Evaluation of Bank Efficiency in Brazil: A Dea Approach

Abstract

Este artigo pode ser copiado, distribuído, exibido, transmitido ou adaptado desde que citados, de forma clara e explícita, o nome da revista, a edição, o ano, e as páginas nas quais o artigo foi publicado originalmente, mas sem sugerir que a RAM endosse a reutilização do artigo. Esse termo de licenciamento deve ser explicitado para os casos de reutilização ou distribuição para terceiros. Não é permitido o uso para fins comerciais. The research aims to evaluate the efficiency of banks that operated in the Brazilian market in 2013. To achieve this goal, efficient banks were identified according to the production approach. To detect and explain efficiency standards, additional analyses were carried out related to: 1. capital origin, 2. size, 3. business segment, and 4. risk rating. Originality/gap/relevance/implications: The Brazilian literature on bank efficiency features several studies linking the efficiency of banks to capital origin and size. However, the relationship between efficiency and business segment has been poorly explored and the relationship between efficiency and risk rating is scarce. In this sense, this research contributes to the literature by exploring the relationship between efficiency and business segment, as well as the relationship between efficiency and risk rating.ISSN 1518-6776 (impresso) • ISSN 1678-6971 (on-line) • http://dx.doi.org/10.1590/1678-69712016/administracao.v17n4p61-83. Submissão: 24 abr. 2015. Aceitação: 1º fev. 2016. Sistema de avaliação: às cegas dupla (double blind review Key methodological aspects:The research uses a quantitative approach and employs the Data Envelopment Analysis (DEA) technique to calculate efficiency scores. The data were obtained from the Central Bank of Brazil (Bacen). Summary of key results:Federal public banks and large banks are, on average, more efficient. Banks operating in foreign exchange and retail, as well as banks with high credit ratings, also achieved high levels of efficiency. Key considerations/conclusions: Efficient banks proved to be more profitable, lent less money in proportion to their total assets, and received fewer complaints filed with the Central Bank of Brazil in 2013.

Evaluation of Old Oyo National Park efficiency using DEA Approach

“…Four variables were selected as inputs: number of staff, operating expenses (budget), number of offences and number of staff trained. Number of staff represents labor, the human resources providing services in the park (Saha and Ravisankar, 2000;Sathye, 2003;Macedo and Barbosa, 2009;Cava et al, 2016). Operating expenses represent the cost of the park's operations (Sathye, 2003;Liu, 2009;Wanke et al, 2015).…”

Institutional factors and efficiency performance in the global microfinance industry

“…To account for the effect of size on efficiency, we have utilised the natural logarithm of net fixed assets (LNFASST) as a proxy. The inclusion of this variable is crucial to capture the competitive capacity of MFIs, market awareness, technological variation, diversification, opportunities and networking capability (Berger and Di Patti, 2006;Cava et al, 2016;Nhung and Okuda, 2015;Wijesiri et al, 2017). Generally, it is understood that large-scale MFIs often lead the industry from various angles; hence, a positive association is envisaged between MFIs' size and efficiency.…”

Market share and performance in Taiwanese banks: min/max SBM DEA

“…Bank efficiency was found to be specifically related to the scale and the production method but was not affected by the use of the intermediation approach or the profitability approach. Cava et al (2016) used a product approach to also study the efficiency of Brazilian banks, and found that the average efficiency of the large-scale banks was high, and that the capital use average efficiency of the public banks was the highest. Baten et al (2015) examined the efficiency of commercial banks and private banks in Bangladesh and found that the profit DEA was higher than the cost DEA for the average annual bank productivity and efficiency changes and for technology efficiency.…”

“…Most previous research on bank performance evaluations has employed the stochastic frontier approach (SFA) and/or data envelopment analysis (DEA). DEA models can be classified into basic five categories: (1) radial efficiency such as CCR and BCC models (Tuskan and Stojanovic 2016;Singh and Kaur 2016;de Branco et al 2016;Cava et al 2016;Baten et al 2015;Radojicic et al 2015;Erasmus and Makina 2014;Benli and Degirmen 2013;Tsolas 2011;Sanjeev 2007;Schaffnit et al 1997); (2) non-radial efficiency such as the SBM model (Bahrini 2017;Lee et al 2015); (3) hybrid DEA models (Wong and Deng 2016;Chen et al 2015); (4) two-stage DEA and dynamic DEA models (Hassan and Jreisat 2016;Fukuyama and Weber 2015), and (5) others. As the radial efficiency CCR and BCC models assume that the inputs and outputs increase or decrease in equal proportions, there is a slacks problem, and as the non-radial efficiency embedded in the SBM model employs a furthest frontier point method (Tone 2001), there is an over and underestimation problem in the efficiency value improvement degree.…”