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Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units

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Listed:
  • Noémi Kreif
  • Richard Grieve
  • Dominik Hangartner
  • Alex James Turner
  • Silviya Nikolova
  • Matt Sutton

Abstract

This paper examines the synthetic control method in contrast to commonly used difference-in-differences (DiD) estimation, in the context of a re-evaluation of a pay-for-performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re-weighting the control group so that it has similar pre-intervention characteristics to the treated group. We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re-analyse a recent study evaluating the effects of a hospital P4P scheme on risk-adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non-incentivised conditions. This result was robust to alternative specifications of the synthetic control method
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Suggested Citation

  • Noémi Kreif & Richard Grieve & Dominik Hangartner & Alex James Turner & Silviya Nikolova & Matt Sutton, 2016. "Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units," Health Economics, John Wiley & Sons, Ltd., vol. 25(12), pages 1514-1528, December.
  • Handle: RePEc:wly:hlthec:v:25:y:2016:i:12:p:1514-1528
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    Cited by:

    1. Olper, Alessandro & Curzi, Daniele & Swinnen, Jo, 2015. "Trade Liberalization and Child Mortality: A Synthetic Control Method," 2015 Conference, August 9-14, 2015, Milan, Italy 212597, International Association of Agricultural Economists.
    2. Ferman, Bruno & Pinto, Cristine Campos de Xavier & Possebom, Vitor Augusto, 2016. "Cherry picking with synthetic controls," Textos para discussão 420, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    3. Ferman, Bruno & Pinto, Cristine, 2016. "Revisiting the Synthetic Control Estimator," MPRA Paper 73982, University Library of Munich, Germany.
    4. repec:eee:jhecon:v:53:y:2017:i:c:p:131-155 is not listed on IDEAS
    5. Ziebarth, Nicolas R., 2017. "Social Insurance and Health," IZA Discussion Papers 10918, Institute for the Study of Labor (IZA).
    6. Roesel, Felix, 2017. "Do mergers of large local governments reduce expenditures? – Evidence from Germany using the synthetic control method," European Journal of Political Economy, Elsevier, vol. 50(C), pages 22-36.
    7. repec:eee:jeeman:v:88:y:2018:i:c:p:283-299 is not listed on IDEAS
    8. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.
    9. Alena Bachleitner, 2017. "Abolishing the Wealth Tax. A Case Study for Germany," WIFO Working Papers 545, WIFO.

    More about this item

    JEL classification:

    • N0 - Economic History - - General

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