arXiv 28 Feb 2022 · Econometrics
arXiv:2202.13545 · PDF · DOI · OpenAlex · Extracted main text
Subsidies are commonly used to encourage behaviors that can lead to short- or long-term benefits. Typical examples include subsidized job training programs and provisions of preventive health products, in which both behavioral responses and associated gains can exhibit heterogeneity. This study uses the marginal treatment effect (MTE) framework to study personalized assignments of subsidies based on individual characteristics. First, we derive the optimality condition for a welfare-maximizing subsidy rule by showing that the welfare can be represented as a function of the MTE. Next, we show that subsidies generally result in better welfare than directly mandating the encouraged behavior because subsidy rules implicitly target individuals through unobserved heterogeneity in the behavioral response. When there is positive selection, that is, when individuals with higher returns are more likely to select the encouraged behavior, the optimal subsidy rule achieves the first-best welfare, which is the optimal welfare if a policy-maker can observe individuals' private information. We then provide methods to (partially) identify the optimal subsidy rule when the MTE is identified and unidentified. Particularly, positive selection allows for the point identification of the optimal subsidy rule even when the MTE curve is not. As an empirical application, we study the optimal wage subsidy using the experimental data from the Jordan New Opportunities for Women pilot study.
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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 | Heckman, J. J. and E. Vytlacil (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 1.000 | 6 | 3 | 100% |
| 2 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.950 | 7 | 3 | 86% |
| 3 | Groh, M., N. Krishnan, D. McKenzie, and T. Vishwanath (2016) Do wage subsidies provide a stepping-stone to employment for recent college graduates? evidence from a randomized experiment in… | 0.874 | 7 | 2 | 100% |
| 4 | Carneiro, P. and S. Lee (2009) Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enro… | 0.843 | 5 | 4 | 60% |
| 5 | Carneiro, P., J. J. Heckman, and E. J. Vytlacil (2011) Estimating marginal returns to education | 0.843 | 5 | 4 | 60% |
| 6 | Kasy, M (2016) Partial identification, distributional preferences, and the welfare ranking of policies | 0.811 | 4 | 2 | 100% |
| 7 | Sasaki, Y. and T. Ura (2020) Welfare analysis via marginal treatment effects | 0.811 | 4 | 2 | 100% |
| 8 | Brinch, C. N., M. Mogstad, and M. Wiswall (2017) Beyond late with a discrete instrument | 0.737 | 4 | 3 | 50% |
| 9 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.737 | 3 | 2 | 100% |
| 10 | Heckman, J., J. L. Tobias, and E. Vytlacil (2003) Simple estimators for treatment parameters in a latent-variable framework | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning under Endogeneity Using Instrumental Variables | 0.928 | 4 | 3 |
| 2 | Uniform Confidence Band for Marginal Treatment Effect Function | 0.405 | 1 | 1 |
| 3 | Policy Learning under Unobserved Confounding: A Robust and Efficient Approach | 0.405 | 1 | 1 |