Productivity & Innovation

Feminisation of India’s industrial workforce

  • Blog Post Date 22 January, 2025
  • Perspectives
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Suresh Chand Aggarwal

Institute of Human Development

sureshchag@yahoo.com

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Bishwanath Goldar

Institute of Economic Growth

b_goldar77@yahoo.com

In recent years, the average annual growth rate in manufacturing employment has exceeded that of aggregate employment in India. In this post, Goldar and Aggarwal demonstrate that this trend is accompanied by an increase in the share of women in the industrial workforce – largely driven by a rise in the proportion of women-owned manufacturing enterprises. Their findings also highlight the need for measures to enhance productivity of women-owned units.

Between 1993-94 and 2019-20, the annual growth rate in employment in Indian manufacturing was about 2.2% (in comparison, that of aggregate jobs was about 1.4% per year).1  In the years 2020-21 through 2023-24, the average annual growth rate in manufacturing employment was about 5.5% (Goldar and Aggarwal 2024), exceeding the aggregate employment growth rate of 4.3%.

The growth rate in manufacturing employment in different states in recent years differed significantly (Figure 1). Some states recorded a growth rate of about 10% per annum or above (Rajasthan, Jharkhand, Madhya Pradesh, and Bihar), whereas some other states recorded negligible growth in manufacturing employment or even a fall (for example, Tamil Nadu). The inter-state pattern of manufacturing employment growth is positively correlated with the increase in rural female workforce participation rate (RFWPR) (Figure 2).2  This is expected because of the significant rise in RFWPR between 2017-18 and 2023-24, resulting in an increase in rural female workers by about 80 million, 9% of whom took up manufacturing sector jobs (Goldar and Aggarwal 2024).

Figure 1. Manufacturing employment growth rate, 2018-19 to 2023-24 (% per year) 

Source: Authors’ computations based on PLFS data.

Figure 2. Change in RFWPR and growth rate in manufacturing employment

Source: Authors’ computations based on PLFS data.

Feminisation of the industrial workforce 

Between 1999-2000 and 2011-12, female share in incremental manufacturing employment was 22%. Between 2017-18 and 2023-24, the female share in incremental manufacturing employment was 74%, of which rural females accounted for 46 percentage points. This pattern seen in the growth in manufacturing employment in recent times is a manifestation of the feminisation of industrial labour in India.

The share of females in incremental manufacturing employment differed among states (Figure 3). Gujarat, Maharashtra and Rajasthan accounted for about 45% of the increase in aggregate manufacturing employment between 2018-19 and 2023-24. The female share in incremental manufacturing employment in these states was relatively small (an average of about 36%). On the other hand, the female share in incremental manufacturing employment was about 90% in Bihar and 85% in Assam. In several states, the increase in female employment in manufacturing exceeded that in total employment because the number of male manufacturing workers declined. In Kerala, for example, total employment in manufacturing increased by 30,000 between 2018-19 and 2023-24, whereas female employment increased by 65,000. Such a pattern was also found in Karnataka and Andhra Pradesh. Evidently, the feminisation of industrial labour in India was geographically segmented.

Figure 3. Aggregate increase in manufacturing employment and the share of females in incremental manufacturing employment, by state

Note: Only those states in which manufacturing employment increased by 500,000 or more between 2018-19 and 2023-24 are considered.

Source: Authors’ computations based on PLFS data.

A deeper analysis reveals that the states in which organised or formal manufacturing has a relatively high share (for example, Haryana) did not experience any significant feminisation of industrial labour (Figure 4). On the other hand, the states in which the share of the formal sector in manufacturing employment is low (such as Assam, Jharkhand, West Bengal and Bihar) there was significant feminisation of industrial labour. The negative correlation between the share of the formal segment in manufacturing employment in 2022-23 and the share of females in incremental manufacturing employment between 2018-19 and 2023-24 is attributable mostly to the fact that the feminisation of the industrial workforce in recent years has taken place in the informal sector of manufacturing but not in the formal sector. The relative share of male workers in manufacturing enterprises having ten workers or more increased from 37.5% to 47% between 2018-19 and 2023-24 (Figure 5), whereas the corresponding share for female workers has remained at about 20% (further, Annual Survey of Industries (ASI), which covers organised manufacturing, shows that the share of females among total workers employed directly has not increased).   

Figure 4. Size of formal segment and the share of females in incremental manufacturing employment, by state

Source: Authors’ computations based on PLFS data.

Figure 5. Proportion of workers working in manufacturing enterprises with over 10 workers, by gender

Source: Authors’ computations based on PLFS data.

Increase in female-owned manufacturing enterprises

A related dimension of the feminisation of the industrial workforce in India is the increase in the proportion of manufacturing enterprises that females own. According to data from the Annual Survey of Unincorporated Sector Enterprises (ASUSE) for 2022-23, about 55% of proprietary manufacturing enterprises were owned by females in that year, up from about 45% in 2015-16 (National Sample Survey 73rd Round). Since the employment of female workers is relatively higher in female-owned manufacturing enterprises than in male-owned manufacturing enterprises, this boosts female employment in manufacturing.   

At the all-India level, the proportion of female-owned manufacturing enterprises increased by about 10 percentage points between 2015-16 and 2022-22. However, the increase differed from state to state and was much greater in some states, such as Bihar (Figure 6).3 The states where there was a substantial increase in the proportion of female-owned manufacturing enterprises include Bihar, Madhya Pradesh, Uttar Pradesh, Himachal Pradesh and Assam. This is highly correlated with the increase in female employment share. Further, the pattern seen in Figure 6 matches the pattern seen in Figure 1 to some extent. Arguably, the increase in the number of female-owned manufacturing enterprises and in the proportion of female-owned manufacturing enterprises, is a major factor behind the feminisation of the industrial workforce in India. 

Figure 6. Increase in the proportion of female-owned manufacturing enterprises and female share in informal manufacturing employment, by state (2015-16 to 2022-23) 

Source: Authors’ computations based on ASUSE, 2022-23, and NSS 73rd Round.

A concern and the possible remedial action

The increase in the proportion of female-owned manufacturing enterprises has led to increased employment of female workers in manufacturing, which is a welcome development. However, there is a concern. A typical female-owned informal manufacturing unit is about half the size of a male-owned unit, and the gross value added (GVA) per worker is about one-third. The implication is that the increase in the proportion of female-owned units may lead to productivity loss.

We conduct an econometric analysis of the determinants of labour productivity in female-owned informal sector manufacturing enterprises based on unit-level data from ASUSE 2022-23 (Goldar and Aggarwal 2025). The results show that enterprise size positively impacts productivity, pointing to the need for raising the size of female-owned enterprises. Access to finance could be a major hurdle. Gupta et al. (2024) underscore the need for business development services to foster women’s entrepreneurship and recommend that, in priority-sector lending, a further sub-segment of the microenterprises category should focus on women-owned growth-oriented enterprises to encourage commercial banks to lend to this segment.

We also find that the level of education of the entrepreneurs and workers positively impacts the productivity of female-owned manufacturing enterprises, and that productivity in an enterprise will be higher if the enterprise uses the internet and there is sufficient ICT (information and communication technology) capability among the adult population of the state where the enterprise is located. Kapoor (2024) notes that there is significant scope to leverage the digital advancements made by India to address the challenges women entrepreneurs face in accessing finance, skills, networks and markets, thereby unshackling the potential of women entrepreneurship. Thus, to enhance the productivity of female-owned manufacturing enterprises, there is a need for investment in digital infrastructure, and policies to enhance digital literacy and augment access to mobile phones among adult females in rural areas, as well as raising the level of general education and technical training of entrepreneurs and the working-age rural female population. 

Based on their analysis of the barriers to labour force participation and entrepreneurship faced by Indian women, Chiplunkar and Goldberg (2024) conclude that policies focused exclusively on increasing female labour force participation may have unintended adverse effects on female wages and profits of female entrepreneurs. Hence, such policies must be complemented with measures supporting female entrepreneurship, resulting in larger benefits for women. They also note that interventions to support female entrepreneurship will be more effective if they target supporting existing female-owned enterprises rather than encouraging the entry of new female entrepreneurs.

The views expressed in this post are solely those of the authors, and do not necessarily reflect those of their organisations or of the I4I Editorial Board.

Notes:

  1. The growth rate in aggregate employment is based on India KLEMS data which uses PLFS (Periodic Labour Force Survey) data for making employment estimates. Computation of manufacturing employment growth rate is based on Goldar (2024) and PLFS data for 2019-20 to 2023-24.
  2. This is also connected with reverse (urban-to-rural) migration (Aggarwal and Goldar 2024).
  3. For a discussion on the performance of Bihar vis-à-vis other states, see Aggarwal and Goldar (2024).

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