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Computerization, Obsolescence, and the Length of Working Life

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  • Péter Hudomiet
  • Robert J. Willis

Abstract

This paper analyzes how computerization affected the labor market outcomes of older workers between 1984 and 2017. Using the computerization supplements of the Current Population Survey (CPS) we show that different occupations were computerized at different times, older workers tended to start using computers with a delay compared to younger workers, but computer use within occupations converged to the same levels across age groups eventually. That is, there was a temporary knowledge gap between younger and older workers in most occupations. We estimate how this knowledge gap affected older workers’ labor market outcomes using data from the CPS and the Health and Retirement Study. Our models control for occupation and time fixed effects and in some models; we also control for full occupation-time interactions and use middle aged (age 40-49) workers as the control group. We find strong and robust negative effects of the knowledge gap on wages, and a large, temporary increase in transitions from work to non-participation, consistent with a model of creative destruction in which the computerization of jobs made older workers’ skills obsolete in birth cohorts that experienced computerization relatively late in their careers. We find larger effects on females and on middle-skilled workers.

Suggested Citation

  • Péter Hudomiet & Robert J. Willis, 2021. "Computerization, Obsolescence, and the Length of Working Life," NBER Working Papers 28701, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28701
    Note: AG LS
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    Cited by:

    1. Casas, Pablo & Román, Concepción, 2023. "Early retired or automatized? Evidence from the survey of health, ageing and retirement in Europe," The Journal of the Economics of Ageing, Elsevier, vol. 24(C).
    2. Allen, Steven G., 2023. "Demand for older workers: What do we know? What do we need to learn?," The Journal of the Economics of Ageing, Elsevier, vol. 24(C).
    3. Laub, Natalie & Boockmann, Bernhard & Kroczek, Martin, 2023. "Tightening Access to Early Retirement: Who Can Adapt?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277625, Verein für Socialpolitik / German Economic Association.
    4. Pablo Casas & Concepción Román, 2024. "The impact of artificial intelligence in the early retirement decision," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 583-618, August.
    5. Bernhard Boockmann & Martin Kroczek & Natalie Laub, 2023. "Tightening access to early retirement: who can adapt?," IAW Discussion Papers 142, Institut für Angewandte Wirtschaftsforschung (IAW).
    6. Bavafa, Hessam & Mukherjee, Anita & Welch, Tyler Q., 2023. "Inequality in the golden years: Wealth gradients in disability-free and work-free longevity in the United States," Journal of Health Economics, Elsevier, vol. 92(C).

    More about this item

    JEL classification:

    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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