arXiv 14 May 2026 · Econometrics
arXiv:2605.15119 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a difference-in-differences framework for staggered policy adoption when units can be affected by other units' adoption. For each treated cohort and event time, the framework separates the effect of own adoption, the spillover effect generated by other adopters, and the total effect under the realized rollout. Identification uses a prespecified summary of spillover exposure and parallel trends comparisons among units with the same exposure at the baseline and target dates. Spillover effects are learned from never-treated units and evaluated for treated cohorts under the exposure distribution they face. We construct estimators for these effects and an inference procedure that allows for spatial dependence. Monte Carlo simulations illustrate that standard DID estimators that ignore spillovers can miss the total effect, whereas the proposed estimators have small bias for these effects and the associated confidence intervals have coverage close to the nominal level. In an empirical study of the Community Health Centers rollout, estimated spillovers account for a substantial share of the effect on older-adult mortality.
appendix boundary found by appendix_command · 75% of the source is main text. Read the extracted text to check this.
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 | Callaway, Brantly and Sant'Anna, Pedro HC (2021) Difference-in-differences with multiple time periods | 0.874 | 5 | 2 | 100% |
| 2 | Xu, Ruonan (2025) Difference-in-Differences with Interference | 0.817 | 11 | 3 | 55% |
| 3 | Bailey, Martha J and Goodman-Bacon, Andrew (2015) The War on Poverty's experiment in public medicine: Community health centers and the mortality of older Americans | 0.811 | 4 | 2 | 100% |
| 4 | Peter M. Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 2 | 100% |
| 5 | Butts, Kyle (2024) Difference-in-differences with spatial spillovers | 0.737 | 3 | 2 | 100% |
| 6 | Denis Kojevnikov and Vadim Marmer and Kyungchul Song (2021) Limit theorems for network dependent random variables | 0.644 | 3 | 2 | 67% |
| 7 | Goodman-Bacon, Andrew (2021) Difference-in-differences with variation in treatment timing | 0.644 | 3 | 2 | 67% |
| 8 | Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation | 0.511 | 2 | 1 | 100% |
| 9 | Gardner, John and Thakral, Neil and Tô, Linh T and Yap, Luther (2025) Two-Stage Differences in Differences | 0.511 | 2 | 1 | 100% |
| 10 | Leung, Michael P (2022) Causal inference under approximate neighborhood interference | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 23 scored citations.