Gayani Rathnayake, Akanksha Negi, Otavio Bartalotti, Xueyan Zhao
arXiv 14 Nov 2024 · Econometrics
arXiv:2411.09221 · PDF · DOI · OpenAlex · Extracted main text
We consider identification of average treatment effects on the treated (ATT) within the difference-in-differences (DiD) framework in the presence of endogenous sample selection. First, we establish that the usual DiD estimand fails to recover meaningful treatment effects, even if selection and treatment assignment are independent. Next, we partially identify the ATT for individuals who are always observed post-treatment regardless of their treatment status, and derive bounds on this parameter under different sets of assumptions about the relationship between sample selection and treatment assignment. Extensions to the repeated cross-section and two-by-two comparisons in the staggered adoption case are explored. Furthermore, we provide identification results for the ATT of three additional empirically relevant latent groups by incorporating outcome mean dominance assumptions which have intuitive appeal in applications. Finally, two empirical illustrations demonstrate the approach's usefulness by revisiting (i) the effect of a job training program on earnings(Calonico & Smith, 2017) and (ii) the effect of a working-from-home policy on employee performance (Bloom, Liang, Roberts, & Ying, 2015).
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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 | Bartalotti, O., D. Kédagni, and V. Possebom (2023) Identifying marginal treatment effects in the presence of sample selection self | 1.000 | 7 | 3 | 100% |
| 2 | Chen, X. and C. A. Flores (2015) Bounds on treatment effects in the presence of sample selection and noncompliance: the wage effects of Job Corps | 1.000 | 5 | 4 | 100% |
| 3 | Lee, D. S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.885 | 26 | 8 | 69% |
| 4 | Huber, M. and G. Mellace (2015) Sharp bounds on causal effects under sample selection | 0.851 | 13 | 5 | 62% |
| 5 | Sant’Anna, P. H. and Q. Xu (2026) Difference-in-differences with compositional changes | 0.843 | 4 | 3 | 75% |
| 6 | Semenova, V (2025) Generalized lee bounds | 0.843 | 10 | 3 | 60% |
| 7 | Bloom, N., J. Liang, J. Roberts, and Z. J. Ying (2015) Does working from home work? Evidence from a Chinese experiment | 0.811 | 4 | 2 | 100% |
| 8 | Shin, S (2024) Difference-in-differences Design with Outcomes Missing Not at Random | 0.811 | 4 | 2 | 100% |
| 9 | Abadie, A (2005) Semiparametric difference-in-differences estimators | 0.737 | 5 | 4 | 40% |
| 10 | Imai, K (2008) Sharp bounds on the causal effects in randomized experiments with “truncation-by-death” | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating the Intensive Margin Effect in Panel Data Settings | 0.811 | 4 | 2 |
| 2 | Difference-in-Differences with Compositional Changes | 0.405 | 1 | 1 |
| 3 | Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random | 0.405 | 1 | 1 |