arXiv 16 Dec 2025 · Econometrics
arXiv:2512.14515 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the identification and estimation of heterogeneous effects of an endogenous treatment under interference and spillovers in a large single-network setting. We model endogenous treatment selection as an equilibrium outcome that explicitly accounts for spillovers and derive conditions guaranteeing the existence and uniqueness of this equilibrium. We then identify heterogeneous marginal exposure effects (MEEs), which may vary with both the treatment status of neighboring nodes and unobserved heterogeneity. We develop estimation strategies and establish their large-sample properties. Equipped with these tools, we analyze the heterogeneous effects of import competition on U.S. local labor markets in the presence of interference and spillovers. We find negative MEEs, consistent with the existing literature. However, these effects are amplified by spillovers in the presence of treated neighbors and among localities that tend to select into lower levels of import competition. These additional empirical findings are novel and would not be credibly obtainable without the econometric framework proposed in this paper.
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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 | Leung, M. P (2022) Causal Inference Under Approximate Neighborhood Interference | 1.000 | 5 | 3 | 100% |
| 2 | Xu, H (2018) Social Interaction in Large Networks: A Game Theoretic Approach | 0.928 | 4 | 3 | 100% |
| 3 | Hoshino, T. and T. Yanagi (2023) Treatment effect models with strategic interaction in treatment decisions | 0.843 | 3 | 3 | 100% |
| 4 | Lin, Z. and Y. Hu (2024) Binary choice with misclassification and social interactions, with an application to peer effects in attitude | 0.843 | 3 | 3 | 100% |
| 5 | Sasaki, Y (2025) GMM and M Estimation under Network Dependence, ArXiv 2503.00290 self | 0.830 | 7 | 3 | 57% |
| 6 | Autor, D. H., D. Dorn, and G. H. Hanson (2013) The China Syndrome: Local Labor Market Effects of Import Competition in the United States | 0.811 | 4 | 2 | 100% |
| 7 | Cai, J., A. De Janvry, and E. Sadoulet (2015) Social Networks and the Decision to Insure | 0.811 | 4 | 2 | 100% |
| 8 | Hoshino, T. and T. Yanagi (2023) Causal Inference with Noncompliance and Unknown Interference | 0.811 | 4 | 2 | 100% |
| 9 | Newey, W. K. and D. McFadden (1994) Chapter 36 Large sample estimation and hypothesis testing, in | 0.737 | 4 | 2 | 75% |
| 10 | Lee, L.-f., J. Li, and X. Lin (2014) Binary Choice Models with Social Network under Heterogeneous Rational Expectations | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 28 scored citations.