Analysis of the Allocation Process of a Public Financial Incentive with Data Mining Techniques

Abstract

While public financial incentive to outward internationalisation are of fundamental importance, they have so far been neglected by academic circles, and scarce empirical evidence exists on the processes that drive agencies in the allocation of public incentives among firms. Policy makers should instead be concerned about agency behaviour when allocating subsidy, since that the process has important implications for evaluation strategies and it can reveal possible misalignments between policy goals and allocation outcomes. To this end and using multivariate statistical analysis together with data mining algorithms (Regression Trees), in this study we empirically inspect the complex relationship between Italian public financial support and the key factors governing the overall allocation process.