Robust Bounds on Optimal Tax Progressivity

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.