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Purpose: The purpose of this paper is to analyse the labour force participation among the elderly population (aged 60 and above) in India from data available for the most recent time periods. The paper reviews the types of employment, industries and occupations that the elderly in India are employed in presently and also examines the impact of different factors on the decision of the elderly to be a part of the labour force. Design/methodology/approach: Unit-level data has been extracted from the most recent data available on employment and labour force in India provided by the Periodic Labour Force Survey (PLFS), from 2017 to 2018 to 2019–2020. Binary logistic regression model has been used to identify some of the socio-economic and demographic factors that influence the odds of the elderly being a part of the labour force along with the extent of their influence, and Wald statistic has been used to examine the dynamics. Findings: The study finds that among the elderly population, males, individuals living in rural areas, those living alone, those who are currently married and those with low educational attainment are more likely to be a part of the labour force. Again, elderly females are found to be employed mostly in the informal or unorganised sector which provide minimal or nil social security benefits. Between 2017–2018 and 2019–2020, the coefficients for sector, gender, marital status, education and income are found to be significantly different. Originality/value: This paper attempts to shed some light on the scanty literature with regards to studies on the elderly in India, and also to reveal the possible factors which induce the elderly in India to participate in the labour force with the help of the most recent data available. Peer review: The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2021-0350. [ABSTRACT FROM AUTHOR]

Purpose: In the process of school-to-work transition, the role of general education and vocational education and training (VET) remains quite central. Based on the human capital theory, we estimate whether investment in VET brings additional returns for workers across the age cohorts. Design/methodology/approach: The focus of our study being the labour market in India, the data from the Periodic Labour Force Survey 2018–19, conducted by the National Statistical Office, has been used for analysis. We have applied the ordinary least square method with sample selection correction, the quasi-experimental technique of propensity score matching and heteroskedasticity based instrumental variable approach to estimate the returns with respect to no VET, formal VET and informal VET. Findings: Our study shows that workers with formal VET earn higher wages than workers with no VET or informal VET. The study finds that workers with informal VET do not earn higher wages than workers with no VET. Moreover, from the age cohort analysis, we have deduced that wage advantage of workers with formal VET persists across all age cohorts and, in fact, accentuates with an increase in age. Originality/value: We have estimated that VET being complemented with basic general education fetches higher returns in the labour market, especially when provided through formal channels. Moreover, to the best of our knowledge, in the case of developing countries where informal VET is widely provided, this is one of the first studies that captures the return to informal VET. Lastly, complementing the existing studies on the developed countries, we have estimated the returns to VET over the life cycle of the workers. [ABSTRACT FROM AUTHOR]

Purpose: This research contributes to current debates on automation and the future of work, a much-hyped but under researched area, in emerging economies through a particular focus on India. It assesses the national strategy on artificial intelligence and explores the impact of automation on the Indian labour market, work and employment to inform policy. Design/methodology/approach: The article critically assesses the National Strategy on AI, promulgated by NITI Aayog (a national policy think tank), supported by the government of India and top industry associations, through a sectoral analysis. The key dimensions of the national strategy are examined against scholarship on the political economy of work in India to better understand the possible impact of automation on work. Findings: The study shows that technology is not free from the wider dynamics that surround the world of work. The adoption of new technologies is likely to occur in niches in the manufacturing and services sectors, while its impact on employment and the labour market more broadly, and in addressing societal inequalities will be limited. The national strategy, however, does not take into account the nature of capital accumulation and structural inequalities that stem from a large informal economy and surplus labour context with limited upskilling opportunities. This raises doubts about the effectiveness of the current policy. Research limitations/implications: The critical assessment of new technologies and work has two implications: first, it underscores the need for situated analyses of social and material relations of work in formulating and assessing strategies and policies; second, it highlights the necessity of qualitative workplace studies that examine the relationship between technology and the future of work. Practical implications: The article assesses an influential state policy in a key aspect of future of work–automation. Social implications: The policy assessed in this study would have significant social and economic outcomes for labour, work and employment in India. The study highlights the limitations of the state policy in addressing key labour market dimensions and work and employment relations in its formulation and implementation. Originality/value: This study is the first to examine the impact of automation on work and employment in India. It provides a critical intervention in current debates on future of work from the point of view of an important emerging economy defined by labour surplus and a large informal economy. [ABSTRACT FROM AUTHOR]