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Analysis of Labour Productivity in Selected Nifty 50 Companies CMA (Dr.)Meenu Maheshwari Associate Professor, Department of Commerce and Management, University of Kota, Kota (Rajasthan) Email: drmeenumaheshwari@gmail.com Dr. Ashok Kumar Gupta Principal, Government College, Ramganjmandi Email: drashokkr.gupta@gmail.com CA Priya Taparia Research Scholar, Department of Commerce and Management, University of Kota, Kota (Rajasthan) Email: tapariapriyataparia@gmail.com Abstract Research Issue: Productivity represents the relationship ratio of output and input. It reflects how efficiently resources are being used in creating outputs. The present study aims to measure labour productivity in selected Nifty 50 companies. Labour productivity is the ratio of output to the labour input. Therefore, an attempt has been made to analyse and interpret the labour productivity from 2010-11 to 2017-18 i.e. for eight years in selected 24 companies included in Nifty 50. Research Findings: For intra-company comparison non parametric chi- square test has been used and results indicated that the null hypothesis drawn is accepted indicates that the labour productivity indices of the company for the study period are approximately equal and can be represented by straight line trend or line of best fit. For inter-company comparison Kruskal Wallis One Way Analysis of Variance Test has been used and results indicated that null hypothesis is rejected in all the sectors represents the significant difference in the labour productivity ratios of the companies. Conclusion and Suggestions: On analysing the labour productivity of all companies during all the years under study, it has been observed that overall labour productivity is the best in Reliance Industries Ltd., followed by Hindustan Petroleum Corporation Ltd. and Bharat Petroleum Corporation Ltd. Possible savings has also been calculated and result indicated that the total possible savings in labour input of Tata Consultancy Ltd. would have been as high as ₹ 30793 crore while it would have been as low as ₹ 552 crore of Vedanta Ltd. during the study period of eight years. Theyear of lowest labour input output ratio has been taken as the base yearfor calculating possible savings. Further it has been suggested that labour productivity can be improved by optimally utilizing the labour cost. To improve the labour productivity a company can adopt the measures such as quality circles, management by objective, employee’s flexitime technique of work, incentives schemes to the workers attaining the target, etc. Keywords: Labour Productivity, Index Number, Output, Labour Input, Chi-Square Test, Kruskal Wallis One Way Analysis of Variance Test, Possible Savings. Introduction Productivity is regarded as an essential source of growth in an economy. A country having a productive economy has comparatively high ratio of surplus to capital, high level of labour productivity, low level of labour input output ratio and high level of profit rate. Thus productivity determines the efficiency and effectiveness of the companies factors of production. It represents the relationship ratio of output and input. It reflects how efficiently resources are being used in creating outputs. As quoted by Maheshwari and Taparia, in the words of B. B. Lal, “Productivity is a measurable relationship between well-defined outputs and inputs, i.e., between the production results and the relative production agents in both the financial and physical terms in relation to given terms and conditions.” Measurement of productivity comprises of various inputs viz., material, labour, overhead and investor input. Labour is considered as an essential factor in production as without it other factors will remain idle. Labour productivity is the ratio of revalued output to the revalued labour input. Labour Productivity can be denoted as Labour Productivity = Total Output Labour Input Hence, this study aims to measure labour productivity in selected companies of Nifty 50. Review of Literature Many studies related to productivity at national and international level have been carried out over the last few decades. Few studies are being summarised below: Ferreira and Martinez (2011) focused on the employees perceptions of productivity or company investments in respect of intellectual capital. The Bontis model of intellectual capital has been adopted. As per the model, intellectual capital has been categorised into three components, viz. human capital referring to knowledge and skills of the individual, structured capital comprising internal processes and information of organisation and customer relational capital which refers to the inter relationship of organisation and its stakeholders. The data were obtained from the survey conducted on 440 employees of 13 Portuguese companies. The statistical tools and techniques adopted in the study were ANOVA and regression analysis. It has been concluded by the results that the companies with higher structured capital have a lower perception of in human resources while higher perception of investment in marketing and sale and also higher perception of productivity. Manonmani (2012) highlighted that the wage-productivity relationship in Indian industries has been ongoing and indecisive issue. The study therefore, highlights the wage productivity linkages in rural, urban and aggregate industries of India covering the periods from 1998-1999 to 2007-2008. The regression model has been used to understand the links between wages and productivity. The study computed the partial as well as total factor productivity indices. The variables used in the study are output and input. NVA (Net Value Added) has been used as output. Input includes labour and capital element where labour consists of workers directly or indirectly involved in production while capital consists of invested capital. Taparia and Maheshwari (2015)reviewed in their study the literature related to productivity. According to the study, there are many studies available at international, national and regional level related to productivity. The study reviewed the selected literature from the year 1975 till the end of the year 2015. The study concluded that the methodology employed, nature of data used, number of variables examined, estimation procedure adopted, conclusion drawn vary widely with respect of time. Maheshwari and Taparia (2019)analysed the material productivity of pharmaceutical sector companies from 2008-09 to 2015-16. Both intra sector and inter sector comparison has been drawn and for intra-sector hypothesis, Chi-Square Test has been used and for inter-sector hypothesis, Kruskal Wallis One Way Analysis of Variance Test has been used. Research Gap: As per the above reviews and many more studies studied related to the topic, there is no study on labour productivity in selected Nifty 50 companies for this particular study period has been conducted. So in this present research an attempt has been made to measure the labour productivity of selected companies. Research Methodology Main Objectives of the Research The main objectives are being summarized as follows:- 1) To analyse and interpret the labour productivity of the selected companies included in Nifty 50. 2) To make intra-company and inter-company comparison of labour productivity of the sampled companies for the study period. 3) To recommend ways for the improvement in labour productivity. Sample and Collection of Data The study is based on the secondary data extracted from a sample of 24 companies has been selected from the Nifty 50. These companies have been selected from Automobile, Energy, Information Technology, Metals, Pharmaceutical and Refineries sector which has a great impact on the economy of country. The data and information regarding output (includes sales, other income and change in the inventory of finished goods, work in progress and traded goods), labour input(includes salary, wages, bonus and benefits, contribution to provident and other funds and employees welfare expenses and others) and all other financial variables have been obtained from the annual reports of the selected companies. The index numbers used in the study have been collected from the various bulletins published by Reserve Bank of India on its website. Model to be used Productivity Accounting Model is being used for measuring productivity as it considers all the elements of output and input, ignoring the effect of inflation. But here in the present research, only one element of input i.e. labour is considered and analysed. Selection of Base Year The study covers a period of eight years i.e. from 2010-11 to 2017-18. The year 2010-11 has been taken as the base year which is used for the revaluation of output as well as of labour input. Research Hypotheses and Testing In accordance with the objectives of the research, following hypotheses have been developed which will be tested. Intra Company Hypothesis: It is tested with the help of Non Parametric Test “Chi-Square Test”. Null Hypothesis (H0): There is no significant difference in the labour productivity indices of the sampled company for the study period and can be represented by straight line trend or line of best fit. The acceptance of null hypothesis would reveal that the labour productivity indices of the sampled company for the study period are approximately equal. Inter Company Hypothesis:It is tested with the help of Kruskal Wallis One Way Analysis of Variance Test. Null Hypothesis (H0): There is no significant difference in the labour productivity ratios for the sampled companies. The acceptance of null hypothesis would reveal that the labour productivity ratios of sampled companies are approximately equal. Calculation of Index Numbers and Conversion Factors To remove the inflation effect in the data, index numbers published by various RBI Bulletins and conversion factors calculated accordingly have been used, for the revaluation of data on the base year’s prices for eight years from 2010-11 to 2017-18.Here the year 2010-11 has been taken as base year and Backward Splicing technique has been used for calculating the index numbers of 2010-11. Following formula has been used to calculate conversion factors: Index number of the base year Index number for the current year Table 1: Index Numbers and the Conversion Factors for Revaluation of Data Source: Author’s Calculation with the help of RBI Bulletin Revaluation of Output The output of the companies has been revalued by multiplying the output values with the conversion factors. Output includes sales, other income and change in the inventories of finished goods, work in progress and traded goods.Wholesale price index has been used for revaluating the output. Revaluation of labour Input The labour input of the companies has been revalued by multiplying the input values with the conversion factors. Here for the purpose of this study, the labour input includes salary, wages, bonus and benefits, contribution to provident and other funds and employees’ welfare expenses and others and it is revalued with the consumer price index for industrial workers. Labour Productivity Analysis A comparative labour productivity analysis has been drawn and also its average performance for the study period has been evaluated. Table 2 shows that the labour productivity as well as its average overall labour productivity. Table 2: Labour Productivity Ratios from 2010-11 to 2017-18 According to the above table, labour productivity is the best in Reliance Industries Ltd., followed by Hindustan Petroleum Corporation Ltd. and Bharat Petroleum Corporation Ltd. in 2010-11, 2011-12 and 2013-14. It is the best in Reliance Industries Ltd., followed by Bharat Petroleum Corporation Ltd. and Hindustan Petroleum Corporation Ltd. in 2012-13. It is the best in Bharat Petroleum Corporation Ltd., followed by Reliance Industries Ltd. and Hindustan Petroleum Corporation Ltd. in 2014-15. It is the best of Hindustan Petroleum Corporation Ltd., followed by Bharat Petroleum Corporation Ltd. and Vedanta Ltd. in 2015-16 while in 2016-17 Hindustan Petroleum Corporation Ltd. has the highest productivity followed by Vedanta Ltd. and Bharat Petroleum Corporation Ltd., in 2017-18 Hindustan Petroleum Corporation Ltd. has the highest productivity followed by Bharat Petroleum Corporation Ltd. and Reliance Industries Ltd. By analysing the overall labour productivity of all companies during all the years under study, it has been observed that overall labour productivity is the best in Reliance Industries Ltd., followed by Hindustan Petroleum Corporation Ltd. and Bharat Petroleum Corporation Ltd. Intra-company comparison of Labour Productivity Intra-company comparison of labour productivity of the selected companies of six sectors of Nifty 50 has been calculated and tested with the help of Chi square test and results has been shown in table 3. Table 3: Intra-Company Comparison of Labour Productivity of Companies from 2010-11 to 2017-18 through Chi-Square Test S. No. Company Name Chi Square Value Hypothesis Testing 1 Bajaj Auto Ltd. 11.041 Null Hypothesis is Accepted 2 Mahindra & Mahindra Ltd. 4.749 Null Hypothesis is Accepted 3 Maruti Suzuki India Ltd. 5.068 Null Hypothesis is Accepted 4 Tata Motors Ltd. 13.193 Null Hypothesis is Accepted 5 GAIL (India) Ltd. 26.646 Null Hypothesis is Rejected 6 NTPC Ltd. 1.443 Null Hypothesis is Accepted 7 Oil and Natural Gas Corporation Ltd. 9.346 Null Hypothesis is Accepted 8 Power Grid Corporation of India Ltd. 10.289 Null Hypothesis is Accepted 9 Infosys Ltd. 1.972 Null Hypothesis is Accepted 10 Tata Consultancy Services Ltd. 11.706 Null Hypothesis is Accepted 11 Tech Mahindra Ltd. 8.528 Null Hypothesis is Accepted 12 Wipro Ltd. 2.177 Null Hypothesis is Accepted 13 Coal India Ltd. 107.206 Null Hypothesis is Rejected 14 Hindalco Ltd. 1.324 Null Hypothesis is Accepted 15 Tata Steel Ltd. 8.106 Null Hypothesis is Accepted 16 Vedanta Ltd. 72.090 Null Hypothesis is Rejected 17 Cipla Ltd. 10.719 Null Hypothesis is Accepted 18 Dr. Reddy’s laboratories Ltd. 3.294 Null Hypothesis is Accepted 19 Lupin Ltd. 9.013 Null Hypothesis is Accepted 20 Sun Pharmaceutical Industries Ltd. 12.135 Null Hypothesis is Accepted 21 Bharat Petroleum Corporation Ltd. 69.482 Null Hypothesis is Rejected 22 Hindustan Petroleum Corporation Ltd. 32.335 Null Hypothesis is Rejected 23 Indian Oil Corporation Ltd. 33.676 Null Hypothesis is Rejected 24 Reliance Industries Ltd. 19.682 Null Hypothesis is Rejected If the calculated value of chi square is less as compared to the table value 14.067 at 5% level of significance with (8-1) = 7 degree of freedom, null hypothesis is accepted. This reveals that the labour productivity indices of the company for the study period are approximately equal and can be represented by straight line trend or line of best fit. If the calculated value of chi square is more as compared to the table value, null hypothesis is rejected. This reveals that the labour productivity indices of the company for the study period are not equal and cannot be represented by straight line trend or line of best fit. Inter-company comparison of Labour Productivity For inter-company comparison, Kruskal Wallis One Way Analysis of Variance Test has been used. According to which the labour productivity of all the samples is combined and arranged in order of increasing size and given a rank number and H Value is calculated as shown in table 4. Table 4: Inter-Company Comparison of Labour Productivity Ratios S. No. Sector Name H Value Hypothesis Testing 1 Automobile Sector 25.912 Null Hypothesis is Rejected 2 Energy Sector 26.466 Null Hypothesis is Rejected 3 Information Technology Sector 13.935 Null Hypothesis is Rejected 4 Metals Sector 21.872 Null Hypothesis is Rejected 5 Pharmaceutical Sector 9.281 Null Hypothesis is Rejected 6 Refineries Sector 11.514 Null Hypothesis is Rejected The calculated value is greater than the table value 7.815at 5% level of significance with (4-1) = 3 degrees of freedom for all the companies hence null hypothesis is rejected. This means that the labour productivity ratios of the companies included in Nifty 50 are not same that is there is significance difference in the labour productivity ratios. Possible Savings in Labour Input Possible savings in labour input has been calculated to analyse what would have been saved if the labour input is optimally utilized. To view the performance of the companies in respect of the labour input an attempt has been made to calculate the possible savings. The possible savings in labour input can be calculated on the basis of following formula: • Possible Saving in Labour Input = Actual labour input – Standard labour input • Standard Labour Input = minimum requirement of labour input per unit of output X Actual Output revalued according to the base year • Actual labour input means the actual revalued labour input according to base year prices Table 5: Possible Savings in Labour Input from 2010-11 to 2017-18 Amount in ₹ crore Note: Amount has been rounded off to nearest ₹ Table 5 suggests that the total possible savings in labour input of Tata Consultancy Ltd. would have been as high as ₹ 30793 crore while it would have been as low as ₹ 552 crore of Vedanta Ltd. during the study period of eight years. For calculating possible savings year of the lowest labour input output ratio has been taken as the base year. Conclusion and Suggestions:On analysing the labour productivity of all companies during all the years under study, it has been observed that overall labour productivity is the best in Reliance Industries Ltd., followed by Hindustan Petroleum Corporation Ltd. and Bharat Petroleum Corporation Ltd. Although it is suggested that labour productivity of a company can be improved by utilizing its labour cost optimally with reduction in idle labour hours. To improve the labour productivity a company can adopt the measures such as quality circles, management by objective, employee’s flexitime technique of work, incentives schemes to the workers attaining the target, etc. Also a company should promote leisure or recreational activities which are mainly categorized as physical, social, cultural and intellectual activities. These activities include sports, games, vacation, family get together, creating clubs for employees entertainment and encouraging them to join, etc. By adopting the above suggested methods for improving the labour productivity, a company can save the amount of possible savings. References 1. Ferreira, A.I. & Martinez, L.F. (2011). Intellectual Capital: Perceptions of Productivity and Investment. RAC, Curitiba, 15(2), art. 5, 249-260. Retrieved from http://www.anpad.org.br/periodicos/arq_pdf/a_1165.pdf 2. Grewal, T.S., Grewal, H.S., Grewal, G. S. & Khosla, R. K. (2019). T.S. Grewal’s Analysis of Financial Statements. Sultan Chand and Sons, New Delhi, India. 3. Gupta, S. P. (2001). Statistical Methods by S. P. Gupta. Sultan Chand and Sons, Delhi, India. 4. Jain, S.P.&Narang, K.L. (2015). Cost and Management Accounting. (15th Ed.),Kalyani Publications,New Delhi. 5. Kothari, C., R., & Garg, G. (2014). Research Methodology: Methods and Techniques. New Age International (P) Ltd., New Delhi. 6. Maheshwari, M. (1998). Productivity Accounting in Engineering Industries in Rajasthan. Submitted to the University of Rajasthan for the degree of Doctor of Philosophy. 7. Maheshwari, M. & Taparia, P. (2019). Measurement of Material Productivity: A Case Study of Pharmaceutical Sector Companies included in Nifty 50, Productivity, 60 (2), 175-194. Retrieved from https://doi.org/10.32381/PROD.2019.60.02.7 8. Maheshwari, M. & Taparia, P. (2019). Measurement of Material Productivity: A Case Study of Automobile Sector Companies included in Nifty 50, International Journal of Research and Analytical Reviews (IJRAR), 6 (2), 964-981. Retrieved from www.ijrar.org. 9. Manonmani, M. (2012). Wage - Productivity Linkages in Indian Industries, Indian Journal of Industrial Relations, 47(3), 450-458. Retrieved from http://www.jstor.org/stable/23267336 10. Taparia, P. & Maheshwari, M. (2015). Productivity Accounting: A Review of Literature, INSPIRA – Journal of Commerce, Economics and Computer Science, 01(4), 68-76. Reports 1. Annual Reports of the companiesfrom 2010-11 to 2017-18. 2. Wholesale Price Index and Consumer Price Index for industrial workers from the various bulletins of Reserve Bank of India. Acknowledgement This is our original research work and it has been extracted from the unpublished and unsubmittedwork of my thesis (Priya Taparia) and from our other papers and has been checked on the Urkund from University of Kota, Rajasthan.