Abstract
This paper examines whether the subjective well-being of migrants is responsive to fluctuations in macroeconomic conditions in their country of origin. Using the German Socio-Economic Panel for the years 1984 to 2009 and macroeconomic variables for 24 countries of origin, we exploit country-year variation for identification of the effect and panel data to control for migrants’ observed and unobserved characteristics. We find strong evidence that migrants’ well-being responds negatively to an increase in the GDP of their home country. That is, migrants seem to regard home countries as natural comparators, which grounds the idea of relative deprivation underlying the decision to migrate. The effect declines with years-since-migration and with the degree of assimilation in Germany.
I. Introduction
The behavior of migrants regarding labor market decisions, the timing of return to the home country, or the incentives behind “circular” migration probably are better understood if one looks to both the process of assimilation and to its natural counterpart—namely, the process of “disintegration” from their home countries (Nekoei 2013). The latter, which describes how migrants’ home country ties weaken over time, is less studied in the economic literature. Migrants may keep noneconomic links with their home land (culture, altruism, patriotic feelings during soccer games) but also may experience adverse or competing feelings if the home country is taken as a natural comparator regarding economic performances.
We suggest investigating this particular dimension using subjective well-being (SWB) data. Self-reported measures of life satisfaction have been increasingly used as proxies for utility during the last decade. (See Clark, Frijters, and Shields 2008.) This literature has established the importance of relative or positional concerns, notably the influence of a person's relative income compared to a reference group on her welfare. (See Easterlin (1995); McBride (2001); Senik (2004); Ferrer-i-Carbonell (2005); Luttmer (2005); Clark and Senik (2010); among others.) Admittedly, it is difficult to identify the relevant reference point for a given population. However, migrants offer an interesting case study. They are indeed possibly confronted with multiple and switching reference groups between home countries and regions of destination. This question is related to the migration decision itself and to the close concept of “relative deprivation” often cited in the migration literature (Stark and Taylor 1991). Indeed, migration is often undertaken to improve a person's income relative to members of her reference group in the source country. To our knowledge, the literature has not yet studied relative deprivation (and the net gains from migrating) using SWB measures, or whether home countries are relevant reference points for international migrants.1
In this paper, we test whether migrants are sensitive to the economic performances of both their home country and destination locations using the German Socio-Economic Panel (GSOEP) over 26 years and for 24 origin countries. Time and home-country variation is used to identify the effects of macroeconomic fluctuations on migrants’ well-being.2 While the approach suggested in this paper could be replicated for other countries, we believe that Germany is interesting for at least two reasons. First, it has one of the highest immigrant populations in Western countries, with 7.72 million persons (9.5 percent of the total population) coming from 194 countries.3 Second, the GSOEP is a large representative data set including SWB measures, very detailed individual and household information, a panel dimension and excellent representativeness of migrants. Our main application consists in estimating migrants’ SWB on a large set of individual determinants of well-being (such as household income and health status) and the macroeconomic variables of home countries. We also control for migrants’ family circumstances in both the host and home countries, for (overall and country-specific) time trends and, using panel information, for migrants’ time-invariant unobservables.
We originally show that home countries indeed act as a natural comparator for migrants. We find a marked and statistically robust effect of the home countries’ macroeconomic conditions on migrants’ well-being. It is fully in line with the relative concerns/deprivation hypothesis: Migrants’ well-being decreases with home country GDP per capita. We extensively check the robustness of our results as well as the validity of alternative interpretations (in particular the role of remittances and a correction for possible nonrandom selection into return migration). We also examine heterogeneous effects of GDP on migrants’ well-being, along dimensions like years-since-migration (YSM hereafter) and objective and subjective measures of the degree of assimilation in Germany. We unveil that competing feelings toward home countries decrease after some years in the host country. Consistently, less-assimilated migrants keep strong transnational ties, and origin countries are likely to remain their key reference group. Our conclusions are reinforced through finding an effect of opposite direction regarding local economic performances: Migrants’ well-being increases along with the GDP of the German counties in which they live. Interestingly, this “signal effect” also declines with YSM, as if gradually replaced by relative concerns toward the local environment. These results are consistent with the existence of multiple reference points and a possible switch over time and with the assimilation process. We derive important labor market and migration policy implications from these results.
The paper is organized as follows. Section II presents the data and the empirical methodology. Section III reports the main results, robustness checks, and additional results using migrants’ heterogeneity. Section IV concludes.
II. Empirical Approach
A. Data and Selection
Our analysis is based on the German Socio-Economic Panel (GSOEP), a well-known survey of individuals in households living in Germany. It has been used in important analyses in the SWB literature. See, for instance, van Praag, Frijters, and Ferrer-i-Carbonell (2003); Frijters, Haisken De-New, Shields (2004a, 2004b); Ferrer-i-Carbonell (2005). It is a representative survey of the entire German population and an exceptionally long panel, of which we are using 26 years from 1984 to 2009. It contains a wealth of information at the individual or household level, including data on education, health, labor market conditions and incomes, as well as various subjective measures of well-being. The data set was started in 1984 in West Germany (with around 10,000 respondents per wave) and has covered the entire reunited Germany since 1990 (with around 14,000 respondents per wave after 1990 and more than 20,000 after 2000).4
We select all the waves of the GSOEP, keeping all adult first-generation immigrants aged 17 or older and living in West or East Germany.5 Although more than 100 nationalities are reported, we restrict our study to the main migration groups, resulting in 24 different countries of origin. These correspond to the largest groups in terms of their population size in Germany and countries for which we have at least 100 observations in the data. Our dependent variable (subjective well-being of individual i of country h at year t, SWB iht) derives from the question “How satisfied are you with your life as a whole, all things considered?” The answer is reported on an 11-point scale (0 signifies “completely dissatisfied” and 10 means “completely satisfied”). Life satisfaction is highly correlated with other subjective measures of well-being like self-reported happiness or aggregated answers about mental health such as the GHQ-12. (See Clark and Oswald 1994.) It has been shown that SWB information is a solid proxy for individual well-being, notably because of the strong correlation with other, more objective measures of well-being. (See Oswald and Wu 2010.)6 We combine SWB and other individual characteristics with macroeconomic variables for the migrants’ 24 countries, drawn from annual time series data of the World Bank indicators. We focus on the main variables of interest, including log real GDP per capita of country h in year t (denoted GDPht hereafter),7 growth in real GDP per capita (denoted DGDP), price levels measured by the GDP deflator (Pht), and unemployment rates Uht.8 The resulting sample includes a total of 51,171 individual X year observations obtained over 26 years of data and migrants from 24 origin countries. We lose a small fraction of this data set due to missing information so our final sample contains 47,557 individual X year observations.
B. A First Look at the Data
Appendix Table A1 provides some aggregate statistics by country of origin, including the main macroeconomic indices (log real GDP per capita expressed in PPP-adjusted 2005 international dollars, nominal GDP per capita and unemployment rates) and migrants’ SWB (average SWB over all migrants of a country for the period 1984-2009). We also provide the ratio of real GDP per capita for each country compared to Germany. This reflects the huge variation in development levels across immigration countries,9 and the convergence process (18 countries out of 24 have caught up with Germany over the period). A lot of variation also can be observed concerning reported well-being. On the 0-10 scale, migrants’ SWB scores 7.1 on average over all years and origin countries. Using the country average over 1984-2009, we see that SWB varies from 5.8 for Iranian migrants to 7.6 for Dutch migrants, which partly reflects the large variation in living conditions (as proxied by GDPht) across nations. This is illustrated by the cross-country correlation between average migrant SWB and absolute real GDP (respectively unemployment rate), which amounts to 0.46 (-0.40).10
In Appendix Table A1, we also report correlation over time between yearly migrants’ average SWBht and home country GDPht (or unemployment Uht). Interestingly, for GDP (respectively unemployment), the time correlations are negative (positive) in the majority of countries, as if increases in GDP per capita (unemployment) were associated with a decline (rise) in the well-being of the corresponding migrants. This unexpected result is illustrated in Figure 1 for the five largest migrant groups (those from Turkey, Greece, Italy, Spain, and Poland). We plot log real GDP per capita, GDPht, against yearly migrants’ average SWB, SWBht, for all our panel years (years are indicated next to the data points). The negative relationship between home country GDP and migrants’ SWB seems to characterize the whole period (with a few exceptions) and most immigration countries.11 We do the same for unemployment rates (Appendix Figure A1): The pattern is not as pronounced as for GDP, yet it seems as though increases in unemployment rates are associated with an increase in SWB.
These preliminary results directly align with the interpretation in terms of relative concerns or relative deprivation suggested in this paper. With the Easterlin paradox (Easterlin 1995), the fact that a country like Germany has experienced GDP growth but a flat trend in SWB over the past 30 years often pertains to the classic explanation in terms of “positionality.” That is, after some point, well-being would depend more on relative income than on absolute income, so that absolute increases in national wealth would not improve well-being over time. First, this argument does not mean that relative concerns kick in only above a certain level of income, just that they tend to overcome absolute income effects at this point. Status indeed plays a considerable role in the context of poorer countries as well. (See Clark and Senik 2015 ed., for recent evidence.) Second, for migrants (from poorer or other rich countries in the case of Germany), one could in fact expect an even more radically opposed association between SWBht and GDPht. Indeed, if home countries act as reference points and if most countries “catch up” with Germany, the relative position of migrants declines over time and their SWB can be negatively affected. This is exactly what Figure 1 illustrates. In the following, we attempt to better characterize this effect by means of regressions while controlling for migrants’ characteristics. We shall demonstrate that these co-movements in SWB and home GDP are causally linked by the fact that origin countries serve as a reference point against which migrants assess their own well-being.
SWB versus GDP Across Time for Selected Ethnic Groups
Notes: Figures indicate years. GDP per capita is taken from the World Bank Indicators and SWB (Subjective well-being) from the German Socio-Economic Panel (life satisfaction question). In the legend, we report for each country the intertemporal correlation between migrants’ SWB and their home country GDP per capita.
C. Modeling the Well-Being of Migrants
Using our selected panel of migrants living in Germany, we estimate the well-being SWB* of migrant i from home country h at time t as follows:
.(1)Latent well-being SWB* is considered as a proxy for the unobserved utility of a migrant, for which we observe an ordinal metric SWBiht=j on an ordered scale of well-being categories j = 1,…,J. The model combines both characteristics of migrant i at year t, Xit, and macroeconomic variables of her home country h at year t, Macroht. Individual time- varying variables in Xit include the usual determinants of SWB—namely log household income, work status, marital status and family circumstances, health status, education, other characteristics related to the home country (children and spouse in the home country, refugee status, remittance receipt), and German states (Lander).12 We also control for year dummies θt (they pick up the effect of German GDP as well as of any global shocks that are common to all migrants’ countries in each year), country-specific linear time trends t × δh (δh denotes country fixed effects), individual effects φi and a usual i.i.d. error term εiht . Country time trends may capture, for instance, cultural attitude toward changes in well-being or country-specific unobservable assimilation patterns of migrants of country h.13
Our baseline estimation strategy consists of linear panel estimations with fixed effects (FE), denoted by ji. Alternatively, we shall experiment with the Mundlak's “quasi-fixed effects” (QFE) model, which combines both between and within variation. This model allows for the inclusion of variables that cannot be introduced in FE estimations, notably country effects and immigrant arrival cohorts.14 Hence, the overall individual effect is based on a slightly more structural specification where φi = δh + Zi + Ageit + YSMit + ui, with home country effects δh (for unchanging cultural influences of origin country on reported well-being), time-invariant characteristics Zi (gender and cohort effects), two time variables (age and YSM, which are not identified when using FE time-demeaning panel estimation with year effects), and the Mundlak QFE U,. 15 Finally, we consider that J = 10 is large enough to treat reported well-being as a continuous variable so that Equation 1 can be estimated linearly.16 Yet we also provide checks where we acknowledge the ordinal nature of the dependent variable, allowing for unobserved individual effects in this nonlinear context by using the QFE ordered probit and the “Blow-Up and Cluster” FE ordered logit estimators. (See Baetschmann, Staub, and Winkelmann 2015.)
III. Results
A. Main Results
We first present our main results, namely the estimation of Model 1 on panel data. It relates the macroeconomic conditions of home countries to individual SWB, conditional on various individual and family circumstances in both the host and home countries.
1. Effect of GDP: Baseline estimations
In this section, we shall present summary tables in which we report estimates of the coefficient g only. Our main result is in the two first columns of Table 1.17 We report panel estimations of the effect of log real GDP per capita on SWB while controlling for year effects, state effects, and time-invariant unobservables (FE). We obtain an estimate of -0.303, which is significant at the 1 percent level.18 The next column additionally controls for country-specific time trends to filter out the spurious correlation between macroeconomic indices and SWB. The magnitude of the effect is basically unchanged (-0.212) but the effect is less precisely estimated, even if still significant at the 10 percent level.19 This finding suggests that macroeconomic movements in the home countries feed through into migrants’ feelings of well-being. This may be seen as an unexpected result if one believes that migrants are bounded to homelands by a sense of pride, identity, and patriotic ties. Yet it is likely that this altruistic or emotional link pertains to noneconomic aspects.20 As far as economic conditions are concerned, our results do consolidate previous findings in the literature showing that people's wellbeing is evaluated against natural comparison points (for instance Luttmer 2005)—and we show that home countries are an important one. This also relates to the fact that mean income in home countries is a marker with respect to which migrants can gauge the success of their migration experience. Migrants from countries characterized by better macroeconomic performances experience lower gains from migration and, other things being equal, lower levels of well-being. Arguably, this effect may be attenuated when migrants decide to stay forever in Germany or become assimilated enough for their reference point to shift from home countries to other comparators within Germany. We investigate this point below.
2. Magnitude
To gauge the magnitude of the effect, we suggest alternative metrics and a brief comparison with other studies. We base our calculation on the FE model with country- specific time trends. First, a one standard deviation increase in the home country's (log) GDP per capita is associated with a decline of 2 percent of a standard deviation of SWB (or a 0.5 percent decrease in mean SWB). While this may seem modest, it is very much in line with measures of relative concerns or socioeconomic status in the literature. For instance, Di Tella, Haisken-De New, and MacCulloch (2010) find that a one standard deviation change in status (that is, an individual's relative standing to others measured by job prestige) explains 3.1 percent of the standard deviation in SWB. An alternative way to gauge the effect is to take the ratio of the coefficient on log GDP per capita over the coefficient on log household income.21 We obtain a ratio of -0.553, which can be interpreted as an equivalent income variation, that is, a 1 percent increase in the home country's real GDP per capita is equivalent to a 0.55 percent decrease in household income. Drawing from estimates of absolute and relative income effects in the literature, we find equivalent income measures of a similar order of magnitude, such as -0.58, -0.76 and -0.82 in Akay and Martinsson (2011), Ferrer-i-Carbonell (2005), and Luttmer (2005) respectively.
3. Alternative estimators and specifications
Our baseline results are obtained with FE linear estimations and treating SWB as a continuous variable. We check the sensitivity of our results with respect to alternative estimators. Appendix Table A4 reports a series of estimates, starting with the FE model without and with country-specific time trends. Acknowledging the ordinal nature of SWB data, we also show estimates of the “Blow-Up and Cluster” FE ordered logit. The coefficient is still negative and significant. We could not calculate marginal effects but we can check the equivalent income measure, -0.972, which turns out to be only slightly larger than the linear FE estimation without country time effects. Then we move to QFE estimates showing very similar results compared to the baseline (-0.281 and -0.224 for QFE models without and with country time effects, respectively). Equivalent incomes are also almost identical. The penultimate model augments QFE with information on personality traits based on the so-called “Big Five” model. Psychological traits are increasingly used as a time-invariant and potentially important determinant of wellbeing (Boyce 2010). “Big Five” traits are reported in Waves 2004 and 2009 only, so cannot be used for all individuals in the panel. Despite the resulting drop in sample size, the coefficient of -0.321 is close to the baseline. Finally, we estimate an ordered probit with QFE: The coefficient of -0.215 is not directly comparable but the equivalent income is again very similar to the baseline.
4. Timing and adaptation
Turning back to Table 1, we provide additional results starting with the timing of the effect. It may be the case that migrants are affected by the dynamics of their country's economic performances more than its actual level. We introduce the potential role of DGDP alone or together with GDPht (Columns 3 and 4 of Table 1). The negative sign on the former term indicates that an increase in home country growth negatively affects the well-being of migrants, yet it is not significant. If introduced simultaneously, the GDP effect remains significant and close to the baseline. A more flexible way to account for dynamics is to introduce lagged GDP. Macroeconomic fluctuations may be perceived with a delay or their impact on SWB could depend on longer-term trends rather than on current economic conditions. Lagged macroeconomic variables also can relate to adaptation effects (Di Tella, MacCulloch, and Oswald 2003; Di Tella, Haisken-De New, and MacCulloch 2010), stemming from the idea that migrants may adjust to the home country GDP after a period of time and only thereafter derive negative positional feelings from increases in GDP. Columns 5 and 6 show results with one-year and two- year lags of GDP respectively. Di Tella, Haisken-De New, and MacCulloch (2010) interpret the sum of lagged effects as the amount of adaptation. We observe that lagged GDP effects change sign. Only the two-year lag is significant but a F-test of whether the joint effect of all GDP variables (that is, current and lagged) is zero can be rejected. With one lag (two lags), 17 percent (9 percent) of an initial increase of GDP is lost over the ensuing year(s), leaving a long-lasting effect of-0.337 (-0.426) on SWB, which is very similar to our baseline result. We draw two lessons from these results. First, it is obviously not possible to identify the precise timing due to the high correlation between GDPht, GDPht-1, and GDPht-2. This is no impediment to our analysis, as cumulated effects do not change our conclusions. Second, we find no evidence of an adaptation effect to individual positional concerns toward the home country.22
5. Price effects and exchange rates
In place of real GDP, it would make sense to include log nominal GDP per capita, denoted 
, to check if migrants are to some extent victims of money illusion (Boes, Lipp, and Winkelmann 2007). That is, migrants should be affected by the success of their home country in terms of nominal GDP but they also should know that a price increase in their home country reduces their relative deprivation as it decreases the relative cost of living in Germany. Because 
as the log price index (log GDP deflator), we can simply introduce the latter in the SWB regression together with GDPht
. Column 7 in Table 1 shows that the effect of log real GDP per capita is unchanged while the log price level has no significant effect. Even if not a definitive proof, this is suggestive evidence that real GDP is what truly matters for well- being—that is, migrants do not suffer from money illusion. Regardless, other interpretations should be mentioned. In particular, migrants from countries with lower relative prices could take advantage of the higher relative purchasing power of their income when they vacation at home. In this case, higher prices in the home country should decrease rather than increase SWB, an effect that partly may counteract the relative concern effect described above. For one thing, migrants could equally go to any other low-price country to take advantage of their German salaries. We nonetheless replicate estimations while including exchange rates between the home country and Germany. In Column 8, the GDP effect is slightly larger than the baseline (but not significantly so) while the coefficient on exchange rates is insignificant.
6. Unemployment
The effect of home country unemployment rates on migrants’ SWB is reported in Appendix Table A5, using alternative specifications including simultaneous estimation of unemployment and GDP effects. The overall picture is that results are much less pronounced in the case of unemployment. Consistently with our positionality interpretation, the coefficient on unemployment is positive. Yet it is small enough, or the effect imprecisely estimated, so that it becomes insignificant as soon as individual effects (FE or QFE) are introduced.23 This could be explained by the fact that the unemployment effect also relates to migrants’ own labor market prospects in the case of return migration. Another explanation is that informal work, which affects many of the low-income countries sending migrants to Germany, might leave unemployment as a less reliable proxy for their labor market conditions.
B. Sensitivity Analysis
1. Basic checks
First, it may come to mind that such a positional concern vis-a-vis home countries can be mitigated by the fact that some of the migrants’ close relatives still live there and may be negatively affected by macroeconomic shocks. For this reason, our estimations control both for the presence of close relatives in the home country and for the level of remittances sent by migrants to help face income shocks. (See Appendix Table A2.) Second, the effect could be driven by the fact that household income is partly determined by home country GDP if a migrant has investments in the home country. In the absence of information in GSOEP about the specific nature of investments, we can nonetheless replicate baseline estimations whereby investment income is excluded from household income. In this case, the coefficient on GDP could now capture both the investment income effect (positive) and relative concerns (negative). Results show hardly any difference with the baseline estimates (that is, coefficient of -0.307, standard error of 0. 112), which conveys that the former effect is certainly marginal. Third, the effect of home country GDP implies that origin countries are different from other countries in terms of migrant comparisons. To check this, we conduct a placebo test whereby each migrant is randomly assigned to another country's GDP. In this case, the estimated coefficient on the placebo GDP figure is insignificant.
2. Regions of origin
Next, we investigate the sensitivity of our results to country and year selection. First, Turkish migrants are by far the largest group among all migrants in Germany (25.1 percent of the total foreign population, see Table A.1). We check if this group drives the results. In Columns A and B of Table 2, we report estimates of the FE model on our sample excluding Turkish migrants and on Turkish migrants alone. The effect of GDPht is negative and significant in both cases. It is very similar to the baseline in the model without Turkey, conveying that results are not driven by Turkish migrants alone. The coefficient is very large (but less precisely estimated) when using only time variation among Turkish migrants.24
Next, we check whether the effect varies with geographical distance to Germany. Closer countries are in general richer (so the rate by which they may converge toward German GDP is lower), yet GDP comparisons can be easier to do. Countries located farther away are poorer but make circular migration more difficult (especially in the early years of our panels during which possibilities of air travel were not as developed as today). Columns C and D in Table 2 show estimates using a threshold of 2,100 kilometers from Germany (the median), which excludes countries like Turkey, Iran, Ukraine, and Russia. The effect is larger in the more distant group, but not significantly so, compared to countries in the vicinity of Germany. Finally, we distinguish countries of origin by level of economic development: OECD/rich countries (real GDP above 65 percent of German real GDP), middle income (35-65 percent), and poor countries (below 35 percent). Estimates in Columns E-G display a U-shaped pattern that is stronger effects from less developed countries, an insignificant effect in the middle group, and the largest effect from rich countries. The latter may correspond to the fact that the economic performances of neighboring countries are most visible (see also Becchetti et al. 2013) and generate the most regret among migrants who do not benefit from the positive dynamics at home.
3. Asymmetrical effects
We also verify if selected years make a difference. As previously seen in Figure 1, most countries in our sample experience economic growth for a majority of the years 19842009. We investigate whether our results are driven by these episodes of growth or whether the recession years tell us a similar story. While upturns in home countries are expected to trigger relative concerns among migrants, downturns may have an asymmetrical effect if migrants experience more sympathy toward their nation during bad years. We interact macroeconomic conditions with dummy variables for upward or downward changes in these variables. The results are reported in Columns H and I of Table 2. Both upward and downward changes in the home country GDP affect migrants’ well-being. Whereas the effect generated by economic downturns in home countries is smaller, as conjectured above, the difference with upturns is neither large nor significant.
C. Alternative Interpretations
Di Tella, MacCulloch, and Oswald (2003) discuss the possible endogeneity of national GDP effects on citizens’ life satisfaction. They reckon that it is difficult to find believable macroeconomic instruments and therefore suggest instead to experiment with different forms of lag structures. In the present context, there is much less concern for endogeneity given the minimal influence of migrants on their home country's GDP. Nonetheless, relative changes in the home country's GDP may affect migrants through three other channels besides positional concerns—namely, migration flows, remittances, and the option to return home. We now investigate whether migrants responding to country-of-origin conditions through these variables challenge our interpretations.
1. Inflow of home country peers
A potential effect of bad economic conditions in the home country is that more potential immigrants from that country may be interested in migrating to Germany. Possibly they migrate to the same regions where their conationals already live. In this case, an increased flow of new migrants may enhance the well-being of existing migrants (which would reduce our effect) or decrease it (which would explain our effect). Additional, unreported estimations depart from our baseline model by including the proportion of immigrants in local labor markets. They show no effect of the latter, interpreted as a change in migrants’ proportion in our FE estimations, whereas the effect of GDPht is basically unchanged. This is also true when including local labor market conditions (mainly the local unemployment rate). More generally, the formation of enclaves requires long-lasting dynamics, probably mixing people of different nationalities. Also, migration inflows cannot respond freely to changes in the home country's economic conditions.
2. Return migration
A second channel is return migration, which we treat as a more serious challenger in terms of interpreting our results. Indeed, the potential return decision concerns each migrant directly. We first empirically check whether return migration depends on changes in the home countries’ macroeconomic performances. We suggest the following model:
,(2)where riht is an indicator variable taking value 1 if migrant i from country h leaves Germany in year t (and drops from the panel for this reason), and 0 otherwise. The model combines individual characteristics, Xit , including cohort and state fixed effects, a macroeconomic index of the home country, Macroht , individual effects (modeled as QFE), vi , country and time fixed effects, ξh and pt respectively, and an i.i.d. normally distributed random term, uiht. Unreported results show that m is positive but insignificant.25 Next, we reestimate SWB regressions accounting for possible return—and nonrandom sample attrition due to return migration—as a function of home country macroeconomic conditions. We use the Heckman procedure adapted to panel data by simultaneously estimating selection into return migration and the SWB equation by Maximum Likelihood (for a more structural approach, see Bellemare 2007). A complete discussion on the instrumentation is provided in the Appendix. The first column of Appendix Table A6 reports the effect of GDPht on migrants’ SWB when controlling for selection into return migration. It is very much in line with the baseline results and significant in all cases.
3. Remittances
Remittances constitute a third channel linking migrants to their home countries. First, remittances sent by migrants directly can affect home country macroeconomic conditions and influence, at the same time, their own well-being. Yet, the latter effect is of significant magnitude only for a limited set of countries and years.26 Moreover, our GDP measure already includes total annual remittances sent by migrants in Germany and other destination countries. Second, if per-capita income in the home country increases, migrants may need to compensate their relatives left behind less and, hence, their SWB would increase. Note, however, that our baseline estimations already control for the amount of remittances sent by migrants, and we find hardly any difference in the GDP effect whether we include this variable or not. Additionally, we have run estimations of the probability to send remittances on individual characteristics and macroeconomic variables. Remitting does not significantly depend on (current or lagged) GDP. Third, even if remitting behavior does not respond much to home-country economic conditions, the implicit value of remittances may change with it. If economic conditions improve, migrants’ status may decrease (along with their SWB) to the extent that their role as supporting their extended family in the origin region becomes less prominent. In fact, replicating our estimations on migrants who do not send remittances provides results that are very similar to the baseline. These various checks convey that the channel of remittances does not affect our results nor our interpretation in terms of relative concerns/deprivation.
D. Heterogeneity among Migrants and Additional Outputs
We now examine how the migration history of migrants and their connection to the home country may affect the results. To capture migrants’ heterogeneity, we first linearly interact GDP with migrants’ duration of stay (YSM), then with a set of characteristics on intentions to stay in Germany, objective and subjective measures of assimilation and attachment to host versus home countries.
1. Duration of stay
We first check how duration into migration influences the GDP effect. We use a flexible specification with four groups of YSM interacted with the GDP coefficient (less than 10 years, 10-20, 20-30 and more than 30 years). FE estimations with year effects do not allow the inclusion of time variables like age or YSM, so our interaction terms would not have a clear interpretation. Therefore, we rely on QFE in this exercise. The results correspond to the solid curve in Figure 2. The effect of the home country GDP per capita is negative and very large (around -0.5) in the first ten years, declines a bit in the following years, then becomes virtually zero after 20 years. That the effect of the home country GDP only affects migrants’ SWB in the first two decades after arrival most likely can be interpreted in two ways: (i) as migrants assimilate into the host country, the effect of the home country GDP as a reference group fades away; (ii) migrants who arrived young in the host countries are more assimilated and ignore their home country as a reference point. Alternative, less convincing explanations pertain to the changing composition of the migrant community due to cohort effects27 or to return migration.28
Effect of Home GDP versus Local German GDPon Migrants' SWB according to Years Since Migration
Notes: Square points with solid lines represent home country GDP effect; Triangle point with short-dashed lines represent home country GDP effect excluding return migrants; Circle points with long-dashed lines represent German ROR level GDP effect; and dotted lines represent the 90% confidence bounds of GDP effect on subjective well-being (SWB). Horizontal axis shows results by discrete groups of years since migration.
2. Assimilation and fading connection with home countries
We further explore the assimilation process that potentially explains the pattern in Figure 2. First, Interpretation 2 above suggests that relative concerns are necessarily smaller for those who arrived as a child in the host country and feel disconnected from the home countries. In unreported estimations, we have interacted GDP with dummies for the age at which migrants arrived in Germany: as a child (under 12), teenager (1218), young adult (18-39), or older. Results are not inconsistent with this explanation. Although those who arrived as children are not affected by home country GDP, the GDP effect remains significant at older ages (12-18 and 18-39). Yet we cannot provide a definite answer to the question of whether stronger assimilation for people who migrated younger is due to (1) duration of exposure or (2) exposure during a sensitive period for acculturation.29
Second, the assimilation process may have more implications than just “forgetting” home countries. It also may imply a switch in the reference group over time, with the local economic environment becoming the new natural comparator for long-term migrants. To check this, we exploit variation in economic performances across German ROR (Raumordnungsregionen). ROR are spatially organized units based on various criteria to represent local markets. We match information about 96 German ROR with our microdata and regress migrants’ SWB on ROR-level GDP interacted with YSM.30 The long-dashed curve in Figure 2 shows that for short-term migrants, local GDP has a positive and significant effect. This is consistent with an interpretation in terms of signal effect, that is, urban residents’ higher incomes may be informative about migrants’ own future income. (See also Ravallion and Lokshin 2000; Senik 2004; Akay, Bargain, and Zimmermann 2012.) It may appear as opposed to the (negative) effect of local income when taking the population as a whole (Ferrer-i-Carbonell 2005). Yet, we observe that this effect also exhausts over time, possibly as migrants assimilate and start to consider their local environment as competitors. Interestingly, the declining (positive) signal effect is symmetrical to the decline of competing feelings vis-a-vis home countries.31
Third, we investigate the assimilation process in a more qualitative way. We estimate the potential heterogeneity of the GDP effect among migrants by using different proxies for their connection to their home country. In separate estimations, we use information about the intention to migrate back (wish to stay temporarily or permanently), migrants’ attachment to the host country (do you feel like a German?), whether migrants have purchased their dwelling (which may indicate a long-term commitment to stay), objective measures of sociocultural assimilation (language skills), and on the presence of children and spouses in home versus host countries. Results are reported in Figure 3. The effect of GDP per capita on migrant SWB is ordered, for each of the questions above, from the highest to the lowest connection to the home country. Strikingly, all questions point to the same conclusion: Migrants characterized by a strong connection with their homelands show greater relative concerns. Admittedly, the difference with other migrants is not significant when each item is taken separately. Nonetheless, the fact that all measures systematically point to the same direction seems to corroborate our interpretation: Those who lose touch with the homeland, intentionally or not, also treat it less as a reference point. This is highly consistent with the time pattern discussed above.
Effect of Home GDP on Migrants’ SWB: Heterogeneity
3. Additional outcomes
Our paper is also related to the burgeoning literature on the effect of migration on wellbeing. Even if migrants see their economic conditions improve, they may experience a declining SWB due to a fall in their relative position when migrating (Knight and Gunatilaka 2012). Yet our results suggest that a shift in reference frame may take time. Stillman et al. (2015) obtain causal effects, thanks to a lottery randomization, which also show that the mere impact of migration on subjective welfare is complex, emphasizing a sensitivity of the SWB impact to the well-being measure at use. Unfortunately our data set does not include other SWB like mental health. Additional regressions nonetheless show that domains of satisfaction (job, income, health) point to the same result as life satisfaction (a negative effect of log GDP per capita), yet with insignificant estimates. A question on whether the person is concerned with the economic environment in Germany (3-very concerned, 2-somewhat concerned, 1-not concerned at all) shows a positive significant response to home country log GDP per capita (p-value of 0.09). We also have explored the impact of home country GDP on behavior. Although remittances and return migration are discussed above, other dimensions may be of interest. A migrant may, for example, work harder or acquire skills as a response to changes in the home country performances, possibly to improve her relative position to the average fellow in the home country. We indeed find that the log GDP per capita increases both hours worked (p-value of 0.01) and education (the effect is significant when restricting estimations to migrants below 40 years old, with a p-value of 0.04).
IV. Concluding Discussion
We investigate whether a country's macroeconomic performances matter for those who have migrated. Using various groups of migrants in Germany observed over 26 years, we find a significant and negative effect of home country GDP per capita on migrants’ reported well-being. This result is well explained by positional concerns and the idea of relative deprivation of international migrants. Migrants leave their country to improve living conditions, potentially in relation to what they could have achieved in the homeland. We also show that both relative concerns toward home countries and a signal effect from migration regions are stronger upon arrival or for those with a low degree of assimilation in Germany. Both effects tend to disappear as migrants lose ties with home countries, possibly forming new reference groups among destination regions.
These results bear some interesting implications and suggest further natural developments. First, relative income effects provide an original way to measure assimilation in relation to such policies. Indeed, our approach may allow for the identification of different types of migration dynamics, as discussed in Clark, Frijters, and Shields (2008). The modern brain drain view—and the type of workers targeted by migration policy in Canada and the United States—would correspond to high-skilled migrants who voluntarily migrate, quickly assimilate, and rapidly switch their reference frame. Other migrants from poorer regions and less easily assimilated may keep home countries as the reference point for a longer time. The different types will have different economic and cultural implications for the host country.
Second, the macroeconomic conditions in the home country are one of the most important sources of information to make a cost-benefit calculation not only for initial migration decisions but also for return migration decisions. We could examine how macroeconomic conditions of home countries affect “circular migration,” which is an important phenomenon of the last decade (Constant, Nottmeyer, and Zimmermann 2013). As noted by Clark, Frijters, and Shields (2008), relative concerns also can explain why migrants continue to visit their home countries: This is when they can cash in as relatively high earners compared to those in the home country.
Last, our SWB-based test of the “relative deprivation hypothesis” was only partial. We simply check whether the migrant's relative position with respect to her origin country as a whole—proxied by GDP per capita—may have an effect on her well-being. We could not say anything about how migration improves the relative position of a migrant or her family within the home country income distribution (the internal relative deprivation hypothesis, as described in the studies of Stark and Taylor 1991). Interestingly, this hypothesis potentially generates further testable implications. In particular, it implies that the characteristics of the migrants’ home country income distribution can influence both the decision to migrate and to return. Further research should attempt to gather more specific information on a migrant's expected labor income in the home country, on her family's position within the home country distribution and on how differential income growth between host and home countries affects this position.
Acknowledgments
The authors are grateful to Derek Stemple and Victoria Finn for editorial assistance, and to two anonymous referees and seminar participants at CEPS-INSTEAD, DIAL, AMSE, the Institute for the Study of Labor (IZA), the 2013 AM2 conference of IZA with Hebrew University, the 2015 annual conference of the European Economic Association (EEA) in Mannheim, the National University of Singapore, American University in Washington DC and Yale University in New Haven for very valuable comments. The data used in this article can be obtained beginning October 2017 through September 2020.
Footnotes
↵⍈ Supplementary materials are freely available online at: http://uwpress.wisc.edu/journals/journals/jhr-supplementary.html
↵1 An exception is Gelatt (2013), who uses data on Latino and Asian Americans to test the location of immigrants’ reference groups and the relationship between various measures of subjective social standing and SWB. Akay, Bargain, and Zimmermann (2012) also study the role of positional concerns of migrants within a country (China).
↵2 Another recent study (Nekoei 2013) exploits 16 years x 73 origin-countries to study the effect of exchange rate volatility on migrants’ labor supply in the United States. Other studies check how movements in GDP, unemployment, or inflation directly affect individual happiness (Clark and Oswald 1994; Di Tella, MacCulloch, and Oswald 2001,2003; and Wolfers 2003). We relate especially to the Di Tella, MacCulloch, and Oswald papers, and to Becchetti et al. (2013), who study the correlation between citizens’ (not solely migrants’) SWB and their country's macroeconomic fluctuations. Di Tella, MacCulloch, and Oswald (2003) use 17 years x 13 countries to capture enough regional and time variation in macroeconomic conditions. They report that GDP (unemployment and inflation) is positively (negatively) associated with citizens’ well-being and explain this correlation with feelings of national prestige (for GDP), corroding purchasing power (for inflation) and loss of self-esteem, depression, anxiety, and social stigma (for unemployment). Becchetti et al. (2013) show that neighboring countries can be reference groups and generate negative feelings if they experience higher economic success.
↵3 Figures extrapolated to the recent years (before the refugees’ crisis) on the basis of the 2011 microcensus by the Federal Statistical Office (www.destatis.de).
↵4 Sample weights are provided and used to guarantee the representativeness of the sample. Representativeness of the migrantpopulation is excellent according to the detailed assessment ofLelkes and Zolyom (2010). Attrition in GSOEP is discussed in Spiess and Kroh (2004) and, in relation with SWB estimations, in Frijters, Haisken DeNew, and Shields (2004b). Nonrandom attrition due to return migration is addressed in our analysis below.
↵5 We select first generation migrants using information from the “migration background” module of the GSOEP. The migration status of an individual is obtained by combining information on his/her country ofbirth, citizenship, migration history, and parental information. We also have an exact information about the arrival year in Germany, which is used to define the year-since-migration variable and arrival-cohort dummies.
↵6 In addition, Krueger and Schkade (2008) provide extensive evidence about the robustness of SWB measures compared to more usual data used by economists. Di Tella, MacCulloch, and Oswald (2003) report a high regularity in SWB equation regressions across different nations (as we do for the different migrant groups in our data) while Clark, Frijters, and Shields (2008) show that changes in SWB are good predictors of behavior responses. All these checks convey that SWB is not mere statistical noise but rather contains meaningful information. Nonetheless, we keep in mind the possible lack of interpersonal comparability in the perception of (and answers about) well-being. We treat this as a measurement error, namely by using large samples and by controlling for individual fixed effects in our regressions. Notice that we are not interested in SWB scales so much as the effect of home-country macroeconomic performances, or in their relative effect. The latter, the tradeoff between these performances and individual income, can be calculated as an “equivalent income” measure of relative concerns, as explained below.
↵7 In all the estimations hereafter, we use the log of real GDP per capita divided by 10,000, for comparability with Di Tella, MacCulloch, and Oswald (2003).
↵8 See http://data.worldbank.org/indicator.
↵9 For instance, the ratio is as little as 30 percent (respectively 28 percent) of the German real (nominal) GDP per capita for Iran and up to 113 percent (99 percent) for the Netherlands.
↵10 However, differences in income levels do not perfectly explain the well-being gap. The relationship between income and well-being may not be linear: Beyond a certain income level, income differences have smaller effects on perceived well-being (this pattern is found in Easterlin (1995), but are questioned more recently by Stevenson and Wolfers (2008), who do not reject linearity). For instance, the correlation between mean SWB and real GDP per capita is smaller when GDP is expressed in logs (0.36). Moreover, if we focus on Western European countries and the US, this correlation drops to 0.07.
↵11 This result is not only driven by the periods of economic growth. While not visible in Figure 1, we observe in source data that, for instance, the downturns of 1993-94 and 2000-2001 in Turkey and the 2008-2009 recession in Italy are associated with an increase in SWB among migrants from these countries.
↵12 State effects account for possible migration patterns within Germany. Evidence in GSOEP shows, however, that geographical mobility of migrants is extremely limited. (See Akay, Constant, and Giuletti 2013.)
↵13 Together with flexible time trends 0t , they also represent deterministic functions of time that are used to render the data stationary. (Di Tella, MacCulloch, and Oswald 2003 stress that for usual unit-root reasons, untrended SWB should not be regressed on trended macroeconomic indices like GDP.)
↵14 Migrants may vary in unobservable characteristics depending on the year they arrived in Germany (Borjas 1999). Therefore, migrants are grouped into nine cohorts taken five years apart (nine dummy variables starting from pre-1960 arrivals until the last cohort corresponding to the last ten years). These cohort dummies aim to capture cohort-specific unobserved characteristics affecting migrants’ well-being. Grouping is necessary for identification.
↵15 Following Mundlak's approach, the latter combines a normally distributed term and within-means of relevant time-variant variables. (We use household income, household size, age, amount of remittances sent to the home country, education and working hours.)
↵16 The advantage of the linear approach is that it makes the required extensions to panel estimations much more transparent and allows including unobserved individual heterogeneity in a flexible way. Notwithstanding, Ferrer-i-Carbonell and Frijters (2004) show that results are typically similar using both linear and ordinal models; the present study shares this conclusion.
↵17 The complete set of SWB estimates is shown and discussed in the Appendix (Table A2). Models I and II relate to Models 1 and 2 in Table 1 while odel 0isa variant without home country GDP. (See detailed discussion in the Appendix.)
↵18 In all specifications, clustering is made at the year and home country level to account for possible bias due to repeated observations for the same country of origin (and to control for the correlation between errors in the same country). Alternatively, we have clustered standard errors at the individual level due to the panel nature of the data. The standard errors only slightly increased in both cases.
↵19 Alternatively, we also have used the Hodrick-Prescott filter to detrend macroeconomic variables before estimations (detailed results available from the authors). By doing so, we obtain an effect of -0.303 (standard error of 0.145) for detrended GDP per capita in levels and -0.256 (standard error of 0.137) for GDP per capita in logs. Hence, the results are still significant in this case and the log GDP effect is of similar magnitude as in the baseline.
↵20 We perform separate estimations of the effect of battle-related deaths (log number of people) and life expectancy (number of years) on migrants’ SWB, using the same controls as in the baseline model. The former is significant (estimates of -0.016 with a standard error of 0.006), suggesting that there may be feelings of sympathy toward home countries when it comes to noneconomic domains.
↵21 The latter is 0.38 in the baseline, which is of the same order as in related studies. For instance, Akay and Martinsson (2011), Ferrer-i-Carbonell (2005), and Di Tella, Haisken-De New, and MacCulloch (2010) report 0.36, 0.25, and 0.20 respectively.
↵22 If any, this is a very partial adaptation process, which is consistent with the findings in Di Tella, Haisken-De New, and MacCulloch (2010) or Ferrer-i-Carbonell and van Praag (2008). These authors show that while people almost fully adapt to changes in absolute living standards, they do not (or only partly) adapt to changes in status.
↵23 This is true in general and for separate estimations on working age, employed and unemployed migrants.
↵24 This is not necessarily reflecting a larger effect in this country. Indeed, in this case, the estimation is different as coefficient g accounts for GDP time variation only, in a specification where time dummies 0t must be dropped (as they would pick up Turkish GDP over time) and a linear time t trend is kept.
↵25 We obtain the same conclusion with lagged GDP. Only the lagged change in GDP, namely GDPh,t-1 — GDPh,t-2, is found to significantly affect the probability of return in year t. Note that this variable does not affect migrants’ SWB in the main equation.
↵26 This concerns especially Turkey, given the size of its migrant community in Germany. For instance in 2002, remittances sent by Turkish migrants living in Germany accounted for 0.4 percent of the total GDP of Turkey. We have checked above that this country does not drive the results.
↵27 For instance, newcomers due to family reunification would have different assimilation potentials than first- round migrants attracted by bilateral guest-worker programs (Borjas 1999). Yet we control for unobservable differences between different migrant cohorts by using arrival cohort fixed effects in our QFE estimations.
↵28 Those experiencing greater relative concerns also could be more likely to eventually return to their home countries. Yet, we have seen that accounting for nonrandom return migration did not change our result at the mean. Moreover, the short-dashed line in Figure 2 plots the GDP effect estimated on a subsample excluding all the observations for those who return to home countries at some point in the panel (930 return migrants over the period of study and 6,118 individual x year observations). The results are basically unchanged.
↵29 Some evidence, providedby Cheung, Chudek, and Heine (2011), tendsto showthatthesemechanisms are cumulative: People are better able to identify with a host culture the longer their exposure to it, but only if this exposure occurs when they are relatively young.
↵30 ROR information is unfortunately limited to 12 years, 1998-2009, which reduces the sample to around 21,145 migrant-year observations. (This is another reason to use QFE rather than FE in this extension.)
↵31 We believe that such suggestive evidence of a switch in reference groups—theorized by Piore (1979) and Stark (1991)—is original in the literature. The study by Gelatt (2013) suggests that Latino and Asian migrants maintain simultaneous reference groups in both the United States and the country of origin. Yet she does not find clear evidence of a shift in reference groups, which may be due to small sample sizes (lowtestpower) or the fact that she cannot capture changes in reference points occurring within migrants’ earliest years in the country.
- Received January 2015.
- Accepted January 2016.
