The Directional Distance Function and Measurement of Super-Efficiency: An Application to Airlines Data

Lovell and Rouse (LR) have recently proposed a modification of the standard DEA model that overcomes the infeasibility problem often encountered in computing super-efficiency. In the LR procedure one appropriately scales up the observed input vector (scale down the output vector) of the relevant super-efficient firm thereby usually creating its inefficient surrogate. An alternative procedure proposed in this paper uses the directional distance function introduced by Chambers, Chung, and Färe and the resulting Nerlove-Luenberger (NL) measure of super-efficiency. The fact that the directional distance function combines features of both an input-oriented and an output-oriented model, generally leads to a more complete ranking of the observations than either of the oriented models. An added advantage of this approach is that the NL super-efficiency measure is unique and does not depend on any arbitrary choice of a scaling parameter. A data set on international airlines from Coelli, Perelman, and Griffel-Tatje (2002) is utilized in an illustrative empirical application.

Comparing Input- and Output-Oriented Measures of Technical Efficiency to Determine Local Returns to Scale in DEA Models

This paper shows how one can infer the nature of local returns to scale at the input- or output-oriented efficient projection of a technically inefficient input-output bundle, when the input- and output-oriented measures of efficiency differ.

Are Indian Firms too Small? A Nonparametric Analysis of Cost Efficiency and Industry Structure of Indian Manufacturing

In this paper we use the 2004-05 Annual Survey of Industries data to estimate the levels of cost efficiency of Indian manufacturing firms in the various states and also get state level measures of industrial organization (IO) efficiency. The empirical results show the presence of considerable cost inefficiency in a majority of the states. Further, we also find that, on average, Indian firms are too small. Consolidating them to attain the optimal scale would further enhance efficiency and lower average cost.

Are Some Indian Bank Too Large? A Examination of Size Efficiency in Indian Banking

In this paper we use data from the years 1997 through 2003 to evaluate the size efficiency of Indian banks. Following Maindiratta (1990) we consider a bank to be too large if breaking it up into a number of smaller units would result in a larger output bundle than what could be produced from the same input by a single bank. When this is the case, the bank is not size efficient. Our analysis shows that many of the banks are, in deed, too large in various years. We also find that often a bank is operating in the region of diminishing returns to scale but is not a candidate for break up.

Input Price Variation Across Locations and a Generalized Measure of Cost Efficiency

We propose a nonparametric model for global cost minimization as a framework for optimal allocation of a firm's output target across multiple locations, taking account of differences in input prices and technologies across locations. This should be useful for firms planning production sites within a country and for foreign direct investment decisions by multi-national firms. Two illustrative examples are included. The first example considers the production location decision of a manufacturing firm across a number of adjacent states of the US. In the other example, we consider the optimal allocation of US and Canadian automobile manufacturers across the two countries.

Technical Efficiency in Public and Private Sectors in India: Evidence from the Post-Reform Years

While India's state-owned enterprises are widely believed to be inefficient, there is a dearth of studies that document such inefficiency on any rigorous basis. Yet, since improvement in firm efficiency is one of the basic objectives of privatization, it is important to assess whether efficiency is indeed lower in the public sector than in the private sector. This paper compares the performance of state-owned enterprises with those of private sector firms in respect of technical efficiency. The comparison is made in eight different sectors over the period 1991-92 to 1998-99. We measure technical efficiency using the method of Data Envelopment Analysis. Judging by the average levels of technical efficiency, no conclusive evidence of superior performance on the part of the private sector is found.

Technical Efficiency of State Owned Enterprises in China (1980-1989): An Assessment of the Economic Reforms

This paper uses Data Envelopment Analysis to examine changes in levels of technical efficiency over time in China's state-owned enterprises (SOEs) during 1980-1989. Our paper adds to the growing body of literature in this area by obtaining measures of technical efficiency of individual SOEs over years and by identifying how different aspects of the reforms have affected efficiency. We estimate a Tobit regression model, using the technical efficiency score as the dependent variable and a set of reform variables and firm attributes as regressors. We find that specific aspects of the reforms were very effective in improving technical efficiency.

Technical Efficiency in the Indian Textiles Industry: A Nonparametric Analysis of Firm-Level Data

The Indian textiles industry is now at the crossroads with the phasing out of quota regime that prevailed under the Multi-Fiber Agreement (MFA) until the end of 2004. In the face of a full integration of the textiles sector in the WTO, maintaining and enhancing productive efficiency is a precondition for competitiveness of the Indian firms in the new liberalized world market. In this paper we use data obtained from the Annual Survey of Industries for a number of years to measure the levels of technical efficiency in the Indian textiles industry at the firm level. We use both a grand frontier applicable to all firms and a group frontier specific to firms from any individual state, ownership, or organization type in order to evaluate their efficiencies. This permits us to separately identify how locational, proprietary, and organizational characteristics of a firm affect its performance.