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Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data

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  • Rajiv Banker
  • Hsihui Chang
  • Ram Natarajan

Abstract

In this paper, we address the question of Data Envelopment Analysis (DEA) evaluation of efficiency when aggregate cost or revenue data must be used. We show that the DEA technical inefficiency measure using total revenues as the single output variable or total costs as the single input variable equals the aggregate technical and allocative inefficiency. We employ this result to estimate allocative inefficiency and construct statistical tests of the null hypothesis of no allocative inefficiency analogous to those of the null hypothesis of no scale inefficiency. We illustrate our method using revenue and personnel data for the top U.S. public accounting firms over 1995–1998. Our empirical results indicate the existence of statistically significant allocative inefficiency in the public accounting industry. Copyright Springer Science+Business Media, LLC 2007

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  • Rajiv Banker & Hsihui Chang & Ram Natarajan, 2007. "Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data," Journal of Productivity Analysis, Springer, vol. 27(2), pages 115-121, April.
    • Handle: RePEc:kap:jproda:v:27:y:2007:i:2:p:115-121
    DOI: 10.1007/s11123-006-0027-1
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    References listed on IDEAS

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    2. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    3. Rajiv D. Banker & Hsihui Chang & Ram Natarajan, 2005. "Productivity Change, Technical Progress, and Relative Efficiency Change in the Public Accounting Industry," Management Science, INFORMS, vol. 51(2), pages 291-304, February.
    4. Schaffnit, Claire & Rosen, Dan & Paradi, Joseph C., 1997. "Best practice analysis of bank branches: An application of DEA in a large Canadian bank," European Journal of Operational Research, Elsevier, vol. 98(2), pages 269-289, April.
    5. Banker, Rajiv D & Maindiratta, Ajay, 1988. "Nonparametric Analysis of Technical and Allocative Efficiencies in Production," Econometrica, Econometric Society, vol. 56(6), pages 1315-1332, November.
    6. Banker, Rajiv D. & Chang, Hsihui & Cunningham, Reba, 2003. "The public accounting industry production function," Journal of Accounting and Economics, Elsevier, vol. 35(2), pages 255-281, June.
    7. Bhattacharyya, Arunava & Lovell, C. A. K. & Sahay, Pankaj, 1997. "The impact of liberalization on the productive efficiency of Indian commercial banks," European Journal of Operational Research, Elsevier, vol. 98(2), pages 332-345, April.
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    Cited by:

    1. Subhash C. Ray & Abhiman Das & Kankana Mukherjee, 2018. "Measures of Labor Use Efficiency from a Cost-Based Dual Representation of the Technology: A Study of Indian Bank Branches," Working papers 2018-17, University of Connecticut, Department of Economics.
    2. Bao-Guang Chang & Tai-Hsin Huang & Chun-Yi Kuo, 2015. "A comparison of the technical efficiency of accounting firms among the US, China, and Taiwan under the framework of a stochastic metafrontier production function," Journal of Productivity Analysis, Springer, vol. 44(3), pages 337-349, December.
    3. Silva Portela, Maria Conceição A., 2014. "Value and quantity data in economic and technical efficiency measurement," Economics Letters, Elsevier, vol. 124(1), pages 108-112.
    4. Ray, Subhash C. & Chen, Lei & Mukherjee, Kankana, 2008. "Input price variation across locations and a generalized measure of cost efficiency," International Journal of Production Economics, Elsevier, vol. 116(2), pages 208-218, December.
    5. Dario Pinello & Angelos Liontakis & Alexandra Sintori & Irene Tzouramani & Konstantinos Polymeros, 2016. "Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece," Sustainability, MDPI, vol. 8(7), pages 1-11, July.
    6. Rajiv Banker & Seok-Young Lee & Gordon Potter & Dhinu Srinivasan, 2010. "The impact of supervisory monitoring on high-end retail sales productivity," Annals of Operations Research, Springer, vol. 173(1), pages 25-37, January.
    7. Mircea Epure, 2016. "Benchmarking for routines and organizational knowledge: a managerial accounting approach with performance feedback," Journal of Productivity Analysis, Springer, vol. 46(1), pages 87-107, August.
    8. Banker, Rajiv D. & Chang, Hsihui & Lee, Seok-Young, 2010. "Differential impact of Korean banking system reforms on bank productivity," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1450-1460, July.
    9. Kourtesi, Sofia & De Witte, Kristof & Polymeros, Apostolos, 2016. "Technical Efficiency in the Agricultural Sector - Evidence from a Conditional Quantile-Based Approach," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 17(2), June.
    10. Ray, Subhash C., 2015. "Nonparametric measures of scale economies and capacity utilization: An application to U.S. manufacturing," European Journal of Operational Research, Elsevier, vol. 245(2), pages 602-611.
    11. Padilla-Hernández, Salvador & Venegas-Martínez, Francisco & Gómez-Monge, Rodrigo (ed.), 2011. "Avances recientes en teoría y práctica económica," Sección de Estudios de Posgrado e Investigación de la Escuela Superios de Economía del Instituto Politécnico Nacional, Escuela Superior de Economía, Instituto Politécnico Nacional, edition 1, volume 2, number 006, July.
    12. Subhash C. Ray, 2022. "Choice of Inputs and Outputs for Production Analysis," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 26, pages 1083-1116, Springer.
    13. Banker, Rajiv & Chen, Janice Y.S. & Klumpes, Paul, 2016. "A trade-level DEA model to evaluate relative performance of investment fund managers," European Journal of Operational Research, Elsevier, vol. 255(3), pages 903-910.
    14. Amin, Gholam R. & Emrouznejad, Ali & Rezaei, S., 2011. "Some clarifications on the DEA clustering approach," European Journal of Operational Research, Elsevier, vol. 215(2), pages 498-501, December.
    15. Aldanondo, Ana M. & Casasnovas, Valero L., 2016. "A note on the impact of multiple input aggregators in technical efficiency estimation," MPRA Paper 75290, University Library of Munich, Germany.
    16. Rodrigo Gómez Monge, 2012. "El sector bancario en México, los depósitos a plazo y las cuentas de ahorro: un análisis de eficiencia durante el periodo de internacionalización a través de la envolvente de datos (dea)," Revista Ciencias Estratégicas, Universidad Pontificia Bolivariana, June.
    17. Samagaio, Antonio & Rodrigues, Ricardo, 2016. "Human capital and performance in young audit firms," Journal of Business Research, Elsevier, vol. 69(11), pages 5354-5359.

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    More about this item

    Keywords

    Data envelopment analysis; Aggregate cost data; Aggregate revenue data; Technical inefficiency; Allocative inefficiency; Public accounting; D24; L11; M41;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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