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

Author

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  • 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
    Note: ED LS PE
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    References listed on IDEAS

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    Cited by:

    1. Jan David Bakker & Stephan Maurer & Jörn-Steffen Pischke & Ferdinand Rauch, 2018. "Of Mice and Merchants: Trade and Growth in the Iron Age," NBER Working Papers 24825, National Bureau of Economic Research, Inc.
    2. Stephan E. Maurer & Andrei V. Potlogea, 2017. "Male-biased Demand Shocks and Women’s Labor Force Participation: Evidence from Large Oil Field Discoveries," Working Paper Series of the Department of Economics, University of Konstanz 2017-08, Department of Economics, University of Konstanz.
    3. Mariana Lopes da Fonseca, 2017. "Tax Mimicking in Local Business Taxation: Quasi-experimental Evidence from Portugal," CESifo Working Paper Series 6647, CESifo Group Munich.
    4. Kassenboehmer, Sonja C. & Schurer, Stefanie, 2018. "Survey Item-Response Behavior as an Imperfect Proxy for Unobserved Ability: Theory and Application," IZA Discussion Papers 11449, Institute for the Study of Labor (IZA).
    5. Schwandt, Hannes, 2017. "The Lasting Legacy of Seasonal Influenza: In-utero Exposure and Labor Market Outcomes," COHERE Working Paper 2017:5, University of Southern Denmark, COHERE - Centre of Health Economics Research.
    6. Josh Angrist & David Autor & Sally Hudson & Amanda Pallais, 2015. "Evaluating Econometric Evaluations of Post-Secondary Aid," American Economic Review, American Economic Association, vol. 105(5), pages 502-507, May.
    7. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Comments on “Unobservable Selection and Coefficient Stability-Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”," EIEF Working Papers Series 1802, Einaudi Institute for Economics and Finance (EIEF), revised Feb 2018.
    8. Sonja C. Kassenboehmer & Stefanie Schurer, 2018. "Survey item-response behavior as an imperfect proxy for unobserved ability: Theory and application," Working Papers 2018-035, Human Capital and Economic Opportunity Working Group.

    More about this item

    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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