A Two-Step Synthetic Control Approach for Estimating Causal Effects of Marketing Events

Marketing researchers are often interested in estimating causal effects when a randomized experiment is infeasible. The synthetic control (SC) method has emerged as a powerful tool in these quasiexperimental settings. It is important to verify the SC parallel pretrends assumption, the testable part of the identifying assumption, because its violation may lead to biased estimates. However, no formal test exists, so researchers have to rely on visual inspection. Even with a formal test, researchers still need to know how to balance the bias-efficiency trade-off for the estimate. We fill this void and advance the two-step synthetic control (TSSC) approach that comprises a formal test for the SC pretrends assumption in the first step and recommends an appropriate method that balances the dual goal of reducing bias and increasing efficiency in the second step. Simulations show that the TSSC approach performs favorably in the bias-variance (bias-efficiency) trade-off. Applying the TSSC approach, we find that New York State’s repeal of the tampon tax caused a positive and significant (2.08%) increase in weekly tampon sales. Using theory, simulations, and empirics, we demonstrate the importance, validity, and usefulness of the TSSC approach.