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Taxation under Learning by Doing

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  • Miltiadis Makris
  • Alessandro Pavan

Abstract

We study optimal income taxation when workers’ productivity is stochastic and evolves endogenously because of learning by doing. Learning by doing calls for higher wedges and alters the relation between wedges and tax rates. In a calibrated model, we find that reforming the US tax code brings significant welfare gains and that a simple tax code invariant to past incomes is approximately optimal. We isolate the role of learning by doing by comparing the aforementioned tax code to its counterpart in an economy that is identical to the calibrated one except for the exogeneity of the productivity process. Ignoring learning by doing calls for fundamentally different proposals.

Suggested Citation

  • Miltiadis Makris & Alessandro Pavan, 2021. "Taxation under Learning by Doing," Journal of Political Economy, University of Chicago Press, vol. 129(6), pages 1878-1944.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/713745
    DOI: 10.1086/713745
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    Cited by:

    1. Paweł Doligalski & Abdoulaye Ndiaye & Nicolas Werquin, 2023. "Redistribution with Performance Pay," Journal of Political Economy Macroeconomics, University of Chicago Press, vol. 1(2), pages 371-402.
    2. Evangelos Dioikitopoulos & Dimitrios Varvarigos, 2023. "Delay in childbearing and the evolution of fertility rates," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(3), pages 1545-1571, July.
    3. Tao Zhang & Quanyan Zhu, 2022. "On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment," Dynamic Games and Applications, Springer, vol. 12(2), pages 701-745, June.

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