A1019
Title: Using multiple outcomes to improve the synthetic control method
Authors: Liyang Sun - UCL and CEMFI (Spain) [presenting]
Eli Ben-Michael - Carnegie Mellon University (United States)
Avi Feller - University of California at Berkeley (United States)
Abstract: When there are multiple outcome series of interest, synthetic control analyses typically proceed by estimating separate weights for each outcome. Estimating a common set of weights across outcomes is proposed instead by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, it is shown that these approaches lead to lower bias bounds than separate weights and that averaging leads to further gains when the number of outcomes grows. This is illustrated via simulation and in a re-analysis of the impact of the Flint water crisis on educational outcomes.
