Abstract
Public credit guarantee schemes are set up with the purpose of facilitating access to credit by Small and Medium-sized Enterprises (SMEs). The aim of the paper is to study the effectiveness and impacts of the Italian Central Guarantee Fund (CGF)’s activity, one of the main public guarantee schemes in Europe. This is even more important in the light of the 2018 CGF reform. 
 
 Analyzing a sample which includes all the guarantees issued by the CGF from 2012 to 2018 on loans made to manufacturing companies, we find that the CGF methodology is partially able to capture the variables affecting the probability of default of SMEs. The CGF scores before the reform show poor capability to forecast risk in the medium term, above all for micro and small enterprises. The post-reform model shows better forecasting ability and a greater consistency with the Z’’-score, one of the most recognized model in the distress prediction literature. The new CGF model may indirectly control the behaviour of lenders and first-level guarantors. In particular, our findings show that the probability of default on exposures covered by a mutual guarantee institution and counter-guaranteed by the CGF is lower than the probability of default of loans granted by a bank and directly guaranteed by the CGF. As a consequence, the direct guarantees need to be more monitored by the CGF and potential effects on the bank behaviour may derive, strengthening ECB’s supervision activities.
Highlights
It is known that small firms, because of information asymmetry and adverse selection, frequently experience difficulties in accessing the credit market (Beck & Demirguc-Kunt, 2006; Zaho, 2008)
Because there are different types of Central Guarantee Fund (CGF) intervention, the analysis distinguishes between the channels of direct guarantee to banks and counter-guarantee to mutual guarantee institutions (Confidi)
We verify whether the scores calculated using CGF assessment methodologies, developed before and after its recent reform, are consistent with one of the best-known distress prediction models, the Altman Z’’-score
Summary
It is known that small firms, because of information asymmetry and adverse selection, frequently experience difficulties in accessing the credit market (Beck & Demirguc-Kunt, 2006; Zaho, 2008). SMEs are often unable to provide information on their creditworthiness, and gathering information on SMEs can be challenging and costly for banks This is true for start-ups, given the level of uncertainty on the expected rates of return and the integrity of the borrower (Korosteleva and Mickiewicz, 2011). The CGF does not intervene in the relationship between the bank and the SME, but provides a public guarantee on financial operations. This guarantee can cover up to 80% of the loan, or up to €2.5 million, with the aim of improving the financial conditions applied to the borrowers by banks and Confidi (e.g. loan amount, required collateral, interest rate levels). In 2018, CGF was reformed to include an internal credit rating model, similar to those developed by banks, and more accurate than the previous scoring system used for identifying eligible ijbm.ccsenet.org
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