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National Institute of Public Finance and Policy NIPFP One pager
No. 11

May, 2014

Integrating Time Use in Public Policy

LEKHA CHAKRABORTY, Professor, National Institute of Public Finance and Policy, New Delhi, India.

Integrating time in macro policy making is a difficult area of research. Data paucity is often cited as the major constraint. What we cannot measure and price, remains invisible for meaningful public policy making.


Such sectors are often regarded as “hard-to-price” sectors and absent from Systems of National Accounts (SNA) till 1993. Time use statistics presents a solution to correct this flaw, especially by providing labour force participation estimates for “invisible” economic activities. Chakraborty (2013), the paper on which this One Pager is based, examines the role of time as a significant element in gender-specific public expenditure analysis.


Public budgeting processes often fail to visualise or account for the gender differential impacts of expenditures. Innovative strategies adopted recently strengthen the “gender lens” of expenditure decisions and show how gender differential outcomes of fiscal policy are measured. Gender responsive budgeting (GRB), a recent policy tool for accountability to engage in such critical areas of public spending effectiveness, is a fiscal innovation that translates gender commitments to budgetary commitments. It is not an approach either to earmark functional finance specifically for women, nor target outcomes for women. It is rather analysing the budget in its entirety through a ‘gender lens’.


Government budgets are constructs of a specific sociocultural context. Prima facie the budget may appear to be gender neutral. However the asymmetry in the socially determined economic activity often results in unintended gender-equity outcomes. GRB seeks to rectify this asymmetry by unpacking the ‘social’ content of the macro policies and getting the policies right in terms of human development goals. Three components are core to this process: gender diagnosis, an analysis of contribution of women to the market economy and care economy and an analysis of the gender differential outcomes of public expenditure. Time use statistics play a significant role in each of these components of GRB.


GRB is often conducted across countries on a highly restricted assumption that ‘all public expenditure cannot be gender partitioned’. This assumption is highly controversial. Chakraborty (2013) argues that gender disaggregated public expenditure benefit incidence studies reveal distinct gender specific outcomes of public spending across quintiles. Analysis of mainstream expenditure for its gender differential impacts (other than education and health) is scarce due to unavailability of ‘unit utilised’ data. Time use data is helpful for benefit incidence especially for water and energy budgets.


Gender diagnosis forms the basic premise for any gender budgeting policy. The Gender Development Index or Gender Inequality Index, the popular measures to capture gender specific outcomes at national level remain partial as it has not incorporated time use statistics for the unpaid care and non-market work done by women and men.


The quantification of the statistical invisibility of women’s unpaid work is quite challenging. Time use statistics capture the actual time spent by individuals on paid work, unpaid work and personal care. 


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The ‘unaccounted work’ in the care economy is valued by imputing a unit price or unit wage to the household non- market time. The imputed wage may be a global substitute of wages in the wage hierarchy in markets, specialised wages or opportunity cost of wages.


Time use has significant policy implications. For instance, Bredie and Beehary (1998) revealed that in Madagascar, 83 per cent of the girls who did not go to school spent their time collecting water. Ilahi and Grimard (2000) and Chakraborty (2013) indicated that worsening water gathering infrastructure caused an increase in the total work burden of women respectively in Pakistan and India. Chakraborty (2008) revealed that public investment policy can redress intrahousehold inequalities, in terms of labour-supply decisions, by supporting initiatives that reduce the allocation of time in nonmarket work.


The Centre for Statistical Organization (CSO) has conducted the first macro level time use survey in India, covering 77,593 persons in 18,591 households in Gujarat, Tamil Nadu, Madhya Pradesh, Meghalaya, Orissa, and Haryana. However, this unit record data is yet to be explored to its full potential by researchers, and in turn the inferences derived for public policy making remain partial.


Findings and actions


  1. Integrating time use survey in statistical system of India and estimating time poverty could be the plausible first step.
  2. Time poverty affects income poverty. This suggests that mere employment policies cannot stem poverty. It needs to be coupled with policies related to releasing the time stress of poor and make them available for work.
  3. Judiciously designed employment policies taking into account of ‘care infrastructure’ and the pay packages sensitive to ‘care economy policies’ can lead to inclusive growth and gender sensitive human development.
  4. Poverty-inducing effects of time deficits are substantial, if someone is unemployed because of household’s inability to meet the basic needs like water and fuel. Infrastructure investment can reduce time deficit.



Bredie, J., and G. Beehary, 1998. School Enrolment Decline in Sub-Saharan Africa. World Bank Discussion Paper No. 395. The World Bank.


Chakraborty, Lekha, 2008. Deficient Public Infrastructure and Private Costs: Evidence from a Time-Use Survey for the Water Sector in India, Working Paper 536, The Levy Economics Institute, New York. 


Chakraborty, Lekha, 2014. Integrating Time in Public Policy, Working Paper 127, NIPFP, New Delhi (October).


Ilahi, Nadeem, and Franque Grimard. 2000. “Public Infrastructure and Private Costs: Water Supply and Time Allocation of Women in Rural Pakistan.” Economic Development and Cultural Change 49(1); 45–76.


Government of India, 2000. Time Use Survey, Centre for Statistical Organization, New Delhi.

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