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Alternatives to Polynomial Trend-Corrected Differences-In-Differences Models

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  • Vincent Vandenberghe

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

Abstract

A common problem with differences-in-differences (DD) estimates is the failure of the parallel-trend assumption. To cope with this, most authors include polynomial (linear, quadratic…) trends among the regressors, and estimate the treatment effect as a once-in-a-time trend shift. In practice that strategy does not work very well, because inter alia the estimation of the trend uses post-treatment data. An extreme case is when sample covers only one period before treatment and many after. Then the trend's estimate relies almost completely on post-treatment developments, and absorbs most of the treatment effect. What is needed is a method that i) uses pre-treatment observations to capture linear or non-linear trend differences, and ii) extrapolates these to compute the treatment effect. This paper shows how this can be achieved using a fully-flexible version of the canonical DD equation. It also contains an illustration using data on a 1994-2000 EU programme that was implemented in the Belgian province of Hainaut.

Suggested Citation

  • Vincent Vandenberghe, 2018. "Alternatives to Polynomial Trend-Corrected Differences-In-Differences Models," LIDAM Discussion Papers IRES 2018001, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2018001
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    References listed on IDEAS

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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
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    4. Timothy Besley & Robin Burgess, 2004. "Can Labor Regulation Hinder Economic Performance? Evidence from India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 91-134.
    5. Vincent Vandenberghe, 2016. "Treatment-Effect Identification Without Parallel paths An illustration in the case of Objective 1- Hainaut/Belgium, 1994-2006," Working Papers CEB 16-051, ULB -- Universite Libre de Bruxelles.
    6. Justin Wolfers, 2006. "Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results," American Economic Review, American Economic Association, vol. 96(5), pages 1802-1820, December.
    7. Mora, Ricardo & Reggio, Iliana, 2012. "Treatment effect identification using alternative parallel assumptions," UC3M Working papers. Economics we1233, Universidad Carlos III de Madrid. Departamento de Economía.
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    Cited by:

    1. Vandenberghe, Vincent, 2018. "Treatment-effect identification without parallel paths," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-19.
    2. Jo, Wooyong & Nam, Hyoryung & Choi, Jeonghye, 2022. "Opening the OTC drug market: The effect of deregulation on retail pharmacy’s performance," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 847-866.
    3. Annarita Macchioni Giaquinto, 2022. "The power of the (red) pill in Europe: pharmaceutical innovation and female empowerment," Working Papers 2022:09, Department of Economics, University of Venice "Ca' Foscari".

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    More about this item

    Keywords

    Treatment-Effect Analysis; Differences-in-Differences Models; Correction for trend differences;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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