Robust Bounds on Optimal Tax Progressivity
Year: 2024
Type: article
Abstract: We study the problem of a robust planner who designs an optimal taxation scheme for a heterogeneous population in presence of uncertainty about the shape of the distribution of underlying types. Low-income workers are well insured under the optimal scheme, and so concerns about the left tail of the type distribution are negligible. On the other hand, the planner fears misspecification of the right tail of the type distribution emerging from budgetary concerns. Even when the tail of the distribution is Pareto, arbitrarily small misspecification concerns lead to zero marginal taxes at the top. A quantitative calibration shows that a plausible degree of uncertainty leads to an optimal tax scheme with substantially reduced marginal tax rates for high-income earners and a peak marginal tax rate much lower than in the model without uncertainty.
Source: Stanford Digital Repository
DOI: 10.25740/sp398mq3328
Authors: Bhandari, Anmol, Borovička, Jaroslav, Yao, Yuki
Language: English
Topic: Economic and Fiscal Studies
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6945946185
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