Split-Sample Instrumental Variables Estimates of the Return to Schooling
提出分样本工具变量估计量，解决两阶段最小二乘法在估计教育回报时存在的偏误问题，并用该方法重新评估了已有研究结果。
Abstract This article reevaluates recent instrumental variables (IV) estimates of the returns to schooling in light of the fact that two-stage least squares is biased in the same direction as ordinary least squares (OLS) even in very large samples. We propose a split-sample instrumental variables (SSIV) estimator that is not biased toward OLS. SSIV uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample. SSIV is biased toward 0, but this bias can be corrected. The splt-sample estimators confirm and reinforce some previous findings on the returns to schooling but fail to confirm others. KEY WORDS: Finite-sample biasHuman capital and wagesTwo-stage least squares