Why do migrants go back and forth?

A Review and Empirical Tests of Micro-related Theories

Rosa Weber and Jan Saarela

Rosa Weber

Stockholm University, Sweden

Department of Sociology

Jan Saarela

Åbo Akademi University, Finland

Abstract

Circular migration has received increasing attention among policy makers over the last two decades. However, to date we know relatively little about the mechanisms underlying circular migration, largely because of data constraints and lack of a unified theoretical approach. The aim of this paper is to synthesise the growing body of research under a common analytical framework and to investigate the hypotheses empirically using detailed linked Finnish and Swedish register data. This data set allows us to observe individuals in both countries and thus to follow migrants across national borders. We use an event history framework and find that migrants who move more than once or twice are very mobile. Similarly, we observe strong selection effects and seasonality in movement back and forth. With this, we hope to lay the ground for more thorough empirical investigation of circular migration.

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1. Introduction

Over recent decades, we have observed a vast increase in migration flows. Cross-country transportation has proliferated both in scope and speed. Last minute Ryanair and Eurostar tickets, among others, have made the decision to move ever easier. Simultaneously have numerous governments initiated programs promoting circular migration. In the European Union in particular, incentives to increase cross-border movement and temporary or circular migration have entailed the granting of the European citizenship allowing citizens to move freely between the EU-member states, as well as, incentivizes for targeted groups.

Even though free movement laws in the EU have removed prior constraints to settling abroad, the level of geographical mobility of EU citizens remains lower than expected (Recchi 2005). Migrants from other EU member states make up 1.6% of the residents, compared to the 3.4% that immigrants from outside of the EU constitute, a large share of who come to EU-member states as refugees and asylum seekers (Eurostat 2004). However, it remains unclear whether intra-EU movement is only less ‘visible’ in current data sets than from outside of the EU. Predominant forms of intra-EU movement are short-term, commuting and rotation mobility, which are more difficult to capture empirically than permanent migration (Benton and Petrovic 2013; Castro-Martin and Cortina 2015). Indeed, many data sets define migration as a movement after being resident in a country for one or five years (Recchi 2005; see also Deshingkar 2008 and Hugo 2008). Similarly, data sets that have information of temporary movements often rely on self-reported information regarding the time abroad (Nekby 2006; Bratsberg et al. 2007; Vadean and Piracha 2009).

In this paper, we are able to overcome this problem by using unique linked Finnish and Swedish register data that allow us to follow migrants across national borders. We measure migration on a monthly basis and, moreover, have detailed information on background characteristics. Specifically, we analyse a sample of Finnish migrants who moves back and forth between Finland and Sweden between the years 1988 and 2005. Even though various theories have been proposed to explain circular migration, there is no single, coherent theory that allows us to analyse certain hypotheses. The main aim of this paper is, therefore, first to

2 compile different ideas that have been proposed into a number of empirically verifiable hypotheses and in a second step to test these empirically. With this, we hope to lay the ground for more thorough empirical investigation of circular migration.

The paper is set up as follows: We begin by a discussion of the mechanisms underlying migration in order to facilitate the discussion on the decision to move back and forth. In the interest of space, we do not delve into return migration. Then, we provide a description of the data set, as well as, the Finnish-Swedish migration context. Finally, we provide an empirical investigation of the hypotheses and a detailed outline for future research in the field.

2. Why do people move and where do they go?

Theoretical and empirical investigations of migration have been challenged by the complexity of factors at play in any decision to migrate. A migration can be a dream of self-realisation, a gesture of escape, an adventure, a rite of passage; it may be undertaken for love, excitement, experience, leisure or ‘seeing the world’ (King 2002). In an attempt to understand this wide array of reasons underlying migration, we take a holistic theoretical approach and focus on neo-classical economics, new economics of migration and social network theory. Much of the empirical migration research on the -US context leans on these theories and they are, moreover, well-apt to incorporate more recent literature on intra- EU migration.

Four restrictions should be kept in mind. First, we predominantly discuss the micro-part of migration theories, sometimes considering also the meso-level. This choice is largely data driven, but allows us to investigate the mechanisms underlying individual decisions. Second, we adopt a broader position that causal processes relevant to migration may operate on multiple levels simultaneously, and that sorting out which of the explanations are useful is an empirical and not only a logical task. Hence, we first considered each model separately to derive empirically testable propositions and evaluate them empirically in a subsequent step. Third, our focus is directed by theories’ applicability to the European setting, as well as, movement between countries with similar GDP per capita. Forth, the theories discussed are all

3 rational choice theories. Rational choice theories have been subject to heavy criticism in some disciplines, but they provide us with a useful framework for an empirical assessment and, moreover, linking this to the existing literature.

Neo-classical economics

Neo-classical economics posits that migrants base their decision to move on a cost-benefit calculation, which leads them to expect a positive net return, usually monetary, from movement. Wage differentials are expected to be important factors in the decision to move. Indeed, according to the EIMSS work-related reasons are common determinants of migration (Recchi 2009, pp. 217). Moreover, according to neo-classical economics characteristics that are more remunerated in the host than the home country increase the likelihood of international movement (Massey et al. 1993, pp. 435). In short, the benefit of moving increases if your skills are valued abroad. Transferability of human capital is still relatively low in the EU. Many migrants hence decide to move between countries that are culturally close, which facilitates the transfer of human capital. For example, migration between Germany and Austria is high because of language and institutional similarities (Recchi 2005, pp. 15). Other migrants move in order to accumulate human capital. A common example of this is student migration.

Another group of migrants that has received attention in the EU migration literature consists of retirement and resort migrants. These migrants are driven by preferences in climate and life style (see Klinthäll 1999 and King 2002 for a more detailed discussion), as well as, by lower prices in the host country. British pensioners who move to Spain exhibit capital that is worth more in Spain than the UK. Indeed, neo-classical economics expects that the flow of workers from labor-abundant to labor-scarce countries is mirrored by a flow of investment capital from capital-rich to capital-poor countries (Massey et al. 1993, pp. 433).

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New Economics of Migration

According to New Economics of Migration, migration decisions are taken by larger units of related people – typically families or households. The decision to migrate is hypothesised to be taken collectively with a common interest in mind, rather than by isolated individual actors as theorists of neo-classical economics assume. These groups of people, moreover, do not only want to maximise expected income, but also to minimise risks and to loosen constraints associated with a variety of market failures (Stark and Bloom 1985). In the EU literature, family and love related reasons are common explanations for migration. In this way, it is clear that migrants do not make their decision as isolated individuals. A second tenant is that people move to improve their situation or income in relative rather than absolute terms. They compare themselves to actors in their reference group, as other households in the home country.

Social capital theory

Many scholars have questioned the explanatory power of the two above theories when analysing factors governing migration decisions (e.g., Massey and Espinosa 1997; Saarela and Scott 2015). They find stronger empirical support for theory, which posits a direct connection between networks and the costs and benefits of migration.1 According to this theory, migrant networks increase the likelihood of international movement because they lower the costs and risks of movement and increase the expected net returns to migration. Network connections, furthermore, constitute a form of social capital that people can draw upon to gain access to foreign employment. More specifically, they are sets of interpersonal ties that connect migrants, former migrants and non-migrants in origin and destination areas through ties of kinship, friendship and share community origin. This theory helps us explain the role of the social context more broadly in the decision to migrate. Each act of migration, additionally, alters the social context within which subsequent migration decisions are made, typically in ways that make additional movement move likely. Recchi (2005, pp. 17) similarly

1 Variants but closely related theories and concepts are network theory, cumulative causation and the self- perpetuation of migration. 5 finds that intra-EU migrations do not change much with year to year variations in wage and differentials between countries. These movements are, therefore, rather due to inertia and other ties to the destination than based on economic calculations.

However, the feedback from social capital is not necessarily positive. It may be either positive or negative depending on the profitability of moving to the host country. Network connections act to promote or inhibit migration; progressively overshadowed by the falling costs and risks of movement stemming from the growth of migrant networks over time.

3. Why do migrants move back and forth?

After having moved for the first time, migrants may stay permanently in the host country or decide to return, and then possibly to move back and forth multiple times. The conditions that initiate international movement are quite different from those that perpetuate it across time and space and therefore require independent investigation. While the initial move to the host country is governed by uncertainty, return and circular migration decisions are operating under a more complete set of information, thereby reducing search, relocation and psychic costs (Constant and Zimmermann 2011). Furthermore, the decision framework becomes increasingly complex. The choice to move again may be an integral part of the initial migration decision or an ad hoc decision based on a change in circumstances. The extent to which these chains are part of a pre-planned strategy and the extent to which they evolve with time and experience vary between migrants, but both are important.

Neo-classical economics

According to neo-classical economics, migrants may choose to maximise life-time earnings by staying permanently in the host country or by moving back and forth between home and host country (Hill 1987; Dierx 1988). Based on empirical evidence from Australia, Hugo (2008) finds considerable business and other economic activity associated with temporary

6 migration. Similarly, previous studies have found that seasonal workers tend to move back home while they are not working. The benefit of seeing one’s family and lower spending at home may compensate the cost of migrating back (Constant, Nottmeyer and Zimmermann 2012). It is, thus, hypothesised that if the cost of migration is low enough, migrants move with the business cycle. Empirical research on migration between Mexico and the US and on guest workers’ return migration underlines that strict visa regulations induce migrants to stay for a longer time in the host country. In this way, legal restrictions increasing the cost of migration also increase the immigrant stock in the host country (i.e., Donato, Durand and Massey 1992; Massey and Espinosa 1997; Massey and Zenteno 1999; Constant and Massey 2002; Constant, Nottmeyer and Zimmermann 2012; Görlach 2015).

Human capital accumulation also plays an important part in circular migration. In a study on migration from Poland to Germany, Kalter (2011) finds evidence that migrants accumulate capital that is specific for the place of destination with each additional trip and in this way increase their productivity in the foreign labour market by gaining knowledge, skills and information. However, human capital accumulation becomes especially interesting in terms of selection effects. Migrants with the most host country-specific human capital are predicted to stay permanently, while the best of those who returned undertake move 3 (Borjas and Bratsberg 1996).2

Based on neo-classical economics, we, therefore, predict that (1) circular migration is more common in a setting of free mobility than between countries separated by strict border controls. (2) In a setting of free mobility, migrants’ decision to move is affected by macro- economic shocks, as well as, (3) fluctuations in individual labour market productivity. In particular, seasonal workers may find it profitable to make back home in order to reduce spending while not working abroad. (4) Furthermore, circular migrants are likely to be selected by their human capital characteristics. Generally speaking, return migrants are negatively selected, meaning that the best remain permanently in the host country. Migrants who circulate are, then, expected to be positively selected among returnees. In other words, circular migrants are the best of the worst. (5) Migration between culturally close countries may not be as strongly selective, as the decision to move is taken more easily.

2 We consider migration-specific human capital and host country human capital as synonyms. 7

New economics of labour

According to new economics of labour, migrants return because they have achieved their earnings target. Migrants would then only move abroad again if the conditions at home changed while abroad or the migration experience altered the migrant’s tastes and motivations (Piore 1979). However, circular migration may similarly be a contract of mutual cooperation and insurance between sending families and the migrants themselves (Deshingkar 2008, pp. 172). The movement may either entail entire families or only one family member. The psychic costs endured if the family stays at home may play an important role for the decision to move back and forth (Constant and Zimmermann 2011). Indeed, long periods away from family and home environment may create a cost for the migration that increases with separation but returns to its initial value once the individual has spent some time at home (Dustmann and Görlach 2015).

New economics of labour leads us to expect that circular migrants (1) move for a third time because of a failed or premature return. (2) Circular migration may also be influenced by macro-level changes in the situation in the home country, which induce the migrant to move again. (3) According to new economics of labour, the family context, moreover, plays a central role in the decision to circulate. If the family stays at home and only one family moves, the mover is more likely to circulate. On the other hand, migrants who move with their family are somewhat more likely to relocate permanently.

Social capital theory

Social capital theory posits that circular migration is self-perpetuating. Namely, skills, ties and knowledge acquired as a direct result of having lived in the host country decrease the cost of a further trip and in this way every migration makes someone more likely to move again. Circular migration may indicate strong preferences for frequent location changes in maximizing utility or simply the feeling that a true home does not exist anymore (Constant and Zimmermann 2011, pp. 499; King 2002, pp. 93). Perhaps we can call this a state of migrancy (Chambers 1994). The construction of transnational communities can be seen as

8 another expression of this condition of being neither (or both) here and there, with the migrant moving back and forth across and within this transnational social and cultural space.

In addition to migrants’ personal traits and social ties in the host country, the process of migration itself creates new and more specific forms of human and social capital accessible only to those who have undertaken a trip. Massey and Espinosa (1997) call this form of human and social capital, migration-specific capital.3 This includes having social connections who are similarly migrants, in contrast to the general notion of being influenced by a family at home.

According to social capital theory, we hypothesise that (1) many migrants only move a few times. However, those who move often, migrate quite a lot. (2) Similarly, migrants who relocate often migrate in short time intervals. (3) Even though the family plays an important role, friends and other social connections may also be important factors in the decision to circulate, (4) especially if they themselves are very mobile. In this way, it is likely that migrants who circulate have relatively low levels of attachment to both the home and the host country, because it is difficult to keep connections when moving frequently. (5) Students move to accumulate human capital and diversify their social and professional network. They are predicted to be more mobile, because of the human and social capital that they have acquired abroad.

4. Data and Methods

In the empirical part of the paper, we analyse a unique data set that was constructed by integrating records on Finnish immigrants in Sweden from population registers in both Sweden and Finland (the permission number from Statistics Sweden is 8547689/181453 and

3 Massey and Espinosa (1997) differentiate between migration-specific human and migration-specific social capital, considering that we do not focus on there we will treat them as synonyms here. Furthermore, human capital is a concept predominantly talked about in neo-classical economics, however, the self-perpetuation of migration-specific human capital makes it more compatible with social capital theory, so these appear in both parts. 9 the permission number from Statistics Finland is TK-52-215-11). The data from Sweden cover the period 1985-2005 and contain rich information on socio-economic, demographic and labour market characteristics of each individual registered in the country. The data from Finland is similar in terms of variables and extends from 1987 to 2007. These two data sets were linked by the identification of immigrants coming from Finland in the Swedish registers. This data set thus provides us with unique information from both before and after the migration, as well as, on repeated moves between the two countries occurring during the time period of observation.

We measure migratory trips by registration and deregistration from the population registers in the respective country. The registers allow us to measure migration on a monthly basis and to determine the source country and the country of destination. These variables, supplemented by a proxy for whether the person lived in Sweden or Finland during the year in question provide us with a highly reliable measure of migration. We define the first migration as the first move from Finland to Sweden. Return, we measure by emigration from Sweden and reappearing in the Finnish registers. Circular migration is, then, the repeated migration from Finland to Sweden and the forth move is the second return to Finland. We use an event history framework and focus on these first four moves. In order to avoid confusion, we will denote the separate moves by their numerical order in the rest of the paper. That is, move 1 is the first move from Finland to Sweden, move 2 is the first return to Finland, move 3 is the second move to Sweden and move 4 is the second return to Finland.

In order to reduce heterogeneity in the sample, we restrict our analysis to Finnish migrants who move back and forth between the two countries. We restrict our observation period to the years for which we have information from both countries, namely1987-2005. We, furthermore, restrict our analysis to all migrations that occur after 1988, so as to have information from Finland prior to the first move. The longest total follow-up period since migration is then 18 years. The Swedish data set, furthermore, provides us with information on any previous migration, which allows us to establish migrants’ first trip and to focus on migrants who we observe moving for the first time. We also drop migrants who undertake their first trip when they are below the age of 18, as we these migrations are primarily the

10 result of parents’ decisions, and persons, who are older than 45 during the year of their first migration.

The final sample consists of 25,000 persons moving at least once from Finland to Sweden in the period 1988-2005, 13,462 persons moving at least twice, 2,585 persons moving at least three times and 1,261 persons moving at least four times. All persons migrate for the first time in 1988 or later.

5. The Swedish-Finnish migration context

Migration from Finland to Sweden has a strong history. During the 1950s and 60s, many Finns moved to Sweden for work related reasons. The labour shortage in Sweden attracted lots of Finnish migrants, as Finland, as well as, many other countries in Europe had not yet recovered from World War II. Since the 1970s, Finland has experienced an economic boom and immigration from third countries has become increasingly important in both countries. Finnish immigrants still make up the largest immigrant stock in Sweden but are no longer the largest group of immigrants.

Sweden and Finland have been part of the Common Nordic Labour market since 1954. This has facilitated free movement between the countries and allows us to analyse migration in a setting of free mobility. This is of particular interest in the EU context, but, also when wanting to find out more about migration decisions independent of legal restrictions.

The institutional setting in the two countries is very similar. This means that skills and human capital are easily transferred. Although Finnish and Swedish are very different languages, Swedish is an official language in Finland and there is a sizable minority of Swedish-speakers lives in Finland. Research has shown that Swedish and Finnish-speakers have rather distinct migration and integration patterns in Sweden, because of knowledge of language and more

11 general human capital that is valued in Sweden (for a more detailed discussion, see Rooth and Saarela 2007; Saarela and Rooth 2012; Saarela 2015; Saarela and Scott 2015).

6. Empirical findings

In this section, we examine some descriptive statistics of circular migration and link these to the theoretical predictions outlined above. First, we investigate how many migrants undertake multiple trips and how quickly the move again after the previous trip. Then, we explore the age distribution of the migrants to find out more about the life stage in which the decision was made. Subsequently, we delineate the sample by gender, income, month of migration and primary language spoken.

Number of moves

Social capital theory predicts that migration becomes self-perpetuating as migrants move more times they become increasingly likely to move again. Indeed, according figure 1, more than half of the migrants return to Finland, while only 20% of those who return move for a third time after this. Specifically, out of 20,500 migrants who we observe moving for the first time, more than 65% move for a second time. But when looking at move 3, we observe a substantial decrease in the numbers. Only 20% of migrants who return move for a third time. After this, the number decreases at a smaller rate. About 50% of migrants who move three times move for a forth time.

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Figure 1. Number of moves 25,000 20,500 20,000

15,000 13,462

10,000

5,000 2,585 1,261 289 148 46 22 0 Mig 1 Mig2 Mig 3 Mig4 Mig5 Mig6 Mig 7 Mig 8 Figure based on authors' calculaons.

Time abroad

Both new economics of migration and social capital theory predict that migrants move in short time intervals if they move a lot. For social capital theory, this is linked to the idea of the self-perpetuation of migration, while new economics of migration expects this to be the case because the migrant moves due to unexpected circumstances encountered in the home country. Plot 2a shows that most migrants return to Finland within the first five years. Figures 2b and 3b show that move 3 occurs even more quickly after move 2. The hazard of making move 3 is already high two months after the move 2. Figures 2c and 3c show a similar picture for move 4. The numbers become smaller and migrants seem to spend a bit more time in the country before moving again. Still, about 50% of migrants have moved again after 2 years in Sweden. On a general note, this indicates the difficulty of capturing these movements with less precise information on migration. Considerable numbers of movement would remain uncounted if we measured migration on a yearly basis. Hence, this leads us to expect that intra-EU migration is higher than that measured in the current statistics.

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Figure 2a. Years between Move 1 and 2 700 600 500 400 300 200 100 0 0 1 year 2 3 4 5 6 7 8 9 10 11 12 13 14 15 years years years years years years years years years years years years years years Figure based on authors' calculations. Figure 2b. Years between Move 2 and 3 120 100 80 60 40 20 0 0 1 year 2 3 4 5 6 7 8 9 10 11 12 14 years years years years years years years years years years years years Figure based on authors' calculations. Figure 2c. Years between Move 3 and 4 70 60 50 40 30 20 10 0 0 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years Figure based on authors' calculations.

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Figure 3a. Hazard to make Move 2 Figure 3b. Hazard to make Move 3 .04 .01 .008 .03 .006 .02 Hazard Hazard .004 .01 .002 0 0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 02 m. 1 2 3 4 5 6 7 8 9 10 Years between Move 1 and 2 Years between Move 2 and 3

Figure 3c. Hazard to make Move 4 .03 .02 Hazard .01 0

0 1 2 3 4 5 6 7 8 9 10 Years between Move 3 and 4

Age distribution of Migrants

Different migration options are included in the assessment of life chances. Remigration decisions are hence embedded in the other choices taken at that stage in a migrant’s life. For instance, it may be the result of career advancement or a way for students to see their friends again. Social capital theory, specifically, predicts that student migrants are very mobile due to the human and social capital acquired in the host country. On the other hand, new economics of migration puts more emphasis on the family in the decision to migrate. Figure 4 shows that our sample of migrants is extremely young. Most migrants move for the first time between the ages of 18 and 25. Migrants naturally age between each move, but the majority of migrants remains relatively young. This is expected, as often only a short time interval separates the individual moves. Based on this information, we might assume that student migration is a common form of migration, while for later moves the family situation may also become increasingly important. A more detailed exploration of student migration and the family situation are required to disentangle the different theoretical predictions.

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Figure 4a. Age at Move 1 (prior to restrictions) Figure 4b. Age at Move 1 2000 2000

1500 1500

1000 1000

500 500

0 0 1 11 21 31 41 51 61 71 81 91 18 23 28 33 38 43

Figure 4c. Age at Move 2 Figure 4d. Age at Move 3 1000 250

800 200

600 150

400 100

200 50 0 0 18 23 28 33 38 43 48 53 58 19 24 29 34 39 44 49 54 Authors' calculations. Figure 4e. Age at Move 4 100 80 60 40 20 0 20 25 30 35 40 45 50 55 Authors' calculations, sample

Selection by Gender

The assessment of life options at different stages in a migrant’s life are likely to differ by gender. Figure 5 provides the smoothed hazard curves of the risk of undertaking move 2, move 3 and move 4, respectively. While men are more likely to make move 2, women have a somewhat higher risk of moving for a third time. The hazard to make move 4 is then again slightly higher for men than women.

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Figure 5. Smoothed hazard to migrate by gender

a. Move 2 b. Move 3 .02 .005 .004 .015 .003 .01 .002 .005 .001 0 0

0 2 4 6 8 10 0 1 2 3 4 5 6 7 8 9 10 Years between Move 1 and 2 Years between Move 2 and 3

Male Female Male Female

c. Move 4 .02 .015 .01 .005 0

0 1 2 3 4 5 6 7 8 9 10 Years between Move 3 and 4

Male Female

Selection by Income

Neo classical economics and new economics of migration have different predictions on the effect of income on circular migration. While theorists of neo classical economics posit that the best of the worst circulate, new economics of migration predicts that migrants with the highest income abroad return the fastest as they achieve their earnings target the quickest. However, those who had the lowest earnings target are the most likely to require undertaking another trip to abroad as their funds at home do not suffice.

Figure 6 shows rather different trends in how quickly migrants make move 2, move 3 and move 4, respectively, after the previous move. Income is an average measure of earnings in Sweden during the first trip abroad. It is inflation adjusted and the first quintile consists only of migrants with zero income. Migrants with zero income are the most likely to undertake all three of the moves (some of these are likely to be student migrants). Migrants in income

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quintile 2 are also very likely to undertake move 2, while move 3 seems more common among those who were in income quintile 4 during their first stay in Sweden. Figure 4c becomes somewhat less clear but both migrants seem to have had zero income or been in quintiles 3 and 4 while the first time in Sweden.

Figure 6. Hazard to move by income. a. Move 2 300

250

200

150

100

50

0 0 1 year 2 years 3 years Zero income Inc quin 2 Inc quin 3 Inc quin 4 Inc quin 5

b. Move 3 50 45 40 35 30 25 20 15 10 5 0 0 1 year 2 years 3 years Zero income Inc quin 2 Inc quin 3 Inc quin 4 Inc quin 5

c. Move 4 30

25

20

15

10

5

0 0 1 year 2 years 3 years Zero income Inc quin 2 Inc quin 3 Inc quin 4 Inc quin 5

Seasonality of movement

However, an investigation of total income seems less indicative for circular migration than to additionally determine the seasonality of movements. Neo-classical economics posits that

18 seasonal migration is very common among the very mobile. According to figure 7 below, we see clear seasonal trends. Specifically, move 1 is much more common to be undertaken in August, September and October than during other months of the year. Return to Finland is more evenly distributed throughout the year with slight peaks in January and then other peaks in June and August. Move 3 peaks again very strongly in August and September and move 4 is likely to occur in different months of the year.

This information indicates are considerable movement with the academic cycle. Movement is common in August and September at the start of the academic year. Students then return in January or June at the end of the first or the second semester. Of course, this requires further empirical investigation. An investigation of movement by occupation would, furthermore, be of interest. Certain jobs may require more work force during the summer and fall months.

Figure 7. Month of migration a. Month of Move 1 b. Month of Move 2 3,500 1,800 3,000 1,600 1,400 2,500 1,200 2,000 1,000 1,500 800 600 1,000 400 500 200 0 0

c. Month of Move 3 d. Month of Move 4 500 160 400 140 120 300 100 80 200 60 100 40 20 0 0

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Strong selection by primary language

As aforementioned, there is a large minority of Swedish-speakers in Finland. They exhibit very distinct migration patterns and integrate much more easily into the Swedish labour market. Thus, primary language provides us with a rough measure of human and social capital. However, considering that Swedish is an official language in Finland, knowledge of the Swedish language is a human capital characteristic that is also remunerated in Finland as many governmental jobs require knowledge of both languages. The Finnish data set allows us to distinguish the main language spoken by the migrants and shows strong selection effects. In Figure 8a, we see that Finnish-speakers have a much higher risk of returning, while figure 8b shows roughly the opposite. Swedish- speakers are much more likely to make move 3 than their Finnish-speaking counterparts. For move 4 in figure 8c we observe again a higher hazard among Finnish-speakers. The selection effects resemble those that we observed when delineating the sample by gender, though the differences are noticeably larger.

Figure 8. Smoothed hazard to migrate by language

a. Move 2 b. Move 3 .008 .02 .006 .015 .01 .004 .002 .005 0 0

0 2 4 6 8 10 0 2 4 6 8 10 Years between Move 1 and 2 Years between Move 2 and 3

Finnish-speakers Swedish-speakers Finnish-speaker Swedish-speaker Other language Other language

c. Move 4 .02 .015 .01 .005 0

0 2 4 6 8 10 Years between Move 3 and 4

Finnish-speakers Swedish-speakers

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Discussion and Conclusions

To be completed with a summary and empirically testable hypotheses for future research

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