Does Migration Make You Happy? The Influence of Migration on Subjective Well-Being. Silvia Maja Melzer

Transcription

1 Does Migration Make You Happy? The Influence of Migration on Subjective Well-Being Silvia Maja Melzer When choosing a new location migrants usually improve their economic situation, but what about their subjective well being (SWB)? Based on longitudinal data from the German Socio- Economic Panel Study, I investigate the impact of migration from eastern to western Germany on SWB. Hypotheses are derived from human capital theory but also from psychological approaches. Fixed-effects models enable me to distinguish between the impact of personality and migration on SWB. Migration has a positive lasting effect on SWB. The favorable conditions of the labor market in western Germany account for the increase in reported SWB of males but not of females. 0

2 1. Introduction Migrations are usually motivated financially. When choosing a new location migrants usually improve their economic situation. But can such financial gains compensate the loss of friendship and family networks? What s about the SWB of migrants; are they happier after the move? Few studies have addressed this question (De Jong, Chamratrithirong, and Tran 2002). Reasons for this are the high requirements concerning the data that are needed to study the impact of migration on SWB. Ideally, longitudinal data containing information on individuals SWB before and after a move are necessary. However, such data rarely exist. Immigrants usually become part of a survey after the move, while they drop out of the data set in their country of origin. Most studies therefore rely on cross-sectional data collected after the move and containing no information on SWB in the past. Hence, migrants are compared with natives (Amit 2010; older immigrants Amit and Litwin 2009; Bălţătescu 2007; Bertram 2010; second generation Neto 1995). 1 Other studies ask the respondents directly about their SWB before and after a move (De Jong, Chamratrithirong, and Tran 2002; Lundholm and Malmberg 2006). Problems arise from both of these methodical settings. First, personality matters for SWB (Diener, Eunkook, Lucas, and Smith 1999). 2 Analyzing cross-sectional data, it is not possible to distinguish between the impact of personality on SWB and the situation that individuals report. Second, when asked to compare two situations directly most people report that they are better off now than they used to be (Hagerty 2003) and migrants do not differ from the general population in this regard (Scott and Scott 1989). Individuals would report higher SWB after the move in order to avoid acknowledging any dissonance (Festinger 1957: theory of cognitive dissonance). These problems emphasize the importance of using longitudinal data to conduct research on SWB as they raise doubts on the reliability of crosssectional samples for this subject. Based on longitudinal data from the German Socio- Economic Panel Study (SOEP), this study investigates the impact of migration on SWB with regard to relocation from eastern to western Germany. The reunification in Germany, which several economists call a natural experiment, provides the unique possibility to study the impact of migration on SWB based on longitudinal data containing information for persons who migrated from eastern to western Germany before and after a move. 1 Bălţătescu (2007) and also Safi (2010) use the European Social Survey pooled over waves, they also have only information on life satisfaction after migration. 2 For example neurotic individuals might report lower satisfaction than those who are not neurotic (Diener et al. 1999). 1

3 After the collapse of the Eastern Bloc and the fall of the Berlin Wall on November 9, 1989 the former German Democratic Republic (GDR) found itself in a unique position (Mayer 2006). After the first free election in March 1990, its reconstruction was controlled by the government of the Federal Republic of Germany (FRG). Despite sharing a common past and language, East and West Germany developed in different directions after the Second World War and shared as many similarities as differences in West Germany developed a conservative welfare regime (Esping-Andersen 1990), while East Germany adopted a socialist system. The socialist system in East Germany did not favor change, while West German policy supported social and economic development (Nazio and Blossfeld 2003). The socialist regime and the requirements of a shortage economy encouraged women to participate in the labor force, while the conservative political forces and the institutions of the welfare state in West Germany hindered gender equality in the labor market. 3 Most important, the socialist system was never able to compete with its capitalist counterpart, and the eastern per capita income lagged far behind western standards. Even today, 20 years after reunification, the eastern German labor market shows weaker performance compared to the western one, whereas eastern Germans face large incentives to migrate. 4 Substantial and permanent movement from eastern to western Germany was the consequence of the reunification and the fall of the wall. Compared to the population level in , the former GDR had lost 4.3 percent of its population by 1992, 7.9 percent by the year 1995, 10.7 percent by 2000 and 14.1 percent by For example because of the tax system in West Germany in which husband and wife each pay income tax on half the total of their combined incomes (Ehegattensplitting), which reduces the labor market participation of women. 4 In 1991 the gross domestic product (GDP) of the new federal states excluding Berlin accounted for only seven per cent of the GDP of united Germany (eleven percent if East and West Berlin are included) while comprising approximately one third of the territory and about one fourth of the population. In the past fifteen years GDP rose by only five percentage points to a level of twelve per cent (fifteen percent if East and West Berlin are included). Calculations on the basis of data from the Federal Statistical Office and the Statistical Offices of the Länder. Also eastern wage levels lagged far behind western standards. Shortly after reunification in 1991 eastern German gross pay reached 51 per cent of the western German wage level (calculations for men and women together). In 1992 the average daily income of eastern men amounted to 57 percent of the daily income in the west (68 per cent for women). Despite wages rising quickly between 1992 and 1995 they still failed to reach the western level. In 1996 the rapid growth stopped and the differences between the living standards in the East and the West have persisted. In 2006, gross wages for men were still 32.5 per cent lower in the East (19 per cent lower for women). Calculations on the basis of the IAB Employee History (IAB Beschäftigten-Historik - BeH) V7.01, Nuremberg For the population levels of the GDR, see Statistisches Jahrbuch der DDR (1989): 335. For more recent figures, see the Federal Statistical Office Wiesbaden (2006). The figures presented do not include East Berlin, as it is not possible to differentiate between East and West Berlin after By that year, eastern Germany including Berlin had lost 10.1 percent of its former population. 2

4 While previous research on the impact of migration o regional mobility on SWB provides a first insight into the topic and deepens our understanding of the process, it is limited in several aspects: First, there are few studies linking the impact of migration on SWB with a theoretical background. Mostly, the hypotheses tested are derived ad hoc from previous findings (e.g. Bertram 2010; Lundholm and Malmberg 2006). The few contributions to the literature which do provide a theoretical background concentrate on specific aspects. In some cases the focus is on the comparison of migrants and natives using varieties of assimilation frameworks (Safi 2010) or discussions of the integration process using concepts such as Bourdieu s (1986) social capital (Amit 2010; Amit and Litwin 2009). Other articles focus on the relocation process by investigating residential mobility or migration. As an example, Lu (2002), analyzing residential mobility, followed the housing career thesis and therefore understood residential mobility as a path to achieving the preferred housing type. Here the long-term trajectory is associated with the individual life-course. Individuals move house to adapt to their present needs, and simultaneously to their desired housing type (Davies and Pickles 1991; Gober 1992). Interregional relocations are made for economic or family-related reasons (Zax 1994) but follow a similar pattern. 6 Only, De Jong et al. (2002) explained the impact of internal migration on SWB using a human capital framework in accordance with Sjaastad (1962), where relocations are treated as investments in the productivity of human capital. Using a link between human capital theory and SWB (see Ziegler and Britton 1981), the authors expect individuals to migrate when they assume an improvement in some subjective sense and therefore expect them to be more satisfied after the move. According to De Jong et al. (2002) more highly educated and more permanent migrants should also reveal higher SWB compared to less well educated or temporary migrants. 7 De Jong et al. (2002) provides a link between migration and SWB and derivers hypothesis on some features of migration, other features like family migration remain unexplained. Second, there are some features of migration that have not been addressed at all. Questions regarding the impact of regional characteristics or the length of the stay in the new host region on SWB have not yet been answered. And this although studies conducted at the macro level show the importance of regional income or unemployment levels for individual satisfaction 6 As Roseman (1971) pointed out, the greater the distance between origin and destination, the higher the information costs and also the time pressure are likely to be. 7 For hypotheses on the SWB of temporary migrants, De Jong et al. (2002) investigate the risk minimization approach within households in developing countries which was described by Stark (1991). 3

5 (for the USA: Alesina, Di Tella, and MacCulloch 2004; for Europe: Di Tella, MacCulloch, and Oswald 2001; and for Germany: Easterlin and Plagnol 2008). Third, previous studies were usually based on cross-sectional data. Using cross-sectional data, the causality between the described factors and SWB is not always obvious (c.f. Frey and Stutzer 2005). While it is clear that, for example, gender has an impact on satisfaction, other factors such as marriage or migration might show a reverse causality. So far our knowledge about the impact of migration on SWB is based on one longitudinal study for eastern and western Germany, which provides a general overview of the impact of major life events rather than migration. Moreover, this study is based on a rather short time period, 8 and one where levels of life satisfaction in eastern and western Germany were still converging. This study attempts to close these gaps. The hypotheses describing the relationship between migration and subjective well-being (SWB) are derived from human capital theory and the New Household Economy, but approaches from psychology are used as well. More precisely, the study aims to answer the following questions: How does migration influence SWB? Are there differences in SWB for different groups migrating? What impact do regional labor market characteristics have on SWB? How does the change in SWB after migration (if there is any) develop over time? The empirical investigations are based on the German Socio-Economic Panel Study (SOEP) and the waves between 1992 and 2006; including all individuals migrating from eastern to western Germany between 1990 and The SOEP data provide repeated interviews which are nested within an individual. Fixed effects models are used to disentangle the impact of personality and migration, relying on the assumption that personality tends to be stable over time. Moreover, using fixed effects models enables me to account for unobserved heterogeneity between individuals and to ensure that it is not the unobserved happy person who migrates, but that migration does indeed have an impact on SWB. In the analyses I control for individual and regional labor market characteristics in eastern and western Germany. Variables indicating how long individuals have already been in the West account for the influence of time on the change in SWB. The analyses are conducted separately for 8 For western Germany the data extend from 1984 to 1999, for eastern Germany the data cover the period from 1991 to From 1991 to 1999, the levels of life satisfaction in eastern and western Germany were converging (Easterlin and Plagnol 2008). 9 Individuals who migrated to western Germany between 1990 and 1991 are included in the estimation from 1992 onwards, their second year in western Germany. Those who migrated between 2006 and 2007 are just included in the analysis in their last year (2006) in eastern Germany. 4

6 men and women, as previous studies found gender-specific differences in the impact of migration (e.g. Frijters, Haisken-DeNew, and Shields 2004). 2. Previous Research Research on the subjective well-being (SWB) of immigrants is motivated differently in sociological and economic literature. 10 Research in sociology goes as far back as 1928, to Park s (1928) marginal man, or to the research on the uprooted (Handlin 1951) focusing on the integration process of migrants. The first economic research which addresses migration in the context of SWB, in contrast, is motivated by differences in SWB between countries. 11 In fact, the average happiness of individuals differs between countries; individuals from Western Europe and the USA score higher on well-being scales than those from Eastern Europe (Blanchflower and Oswald 2008). 12 Some researchers explain the higher well-being in wealthier countries with the higher livability of those states (e.g. Veenhoven 1991). However, as Bertram (2010) pointed out, it would be an ecological fallacy to conclude that migration from economically disadvantaged to wealthier countries would make one happy. Analyzing five Nordic countries (Denmark, Finland, Iceland, Norway, Sweden) Lundholm and Malmberg (2006) reveal a positive relationship between residential mobility and SWB. Among the few people who were less satisfied after the move (eight percent), single people were overrepresented. Most individuals reported an increase in satisfaction with their environment, while they were less satisfied with their social life. Those who reported higher satisfaction with their social life after the move were most satisfied on the whole, as social life has the largest effect on overall satisfaction. As the authors pointed out, the positive relationship between residential mobility and SWB is probably due to the extremely low percentage of persons relocating because of economic constraints (16 percent) (Lundholm and Malmberg 2006). Usually, residential moves are motivated by social and environmental reasons. Therefore, migration in Nordic countries is not a trade-off situation, where social and environmental cuts are accepted in return for higher incomes, but as already found in Lu s (2002) analysis for the USA, relocations serve as an opportunity to achieve the preferred type 10 For a general overview of the factors influencing SWB see Dolan, Peasgood and White (2008). For an overview validating the theoretical importance and the measurement of SWB see Blanchflower and Oswald (2004a), Di Talla and MacCulloch (2006), Frey and Stutzer (2002) as well as Kahneman and Krueger (2006). 11 In general, economic research on SWB goes back to Richard Easterlin (1974), who pointed out that despite increasing personal income over time individuals do not report increased SWB. 12 Moreover, at the macro level life satisfaction is an excellent predictor of international migration (Blanchflower and Oswald 2008). 5

7 of housing. Other studies, such as a study of repeat, temporary and permanent migration in Thailand, reveal a mixed impact of migration on life satisfaction (De Jong, Chamratrithirong, and Tran 2002). Mobility was found to increase, decrease and leave life satisfaction stable, with approximately one third of the migrants accounting for each group. Job satisfaction, on the other hand, increased after migration. What is also interesting is a negative relationship between life satisfaction and education, found in this study to be explained by unrealistic expectations of the highly educated group regarding living conditions after the move. Finally, earlier research in this field concentrating on depression, which could be understood as the opposite of SWB, reveals that residential mobility increases the likelihood of becoming depressed and that this effect is especially strong for women (Magdol 2002). 13 The most recent study applying a different methodology focuses on the life satisfaction of immigrants and natives in the USA. Bertram (2010) states that when the move is made for the right reasons, with the right amount of information and without unrealistic expectations regarding the destination country, migrants should be happier after the move than in the country of origin. The results obtained by Bertram (2010) are in line with other studies comparing the SWB of migrants and natives, which show lower life satisfaction among immigrants than natives (e.g. Amit 2010; Amit and Litwin 2009; Bălţătescu 2007; Neto 1995; Safi 2010). Comparing the countries of origin, immigrants from poorer countries show lower levels of life satisfaction, while immigrants from Europe or Canada do not differ significantly from natives. Moreover, the satisfaction of immigrants from poorer countries is defined to a higher degree by absolute income. Conversely, they constitute a group with modest earnings and they are therefore more frustrated than natives for not being unable to achieve higher incomes (Bertram 2010). So far only one study has included a variable measuring life satisfaction before and after migration on the basis of longitudinal data. Frijters et al. (2004) investigated determinants of life satisfaction in eastern and western Germany and measured the impact of migration on life satisfaction after a move. Using ordered logit fixed effects models the authors found a positive effect of migration from eastern to western Germany and a negative effect of relocation from West to East for males only (Frijters, Haisken-DeNew, and Shields 2004). 13 For a recent analysis of the depressive symptoms among young natives and immigrants in Norway see Fandrem et. al (2009). 6

8 3. Theoretical considerations Migration represents a risky future investment allowing for more productive use of human capital. Individuals maximize their utility by choosing the most beneficial location. In this respect, migrants must be willing to bear present costs in order to benefit in the future (Borjas 1987; Chiswick 1999; Chiswick 1978; Sjaastad 1962). 14 Individuals compare these costs and gains of migration; those profiting from a move and willing to take the risk will migrate. The costs of migration are monetary and non-monetary. While the monetary costs are mostly transportation costs, non-monetary costs are more substantial. They cover the loss of locationspecific human capital and the loss of family and friendship networks (DaVanzo 1983). But it is not only the costs that are monetary or non-monetary, the gains can be non-monetary as well. Individuals may therefore migrate because of a better climate or for family-related reasons and accept financial drawbacks in order to live in the new location. When people make their decisions with plenty of information and without unrealistic expectations, and taking into account both monetary and non-monetary costs and gains, only those profiting from the migration in some subjective sense will migrate (Ziegler and Britton 1981). So, as De Jong et al. (2002) pointed out: migrants should report higher subjective well-being (SWB) after the move compared to the situation before (hypothesis 1). Moreover, the increase in SWB after the move should be lasting (hypothesis 2). Migration is an investment in the long run, like any other investment in human capital. An alternative view on the development of SWB in the long run is provided by psychological literature. The view here is that following an initial increase in SWB in the period immediately after migration, mechanisms like adaption, aspiration and comparison reduce SWB in later periods. The view here is that following an initial increase in SWB in the period immediately after migration, mechanisms like adaption, aspiration and comparison reduce SWB in later periods. First, individuals adapt to repeated stimuli (Scitovsky 1992), such as for example the higher consumption level. After a move, they should adapt quickly to their improved living standard, which should result in a decline in. Second, obtaining higher incomes may only trigger even higher aspirations regarding earnings and economic status (Stutzer 2003; van Praag 1993). And third, individuals change their reference category after an increase in income, thereafter comparing themselves to even wealthier individuals (Clark, Diener, Georgellis, and Lucas 2008). Migrants who relocated from eastern to western Germany should also change their peer group 14 In this sense migration is equivalent to any other investment in human capital, such as schooling or on-the-job training. 7

9 from eastern to western Germans. After the move they should compare themselves to the wealthier western Germans. 15 Therefore, after an increase in SWB in the first period following the migration, the three discussed mechanisms; adaption, aspiration and comparison should decrease SWB to its previous level in the subsequent periods. Alternative hypothesis 2 states: Following an initial increase in SWB immediately after migration, migrants should report decreasing SWB in later periods (alternative hypothesis 2a). Following an initial increase in SWB immediately after migration, migrants should report decreasing SWB in later periods Depending on the income distribution in the locations of origin and destination, the gains and costs of migration differ among individuals depending on their education (Borjas 1987; Chiswick 1999; Chiswick 1978). Highly educated individuals should profit the most from relocations form eastern to western Germany, due to the higher marginal value of their education in the West (c.f. Melzer 2010). Moreover, highly educated individuals also have regionally broader networks (Borjas 1987; Brücker and Trübswetter 2007; Massey, Arango, Hugo, Kouaouci, Pellegrino, and Taylor 1998). This makes them regionally more independent and reduces their costs of migration. The low costs combined with high gains should not only motivate more highly educated individuals to migrate but also raise their profits and SWB after the move. In addition, highly educated individuals should be more able to gather the necessary information for the move and to weight up the gains and losses correctly. Hence they are more likely to make well founded decisions and to avoid disappointment. Therefore, highly educated individuals should report more positive change in SWB than migrants with lower educational levels (hypothesis 3). According to Mincer (1978) the migration of couples is subject to the same maximization strategy as that of individuals. The only difference is that the decision to migrate is now based on the entire household, and the monetary and non-monetary gains and losses of the household members are accumulated. The migration takes place when the household as a unit gains from it irrespective of the individual gains and losses of individual household members. One partner usually takes the initiative for the move, while the other follows (the so-called tied mover), as it is unlikely that both partners can improve their situations simultaneously in a new destination (Kalter 1998). Due to lower earnings and interrupted labor force 15 Oded Stark (1991) developed a concept which is similar to the described mechanism of comparison but was developed especially with regard to migration. The concept is called relative deprivation. He shows that families who migrate to maximize their absolute income and ignore the relative wage distribution in the locations of origin and destination can end up reducing their SWB. Despite higher incomes in the new location, their social position as defined by the position occupied in the income structure of the region may be much lower than before and reduce their SWB. 8

10 participation, women are usually in the position of the tied mover (e.g. Bielby and Bielby 1992; Shihadeh 1991) even if Mincer s (1978) theoretical setting is gender-neutral. Women migrating together with a partner could thus be expected to reveal fewer positive changes in SWB after the move. However, if we take Mincer s (1978) theoretical framework seriously, compensation payments are exchanged between members of the household gaining and losing from the relocation. Therefore, it is hypothesized that: after the migration women migrating together with their partners should not report fewer positive changes in their SWB (hypothesis 4). 4. Data and methods 4.1 Data The empirical analyses are based on the German Socio-Economic Panel Study (SOEP), which is a representative longitudinal survey of private households that started in 1984 for West Germany and West Berlin. In 1990, the sample was extended to include the former GDR. The population of the new federal states was oversampled to allow separate analysis of eastern Germany (Haisken-DeNew and Frick 2005). The sampling procedure is based on a random selection of households; within a household every household member over 16 is surveyed. An unbalanced sample of persons from the new federal states serves as the basis of the investigation, all individuals who entered the sample after 1992 or were absent in one or more waves are included in the estimations. All persons who left the new federal states for western Germany between 1990 and 2007 are identified as migrants. Individuals who left eastern Germany between 1990 and 1991 are investigated, like any other person in the sample, from 1992 and therefore from their second year in western Germany onwards. 16 The dataset contains 7,640 persons aged between 18 and 60, hereof 653 which migrated. The ratio of men to women in the dataset is balanced: 51.5 percent of the individuals surveyed are female. The sample used contains 45,992 person years for stayers and an additional 4786 person years for migrants (2510 of these person years are from the period following the move). 17 After the move, individuals in western Germany continue to be interviewed on a yearly basis. The questionnaires for eastern and western Germany are identical. 40 percent of male and Information on the regional characteristics is only available from 1992 onwards, which limits the observation period. 17 If all variables are included. 9

11 percent of female migrants stay in western Germany for more than 5 years while 12 percent stay there for more than 10 years. Only the first relocation is taken into account in order to be able to compare the SWB before and after the move. Individuals who moved from eastern to western Germany drop out of the sample if they return to the East. 18 Regional mobility within western Germany does not lead to exclusion from the sample. The following question from the SOEP was used to operationalize subjective well-being (SWB): How satisfied are you with your life, all things considered. The interviewee could answer on a 0 to 10 integer scale where 0 represented the lowest level of life satisfaction. To capture the impact of migration on SWB, information on the part of Germany in which the individuals live is included in the analysis via the dummy variable migrated to western Germany. An additional variable was created in order to be able to determine the impact of time after the move on SWB. This variable indicates whether an individual has already spent no time, less than two, two to four, four to six, six to nine or more than ten years in western Germany. The analyses control for age 19, education, household income, employment as well as the marital status. I also include variables measuring regional unemployment rates, regional income levels and whether more than 100,000 habitants live in the municipality. Information on the construction and distribution of these variables can be found in the Appendix. Finally, to capture the differences in SWB between different groups of migrants, additional variables were constructed by interactions with the variable migrated to western Germany. The interaction term partner*migrated takes the value one (1) if a person migrated together with his or her partner (cohabitation) and is otherwise zero (0). The same applies to the variables married* migrated and tertiary education* migrated. 4.2 Methods 18 As this may be the group which is particularly dissatisfied with their decision to migrate I have conducted some sensitivity analyses, where I do not exclude the individuals returning to eastern Germany. The influence of migration on SWB remains stable. Additional variable tests were conducted to control for the possibility that persons who drop out of the data are not a random selection but are those who are less satisfied (c.f. Wooldrige 2001 S. 581). Two of the three constructed test find no selection on SWB of persons who drop out and support the view that persons drop out of the data at random. 19 Despite using fixed effects analysis I include age squared in the analysis, following an empirical approach described by Wooldrige (2002 S.??) and used to analyze SWB by Frijters et al. (2004). 10

12 Fixed effects models (or multilevel regressions) are used to estimate the relationship between subjective well-being (SWB) and migration. 20 Fixed effects models have several desirable statistical implications for the analysis of SWB. The estimations of fixed effects models are based on the within variation only. Using fixed effects specification takes into account timeinvariant observable and unobservable heterogeneity between individuals. Consequently, the impact on SWB of time-invariant characteristics like gender or race can no longer be estimated but it is not necessary to control for such characteristics either. This implication of the statistical model serves as a major advantage for the analysis of SWB, as data usually contain only limited information about personality traits, but also because it is virtually impossible to control for all features of personality that might influence SWB. Moreover, fixed effects models control for potential sample selection on time-invariant characteristics. This statistical implication is also essential for investigating the influence of migration on SWB, as migration is a highly selective procedure. Individuals who migrate from eastern to western Germany are younger, more highly educated and are also more likely to be female and single (e.g. Hunt 2006; Melzer 2010). Moreover, not all characteristics are indeed observable. If not all observable and unobservable differences between migrants and nonmigrants are controlled for, the differences in SWB resulting from the selection into migration could be wrongly ascribed to the impact of migration on SWB. Using fixed effects models the selection according to time-invariant characteristics is no longer a concern. The following model is fitted: SWB migration tertiary Z migration tertiary (1) it 1 it 2 it it it * it i it The dependent variable SWB (operationalized through life satisfaction) is observed for respondent i at the point in time t. 1 indicates the estimated parameter that informs us about the impact of migration on life satisfaction, i.e. our key interest. While 2 provides information on the impact of a university degree on life satisfaction, estimates the impact of the interaction effect between tertiary education and migration, indicating whether migration 20 Even if the trend in the research on SWB, especially in the economic literature, is heading towards ordered logit models, in this article I refrain from using ordered logit models. Blanchflower and Oswald (2004) showed that simple OLS regressions achieve similar results as ordered logit models already for three-point scales and the life satisfaction scale used here is measured on an eleven-point scale. These results are also supported by Ferreri-Carbonell and Frijters (2004), who use SOEP data to show that the results of ordered logit models and OLS regressions do not differ considerably. On the other hand, the authors emphasize the importance of fixed-effects regressions, as these do change the results substantially (Ferrer-i-Carbonell and Frijters 2004). 11

13 has an additional effect on the life satisfaction of people with tertiary education. Other observable time-dependent individual and regional characteristics enter the model via Z it. I allow the individuals to vary in their intercept but not in their slope. A separate intercept for each individual included in the data is therefore estimated in i ; the deviation of the individual values over time from the general mean is captured in the observation-specific error term it. 5. Results 5.1 Descriptive results Until today the average life satisfaction in eastern Germany is lower than that in the West. This is true despite the fact that a convergence could be observed between the two parts of Germany in the period following reunification. Life satisfaction increased between 1991 and 1999 in the East and declined simultaneously in the West. The increase in life satisfaction in the East is associated with an increase in both relative and absolute income, while the decline in life satisfaction in the West is mostly linked to the unemployment rates (Easterlin and Plagnol 2008). 21 The course of life satisfaction in Figure 1 starts with a sharp drop in life satisfaction in both parts of Germany between 1990 and People probably realized at that time that reunification brought not only wealth but also new life risks such as unemployment, which was not known before (at least in the eastern part of Germany). In 1999 eastern Germans regained the level of life satisfaction of 1990, while the same applied for western Germany in From then onwards no further convergence took place, instead the curves for eastern and western Germans followed the same pattern of up and downs for the next six years. Not until 2008, the last year under observation, did the life satisfaction of eastern Germans rise slightly higher than that of western Germans. Besides the life satisfaction of eastern and western Germans, a third dotted line is included in Figure 1, which displays the average life satisfaction of people who migrated from eastern to western Germany after their move. The first observation in this group is from 1991, as the first migrations took place between 1990 and We can see that eastern Germans who relocated to the western part of the country are more satisfied than the remaining population 21 dthe decline in life satisfaction in western Germany might also be connected to a decline in relative earnings, as the gap between eastern and western German earnings shrank after reunification and western German earnings lost in relative terms to those of eastern Germans. 12

14 in eastern Germany, but they are less satisfied than the western population. From 1993 onwards they show an average satisfaction level which is always above that of eastern but also always below that of western Germans. At this point in the analysis it is not clear whether the migrants are generally more satisfied than the remaining eastern population, whether characteristics which make them more likely to relocate also make them happier, or whether their higher satisfaction indeed results from the migration. More sophisticated methods, such as fixed effects regressions, are needed to answer this question. ***Figure 1 around here*** 5.2 Analytical results The results of the fixed effects regressions for males are presented in Table 1 and those for females in Table 2. I first discuss the impact of migration on subjective well-being (SWB) for males and then for females and compare the findings. I start the analyses with Models 1 and 2, where the estimations only include individual characteristics. In Model 2 (as in Model 6) the duration of the stay in western Germany is considered to allow for possible changes in SWB over time. In Models 3 and 4 additional interaction effects are included to take into account group-specific differences between migrants. Finally, in Models 5 and 6 regional characteristics are also controlled for. ***Table 1 around here*** Even when controlling for individual observed and unobserved characteristics, migration from eastern to western Germany increases life satisfaction by 0.3 scale points (see Model 1), with an average life satisfaction in eastern Germany of around 6.5 points. It is important to bear in mind that, due to the usage of fixed effects models, it is possible to ensure that it is not the unobserved happy person who migrates, but that migration does indeed influence SWB. Migration from eastern to western Germany increases SWB fundamentally; only becoming unemployed or working part-time has a greater impact on the life satisfaction of males. 13

15 Neither highly educated males nor those who move to western Germany with a partner differ from other groups of migrants. Married individuals who relocate, on the other hand, show higher levels of satisfaction (see Model 4). What is more important is that the change in life satisfaction is lasting. Male migrants report higher levels of life satisfaction even six years after migration. Note that after controlling for labor market characteristics in Model 5 and 6 the effect of migration is no longer significant for males. The increased SWB after migration can therefore be explained by the better labor market characteristics in western Germany. Males seem to benefit from the higher and more secure earnings in western Germany. The squared age shows a positive and significant influence on life satisfaction, which indicates that the U-shaped structure of life satisfaction over age can be found in eastern Germany as well. 22 Males who found a partner or got married in the past year are not happier than single people. Males who get divorced or are widowed reveal lower satisfaction with their lives than single men. Note that the effect of widowhood is statistically insignificant what can probably be explained by the small number of cases. Males who gained upper secondary school certificates or completed tertiary education in the past year report statistically lower life satisfaction. This is probably due to the new challenges and adaptations necessary following graduation. This result does not necessarily contradict the positive effect of education found in cross-sectional analyses (e.g. Blanchflower and Oswald 2004b), as the fixed effects models take into account only the change that occurs between two years, and are consequently based on within variance. Highly educated males might still be happier than those with lower educational qualifications, although other methods, for example a random effects analysis, would be necessary to carry out a comparison between groups. 23 *** Table2 around here*** 22 The effect is in line with the findings of Frijters et a. (2004). 23 Using random effects models, however, would not permit controlling for the unobserved time-stable characteristics like personality. 14

16 In general the patterns of SWB found in this study are similar for men and women. Migration increases SWB for both sexes. The effect is lasting and only persons migrating with a partner to whom they are married show higher SWB. However, the effects seem to be slightly larger and also more stable for females. Few differences between men and women can be found. The most important one is that for females the impact of migration on SWB remains stable even if regional features are controlled for (see Table 2, Models 5 and 6). Also, the impact of partnerships differs between the sexes. While males who found a partner in the past year were not more satisfied, the life satisfaction of females increased by approximately 0.16 points after finding a partner (see Models 3 to 4). Finally, there are also differences between men and women with regard to education and employment status. Gaining a secondary upper school certificate and competing tertiary degree have a significant negative impact on life satisfaction for males. For females no school certificate shows a significant effect on life satisfaction. The absence of significant values or weak effects on life satisfaction of the educational variables might be explained by the fact that most of the individuals had completed their education before entering the survey, which reduces the effect of education found in fixed effects models (c.f. Meier and Stutzer 2006). Both men and women who became unemployed, started an apprenticeship or began to work part-time are less satisfied. At first this pattern shows no gender-specific differences. Comparing the impact of migration and the employment status on life satisfaction, on the other hand, reveals differences between men and women. While for men unemployment or part-time work showed the strongest effect on SWB, migration shows the strongest effect on SWB for females Conclusion The purpose of this article was to investigate the impact of migration on subjective well-being (SWB). Hypotheses regarding the impact of migration on SWB were derived from human 24 In addition, some robustness tests were carried out. For example a comparison of the group leaving eastern Germany up until 1999 with the group that left later was conducted to take into account possible differences in SWB depending on the time when the individuals left. These analyses, too, provided comparable results, although the results for females seem more robust. Fixed effects regressions take into account an average level of SWB of individuals. The influence of migration in Tables 1 and 2 indicates an increase above the average after migration. Additional robustness tests were conducted to take into account the possibility that the effect is dictated by a strong deflection in the years before and after the migration. Here, the last year in eastern Germany or the first year in western Germany was excluded. These results too provided a similar picture. 15

17 capital theory and the New Household Economy, but also refer to concepts developed in psychology. An approach developed by Ziegler and Bitton (1981) provides the theoretical link between SWB and the listed theories, assuming that individuals take into account monetary and non-monetary costs and gains. In this respect, migration is a strategy to maximize utility and occurs when individuals anticipate an improvement in some subjective sense. The main prediction derived from the human capital framework is that migrants should be more satisfied after the move. Additional hypotheses predicting differences between groups and the development of the change in SWB over time are derived. Even though research generally points out the importance of longitudinal data for analyzing the impact of migration on SWB, due to the absence of alternatives, previous research was usually carried out on the basis of cross-sectional data. This study, in contrast, was based on longitudinal data (SOEP ) which include information on individuals living in eastern Germany as well as on people who moved from eastern to western Germany. Fixed effects hierarchical models were used to disentangle the impact of personality and mobility. Moreover, using fixed effects regression ensures that the change in SWB found shows the effect of migration and that it is not the unobserved happy individual who is more likely to migrate. Regional features of the locations of origin and destination were included to control for the impact of labor market characteristics on SWB. Additional control variables were included to take into account a change in SWB in the periods following migration. The analysis was carried out separately for men and women. Migration has a positive impact on SWB. Therefore, hypothesis 1 predicting that migrants should report higher SWB after the move compared to the situation before, could be verified. While the favorable conditions on the western German labor market, such as higher regional income levels, account for the increase in the reported SWB of males, this is not true for females. From previous studies it is known that males and females gain SWB partly through different factors. For example, Fandrem et al. (2009) showed that young females gain higher satisfaction from housing. It is possible that, for example, the quality of housing differs in eastern and western Germany and that females therefore gain higher SWB in the West. Research addressing mobility to urban municipalities states that women are more likely to relocate to bigger cities, as males living there have higher education levels and earnings, which make them more attractive for females (Edlund 2005). Wages in western Germany are higher on average than in eastern Germany. Therefore, moving to the West seems to be driven by a similar mechanism as moving to cities. Single females migrating to western Germany or 16

18 bigger cities may profit not only from the increase in their own wages but also from the higher incomes of their potential partners (Edlund 2005). For women living in partnerships a different mechanism could account for the higher SWB of female migrants. One possible explanation for the increase in SWB for females migrating with their partner or spouse can be found in the research on over-qualification, which shows that married women living in more highly populated municipalities are less likely to work in jobs for which they are overqualified (Büchel 2000). If eastern and western Germany differ regarding the quality of jobs available, women migrating with a partner or spouse might be more satisfied because of the better job opportunities available. Hereby, the regional control variables used may not capture the better job opportunities in West Germany. The positive effect of migration on SWB can be found for males even six (for females even ten) years after relocation. Even if migration should be painful for the greater part of the population as it destroys family and friendship networks, migrants are more satisfied not only temporary but also on the long run after the relocation. One explanation therefore is that migrants are a highly selective group, it seems that those persons who indeed decide to migrate are profiting from the entire new situation and that not only from the improved financial means. We can see that individuals SWB does not adapt to the improved situation after the move. This result seems surprising at first, especially when it is compared to earlier research on SWB, which demonstrated that individuals SWB resumed its previous level even after high lottery winnings or disability (Brickman, Coates, and Janoff-Bullman 1978). These results also contradict the predictions of the psychologist s adaption, aspiration and comparison mechanisms used in alternative hypothesis 2 (Migrants should report decreasing SWB in periods immediately following migration).when the results are compared to more recent research using superior empirical methods and larger samples we find a harmonized picture, showing that individuals do not adapt entirely to non-monetary life events such as marriage, divorce or even disability 25. What is against a complete adaption is also the fact that individuals should be able to accumulate location-specific capital, for example to establish new friendship networks in the periods following migration, which should increase their SWB further. Indeed, the findings in this paper regarding the lasting increase in SWB are better 25 Recent research shows that while individuals adapt quickly to income changes (within four years) (Easterlin 2003), they do not become entirely accustomed to major life events, such as disability (Easterlin 2003) marriage, (Lucas et al. 2003) or even to a new job status (Di Tella et al. 2007). There is, of course, also recent research that takes a different view see e.g. Kahneman and Krueger (2006). 17

19 explained by the human capital approach, which indicates that migration, like any other investment in human capital, should result in lasting future improvements. Therefore, hypothesis 2 can be confirmed. Highly educated migrants did not reveal higher SWB after the move, so hypothesis 3 has to be rejected. Hypothesis H can be confirmed, as women migrating together with a partner do not show lower SWB after the move. In contrast the SWB of married women who migrated with their spouse seems to be even higher. Using longitudinal data, this study was able to disentangle the influence of migration and personality on SWB. Some findings obtained from cross-sectional studies were supported (Lundholm and Malmberg 2006), while other studies were questioned (De Jong, Chamratrithirong, and Tran 2002). While this study compared the SWB of individuals remaining in their country of origin which that of those leaving it, the link between migrants and individuals from the country of destination is still missing and should be analyzed in future research. 18

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