IMPACT OF AI ON LABOR PRODUCTIVITY IN THE MANUFACTURING SECTOR IN INDIA
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.4413Keywords:
Labor Productivity, Artificial Intelligence, Labor, Productivity, Indian Manufacturing Sector, Short-Run, Capital Stock, Development, Statistical Interpretation, DistributionAbstract [English]
This research paper primarily analyses the relationship between labor productivity and Artificial intelligence in the Indian manufacturing sector. For this purpose, variables such as the amount of capital stock and expenditure on research and development were chosen as independent variables. Here the expenditure on R&D is considered as a proxy for investment in AI. Although Artificial Intelligence has been playing a vital role in output maximization, here the attempt is made to capture AI’s impact on Labor productivity, The data of capital stock and labor productivity were taken from the RBI KLEMS database and the expenditure on research and development taken from NSTMIS, Department of Science & Technology. The data has been filtered for the research period from 1991 to 2022. The data was converted into log form for easier statistical interpretation. The measure of central tendency and correlation matrix were executed. For each variable stationery of the data has been worked out using the Augmented dickey fuller unit root test. It was observed that there were mixed differences. Hence the ARDL (Autoregressive Distributed log) model was chosen for accurate model building. The results of correlation analysis showed a strong relationship between capital stock and labour productivity at 0.92 percent. And the expenditure on R&D and Labour productivity had a strong correlation of 0.91 percent. That of capital stock and R&D had 0.95 percent. Tests for serial correlation and multicollinearity were executed. The short-run results of Auto Regressive Distributed Lag showed that the coefficient of past labor productivity has a positive impact on the current labour productivity at P<0.005. Whereas in the long run, the research and development investment coefficient suggests the relationship with labor productivity is positive but only with a marginal significance because the P-value is a little above the benchmark. So, the result should be carefully inferred. By and large, the short-run dynamics show persistency in the labor productivity changes and a potential negative influence of the temporary increase in capital stock on the current variation in labor productivity.
References
Bhattacharya, M, & Narayan, P. (2015). Output and labour productivity in organized manufacturing: A panel cointegration analysis for India. International Journal of Production Economics, 170, 171–177. https://doi.org/10.1016/j.ijpe.2015.09.020 DOI: https://doi.org/10.1016/j.ijpe.2015.09.020
Bhattacharya, M., Narayan, P. K., Popp, S., & Rath, B. N. (2011). The productivity-wage and productivity-employment nexus: a panel data analysis of Indian manufacturing. Empirical Economics, 40(2), 285–303. https://doi.org/10.1007/s00181-010-0362-y. DOI: https://doi.org/10.1007/s00181-010-0362-y
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. DOI: https://doi.org/10.1016/j.futures.2017.03.006
Mitra, D., & Ural, B. P. (2008). Indian manufacturing: A slow sector in a rapidly growing economy. The Journal of International Trade & Economic Development, 17(4), 525–559. https://doi.org/10.1080/09638190802250282 DOI: https://doi.org/10.1080/09638190802250282
Rizvi, A. T., Haleem, A., Bahl, S., & Javaid, M. (2021). Artificial intelligence (AI) and its applications in Indian manufacturing: A review. In Lecture Notes in Mechanical Engineering (pp. 825–835). Springer Singapore. DOI: https://doi.org/10.1007/978-981-33-4795-3_76
Li, B.-H., Hou, B.-C., Yu, W.-T., Lu, X.-B., & Yang, C.-W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86–96. https://dx.doi.org/10.29121/shodhkosh.v5.i1.2024.4413
Alderucci, D., Branstetter, L. G., Hovy, E., Runge, A., Ryskina, M., & Zolas, N. (2019). Quantifying the impact of AI on productivity and labour demand: Evidence from U.s. census Microdata1.https://www.semanticscholar.org/paper/49d415cf593be38c6cd97a183dadc7d7b48bab72.
Erumban, A. (2007). Productivity and unit labour cost in Indian manufacturing: A comparative perspective. https://www.semanticscholar.org/paper/3cafb17581352c4bb391ad2f55ff898a10daa289.
Alderucci, D., Branstetter, L. G., Hovy, E., Runge, A., Ryskina, M., & Zolas, N. (2019). Quantifying the impact of AI on productivity and labour demand: Evidence from U.s. census Microdata1.https://www.semanticscholar.org/paper/49d415cf593be38c6cd97a183dadc7d7b48bab72.
Erumban, A. (2007). Productivity and unit labour cost in Indian manufacturing: A comparative perspective.https://www.semanticscholar.org/paper/3cafb17581352c4bb391ad2f55ff898a10daa289.
Li, B.-H., Hou, B.-C., Yu, W.-T., Lu, X.-B., & Yang, C.-W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86–96. https://doi.org/10.1631/fitee.1601885. DOI: https://doi.org/10.1631/FITEE.1601885
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Copyright (c) 2024 Dr. Prakashchandra M. Parmar, Dr. Justin John Stephen
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