An innovative approach to analysing data
Georgina Rawle, Madhumitha Hebbar, Michele Binci, Paul Jasper
When evaluating programme impact in a context where a randomised control trial is either infeasible or not appropriate, the quasi-experimental approach of Propensity Score Matching (PSM) is often used to construct a counterfactual. However, if there are imbalances remaining after PSM, selection bias may persist.
Increasingly, researchers combine PSM and Difference-in-Differences (DID) to counter such imbalances. While there is guidance on applying this combined approach using panel data, applications of this approach in repeated cross-section settings are less frequent. In this paper, we present an innovative approach to combining PSM and DID when only cross-sections of data are available.