Sunny Karim, Matthew D. Webb, Nichole Austin, Erin Strumpf
arXiv 23 Mar 2024 · Econometrics
arXiv:2403.15910 · PDF · DOI · OpenAlex · Extracted main text
Difference-in-differences (DID) is commonly used to estimate treatment effects but is infeasible in settings where data are unpoolable due to privacy concerns or legal restrictions on data sharing, particularly across jurisdictions. In this study, we identify and relax the assumption of data poolability in DID estimation. We propose an innovative approach to estimate DID with unpoolable data (UN-DID) which can accommodate covariates, multiple groups, and staggered adoption. Through analytical proofs and Monte Carlo simulations, we show that UN-DID and conventional DID estimates of the average treatment effect and standard errors are equal and unbiased in settings without covariates. With covariates, both methods produce estimates that are unbiased, equivalent, and converge to the true value. The estimates differ slightly but the statistical inference and substantive conclusions remain the same. Two empirical examples with real-world data further underscore UN-DID's utility. The UN-DID method allows the estimation of cross-jurisdictional treatment effects with unpoolable data, enabling better counterfactuals to be used and new research questions to be answered.
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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 | Abadie, A (2005) Semiparametric difference-in-differences estimators | 1.000 | 6 | 4 | 100% |
| 2 | Callaway, B., and P. H. Sant’Anna (2021) Difference-in-differences with multiple time periods | 0.980 | 17 | 7 | 94% |
| 3 | Karim, S., and M. D. Webb (2024) Good controls gone bad: Difference-in-differences with covariates self | 0.961 | 9 | 3 | 89% |
| 4 | De Chaisemartin, C., and X. d’Haultfoeuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.941 | 6 | 4 | 83% |
| 5 | Karim, S., E. Strumpf, N. Austin, and M. D. Webb (2025) Which policy works and where? estimation and inference of state level treatment effects using difference-in-differences, Slides… self | 0.928 | 4 | 4 | 100% |
| 6 | Roth, J., P. H. Sant'Anna, A. Bilinski, and J. Poe (2022) What's trending in difference-in-differences? a synthesis of the recent econometrics literature | 0.928 | 4 | 3 | 100% |
| 7 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.644 | 2 | 2 | 100% |
| 8 | Conley, T. G., and C. R. Taber (2011) Inference with “difference in differences” with a small number of policy changes | 0.644 | 2 | 2 | 100% |
| 9 | Heckman, J. J., H. Ichimura, and P. E. Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.644 | 2 | 2 | 100% |
| 10 | Sabia, J. J., J. Swigert, and T. Young (2017) The effect of medical marijuana laws on body weight | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Good Controls Gone Bad: Difference-in-Differences with Covariates | 0.737 | 3 | 2 |