Evaluating direct and indirect treatment effects in Italian R&D expenditures

During the last decades SUTVA has represented the "gold standard" for the identification and evaluation of causal effects. However, the presence of interferences in causal analysis requires a substantial review of the SUTVA hypothesis. This paper proposes a framework for causal inference in presence of spatial interactions within a new spatial hierarchical Difference-in-Differences model (SH-DID). The novel approach decomposes the ATE, allowing the identification of direct (ADTE) and indirect treatment effects. In addition, our approach permits the identification of different indirect causal impact both on treated (AITET) and on controls (AITENT). The performances of the SH-DID are evaluated by a Montecarlo Simulation. The results confirm how omitting the presence of interferences produces biased parameters of direct and indirect effects, even though the estimates of the ATE in the traditional model are correct. Conversely, the SH-DID provides unbiased estimates of both total, direct and indirect effects. On this basis, we provide empirical evidence on the effectiveness of public policies in Italy. The estimates show the additionality of the policies on R&D expenditures. Decomposing the ATE, we demonstrate positive and significant direct effects, while the indirect impact is negative and meaningful, even if limited to the treated.