A blurred mapping of internal female migration Premium

A change in narrative is required in looking at female migration, starting with an increased collection of female-specific data

Internal migration is a crucial form of physical and social transaction in India. The Periodic Labour Force Survey (PLFS), which collects data on employment and unemployment indicators in the country, has estimated it to be 27% from June 2020 to 2021. Normative literature (Rajan et al., 2020; Jesline et al., 2021) usually documents it as a male-dominated narrative. However, women, especially of working age, comprise a greater share of the migrant pool but there is little dialogue surrounding them. This is a concern given India’s falling Female Labour Force Participation Rate (FLFPR). It also raises the question of whether women face employment barriers due to post-migration conditions.

National surveys such as the PLFS capture information about female migrants but often convey an inaccurate picture. For instance, surveys only ask the respondents regarding their primary reason for migration. PLFS data suggest that the leading reason for migration among women is marriage (81%), followed by migration of family members (10%), employment (2.42%), and migration for education opportunities (0.48%). There is no provision to know the secondary reasons/motivations such as climate shocks and food insecurity, which can be a crucial driver of migration for women.

In the same vein, data from these surveys regarding migrant women’s labour force participation can be misinforming. According to the PLFS, approximately three quarters of migrant women are unemployed, approximately 14% of migrant women are in self and wage-employed jobs and approximately 12% are in casual labour. This round of data collection was during the COVID-19 pandemic, which might explain the low numbers, but does not adequately underscore the problem of underreporting of their employment status. Anecdotal evidence and findings from the respective works of researchers Sonalde Desai and Ashwini Deshpande suggest that it is not uncommon for migrant women to engage in casual employment, indicating the underestimation of the number of migrant women involved in the various sectors that might be categorised as causal (or even informal) such as agriculture, construction, and domestic help.

Second, definitional issues and women’s own beliefs also lead to an underreporting of employment of migrant women. According to the definition of employment used by national surveys, only those with some form of verbal or written contract with their employer are considered part of the labour force. Consequently, women are largely classified as unemployed.

However, what is often overlooked is that women choose forms of employment that allow them to handle their domestic duties while contributing to the household’s production or finances. Thus, working as unpaid family workers, in household enterprises, or being self-employed is common amongst them (S. Rukmini, 2023). But they may view that as an extension of their domestic commitment instead of a form of employment which leads to them misreporting their employment status.

Notwithstanding these arguments, if indeed entry to the formal labour force is challenging, one important factor restricting their entry into the labour force could be the need for more human and social capital. In the PLFS data, 85% of the women have less than 10 years of education, which can create problems. While there is no significant difference in the educational levels of migrant and non-migrant women, migrant women are proportionally less employed than the non-migrant women. Coupled with the lack of social networks, especially after they migrate, these factors can significantly hinder their employment chances.

Such barriers might also explain the dismal recovery of women’s labour activity after the pandemic. A study by Yale University on this issue observed that after the COVID-19-induced lockdown, 55% of women never returned to their places of employment, and those who did so, earned only 56% of their pre-pandemic income levels.