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Birthplace diversity and economic prosperity

Abstract

We propose an index of population diversity based on people’s birthplaces and decompose it into a size (share of immigrants) and a variety (diversity of immigrants) component. We show that birthplace diversity is largely uncorrelated with ethnic, linguistic or genetic diversity and that the diversity of immigrants relates positively to measures of economic prosperity. This holds especially for skilled immigrants in richer countries at intermediate levels of cultural proximity. We address endogeneity by specifying a pseudo-gravity model predicting the size and diversity of immigration. The results are robust across specifications and suggestive of skill-complementarities between immigrants and native workers.

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Notes

  1. 1.

    See Ozden et al. (2011) for a picture of the evolution of international migration over the last fifty years, and Docquier and Rapoport (2012) for a focus on high-skill migration and its effects on source and host countries.

  2. 2.

    That is, a 17-% increase. 22 out of 27 OECD countries saw increases in the diversity of their skilled workforce between 1990 and 2000 (the only exceptions being Estonia, Greece, New Zealand, Poland and Slovakia).

  3. 3.

    This inverted u-curve for cultural proximity mirrors the results of Ashraf and Galor (2013a) on genetic diversity.

  4. 4.

    Bellini et al. (2013) apply the same methodology to European regions and find broadly consistent results for Europe as well.

  5. 5.

    See also Bandiera et al. (2013) and Abramitzky et al. (2012, 2013), respectively, on the measurement of entry and return flows and on migrants’ self-selection.

  6. 6.

    See Laitin and Jeon (2013) for a recent overview of social psychology research on the effects of diversity.

  7. 7.

    See ADOP (2015) for more details.

  8. 8.

    We conduct a robustness check restricting our OLS and IV models to non-estimated observations only. The results (available upon request) remain virtually unchanged.

  9. 9.

    This also holds in first differences: the correlation between changes in size and diversity of skilled immigration 1990–2000 is low and even negative at \(-\)0.14.

  10. 10.

    See McKenzie and Rapoport (2010) and Bertoli (2010) for micro evidence on the role of migrant networks in determining self-selection patterns, and Beine et al. (2011) for macro evidence.

  11. 11.

    See the online appendix for details on the definitions and sources for all variables.

  12. 12.

    Following Ashraf and Galor (2013a) we also include a squared term for genetic diversity.

  13. 13.

    See, e.g., Hall and Jones (1999), Gallup et al. (1998), Rodriguez and Rodrik (2001), Sachs (2003), Rodrik et al. (2004).

  14. 14.

    We use the standard measure of trade volume: real trade openness (exports + imports) in percentage of GDP in real PPP prices. This indicator correlates most robustly with GDP growth (Yanikkaya 2003).

  15. 15.

    This definition follows the literature on trade concentration. See, e.g., Kali et al. (2007) for the effect of trade concentration on income or Frankel et al. (1995) on transportation costs.

  16. 16.

    Typical countries that drop out of this sample are small island states or territories.

  17. 17.

    See the Online Appendix, Table 13, for a specification sequentially introducing covariates in a model of skilled birthplace diversity.

  18. 18.

    The difference in \(Div_{mig}\) (skilled) between the rich and poor country subsample is significant at the 1 % level (unlike the diversity of unskilled migrants).

  19. 19.

    Still, we obtain qualitatively similar results in our rich country subsample when using country fixed effects (see Appendix).

  20. 20.

    This test relies on the assumption that selection on observables from a basic model towards a full model is proportional to selection on unobservables.

  21. 21.

    See the Online Appendix Table 13 for a similar analysis for the full sample. It shows that the full sample model is relatively more susceptible to a remaining positive omitted variables bias than our main results for rich countries only.

  22. 22.

    The sample thus includes all countries with patenting activity as covered by WIPO (2010). Hence, our estimates are best interpreted as effect on the intensive margin of patenting.

  23. 23.

    Note that Ozden et al. (2011) do not provide a skill decomposition of immigration in 1960, we hence rely on diversity of immigrants of all skill groups.

  24. 24.

    In particular, we test for robustness to continental fixed effects as employed by Ashraf and Galor (2013a).

  25. 25.

    See Grogger and Hanson (2011) for a deeper discussion on such sorting across destinations.

  26. 26.

    In line with our priors, in a basic model as in Table 11, column (2), both indices hold independent explanatory power and correlate highly positively with income (available upon request).

  27. 27.

    See www.oecd-ilibrary.org (PISA 2009 results at a glance).

  28. 28.

    See Filmer et al. (2006), for an illustrative review of test score results. They report, among many other examples, that “the average science score among students in Peru [is] equivalent to that of the lowest scoring 5 % of US students”.

  29. 29.

    See the appendix for a simulation. The figures show that the vast majority of countries—even under the assumption that all high-ability math/science students had left—mostly sent non- highly math-skilled people abroad.

  30. 30.

    We build on the trade (e.g., Tinbergen 1962; Frankel and Romer 1999) and migration (e.g., Grogger and Hanson 2011; Beine et al. 2011) gravity literatures.

  31. 31.

    See, Lewer and Van den Berg 2008; Felbermayr et al. 2010; Mayda 2010; Grogger and Hanson 2011; Beine et al. 2013; Ortega and Peri 2009 and 2014.

  32. 32.

    While the use of origin FE largely suffices to account for multilateral resistance in trade, Bertoli and Fernández-Huertas Moraga (2013) show this to hold for migration only under more restrictive distributional assumptions.

  33. 33.

    This bias is particularly salient with data that are heteroskedastic (e.g., due to many zero cells). Overall, the degree of OLS bias relative to PPML depends on the underlying features of the data.

  34. 34.

    To avoid violating the exclusion restriction via inclusion of a lagged measure of population size, we fully rely on the more parsimonious model excluding this variable.

  35. 35.

    As is well known, the Stock and Yogo (2005) critical values are are appropriate under homoskedasticity only. We report heteroskedasticity-robust clustered standard errors, which tend to be higher than those obtained under the assumption of homoskedasticity.

  36. 36.

    F-Tests on the excluded instruments and the joint instruments are well above the respective Stock and Yogo (2005) critical values.

  37. 37.

    Weak instruments could also drive this result. Note, however, that models 3 and 4 are still relatively strongly identified (Kleibergen-Paap exceeding or close to Stock Yogo 15 % maximal IV size critical value).

  38. 38.

    A one standard deviation increase in the birthplace diversity of skilled immigrants generates an increase of about 25 % in long run economic output.

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Correspondence to Hillel Rapoport.

Additional information

Our birthplace diversity dataset can be found at www.nber.org/data/population-birthplace-diversity/.

We thank the editor, Oded Galor, and three anonymous referees for excellent comments which greatly improved the paper. We are also grateful to Amandine Aubry, Simone Bertoli, François Bourguignon, Matteo Cervellati, Frédéric Docquier, Jesús Fernández-Huertas Moraga, Gordon Hanson, Frédéric Jouneau, Thierry Mayer, Lant Pritchett, Yona Rubinstein, Joao Santos-Silva, Jacques Silber, Sylvana Tenreyro, Nico Voigtlaender, Ekaterina Zhuravskaya, participants at the 5th AFD-World Bank Conference on Migration and Development, Paris, June 2012, the CEMIR conference at CESifo, Munich, December 2012, the NBER Economics of Culture and Institutions Meeting, Cambridge, April 2013, the 10th IZA Migration Meeting in Jerusalem, June 2013, the 4th TEMPO-CEPR Conference in Nottingham, September 2013, the NBER Conference on High Skill Immigration, October 2013, the workshop on “Deep Rooted Factors in Comparative Economic Development” at Brown University, May 2014, the CESifo workshop on “Demographic Changes and Long-Run Growth” in Venice, July 2014, and seminar audiences at PSE, Louvain, Geneva, Luxembourg, Milan, Hebrew University, Tel-Aviv, EUI, IDC, and the Kiel Institute for comments and suggestions. We also thank Quamrul Ashraf and Frédéric Docquier for sharing their data with us.

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Alesina, A., Harnoss, J. & Rapoport, H. Birthplace diversity and economic prosperity. J Econ Growth 21, 101–138 (2016). https://doi.org/10.1007/s10887-016-9127-6

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Keywords

  • Birthplace diversity
  • Immigration
  • Culture
  • Economic development

JEL Classification

  • O1
  • O4
  • F22
  • F43