arXiv 24 Sep 2026 · Econometrics
arXiv:2609.30007 · PDF · Extracted main text
I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.
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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 | Wooldridge, Jeffrey M (2025) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators | 1.000 | 15 | 3 | 100% |
| 2 | De Chaisemartin, Clément and D'Haultfœuille, Xavier Difference-in-Differences Estimators of Intertemporal Treatment Effects | 1.000 | 10 | 3 | 100% |
| 3 | Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann Revisiting Event-Study Designs: Robust and Efficient Estimation | 1.000 | 6 | 4 | 100% |
| 4 | Callaway, Brantly and Sant'Anna, Pedro H.C (2021) Difference-in-Differences with Multiple Time Periods | 1.000 | 6 | 3 | 100% |
| 5 | Sun, Liyang and Abraham, Sarah (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects | 0.874 | 5 | 2 | 100% |
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| 7 | Hsiang, Solomon M. and Jina, Amir S (2014) The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence from 6,700 Cyclones | 0.644 | 2 | 2 | 100% |
| 8 | Krolikowski, Pawel Choosing a Control Group for Displaced Workers | 0.644 | 2 | 2 | 100% |
| 9 | Patel, Dev (2024) Floods | 0.644 | 2 | 2 | 100% |
| 10 | Sandler, Danielle H. and Sandler, Ryan (2014) Multiple Event Studies in Public Finance and Labor Economics: A Simulation Study with Applications | 0.644 | 2 | 2 | 100% |
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