The Importance of Borrowers’ History on Credit Behavior: The Mexican Experience

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 developedcredit information, probity modelling, Mexico

Universidad y campo

El libro reúne los 14 trabajos presentados en el seminario "Universidad y Campo", que tuvo lugar en el ITESO en abril de 1983. No es ésta una exposición de los hechos. Tampoco una memoria. Se incluye aquí, en una forma organizada, el producto de algunas reflexiones acerca del campo hechas por un grupo de universitarios

#GlobalCollembola - full sample-level database

Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. The database will be further expanded to include underrepresented regions and linked with trait data