Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector-Reference-Cited by-同舟云学术

Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector

Published:2020-01-01 Issue:1 Volume:3 Page:50-69
ISSN:2451-3458
Container-title:Open Economics
language:en
Short-container-title:

Author:

Sakouvogui Kekoura¹,Shaik Saleem²,Addey Kwame Asiam²

Affiliation:

1. Mathematical Statistician, U.S Census Bureau, Washington D.C.,

2. Department of Agribusiness and Applied Economics , North Dakota State University

Abstract

Abstract This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap statistic and Elbow methods. We use the December quarterly panel data consisting of 122 U.S agricultural banks across 37 states from 2000 to 2017 to estimate the cluster-adjusted DEA model. Empirical results show differences in the estimated DEA efficiency measures with and without a clustering approach. Furthermore, using non-parametric tests, the results of Ansari-Bradley, Kruskal Wallis, and Wilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better efficiency measures in comparison to the DEA model without a clustering approach.

Publisher

Walter de Gruyter GmbH

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference45 articles.

1. Alirezaee, M., & Sani, M. (2011). New analytical hierarchical process/data envelopment analysis methodology for ranking decision-making units. International Transactions in Operational Research, 18(5), 533-544. https://doi.org/10.1111/j.1475-3995.2011.00812.x

2. Aly, H., Grabowski, R., Pasurka, C., & Rangan, R. (1990). Technical scale and allocative efficiencies in US banking: An empirical investigation. Review of Economics and Statistics, 72 (2), 211-218. https://doi.org/10.2307/2109710

3. Amirteimoori, A,. & Kordrostami, S. (2014). Data envelopment analysis with discrete- valued inputs and outputs. Expert Systems, 31(4), 335-342. https://doi.org/10.1111/exsy.12045

4. Atwood, J., & Shaik, S. (2018). Quantile DEA: Estimating qDEA-alpha Efficiency Estimates with Conventional Linear Programming. In: Greene W., Khalaf L., Makdissi P., Sickles R., Veall M., Voia MC. (eds) Productivity and Inequality. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-68678-3_14.

5. Banker, R., Charnes, A., & Cooper, W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. COVID-19 Quarantine Measures Efficiency Evaluation by Best Tube Interval Data Envelopment Analysis;Operations Research Forum;2023-03-14

2. Measuring the efficiency of the banking sector by the method of Data Envelopement Analysis;InterConf;2023-02-19

3. Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach;Transportation Research Part E: Logistics and Transportation Review;2022-06

4. How are the United States Banks faring during the COVID-19 Pandemic? Evidence of Economic Efficiency Measures;Open Economics;2022-01-01

5. Modeling a shared hierarchical structure in data envelopment analysis: An application to bank branches;Expert Systems with Applications;2020-12