학술논문
Cost and Profit Efficiency of Banks: Stochastic Frontier Analysis vs Data Envelopment Analysis
이용수 3
- 영문명
- 발행기관
- 강원대학교 경영경제연구소
- 저자명
- Azizul Baten Maznah Mat Kasim Mafizur Rahman
- 간행물 정보
- 『아태비즈니스연구』제6권 제2호, 1~17쪽, 전체 17쪽
- 주제분류
- 인문학 > 문학
- 파일형태
- 발행일자
- 2015.12.30
4,840원
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국문 초록
영문 초록
This study compares the most widely used parametric and non-parametric techniques to measure cost and profit efficiency of banks, namely the Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We formulate the specification form of both stochastic cost and profit frontier models and constant return to scale Cost DEA and Profit DEA models and provide an empirical assessment of the cost and profit frontiers based on a panel dataset of National Commercial Banks (NCBs) and Private Banks (PBs) in Bangladesh over the 2001-2010 period. The cost inefficiency and profit efficiency are slightly higher for PBs than NCBs in case of both SFA and DEA. The coefficients of advance and off-balance sheet items are significant that positively influence the banks in stochastic cost frontier model while the advance, other earning assets, price of borrowed fund are significant and negative effects on the banks in stochastic profit frontier model. The average cost inefficiency and average profit efficiency are recorded with 16.3% and 91% respectively.
The highest and lowest cost inefficiency are observed for Janata Bank and United Commercial Bank Limited whilst the highest and lowest profit efficiency are recorded for Eastern Bank Limited and Janata Bank respectively. The average technical and allocative efficiency are 68.8% and 35.9%, respectively in case of CRS cost-DEA model whereas they are 70.3% and 31.8% in case of CRS profit-DEA model. The average cost inefficiency is recorded 6.3% by SFA whereas it is 24.5% by DEA. The average profit efficiency is found 91% by SFA while it is 22.1% by DEA, and SFA method shows better bank efficiency than DEA.
목차
Ⅰ. Introduction
Ⅱ. Efficiency: Concepts and Measurement
Ⅲ. Data and Definition of Variables
Ⅳ. Empirical Results and Discussion
Ⅴ. Conclusions
키워드
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