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Genetic variation in health insurance coverage

  • George L. WehbyEmail author
  • Dan Shane
Research article

Abstract

We provide the first investigation into whether and how much genes explain having health insurance coverage or not and possible mechanisms for genetic variation. Using a twin-design that compares identical and non-identical twins from a national sample of US twins from the National Survey of Midlife Development in the United States, we find that genetic effects explain over 40% of the variation in whether a person has any health coverage versus not, and nearly 50% of the variation in whether individuals younger than 65 have private coverage versus whether they have no coverage at all. Nearly one third of the genetic variation in being uninsured versus having private coverage is explained by employment industry, self-employment status, and income, and together with education, they explain over 40% of the genetic influence. Marital status, number of children, and available measures of health status, risk preferences, and prevention effort do not appear to be important channels for genetic effects. That genes have meaningful effects on the insurance status suggests an important source of heterogeneity in insurance take up.

Keywords

Health insurance Genetic variation Health determinants Risk taking 

JEL Classification

D1 D82 I10 I12 I13 

Supplementary material

10754_2018_9255_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 18 kb)

References

  1. Agarwal, A., Williams, G. H., & Fisher, N. D. (2005). Genetics of human hypertension. Trends in Endocrinology and Metabolism,16(3), 127–133.CrossRefGoogle Scholar
  2. Anokhin, A. P., Golosheykin, S., Grant, J. D., & Heath, A. C. (2011). Heritability of delay discounting in adolescence: A longitudinal twin study. Behavior Genetics,41(2), 175–183.CrossRefGoogle Scholar
  3. Barnes, J. C., Wright, J. P., Boutwell, B. B., Schwartz, J. A., Connolly, E. J., Nedelec, J. L., et al. (2014). Demonstrating the validity of twin research in criminology. Criminology,52(4), 588–626.CrossRefGoogle Scholar
  4. Benjamin, D. J., Cesarini, D., Chabris, C. F., Glaeser, E. L., Laibson, D. I., Guðnason, V., et al. (2012). The promises and pitfalls of genoeconomics. Annual Review of Economics,4, 627.CrossRefGoogle Scholar
  5. Boardman, J. D., Blalock, C. L., & Pampel, F. C. (2010). Trends in the genetic influences on smoking. Journal of Health and Social Behavior,51(1), 108–123.CrossRefGoogle Scholar
  6. Boardman, J. D., Domingue, B. W., & Daw, J. (2015). What can genes tell us about the relationship between education and health? Social Science and Medicine,127, 171–180.CrossRefGoogle Scholar
  7. Branigan, A. R., McCallum, K. J., & Freese, J. (2013). Variation in the heritability of educational attainment: An international meta-analysis. Social Forces,92(1), 109–140.CrossRefGoogle Scholar
  8. Brim, O. G., Baltes, P. B., Lachman, M. E., Markus, H. R., Shweder, R. A., Marmot, M. G., et al. (2017). Midlife in the United States (MIDUS 1), 1995–1996. Ann Arbor, MI: Inter-University Consortium for Political and Social Research [distributor], 2017-11-16.  https://doi.org/10.3886/icpsr02760.v12.
  9. Burt, C. H., & Simons, R. L. (2014). Pulling back the curtain on heritability studies: Biosocial criminology in the postgenomic era. Criminology,52(2), 223–262.CrossRefGoogle Scholar
  10. Carlsson, S., Ahlbom, A., Lichtenstein, P., & Andersson, T. (2013). Shared genetic influence of BMI, physical activity and type 2 diabetes: A twin study. Diabetologia,56(5), 1031–1035.CrossRefGoogle Scholar
  11. Cesarini, D., Dawes, C. T., Johannesson, M., Lichtenstein, P., & Wallace, B. (2009). Genetic variation in preferences for giving and risk taking. Quarterly Journal of Economics,124, 809–842.CrossRefGoogle Scholar
  12. Cesarini, D., Johannesson, M., Lichtenstein, P., Sandewall, Ö., & Wallace, B. (2010). Genetic variation in financial decision-making. The Journal of Finance,65(5), 1725–1754.CrossRefGoogle Scholar
  13. Charney, E., & English, W. (2013). Genopolitics and the science of genetics. American Political Science Review,107(02), 382–395.CrossRefGoogle Scholar
  14. Congressional Budget Office (CBO). (2017). Repealing the individual health insurance mandate: An updated estimate. https://www.cbo.gov/system/files/115th-congress-2017-2018/reports/53300-individualmandate.pdf. Accessed June 20, 2018.
  15. Cronqvist, H., & Siegel, S. (2015). The origins of savings behavior. Journal of Political Economy,123(1), 123–169.CrossRefGoogle Scholar
  16. Cutler, D., Finkelstein, A., & McGarry, K. (2008). Preference heterogeneity and insurance markets: Explaining a puzzle of insurance. American Economic Review: Papers and Proceedings,98(2), 157–162.CrossRefGoogle Scholar
  17. Cutler, D. M., & Lleras-Muney, A. (2006). Education and health: Evaluating theories and evidence (No. w12352). National Bureau of Economic Research.Google Scholar
  18. Cutler, D. M., & Zeckhauser, R. J. (1998). Adverse selection in health insurance. Forum for Health Economics & Policy,1(1), 1–31.  https://doi.org/10.2202/1558-9544.1056.CrossRefGoogle Scholar
  19. Domingue, B. W., Fletcher, J., Conley, D., & Boardman, J. D. (2014). Genetic and educational assortative mating among US adults. Proceedings of the National Academy of Sciences,111(22), 7996–8000.CrossRefGoogle Scholar
  20. Elks, C. E., Den Hoed, M., Zhao, J. H., Sharp, S. J., Wareham, N. J., Loos, R. J., et al. (2012). Variability in the heritability of body mass index: A systematic review and meta-regression. Frontiers in endocrinology,3, 29.CrossRefGoogle Scholar
  21. Fang, H., Keane, P. M., & Silverman, D. (2008). Sources of advantageous selection: Evidence from the medigap insurance market. Journal of Political Economy,116(2), 303–350.CrossRefGoogle Scholar
  22. Kaiser Family Foundation (KFF). (2018b). Uninsured rate among the nonelderly population, 1972–2017. https://www.kff.org/uninsured/slide/uninsured-rate-among-the-nonelderly-population-1972-2017/. Accessed June 20, 2018.
  23. Kendler, K. S., Gatz, M., Gardner, C. O., & Pedersen, N. L. (2006). A Swedish national twin study of lifetime major depression. American Journal of Psychiatry,163(1), 109–114.CrossRefGoogle Scholar
  24. Kosova, G., Abney, M., & Ober, C. (2010). Heritability of reproductive fitness traits in a human population. Proceedings of the National Academy of Sciences,107(suppl 1), 1772–1778.CrossRefGoogle Scholar
  25. Kuhnen, C. M., & Chiao, J. Y. (2009). Genetic determinants of financial risk taking. PLoS ONE,4(2), e4362.CrossRefGoogle Scholar
  26. Maes, H. H., Sullivan, P. F., Bulik, C. M., Neale, M. C., Prescott, C. A., Eaves, L. J., et al. (2004). A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence. Psychological Medicine,34(07), 1251–1261.CrossRefGoogle Scholar
  27. McGue, M., Zhang, Y., Miller, M. B., Basu, S., Vrieze, S., Hicks, B., et al. (2013). A genome-wide association study of behavioral disinhibition. Behavior Genetics,43(5), 363–373.CrossRefGoogle Scholar
  28. Moor, M. M., Willemsen, G., Rebollo-Mesa, I., Stubbe, J., Geus, E. C., & Boomsma, D. (2011). Exercise participation in adolescents and their parents: Evidence for genetic and generation specific environmental effects. Behavior Genetics,41, 211–222.CrossRefGoogle Scholar
  29. Oster, E., Shoulson, I., Quaid, K., & Dorsey, E. A. (2010). Genetic adverse selection: Evidence from long-term care insurance and Huntington disease. Journal of Public Economics,94, 1041–1050.CrossRefGoogle Scholar
  30. Rabe-Hesketh, S., Skrondal, A., & Gjessing, H. K. (2008). Biometrical modeling of twin and family data using standard mixed model software. Biometrics,64, 280–288.CrossRefGoogle Scholar
  31. Romeis, J. C., Heath, A. C., Xian, H., Eisen, S. A., Scherrer, J. F., Pedersen, N. L., et al. (2005). Heritability of SF-36 among middle-age, middle-class, male-male twins. Medical Care,43(11), 1147–1154.CrossRefGoogle Scholar
  32. Romeis, J., Scherrer, J., Xian, H., Eisen, S., Bucholz, K., Health, A., et al. (2000). Heritability of self-reported health status. Health Services Research,35(5 Part 1), 995–1010.PubMedPubMedCentralGoogle Scholar
  33. Stacey, D., Clarke, T. K., & Schumann, G. (2009). The genetics of alcoholism. Current Psychiatry Reports,11(5), 364–369.CrossRefGoogle Scholar
  34. Stubbe, J. H., Boomsma, D. I., Vink, J. M., Cornes, B. K., Martin, N. G., Skytthe, A., et al. (2006). Genetic influences on exercise participation in 37,051 twin pairs from seven countries. PLoS ONE,1(1), e22.CrossRefGoogle Scholar
  35. Thomsen, S. F., Van Der Sluis, S., Kyvik, K. O., Skytthe, A., & Backer, V. (2010). Estimates of asthma heritability in a large twin sample. Clinical and Experimental Allergy,40(7), 1054–1061.CrossRefGoogle Scholar
  36. Treloar, S. A., McDonald, C. A., & Martin, N. G. (1999). Genetics of early cancer detection behaviours in Australian female twins. Twin Research,2(01), 33–42.CrossRefGoogle Scholar
  37. True, W. R., Romeis, J. C., Heath, A. C., Flick, L. H., Shaw, L., Eisen, S. A., et al. (1997). Genetic and environmental contributions to healthcare need and utilization: A twin analysis. Health Services Research,32, 37–53.PubMedPubMedCentralGoogle Scholar
  38. Trumbetta, S. L., Markowitz, E. M., & Gottesman, I. I. (2007). Marriage and genetic variation across the lifespan: Not a steady relationship? Behavior Genetics,37(2), 362–375.CrossRefGoogle Scholar
  39. van der Loos, M., Rietveld, C. A., Eklund, N., et al. (2013). The molecular genetic architecture of self-employment. PLoS ONE,8(4), e60542.  https://doi.org/10.1371/journal.pone.0060542.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Verhulst, B. (2013). Gene-environment interplay in twin models. Political Analysis,21(3), 368–389.CrossRefGoogle Scholar
  41. Wehby, G. L., Domingue, B. W., & Boardman, J. D. (2015). Prevention, use of health services, and genes: Implications of genetics for policy formation. Journal of Policy Analysis and Management,34(3), 519–536.CrossRefGoogle Scholar
  42. Wehby, G. L., Domingue, B. W., Ullrich, F., & Wolinsky, F. D. (2017). Genetic predisposition to obesity and medicare expenditures. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences,73(1), 66–72.CrossRefGoogle Scholar
  43. Wehby, G. L., Domingue, B. W., & Wolinsky, F. D. (2018). Genetic risks for chronic conditions: Implications for long-term wellbeing. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences,73(4), 477–483.CrossRefGoogle Scholar
  44. Wolinsky, F. D., Jones, M. P., Ullrich, F., Lou, Y., & Wehby, G. L. (2014). The concordance of survey reports and medicare claims in a nationally representative longitudinal cohort of older adults. Medical Care,52(5), 462–468.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Health Management and PolicyUniversity of IowaIowa CityUSA
  2. 2.Department of EconomicsUniversity of IowaIowa CityUSA
  3. 3.Department of Preventive and Community DentistryUniversity of IowaIowa CityUSA
  4. 4.Public Policy CenterUniversity of IowaIowa CityUSA
  5. 5.National Bureau of Economic ResearchCambridgeUSA

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