A STUDY ON PRODUCTIVITY AND GROWTH OF STEEL INDUSTRY IN INDIA

Transcription

1 International Journal of Business Management & Research (IJBMR) ISSN: Vol. 3, Issue Mar 203, TJPRC Pvt. Ltd. A STUDY ON PRODUCTIVITY AND GROWTH OF STEEL INDUSTRY IN INDIA S. SIVAKUMAR Assistant Professor in Management, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India ABSTRACT The back bone of any economy is its industries. It is the industrial growth of a country that contributes to the faster growth of the economy. This realization has made the economic planners and practioners to implement various policies and programmes that are highly favorable for the development of industries. In the context of India, there are a few traditional industries which contribute to the faster industrial development and the steel industry is one. The government framed and implemented National Steel Policy in The long term strategic goal of NSP is that India should have a modern and efficiency steel industry of world standards, catering to diversified steel demand. The study on productivity becomes important in view of the limited availability of factors of production, particularly the capital. The proportion of the factors of production or the inputs will be different in the different industries depending on the nature of product. The labour intensive industries with the abundant labour and relatively low wages, the emphasis is on the increasing capital productivity. On the other hand, in the capital intensive industries, the concern is to increase the labour productivity. The productivity growth is the only plausible route to increase the standard of life of the society. Economic and industrial growth is the result of the interaction of two prime factors: the investment capabilities, which are a function of savings and the productivity with which these capabilities are utilized. Productivity is a major source of high levels of production. It is a measure of the efficiency of factors or inputs in production. An increase in the levels of productivity in an economy implies that its factors of production and commodity inputs are manifesting increase in their output efficiency. The prosperity of new developed nations has been attributed mainly to the sustained growth of its total factor productivity (Prescott 997). The performance of an industry can be understood with the estimation of Total Factor Productivity Growth (TFPG) and its behaviour over a period of time. Productivity growth is an indicator of the utilization of the factor inputs and in turn a measure of performance of the firm or industry concerned. The labour productivity was estimated at 0.24 at the industry level. Of the 29 firms, it was found below the industry average for 7 firms. The estimates of mean LP was higher at for the firm JSW Steels and least at to Super Forgings during to In the case of capital productivity, the estimates were comparatively higher than the LP. The mean CP was estimated at at the industry level. It was noticed that 8 firms registered CP higher than the industry average with highest CP of.734 (Garg Furnace) and the lowest CP was obtained by Lloyds Steels industry at Similarly capital-labour ratio was also estimated during the period of study and was observed that there were wide fluctuations in the K/L across the firms. The range was found between (SAIL) and (JSW Steels). It showed the fact that the availability of capital per unit of labour was too low in SAIL and much higher in JSW Steels. The DEA will estimate the Malmquist Productivity indeed and will further decompose it into two major sources names, efficiency change and technical change. Malmquist productivity index is inferred using the rule that if the estimate is greater than, then the productivity growth is positive and it is less than, then productivity growth is negative. Accordingly, TFPG was estimated both at the aggregate and firm level during to The mean TFPG for the entire Iron and Steel industry was found positive at. per cent per annum as was mainly contributed by the technical change. Over the period of 0 years of the study, the negative TFPG was found higher at 5.8 per cent in and positive TFPG was estimated at 33 per cent in

2 76 S. Sivakumar KEYWORDS: Cost Benefit Analysis, DEA, Econometrics, Efficiency, Forecasting, Cost Models INTRODUCTION The prime objective of the present piece of research is to examine the productivity of the large, medium and small scale industries. This is being carried out in the present chapter. There are various measures of productivity index which get differed in terms of the model building, techniques used. However, measures have got their own advantages and disadvantages. Nevertheless, the present study aims at understanding whether there is increase in the factor productivity and not in the magnitude of it. Hence, the study has made use of all the major techniques of productivity growth. These include, Kendrick model, Solow model, Translog model, and Malmquist productivity index. These measures are broadly used in the empirical studies as they assign different weights to different variables. The present study, so as to catch hold of the effect of all the variables, has made use of all the models and hence measures the partial and total factor productivity growth of Indian Iron and Steel Industry for the periods from to OBJECTIVE OF THE STUDY To estimate the trends in the productivity growth of the Indian steel industry. To compare the sources of productivity growth in the large, medium and small scale steel industries of India. To provide suggestions based on the issues identified. SOURCE OF DATA From the official sources it is found that there are totally 279 steel industries with a distribution of 48 large and 23 medium and small scale industries are getting operated in India. However, for the purpose of collecting a reliable secondary data, the researcher could identify that there are only 5 large scale industries and 9 medium and small scale industries listed in Bombay Stock Exchange and National Stock Exchange. Hence, the study is confined to these 5 large scale and 9 medium and small scale industries. The study focuses on the total productivity growth of steel industries of India through the sample of 29 industries. The required secondary data were collected from Capital Line Database and the Annual Reports of BSE, the Annual Reports of Steel, Ministry of Steel, the annual Reports of Corporate Sector, Capital Markets and Market Shares and Size of Industrial Product published by Centre for Monitoring Indian Economy (CMIE), Annual Survey of Industries, published by Ministry of Industries, Economic survey and the Annual Reports of respective industries under study. The secondary data pertaining to the level of output of the selected firms, number of labours employed, investment made, exports, imports, the data on financial indicators were collected. TOOLS AND TECHNIQUES To study the partial and total factor productivity have been calculated using the sophisticated techniques like, Kendrick model, Solow model, translog model, Data Envelope Analysis (DEA) and Malmquist Index. A discussion on this model can be given as below: There are a number of measures to estimate the factor productivity namely Kendrick, Solow, Translog and Malmquist productivity index. These measures are broadly used in the empirical studies as they assign different weights to different variables. The present study, so as to catch hold of the effect of all the variables, has made use of all the models.

3 A Study on Productivity and Growth of Steel Industry in India 77 Kendrick Model The Kendrick Model of the estimation of Total Factor Productivity Growth takes the following form. A (t) = Y (t) / W o L (t) + R (o) K (t) A (t) = Total Factor Productivity Growth Y (t) = Gross value of production in the year t W o L (t) = Wage rate in the base year = Number of workers in the year t R (o) = Rate of interest in the base year K (t) = Capital in the year t Solow Model Solow provided an elementary method of segregating variations in output per head due to technical change from those due to change in the availability of capital per head. Solow index of TFP is based on Cobb-Douglas production function under the assumptions of constant returns to scale, autonomous Hicks-neutral technological progress and payment to factors according to their marginal product. The discrete method of measurement of productivity due to Solow model is obtained as, A (t)/a (t) = V (t)/ V (t) - [S L (t) ( L (t) / L (t)) + S K (t) ( K (t) / K (t))] Where A (t) / A (t) V (t) / V (t) S L (t) L (t) / L (t) S K (t) = Annual rates of TFP growth = rate of change of real gross value of production = share of labour in gross value of production in year t = rate of change of labour = share of capital in gross value of production in year t K (t) / K (t) = rate of change in real gross fixed capital Translog Model The translog index is a discrete version of the continuous Divisia index and is obtained as, P (t) / P (t) = V (t) / V (t) - [/2 (S L (t-) + S L (t)) ( L (t)/l (t)] + [/2 (S K (t-) + S K (t)) ( K (t)/k (t)] Here, V (t) / V (t), L(t) / L(t) and K(t) / K(t) are approximated by corresponding logarithms of ratios of variables over successive year i.e., V (t) / V (t) ~ LN [ V (t) / V (t-)] = LN V (t) - LN V (t-) = LNV (t)

4 78 S. Sivakumar L (t) / L (t) ~ LN [ L (t) / L (t-)] = LN L (t) - LN L (t-) = LNL (t) K (t) / K (t) ~ LN [ K (t) / K (t-)] = LN K (t) - LNK (t-) = LNK (t) S L (t) and S K (t) being the shares of labour and capital in the real gross value added P (t) / P (t) is thus the translog index of TFP growth. P (t)/p (t) = LN V (t) [(½ * (S L (t-) + S L (t)) * LN L (t) + ((½ * S K (t-) + S K (t) * LN K (t))] Dea and the Malmquist Index The Data Envelopment Analysis (DEA) is a special mathematical linear programming model and test to assess efficiency and productivity. It allows use of panel data to estimate changes in total factor productivity and breaking it down into two components namely, technological change (techch) and technical efficiency change (effch). The Malmquist index measures the total factor productivity change (TFPCH), between two data points over time, by calculating the ratio of distances of each data points relative to a common technology. The Malmquist productivity change index as can be given as: m o t t+ t+ t+ t+ t t do ( ) ( x, y ) y x, y, x t t t d ( x, y ) t+ t+ ( x, y ) t t ( x, y ) t+ d o, = Χ t+ o d o The subscript o has been introduced to remind us that these are output-oriented measures 2.. () The CRS output-oriented Linear Programming (LP) used to calculate d t o (x t, y t ) is identical to equation (), except that the convexity (VRS) restriction has been removed and time subscripts have been included. That is, subject to t [ d ( x y )] max φ, o t, t φ, λ φy it + Y λ 0, t λ 0, X X t it λ 0,..(2) The remaining three LP problems are simple variants of this: t+ [ d ( x y )] = max φ, o t+, t+ φ, λ subject to φy + + Y λ 0, i, t t X X, t + i t + λ 0,

5 A Study on Productivity and Growth of Steel Industry in India 79 subject to 0, λ.. (3) t [ d ( x y )] = max, o t+, t+ φ, λ φ φ yi, t+ + Yt + λt λ i, t+ X X t 0, 0, 0, t+ [ d ( x y )] = max φ, subject to φy it + Yt + λ 0, o t, t φ, λ X X t + λ it 0, λ.. (4) 0, λ.. (5) The Malmquist index of total factor productivity change (tfpch) is the product of technical efficiency change (effch) and technological change (techch) as expressed (Cabanda, 200): tfpch = effch x techch (2) The Malmquist productivity change index, therefore, can be written as: M 0 (yt+, xt+, yt, xt) = effch x techch (3) REVIEWS RELATING TO PRODUCTIVITY Mehta also estimated a productivity growth of 8.8 per cent in the Indian steel industry during the period 953 to 965. He also found the evidence of capital deepening in the production process of steel during this period. Brahmananda 2 estimated the single and total factor productivity for the sectors and sub-sectors of Indian economy during to During this period, he found that the capital productivity declined by as much as 40 per cent, but the labour productivity went up to 2 ¼ times in the registered Indian manufacturing sector. He estimated that this sector witnessed an increase in the total factor productivity at an annual rate of 0.70 per cent during to and thereafter he found it declining during to Ahluwalia 3 studied the productivity growth in Indian manufacturing during and He applied both Solow and Translog measures and found that the results of both the measures were similar. The study estimated that rubber products and miscellaneous manufacturing industries suffering a sharp decline in the total factor productivity growth whereas the footwears and furniture industries registered a high growth rate at around 2.00 and 3.00 per cent per annum. The study estimated the declining total factor productivity growth at a rate between 0.2 and.3 per cent per annum during mid 60s and 70s.

6 80 S. Sivakumar Alagh 4 studied the performance of Indian industrial sector at the sectoral level by the growth rates of three measures namely index of industrial production (IIP), valued of output and net value added. He found similar pattern with varying rates of growth. In his study, he considered two time periods, to and to At the aggregate level, the industrial growth increased from 3 per cent to 4.58 per cent in IIP and 4.6 per cent to 7.6 per cent in value of production and in the case of net value added, it increased from 3.49 per cent to 5.76 per cent during the study period. Romer 5 suggested that the technological change has been an important factor to contribute output growth. Technological change arises in large part because of intentional actions taken by people who respond to market incentives and hence the technical change happens more to be endogenous rather than exogenous. In his study, he concluded that the stock of human capital (levels of education and experience) accelerated the growth but the growth did not depend on total size of labour force or the population. He found that international trade facilitates free flow of new ideas and technologies and reduces the idea-gap, which was a major source of spillovers and growth. Most of the new ideas and technologies were developed in developed countries and trade with them helped in realising these dynamic gains to promote productivity. He further found that the use of non-rivalry nature (use of a blue print of a technology or new idea by one agent does not prelude use by other agents) of technological change was a source of increasing returns to scale and sustained long run growth. Kumari 6 estimated total and partial factor productivity and elasticity of factor substitution of public sector enterprises for groups of industries in India during and In the estimation, she applied the three basic measures of productivity estimation, Kendrick, Solow and Divisia index. The study found significant variations in the growth levels of factor productivity and substitution. For chemical industries, the study estimated the annual growth of total factor productivity at 4.9 percent, 4.93 per cent and 4.80 per cent in Kendrick, Solow and Translog measures respectively. The annual growth of labour productivity at 8.39 and capital productivity at 2.82 percent was also estimated by the study. In the estimation of factor substitution, the Cobb-Douglas production function estimated constant returns to scale and the CES production function estimated unit elasticity of factor substitution for chemical industries in the Indian public sector. Majumdar 7 studied the pattern of productivity growth of Indian Industrial sector since 950s. The study empirically proved the positive impact of liberalisation measures on productivity. The reforms process was not exacerbated entry threats for the sitting incumbents in Indian industry, but the environment was equally competitive for the new entrants. Attainment of efficiency was a key survival criterion in such situations and the Indian firms had so far yielded positive efficiency out comes. The adoption of technological and organisational innovations had a very large impact on productivity at the firm level. The policy changes that took place in India in the 990s did significantly enhance potential opportunities on one hand and increase the uncertainties and ambiguities levels on the other. Aitken and Harrison 8 found two offsetting effects of FDI. Domestically owned firms might 'benefit' from the presence of foreign firms, when the workers of foreign firms left the foreign firms, human capital might become available to domestic firms. Firm specific knowledge of foreign firms (technology) might 'spillover' to domestic firms as the domestic firms were exposed to new products, production and marketing techniques. Foreign firms might also act as a stable source of demand for inputs in an industry, which could benefit upstream domestic firms by allowing them to train and maintain relationships with experienced employees. In all these cases, foreign presence would raise the productivity of domestically owned firms. On the contrary, foreign presence could also 'reduce' productivity of domestic firms particularly in short run. A foreign firm with lower costs would have an incentive to increase production relative to its domestic competitor. In this

7 A Study on Productivity and Growth of Steel Industry in India 8 environment, entering foreign firms producing for the local market could draw demand from domestic firms, causing them to cut production and to a fall in domestic productivity. Desai 9 studied the problems in the technological transfers in India. According to him, technological transfers did not take place properly. He highlighted the major problems in the technological transfers, the inadequacy of knowledge and skills to exploit the existing technology, lack of confidence in the successful exploitation of the projects on commercial basis and poor R&D activities. Bureaucratic inefficiency, high price of technology and import restrictions are some of the impediments to the effective technological transfers. Mongia and Sathaye 0 studied the productivity trends in selected 6 energy intensive industries including Steel industry with an elaborate survey on productivity during the period They applied Kendrick, Solow and Translog index to estimate productivity growth and found the total factor productivity in steel industry was in the range of -.6 per cent and 0.07 per cent. Athreya and Kapur studied the linkage between the policy towards foreign capital and its contribution to the Indian economy. They also explained the long run conduct and performance of foreign controlled firms relative to domestic firms. In 950s, the Indian government, in order to achieve the plan targets, allowed foreign equity participation to meet the foreign exchange needs of investment projects. In 960s, the selectivity of government policy changed the pattern of foreign capital towards manufacturing and technology intensive industries. In 970s, the intervention of FERA to dilute the 40 per cent of foreign equity and the exception of technology intensive export intensive and core sector, proved more hostile to new foreign investment than the existing foreign affiliates. In 980s, the policies of India were softened to attract foreign investment but there was only a slight increase and most part, Indian industry came to rely on foreign debt capital to meet its foreign exchange needs. The enormous increase in FDI was realised only in 990s when India agreed to implement the reform measures in tune with IMF. The study found that the advertising intensity was greater for foreign controlled firms while expenditure on technology imports was greater for domestic firms. Export intensity was quite similar for both the firms. Technology inflows could also improve the productivity of domestic firms through spillovers as better productions and management techniques in the host country. Mahadevan and Kalirajan 2 examined the criticism leveled against Singapore for experiencing insignificant total factor productivity (TFP) growth. This paper examines whether this criticism is valid in the context of the manufacturing sector of Singapore. Using new data and the stochastic production frontier approach, TFP growth is ecomposed into technological progress and changes in technical efficiency. While the results could not reject the hypothesis that Singapore s output growth is mostly input-driven, they show that, despite technological progress, technical inefficiency is the cause for the low and declining TFP growth in the manufacturing sector. Singh 3 estimated the total factor productivity (TFP) in Indian manufacturing sector during to for the ten industries, which constituted about 70 per cent of GDP in India. He found that food products industry showed improvement in TFP during the period and recorded a trend growth rate of 2.68 per cent followed by transport equipments industry at 2.9 per cent. The chemical industries having the highest weight of.4 per cent in GDP showed moderately rising trend in TFP and worked out to be 0.28 per cent per annum during the period. The study listed out the factors responsible for the growth in productivity and output namely, ) increase in capacity utilisation, 2) efficient allocation of resources, 3) generation of economies of scale, 4) spillovers of external economies among industries, 5) increase in specialization and technological improvements in response to greater competition abroad, 6) increase in R&D expenditure and 7) increase in export and trade orientation. In addition to these factors, the policy initiatives of economic reforms

8 82 S. Sivakumar namely ) removal of economic controls, 2) entry of MNCs, 3) FDI, 4) financial sector reforms and 5) liberalisation of trade etc., would also enhance the TFP in the country. The study concluded that the recent policy initiatives aimed at the removal of controls and creation of competition in the industrial sector had important implications for the TFP and the process of economic growth. These changes have created a more conducive and competitive environment in the economy and this would have favourable effects on the total factor productivity. Sharma and Upadhyay 4 studied the components of total factor productivity in the Indian fertilizer industry. They used the cost function to estimate the scale of economies, technical progress, elasticity of substitution, scale bias and technical bias. The study estimated decreasing returns to scale and increasing technical progress in the fertilizer industry. The study further found that the technical bias and scale bias were in favor of material input. With regard to factor substitution, they found that the substitution between capital and energy and capital and material led to an important implication that output could be increased by using more material and even without increasing the capacity. Goldar and others 5 in their paper studied the effect of ownership on efficiency of engineering firms in India with a comparison of technical efficiency among three groups of firms viz., firms with foreign ownership, domestically owned private sector firms and public sector firms. The study explained that the foreign ownership firms had greater efficiency than the domestic firms. It was so because, in a developing country, the foreign firms had relatively better access to advanced technology. The study concluded that the foreign firms in Indian engineering sector had greater technical efficiency than that of domestic firms and there was no significant variation in technical efficiency between private and public sector firms. The study pointed out a fact that there were indications of a process of efficiency convergence, that is, the domestic firms tended to 'catch-up' with foreign firms in terms of technical efficiency. Among the various factors responsible for inter firm variation in technical efficiency, the import intensity played a significant role. The liberalization of imports increased the access of firm to imported inputs and capital goods and thus contributed considerably to increase the efficiency of engineering firms. Sampathkumar 6 examined the assumption of homogeneity in the estimation of total factor productivity at the aggregate level in the Indian chemical sector. He classified the entire sector into five major sub-sectors and each subsector has further been divided into small and large firms. The study further found that there are productivity variations as the size of the firms differs. It was found in his study that large firms tend to have higher level of TFPG than the small firms. Kim 7 decomposed total factor productivity (TFP) growth into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC) to Malaysian manufacturing data from 2000 to The paper also identified the factors that determine each TFP component. Empirical results show that TFP was driven mainly by TP, but plagued by deteriorating TEC. The skill and quality of workers represent the most important determinants of TE, whereas foreign ownership, imports and employee quality represent those of TP. The impact of firm size on SEC differed across industries, and AEC determinants were identified. Nwaokoro 8 examined the impact of the trade restrictions on steel imports in order to protect the US steel industry. During the period of 963 to 988, the industry experienced a tremendous decline in its output. Trade restrictions are implemented to limit steel imports. The overall goal of this study is to estimate the impact of the steel trade restriction regimes on the output of the industry. Beside foreign competition, the study addresses the impact of other factors - other shipments (nonsteel shipments) and the prices of steel substitutes - aluminum, and plastic and rubber that may have also caused variation in steel production. The study estimated insignificant regression results which implied that the protection regimes were not statistically significant to enhance output expansion.

9 A Study on Productivity and Growth of Steel Industry in India 83. Equity intensives (retained cash flow from operations to tangible net worth) 2. Return on investment (profit before depreciation, interest and tax to total tangible assets) 3. Sales efficiency (profit after tax to net sales) and Their study observed a declining trend in profitability in relation to sales shareholders equity and total investment the impact of which increased with the increasing interest burden. It was also found that these 3 groups of ratios of profitability showed a consistent declining trend across most of the firms. PARTIAL FACTOR PRODUCTIVITY Table. presents the mean estimates of partial factor productivity of the Iron and Steel Industry both at the firm and Industry level for the period of study. Both the labour and capital productivity were estimated for all the 29 firms in the industry. Table.: Mean Partial Productivity of Sample Firms from to Sl. No. Firm LP KP K/L Welspun guj stah Uttam Galva Tata Steels Surya Roshini Sunflag iron SAIL Natl Steel and agro Mukand Man Inds Lloyds steel Inds JSW steels Ispat Steels Essar Steels Bhusan steels Ajmera Realty Surana Indusries Super Forgings Ruchi Strips Ratnamani Metals Pennar Inds Panchmahal steels MUSCO Jai Corpn India steel Goodluck steel Garg furnace Gandhi spl Tube Bilpower Anil spl steel Mean Source : Compiled from Annual Reports

10 84 S. Sivakumar LABOUR PRODUCTIVITY (LP) It is a measure of average productivity or producing ability of labour per unit. It could be noticed from the Table. that the estimates of labour productivity seems to be very low as the mean labour productivity was 0.24 for the entire Iron and Steel industry. But at the firm level, it differs and ranges from (Super Forgings) to (JSW Steels). Of the 29 firms, the mean labour productivity of 7 firms were found to be less than the industry average of 0.24 and other 2 firms were observed to have comparatively higher levels of labour productivity. The fimrs, JSW Steels (0.379), Essar Steels (0.337) ranked top in the levels of LP. These estimates reveal the fact that these firms were able to produce more output with the employment of each unit of labour. CAPITAL PRODUCTIVITY The estimates of capital productivity (CP), the efficiency of capital to produce, are also estimated and presented in Table.. The mean Capital productivity was estimated at for the entire Iron and Steel Industry for the period to The estimates of Capital productivity also differed widely across the firms from 0.48 to.734. Lloyds Steel industry was found to have lowest Capital productivity at.48 which was far below the average CP of the industry as a whole (0.464). Similarly, Garg Furnace was noticed to have highest level of Capital productivity at.734, much above the mean Capital productivity of the industry. From the table-, it could be observed that only 8 out of 29 firms were estimated to have higher Capital productivity levels than the industry average. Majority of the firms (2 out of 29) have lower Capital productivity levels which are an indication of under utilization of this prime factor of production. CAPITAL-LABOUR RATIO (K/L) Capital-Labour ratio (K/L) is the availability of capital per unit of labour and is often described as measure of technology. A higher level of K/L is always preferred as it would increase the labour efficiency. The estimates of K/L for the 29 firms considered in the study are presented in Table. as the mean values for the period to While the mean ratio of capital to labour ratio (K/L) of the industry as a whole was estimated at 0.434, SAIL was found to have a K/L ratio at 0.082, the least valued and JSW steels was noticed to have a highest K/L ratio of The observation that could be made was that there were wide variations in the K/L ratios across the firms during the study period ranging from to It could be seen from the table that only 8 firms were estimated higher K/L ratio much higher than the industry average of and 2 firms out of 29 were found to have lower K/L ratio even below the industry average. Despite being the capital intensive industry, the availability of capital per unit of labour is widely dispersed among the firms. There is a close relationship between K/L and labour productivity. A higher K/L implies the availability of higher amount of capital to the labour which in turn increases the labour productivity. Higher the measures of capital-labour ratio, higher will be labour productivity (Lall and Streeten 977). But in this study the labour productivity was estimated at low levels both at the firm and industry levels. It may be perhaps due the lower availability of capital to the labour which was also substantiated by the estimates of lower levels of K/L in the study. ESTIMATES OF MEAN TOTAL FACTOR PRODUCTIVITY GROWTH (TFPG) Table.2 presents the estimates of annual means of TFPG at the aggregate level. During to , the TFPG was estimated at. percent (.0) at the industry level. The TFPG recorded a highest growth of 33 percent in and was found negative at 6 per cent in The positive growth of TFP was estimated during (2.97 percent), (33 per cent), (9.4 per cent) and in (.28 per cent). There was negative productivity growth in the remaining years of the study.

11 A Study on Productivity and Growth of Steel Industry in India 85 Table.2: Trends in Annual Average of Malmquist Index of Sample Firms from to Year effch techch Tfpch Mean Source : Compiled from Annual Reports The advantage of Malmquist productivity index is the decomposition of productivity growth into efficiency change and technical change. Efficiency change is defined as the contribution of factor inputs to the output growth when there is no change in the factor proportions. Technical change is the contribution of factor inputs to the output growth due to the change in the factor proportions. TFPG is thus the product of both efficiency change and technical change. From Table 5.2, it could be noticed that the dominating factor for the productivity growth at the aggregate level was the technical change. While the efficiency change was negative at 5.7 per cent, the technical change was positive at 7.3 per cent. It was due to higher technical change and there was positive productivity growth in the Indian Iron and Steel Industry during the period of study. The annual mean estimates of efficiency change and technical change for the period to showed that it was the technical change which was the driving force of productivity in many of the years (except in , and ). The greater contribution of technical change in increasing productivity growth indicates that Indian Iron and Steel industry has undergone technological advancements by way of greater access to capital equipments and raw material and R&D efforts during the period of study. Similarly the negative contribution of efficiency change also indicates a point to understand that the factor inputs are yet to be fully utilized and there is a greater scope for the optimum use of factor inputs, labour and capital, in the iron and steel industry. FIRM WISE MEAN FACTOR PRODUCTIVITY GROWTH THE MALMQUIST INDEX Table.3 depicts the mean TFPG of 29 firms during to While the overall mean TFPG was positive at. per cent 2/3 rd of the total firms (9 out of 29) tend to move in accordance with the industrial average registering positive TFPG during the period of study. It could be inferred that though majority of firms registered positive growth of total factor productivity, the rate of growth differed among them. From the Table 5.3, it could be noticed that Ajmera Realty was found to have highest mean productivity at 27.2 per cent followed by Bilpower at 3.7 per cent during the study period. Of the 9 firms which recorded positive TFPG, the rate of growth was too marginal for the firms MUSCO (0.2 per cent), Uttam Galva (0.3 per cent), Mukand (0.7 per cent), Ispat Steels (0.8 per cent) and Ruchi Strips (0.9

12 86 S. Sivakumar per cent) but it was comparatively better for other firms. In the case of firms which experienced declining productivity growth, it was Welspun Gujarat which marked a highest decline to the extent of. per cent during to Table.3: Firm Wise Average of Malmquist Index Sl. No. Firm effch techch tfpch Welspun guj stah Uttam Galva Tata Steels Surya Roshini Sunflag iron SAIL Natl Steel and agro Mukand Man Inds Lloyds steel Inds JSW steels Ispat Steels Essar Steels Bhusan steels Ajmera Realty Surana Indusries Super Forgings Ruchi Strips Ratnamani Metals Pennar Inds Panchmahal steels MUSCO Jai Corpn India steel Goodluck steel Garg furnace Gandhi spl Tube Bilpower Anil spl steel Mean Source : Compiled from Annual Reports It could also be observed from the Table.3, that the technical change was positive for all the firms with varying rates. While the industry average was 7.3 per cent, 7 firms recorded higher rates of technical change than the industry average.

13 A Study on Productivity and Growth of Steel Industry in India 87 It was higher at 2.3 per cent for the firm Bilpower followed by 0.5 per cent for Surya Roshini and 0.3 per cent for Panchamahal Steels and Goodluck Steel. In the case of efficiency change, it was a contributing factor only to Ajmera Realty (8.8 pr cent), Bilpower (.3 per cent) and Gandhi Special Tube (0. per cent). For all other firms, the contribution of efficiency change was negative. It is interesting to note that the firms which registered higher TFPG have higher levels of both efficiency change and technical change (Ajmera Realty and Bilpower). Again the factor responsible mainly due to negative efficiency change which accounted for 6.5 per cent for these firms. The analysis of TFPG and its driving forces can be concluded with the inference that at an average, Indian Iron and Steel industry registered positive productivity growth and the main cause of this growth was technical change rather than efficiency change. At the firm level, it was again the technical change, the main contributing factor for the productivity growth. Similarly the firms which recorded negative productivity growth was mainly due to negative efficiency change experienced by these firms during the period of study. ESTIMATES OF ANNUAL TFPG The previous section of the study analyzed the TFPG and its components at the aggregate level either for one particular year (Table.2) or for one particular firm (Table.3). The results obtained from the analysis made on the basis of aggregates cannot be generalized for all the firms in all the years. In order to get an in depth understanding, an attempt was made in this section to study TFPG of the firms by years. The estimates of annual TFPG and its decomposed measures are presented in the Tables.4 and.5 for clear understanding of the trends in the productivity growth during the period of study. Tables.4 and.5 do not differ in the estimates except for its presentation. While Table.4 provides the TFPG of all the firms in one particular year, Table.5 presents TFPG of one particular firm in all the years. From such presentation, both firm wise and year wise observation can easily be made. The estimates of firm wise TFPG and its sources presented in Table.4 indicate wide fluctuations in the growth rates during the period to In , the industry average TFPG was estimated to be negative at 2.9 per cent caused by technical change (0.5 per cent) and more by efficiency change (2.5 per cent). But this negative growth was not experienced in 0 firms which in turn registered positive growth, highest being 26 per cent (Man Industry) and least by 0.9 per cent (Garg Furnace). Among the firms which experienced negative growth of TFP, the rate of decline was worst in the case of Welspun Gujarat (84.5 per cent). With regard to the sources of productivity growth, it was efficiency change which was responsible for the firms which recorded positive TFPG in The extent of negative TFPG declined steeply from 2.9 per cent in to.9 per cent in Similarly, the negative contribution of technical change became positive (8. per cent) and contribution of efficiency change also improved marginally by a reduction in the negative contribution from 2.5 per cent in to 9.2 per cent in

14 88 S. Sivakumar Table.4: Trends in the Malmquist Index by Firms and by Year effch Chch tfpch Effch techch tfpch Effch techch tfpch effch techch tfpch effch techch Tfpch effch Echch tfpch Effch techch tfpch Effch echch tfpch effch techch tfpch ffch techch fpch Welspun guj stah Uttam Galva Tata Steels Surya Roshini Surya Roshini SAIL Natl Steel and agro Mukand Man Inds Lloyds steel Inds JSW steels

15 A Study on Productivity and Growth of Steel Industry in India 89 Ispat Steels Essar Steels Bhusan steels Ajmera Realty Surana Indusries Super Forgings Ruchi Strips Ratnamani Metals Pennar Inds Panchmahal steels MUSCO

16 90 S. Sivakumar Mean Anil spl steel Bilpower Gandhi spl Tube Garg furnace Goodluck steel India steel Jai Corpn This improvement was also observed in the TFPG of firms as the rate of productivity growth increased significantly to 2.99 per cent in Ajmera Realty followed by 2.75 per cent in JSW Steels. Similarly 25 out of 29 firms witnessed positive technical change which was responsible for greater rates of productivity growth in The negative TFPG estimated in (.9 per cent) became positive at 2.9 per cent due to both efficiency change (2.7 per cent) and technical change (0. per cent). This positive growth further increased to 6.4 per cent in (mainly by technical change by 23.5 per cent). Many firms which experienced decline in their productivity levels have recorded positive growth of productivity and the extent of positive growth further increased in case of other firms. Tata Steels, Surya Roshini, Sunflag Iron, India Steels, Bilpower are the few firms whose negative productivity became positive both in and Similarly the extent of technical change as a source of productivity growth was positive and improved to 23.5 per cent in from 8. per cent in But the increasing trend slipped down to negative in as the productivity growth became negative. In , only 5 out of 29 firms marked positive growth (Man Industry 8 per cent, Ajmera Realty 25.8 per cent, Surana Industry 6 per cent, Ruchi Strips 9.8 per cent and Good luck Steel 22.6 per cent) and it was efficiency change which drove these firms to record higher productivity growth. In , the trend changed towards both efficiency change and technical change to contribute for positive growth for the firms and for the industry as a whole.

17 A Study on Productivity and Growth of Steel Industry in India 9 At the industry level, TFPG was estimated at 33 per cent and the share of efficiency change was 9.4 per cent and technical change was 2.6 per cent. Similar trend was observed in and But the negative growth of TFP (3.7 per cent in ) became positive at 9.4 per cent in Technical change was found to be the driving force of this higher and significant positive productivity growth at the firm level. It was positive for all the firms except for SAIL and Garg Furnace (-.7 per cent). In the subsequent years, and , the dominance of technical change was found significant. The contribution of technical change was marginal at 4.8 per cent in and was significantly increased to 46.8 per cent to drive the TFPG of the Iron and Steel industry to 2.8 per cent in All the firms marked significant improvement in the technical change as it could be observed from the table-4 that it was positive for all the firms. The marked improvement in the technical change significantly increased the productivity of the industry to record a productivity growth of 2. 8 per cent during The analysis of the growth of TFP and its contributing factors at the firms level in Iron and Steel industry reveals more or less similar pattern of results during the period to It could, therefore, be inferred that there was an improvement in the total factor productivity growth in the Iron and Steel industry both at the aggregate and firm level. With the introduction of set of economic reforms in 99, there was greater scope for the technological advancements, greater access to the markets even beyond the national border. The import of better and sophisticated capital equipments, high quality raw materials, transfer of technology and more R&D efforts could be the reasons for the significant TFPG attained by the Iron and Steel industry. Table.5: Firmwise and Yearwise Trends in the Malmquist Index Welspun guj stah Uttam Galva Tata Steels Surya Roshini Sunflag iron SAIL Natl Steel and agro Mukand Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch

18 92 S. Sivakumar Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch Effch Techch Tfpch Mean Effch Techch Tfpch Man Inds Lloyds steel Inds JSW steels Ispat Steels Essar Steels Bhusan steels Ajmera Realty Surana Indusries Super Forgings effch techch tfpch effch techch tfpch effch techch tfpch effch techch tfpch effch techch tfpch effch techch tfpch effch techch tfpch effch techch tfpch effch