Optimal taxation and insurance using machine learning - Sufficient statistics and beyond

• 成果类型: Article
• 署名作者: Kasy, Maximilian
• 署名单位: Harvard University
• 刊物名称: JOURNAL OF PUBLIC ECONOMICS
• ISSN/ISSBN: 0047-2727
• DOI: 10.1016/j.jpubeco.2018.09.002
• 发表日期: 2018
• 页码: 205-219
• 关键词: Optimal policy; Gaussian process priors; Posterior expected welfare

摘要

How should one use (quasi -)experimental evidence when choosing policies such as tax rates, health insurance copay, unemployment benefit levels, and class sizes in schools? This paper suggests an approach based on maximizing posterior expected social welfare, combining insights from (i) optimal policy theory as developed in the field of public finance, and (ii) machine learning using Gaussian process priors. We provide explicit formulas for posterior expected social welfare and optimal policies in a wide class of policy problems. The proposed methods are applied to the choice of coinsurance rates in health insurance, using data from the RAND health insurance experiment. The key trade-off in this setting is between transfers toward the sick and insurance costs. The key empirical relationship the policy maker needs to learn about is the response of health care expenditures to coinsurance rates. Holding the economic model and distributive preferences constant, we obtain much smaller point estimates of the optimal coinsurance rate (18% vs. 50%) when applying our estimation method instead of the conventional sufficient statistic approach. (C) 2018 Elsevier B.V. All rights reserved.