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

Maximilian Kasy

Journal of Public Economics, 2018, vol. 167, issue C, 205-219

Abstract: 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.

Keywords: Optimal policy; Gaussian process priors; Posterior expected welfare (search for similar items in EconPapers)
JEL-codes: C11 C14 H21 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:167:y:2018:i:c:p:205-219

DOI: 10.1016/j.jpubeco.2018.09.002

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