improves or worsens the possibilities for refugees on the labour market in Sweden.

16Of course, internal migration decision might also be aﬀected by other factors, such as love and curiosity.

28 2.3 Data and descriptive statistics

The empirical analysis in this paper utilises a dataset from Statistics Swe- den (SCB) identifying all individuals who were registered as refugees from

Bosnia-Herzegovina during the years 1993 and 1994. From the longitudinal database LOUISE, we have information about variables such as sex, education, employment, income, tax payments, transfers, family status, workplace, citizenship, labour market status, and municipality of residence registered in

November each year for any given year during the period 1993–2003.

The original data consisted of 36,401 individuals. But only those aged between 25 and 54 in year t0, the year of arrival, were selected.17 The age restriction was adopted in order to reduce the number of persons leaving the labour force due to retirement and the number of persons in education.

Besides the age restriction, there is a reduction of individuals leaving the sample during the period.18 The main reasons for an individual to drop from the sample are death or emigration. Both causes are identiﬁable in the data. As a result, the ﬁnal sample consisted of 18,267 individuals.19

In order to study who migrates, the variable MOVER has been constructed as a dependent variable. This variable is based on a classiﬁcation of labour markets and employment zones being used for diﬀerent purposes by

Statistics Sweden (SCB). In this classiﬁcation, the 284 municipalities in Swe- den have been aggregated to 87 Local Labour Markets (LLMs) with largely

1716,968 individuals were dropped due to the age restriction. 18286 individuals were thereafter dropped because of death during the investigated period and 429 individuals because of re-emigration. 451 individuals were excluded from the sample because information about the municipality of residence was missing for at least one year. 19Of which 67 individuals are classiﬁed as tied movers.

29 internal commuting. SCB’s primary objective in demarcating local labour markets was to create areas of reference that are suitable for use as a means of comparing different regional areas as regards, for example, the functioning of the labour market.20 If one is to define migration in terms of moving due to labour market prospects, then a change of LLM is, compared to a change of municipality, a very appropriate definition unit. Thus, a mover in this paper is defined as a person changing LLM from one year to the next. How- ever, since some of the local labour markets are very large and include many municipalities, while others are made up of just one, we use municipalities in a sensitivity analysis.

In order to study the labour market outcome, two different types of measures have been constructed. The first measure is EMPLOYED, i.e. whether an individual is employed or not. EARNINGS is defined as yearly income from work and/or income from self employment.

A number of variables are used as explanatory variables. Those variables are: age, children in the household, marital status, educational attainment, citizenship, cohort and region of residence. The scoring of nominal variables is shown in Table 2.1.

In Table 2.2, descriptive statistics of the explanatory variables used in the study are presented. The characteristics of men and women are presented separately. The table shows the mean of the variable or the proportion of the sample with the speciﬁc characteristic. Note that those values are observed at the ﬁrst location of residence in Sweden and in the initial year.

The mean age is about 36 years, which is about the same for both men and

20See Appendix.

30 Table 2.1: Variable deﬁnitions.

Variable Description AGE The individual’s age

MARRIED 1iftheindividualismarried(orcohabitingcouples with children), 0 otherwise

CHILDREN 1ifchildreninthehousehold,0otherwise

FEMALE 1 if female, 0 if male

CITIZENSHIP 1iftheindividualisaSwedishcitizen,0otherwise

EDUCATION Compulsory School 1 if the individual has 9-year compulsory school, or less, as the highest level of education, 0 otherwise SecondarySchool 1iftheindividualhassecondaryeducation or upper secondary education, 0 otherwise University 1iftheindividualhasauniversitydegree, 0 otherwise RESIDENCE Stockholm 1iftheindividualresidesinStockholm, 0 otherwise Göteborg 1iftheindividualresidesinGöteborg, 0 otherwise Malmö 1 if the individual resides in Malmö, 0 otherwise

L3 1 if the individual resides in a L3-region, 0 otherwise

L4 1 if the individual resides in a L4-region, 0 otherwise

L5 1 if the individual resides in a L5-region, 0 otherwise

L6 1 if the individual resides in a L6-region, 0 otherwise

L7 1 if the individual resides in a L7-region, 0 otherwise

L8 1 if the individual resides in a L8-region, 0 otherwise

L9 1 if the individual resides in a L9-region, 0 otherwise

EMPLOYED 1iftheindividualisinwork,0otherwise

EARNINGS inSEK

MOVER 1 if the individual has changed LA-region, 0 otherwise

COHORT93 1iftheindividualarrivedin1993,0otherwise,i.e.1994

Note: The residence areas are deﬁned in Appendix.

31 Table 2.2: Mean values or proportions of individuals’ characteristics in period t0.

Sample characteristics Men(st.dev) Women(st.dev) AGE 36.07 35.86 (7.399) (7.464)

25 - 34 46.34% 48.72% 35 - 44 38.97% 36.80% 45 - 54 14.69% 14.48%

CHILDREN .6647 .7461 (.4721) (.4353)

MARRIED .7583 .7550 (.4281) (.4301)

EDUCATION at t Compulsory School, or less 12.49% 29.75%

Secondary School 55.97% 47.68%

University 31.55% 22.58%

RESIDENCE Stockholm .0732 .0653 (.2605) (.2470) Göteborg .0658 .0694 (.2479) (.2541) Malmö .0464 .0469 (.2103) (.2115)

L3 .2294 .2249 (.4205) (.4176)

L4 .0617 .0619 (.2406) (.2410)

L5 .0442 .0432 (.2056) (.2034)

L6 .1382 .1410 (.3452) (.3481)

L7 .1509 .1576 (.3480) (.3643)

L8 .1233 .1225 (.3288) (.3278)

L9 .0669 .0673 (.2498) (.2505)

COHORT 93 45.28% 44.97%

N 9,318 8,949

32 women. There is a concentration of individuals at the lower ages. We can also observe that men are less likely to have children in their household but the sexes are equally likely to be married or cohabiting. Men seem to have a higher educational level, but the distribution of initial location of residence seems to be quite identical when looking at gender.

Table 2.3–2.6 below gives some descriptive statistics regarding the outcome variables: ’movers’, annual earnings and employment. Table 2.3 shows the internal migration ﬂows up to eight years after placement at the initial location in Sweden. The table reveals that on average movers make the internal migration investment during the ﬁrst years after initial settlement.21

The internal migration rates are at maximum in the ﬁrst year, and almost

20 percent of the refugees change labour market during t0. In the following years, the ﬂow declines.22 This is consistent with the assumption that recent migrants are inherently more prone to move; refugees tend to move a relatively short time after arrival, but over time their propensity to migrate declines.23

Table 2.4 illustrates the internal migration pattern. It becomes apparent that the migration pattern implies very high positive net in-migration rates for the metropolitan areas. As one can see, the ﬂow is highly positive for the three biggest cities (Stockholm, Malmö and Göteborg) but also for other large cities, named L3. The largest changes (in per cent) of individuals are

21In addition to moving costs, an early relocation involves potential costs in the form of having to wait before being admitted into a new language course. 22Note that the decreasing number of movers in t1 and the increasing number in t2 may in part be due to the fact that some refugees waited to move until the end of the introduction programme, which in general lasted for 18–24 months. 23This is also shown by Ekberg (1995).

33 Table 2.3: Movers and stayers per period t .

Stayers Movers Period Number Share Number Share t0 14,647 0.8018 3,620 0.1982 t1 17,737 0.9710 530 0.0290 t2 16,902 0.9253 1,365 0.0747 t3 17,108 0.9366 1,159 0.0634 t4 17,349 0.9497 918 0.0503 t5 17,604 0.9637 663 0.0268 t6 17,777 0.9732 490 0.0334 t7 17,901 0.9800 366 0.0200 t8 17,994 0.9851 273 0.0149 Note: All movements are included. In other words, some individuals are counted more than once because of repeat movements. The definition of being a mover in t0 is to have changed LLM during the first full year in Sweden, i.e. before the second observation is made. represented by the inflow to Malmö and the outflow from sparsely populated municipalities, L5. Notable is that manufacturing areas, L6, have an almost constant number of refugees, whereas other small, or medium-sized, cities have high outflow.

According to economic theory, immigrants changing location (i.e. migrating internally) tend to be less attached to the labour market compared to non-movers ("stayers"). Table 2.5 presents numbers for how internal migration relates to initial employment in Sweden. In this table, the individuals are identiﬁed as employed if they had positive earnings in the particular year. The main argument for using this generous deﬁnition is that a large fraction of the sample has zero earnings and any attachment to the labour market must be assumed to be better than having no connection at all to the labour market. The table shows the proportions of stayers and movers with positive earnings during the years in Sweden after the initial placement.

There is a diﬀerence between stayers and movers, especially in the case of

34 Table 2.4: Internal migration ﬂows among Bosnian refugees in Sweden, period t0 to period t9.

Observations Difference, t0 and t9 Residence area Period t0 Period t4 Period t9 Change Stockholm 1,266 1,494 1,560 +23.2% Malmö 852 1,560 1,785 +109.5% Göteborg 1,234 2,006 2,229 +80.6% L3 4,151 5,295 5,421 +30.6% L4 1,129 853 807 -28.5% L5 799 212 92 -88.5% L6 2,550 2,373 2,618 +2.3% L7 2,816 2,749 2,463 -12.5% L8 2,245 1,243 948 -57.8% L9 1,225 482 344 -72.0% Note: See APPENDIX for the definitions of the regions. early movers. The proportion of movers working for pay during their first full years is more than four percentage points less than the corresponding proportion for stayers. Thus, in terms of employment, movers in general are overrepresented in the zero-earnings category. This indicates that internal movers are substantially less attached to the Swedish workforce than stayers, at least when it comes to the early movers.

The ﬁgures in the table also indicate that movers have lower average earnings than non-movers when comparing those with positive earnings. Movers in this table are deﬁned as individuals changing LLM at least once during the nine years, but are excluded the year after the change of LLM. This implies that the number of observations in the ’movers’ category decreases over time.

Table 2.6 shows the number of refugees with Swedish citizenship in every period of time. As shown, most of the Bosnian refugees became Swedish citizens during their ﬁfth year in Sweden. At the end of the period, 84 per

35 Table 2.5: Diﬀerences in employment and log annual earnings between movers and stayers during their ﬁrst years in Sweden. The employment numbers show the fraction of the group with positive earnings.

Employment(%) Log earnings Stayers Movers Stayers Movers t0 5.5 3.6 9.23 9.17 t1 18.4 16.1 9.79 9.69 t2 27.6 25.5 10.29 10.01 t3 38.8 32.7 10.71 10.39 t4 51.3 46.9 11.10 10.69 t5 64.0 62.8 11.37 11.08 t6 72.2 73.0 11.62 11.44 t7 76.3 81.9 11.80 11.79 t8 77.4 86.2 11.90 11.85 t9 77.4 11.90 Note: Employment is deﬁned as positive annual earnings from employment or self- employment. Positive earnings are deﬁned as larger than 0. Also, note that all numbers refer to the initial location. Reported are average log annual earnings given that the individual has positive earnings the year of interest.

Table 2.6: Number of refugees with Swedish citizenship

Period Total Number Share t0 0 0 t1 0 0 t2 0 0 t3 251 0.01 t4 2,258 0.12 t5 7,453 0.41 t6 11,655 0.64 t7 13,616 0.75 t8 14,643 0.80 t9 15,418 0.84 Note: The table shows the number of refugees with a Swedish citizenship in every period.

36 cent of the individuals had acquired citizenship.

2.4 Empirical speciﬁcation

2.4.1 Factors aﬀecting the internal migration decision and labour market status

In this part of the paper, we study refugees’ internal migration propensities and their labour market status. We do this with the help of diﬀerent econometric speciﬁcations and all the variables presented in Table 2.1.

When we study internal migration propensities, the variable MOVER is used as a dependent variable. When we study the labour market status, two diﬀerent variables are used as dependent variables. We study employment status, i.e. whether the individual is employed, as well as income from work, i.e. the individual’s yearly earnings. When employment status is studied, the variable EMPLOYED is the dependent variable. When income from work is studied, the variable EARNINGS is the dependent variable. All other variables presented in Table 2.1 are used as explanatory variables.

The variables AGE and AGE × AGE are included in order to capture how the internal migration decision and labour market status are aﬀected by the individual’s age.

In other countries, CITIZENSHIP has been shown to aﬀect labour market status.24 Moreover, it is also reasonable to believe that citizenship aﬀects the probability of being a mover, since the probability of being a Swedish citizen increases over time spent in the country. Table 2.6 also illustrates

24See Chiswick and Miller (1995).

37 this.

Citizenship may reﬂect a stronger commitment to the immigration country. Such commitment is associated with a greater investment in Sweden- speciﬁc human capital and, thus, improves the labour market prospects. Ac- cording to Table 2.6, the refugees started to become Swedish citizens in t3.

At the end of the period studied, 84 per cent of the refugees had a Swedish citizenship. The variable COHORT is included to deﬁne the immigrant’s year of arrival.

In order to check whether individuals with children in the household act in diﬀerent ways than those who do not as regards internal migration, or get a penalty due to their lower availability when it comes to employment, a dummy variable CHILDREN is introduced.

It has also been shown that a migrant accompanied by his or her spouse has greater ﬂexibility in following a family investment strategy where, for instance, the one spouse takes up work to support the other’s investment in host country-speciﬁc human capital.25 A dummy variable for marital status,

MARRIED, is therefore included. Even cohabiting individuals are included in this variable. The variables CHILDREN and MARRIED should capture family eﬀects on the probability of internal migration as well as labour market status.

EDUCATION is expected to have a positive effect on migration, partly because educated people may be more efficient at gathering information about conditions in different regions. In addition, it is a well-known fact that education improves an individual’s opportunities in the labour market.

25See Duleep and Sanders (1993).

38 To account for the highest level of completed education for each individual, a dummy variable Secondary School is included. A second dummy variable is University. The education category excluded from the regression equation covers those whose highest completed education level is below secondary school or those whose level is unknown.

It is evident in several studies that those in employment have a lower propensity to migrate than those who are unemployed.26 Thus, the variable

EMPLOYED is included as an independent variable when internal migration propensities are studied. Since it is of importance to study to what extent an individual can improve his/her situation by internal migration, the variable

MOVER is included as an explanatory variable when the labour market situation is studied.

To examine the eﬀect of RESIDENCE location, a number of dummy variables are included: Stockholm, Göteborg, L3, L4, L5, L6, L7, L8 and

L9. These variables represent diﬀerent kinds of municipalities and cities, all of which are deﬁned in APPENDIX (Table A.1). The omitted variable is

Malmö. One reason for using Malmö as a reference category is the high concentration of immigrants in that area. Another is that the internal migration inﬂow to Malmö seems to be substantially high (See Table 2.4). These at- tributes are also used as explanatory variables in the estimation of the labour market status.

Finally, all estimations are carried out for males and females separately.

This is because previous studies have shown that males and females act dif- ferently as regards internal migration and also have diﬀerent possibilities

26See Holmlund (1984).

39 to succeed in the labour market.27 This might be due to either demand side factors (such as the nature of the work or gender discrimination in the labour market) or supply side factors (such as motivational or attitudinal diﬀerences between men and women). By making separate estimations of males and females, we are able to interpret the results for each gender separately. Moreover, we are also able to compare the results with the result from previous studies.

2.4.2 Estimating internal migration propensities

As regards internal migration propensities, a logit model is used. Nine cross- sections for the ﬁrst year through the ninth year in Sweden are carried out.

Estimation of regressions for each year gives an opportunity to check for potential time eﬀects.28

The logit model is speciﬁed as follows:

′ P (Movert =1)= φ(xiβ) (2.1) where Movert takes value 0 or 1, xi is a vector of individual observed characteristics that are relevant for explaining internal migration behaviour (see

Table 2.1), β is a vector of parameters to be estimated and φ[.] is a function following a logistic distribution.

Another way to study the migration pattern over time would have been by using a duration model. But since the available data only includes one observation a year and according to the fact that using repeated cross-sections es-

27See Jones and McAllister (1991). 28See Greene (2003).

40 timations also make the pattern of interest possible to describe, this method is used.

2.4.3 Estimating labour market outcomes

Probability of being employed

As we established in Section 2, one important reason for internal migration could be to improve labour market outcomes in terms of employment probabilities and/or income from work. To be able to investigate this, two econometric models will be estimated. In them, the dependent variable is either employment or log earnings and the explanatory variable of interest is whether the individual has changed labour market (i.e. MOVER). The ﬁrst focuses on the individual’s probability to be employed given that he/she was unemployed in the previous period. Since the variable EMPLOYED takes two values, 1 if the individual is employed and 0 if not, one appropriate way to estimate the probability is by using a logit model.

′ P (Employedt = 1|Employedt−1 =0)= φ(xiβ) (2.2) where the dependent variable takes value 0 or 1, xi is a vector of observed characteristics relevant for explaining employment. β is a vector of parameters to be estimated and φ[.] is a function following a logistic distribution.

The explanatory variable of interest here is MOVERt−1 as we are inter- ested in the relation between internal migration and employment status. If the individual migrates in period t-1 and still is unemployed in the period after we can conclude that the migration decision was not a direct consequence

41 of acquiring employment in an other LLM. Still, the internal migration decision might be a result of an expectation to increase the possibilities to get a job at the new location. Thus, if the individual is employed the period after the change of LLM, we can show a positive impact on employment status but we cannot conclude anything about the causality mechanism.

The earnings of movers vs. non-movers

To study if internal migration inﬂuences income from work, we estimate regressions of log earnings conditional on employment. Employment equals

1 if the individual has annual earnings greater than the basic amount.29 The earnings are assumed to follow the standard Mincerian form:

′ lnyi = xiβ + δiMOVER + ǫi (2.3) where lnyi is the individual’s earnings in the last period, i.e. t9, in loga- rithmic form, xi is vector of characteristics, βi is its corresponding vector of coefficients, MOVER is a dummy variable with value 1 if the refugee immigrant has migrated internally and zero otherwise, δi is the coefficient measuring the effect of such a movement, and ǫi is the error term.

The reason for estimating earnings regressions conditional on employment, instead of applying a selection-correction model, is that we do not have access to an identifying instrument for the selection equation into employment.

29i.e. if the income is higher than 37,900 Swedish krona (SEK) year 2002 respectively 38,600 SEK year 2003. This is a lower earnings limit corresponding to the minimum amount of earnings that qualiﬁes to the earnings-related part of the public system. We have also performed a sensitivity analysis using an alternative threshold.

42 2.5 Empirical results

2.5.1 Internal migration propensities

Tables 2.7–2.8 show results from the logit estimations for men and women respectively for each year. As argued in Greene (2003), the sign of the coefficients only informs us as to how the variable influences the direction of the change in the probability (positively if the coefficient is positive and negatively if the coefficient is negative). However, to draw conclusions of some economic importance, the marginal effects are also reported.30

For males, the age eﬀect is in most cases not signiﬁcant. This is probably a result of the small variation in age.31 Furthermore, possessing a

Swedish citizenship seems to decrease the incentives to migrate internally for males. As regards females, there is no statistically signiﬁcant eﬀect of being a Swedish citizen.

Being a part of a family affects men and women in the same direction: it lowers the probability of migrating in those cases when statistical significance arises. A stronger effect is observed for employment. Unemployed are more inclined to migrate internally, which is not very surprising.

An important relationship in the economic theory is that migration propensities rise with education. In our estimates, this is, in some cases, signiﬁcant for the highest level of education.

Focusing on the remaining parameters, we ﬁnd that the dummy variables

30See McCloskey (1985) for a discussion about economic signiﬁcance vs. statistic sig- niﬁcance. 31When we used a sample with individuals between 16–54 years of age, a non-linear relation of age and migration was found.

43 for the residence areas have positive signiﬁcant signs, with the exception of Stockholm and Göteborg. Thus, except for individuals that resides in

Stockholm and Göteborg, refugees in all areas have a higher probability to change location in relation to refugees living in Malmö (the reference category). These results are in line with the results presented in Table 2.4, where the internal ﬂows were observed. We can also conclude that there are no diﬀerence in signs between men and women in most cases.

To sum up, it seems that the share of movers is strongly and negatively correlated with the initial municipality size and character. This suggests that small municipalities have characteristics that push refugees away. How- ever, when those with a desire to change location have made their internal movement, these eﬀects fade out.

After t5, the probability of moving from different types of smaller municipalities has decreased markedly. In t9, the effects are not significant in most cases. Explanations for this are that moving costs increase with the time spent in Sweden and that the refugee’s labour market position improves as time spent in Sweden elapses. Another explanation might be that the out- placed refugees tended to move shortly after the initial placement.

To conclude, the internal migration decision is negatively correlated with factors that are connected with stability, such as family and employment.

Nonetheless, the type of initial residence area seems to have the largest impact when it comes to the relocation decision. Finally, the internal migration pattern does seem to change over time, and we do not find much difference between males and females.32 32In another specification, MOVER was defined as an individual changing municipality.

44 Table 2.7: Marginal eﬀects from logit estimations of the probability of being a mover in diﬀerent periods, men

Marginal eﬀects for men a t t1 t2 t3 t4 t5 t6 t7 t8 AGE .00086 -.00065 -.00863*** -.00485 .-.00307 .00160 .00080 .00212 .00108 (.0044) (.0019) (.0028) (.0029) (.0023) (.0023) (.0019) (.0019) (.0016) 2 AGE -.00002 -.00000 .00009** -.00001 .00003 -.00003 -.00002 -.00003 -.00002 (.0001) (.0000) (.0000) (.0000) (.0000) (.0000) (.0000) (.0000) (.0000)

CITIZENSHIP - - - .00616 .00193 -.00642** -.00453* -.00131 .00000 (.0148) (.0052) (.0031) (.0026) (.0027) (.0000)

COHORT .23827*** - -.03973*** -.00911** .01970*** -.00240 .00274 .00461** .00389** (.0097) (.0044) (.0039) (.0035) (.0030) (.0024) (.0024) (.0020)

EMPLOYMENT -.06506*** -.01170*** -.02210*** -.02963*** -.02260*** -.01917*** -.01102*** -.01116*** -.00405 (.0225) (.0034) (.0044) (.0039) (.0036) (.0035) (.0033) (.0036) (.0027)

CHILDREN -.03028*** .00210 .00502 -.00453 .00484 -.00496 -.00207 -.00173 -.00455 (.0118) (.0047) (.0068) (.0070) (.0051) (.0050) (.0041) (.0040) (.0031)

MARRIED .00517 -.00212 -.00846 .00106 -.01563** -.00597 -.01827*** -.01185** -.01007** (.0117) (.0055) (.0084) (.0075) (.0075) (.0058) (.0065) (.0056) (.0046)

EDUCATION Secondaryschool -.01922* .00433 .00453 -.00621 -.00298 .00256 -.00476 .00367 -.00301 (.0104) (.0043) (.0062) (.0059) (.0047) (.0046) (.0038) (.0041) (.0032) University -.01198 .01298** .01804** .00034 -.00319 .00314 .00031 .00436 -.00165

45 (.0105) (.0057) (.0075) (.0063) (.0049) (.0053) (.0003) (.0050) (.0031)

RESIDENCE Stockholm .31993* .00980 -.03150** -.00614 -.02404*** -.02126*** -.00534 .00586 .00672 (.1662) (.0344) (.0141) (.0209) (.0085) (.0066) (.0080) (.0122) (.0104) Göteborg .22764 .09640 .00742 .02856 -.00603 .00582 -.00868 .00627 .00712 (.1589) (.0932) (.0229) (.0281) (.0124) (.0121) (.0067) (.0116) (.0099) L3 .42731*** .12665 .12724*** .11291*** .06817*** .03915*** .02951** .03339*** .01584 (.1432) (.0842) (.0350) (.0334) (.0214) (.0142) (.0122) (.0147) (.0098) L4 .62679*** .29665 .18120*** .28316*** .12730*** .04505* .01940 .03307 .00200 (.1343) (.1979) (.0619) (.0783) (.0483) (.0265) (.0186) (.0260) (.0112) L5 .83547*** .66700*** .45973*** .46161*** .42168*** .25120*** .22048*** .34412*** .19546*** (.0451) (.2042) (.0925) (.1002) (.0977) (.0845) (.0904) (.1256) (.1095) L6 .65380*** .18343 .22040*** .20211*** .09901*** .06269*** .05877** .07575** .04083** (.1240) (.1358) (.0589) (.0586) (.0347) (.0233) (.0228) (.0315) (.0212) L7 .56194*** .19533 .17431*** .22886*** .15056*** .08194*** .07383*** .05756** .04059* (.1428) (.1346) (.0496) (.0589) (.0423) (.0263) (.0263) (.0268) (.0215) L8 .67821*** .31044 .30560*** .33816*** .18583*** .14929*** .09445** .07367** .05821* (.1176) (.1928) (.0718) (.0782) (.0557) (.0449) (.0370) (.0384) (.0335) L9 .79357*** .39462* .39500*** .39837*** .22401*** .15216*** .11940** .03356* .06400 (.0704) (.2260) (.0843) (.0910) (.0714) (.0553) (.0531) (.0338) (.0450) -2(Loglikehood) 7,181 2,387 4,486 4,162 3,427 2,806 2,236 1,856 1,392 b LR chi2(17)(16)(18) 2,230*** 189*** 479*** 354*** 378*** 203*** 207*** 129*** 95*** N 9,318 9,318 9,318 9,318 9,318 9,318 9,318 9,318 9,318

Note:*significant at 10 per cent **significant at 5 per cent and ***significant at 1 per cent.

a The variable COHORT is here excluded due to collinearity. b Likelihood ratio test, testing significance of model. Degrees of freedom for respective model specification noted in paranthesis. Table 2.8: Marginal effects from logit estimations of the probability of being a mover in different periods, women

Marginal eﬀects for women (st.dev.) a t0 t1 t2 t3 t4 t5 t6 t7 t8 AGE .00575 -.00198 -.00136 -.00515** -.00168 .00026 -.00148 .00302* -.00142 (.0043) (.0017) (.0027) (.0022) (.0022) (.0021) (.0016) (.0017) (.0013) 2 AGE -.00008 .00002 .00000 .00005* .00002 -.00001 .00001 -.00004** .00001 (.0001) (.0000) (.0000) (.00) (.0000) (.0000) (.0000) (.0000) (.0000)

CITIZENSHIP - - - .01591 -.00319 -.00425 -.00164 -.00075 -.00187 (.0175) (.0049) (.0030) (.0024) (.0024) (.0024)

COHORT .22149*** - -.04064*** -.00304 .01672*** .00160 .00116 .00274 .00453** (.0093) (.0044) (.0034) (.0035) (.0030) (.0023) (.0020) (.0018)

EMPLOYMENT -.03454 -.00353 -.01749*** -.01626*** -.01311*** -.00898*** -.01099*** -.00318* -.00031 (.0243) (.0048) (.0061) (.0041) (.0034) (.0030) (.0027) (.0023) (.0020)

CHILDREN -.01620* -.00308 -.00766 -.00275 .00498 -.00295 -.00303 -.00017 -.00776*** (.0094) (.0040) (.0060) (.0051) (.0043) (.0042) (.0031) (.0026) (.0026)

MARRIED .00069 .00390 -.01223** -.00624 -.00623 -.00225 -.00979*** -.00371 -.00785*** (.0078) (.0031) (.0057) (.0048) (.0044) (.0038) (.0035) (.0028) (.0028)

EDUCATION Secondaryschool .00259 .00042 .00273 -.00906** -.00520 -.00197 -.00134 -.00372 -.00075 (.0077) (.0031) (.0048) (.0040) (.0037) (.0035) (.0027) (.0025) (.0022) University -.00389 .00949** .01183* .00049 -.00219 -.0041 .00355 .00146 .00193 (.0089) (.0044) (.0063) (.0048) (.0044) (.0041) (.0036) (.0029) (.0027)

RESIDENCE 46 Stockholm .11780 .02331 .01496 .00349 -.02245** -.01023 .00556 .00162 -.00491 (.1029) (.0427) (.0395) (.0323) (.0107) (.0101) (.0169) (.0107) (.0046) Göteborg .10729 .05202 .05833 .04232 .01566 -.00817 .01393 .00305 -.00241 (.1025) (.0611) (.0501) (.0444) (.0209) (.0097) (.0182) (.0103) (.0053) L3 .28470*** .09106 .20766*** .17625*** .08279*** .04385*** .06198** .02703* .01284 (.1109) (.0652) (.0657) (.0640) (.0283) (.0164) (.0268) (.0146) (.0080) L4 .52184*** .20087 .33553*** .44909*** .14447** .04368 .08232 .02407 .00852 (.1311) (.1581) (.1144) (.1349) (.0625) (.0291) (.0565) (.0250) (.0130) L5 .79992*** .55107** .68726*** .66540*** .46821*** .25413*** .41414*** .39874*** .26174** (.0553) (.2341) (.0992) (.1221) (.1147) (.0958) (.1595) (.1524) (.1204) L6 .55068*** .15543 .36990*** .30153*** .11690*** .06726** .12278** .06224* .03018* (.1198) (.1202) (.1050) (.1095) (.0456) (.0271) (.0570) (.0332) (.0168) L7 .43057*** .14514 .31781*** .17523***** .1586 .09876*** .10791** .05665* .03144* (.1263) (.1078) (.0944) (.1088) (.0550) (.0326) (.0511) (.0307) (.0170) L8 .56687*** .27406 .46747*** .48927*** .24508*** .12985*** .20301** .09228* .04221 (.1193) (.1795) (.1133) (.1284) (.0763) (.0461) (.0916) (.0523) (.0259) L9 .72500*** .37258* .56266*** .57213*** .27890*** .11816** .20480* .05898 .02962 (.0845) (.2211) (.1157) (.1324) (.0930) (.0543) (.1074) (.0482) (.0279) -2(Loglikehood) 6,657 2,061 4,291 3,749 3,189 2,527 1,916 1,478 1,239 b LR chi2(17)(16)(18) 2,116*** 157*** 450*** 368*** 283*** 160*** 148*** 119*** 111*** N 8,949 8,949 8,949 8,949 8,949 8,949 8,949 8,949 8,949

Note:*significant at 10 per cent **significant at 5 per cent and ***significant at 1 per cent.

Probability of being employed

Tables 2.9–2.10 show results from logit estimations of an employment equation for men and women respectively. The dependent variable is EMPLOYED.

However, since only individuals unemployed in the previous period are included, the dependent variable can be interpreted as the probability to change state in the labour market (i.e. the probability of having work the period after being unemployed).

Table 2.9 gives marginal eﬀects for men. It measures the probability of changing employment state over one period of time. The same is reported for women in Table 2.10.

As regards the variables age, marital status and children there are simi- larities as well as diﬀerences between men and women. Employment chances are generally positively associated with age, but the negative sign on the

AGE ×AGE variable indicates that employment probabilities diminish with age. Being married or cohabiting is positively associated with employment for both men and women. On the other hand, having children in the household is, in most cases, negatively correlated to employment chances for women but have no signiﬁcant eﬀects for men.

Higher educational levels generally improve individuals’ chances of being employed. This is signiﬁcant for both men and women. As expected, the education variables, SECONDARY and UNIVERSITY, are highly signiﬁ- cant for the probability to go from unemployment to employment, and the

Those results show very similar eﬀects.

47 marginal effects are relatively large. Also, individuals with Swedish citizenship seem to have a higher probability of gaining employment. One manner of explaining this is to assume that citizenship signals a commitment and a will to be integrated. This, in turn, increases the probabilities of getting employed. However, there are substantial difficulties in establishing that citizenship reflects a causal mechanism running from the individual’s citizenship to employment status. The essence of the problem is that a well-integrated individual, already in employment, may feel a stronger commitment to the new country and thereby acquire citizenship. Thus, citizenship and employment as a whole are reflections of one another. This leads to a correlation between employment and citizenship that lacks a causal interpretation. Here, we treat citizenship as pre-determined.

As far as residence is concerned, the living area has an impact on employment, or on any change of employment state. Refugees residing in areas other than the reference area have signiﬁcantly higher employment probabilities compared to refugees living in Malmö. A plausible explanation for this is that the economy in Malmö has undergone structural changes during the last 20 years that have resulted in high unemployment, even among natives.

Among the big cities, it would seem that the labour market in the Stockholm area has been the best when it comes to integrating refugees.

The highest marginal eﬀects are found in areas like L6 for men and L4 and

L6 for women.33 Regional differences in the general demand for labour are important for the differences in the probability of finding a job. But a more

33The marginal effects for L5 areas (sparsely populated municipalities) are almost the same. However, this is not surprising because of the high outflow from the areas. The change of employment status is probably a result of the outflow and not of good integration.

48 Table 2.9: Marginal eﬀects from logit estimations of the probability of being employed in t given that the individual was unemployed in t-1 (including MOVERt-1), men

Marginal eﬀects for men (st.dev.), 25–54 years in t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 AGE -.00186 .01306** .02289*** .01841** .04331*** .04547*** .05618*** .04914*** .03953*** (.0028) (.0053) (.0065) (.0083) (.0101) (.0119) (.0129) (.0124) (.0110) 2 AGE -.00000 -.00022*** -.00037*** -.00036*** -.00067*** -.00072*** -.00075*** -.00066*** -.00053*** (.0000) (.0000) (.0001) (.0001) (.0001) (.0001) (.0001) (.0001) (.0001)

CITIZENSHIP - - .0005 .06653*** .05577*** .08283*** .03524*** .00775 .05049*** (.0361) (.0182) (.0135) (.0148) (.0172) (.0174) (.0146)

COHORT -.04951*** .01100* -.02673*** -.06831*** -.02902** -.06809*** .07349*** .02141 .05267*** (.0046) (.0066) (.0082) (.0102) (.0126) (.0149) (.0168) (.0162) (.0416)

CHILDREN -.00029 .01471 -.01538 .02450 -.00040 .02500 .05434** .03644 -.00670 (.0071) (.0117) (.0145) (.0172) (.0198) (.0226) (.0239) (.0232) (.0192)

MARRIED .00483 .02395** .05587*** .06661*** .11165*** .08449*** .08285*** .04905** .05830*** (.0072) (.0121) (.0132) (.0173) (.0189) (.0229) (.0237) (.0221) (.0180)

EDUCATION Secondaryschool .01553** .05480*** .07684*** .08213*** .09222*** .13694*** .16498*** .06975*** .04497** (.0066) (.0121) (.0138) (.0162) (.0192) (.0229) (.0259) (.0232) (.0205) University .02813*** .09398*** .09569*** .12439*** .14100*** .19967*** .23974*** .09920*** .05274** (.0088) (.0168) (.0182) (.0204) (.0232) (.0280) (.0325) (.0294) (.0255) 49 RESIDENCE Stockholm .04054* .07023** .12974** .19752*** .17996*** .21757*** .06694 .15494*** .07009* (.0247) (.0276) (.0311) (.0349) (.0354) (.0411) (.0423) (.0487) (.0399) Göteborg .05252** .02765 .07210*** .04313 .02854 .14712*** .09673*** .10286*** .06824** (.0259) (.0228) (.0267) (.0278) (.0283) (.0335) (.0351) (.0382) (.0324) L3 .04469** .02797 .02617 .07597*** .07067*** .11566*** .08205*** .11011*** .05067** (.0183) (.0189) (.0196) (.0230) (.0241) (.0273) (.0283) (.0303) (.0242) L4 .16383*** .06698** .17009*** .16180*** .19161*** .23045*** .04027 .16995** .01900 (.0480) (.0298) (.0370) (.0410) (.0439) (.0529) (.0571) (.0667) (.0483) L5 .10787** .07976* .09507* .22926*** .10715 .34050*** -.06787 -.03583 .09997 (.0506) (.0438) (.0522) (.0676) (.0840) (.0950) (.1094) (.1529) (.1604) L6 .15231*** .18143*** .19545*** .35462*** .30869*** .33681*** .17480*** .20475*** .06586* (.0398) (.0331) (.0315) (.0327) (.0340) (.0391) (.0476) (.0528) (.0394) L7 .04729** .05193** .05963** .12759*** .11574*** .15091*** .08873** .13657*** .04363 (.0220) (.0224) (.0235) (.0283) (.0294) (.0342) (.0357) (.0405) (.0308) L8 .08724*** .03745 .09570*** .22006*** .23848*** .18940*** .18438*** .23014*** .09599* (.0326) (.0242) (.0298) (.0368) (.0396) (.0495) (.0544) (.0642) (.0565) L9 .11437** .13109*** .20941*** .28510*** .30161*** .32083*** .05969 .22313** .16483* (.0431) (.0383) (.0449) (.0519) (.0583) (.0747) (.0875) (.0985) (.0961)

MOVERt-1 -.01140** -.00501 .01753 .06115*** .05214** .12628*** .11739** .12267** .03827 (.0055) (.0171) (.0154) (.0212) (.0271) (.0394) (.0504) (.0580) (.0577) -2(Loglikehood) 3,747 6,116 6,857 6,861 6,071 4,610 3,369 2,503 2,056 a LR chi2(17)(18) 313*** 312*** 341*** 665*** 597*** 651*** 433*** 293*** 247*** N 9,204 8,731 7,886 6,800 5,469 4,164 3,098 2,550 2,404

Note:*significant at 10 per cent **significant at 5 per cent and ***significant at 1 per cent.

a Likelihood ratio test, testing significance of model. Degrees of freedom for respective model specification noted in paranthesis. Table 2.10: Marginal effects from logit estimations of the probability of being employed in t given that the individual was unemployed in t-1 (including MOVERt-1), women

Marginal eﬀects for women (st.dev.), 25–54 years in t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 AGE .00639** .00873** .03546*** .04080*** .10003*** .08725*** .07295*** .05480*** .04599*** (.0027) (.0034) (.0050) (.0064) (.0086) (.0100) (.0107) (.0092) (.0078) 2 AGE -.00009** -.00013*** -.00048*** -.00059*** -.00133*** -.00116*** -.00098*** -.00073*** -.00060*** (.0000) (.0000) (.0000) (.0001) (.0001) (.0001) (.0001) (.0001) (.0001)

CITIZENSHIP - - .02321 .02229* .04319*** .05590*** .04181*** .02186* .01022 (.0285) (.0126) (.0100) (.0120) (.0140) (.0128) (.0119)

COHORT .01785*** -.00034 -.01537*** -.04482*** -.05255*** .00626 .03263** .04500*** .02192** (.0078) (.0044) (.0059) (.0076) (.0094) (.0120) (.0132) (.0117) (.0098)

CHILDREN -.01283** -.00976 -.02473*** -.04350*** .01660 -.00984** -.00928 .03176** -.00869 (.0060) (.0068) (.0093) (.0119) (.0050) (.0159) (.0171) (.0151) (.0117)

MARRIED -.00049 .00257 .01572** .04720*** .04580*** .06247*** .07501*** .04883*** .02309** (.0048) (.0056) (.0073) (.0088) (.0110) (.0135) (.0140) (.0116) (.0098)

EDUCATION Secondaryschool .01129** .03873*** .04588*** .06250*** .08483*** .11866*** .13506*** .07137*** .06340*** (.0050) (.0068) (.0082) (.0097) (.0120) (.0149) (.0168) (.0147) (.0135) University .02653*** .06843*** .09757*** .08591*** .08789*** .12586*** .15379*** .08810*** .11971*** (.0074) (.0120) (.0135) (.0146) (.0166) (.0202) (.0234) (.0051) (.0222)

RESIDENCE Stockholm .05207* .06210** .08157** .14125*** .19824*** .19644*** .09798** .03508 .01720 50 (.0052) (.0252) (.0257) (.0311) (.0347) (.0383) (.0394) (.0327) (.0262) Göteborg .07432** .02075 .05243** -.00606 .01052 .06119** .05682** .03508 .02101 (.0356) (.0171) (.0210) (.0119) (.0192) (.0275) (.0280) (.0233) (.0202) L3 .01766 .01211 .01761 .04049** .08584*** .09079*** .04495** .00091 .01059 (.0167) (.0128) (.0145) (.0178) (.0209) (.0236) (.0280) (.0182) (.0162) L4 .12456** .06673** .07179*** .16356*** .16610*** .18348*** .16494**** .07890* .16464*** (.0516) (.0280) (.0276) (.0366) (.0406) (.0479) (.0547) (.0490) (.0599) L5 .13111** .07218* .07801* .29826*** .16582** 0.19407* .01576 .07890 - (.0631) (.0397) (.0447) (.0657) (.0800) (.1101) (.1104) (.1069) L6 .07814** .05565** .07071*** .15841*** .22983*** .21815*** .20630*** .07467** .03381 (.0346) (.0218) (.0222) (.0278) (.0309) (.0334) (.0379) (.0313) (.0245) L7 .07040** .02357 .00804 .04098** .07626*** .08708*** .04137 .00112 .03245 (.0307) (.0158) (.0155) (.0204) (.0243) (.0278) (.0274) (.0210) (.0211) L8 .05417* .00525 .04473** .05768** .09227*** .12910*** .09262** .07050* .02677 (.0310) (.0150) (.0218) (.0262) (.0320) (.0380) (.0416) (.0383) (.0315) L9 .02892 .05740** .08568** .14105*** .15085*** .13031** .10753* .05344 .01304 (.0287) (.0285) (.0338) (.0431) (.0504) (.0614) (.0637) (.0549) (.0455)

MOVERt-1 -.01071*** .00787 -.02713*** -.02419* -.04361** .00399 .05820 -.03452 -.00010 (.0041) (.0141) (.0094) (.0138) (.0183) (.0303) (.0402) (.0307) (.0351) -2(Loglikehood) 2,895 3,465 5,126 6,244 6,522 5,668 4,169 2,883 2,274 a LR chi2(17)(18) 123*** 151*** 259*** 470*** 746** 739*** 692*** 570*** 433*** N 8,793 8,536 8,294 7,661 6,650 5,344 4,137 3,388 3,063

Note:*significant at 10 per cent **significant at 5 per cent and ***significant at 1 per cent.

a Likelihood ratio test, testing significance of model. Degrees of freedom for respective model specification noted in paranthesis. significant factor might be the structure of the economy in the different areas.

The manufacturing districts, L6-areas, distinguish themselves through small- scale industrial production. Meanwhile, the Stockholm area, for example, has a high proportion of knowledge-intensive production and also a large service sector. It can therefore be assumed that Sweden-speciﬁc knowledge is a more important requirement for those seeking work in the big cities, compared to those going to areas dominated by small-scale industries.

However, the main interest of this study is to evaluate to what extent internal migration improves the probability of gaining employment. Table

2.9 and 2.10 show how an internal migration influences the possibility for men and women to go from unemployment to employment after residence at the new location for less than one year. This is done with the help of the variable MOVERt−1. The results differ according to gender and the time of the internal movement. For women, the relocation is either significantly negative or has no effect on the probability of improving their labour market situation in terms of employment. Men, on the other hand, do have the opportunity to increase the chances to find a job by moving as long as they do not move during their first years in Sweden.34 Thus, individuals gain in terms of integration into the labour market if they stay at the initial placement for some time before they make the migration decision. One explanation is that an early internal migration decision is based on imperfect information and therefore can lead to a mismatch on the labour market. Another is that early movements are driven by other motives than the maximization of economic

34Note that only the ﬁrst cross-section estimation shows a negative signiﬁcant relation between internal migration and employment status for men.

51 returns, while later movers incur higher migration costs when relocating.

This hypothesis is supported by the employment rates (i.e. fractions with positive earnings) described in Table 2.5. It is shown that, after t5, movers are more attached to the labour market than non-movers. Of course, the investment in migration then needs to reap beneﬁts in terms of an improved labour market outcome.

The diﬀerent impact of an internal migration decision on male and female employment supports the theory that migration decisions are based on family utility maximization. In this study, where about 75 per cent of the individuals are married, it seems as though the decision to migrate internally is a result of the men’s possibility to improve their labour market situation rather than the females’.

The earnings of movers vs. non-movers

An income regression on period t9 is estimated and presented in Table 2.11 in order to study if the successful movers (the employed ones) realize some internal migration premium, such as higher earnings than employed non- movers. Here, the coeﬃcient, β, is the eﬀect on income from work.

According to Table 2.11, internal migration has a significant and negative impact on job earnings for men, since we have negative significant coefficients for the dummy variable MOVER. For women, no significant effect is found.

If we look at the other estimators, we find that education and region of residence seem to be important factors for determining the earnings. Moreover, being married has a statistical significant effect for men. Finally, obtaining

Swedish citizenship is of great importance for both men’s and women’s earn-

52 ings. Again, the acquisition of Swedish citizenship might signal unobservable ability in terms of commitment. Employment is here deﬁned as if the yearly earnings are higher than the basic amount. If we lower the threshold for being classiﬁed as employed from the basic amount to individuals with positive earnings, we get the same pattern.

Thus, we do not find any evidence that movers improve their labour market situation, in terms of earnings, by internal migration. The other coefficients simply underline the results already presented: education, age, marital status, residence area, and citizenship exercise an important influence on the degree of success on the Swedish labour market.

53 Table 2.11: Income estimations of women and men in t9, 34–64 years of age. Income from work in SEK (logaritic form).

Men Women Coeﬃcients Coeﬃcients (st.dev.) (st.dev.) AGE .05061*** .08341*** (.0107) (.0127) 2 AGE -.00065*** -.00098*** (.0001) (.0001)

CITIZENSHIP .05467*** .04029** (.0160) (.0187)

CHILDREN .00388 -.01433 (.0155) (.0155)

MARRIED .09154*** .02324 (.0248) (.0281)

EDUCATION Secondary school .09335*** .06129*** (.0248) (.0153) University .20267*** .19645*** (.0260) (.0184)

RESIDENCE Stockholm .21890*** .15171*** (.0314) (.0271) Göteborg .22148*** .05157** (.0308) (.0265)

L3 .14130*** .03920* (.0284) (.0230) L4 .19929*** .07276*** (.0343) (.0304) L5 .18627** .04512 (.0593) (.0752) L6 .23388*** .08132*** (.0287) (.0246) L7 .13387*** -.00148 (.0311) (.0259) L8 .18867*** .04288 (.0323) (.0323) L9 .18934*** -.00346 (.0382) (.0466)

MOVER -.04513*** -.01749 (.0117) (.0121)

Constant 10.892*** 10.111*** (.2346) (.2847) N 7,024 5,992

Note:*significant at 10 per cent **significant at 5 per cent and ***significant at 1 per cent. Estimates from the income regression conditional on positive yearly earnings are available upon request.

54 2.6 Conclusions

This study has analysed the internal migration propensities and the labour market outcomes of movers and non-movers among refugees in Sweden. The results show that refugees who were initially placed in labour markets on the countryside moved away from such labour markets to labour markets in the big cities of Stockholm, Göteborg and Malmö. It is a fact that a large number of Bosnian immigrants live in these cities. Thus, this study gives some support to the claim that refugees migrate internally toward ethnic enclaves.

As regards the propensity to migrate internally, the results presented in this paper indicate that, among refugees in Sweden, the decision to move from one labour market to another is affected by a number of individual characteristics. Among males, factors connected with different aspects of stability (such as having obtained Swedish citizenship, being employed or being married) decrease the probability of being a mover. Among females, being employed reduces the probability of being a mover, while the effect of possessing Swedish citizenship or being married is unclear.

It is also worth pointing out that there are results in the study indicating that internal movers to some extent are positively selected. The results suggest that internal migration is more common among refugees who have obtained a university degree than among refugees with lower education. This is the case for males as well as for females.

As regards the labour market outcome, the results show that refugees who are married and well educated have the highest probabilities of being

55 employed, as well as the highest earnings, at the end of the observed period.

When studying the eﬀect of being a mover, no clear pattern emerges in the results. There is some evidence of the fact that internal migration increases the probabilities of succeeding on the Swedish labour market for male refugees from Bosnia. But there are also results pointing in the opposite direction. It is, for example, a fact that male refugees that migrated internally had lower earnings at the end of the observation period than refugees who did not migrate within Sweden.

One explanation for the observed results might be that refugees migrate internally for other reasons than to improve their labour market situation.

One such reason might be a wish to join compatriots in ethnic enclaves. It is also worth noting that male refugees that migrated internally at the end of the observation period had a higher probability of being employed after the internal migration. This was not the case for refugees who migrated early in the observation period. The explanation for this can be that an early migration decision is based on imperfect information and therefore leads to a mismatch on the labour market.

Against this background, some policy conclusions can be drawn. If refugees want to improve their labour market position with respect to employment and earnings in the long run, internal migration is not enough.

The results point toward the fact that refugees need time and information about the Swedish labour market before an internal migration can meet with success regarding the labour market position. Therefore, it is a better strategy for them to gather information about the Swedish society at their initial place of residence. Such information might be information about labour

56 market situations in diﬀerent regions, etc. Furthermore, it might also be a worthwhile strategy to improve their knowledge in the Swedish language before migrating internally.

Previous studies have also stressed the importance of having belonged to the labour force in Sweden.35 One way to become integrated, therefore, is to accept a job oﬀer without hesitation even if the job lies below the individual’s formal competence level. Other ways are of course to acquire a formal education and/or to signal integration in the Swedish society by acquiring Swedish citizenship.

Finally, we can conclude that this paper has documented some patterns in the internal migration and determinants behind labour market outcomes among refugees in Sweden. However, the eﬀect of internal migration on labour market outcomes to some extent remains unclear. This suggests that further research in this area is needed.

35See Rooth (1999).

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Appendix

2.A Local labour market regions (LLM)

All the data have been collected at the municipal level. However, the data have been aggregated up to match Statistic Sweden’s definitions of local labour markets (LLMs). The classification is generated by using data on commuting habits and the 284 municipalities in Sweden have been aggregated to 87 Local Labour Markets (LLMs) with frequent cross-border commuting. SCB’s objective of demarcating local labour markets was primarily to create areas of reference that are suitable for use as a means of comparing different regional areas as regards, for example, the functioning of the labour market. Statistic Sweden has updated the LLM definition every five years since 1988. The definition used in this paper is from 2005. The LLMs are presented in Figur A1.

2.B Classiﬁcation of municipalities

The following classiﬁcation of municipalities was partly made by the Swedish

Association of Local Authorities. The municipalities were originally divided

69 Figure A1: Local Labour Markets

The ﬁgure illustrates the diﬀerent Local Labour Markets in Sweden, 2005. Source: Statistics Sweden, (2005).

70 into nine categories on the basis of structural parameters such as population, commuting patterns and economic structure. The only difference here is that we have, instead of a metropolitan category and a suburban category, three metropolitan regions including their suburban areas. Thus, we still have nine categories. The following tables show the classifications and definitions of the different categories.

71 Table A1: Classiﬁcation of municipalities, 1 January 2005

Variable Description

Metropolitan municipalities STOCKHOLM including suburbs.

GÖTEBORG including suburbs.

MALMö including suburbs.

L3 Larger cities (27 municipalities) Municipalities with 50,000-200,000 inhabitants and more than 70 per cent of urban areas. (Example: Uppsala, Norrköping, Växjö, and Ume˙a.)

L4 Commuter municipalities (41 municipalities) Municipalities in which more than 40 per cent of the nocturnal population commute to work in another municipality.

L5 Sparsely populated municipalities (39 municipalities) Municipalities with less than 7 inhabitants 2 per km and less than 20,000 inhabitants (Example: Härjedalen, Strömsund, Vilhelmina)

L6 Manufacturing municipalities (40 municipalities) Municipalities where more than 40 per cent of the nocturnal population between 16 and 64 are employed in manufacturing and industry. (Example: Finsp˙ang, Gnosjö, Oskarshamn.)

L7 Other municipalities, more than 25,000 inhabitants (34 municipalities) Municipalities that do not belong to any of the previous categories and have a population of more than 25,000. (Example: Gotland, Hudiksvall, Västervik.)

L8 Other municipalities, 12,500—25,000 inhabitants (37 municipalities) Municipalities that do not belong to any of the previous categories and have a population of 12,000—25,000. (Example: Flen, Leksand, Sölvesborg.)

L9 Other municipalities, less than 12,500 inhabitants (34 municipalities) Municipalities that do not belong to any of the previous categories and have a population of less than 12,500. (Example: Bengtsfors, Torsås, Hjo.)

72 Table A2: Deﬁnitions of residences variables

L3 L4 L5 L6 L7 L8 L9 Borås Bjuv Arjeplog Alvesta Alingsås Arboga Aneby Eskilstuna Boxholm Arvidsjaur Emmaboda Arvika Avesta Askersund Falun Bromölla Berg Fagersta Boden Båstad Bengtsfors Gävle Eslöv Bjurholm Finspång Bollnäs Eksjö Borgholm Halmstad Essunga Bräcke Gislaved Borlänge Flen Degersfors Helsingborg Forshaga Dals-Ed Gnosjö Enköping Hagfors Eda Jönköping Gagnef Dorotea Grums Falkenberg Hallsberg Filipstad Kalmar Gnesta Gällivare Götene Falköping Hallstahammar Färgelanda Karlskrona Grästorp Härjedalen Herrljunga Gotland Heby Gullspång Karlstad Habo Jokkmokk Hofors Hudiksvall Hedemora Haparanda Kristianstad Hammarö Ljusdal Hylte Härnösand Hultsfred Hjo Linköping Höganäs Lycksele Laxå Hässleholm Kalix Hällefors Luleå Hörby Malung Lessebo Karlshamn Kiruna Högsby Lund Höör Malå Ljungby Karlskoga Klippan Karlsborg Norrköping Kil Nordmaling Markaryd Katrineholm Kramfors Kinda Skellefteå Knivsta Norsjö Mönsterås Landskrona Kristinehamn Ljusnarsberg Sundsvall Krokum Ockelbo Nybro Lidköping Köping Mellerud Södertälje Kumla Orsa Nässjö Ludvika Laholm Munkfors Trollhättan Kungsör Ovanåker Olofström Mark Leksand Nora 73 Umeå Kävlinge Pajala Osby Mjölby Lindesberg Nordanstig Uppsala Lekeberg Ragunda Oskarshamn Motala Skinnskatteberg Varberg Mullsjö Robertsberg Oxelösund Norrtälje Lysekil Smedjebacken Västerås Munkedal Rättvik Perstorp Nyköping Mariestad Strömstad Växjö Mörbylånga Sorsele Sotenäs Piteå Mora Tanum Örebro Norberg Storuman Surahammar Ronneby Sala Torsås Örnsköldsvik Nykvarn Strömsund Svenljunga Sandviken Simrishamn Töreboda Östersund Nynäshamn Torsby Sävsjö Skövde Skara Vadstena Orust Vansbro Tibro Strängnäs Sollefteå Valdemarsvik Sigtuna Vilhelmina Tranemo Söderhamn Sunne Vingåker Sjöbo Vindeln Tranås Trelleborg Säﬄe Åtvidaberg Stenungssund Ydre Ulricehamn Uddevalla Sölvesborg Ödeshög Storfors Ånge Uppvidinge Västervik Tidaholm Svalöv Åre Vaggeryd Ystad Tierp Säter Årjäng Vara Ängelholm Tingsryd Söderköping Åsele Vetlanda Tomelilla Timrå Älvdalen Vårgårda Vimmerby Trosa Älvsbyn Värnamo Åmål Vänersborg Överkalix Älmhult Östhammar Vännäs Övertorneå Örkelljunga Åstorp Östra Göringe Älvkarleby