Factors affecting the technical efficiency of production of the Brazilian banking system : a comparison of four statistical models in the context of data envelopment analysis

This paper uses an output oriented Data Envelopment Analysis (DEA) measure of technical efficiency to assess the technical efficiencies of the Brazilian banking system. Four approaches to estimation are compared in order to assess the significance of factors affecting inefficiency. These are nonparametric Analysis of Covariance, maximum likelihood using a family of exponential distributions, maximum likelihood using a family of truncated normal distributions, and the normal Tobit model. The sole focus of the paper is on a combined measure of output and the data analyzed refers to the year 2001. The factors of interest in the analysis and likely to affect efficiency are bank nature (multiple and commercial), bank type (credit, business, bursary and retail), bank size (large, medium, small and micro), bank control (private and public), bank origin (domestic and foreign), and non-performing loans. The latter is a measure of bank risk. All quantitative variables, including non-performing loans, are measured on a per employee basis. The best fits to the data are provided by the exponential family and the nonparametric Analysis of Covariance. The significance of a factor however varies according to the model fit although it can be said that there is some agreements between the best models. A highly significant association in all models fitted is observed only for nonperforming loans. The nonparametric Analysis of Covariance is more consistent with the inefficiency median responses observed for the qualitative factors. The findings of the analysis reinforce the significant association of the level of bank inefficiency, measured by DEA residuals, with the risk of bank failure.

Bank networks and firm credit: an agent based model approach

Starting from the idea that economic systems fall into complexity theory, where its many agents interact with each other without a central control and that these interactions are able to change the future behavior of the agents and the entire system, similar to a chaotic system we increase the model of Russo et al. (2014) to carry out three experiments focusing on the interaction between Banks and Firms in an artificial economy. The first experiment is relative to Relationship Banking where, according to the literature, the interaction over time between Banks and Firms are able to produce mutual benefits, mainly due to reduction of the information asymmetry between them. The following experiment is related to information heterogeneity in the credit market, where the larger the bank, the higher their visibility in the credit market, increasing the number of consult for new loans. Finally, the third experiment is about the effects on the credit market of the heterogeneity of prices that Firms faces in the goods market.