Carl Bonander
arXiv 23 Sep 2026 · Econometrics
arXiv:2609.28678 · PDF · Extracted main text
Policy reforms are sometimes accompanied by detailed individual-level data in the implementing jurisdiction, while only aggregate outcomes are available for potential comparison jurisdictions. This article develops an identification framework for heterogeneous policy effects when individual-level data are unavailable for the comparison units. The framework combines treatment-effect contrasts from difference-in-differences comparisons within the treated jurisdiction with a compatible population-average effect identified from aggregate panel data. Identification requires relative parallel trends within the treated jurisdiction together with the assumptions needed to identify the population-average effect from the aggregate panel. The within-jurisdiction component can be estimated from repeated cross-sections with a single pretreatment period.
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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 | Xu, Y (2017) Generalized synthetic control method: Causal inference with interactive fixed effects models | 0.874 | 6 | 5 | 67% |
| 2 | Long, S. K (2008) On the road to universal coverage: Impacts of reform in Massachusetts at one year | 0.737 | 3 | 2 | 100% |
| 3 | Shahn, Z (2023) Subgroup difference in differences to identify effect modification without a control group | 0.644 | 2 | 2 | 100% |
| 4 | Shahn, Z. and Hatfield, L (2024) Generalizing difference-in-differences to non-canonical settings: Identifying an array of estimands | 0.644 | 2 | 2 | 100% |
| 5 | Xu, Y., Zhao, A., and Ding, P (2026) Factorial difference-in-differences | 0.644 | 2 | 2 | 100% |
| 6 | Liu, L., Wang, Y., and Xu, Y (2024) A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data | 0.511 | 2 | 2 | 50% |
| 7 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program | 0.405 | 1 | 1 | 100% |
| 8 | Abadie, A. and L'Hour, J (2021) A penalized synthetic control estimator for disaggregated data | 0.405 | 1 | 1 | 100% |
| 9 | Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., and Wa… (2021) Synthetic difference-in-differences | 0.405 | 1 | 1 | 100% |
| 10 | Athey, S. and Imbens, G. W (2006) Identification and inference in nonlinear difference-in-differences models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 29 scored citations.