Dalia Ghanem, Sarojini Hirshleifer, Désiré Kédagni, Karen Ortiz-Becerra
arXiv 23 Mar 2022 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2203.12740 · PDF · DOI · OpenAlex · Extracted main text
Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically impose restrictions on whether or how response depends on treatment, our proposed attrition correction exploits restrictions on the outcome model. We further show that the conditions required for our correction can accommodate a broad class of response models that depend on treatment in an arbitrary way. We illustrate the implementation of the proposed corrections in an application to a large-scale randomized experiment.
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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 | Behaghel, Crépon, Gurgand and Barbanchon (2015) Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models | 1.000 | 5 | 3 | 100% |
| 2 | Millán and Macours (2021) Attrition in Randomized Control Trials: Using Tracking Information to Correct Bias | 0.950 | 7 | 4 | 86% |
| 3 | Lee (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.924 | 19 | 5 | 79% |
| 4 | Ghanem, Hirshleifer and Ortiz-Beccera (2023) Testing Attrition Bias in Field Experiments | 0.920 | 9 | 5 | 78% |
| 5 | Athey and Imbens (2006) Identification and Inference in Nonlinear Difference-in-Differences Models | 0.874 | 9 | 5 | 67% |
| 6 | Horowitz and Manski (1995) Identification and Robustness with Contaminated and Corrupted Data | 0.644 | 2 | 2 | 100% |
| 7 | Manski (1989) Anatomy of the Selection Problem | 0.511 | 4 | 2 | 25% |
| 8 | Gertler, Martinez and Rubio-Codina (2012) Investing Cash Transfers to Raise Long-Term Living Standards | 0.511 | 2 | 1 | 100% |
| 9 | Attanasio, Meghir and Santiago (2012) Education Choices in Mexico: Using a Structural Model and a Randomized Experiment to Evaluate PROGRESA | 0.405 | 1 | 1 | 100% |
| 10 | Behrman and Todd (1999) Randomness in The Experimental Samples of Progresa - Education, Health, and Nutrition Program | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Difference-in-differences Design with Outcomes Missing Not at Random | 0.511 | 2 | 1 |
| 2 | Extreme Changes in Changes | 0.405 | 1 | 1 |
| 3 | Difference-in-Differences with Sample Selection | 0.405 | 1 | 1 |
| 4 | Estimating the Intensive Margin Effect in Panel Data Settings | 0.405 | 1 | 1 |