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Difference-in-difference estimation by FE and OLS when there is panel non-response

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  • Michael Lechner
  • Nuria Rodriguez-Planas
  • Daniel Fernández Kranz

Abstract

We show that the ordinary least squares (OLS) and fixed-effects (FE) estimators of the popular difference-in-differences model may deviate when there is time-varying panel non-response. If such non-response does not affect the common-trend assumption, then OLS and FE are consistent, but OLS is more precise. However, if non-response is affecting the common-trend assumption, then FE estimation may still be consistent, while OLS will be inconsistent. We provide simulation as well as empirical evidence for this phenomenon to occur. We conclude that in case of unbalanced panels, deviating OLS and FE estimates should be considered as evidence that non-response is not ignorable for the differences-in-differences estimation.

Suggested Citation

  • Michael Lechner & Nuria Rodriguez-Planas & Daniel Fernández Kranz, 2016. "Difference-in-difference estimation by FE and OLS when there is panel non-response," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2044-2052, August.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2044-2052
    DOI: 10.1080/02664763.2015.1126240
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    References listed on IDEAS

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    1. Fernández-Kranz, Daniel & Rodríguez-Planas, Núria, 2013. "Can Parents' Right to Work Part-Time Hurt Childbearing-Aged Women? A Natural Experiment with Administrative Data," IZA Discussion Papers 7509, Institute of Labor Economics (IZA).
    2. Timothy Besley & Robin Burgess, 2004. "Can Labor Regulation Hinder Economic Performance? Evidence from India," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 91-134.
    3. David H. Autor, 2003. "Outsourcing at Will: The Contribution of Unjust Dismissal Doctrine to the Growth of Employment Outsourcing," Journal of Labor Economics, University of Chicago Press, vol. 21(1), pages 1-42, January.
    4. Amuedo-Dorantes, Catalina & Serrano-Padial, Ricardo, 2007. "Wage Growth Implications of Fixed-Term Employment: An Analysis by Contract Duration and Job Mobility," Labour Economics, Elsevier, vol. 14(5), pages 829-847, October.
    5. Bedard, Kelly & Kuhn, Peter, 2015. "Micro-marketing healthier choices: Effects of personalized ordering suggestions on restaurant purchases," Journal of Health Economics, Elsevier, vol. 39(C), pages 106-122.
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    Cited by:

    1. Marcus, Jan & Reif, Simon & Wuppermann, Amelie & Rouche, Amélie, 2020. "Increased instruction time and stress-related health problems among school children," Journal of Health Economics, Elsevier, vol. 70(C).
    2. Lea Toulemon, 2018. "The effect of group purchasing on prices hospitals pay for medicines," Health Economics, John Wiley & Sons, Ltd., vol. 27(9), pages 1380-1393, September.
    3. Egbert Jongen & Arjan Lejour & Gabriella Massenz, 2018. "Cheaper and More Haircuts After VAT Cut? Evidence from the Netherlands," De Economist, Springer, vol. 166(2), pages 135-154, June.
    4. Julia Bredtmann & Christina Vonnahme, 2019. "Less money after divorce – how the 2008 alimony reform in Germany affected spouses’ labor supply, leisure and marital stability," Review of Economics of the Household, Springer, vol. 17(4), pages 1191-1223, December.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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

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