Caring for household members. Well-being of the family. Putting food on the table. Washing clothes. Making ends meet. Looking after the young, the old, and differently abled family members. These are everyday activities performed by someone or some people for someone or some people. The questions to ask (but more often not asked) are, by whom and why?
For anyone to be able to engage in, or look for, work for monetary income a number of factors are at play: level of skill, level of education, availability of paid work, mobility, access to transport, access to raw materials, the demands of the labour market (agriculture, industry, services). Socio-cultural factors – the person’s age, marital status, number of dependents in the family, access to care services or support networks – are crucial enablers too. When at least a few of these factors combine, it is likely a person can find paid work.
We can look at the distribution of women and men in the national labour force for information on how the working population is captured through statistical data. Labour force participation data is key to State level employment policies, for welfare interventions such as poverty alleviation programmes, national budgetary allocations for elder care and care of persons with disabilities, and for development policy formulations. It is the primary source of sex disaggregated labour force data.
However, mainstream definitions in the national labour force surveys appear to be founded on prevailing norms on, and practices of, social reproduction which are in effect blind to gender based inequality and inequity.
The 2021 Labour Force Survey found that out of a “working age” population of 17.1 million, 7.9 million were males and 9.2 million women.[i] However, economically active women comprise only 2.9 million (31.8%) as against 5.6 million (71.0%) men in the labour force. The remaining 6.2 million (73.3%) women are categorised as “economically inactive”, involved in activities that have no economic value. Out of this number 59.4% are listed under the category “engaged in housework”. The corresponding figure for men in this category is 3.4%. By the sheer disparity of these numbers, it is apparent there are issues that need to be further examined.
Why is it that work done in the home around taking care of household members is not recognised for its economic value? What we take as accepted norms and practices of everyday life in a household or a family, are in fact deeply rooted in the fabric of gendered patriarchal social norms. The family is constructed as a heteronormative harmonious unit.[ii] From childhood, girls are socialised into internalising that their roles within the family revolve around cooking, taking care of children, looking after the elderly, etc. (Federici 1975). Boys are not socialised to take responsibility for such household care work. Instead, boys are raised to understand such work as being ‘women’s work’.
Definitions of ‘Work’ and ‘Labour’
The issue here is the definitions of ‘work’ and ‘labour’. Unpaid care work is not considered to have economic value, because it is presented to us as care work for the family. ‘Care’ is often conflated with notions of altruism or unselfishness and self-sacrifice rooted in the family and related to a sexual division of labour where women are ‘nurturing’ care givers. For a woman to be able to look for and take up wage or salaried work, she has to ensure that gendered ‘reproductive’ work, the work of social reproduction she is ‘responsible’ for, is managed. When this is not possible, she may not take on direct paid work. Then, in mainstream economics, she belongs outside the labour force, is economically inactive, “engaged in housework”, not contributing an income, and hence not productive. Socialisation of girls and women also means that women themselves accept their gendered roles uncritically as they see their work being carried out of love or duty or obligation.
WMC-SSA Time Use Survey[iii]
In 2019, the Women and Media Collective (WMC) with the Social Scientists’ Association (SSA) conducted a Time Use Survey (TUS) with 800 women and 120 men living across six districts in Sri Lanka in order to study unpaid care work.[iv] The women respondents were involved in agriculture, paddy farming, home gardening, and industries such as Free Trade Zones and tea plantations. They represented urban low-income families, internal migrants, survivors of the war, and returnee migrant workers.
The survey asked respondents to maintain a diary to capture the time used for a range of activities carried out by them in and around the home. We aimed at recording the simultaneous activities that women undertake as ‘care’ work.
The Time Use Survey found that the time spent on average per day for all unpaid housework, care work, and voluntary activities was 8.98 hours for men and 13.77 hours for women.[v] Women ‘allocated’ their time and labour for household activities for their respective families while ‘balancing’ their daily engagement with remunerative work, if any, as well as social or political activities.
Based on these findings, the researchers attempted to compute the value of unpaid care work that was captured by the survey. It was found that the value of unpaid work performed by the average person in Sri Lanka ranges from 30% to 100% of Gross Domestic Product per capita. In other words, if unpaid work were included in the accounting of the size of the national economy (as measured in GDP), it would at the very least increase by 30%, and at the top-end double itself.[vi] Valuing of unpaid care work will enhance the recognition of women’s economic contributions to households and to the national economy.
There needs to be accompanying acknowledgement of the impact of time poverty in women’s lives (Ranatunga 2020). Time poverty brings into focus the impact of financial constraints on families where women’s care work increases to the detriment of their physical and social wellbeing. Muchhala et al. note that “social reproduction buffers communities from the economic, social and physical effects of crises by taking on additional caring labour both paid and unpaid, inside and outside the household, including the informal sector” (Muchhala et al. 2022: 1). Mainstream development approaches to women’s labour force participation focus on increasing women’s labour force participation with less acknowledgement of the equally important re-assignment of gendered roles within households. Programmes for childcare arrangements are being advocated to ‘free’ women so that they can enter the labour force, for example, by having women from low-income households take up paid care work in middle class households. Yet, without an accompanying push to redistribute unpaid care work in the private and public sectors, patriarchal norms (and class dynamics) are left unchallenged. Women’s time poverty continues to chip away at their human rights; and gendered norms demand that women accept the multitude of domestic work and responsibilities with little or no question as to whether they are full time ‘housewives’ or ‘paid workers’.
Recognising, reducing, and redistributing unpaid care work is integral to challenging the discourses on social, economic, and political structures on, and for, gender equality and equity (Elson 2017). It is a pathway for women, and men, to understand better those factors such as love, family, duty, obligations framed within patriarchal ideology inherently take away, or at best curtail, women’s power in the private and public spheres. Women do not need to be told that they are the queens in the kitchen at home; when men are kings in the kitchens of the hotel industry – where there is recognition of skills and rewards both professionally and remuneratively. Unpaid care work must be reduced and redistributed in the public and private spheres for effective political power sharing, social cohesion, and for ensuring the human rights of women.
Sepali Kottegoda (DPhil, Sussex) is Director of Programmes on Women’s Economic Rights at the Women and Media Collective, Sri Lanka.
References
Budlender, Debbie. (2010). “What do Time Use Studies Tell Us about Unpaid Care Work? Evidence from Seven Countries”. In Debbie Budlender. (Ed.). Time Use Studies and Unpaid Care Work (1-54). New York: Routledge.
Department of Census and Statistics. (2006 and 2021). Sri Lanka Labour Force Participation Survey. Available at statistics.gov.lk/LabourForce/StaticalInformation/AnnualReports/2021
Department of Census and Statistics. (2020). Sri Lanka Time Use Survey. Final Report Available at statistics.gov.lk/PressReleases/TUS_FinalReport_2017
Elson, Diane. (2017). “Recognise, Reduce and Redistribute Unpaid Care Work: How to close the Gender Gap”. New Labour Forum, 26(2): 52-61.
Federici, S. (1975). Wages Against Housework. Berlin: Power of Women Collective and the Falling Wall Press.
Gunawardena, Dileni and A. Perera. (Forthcoming). Economic Value Assessment of the Women and Media Collective Study on Unpaid Care Work. Colombo: Women and Media Collective.
Muchhala, Bhumika, Vanessa Das Castello and Andrea Guillen. (2022). “Gendered Austerity in the Covid-19 era: A Survey of fiscal consolidation in Ecuador and Pakistan” (TWN Briefing Paper.) Available at twn.my/title2/briefing_papers/twn/Gendered%20austerity%20TWNBP%20Apr%202022%20Muchhala%20et%20al.pdf
Ranatunga, R. and Priyanga Dunusinghe. (2020). “Nature of Time Poverty in Sri Lanka”. Sri Lanka Journal of Economic Studies, 9(1). DOI: sljer.sljol.info/articles/abstract/10.4038/sljer.v9i1.155/
Women and Media Collective and the Social Scientists’ Association. (Forthcoming). Recognise, Reduce, and Redistribute Unpaid Care Work: Study of Six Districts in Sri Lanka. Colombo: WMC and SSA.
[i] Department of Census and Statistics. (2006 and 2021). Sri Lanka Labour Force Participation Survey. This survey of households is conducted through a scientifically selected sample designed to represent the civilian non-institutional population. Respondents are interviewed to obtain information about the employment status etc. of each member of the household of 15 years of age and over (82). From January 2013 onwards, the lower bound of working age populations is considered as age 15; hence age 15 and over population is considered as working age population (85). Available at statistics.gov.lk/LabourForce/StaticalInformation/AnnualReports/2021
Notes
[ii] However, this does not preclude non-heternormative households from being included in the Census of the Labour Force Survey.
[iii] See Budlender, Debbie. (2010). “What do Time Use Studies Tell Us about Unpaid Care Work? Evidence from Seven Countries”. In Debbie Budlender. (Ed.). Time Use Studies and Unpaid Care Work (1-54). New York: Routledge. Available at https://www.unrisd.org/en/library/publications/time-use-studies-and-unpaid-care-work/chapter-1-what-do-time-use-studies-tell-us-about-unpaid-care-work-evidence-from-seven-countries
[iv] Women and Media Collective and the Social Scientists’ Association. Recognise, Reduce, and Redistribute Unpaid Care Work: Study of Six Districts in Sri Lanka. Colombo. (Forthcoming).
[v] Gunawardena, Dileni and A. Perera. Economic Value Assessment of the Women and Media Collective Study on Unpaid Care Work. Colombo: Women and Media Collective (forthcoming). This was higher than the data captured by the Time Use Survey of the Department of Census and Statistics where it was 1.6 hours for men and 5.7 hours for women. This data is available at: Department of Census and Statistics. (2020). Sri Lanka Time Use Survey. Final Report <statistics.gov.lk/PressReleases/TUS_FinalReport_2017>
[vi] Gunawardena, D. and A. Perera. Economic Value Assessment of the Women and Media Collective Study on Unpaid Care Work. Colombo: Women and Media Collective (forthcoming). Gunawardena clarifies that “unpaid care work involves two main processes: (1) the measurement of unpaid care work through the careful collection of data on time spent by individuals in unpaid care (which in itself involves defining what constitutes care work, and identifying different types of care work) and (2) imputing value to the measures of time use. The first process requires time use data and the second requires wage data at the appropriate disaggregated level”.