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GUESTWORKERS OF THE 1980's AND 1990's: WHO IS GOING BACK AND WHOIS STAYING IN GERMANY

BY

DOUGLAS MASSEY

Population Studies Center, University of Pennsylvaniadmassey@pop.upenn.edu

AND

AMELIE CONSTANT

Population Studies Center, University of Pennsylvaniaaconstan@pop.upenn.edu

Prepared to be presented at the 2001 meetings of the European Society of PopulationEconomics, in Athens, Greece, June 14-17, 2001.

1Treaties for recruitment were signed with Italy in 1955, Spain and Greece in 1960,Turkey in 1961, Portugal in 1964, and Yugoslavia in 1968.

2Guestworkers and immigrants are synonymous terms in this paper; they include boththe foreign-born and their offsprings who are born in Germany. They all are legal residents,and some are German citizens.

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ABSTRACT:

Analysis and estimation of emigration is very important when assessing the successof immigrants in the host country. The big debate raging in the migration field is whetherthe wage-experience earnings trajectory estimated by Chiswick and others using cross-sectional data actually reflects a process of economic assimilation characterized bysteadily increasing returns to host-country experience (as predicted by Mincer, Chiswick,and other standard human capital theorists), or whether it reflects the changing selectivityof migration across successive cohorts (i.e. a drop in cohort quality as argued by Borjas),or a selective process of emigration (wherein economic failures steadily return home, asmentioned by Jasso and Rosenzweig and others). In this paper we address this questionby looking at the actual emigration rates of the guestworkers in Germany and theircharacteristics.

Using the German panel data we model and control some of the selectiveprocesses and calculate the actual probabilities of return migration as opposed to stayingemployed or unemployed in Germany. We create an event history file of person-yearsobserved from 1984 through 1997 by following men and women immigrants from their pointof entry until exit or censoring. We estimate a logit model to predict the probability ofemigration as a function of human capital, time in Germany, demographic, and labormarket characteristics.

Our results show that emigration processes are not random but highly selective.Overall, the probability to emigrate decreases with additional time in Germany, highereducation, higher wages, and secured prestigious jobs. We found that the least successfulguestworkers have the higher probability of emigration, and the probability of emigrationremains strong for male guestworkers. Finally, we were not able to confirm the hypothesisthat selective emigration biases cross-sectional results.

I. INTRODUCTION

Since the late 1950's Germany has experienced massive migration. The immigrantsof the 1950's, 1960's, and 1970's, the guestworkers, were recruited by German employersto work in the German factories and relieve Germany from labor shortages. They camefrom Italy, Spain, Greece, Yugoslavia, and Turkey according to bilateral treaties1 with therespective sending countries. The term guestworkers2 also reflects the fundamentaldifference between immigrants in Germany and in other countries that experience

3Emigration, return migration, remigration, outmigration, and repatriation, aresynonymous terms in this paper. They indicate that the immigrants move back to their nativecountry after they have stayed in the host country.

4The option of moving on to a third country is not considered here.

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immigration influxes - like the U.S. Guestworkers were recruited on a short term temporarybasis according to the ‘rotationprinzip.’ At the same time, the guestworkers migratoryintentions were also short term; they were to return to the ‘native soil’ within 5 years. Theirmotive was to make enough money abroad, so as to improve their economic plight athome. The first streams of guestworkers, were dominated by young men mostly single ormarried but not accompanied by their families. This phase lasted up until the halt ofrecruitment in 1973. After 1973, virtually all migration to Germany was due to familyreunification. Guestworkers in Germany can, therefore, be considered as a homogeneousgroup vis-à-vis their legal status and immigrant careers.

The enlargement of the European Union in the 1980's and 1990's allowed statemembers to legally live and work in Germany. From the group of guestworkers thisincluded Italians, Spaniards, and Greeks. The fall of the iron curtain in the 1990's, alsoprompted a plethora of immigrants in Germany, mostly ethnic Germans. By the end of themillennium the immigrant population in Germany has risen to more than 10 percent. Still,guestworkers remain a large and distinct group of legal immigrants, the majority of whomare Turks.

Estimation of emigration is very important in assessing the success and assimilationof immigrants in the host country. Regardless of whether immigrants are positively ornegatively selected, the selective character of emigration amplifies their initial selection,and can, thus, undermine the validity of cross-sectional studies (Borjas (1985)), givingerroneous results on assimilation. Moreover, understanding emigration is extremely usefulwhen studying the economic impact of immigration on the natives while it also hassignificant fiscal implications (Reagan and Olsen (2000) and Duleep (1994)). Lastly,research on emigration can improve the ability to forecast trends in immigration.

Return migration3 is inherent in the migrant’s career. From the moment a personmigrates, he or she is confronted with the decision to stay in the new country or to returnback to the home country.4 In fact, return is the last phase in the social process ofmigration (Massey (1987)). This ‘return ideology’ has always been very prominent amongthe guestworkers in Germany. Bohning (1981) estimates that more than two thirds of theforeign workers admitted to the FRG have returned. In particular, during the years 1961-1976 9 in 10 Italians, 8 in 10 Spaniards, 7 in 10 Greeks, 5 in 10 Yugoslavs, and 3 in 10Turks have returned to their home countries. Still, the guestworker population constitutesmore than 8 percent of the German workforce. In spite the official statistics of theirpermanent migration, many guestworkers still believe they will return home.

In this paper we seek to answer the following research questions. First, who are theimmigrants who go back to their home country and what are their characteristics,compared to those who stay in the host country; is emigration a random or a self-selected

5Chiswick (1986b) found little evidence that emigration was selective, at least withrespect to schooling. Reagan and Olsen (2000) found no evidence for a skill bias in returnmigration.

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process? Second, if emigration is selective, does this selection bias cross-sectionalassimilation results? Our aim is to improve the existing empirical basis and offer robustsolutions to the ongoing debate on immigrant emigration. To answer these questions weanalyze the economic and social longitudinal determinants of the propensity ofguestworkers to return to their homeland. In this 14 year longitudinal study, we are ableto study the life cycle events of men and women guestworkers. We control for the standardhuman capital variables, like years of education, vocational training, language proficiency,and years of residence in Germany. We augment the analysis to account for labor marketcharacteristics, demographics, social, and psychological ties. Finally, we control forgeographic location.

Our results indicate that between the years 1984 and 1997 about 10 percent ofguestworkers have left Germany to return to their home country. We find distinctdifferences between the guestworkers who choose to emigrate and those who choose tostay in Germany. The odds of returning are highest among the least educatedguestworkers, the lowest paid, and those in low prestige jobs. Moreover, we find that menare the most likely to return, and that the probability of emigration increases with age andis a positive function of retirement. We could not accept the hypothesis that emigration isa function of poor labor market conditions in the host country or unemployment. Theprobability of emigration decreases considerably with additional years of residence inGermany, with the presence of young children, and with property ownership. In sum, allthe assimilation indicators (economic, social, and psychological) are significant, and exerta negative effect on the probability of emigration. Our results are in congruence with theliterature affirming that emigration is negatively selective with respect to human capital andwages. Our results on cross-sectional earnings estimation could not able to confirm thehypothesis that selective emigration biases cross-sectional results.

The paper is structured as follows: In the next section we review the literature onreturn migration. In section III we present the theoretical and methodologicalconsiderations. In Section IV we describe the data base and the variables employed in theanalysis, and discuss the predictions of the model. In Section V we describe the samplepopulation. In Section VI we present the results, and in Section VII we conclude.

II. LITERATURE REVIEW

Research on return migration has produced a wealth of studies with the mainconclusion being that emigration rates are high, vary considerably by nationality, andemigrants are a self-selected group of immigrants.5 Most empirical studies have beengender blind, considering only male immigrants. Historically for the U.S., according to theSocial Security actuarial assumption, 30 percent of all legal immigrants emigrate, 83percent of them emigrate during their first 10 years, and 17 percent emigrate after residing

6The Census Bureau’s projections for the years 1992-2050, based on the assumptionthat high levels of immigration are associated with low emigration rates, imply a much loweremigration rate of 15.38 percent (Duleep (1994)).

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at least 10 years in the U.S. (Duleep (1994)).6 In the 1960s, return immigrants were aboutone third of legal immigrants in the U.S., and emigration rates were higher for the morerecent immigrants (Warren and Peck (1980)). In the 1970's, emigration rates dropped to27 percent, and emigrants were found to leave the country within a decade after theirarrival (Warren and Kraly (1985)). Emigration rates varied substantially by nationality (from20 to 50 percent); proximity as well as relative attractiveness of the immigrant’s homecountry are better predictors of emigration than address reporting (Jasso and Rosenzweig(1982)). Jasso and Rosenzweig (1990) in their study of permanent resident immigrants inthe U.S. between 1960 and 1980 found that European immigrants are the most likely toemigrate, Asian immigrants are the least likely to emigrate, and immigrants from theWestern Hemisphere are in between. Emigration rates are even lower in the 1980's but,still, they vary by nationality from 3.5 percent for Asians to 34.5 percent for North Americanimmigrants (Borjas and Bratsberg (1994)).

With regards to the quality of emigrants research has produced conflicting results.Jasso and Rosenzweig (1988) found that the immigrants who are the most skilled do notnaturalize and are these immigrants who have the higher probability of return migration.In sharp contrast, Borjas’ (1989) longitudinal study on immigrant scientists and engineersand Massey’s (1987) study on Mexicans found that return migrants are the least successfulimmigrants economically. Mexican emigrants are highly negatively selective with respectto both wages and human capital (Lindstrom and Massey (1994)). Borjas and Bratsberg’s(1994) theoretical study on immigrants in the U.S. showed that whether the initialimmigrants are positively or negatively selected, the selective character of emigrationamplifies their selection at either tail of the distribution. Specifically, if the immigrants arepositively selected the return immigrants are the worst of the best but if the immigrants arenegatively selected the return immigrants are the best of the worst. Outmigrants are morelikely to return to wealthy countries that are geographically close to the U.S. Thesetheoretical findings are verified by Ramos’ (1992) study on the Puerto Ricans in the U.S.,who are a negatively selected group but the returnees are the most skilled among them.

Reagan and Olsen’s (2000) study on both men and women did not find any genderdifferential effects but found that the immigrants who migrate at a younger age, who havea higher potential wage, more years of residence in the U.S., and participate in welfare areless likely to return. They also found that Mexicans and immigrants with a college degreeare more likely to emigrate. In general, in the case of “mistaken migration” most emigrationoccurs soon after entry and in the case of work in the U.S. - retire in the home country theolder one arrives, the higher the probability of emigration at retirement age (Duleep(1994)). Additional years of residence in the U.S. lower the probability of emigration, whileemigration rates are higher for Western European countries, lower for Third Worldcountries and the lowest for refugees.

Research on emigration rates and the quality of emigrants in countries other than

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the U.S. has also found similarly conflicting results. For example, Irish return migrants,compared to immigrants who stay abroad and to the non-migrants, are positively selectedwith respect to education (Barrett and Trace (1998)), while Egyptian return migrants arenegatively selected with respect to skills (Bauer and Gang (1998)). Contrary to studies inthe U.S. Bauer and Gang (1998) also found that additional time spent abroad and theexistence of social and informational networks abroad shorten the migration durationabroad, while remittances increase the migration duration. For Germany, the few studieson return migration are based on selected subsamples of the immigrant population andfocus on the self-reported expected duration of stay, which is used as synonymous toreturn migration. Overall, these studies concur that the more integrated the guestworkersare the longer their expected duration of stay. Dustmann (1993) in his theoretical life-cyclemodel with endogenous return intentions compares the environment at home and abroadand asserts that return migration takes place even without changes in the wagesdifferentials between the two countries. His empirical cross-sectional analysis uses thereturn intentions of male immigrant heads of households to predict their optimal returnpoint. He finds that the integration indicators, such as YSM, speaking German, beingmarried to a German, and having young children, have a prolonging effect on the intendedduration of stay in Germany, and that duration of stay varies with nationality. Based on thefirst 6 waves of the GSOEP Schmidt’s (1994) study on blue collar immigrants estimateda return migration of 21 percent, assuming panel attrition is orthogonal to return migration.He showed that return probabilities differ by country of origin, age is convex in theprobability to return and decreases with education but additional years-since-migration donot lead to reduction in return propensities. Naturally, when the spouse is living abroad theprobability to return increases.

Along the same vein, Steiner and Velling’s (1994) study on male and femalehousehold heads emphasize permanent and temporary intentions of stayers. They findthat the duration of stay increases with more years-since-migration, with higher education,speaking German well, property ownership, feeling good in Germany, and the presenceof young children in the household. Having children in the home country decreases theduration of stay as does remitting, and being unemployed. They also found that theprobability of return increases at retirement but could not find any gender effect. In arelated household study on family reunification, Velling (1994) found no gender, education,nationality, or ysm effect on the probability to return home for family reunification. Instead,the less economically successful immigrants return home first. Moreover, remigration toreunify in the home country is less likely the older the household head and more likely ifthey remit.

Our study is a comprehensive study of both men and women that models the actualreturn migration of immigrants in Germany. With the availability of 14 years of the GSOEPwe were able to implement a survival analysis and calculate the probability of emigrationat each point in time. Because we are using the documented return migration we did nothave to make any assumptions about panel attrition (Borjas (1989)), create upper or lowerbounds of emigration (Jasso and Rosenzweig (1982)), or use the return intentions ofimmigrants (similar to the German studies), which might be endogenous with time spent

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in Germany. In the next section we present the theoretical and econometric framework ofour study.

III. THEORETICAL CONSIDERATIONS, ECONOMETRIC METHODOLOGY, ANDEMPIRICAL IMPLEMENTATION

Our theoretical considerations are based upon the principles of economics andsociology. The underlying economic theory posits that rational economic agents willmigrate if the expected present value of total benefits is greater than the total costs ofmigration, subject to information constraints. Total benefits entail both pecuniary (higherearnings) and non pecuniary benefits (family and cultural ties, better climate, sentimentalbonds with a region, and political regimes). Similarly, costs include direct costs (costs ofmoving) and opportunity costs. In sociology, the deciding factor for migration to take placeis social networks that are maintained and reinforced by a constant circulation of people,goods, information, and capital between sending and receiving areas (Massey (1987)).Social networks and ties can effectively explain why migration takes place even in theabsence of wages differentials.

The theory of emigration is very much like the theory of migration or first move toanother country. However, there are three main differences between the decision tomigrate and the decision to emigrate. First, the emigrant is inherently more prone to movebecause he or she has already experienced one move. Second, with regards to both thehost and destination (home) countries, the emigrant has a more accurate information set.He or she knows the wage distribution, the language, the culture, and the climate of hisnative country. He also knows the realities of the host country and might, thus, incur lowerpsychic costs than in his first move. Third, higher wages and employment opportunities inthe destination country are neither a necessary nor a sufficient condition for emigration.Instead, familial and sociogeographic considerations are more important for the decisionto return. Social networks are central in the return behavior of immigrants. The emigrantmight, therefore, return in spite the existing wage differentials. Finally, the proximity of thetwo countries is not a significant determinant of the decision to move.

The theory of emigration affirms that emigrants can come from the upper or thelower end of the socio-economic distribution. The big theoretical debate hinges upon thequality of the emigrants. Specifically, the question is whether the wage-experienceearnings trajectory estimated by Chiswick and others using cross-sectional data actuallyreflects a process of economic assimilation characterized by steadily increasing returnsto host-country experience, or whether it reflects the changing selectivity of migrationacross successive cohorts, or a selective process of emigration wherein economic failuressteadily return home.

In this paper we model the choice behavior of individual migrants facing twoalternatives: the option of returning to one’s home country versus the option of staying inthe host country. Such behavior is described in probabilistic terms. We estimate theprobability of emigration based on values of a set of explanatory variables. The probabilityof emigration is not directly observed. Logistic regression model is an advantageous

7The parameters of the logit are not necessarily the marginal effects, but vary with the

values of x as: . This is why the odds ratio�E[ y]�x

� (�x) [1� (�x)] � or P(Y1)[1P(Y1)]�

is used.

8The odds are the ratio of two probabilities for any mutually exclusive events or P/(1-P)

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P(Y1 |X) e �/xi

1�e �/xi

� (�/x) (1)

P(Y1) 1

1�e�/x

ln P1P

�/ x

technique for estimating models with a binary dependent variable. Our dependent variableis a categorical variable that takes the value of one if an individual has emigrated and thevalue of zero if an individual is still in Germany.

The econometric method is discrete-time logistic regression for event history (orsurvival) analysis. In particular, we focus on nonrepeatable one-way transition events. Thatis, the event (emigration) occurs only at discrete time points and the transition from onediscrete state (living in Germany) to another (emigrating) occurs only once for eachperson. We model the risk of the event occurring at time t, given that the event did notoccur before time t. By definition this is the ratio of the probability of the event occurringat time t for individual i (Pit) divided by the probability of nonoccurence of the event priorto time t (1 - Pit). The risk of the event depends on the independent variables X. The choiceprobability, assuming a logistic distribution, is:

where i indexes the individuals. The parameters � reflect the impact of changes in X on theprobability that Y = 1.7 The virtue of equation (1) is that its inverse (or the log odds ratio8)has the following closed form:

The explanatory variables in X consist of a set of human capital, individual specificcharacteristics, and labor market characteristics. These independent variables areexpected to affect the individual’s probability to emigrate. The model is solved withmaximum likelihood estimation. Since likelihood equations are non-linear in theparameters � an iterative algorithm is used for the maximization of the likelihood function.The resulting estimates are asymptotically unbiased, consistent, normal, and efficient.Finally, we compute the actual emigration probabilities, according to the followingequation:

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In the following section we describe the database and the determinants of theprobability of emigration.

IV. DATABASE AND CONSTRUCTION OF VARIABLES

Database

The empirical analysis is based on a nationally representative data set, the GermanSocioeconomic Panel (GSOEP). The GSOEP was developed at the Universities ofFrankfurt and Mannheim in cooperation with the German Institute for Economic Research(DIW) in Berlin. It is an ongoing longitudinal database that started in 1984 in the formerFRG with a sample of about 12000 respondents, 3000 of whom were legal immigrants. Thelatter were immigrants whose head of the household was from Italy, Greece, Spain,Yugoslavia, and Turkey, or the so-called guestworkers. The first wave was based on thenon-institutionalized population. In later years, as the GSOEP follows individuals, someinstitutionalized individuals are included. In this ongoing project all individuals aged 16 orolder are interviewed annually. Respondents were selected by random walk. The GSOEPcontains rich socio-economic information on both native Germans and legal immigrants.Since the 1990 reunification, the data base refers to all Germans - West and East. In 1996the immigrant data base was expanded to include immigrants from other countries,especially eastern Europeans. The most important feature of the GSOEP is that itoversamples guestworkers. The survey provides excellent information on guestworkerspre-immigration profiles and level of socio-political integration into the German community(Wagner et al. (1993)).

The GSOEP is especially suitable for analyzing emigration probabilities becauseit has a good record of following individuals who move within Germany, and a good recordof tracking immigrants who returned back to Germany after they had gone to theirhomeland (to serve in the military, for example). Temporary drop-outs or persons andhouseholds which could not be successfully interviewed in a given year are followed untilthere are two consecutive temporary drop-outs of all household members or a final refusal.The longitudinal development of the database is influenced by demographic and field-workrelated factors. Specifically, attrition is related to mortality and migratory movements. It isalso related to unsuccessful interviews and unsuccessful tracking of individuals throughoutthe survey. Overall, GSOEP has a relatively low attrition rate.

Our Sample

The empirical analysis is based on all 14 waves available (1984-1997). The unit ofanalysis is the individual. For the purpose of this paper we focus on men and womenguestworkers only, because they have been in the GSOEP since its inception. Eventhough since 1990 the GSOEP refers to the new united Germany, our guestworker sample

9Guestworkers did not move to the new states after the reunification but have remainedconcentrated in the former West German territory.

10In the case of right-censored observations we include information about their survivalup to the time of censoring without making any assumptions about the timing of the event’soccurrence in the future (Yamaguchi (1991)).

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is from the former FRG.9 Each year we consider all individuals over 16 years of age, whohave given successful interviews. Further, we excluded those immigrants in the military.Military personnel follow different moving trajectories and may skew our emigrationestimates. Throughout this longitudinal study immigrants in the military were no more thana dozen. The final longitudinal sample, adjusted for mortality, contains 4485 guestworkers,of whom 2311 are men and 2174 are women (Table 1). Out of these 4485 individuals, 830are with the panel for all 14 years. From the remaining 3655 (the unbalanced panel) somestarted with the panel and then left for good or they came back to the panel after atemporary drop-out, some entered the panel in later waves and left or stayed, and someare repeat entrants. The GSOEP documents about 750 guestworkers who have movedabroad. They are the emigrants of our sample.

To implement the event history (or survival) analysis we restructured the GSOEPdata into “person-years,” which are the effective unit of analysis. A person-year is a one-year fraction of a person’s life during which the event (emigration) may or may not haveoccurred. Each fraction of a person’s life is treated as a distinct observation. The person-year file or the person-period record file contains information about the occurrence ornonoccurence of the emigration event, as well as the values of all the relevantindependent variables (with or without temporal variation); it is the life history of eachperson. By the very definition of being a migrant, all immigrants are at risk of returning totheir home country from the moment they arrive in Germany. We assume that allimmigrants enter the risk of having the event or emigrating at age 16 (when they enter theGSOEP) and they remain at risk until the time of emigration or censoring.10 It is not,however, necessary that every person experience the event. We also assume that right-censoring is random so that the time between the beginning and end of an observation isindependent of the timing of events (Massey (1987)). The person-year file has 32555observations and 3000 of them have emigrated.

Variables

The variables that change from year to year for each person - for example, age,YSM, education, and weekly wages - are allowed to vary across years in the event historyfile. The variables referring to fixed characteristics such as sex, and education beforemigration remain constant over all person-years.

11A move is an investment in human capital (Sjastaad (1962) that is justified accordingto a cost-benefit analysis. The rationale is that younger immigrants will be able to reap therewards of their investment.

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G � IA�N

A D�E�L

Construction of the dependent variable:

In general, the guestworker population in the GSOEP in 1997 is described by thefollowing identity:

where G is the guestworker population in 1997, I is the initial guestworker population in1984, A is the sample attrition, and N is the new guestworkers who entered the sampleafter 1984 and throughout this longitudinal study. We define sample attrition as:

where D is the sample attrition due to mortality, E is attrition due to emigration, and L isattrition due to unsuccessful tracking or interviewing of individuals. In our analysis, weadjust for mortality and classify as emigrants those who are documented by the GSOEPas having moved abroad. We implicitly assume that those individuals lost to follow-up havestayed in Germany. Our dependent variable is a categorical variable that takes the valueof one if an individual has emigrated and the value of zero if an individual is still inGermany. The resulting emigration rate for our person-year observations is 9 percent. Independent variables

A standard set of human capital and socioeconomic status (SES) measures areentered as covariates in the model. Our main interest is how these characteristicsinfluence the individuals’ probability to emigrate. According to the theory of human capital,the young, the more educated, the recently married, and the wage earners are more likelyto migrate.11 Emigrants are self-selected, and those who invest more in the host countrywill be less likely to emigrate. By investment in the host country we mean investment inskills, such as education and training, in physical capital, such as owning property orbusiness, and in social capital such as the creation of family ties and professionalcontacts, attachment to neighborhood and way of living. Finally, emigration rates will behigher for the most recent immigrants than the immigrants who live in the host country forseveral years. Table 2 presents the variables and the predicted outcomes.

AGE: The effect of age on the odds of emigrating is non-linear. We expect the propensityto emigrate to be convex in age, that is, the younger immigrants are more likely toemigrate, while at prime working age, emigration rates drop and increase again as onebecomes older and retires from the labor market (Schmidt 1994)).

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HUMAN CAPITAL: The GSOEP allows us to differentiate between the effects of years ofschooling and vocational training in the home country and years of schooling andvocational training in Germany. We, thus, control for differences in the initial stock ofhuman capital (education before migration) and render immigrants' education in Germanyqualitatively similar among themselves. Our measure of human capital includes the effectof training in addition to formal education. Vocational training is a unique feature ofGermany's educational system and an important part of formal education for non-University goers who want to access skilled jobs. It includes trade/agriculturalapprenticeship, business school, technical college, and college or university education.

Education acquired in the country of origin, eduhome, is entered as a continuousvariable and includes both formal schooling and vocational training. The average yearsof schooling and vocational training for all five groups of guestworkers were assigned tothis variable (Constant (1998)). We expect that immigrants who went to school in theirhome country will be more likely to return to their home country. They will not face theproblems of the nontransferability of credentials and will have less difficulty finding a jobafter remigration.

To capture non linearities of formal schooling in Germany we create five dummyvariables following the GSOEP questionnaire. They indicate investment in human capital,which may contribute to better job opportunities and higher earnings in Germany. Weexpect that immigrants who went to school in Germany will be less likely to emigrate.Obtaining no degree in Germany is the reference category.(This is also important becauseit reflects the age at entry effect. The younger they arrive the lower the emigration rates.)Vocational training in Germany is another dummy variable. Because vocational trainingis more country specific, we expect that immigrants with vocational training in Germany willbe less likely to emigrate.

LANGUAGE: The literature on assimilation has established that speaking the hostcountry’s language fluently facilitates the transfer of skills and the acquisition of humancapital, as well as it may reduce employer discrimination. German language proficiencyis important in determining job prospects, occupational advancement, and earnings, andreflects the ease with which guestworkers are assimilated. The dummy variable speakingGerman fluently is constructed from a self-assessed skill question. We expect languagefluency to decrease the probability to emigrate.

YSM: The years since migration variable is constructed from the year of immigration andthe current wave. For those born in Germany the birth year is subtracted from the currentwave. It captures investment in human and social capital in the host country as well asother intangibles such as a bond or attachment with the new country and making newroots. Besides capturing assimilation, additional years of residence in Germany alsoincrease the probability of being employed in Germany. Finally, this variable captures theestrangement or distancing of immigrants from their home country. We expect to find thatthe propensity to emigrate is convex in ysm, and that additional years of residence inGermany constitute a strong deterrent to return migration.

12The married categories include those who are separated.

13Dustmann (1993) views children as less of an integrating factor and more as aconstraint in obligating parents to remain in the host country until they finish their education.

14We classified those on maternity leave as being full time workers.

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SEX: This variable is measured as a dummy variable equal to 1 if the respondent is male.We expect a strong differential effect on the probability of emigration by gender becausethe opportunity structures in the home countries are different for men and women. Ingeneral, the guestworkers home countries are more patriarchic than Germany, there issexual stereotyping, and the social and cultural norms favor men. Male immigrants havehigher probability to emigrate because they benefit more from this setting. Similarly,women are more apprehensive about returning if they feel that there will be lessopportunities for them and their daughters.

FAMILY STATUS: Familial reasons are very important in the emigration decision-makingprocess. We distinguish familial reasons between marital status and information onchildren. With regards to marital status we constructed the following categorical variables:married with the spouse living in Germany, married with the spouse not in Germany, andsingle, divorced, and widower which are the reference category.12 Being married with thespouse in Germany indicates a permanent migration and should lower the probability ofemigration. On the other hand, if the spouse lives in the home country this indicates astrong link with the home country and should increase the probability of emigration.

The presence of children under 16 years old in the household is a dummy variable.Having children in the host country creates strong social ties. In addition, if these childrenare born in the host country this is equivalent to planting new roots. Children are, also theacculturation link between the host country and the parents, and can influence the parentsto stay in the host country.13 Another dummy variable stands for whether immigrants havechildren under 18 years of age in their home country (kidsnative). Children in the homecountry will increase the likelihood of return.

EARNINGS: Weekly gross labor income is another determinant of the probability ofemigration. According to the theory of emigration the effect of this variable is ambiguous.Economically successful immigrants, who feel that their mission has been accomplishedabroad might decide to return home. In this case, higher earnings increase the probabilityto return. On the other hand, return migrants may be those who were unable to ‘make’ itin the new country and are disappointed about their migrant career. Lower earnings will,thus, increase the probability of emigration.

EMPLOYMENT STATUS: The GSOEP asked respondents to indicate their employmentstatus classified as: full time, part time, in training, marginally employed, registered asunemployed and not employed.14 Combining information on these categories and on age,

15Because our analysis is based on the legal guestworker population this variable dosnot reflect legal status.

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we carefully recoded the employment status variable to include a retired category. Workingfull time is the reference category. We expect full time status to vary negatively with theprobability of emigration.

PRESTIGE. Occupation is viewed as a form of human capital that determines thepropensity to emigrate (Massey (1987)). This is a continuous variable of the Treiman’soccupational prestige scale, a highly accurate measure of the occupational prestigehierarchies of individual countries. The highest score corresponds to the highestoccupational standing, such as the Chief of State, and the lowest score to a garbagecollector, or a beggar. It captures social standing, power, respect, life-style, and lifechances. In a traditional society like Germany job hierarchies are paramount. The higherthe prestige score, the more immigrants are successfully integrated, and the less likely toemigrate.

EMPLOYMENT BEFORE EMIGRATION: Is a dummy variable for whether an individualwas working full or part time the year before emigration. The hypothesis is that those whoare unemployed or not employed may find it to their interest to stay in Germany and benefitfrom Germany’s generous social services and assistance. Therefore, full time workers willhave a higher probability to emigrate.

REMITTANCES: Whether a migrant remitted to family members in the country of origin isemployed as another dummy variable to capture differences in migration intent.Remittances indicate two effects. First, that immigrants keep strong ties with family andfriends in their home country. Therefore, they remit either to repay a debt, to pay for asister’s dowery, for the parents’ health care, for financial support of close relatives, or tofulfill previous financial arrangements. Second, immigrants remit for their own financialbenefit in the home country. That is, to help build their own house, to buy a taxi, start anew business when they return, to be able to afford more capital goods, or invest in newventures. We expect that those who remit will have a higher probability of returning.

GERMAN BORN: A dummy variable, longitudinally checked, that is one if the guestworkeris born in Germany or has migrated in Germany before 1949. This mainly reflects the ageat entry effect. Given the idiosyncracies of the German naturalization law this variabledoes not necessarily indicate German citizenship but can be thought of as a dimention ofsocial stratification.15 Guestworkers who are German born will be more assimilated andfeel that Germany is their home. Moreover, German citizenship may open the door to jobsand opportunities not available to immigrants. The German born will, therefore, have alower probability of emigration.

FEEL GERMAN: A dummy variable that indicates a successful social integration. It is

16This geographic classification is analogous to the SMSAs in the United States. SMSAstructures do not exist in European countries.

15

constructed from a self-reported question and reflects the perception that the guestworkershave about themselves as residents in Germany, and how they think they fit in the Germansociety. We expect those who feel entirely German to have a lower probability ofemigration.

ASSETS: Whether immigrants own their dwelling in Germany is another dummy variable.In general, property ownership deters people from moving. We expect home ownershipto decrease the probability of emigration.

GEOGRAPHIC LOCATION: We constructed 10 dummy variables for geographic location,following the GSOEP classification of the former western federal states and the city-statesof Berlin and Hamburg.16 These variables refer to contextual factors, and capture the jobopportunities, the regional unemployment rate, population density of place of residence,and the amenities and disamenities of a region . Berlin, the capital, is the omitted location.

The general treatment of the variables employed in this analysis is that if somecontrol variables had missing values we replaced the missing value with the mean valueand flagged them with dummy variables indicating missing values (Lillard and Willis,(1994)).

V. CHARACTERISTICS OF THE SAMPLE POPULATIONS

Table 3 presents the selected demographic and labor market characteristics of oursample populations for 1984 (the first year of the study) and 1995. We chose 1995because many emigrants are still in the sample. The first two columns present and contrastthe characteristics of the entire population in the two cross-sections. Columns 3 and 4 referto men, and Columns 5 and 6 to women in the respective cross-sections. Overall, menguestworkers work more hours per week and earn more per week than womenguestworkers. Although both men and women have been in Germany for 15 years by1984, men are, on average, older than women and have slightly more education beforemigration. Both men and women have the same basic education in Germany (primary,secondary, and technical) and only 1 percent of guestworkers finishes the academic highschool (Abitur). By 1995 a larger percentage of men and women have obtained degreesin Germany but the percentage of guestworkers who finish high school remains remarkablyat the low 1 percent. Further, a larger percentage of men than women have obtainedvocational training in Germany. According to their self-assessment more men speakGerman fluently. It is interesting to note that speaking fluency has increased by 86 percentin 10 years.

The majority of guestworkers (about 73 percent) are married and live with their

17Both Figures 1 and 2 have been calculated at the mean value of the rest of theregressors.

16

spouses in Germany. A small percentage of them have a spouse in the home country andthis is true more for men than for women. It is interesting to note that this percentage hasdropped by half in a decade. Similarly, the percentage of guestworkers who have childrenin the home country fell from 10 to 2 percent in a decade. These statistics confirm the defacto permanency of the guestworker population in Germany. In particular, the percentageof guestworkers who are German born increased from 7 in 1984 to 17 percent in 1995, anda striking 13 percent own their dwelling by 1995 (as opposed to 6 percent in 1984). Lastly,a smaller percent of guestworkers remit by 1995. In sum, the summary statistics from Table3 point to an aging immigrant population that has settled in Germany permanently.Nonetheless they hardly identify themselves as Germans; only 3 percent reported feelingtotally German and this has not changed through the years.

VI. EVENT HISTORY RESULTS

Logistic regression results of the probability of emigration

Table 4 presents the results of the survival analysis for men and womenguestworkers. The first column shows the estimated coefficient and, thus, the impact(positive or negative) of the independent variables on the log odds of emigration. The $2-Wald test result is in parenthesis underneath. The second column shows the odds ratioestimates. Overall, the results are in accordance with the theoretical expectations andsimilar to results in the U.S. and Germany. From the age coefficient we see that the oddsof emigration decrease with age. Figure 1 plots the odds ratio as a function of age, holdingeverything else constant.17 Accordingly, the odds of returning to the home countrydecrease when the individual is young, reach a minimum by the mid-twenties, and increaseat an increasing rate afterwards. This profile indicates that younger immigrants want tostay in Germany and take advantage of the quality education and vocational training thatit offers in the hopes of a good secure job with high pay. If these expectations are notrealized in Germany by the time they reach mid-twenties, they will be better off emigratingand starting a new life in the home country while they are still young. At older ages, whenimmigrants approach retirement and especially after retirement, the odds of emigratingskyrocket. At these ages, pecuniary reasons are not as important as are thesociogeographic ambience of their home country, and being surrounded by family andfriends. This result is similar to Borjas (1989) and Lindstrom and Massey (1994) who findthat age is convex.

As expected, we find that the odds of emigration decrease steadily and rapidly withadditional years-since-migration. That is, each additional year one resides in Germanydecreases the chances of emigration. In Figure 2 we depict this convex relationship,holding all other variables constant. The odds of emigrating are the highest for the most

18We also experimented with a specification of the natural logarithm of weekly wagesbut the results indicated a worse fit.

17

recent immigrants (those with less than five years of residence in Germany). Clearly,additional years of living in Germany have a strong negative influence on the odds ofleaving Germany. This effect prevails throughout the entire lives of immigrants. The oddsof emigrating are practically zero after 50 years of residence in Germany. This result issimilar to Borjas (1989) who finds a convex pattern of the years-since-migration variable,and to Dustmann (1993).

Table 4 also shows a gender effect. Being male is associated with 8 percentincrease in the odds of emigration, controlling for other effects. We believe that this findingis due to the different incentives by men and women. Guestworker women may fear thatthey will face stark social pressures when they return home, due to gender-role norms andpatriarchic structures of the home societies. They will, thus, be reluctant to return.Similarly, men, who benefit from this system, will have a higher propensity to return.Besides fearing restricted opportunities for advancement upon return, women also facefertility considerations that might affect their decision to migrate. Lastly, these genderdifferences might be related to the fact that women are more assimilated in Germany thantheir male counterparts (Constant (1998)).

Next, we find that weekly wages18 have the largest effect on the odds of emigrating(as indicated by the $2-Wald test). Higher wages are associated with a lower probabilityof emigration. In other words, insofar as wages mirror economic success, we find that themore economically successful guestworkers stay in Germany. This particular finding is inaccordance with the literature that finds returnees to be the unsuccessful immigrants(Borjas (1987), Massey (1987), and Lindstrom and Massey (1994)). Similarly, thoseworkers in jobs that command higher prestige scores - according to the Treiman prestigescores - are more likely to stay in Germany. All six dummy variables representingemployment status (with the reference status being full time workers) have significant andnegative effects on the odds of emigration. That is, full time workers (compared to all othercategories) have the highest probability of emigrating. This finding shows that it is not“laziness” or unemployment that prompts immigrants to return to their home country.Instead, it is rather holding low-level, low-pay, dead-end jobs. Surprisingly, we find thatworking full or part time the year before emigration decreases the odds of returning homeby 20 percent.

With respect to the human capital variables, we find that those who speak theGerman language fluently and are well educated are more likely to stay in Germany.Speaking German fluently decreases the odds of emigrating by 28 percent. From thenegative coefficients on the dummy variables on education in Germany, we see that themore educated guestworkers are less likely to emigrate. Specifically, those who haveobtained a technical or other degree in Germany, as opposed to those who did not go toschool in Germany or they went to school but did not obtain a degree (the referencecategory) have lower probability of emigrating. Stated differently, those guestworkers whodid not go to school in Germany or never obtained a degree in Germany are more likely

18

to emigrate. This result is similar to Lindstrom and Massey (1994), Schmidt (1994), andVelling (1994). Obtaining a high school degree that leads to academia (Abitur) is notsignificant probably due to the few observations we had in this category and the smallvariability of this variable. The effect of having acquired vocational training in Germany hasa negative effect on the odds of emigration but is not significant. As expected, we find thatpre-migration human capital increases the odds of emigrating, although it is not significant.

Results on the family status of the guestworkers are also as expected. Out of themarried group of guestworkers, those whose spouses live in their home countries have a51 percent higher probability of emigrating (the reference category is single, divorced, andwidower). Having children exerts a strong influence on the odds of emigrating. Specifically,having children less than 18 years old in the home country increases the odds ofemigrating by 61 percent. According to our hypothesis, the odds in favor of emigrating forthose who have young children in the household (less than 16 years old) decrease by 28percent. This reflects the fact that immigrant parents want their children to take advantageof the opportunities that Germany offers and obtain a good education. This result is similarto Dustmann (1993).

According to the theoretical predictions, we find that those guestworkers whoremitted have a higher chance of returning home (19 percent) than those who did notremit. Being German born decreases the odds of emigration by 99 percent. Thoseguestworkers who said that they feel totally German have a 46 percent lower chance ofreturning home. With regards to real estate, we find that those who own their home inGermany are less likely to emigrate. In fact, owning their dwelling decreases the odds ofemigrating by 18 percent.

The estimated coefficients on the geographic locations are all positive, with theexception of Bremen. We find that those guestworkers who live in Holstein, lower Saxony,Pfalz, and Saarland have a higher probability of emigrating compared to those who live inBerlin (the reference location). In sum, the model has good predictive power as indicatedby the AIC and the likelihood ratio test.

Calculated probabilities of emigration

From the above results we calculate the predicted value of the probability ofemigration for different combinations of values of the independent variables. Table 5shows the calculated actual probabilities of emigration separately for men and women withdifferent assumed characteristics. It shows how gender, age, ysm, and wages affect aguestworker’s probability of emigration. Clearly, men exhibit higher probabilities ofemigration than women, and older men exhibit the highest probabilities. In the first row weconsider a hypothetical immigrant who has 5 years of residence in Germany, earns belowaverage wages, is employed full time in a below average prestige job, has no educationaldegree in Germany, does not speak German fluently, has average education beforemigration, is married with spouse and kids in the home country, remits, is not Germanborn, does not feel as a German, is not a homeowner, and lives in Berlin. If this person isa 23 year old man his probability of emigration is 30 percent whereas if he is 50 years old

19

his probability increases to 50 percent. On the other hand, if this person is a 23 year oldwoman her probability of emigration is 28 percent and increases to 48 percent if she is 50years old. From the first and third column we see that women have a 5.8 percent lowerprobability of emigration than men.

In the second row of Table 5 we consider a migrant who has 10 years of residencein Germany and has obtained some schooling degree while the rest of the characteristicsare the same as above. We find that all respective probabilities decrease dramatically. Inessence, this row shows that education and residence in Germany account for about a 90percent drop in the probability of emigration, and they affect women’s probabilities morethan men’s. In the third row we consider the same immigrant who now earns aboveaverage wages, is in an above average prestige scale, does not remit, speaks Germanfluently, and whose spouse and children are in Germany in the household. Once again theprobabilities of emigration drop by 67 percent, while the disparities due to age and genderare amplified. In general, we find that higher wages and better jobs with higher socialstanding decrease the probability of emigration even further. In the next row we considerthe same immigrant, who has been living in Germany for 20 years, is German born, andhas become a homeowner in Germany. We find, that additional time in Germany alongwith German nationality and home ownership account for a remarkable 48 percent dropin the probabilities of emigration (compared to the previous row). The probability ofemigration for a 23 year old man is 0.79 percent, for a 50 year old man is 1.84 percent, fora 23 year old woman is 0.73 percent, and for a 50 year old woman is 1.69 percent.

In the last row we consider a similar migrant who has obtained a technical schooldegree and vocational training, and feels like German. In this last scenario theprobabilities of emigration drop by an additional 13 percent and women have a lowerprobability of emigration than men. We believe that the traditional and occasionallydespotic beliefs that govern the guestworker countries make them uninviting to women. Insummary, a clear pattern emerges from the above analysis. The probability of returnmigration for guestworkers diminishes with additional time in Germany, higher education,higher wages and secured prestigious jobs, and the perception of feeling German but itincreases with age, remittances, and for males. The economically and sociallyunsuccessful guestworkers are more likely to return to their native countries. These resultsare similar to Massey (1987) for the Mexican migration to the U.S., to Jasso andRosenzweig (1982) for the U.S., to Dustmann (1993) for Germany, and are consistent withthe social processes of emigration.

Detection of selection processes

Because we found strong selection processes vis-à-vis wages, we estimated astandard human capital earnings regression for the year 1995. With this cross-sectionalwage regression we can test the hypothesis that cross-sectional results suffer fromselective emigration bias. First, we estimated an earnings regression for the entireguestworker population in the GSOEP in 1995. Secondly, we considered only thepopulation who actually stayed in Germany up to 1995, by omitting from the entire

19To find the effect of YSM on the earnings of guestworkers in 1995, we calculated thepartial effect of YSM at different levels of YSM. Holding other variables constant, we find that,for the entire population, the earnings of guestworkers increase by 6.7, 5.1, and 3.6 percent at10, 15, and 20 years of YSM respectively. The corresponding calculated results for therestricted set show increases of 7.1, 5.2, and 3.3 percent at 10, 15, and 20 years of YSMrespectively. Clearly, for the immigrants who stay in Germany, earnings increase faster withYSM during the first 15 years of residence, but they drop faster afterwards, as well.

20

guestworker population those who died and the emigrants (traced from the next year). Thedependent variable in this analysis is the natural logarithm of weekly wages. Theexplanatory variables include human capital variables, assimilation variables, and familystatus variables. The estimated results are presented in Tables 6; t-ratios are underneaththe coefficients in parentheses, and the asterisk indicates a significance level of five percent in a two-tailed test.

All specifications of Table 6 show similar results. The wages of guestworkers areconcave in age. Wages increase with age at a decreasing rate, they reach a maximum atage 37, and start decreasing thereafter. A similar pattern emerges form the coefficients onthe YSM variables. Immigrant earnings increase with each additional year of residence inGermany until 30 years of YSM, and decrease thereafter. Both education before and aftermigration significantly increase wages, as does fluency in German language. Men earnmore than women. Controlling for human capital and YSM, marital status has no effect onthe wages of guestworkers. The most important result from this analysis is that thecoefficients for both the restricted and the entire population are the same. Overall, Table6 presents the same portrait of immigrant earnings.19

In sum, similar to Lindstrom and Massey’s (1994) study, we were not able to confirmthat selective emigration significantly biases cross-sectional results.

VII. CONCLUSION

In this paper we addressed the question of selective emigration and how it affectscross-sectional earnings results. Using the German panel data we estimated a logit modelto predict the probability of emigration as a function of human capital, time in Germany,demographic, and labor market characteristics. Our results showed that emigrationprocesses are not random, and emigration rates are about 10 percent. We found distinctdifferences between the guestworkers who choose to emigrate and those who choose tostay in Germany. The probability to emigrate decreases with additional time in Germany,higher education, higher wages, and secured prestigious jobs. Men are the most likely toreturn, and the probability of emigration increases with age and is a positive function ofretirement.

In sum, all the integration indicators (economic, social, and psychological) aresignificant, and exert a negative effect on the probability of emigration. Our results are incongruence with the literature affirming that emigration is negatively selective with respect

21

to human capital and wages. Finally, we were not able to confirm the hypothesis thatselective emigration biases cross-sectional results. The next step will be to estimate theprobability of emigration versus the probability of staying employed or unemployed inGermany as well as the probability of being lost to follow up with a multinomial logit. Futureresearch should address the question of repeat migration.

22

TABLE 1. YEARLY OBSERVATIONS BY SEX

WAVES MEN(1)

WOMEN(2)

TOTAL(3)

1984 1592 1418 3010

1985 1355 1137 2492

1986 1312 1080 2392

1987 1301 1093 2394

1988 1236 1062 2298

1989 1195 1059 2254

1990 1194 1032 2226

1991 1192 1029 2221

1992 1167 1010 2177

1993 1152 998 2150

1994 1074 960 2034

1995 1003 907 1910

1996 1027 1008 2035

1997 1000 969 1969

All 14 Waves (excluding the deceased) 4485

Person-year observations 32555

Source: Own calculations from GSOEP 1984-1997

23

TABLE 2. DESCRIPTION OF VARIABLES

VARIABLE LABEL VARIABLE DESCRIPTION PREDICTED SIGN

WWAGE Weekly gross earnings (in Deutsche Marks) + or -

FULLTIME 1 if individual works full time or more than 35 hrs per week, zerootherwise Category includes maternity leave (omitted category)

+

PARTTIME 1 if individual works part time, zero otherwise -

INTRAIN 1 if individual is in training, zero otherwise -

MARGEMPL 1 if individual works irregularly or is minimally employed, zerootherwise

-

REG_UN 1 if individual is registered as unemployed, zero otherwise -

RETIRED 1 if individual is > 60 yrs old and is unemployed or does not work,zero otherwise

+

NOTEMPL 1 if individual does not work, zero otherwise -

PRESTIGE Treiman’s Occupational scale. Ranges from 18-78. Those not inthe LF score an 8; those with missing data score a 9.

-

EMPLBEFO 1 if individual is working full or parttime the year before emigrated,zero otherwise

-

SPKGFLU 1 if individual speaks German fluently, zero otherwise -

NO DEGREE 1 if individual did not go to school in Germany or went to schoolbut obtained no degree, zero otherwise (Omitted category)

+

PRIMSEC 1 if individual went to primary or secondary school in Germany,zero otherwise

-

TECHNI 1 if individual went to technical school, zero otherwise -

ABITUR 1 if individual finished the academic high school, zero otherwise -

OTHER 1 if individual obtained other degree in Germany, zero otherwise -

EDUHOME Total years of schooling and vocational training in home country +

MARING 1 if individual is married with the spouse living in Germany, zerootherwise. Category includes those separated

-

MARNOING 1 if individual is married with the spouse living in the homecountry, zero otherwise

+

KIDSINHH 1 if there are kids < 16 years old in the household, zero otherwise -

KIDSNATIVE 1 if there are kids < 18 years old in the home country, zerootherwise

+

TABLE 2. DESCRIPTION OF VARIABLES

VARIABLE LABEL VARIABLE DESCRIPTION PREDICTED SIGN

24

REMIT 1 if individual sent money to relatives or friends in the homecountry, zero otherwise

+

DEUTSCH 1 if individual is born in Germany or migrated before 1949, zerootherwise

-

FEEL_GER 1 if individual feel totally German, zero otherwise -

OWNDWELL 1 if individual owns the dwelling his is residing in, zero otherwise -

BERLIN 1 if individual resides in Berlin, zero otherwise (omitted location) -

S_HOLSTEIN 1 if individual resides in Schleswig-Holstein, zero otherwise

HAMBURG 1 if individual resides in Hamburg, zero otherwise

LOSAXONY 1 if individual resides in lower Saxony, zero otherwise

BREMEN 1 if individual resides in Bremen, zero otherwise

NR_WESTFALIA 1 if individual resides in North Rhein Westfalia, zero otherwise

HESSEN 1 if individual resides in Hesse, zero otherwise

RP_SAAR 1 if individual resides in Rheineland-Pfalz and Saarland, zerootherwise

B_WURTTEM 1 if individual resides in Baden-Wurttemberg, zero otherwise

BAVARIA 1 if individual resides in Bavaria, zero otherwise

25

TABLE 3. SELECTED CHARACTERISTICS OF GUESTWORKERS IN 1984 AND 1995

TOTAL MEN WOMEN

1984 1995 1984 1995 1984 1995

Weekly wages (in DM) 391.38 499.71 547.17 654.35 216.47 328.70

Hours worked per week 27.25 23.42 35.25 28.78 18.27 17.49

Age in yrs 36.45 39.09 37.25 40.09 35.55 37.98

YSM 14.54 21.57 14.82 22.58 14.22 20.46

Education in Country of Origin (in yrs) 5.59 4.67 6.15 4.91 4.97 4.40

No degree (or school) in Germany (in %) 81 70 81 67 80 74

Primary or Secondary degree in Germany 14 18 14 20 14 15

Technical School degree in Germany 4 6 4 6 5 6

Academic High School degree (Abitur) 1 1 1 1 1 1

Other degree 0 5 1 6 0 4

Vocational Training in Germany 16 16 19 19 14 11

Speak German fluently (in %) 14 26 15 27 12 25

Married-Spouse in Germany 73 72 69 71 77 72

Married-Spouse not in Germany 4 2 6 2 1 1

Children in Household < 16 yrs old 63 52 60 49 67 55

Children in Home country < 18 yrs old 10 2 12 2 9 1

German born (in %) 7 17 5 16 9 17

Feel totally German 3 4 3 4 3 3

German Citizen 3 12 2 12 4 13

Remit 31 22 43 28 17 16

Own dwelling in Germany 6 13 6 12 6 12

Number of Obs 3010 1910 1592 1418 1003 907

Source: Own calculations from GSOEP 1984-1997

26

TABLE 4. COEFFICIENT ESTIMATES OF LOGIT EVENT HISTORY RESULTSParameters Logit Estimates

($2 Wald test)Odds Ratio Estimates

INTERCEPT -0.3322 -(1.5695)

AGE -0.0627 ** 0.939(24.0118)

AGE2 0.00129 ** 1.001(72.0561)

YSM -0.0508 ** 0.950(24.3089)

YSM2 -0.00026 ** 1.000(0.9472)

SEX 0.0842 * 1.088 (3.0540)

WWAGE -0.00126 ** 0.999(90.4738)

PRESTIGE -0.00859 ** 0.991(19.8297)

PARTTIME -0.6653** 0.514 (26.1363)

INTRAIN -1.0659 ** 0.344(36.4076)

MARGEMPL -0.6042 ** 0.547(7.7339)

REG_UN -0.7025 ** 0.495(36.8912)

RETIRED -1.6843 ** 0.186(114.5727)

NOTEMPL -0.6249 ** 0.535(37.1568)

EMPLBEFO -0.2349** 0.791(22.0937)

SPKGFLU -0.3275** 0.721(18.2101)

PRIMSEC 0.1046 1.110(1.5199)

TECHNI -0.4368 ** 0.646(6.0739)

ABITUR -0.0564 0.945(0.0285)

OTHER -1.043 ** 0.352(21.8421)

VOCING -0.0808 0.922(1.0223)

TABLE 4. COEFFICIENT ESTIMATES OF LOGIT EVENT HISTORY RESULTSParameters Logit Estimates

($2 Wald test)Odds Ratio Estimates

27

EDUHOME 0.00912 1.009(1.4994)

MARING -0.0484 0.953(0.5791)

MARNOING 0.4118 ** 1.510(13.6530)

KIDSNATIVE 0.4789 ** 1.614(42.7854)

KIDSINHH -0.3227 ** 0.724(52.2234)

REMIT 0.1769 ** 1.193(12.4573 )

DEUTSCH -0.1930 * 0.825(2.8108)

FEEL_GER -0.6248 ** 0.535(12.2915)

OWNDWELL -0.1988 ** 0.820(5.8088)

S_HOLSTEIN 0.8004 ** 2.226(18.7217)

HAMBURG 0.1603 1.174(0.8489)

LOSAXONY 0.4302 ** 1.538(10.3811)

BREMEN -0.5537 * 0.575(2.7189)

NR_WESTFALIA 0.1822 * 1.200( 2.4552)

HESSEN 0.1223 1.130(0.9796)

RP_SAAR 0.6890 ** 1.992(25.7938)

B_WURTTEM 0.1338 1.143(1.3139)

BAVARIA 0.0268 1.027(0.0468)

Akaike Info. Criterion 17982.024Likelihood Ratio 1782.709No. of Person-years Obs. 32555 (2988 or 9.17 % have emigrated)

Note:** indicates significance at the 5 per cent level in two-tailed test (p<0.05) * indicates significance at the 10 per cent level in two-tailed test (p<0.1)

28

TABLE 5. PREDICTED PROBABILITIES OF EMIGRATION UNDER VARIOUS SCENARIOS,IN PERCENT

MEN WOMEN

Characteristics 23-year-old 50-year-old 23-year-old 50-year-old

5 YSM, below ave wages and prestige,no degree, no vocational school, notfluent in German, ave eduhome,fulltime, no emplbefo, Berlin, remit, nohomeowner, marnoing, kidsnative

29.63 49.61 27.90 47.51

10 YSM, below ave wages and prestige,other degree, no vocational school, notfluent in German, ave eduhome,fulltime, emplbefo, Berlin, remit, nohomeowner, maring, kidsinhh

2.46 5.58 2.26 5.15

10 YSM, high wages and prestige, otherdegree, ave eduhome, no vocationalschool, fluent, fulltime, emplbefo, Berlin,no remit, no homeowner, maring,kidsinhh

0.79 1.84 0.73 1.69

20 YSM, high wages and prestige, otherdegree, no vocational school, aveeduhome, fluent, fulltime, emplbefo,Berlin, no remit, Deutsch, homeowner,maring, no kidsinhh

0.41 0.96 0.38 0.89

20 YSM, high wages and prestige, technidegree, vocational school, no eduhome,fluent, fulltime, emplbefo, Berlin, noremit, Deutsch, homeowner, feelGerman

0.36 0.83 0.32 0.76

29

TABLE 6. ESTIMATED EARNINGS EQUATIONS RESULTS1995ENTIRE DATA SET RESTRICTED DATA SET

PARAMETERS COEFFICIENTS(t-ratio)

COEFFICIENTS(t-ratio)

INTERCEPT -5.026 -4.889 -5.191 -5.092(-8.20) (-7.77) (-8.05) (-7.73)

AGE 0.3873* 0.4001* 0.3942* 0.4099*(11.63) (10.98) (11.24) (10.70)

AGE2 -0.0052* -0.0054* -0.0053* -0.0055*(-13.81) (-13.22) (-13.19) (-12.75)

YSM 0.0984* 0.0939* 0.1087* 0.1049*(3.15) (2.97) (3.06) (2.92)

YSM2 -0.0016* -0.0015* -0.0019* -0.0019*(-2.15) (-2.09) (-2.21) (-2.17)

SEX 1.5252* 1.5081* 1.5053* 1.4904*(11.29) (11.17) (10.65) (10.55)

PRIMSEC 0.6403* 0.6589* 0.4980* 0.5122*(3.00) (3.08) (2.23) (2.30)

TECHNI 0.4775 0.4297 0.4254 0.3763(1.50) (1.35) (1.30) (1.14)

ABITUR 0.0645 0.0178 -0.3072 -0.3812(0.11) (0.03) (-0.49) (-0.60)

OTHER 0.3749 0.2995 0.5289 0.4395(1.17) (0.93) (1.56) (1.29)

VOCING 0.2145 0.2116 0.3377 0.3398(1.08) (1.07) (1.62) (1.63)

EDUHOME 0.102* 0.101* 0.1085* 0.1094*(4.18) (4.15) (4.22) (4.25)

SPKGFLU 0.6075* 0.5620* 0.6631* 0.6166*(3.42) (3.14) (3.59) (3.32)

MARING -0.0646 -0.0834(-0.33) (-0.41)

MARNOING 0.4849 0.6514(0.85) (1.09)

KIDS16 -0.3673* -0.3689*(-2.60) (-2.49)

KIDSNATIVE 0.5729 0.2957(1.06) (0.52)

R2 0.2216 0.2257 0.2261 0.2303F statistic 45.01 34.49 41.59 31.87Mean of LnWWage 4.01056 4.07386Number of Obs 1910 1721

Note:* indicates significance at the 5 per cent level in two-tailed test (p<0.05)

30

Figure 1 : Odds of emigration with age

31

Figure 2 : Odds of emigration with Years-since-Migration

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