According to the recently released Global Gender Gap Report 2022, India ranks 135 out of 146 countries and it has slightly improved its position in the overall ranking compared to the last year. However, India is one of the worst performers on gender equality in South Asia as only Iran, Pakistan and Afghanistan perform worse in the region. This is largely due to the lowest gender parity in health and survival, poor representation of women in politics and a low labour force participation rate of women. Understanding women’s role in the workforce is critical to promote gender equality and realise economic growth in India. But the data to aid this understanding is missing, incomplete or inadequate.

In India, information about labour is collected and compiled by several agencies. The Census collects data every ten years from all Indians, while the National Sample Survey Office (NSSO) collects it every five years from a large sample of households covering a wide range of variables. Considering the importance of labour force data, from 2017, the Government of India launched an annual statistics series called the Periodic Labour Force Survey (PLFS). But women’s work is underreported in all these surveys. Women may not necessarily participate in the formal labour market, meaning their contribution to the household economy, and economic activity more broadly, remains invisible.

The most recent PLFS data showed that more than half of the women were engaged in different unpaid domestic activities and most of them were involved in household chores (such as cooking, cleaning, caring for the children and elderly) along with a decrease in uncounted activities like collection of vegetables, firewood, cattle feed, and sewing, tailoring, weaving. The previous NSSO reported significant engagement of women these kinds of activity, so the disparity needs a more detailed analysis. Perhaps the women had improved access to infrastructure like drinking water and fuel (as the government’s Ujjwala scheme had intended). But without more detail, any conclusions remain difficult.

To capture women’s work, the Central Statistical Office undertook the first national Time Use Survey (TUS) during January-December 2019. This survey interviewed participants about their recent activities and asked respondents to assess the amount of time spent at each. The TUS highlighted the inadequacy of conventional employment and unemployment surveys and the Census in measuring women’s unpaid work. And while concerns regarding the methodology were raised, it was described as the best available method in a country such as India with a low literacy level.

In India, work has been increasingly informal in nature. Given the huge size of the informal sector, it is important to collect data on the conditions of such work (for example paid leave and access to job contracts) but data is only available at the state and national level. Data should also be collected on the proportion of women workers who need social security benefits and those who are getting them. There is no data on the number of women workers who received training and were promoted to a higher position in regular employment, and the number of women cultivators and agricultural labourers who received agricultural machinery and agricultural extension training.

Similarly, there is no information on the number of cases registered against employers paying lower than minimum wages to women workers; percentage of women workers with ‘decent’ work conditions; the shortfall in access to working women’s hostels; creche facilities available at the workplace; and whether lactating mothers are allowed breaks to feed their children during work hours.

The Annual Survey of Industries collects data on the organised manufacturing sector, but it provides gender-disaggregated data only for directly employed workers. There is no gender-disaggregated data for contract workers or their wages. Given the large increase in the proportion of contractual workers, and a sizable proportion of women among contract workers, gender-disaggregated data on India’s factory sector is required to understand the composition and characteristics of the workforce.

Migration for employment is another important aspect of economic empowerment. The inability of the official data to delineate the scope, scale and patterns of female labour migration has been central to making women invisible. In India, the Census and NSSO are the two official data sources on migration. However, they provide figures for long-term migration (migration for more than six months) and capture only one reason for migration. Usually, respondents give a social reason — marriage, migration with parents — as the primary reason for migration, which means that even if a woman also migrates for economic reasons, it is not captured. Also, the surveys do not differentiate between circular and seasonal migration and commuting for work, which is more common among women than long-distance, long-term migration.

Ownership of assets (land, housing and livestock) is an indicator of the status and power of an individual in a household but there is no gender-disaggregated data on asset ownership. Similarly, the gender dimension of access to basic amenities is often ignored in the official statistics. For all the data that is collected, the unit of analysis is the household, and often the only gender disaggregation is in terms of the sex of the head of household. The NSSO, National Family Health Survey and the Census collect information on whether a household has access to a latrine (owned/shared) but there is no information in any of these surveys on whether women use the latrine facility and whether they have access to it throughout their life. This is important as India is currently focusing on toilet building, but ensuring its use is not considered.

There is no data on whether people are also defecating openly despite having a latrine at home. Further, there is no information available on workplace amenities. In short, data on individual access to water and actual toilet use are two basic amenities that are particularly relevant to women’s lives and data on these two variables is absent. Surveys could also focus on individual access to these facilities.

India’s decline in women’s labour force participation could be due to social or economic factors influencing demand and supply. However, the available data does not allow analysis of the factors that lead occupations to becoming being segregated by gender and the ensuing wage discrimination. Information on hiring practices would help understand such disparities and formulate policies to ensure the presence of women in non-traditional occupations.

For a better understanding and analysis of women’s empowerment in India, adequate and good quality data is required. The TUS attempted to fill some of the gaps. Adding this survey method to forthcoming labour force surveys, or an independent TUS, would help to fill in missing data. The many reasons for migration, data collection on ownership, management of assets and business at the individual level instead of the household level are also recommended for women’s empowerment and gender equality.

(The writer is Assistant Professor, Amity Law School, Amity University, Noida.)