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


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  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).


  1. Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press.

    Google Scholar 

  2. Barker, D. J. (1990). The fetal and infant origins of adult disease. BMJ, 301, 1111.

    Article  Google Scholar 

  3. Barker, D. J. (1995). Fetal origins of coronary heart disease. BMJ, 311, 171–174.

    Article  Google Scholar 

  4. Bauer, T. K., Braun, S., & Kvasnicka, M. (2013). The economic integration of forced migrants: Evidence for post-war Germany. Economic Journal, 123, 998–1024.

    Article  Google Scholar 

  5. Besser, C. (2008). Grunddaten zur Bevölkerungsstatistik Deutschlands von 1871 bis 1939 [ZA8295 Datenfile Version 1 (0)] [Basic data on the demographic statistics of Germany from 1871 to 1939]. Cologne, Germany: GESIS.

    Google Scholar 

  6. Borjas, G. J. (1985). Assimilation, changes in cohort quality, and the earnings of immigrants. Journal of Labor Economics, 3, 463–489.

    Article  Google Scholar 

  7. Borjas, G. J. (1987). Self-selection and the earnings of immigrants. American Economic Review, 77, 531–553.

    Google Scholar 

  8. Borjas, G. J. (1999). The economic analysis of immigration. In O. C. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3A, pp. 1697–1760). Amsterdam, the Netherlands: Elsevier.

  9. Braun, S. T., & Dwenger, N. (2017). The local environment shapes refugee integration: Evidence from post-war Germany (School of Economics and Finance Discussion Paper No. 1711). St. Andrews, UK: University of St. Andrews.

    Google Scholar 

  10. Case, A., & Deaton, A. (2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112, 15078–15083.

    Article  Google Scholar 

  11. Case, A., & Deaton, A. (2017). Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity, 2017(Spring), 397–476.

  12. Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., . . . Cutler, D. (2016). The association between income and life expectancy in the United States, 2001–2014. JAMA, 315, 1750–1766.

  13. Connor, I. (2007). Refugees and expellees in post-war Germany. Manchester, UK: Manchester University Press.

    Google Scholar 

  14. Deaton, A. (2003). Health, inequality, and economic development. Journal of Economic Literature, 41, 113–158.

    Article  Google Scholar 

  15. Douglas, R. M. (2012). Orderly and humane: The expulsion of the Germans after the Second World War. New Haven, CT: Yale University Press.

    Google Scholar 

  16. Eberhardt, P. (2011). Political migrations on Polish territories (1939–1950) (Monograph 12). Warsaw, Poland: Polska Akademia Nauk, Instytut Geografii i Przestrzennego Zagospodarowania im. Stanislawa Leszczyckiego.

    Google Scholar 

  17. Falck, O., Heblich, S., & Link, S. (2012). Forced migration and the effects of an integration policy in post-WWII Germany. B. E. Journal of Economic Analysis & Policy, 12(1), 1–29.

    Article  Google Scholar 

  18. Federal Statistical Office. (2016). Statistik der Sterbefälle, Deutschland 1970–2014 [Statistics on deaths, Germany 1970–2014] (Technical report). Wiesbaden, Germany: Statistisches Bundesamt.

  19. Federal Statistical Office. (2018). Population, persons in employment, unemployed persons, economically active population, economically inactive population (aged 15 to under 65): Germany, years, sex. Wiesbaden, Germany: Federal Statistical Office. Retrieved from

    Google Scholar 

  20. Fiala, N. (2015). Economic consequences of forced displacement. Journal of Development Studies, 51, 1275–1293.

    Article  Google Scholar 

  21. Forschungsdatenzentrum Deutsche Rentenversicherung. (2017). Codeplan SUFDemografie Rentenbestand & Rentenwegfall 1993–2014 [Code plan for SUF pension demographics 1993–2014] (Technical Report). Berlin/Würzburg, Germany: Deutsche Rentenversicherung Bund.

  22. Haukka, J., Suvisaari, J., Sarvimäki, M., & Martikainen, P. (2017). The impact of forced migration on mortality: A cohort study of 242,075 Finns from 1939–2010. Epidemiology, 28, 587–593.

    Article  Google Scholar 

  23. Kalwij, A. S., Alessie, R. J. M., & Knoef, M. G. (2013). The association between individual income and remaining life expectancy at the age of 65 in the Netherlands. Demography, 50, 181–206.

    Article  Google Scholar 

  24. Kibele, E., Klüsener, S., & Scholz, R. (2015). Drastic changes in regional life-expectancy disparities in Germany: In search of the determinants (Technical report). Rostock, Germany: Max Planck Institute for Demographic Research.

  25. Lubotsky, D. (2007). Chutes or ladders? A longitudinal analysis of immigrant earnings. Journal of Political Economy, 115, 820–867.

    Article  Google Scholar 

  26. Lüttinger, P. (1986). Der Mythos der schnellen Integration: Eine empirische Untersuchung zur Integration der Vertriebenen und Flüchtlinge in der Bundesrepublik Deutschland bis 1971 [The myth of rapid integration: An empirical study on the integration of displaced persons and refugees in the Federal Republic of Germany until 1971]. Zeitschrift für Soziologie, 15(1), 20–36.

    Article  Google Scholar 

  27. Müller, W., & Simon, H. (1959). Die Vertriebenen in Westdeutschland: Ihre Eingliederung und ihr Einfluss auf Gesellschaft, Wirtschaft, Politik und Geistesleben [Displaced persons in West Germany: Their integration and their influence on society, economy, politics and intellectual life]. Kiel, Germany: Ferdinand Hirt.

  28. Palme, M., & Sandgren, S. (2008). Parental income, lifetime income, and mortality. Journal of the European Economic Association, 6, 890–911.

    Article  Google Scholar 

  29. Reichling, G. (1958). Die Heimatvertriebenen im Spiegel der Statistik [The expellees in the mirror of statistics]. Berlin, Germany: Duncker & Humblot.

  30. Ruiz, I., & Vargas-Silva, C. (2013). The economics of forced migration. Journal of Development Studies, 49, 772–784.

    Article  Google Scholar 

  31. Ruiz, I., & Vargas-Silva, C. (2015). The labor market impacts of forced migration. American Economic Review: Papers and Proceedings, 105, 581–586.

    Article  Google Scholar 

  32. Saarela, J., & Finnäs, F. (2009). Forced migration and mortality in the very long term: Did Perestroika affect death rates also in Finland? Demography, 46, 575–587.

    Article  Google Scholar 

  33. Saarela, J. M., & Elo, I. T. (2016). Forced migration in childhood: Are there long-term health effects? SSM—Population Health, 2, 813–823.

    Google Scholar 

  34. Sarvimäki, M., Uusitalo, R., & Jäntti, M. (2009). Long-term effects of forced migration (IZA Discussion Paper No. 4003). Bonn, Germany: Institute for the Study of Labor.

  35. Schumann, A. (2014). Persistence of population shocks: Evidence from the occupation of West Germany after World War II. American Economic Journal: Applied Economics, 6(3), 189–205.

    Google Scholar 

  36. Schwandt, H., & von Wachter, T. (2017). Unlucky cohorts: Earnings, income, and mortality effects from entering the labor market in a recession. Unpublished manuscript, University of California, Los Angeles, CA.

  37. Smith, J. P. (1999). Healthy bodies and thick wallets: The dual relation between health and economic status. Journal of Economic Perspectives, 13(2), 145–166.

    Article  Google Scholar 

  38. United Nations High Commissioner for Refugees. (2016). Global trends: Forced displacement in 2015 (Technical report). Geneva, Switzerland: United Nations High Commissioner for Refugees.

  39. van den Berg, G. J., Lindeboom, M., & Portrait, F. (2006). Economic conditions early in life and individual mortality. American Economic Review, 96, 290–302.

    Article  Google Scholar 

Download references


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.

Author information



Corresponding author

Correspondence to Matthias Giesecke.

Electronic supplementary material


(PDF 119 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation


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