| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17364 |
| DP17364 (Machine) Learning What Policies Value | |
| Joshua Blumenstock; Dan Bjorkegren; Samsun Knight | |
| 发表日期 | 2022-06-06 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Development Economics ; Industrial Organization ; Public Economics |
| 关键词 | Targeting Welfare Heterogeneous treatment effects |
| URL | https://cepr.org/publications/dp17364 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/546415 |
| 推荐引用方式 GB/T 7714 | Joshua Blumenstock,Dan Bjorkegren,Samsun Knight. DP17364 (Machine) Learning What Policies Value. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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