in ,

Heritability of lifetime earnings, Hacker News

Heritability of lifetime earnings, Hacker News


Open Access

Article

First Online:

Abstract

Using twenty years of earnings data on Finnish twins, we find that about 40% of the variance of women’s and little more than half of men’s lifetime labor earnings are linked to genetic factors. The contribution of the shared environment is negligible. We show that the result is robust to using alternative definitions of earnings, to adjusting for the role of education, and to measurement errors in the measure of genetic relatedness.

Keywords

Earnings inequalityHeritabilityTwinsGenetics

Download to read the full article text

Notes

Acknowledgments

We would like to thank anonymous referees, Anders Björklund, Markus Jäntti, Jaakko Kaprio, Tomi Kyyrä, Tuomas Pekkarinen, Roope Uusitalo, as well as seminar participants at the Summer Meeting of the Finnish Economists (Jyväskylä), EALE Conference (Bonn), EEA Conference (Gothenburg), VATT (Helsinki), and SOFI (Stockholm) for useful comments. The usual caveat applies. We are thankful to Professor Jaakko Kaprio (University of Helsinki) for access to the twins data (Older Finnish Twin Cohort Study of the Department of Public Health in the University of Helsinki), to Statistics Finland for access to the register data (Finnish Longitudinal Employer -Employee Data FLEED), and to the Research Services unit of Statistics Finland for linking of the data sets. The Ethics Committee of Statistics Finland has given permission to use the data and all data work has been carried out following the terms and conditions of confidentiality of Statistics Finland.

Funding

Open access funding provided by Aalto University. This research has been financially supported by the Academy of Finland (project 127796), the Strategic Research Council (project Work, Inequality, and Public Policy, 293120), Jenny and Antti Wihuri Foundation, and Palkansaajasäätiö Foundation. The opinions expressed in the article are those of the authors and do not necessarily reflect the views of the funding sources.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

References

  1. Ashenfelter, O., Krueger, A .: Estimates of the economic return to schooling from a new sample of twins. Am. Econ. Rev.84, 1157 – 1173 (1994)Google Scholar

  2. Ashenfelter, O., Rouse, C .: Income, schooling and ability: evidence from a new sample of identical twins. Q. J. Econ.113, 253 – 284 (1998)CrossRefGoogle Scholar

  3. Autor, D., Figlio, D. Karbownik, K., Roth, J., Wasserman, M., Family disadvantage and the gender gap in behavioral and educational outcomes. NBER Working Paper No. 22267 (2016)Google Scholar

  4. Barnea, A., Cronqvist, H., Siegel, S .: Nature or nurture: what determines investor behavior? J. Financ. Econ.98, 583 – 604 (2010)CrossRefGoogle Scholar

  5. Behrman, S., Taubman, P .: Is schooling “mostly in the genes?” Nature-nurture decomposition using data on relatives. J. Polit. Econ.97, 1427 – 1446 (1989)Google Scholar

  6. Benjamin, DJ, Cesarini , D., Chabris, CF, Glaeser, EL, Laibson, DI, Gudnason, V., Harris, TB, Launer, LJ, Purcell, S., Smith, AV, Johannesson, M., Magnusson, PKE, Beauchamp, JP , Christakis, NA, Atwood, CS, Hebert, B., Freese, J., Hauser, RM, Hauser, TS, Grankvist, A., Hultman, CM, Lichtenstein, P .: The promises a nd pitfalls of genoeconomics. Annu. Rev. Econ.4, 627 – 662 (2012)CrossRefGoogle Scholar

  7. Bertrand, M .: New perspectives on gender. In: Ashenfelter, O., Card, D. (eds.) Handbook of Labor Economics, vol. Volume 4B, pp. 1543 – 1590. Elsevier, Amsterdam 2011)Google Scholar

  8. Bingley, P., Cappelari, L., Alike in many ways: Intergenerational and sibling correlations of brothers’ earnings. IZA Discussion Paper No. 6987 (2012)Google Scholar

  9. Bishop, DVM: DeFries-Fulker analysis of twin data with skewed distributions: cautions and recommendations from a study of children’s use of verb inflections. Behav. Genet.35, 479 – 490 (2005CrossRefGoogle Scholar

  10. Björklund, A., Jäntti, M .: Intergenera tional income mobility and the role of family background. In: Salverda, W., Nolan, B., Smeeding, T.M. (eds.) The Oxford Handbook of Economic Inequality, pp. 491 – 521. Oxford University Press, Oxford 2009)Google Scholar

  11. Björklund, A., Jäntti, M .: How important is family background for labor-economic outcomes? Labor Econ.19, 465 – 474 (2012)CrossRefGoogle Scholar

  12. Björklund, A., Jän tti, M., Solon, G .: Influences of nature and nurture on earnings variation: a report on a study of various sibling types in Sweden. In: Bowles, S., Gintis, H., Osborne, M. (eds.) Unequal Chances: Family Background and Economic Success, pp. 145 – 164. Russell Sage Foundation, New York 2005)Google Scholar

  13. Björklund, A., Lindahl, M., Plug, E .: The origins of intergenerational associations: lessons from Swedish adoption data. Q. J. Econ.121, 999 – 1028 (2006)CrossRefGoogle Scholar

  14. Björklund, A., Jäntti, M., Solon, G., Nature and nurture in the intergenerational transmission of socioeconomic status: Evidence from Swedish children and their biol ogical and rearing parents. BE J. Econ. Anal. Poli .: Advances 7, Article 4 (2007)Google Scholar

  15. Björklund, A., Jäntti, M., Lindquist, M .: Family background and income during the rise of the welfare state: brother correlations in income for Swedish men born 1932 – 1968. J. Public Econ.93, 671 – 680 (2009)CrossRefGoogle Scholar

  16. Björklund, A., Roine, J., Waldenström, D .: Intergenerational top income mobility in Sweden: capitalist dynasties in the land of equal opportunity? J. Public Econ.96, 474 – 484 (2012)CrossRefGoogle Scholar

  17. Black, SE, Devereux, PJ: Recent developments in in tergenerational mobility. In: Ashenfelter, O., Card, D. (eds.) Handbook of Labor Economics, vol. 4B, pp. 1487 – 1541. Elsevier, Amsterdam 2011)Google Scholar

  18. Böckerman, P., Viinikainen, J., Vainiomäki, J., Hintsanen, M., Pitkänen, N., Lehtimäki, T., Pehkonen, J., Rovio, S., Raitakari, O .: Stature and long-term labor market outcomes: evidence using Mendelian randomization. Econ. Hum. Biol.24, 18 – 29 (2017)CrossRefGoogle Scholar

  19. Böhlmark, A., Lindquist, M .: Life-cycle variations in the association between current and lifetime income: replication and extension for Sweden. J. Labor Econ.24, 879 – 896 (2006)CrossRefGoogle Scholar

  20. Bowles, S., Gintis, H .: The inheritance of inequality. J. Econ. Perspect.16, 3 – 30 (2002)CrossRefGoogle Scholar

  21. Branigan, AR, McCallum, KJ, Freese, J .: Variation in the heritability of educational attainment: an international meta-analysis. Soc. Forces.92, 109 – 140 (2013)CrossRefGoogle Scholar

  22. Cesarini, D., Essays on genetic variation and economic behavior. Ph.D thesis, MIT (2010)Google Scholar

  23. Cesarini, D., Dawes, C., Johannesson, M., Lichtenstein, P., Wallace, B .: Genetic variation in preferences for giving and risk taking. Q. J. Econ.124, 809 – 842 (2009)CrossRefGoogle Scholar

  24. Cesarini, D., Johannesson, M., Lichtenstein, P., Sandewall, Ö., Wallace, B .: Genetic variation in financia l decision making. J. Financ.65, 1725 – 1754 (2010CrossRefGoogle Scholar

  25. Chetty, R., Hendren, N., Kline, P., Saez, E., Turner, N .: Is the United States still a land of opportunity? Recent trends in intergenerational mobility. Am. Econ. Rev.104(Papers and Proceedings, 141 – 147 (2014)CrossRefGoogle Scholar

  26. Chetty, R., Hendren, N., Lin, F., Majerovitz, J., Scuderi, B. .: Childhood environment and gender gaps in adulthood. Am. Econ. Rev.106(Papers and Proceedings, 282 – 288 (2016)CrossRefGoogle Scholar

  27. Cronqvist, H., Siegel, S .: The origins of savings behavior. J. Polit. Econ.123, 123 – 169 (2015)CrossRefGoogle Scholar

  28. DeFries, J., Fulker, D .: Multiple regression analysis of twin data. Behav. Genet.15, 467 – 473 (1985)CrossRefGoogle Scholar

  29. Goldberger , A .: Heritability. Economica.46, 327 – 347 (1979)CrossRefGoogle Scholar

  30. Goldin, C .: A grand gender convergence: its last chapter. Am. Econ. Rev.104, 1091 – 1119 (2014)CrossRefGoogle Scholar

  31. Haider, S., Solon, G .: Life-cycle  variation in the association between current and lifetime earnings. Am. Econ. Rev.96, 1308 – 1320 (2006)CrossRefGoogle Scholar

  32. Harding, D., Jencks, C., Lopoo, LM, Mayer, SM: The changing effect of family bacground on the incomes of American adults. In: Bowles, S., Gintis, H., Osborne, M. (eds.) Unequal Chances: Family Background and Economic Success, pp. 100 – 144. Russell Sage Foundation, New York 2005)Google Scholar

  33. Heckman, J., Stixrud, J., Urzua, S .: The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. J. Labor Econ.24, 411 – 482 (2006)CrossRefGoogle Scholar

  34. Hyytinen, A., Ilmakunnas, P., Toivanen, O .: The returns to entrepreneurship puzzle. Labor Econ.20, 57 – 67 (2013)CrossRefGoogle Scholar

  35. Isacsson, G .: Estimates of the return to schooling in Sweden from a large sample of twins. Labor Econ.6, – 489 (1999)CrossRefGoogle Scholar

  36. Jäntti, M., Jenkins, SP: Income mobility. In: Atkinson, A.B., Bourguignon, F. (eds.) Handbook of Income Distribution, vol. 2, pp. 807 – 935. Elsevier, Amsterdam 2015)Google Scholar

  37. Jäntti, M., Österbacka, E ., Raaum, O., Eriksson, T., Björklund, A .: Brother correlations in earnings in Denmark, Finland, Norway and Sweden compared to the United States. J. Popul. Econ.15, 757 – 772 (2002)CrossRefGoogle Scholar

  38. Johnson, W., Krueger, R.F .: Genetic effects on physical health: lower at higher income levels. Behav. Genet.35, 579 – 590 (2005)CrossRefGoogle Scholar

  39. Kaprio, J .: The Finnish twin cohort study: an update. Twin Res. Hum. Genet.16, 157 – 162 (2013)CrossRefGoogle Scholar

  40. Kaprio, J., Koskenvuo, M .: Genetic and environmental factors in complex diseases: the older Finnish twin cohort. Twin Res.5, 358 – 365 (2002)CrossRefGoogle Scholar

  41. Kaprio, J. , Koskenvuo, M., Artimo, M., Sarna, S., Rantasalo, I., The Finnish twin registry: Baseline characteristics. Section I. Materials, methods, representativeness and results for variables special to twin studies. Department of Public Health, University of Helsinki, Series M 47 (1979)Google Scholar

  42. Killingsworth, MR, Heckman, JJ: Female labor supply: a survey. In: Ashenfelter, O.C., Layard, R. (eds.) Handbook of Labor Economics, vol. 1, pp. 103 – 204. Amsterdam, Elsevier (1986CrossRefGoogle Scholar

  43. Kohler, H., Rodgers, G .: DF-analyses of heritability with double-entry twin data: asymptotic standard errors and efficient estimation. Behav. Genet.31, 179 – 192 (2001)CrossRefGoogle Scholar

  44. LaBuda, MC, DeFries, JC, Fulker, DW: Multiple regression analysis of twin data obtained from selected samples. Genet. Epidemiol.3, 425 – 433 (1986)CrossRefGoogle Scholar

  45. Landersø, R., Heckman, JJ: The Scandinavian fantasy: The sources of intergenerational mobility in Denmark and the US Scand. J. Econ.119, 178 – (2017)CrossRefGoogle Scholar

  46. Lucas, REB, Pekkala Kerr, S .: Intergenerational income immobility in Finland: contrasting roles for parental earnings and family income. J. Popul. Econ.26, 1057 – 1094 (2013)CrossRefGoogle Scholar

  47. Manski, C .: Genes, eyeglasses, and social policy. J. Econ. Perspect.25, 83 – 94 (2011)CrossRefGoogle Scholar

  48. Mazumder, B .: Fortunate sons: new estimates of intergenerational mobility in the United States using social security earnings data. Rev. Econ. Stat.87, 235 – 255 (2005)CrossRefGoogle Scholar

  49. Miller, P., Mulvey, C., Martin, N .: What do twins studies reveal about the economic returns to education? A comparison of Australian and U.S. findings. Am. Econ. Rev.85, 586 – 599 (1995)Google Scholar

  50. Miller, P. , Mulvey, C., Martin, N .: Multiple regression analysis of the occupational status of twins: a comparison of economic and behavioral genetic models. Oxford B. Econ. Stat.58, 227 – 239 (1996)CrossRefGoogle Scholar

  51. Miller, P., Mulvey, C., Martin, N .: Family characteristics and the returns to schooling: evidence on gender differences from a sample of Australian twins. Economica.64, 137 – 154 (1997)CrossRefGoogle Scholar

  52. Miller, P., Mulvey, C., Martin, N .: Genetic and environmental contributions to educational attainment in Australia. Econ. Educ. Rev.20, 211 – (2001)CrossRefGoogle Scholar

  53. Miller, P., Mulvey, C., Martin, N .: The returns to schooling: estimates from a sample of young Australian twins. Labor Econ.13, 571 – 587 (2006)CrossRefGoogle Scholar

  54. Moffitt, RA, Gottschalk, P .: Trends in the transitory variance of male earnings: methods and evidence. J. Hum. Resour.47, 204 – 236 (2012)Google Scholar

  55. Nicolaou, N., Shane, S., Cherkas, L ., Hunkin, J., Spector, T .: Is the tendency to engage in entrepreneurship genetic? Manag. Sci.54, 167 – 179 (2008)CrossRefGoogle Scholar

  56. Nilsen, Ø.A., Vaage, K ., Aakvik, A., Jacobsen, K .: Intergenerational earnings mobility revisited: estimates based on lifetime earnings. Scand. J. Econ.114, 1 – 23 (2012)CrossRefGoogle Scholar

  57. Ørstavik, RE, Czajkowski, N., Røysamb, E., Knudsen, GP, Tambs, K., Reichborn-Kjennerud, T .: Sex differences in genetic and environmental influences on educational attain ment and income. Twin Res. Hum. Genet.17, 516 – 525 (2014)CrossRefGoogle Scholar

  58. Pekkarinen, T., Uusitalo, R., Pekkala Kerr, S .: School tracking and intergenerational income mobility: evidence from the Finnish comprehensive school reform. J. Public Econ.93, 965 – 973 (2009)CrossRefGoogle Scholar

  59. Piketty, T., Saez, E .: Income inequality in the United States, 1913 – 1998. Q. J. Econ.118, 1 – 41 (2003)CrossRefGoogle Scholar

  60. Plomin, R .: Commentary: why are children in the same family so different? Non-shared environment three decades after. Int. J. Epidemiol.40, 582 – 592 (2011)CrossRefGoogle Scholar

  61. Plomin, R., Kovas, Y .: Generalist genes and learning disabilities. Psychol. Bull.131, 592 – 617 (2005)CrossRefGoogle Scholar

  62. Plomin, R., Shakeshaft, NG, McMillan, A., Trzaskowski, M .: Nature, nurture, and expertise. Intelligence.45, 46 – 59 (2014)(CrossRef) ********Google Scholar

  63. Plug, E., Vijverberg, V .: Schooling, family background, and adoption: is it nature or is it nurture? J. Polit. Econ.111, 611 – 641 (2003)CrossRefGoogle Scholar

  64. Posthuma, D., Beem, AL, de Geus, EJC, van Baal, GCM , von Hjelmborg, JB, Iachine, I., Boomsma, DI: Theory and practice in quantitative genetics. Twin Res.6, 361 – 376 (2003)CrossRefGoogle Scholar

  65. Rodgers, J., Kohler, H .: Reformulating and simplifying the DF analysis model . Behav. Genet.35, 211 – 217 (2005)CrossRefGoogle Scholar

  66. Rodgers, J., McGue, H .: A simple algebraic demonstration of the validity of the DeFries-Fulker analysis in unselected samples with multiple kinship levels. Behav. Genet.24, 259 – 262 (1994)CrossRefGoogle Scholar

  67. Rodgers, J., Kohler, H., Kyvik, K., Christiansen, K .: Modeling of human fertility: findings from a contemporary Danish twin study. Demography.38, 29 – 42 (2001)CrossRefGoogle Scholar

  68. Sacerdote, B .: The nature and nurture of economic outcomes. Am. Econ. Rev.92(Papers and Proceeedings, 344 – 348 (2002)CrossRefGoogle Scholar

  69. Sacerdote, B .: How large are the effects from changes in family environment? A study of Korean American adoptees. Q. J. Econ.122, 119 – 157 (2007)CrossRefGoogle Scholar

  70. Sacerdote, B. .: Nature and nurture effects on children’s outcomes: what have we learned from studies of twins and adoptees? In: Benhabib, J., Bisin, A., Jackson, M.O. (eds.) Handbook of Social Economics, pp. 1 – 30. Elsevier, Amsterdam 2011)Google Scholar

  71. Schnittker, J .: Happiness and success: genes, families, and the psychological effects of socioeconomic position and social support. Am. J. Sociol.114, 233 – 259 (2008)CrossRefGoogle Scholar

  72. Shakeshaft, NG, Trzaskowski, M., McMillan, A ., Krapohl, E., Simpson, MA, Reichenberg, A., Cederlöf, M., Larsson, H., Lichtenstein, P., Plomin, R .: Thinking positively: the genetics of high intelligence. Intelligence.48, 123 – 132 (2015)CrossRefGoogle Scholar

  73. Simonson, I., Sela, A .: On the heritability of consumer decision making: an exploratory approach for studying genetic effects on judgment and choice. J. Consum. Res.37, 951 – 966 ( 2011)CrossRefGoogle Scholar

  74. Solon, G .: Intergenerational mobility in the labor market. In: Ashenfelter, O.C., Card, D. (eds.) Handbook of Labor Economics, vol. 3A, pp. 1761 – 1800. Elsevier, Amsterdam 1999)Google Scholar

  75. Stenberg, A .: Interpreting estimates of heritability – a note on the twin decomposition. Econ. Hum. Biol.11, 201 205 (2013)CrossRefGoogle Scholar

  76. Taubman, P .: The determinants of earnings: genetics, family, and other environments: a study of white male twins. Am. Econ. Rev.66, 858 – 870 (1976)Google Scholar

  77. Visscher, PM, Medland, SE, Ferreira, MAR, Morley, KI, Zhu, G., Cornes, BK, Montgomery, GW , Martin, NG .: Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet.2(3), E 41 (2006)CrossRefGoogle Scholar

  78. Waller, N .: A DeFries and Fulker regression model for genetic nonadditivity. Behav. Genet.24, 149 – 153 (1994)CrossRefGoogle Scholar

Copyright information

© The Author (s) 2019

(Open Access) This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author (s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Ari Hyytinen
      (1) (2)
  • Pekka Ilmakunnas
      (2) (3)

    Email author

  • Edvard Johansson
      (4)
  • (Otto Toivanen)
      (2) (5)
  1. 1.Department of EconomicsHanken School of EconomicsHelsinkiFinland
  2. 2.(Finland and Helsinki GSE)HelsinkiFinland
  3. Aalto University School of BusinessEspoo(Finland)

  4. 4.Faculty of Social Sciences, Business and EconomicsÅbo Akademi UniversityTurkuFinland
  5. 5.Aalto University School of BusinessHelsinkiFinland

                        
Brave Browser
(Read More) ********************
Payeer

What do you think?

Leave a Reply

Your email address will not be published. Required fields are marked *

GIPHY App Key not set. Please check settings

General Electric (GE) Stock's Decade of Pain May Be Over After Encouraging Q3 Earnings, Crypto Coins News

General Electric (GE) Stock's Decade of Pain May Be Over After Encouraging Q3 Earnings, Crypto Coins News

Researchers unearth malware that siphoned SMS texts out of telco’s network, Ars Technica

Researchers unearth malware that siphoned SMS texts out of telco’s network, Ars Technica