WAGE DISPARITY AND DETERMINANTS OF WAGES IN THE INDIAN INDUSTRY

Transcription

1 The Indian Journal of Labour Economics, Vol. 51, No. 2, 2008 WAGE DISPARITY AND DETERMINANTS OF WAGES IN THE INDIAN INDUSTRY Hina Sidhu* Wage disparities among different categories of workers have widened substantially within and across the industry groups. By establishing the linkage between labour productivity and wage rate, it can be argued that labour productivity is an important determinant for the wage rates. However, the data analysis revealed a strong association between technology and labour productivity. Therefore, from the comparative analysis of the coefficients of correlations, the study concludes that technology is a critical factor for the determination of wage rates. I. INTRODUCTION Studies on wage structure have empirically established the existence of substantial wage differentials among the workers with different levels of skills in different industrial units. Some of the important studies on the subject are those undertaken by Dickens and Katz (1986), Krueger and Summers (1986), Holzer, et al., (1988), Katz and Summers (1988), Katz and Murphy (1991), Krueger (1991), Krugman (1994), Lowe (1995), Murphy, et al. (1998), Krueger (1999), Jean and Nicoletti (2002), and Virén (2005). The wage rate acts as the motivating factor for the improvement of workers efficiency. Improvement in efficiency, i.e. productivity, is the chief determinant for the expansion of production activities and the upgradation of technologies. The implementation of advanced technologies necessitates the recruitment of skilled workers and training of workers on the payroll. Skilled and experienced workers are available at relatively higher payments in the competitive labour markets. Therefore, studies related to wage structure and the factors influencing wage rates are vital in the formulation of wage policies. Considering the limitations of data availability, this study has been restricted to the empirical examination of wage differential and the influence of labour productivity as well as technology in the determination of wage. As per the structure of this article, Section II presents the data source and classification of registered industrial units. In Section III, structural changes in the industrial sector are discussed. Arithmetic of wage and labour productivity relationship and the share of wages in value added are analysed in Section IV. Section V examines the wage disparity within and across the industry sectors. Section VI is devoted to identifying the factors which influence the wage rate as well as labour productivity. Finally in Section VII, some important conclusions, which are derived from the study, are presented. II. DATA SOURCE AND CLASSIFICATION OF INDUSTRIES This study is based on the data obtained from the Annual Survey of Industries (ASI). The ASI is an important survey of the Central Statistical Organisation (CSO) under the Ministry of Statistics and Programme Implementation of the Government of India. ASI covers the industrial units registered under the Factories Act, As per the ASI report, the design of data collection classifies the industrial units into two categories (i) Census sector units, and (ii) Sample sector units. Prior to , the Census sector covered all the units with 50 or more workers operating * Reader, Department of Economics, School of Social Sciences, Gujarat University, Ahmedabad.

2 250 THE INDIAN JOURNAL OF LABOUR ECONOMICS with power, and units having 100 or more workers operating without power. Since ASI , the definition of the Census sector has been changed to cover all the units having 100 or more workers, irrespective of their operation with or without power. Since ASI , the Census sector has been redefined to include all the units with 200 or more workers, and also some important units which contribute significantly to the value of the output even if they employ less than 200 workers. Besides, the Census sector also covers all the public sector undertakings. The remaining universe of the registered industrial sector is covered by the Sample sector following some specific formula for determining the sample size. The revision of the Census sector and change in the sample size of Sample sector units has been undertaken carefully in order to maintain a fair degree of precision for estimates up to the state level (ASI, ). The compilation of data in the ASI statistics is done on the basis of the National Industrial Classification (NIC), which in turn, is based on the United Nations International Standard Industrial Classification (UNISIC). With the change of three-digit NIC codes from the year onwards, the clubbing of some industries is inevitable for pooling the time series data at the twodigit NIC level. After the clubbing of industries, the number of industry groups for the present study has reduced to fourteen. These groups are: (1) Food products, beverages and tobacco products, (2) Textiles, wearing apparel, dressing and dyeing of fur, (3) Leather and leather products, (4) Wood and wood products including furniture, (5) Paper and paper products including publishing and printing, (6) Rubber, plastic, petroleum, nuclear fuels and coal products, (7) Chemicals and chemical products, (8) Non-metallic mineral products, (9) Basic metals, (10) Fabricated metal products except machinery, (11) Machinery and equipment, (12) Computers, electrical machinery, radio, TV, communication equipment, etc., (13) Motor vehicles, transport equipment and parts, and (14) Other industries. The ASI data considered for the present study pertain to the: (a) Number of industrial units, (ii) Number of workers and employees, (iii) Payment of wages, salaries, provident fund, bonus, etc., (iv) Value of fixed capital, and (v) Net value added. The term worker refers to all the workers including contract labourers, while the term employee refers to the total number of both workers and employees in the non-worker category. The yearly data on capital stock, net value added and payment of wages, etc., are available at the current prices in the ASI statistics. Therefore, in order to make the analysis of time series data comparable, the nominal values of fixed capital, payment of wages and salaries, and net value added were deflated with the appropriate price indices, which are specified at appropriate places in each section. However, at this stage, it may be noted that all the value related variables were measured at the prices. It is also important to note that due to significant variations in the year on year coverage of industrial units from each sector of the industry under the ASI, the time series data for each industry were transformed into the industry level averages in order to obtain realistic estimates that could be used for comparative analysis. III. STRUCTURAL CHANGES IN THE INDUSTRIAL SECTOR The structure of the industrial sector in India has undergone considerable changes over the period The average size of the units in terms of employment has declined by nearly one per cent per annum while the capital intensity, i.e. the capital-labour ratio has increased by 7 per cent per annum (Table 1). This indicates the establishment of a relatively large number of medium and small size units with modern technology, and the upgradation of technology by the old ones. However, capital deepening in most of the industry groups has failed to yield a proportionate increase in the industrial output due to which the output-capital ratio has recorded negative growth in most of the industries. The output-capital ratio recorded positive growth in only three industry groups, viz. wood and wood products (1.41 per cent per annum), rubber and allied industries

3 WAGE DISPARITY IN THE INDIAN INDUSTRY 251 (0.85 per cent per annum), and computers and allied activities (0.59 per cent per annum). At the aggregated level, the output per unit of capital recorded a decline of 0.8 per cent per annum during the period Table 1 shows that the capital intensity in the categories of wood and wood products, and computers and allied activities, respectively, has not only created additional employment but also led to relatively higher growth in output. Relatively higher growth in the capital-labour ratio in the product categories of wood and allied products, chemicals and chemical products, non-metallic mineral products, computers and allied activities, and the group of other industries indicates that significant modernisation has taken place in the production technologies in these industries. Relatively higher growth in capital vis-à-vis negative growth in employment in the categories of textiles, chemicals, non-metallic mineral products and the group of other states indicates that investments in these industries were directed towards the implementation of labour- saving technologies. Table 1 also shows that due to improvement in production technology, the labour productivity has recorded appreciable improvement in most of the industries. Table 1 Structural Changes during ( Growth in % per annum) Industry Employ- Fixed Fixed Value of Output- Net Labour ment capital capital- gross fixed value produlabour output capital added ctivity ratio ratio Food, beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries IV. SHARE OF WAGES IN VALUE ADDED There is an agreement among scholars that the relevance of the term wage is different from the perspectives of the employer and the employee. The consumption price is relevant for the employees as the wage earners are interested in their purchasing power. On the other hand, the relevant price for the employers is the value added price as this is the price of their production. Since the linkages between wages and labour productivity has been established, a common deflator, i.e. the wholesale price index (WPI) of Manufactured Products is used. It is important to note that in case the wage rate is calculated from the data (Total Emoluments) deflated by the consumer price index and labour productivity is calculated from the data (Net Value Added) deflated by the WPI of Manufactured Products, then the results would be unbelievable and questionable.

4 252 THE INDIAN JOURNAL OF LABOUR ECONOMICS As per the concepts and definitions of the terms used for collection of information in the ASI, the term employee means the workers as well as non-workers. The category of non-workers includes the managers, supervisors, and office and field staff. The term emoluments means total wages and salaries plus non-wage benefits like bonus, provident fund, etc., and the term net value added implies the surplus which is shared between the employees and the employer. The reasons for considering the aforesaid data are that: (a) all the employees are involved in the production process, and (b) all the employees receive wages and salaries, and non-wage benefits. Considering these terms, the relationship between the wage rate and labour productivity can be established as follows: Average wage = Total emoluments/total number of employees...(1) Labour productivity = Net value added/total number of employees (2) Dividing Equation 1 by Equation 2, we get the share of wages in labour productivity: Share of wage = Total emoluments/ Net value added or = Average wage/labour productivity (3) Equation (3) implies that Ä Share of Wage = Ä Average wage Ä Labour productivity (4) Equation (4) shows that the change in wage share is the difference between the change in average wage and the change in labour productivity. For example, if the average wage rises by 8 per cent and labour productivity increases by 5 per cent, then the share of wages will increase by 3 per cent. On the other hand, if the average wage rises by 5 per cent and labour productivity increases by 8 per cent, then the share of wages will decrease by 3 per cent. Thus, the wage share will not change as long as the variations in labour productivity are at par with the variations in the average wage. The above association also states that higher labour productivity allows for high wage rates. Table 2 presents the industry-wise labour productivity vis-à-vis the average emoluments during some selected years. It is apparent from the table that in most of the industry groups wherein the Table 2 Labour Productivity and Average Emoluments (Rs./month*) Industry Labour productivity Average emoluments Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries * WPI of all manufactured products was used for estimation.

5 WAGE DISPARITY IN THE INDIAN INDUSTRY 253 labour productivity is higher, the employees also receive relatively higher payments. It is also clear from the table that there is no uniformity in the share of wages in labour productivity across the industries. The prevalence of job specialisation and restricted mobility of workers outside the sector may be the reason for high wage differentials across the industry groups. The growth rates of labour productivity and average emoluments received by the employees were calculated to examine whether the share of wages in labour productivity has increased or declined during over the period Table 3 shows that in the industry groups other than those of machinery and equipment, and motor vehicles and allied industries, the growth in labour productivity was relatively higher than the growth in average wages. This means that the share of wages in value added or the share of the average wage rate in labour productivity has declined over the period The highest decline in the share of wage was recorded in the product category of fabricated metal products except machinery and equipment (-8.48 per cent per annum). At the aggregated level of the industry, the share of wages in value added decline by 0.48 per cent per annum during the period Table 3 Estimated Growth during (in % per annum) Industry Labour Average Share of productivity emoluments wages in VA Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries V. WAGE DISPARITIES From the time series ASI data, a distinction can be made between the average wage received by the workers and the average salary paid to employees in the non-worker category. A distinction can be made between workers and non-workers by considering the availability of data in the ASI statistics. The terms worker and non-worker are already defined in Section II. Considering the limitations of the ASI data, an attempt is made in this section to understand the wage disparities between the worker and non-worker categories of employees within and across the industry groups. Table 4 shows that textiles and allied industries, food and allied activities, leather and leather products, and non-metallic mineral products demand relatively less supervision. On the other hand, in the case of machinery and equipment, computers and allied products, and rubber and allied industries, the nature of production necessitates relatively greater supervision due to which the proportion of managerial and supervisory staff is relatively high in these industries as compared to the others. This reveals considerable variations in the process technologies and the demand for

6 254 THE INDIAN JOURNAL OF LABOUR ECONOMICS Table 4 Industry-wise Average Size of Establishments (in Nos.) Industry Average number of employees Ratio of workers to non-workers Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries skilled workers, which are critical for the determination of wage rates among different categories of workers. The average monthly payments of wages and salaries, growth in wages and salaries, and coefficients of variations in wages and salaries across different industry groups were calculated to examine wage disparities (Table 5). The time series data on wages and salaries were deflated by the WPI of industrial workers in order to obtain a comparative picture across the years. Table 5 seems to indicate that wage disparities between the payments made to the worker and non-worker categories are relatively high in the case of food and allied activities, non-metallic mineral products, rubber and allied products, and computers and allied activities. On an average, the non-worker categories of employees in these industries receive more than three times the average wage of the workers. Across the industry groups, the wage rates are significantly higher in the case of motor vehicle and allied as well as basic metal industries. On the other hand, the wage rates are relatively low in the case of leather and leather products, food and allied activities, and the group of Other Industries. An important observation that can be made from Table 5 is that the average wage of workers in the case of motor vehicles and allied, and basic metal industries is comparable with the payments received by employees in the non-worker category in the case of food and allied activities, and the group of Other Industries. This reveals high inter-industry wage differentials. The coefficient of variation calculated to understand the wage disparities among each category of employees across the industry groups indicates that the disparities are relatively high in the case of the wages of workers. Moreover, the inter-industry wage disparities have recorded an increase over a period of time in the case of payments to both workers as well as non-workers. This may be due to significant variations in job specialisations and the industry-level demand for skilled workers. The ideal approach for understanding the shift in wage disparities over a period of time would be to carry out a comparative analysis of the growth rates. Prior to the calculation of growth rates, the wage-related data were deflated by the wholesale price index of industrial workers.

7 WAGE DISPARITY IN THE INDIAN INDUSTRY 255 Table 5 Wage Differential In the Industrial Sector in India (Rs./month) Industry Worker Non-worker Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries Coefficient of Variation 32% 33% 37% 31% 30% 36% All Industries Since the structural changes in technology are biased towards skilled professionals, the growth in payments to non-workers is expected to be relatively higher than those made to workers. Several studies have established that the implementation of skill-biased technologies has widened the wage disparities among the workers having different levels of education and technical skills. The important studies on this subject are those by Dickens and Katz (1986), Krueger (1991), Krugman (1994), Murphy, et al. (1998) and Virén (2005). In this study, the growth rates are calculated through the least-squares method. The leastsquares growth rate is estimated by fitting a linear regression trend line to the logarithmic annual values of the variable during the relevant period. For the calculation of growth in wage rates, the regression equation takes the following form: Ln W t = a + r t where, W t is the average wage rate during the relevant year, t represents time or the relevant year, a is the intercept or constant, r is the growth rate, and Ln is the natural log. The industry group-wise growth in the average wages received by employees in both are presented in Table 6. In this table, the shift in wage disparity is obtained by estimating the growth of the ratio of non-worker to worker wage payments. Positive growth implies that the wage disparities have widened while negative growth means that the wage disparities have narrowed down. The estimates reveal that wage disparities have widened in all the industries over the period It is also apparent from Table 6 that the widening of wage disparity was relatively high in the groups of other industries, paper and allied activities, computers and allied activities, and textiles and allied industries.

8 256 THE INDIAN JOURNAL OF LABOUR ECONOMICS Table 6 Growth during (in % per annum) Industry Wage per Salary per Shift in worker non-worker wage disparity Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries VI. DETERMINANTS OF WAGES Technology has a crucial impact on labour productivity. The association of both these factors is so strong that they are both used as the determinants of wage rates. The studies by Philip Lowe (1995), Joost Verlinden (1997), Alan Carruth, et al. (1999), and Jesus Felipe (2005) have examined the wage productivity relation while those by Dickens and Katz (1986), Paul Krugman (1994), K.M. Murphy, et al. (1998), and Jean and Nicoletti (2002) have supported the conclusion that wages are determined by technology. Capital intensity is taken as the proxy for technology. In the context of Indian industry, some of the important studies on the determinants of wages are those undertake by T.S Papola (1972), B.H Dholakia (1976), Verma and Subbayamma (1985), Veena Bhatnagar (1988), Lakhwinder Singh (1991), Hina Sidhu (1997), and Bhandari and Heshmati (2006). It is generally believed that the wage rates in the organised sectors in India are not determined by the market conditions due to stringent labour laws and government protections. Periodic changes in the minimum wage rates by the state governments and the revision of pay scales and other benefits for the Central Government employees every 10 years following the Pay Commission Reports also determine the wage rates and pay revisions in the private and other sectors. It is also argued that the influence on wage rates by the factors other than labour productivity may adversely affect the health of the industrial units. In this context, the study of the wage and labour productivity link assumes more importance. The influence of technology on labour productivity has also been widely recognised. Therefore, it is equally important to examine the impact of technology on the wage rate and the association between technology and labour productivity. The aforesaid relations have been empirically examined in this section. 1. Impact of Labour Productivity on Wage Rate The ability to pay hypothesis states that labour productivity is an important determinant of the wage rate. As per the productivity theory, assuming competitive market conditions, the wage rate should be equal to the marginal product of labour. In the Cobb-Douglas production function, the marginal product of labour is proportional to the average product of labour. This suggests a linear

9 WAGE DISPARITY IN THE INDIAN INDUSTRY 257 relation between the wage rate and labour productivity. This relationship was examined for each group of industries with the following log linear regression equation: Ln W i = α + β Ln P Li where, W i is average wage paid in industry i, P Li is labour productivity in industry i, α is constant or intercept of the regression line, β is the elasticity of wage with respect to labour productivity, and Ln is the natural log. Industry-wise ASI time series data for the period were used to test the aforesaid relation. The regression results are presented in Table 7. The interpretation of the regression results is done cautiously, keeping in view the tests of significance. The F statistic of regression results for each sector were significant at 5 per cent. This means that the regression equation for each sector was statistically significant. The significance of the F statistic strengthens the prediction power of the regression equation. The value of R 2 indicates the strength of the association between the dependent and the explanatory variables. In Table 7, the values of R 2 ranged from 0.15 to This means that there are considerable differences in the strength of the wage productivity relation in different groups of the industry. The relatively lower value of R 2 for the wood and wood products industry (0.15) indicates that the influence of labour productivity on the determination of the wage rate is weak. On the other hand, the relatively higher value of R 2 for the product category food and allied industries (0.94) means that labour productivity is an important determinant of wage rates in this industry. The significantly higher value of R 2 (0.94) for the aggregated industrial sector implies that 94 per cent of the variation in wage rates in the Indian industry was explained by labour productivity. The value of the coefficient for labour productivity, i.e. β, is positive and statistically significant at one per cent in the regression equations for respective industry groups other than the wood and Table 7 Regression Results of Wage and Labour Productivity Relation Industry Constant β t-value of β R 2 F Value d.f (n-k-1) Food, Beverages and Tobacco Products *** Textiles, Wearing Apparel, etc *** Leather and Leather Products *** Wood and Wood Products Furniture, etc * Paper and Paper Products, and Publishing *** and Printing Rubber, Plastic, Petroleum Products, etc *** Chemicals and Chem. Products *** Non-metallic Mineral Products *** Basic Metals *** Fabricated Metal Products except Machinery *** and Equipment Machinery and Equipment *** Computers, Electrical Machinery, Radio, TV, *** Motor Vehicles, Transport Equipment and Parts *** Other Industries *** All Industries *** Note: *** Significant at 1%; * Significant at 10%; F Significant at 5%

10 258 THE INDIAN JOURNAL OF LABOUR ECONOMICS wood products industry. The significance of the coefficient β, which is determined by the value of t-statistic, shows that the increase in labour productivity would bring about an increase in the wage rates. However, the expected increase in the wage rates depends on the value of the coefficient β, i.e. the elasticity of the wage rate with respect to the labour productivity. The value of the coefficient β ranges from 0.13 to For the aggregated industrial sector, it is 0.87, which means that a one per cent increase in labour productivity would bring about a 0.87 per cent increase in wage rates. Thus, the regression results establish that labour productivity has a strong influence on the determination of wages in most of the industry groups. 2. Impact of Technology on Wage Rate Variations in the capital-labour ratio are used as proxies to determine technological change. The capital-labour ratio increases with the installation of new technology or upgradation of the existing machinery. Advanced technology demands more skilled workers and supervisors. This necessitates the imparting of training to the existing workers and the recruitment of more skilled workers and supervisors. Experienced and skilled labour is available at higher payment in the competitive labour markets. With the implementation of improved technology, labour productivity tends to rise as a result of which the wage rates would go up. Thus, technological progress and wage rates are expected to have a strong linkage. The impact of technology on the wage rates was examined for each sector of the industry with the following regression equation: Ln W i = α + β Ln K/L i where W i is the average wage paid in industry i, K/L i is the capital-labour ratio in industry i, α is constant or intercept of the regression line, β is the elasticity of wage with respect to capital intensity, and Ln is the natural log. The data source and interpretation of regression results in this section follow the same pattern as in the previous section. Table 8 shows that the F statistic of regression results for all groups of industries were significant at 5 per cent. This means that there is a statistically significant association between capital intensity and wage rates. The value of R 2 is weak for the wood and wood products industry, which means that technology does not influence wage rates in this industry. In Table 8, the values of R 2 ranged from 0.20 to 0.96, which means there are considerable variations in the impact of technology on the wage in different groups of industries. The relatively higher value of R 2 for the food and allied industry (0.96) and non-metallic mineral products (0.88) prove that technology is an important determinant of wage rates in these industries. The coefficient for capital intensity, i.e. β, is statistically significant in the regression equations for all the industry groups. The value of the coefficient β ranges from 0.13 to For the aggregated industrial sector, the value of β is 0.65, which means that a one per cent increase in the capital-labour ratio would bring about a 0.65 per cent increase in the wage rates. Thus, the regression results reveal that other than in the case of the wood and wood products industry, technology has a strong influence on the determination of wages in the industrial sector in India. 3. Relative Importance of Technology and Labour Productivity From the regression results for the wage-productivity and wage-technology relations, it is difficult to conclude whether labour productivity or technology is a relatively more critical factor in the determination of wages. Therefore, in order to draw an emphatic conclusion, the coefficients of correlation r were calculated to examine the strength of the relationship between: (a) technology

11 WAGE DISPARITY IN THE INDIAN INDUSTRY 259 Table 8 Regression Results of Wage and Fixed Capital Relation Industry Constant β t-value of β R 2 F Value d.f (n-k-1) Food, Beverages and Tobacco Products *** Textiles, Wearing Apparel, etc *** Leather and Leather Products *** Wood and Wood Products, Furniture, etc ** Paper and Paper Products, and Publishing *** and Printing Rubber, Plastic, Petroleum Products, etc *** Chemicals and Chem. Products *** Non-metallic Mineral Products *** Basic Metals *** Fabricated Metal Products except Machinery *** and Equipment Machinery and Equipment *** Computers, Electrical Machinery, Radio, TV, *** Motor Vehicles, Transport Equipment and Parts *** Other Industries *** All Industries *** Note: *** Significant at 1%; ** Significant at 5%; F Significant at 5% and labour productivity, (b) technology and wage rates, and (c) labour productivity and wage rates. The results (Table 9) show that the correlation between capital intensity and labour productivity is very strong in all the industry groups. It ranges from 0.80 in the case of chemical and allied industries to 0.99 in rubber and allied industries. As regards the relationship between technology and wage rates, it is very strong in all the industries other than that of wood and wood products. The value of r was relatively low (0.20) for the wood and wood products industry and ranged from 0.69 to 0.98 for the remaining industry groups. The highest value of r (0.98) was observed for the food and allied industries. For the Table 9 Coefficient of Correlation Industry Capital-labour ratio Emoluments and Emoluments and and labour productivity capital-labour ratio labour productivity Food, Beverages and Tobacco Products Textiles, Wearing Apparel, etc Leather and Leather Products Wood and Wood Products, Furniture, etc Paper and Paper Products, and Publishing and Printing Rubber, Plastic, Petroleum Products, etc Chemicals and Chem. Products Non-metallic Mineral Products Basic Metals Fabricated Metal Products except Machinery and Equipment Machinery and Equipment Computers, Electrical Machinery, Radio, TV, Motor Vehicles, Transport Equipment and Parts Other Industries All Industries

12 260 THE INDIAN JOURNAL OF LABOUR ECONOMICS aggregated industrial sector, the value of r was This implies that technology has a strong influence on the determination of wages. The value of r, calculated for the wage and labour productivity relation, was weak only in the case of the wood and wood products industry (0.25). For the remaining industry groups, the value of r ranged from 0.55 to The relatively high correlation between wage and labour productivity (0.96) was observed for the food and allied industry. For the aggregated industrial sector, the value of r was 0.94, which indicates that labour productivity also has a strong influence on the determination of wage rates. From the aforesaid analysis and Table 9, it is apparent that there is a strong relation between: (a) technology and labour productivity, (b) technology and wage rates, and (c) labour productivity and wage rates. However, a comparative analysis of the coefficients of correlation between technology and wage rates vis-à-vis labour productivity and wage rates (Table 9) reveals that in most of the industry groups, technology has a relatively greater influence on the wage rate. Thus, it may be concluded that technology is the key determinant of wage rates in the industrial sector in India. VII. CONCLUSION The study concludes that wage disparities in each sector of the Indian industry have widened during the period Across the industry groups also, the wage disparities have widened, which may be due to considerable variations in technology and labour productivity. The study revealed that during the period , the growth in wage rate was relatively lower than the growth in labour productivity, which is an indicator of the decline in the share of wages in value added. The analysis has established a strong relation between technology and labour productivity. Thus, both wage rate and labour productivity as well as wage rate and capital intensity are closely linked to each other. From the comparative analysis of the coefficients of correlation, the study concludes that technology is a critical factor in the determination of wage rates in the industrial sector in India. References Bhandari, A.K. and Heshmati, A. (2006), Wage Inequality and Job Insecurity among Permanent and Contract Workers in India: Evidence from Organised Manufacturing Industries, Discussion Paper No. 2097, Institute for the Study of Labour (IZA), Bonn, April. Carruth, A.; Collier, B. and Dickerson, A. (1999), Inter-Industry Wage Differences and Individual Heterogeneity: How Competitive is Wage Setting in the UK?, ESRC Research Centre on Micro-Social Change at the University of Essex, December. Dholakia, B.H. (1976), Determinants of Inter-industry Wage Structure in India, The Indian Journal of Industrial Relations, Vol. 11, No. 4, April. Dickens W.T. and Katz, L.F. (1986), Inter-industry Wage Differences and Industry Characteristics, Working Paper No. 2014, National Bureau of Economic Research, Cambridge, September. Felipe, J. (2005), A Note on Competitiveness, Unit Labour Costs and Growth: Is Kaldor s Paradox a Figment of Interpretation?, CAMA Working Paper 6/2005, Centre for Applied Macro-economic Analysis, The Australian National University, May. Holzer, H.J.; Katz, L.F. and Krueger, A.B. (1988), Job Queues and Wages: New Evidence on the Minimum Wage and Inter-Industry Wage Structure, Working Paper No. 230, Industrial Relations Section, Princeton University, April. Jean, S. and Nicoletti, G. (2002), Product Market Regulation and Wage Premia in Europe and North America: An Empirical Investigation, Working Paper No. 318, Economics Department, Organisation for Economic Cooperation and Development, January. Katz, L.F. and Murphy, K.H. (1991), Changes in Relative Wages, : Supply and Demand Factors, Working

13 WAGE DISPARITY IN THE INDIAN INDUSTRY 261 Paper No. 3927, National Bureau of Economic Research, Cambridge, December. Katz, L.F. and Summers, L.H. (1988), Can Inter-Industry Wage Differentials Justify Strategic Trade Policy, Working Paper No. 2739, National Bureau of Economic Research, Cambridge, October. Krueger, A.B. (1991), How Computers Have Changed The Wage Structure: Evidence From Micro Data , Working Paper No. 3858, National Bureau of Economic Research, Cambridge, October. (1999), Measuring Labour s Share, Working Paper No. 413, Princeton University, January. Krueger A.B. and Summers, L.H. (1986), Reflections on the Inter-Industry Wage Structure, Working Paper No. 1968, National Bureau of Economic Research, Cambridge, June. Krugman, P. (1994), Past and Prospective Causes of High Unemployment, Federal Reserve Bank of Kansas City, Economic Review, Fourth Quarter, pp Lowe, P. (1995), Labour-Productivity Growth and Relative Wages: , Productivity and Growth, Reserve Bank of Australia Conference, July, pp Murphy, K.M.; Riddell, W.C. and Romer, P.M. (1998), Wages, Skills and Technology in the United States and Canada, Working Paper No. 6638, National Bureau of Economic Research, Cambridge, July. Papola, T.S. (1972), Inter-industry Wage Structure Technology Hypothesis, Anveshak, Vol. 2, No. 1, June. Sidhu, Hina (1997), Wage Differentials in SSI Sector in Gujarat, Indian Journal of Industrial Relations, Vol. 33, No. 2, October. Singh, L. (1991), Changes in the Inter-Industry Structure of Wages: The Case of Punjab, Indian Journal of Industrial Relations, Vol. 27, No. 2, October. Verlinden, J. (1997), Concept, Measurement and Policy Implications of the Nairu-Perspective from Belgium, Working Paper No. 184, Organisation for Economic Co-Operation and Development, Paris. Verma, P. and Subbayamma, G. (1985), The Inter-Industry Wage Structure in India: Recent Experience, The Indian Journal of Labour Economics, Vol. XXVIII, No. 3, October. Virén, M. (2005), Why Do Capital-intensive Companies Pay Higher Wages?, Discussion Papers 5/2005, Bank of Finland Research, Helsinki, Finland.

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