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Twisting the Demand Curve: Digitalization and the Older Workforce

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

Abstract

This paper uses U.S. Census Bureau panel data that link firm software investment to worker earnings. We regress the log of earnings of workers by age group on the software investment by their employing firm. To unpack the potential causal factors for differential software effects by age group we extend the AKM framework by including job-spell fixed effects that allow for a correlation between the worker-firm match and age and by including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, software capital raises earnings at a rate that declines post age 50 to about zero after age 65. By contrast, the effects of non-IT equipment investment on earnings increase for workers post age 50. The difference between the software and non-IT equipment effects suggests that our results are attributable to the technology rather than to age-related bargaining power. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and the earnings in high-wage firms relative to low-wage firms, and may thus widen earnings inequality within and across firms.

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  • Erling Barth & James C. Davis & Richard B. Freeman & Kristina McElheran, 2020. "Twisting the Demand Curve: Digitalization and the Older Workforce," NBER Working Papers 28094, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28094
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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.

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    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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