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Poorly Measured Confounders are More Useful on the Left Than on the Right

Author

Listed:
  • Zhuan Pei
  • Jörn-Steffen Pischke
  • Hannes Schwandt

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 various strategies which have been suggested to identify the returns to schooling.

Suggested Citation

  • Zhuan Pei & Jörn-Steffen Pischke & Hannes Schwandt, 2017. "Poorly Measured Confounders are More Useful on the Left Than on the Right," NBER Working Papers 23232, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23232
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    References listed on IDEAS

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    1. Anderson, Michael L., 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1481-1495.
    2. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    3. Jonah B. Gelbach, 2016. "When Do Covariates Matter? And Which Ones, and How Much?," Journal of Labor Economics, University of Chicago Press, vol. 34(2), pages 509-543.
    4. Henry S. Farber & Robert Gibbons, 1996. "Learning and Wage Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(4), pages 1007-1047.
    5. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    6. Matias D. Cattaneo & Michael Jansson & Whitney K. Newey, 2018. "Inference in Linear Regression Models with Many Covariates and Heteroscedasticity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1350-1361, July.
    7. Nicola Persico & Andrew Postlewaite & Dan Silverman, 2004. "The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 1019-1053, October.
    8. J. A. Hausman & W. E. Taylor, 1980. "Comparing Specification Tests and Classical Tests," Working papers 266, Massachusetts Institute of Technology (MIT), Department of Economics.
    9. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    10. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    11. Nicola Persico & Andrew Postlewaite & Dan Silverman, 2001. "The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height, Third Version," PIER Working Paper Archive 04-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 15 Mar 2004.
    12. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    13. Joshua D. Angrist & Jörn-Steffen Pischke, 2015. "The path from cause to effect: mastering 'metrics," CentrePiece - The magazine for economic performance 442, Centre for Economic Performance, LSE.
    14. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    15. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    16. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
    17. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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