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Public pensions in the age of automation

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We analyze the impact of improved automation on the size and distribution of pension benefits, and on the optimal size of public pension systems. To this end, we build an overlapping generations model with heterogeneous agents. Automation is either conceptualized in a capital-skill complementarity (CSC) or task-based (TB) fashion. We find that any productivity gains of automation realized as increased returns to savings disproportionately benefit high-skilled workers who are less dependent on illiquid public pensions. A redistributive pension system can reduce public pension inequality but increase inequality in private retirement savings. The optimal size of the pension system is larger in the TB specification where displacement effects of automation are accounted for. We do not find that automation-driven growth warrants any change to the optimal size of the public pension system.

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  • Gustafsson, Johan & Lanot, Gauthier, 2024. "Public pensions in the age of automation," Umeå Economic Studies 1030, Umeå University, Department of Economics.
    • Handle: RePEc:hhs:umnees:1030
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    1. Kim, Jae-Yoon & Lee, Dongwoo, 2024. "Pension systems revisited in the age of automation and an aging economy," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    2. Alessandro Sommacal, 2006. "Pension systems and intragenenerational redistribution when labor supply is endogenous," Oxford Economic Papers, Oxford University Press, vol. 58(3), pages 379-406, July.
    3. Disney, Richard, 2000. "Declining public pensions in an era of demographic ageing: Will private provision fill the gap?," European Economic Review, Elsevier, vol. 44(4-6), pages 957-973, May.
    4. Prettner, Klaus & Strulik, Holger, 2020. "Innovation, automation, and inequality: Policy challenges in the race against the machine," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 249-265.
    5. Hans Fehr & Christian Habermann, 2008. "Risk Sharing and Efficiency Implications of Progressive Pension Arrangements," Scandinavian Journal of Economics, Wiley Blackwell, vol. 110(2), pages 419-443, June.
    6. James Bessen, 2019. "Automation and jobs: when technology boosts employment," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 589-626.
    7. Prettner, Klaus, 2019. "A Note On The Implications Of Automation For Economic Growth And The Labor Share," Macroeconomic Dynamics, Cambridge University Press, vol. 23(3), pages 1294-1301, April.
    8. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    9. Bas Jacobs, 2009. "Is Prescott right? Welfare state policies and the incentives to work, learn, and retire," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 16(2), pages 253-280, April.
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    JEL classification:

    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies

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