Claudia Goldin, Nobel laureate and American labour economist, calls it ‘greedy’ work. This is work that requires long, inflexible working hours and pays disproportionately more on a per-hour basis. It includes the work of professionals such as doctors, lawyers and those in the finance industry.
For instance, the positions that women find hard to enter, such as those in finance, are precisely those that have seen the greatest increases in income in the last several decades.
The reason women are losing out is because they are spending a disproportionate amount of time on domestic and caregiving responsibilities such as childcare. Their ability to perform in jobs that demand ‘greedy work’ is constrained by their caregiving responsibilities.
For instance, 53 percent of women in India cite care responsibilities as reason for not participating in the labour force, while the corresponding figure for men stands at 1.1 percent.
This link between greedy work, economic opportunities for women and the gendered pay gaps has brought to the fore discussions around ‘women’s time’ and what they do, or rather, can do with it.
Working from, and for, home:
Women contribute a major chunk of household labour in the form of unpaid labour — they carry out at least two and a half times more unpaid household and care work than men.
But conventional national statistics of work and production ignore this, excluding two-thirds of work and economic production by women.
To account for household production, the United Nations’ statistical commission in 1993 suggested creating satellite accounts that are separate from — but consistent with — the main accounts of the market economy.
Satellite accounts help measure the size of economic sectors such as unpaid domestic and care work that are not classified as industries in national accounts.
The Platform for Action adopted by the Fourth World Conference on Women in Beijing, 1995 — considered the most progressive blueprint ever for advancing women’s rights — also demanded the full recognition of women’s economic production.
It called for several measures including improving data collection on unpaid labour in non-market production, and measuring and quantifying care work.
It is in this context — push for better statistics to understand the gendered nature of household labour — that Time Use Surveys gained prominence.
Time-use surveys are designed to collect quantitative summaries of how individuals spend or allocate their time over a specified period, typically over a day or a week.
They describe the daily life of a population in terms of what people do, how much time they spend on each activity, and the context of those activities.
These surveys have helped make gendered labour visible by quantifying it, and thus contributing to valuable inputs for policy and business decisions.
Data generated by these surveys has been used in the system of national accounts and labour statistics in some countries such as Ecuador, Japan, Mexico, Vietnam and Ghana.
Quality, not just quantity:
Despite their utility, these surveys have their own challenges, especially in the context of developing countries.
Both the quantity and quality of time are important, but these surveys do not adequately capture the qualitative aspects.
For instance, time spent on work that people enjoy differs from time spent on work they dislike — an hour of work doing a task you dread is not the same as an hour doing a task you love.
The survey may then miss the distinction between slow and fast workers and does not capture information on the quality and efficiency of work.
Social and cultural norms can also influence responses. For instance, men may answer questions on behalf of women, leading to an underreporting of their actual contributions when it comes to unpaid work.
Illiteracy and language barriers also impact both the quality of the data and the respondents’ ability to participate. Some respondents may struggle to understand the questions or may be unable to articulate their answers clearly.
Women face persistent pay gaps and economic inequalities due to the disproportionate share of unpaid work they do. Quantifying it through time-use surveys and using the data generated to inform policy decisions is one way to address this issue.
About authors: Manjula M is faculty at the Azim Premji University, Bengaluru, where she teaches courses in the broad domains of sustainability and livelihoods. Sangita Dutta Gupta is Professor, School of Management, and Assistant Dean, Research at BML Munjal University, Haryana. She also serves on the editorial review board of the International Journal of Emerging Markets.
This is an edited version. Originally published under Creative Commons by 360info™.
