NEIGHBORHOOD EFFECTS ON THE PROPENSITY SCORE MATCHING

The focus of our paper is the identification of the regional effects of industrial subsidies when the presence of subsidized firms is spatially correlated. In this case the stable unit treatment value assumption (SUTVA) in the Rubin model is not valid and some econometric methods should be used in order to detect the consistent policy impact in presence of spatial dependence. We propose a new methodology for estimating the unbiased “net” effect of the subsidy, based on novel “spatial propensity score matching” technique that compare treated and not treated units affected by similar spillover effects due to treatment. We offer different econometrical approaches, where the “spatial” propensity score is estimated by standard or spatial probit models. Some robustness tests are also implemented, using different instrumental variable spatial models applied to a probit model. We test the model using an empirical application, based on a dataset that incorporates information on incentives to private capital accumulation by Law 488/92, mainly devoted to SME, and Planning Contracts, created for large projects, in Italy. The analysis is carried out on a disaggregated territorial level, using the grid of the local labour system. The results show a direct effect of subsidies on subsidized firms. The sign of the impact is generally positive, the output effect outweighing the substitution effect. Confronting the standard and the “spatial” estimation, we observed a positive but small crowding out effect across firms in the same area and across neighbouring areas, mostly in the labour market. However, due to the small sample, the standard and the “spatial” effect of subsidies is not statistically significant.