Locally Robust Policy Learning: Inequality, Inequality of Opportunity and Intergenerational Mobility

TL;DR

This paper develops a flexible framework for policy learning using semiparametric social welfare functions, enabling optimal treatment decisions that account for inequality, opportunity, and mobility, with applications to preschool attendance and income data.

Contribution

It introduces a general theory for policy learning with locally robust moments for a broad class of social welfare functions, extending prior work and accommodating diverse distributional preferences.

Findings

Optimal policies for preschool attendance improve adult earnings.

The framework effectively incorporates inequality and opportunity considerations.

Empirical analysis demonstrates practical policy recommendations.

Abstract

Policy makers need to decide whether to treat or not to treat heterogeneous individuals. The optimal treatment choice depends on the welfare function that the policy maker has in mind and it is referred to as the policy learning problem. I study a general setting for policy learning with semiparametric Social Welfare Functions (SWFs) that can be estimated by locally robust/orthogonal moments based on U-statistics. This rich class of SWFs substantially expands the setting in Athey and Wager (2021) and accommodates a wider range of distributional preferences. Three main applications of the general theory motivate the paper: (i) Inequality aware SWFs, (ii) Inequality of Opportunity aware SWFs and (iii) Intergenerational Mobility SWFs. I use the Panel Study of Income Dynamics (PSID) to assess the effect of attending preschool on adult earnings and estimate optimal policy rules based on parental years of education and parental income.