arXiv 9 Jul 2022 · Econometrics
arXiv:2207.04314 · PDF · DOI · OpenAlex · Extracted main text
This paper studies identification and inference of the welfare gain that results from switching from one policy (such as the status quo policy) to another policy. The welfare gain is not point identified in general when data are obtained from an observational study or a randomized experiment with imperfect compliance. I characterize the sharp identified region of the welfare gain and obtain bounds under various assumptions on the unobservables with and without instrumental variables. Estimation and inference of the lower and upper bounds are conducted using orthogonalized moment conditions to deal with the presence of infinite-dimensional nuisance parameters. I illustrate the analysis by considering hypothetical policies of assigning individuals to job training programs using experimental data from the National Job Training Partnership Act Study. Monte Carlo simulations are conducted to assess the finite sample performance of the estimators.
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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 | 1.000 | 9 | 3 | 100% |
| 2 | Kasy (2016) Partial identification, distributional preferences, and the welfare ranking of policies | 0.737 | 3 | 2 | 100% |
| 3 | Athey and Wager (2020) Policy Learning with Observational Data | 0.644 | 2 | 2 | 100% |
| 4 | Manski (2004) Statistical treatment rules for heterogeneous populations | 0.644 | 2 | 2 | 100% |
| 5 | Manski (1990) Nonparametric bounds on treatment effects | 0.585 | 3 | 1 | 100% |
| 6 | Manski (2003) Partial identification of probability distributions | 0.585 | 3 | 1 | 100% |
| 7 | Ichimura and Newey (2017) The influence function of semiparametric estimators | 0.511 | 4 | 2 | 25% |
| 8 | Molchanov and Molinari (2018) Random Sets in Econometrics | 0.511 | 3 | 2 | 33% |
| 9 | Beresteanu and Molinari (2008) Asymptotic properties for a class of partially identified models | 0.511 | 2 | 1 | 100% |
| 10 | Beresteanu, Molchanov, and Molinari (2011) Sharp identification regions in models with convex moment predictions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 65 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Orthogonal Policy Learning Under Ambiguity | 0.874 | 6 | 2 |
| 2 | Personalized Subsidy Rules | 0.405 | 1 | 1 |
| 3 | Policy Learning under Endogeneity Using Instrumental Variables | 0.405 | 1 | 1 |
| 4 | Inference for Interval-Identified Parameters Selected from an Estimated Set | 0.405 | 1 | 1 |
| 5 | Welfare at Risk: Distributional impact of policy interventions | 0.405 | 1 | 1 |