DP17364 (Machine) Learning What Policies Value

Author(s): Daniel Bjorkegren, Joshua Blumenstock, Samsun Knight
Publication Date: June 2022
Keyword(s): Heterogeneous Treatment Effects, targeting, Welfare
JEL(s): H53, I38, O10, Z18
Programme Areas: Public Economics, Industrial Organization, Development Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=17364

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.