Assessing Spillover Effects of Spatial Policies with Semiparametric Zero-Inflated Models and Random Forests

The aim of this work is to estimate the variation over time of the spatial spillover effects of a public policy that was devoted to boost rural development in France over the period 1993–2002. At a micro data level, it is often observed that the dependent variable, such as local employment in a municipality, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a classical continuous response model or propensity score approaches. We consider two recent non parametric techniques that are able to deal with that estimation issue. The first approach consists in fitting two generalized additive models to estimate both the probability of no variation as well as the variation along time of the continuous part of the response. The second approach is based on the use of random forests which can naturally handle the observation of a mixture of a discrete response as well as a continuous one. Instead of estimating average treatment effects, we take advantage of the flexibility of the non parametric approaches to estimate what would have been the potential outcome under treatment, as well as treatment of the neighboring municipalities, on some particular municipalities chosen as being representative or as being of particular interest. The results indicate the evidence of interesting patterns of temporal spatially-mediated spillover effects of the policy with relevant nonlinear effects. Policy spillovers matter, even if they are generally not high in magnitude, for some municipalities with specific demographic and economic characteristics.