(Machine) Learning What Policies Value

Daniel Björkegren, Joshua Blumenstock and Samsun Knight

No 17364, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Targeting; Welfare; Heterogeneous treatment effects (search for similar items in EconPapers)
JEL-codes: H53 I38 O10 Z18 (search for similar items in EconPapers)
Date: 2022-06
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Working Paper: (Machine) Learning What Policies Value (2022)
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