arXiv 29 Mar 2021 · Econometrics · 4 citations (OpenAlex)
arXiv:2103.15298 · PDF · DOI · OpenAlex · Extracted main text
Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from the setting without a budget constraint where uniformity is achievable. I propose an alternative trade-off rule and illustrate it with Medicaid expansion, a setting with imperfect take-up and varying program costs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kitagawa and Tetenov (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.909 | 12 | 5 | 75% |
| 2 | Athey and Wager (2021) Policy Learning With Observational Data | 0.843 | 4 | 3 | 75% |
| 3 | Hendren and Sprung-Keyser (2020) A Unified Welfare Analysis of Government Policies | 0.811 | 4 | 2 | 100% |
| 4 | Finkelstein, Taubman, Wright, Bernstein, Gruber, Newhouse, Allen and… (2012) The Oregon Health Insurance Experiment: Evidence from the First Year | 0.693 | 6 | 1 | 100% |
| 5 | Hirano and Porter (2009) Asymptotics for Statistical Treatment Rules | 0.644 | 2 | 2 | 100% |
| 6 | Bhattacharya and Dupas (2012) Inferring welfare maximizing treatment assignment under budget constraints | 0.585 | 3 | 1 | 100% |
| 7 | Stoye (2009) Minimax regret treatment choice with finite samples | 0.585 | 3 | 1 | 100% |
| 8 | Rai (2019) Statistical Inference for Treatment Assignment Policies | 0.511 | 2 | 2 | 50% |
| 9 | Finkelstein, Hendren and Luttmer (2019) The Value of Medicaid: Interpreting Results from the Oregon Health Insurance Experiment | 0.511 | 2 | 1 | 100% |
| 10 | Manski (2004) Statistical Treatment Rules for Heterogeneous Populations | 0.511 | 2 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.