Working Paper: CEPR ID: DP17364
Authors: Joshua Blumenstock; Daniel Bjorkegren; Samsun Knight
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
JEL Codes: I38; Z18; H53; O10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| allocation of benefits (H55) | benefits received (H55) |
| allocation of benefits (H55) | welfare weights (I38) |
| household income (D19) | welfare weights (I38) |
| household size (D10) | welfare weights (I38) |
| education level (I24) | welfare weights (I38) |
| treatment effects vary across household characteristics (C21) | welfare weights (I38) |
| welfare weights (I38) | policy allocation (E60) |