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

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 confirm its finite-sample validity, and an application to China's New Rural Pension Scheme uncovers a significantly negative within-household spillover of pension participation on co-residents' labour income.