Keywords: Synthetic control method, difference-in-differences, policy evaluation
Difference-in-Differences (DiD) estimation and the Synthetic Control (SC) method have been widely used to evaluate the effects of changes to health policy. However, DiD estimation relies on a parallel trends assumption that is often questionable. While the SC method can provide approximately unbiased estimates under non-parallel trends given sufficient pre-intervention data, SC estimates can be relatively inefficient. We consider two promising modelling approaches which have been largely overlooked in the health policy evaluation literature. First, including Interactive Fixed Effects (IFE) in place of the additive fixed effects commonly employed in DiD models, allows for non-parallel trends and can be more efficient than using the SC method. However, where policy effects are heterogeneous, IFE models provide biased estimates. The second approach, the Generalised Synthetic Control (GSC) method, combines insights from the literature on synthetic controls with an IFE model for the control units, permitting non-parallel trends while avoiding bias when policy effects are heterogeneous. We contrast the DiD, SC, IFE and GSC methods through a Monte Carlo simulation study and a case study.