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Replication data for: Split-Sample Instrumental Variables Estimates of the Return to Schooling

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Title Replication data for: Split-Sample Instrumental Variables Estimates of the Return to Schooling
 
Identifier https://doi.org/10.7910/DVN/6LX9OE
 
Creator Joshua D. Angrist
Alan B. Krueger
 
Publisher Harvard Dataverse
 
Description 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.
 
Date 1995
 
Relation Joshua D. Angrist, Alan B. Krueger. 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?" The Quarterly Journal of Economics, 106(4), 979-1014
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