Centre for Internet & Society

This paper aims to contextualise the narrative around digitalisation and automation with reference to women's labour in India. The paper has been authored by Ambika Tandon and Aayush Rathi, edited by Elonnai Hickok and Rakhi Sehgal. Research assistance has been provided by Divya Kushwaha.


Studies around the future of work have predicted technological disruption across industries, leading to a shift in the nature and organisation of work, as well as the substitution of certain kinds of jobs and growth of others. This paper seeks to contextualise this disruption for women workers in India. The paper argues that two aspects of the structuring of the labour market will be pertinent in shaping the future of work: the gendered nature of skilling and skill classification, and occupational segregation along the lines of gender and caste. We will take the case study of the electronics manufacturing sector to flesh out these arguments further. Finally, we bring in a discussion on the platform economy, a key area of discussion under the future of work. We characterise it as both generating employment opportunities, particularly for women, due to the flexible nature of work, and retrenching traditional inequalities built into non-standard employment.


The question on the future of work across the global North - and parts of the global South - has recently been raised with regards to technological disruption, as a result of digitisation, and more recently, automation (Leurent et al., 2018). While the former has been successively replacing routine cognitive tasks, the latter, defined as the deployment of cyber-physical systems, will enable the replacement of manual tasks previously being performed using human labour (Leurent et al., 2018). In combination, these are expected to have a twofold effect on: the “structure of employment”, which includes occupational roles and nature of tasks, and “forms of work”, including interpersonal relationships and organization of work (Piasna and Drahokoupil, 2017). Building from historical evidence, the diffusion of digitising or automative technologies can be anticipated to take place differently across economic contexts, with different factors causing varied kinds of technological upgradation across the global North and South. Moreover, occupational analysis projects occupations in the latter to be at a significantly higher risk of being disrupted than the former (WTO, 2017). 

However, these concerns are somewhat offset by the barriers to technological adoption that exist in lower income countries such as lower wages, and a relatively higher share of non-routine manual jobs (WTO, 2017). 1 With the global North typically being early and quicker adopters of automation technologies, the differential technology levels in countries have been in fact been utilised to understand global inequality (Foster and Rosenzweig, 2010). Consequently, the labour-cost advantage that economies in the global South enjoy may be eroded, leading to what may be understood as re-shoring/back shoring - a reversal of offshoring (ILO, 2017). This may especially be the case in sectors where there has been a failure to capitalise on the labour-cost advantage by evolving supplier networks to complement assembly activities (such as in manufacturing) (Milington, 2017), or production of high-value services (such as in the services sector). 

Extensive work over the past three decades has been conducted on the effects of liberalisation and globalisation on employment for women in the global South. This has explored conditional empowerment and exploitation as women are increasingly employed in factories and offices, with different ways of reproducing and challenging patriarchal relations. However, the effects of reshoring and technological disruption have yet to be explored to any degree of granularity for this population, which arguably will be one of the first to face its effects. This can be seen as a consequence of industries that rely on low cost labour being impacted first by re-shoring, such as textile and apparel and electronics manufacturing (Kucera and Tejani, 2014).

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