Credit sharing information mechanisms represent the institutional answer to the asymmetric information problems inherent to credit markets. It is generally accepted that sharing information is beneficial for the participant institutions, however, there are few studies that have measured the impact of past behavioral information on risk analysis. Applying a Probit model to the micro level database gathered by the Mexican Public Registry of Credit Information we find that historical variables, like previous defaults and previous missing payments are highly significant in explaining the probability of default. In particular, having defaulted a loan in the past, increases current loan’s default probability in 30 percentage points. We also find that the longer the borrower has been in the market and the larger the loan, the less likely it is that the current loan will be defaulted on. Additionally, we measure the effects of macroeconomic fluctuations over individual loans’ probability of default; we find that inflation significantly increases it while economic growth reduces it. Our results imply that effort should be exerted to develop more complete databases on individuals’ past behavior. This is particularly relevant in the Latin American context were the credit sharing industry is not very developed
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