بررسی بهره وری و رشد بهره وری در ریخته گری و استخراج زغال سنگ از معادن زیرزمینی در هند:آنالیز تحلیل پوششی داده ها

This paper attempts to study efficiency and productivity of coal mining in the Indian coal sector using detailed input and output data for underground and opencast coal mining for the period between 1985 and 1997. The non-parametric approach of data envelopment analysis (DEA) is adopted for performance analysis of different coal mining regions. Total factor productivity growth was analysed using the Malmquist index by decomposing productivity change into efficiency and technical change. Results of the analysis do not conform to the prevailing notion of opencast (OC) mining having shown more productivity growth than underground mining in India. An increasing percentage of OC mining regions showed a decline in efficiency over the period of analysis. Approximately 58%, 59% and 67% of the mining regions showed decline in productivity between 1985 and 1990, 1990 and 1995 and 1995 and 1997, respectively. Technical progress seems to have been the major driving factor behind productivity growth in opencast mining, while efficiency growth has been the most important factor in growth of underground mine productivity. Underground mines seem to have adopted a more efficient practice of operation to compensate for the lag in technical change. On the other hand, operational efficiency of opencast mines seems to have been overlooked in the process of increasing production through technological improvement in OC mining.

Productivity measures of Indian coal mining activity in underground and opencast mining have shown a steady increase (especially from the 1980s onwards), when measured in output per manshift (OMS). However, this cannot be fully attributed to improvement in actual productivity of any of the inputs of production. In response to the rising targets of production in the coal sector during the 1980s, the policy decisions emphasised primarily on capital accumulation as the prime driving force to growth in production (BICP, 1988). Massive investments have been made towards mechanisation of both opencast (OC) and underground (UG) mining operations and import of foreign technology (Chari, 1988). Hence, productivity analysis needs to be done on usage of all inputs, both labour and machineries. This study attempts performance analyses of mining activity by both opencast and underground processes in the various coal producing districts across the country. Performance of the different regions depends on the state of technology and economic efficiency of the regions. The technology is depicted by the best practice frontier of production and economic inefficiency is related to misallocation of resources relative to the frontier (Lovell, 1993). Two kinds of analyses would be of interest. Firstly to study the levels of economic efficiency and productivity and second to analyse growth in factor productivity. Farrell (1957) proposed that productive efficiency has two components. The purely technical or physical component refers to the ability to avoid waste through output augmentation with a given set of inputs and/or input conservation for a given amount of output. Koopmans (1951) defines technical efficiency as a feasible input output vector where it is technologically impossible to increase any output (or reduce any input) without simultaneously reducing another output (or increasing another input). The other is the allocative efficiency which refers to the ability to combine inputs and outputs in optimal proportions at their prevailing prices, under a behavioural assumption for the decision making units (DMUs), for example, cost minimisation, revenue maximisation, etc. This study is primarily concerned with technical efficiency without assuming any behavioural goal dictating the input and output decisions of the mining activity. The coal sector being a public sector unit has administered prices of output and also input accumulation is not based on profit maximisation considerations that usually guide private sector units. Hence, operational efficiency in usage of various inputs in coal mining is studied. The objective is to derive performance measures for the different mining regions and also to isolate the effects of efficiency and productivity growth from the production environment and identify the sources of efficiency and productivity differentials. Identification of the sources of productivity is essential in adopting a right approach to policy decisions to improve performance. Hence, a measurement which quantifies the differentials that are predicted qualitatively by theory is required (Lovell, 1993). Input based indices of technical efficiency were developed by Farrell (1957) who measured the maximum equiproportionate reduction in all inputs consistent with equivalent production of observed output. A non-parametric approach to frontier analysis, the DEA, is used in the paper. Data envelopment analysis (DEA) is a widely used tool for performance analysis. The DEA pioneered by Charnes et al. (1978) and extended by Banker et al. (1984), Fare et al., 1985 and Fare et al., 1994 is a mathematical programming approach which characterises the relationship among multiple inputs and multiple outputs by envelopment of the observed data to determine a best practice frontier for production. DEA involves the use of linear programming methods to construct a non-parametric piecewise surface or frontier over the data. Efficiency measures are then calculated relative to this surface which can be perceived as the production possibility frontier. It is important to recognise the relation between inefficiency and productivity. Inefficiency can be defined as a measure of the variability of performance within an industry relative to a theoretical production frontier. This measure may not be directly comparable with productivity, measured as ratio of output to input. As Lansbury and Mayes (1996) observed, if the production frontier shifts over the period of observation, the average level of productivity would rise without there necessarily being a fall in technical inefficiency. In this analysis, attempt has also been made to distinguish between the potential contribution of efficiency change and technical change to overall productivity change. Solow (1957) identified the contributions of technical change by distinguishing movements along a production frontier from shifts in the frontier. Nishimizu and Page (1982) decomposed productivity growth into shifts in the production frontier and movements towards or away from it. They attempted to incorporate efficiency change into a model of productivity change. Measuring total factor productivity using a Malmquist-type index is a non-parametric approach to productivity measurement, which explicitly allows for inefficiency. The Malmquist index is a useful tool for calculating productivity growth in the presence of inefficiency. Constructed from distance functions, it allows explicit calculation and isolation of changes in efficiency. It does not require price or share data, and hence is especially suitable for public sector production activities where prices are mostly administered. Panel data is required for isolation of efficiency and technical change. Deterioration in performance over time is associated with a Malmquist index with value less than unity. Our study seeks to answer questions like the following: 1 What has been the trend of growth of total factor productivity (TFP) in opencast and undermine coal production in India? 2 Can we isolate the contributions of technical change and efficiency change in the productivity development? 3 Which of the coal mining regions have improved in productivity over the years? What has been their performance in underground and opencast mining? 4 Which of the coal mining regions are efficient in operation? 5 Which are the inefficient ones? What is the extent of inefficiency? How much reduction in input use is required in each of these regions to reach the best practice frontier of input use? In an attempt to answer these questions, firstly, total factor productivity growth is analysed using the Malmquist index for the periods 1985–1990, 1990–1995 and 1995–1997. Total factor productivity growth in each of these periods is decomposed into efficiency change and technical change. Next, we do a non-parametric frontier analysis to distinguish between the best practice districts of coal mining and the inefficient ones. This is done using DEA on the coal mining districts separately for opencast and underground mining, for the years 1985, 1990, 1995 and 1997. The paper is organised as follows. Section 2 discusses the sources of data. Section 3 discusses the methodologies applied for the analyses. Section 4 discusses the results and in Section 5 conclusion and policy implications have been provided.

This study attempted to carry out an in-depth performance analysis of coal mining in approximately 30 regions of India. A non-parametric approach to frontier analysis was adopted and efficiency scores for the different mining regions were determined using DEA, separately for underground and opencast mining. A Malmquist productivity index approach was used to study productivity development in the different regions over the periods from 1985 to 1990, 1990 to 1995 and 1995 to 1997. Total factor productivity was decomposed into the effects of technical change and pure efficiency change. The results of the analysis indicated that contrary to the common belief of higher productivity growth in opencast mines, the OC mines have not displayed productivity development very significantly better then underground mines. There has been, however, remarkable technical change in opencast mines with approximately 18 out of 24 regions displaying improvement in technology by more than 20% between 1995 and 1997. The increasing share of output from opencast mines in the country has been more due to the conscious efforts at technological improvement of opencast mining, aimed at a fast recovery of output vis-à-vis the underground mines which require more intensive efforts. Productivity growth in opencast activity seems to have been driven primarily by technical change rather than growth in efficiency as is evident from the fact that only five out of 24 regions indicated growth in technical efficiency during the period 1995–1997. On the other hand, the underground mining seems to have improved in efficiency to compensate for the deteriorating technical change. In UG mines only 10 out of 22 mines showed positive technical change between 1990 and 1995, while 19 regions showed growth in operational efficiency. Thus, it is imperative that efforts at optimizing input use be made in the opencast mines to fully exploit the benefits of improving technology. The underground mines should not be overlooked and equal effort needs to be made at technology upgradation in underground mines. This is especially so because underground mines have large deposits of superior quality coal and quality gets compromised in opencast mining. Also, to improve operational efficiency of opencast mines, close monitoring of input purchase and use is required. CMPDIL, the subsidiary of CIL, which is mainly responsible for research and development work and introduction of new technologies, takes consultancy and collaborations from research organizations in India and abroad. However, the total annual budget of coal companies for upgradation have been in the range of Rs 80–150 million only. Considering the volume and infrastructure of coal companies, budget allocation for these activities is proportionately far too little and needs to be raised appropriately.