Akanksha Negi, Digvijay Singh Negi
arXiv 4 Aug 2022 · Econometrics · publishedJournal of Applied Econometrics (2025) · 5 citations (OpenAlex)
arXiv:2208.02412 · PDF · DOI · OpenAlex · Extracted main text
This paper studies identification and estimation of the average treatment effect on the treated (ATT) in difference-in-difference (DID) designs when the variable that classifies individuals into treatment and control groups (treatment status, D) is endogenously misclassified. We show that misclassification in D hampers consistent estimation of ATT because 1) it restricts us from identifying the truly treated from those misclassified as being treated and 2) differential misclassification in counterfactual trends may result in parallel trends being violated with D even when they hold with the true but unobserved D*. We propose a solution to correct for endogenous one-sided misclassification in the context of a parametric DID regression which allows for considerable heterogeneity in treatment effects and establish its asymptotic properties in panel and repeated cross section settings. Furthermore, we illustrate the method by using it to estimate the insurance impact of a large-scale in-kind food transfer program in India which is known to suffer from large targeting errors.
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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 | Aigner, D. J (1973) Regression with a binary independent variable subject to errors of observation | 1.000 | 5 | 3 | 100% |
| 2 | Lewbel, A (2007) Estimation of average treatment effects with misclassification | 0.928 | 4 | 3 | 100% |
| 3 | Botosaru, I. and F. H. Gutierrez (2018) Difference-in-differences when the treatment status is observed in only one period | 0.874 | 7 | 2 | 100% |
| 4 | Acerenza, S., K. Ban, and D. Kédagni (2021) Marginal Treatment Effects with Misclassified Treatment | 0.843 | 3 | 3 | 100% |
| 5 | Nguimkeu, P., A. Denteh, and R. Tchernis (2019) On the estimation of treatment effects with endogenous misreporting | 0.843 | 3 | 3 | 100% |
| 6 | Wooldridge, M. J (2021) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators | 0.843 | 3 | 3 | 100% |
| 7 | Coady, D., M. E. Grosh, and J. Hoddinott (2004) Targeting of transfers in developing countries: Review of lessons and experience | 0.811 | 4 | 2 | 100% |
| 8 | Gadenne, L., S. Norris, M. Singhal, and S. Sukhtankar (2021) In-kind transfers as insurance, Tech | 0.811 | 4 | 2 | 100% |
| 9 | Battistin, E. and B. Sianesi (2011) Misclassified treatment status and treatment effects: an application to returns to education in the United Kingdom | 0.737 | 3 | 2 | 100% |
| 10 | Jha, S., A. Kotwal, and B. Ramaswami (2013) Safety Nets and Food Programs in Asia: A Comparative Perspective | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Misclassification in Difference-in-Differences Models | 0.644 | 2 | 2 |
| 2 | 2106.00536 | 0.405 | 1 | 1 |
| 3 | Staggered Adoption DiD Designs with Misclassification and Anticipation | 0.405 | 1 | 1 |