Determinants of efficiency in state-chartered financial institutions: Why financial education and freedom matter

• Main Authors: Emmanuel Sousa de Abreu, Herbert Kimura
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Heliyon
• Subjects: Banking efficiency; State-chartered financial institutions; SBM DEA network; Machine learning; Fractional logistic regression
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405844020326384

In this paper, we verify which qualitative banking attributes can determine the level of American state-chartered Financial Institutions (FIs) and evaluate its underlying variables. The methodology followed three procedures of analysis. First, we measured banking efficiency using a two-stage SBM network data envelopment analysis (NDEA). Subsequently, we used machine learning methods to predict efficient FIs from qualitative attributes. Finally, we tested the variables related to the attributes, using a fractionated logistic regression controlled by economic-financial variables. As main results, we found that attributes linked to political-administrative localization criteria were the more important attribute in predicting if the FI was in the efficient group; we confirmed the recent findings of the literature that state that less governmental influence (freedom) is related to more efficient institutions. Besides that, we found that a population with a higher financial education have FIs with higher levels of efficiency.