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Alternatives to polynomial trend-corrected differences-in-differences models

Author

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  • Vincent Vandenberghe

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 pretreatment observations to capture linear or nonlinear trend differences, and ii) extrapolates these to compute the treatment effect. This article 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, 2019. "Alternatives to polynomial trend-corrected differences-in-differences models," Applied Economics Letters, Taylor & Francis Journals, vol. 26(5), pages 358-361, March.
    • Handle: RePEc:taf:apeclt:v:26:y:2019:i:5:p:358-361
    DOI: 10.1080/13504851.2018.1478386
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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, 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".

    More about this item

    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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