Public beliefs in social mobility and high-skilled migration


This paper investigates how beliefs of the destination country’s population in social mobility may influence the location choice of high-skilled migrants. We pool macro data from the IAB brain-drain dataset with population survey data from the ISSP for the period 1987–2010 to identify the effect of public beliefs in social mobility on the share of high-skilled immigrants (stocks) in the main OECD immigration countries. The empirical results suggest that countries with higher “American Dream” beliefs, i.e., with stronger beliefs that climbing the social ladder can be realized by own hard work, attracted a higher proportion of high-skilled immigrants over time. This pattern even holds against the fact that existing social mobility in these countries is relatively lower.

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  1. 1.

    Thereby, we do not assume that immigrants and the OECD countries’ general population are differently informed about existing social mobility. We rather follow Stiglitz’ (2012) description that public beliefs in social mobility are—irrespective of existing social mobility—relatively persisting (for whatever reason: either being mis- or uninformed about existing social mobility or neglecting existing social mobility; Stiglitz (2012) calls this phenomenon “cognitive dissonances”).

  2. 2.

    The 20 destination countries are also source countries which leads to the fact that our sample of country-pairs includes 194 source countries for each destination country.

  3. 3.

    See the Methodology Report by Bruecker et al. (2013) for a detailed description of the imputation procedure.

  4. 4.

    For the nine destination countries, we have 23 observations in the three considered years which we multiply with the number of 194 source countries.

  5. 5.

    The density function of the share of high-skilled over total migrant stocks (all migrants) is bell-shaped with a skewness to the right. Nearly 20% of the observations represent a share of zero whereas in only 3% of the observations migrants are exclusively high-skilled (cf. Fig. 1).

  6. 6.

    Only exception is New Zealand with a very volatile share that decreased from 44.7% in 1990 to 41.7% in 2010 (− 3.0%), but still shows a profound increase if we compare 2010 to 1980 (+ 17.1%).

  7. 7.

    Note that Peri (2005) uses a slightly different definition for the “V-shape”, i.e., shares of foreign-borns in the three different skill groups (low, medium, high), and that his study includes the years 1990–2000 on the basis of the European Labor Force Survey for the European countries and the IPUMS for the United States.

  8. 8.

    We select the ISSP instead of the World Value Survey (WVS) that has been carried out together with the European Value Survey (EVS) because i) the ISSP covers a higher number of destination countries and ii) because the formulation of the question is more explicit with regard to our purposes. The question of the WVS/EVS is a relative question on a scale from 1 to 10 measuring whether a better life stems from hard work or from luck and connections: “Now I’d like you to tell me your views on various issues. How would you place your views on this scale? (1) In the long run, hard work usually brings a better life. (10) Hard work doesn’t generally bring success—it’s more a matter of luck and connections.”

  9. 9.

    We omit two further possible answers “Don’t know” and “Not answered” that represent only 1.0% of all responses from our sample.

  10. 10.

    We calculate an aggregated mean for East and West Germany together because immigrant stocks for Germany are also aggregated in the migration dataset.

  11. 11.

    For further eight countries, the ISSP includes only 1 year of observation (the Netherlands in 1987, Canada in 1992, and Chile, Denmark, Finland, France, Portugal, and Spain in 2009).

  12. 12.

    In adding social status to the utility function we go beyond the studies of Grogger and Hanson (2011), Ortega and Peri (2013), and Gorinas and Pytliková (2017) which focus solely on income and costs. For reasons of simplicity and as we consider public beliefs in social mobility, we assume a linear utility function and deviate at this point from Lumpe et al. (2016) whose function of expected utility in the destination country is inverse u-shaped due to a quadratic cost function. Moreover, in their model, high-skilled migrants maximize expected utility over effort.

  13. 13.

    See the model of choice behavior of McFadden (1974).

  14. 14.

    The assumption of independence of irrelevant alternatives (IIA) is satisfied if the estimated regression coefficients are stable across choice sets (cf. Hausman and McFadden 1984). We checked for violations of IIA by re-estimating our model in each empirical specification nine times, each time dropping one of the nine destination countries from our sample. The resulting coefficients of our belief variable are quite similar across samples which suggests that the IIA property is not violated.

  15. 15.

    For the most recent year in the dataset, 2010, endogeneity can not be ruled out completely for the USA and Germany. Record date of the American Community Survey for migration stocks is July 1, 2010 and ISSP-data about public beliefs have been gathered in the same year between March, 18 and August, 14. Thus, migration stocks were recorded in the last third of the ISSP period and the time lag has been kept at least partly. For Germany, the Mikrozensus published migration stocks as of December 31, 2009. However, according to information given by the authors of the IAB brain-drain dataset, the Mikrozensus 2009 has been taken as a proxy for 2010 as the difference is minimal. Due to these facts and because we use stocks and not inflows, we keep the observations in our sample.

  16. 16.

    We gather the data for the control variables from various sources, thereof especially from the World Bank (2016a) (see Table 8 for all data sources and summary statistics).

  17. 17.

    According to our model, high-skilled migrants would choose the destination country with the highest wage gap compared to their wage in the source country. Alternatively to the difference of GDP per capita between d and s, we estimated Eqs. 3 and 4 with GDP per capita in d and with GDP per capita in s, which does not alter our results.

  18. 18.

    Harmonized ILO estimates which are also reported by the World Bank are only available since 1991 (cf. ILO 2018a, b; World Bank 2016a).

  19. 19.

    This applies for Australia since 1989, for New Zealand since 1991, and for the UK since 2008 (cf. Czaika and Parsons 2016; Eichhorst et al. 2011; Humpert 2015).

  20. 20.

    Other databases on comparative immigration policies, e.g., the IMPALA database (International Migration Policy And Law Analysis, see Beine et al. (2014, 2016)) or the MIPEX (Migration Integration Policy Index, cf. Huddleston et al. 2015) show only a limited coverage of countries and time frames with regard to our data (see also Gest et al. (2014) for a comprehensive overview of existing studies on immigration policies). Furthermore, the measure of Mayda (2010) that has been applied by, e.g., Ortega and Peri (2009), is not suitable for studying migration stocks, as it only captures changes in immigration policy without information on initial policy levels.

  21. 21.

    This applies between Australia and New Zealand (for the whole period considered) and between the member states of the European Union plus Norway and Switzerland (for certain years, cf. OECD 2012; Eurofound 2014). E.g., in 2004, the UK, Sweden, and Norway opened their labor markets directly to the eight Eastern European accession countries whereas the remaining countries opted for a transition phase (Austria and Germany as well as Switzerland until 2011).

  22. 22.

    The proportion of women who migrate independently from male family members and seek for employment on their own has significantly risen over the last decades (see, e.g., ILO 2010).

  23. 23.

    The results are robust if we exclude in this as well as in the other empirical specifications each of the nine destination countries separately (see footnote 14).

  24. 24.

    Broadening the scope to 17 destination countries (additionally Canada, Chile, Denmark, Finland, France, Netherlands, Portugal, and Spain, see Section 2.1) changes these results only for the year 2010. Now, the coefficient of our belief variable is still significant but lower in absolute terms (at 0.7). This is due to the fact that we add especially in 2010 destination countries with relatively lower shares of high-skilled migrants, and at the same time relatively weaker “American Dream” beliefs.


  1. Alesina A, Glaeser E, Sacerdote B (2001) Why doesn’t the United States have a European-style welfare state? BPEA 2:187–254

    Google Scholar 

  2. Bauer TK, Lofstrom M, Zimmermann KF (2000) Immigration policy, assimilation of immigrants, and natives’ sentiments towards immigrants: evidence from 12 OECD countries. Swed Econ Policy Rev 7:11–53

    Google Scholar 

  3. Bauer TK, Epstein G, Gang IN (2007) Herd effects or migration networks? The location choice of Mexican immigrants in the US. Res Labor Econ 26:199–229

    Article  Google Scholar 

  4. Beine M, Docquier F, Özden C (2011) Diasporas. J Dev Econ 95:30–41

    Article  Google Scholar 

  5. Beine M, Burgoon B, Crock M, Gest J, Hiscox M, McGovern P, Rapoport H, Tielemann E (2014) Measuring immigration policies: preliminary evidence from IMPALA. CESifo Working Paper Series 5109:1–41

    Google Scholar 

  6. Beine M, Boucher A, Burgoon B, Crock M, Gest J, Hiscox M, McGovern P, Rapoport H, Schaper J, Tielemann E (2016) Comparing immigration policies: an overview from the IMPALA database. Int Migr Rev 50 (4):825–1076

    Article  Google Scholar 

  7. Bénabou R, Tirole J (2006a) Belief in a just world and redistributive politics. Q J Econ 121(2):699–746

    Article  Google Scholar 

  8. Bénabou R, Tirole J (2006b) Incentives and prosocial behavior. Am Econ Rev 96(5):1652–1678

    Article  Google Scholar 

  9. Bjerre L, Helbling M, Roemer F, Zobel M (2015) Conceptualizing and measuring immigration policies: a comparative perspective. Int Migr Rev 49(3):555–600

    Article  Google Scholar 

  10. Bloom DE, Stark O (1985) The new economics of labor migration. AEA Pap Proc 75(2):173–178

    Google Scholar 

  11. Borjas GJ (1987) Self-selection and the earnings of immigrants. Am Econ Rev 77(4):531–553

    Google Scholar 

  12. Borjas GJ (1999a) Immigration and welfare magnets. J Labor Econ 17 (4):607–637

    Article  Google Scholar 

  13. Borjas GJ (1999b) The economic analysis of immigration. In: Ashenfelter OC, Card D (eds) Handbook of labor economics. North-Holland, Amsterdam, pp 1697–1760

  14. Bruecker H, Capuano S, Marfouk A (2013) Education, gender and international migration: insights from a panel-dataset 1980–2010. Mimeo, New York

    Google Scholar 

  15. Card D, Dustmann C, Prestin I (2012) Immigration, wages and compositional amenities. J Eur Econ Assoc 10:78–119

    Article  Google Scholar 

  16. Chiswick BR (1999) Are immigrants favorably self-selected? Am Econ Rev 89:181–185

    Article  Google Scholar 

  17. Chiswick BR, Miller PW (2015) International migration and the economics of language. In: Chiswick BR, Miller PW (eds) Handbook of the economics of international migration, 1A. Elsevier, Amsterdam, pp 211–269

  18. Corak M (2013) Income inequality, equality of opportunity, and intergenerational mobility. J Econ Perspect 27(3):79–102

    Article  Google Scholar 

  19. Czaika M, Parsons CR (2016) The gravity of high-skilled migration policies. KNOMAD Working Paper 13:1–33

    Google Scholar 

  20. Docquier F, Rapoport H (2012) Globalization, brain drain, and development. J Econ Lit 50(3):681–730

    Article  Google Scholar 

  21. Eichhorst W, Giulietti C, Guzi M, Kendzia MJ, Monti P, Frattini T, Nowotny K, Huber P, Vandeweghe B (2011) The integration of migrants and its effects on the labor market. European Parliament Study Policy Department A - Economic and Scientific Policy, pp 1–117

  22. Eurofound (2014) Labour migration in the EU: recent trends and policies. Publications Office of the European Union, Luxembourg

    Google Scholar 

  23. Fertig M, Schmidt CM, Sinning MG (2009) The impact of demographic change on human capital accumulation. Labor Econ 16:659–668

    Article  Google Scholar 

  24. Freedom House (2016) Freedom in the world 2016. Accessed 20 April 2017

  25. Geis W, Uebelmesser S, Werding M (2011) Why go to France or Germany, if you could as well go to the UK or the US? Selective features of immigration to four major OECD countries. J Comm Market Stud 49:767–796

    Article  Google Scholar 

  26. Geis W, Uebelmesser S, Werding M (2013) How do migrants choose their destination country? An analysis of institutional determinants. Rev Int Econ 21:825–840

    Article  Google Scholar 

  27. Gest J, Boucher A, Challen S, Burgoon B, Thielemann E, Beine M, McGovern P, Crock M, Rapoport M, Hiscox M (2014) Measuring and comparing immigration policies globally: challenges and solutions. Glob Policy 5 (3):1–14

    Article  Google Scholar 

  28. Gorinas C, Pytliková M (2017) The influence of attitudes towards immigrants on international migration. Int Migr Rev 51(2):416–451

    Article  Google Scholar 

  29. Grogger J, Hanson GH (2011) Income maximization and the sorting of international migrants. J Dev Econ 95:42–57

    Article  Google Scholar 

  30. Hatton TJ, Williamson JG (2003) Demographic and economic pressure on emigration out of Africa. Scand J Econ 105(3):465–486

    Article  Google Scholar 

  31. Hausman J, McFadden D (1984) Specification tests for the multinomial logit model. Econometrica 52(5):1219–1240

    Article  Google Scholar 

  32. Helbling M, Bjerre L, Roemer F, Zobel M (2017) Measuring immigration policies: the IMPIC database. Eur Polit Sci 16(1):79–98

    Article  Google Scholar 

  33. Huddleston T, Bilgili O, Joki A, Vankova Z (2015) Migrant integration policy index 2015. Accessed 27 February 2018

  34. Humpert S (2015) Fachkraeftezuwanderung im internationalen Vergleich, Working Paper No. 62 of a research group of the Federal Office for Migration and Refugees, pp 1–117

  35. ILO (2010) International labour migration: a rights-based approach. Accessed 27 February 2018

  36. ILO (2018a) Global employment trends 2014: supporting data sets. Accessed 27 February 2018

  37. ILO (2018b) Total unemployment. Accessed 27 February 2018

  38. Isphording IE, Otten S (2013) The costs of Babylon – Linguistic distance in applied economics. Rev Int Econ 21(2):354–369

    Article  Google Scholar 

  39. ISSP Research Group (1989) International social survey programme: social inequality I – ISSP 1987, GESIS data archive, cologne, ZA1680 Data file Version 1.0.0

  40. ISSP Research Group (2012) International social survey programme: social inequality IV – ISSP 2009, GESIS data archive, cologne, ZA5400 Data file Version 3.0.0

  41. ISSP Research Group (2014) International social survey programme: social inequality I-IV – ISSP 1987-1992-1999-2009, GESIS data archive, cologne, ZA5890 Data file Version 1.0.0

  42. Krueger AB (2012) The rise and consequences of inequality, Presentation made to the Center for American Progress, January 12th, Accessed 27 February 2018

  43. Lumpe C, Lumpe C, Meckl J (2016) Social status and public expectations: self-selection of high-skilled migrants, Ruhr Economic Papers No. 614, RWI, Essen, pp 1–28

  44. Mayda AM (2006) Who is against immigration? A cross-country investigation of individual attitudes towards immigration. Rev Econ Stat 88(3):510–530, 1–12

    Article  Google Scholar 

  45. Mayda AM (2010) International migration: a panel data analysis of the determinants of bilateral flows. J Popul Econ 23:1249–1274

    Article  Google Scholar 

  46. Mayer T, Zignago S (2011) Notes on CEPII’s distances measures: the GeoDist database, CEPII Working Paper No. 2011–25

  47. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142

  48. Melitz J, Toubal F (2014) Native language, spoken language, translation and trade. J Int Econ 92(2):351–363

    Article  Google Scholar 

  49. Mincer J (1978) Family migration decisions. J Polit Econ 86:749–773

    Article  Google Scholar 

  50. OECD (2012) Free movement of workers and labour market adjustment. Recent experiences from OECD countries and the European Union. OECD Publishing, Paris

    Google Scholar 

  51. OECD (2013) Recruiting immigrant workers: Germany 2013. OECD Publishing, Paris

    Google Scholar 

  52. Ortega F, Peri G (2009) The causes and effects of international migrations: evidence from OECD countries, 1980–2005, NBER Working Paper No. 14833, NBER, Cambridge, MA, pp 1–42

  53. Ortega F, Peri G (2013) The effect of income and immigration policies on international migration. Migr Stud 1(1):47–74

    Article  Google Scholar 

  54. O’Rourke K, Sinnott R (2006) The determinants of individual attitudes towards immigrants. Eur J Polit Econ 22:838–861

    Article  Google Scholar 

  55. Pedersen P, Pytliková M, Smith N (2008) Selection and network effects—migration flows into OECD countries 1990–2000. Eur Econ Rev 52:1160–1186

    Article  Google Scholar 

  56. Peri G (2005) Skills and talents of immigrants. A comparison between the European Union and the United States, Working Paper No. 524, University of California, Davis, Department of Economics, pp 1–32

  57. Piketty T (1998) Self–fulfilling beliefs about social status. J Pub Econ 70:115–132

    Article  Google Scholar 

  58. Roy AD (1951) Some thoughts on the distribution of earnings. Oxf Econ Pap 3:135–146

    Article  Google Scholar 

  59. Sjastaad LA (1962) The costs and returns of human migration. J Polit Econ 70(5):80–93

    Article  Google Scholar 

  60. Stiglitz JE (2012) The price of inequality: how today’s divided society endangers our future. Norton, New York

    Google Scholar 

  61. World Bank (2016a) World development indicators. Accessed 20 April 2017

  62. World Bank (2016b) World Bank country and lending groups. Accessed 20 April 2017

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This paper has been written during a research visit at RWI, Essen, and I am very grateful to RWI for its hospitality. I also thank Thomas K. Bauer, Julia Bredtmann, Christian Lumpe, Juergen Meckl, Matthias Goecke, Lisa Hoeckel, Jana Brandt, Caroline Schwientek, participants of the 19th Workshop on International Economics in Goettingen and the 29th EALE conference in St. Gallen as well as two anonymous referees for very helpful suggestions and comments on this paper. Financial support from the Fritz Thyssen Foundation within the framework of the project “Public attitudes and migration” is also gratefully acknowledged. All remaining errors are my own.


This study was funded by the Fritz Thyssen Foundation within the framework of the project “Public attitudes and migration.”

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Correspondence to Claudia Lumpe.

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Responsible editor: Klaus F. Zimmermann



Table 8 Descriptive statistics, definitions and sources of variables
Fig. 1

Density function share high-skilled over total migrants

Table 9 Share high-skilled over total migrants (mean values)
Table 10 High-skilled and total migrants (in absolute numbers)
Table 11 Frequencies ISSP survey question “How important you think is hard work for getting ahead in life?”
Table 12 Fixed-effects estimates (destination and source country)

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Lumpe, C. Public beliefs in social mobility and high-skilled migration. J Popul Econ 32, 981–1008 (2019).

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  • Immigration
  • Public beliefs
  • Social mobility
  • Social status

JEL Classification

  • F22
  • J62
  • J15