Poorly Measured Confounders are More Useful on the Left than on the Right
By Zhuan Pei, Jörn-Steffen Pischke, H. Schwandt. Journal of business & economic statistics, 2017
253 citations recorded by the source corpus.
ABSTRACT Researchers frequently test identifying assumptions in regression-based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right-hand side of the regression. If such additions do not affect the coefficient of interest (much), a study is presumed to be reliable. We caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor measures of the potential underlying confounders. In this case, a more powerful test of the identifying assumption is to put the variable on the left-hand side of the candidate regression. We provide derivations for the estimators and test statistics involved, as well as power calculations, which can help applied researchers interpret their findings. We illustrate these results in the context of estimating the returns to schooling.
Subfields: Education, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f5778a3a89fb0c69bf2209486160efd4d62419d2
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Citations in the corpus
- Unlucky Cohorts: Estimating the Long-Term Effects of Entering the Labor Market in a Recession in Large Cross-Sectional Data Sets (2018, methodology): “We tested for such selection effects with balancing regressions that use the racial or gender composition as a dependent variable in our Web Appendix (Section IV), and found numerically small effects (Pei et al., 2018).”