AI 中文总结
研究在部分干扰下的有效双重差分估计，采用聚类增量倾向得分策略，定义并识别相关效应，推导影响函数构建估计量，经模拟验证有效性，应用于新农保发现家庭内养老金参与对居民劳动收入有负向溢出。
AI 中文摘要
本文提出了在部分干扰下采用聚类增量倾向得分（CIPS）策略的有效双重差分（DID）估计方法。定义了对处理组的直接和溢出平均处理效应，确定其识别方法并推导有效影响函数，构建交叉拟合估计量。模拟证实其有限样本有效性，对中国新型农村养老保险计划的应用揭示了养老金参与对同住居民劳动收入的显著负向家庭内溢出效应。
英文摘要
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.
