Women’s Employment and Digital Technology: A Regional Analysis in India

Abstract

The article explores women’s employment and the future of work due to the changing nature of jobs as a result of the onslaught of new technologies. Adoption of new digital technologies, industry 4.0 technologies and the increasing influence of platform or gig economy has had intense effects on the ‘future of work’, causing dramatic changes. Further, COVID-19 has severely impacted the economy, especially women, reflected in the consistent fall in female labour force participation across states. The unemployment rate (UR) is significantly higher among urban women. A large proportion of woman workers are vulnerable from automation because of their low-skill and the unskilled and routine nature of their work. The risk of automation is much higher in the case of women working in manufacturing and modern services. Women in India are engaged largely in traditional jobs with low level of education and skill, and having limited or negligible social security, particularly in the unorganised sector. The article is based on the secondary data provided by the National Sample Survey Organisation (NSSO) and uses the International Labour Organization (ILO) skill framework. The analysis clearly shows that skill sets among women is abysmally low. Noticeably, enhancing skill development as per the emerging market demand, including digital literacy, will go a long way to expand job opportunities for women.

A Systematic Review Discussing the Sustainability of Men and Women’s Work in Industry 4.0: Are Technologies Gender-Neutral?

“…Mehta et al [49] also used secondary national data (in this case, from the Indian National classification of occupations and from the Periodic Labour Force Survey, collected from 2018 to 2019) to predict employment prospects. Nevertheless, while Ramos et al [53] used gender as one of many mere sociodemographic factors, Mehta et al [49] specifically focused on how I4.0 s predicted impacts could be different for men and women.…”

“…Mehta et al [49] also used secondary national data (in this case, from the Indian National classification of occupations and from the Periodic Labour Force Survey, collected from 2018 to 2019) to predict employment prospects. Nevertheless, while Ramos et al [53] used gender as one of many mere sociodemographic factors, Mehta et al [49] specifically focused on how I4.0 s predicted impacts could be different for men and women. Their idea was to explore women's employment in the context of new I4.0 technologies that were being introduced into India by focusing on the labor distribution according to sector (e.g., service, agriculture), territory (rural and urban), skill level (unskilled, low-skilled, medium-skilled and high-skilled), and type of tasks (e.g., physical routine tasks) [49].…”

“…The findings of both of these studies in terms of the job loss for men and women were distinct and were also according to country-level specificities and the type of data considered. Mehta et al [49] found differentiated risks that could lead to an increased polarization between jobs in terms of skill levels and gender. That is, taking into account the large proportion of women in low-skill and unskilled jobs working in the manufacturing and service sector, by developing the so-called 'routine' tasks, the risk of automation seemed to be much higher for the case of women [49].…”

“…Mehta et al [49] found differentiated risks that could lead to an increased polarization between jobs in terms of skill levels and gender. That is, taking into account the large proportion of women in low-skill and unskilled jobs working in the manufacturing and service sector, by developing the so-called 'routine' tasks, the risk of automation seemed to be much higher for the case of women [49]. Ramos et al [53], however, did not find a negative relationship between the likeliness of automation and having a lower educational level, as commonly found in the literature, e.g., [57], nor did they find that women were more exposed to this risk.…”

“…However, the results did have some variations. For instance, some studies mention the service sector as more highly prone to automation, e.g., [49], and others find the opposite, e.g., [46]; alternatively, lower wages were flagged as an obstacle to automation, but women with lower wages were still at higher risk of being automated, e.g., [44]. This is also a consequence in part to the limited data regarding the long-term and large-scale implementations of I4.0 technology, which is also a consequence of the novelty of these technologies and of the fact that they are still under development, e.g., [66].…”