Optimal taxation and insurance using machine learning

Maximilian Kasy

Working Paper from Harvard University OpenScholar

Abstract: How should one use (quasi-)experimental evidence when choosing policies such as top tax rates, health insurance coinsurance rates, unemployment benefit levels, class sizes in schools, etc.? This paper provides an answer that combines insights from (i) optimal policy theory as developed in the field of public finance, and (ii) machine learning using Gaussian process priors. We propose to choose policies which maximize posterior expected social welfare. 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 the data of the RAND health insurance experiment. The key tradeoff in this setting is between redistribution toward the sick and insurance revenues. The key empirical relationship the policymaker needs to learn about is the response of health care expenditures to coinsurance rates. Holding everything constant except the estimation method, we obtain much smaller estimates of the optimal coinsurance rate (18% vs. 50%) than those obtained using a conventional ``sufficient statistic'' approach.

Date: 2017-01
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://scholar.harvard.edu/kasy/node/56221

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qsh:wpaper:56221

Access Statistics for this paper

More papers in Working Paper from Harvard University OpenScholar Contact information at EDIRC.
Bibliographic data for series maintained by Richard Brandon ( this e-mail address is bad, please contact ).