Impact of Automation on Workers in Informal Industrial Work in North India
Abstract
Automation is transforming the manufacturing sector across the world. However the research on its effects on workers is insufficient, particularly in the developing economies.In this regard, this study focuses on automation awareness, perceptions of job insecurity, skills transformation, and well-being of workers amongst 67 shop floor workers working at an automobile parts manufacturing firm in Jaipur, India. The survey was conducted in Hindi language with a physical questionnaire amongst the workers in April 2026 focusing on twelve dimensions of automation such as awareness about automation, benefits and limitations, training needs, willingness to invest in skills development, and preferred adaptation methods in case of job displacement. This study found workers aware of automation but confused about what it entails with most of the respondents having a narrow view of automation as machines replacing tasks rather than as digital transformation of industrial processes. Moreover, the workers were concerned about productivity aspects of automation on one hand and fear of losing their jobs, income disparity, and inability to afford the training costs on the other. There was not a single worker who thought that their skills would be adequate enough for an automated environment. Taking into account recent research on automation and its impact on worker well-being (Nazareno & Schiff, 2021; Li et al., 2026), the analysis shows that the above perceptions have practical implications for the well-being of workers, and, therefore, the policies aimed at tackling the consequences of automation should pay due attention to these problems. The results of this paper highlights the importance of targeted training programs, well-funded retraining opportunities so that they could be affordable for the masses and a stronger social safety net protection in order to account for the human side of industrial automation. In accordance with Kasy and Sautmann (2022), the optimal approach to solving problems related to automation should be context-specific, and the current research provides a solid basis for that.
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. https://doi.org/10.1257/jep.33.2.3
Bessen, J. E., Goos, M., Salomons, A., & Van den Berge, W. (2022). Firm-level automation: Evidence from the Netherlands. AEA Papers and Proceedings, 112, 389-93. https://doi.org/10.1257/pandp.20221026
Boeren, E. (2020). Understanding adult lifelong learning participation as a lay person: the view from biographical research. Revista Internacional de Formacao Profissional, 1(3), 7-25. https://doi.org/10.37927/rifp.v1i3.2
Bonnefon, J. F., Shariff, A., & Rahwan, I. (2019). The social dilemma of autonomous vehicles. Science, 352(6293), 1573-1576. https://doi.org/10.1126/science.aah5654
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company. https://doi.org/10.22138/ijpm.v5i1.12
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. https://doi.org/10.1126/science.aap8062
Centre for Sustainable Employment, Azim Premji University. (2026). State of Working India 2026. Azim Premji University.
Cramarenco, R. E., Horatiu, L., & Osoian, C. (2023). New organizational roles needed for up-skilling and re-skilling endeavors. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1133255
Giuntella, O., Konig, I., & Stella, L. (2023). Artificial intelligence adoption and worker well-being. Available at SSRN. https://doi.org/10.2139/ssrn.4313820
Hanushek, E. A., Schwerdt, G., & Zhang, L. (2022). Artificial intelligence and the future of work: A perspective from economics. In Machines We Trust (pp. 173-200). MIT Press.
International Labour Organization. (2018). The economics of artificial intelligence: Implications for the future of work. https://www.ilo.org/global/topics/future-of-work/publications/research-papers/WCMS_647306/lang--en/index.htm
International Labour Organization. (2019). Technological change and employment: Creative destruction. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_727055.pdf
International Labour Organization. (2020). Global employment trends for youth 2020: Technology and the future of jobs. https://www.ilo.org/global/publications/books/WCMS_737648/lang--en/index.htm
Kasy, M., & Sautmann, A. (2022). Adaptive treatment assignment in experiments for policy choice. Econometrica, 90(6), 2735-2763. https://doi.org/10.3982/ECTA19800
Li, Y., Wang, M., & Zeng, D. (2026). Automation and worker happiness: Evidence from the Chinese manufacturing industry. Journal of Happiness Studies, 27(3), 1095-1115.
McKinsey Global Institute. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. https://www.mckinsey.com/~/media/McKinsey/Industries/Public%20and%20Social%20Sector/Our%20Insights/What%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/MGI-Jobs-Lost-Jobs-Gained-Executive-summary-December-6-2017.pdf
McKinsey Global Institute. (2022). The future of work after COVID-19. https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19
Ministry of Statistics and Programme Implementation. (2022). Annual Survey of Industries 2019-20. http://www.csoisw.gov.in/CMS/cms/Home.aspx
Ministry of Statistics and Programme Implementation. (2024) Workforce status of graduates by gender. Periodic Labour Force Survey. Government of India.
Ministry of Statistics and Programme Implementation. (2023). Periodic Labour Force Survey (PLFS) - Annual Report (July 2021 - June 2022). http://mospi.nic.in/publication/periodic-labour-force-survey-plfs-annual-report-july-2021-june-2022
Nazareno, J., & Schiff, E. (2021). Exploring the implications of AI for worker well-being. Available at SSRN. https://doi.org/10.2139/ssrn.3978220
OECD. (2021). OECD digital outlook 2021. https://doi.org/10.1787/bb167041-en
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Ruiz-Vanoye, J. A., Estrada-Vargas, E., & Díaz-Parra, O. (2025). Artificial intelligence and human wellbeing: Understanding the impact of AI through the happiness pyramid framework. Journal of Happiness Studies, 26(8), 2703-2727.
Toker, S. (2023). Upskilling through self-development for future of work: A review. Human Resource Development Review, 22(1), 7-32. https://doi.org/10.1177/15344843221104897
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124
World Bank. (2019). World development report 2019: The changing nature of work. https://doi.org/10.1596/978-1-4648-1328-3
World Economic Forum. (2023). Future of jobs report 2023. https://www.weforum.org/reports/future-of-jobs-report-2023
Authors retain copyright of their published work and grant the International Journal of Social Science Research and Review (IJSSRR) the right of first publication.
Articles published in IJSSRR are distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.
Authors are permitted to share, archive, and distribute the published version of their work, provided that proper acknowledgement of the original publication in IJSSRR is given.
