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Using Multiple Outcomes to Improve the Synthetic Control Method

Liyang Sun¹; Eli Ben‐Michael²; Avi Feller³

¹ Department of Economics, University College London and CEMFI [email protected] · ² Department of Statistics & Data Science and Heinz College of Information Systems & Public Policy, Carnegie Mellon University · ³ Goldman School of Public Policy & Department of Statistics, University of California, Berkeley

When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to further gains when the number of outcomes grows. We illustrate this via a re-analysis of the impact of the Flint water crisis on educational outcomes.

DOI

10.1162/rest_a_01592

Pages

1-29

Language

en

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