Book
Sengupta, J. K. and Sahoo, B. K. (2006), Efficiency Models in Data Envelopment Analysis: Techniques of Evaluation of Productivity of Firms in a Growing Economy, Palgrave Macmillan, United Kingdom.
Abstract: Productivity and efficiency gains are central to the growth of firms in any industry. The industry growth depends on the markets and innovations in products and services and technology. The present volume discusses the various techniques for evaluating firm productivity and its impact on the industry evolution.
Economic analysis has been integrated here with the techniques of management science and operations research, which are called ‘data envelopment analysis’ (DEA). These techniques are useful to the managers and economic policy makers in designing optimal strategies and policies for improving productivity.
Some of the distinctive features of this volume are: (1) a synthesis of the two approaches: the econometric and the mathematical programming, (2) a dynamic cost frontier approach for measuring the optimal capacity utilization level, where the role of variable, semi-fixed and fixed costs can be separately estimated, (3) an integrated approach for linking the efficient firms with the industry equilibrium, where the ‘consumer surplus’ criterion can be applied for evaluating the welfare implications of both competitive and non-competitive market structures faced by the firms, (4) a production frontier approach estimating the effects of congestion, capacity utilization and technical efficiency, (5) finally, a new way of incorporating the impact of R&D investment and learning by doing on both demand and costs. This is especially important for the high-growth and technology-intensive firms.
The central focus on the applications side is to discuss the structure of efficiency gains or losses, the static and dynamic changes in productivity and an economic evaluation of policy measures. The industries selected here include modern industries like computer, pharmaceuticals, banking and life insurance. The banking and life insurance sectors in India have been studied in some detail in order to evaluate recent policy measures adopted by the government by way of economic reforms.
The analytical models developed in this volume discuss in some details some of the following concepts that are central to the optimal policy making objectives: (a) the pattern of distribution of efficiency across firms in an industry, (b) the impact of learning effects and R&D externalities, (c) the concepts of congestion and input surplus (surplus labor in the banking sector in India) and finally (d) the level and growth efficiency separating the static and dynamic productivity gains.
Research Papers
1. Sahoo, B. K., Mohapatra, P. K. J. and Trivedi, M. L. Trivedi (1999), “A Comparative Application of Data Envelopment Analysis and Frontier Translog Production Function for Estimating Returns to Scale and Efficiencies”, International Journal of Systems Science, Vol. 30, No. 4, pp. 379-394.
Abstract: This paper critically analyses, in the context of classical and neoclassical perspectives, the concepts of ‘returns to scale’ and ‘economies of scale’ by relating the former to the concept of ‘production unit’ and the latter to the concept of ‘firm’, and concludes that returns to scale constitutes only one component of economies of scale. The estimation models of frontier translog production function and DEA are applied to data on the Indian steel industry for a period of eight years in order to compare inferences about the production correspondence of the steel industry. Our results indicate that DEA is advantageous over the translog method in providing information relating to returns to scale possibilities of each of the individual production units. With regards to the efficiency evaluation, both the models are found to give statistically significant ‘technical’ and ‘technical and scale’ efficiency ratings.
2. Sahoo, B. K. (2000), “Returns to Scale and Technical Efficiency in Indian Agriculture”, Anvesak, Vol. 30, No. 1, pp. 39-60.
Abstract: An attempt has been made in this paper to examine the trend of productivity variations over years in Indian agriculture using Data Envelopment Analysis (DEA) and Econometric method. It is argued that DEA has merits over the econometric method in estimating efficiencies, and for year specific information regarding to its returns to scale possibilities. Finally, the results suggest that scope exist for Indian agriculture for improving the productivity growth with further production expansion and proper allocation of the existing resources for the time to come.
3. Sahoo, B. K. and Mohapatra, P. K. J. (2001), “Measuring Technical Efficiency in Data Envelopment Analysis”, OPSEARCH, Vol. 38, No. 5, pp. 456-483.
Abstract: In this study an attempt is made to examine, using data envelopment analysis, the productivity trends in the Indian Pig Iron and Sponge Iron industry for the period after economic liberalization. The methods of cross-efficiency matrix, distribution of virtual inputs and returns to scale are also used to get further insights into the performances of the individual units. The results show that the there has been declining efficiency trend since 1996 which could be taken as the evidence of rapid capacity addition in the early 1990s coupled with the recession in the steel sector. Though our DEA results on peer group and target provide limited information as to how performance can be improved through on-site inspection of operations these are not robust in the sense that some of the peers identified are not real peers. However, the results on cross efficiency matrix and the distribution of virtual inputs provide information concerning not only the real peers but also their truly efficient operating practices where there is high probability that both efficient as well as inefficient companies would improve upon.
Key Words: DEA; Technical Efficiency; Peer Group; Cross-Efficiency Matrix; Distribution of Virtual Inputs.
4. Mohanty, P. and Sahoo, B. K. (2002), “An Alternative to CRISIL Credit Rating Using Discriminant Analysis”, Journal of Applied Finance, No. 8, No. 1. pp. ----.
Abstract: Not available
5. Sahoo, B. K. (2003), “An Empirical Comparison of Farrell and Koopmans Measures of Technical Efficiency in Data Envelopment Analysis”, Anvesak, Vol. 33, No. 2, pp. 81-110.
Abstract: In this paper we attempt to provide theoretical justifications for distinguishing and comparing the two definitions of technical efficiency offered by Farrell and Russell, and empirically examine the two efficiencies in the context of Indian Pig Iron and Sponge Iron Industry. The empirical analysis shows that the technical efficiency ratings of the problematic DMUs (those operating not in the efficient subset of the production technology) in the radial measure of Farrell have drastically gone down in the non-radial Russell measure. An effort has also been made to see how far the extremely efficient DMUs are robust in the face of input perturbations. The results indicate that only two units are robust in the face of input increase, under the assumption of constant returns to scale.
Key Words: DEA; Farrell Efficiency; Koopmans Efficiency; Excess Slack
6. Tone, K. and Sahoo, B. K. (2003), “Scale, Indivisibilities and Production Function in Data Envelopment Analysis”, International Journal of Production Economics, Vol. 84, No. 2, pp. 165-192.
Abstract: This paper critically re-examines the concept of returns to scale vis-à-vis economies of scale since the writings of Adam Smith by relating the former to the concept of production unit and the latter to the concept of firm. Though to date some valuable progress has been made exploring the economic theory underlying DEA models, these developments have remained in their infancy and have not found enough application. An attempt is made here to examine the DEA model of efficiency measurement and its application from an economic viewpoint. We show here that the presence of indivisibilities in all multi-stage production processes makes the technology structure non-convex, and therefore, the standard convex DEA production models (e.g., CCR and BCC) fail to exhibit increasing returns due to indivisibilities. However, the non-convex technology embedded in FDH model helps revealing process indivisibilities arising from task-specific processes whereas a homogeneous characterization of production function fails to do so.
Key Words: Returns to scale; economies of scale; process indivisibility; data envelopment analysis.
7. Sahoo, B. K. (2004), “Measuring Productivity Growth and Capacity Utilization in Data Envelopment Analysis: An Application”, International Journal of Applied Economics and Econometrics, Vol. XII, No. 2, pp. 165-200.
Abstract: This article first reviews the well-known nonparametric methods of decomposing productivity growth and finally argue that Färe et al. (1994)’s method of computing productivity growth is superior in spite of the fact that this model model is based on convexity postulate, which assumes away some important technological features such as indivisiblities, economies of scale, and economies of specialization, which all arise from concavities in production. Second, we find the measurement of capacity utilization by Färe et al. (1989) not very practical, and therfore suggest a new method to measure capacity utilization, and then decompose the cost gap between actual and minimum cost into various components that are of practical use to managers, with special emphasis on that component due to unused capacity. Finally, this study applies Färe et al. method to a sample of 60 steel units in India over the period 1989-1996 to decompose the productivity growth into its various components. The results reveal that despite technological progress, there is a decline in productivity growth due to growing inefficiency over the period. This article finally concludes by suggesting that bold institutional policy changes is to be introduced as a part of the liberalization process in order that inefficiency is substantially reduced, resulting productivity growth to increase for the time to come.
Key Words: Productivity Growth, Technical Progress, Efficiency Change, Capacity Utilization
8. Tone, K. and Sahoo, B. K. (2004), “Degree of Scale Economies and Congestion: A Unified DEA Approach”, European Journal of Operational Research, Vol. 158, No. 3, pp. 755-772.
Abstract: There are increasing concerns about how increase in congestion can adversely affect output as well as about the relative benefit-cost ratio or return on investment associated with alternative projects or policies to address those problems. Regardless of what policy strategies are used to address congestion, the fact remains that we cannot assess the economic benefits of congestion-reduction strategies unless we are able to measure the extent to which congestion affects productivity in general, and scale economies in particular. This paper makes a novel attempt to suggest a method in a non-parametric framework to measure scale elasticity in production in the presence of congestion.
Keywords: DEA, scale elasticity, degree of scale economies, congestion.
9. Tone, K. and Sahoo, B. K. (2005), “Evaluating Cost Efficiency and Returns to Scale in the Life Insurance Corporation of India Using Data Envelopment Analysis”, Socio-Economic Planning Sciences, Vol. 39, No. 4, pp. 261-285 (This paper is ranked among top 25 articles within the journal: Socio-Economic Planning Sciences).
Abstract: This paper applies a new variant of data envelopment analysis model to examine the performance of Life Insurance Corporation (LIC) of India. The findings show a significant heterogeneity visible in the cost efficiency scores within 19 years. The decline in performance after 1994-95 can be taken as an evidence of increasing allocative inefficiencies arising from the huge initial fixed cost undertaken by LIC in modernizing the operations. But the significant increase in cost efficiency in 2000-01 is the basis for an optimism that LIC may be beginning to benefit from such a modernization, which will stand in good stead in terms of future competition. The results obtained from the sensitivity analysis are in broad agreement with the main findings of this study.
Key Words: LIC, DEA; Technical Efficiency, Returns to Scale; Economies of Scale.
10. Sengupta, J. K. and Sahoo, B. K. (2005), “Evaluation of Alternative Efficiency Measures: An Application”. In R. Ghosh and C. Neogi (eds.) Theory and Applications of Productivity and Efficiency: Econometric and DEA Approach, Macmillan.
Abstract: This paper critically reviews and compares the radial and non-radial measures of technical efficiency in the context of two schools of thought: Shephard School and Charnes School; and empirically examines them in the context of Indian Pig Iron and Sponge Iron Industry. We find here that first, both the Debreu-Farrell (DF) measure (with no slacks) and the non-radial Russell measure fail to declare some units efficient as identified efficient by the non-radial Mix-Adjusted DF measure (MA). Second, the MA measure in the variable returns to scale technology structure outperforms, in terms of mean, standard deviation and rank correlation, the remaining radial and non-radial measures, including the Weighted Russell measure (WRM) when slacks exist and inputs have differential importance in the production process. However, no measure is found to excel in all fronts in the case of constant returns to scale technology. These two findings provide some evidence to consider the MA measure as a superior method while pursuing efficiency studies.
Keywords: DEA; Debreu-Farrell Efficiency; Russell Efficiency; Mix-Adjusted Efficiency; Excess Slacks.
11. Tone, K. and Sahoo, B. K. (2006), “Re-examining Scale Elasticity in DEA”, Annals of Operations Research, First Published Online: June 27, 2006.
Abstract: We propose a new scheme for measuring scale elasticity of production based on a new cost efficiency model developed in Tone (2002). Comparing our model with classical model we establish the superiority of our model over the latter based on the premise that the classical estimates of cost efficiency and scale elasticity can be illusory.
Keywords: DEA, cost efficiency, returns to scale, scale elasticity
12. Sahoo, B. K. (2006) “Evaluating Technical Change: An Application to Indian Sunrise Industries”, Anvesak, Vol. 36, No. 1, pp. 59-86.
Abstract: In this paper an attempt is made to use the technique of Data Envelopment Analysis (DEA) to measure neutral technological progress (NTP) of a few technically efficient Indian Sunrise industries during the period 1978-79 to 1992-93 as also in the two sub-periods spanning 1978-79 to 1985-86 (pre-liberalization) and 1985-86 to 1992-93 (transition). The average rate of NTP, so obtained, is compared with the industry-wise progress for a wider set of such industries to find out the lagging and/or surpassing periods, which, in turn, reflect their change in output technical efficiency. The empirical results show that most of the industries are crippled by growing inefficiency despite a greater improvement in productivity growth in the transition period whereas the scenario is just the reverse in the pre-liberalization period. So bold institutional changes are to be introduced as a part of the on-going liberalization process in order that inefficiency is substantially reduced, making further addition to productivity growth.
Key Words: Technical Efficiency, Technical Progress, Total Factor Productivity Growth, Data Envelopment Analysis
13. Sahoo, B. K., Sengupta, J. K. and Manda, A. (2006) “Productive Performance Evaluation of the Banking Sector in India Using Data Envelopment Analysis”, Accepted for publication in International Journal of Operations Research.
Abstract: This paper attempts to examine, using data envelopment analysis, the productivity performance trends of the Indian commercial banks for the period: 1997-98 – 2004-05. Our broad empirical findings are indicative in many ways. First, the increasing average annual trends in TE for all ownership groups indicate an affirmative gesture about the effect of the reform process on the performance of the Indian banking sector. Second, the higher cost efficiency accrual of private banks over nationalized banks indicate that nationalized banks, though old, do not reflect their learning experience in their cost minimizing behavior due to X-inefficiency factors arising from government ownership. This finding also highlights the possible stronger disciplining role played by the capital market indicating a strong link between market for corporate control and efficiency of private enterprise assumed by property right hypothesis. And, finally, concerning the scale elasticity behavior, the technology- and market-based results differ significantly supporting the empirical distinction between returns to scale and economies of scale, often used interchangeably in the literature.
Key Words: Banks, Efficiency, Scale elasticity, Data Envelopment Analysis.
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