Machine learning in the service of policy targeting: the case of public credit guarantees

We use Machine Learning (ML) predictive tools to propose a policy-assignment rule designed to increase the effectiveness of public guarantee programs. This rule can be used as a benchmark to improve targeting in order to reach the stated policy goals. Public guarantee schemes should target firms that are both financially constrained and creditworthy, but they often employ naïve assignment rules (mostly based only on the probability of default) that may lead to an inefficient allocation of resources. Examining the case of Italy’s Guarantee Fund, we suggest a benchmark ML-based assignment rule, trained and tested on credit register data. Compared with the current eligibility criteria, the ML-based benchmark leads to a significant improvement in the effectiveness of the Fund in gaining credit access to firms. We discuss the problems in estimating and using these algorithms for the actual implementation of public policies, such as transparency and omitted payoffs.