Otávio Bartalotti, Désiré Kédagni, Vitor Possebom
arXiv 13 Dec 2021 · Econometrics · publishedJournal of Econometrics (2021) · 5 citations (OpenAlex)
arXiv:2112.07014 · PDF · DOI · OpenAlex · Extracted main text
This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Our analysis extends to discrete instruments. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee's (2009) trimming procedure to the MTE context. We propose estimators for the bounds derived and use data made available by Deb, Munking and Trivedi (2006) to empirically illustrate the usefulness of our approach.
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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 | Imai, Kosuke (2008) Sharp Bounds on the Causal Effects in Randomized Experiments with Truncation- by- Death | 1.000 | 6 | 4 | 100% |
| 2 | Deb, Partha, Murat K. Munkin, and Pravin K. Trivedi (2006) Bayesian Analysis of the Two-part Model with Endogeneity: Application to Health Care Expenditure | 0.981 | 18 | 5 | 94% |
| 3 | Lee, David S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.976 | 14 | 6 | 93% |
| 4 | Chen, Xuan and Carlos A Flores (2015) Bounds on treatment effects in the presence of sample selection and noncompliance: the wage effects of job corps | 0.965 | 10 | 6 | 90% |
| 5 | Heckman, James J. and Edward Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation 1 | 0.928 | 10 | 4 | 80% |
| 6 | Carneiro, Pedro, James J. Heckman, and Edward J Vytlacil (2011) Estimating marginal returns to education | 0.811 | 4 | 2 | 100% |
| 7 | Heckman, James J. and Edward Vytlacil (1999) Local Instrumental Variable and Latent Variable Models for Identifying and Bounding Treatment Effects | 0.737 | 4 | 3 | 50% |
| 8 | Tamer, Elie (2010) Partial Identification in Econometrics | 0.737 | 3 | 2 | 100% |
| 9 | Imbens, Guido W. and Joshua D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.737 | 3 | 2 | 100% |
| 10 | Carneiro, Pedro and Sokbae Lee (2009) Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enro… | 0.693 | 9 | 6 | 33% |
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