Taxation under Learning by Doing

• 成果类型: Article
• 署名作者: Makris, Miltiadis; Pavan, Alessandro
• 署名单位: University of Kent; Northwestern University
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/713745
• 发表日期: 2021
• 页码: 1878-1944
• 关键词: Dynamic mechanism design; Optimal income taxation; tax; experience; wages; redistribution; progressivity; exploration

摘要

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.