Krishna Mitra

Bio: Krishna Mitra is an academic researcher from University of Burdwan. The author has contributed to research in topics: Technological change & Factor price. The author has an hindex of 1, co-authored 1 publications receiving 20 citations.

Decomposition of Technological Change and Factor Bias in Indian Power Sector: An Unbalanced Panel Data Approach

Abstract: Technological change and factor bias in the Indian power sector are analyzed using a translog cost function. Various components of technological progress and factor bias are identified and estimated, using a 21 year unbalanced panel data of Indian states and union territories. Heterogeneity across states is incorporated in the model using a variance component model. Appropriate corrections are made for unbalanced panel data. Empirical results show that the annual average rate of technological progress has been 2.4% for the country as a whole. Accumulation of knowledge and increasing scale are found to be the major factors contributing to technological progress. In contrast, the effects of factor price changes and fixed capital accumulation on technological progress have been unfavorable. Pure factor bias measure indicate saving in the use of fuel and labor, and increased use of materials. Tests are performed to check the curvature properties of the underlying technology.

The influence of ownership on the cost of bus service provision in Switzerland - an empirical illustration

Abstract: In deregulated transport markets, a firm's ownership status and management system represent an important issue. Property right theory suggests that productivity and performance are higher in the private than in the public sector. In Switzerland, providers of bus transportation are traditionally corporations, though a large part of their equity shares are still held by the public sector (federal government, cantons, municipalities). This paper examines the potential impact of ownership on the cost of bus service provision for a sample of private, public and mixed bus companies in Switzerland. The estimation of a translog cost model has been considered for 34 bus transit companies observed over 5 years (1991–1995). The results only partially confirm that if the private sector holds shares in the company's capital, efficiency is enhanced. In addition, measures of economies of scale and density are derived and discussed within the actual public transport policy.

with Data Envelopment Analysis

Abstract: Data Envelopment Analysis (DEA) is an increasingly popular management tool. DEA is commonly used to evaluate the efficiency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer. In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU. DEA is not always the right tool for a problem but is appropriate in certain cases.

The impact of ownership on the cost of bus service provision: an example from Italy

Abstract: This article examines the potential impact of ownership on the cost of bus service provision for a sample of 65 private and 12 public companies providing local public transit (LPT) in Piedmont (Italy) from 1998 to 2002. A translog cost frontier is estimated using the model in Battese and Coelli (1995) where inefficiency scores are allowed to vary across firms and over time. A public ownership dummy is included in the inefficiency model and it is always positive and significant. Density and scale economies and cost inefficiencies are then computed. Private companies seem to experience higher density and scale economies than public ones. Cost inefficiencies appear higher in the public sample.

Is Firm Productivity Related to Size and Age? The Case of Large Australian Firms

Abstract: We investigate the relationship between productivity, size and age of large Australian firms employing more than 100 employees or holding assets in excess of $100 million. In addition, we also investigate the extent of productivity persistence among these firms by looking at transition matrices of productivity distribution and productivity-rank mobility. The empirical study is based on the IBISWorld database used to estimate translog cost function to measure (a residual based) productivity. We find evidence, though somewhat weak, that larger and older firms are on average less productive. Further, we find strong evidence for a high degree of inertia in terms of productivity ranking within an industry.