(Machine) Learning What Policies Value

TL;DR

This paper introduces a machine learning-based method to analyze and uncover the underlying values and welfare considerations behind observed policy decisions, demonstrated through an anti-poverty program case study.

Contribution

It develops a novel approach to infer welfare weights and heterogeneous effects from policy data, enabling policy auditing and better alignment with societal values.

Findings

Identified that indigenous households benefited more despite lower welfare weights.

Demonstrated the method's ability to audit and inform policy design.

Applied the approach to Mexico's PROGRESA program successfully.

Abstract

When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We use machine learning methods to estimate how much each individual benefits from an intervention, and then reconcile its allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico's PROGRESA anti-poverty program. The analysis reveals that while the program prioritized certain subgroups -- such as indigenous households -- the fact that those groups benefited more implies that they were in fact assigned a lower welfare weight. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.