Efficient difference-in-differences estimation under partial interference with incremental propensity score policies

Junjie Li and Yukitoshi Matsushita

Papers from arXiv.org

Abstract: This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and spillover average treatment effects on the treated, establish their identification, and derive their efficient influence functions, from which we construct a cross-fitted estimator. Simulations evaluate its finite-sample performance. An application to China's New Rural Pension Scheme recovers the reduction in farmwork among pension recipients reported by the original county-level analysis, separately estimates a within-household spillover alongside the direct effect, and traces both effects across the policy parameter.

Date: 2026-07, Revised 2026-09
New Economics Papers: this item is included in nep-cna and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2607.19925

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