Spatial differencing for sample selection models with ‘site-specific’ unobserved local effects
提出一种结合空间差分与两步样本选择估计量的方法，处理无法被行政边界捕捉的地点特定未观测效应，并通过蒙特卡洛实验和美国女性工资方程应用验证其有效性。
Summary This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from ‘site-specific’ unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator is consistent and asymptotically normal. A Monte Carlo experiment illustrates the small sample properties of our estimator. We apply our procedure to the estimation of a female wage offer equation in the United States and the results show the relevance of spatial differencing to account for ‘site-specific’ unobserved effects.