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Population Aging, Labor Demand, and the Structure of Wages

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Abstract

One consequence of demographic change is substantial shifts in the age distribution of the working-age population. As the baby boom generation ages, the usual historical pattern of there being a high ratio of younger workers relative to older workers has been replaced by a pattern of there being roughly equal percentages of workers of different ages. One might expect that the increasing relative supply of older workers would lower the wage premium paid for older, more experienced workers. This paper provides strong empirical support for this hypothesis. Econometric estimates imply that the size of one’s birth cohort affects wages throughout one’s working life, with members of relatively large cohorts (at all stages of their careers) earning a significantly lower wage than members of smaller cohorts. Estimated elasticities of wages with respect to the relative size of one’s own cohort are generally between −0.05 and −0.10, and are of similar magnitude for men and for women. Our results suggest that cohort size effects are quantitatively important and should be incorporated into public policy analyses.

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Notes

  1. Welch (1979) and others.

  2. Easterlin (1961).

  3. Easterlin’s main focus was on how economic conditions affect fertility, and he correctly predicted that the relatively unfavorable conditions created by the entry of the baby boom into the labor market would depress fertility rates.

  4. Welch’s (1979).

  5. Freeman (1979).

  6. Berger (1985).

  7. Murphy et al. (1988).

  8. Katz and Murphy (1992).

  9. Murphy and Welch (1992).

  10. Herd (2005).

  11. Details are provided in the Appendix.

  12. For this reason, we do not show birth cohorts more recent than 1970.

  13. The lines shown in these figures are smoothed versions of the underlying data. A locally weighted scatterplot smoothing (LOWESS) procedure was used, with a bandwidth of 0.2.

  14. For women, increases in labor force participation over time also play an important role.

  15. We define full-time, full-year workers as those who report working at least 45 weeks in the previous year, and report that they normally work at least 35 hours per week. We use the CPI-W series to express nominal wage rates in 2004 dollars. As described in the appendix, experience is imputed for each gender-education-age-birth year group. A LOWESS procedure, with bandwidth equal to 0.2, was used to smooth the plotted data.

  16. The median, rather than the mean, of individual wages is primarily used to lessen the impact of outliers.

  17. We are not able to display the experience premium for the “less than high school” women for early years due to the insufficient average work experience of this group in those years.

  18. Our specific specification most closely follows that of Card and Lemieux (2001).

  19. Each observation is formed by aggregating the underlying CPS micro-data by gender, birth year, and educational attainment.

  20. An earlier version of this paper that used data only for men included nodes at 20 and 30 years of experience, but the distribution of female labor force experience necessitated that our highest node be at 15 years.

  21. That is, although the observations are formed by aggregating by birth year and educational attainment, and so effectively by potential experience, the value of actual labor market experience that is imputed for each observation is used in the regressions.

  22. Goldin (1992).

  23. Falaris and Peters (1992).

  24. Connelly and Gottschalk (1995).

  25. To smooth over sampling variation, cohort size is calculated as a 5-year centered moving average, with weights equal to 1/9, 2/9, 1/3, 2/9, and 1/9.

  26. We do not include knots in the spline at points beyond 15 years because the experience is measured as within-group averages, and generally does not extend much beyond 15 years for women.

  27. Munnell and Sundén (2004).

  28. The test was conducted by estimating instrumental variables regressions in which the gender indicator variable is interacted with the relative cohort size and experience spline variables. The hypothesis that the coefficients on both the relative cohort size and age spline variables are equal for both genders was also rejected at 0.0000 significance level for each educational attainment level.

References

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Acknowledgements

The research reported herein received support from The Atlantic Philanthropies and from the U.S. Social Security Administration (SSA) funded as part of the Retirement Research Consortium (RRC). The findings and conclusions expressed are solely those of the authors and do not necessarily represent the views of the Federal Reserve Bank of Boston, the Federal Reserve System, Charles River Associates, The Atlantic Philanthropies, the SSA, any agency of the Federal Government or the RRC. The authors thank Jamie Lee and Brendan Mackoff for very helpful research assistance, and Melinda Pitts, Ron Lee, Alicia Munnell, Joe Quinn, Steve Sass, Tony Webb and two anonymous referees for helpful comments.

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Authors and Affiliations

  1. New School for Social Research, New York, NY, USA

    Michael Papadopoulos

  2. Charles River Associates, Boston, MA, USA

    Margarita Patria

  3. Research Department, Federal Reserve Bank of Boston, 600 Atlantic Avenue, Boston, MA, 02210, USA

    Robert K. Triest

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  1. Michael Papadopoulos
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  2. Margarita Patria
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  3. Robert K. Triest
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Correspondence to Robert K. Triest.

Appendix: Imputing labor market experience

Appendix: Imputing labor market experience

Past labor market experience is not reported in the CPS, and so we imputed average years of full-time labor market experience based on synthetic labor participation histories that we constructed by gender and educational attainment for each single-year birth cohort.

The synthetic labor participation histories are based on decennial population census micro-data (IPUMS) samples for the years 1940, 1950, and 1960, along with March CPS data for 1964–2016. Census data prior to 1940 do not include information on educational attainment comparable to that which we use in this study, and so could not be used. For the years available (1940, 1950, 1960, and 1964–2016), we calculate the mean full-time employment ratio for each year/gender/age/education group. That is, for each cell in the year/gender/age/education matrix, we find the percent of people working full time (which we define as working 45 or more weeks per year and 35 or more hours of work in the previous week; in 1960, we treat 40 or more weeks per year as full time due to data limitations). Because we lack data for years prior to 1940, we assume that full-time participation rates were constant from 1900 to 1940. We use linear interpolation to impute mean full-time participation rates for years between the decennial censuses and between 1960 and 1964. The final step in the imputation is to create a running sum of the full-time participation rates for each birth-year cohort (by gender and educational attainment). This yields a measure of mean years of full-time labor market experience for each birth-year cohort/age/gender/educational attainment combination.

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Papadopoulos, M., Patria, M. & Triest, R.K. Population Aging, Labor Demand, and the Structure of Wages. Geneva Pap Risk Insur Issues Pract 42, 453–474 (2017). https://doi.org/10.1057/s41288-017-0059-y

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