Journal of Economic Growth

, Volume 23, Issue 3, pp 307–339 | Cite as

High-school genetic diversity and later-life student outcomes: micro-level evidence from the Wisconsin Longitudinal Study

  • C. Justin CookEmail author
  • Jason M. Fletcher


A novel hypothesis posits that levels of genetic diversity in a population may partially explain variation in the development and success of countries. Our paper extends evidence on this question by subjecting the hypothesis to an alternative context that eliminates many competing hypotheses. We do this by aggregating representative individual-level data for high schools from a single US state (Wisconsin) in 1957, when the population was composed nearly entirely of individuals of European ancestry. Using this sample of high school aggregations, we too find a strong association between school-level genetic diversity and a range of student socioeconomic outcomes. Our use of survey data also allows for a greater exploration into the potential mechanisms of genetic diversity. In doing so, we find positive associations between genetic diversity and indexes for openness to experience and extraversion, two personality traits tied to creativity and divergent thinking.


Genetic diversity Years of schooling Income Personality Survey data 

JEL Codes

J11 N30 O10 Z13 

Supplementary material

10887_2018_9157_MOESM1_ESM.pdf (241 kb)
Supplementary material 1 (PDF 240 kb)


  1. Ager, P., & Brueckner, M. (2013). Cultural diversity and economic growth: Evidence from the US during the age of mass migration. European Economic Review, 64, 76–97.CrossRefGoogle Scholar
  2. Ager, P., & Brueckner, M. (2018). Immigrants’ genes: Genetic diversity and economic development in the United States. Economic Inquiry, 56(2), 1149–1164.CrossRefGoogle Scholar
  3. Alesina, A., Harnoss, J., & Hillel, R. (2016). Birthplace diversity and economic prosperity. Journal of Economic Growth, 21(2), 101–138.CrossRefGoogle Scholar
  4. Alesina, A., & La Ferrara, E. (2005). Ethnic diversity and economic performance. Journal of Economic Literature, 43(3), 762–800.CrossRefGoogle Scholar
  5. Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy, 113(1), 151–184.CrossRefGoogle Scholar
  6. Arbatli, E., Ashraf, Q., Galor, O., & Klemp, M. (2018). Diversity and Conflict. Working Paper. Accessed 17 June 2017.
  7. Ashraf, Q., & Galor, O. (2013a). The ‘out of Africa’ hypothesis, human genetic diversity, and comparative economic development. American Economic Review, 103(1), 1–46.CrossRefGoogle Scholar
  8. Ashraf, Q., & Galor, O. (2013b). Genetic diversity and the origins of cultural fragmentation. American Economic Review, Papers and Proceedings, 103(3), 528–533.CrossRefGoogle Scholar
  9. Ashraf, Q., & Galor, O. (2018). The macrogenoeconomics of comparative development. Journal of Economic Literature, 56(3).Google Scholar
  10. Ashraf, Q., Galor, O., & Klemp, M. (2014). The out of Africa hypothesis of comparative development reflected by light intensity. Working Paper. Accessed 17 June 2017.
  11. Ashraf, Q., Galor, O., & Klemp, M. (2015). Heterogeneity and productivity. Working Paper. Accessed 17 June 2017.
  12. Cavalli-Sforza, L. L. (2005). The human genome diversity project: Past, present and future. Nature Reviews Genetics, 6(4), 333–340.CrossRefGoogle Scholar
  13. Chabris, C., Hebert, B. M., Benjamin, D. J., Beauchamp, J. P., Cesarini, D., van der Loos, J. H. M. M., et al. (2012). Most published genetic associations with general intelligence are probably false positives. Psychological Science, 23(11), 1314–1323. Scholar
  14. Chabris, C., Lee, J., Benjamin, D., Beuchamp, J., Glaeser, E., Borst, G., et al. (2013). Why is it hard to find genes that are associated with social science traits? Theoretical and empirical considerations. American Journal of Public Health, 103(S1), S152–S166.CrossRefGoogle Scholar
  15. Costa, P. T., Jr., & McCrae, R. R. (1994). Set like plaster: Evidence for the stability of adult personality. In T. F. Heatherton & J. L. Weinberger (Eds.), Can personality change? (pp. 21–40). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  16. Depetris-Chauvin, E., & Özak, Ö. (2018). The Origins of the Division of Labor in Pre-modern Times.
  17. Domingue, B. W., Belsky, D. W., Harrati, A., Conley, D., Weir, D. R., & Boardman, J. D. (2017). Mortality selection in a genetic sample and implications for association studies. International Journal of Epidemiology. Scholar
  18. Duncan, O. D. (1961). A Socioeconomic index for all occupations. In occupations and social status. New York: Free Press of Glencoe.Google Scholar
  19. Easterly, W., & Levine, R. (2016). The European origins of economic development. Journal of Economic Growth, 21(3), 225–257.CrossRefGoogle Scholar
  20. Feist, G. J. (1998). A meta-analysis of the impact of personality on scientific and artistic creativity. Personality and Social Psychological Review, 2, 290–309.CrossRefGoogle Scholar
  21. Freeman, R. B., & Huang, W. (2015). Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics, 33(S1), S289–S318.CrossRefGoogle Scholar
  22. Furnaham, A., & Chamorro-Premuzic, T. (2004). Personality, intelligence, and art. Personality and Individual Differences, 36, 705–715.CrossRefGoogle Scholar
  23. Furnham, A., & Bachtiar, V. (2008). Personality and intelligence as predictors of creativity. Personality and Individual Differences, 45, 613–617.CrossRefGoogle Scholar
  24. Hirsh, J. B., DeYoung, C. G., & Peterson, J. B. (2009). Metatraits of the big five differentially predict engagement and restraint of behavior. Journal of Personality, 77(4), 1085–1102.CrossRefGoogle Scholar
  25. Hong, L., & Page, S. (2001). Problem solving by heterogeneous agents. Journal of Economic Theory, 97, 123–163.CrossRefGoogle Scholar
  26. Hong, L., & Page, S. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. PNAS, 101, 16385–16389.CrossRefGoogle Scholar
  27. Hunt, J., & Gauthier-Loiselle, M. (2010). How much does immigration boost innovation. American Economic Journal: Macroeconomics, 2, 31–56.Google Scholar
  28. Kaufman, S. B., Quilty, L. C., Grazioplene, R. G., Hirsh, J. B., Gray, J. R., Peterson, J. B., et al. (2016). Openness to experience and intellect differentially predict creative achievement in the arts and sciences. Journal of Personality, 84(2), 248–258.CrossRefGoogle Scholar
  29. Kemeny, T. (2017). Immigrant diversity and economic performance in cities. International Regional Science Review, 40(2), 164–208.CrossRefGoogle Scholar
  30. King, L., Walker, L., & Broyles, S. (1996). Creativity and the five factor model. Journal of Research in Personality, 30, 189–203.CrossRefGoogle Scholar
  31. Lazear, E. (1999). Globalization and the market for teammates. The Economic Journal, 109, 15–40.CrossRefGoogle Scholar
  32. McCrae, R. R., & Costa, P. T., Jr. (1996). Toward a new generation of personality theories: Theoretical contexts for the five-factor model. In J. S. Wiggins (Ed.), The five-factor model of personality: Theoretical perspectives (pp. 51–87). New York: Guilford Press.Google Scholar
  33. McCrae, R. R., Costa, P. T., Ostendorf, F., Angleitner, A., Hrebickova, M., Avia, M. D., et al. (2000). Nature over nurture: Temperament, personality, and life span development. Journal of Personality and Social Psychology, 78, 173–186.CrossRefGoogle Scholar
  34. Nunn, N., & Wantchekon, L. (2011). The slave trade and the origins of mistrust in Africa. American Economic Review, 101(7), 3221–3252.CrossRefGoogle Scholar
  35. Olson, C., & Ackerman, D. (1998). Wisconsin high school district information for 1954–1957. Accessed 17 June 2017.
  36. Ottaviano, G., & Peri, G. (2006). The economic value of cultural diversity: Evidence from US cities. Journal of Economic Geography, 6, 9.CrossRefGoogle Scholar
  37. Parrotta, P., Pozzoli, D., & Pytlikova, M. (2014). The nexus between labor diversity and firm’s innovation. Journal of Population Economics, 27, 303–364.CrossRefGoogle Scholar
  38. Peri, G. (2012). The effect of immigration on productivity: Evidence from US states. The Review of Economics and Statistics, 94, 348–358.CrossRefGoogle Scholar
  39. Peri, G., & Sparber, C. (2009). Task specialization, immigration, and wages. American Economic Journal: Applied Economics, 1, 135–169.Google Scholar
  40. Pickering, A. D., Smillie, L. D., & DeYoung, C. G. (2016). Neurotic individuals are not creative thinkers. Trends in Cognitive Science, 20(1), 1–2.CrossRefGoogle Scholar
  41. Ruggles, S., Genadek, K., Goeken, R., Grover, J., & Sobek, M. (2015). Integrated public use microdata series: Version 6.0 [dataset]. Minneapolis: University of Minnesota. Scholar
  42. Sampson, R. J., & Sharkey, P. (2008). Neighborhood selection and the social reproduction of concentrated racial inequality. Demography, 45(1), 1–29.CrossRefGoogle Scholar
  43. Siegel, P. M. (1971). Prestige in the American occupational structure. Doctoral dissertation, University of Chicago.Google Scholar
  44. Srivastava, S., John, O., Gosling, S., & Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84(5), 1041–1053.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of California-MercedMercedUSA
  2. 2.University of Wisconsin-MadisonMadisonUSA

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