Replication data for: Split-Sample Instrumental Variables Estimates of the Return to Schooling
Year: 2008
Type: dataset
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.
Source: Harvard Dataverse
DOI: 10.7910/dvn/6lx9oe
Authors: D. Angrist, Joshua, Alan B. Krueger
Institutions: Massachusetts Institute of Technology, Princeton University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4398685066