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Ethnic diversity and well-being

Journal of Population Economics volume 30, pages 265–306 (2017)Cite this article

Abstract

This paper investigates how ethnic diversity, measured by immigrants’ nationalities, influences the well-being of the host country. Using panel data from Germany from 1998 to 2012, we find a positive effect of ethnic diversity on the well-being of German natives. Our finding is robust to alternative definitions of ethnic diversity and to the non-random selection of natives and immigrants into regions. The positive effect of ethnic diversity is stronger for immigrant groups that are culturally and economically closer to Germany. Consistent with this result, we document the existence of two mechanisms explaining the influence of ethnic diversity on well-being: productivity—as captured by immigrants’ skills and assimilation—and social capital—particularly in relation to the creation of a multicultural environment.

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Notes

  1. 1.

    http://www.statistik-portal.de/Statistik-Portal/en/en_jb01_jahrtab2.asp Last access: September 5th 2016. The number of individuals with “immigration background” is much higher, and was approximately 16.4 million, representing about 20.3 % of the population (according to the 2014 microcensus). The majority of people with a migrant background (56 %) have also a German passport passport.

  2. 2.

    We focus on the subjective well-being of individuals who report being born in Germany. We use the term Germans and natives interchangeably.

  3. 3.

    We use the term foreigner interchangeably with immigrant.

  4. 4.

    Kreise are administrative units that are self-contained within RORs. In rare cases, there were small changes in the geography of Kreise, with some of them being classified in different RORs over time. We were able to match Kreise to the correct ROR thanks to lookup files provided with the data.

  5. 5.

    Note that up to 2007 immigration data were supplied by the statistical offices of each state (Länd), but since 2008 data come from the German Federal Statistical Office. The major implication is that the number of nationalities available is different across the 16 states for the first period, while it is homogeneous for the period when federal-level data are used. The robustness checks presented in Section 4 show that the different number of available nationalities does not influence our results.

  6. 6.

    AZR does not collect data on country of birth or on ethnicity; hence, it is not possible to construct a diversity index based on these alternative dimensions. However, in our robustness analysis, we provide sensitivity checks around the definition of our index.

  7. 7.

    At the time of writing, AZR data are available until 2014, while INKAR until 2012; hence, we restrict our analysis up to this year. AZR data for the State Saxony-Anhalt are only available from year 2007.

  8. 8.

    Note that the argument of the sum operator can also be represented as: \(\frac {m_{gr}}{m_{r}}~=~\left (\frac {m_{gr}}{m_{r}}~\Big /~\frac {m_{g}}{m}\right )~\times ~\frac {m_{g}}{m}\). The first component in brackets measures the spatial distribution of immigrants and in particular whether immigrants of a certain nationality are over- or under-represented (values above and below 1, respectively) in a region. This is sometimes referred to as the relative clustering index (see e.g., Borjas 2000). The second component is the share of immigrants over the total number of immigrants in Germany and captures the relative size of each nationality group.

  9. 9.

    A diversity index based on birthplace would differ from one based on nationality because of citizenship dynamics. If no immigrants naturalize, then there would be virtually no difference since country of birth would coincide with nationality. If one considers the extreme example of immigrants coming from a small country who all naturalize, then the number of immigrant groups would be different since in this hypothetical case one particular nationality would disappear. To understand exactly how the two indices would differ, one would need to either obtain exact counts of immigrants by birthplace (which to our knowledge do not exist at the same level of our nationality data) or to carefully track for citizenship dynamics (which is hard to do even with a good dataset like the GSOEP). To provide a rough idea of the potential differences between the two indices, we have calculated an index based on ethnic diversity (i.e., using reported nationality) and one based on birthplace diversity (i.e., using reported country of origin) based on pooled data from the GSOEP over the period of our analysis. The ED index is constructed like the one used in the paper but uses the GSOEP instead of data from population registers. The birthplace diversity index is constructed in the same manner but uses information on the country of origin. After calculating the two indices, we note that for the same sample, there are 120 reported countries of origin and 104 nationalities. This is likely due to some small group transiting into German citizenship. Not surprisingly—since it uses a larger number of groups—the birthplace diversity index is slightly higher (0.91) than the ED index (0.85). While we recognize that it is difficult to speculate how our results would be had we had the possibility of using a birthplace diversity index, we would expect them not to be substantially different than those obtained with our ED index.

  10. 10.

    Estimates from the OECD were downloaded from https://stats.oecd.org/Index.aspx?DataSetCode=MIG. Last access September 4th 2016.

  11. 11.

    The time-variant characteristics that we use for the QFE specification are averages over time of household size, household income after tax, and weekly working hours.

  12. 12.

    From the maps in Fig. 2, one notices that ethnic diversity and immigrant share are somewhat negatively correlated. The estimated correlation is -0.25.

  13. 13.

    We have also estimated a “Blow and Cluster” fixed-effects ordered logit model (Baetschmann et al. 2015). The aim of this specification is to allow controlling for individual heterogeneity by taking the ordinal nature of SWB into account. Note that in this model—very much like in the fixed-effects model—we omit time-invariant characteristics such as sex and those that are (quasi-)collinear with the time dummies, such as age. The estimate is 1.3319 (s.e. 0.3163), which suggests once again a positive and statistically significant relationship between ethnic diversity and well-being. Even in this case, the point estimate is only qualitatively comparable with those of the fixed-effects model.

  14. 14.

    We have performed an Hausman test between the FE and RE models and between the FE and QFE models, finding that in both cases we cannot reject the hypothesis that the FE model provides consistent estimates.

  15. 15.

    Glitz (2014) reports that both workplace and residential segregation among natives and immigrants are persistent over time, with the former being more pronounced. The author also shows that residential segregation does not vary by skills of immigrants, but differences are observed across nationalities, with Turkish, Greek, and African immigrants being the groups that are more segregated.

  16. 16.

    The weighted ED index can be written as \(\text {ED}_{r}=\sum \limits _{g}\left (\frac {m_{gr}}{m_{r}}\right )\times \left (1-\frac {m_{gr}}{m_{r}}\right )\times \omega _{g}\)

  17. 17.

    Data were obtained from http://wdi.worldbank.org.

  18. 18.

    According to the AZR data, the top ten nations in terms of immigrants in Germany are Austria, France, Greece, Italy, Poland, Portugal, Serbia, Spain, Turkey, and the UK.

  19. 19.

    The estimate for the interaction term (which captures potential changes in the regime of data) is somewhat lower, but still positive and significant (0.613, s.e. 0.212).

  20. 20.

    In unreported regressions, we test the sensitivity to the exclusion of second generation migrants from the sample of Germans. A subset of individuals, while reporting being born in Germany and having a German nationality, might feel attached to the country of origin of their parents. To explore the sensitivity of our results, we perform a check by excluding individuals born in Germany whose father or mother report being born abroad. Information on parental background is only available after 2006 and for a subset of individuals. Hence, we first perform a regression on the sample reporting parental background information (17,048 individuals), obtaining an estimate stronger than the baseline (1.240, s.e. 0.662). We then estimate a specification excluding second-generation migrants (the sample decreases to 13,923 individuals), obtaining an estimate of 1.435 (s.e. 0.655). This result, although referred to a subset, suggests that including second generation migrants might annihilate the true effect of ethnic diversity.

  21. 21.

    The top ten cities are Berlin, Hamburg, Munich, Cologne, Frankfurt, Stuttgart, Dusseldorf, Dortmund, Essen, and Bremen.

  22. 22.

    The five RORs with highest values of the ED index are Trier, Havelland-Flaeming, Prignitz-Oberhavel, Oberes Elbtal/Osterzgebirge, and West Saxony. The five RORs with highest values of immigrant share are Hamburg, Rhine-Main, Stuttgart, Unterer Neckar, and Munich. Excluding either the top five RORs in terms of ED index or in terms of immigrant share would produce similar results. Similarly, if we exclude the top ten regions in terms of ethnic diversity and/or immigration, results would be very stable.

  23. 23.

    Interestingly, the propensity of internal migration is fairly similar across ethnic groups (being 0.13 on average).

  24. 24.

    Ideally and to be more rigorous, one would calculate an ED index that excludes all immigrants who migrate internally. This is not feasible in our case, since the only possible way of obtaining estimates of mobility of immigrants is via the GSOEP which—due to the small sample size of immigrants who move—would produce imprecise predictions to be used to correct the official statistics we use in this paper.

  25. 25.

    The top ten SWB RORs are Oberfranken-Ost, Schleswig-Holstein Mitte, Paderborn, Hamburg, Osnabrueck, Bodensee-Oberschwaben, Ostwuerttemberg, Oberland, Bremen, and Rheinpfalz.

  26. 26.

    The estimate of the lagged ethnic diversity in the first stage is 0.675 (s.e. 0.002). The F-stat is 119,912 and the partial R 2 is 0.474. Full estimation results can be obtained from the authors upon request.

  27. 27.

    To test the statistical difference of the estimates, we pool the various groups (e.g., males and females), create an indicator for the group (e.g., a male dummy), and estimate a fully interacted model where we interact the group indicator with all explanatory variables. We test the null hypothesis that the interaction between the group indicator and the ED index is equal to zero. If this is not rejected, the estimates are not statistically different from each other.

  28. 28.

    We constructed the wage assimilation measure following Akay et al. (2014) closely. In practice, using GSOEP, we perform wage regressions separately for natives and immigrants. We then predict the wages for the two groups and calculate averages for each ROR. Finally, we construct the ratio between the average predicted wages of immigrants and that of natives. Values of the ratio below 1 indicate lower wage assimilation, while values of 1 or larger a higher level of assimilation. Finally, we derive the indicator for values above and below the median of the assimilation ratio.

  29. 29.

    The trust questions have been experimentally validated (see Fehr et al. 2003, for more details).

  30. 30.

    Respondents can agree or disagree (on a 4-point scale, higher values correspond to stronger agreement). We harmonize the scales so that higher values mean strong agreement to the first question and strong disagreement to the second and third. We obtain similar results if we perform principal component analysis. Since trust questions are asked only in 2003 and 2008, we use only data that refer to these two periods.

  31. 31.

    It is possible that there is a causal relationship between ethnic diversity and trust. While this has been found in some studies (e.g., Putnam 2007), it does not seem of particular concern in our case, since the correlations between the three measures of trust with ethnic diversity are all small and insignificant (-0.005 (s.d. 0.406), 0.001 (s.d. 0.920), and 0.002 (s.d. 0.692), respectively).

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Acknowledgments

We are grateful to two anonymous referees and the Editor Klaus F. Zimmermann for constructive feedback. We thank Peter Huber, Ruud J. A. Muffels, Jackie Wahba, and participants to the seminar at the Beijing Normal University, IOS Regensburg, Reflect at Tilburg University, and WIFO in Vienna for their useful comments. We are also indebted to Georgios Tassoukis for helping us collecting data from the German Federal Statistical Office and the statistical offices of the Länder.

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Affiliations

  1. University of Gothenburg, Gothenburg, Sweden

    Alpaslan Akay

  2. Princeton University, Princeton, NJ, USA

    Amelie Constant

  3. University of Southampton, Southampton, UK

    Corrado Giulietti

  4. Masaryk University, Brno, Czech Republic

    Martin Guzi

Authors
  1. Alpaslan Akay
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  2. Amelie Constant
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  3. Corrado Giulietti
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Correspondence to Corrado Giulietti.

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Responsible editor: Klaus F. Zimmermann

Appendix:

Appendix:

Fig. 4
figure4

Components of Shannon entropy index

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Table 8 Multiculturality and happiness—full estimates
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Table 9 Predictions from main effects and interaction models
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Akay, A., Constant, A., Giulietti, C. et al. Ethnic diversity and well-being. J Popul Econ 30, 265–306 (2017). https://doi.org/10.1007/s00148-016-0618-8

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Keywords

  • Ethnic diversity
  • Subjective well-being
  • Assimilation
  • Multiculturality

JEL Classification

  • C90
  • D63
  • J61