Taxation under Learning by Doing
研究了当工人生产率因干中学而内生演化时，最优所得税的设计。发现干中学要求更高的楔子，并改变了楔子与税率的关系。校准模型显示，改革美国税制能带来显著福利收益，且一个不依赖过去收入的简单税制近似最优。
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.