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Twisting the demand curve: Digitalization and the older workforce

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  • Barth, Erling
  • Davis, James C.
  • Freeman, Richard B.
  • McElheran, Kristina

Abstract

Software represents a major and fast-growing share of firms’ capital investment, impacting demand for labor and what workers do on their jobs. Using U.S. Census Bureau panel data that link firms and workers, this paper estimates the effect of firm software capital on the earnings of workers by age group. We extend the AKM framework to include job-spell fixed effects that account for potential correlation between the worker–firm match and employee age, as well as including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, capitalized software investment raises worker earnings. However, it does so at a rate that declines after the age of 50, to about zero beyond 65. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and earnings in high-wage firms relative to low-wage firms, thereby widening earnings inequality within and across firms.

Suggested Citation

  • Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    • Handle: RePEc:eee:econom:v:233:y:2023:i:2:p:443-467
    DOI: 10.1016/j.jeconom.2021.12.003
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    2. Herbert Dawid & Jasper Hepp, 2022. "Distributional effects of technological regime changes: hysteresis, concentration and inequality dynamics," Review of Evolutionary Political Economy, Springer, vol. 3(1), pages 137-167, April.
    3. 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).
    4. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    5. Julia Varlamova & Ekaterina Kadochnikova, 2023. "Modeling the Spatial Effects of Digital Data Economy on Regional Economic Growth: SAR, SEM and SAC Models," Mathematics, MDPI, vol. 11(16), pages 1-31, August.
    6. Pawe{l} Gola & Yuejun Zhao, 2024. "A Firm Link: Overall, Between- and Within-Firm Inequality Through the Lens of a Sorting Model," Papers 2410.11532, arXiv.org.

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    More about this item

    Keywords

    Software investments; Earnings equations; The AKM model; Age-biased technical change; Digitization;
    All these keywords.

    JEL classification:

    • J0 - Labor and Demographic Economics - - General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • O0 - Economic Development, Innovation, Technological Change, and Growth - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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