Using register data to estimate causal effects of interventions: An ex post synthetic control-group approach.

AIMS: It is common in the context of evaluations that participants have not been selected on the basis of transparent participation criteria, and researchers and evaluators many times have to make do with observational data to estimate effects of job training programs and similar interventions. The techniques developed by researchers in such endeavours are useful not only to researchers narrowly focused on evaluations, but also to social and population science more generally, as observational data overwhelmingly are the norm, and the endogeneity challenges encountered in the estimation of causal effects with such data are not trivial. The aim of this article is to illustrate how register data can be used strategically to evaluate programs and interventions and to estimate causal effects of participation in these.
METHODS: We use propensity score matching on pretreatment-period variables to derive a synthetic control group, and we use this group as a comparison to estimate the employment-treatment effect of participation in a large job-training program.
RESULTS: We find the effect of treatment to be small and positive but transient.
CONCLUSIONS: Our method reveals a strong regression to the mean effect, extremely easy to interpret as a treatment effect had a less advanced design been used (e.g. a within-subjects panel data analysis), and illustrates one of the unique advantages of using population register data for research purposes.