The Impact of Forced Migration on Mortality: Evidence From German Pension Insurance Records

Abstract

We examine the long-run effects of forced migration for individuals who were displaced from Eastern Europe to Germany in the aftermath of World War II. Evidence suggests that displaced individuals were worse off economically, facing a considerably lower income and a higher unemployment risk than comparable nondisplaced Germans, even 20 years after being expelled. We extend this literature by investigating mortality outcomes. Using social security records that document the exact date of death and a proxy for pre-retirement lifetime earnings, we estimate a significantly and considerably higher mortality risk among forced migrants compared with nondisplaced West Germans. The adverse displacement effect persists throughout the earnings distribution except for the top quintile. Although forced migrants were generally worse off regarding mortality outcomes, those with successful labor market histories seem to have overcome the long-lasting negative consequences of flight and expulsion.

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Notes

  1. 1.

    An extensive literature has examined voluntary migration. However, the process behind forced migration differs in many respects from voluntary migration decisions such that many of the policy implications and conclusions from studies on voluntary migrants may not be applicable to forced migrants (for an overview on the forced migration literature, see Ruiz and Vargas-Silva 2013).

  2. 2.

    Positive effects on long-term income of males in Finland have been argued to be due to an accelerated transition from traditional to modern occupations (Sarvimäki et al. 2009). A similar result was documented for those forced migrants in Germany who worked in the agricultural sector before migrating to West Germany (Bauer et al. 2013).

  3. 3.

    Individuals born in a recession die earlier than individuals born in times of economic prosperity (van den Berg et al. 2006). Recent evidence has suggested that entering the labor market in a recession increases mortality later in life even if initial losses in earnings have faded (Schwandt and von Wachter 2017).

  4. 4.

    Case and Deaton (2017) used the term cumulative disadvantage to describe recently increasing middle-age mortality rates among non-Hispanic whites without a college degree in the United States (1998–2015), triggered by increasingly poor labor market conditions at the time of labor market entry.

  5. 5.

    For recent empirical evidence on the link between income and health, see Chetty et al. (2016). What remains controversial is measuring the causal impact of income on health (or mortality) and vice versa. Low income could explain poor access to health care or less healthy nutrition. One could also think of poor health as reducing the ability to be gainfully employed or even as preventing people from investing in human capital. See Smith (1999) and Deaton (2003) for discussions.

  6. 6.

    The historical details regarding this mass migration have been documented thoroughly. Douglas (2012) provided a detailed overview about the historical background of the mass migration, and Connor (2007) focused on the integration of forced migrants into postwar Germany. An alternative source from the Eastern European perspective is Eberhardt (2011), who reported the number of Germans who were displaced from Poland and territories incorporated into Poland. Finally, Lüttinger (1986) and Reichling (1958) summarized important aggregate statistics on forced migrants in Germany that were drawn from censuses of the postwar period.

  7. 7.

    A small share of displaced persons who were initially located in the Soviet occupation zone eventually arrived in West Germany. This type of east-west sorting amounted to a total of approximately 500,000 individuals who moved from East Germany (Soviet occupation zone including Berlin) to West Germany from 1950 to 1955 (Reichling 1958:357–358).

  8. 8.

    The expulsion was not entirely universal regarding some ethnic Germans who remained in Eastern Europe for several decades (Aussiedler/Spätaussiedler), also including German minorities (e.g., in Upper Silesia or Transylvania).

  9. 9.

    For example, some Catholic Sudeten Germans were placed in Protestant North Hesse and Franconia, and many Protestant migrants were settled to Catholic areas in Lower Bavaria (Connor 2007).

  10. 10.

    Precisely, the covered period is from December 1993 to November 2013. Because of the nature of the administrative process, each annual wave of pension shortfall records samples all cases of death from January to November of a given calendar year and adds those cases documented for December of the preceding year. For example, the 2012 wave includes all deaths that were documented from December 2011 to November 2012.

  11. 11.

    For a detailed description of the sampling design (in German) and a codebook, see Forschungsdatenzentrum Deutsche Rentenversicherung (2017).

  12. 12.

    We obtain these shares from relating the total number of deaths reported in the official mortality statistics for Germany (Federal Statistical Office 2016) to the number of deaths in the pension shortfall records that we use as primary data source.

  13. 13.

    Only 4 % of male cases of death documented in official mortality statistics are not covered by the pension shortfall. Probably the most plausible explanation is that a considerable share of these men worked as civil servants from the beginning of their employment biography. Pensions of civil servants are tax-financed and handled separately from the public (pay-as-you-go) system.

  14. 14.

    We use an electronically preprocessed version of the original print by Besser (2008).

  15. 15.

    The cohort distribution is depicted in Fig. B.1 (online appendix), showing the percentage of each cohort in the sample.

  16. 16.

    For example, the population at risk also includes persons who out-migrated. They need only to have accumulated pension claims in Germany at some time—for example, by employment that is subject to social security contributions or by periods of child-rearing. In this case, a persons’ death is documented in the shortfall records because pension payments are terminated.

  17. 17.

    Comparing earnings biographies is more feasible if they are completed by the date of retirement because this rules out false comparisons of earnings biographies that differ only because of age differences.

  18. 18.

    This is necessary to make these population counts comparable with the pension shortfall records that document only participants of the public pension system.

  19. 19.

    The mortality rates are higher among men (95 % for displaced and 75 % for nondisplaced men) than among women (74 % for displaced and 50 % for nondisplaced women). The calculation assumes that everyone has deceased in the observed birth cohorts by the end of the observation period. The calculations are available from the authors upon request.

  20. 20.

    Because of the small number of observations at the upper margin of the age-at-death distribution, we use 40 duration indicator variables for the ages a = 68, . . . , 107. Estimating δa for ages above 107 is difficult because we observe very few persons to survive this age (e.g., the maximum age reached by one single person in the sample is 111).

  21. 21.

    To further support that interregional variation reflected by the displacement indicator matters, we implemented a multilevel mixed-effects linear regression with regions of origin (Eastern Europe vs. West Germany) at the higher level and individuals at the lower level. From this exercise, we infer that the share of variation in mortality explained at the regional level ranges from 2.7 % to 7.7 % (men) and 0 % to 7 % (women); detailed results are available from the authors upon request. Although the random effects parameters indicate only a small variance contribution at the regional level for women after birth cohort dummy variables (close to 0) are included, the overall results indicate that interregional variation matters. A large fraction of variation is explained at the individual level; but given that mortality is determined by various—presumably individually driven—factors, the estimated variance shares at the regional level can still be considered as substantial.

  22. 22.

    Lifetime earnings may represent a “bad control” (for a discussion, see Angrist and Pischke 2009) because they may themselves be an outcome and thus be affected by displacement. In particular, comparing displaced with nondisplaced individuals at a given value of lifetime earnings may differ by some unobserved characteristics that compensate the initial disadvantage in the earnings potential of the displaced. This problem is arguably a minor one because including earnings in the baseline specifications does not considerably change the estimated hazard ratios (see Table 2, comparing columns 1 and 2 for men and columns 4 and 5 for women). Both of these variables covary positively with the mortality rate and the displacement indicator given that prewar birth and death rates were considerably higher in Eastern Europe than in West Germany (see Fig. 2).

  23. 23.

    Arguably, this would be the case because the displaced person ended up earning the same despite of being displaced.

  24. 24.

    A total of 1,937,297 displaced persons were registered in Bavaria by 1950. In relative terms, this amounted to 21 % of the Bavarian population at that time (Reichling 1958).

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Acknowledgments

We are grateful to Ronald Bachmann, Gerard van den Berg, Julia Bredtmann, Bernd Fitzenberger, Joseph-Simon Görlach, Elke Jahn, Rainer Kotschy, and Regina Riphahn for helpful suggestions, and we thank the editors and three anonymous referees for their valuable comments that helped to improve this article. We also thank the participants of the meetings of the DFG-SPP 1764 Priority Program (Essen, 2016), RWI Research Seminar (2016), RGS Conference (Dortmund, 2017), ESPE Conference (Glasgow, 2017) and the DFG-SPP 1764 International Conference (Nürnberg, 2018) for insightful discussions. We further thank the team of the research center of the German Federal Pension Insurance (FDZ-RV), in particular Ute Kirst-Budzak, Torsten Hammer, and Ingmar Hansen, for supporting the data processing. Fabian Dehos, Gökay Demir, and Jan Wergula provided excellent research assistance. Financial support from the German Research Foundation (DFG-SPP 1764) is gratefully acknowledged.

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Correspondence to Matthias Giesecke.

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Bauer, T.K., Giesecke, M. & Janisch, L.M. The Impact of Forced Migration on Mortality: Evidence From German Pension Insurance Records. Demography 56, 25–47 (2019). https://doi.org/10.1007/s13524-018-0742-z

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Keywords

  • Forced migration
  • Differential mortality
  • Lifetime earnings
  • Economic history