(Machine) Learning What Policies Value

Daniel Bj\"orkegren, Joshua Blumenstock and Samsun Knight
Authors registered in the RePEc Author Service: Daniel Björkegren

Papers from arXiv.org

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.

Date: 2022-06
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (2)

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http://arxiv.org/pdf/2206.00727 Latest version (application/pdf)

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Working Paper: (Machine) Learning What Policies Value (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2206.00727

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