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The "End of Men" and Rise of Women in the High-Skilled Labor Market

Author

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  • Guido Matias Cortes
  • Nir Jaimovich
  • Henry E. Siu

Abstract

We document a new finding regarding changes in labor market outcomes for high-skilled men and women in the US. Since 1980, conditional on being a college-educated man, the probability of working in a cognitive/high-wage occupation has fallen. This contrasts starkly with the experience for college-educated women: their probability of working in these occupations rose, despite a much larger increase in the supply of educated women relative to men. We show that one key channel capable of rationalizing these findings is a greater increase in the demand for female-oriented skills in cognitive/high-wage occupations relative to other occupations. Using occupation-level data, we find evidence that this relative increase in the demand for female skills is due to an increasing importance of social skills within such occupations. Evidence from both male and female wages is also indicative of an increase in the demand for social skills. Finally, we document how these patterns change across the early and latter portions of the period.

Suggested Citation

  • Guido Matias Cortes & Nir Jaimovich & Henry E. Siu, 2018. "The "End of Men" and Rise of Women in the High-Skilled Labor Market," NBER Working Papers 24274, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24274
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    References listed on IDEAS

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    1. Paul Beaudry & David A. Green & Benjamin M. Sand, 2016. "The Great Reversal in the Demand for Skill and Cognitive Tasks," Journal of Labor Economics, University of Chicago Press, vol. 34(S1), pages 199-247.
    2. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    3. Peter B. Meyer & Anastasiya M. Osborne, 2005. "Proposed Category System for 1960-2000 Census Occupations," Working Papers 383, U.S. Bureau of Labor Statistics.
    4. Paul Beaudry & Ethan Lewis, 2014. "Do Male-Female Wage Differentials Reflect Differences in the Return to Skill? Cross-City Evidence from 1980-2000," American Economic Journal: Applied Economics, American Economic Association, vol. 6(2), pages 178-194, April.
    5. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    6. Guido Matias Cortes, 2016. "Where Have the Middle-Wage Workers Gone? A Study of Polarization Using Panel Data," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 63-105.
    7. Shintaro Yamaguchi, 2018. "Changes in Returns to Task-Specific Skills and Gender Wage Gap," Journal of Human Resources, University of Wisconsin Press, vol. 53(1), pages 32-70.
    8. George-Levi Gayle & Limor Golan, 2012. "Estimating a Dynamic Adverse-Selection Model: Labour-Force Experience and the Changing Gender Earnings Gap 1968--1997," Review of Economic Studies, Oxford University Press, vol. 79(1), pages 227-267.
    9. repec:hrv:faseco:33973831 is not listed on IDEAS
    10. Claudia Goldin & Lawrence F. Katz, 2016. "A Most Egalitarian Profession: Pharmacy and the Evolution of a Family-Friendly Occupation," Journal of Labor Economics, University of Chicago Press, vol. 34(3), pages 705-746.
    11. Francine Blau & Peter Brummund & Albert Liu, 2013. "Trends in Occupational Segregation by Gender 1970–2009: Adjusting for the Impact of Changes in the Occupational Coding System," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 471-492, April.
    12. Ross, Matthew B., 2017. "Routine-biased technical change: Panel evidence of task orientation and wage effects," Labour Economics, Elsevier, vol. 48(C), pages 198-214.
    13. Francine Blau & Peter Brummund & Albert Liu, 2013. "Erratum to: Trends in Occupational Segregation by Gender 1970–2009: Adjusting for the Impact of Changes in the Occupational Coding System," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 493-494, April.
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    Cited by:

    1. Gregory Verdugo & Guilllaume Allègre, 2017. "Labour force participation and job polarization : evidence from Europe during the Great Recession," Documents de Travail de l'OFCE 2017-16, Observatoire Francais des Conjonctures Economiques (OFCE).
    2. Nir Jaimovich & Itay Saporta-Eksten & Henry Siu & Yaniv Yedid-Levi, 2020. "The macroeconomics of automation: data, theory, and policy analysis," ECON - Working Papers 340, Department of Economics - University of Zurich.
    3. Bhalotra, Sonia R. & Karlsson, Martin & Nilsson, Therese & Schwarz, Nina, 2016. "Infant Health, Cognitive Performance and Earnings: Evidence from Inception of the Welfare State in Sweden," IZA Discussion Papers 10339, Institute of Labor Economics (IZA).
    4. Atalay, Enghin & Phongthiengtham, Phai & Sotelo, Sebastian & Tannenbaum, Daniel, 2018. "New technologies and the labor market," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 48-67.
    5. Devereux, Kevin, 2018. "Identifying the value of teamwork: Application to professional tennis," CLEF Working Paper Series 14, Canadian Labour Economics Forum (CLEF), University of Waterloo.

    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand

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