Efficient semiparametric estimation for Gini inequality treatment effects

- Evaluate the effect of a program on Gini inequality under the treatment setup.
- Allow the membership of treated and control groups depending on covariates.
- Propose a two-step procedure to estimate the Gini inequality treatment effect.
- Investigate the asymptotic properties of the two-step estimator.
- Run simulations to examine the finite sample performance.

This paper evaluates the effects of a program on Gini index where the selection to treatment depends on covariates. We propose a two-step nonparametric estimation procedure for the Gini inequality treatment effects. The proposed new estimator is shown to be consistent and has asymptotical normal distribution. We also show that the proposed estimator achieves semiparametric efficiency bound. Simulations confirm the theoretical results and show that the proposed estimator has good finite sample performance.