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Synthetic Controls with Multiple Outcomes: Estimating the Effects of Non-Pharmaceutical Interventions in the COVID-19 Pandemic

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  • Wei Tian
  • Seojeong Lee
  • Valentyn Panchenko

Abstract

We propose a generalization of the synthetic control method to a multiple-outcome framework, which improves the reliability of treatment effect estimation. This is done by supplementing the conventional pre-treatment time dimension with the extra dimension of related outcomes in computing the synthetic control weights. Our generalization can be particularly useful for studies evaluating the effect of a treatment on multiple outcome variables. To illustrate our method, we estimate the effects of non-pharmaceutical interventions (NPIs) on various outcomes in Sweden in the first 3 quarters of 2020. Our results suggest that if Sweden had implemented stricter NPIs like the other European countries by March, then there would have been about 70% fewer cumulative COVID-19 infection cases and deaths by July, and 20% fewer deaths from all causes in early May, whereas the impacts of the NPIs were relatively mild on the labor market and economic outcomes.

Suggested Citation

  • Wei Tian & Seojeong Lee & Valentyn Panchenko, 2023. "Synthetic Controls with Multiple Outcomes: Estimating the Effects of Non-Pharmaceutical Interventions in the COVID-19 Pandemic," Papers 2304.02272, arXiv.org.
    • Handle: RePEc:arx:papers:2304.02272
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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