Inequality Sensitive Optimal Treatment Assignment

TL;DR

This paper develops optimal treatment assignment rules considering inequality aversion, extending existing frameworks to incorporate egalitarian equivalent measures, and demonstrates their application in social programs for disadvantaged groups.

Contribution

It introduces a novel extension of treatment choice models to inequality-averse welfare measures, deriving optimal rules for Bayesian, maximin, and minimax regret evaluators.

Findings

Optimal treatment rules under inequality aversion are derived.

Methodology applied to education and microcredit program evaluations.

Shows how inequality sensitivity influences treatment assignment decisions.

Abstract

The egalitarian equivalent, $ee$, of a societal distribution of outcomes with mean $m$ is the outcome level such that the evaluator is indifferent between the distribution of outcomes and a society in which everyone obtains an outcome of $ee$. For an inequality averse evaluator, $ee < m$. In this paper, I extend the optimal treatment choice framework in Manski (2024) to the case where the welfare evaluation is made using egalitarian equivalent measures, and derive optimal treatment rules for the Bayesian, maximin and minimax regret inequality averse evaluators. I illustrate how the methodology operates in the context of the JobCorps education and training program for disadvantaged youth (Schochet, Burghardt, and McConnell 2008) and in Meager (2022)'s Bayesian meta analysis of the microcredit literature.