Data Envelopment Analysis Journal > Vol 5 > Issue 1

Measurement of Organizational Ability in the Banking Industry

Rajiv D. Banker, Department of Accounting, Temple University, USA, , Muktak K. Tripathi, Department of Accounting, Temple University, USA,
Suggested Citation
Rajiv D. Banker and Muktak K. Tripathi (2021), "Measurement of Organizational Ability in the Banking Industry", Data Envelopment Analysis Journal: Vol. 5: No. 1, pp 183-242.

Publication Date: 22 Jun 2021
© 2021 R. D. Banker and M. K. Tripathi
Applications and case studies,  Optimization,  Management control,  Performance measurement,  Estimation frameworks,  Financial econometrics,  Microeconometrics,  Panel data,  Productivity measurement and analysis,  Corporate finance,  Financial markets :Financial intermediation,  Financial markets :Market microstructure,  Industrial Organization:Market Structure,  Industrial Organization:Regulatory Economics,  Economic Theory:Microeconomic Theory,  Imperfect information,  Principal-Agent,  Financial Services
Organizational abilityefficiencybankingperformancedata envelopment analysisbenchmarkingDEA applicationcontextual factorsinput–output resourcesproduction function


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In this article:
1 Introduction 
2 Background 
3 Approaches to Banking Production 
4 Estimation of Models 
5 Data and Variables 
6 Validation Tests 
7 Application: Ability and Future Banking Investments 
8 Conclusion 
Appendix II.A: Production (DEA) and its Estimation (OLS) 
Appendix II.B: Random Process: DEA + OLS 
Appendix III: Banks on the Production Frontier: Highest (=1) Banking Efficiency — Ability and Input–Output 


This study provides a measure of organizational ability in the commercial banking industry. The current measurement of organizational ability developed by Demerjian et al. (2012) is based on a sample of firms that excluded financial firms and utilities due to their unique production approach. We use the banking-specific production function operating-based approach to estimate bank efficiency using Data Envelopment Analysis (DEA). We also identify and evaluate contextual factors as determinants of the relative technical efficiency using OLS. The estimated residuals, part of bank efficiency, is considered a noisy measure of the unobserved organizational ability. We show that the DEA + OLS model residuals are approximately normally distributed and are consistent estimates of banks' ability. We validate our measure of organizational ability by documenting its strong persistence and predictive power in future periods. We find that a one standard deviation change in organizational ability corresponds to a change in return on assets and revenues annually of 18.92% and 9.35%, respectively. The findings suggest that more (less) capable banking organizations are associated with higher (lower) contemporaneous and subsequent performance.