Natural experiments provide explicit and robust identifying assumptions for the estimation of treatment effects. Yet their use for policy design is often limited by the difficulty in extrapolating on the basis of reduced-form estimates of policy effects. On the contrary, structural models allow us to conduct ex ante policy analysis but their internal validity is often questioned. In this paper, we suggest combining the two approaches by putting structure on a regression discontinuity (RD) design. We start with a RD estimation, exploiting the fact that childless single individuals under 25 years of age are not eligible for social assistance in France. A behavioral model is then identified using the same age discontinuity. While this model replicates well the employment effect obtained by RD, it can also be used to predict actual policy reforms and, hence, to check external validity. Showing good performances in this regard, it is finally used to simulate important counterfactual policies, namely the extension of social assistance to young people and the employment effects of a large in-work benefit reform.
