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Gender, Time Use, and Poverty
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Edited by C. Mark Blackden and Quentin Wodon
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W O R L D
B A N K
W O R K I N G
Gender, Time Use,
and Poverty
in Sub-Saharan Africa
Edited by C. Mark Blackden and Quentin Wodon
THE WORLD BANK
Washington, D.C.
P A P E R
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Copyright © 2006
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ISBN-10: 0-8213-6561-4
eISBN: 0-8213-6562-2
ISSN: 1726-5878
ISBN-13: 978-0-8213-6561-8
DOI: 10.1596/978-0-8213-6561-8
Cover Photo by C. Mark Blackden. Batik from Burkina Faso, c. 1996. Artist unknown, presented
as a gift to Callisto Madavo.
C. Mark Blackden and Quentin Wodon are Lead Specialists in the Office of the Sector Director,
Poverty Reduction and Economic Management, Africa Region of the World Bank.
Library of Congress Cataloging-in-Publication Data has been requested.
Contents
Foreword
ix
Abstract
xi
xiii
Acknowledgments
1
Gender, Time Use, and Poverty: Introduction
1
Time Use and Africa’s Development
1
Brief Overview of the Contributions in This Volume
5
What Next? Some Areas for Further Research
7
Part I: Reviews of the Literature
2
3
Gender and Time Poverty in Sub-Saharan Africa
13
Conceptual Framework Linking Time Use and Poverty
14
Gender and Time Use Patterns in Sub-Saharan Africa
17
Gender, Time Use, and Agriculture
18
Household Fuel and Water Provisioning
19
Care and Domestic Work
19
HIV-AIDS Epidemic and the Burden of Care
20
Challenges to Reducing Women’s Time Poverty
21
Methodologies Used in Time Use Surveys
24
Conclusions and Recommendations
26
A Review of Empirical Evidence on Time Use in Africa
from UN-Sponsored Surveys
39
Definitions of Work in the System of National Accounts
40
Measurement of Work in Time Use Surveys in Sub-Saharan Africa
44
Time Use Patterns and Key Development Variables
63
Measuring and Analysing Time Poverty
66
Part II: Measuring Time Poverty
4
Measuring Time Poverty and Analyzing Its Determinants:
Concepts and Application to Guinea
75
Analytical Framework
78
Data and Results
81
Time Use Statistics
81
iii
iv
5
Contents
Time Poverty
87
Correlates of Time Poverty
87
Conclusion
91
Labor Shortages Despite Underemployment? Seasonality in Time
Use in Malawi
97
Data and Empirical Results
101
Conclusion
114
Part III: Time Use and Development Outcomes
6
7
Poverty Reduction from Full Employment: A Time Use Approach
119
Data
121
Analytical Framework
122
Results
125
Impact on Consumption
125
Impact on Poverty and Inequality
128
Conclusions
131
Assessing the Welfare of Orphans in Rwanda: Poverty, Work,
Schooling, and Health
135
Number of Orphans and Qualitative Findings
138
Number of Orphans
138
Qualitative Evidence on Living Conditions
140
Living Conditions of Orphans: Quantitative Empirical Results
141
Household Consumption
141
Education and Child Labor
143
Nutrition
145
Conclusion
145
LIST OF TABLES
2.1 Time Devoted to Economic Activity and to Work, By Gender in Benin
(1998), South Africa (2000), Madagascar (2001), and Mauritius (2003)
17
2.2 Average Time Spent in Agricultural Activities, By Gender in Burkina Faso,
Kenya, Nigeria, and Zambia
18
2.A1 Inventory and Design Components of All Cross Section and Panel Time
Use Data Sources in Sub-Saharan African Countries
33
2.A2 Matrix of Empirical Studies with Data Sources, Methodology,
and Outcomes
35
2.A3 Select Methodologies of Time Use Data Collection
37
Contents
v
3.1 Characteristics of the Time Use Surveys in Five Sub-Saharan
African Countries
46
3.2 Time Devoted per Day to Economic Activity and to Work
by Gender in Various Countries
47
3.3 Classifications of SNA Non-Market Activities Used
in South Africa, Benin and Madagascar
48
3.4 Time Spent per Day on SNA Non-Market Activities in Three Countries
49
3.5 Time Spent per Day in SNA Non-Market Activities in Three Countries
as a Share of Total SNA Production
49
3.6 Time Spent by Women in Food Crops Work and by Men in Food Crop
and Export Crops, Center-South Cameroon, 1976
50
3.7 Time Spent on Fetching Water and Collecting Firewood
by Women and Men
53
3.8 Time Spent on Fetching Water and Collecting Firewood by Women
and Men Engaged in the Activity
53
3.9 Time Spent Per Day on Fetching Water and Collecting Firewood by Girls
and Boys Aged 6 to 14 (Benin and Madagascar) or 7 to 14 (Ghana)
54
3.10 Trends in Number of Persons Involved and Time Spent per Day in Water
and Wood Fetching in Ghana, 1991–92 and 1998–99
55
3.11 Classifications of Non-SNA Activities Used in South Africa, Benin,
Madagascar and Ghana
57
3.12 Time Spent on Non-SNA Productive Activities in Five African Countries
59
3.13 Comparisons of Daily Time Use for Women and Men in Four
Sub-Saharan African Countries
61
3.14 Time Use among the Yassa of Campo (Southwest Cameroon) in 1984
62
3.15 Time Use among the Mvae of Campo (Southwest Cameroon) in 1984
63
3.16 Comparisons of Daily Time Use for Women and Men in Four
Sub-Saharan African Countries
64
3.17 Time Use among the Yassa of Campo (Southwest Cameroon) in 1984
65
3.A1 Time Use Patterns for Household Members from 6 to 65 Years Old
in Urban Areas by Province (Faritany), Sex, and Activity
69
3.A2 Time Use Patterns for Household Members from 6 to 65 Years Old
in Rural Areas by Province (Faritany), Sex, and Activity
71
4.1 Average Number of Weekly Hours Spent for Various Activities,
by Sex and Age
83
4.2 Selected Values in the Cumulative Distribution of Working Time
for Various Groups
85
4.3 Time Poverty Rates
86
4.4 Time Poverty Gap and Squared Time Poverty Gap
87
4.5 Probit Regression for the Probability of Being Time Poor
88
vi
Contents
4.A1 Number of Weekly Hours Spent for Various Activities, by Sex,
Time Spent Collecting Water, and Urban/Rural Area
94
4.A2 Number of Weekly Hours Spent for Various Activities, by Sex,
Time Spent Collecting Wood, and Urban/Rural Area
95
5.1 Seasonality in Cropping Activities, Kasungu, Northern Malawi
99
5.2 Seasonality in Cropping Activities, Zomba, Southern Malawi
100
5.3 Total Time Spent Working by Area and Consumption Quintile,
National Sample
104
5.4 Total Time Spent Working by Area and Consumption Quintile,
Rural Areas
105
5.5 Work Time by Gender, Month, and Age According to the Categories
of Time Recorded in the Survey, Malawi–National, 2004
106
5.6 Work Time by Gender, Month, and Age According to the Categories
of Time Recorded in the Survey, Malawi–National, 2004
108
5.7 Work Time by Gender, Month, and Age According to the Categories
of Time Recorded in the Survey, Malawi–National, 2004
110
5.8 Work Time by Gender, Month, and Age According to the Categories
of Time Recorded in the Survey, Malawi–National, 2004
112
6.1 Working Time per Week, Adult Population by Consumption Quintile
and Location
123
6.2 Average Increase in per Capita Consumption Following an Increase
in Individual Working Time, by Quintiles of per Capita Consumption
126
6.3 Results of Decomposition for Full Sample
126
6.4 Contribution of Men and Women to Average Increase in per Capita
Consumption, by Quintiles of per Capita Consumption
127
6.5 Results of Decomposition for Men and Women
128
6.6 Average Increase in per Capita Consumption Following
an Increase in Individual Working Time, by Quintiles
of Current per Capita Consumption
128
6.7 Results of the Decomposition for Full Sample
129
6.8 Contribution of Men and Women to the Average Increase in per Capita
Consumption, by Quintiles of Current per Capita Consumption
129
6.9 Results of Decomposition for Men and Women
130
6.10 Increase in Average Consumption and Changes in Poverty Rate
and Inequality Following an Increase in Individual Working
Time Under Various Hypotheses
130
6.A1 Wage Regressions, by Gender
133
7.1 Incidence of Orphanhood by Age, Area, and Poverty status,
Rwanda 2000–01
139
7.2 Selected Characteristics of Households with and Without Orphans
Rwanda 2000–01
142
Contents
vii
7.3 School Enrollment and Child Labor for Children Aged 7–15,
Rwanda 2000–01
144
7.4 Determinants of School Enrollment among Children Aged 7–15,
Rwanda 2000–01
146
7.5 Selected Health Indicators for Children Below 5 Years of Age,
Rwanda 2000–01
149
LIST OF FIGURES
2.1 A Framework for Analyzing Time Use and Time Poverty
15
3.1 To What Extent Do the Notions of Market/Non-Market Work, Paid/Unpaid
Work, and SNA/Non-SNA Work Overlap?
43
4.1 Distribution of Individual Working Time by Sex and Area
82
5.1 Distribution of Individual Working Time by Sex and Area
103
5.2 Seasonality of Labor Hours among Rural Households by Land Holdings
115
7.1 Impacts of Parental Loss
137
LIST OF BOXES
2.1 Time Can be Saved through Better Infrastructure: Examples
from Uganda and Zambia
22
2.2 Diesel-powered Multifunctional Platforms Reduce the Burdens
on Women in Mali
23
2.3 Manual Versus Mechanized Food Processing
23
2.4 Women’s Adoption of Appropriate Food Processing Technologies
in Tanzania
24
2.5 Valuing Unpaid Non-SNA Work
26
2.6 Making Extension Services Tailored to Women’s Needs
27
2.7 Using Time Use Data in Project Evaluations
28
Foreword
ender, Time Use, and Poverty in Sub-Saharan Africa sheds light on a critical dimension
of poverty in Sub-Saharan Africa: time poverty. Although the concept of time poverty
has been used in the development literature, it is not always clear what is meant by time
poverty, how it can be measured, what impact it has on other areas, and what actions are
most effective in addressing it. This volume tackles these questions by exploring the concept of time poverty, reviewing existing studies on time use in Africa, developing tools and
approaches for analyzing time use and time poverty, and assessing the impact of time use
and time poverty on other development indicators. The insights provided in the various
papers included in this volume show that a better understanding of time poverty is required
to inform poverty diagnostics, national poverty reduction strategies, and the design and
implementation of development interventions.
As argued by the editors of the volume in their introduction, the lack of data on time
use and the omission of the household economy from conventional development planning
mean that the picture of the development process is incomplete and our understanding of
the labor supply of households is insufficient—much of what we are (or should be) concerned with occurs in an invisible realm. There is therefore a tendency to make misleading
assumptions about labor availability and labor mobility. Overlooking the differences in
men’s and women’s contributions to “household time overhead” can lead to inappropriate
policies which have the unintended effect of raising women’s labor burdens while sometimes lowering those of men. Furthermore, as a community of policymakers and development practitioners, we often do not invest in (or prioritize) what is not visible: so if the
household economy is not visible to policymakers and planners, they are unlikely to prioritize investment in it. This means that we do not recognize the tradeoffs or positive links
among different tasks and activities, and, by extension, do not focus on reducing or minimizing the tradeoffs and on building on the positive linkages.
The papers in this volume outline a challenging agenda for Africa. For example, seasonality in rural work and the combination of underemployment and labor shortages
within a given population at different times of the year call for appropriately designed policy responses and programs. The issue of care for sufferers of HIV/AIDS needs to be analyzed further and integrated into the response to the pandemic. The wider question of care,
how care work is captured, how it interacts with other domestic and other work, also needs
more attention in both time use surveys and in policy responses. Finally, infrastructure
investments, already recognized as a priority in the World Bank’s Africa Action Plan, need
to be directed in part toward meeting the requirements of household production and the
household economy, and helping women to reduce their time burdens. Acting on this
agenda will help governments in prioritizing public actions aimed at accelerating progress
toward the targets laid out in the Millennium Development Goals.
G
Sudhir Shetty
Sector Director, Poverty Reduction and Economic Management
Africa Region, World Bank
ix
Abstract
T
he papers in this volume examine the links between gender, time use, and poverty in
Sub-Saharan Africa. They contribute to a broader definition of poverty to include
“time poverty,” and to a broader definition of work to include household work. The papers
present a conceptual framework linking both market and household work, review some of
the available literature and surveys on time use in Africa, and use tools and approaches
drawn from analysis of consumption-based poverty to develop the concept of a time
poverty line and to examine linkages between time poverty, consumption poverty, and
other dimensions of development in Africa such as education and child labor.
xi
Acknowledgments
T
his volume was prepared by C. Mark Blackden and Quentin Wodon, in the Office of
the Sector Director, Poverty Reduction and Economic Management, Africa Region.
Two of the chapters in the volume draw on work commissioned by the World Bank from
the International Center for Research on Women (ICRW), which was carried out by
Jacques Charmes (Consultant, Institut de Recherche le Développement, Paris), and by
Aslihan Kes and Hema Swaminathan, with support from Caren Grown and Elizabeth
Nicoletti. The rest of the papers in the volume draw on work undertaken by the Poverty
Team in the Africa Region as part of contributions to poverty assessments for Guinea,
Malawi, and Rwanda. These papers were prepared by Elena Bardasi, Kathleen Beegle,
Corinne Siaens, Kalanidhi Subbarao, and Quentin Wodon.
We gratefully acknowledge the comments and guidance provided by the peer reviewers
for the papers, namely Andrew Morrison (PRMGE) for the papers in Part I of the volume
and Kathleen Beegle (DECRG) for the papers in Parts II and III except the Malawi paper that
was reviewed by Antonio Nucifora (AFTP1) and the Rwanda paper that was reviewed by
Kene Ezemenari (AFTP3) and Bruno Boccara (AFTP3). A workshop to discuss the papers
in this volume was held at the World Bank in November 2005, and the papers benefited
greatly from the many thoughtful comments and ideas from the participants at this workshop. We also thank Alfia Johnson and Hilda Emeruwa for their valuable support in organizing the workshop.
Financial support was provided by two trust funds. The first trust fund is the Norwegian/
Netherlands fund for gender mainstreaming (GENFUND), which provided an important
impetus to launch this work to address time use issues in poverty analysis, and which has
supported the publication and dissemination of this volume. The second trust fund is the
Belgian Poverty Reduction Partnership (BPRP), which provided support for papers in Parts
II and III of the volume.
This work was prepared under the overall leadership of Paula Donovan, former Sector
Director, PREM, Africa, who unstintingly encouraged greater synergy between work on
poverty and work on gender in the Africa Region.
xiii
CHAPTER 1
Gender, Time Use,
and Poverty: Introduction
C. Mark Blackden and Quentin Wodon
This volume aims to shed light on the question of “time poverty” in Sub-Saharan Africa and its
relationship with consumption-based measures of poverty, as well as other development outcomes. Time poverty, especially as seen in the “double workday” of women, has long been a staple of discussion of women’s situation in Africa. Yet it is not always clear what is meant by time
poverty, how time poverty is measured, or what actions are required to tackle time poverty once
identified. The papers presented in this volume seek to address these questions by reviewing
the existing literature and analyzing new data available in time use modules of household income
and consumption surveys in several African countries. The objective is to provide guidance and
examples of how to define and measure time poverty, and also to address ways through which a
better understanding of time poverty can inform poverty diagnostics, national poverty reduction strategies, and the design and implementation of development interventions.
Time Use and Africa’s Development
Perhaps nowhere is the asymmetry in the respective rights and obligations of men and
women more apparent than in the patterns of time use differentiated by gender, and the
inefficiency and inequity they represent. Both men and women play multiple roles (productive, reproductive, and community management) in society (Moser 1989; Blackden
and Bhanu 1999). Yet while men are generally able to focus on a single productive role,
and play their multiple roles sequentially, women, in contrast to men, play these roles
simultaneously and must balance simultaneous competing claims on limited time for
each of them. Women’s labor time and flexibility are therefore much more constrained
than is the case for men. Comparative time use data reflect these constraints, though it is
particularly difficult to capture “simultaneous” tasks and to measure the “intensity” of
work, whether for men or for women. The gender division of labor defines women’s and
1
2
World Bank Working Paper
men’s economic opportunities, and determines their capacity to allocate labor time for
economically productive activities and to respond to economic incentives. Although some
of these differences in time allocation can be explained through economic factors, in many
societies these are secondary to non-economic factors in determining time use patterns
(Ilahi 2000).
Gender-differentiated time use patterns are affected by many factors, including household composition and life cycle issues (age and gender composition of household members), seasonal and farm system considerations, regional and geographic factors, including
ease of access to water and fuel, availability of infrastructure, and distance to key economic
and social services such as schools, health centers, financial institutions, and markets. But
social and cultural norms also play an important role both in defining, and sustaining rigidity in, the gender division of labor. This is most evident in the division of responsibilities
between productive (market) and reproductive (household) work. In addition to their
prominence in agriculture and in much of the informal sector, women bear the brunt of
domestic tasks: processing food crops, providing water and firewood, and caring for the
elderly and the sick, this latter activity assuming much greater significance in the face of
the HIV/AIDS pandemic. The time and effort required for these tasks, in the almost total
absence of even rudimentary domestic technology, is staggering.1
It is important to examine time use in Sub-Saharan Africa and to address its policy and
operational implications for at least three reasons. First, time use data in Sub-Saharan
African countries show what people actually do in their daily lives, and therefore provide
important information on work and on labor allocation within households. Second, in
doing this, they make apparent not only that there is a division of labor, in that different
people do different things, but also that differences in how men and women use their time
are of considerable importance in understanding poverty in Africa—the gender division
of labor is especially significant. Third, time allocation data reveal not only the substantial
market economy contributions of men and women to Africa’s development, but also, and
just as importantly, the existence of a whole realm of human activity—what is termed here
the “household economy”—that is largely invisible and uncounted in economic data and
in the system of national accounts (SNA).2
Examination of time use data therefore performs the critically important function of
giving policymakers and development practitioners a much more complete and comprehensive picture of employment and labor effort than would otherwise be afforded by labor
force data alone. This is done by making visible and providing quantified estimates of nonmarket contributions to total household production and welfare, alongside market-based
1. Some studies have found high correlation between opportunity costs to the household of children’s
time and enrollment figures. In Tanzania, on average, the opportunity cost for families to send girls to
schools is significantly higher than that of boys. If boys from 13 to 15 years old are in school, households
lose about 25 hours of work per week. For girls of the same age, they lose about 37 hours of work (World
Bank 1999). Boys may drop out of school to herd, farm, fish, hunt or to engage in petty trade. For girls,
the main reasons for dropping out are pregnancy, parental concerns about toilet facilities, long distances,
and lack of security.
2. Ironmonger estimates, for example, that labor market employment statistics cover less than 50 percent
of all work performed, and that the regularly published labor statistics cover perhaps 75 percent of men’s
work and 33 percent of women’s work. See Ironmonger (1999).
Gender, Time Use, and Poverty in Sub-Saharan Africa
3
work. Because these contributions are essential for family survival, it is important for policymakers and development practitioners to focus on them explicitly. Non-market labor
is of particular importance from a gender standpoint, as the household economy is where
women predominantly work.
One of the most important insights from gender analysis of time use in Sub-Saharan
Africa is that there are synergies, and short-term tradeoffs, between and within marketoriented and household-oriented activities—economic production, childbearing and rearing, and household/community management responsibilities. These assume particular
importance because of the competing claims on women’s labor time in most environments. There are interconnections between rural development and transport (Barwell
1996), between education, health, and fertility, between girls’ education and domestic
tasks, and within the population/agriculture/environment “nexus” (Cleaver and Schreiber
1994). Other critical interconnections illuminated by time use studies exist between the
time spent (mainly by women and very young children) preparing and cooking meals in
degraded and polluted environments and health, as reflected in high levels of acute respiratory infections related to exposure to air pollutants (for an articulation of this issue in
Uganda, see Green 2005). Several studies document that workload constraints limit the
likelihood that children will be taken to health posts for vaccinations, or that sick children
or family members will access health care in a timely manner. As argued by the World Bank
(2006), there is a critically small “window of opportunity” for addressing undernutrition
in children, which in turn hinges on timely access to food, including time for breastfeeding
and timely preparation of meals in the first two years of life—a period in which, according
to time use survey data, women with young children are likely to be especially heavily burdened with work. Building on these cross-sectoral interconnections can have positive multiplier effects for growth and poverty reduction.
A related insight is that some time uses are indispensable, as argued by Harvey and
Taylor (2000) when they refer to “household time overhead.” This concept refers to the
minimum number of hours that a household must spend on the basic chores vital to the
survival of the family, that is, the time spent preparing meals, washing clothes, cleaning,
fetching water, and gathering fuel for cooking and heating. They argue that, in general,
a household with low household time overhead will be better off than a household with
high time overhead, though they recognize that the impact of the time overhead will in
turn depend on the number of adults and children available to assist in performing
these tasks.
Tradeoffs in time allocation, and sometimes harsh choices, are at the core of the interrelationship between the “visible” market and “invisible” household economies, given the
simultaneous competing claims on women’s—but not men’s—labor time. There are
tradeoffs between different productive activities, between market and household tasks, and
between meeting short-term economic and household needs and long-term investment in
future capacity and human capital. The work burden on women, and the disproportionate cost borne by women of reproductive work in the household economy not only limits
the time women can spend in economic activities but restricts them (spatially and culturally) to activities compatible with their domestic obligations (Blackden and Morris-Hughes
1993). A review of the relationship between female headship and poverty found that the
reasons for greater poverty among female-maintained families lie not in structural factors
4
World Bank Working Paper
in household composition, such as higher dependency ratios, or in gender-related differences in economic opportunity, but in the combination of the two. Where women heads
of households have no other adult women to fulfill home production or domestic roles,
they face greater time and mobility constraints than do male heads or other women, that
in turn leads to lower paying jobs more compatible with childcare (Buvinic and Rao Gupta
1997). The review cites evidence from Malawi indicating that female farmers were inclined
to limit their labor time in farm activities due to a heavy commitment to domestic chores,
while responsibility for children and housekeeping made it difficult for female heads to opt
for regular or off-farm labor activities to increase their earnings. Because they must carry
out their multiple roles simultaneously, and because the “household time overhead” is not
dispensable, women can only engage in directly productive economic activity (whether
measured or not) after or in conjunction with the discharge of their domestic responsibilities. Balancing competing time uses, in a framework of almost total inelasticity of the
gender division of labor, presents a particular challenge to reducing poverty. In many circumstances, necessary and essential actions, including both directly productive tasks and
meeting the “household time overhead,” must compete for scarce labor time.
Here too, though, the situation is not necessarily straightforward. The idea that poverty
is a function of time as well as money is not new, as this was articulated by Vickery in 1977
(see Harvey and Taylor 2000). Time poverty and income poverty may reinforce each other
with negative consequences for individual and household well-being. For example, the
sheer drudgery and low productivity of many non-market tasks, which are time- and
labor-intensive, reduces the availability of time for household members engaged in such
tasks to participate in more economically productive activities. Given that such tasks are
primarily carried out by women, this means that women in particular are less likely to be
able to take full advantage of economic opportunities, to respond to changing market
conditions and incentives, and to participate in income-generating activities.3 Time
poverty also impedes individuals’ ability to expand their capabilities through education
and skills development that could enhance economic returns in the market place. However, the question can also be asked in a different way, namely: to what extent could more
time spent working (but without making a larger share of the population time poor) help
reduce poverty? The logic is then inverted, by showing that even if many men and women
work a lot, there may be a reserve of time that, if jobs were available, could be tapped to
reduce consumption poverty. Said differently, there are circumstances in which underemployment is widespread, at least at certain periods of the year, and “time poverty” as
such does not appear to be the main constraint that prevents the consumption poor to
escape poverty.
The above discussion suggests that the time problem is a key component of the more
traditional poverty problem, and one which deserves more attention in poverty diagnostics and Poverty Reduction Strategy Papers. A possible avenue for further research is to
define more precisely the “household time overhead” and to link it with other dimensions
of time use. This would allow for more exact specification of when the household time
3. The wider set of issues linking gender inequality and economic growth are beyond the scope of this
paper. Discussion of these issues can be found in World Bank (2001), Blackden and Bhanu (1999), and
Gelb (2001).
Gender, Time Use, and Poverty in Sub-Saharan Africa
5
overhead becomes a constraint on labor use for other tasks, and constitutes a form of time
poverty even in environments of under- or unemployment where total time use does not
appear to be a constraint. More generally, omission of the household economy from conventional development planning means that:
■ The picture is incomplete and our understanding of the total labor effort of households is insufficient—much of what we are (or should be) concerned with occurs
in this invisible realm;
■ There is a tendency to make misleading assumptions about labor availability and
labor mobility treating, for example, women’s capacity to undertake unpaid
domestic labor as “infinitely elastic” (Elson 1993)—overlooking the differences in
men’s and women’s contributions to household time overhead can lead to inappropriate policies which have the unintended effect of raising women’s labor burdens while sometimes lowering those of men;
■ We do not invest in (or prioritize) what is not there—if the household economy is
not visible to policymakers and planners, they are unlikely to prioritize investment
in it; and
■ We do not see the tradeoffs among different tasks and activities, and, by extension,
do not place reducing or minimizing these tradeoffs at the core of our response—
nor do we see sufficiently the positive linkages, and prioritize the benefits from
these linkages in our actions.
Brief Overview of the Contributions in This Volume
Many of the above considerations were reflected in the policy research report “Engendering Development” (World Bank 2001) that addressed the link between time poverty and
income poverty as well as growth. Indeed, “time poverty” is not a new concept. Time constraints have been articulated as a central development problem for Africa for many years.
Data from two village surveys in the Central African Republic in 1960 confirm the longevity
of gender-differentiated time allocation burdens. In one village, men work 5.5 hours per day,
women more than 8 (Berio, 1983). The conclusion of this study is particularly interesting:
Rural modernization for improving productivity increases women’s workload and reduces
men’s working hours. “In these conditions, any programme of rural modernization will
soon reach its limits, unless planners can force men to work more on agriculture, and also
to release women from part of their workload.” Time—women’s time in particular—can
be the scarce production factor in a development process.
The papers presented in this volume are intended to make a contribution to a longstanding debate and field of analysis. The first part of the volume comprises two papers
devoted to reviews of the literature and empirical evidence to date on time use and time
poverty in Sub-Saharan Africa (SSA). Chapter 2 presents an overview of the “time poverty”
problem in SSA. It presents a simplified conceptual framework for analysis of the overlapping domains of work, when market-based and non-market work are combined into a
more comprehensive view of total household production. It discusses methodological
issues associated with time use surveys, paying particular attention to questions of how
work is defined in different frameworks (a topic that is addressed further in Chapter 3).
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World Bank Working Paper
It also raises issues relating to how intensity of work, and simultaneity of tasks can be captured in time use surveys, and how this can be pursued in analysis. The paper then
reviews some of the available literature in SSA on time use, paying particular attention to
time problems associated with care for people suffering from HIV/AIDS, and the paper
finds that this issue has not received sufficient attention in time use analysis, despite its
clear significance in SSA. This is clearly an area requiring much more research and analysis in future, to inform strategies for coping, treatment, and mitigation of the effects of
HIV/ AIDS. The paper focuses in particular on the importance of developing infrastructure in SSA in ways that are more closely aligned with alleviating the time burdens of
household economy production and which are accessible to women, so that they specifically lessen their time burdens.
Chapter 3 picks up on the question of how work is defined, and traces the more recent
changes in, and limitations of, the System of National Accounts (SNA), in terms of how
these changes capture unpaid non-market work. While some unpaid work is included in
the SNA (defined as “contributing” labor), there is still a considerable amount of unpaid,
non-market work in the household economy that is not in the SNA, and that is, economically speaking, invisible. The chapter then documents the findings of national time use
surveys in several SSA countries, which provide a descriptive foundation for looking more
widely at total household production. What these surveys tell us, along with much of the
other time use literature in SSA, is how extensive and significant such non-market, unpaid
time use is, and that the household economy, if counted, would be one of the largest economic sectors in terms of the labor (time) allocated to it, and the output that it produces.
Part II of the volume comprises two chapters devoted to the measurement of time
poverty, with data from Guinea and Malawi. In Chapter 4, it is argued that the availability
of better data on time use in developing countries makes it important to provide tools for
analyzing such data. While the idea of “time poverty” is not new, and while many studies
have provided measures of time use and hinted at the concept of time poverty, we have not
seen in the literature formal discussions and measurement of the concept of time poverty
alongside the techniques used for measuring consumption poverty. Conceptually, time
poverty can be understood as the fact that some individuals do not have enough time for
rest and leisure after taking into account the time spent working, whether in the labor market, for domestic work, or for other activities such as fetching water and wood. Said differently, for those who are working long hours, the time constraint makes it necessary for
individuals to make hard choices in terms of to what they allocate their time, with these hard
choices having implications for the welfare of individuals and the household to which they
belong. Unlike consumption or income, where economists assume that “more is better,”
time is a limited resource—more time spent working in paid or unpaid work-related activities means less leisure, and therefore higher “time poverty.” The objective of Chapter 4 is
to demonstrate with data from Guinea how one may apply the concepts used in the consumption poverty literature to time use, in order to obtain measures of time poverty for a
population as a whole and for various groups of individuals.
Chapter 5 is devoted to the issue of seasonality in time use with an exploration of data
from Malawi. The available empirical evidence for Malawi and for many other developing
countries suggests the existence of labor shortages at the peak of the cropping season, with
negative impacts on the ability of households to make the most of their endowments such as
land. At the same time, for most of the year, there is substantial underemployment, especially
Gender, Time Use, and Poverty in Sub-Saharan Africa
7
in rural areas. It could therefore be argued that seasonality in the demand for labor is leading to both underemployment and labor shortages. To assess the validity of this argument,
Chapter 5 provides basic descriptive data from a 2004 nationally-representative household
survey to assess the typical workload of the population. The data do confirm the presence
of strong seasonality effects in the supply of labor, as well as substantial differences in workload between men and women due to the burden of domestic work, including the time
spent for collecting water and wood.
The last part of the volume also comprises two chapters devoted to the implications of
time use and time poverty for development outcomes. Chapter 6 looks at the link between
underemployment and consumption-based poverty. Despite already long working hours for
many household members, and especially women, underemployment is nevertheless affecting a large share of the population in many developing countries. Using the same data on
time use as in Chapter 4, as well as data on wages and consumption levels from the household survey for Guinea, the chapter provides a simple framework for assessing the potential
impact on poverty and inequality of an increase in the working hours of the population up
to what is referred to as a full employment workload. The framework provides for a decomposition of the contribution to higher household consumption of an increase in working
hours for both men and women. The key message is that job creation and full employment
would lead to a significant reduction in poverty, even at the relatively low current levels of
wages and earnings enjoyed by the population. However, even at full employment levels,
poverty would remain massive, and the higher workload that the full employment scenario
would entail would be significant.
Finally, Chapter 7 looks at the links between welfare, time use, and other development
outcomes in a case study of orphans in Rwanda. One of the aspects of the orphan crisis in
Sub-Saharan Africa indeed relates to time use, both in terms of where orphans end up living and what they spend their time doing in their new household of adoption and in terms
of the burdens of care. While some orphans are welcomed in centers and institutions, many
live with relatives or other members of their communities, and some others are welcomed
by families which are not directly related to them. Orphans are in many ways better off
when welcomed by relatives or other families than when living by themselves or in institutions, but there are also concerns that the orphans (and especially girls) that are welcomed in some families may be required to provide more help for the domestic tasks to be
performed, with the resulting time pressure in terms of workload preventing them from
benefitting from the same opportunities in education and other aspects of their development as other children. The objective of the chapter is to conduct preliminary work to test
this assumption using recent household survey data from Rwanda, paying attention not
only to traditional variables of interest such as school enrollment, child labor and time use,
but also with an eye to assessing other dimensions of welfare.
What Next? Some Areas for Further Research
A workshop was held at the World Bank in November 2005 to discuss all the papers presented in this volume except the Rwanda case study (which was added to broaden the
scope of this volume), and to raise issues and concerns relating to the concept, measurement, and implications of “time poverty.” Some of the key points raised at the workshop
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World Bank Working Paper
are summarized below. Participants noted that even the descriptive data on comparative
time uses of men and women were striking in revealing gender-differentiated work burdens, especially in relation to how things have evolved in the context of HIV/AIDS, and
notwithstanding the continued lack of adequate data in this area. Shaping the analytical
agenda was of concern to many participants, including making better use of existing data,
generating new data, and linking the discussion of time poverty to the wider questions of
gender inequality in access to and control of resources, addressing the implications of
time poverty for women’s labor force participation, looking at child labor issues, and tackling the operational question of what investments are required to have the maximum positive impact on reducing time poverty and improving other development outcomes,
especially those related to the Millennium Development Goals.
A related set of concerns articulated at the workshop was to pay explicit attention to
the time demands of development programs, and to look in a more focused manner at
what development outcomes are affected by time poverty. This was especially the case with
respect to health and child welfare. Some participants suggested that beyond the definition
of a time poverty line (a specific amount of time worked), it will be important to pay more
attention to the productivity of time use and to the ways in which time is used. In addition,
it was pointed out that in some situations, not having the time (opportunity) to engage in
directly productive work can also be seen as an important issue (related to underemployment), just as working more hours and earning more can often be seen as a good thing.
Data quality, measurement error, classification of tasks, and capturing both simultaneity
and intensity of work, were also raised by participants as issues for further work. These
issues all merit further attention in taking the analysis of time poverty forward.
Key challenges identified at the workshop include that of communicating effectively
what the time poverty problem is, and how to address it. It is critical to focus attention on
development outcomes (informing the “results agenda”) that time poverty most affects.
This in turn requires much more focus on technology, including labor-saving technology
accessible to women to reduce the burden and drudgery of household tasks. In this context, the renewed focus on infrastructure, for example in the World Bank’s Africa Action
Plan, while welcome, needs to be directed toward meeting the specific needs of the household economy.
For example, the gender division of labor in transport tasks, as revealed in time allocation data, leaves women with by far the most substantial transport task in rural areas.
These figures equate to a time input for an average adult female ranging from just under
1 hour to 2 hours 20 minutes every day. Water, firewood, and crops for grinding are transported predominantly by women on foot, the load normally being carried on the head.
Village transport surveys in Ghana, Tanzania, and Zambia show that women spend nearly
three times as much time in transport activities compared with men, and they transport
about four times as much in volume (Malmberg-Calvo 1994). What would happen if all
households in SSA were no more than 400 m (about a six minute walk) from a potable water
source—a national target once set by the Government of Tanzania—or if woodlots or
other sources of household energy were no farther than a 30-minute walk? Barwell (1996)
summarizes the results of such analysis in five settings. In the Mbale district in Eastern
Uganda, more than 900 hours/year could be saved if these proximity targets were met. This
represents a considerable outlay of household time and energy, predominantly by women,
amounting to the equivalent of a half year of 40-hour work weeks.
Gender, Time Use, and Poverty in Sub-Saharan Africa
9
Public policies could have a significant impact on the heavy time burden of domestic
work through investment in the household economy. Such investment would aim to
reduce the “household time overhead” discussed earlier, and thereby directly relieve the
time burdens on women and reduce the tradeoffs among competing uses of scarce labor.
Infrastructure to provide clean and accessible water supply, and energy focused on domestic requirements (notably for cooking fuel) is especially critical, in view of its multiple benefits. Labor-saving domestic technology relating to food processing is likely to have a
greater immediate impact in raising the productivity and reducing the time burdens of
many women. Transport interventions need to reflect the different needs of men and
women, so as to improve women’s access to transport services (including intermediate
means of transport), commensurate with their load-carrying responsibilities. These investments in the household economy have substantial payoffs in increased efficiency and
growth in the market economy. Energy policy and investment priorities need to focus on
alternative energy sources, and to address the domestic energy needs of households, especially as concerns fuel for cooking. This will have important multiplier effects on improving health, saving time, and enabling girls to go to (and stay) in school. Investing in
labor-saving technologies accessible to women, and focused on reducing the considerable
time and effort expended to transform and process agricultural and food products—a time
expenditure often greater than the time required to grow and harvest the crops in the first
place—deserve high priority.
A critical task for public policy, as articulated in country Poverty Reduction Strategies,
should then be to promote concurrent investment across a range of critical sectors aimed
at minimizing or eliminating the tradeoffs, and building on synergies identified earlier.
Concurrent investment to alleviate the household labor constraint disproportionately
affecting females will go a long way to helping to realize the benefits of investment in
human development. Investing in cleaner domestic energy sources will have very important multiplier effects on achievement of both education- and health-related Millennium
Development Goals. It could actually be argued that access to basic infrastructure provides
double benefits by reducing the time spent on domestic chores and the fetching of wood
and water, and also increasing the realm of small business opportunities and production
activities made feasible thanks to water and electricity.
References
Barwell, I. 1996. Transport and the Village: Findings from African Village-Level Travel and
Transport Surveys and Related Studies. World Bank Discussion Paper No. 344, Africa
Region Series, Washington, D.C.
Berio, A.J. 1983. Time Allocation Surveys, Paper presented at the 11th International Congress of Anthropology Sciences, Vancouver, Canada.
Blackden, C.M., and E. Morris-Hughes. 1993. Paradigm Postponed: Gender and Economic
Adjustment in Sub-Saharan Africa. Technical Note No. 13, Poverty and Human
Resources Division, Technical Department, Africa Region, The World Bank.
Blackden, C.M., and C. Bhanu. 1999. Gender, Growth, and Poverty Reduction. Special Program of Assistance for Africa 1998 Status Report on Poverty, World Bank Technical
Paper No. 428, Washington, D.C.
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Buvinic, M., and G. Rao Gupta. 1997. “Female-Headed Households and Female-Maintained
Families: Are They Worth Targeting to Reduce Poverty in Developing Countries.”
Economic Development and Cultural Change 45:2, 259–280.
Cleaver, K.M., and G.A. Schreiber. 1994. Reversing the Spiral: The Population, Agriculture,
Environment Nexus in Sub-Saharan Africa. Directions in Development, World Bank.
Gelb, A. 2001. “Gender and Growth: Africa’s Missed Potential.” Findings No. 197, Africa
Region, The World Bank, Washington, D.C. April.
Green, K. 2005. Indoor Air Pollution, Health, Energy and The Environment in Uganda: Perspectives on the Poverty-Health-Gender-Environment Nexus. Factsheets prepared for
the Uganda Country Office of the World Bank, Kampala, Uganda, November.
Harvey, A.S., and M.E. Taylor. 2002. “Time Use.” In M. Grosh and P. Glewwe, eds., Designing Household Survey Questionnaires for Developing Countries, Lessons from 15 Years of
the Living Standards Measurement Survey. Washington, D.C.: The World Bank.
Ilahi, N. 2000. “The Intra-household Allocation of Time and Tasks: What Have We Learnt
from the Empirical Literature?” Policy Research Report on Gender and Development,
Working Paper Series No. 13., The World Bank, Washington, D.C.
Ironmonger, D. 1999. “An Overview of Time Use Surveys.” International Seminar on Time
Use Studies, Center for Development Alternatives, Ahmedabad, India.
Moser, C. 1989. “Gender Planning in the Third World: Meeting Practical and Strategic
Gender Needs.” World Development 17:1799–1825.
Malmberg-Calvo, C. 1994. “Case Study on the Role of Women in Rural Transport: Access
of Women to Domestic Facilities.” SSATP Working Paper No. 11, Technical Department, Africa Region, The World Bank.
Vickery, C. 1977. “The Time-Poor: A New Look at Poverty.” Journal of Human Resources
12:27–48.
World Bank. 1999. Tanzania: Social Sector Review. A World Bank Country Study,
Washington, D.C.
———. 2001. Engendering Development: Through Gender Equality in Rights, Resources, and
Voice. World Bank Policy Research Report, Washington, D.C.
———. 2006. Repositioning Nutrition as Central to Development: A Strategy for Large-Scale
Action. Directions in Development, Washington, D.C.
PART I
Reviews of the Literature
11
CHAPTER 2
Gender and Time Poverty
in Sub-Saharan Africa
Aslihan Kes and Hema Swaminathan4
This paper examines the links between time use and poverty in Sub-Saharan Africa. Drawing on
a broader definition of poverty to include ‘time poverty,’ the paper presents a conceptual framework linking both market and household work, and reviews some of the available literature and
studies on time use in Sub-Saharan Africa. It presents evidence on time use for domestic tasks
and care activities within the household, focusing in particular on the limited data and evidence
currently available in time use surveys on care burdens in the context of HIV/AIDS. The paper
concludes by stressing the importance of investing in infrastructure focused on the needs of the
household economy, including water, fuel provisioning, and labor-saving technology accessible
to women.
T
he definition of poverty has evolved significantly over time. Today, poverty is no
longer viewed as solely an economic phenomenon, based on consumption or
income measures alone. Poverty is seen as multidimensional, encompassing both
income/consumption dimensions and other dimensions relating to human development
outcomes, insecurity, vulnerability, powerlessness, and exclusion. It is also increasingly
recognized that poverty, in its many dimensions, is experienced differently by men and
by women, and that, consequently, gender analysis of poverty is essential for a fuller
understanding of poverty dynamics and for articulating effective poverty reduction
strategies.
4. The authors are with the International Center for Research on Women. The authors gratefully
acknowledge financial support for this study from the World Bank. The authors also would like to thank
Mark Blackden, Diane Elson, Maria Sagrario Floro, and Caren Grown for detailed and very constructive
comments on previous drafts of the paper. The authors also thank Elizabeth Nicoletti for her assistance
in the production of the paper.
13
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World Bank Working Paper
While the relationship between gender, work, and poverty has been explored in the
literature,5 the application of a time lens to understand poverty and to inform poverty
reduction strategies has not been mainstreamed in poverty analysis or strategies. This
paper, along with others in this volume, aims to contribute to the wider effort to incorporate time use analysis into poverty analysis, and to draw insights from this work to inform
the preparation of poverty reduction strategies. This paper examines time poverty in SubSaharan Africa from a gender analytical point of view and explores its implications for
poverty reduction strategies. The paper is organized as follows. The next section presents
a simplified conceptual framework for discussion of time use patterns and their linkages
to poverty, while the section after that reviews some of the available literature on men’s and
women’s time use in various sectors and synthesizes the principal findings from time use
studies in Sub-Saharan Africa. The third section discusses some of the methodological
issues relating to the conduct of time use surveys with a focus on the special requirements
of time use surveys in the region. Lastly, the final section presents some key conclusions
and recommendations for moving forward on the incorporation of time use analysis
and time poverty into broader poverty analysis and poverty reduction strategies in SubSaharan Africa.
Conceptual Framework Linking Time Use and Poverty
The conceptual framework presented in this section explores the linkages between individuals’ time allocation and the concept of time poverty in an agrarian developing country context (Figure 2.1). Currently, there are two main approaches used to define and
measure work. One approach, codified in the System of National Accounts (SNA) as most
recently revised in 1993, defines work in terms of formal and informal market work and
non-market subsistence work for production of goods, and is the basis for calculating the
Gross Domestic Product (GDP). This approach excludes non-market work producing services for own-consumption within the household. A more extensive discussion of what is,
and is not, included in the SNA, and how non-market work is captured, is presented in the
paper by Charmes in this volume (Chapter 3).
A second approach defines work and activity in a wider sense, and attempts to capture
work activities and labor allocations that are not otherwise included in national accounts or
economic analysis. Time use surveys are the principal instrument for capturing this wider
approach. As illustrated in Figure 2.1, individuals’ time use can be broadly classified as market work and non-market work. Production of goods and services for the market is grouped
under market economy activities and includes both formal and informal employment, and
is counted in the SNA and in calculation of GDP. The main activities that are included in
the non-market, or household, economy are subsistence production, reproductive work,
and volunteer work. As used here, subsistence production concerns production of goods
for home use that in principle could be marketed such as food, clothing, soft furnishings,
5. Most recently, Chen and others (2005) provide an analysis of the linkages between gender, work
and poverty. Their analysis focuses on the nature of work that men and women engage in and how these
differences affect their economic security. The fact that women, on average, work fewer hours in paid work
due to their roles in the unpaid household sector and that they are overrepresented in the informal sector where the average earnings are low are viewed as major contributors to women’s poverty.
Gender, Time Use, and Poverty in Sub-Saharan Africa
15
Market Work
Formal work
Informal work
Paid SNA
Unpaid SNA
Non-economic factors – social
and cultural norms
Gender Division of Labor
Economic Factors – wage income,
non-wage income, existence of labor,
and good markets, etc.
Allocation of Time
Non-market work
Productive
Reproductive
Subsistence production
(Water fetching)
Domestic work
Voluntary
Unpaid Non-SNA
Determinants of Time Allocation
Figure 2.1 A Framework for Analyzing Time Use and Time Poverty
Care work
(Firewood collection)
• Overburdening
• Competing Claims
• Multiplicity of Tasks
• Trade-offs
Time Poverty
Source: Authors.
pottery, and housing. Reproductive work includes activities such as preparing meals, laundry, cleaning, household maintenance, and personal care. Voluntary community work
comprises unpaid activity in community and civic associations such as self-help groups of
mothers organizing to run a soup kitchen or to secure improvements in neighborhood
safety (Elson 2002).
Evidence from various time use surveys suggests that there are marked differences in
how men and women allocate their time between market and non-market work. Although
some of these differences in time allocation can be explained through economic factors such
as wages, non-wage income, and the functioning of labor and goods markets, in many societies economic factors are only secondary to non-economic factors in determining time use
patterns (Ilahi 2000). In many settings, social and cultural norms underpin what is often a
fairly rigid gender division of labor, where some tasks are strictly viewed as “men’s work”
and others as “women’s work” (Cagatay 1998; World Bank 2001). The gender division of
labor is most apparent in comparing men’s and women’s productive and reproductive work
responsibilities. Societal norms tend to assign reproductive labor, such as looking after
children, caring for the sick and the elderly, as well as preparing food, cleaning and housework, and collection of fuel and water, to women, while men are viewed as working primarily outside the domestic sphere as the main breadwinners of their households. This
disproportionate allocation of female labor time to the reproductive sphere, because it is not
counted in the SNA, is, in economic terms, invisible. Moreover, the fact that some labor
time is allocated according to non-economic criteria that do not necessarily reflect responses
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World Bank Working Paper
to market or price signals, has implications for the smooth functioning of labor markets.
These gendered patterns of labor use also mean that the capacity of individuals to reallocate
their labor in response to economic incentives and to maximize productivity and efficiency,
may be very limited.
The composition of the household also affects its members’ time use patterns. The size
of the household, the number and the gender distribution of adults, as well as the age and
gender of the children, all contribute significantly to determining the options households
have with respect to allocating their available labor to the full range of tasks that need to be
accomplished, and to how tasks are divided among household members. Because childcare is viewed strictly as women’s responsibility, the presence of young children shapes
women’s time use and labor market options significantly. Women who have young children tend to withdraw from the labor market or to reduce the amount of time they work
outside the home (Ilahi 2000).
Similarly, the number of older children in the household, particularly girls, reduces
the time women spend on various reproductive work activities. Girl children are women’s
main helpers in tasks such as water and fuel collection and care duties. The existence of
other adult female household members also reduces the time each one must allocate to various household tasks and increases the likelihood of their involvement in wage employment (Nankhuni 2004).
The links between individuals’ time use and the circumstances under which this might
constitute “time poverty” have not been fully explored in the literature. Although there are
frequent references to time poverty, especially for women, it is not always clear what exactly
this means. More work is needed to define the concept of time poverty more precisely and
to address its policy and operational implications in poverty diagnostics and poverty reduction strategies. Part II of this volume addresses this issue.
In broad terms, time poverty can be understood in the context of the burden of competing claims on individuals’ time that reduce their ability to make unconstrained choices on how
they allocate their time, leading, in many instances, to increased work intensity and to tradeoffs among various tasks. Individuals and households at all income levels can experience time
poverty as they engage in long hours of market and non-market work and have to choose
between various activities. However, it can be surmised that where these tradeoffs become particularly severe, as is likely to be the case in households that are income poor, have fewer assets,
and less available labor, time poverty may become a particularly important problem.
Poor households depend heavily on their members’ time and labor for the provision of
goods and services that are essential for their well-being and survival. When faced with severe
time constraints, and lacking the economic resources to access market substitutes, these
households may have to resort to making tradeoffs between activities which may directly
affect their members’ well-being. These may be short-term intersectoral tradeoffs as well as
intergenerational tradeoffs with far reaching consequences. The negative impact of these
tradeoffs can be observed in various dimensions of “human poverty” such as food security,
child nutrition, health, and education. For instance, time that has to be allocated to care
responsibilities may cause individuals to forego certain responsibilities in subsistence
agricultural production which may adversely affect agricultural output and consequently
threaten household food security and compromise child nutrition and health. Conversely, time spent on agricultural production shaped particularly around seasonal labor
Gender, Time Use, and Poverty in Sub-Saharan Africa 17
requirements may lead to tradeoffs in the form of less time on care and domestic work. This
may impede, among other things, the timely preparation and consumption of adequate food
and adversely affect household and particularly children’s nutrition. Finally, household time
poverty may require children to contribute time and labor to various tasks and therefore
forego an education, which in turn perpetuates the intergenerational transmission of poverty,
and undermines efforts to meet the Millennium Development Goals (MDGs hereafter).
Besides the links that exist between time poverty and other dimensions of human
poverty, there also are several potential direct links between time and income poverty.
Time poverty can exacerbate income poverty in poor households in several ways. First,
low-productivity in many non-market tasks renders them time- and labor-intensive, thus
reducing the availability of time to participate in more economically productive activities.
Second, due to the gendered division of labor that causes poor substitutability of labor
allocation in non-market work, individuals, particularly women, are unable to take full
advantage of economic opportunities and participate in income-generating activities.
Third, time poverty also impedes individuals’ ability to expand capabilities through education and skills development, thereby enhancing economic returns in the market place.
Gender and Time Use Patterns in Sub-Saharan Africa
This section reviews some of the available data and information on time use in Sub-Saharan
Africa. A more extensive treatment of data from recently conducted national time use surveys in Benin, South Africa, Madagascar, and Mauritius, as well as the time use module of
the Ghana Living Measurement Survey, is presented in Chapter 3 by Charmes. The most
critical drawback of the existing national level time use surveys is that they do not collect
any demographic and economic information that would enable an in-depth analysis of
time use patterns of men and women. Overall, the results from these surveys confirm the
sharp gender division of labor between market (SNA) and household (non-SNA) activities, and that, in general, women are relatively more time poor than men, once their household economy (non-SNA) work is taken into account.
Table 2.1. Time Devoted to Economic Activity and to Work, By Gender in Benin
(1998), South Africa (2000), Madagascar (2001), and Mauritius (2003)
(Minutes per day)
Benin
SNA production
Non-SNA production:
care and domestic
activities
Total work
% SNA in total work
Source: Chapter 3.
South Africa
Madagascar
Mauritius
Women
Men
Women
Men
Women
Men
Women
Men
235
208
235
67
115
228
190
75
175
221
290
47
116
277
296
73
443
53.0%
302
77.8%
343
33.5%
265
71.7%
396
44.2%
337
86.1%
393
29.5%
369
80.2%
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World Bank Working Paper
Table 2.2. Average Time Spent in Agricultural Activities by Gender in Burkina Faso,
Kenya, Nigeria, and Zambia (Minutes per day)
Men
Women
Burkina Faso
Kenya
Nigeria
Zambia
420
498
258
372
420
540
384
456
Source: Saito and others 1994.
In Sub-Saharan Africa, both men and women engage in a number of productive and
reproductive work activities. Time use studies from the region reveal that women spend
more time than men at work particularly when their inputs in non-SNA production,
namely domestic and care work, are included.
In Sub-Saharan Africa, children and adolescents, particularly girls, also have important economic roles in their household. In Tanzania, girls at every age have heavier work
burdens than boys (Mason and Khandker in Ritchie, Lloyd, and Grant 2004). In Uganda,
girls work 21.6 hours per week while boys 18.8 hours a week (Uganda DHS in Ritchie,
Lloyd, and Grant 2004). A cross-country study which includes two countries from the
region, South Africa and Kenya also shows that girls spend more time on non-SNA work
in the form of household work compared to boys (Ritchie, Lloyd, and Grant 2004).
Gender, Time Use, and Agriculture
Agriculture is the main source of livelihood in Sub-Saharan Africa. It accounts for 35 percent of the region’s GDP and 70 percent of its employment (World Bank 2000). Women
provide about 50 to 75 percent of all agricultural labor in the region (Saito 1994). A study
conducted by IFPRI indicates that African women undertake about 80 percent of the work
in food storage and transportation, 90 percent of the work of hoeing and weeding, and
60 percent of the work in harvesting and marketing (Quisumbing et al. 1995, in Blackden
and Canagarajah 2003). Women’s average daily hours in agricultural work in four SubSaharan African countries is almost 467 minutes a day, compared with about 371 minutes
a day for men (Table 2.2). An earlier study by Leplaideur (1978) in Cameroon also reveals
that women spend significantly more time on agricultural tasks: they spend 348 minutes a
day on production of food crops, while men spend 270 minutes a day on food and export
crop production.6
Notwithstanding subregional variations, there is an important gender division of
labor among various agricultural tasks. Women are primarily responsible for food processing, crop transportation, and weeding and hoeing, while men do most of the land
clearing. Given these patterns, the effect of productivity-enhancing and time-saving
6. The earlier village level time use studies were designed to measure the diversity of farming work and
gender division of labor. The recent time use surveys diverge from these earlier studies as they do not collect gender disaggregated time use data on various agricultural activities.
Gender, Time Use, and Poverty in Sub-Saharan Africa 19
technological change is unlikely to be gender neutral. However, few empirical studies
examine the impact of technical change on time allocation at the household level, and there
are even fewer studies that disaggregate the impact by gender (Ilahi 2000). A study by
Rubin (1990) of time use patterns of women and men in South Nyanza, Kenya explores
whether changing farming technology—in the form of the introduction of a sugar cane
outgrower scheme—affects women’s time use patterns. The study findings suggest that
women in cane producing households spend more time on domestic work and in craft
work, such as basket weaving, and also have more leisure time compared with women in
non-cane growing households who spend more time on food crop production, marketing,
and transport, as well as in hired agricultural labor. Other studies also show that women
reallocate time saved as a result of technological change in agriculture to other incomegenerating activities, to a variety of community and individual projects, and to domestic
responsibilities (Malmberg-Calvo 1994; Blackden 2002).
Household Fuel and Water Provisioning
Although not consistently implemented by statistical agencies at the national level, since
1993 activities such as water fetching and firewood collection are counted in principle as
part of SNA work. Inclusion of these activities in the SNA is important, not only because
it is one way to make visible a category of work for which women are primarily responsible, but also because, as the time use data show, this represents a very substantial time and
energy allocation on the part of women. This is confirmed in many data sources, including
the five Sub-Saharan Africa national time use surveys presented in Charmes (see Chapter 3)
and in other papers in this volume.
Fetching water and collecting firewood are also associated with child labor, though the
evidence is not conclusive if boys or girls spend more time on these activities. Nankhuni
(2004) studies children’s time allocation on natural resource collection in Malawi and finds
that being female is the most significant determinant of a child participating in natural
resource collection. Her results also indicate that girls are more likely than boys to be burdened by resource collection responsibilities while simultaneously attending school.
Environmental degradation, as manifested by lack of clean water and deforestation, can
significantly increase women’s and girls’ work burdens and total time allocated to firewood
and water collection. The time and distance that women and girls need to travel to collect
water increase substantially as clean water resources are exhausted. Similarly, deforestation
significantly increases the time that needs to be allocated for firewood collection. A recent
study by Nankhuni (2004) in Malawi suggests that women who live in areas with moderate to severe wood deficits spend more time on housework and less time on self or wage
employment.
Care and Domestic Work
Reproductive tasks, such as housework, cooking, care for children, the sick, and elderly
household members, are necessary to maintain families. The time required for these
activities is usually positively correlated with the poverty level of households (Barnett
and Whiteside 2002). Poor households in rural areas depend on female household members for the provision of reproductive tasks since they lack the economic means to access
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market substitutes. Additionally, whenever the household is confronted by a crisis, such as
illness, the time spent on care-giving and domestic work increases significantly. Women
and girls bear a large portion of these unpaid reproductive responsibilities which are often
made more time consuming due to the lack of adequate household technologies. Cooking
and childcare are among the most time-consuming of women’s reproductive responsibilities, as revealed in the time use surveys presented in Charmes (Chapter 3) and in the other
papers included in this volume.7
HIV-AIDS Epidemic and the Burden of Care
HIV/AIDS is a significant—and worsening—health, economic, and social issue in SSA.
There are three critical factors—all interconnected—that place gender issues at the core of
the HIV/AIDS pandemic in Africa, and which must be addressed systematically if the SubSaharan Africa is to halt the spread of AIDS. The first factor is that risk and vulnerability
to HIV/AIDS are substantially different for men and for women, as is most evident in the
marked age- and sex-differentiated HIV prevalence rates, and in the fact that, uniquely in
Africa, women account for the majority of adults (58 percent) living with HIV/AIDS—this
has implications for strategies to reduce overall prevalence in Sub-Saharan Africa and how
and for whom AIDS prevention activities are undertaken. The second is that the impact of
HIV/AIDS differs markedly along gender lines, reflecting men’s and women’s different
roles and responsibilities in household and market activities, and critical gender differences
in access to and control of resources—this has implications for care, support, and treatment programs, and especially for addressing the needs of the 12 million AIDS orphans in
Sub-Saharan Africa. This is the critical link with the care burden and women’s time use in
this paper. The third is that tackling the AIDS pandemic is fundamentally about a radical
change in gender relations in Sub-Saharan Africa, through behavior change that empowers both men and women to “transform” gender relations, though this dimension of the
AIDS pandemic is beyond the scope of this paper.
The impact and consequences of HIV/AIDS differ markedly for men and for women,
and reflect their different roles and responsibilities in household and market activities, as
well as differences in their access to and control of assets and resources, as revealed in the
time use data presented in this volume. What the time use data reveal, once household
economy tasks are included is that the burden of care is substantial, and that it is essential,
as a consequence, that any assessment of the impact of HIV/AIDS in Sub-Saharan Africa
takes full account of the impact not only in the market economy, but also in the largely
invisible and uncounted household economy.
The impact of the disease on patients increases incrementally, as does the burden of the
disease on caretakers. The care-giving tasks resulting from having a household member
affected by HIV/AIDS are numerous. Having a family member with HIV/AIDS increases
the burden of other domestic activities such as housework, shopping, and transportation
(Akintola 2005).
7. It should be noted that in many cases domestic and care work are not accurately captured in time
use surveys as women don’t see these activities as “work” and tend not to report them. Experience from
various time use surveys reveal that these activities tend to be captured better when the questionnaire is
more detailed and contextualized.
Gender and Time Poverty in Sub-Saharan Africa
21
While there is qualitative evidence indicating that women are the primary caregivers
at the household and community levels, the literature is surprisingly thin on the impact of
serious illness on women’s time allocation patterns. The few studies that have considered
this with regard to HIV/AIDS are from the late 1990s, though these often do not disaggregate time allocation by individuals within the household. More recent studies have focused
on understanding the impact of HIV/AIDS on various household-level welfare indicators.
The lack of gender-disaggregated analysis impedes development of policies that are best
suited to help HIV/AIDS-affected households.
Bollinger, Stover, and Seyoum (1999) find that, in Ethiopia, labor losses reduce the time
women spend on agriculture from 33.6 hours per week for non-AIDS affected households
to between 11.6 and 16.4 hours for AIDS-affected households. The study finds that the most
time- consuming activity for women in HIV/AIDS-affected households is nursing at home,
which amounts to 50.2 hours per week on average. The study highlights the tradeoff between
women’s childcare responsibilities and nursing duties. Women in non-AIDS affected
households spend 25.7 hours per week caring for children while women in AIDS affected
households spend between 1.9 and 13.1 hours per week on childcare.
A qualitative study of a farming region of southern Zambia finds that women were
forced to abandon their agricultural work because of their care-giving responsibilities stemming from HIV/AIDS. The study concluded that the rigid division of labor in that environment was a limiting factor in household responsiveness (Waller 1997). Care-giving
responsibilities can also have intergenerational impacts as found by Yamano and Jayne
(2004) in rural Kenya. In their study of working age adult mortality and primary school
attendance, the authors find that adult mortality negatively affects children’s, in particularly
girls’, schooling even in the period directly before mortality, most likely because the children are sharing in the burden of care-giving (Yamano and Jayne in Gillespie and Kadiyala
2005). Hansen and others (1994) study four home care programs in Zimbabwe and estimate the cost incurred by households in caring for a bedridden patient for three months.
The study concludes that the time spent on caring, diverted from other activities such as
food production, employment, education and care of other household members, is the
highest cost burden incurred by these households. In Kagabiro, Tanzania, the labor loss of
households affected by HIV/AIDS is on average 43 percent (Tibaijuka 1997).
Challenges to Reducing Women’s Time Poverty
As the sectoral breakdown of time use among genders in Sub-Saharan Africa reveals, there
are marked differences in how much and on which tasks men and women spend their time.
Women’s tasks are often made more difficult because of inadequate infrastructure for
water, energy, and transport, as well as women’s lack of access to productivity-enhancing
technology that is responsive to their specific needs and work burdens.
The lack of adequate infrastructure, such as feeder roads, water and sanitation systems,
and energy sources, as well as the under-provision of services flowing from these systems,
imposes greater work burdens on women and lengthens the time it takes to perform activities related to household survival and economic production (see Box 2.1).
A 1995 UNDP Report analyzes rural “total transport demand” in the region (all
movement of people and goods by any means, including women headloading water, pack
animals moving relief supplies, and non-motorized vehicles on footpaths and trails).
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Box 2.1: Time Can be Saved through Better Infrastructure: Examples from Uganda
and Zambia
A study conducted in Mbale, Eastern Uganda and Kasama, Zambia illustrates the time saving
effects of better infrastructure: If woodlots were within 30 minutes of the homestead and if the
water source were within 400 meters, Mbale households, specifically women and girls, would save
more than 900 hours/year; around 240 hours in firewood collection and 660 hours in water collection. Similarly, in Kasama, Zambia they would save 125 to 664 hours a year in water collection
and 119 to 610 hours a year in firewood collection.
Source: Barwell 1996.
It finds that total transport requirements in agricultural production and to meet essential
domestic needs (water and fuel collection for example) are much more significant there
than those of crop marketing. This burden largely falls on women, who expend 70 percent
of the time and over 80 percent of the effort devoted to these tasks (Urasa in UNDP 1995).
Similarly, Malmberg-Calvo (1994) in her study of three Sub-Saharan African countries
(Ghana, Tanzania, and Zambia) finds that women account for about 65 percent of the time
spent on all transport activities in the rural household, and about 71 to 96 percent of the
time spent on domestic travel activities. While men’s main transport contributions are in
association with crop establishment, weeding and transport of harvested crops, women are
active in both of these tasks, as well as in transport work associated with basic needs provisioning and crop marketing (Bryceson and Howe 1993).
Improving women’s access to alternative sources of energy other than traditional biofuels can reduce not only their time burdens, but also their exposure to indoor air pollution and
other risks to their health. Cooking fuels such as kerosene and liquefied petroleum gas (LPG)
are good substitutes for traditional biofuels because of their higher thermal efficiency and relative lack of pollutants. The use of such fuels also saves women time for more productive activities by eliminating the need to walk long distances to gather fuel, and by reducing cooking
time. As shown in a study of India, time thus saved can be used for income-earning pursuits,
attention to children, civic participation, or leisure (Barnes and Sen 2003).
Strengthening transitional, low-cost solutions that are already being used by the poor can
also reduce women’s time and effort burdens (Modi 2004). These include diesel-powered
minigrids for charging batteries that can be carried to households and multifunctional platforms powered by a diesel engine for low-cost rural motive power. The multifunctional
platform, implemented in Mali, has been particularly successful (see Box 2.2).
Improvements in the form of labor-saving and productivity-improving technologies in
agriculture can significantly increase productivity, reduce the number of hours worked, and
relieve individuals’ time poverty (see Box 2.3; Von Braun, Swaminathan, and Rosegrant
2005). The amount of time and energy women spend on agricultural tasks such as harvesting, weeding, hoeing, planting, and food processing can be reduced considerably with
the help of adequate inputs such as improved seeds, fertilizers, and pesticides in addition
to labor-saving tools and equipment. As the time they need to spend on agricultural tasks
is reduced, women can allocate their time to other work, including work in the nonagricultural sector.
Gender and Time Poverty in Sub-Saharan Africa
Box 2.2: Diesel-powered Multifunctional Platforms Reduce the Burdens
on Women in Mali
By many measures Mali is one of the poorest and least developed countries in the world. Nearly
three-quarters of its roughly 12 million people live in semi-arid rural areas, where poverty is most
severe. Electrification is virtually nonexistent, and most of the country’s energy supply, particularly
in rural areas, comes from biomass. Women and girls are responsible for the time-consuming and
labor-intensive work of fuel collection.
Beginning in 1993 the UN Industrial Development Organization and the International Fund for
Agricultural Development initiated a program to decrease the burden of fuel collection by supplying labor-saving energy services and promoting the empowerment of women by supplying multifunctional platforms to rural villages. The multifunctional platform is a 10-horsepower diesel
engine with modular components that can supply motive power for time- and labor-intensive work
such as agricultural processing (milling, de-husking) and electricity for lighting (approximately
200–250 small bulbs), welding, or pumping water. Between 1999 and 2004, 400 platforms were
installed, reaching about 8,000 women in villages across the country.
Although the benefits are shared by many in the villages, women’s organizations own, manage,
and control the platform. Capacity building and institutional support by the project, strong in the
early phases, taper off, leaving the women’s groups in charge of platform operation, relying on a
network of private suppliers, technicians, and partners. The women’s groups cover 40–60 percent
of initial cost. The remaining costs are covered by international donors and local partners (nongovernmental organizations, social clubs, and other donors).
A study of 12 villages found several beneficial impacts:
■ The platforms reduced the time required for labor-intensive tasks from many hours to a matter of minutes. The time and labor women saved was shifted to income-generating activities,
leading to an average daily increase in women’s income of $0.47. Rice production and consumption also increased, an indirect benefit arising from time saved.
■ The ratios of girls to boys in schools and the proportion of children reaching grade 5 improved,
as young girls were needed less for time-consuming chores.
■ Increases in time and the mother’s socioeconomic status accompanying the introduction of
the platforms correlate with improvements in women’s health and increases in the frequency
of women’s visits to local clinics for prenatal care.
Overall, the program in Mali offers compelling evidence that time saved in the lives of women
and children, combined with the added socioeconomic benefits to women’s groups of controlling
and managing the platform as a resource, can yield substantial benefits to health and welfare.
Source: Fraccia and others in Modi 2004.
Box 2.3: Manual Versus Mechanized Food Processing
In Nigeria, threshing and milling of grains before pounding may take 2 to 3 hours each day.
82 women hours are needed to process one drum of oil palm fruits. Cassava processing without a
grating machine can take two days of a week. Grinders that can grate a basin of cassava in one minute
compared to two hours by hand are estimated to be present in only 5 percent of Imo State Villages.
Studies carried out in Africa show that it may take up to 13 hours just to pound enough maize to
feed a family for 4 to 5 days.
In the Congo, processing of tapioca and maize took four times as long as all the work hours spent
on the cultivation of these crops.
Source: Ay in Saito and others 1994; Rogers in Blackden 2002.
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Box 2.4: Women’s Adoption of Appropriate Food Processing Technologies
in Tanzania
Most households in Tanzania use traditional sun drying methods. Women’s time is the only
required input. Foods are placed on mats or the bare ground and exposed to direct sunlight.
Unfortunately, the preserved foods often become contaminated and lose their nutritional value.
To improve the food drying process, the Tanzania Food and Nutrition Centre (TFNC) with colleagues in the Ministry of Agriculture and the Ministry of Community Development, Children and
Women’s Affairs developed improved solar dyers to reduce foods’ exposure to contaminants and
direct sun, and the time needed for drying. The quality of food produced through these driers was
more consistent and the nutritional content improved.
The program developed models that responded to women’s needs through providing individual
portable driers. This saved considerable time and also provided substantial flexibility for women.
The program also included an education strategy that ensured all community members received
the same basic information on this new technology.
Over the course of the study, the proportion of women in intervention and control communities
who reported drying vegetables changed somewhat between baseline (88 and 94 percent) and
the next project later (99 and 98 percent). However, adopters produced on average 55 liters after
one year as compared to only 33 liters for non-adopters in the intervention communities.
Adopters also dried significantly larger quantities of a variety of vegetables that were cultivated
in home gardens than did non-adopters. Moreover, the project had the effect of increasing the
consumption of vitamin A and provitamin A rich foods among children whose mothers’ adopted
the improved solar as opposed to children whose mothers were non-adopters.
Source: Mulokozi and others 2000.
In many cases, compared with men, women’s access to time-saving and productivity-enhancing inputs and technologies is very limited. Although this may be linked to a
certain extent to the lack of adequate technologies directly applicable to some of these
tasks, in most cases the technology is available but out of reach of women. Programs
aimed at introducing productivity-enhancing techniques need to identify the community
specific causes of why women are unable to access these technologies and subsequently
address these in their design to make sure women benefit equally from their interventions
(see Box 2.4).
Methodologies Used in Time Use Surveys
In developed countries, time use surveys have had a long history. The surveys are used to
complement the official statistics, which already give a fairly accurate account of market
activities, by providing additional information on time spent on activities such as household work, childcare, and leisure. However, in Sub-Saharan Africa, as noted above, a significant amount of productive activity takes place within the household that is not fully
captured by official statistics. The design, methodology, and implementation of time use
surveys in Sub-Saharan Africa require special attention to the region’s circumstances. In
this region, most of the respondents of time use surveys are likely to be illiterate necessitating use of illustrated survey materials or interviewer-administered surveys. Similarly,
Gender and Time Poverty in Sub-Saharan Africa
25
respondents seldom own or wear watches, lack a modern concept of time, and relate their
activities to fluctuations of nature such as day time or the season (Harvey and Taylor 2000).
To overcome this problem, special tools need to be used to translate local perception of
time into a standard 24 hour timetable. Also, in most countries in the region, people have
overlapping work responsibilities that involve tradeoffs with implications for survival and
growth. This is particularly evident in women’s time use patterns because their time is
already stretched to the limit, and therefore, they are forced to undertake simultaneous
responsibilities.
Women often do not consider domestic and personal care activities as work, and hence,
do not report it (Harvey and Taylor 2000). The omission of such activities may in turn cause
a downward bias in the measurement of intensity of women’s work. Time use surveys need
to be designed to capture individuals’ work intensity and the tradeoffs they face. Finally,
agriculture is the dominant sector in Sub-Saharan Africa and there are distinct seasonal variations in the workloads of women and men. Therefore, it is important to undertake the surveys over a year at different points in time to capture the impact of seasonality.
Certain tradeoffs need to be considered in deciding the type and scope of surveys. A
large-scale time use survey, more likely to be conducted by major statistical agencies, can
reveal patterns of time use by different demographic groups as well as those in different
socio-economic clusters but would be costly to implement. Small-scale studies, although
not as detailed and representative, are important in drawing attention to the need for larger
studies. Qualitative studies of time use are very useful since they provide in-depth information on the sociocultural conditions that determine time use patterns of individuals
within a household. However, they should not be treated as a substitute for quantitative
analysis; rather a combination of both qualitative and quantitative information is desirable
in developing policies and interventions.
Various methodologies have been followed in conducting time use surveys.8 In developing countries, direct observation has been one of the preferred methods for conducting
time use surveys. This method addresses the issues of illiteracy and time measurement since
it uses an outside observer to record the activities of the subjects he or she follows during
the day. It is also helpful in capturing activities that are unstructured and where simultaneous tasks are performed. There are, however, some drawbacks to this method. First of
all, it is highly costly and forces the selection of a smaller sample size. Also knowing that
they are being observed, people tend to change their pattern of behavior. Finally, the
observer may find it difficult to distinguish between market and non-market activities. Due
to the drawbacks of this method others have been explored as well.
Interviewer administered time diaries provide a chronological report of the time studied, provide consistency in time activity data, and forces full accounting. Depending on the
design, this method may allow for recording of both primary and concurrent activities. The
disadvantage of this method is that the design may become too complicated to implement,
especially in developing countries, where interviewers and respondents have low levels of
education (Harvey and Taylor 2000).
8. A list of methods used in time use surveys including the advantages and disadvantages of each of
these methods are provided in Annex 3.
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Box 2.5: Valuing Unpaid Non-SNA Work
Valuing unpaid work is important in capturing the economic contribution of unpaid non-SNA
work. Unpaid work and particularly unpaid care work yield significant positive externalities to
the economy that remain unnoticed unless they are quantified in monetary terms (Budlender
2002). There are several approaches to value unpaid work. These approaches can be grouped
into four broad categories: the mean wage approach, the opportunity cost approach, the generalist approach, and the specialist approach. The mean wage approach calculates the average wage in the economy as a whole and assigns this wage to unpaid non-SNA work. The
average is usually calculated separately for men and women and assigned accordingly. The
opportunity cost approach uses the market wage rate foregone by doing unpaid non-SNA work
as the wage rate. The generalist approach takes as wage rate the average wage of home helpers
meanwhile the specialist approach assumes that different people with different qualifications
would take over different household tasks and uses their wage rates to calculate the wage rate for
housework.
Source: Budlender 2002; Swiebel 1999.
Conclusions and Recommendations
This paper has sought to bring to light a critical gender-relevant dimension of poverty in
Sub-Saharan Africa: time poverty. Its aim has been to inform the analysis of poverty in
Sub-Saharan Africa in a manner that in turn can inform policy and operational priorities.
Time poverty, while not a new concept, adds important insights to the understanding of
poverty dynamics in Sub-Saharan Africa, how these dynamics affect men and women differently, and how they might be acted on in poverty reduction strategies. Time use data
in Sub-Saharan Africa, as shown in the literature review in this paper, and in other papers
in this volume, perform some very important functions. They reveal the co-existence of
both market and household economies, and how they are interdependent. They show not
only the sheer size and significance of the household economy, measured in terms of the
amount of time spent on household economy tasks, but also the disproportionate burden that falls on women for the accomplishment of these tasks, a burden that has been
greatly exacerbated by the HIV/AIDS pandemic. They show that there are important synergies, and critical tradeoffs between and among tasks in these economies, with important implications for poverty reduction and development outcomes. Understanding the
time impact of development interventions becomes a critical dimension of appraisal and
evaluation.
What the time data show us is that there are important differences in gender roles,
which constitute a major obstacle to development and poverty reduction in Sub-Saharan
Africa. Women’s significant, though understated, roles in economic production (agriculture and the informal sector, predominantly) and their pivotal position in household
management and welfare (food preparation, health and hygiene, childcare, and education) are central to Sub-Saharan Africa’s economic development and social survival. Time
use data confirm the evidence available in the agricultural sector showing that women are
indeed the continent’s principal food producers and have primary responsibility for
assuring food availability in the family: they are therefore central to the attainment of
Gender and Time Poverty in Sub-Saharan Africa
27
Box 2.6: Making Extension Services Tailored to Women’s Needs
Extension services are an important means of disseminating information on new techniques, seed
varieties, marketing, and related services in agriculture. PRSPs such as the Ugandan PEAP
acknowledge the importance of these services in improving productivity in agriculture. Although
few studies investigate the direct links between access to agricultural extension and time use, by
improving productivity, these services can reduce the drudgery and time involved in key agricultural tasks undertaken by women. Gender-sensitive training is often required for extension workers, and the content of extension services should be structured to include resources, commodities,
and tasks that are more relevant to women’s crops and agricultural roles. Extension workers need
to specifically target women farmers by moving away from their traditional audience of household heads. Finally, services should be designed to reach women who are constrained by lack of
time and, in certain communities, limited mobility.
Source: Saito and others 1994.
Sub-Saharan Africa’s food security goals and to meeting family nutritional needs. Women
are the principal gatherers and users of wood for fuel and water for washing and cooking: how they do this critically affects the pace and extent of environmental degradation
and the fertility of the soil. Women have primary responsibility for child rearing and
family health: on this the future productivity of the country’s human resource base
depends.
Time use data show that both men and women have multiple roles and responsibilities. What particularly characterizes women’s roles, in contrast to those of men, is that they
must carry out their roles simultaneously, not sequentially. This is evident not only in the
extent of women’s labor burden and the very long working hours, but also in the harsh
choices and tradeoffs that women inevitably have to make because of the simultaneous
competing claims on their time. Addressing time poverty in a way that speaks to these gendered differences therefore needs to be integral to strengthening poverty reduction strategies (PRSs).
As the analysis in this paper also demonstrates, there exist a strong correlation between
gendered differences in time poverty and men and women’s roles in various sectors. The
identification in PRSPs of this relationship with particular attention to gender roles in various sectors is instrumental in devising interventions that can target reductions in time
poverty (see Box 2.6).
Time use analysis can strengthen the policies in sectors that are identified as key to
reducing poverty and improving living and working conditions of women, including agricultural modernization and commercialization, infrastructure, and employment, among
others. Such analysis can also provide guidance in prioritizing sectoral allocation of public expenditures. Time use analysis could support this by being an essential component of
the monitoring and evaluation of the policies and interventions proposed under PRSPs
(see Box 2.7).
Analysis of time use data reveals the significance of the household economy. This in
turn suggests that an important priority for poverty reduction strategies is to invest in
this economy. Such investment would aim to reduce the “household time overhead,”
and relieve the time burdens on women. It would have the added benefit of specifically
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World Bank Working Paper
Box 2.7: Using Time Use Data in Project Evaluations
A review of transport projects supported by the World Bank found that in 2002 four percent
of these projects included a gender component or gender actions, compared with 15 percent
of water supply projects and 35 percent of agriculture projects. Among those World Bank
financed transport programs that pay attention to gender, two are worth mentioning. The
Shova Kalula bicycle project in South Africa aimed at providing low cost mobility solutions
such as non-motorized transport. The project diagnostic incorporated some time use data on
time spent by workers and students on transport. A review of the project found that it significantly reduced the amount of time students spend traveling to school. There was also case
study evidence that women widely used bicycles to access the market to “buy and sell vegetables” (Mahapa 2003). The gender review of the Mubende Fort Portal Trunk Road Rehabilitation project in the Ugandan road sector program also noted the effects of the rehabilitation
project on women’s travel time to markets, trading opportunities, farm inputs, and so forth.
(Tanzarn 2003). Making time use analysis part of project evaluations such as the ones mentioned above can potentially improve their impact and provide better guidance in designing
future interventions.
reducing the tradeoffs among competing uses of scarce labor. Examples of such investment priorities include infrastructure to provide clean and accessible water supply, and
energy focused on domestic requirements (notably for cooking fuel). Labor-saving
domestic technology relating to food processing has the potential to raise women’s labor
productivity and save time. Transport interventions can be oriented to reflect the different needs of men and women, so as to improve women’s access to transport services
(including intermediate means of transport), commensurate with their load-carrying
responsibilities. Energy policy and investment priorities need to focus on alternative
energy sources, and to address the domestic energy needs of households. This will have
important multiplier effects on improving health, saving time, and enabling girls to go
to (and stay) in school.
Of particular importance in Sub-Saharan Africa is the need to address the huge care
burdens facing women in the face of HIV/AIDS, given their disproportionate responsibilities in this area.
As discussed earlier, care for AIDS sufferers occurs in households alongside the other
caring work that women do to sustain their families. As the epidemic progresses, the burden of caring for those living with HIV and AIDS can overtake and displace not only the
other crucial work of the care economy, but also the ability of women to perform their critical economic functions in agriculture and food security.
In recent years international and national level policymakers have begun to recognize
the need for a more coherent, expansive, and inclusive “care agenda” for HIV/AIDS, and
that systems need to be put into place to help households and communities provide care
for those who are sick and dying from AIDS (Ogden, Esim, and Grown 2004). Although
many community-based health care programs are in place, there is an urgent need for these
programs to take account of the time burdens of women, to avoid adding to these burdens,
and to be more strongly integrated with wider family care and production work carried out
by women.
Gender and Time Poverty in Sub-Saharan Africa
29
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Appendix Table 2.A1. Inventory and Design Components of All Cross Section and Panel Time Use Data Sources
in Sub-Saharan African Countries
Country
Sample Size
Type of Survey
Survey
Instrument
1787 households, 5834 respondents in
rural; 1419 households, 6770 respondents
in urban; ages 6–65
Module of survey on labor,
income, and social indicators.
Simplified diary; 63 activities, 15 minute intervals
Survey
Mode of Data
Collection
Face to face recall interview; one diary day
Benin
Time use survey,
1998
Botswana
Rural income distributionsurvey, 1981
Botswana
1984
Cameroon
1984
Cote D’Ivoire
1982
880 women
Cote d’Ivoire
CILSS 1985–1988
1588 households in CILSS85, 1600 households in CILSS86,87,88; ages 7 and older
Module in living standards
survey
Stylized activity list; seven
day recall
Face to face recall
interview
Ghana
Ghana (1991–92,
1998–99)
5998 households 25664 individuals, ages
7 and older
Time use short and incomplete
module in a continuous living
standards measurement survey
Seven day recall; ad hoc
classification.
Face to face recall
interview
Kenya
1985
115 households
Participant observation
Kenya
1990
44 women
Participant observation;
open ended interview
Kenya
1998
Madagascar
2001
2663 households, 7743 individuals; ages 6
to 65.
Specific survey attached to a
permanent survey (parallel
sample)
Prelisting of 77 activities
classified SNA/non SNA
Malawi
Time allocation of
adults; time allocation of children ages
7 through 11; 1995
404 households
Module of Financial Markets
and Household Food Security
Survey
Two day recall
Survey
Interview
Pre-listed Diary
24 hours past day
33
(continued)
Gender and Time Poverty in Sub-Saharan Africa
Interview
34
Country
Malawi
Survey
Adolescents ages 15
through 21; 2004
Sample Size
1000 adolescents
Survey
Instrument
Mode of Data
Collection
Twenty four hour diary; 30
minute intervals; UN
classification
Face to face recall
interview; one diary day
Type of Survey
Subsection in the Malawi
Diffusion and Ideation
Change Project (MDICP).
Ongoing longitudinal study.
Diary
Module of Integrated
household survey
Malawi
Mauritius
Time use survey, 2003
6480 households; 19907 individuals;
ages 10 and older.
Nigeria
1982
69 households
Interview
Nigeria
1992
429 households
Participant observation
Nigeria
Time use in Nigeria, 1998
20 households from 4 states and Lagos;
243 respondents; 10 years and older.
Senegal
1983
139 mothers and children
South Africa
National time use survey,
2000
8564 households; 14553 respondents;
ages 10 and older.
Uganda
ULSS, 1992
Uganda
1993
9929 households
Zimbabwe
1991
132 households
Zimbabwe
1992
331 households
Source: Budlender 2002; UNESCAP 2003.
Module of continuous multipurpose household survey
Independent (pilot)
Open diary with chronological recording of activities
starting; starting and ending times are recorded;
total number of minutes
per activity is also recorded.
Self reporting and face
to face interview; one
diary day.
Spot observation
Independent
Full diary; 30 minute
intervals
Face to face recall interview; one diary day
Module in national integrated household survey
Stylized activity list; seven
day recall
Face to face recall
interview
Interview direct measure
World Bank Working Paper
Appendix Table 2.A1. Inventory and Design Components of All Cross Section and Panel Time Use Data Sources in Sub-Saharan African
Countries (Continued)
Table 2.A2. Matrix of Empirical Studies with Data Sources, Methodology, and Outcomes
Study and
Location
Bollinger, Stover,
and Seyoum (1999)
Ethopia
Rubin
Kenya
Ghana Living
Standards Survey
(GLSS) 87/88,
88/89, 91/92, and
ILO Child Labor
Survey (1996)
Sample
and Design
100 households
Objectives
Differences in workload of women in
HIV/AIDS affected
households versus
in unaffected
women.
Findings
The mean hours women spend on agricultural tasks varies
between 11.6 and 16.4 in affected households compared to
33.6 hours in unaffected households.
Women in affected households spend between 1.9 and
13.1 hours a week on childcare compared to 25.7 hours
in unaffected households.
2876 children in
GLSS1, 3011 children in GLSS2, and
3859 children in
GLSS3
Identify patterns
explaining the
prevalence of child
labor in certain
households
When household chores are included in the definition of work:
Girls are more likely to work
The presence of children younger than 6 increases the probability of working and not attending school.
Presence of female adults increases probability of schooling
and not working.
75 households
interviewed over a
12 month period.
How do time use
patterns between
men and women
differ?
How did the introduction of sugar
cane outgrower
scheme change the
patterns of time use?
Women allocate most of their time on the daily performance
of household duties as well as food crop farming.
Women in cane households spend more time on domestic work
and in craft work. They also had more leisure time
Women in non-sugar households spent more time on food crop
production, marketing and transport as well as in hired agricultural labor.
(continued)
Gender and Time Poverty in Sub-Saharan Africa
Canagarajah and
Coulombe (1993)
Ghana
Data Sources
Household survey
35
Data Sources
1997, 2000, 2002
panel of 1422
households
Sample
and Design
Children ages 7–14
Malawi Integrated
Household Survey
(IHS) 1997/98
10,698 households –
46,128 individuals.
Bhargava
Rwanda
1982–83 longitudinal study
110 households
Ritchie, Lloyd, and
Grant
Multiple countries
including South
Africa and Kenya
from SSA
South Africa (1999);
Kenya (1996);
Pakistan (2001–02),
India (2003),
Guatemala LSMS
(2000), Nicaragua
LSMS (1998)
16,045 adolescents
in Guatemala, 6,
148 in India, 774
in Kenya, 5,115
in Nicaragua,
8,062 in Pakistan,
3051 in South Africa
Nankhuni (2004)
Malawi
Objectives
Does working age
adult mortality
affect children’s
primary school
attendance
Findings
The authors first establish that a high proportion of working
age adult mortality in the household data is AIDS related.
In poor households (measured in terms of asset distribution)
children’s school attendance is adversely affected by the death of
working age adults. The probability that girls from relatively poor
households attend school in the one to two year period before
the death of an adult declines from 90 percent to 62 percent.
One of the conclusions the authors derive is that children especially girls are sharing the burden of caring for sick working
age adults
Is the time children
spend collecting fuel
wood and water a
determinant of their
school attendance?
Being female is the most significant determinant of a child
participating in natural resource collection
Girls are more likely to attend school while burdened by
resource collection responsibilities.
Substitution between women’s agricultural work and their
housework.
Men’s housework is insignificant.
How does time use
change during the
transition to adulthood, does gender
role differentiation
intensify during the
transition, does
school attendance
attenuate gender
differences.
Female students carry a heavier workload and enjoy less leisure
time than male students. But the difference in workloads is
smaller compared to the one between adolescent girls and boys
who do not attend school.
World Bank Working Paper
Study and
Location
Yamano, Jayne
Kenya
36
Table 2.A2. Matrix of Empirical Studies with Data Sources, Methodology, and Outcomes (Continued)
Table 2.A3. Select Methodologies of Time Use Data Collection
Method
Direct Observation
In this method, the researcher observes what
individuals do at particular times and records
their activities.
Advantages
■ Does not require that the person observed is
literate or have a western concept of time.
■ Useful when activities are unstructured
and fractioned in very small segments
and where several activities are performed
simultaneously
Stylized Questions
This method is usually part of a questionnaire
and consists of general questions on time
spent on certain activities such as preparing
meals on a given time period (day/week).
■ Reliable in recording frequency of
participation
■ Less costly to process than diary data
Disadvantages
■ Very researcher intensive.
■ High costs
■ Knowing that they are being observed, people
tend to change their behavior
■ Observer may have problems distinguishing
market and non-market activities.
■ The total hours reported on various activities may
exceed 24 hours.
■ Dependent on perception and subjective calcula-
tion of time use.
number of simultaneous activities
■ Wording of stylized questions is hard and may
lead to misinterpretations.
Stylized Activity List
In this method, the questionnaire includes a
list of daily activities and the person is asked
how much time he/she spends on a given day
doing each of these activities.
Activity Log
In this method, the person is asked to record
on a questionnaire each time he/she engages
in an activity and the time spent on this
particular activity.
■ Reliable in recording frequency of participation
■ Less costly to process than diary data
■ Depending on the design of the list, the
accounting nature is maintained.
■ The list provided may not include all possible
daily activities.
■ Wording of stylized questions is hard and may
lead to misinterpretations.
■ Assumes that the person is literate and that they
own a watch.
■ Relies on the person’s motivation and meticulous-
ness in keeping the log.
37
(continued)
Gender and Time Poverty in Sub-Saharan Africa
■ Time constrained stylized questions omit a large
38
Table 2.A3. Select Methodologies of Time Use Data Collection (Continued)
Stylized Time-Activity Matrix
Similar to stylized activity list, this method
includes a list of all possible activities. In this
case time allocated should be 24 hours.
■ Assumes that the person is able to remember all
of his/her activities and can accurately assign
them to the categories.
■ Assumes good memory and good calculation skills.
Time Activity Matrix
This method is an expanded version of the
stylized time activity matrix. Includes an activity list and a list of time periods. The person
recording the activities marks off the activities
that he/she did in that time period. Each time
period must have at least one activity.
Interviewer Administered Time Diary
In this method, the questionnaire does not
provide a list of activities; the respondent
describes each activity in his/her own words
from the beginning to the end of a day.
■ Provides consistency in time activity data by
forcing full accounting of time.
■ Depending on the design, may provide data on pri-
■ Design may be too complicated especially in
developing countries where the education level
of interviewers and respondents tend to be low.
mary and simultaneous activities as well as sequential, spatial and social dimensions of the activity
■ Minimizes recall bias
Tomorrow or Left Behind Diaries
These are the same as time diaries but the
diaries are left to respondents to fill out
■ Since events can be recorded as they are per-
formed, period covered can be more extensive
than in other methods
■ Difficult to implement in illiterate societies.
Diaries need to include pictures and extensive
training of respondents may be required.
■ These are less expensive methods unless major ■ Respondents may delay recording activities which
review and corrections are needed
may cause inaccuracies.
■ Quality of diary may decline as respondents get
less attentive.
■ Problems in recording the sequence of activities may
occur. Concurrent activities may not be recorded.
Source: Adapted from Budlender (2002) and Harvey and Taylor (2000).
World Bank Working Paper
Disadvantages
Advantages
Method
CHAPTER 3
A Review of Empirical
Evidence on Time Use in Africa
from UN-Sponsored Surveys
Jacques Charmes9
This paper reviews some of the empirical evidence from time use surveys in Sub-Saharan Africa.
Starting from a discussion of the 1993 revision of the System of National Accounts (SNA), the
paper reviews definitions of work, both market-based and non-market work, paid and unpaid,
and how these different types of work are classified and counted within and outside the SNA.
The paper then summarizes the results of national time use surveys in four SSA countries,
Benin, Madagascar, Mauritius, and South Africa, along with results from the time use module
of the Ghana Living Standards Survey, paying particular attention to domestic work, the care
economy, and non-economic activity. In conclusion, the paper examines some of the potential
correlations between time use patterns and other development variables.
T
ime use surveys carried out at national level have for a long time been confined to
industrialized countries where they were designed to measure the transformation
toward a society and an economy of leisure. More recently, with the rise of unemployment rates, their results were used to show the changing roles of men and women in
domestic activities. Yet, it is of course the measurement of domestic activities and of the
care economy which always was their major objective.
This objective moved toward the transformation of these activities with the massive
entry of women into the labor market and the rise of the elderly and the disabled in the
population. In developing countries where time use surveys had been confined to village
studies or very small and often non-representative samples, it is only recently that such
nation-wide surveys have been implemented, with the support of UNDP programmes and
the UN statistics division. India and Nepal (1999), Benin (1998), Nigeria (1999), South
9. The author is with the Institut de Recherche pour le Développement in Paris.
39
40
World Bank Working Paper
Africa (2000), Madagascar (2001), and more recently Mauritius (2003) have carried out
such surveys. In the meantime however, some living standards surveys (especially in
Ghana) included a time use section in their questionnaire in order to capture the main
domestic or non-market activities.
In the context of developing countries, one of the major aims of time use surveys is to assess
the underestimation of female participation in the labor force. In particular they aim at giving
an estimate of women’s contribution to the industrial sectors where they are often engaged in
secondary activities which are not recorded by regular labor force surveys (especially in the processing of agricultural and food products, and also in textiles-clothing activities).
Moreover, results from time use surveys are of great help in the implementation of the
1993 SNA in countries where non-market production for own use consumption (including fetching water and wood) or capital formation is widely spread. But they are also used
for the measurement of domestic activities in these countries as well. More recently, time
use surveys have contributed to illustrate another dimension of poverty: lack of time due
to multiple timetables (domestic work, care work, non-market economic activity) resulting in time poverty and low monetary income.
In this paper, we will first recall the definitions of work, market and non market, paid
and unpaid, and economic activity as measured by the System of National Accounts (SNA)
and its satellite accounts of household production in connection with the objectives of time
use surveys. In the second section, the results of available time use surveys in various countries of Sub-Saharan Africa will be presented with particular reference to domestic work,
care economy, non-market economic activity. Finally we will investigate the potential correlation between time use patterns and key development variables, especially in the perspective of future data collections and analyses.
Definitions of Work in the System of National Accounts
It is not here our purpose to come back into the details of the definitions of work and economic activity which we treated more extensively elsewhere (Charmes and Unni 2004;
Charmes 2005). We would rather like to identify and specify some remaining topics of misunderstanding between feminist economists and national accountants in charge of the definition of what is to be counted in the Gross Domestic Product (GDP). We fully support
the extensive notion of work and we recognize that it must be measured in the national
accounts and the GDP. However, we think that for this purpose and for a better efficiency
in the debates, it is crucial to use a terminology that is not confusing and misleading.
It is very usual to read in academic reports and articles that the exclusion of non-market
work results in a downward bias in GDP calculations and other macroeconomic indicators.
Or that current labor statistics do not capture the informal sector market work or the nonmarket work. Such assertions are incorrect.
Since 1993 (SNA 1993), production, as measured by the SNA, includes:
—the production of all goods and services destined for the market whether for sale or
barter;
—the own-account production of all goods that are retained by their producers for
their own final consumption or gross capital formation;
Gender, Time Use, and Poverty in Sub-Saharan Africa
41
—the own-account production of housing services by owner-occupiers and personal
services produced by households employing paid domestic staff; and
—the production of all goods and services provided free to individual households or
collectively to the community by government units or non profit institutions serving households.
The SNA finds it useful to provide a tentative (but not complete) list of the types of production of goods for own consumption (SNA 1993, §6.24):
—“the production of agricultural products and their subsequent storage; the gathering
of berries or other uncultivated crops; forestry; wood-cutting and the collection of
firewood; hunting and fishing;
—the production of other primary products such as mining salt, cutting peat, the supply of water, etc.;
—the processing of agricultural products; the production of grain by threshing; the production of flour by milling; the curing of skins and the production of leather; the production and preservation of meat and fish products; the preservation of fruit by
drying, bottling, etc.; the production of dairy products such as butter or cheese; the
production of beer, wine, or spirits; the production of baskets and mats; etc.;
—other kinds of processing such as weaving cloth; dress making and tailoring; the production of footwear; the production of pottery, utensils and durables; making furniture or furnishings; etc.”
As to the production of goods and services for own gross fixed capital formation, it includes
the production of machine tools, dwellings and their extensions, and in rural areas such
communal and collective construction activities as small dams, trails, irrigation channels,
and so forth.
It is usually not very well known that such female time-consuming activities as firewood and water fetching fall within the boundaries of measured production. Ever since the
SNA 1968, such activities are included, being considered as extractive activities and the
National Accounts in various West African countries include them in their GDP calculations (for instance Burkina Faso; see Charmes 1989).
Referring now to labor force statistics, the resolution “concerning the economically
active population, employment, unemployment and underemployment” (1982 International Conference of Labor Statisticians) unambiguously states that “the economically
active population comprises all persons (…) who furnish the supply of labor for the production of economic goods and services as defined by the United Nations systems of
national accounts.”
The SNA distinguishes the “general production boundary” and the “production boundary in the SNA.” In the general production boundary, production is defined as the physical
process in which labor and assets are used to transform inputs of goods and services into outputs of other goods and services. Moreover, two conditions are required from goods and services to fall within the definition: “marketability” and adequacy with the “third-party”
criterion. Marketability means that goods and services can (and not must) be sold in markets. The third-party criterion implies that the goods and services are “capable of being provided by one unit to another with or without charge,” which excludes non-productive
42
World Bank Working Paper
activities in an economic sense such as eating, sleeping, studying, and so forth, but includes
the home and care economy (preparation of meals, care and training of children, care of
the sick, handicapped, elderly, and so forth) as well as volunteer work. However, the definition of the production boundary in the SNA, for the purpose of measuring the GDP,
restricts the scope by excluding the production of domestic and personal services by household members for consumption within the same household. Furthermore, the list of these
domestic and personal services to be excluded (unless they are provided by paid employees)
is enumerated (SNA, 1993, §6.20):
—the cleaning, decoration and maintenance of the dwelling occupied by the household,
including small repairs of a kind usually carried out by tenants as well as owners,
—the cleaning, servicing and repair of household durables or other goods, including
vehicles used for household purposes,
—the preparation and serving of meals,
—the care, training and instruction of children,
—the care of sick, infirm or old people, and
—the transportation of members of the households or their goods.
Besides household production, another issue that must be raised in the measurement of
GDP and the definition of work and production is what constitutes “volunteer work.” Here
households are concerned as providers of work, but it is the non-profit institutions serving households (NPISH) which are the beneficiaries of this type of volunteer work. The
value added contributing to the GDP must be attributed to the NPISH sector and not the
household sector. Their output and value added are underestimated in the compilation of
GDP because no monetary value is imputed to volunteer work while work is performed in
the provision of services by these institutions. However, it is different whenever members
of the household are performing such volunteer work for another household (and not for
a collective institution), caring for the neighbor’s children for instance.
Therefore, it is clear that SNA work and non-SNA work, market work and non-market
work, paid work and unpaid work are not substitutable concepts. A part of non-market
work is already included in the GDP (all production of goods for own consumption or capital formation, including collection of firewood and fetching water). This specific part cannot be referred to as “unpaid work” because it contributes to economic production and
the former “unpaid family workers” are now called “contributing family workers” because
their contribution to production has an equivalent on the income side. In particular, it cannot be said that “unpaid work” is not taken into account in the GDP if the contribution to
the production of non-marketed goods (including fetching firewood or water) is part of
“unpaid work.” The following diagram tentatively explains how these various notions
overlap.
The restrictive concept of “unpaid work” is limited to #4 in Figure 3.1: it exactly fits
with the concept of “care economy,” which is not part of the SNA as measured by the GDP.
It is unambiguously outside the GDP. An extended concept of “unpaid work” includes box #3
also extended to all the self-employed engaged in the production of goods for own consumption. The widest concept of “unpaid work” would also extend it to those family workers who are engaged in economic units producing for the market (box #2). Categories 2
Gender, Time Use, and Poverty in Sub-Saharan Africa
43
Figure 3.1. To What Extent Do the Notions of Market/Non-Market Work, Paid/Unpaid
Work and SNA/Non-SNA Work Overlap?
Market work
Paid work
SNA work
Non-SNA work
1
Non-market work
Unpaid work
(contributing)
Unpaid work
(contributing)
2 (family workers)
3 (family workers)
Unpaid work
4 (domestic and
care work)
Notes: (1) Production of goods and services for the market by remunerated labor and remunerated
self-employed. (2) Production of goods and services for the market by contributing family workers
(belonging to economic units producing for the market. (3) Production of goods and services for own
consumption or own capital formation of the household, by contributing family workers (belonging
to economic units not producing for the market. (4) Production of domestic and care services in the
extended SNA.
and 3 are already captured in the GDP even if we can agree that their capture is imperfect
and that their contribution is underestimated.
These few remarks aim to clarify that women per se are not underestimated in the
labor force or that their production is taken for nil in the GDP. There is of course a huge
gap between the concepts as they are defined and their application in the statistical surveys: it is well known that labor force surveys are still undercounting women working in
agriculture in some countries. Yet, progress has been made and the female activity rates
in Sub-Saharan Africa, are among the highest in the world. Even if we admit that the
number of women in the agricultural labor force is underestimated, this does not mean
that their contribution to the agricultural production is underestimated. The reason for
that is that in most countries, the output of agriculture is not based on the performance
of individual farms but on estimates of areas and yields for the main crops. Consequently the contribution of women is really included even if it is not possible to determine their share.
Moreover the tentative measure of subsistence production as a separate aggregate
often turns out to be imperfect because, for the time being, it is more and more difficult
to find pure subsistence farms. Even the smallest farms that produce hardly enough for
own-consumption of the household must sell at the harvest and buy when the storehouses
are empty. In this sense, the fact that many women remain “contributing” family workers in their husband’s farms does not mean that they are involved in subsistence production. As to the agricultural products they grow by themselves, it is well known that they
usually trade them for cash. Therefore the distinction between commercial agriculture and
subsistence agriculture has become more and more fuzzy and most labor force surveys and
national accounts do not use it any more. Even when it is used, it does not mean that this
“subsistence” agriculture is “non-market” agriculture.
However, there are still several reasons for the underestimation of women’s contribution, due to the limits of the current concepts and methods of data collection.
44
World Bank Working Paper
A first reason is that women, more than men, are involved in multiple jobs. The measurement of pluri-activity is still a major challenge for survey statisticians. A better estimate
of women’s contribution to GDP is obtained in the countries where efforts have been made
to measure their secondary activities (multiple jobs). Burkina Faso is a typical example of
a country where the informal sector is principally urban, tertiary, and male as far as the
main activities are taken into account, and becomes principally rural, manufacturing, and
female, when multiple jobs are taken into account. Rural women are engaged in secondary
activities, which mainly consist in the processing of agricultural products and food products.
A second reason results from the first one: statistical surveys generally fail to measure
these female manufacturing activities that are hidden behind agricultural, primary, or trade
activities. The bulk of the female labor force, especially in the informal sector, is in agriculture and in trade. Trade is very often the last stage of diversified female activities starting with growing agricultural products or collecting natural products, processing them
(food products, mats and baskets, textiles, and so forth) and finally selling them. Where
only this last stage (trade) is captured, or the first one (agriculture or gathering), then the
value added of female activities is often underestimated. Moreover where these processing
activities lead into domestic activities (for example, winnowing and crushing cereals for
the preparation of meals), they often remain unmeasured.
Possible directions for future progress in the measurement and understanding of
female pluri-activity lie in time use surveys, but it may require, for instance, a sufficient
decomposition of the activities involved in the preparation of meals.
Measurement of Work in Time Use Surveys in Sub-Saharan Africa
Four time use surveys have been conducted at national level in Sub-Saharan Africa since
1998 (the 1998 time use survey in Nigeria was a pilot survey implemented in four states
and Lagos at 243 households, and the results were not published). The South African survey was a specific ad hoc survey, while the Benin and Madagascar surveys were specific surveys attached to continuous permanent surveys, and the Mauritius survey was a specific
module included in the multi-purpose household questionnaire.
Furthermore, the Ghana Living Standards Survey (GLSS) has included questions on
time use for housekeeping activities in its third round (1991–92), fourth round (1998–99)
and fifth round (2003–04). However in this case, it is not a full-range questionnaire and
the comparability with the other four countries is debatable.10 Before presenting the major
results of these recent time use surveys, it is necessary to recall that, when analyzing time
use data, one must remember that there are two types of results and indicators. A first type
refers to averages covering the total population that has been surveyed (without regarding
whether or not this population was involved in any of the recorded activities; the total
number of hours is divided by the total population). A second type refers to averages covering only the population involved in performing the activities. Moreover, the averages
(presented on a daily or weekly basis) are calculated on a complete year of 365 days:
10. At the World Bank workshop at which this paper was presented in November 2005, other case
studies were presented and are included in Part II and III of this volume.
Gender, Time Use, and Poverty in Sub-Saharan Africa
45
the respondents have been interviewed at random for any day in the week and any week in
the year, so that a part of the surveyed population was interviewed during weekends, holidays, non-working days, days of sickness or days of social events. This is why the figures
presented in the tables may be viewed as lower than expected. For example, a wage-earner
in the formal sector is expected to work at least 8 hours a day, but in a complete year, she
or he may have worked only 6 or 7 hours a day.
Table 3.1 compares the various characteristics of the time use surveys for the five countries, in terms of sample size, age of the respondents, and methods of data collection.
Despite their differences, it can be considered that the results for the four time use surveys
are comparable; only the Ghanaian case is to be looked at separately. It must also be noted
that in Benin and Madagascar, the surveys were carried out in the agricultural off-season.
However, the results seem coherent with the results of other surveys. Table 3.2 provides
the global results of the surveys in the four countries with time use surveys.
Time devoted to production activities as measured by the SNA (and including activities such as fetching water or collecting firewood) varies from a little bit less than 2 hours
(in South Africa and Mauritius) to nearly 3 hours in Madagascar and 4 hours in Benin. It
has to be borne in mind that such statistics include active and inactive persons, the youth
as well as the elderly.
The ratio of females to males is the smallest in Mauritius where women spend only
39 percent of what men spend in SNA production activities and it is the highest in Benin
where women and men spend the same amount of time. In South Africa and Madagascar,
the ratio is around 60 percent.
Regarding work at large, women spend more time than men at work. Duration ranges
from 5 hours and 43 minutes in South Africa to 7 hours and 23 minutes in Benin (and
6 hours and a half in Mauritius and 6 hours and 36 minutes in Madagascar), so that they
exceed men by nearly a half in Benin (47 percent), 29 percent in South Africa, 18 percent
in Madagascar and 6 percent in Mauritius. This is because their involvement in domestic
and care activities is much bigger than men’s: 4.7 times more in Madagascar, nearly 4 times
(3.79) in Mauritius, 3.04 times more in South Africa and 3.1 more in Benin. In Benin,
Madagascar and South Africa, time spent in domestic and care activities is relatively comparable: 3 hours and 28 to 48 minutes (against 4 hours and a half in Mauritius), compared
to a little bit less or more than one hour for men in the four countries.
Finally, the share of SNA activities in work at large ranges from 29.5 percent (Mauritius)
to 33.5 percent (South Africa), 44.2 percent (Madagascar) and 53.0 percent (Benin) for
women, compared with 71.7 percent in South Africa, 77.8 percent in Benin, 80.2 percent in
Mauritius and 86.1 percent in Madagascar for men. In other words, domestic, care, and volunteer activities account for more than 50 percent and up to more than two thirds of work
in its broad sense.
It is interesting to note that in Ghana, when adding up the housekeeping activities
mentioned in the questionnaire (childcare, sweeping, cooking, garbage disposal), the time
spent by women is 5 hours 42 minutes (compared with 3 hours and 8 minutes for men).
The ratio of females to males for domestic and care activities is then comparable to Benin
(1.81 compared with 1.75), but at a much higher level, meaning that the method of data
collection tends to overestimate the time spent in these activities. Or, more likely, it tends
to capture some of these activities as simultaneous activities (especially childcare).
46
Benin (1998)
South Africa (2000)
Madagascar (2001)
Mauritius (2003)
Ghana (1991–92 and 1998–99)
Type of survey
Specific attached to a permanent survey (same sample)
Specific ad hoc survey
Sample size
(households)
Sample size
(individuals)
Age
Time slots
Method of data
collection
3,206
8,564
Specific attached to a
permanent survey
(parallel sample)
2,663
Time use diary module
in a continuous multipurpose household survey
6,480
Short and incomplete time
use module in a continuous
living standard survey
5,998
12,604
14,553
7,743
19,907
25,664
6–65
15mn
Pre-listed Diary 24 hours
past day
10+
30mn
Diary 24 hours past day
6–65
15mn
Pre-listed Diary
24 hours past day
10+
30mn
Diary 24 hours past day
Yes
Yes
Yes
Yes
7+
No
Average time spent in
selected housekeeping
activities in the last 7 days
No
No (March–April)
Yes
No (October—November)
Pre-listing of 63 activities
classified SNA/non SNA
UN classification
Pre-listing of 77 activities
classified SNA/non SNA
Yes (540 households
per month)
UN classification
Simultaneous
activities
Seasonality
activities
Classification
of activities
Yes
Ad hoc classification
World Bank Working Paper
Table 3.1. Characteristics of the Time Use Surveys in Five Sub-Saharan African Countries
Table 3.2. Time Devoted per Day to Economic Activity and to Work, by Gender in Various Countries (In hours and minutes)
Benin (1998)
Women
Men
Women/
Men
3h 55mn
1h 7mn
100%
310%
1h 55mn 3h 10mn
3h 48mn 1h 15mn
61%
304%
5h 2mn
77.8%
147%
68.2%
5h 43mn 4h 25mn
33.5%
71.7%
129%
47.0%
Women
Men
Women/
Men
Madagascar (2001)
Mauritius (2003)
Women/
Men
Women
Men
Women/
Men
2h 55mn 4h 50mn
3h 41mn
47mn
60%
470%
1h 56mn
4h 37mn
4h 56mn
1h 13mn
39%
379%
6h 36mn 5h 37mn
44.2%
86.1%
118%
51.0%
6h 33mn
29.5%
6h 9mn
80.2%
106%
36.8%
Women
Men
Source: Table elaborated on basis of the results of national time use surveys: INSAE/PNUD (1998), Enquête emploi du temps au Bénin, Méthodologie et résultats,
Cotonou; Statistics South Africa (2001), How South African Women and Men spend their time, A survey of time use, Pretoria; INSTAT- DSM/PNUD-MAG/97/007: EPM
2001- Module Emploi du Temps, Antananarivo; Republic of Mauritius, Central Statistics Office (2004), Continuous Multi-Purpose Household Survey 2003, Main
results of the time use study.
Gender, Time Use, and Poverty in Sub-Saharan Africa
SNA production
3h 55mn
Non SNA production: 3h 28mn
Domestic activities
Work
7h 23mn
% SNA in work
53.0%
South Africa (2000)
47
48
World Bank Working Paper
Table 3.3. Classifications of SNA Non-Market Activities Used in South Africa,
Benin and Madagascar
SNA Non-Market Activities
South Africa
Benin, Madagascar
Crop farming
Agriculture,
Gardening
Animal husbandry
Small cattle
Cattle
Poultry
Hunting
Fishing
Gathering
Forestry
Tending animals, fish farming
Hunting, gathering
Digging, stone cutting and carving
Crushing
Food processing and preservation
Preparing/selling food & beverages
Making/selling textiles/craft
Processing agricultural products for food
Drying food products
Other processing for self-consumption
Spinning
Weaving
Embroidering
Basket making
Mat making
Building and extension of dwelling
If we now detail the SNA non-market activities in the various countries, it is necessary
to look at the different classifications used. Table 3.3 compares the two classifications for
South Africa (which follows the UN classification) and for Benin and Madagascar (where
some 70 activities were pre-listed on the questionnaire). Although there is not an exact
correspondence in the wording of activities, (there is one more activity for South Africa:
digging and stone cutting and carving) and the list is more detailed for the two other
countries, it can be assumed that all the spectrum of SNA non-market activities is well
covered.
Table 3.4 is based on the shorter classification (South Africa) and the data for Benin
and Madagascar have been adapted and aggregated accordingly. Fetching water and collecting firewood are classified in SNA non-market activities although South Africa put
them in non-SNA productive activities (see preceding section). Specific tables are prepared
for those two activities (Tables 3.5, 3.6, and 3.7). Moreover, South Africa does not distinguish SNA market from SNA non-market, while Benin and Madagascar have recorded the
Gender, Time Use, and Poverty in Sub-Saharan Africa
49
Table 3.4. Time Spent per Day on SNA Non-Market Activities in Three Countries
(In hours and minutes)
Benin (1998)
Crop farming
Tending animals, fish farming
Hunting, gathering
Digging, stone cutting and carving
Fetching water
Collecting firewood
Food processing and preservation
Preparing/selling food & beverages
Making/selling textiles/craft
Building and extension of dwelling
Total
South Africa
(2000)
Madagascar
(2001)
Women
Men
Women
Men
Women
Men
11
5
1
4
29
13
11
20
9
5
29
37
5
45
16
26
12
4
8
8
6
4
10
1
1
3
3
27
7
20
9
13
3
2
2
2
3
1
24
1
1
3
27
16
6
1h 50
1
1h 37
1h 44
1h 19
Note: For South Africa, the results are based on the final table providing time spent by the persons
engaged in each detailed activity. Consequently total time spent by persons engaged was divided by
the total sample.
National figures are based on a distribution of the population between urban and rural areas as
follows: 30/70% for Madagascar, 36/64% for Benin.
Source: Calculations are based on the same sources as Table 3.2.
non-market activities which were not declared as main economic activities. Notwithstanding these differences, the gap is huge in favor of Benin and Madagascar (meaning for
instance that farming for own-account is nearly negligible in South Africa).
Tables 3.4 and 3.5 highlight huge differences between Benin and Madagascar on the
one hand and South Africa on the other hand.
Table 3.5. Time Spent per Day in SNA Non-Market Activities in Three Countries
as a Share of Total SNA Production (In hours and minutes)
Benin (1998)
Women
Men
South Africa (2000)
Women
/Men Women
Men
Madagascar (2001)
Women
/Men Women
Men
Women
/Men
SNA
3h 55mn 3h 55mn 100.0 1h 55mn 3h 10mn 60.5 2h 55mn 4h 50mn 60.5
production
SNA
1h 44mn 1h 19mn 131.6 24mn
27mn
88.8 1h 50mn 1h 37mn 113.4
non-market
Non-market 44.3
33.6
20.9
14.2
62.9
33.4
in %
Source: See sources for Table 3.2.
50
January February
Women
Number of
observations
Number of days
per month
Number of
hours per day
Men
Number of
observations
Number of
days per month
Number of
hours per day
Women/Men
(hours)
March
April
May
June
July
August
September October November December Average
38
42
64
85
71
72
69
45
38
38
33
33
52.3
15
14
18
19
18
19
19
13
17
15
13
16
16.3
5
4.8
5.5
6.2
5.9
5.6
5.9
5.8
6.3
5.7
6.0
6.4
5.8
39
54
66
68
68
65
62
56
41
38
25
20
50.2
11
13
15
15
17
16
16
14
17
16
17
16
15.3
4.7
4.7
4.6
4.3
4.7
4.2
4.7
4.6
4.2
4.4
4.6
4.6
4.5
106.4%
102.1%
150.0%
129.5%
130.4%
139.1%
127.3%
Source: Leplaideur (1978).
119.6% 144.2% 125.5% 133.3% 125.5% 126.1%
World Bank Working Paper
Table 3.6. Time Spent by Women in Food Crops Work and by Men in Food Crop and Export Crops, Center-South Cameroon, 1976
Gender, Time Use, and Poverty in Sub-Saharan Africa
51
Are these differences due to differences in the level of development? Probably not. All
the more so as the UN classification of time use activities used in the South African survey
does not distinguish non-market activities and only distinguishes between work for establishments and activities not for establishments. For example, “preparing/selling food and
beverages” or “making/selling textiles and craft” are activities to be classified in “market
production” if at least a part of the production is sold on the market and in “non-market
production” if there is no sale at all. We have already stressed that the distinction market/
non-market was more and more misleading because there is no way to identify this distinction in the usual permanent statistical survey. However, in the Benin and Madagascar
time use surveys, these activities have been recorded after the main and secondary
activities had been declared by the respondents: this means that in the absence of time
use survey, these activities would not have been recorded. Yet, the time devoted to them
is high.
The SNA non-market activities account for 44.3 percent in women’s time contribution to SNA production in Benin, and for 62.9 percent in Madagascar (against 20.9 percent in South Africa). In these two countries, men’s time contribution to SNA production
through the SNA non-market activities is about one third. The gap between women and
men is mainly due to the activities of fetching water and collecting firewood.
The sexual division of labor in SNA non-market activities clearly stands out from Table 3.4:
crop farming and especially animal husbandry are male activities, while processing of food
and agricultural products is a female activity. Craft activities are male or female activities
depending on national circumstances and also the seasons. Making mats and baskets is an
important female activity in Madagascar as shown in Table 3.4.
This type of results does not say much about the sexual division of labor in agriculture
(except the one mentioned between crop farming and animal husbandry). It must be noted
here that time use surveys are not tailored and designed to record the details of farming
work. For instance, time use surveys do not record explicitly what type of work was performed by the respondent in his (her) main economic activity: did the furniture-maker
cut wood, or assemble the furniture, or paint the furniture? Did the farmer sow, plough,
hoe, or harvest? The time use surveys do not intend to split up the production process into
its various stages.
In this sense the recent time use surveys diverge from the former village studies
designed to measure the diversity of farming works and the sexual division of labor in agriculture. Yet changes occurring in this field obviously have an impact on time use in general:
for instance, when hoeing was mechanized in rice fields in Madagascar in the early 1970s,
this type of female work became male work.
However, previous surveys conducted in the 1970s and focusing on time use in agricultural activities, provide a different picture: they show that women were spending more
time than men in agricultural production, even though they were excluded from export
crop production. The study carried out in 1976 by Leplaideur (1978) among a hundred
male and female farmers in the center-south of Cameroon over an entire year, illustrates
for example a huge gender gap in the opposite direction within SNA work itself. It shows
that women spent on average more than 16 days per month and 5.8 hours per day in food crop
activities while men spent 15 days per month and 4.5 hours per day in food crops and export
crops. In total, the ratio of women to men in agricultural work rises up to 127.3 percent
with peaks around 150 percent in April and September (Table 3.6).
52
World Bank Working Paper
It must be noted however that such results are not strictly comparable with the results
of the time use surveys analyzed in this paper. In this case, the hundred persons interviewed
are adults entirely dedicated to these activities during the period, while in the time use surveys the results are based on the total population (adults, youth, elderly, active or inactive,
dedicated and not dedicated, captured during working days and not working days). Also
these results show how useful it would be that time use surveys adequately and precisely
capture the various operations included in SNA economic activities.
Fetching water and collecting firewood deserve some more attention not only because
gender differentiation is high in these activities, but also because they often continue to be
classified with non-SNA productive activities. However, “fetching water” accounted for
1 percent of the GDP in the 1974 National Accounts of Burkina Faso (Charmes 1989), an
estimation based on household consumption and not on time use. It has also given rise to
some methodological research. Whittington, Winming, and Roche (1990) propose an
appropriate value of time spent on this activity. Findings of the research conducted in
Ukunda, Kenya, suggest a value approximately equivalent to the wage rate for unskilled
labor, making attractive and profitable a technology based on piped distribution which
would also be an opportunity for saving and sparing women’s time.
Table 3.7 summarizes the results for four countries, while Tables 3.8 and 3.9 provide
the information for the respondents engaged in the activity (and not an average for the
whole population) and for a specific age-group.
The definition of “fetching water” is unambiguous in principle. In fact the collection
of water can also be performed as a task of a production process for the main economic
activity. However, in time use surveys this activity is clearly associated with an activity per se,
to be included either in SNA production (which is correct) or in the domestic production.
It is slightly different for “collecting firewood.” Some surveys or classifications have
opted for a wider denomination such as “collecting fuel.” They also distinguish a category
“chopping wood” and more exactly “chopping wood, lighting fire and heating water not
for immediate cooking purposes,” a wording that might be misleading because women can
go collecting and chopping wood, not for immediate cooking purposes. In South Africa,
this second activity involves many more persons but requires much less time than the activity “collecting fuel.” The two activities have been added up for South Africa in the following tables. However, the impact is limited. Again, “collecting firewood” and “cutting
wood” are intermediate activities of the production process for charcoal or salt extraction.
As was shown for Guinea (Geslin 1997), these two activities take 1 hour and 10 minutes of
women engaged in salt extraction (on a working day of more than 13 hours and a half) and
nearly one hour for men (in a working day of 16 hours).
It is in rural Benin that “fetching water” takes the more time for women (more than
one hour per day on average according to Table 3.7). Women involved in this activity
(72 percent of women of all age-groups; Table 3.8) spend more than 1 hour and a half (1h
38 minutes). At national level (urban and rural) the “fetching water” daily chore takes
45 minutes for women, and the burden of “collecting firewood” takes 7 to 8 times more
time in the countryside than in the cities.
“Collecting firewood” generally takes less time (from 23 minutes in rural Benin to
37 minutes in rural Ghana for women) and this burden is more often shared by men: in
rural Madagascar, men spend 27 minutes a day to this task against 8 minutes for women.
Table 3.7. Time Spent on Fetching Water and Collecting Firewood by Women and Men (In hours and minutes)
Benin (1998)
Fetching
water
Collecting
firewood
South Africa (2000)
Women
Men
Women/
Men
Urban
Rural
16
1h 2
6
16
267%
388%
Urban and rural
45
12
375%
Urban
Rural
Urban and rural
3
23
16
1
5
4
300%
460%
400%
Women
8
6
Men
3
3
Women/
Men
Madagascar (2001)
Ghana (1998–99)
Women
Men
Women/
Men
Women
Men
Women/
Men
16
32
10
8
160%
400%
33
44
31
34
106%
129%
267%
27
9
300%
41
33
124%
200%
3
8
7
6
27
13
50%
30%
54%
44
37
37
51
28
30
86%
132%
123%
Table 3.8. Time Spent on Fetching Water and Collecting Firewood by Women and Men Engaged in the Activity (In hours and minutes)
Benin (1998)
Fetching
water
Collecting
firewood
Urban
Rural
Urban and rural
Urban
Rural
Urban and rural
Women
Men
Women/
Men
47
1h 38
1h 2
1h 5
1h 50
1h 14
40
1h 15
1h 2
1h 11
1h 30
1h 23
118%
131%
100%
92%
122%
89%
Women
Men
Madagascar (2001)
Women/
Men
1h 2
46
135%
2h 17
2h 14
102%
Women
Men
Women/
Men
56
62
1h 2
1h 6
1h 14
1h 12
54
56
55
1h 13
1h 31
1h 26
104%
111%
113%
90%
81%
84%
53
Source: See sources for Table 3.2.
South Africa (2000)
Gender, Time Use, and Poverty in Sub-Saharan Africa
Source: See sources for Table 3.2; Ghana Statistical Service (2000), Ghana Living Standards Survey, Report of the 4th Round (GLSS 4), Accra.
54
Benin (1998)
Fetching
water
Collecting
firewood
Madagascar (2001)
Ghana (1998–99)
Women
Men
Women/
Men
Urban
Rural
16
1h3
10
24
160%
263%
17
37
17
16
100%
231%
Urban and rural
46
19
242%
31
16
Urban
Rural
Urban and rural
2
17
12
2
7
5
100%
243%
240%
2
7
6
6
24
19
Source: See sources for Table 3.6.
Women
Men
Women/
Men
Women
Men
Women/
Men
194%
41
38
108%
33%
29%
32%
30
29
103%
World Bank Working Paper
Table 3.9. Time Spent per Day on Fetching Water and Collecting Firewood by Girls and Boys Aged 6 to 14 (Benin and Madagascar)
or 7 to 14 (Ghana) (In hours and minutes)
Gender, Time Use, and Poverty in Sub-Saharan Africa
55
In urban Ghana as well, men spend 51 minutes daily to collect firewood (against 44 minutes for women).
Such statistics multiplied by the number of days in a year and the total population lead
to a macroeconomic figure of the total number of hours spent in water fetching or collecting firewood at national level. These figures can be valued to provide an estimate of
production, which is equivalent to the value added, provided there is no intermediate
consumption.
The estimate amounts to 2 million hours for men and 4 million hours for women in
Ghana in 1992, which makes 6 million hours in total for water fetching. The total figure is
3.1 million hours for collecting firewood (2.2 million for women and 0.8 million hours for
men; GSS 1995).
Table 3.8 shows that for those persons in charge of the burden, “fetching water” or
“collecting firewood” takes between one hour and more than two hours a day (in South
Africa). “Fetching water” and “collecting firewood” are typically associated with child labor.
In Benin, Madagascar and also in Ghana, girls and boys spend more time fetching water
than the adults (Table 3.9). Furthermore, in Benin and Madagascar where the child population has been classified between those who go to school and those who don’t, it seems
that attending school does not prevent children from being charged with this task. The only
significant difference is for rural girls in Benin who spent 50 minutes per day fetching water
(compared to 63 minutes for all girls aged 6–14). On the contrary, “collecting firewood” is
less time-consuming for boys and girls than for adults. Still, the number of minutes spent
per day on this task is not very far from the population average.
The GLSS gives an opportunity for analyzing a short trend (from 1991 to 1999 and
soon to 2004) for these two activities. Table 3.10 synthesizes the observations in terms of
the number of persons involved and the time spent (in average for the total population,
involved or not) in water fetching and wood fetching. For both activities, the number
of persons involved has decreased by 8 percentage points for women as well as for men.
Table 3.10. Trends in Number of Persons Involved and Time Spent per Day
in Water and Wood Fetching in Ghana, 1991–92 and 1998–99
Number of persons
involved (%)
Number of minutes
per day and per person
1991–92
1998–99
1991–92
1998–99
Water fetching
Women
Men
Both
68
45
57
60.2
37.7
49.1
40
21
31
41
33
38
Wood fetching
Women
Men
Both
43
24
34
34.6
16.0
25.6
22
9
16
37
30
35
Source: Based on Ghana Statistical Service (1995), Ghana Living Standards Survey, Report of the 3rd
Round (GLSS 3), September 1991–September 1992, Accra, and Ghana Statistical Service (2000), Ghana
Living Standards Survey, Report of the 4th Round (GLSS 4), Accra.
56
World Bank Working Paper
At the same time, the number of minutes spent in performing the activities has been
increasing for water fetching (12 minutes for men, but only 1 minute for women) as well
as for wood fetching (15 minutes for women, and 21 minutes for men). Although it is difficult to interpret these results in the absence of qualitative data and surveys, it seems obvious that they are correlated to a better access to sources of drinking water and less need to
resort on wood for cooking. Yet, for those households that did not benefit from the
improvements in infrastructure or equipment (such as tap water or improved stoves for
instance), they had to go further to satisfy their needs, because of deforestation.
Looking now at non-SNA productive activities, we again compare the various classifications used (Table 3.11). The South African and Mauritius classifications (UN classification) are much more detailed than the pre-listed classification used in Benin and
Madagascar, not to mention Ghana, which used only four categories.
A major difference is that the various caring activities in the South African survey distinguish between “spontaneous” caring and “prompted” caring. For instance supervising
children allows for performing another activity while taking care of them is a complete
activity that cannot allow performing another one. The fact that the other classifications
do not make such a distinction means that both forms of activities are recorded in the survey. Furthermore, where (as in Ghana) there is only a short list of activities, there is a risk
of overestimation of the time spent in these activities, particularly for men.
The two classifications identify domestic and care work on the one hand, and volunteer work on the other hand. Here the two classifications are definitely not comparable and
the Ghana survey does not even record such activities. The South African survey and classification are much better and complete and should be used. It must be noted however that,
in National Accounts, the value added by these activities must be split into household production (care of non-household members) and production of the Non-Profit Institutions
serving Households (NPISH).
Regarding the core domestic activities which are preparing meals and washing up,
washing-ironing, and care of children, the gap between women and men is huge: women’s
contribution varies from 13 or 12 times men’s (for preparing meals) and 10 or 9 times (for
both meals and wash up) in Madagascar, Benin and Mauritius, to 2 or 4 times in Ghana
and South Africa respectively.
The gap is even more accentuated for “washing-ironing”: it varies from 38 to 23 times
in Mauritius and Madagascar to 3 to 4 times in Benin and South Africa.
As for childcare, it takes 13 times more time of women than of men in South Africa,
7 times in Benin, 6 in Madagascar, 3 in Mauritius, and nearly 2 times in Ghana.
The duration for the preparation of meals and the wash up are very comparable in the
five countries, notwithstanding the variations in the methodology. It takes generally
between one and a half and two hours per day per woman (this average including those
women who are not engaged in the activity). The methodology makes a difference for men:
in Ghana where the survey did not use the diary, the design of the question results in a
rather high figure for men (55 minutes) which may be exaggerated.
The same remark applies for “washing-ironing”: the duration is approximately the
same in all countries (more or less half an hour per day).
“Care of children” takes from half an hour to three quarters of an hour for women
against 4 to 13 minutes for men. A huge difference is observed in Ghana where time
devoted to care of children takes 3 hours and 24 minutes a day for women, an 1 hour and
Gender, Time Use, and Poverty in Sub-Saharan Africa
57
Table 3.11. Classifications of Non-SNA Activities Used in South Africa,
Benin, Madagascar, and Ghana
Non-SNA Activities (Domestic, care)
South Africa
Benin, Madagascar
Ghana
Preparing food and drinks
Preparing meals
Washing up
Cooking
Cleaning and upkeep of dwelling
Care of textiles
Shopping
Accessing government
services
Household management
Home improvements
Pet care
Household maintenance
Fitting, Maintaining tools
and machinery
Physical care of children
(spontaneous)
Physical care of children (prompted)
Teaching of household
children (spontaneous)
Teaching of household
children (prompted)
Accompanying children
(spontaneous)
Accompanying children (prompted)
Physical care of non-child
household members
Accompanying adults
Supervising those needing
care (spontaneous)
Supervising those needing
care (prompted)
Care of household members n.e.c
Sweeping
Washing, Ironing
Shopping
Accessing government
services
Household maintenance
Other Maintenance
Repairing house
or apparels
Caring for children
Garbage
disposal
Childcare
Teaching of household
children
Accompanying children
Caring for adults,
handicapped, elderly
Non-SNA Activities (volunteer)
Community organised construction
Community organised work
Preparing food for
ceremonies
Other Manufacturing
for ceremonies
(continued)
58
World Bank Working Paper
Table 3.11 Classifications of Non-SNA Activities Used in South Africa, Benin,
Madagascar, and Ghana (Continued)
Non-SNA Activities (Domestic, care)
South Africa
Organizational volunteering
Participation in meetings
Benin, Madagascar
Ghana
Participation in meetings
(associations)
Participation in meetings
(religious)
Involvement in civic responsibilities
Caring for non-household children
(spontaneous)
Caring for non-household children
(prompted)
Caring for non-household adults
(spontaneous)
Caring for non-household adults
(prompted)
Other informal help to other
households
Community services n.e.c
Note: The detailed classification for Mauritius is not given because only the main results of the survey
have been published.
47 minutes a day for men. One may wonder whether the methodology used for data collection can be responsible for such a difference with the other surveys. The capture of
simultaneous activities may be an explanation. In Benin and Madagascar, rural women
involved in the activity spend more than two hours caring for children. Another issue that
can be raised in this respect is the difficulty to record “caring for children” when this activity
is embedded within the main economic activity. In such a case, the methodological efforts
for capturing simultaneous activities might be insufficient because the capture of simultaneous activities is focussing on the activities which are performed at home or in the household, rather than on the work site, especially if the individual works for an establishment.
Here again, it is interesting to note that Geslin (1997), recording the working hours of
Guinean women extracting salt, included more than 1 hour for childcare in a working day
of 13 and a half hours. Even if such a figure seems low, the point here is that simultaneity of
tasks or activities may be missed when they occur during the usual SNA market activities.
From a methodological point of view, it seems clear that the more detailed and contextualized the activities in caring children (physical care, teaching, accompanying; spontaneous, prompted), the longer the time spent and recorded for these activities. The same
remark should be valid for “caring for adults.” However, even in South Africa, time spent on
“caring for adults” is remarkably low for women and men: 6 minutes for women against
2 minutes for men in South Africa, and only 2 minutes against 1 minute in the other countries.
Table 3.12. Time Spent on Non-SNA Productive Activities in Five African Countries (In hours and minutes)
Benin 1998
Women Men
6
4
7
20
2
1250%
425%
314%
110%
50%
1h 47
28
7
1
10
6
6
280%
117%
17%
20
29
4
725%
4
3h 28
Source: See sources in Table 3.6.
55
14
3h 24 1h 48
195%
143%
189%
1h 24
19
443%
26
7
1
6
6
1
403%
116%
48
26
24
6
2
24
2
2
0
1
6
0
2
1h 7
Madagascar (2001)
Mauritius (2003)
Women/
Women/
Women/
Women/
Women/
Men
Women Men
Men Women Men
Men Women Men Men Women Men
Men
1h 15
17
22
22
1
2
South Africa (2000)
200%
310%
5h 31 2h 57
187%
2
2
3
2
4
3h 48 1h 15
1h 34
22
23
18
7
4
1
7
1
1343%
550%
2300%
257%
1h 56
12
967%
38
15
1
20
3800%
75%
200%
403%
23
3
1
4
6
8
575%
50%
13%
1
7
14%
1580%
419%
32
1
5
640%
39
3
11
2
355%
150%
390%
2
2
1
1
200%
200%
2
1
200%
55%
62%
305%
3h 41
2
47
470%
3
3
1
1
4h 36 1h 13
100%
100%
378%
Gender, Time Use, and Poverty in Sub-Saharan Africa
Preparing meals
Washing up
Washing, Ironing
Shopping
Accessing government
services
Household maintenance
Other Maintenance
Repairing house
or apparels
Caring for children
Teaching of household
children
Accompanying children
Caring for adults,
handicapped, elderly
Help to other households
Community services
Total
Ghana (1998–99)
59
60
World Bank Working Paper
When looking at the persons engaged in the task of “supervising those needing care,” it
takes more than one hour, plus another hour for “accompanying” with a number of
women which is five times the number of men. Even if there seems to be a kind of specialization between household members caring for the adults who need it, time required for
these tasks remains limited.
However, the fact that this space of time is three times longer in South Africa compared with the other countries could be related to the burden of caring for the sick and
especially those members of the household who are infected by HIV/AIDS.
Household maintenance is also a female activity, which is somewhat counterbalanced
by other maintenance and repairing house, tools or apparels among men.
Lastly “shopping” is shared rather equally between women and men, except in
Madagascar, while “accessing to government services” is more a male task.
Finally, “volunteer activities” seem to be insufficiently harmonised and data collection
insufficiently systematized to be validly compared among countries. For South Africa and
Mauritius where such harmonization and systematization exist, women and men spend on
average 2 to 3 minutes a day helping other households on a rather equal sharing basis. Help
of the community takes from 1 minute a day for men and women in Mauritius to 4 minutes
for women in Benin and for men in South Africa.
In total, women spend more time than men in non-SNA productive activities: more
than 4 times in Madagascar, more than 3 times in Benin, South Africa and Mauritius, and
nearly twice in Ghana. Time length is the higher in Ghana with 5 hours and 31 minutes for
women against 2 hours and 57 minutes for men (but that could be due to methodological
reasons), and in Mauritius (4 hours 36 minutes for women against 1 hour and 13 minutes
for men). It is lower in Benin, Madagascar and South Africa with respectively 3 hours and
28 minutes, 3 hours and 41 minutes, 3 hours and 48 minutes (against 1 hour and 7 minutes, 47 minutes and 1 hour and 15 minutes for men).
Finally, Table 3.13 provides the distribution of time between SNA work, Work outside
SNA and non-work activities during an average day in four sub-Saharan countries. Within
a 24 hours-day, women devote from 8 to 16 percent of their time to SNA work (against
13 to 20 percent for men) and from 13 to 18 percent to domestic and care work (against 4
to 6 percent for men), which makes from 23 to 30 percent for work (against 20 to 26 percent for men). Physiological needs and personal care take from 49 to 55 percent of women’s
daytime and from 48 to 54 percent for men.
It is interesting to compare these data with the results of surveys conducted in 1984
among two populations in southwest Cameroon (Tables 3.14 and 3.15). These surveys are
very specific because they were looking at activity patterns and energy consumption. To
this end, they measured time spent in the various activities with stopwatches on small samples of individuals. Although the results are not strictly comparable with those of the time
use surveys examined in this paper (regarding the scope and coverage of the surveys, the
date and the type of populations surveyed), it is interesting to note (Tables 3.16 and 3.17)
that time devoted to physiological needs and personal care is higher in these specific surveys (from 53 to 55 percent for women, against 55 to 59 percent for men). The levels of
domestic and care work are much higher for women (from 29 to 34 percent) and lower for
men (from 3 to 5 percent) so that the gender gap is broader. The same observations but in
the reverse sense can be made for SNA work: it is lower for women (from 6 to 10 percent)
Table 3.13. Comparisons of Daily Time Use for Women and Men in Four Sub-Saharan African Countries
South Africa 2000
Activities
Women
Men
Both sexes
Bénin 1998
Women
Both sexes Women
3h 55mn
3h 55mn
15mn
5mn
0
25mn
10mn
0
50mn
15mn
5mn
2h 10mn
3h 45mn
1h
4h 55mn
1h 50mn
1h 55mn
1h 40mn
50mn
12h 50mn
Men
Mauritius (2003)
Both sexes Women
2h 55mn 4h 50mn 3h 50mn
6h 5mn
6h 40mn
1h 30mn 1h 35mn
1h 45mn
35mn
1h 15mn 2h 15mn
45mn
40mn
12h 50mn 13h 10mn
Men
Both sexes
1h 56mn
4h 56mn
3h 25mn
4h 37mn
1h 13mn
2h 56mn
5h 45mn 6h 15mn 6h 33mn
1h 55mn 1h 45mn 1h 5mn
40mn
35mn 1h 45mn
2h 40mn 2h 25mn 1h 48mn
1h
50mn
13h 5mn 13h 5mn 11h 49mn
6h 9mn
1h 7mn
2h 28mn
1h 41mn
6h 21mn
1h 6mn
2h 6mn
1h 45mn
55mn
10mn
5mn
50mn
15mn
5mn
55mn 2h 25mn
11h 35mn 11h 42mn
Source: Table elaborated on basis of the results of national time use surveys : INSAE/PNUD (1998), Enquête emploi du temps au Bénin, Méthodologie et résultats,
Cotonou; Statistics South Africa (2001), How South African Women and Men spend their time, A survey of time use, Pretoria; INSTAT- DSM/PNUD-MAG/97/007: EPM
2001- Module Emploi du Temps, Antananarivo; Republic of Mauritius, Central Statistics Office (2004), Continuous Multi-Purpose Household Survey 2003, Main
results of the time use study.
Gender, Time Use, and Poverty in Sub-Saharan Africa
SNA activities
1h 55mn 3h 10mn 2h 30mn 3h 55mn
Of which:
– non-market
35mn
– fetching water
10mn
5mn
5mn
15mn
– collecting
5mn
firewood
Domestic and
3h 35mn 1h 25mn 2h 35mn 3h 15mn
care activities
Work
5h 30mn 4h 35mn 5h 5mn
7h 10mn
School, studying
1h 35 mn 1h 50mn 1h 40mn 1h 5mn
Social activities
2h 10mn 2h 20mn 2h 15mn 1h 25mn
Leisure
2h 5mn 2h 35mn 2h 20mn
55mn
Commuting
1h
1h 25mn 1h 15mn
30mn
Sleeping, eating, 12h 15mn 12h 5mn 12h 10mn 12h 50mn
resting, personal
care
Men
Madagascar 2001
61
62
Small dry season (July–August)
Sleeping
Personal care
Care of children
Household duties
Agriculture
Fishing
Hunting
Construction
Social activities
Total
Total work SNA
Total work
Women
%
Men
598
190
7
484
41.6%
13.2%
0.5%
33.6%
537
302
11
28
75
8
0
0
77
1439
83
574
%
Women/
Men
Women
37.3% 111.4%
21.0%
62.9%
0.8%
63.6%
1.9% 1728.6%
5.2%
28
1.9%
0.6% 131
9.1%
0.0%
20
1.4%
0.0% 115
8.0%
5.4% 268 18.6%
100.0% 1440 100.0%
5.8% 294 20.4%
39.9% 333 23.1%
Rainy season (November–December)
267.9%
6.1%
0.0%
0.0%
28.7%
99.9%
28.2%
172.4%
Source: Based on data from Pasquet and Koppert (1993 and 1996).
581
210
23
429
71
25
0
0
100
1439
96
548
Dry season (March–April)
Women/
Men
Women
%
Men
%
40.4%
14.6%
1.6%
29.8%
489
310
10
49
34.0%
21.5%
0.7%
3.4%
118.8%
67.7%
230.0%
875.5%
575
189
13
410
4.9%
38
2.6%
1.7% 240 16.7%
0.0%
17
1.2%
0.0%
3
0.2%
6.9% 283 19.7%
100.0% 1439 100.0%
6.7% 298 20.7%
38.1% 357 24.8%
186.8%
10.4%
0.0%
0.0%
35.3%
100.0%
32.2%
153.5%
127
19
0
0
106
1439
146
569
%
Men
%
Women/
Men
40.0%
13.1%
0.9%
28.5%
505
341
12
62
35.1%
23.7%
0.8%
4.3%
113.9%
55.4%
108.3%
661.3%
8.8%
64
4.4%
1.3% 195 13.5%
0.0%
2
0.1%
0.0%
20
1.4%
7.4% 239 16.6%
100.0% 1440 100.0%
10.1% 281 19.5%
39.5% 355 24.7%
198.4%
9.7%
0.0%
0.0%
44.4%
99.9%
52.0%
160.3%
World Bank Working Paper
Table 3.14. Time Use among the Yassa of Campo (Southwest Cameroon) in 1984 (In minutes per day at three different periods of the year)
Gender, Time Use, and Poverty in Sub-Saharan Africa
63
Table 3.15. Time Use among the Mvae of Campo (Southwest Cameroon) in 1984
in minutes per day at three different periods of the year)
Small
dry season
(July–August)
Rainy season
(November–
December)
Dry season (March–April)
Women
%
Men
%
Women/
Men
44.9%
14.9%
595
207
41.3%
14.4%
633
399
44.0%
27.7%
94.0%
51.9%
18
1.3%
15
1.0%
15
1.0%
100.0%
26.1%
334
23.2%
273
19.0%
63
4.4%
433.3%
197
14
12
0
36
13.7%
1.0%
0.8%
0.0%
2.5%
91
63
2
1
70
6.3%
4.4%
0.1%
0.1%
4.9%
250
23
25
0
52
17.4%
1.6%
1.7%
0.0%
3.6%
148
18
66
10
88
10.3%
1.3%
4.6%
0.7%
6.1%
168.9%
127.8%
37.9%
0.0%
59.1%
1439
223
100.0%
15.5%
1440
157
100.0%
10.9%
1440
298
100.0% 1440 100.0%
20.7% 242 16.8%
100.0%
123.1%
603
41.9%
509
35.3%
586
Women
%
Women
Sleeping
Personal
care
Care of
children
642
158
44.6%
11.0%
646
215
5
0.3%
Household
duties
Agriculture
Fishing
Hunting
Construction
Social
activities
Total
Total work
SNA
Total work
375
%
40.7%
320
22.2%
183.1%
Source: Based on data from Pasquet and Koppert (1993 and 1996).
than for men (from 20 to 21 percent). The total time spent on work ranges from 38 to
40 percent for women and from 23 to 25 percent for men, a level much higher than in the
recent time use surveys.
Time Use Patterns and Key Development Variables
The results of time use surveys, which have been presented and analyzed in a previous
section are based on the entire sample population (aged 6 or 10 to 65 or more) or on the
population engaged in the referenced activities. Provided that demographic and socioeconomic characteristics of the respondents have been collected, the reports of the surveys also give time use patterns by age group, by educational level, by region or province.
They can also give them by marital status (single, married, polygamous, divorced), by type
of activity status (active, inactive), employment status (own-account worker, wage earner,
contributing family worker), occupational status, and so forth. The South African survey
also collected the income group and the source of income. The Benin and Madagascar
surveys cross-classify urban/rural areas, provinces, active/inactive, 6–14 attending
64
South Africa 2000
Activities
SNA activities
Domestic and
care activities
Work
School, studying
Social activities
Leisure
Commuting
Sleeping, eating,
resting, personal care
Total
Benin 1998
Madagascar 2001
Mauritius (2003)
Women
Men
Both
Sexes
Women
Men
Both
Sexes
Women
Men
Both
Sexes
Women
Men
Both
Sexes
7.8%
14.6%
12.8%
5.7%
10.1%
10.4%
16.4%
13.6%
16.3%
4.2%
16.2%
9.0%
11.7%
15.1%
19.3%
3.7%
15.4%
9.7%
8.4%
18.6%
21.4%
5.3%
14.9%
12.8%
22.4%
6.4%
8.8%
8.5%
4.1%
49.8%
18.5%
7.4%
9.4%
10.4%
5.7%
48.7%
20.5%
6.7%
9.1%
9.4%
5.1%
49.2%
30.0%
4.5%
5.9%
3.8%
2.1%
53.7%
20.5%
7.6%
8.0%
6.9%
3.5%
53.5%
25.2%
6.2%
7.2%
5.2%
3.1%
53.1%
26.8%
6.4%
2.3%
9.0%
2.7%
52.8%
22.9%
7.6%
2.7%
10.6%
4.0%
52.2%
25.1%
7.0%
2.3%
9.7%
3.3%
52.5%
28.5%
4.7%
7.6%
7.8%
0.0%
51.4%
26.7%
4.9%
10.7%
7.3%
0.0%
50.4%
27.6%
4.8%
9.1%
7.6%
0.0%
50.9%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Source: See sources for Table 3.13.
World Bank Working Paper
Table 3.16. Comparisons of Daily Time Use for Women and Men in Four Sub-Saharan African Countries (In percent of a 24-hour day)
Gender, Time Use, and Poverty in Sub-Saharan Africa
65
Table 3.17. Time Use among the Yassa of Campo (Southwest Cameroon) in 1984
(In minutes per day at three different periods of the year;
in percent of a 24-hour day)
Small dry season
( July–August)
Women
SNA activities
Domestic and
care activities
Work
Men
Rainy season
(November–December)
Women/
Men Women
Men
Dry season
(March–April)
Women/
Men Women
Men
Women/
Men
5.8%
34.1%
20.4%
28.2%
2.7% 1728.6%
6.7%
31.4%
20.7% 32.2%
4.1% 875.5%
10.1%
29.4%
19.5% 52.0%
5.1% 661.3%
39.9%
23.1%
172.4%
38.1%
24.8% 153.5%
39.5%
24.7% 160.3%
Social
5.4% 18.6%
activities
Sleeping,
54.8% 58.3%
Personal care
Total
100.0% 100.0%
28.7%
6.9%
19.7%
35.3%
7.4%
16.6%
44.4%
94.0%
55.0%
55.5%
99.1%
53.1%
58.8%
90.3%
99.9% 100.0% 100.0% 100.0% 100.0% 100.0%
99.9%
Source: See sources for Table 3.14.
school/not attending school, household heads/wives, wives of polygamous, unmarried
women, wage earners and own-account workers. An example of comparative results by
province is provided for Madagascar (see Tables 3.A1 and 3.A2 in annex).
As a matter of fact, descriptive statistical tables do not show much difference in time
use patterns for most of these categories, except at a provincial level. Analytical research
could be undertaken in order to highlight those behaviors.
However, it is certainly possible to go further. The Madagascar survey was carried out
as a parallel sample of the continuous living standards surveys and the Mauritius survey was
part of a multi-purpose household survey. The Benin survey was also part of a continuous
household survey. The Ghana survey is part of the living standard survey. The ongoing
time use survey of Tunisia (2005) is a subsample of the Budget-Consumption household
survey. Therefore, a direct outcome of these surveys should be the presentation of the time
use patterns by socio-economic category of the household.
The socio-economic categories of households used in living standards surveys generally identify the categories through the household heads. They distinguish among them the
subsistence farmers (or rather food crop farmers), the export crop farmers, the public wage
earners, the private wage earners, the non-agricultural employers, the own-account workers in trade, the own-account workers in other non-agricultural activities, the inactive or
unemployed, and so forth. Households can also be categorized, for instance according
to the number of children under age 6, type of access to water and source of energy, the
distance to main services (transport, health, schools), the type of technology used, and
so forth.
Until now no official household survey has collected information on the presence of
an HIV/AIDS infected household member within the household. Even the Demographic
66
World Bank Working Paper
and Health Surveys (DHS) regularly conducted in most African countries have limited
their investigation in this domain to questions of opinion and awareness about the infection and the means to protect from it. Therefore, it does not seem possible to measure the
impact of HIV/AIDS on time use. A possible solution could be to ask a question on sickness in general without detailing the kind of sickness. It has already been stressed that caring for adults takes three times more time in South Africa than in the four other countries,
which seems to be an indication of a correlation with the presence of the infection.
All the usual cross-classifications of income-expenditure/budget-consumption surveys would be interesting to tabulate with respect to time use patterns of the household
members: income groups of the household, expenditure group, extremely poor/poor/nonpoor. At least the Madagascar, Benin, and Ghana surveys, and possibly Mauritius, could
be revisited for more in-depth analyses of micro-data. It would allow looking at the variations and trends in time use patterns in relation to the income (or expenditure) groups for
assessing the impact of poverty in monetary terms, and in relation to access to services for
assessing the impact of human poverty, and finally assessing the impact of time poverty.
Given the scarce number of data usable for capturing trends, it may be important to
better analyze structural changes in time use across income or occupational groups, socioeconomic categories and types of access to drinking water in order to highlight for instance
the shifting of time from fetching water or wood to care of children or to paid work,
depending on the category of household.
Moreover the African Gender and Development Centre (ACGD) of the UN Economic
Commission for Africa has embarked into a five-year programme, an outcome of which
is an Africa-specific Guidebook for Mainstreaming Gender Perspectives and Household
Production in National Planning Instruments and Policies (Charmes 2003a and 2003b;
UNECA, 2005). This compendium of tools and methodologies for time use surveys in particular is going to be applied in several African countries where continuous time use surveys will be implemented. It could be a good opportunity to systematize the analysis of time
use surveys in terms of time poverty.
Measuring and Analyzing Time Poverty
Feminization of poverty has become a major challenge for economic theory and development policies since the middle of the 1990s and the 1995 Beijing Conference when it was
put on the forefront as an important issue to be tackled. Since then it has played the role
of a powerful slogan in parallel with the recognition that “if not engendered, development
is endangered.”
Illustrating and demonstrating the so-called feminization of poverty has remained,
however, a challenge for scholars and researchers given that empirical evidence of gender
inequalities within the household does not exist (or only scarcely and on not very sound
and representative bases). Poverty is measured through household income and expenditures and, if sources of income and expenditures can be recorded individually, it is far more
difficult to know how they have been spent or consumed on an individual basis, especially
food consumption. Consequently, income poverty cannot easily be used to prove the feminization of poverty except that there are more and more female-headed households that
are poor. The gender distribution of males and females within the female-headed and
Gender, Time Use, and Poverty in Sub-Saharan Africa
male-headed households being equal, it is not possible to deduct from this observation
that the number of women in poverty is increasing.
Given the difficulties arising from the income poverty approach, another dimension of poverty was analyzed: access to services, included in what is called “human
poverty.” Yet, unless the gender distribution of frequentation of health services or other
types of services is available as a regular statistical indicator, this approach is also difficult to analyze in a gender perspective because all household members are supposed to
be equal with respect to the distance to the services. This is not true in terms of time
because access to services, access to water in particular—and the duty of fetching water
(or collecting fuel) for instance—are typically feminine tasks. This is why the human
poverty approach inevitably leads to another dimension of poverty, which is “time
poverty.”
The time poverty approach is recent and encompasses the strict dimension of
access to services. Women are poorer than men in terms of time because they must systematically add up domestic and care duties (reproductive work) to their market or
non-market productive work so that this double time-budget makes of time a resource
which is more scarce for women than for men. Therefore, policies oriented toward an
alleviation of female time budgets can have major impacts on resources derived from
income generating activities due to an increased amount of time dedicated to them, or
also on children’s health thanks to an increased amount of time dedicated to care.
There are naturally some domestic activities or SNA non-market activities toward
which efforts should be concentrated in order to spare time that could be devoted to
more gainful or productive work: fetching water and firewood are typically such activities to be spared, as well as commuting.
The time poverty approach thus opens new horizons for policy purposes. More indepth analyses of existing surveys are necessary to provide sound and convincing arguments to policymakers. This is the task ahead of us.
References
Charmes J. 1989. “Trente cinq ans de comptabilité nationale du secteur informel au
Burkina Faso (1954-89). Leçons d’une expérience et perspectives d’amélioration.”
Ministère du Plan et de la Coopération, PNUD-DTCD, rapport n° 13C, Ouagadougou.
———. 2000. “African Women in Food Processing: A Major, But Still Underestimated
Sector of Their Contribution to the National Economy.” IDRC, Ottawa, Nairobi.
———. 2003a. “Application of Time Use to Assess the Contribution of Women to
GDP and to Monitor Impacts of National Budget on Women’s Time Use.” Expert
Group Meeting on a Gender-Aware Macroeconomic Model to Evaluate Impacts
of Policies on Poverty Reduction, May 7–9, Addis Ababa, Ethiopia. United nations
Economic Commission for Africa African Centre for Gender and Development.
———. 2003b. “Easy Reference Guide on Tools for Mainstreaming Gender in Poverty
Reduction Strategies: National Accounts, National Budgets and Time Use Studies.” United Nations Economic Commission for Africa, African Centre for Gender and Development (ACGD), Addis Ababa.
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———. 2005. “Femmes africaines, activités économiques et travail: de l’invisibilité à la
reconnaissance.” Revue Tiers Monde 46(182):255–279.
Charmes, J., and Jeemol Unni. 2004. “Measurement of Work.” In G. Standing and M. Chen
eds., Reconceptualising Work. Geneva: ILO.
Geslin, P. 1997. “L’innovation et le temps. Une approche ethnographique de la réallocation du temps de travail agricole chez les Soussou de Guinée.” In C. Blanc-Pamard and
J. Boutrais, eds. Dynamique des systèmes agraires: Nouvelles recherches rurales au Sud.
Paris: ORSTOM
Ghana Statistical Service. 1995. “Ghana Living Standards Survey, Report of the 3rd Round
(GLSS 3), September 1991–September 1992.” Accra.
———. 2000. “Ghana Living Standards Survey, Report of the 4th Round (GLSS 4).” Accra.
INSAE/PNUD. 1998. Enquête emploi du temps au Bénin, Méthodologie et résultats. Cotonou.
INSTAT. 2002. EPM 2001. Module Emploi du Temps. Antananarivo: INSTAT- DSM/PNUDMAG/97/007.
Leplaideur, A. 1978. Les travaux agricoles chez les paysans du Centre-Sud Cameroun, les techniques utilisées et les temps nécessaires. Paris: IRAT.
Pasquet, P., and G. Koppert. 1993. “Activity Patterns and Energy Expenditure in
Cameroonian Tropical Forest Populations.” In C.M. Hladik, A. Hladik, O.F. Linares,
H. Pagezy, A. Semple, and M. Hadley, Tropical Forests, People and Food. Biocultural
Interactions and Applications to Development. Man and the Biosphere Series, 13. Paris:
UNESCO and Carnforth, The Parthenon Publishing Group.
———. 1996. “Budget-temps et dépense énergétique chez les essarteurs forestiers du
Cameroun.” In C.M. Hladik, A. Hladik, H. Pagezy, O.F. Linares, G. Koppert, and A.
Froment, eds., L’alimentation en forêt tropicale: Interactions bioculturelles et perspectives
de développement. L’Homme et la Biosphère, Editions. Paris: UNESCO.
Republic of Mauritius, Central Statistics Office. 2004. “Continuous Multi-Purpose Household Survey 2003, Main results of the time use study.” Port Louis.
SNA. 1993. System of National Accounts, Commission of the European Communities,
IMF, OECD, UN, WB.
Statistics South Africa. 2001. A Survey of Time Use. How South African Women and Men
Spend Their Time. Pretoria.
United Nations Economic Commission for Africa. 2005. A Guidebook for Mainstreaming
Gender Perspectives and Household Production into National Statistics, Budgets and Policies in Africa. African Centre for Gender and Development. Addis Ababa.
Whittington D., M. Winming, and R. Roche. 1990. “Calculating the Value of Time Spent
Collecting Water: Some Estimates for Ukunda, Kenya.” World Development 18(2):
269–280.
WOMEN
Appendix Table 3.A1. Time Use Patterns for Household Members from 6 to 65 Years Old in Urban Areas by Province (Faritany),
Sex, and Activity (In percent of the total number of hours per day)
Fianarantsoa
Toamasina
Mahajanga
Toliara
Antsiranana
Madagascar
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
9.2
2.5
16.5
9.2
5.2
16.5
8.0
4.7
14.8
10.5
2.9
14.7
6.8
4.7
14.7
7.5
2.6
13.9
8.7
3.4
15.6
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
7.5
2.7
10.6
3.0
52.1
4.4
2.5
9.4
1.9
56.5
5.4
2.3
8.8
3.0
56.8
5.6
2.5
7.6
3.0
55.3
6.4
2.3
7.8
2.0
57.2
8.6
2.2
8.5
4.0
52.6
6.5
2.5
9.3
2.8
54.7
100.0 (25h)
100.0 (25.4h)
100.0 (24.9h)
100.0 (24.5h)
17.7
2.5
4.2
14.7
7.0
3.7
16.4
4.6
3.7
16.1
4.5
3.7
14.1
5.5
2.7
15,3
2.2
5.0
16.3
3.8
3.9
8.6
3.0
13.0
4.3
51.4
7.2
2.8
9.6
3.5
56.2
6.5
2.9
8.0
4.7
57.1
6.4
2.9
7.9
4.6
56.1
9.0
1.7
10.0
4.0
55.1
8.1
2.6
10.1
5.3
51.6
7.8
2.7
11.0
4.3
54.4
100.0 (25.1h)
100.0 (25.1h)
100.0 (24.9h)
100.0 (24.5h)
MEN
Total
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
Total
100.0 (24.5h) 100.0 (24.0h)
100.0 (24.5h) 100.0 (24.1h)
100.0 (24.9h)
100.0 (25.0h)
69
(continued)
Gender, Time Use, and Poverty in Sub-Saharan Africa
Antananarivo
70
BOTH SEXES
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
Total
Antananarivo
Fianarantsoa
Toamasina
Mahajanga
Toliara
Antsiranana
Madagascar
13.2
2.5
10.5
11.7
6.0
10.8
12.6
4.3
9.2
13.2
3.7
9.3
10.4
5.1
8.7
10.5
2.4
10.4
12.3
3.6
10.1
8.0
2.9
11.8
3.6
51.7
5.7
2.7
9.5
2.6
56.5
5.7
2.2
10.0
3.9
56.0
6.0
2.6
7.7
3.8
55.5
7.7
2.0
8.9
3.0
56.1
8.4
2.3
9.2
4.5
52.3
7.2
2.6
10.2
3.5
54.7
100.0 (25h)
100.0 (25.3h)
100.0 (24.9h)
100.0 (24.4)
100.0 (24.5h)
100.0 (24.1h)
100.0 (25.0h)
Note: One percentage point is approximately equivalent to 15 minutes. The total number of hours per day exceeds 24 because of simultaneous activities.
Source: INSTAT (2002), EPM 2001. Module Emploi du Temps, Antananarivo, INSTAT- DSM/PNUD-MAG/97/007.
World Bank Working Paper
Appendix Table 3.A1. Time Use Patterns for Household Members from 6 to 65 Years Old in Urban Areas by Province (Faritany),
Sex, and Activity (In percent of the total number of hours per day) (Continued)
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
Total
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
Total
Antananarivo
Fianarantsoa
Toamasina
Mahajanga
Toliara
Antsiranana
Madagascar
10.2
6.8
15.5
8.7
8.8
13.5
7.5
8.1
13.7
5.0
10.0
17.2
6.6
12.0
13.5
8.0
7.6
13.8
8.1
8.7
14.6
5.8
1.2
5.7
2.8
54.0
4.1
3.2
5.3
1.9
58.0
2.9
3.0
5.3
2.8
59.3
2.1
2.8
6.8
1.8
55.9
3.8
1.5
4.9
1.3
57.2
3.4
3.4
4.0
2.1
57.6
4.0
2.3
5.4
2.1
56.9
100.0 (24.5h)
100.0 (24.8h)
100.0 (24.6h)
100.0 (24.4h)
100.0 (24.2h)
100.0 (24.0h)
100.0 (24.5h)
17.7
7.6
2.9
14.1
9.9
2.2
13.9
9.9
3.1
12.2
12.5
3.2
15.4
13.8
2.0
11.9
10.6
2.4
14.80
10.2
2.7
5.7
1.9
8.0
3.7
54.8
4.6
3.8
7.4
3.9
57.9
2.7
4.0
5.1
4.4
59.2
1.6
4.0
7.2
3.8
57.0
2.7
1.8
6.4
2.7
56.0
4.2
2.6
7.3
3.9
57.0
3.9
3.0
7.0
3.7
56.9
100.0 (24.5h)
100.0 (24.9h)
100.0 (24.6h)
100.0 (24.4h)
100.0 (24.2h)
100.0 (24.0h)
100.0 (24.5h)
71
(continued)
Gender, Time Use, and Poverty in Sub-Saharan Africa
MEN
WOMEN
Appendix Table 3.A2. Time Use Patterns for Household Members from 6 to 65 Years Old in Rural Areas by Province (Faritany),
Sex, and Activity (In percent of the total number of hours per day)
72
BOTH SEXES
SNA market production
SNA non market production
Non-SNA production:
Domestic, care
School
Social activities
Leisure
Travel
Sleeping, resting, eating,
personal care
Total
Antananarivo
Fianarantsoa
Toamasina
Mahajanga
Toliara
Antsiranana
Madagascar
13.9
7.2
9.2
11.3
9.3
8.1
10.6
9.0
8.6
10.6
9.0
8.6
8.5
11.2
10.4
10.8
12.8
8.1
9.9
9.1
8.3
5.7
1.5
6.8
3.2
54.4
4.3
3.5
6.2
2.9
57.9
2.8
3.5
5.2
3.6
59.2
2.8
3.5
5.2
3.6
59.2
1.9
3.5
7.0
2.7
56.5
3.3
1.7
5.6
1.9
56.6
3.8
3.0
5.6
3.0
57.5
100.0 (24.5h)
100.0 (24.8h)
100.0 (24.6h)
100.0 (24.4h)
100.0 (24.2h)
100.0 (24.1h)
100.0 (25.0h)
Note: One percentage point is approximately equivalent to 15 minutes. The total number of hours per day exceeds 24 because of simultaneous activities.
Source: INSTAT (2002), EPM 2001. Module Emploi du Temps, Antananarivo, INSTAT- DSM/PNUD-MAG/97/007.
World Bank Working Paper
Appendix Table 3.A2. Time Use Patterns for Household Members from 6 to 65 Years Old in Rural Areas by Province (Faritany),
Sex, and Activity (In percent of the total number of hours per day) (Continued)
PART II
Measuring Time Poverty
73
CHAPTER 4
Measuring Time Poverty and
Analyzing Its Determinants:
Concepts and Application
to Guinea
Elena Bardasi and Quentin Wodon11
The availability of better data on time use in developing countries makes it important to provide tools for analyzing such data. While the idea of “time poverty” is not new, and while many
papers have provided measures of time use and hinted at the concept of time poverty, we have
not seen in the literature formal discussions and measurement of the concept of time poverty
alongside the techniques used for measuring consumption poverty. Conceptually, time poverty
can be understood as the fact that some individuals do not have enough time for rest and leisure
after taking into account the time spent working, whether in the labor market, for domestic
work, or for other activities such as fetching water and wood. Unlike consumption or income,
where economists assume that “more is better,” time is a limited resource—more time spent
working in paid or unpaid work-related activities means less leisure, and therefore higher “time
poverty.” Our aim in this paper is to provide a simple application of the concepts used in the
consumption poverty literature to time use, in order to obtain measures of time poverty for a
population as a whole and for various groups of individuals.
T
here has been an increase in interest in recent years in analytical work on the economic analysis of time use (see for example the papers in Hamermesh and Pfann
2005). The allocation of time has implications in a wide range of areas, as illustrated
11. The authors are with the World Bank. This work was prepared as a contribution to the Poverty
Assessment for Guinea prepared at the World Bank. The authors acknowledge support from the Trust
Fund ESSDD as well as the Belgian Poverty Reduction Partnership for research on this issue as part of a
small research program on gender, time use and poverty in Sub-Saharan Africa which also benefited from
funding from the GENFUND. Preliminary results from the paper were presented at a three-day workshop
organized in Guinea in October 2005 in collaboration with the country’s National Statistical Office (Direction Nationale de la Statistique), and at a World Bank workshop in November 2005. We are grateful to
Kathleen Beegle and Mark Blackden for comments. The views expressed here are those of the authors and
need not reflect those of the World Bank, its Executive Directors or the countries they represent.
75
76
World Bank Working Paper
for example by work on transportation (Zhang, Timmermans, and Borgers 2005) and taxation (Apps and Rees 2004). In developing countries, the issue of time use has been discussed in relationship among others to the ability of household members to increase their
supply of labor (Newman 2002), given strict time constraints due among others to limited
access to basic infrastructure services. The role of illness in limiting the ability of women
to take advantage of economic opportunities due to the burden of care has also been highlighted (Ilahi 2000 and 2001). A broader discussion of the implications of time use issues
for growth and development is available in the report “Engendering Development” by the
World Bank (2001; see also Blackden and Bhanu 1999; Gelb 2001; Apps 2004).
The importance of time use stems in part from the understanding that the welfare of
individuals and households is a function not solely of their income or consumption, but
also of their freedom in allocating time. Clearly, time use allocation and constraints, especially as they relate to labor markets, have implications for the ability of households to
escape poverty. For example, Vickery (1977) argued that reaching the minimal level of consumption used for poverty measurement in the United States requires both money and
time, which matters when designing income transfer programs. More generally, in their
review of the literature on time use prepared for the World Bank’s manual on Living Standard Measurement Surveys, Harvey and Taylor (2000) argue that households need a minimum number of hours—the “household time overhead” concept—to complete domestic
chores, with a lower such overhead leading to higher levels of welfare.
In Sub-Saharan Africa, the issue of time use is especially important because of the high
workload carried by many and the relationship between time use and consumption
poverty. Households have a high probability of being consumption poor, so that any
opportunity to enable them to make a better livelihood, for example by shifting time from
low- to high-productivity activities should be pursued. Furthermore, time use issues have
strong gender dimensions, as African women often have to work long hours for domestic
chores and the collection of water and wood apart from working in the fields or in other
productive occupations.
On the data front, time use surveys have been implemented for many years in several
developed countries, but in developing countries, their use had been more limited so far,
with much of the evidence coming from small-scale village-level instruments or otherwise
small samples. Recently, thanks to efforts by the United Nations’ statistics division, nationally representative time use surveys have been carried in India and Nepal in 1999, Benin in
1998, Nigeria in 1999, South Africa in 2000, Madagascar in 2001, and Mauritius in 2003.
The results from these surveys are reviewed by Charmes (see Chapter 3). In addition, time
use data have also been available in a range of other surveys similar to the Living Standards
Measurement Surveys (LSMS) promoted by the World Bank. In Sub-Saharan Africa,
examples of recent LSMS-type surveys with time use modules include Ghana in 1991–92
and 1998-99, Guinea in 2002–03, Malawi in 2004, Mauritania in 2000, and Sierra Leone in
2003. This is by no means an exhaustive list, but it does indicate that more data are becoming available to conduct work on these issues. In the Unites States as well, in recognition
of the importance of better analytical work on time use issues, a new time use survey is
being implemented (Hamermesh, Frazis, and Stewart 2005).
The availability of better data for time use analysis in developing countries makes it
important to provide tools for analyzing such data. While the idea of “time poverty” is
Gender, Time Use, and Poverty in Sub-Saharan Africa
77
not new, and while many papers have provided measures of time use and hinted at the
concept of time poverty, we have not seen in the literature much formal discussion and
measurement of the concept of time poverty alongside the techniques used for measuring consumption poverty. Conceptually, time poverty can be understood as the fact that
some individuals do not have enough time for rest and leisure after taking into account
the time spent working, whether in the labor market, for domestic work, or for other
activities such as fetching water and wood. Another way to consider the issue of time
poverty is to argue that individuals who are extremely pressed for time are not able to allocate sufficient time for important activities, and are therefore forced to make difficult
tradeoffs. The analogy with consumption poverty would be a household that, because of
insufficient income, would need to sacrifice some key basic needs in order to be able to
afford other basic needs. However, unlike consumption or income, of which economists
assume that “more is better,” time is a limited resource—more time spent working in paid
or unpaid productive activities means less leisure, and therefore higher “time poverty.”
Our aim in this paper is then to provide a simple application of the concepts used in the
consumption poverty literature to time use, in order to obtain measures of time poverty
for a population as a whole and for various groups of individuals.
Because there is less consensus on the benefits and costs of time spent working than
on the value of a higher consumption or income level for households, the very concept of
time poverty may be challenged. For example, can we consider as time poor relatively
wealthy individuals or households whose members work longer hours in order to achieve
higher levels of income or satisfaction at work? We would argue that time poverty would
apply to such individuals, because long working hours will indeed reduce the time available for leisure, rest, or friends and family. This does not mean that time poor individuals
are worse off than other individuals—simply, time poverty is one of the many dimensions
that may affect an individual’s level of welfare and satisfaction with life.
Another question relates to the treatment of those who are not time poor in the measurement of time poverty. It is important to realize that all poverty measures are censored
variables. That is, for consumption or income poverty, only those below the monetary
poverty line affect the consumption poverty measure, while the individuals above the monetary threshold are assigned a value of zero for their contribution to aggregate consumption poverty. Similarly for time poverty, only those above the time poverty line affect the
time poverty measure, while the individuals below the time poverty line are assigned a
value of zero for their contribution to aggregate time poverty. This means that by considering in the time poverty measure only those individuals who work more hours than the
time poverty line, the measure is itself silent on the situation of the non-time poor, apart
from asserting that they are not time poor. In other words, no assumptions are made in
terms of comparing the welfare in the time use dimension of those individuals who work,
say, 40 hours versus 20 hours per week.
We would argue that precisely because it would be difficult to make comparisons of
time use welfare between individuals who are within the normal range of work hours—
some may prefer to work 20 hours while others may prefer to work 40 hours, the time
poverty concept is the right one to use for the analysis because it does not require such comparisons of time-based welfare below a threshold that would be sufficiently high so as to
ensure that tradeoffs have to be made by individuals above that threshold. Said differently,
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World Bank Working Paper
the fact that poverty measures are censored makes such measures especially well adapted
to the analysis of time poverty by considering only in the measures those who are time poor
and not requiring any specific assumption for the comparison of working hours among
individuals who are not time poor.
Still another question is whether individuals are really time constrained, or whether,
for almost all individuals, there would be an ability to work more, in which case the concept of time poverty would be for practical purposes mostly irrelevant. This is an empirical question, but evidence does suggest the presence of upper bounds on working time for
individuals. For example, using data from Ecuador, Newman (2002) shows that when
women took advantage of new labor market opportunities in the cut flower industry, their
total labor time remained constant, so that men had to provide higher amounts of work in
unpaid tasks. The analysis of seasonality in time use in Malawi provided in this volume by
Wodon and Beegle (Chapter 5) also suggests that there may be labor scarcity at crucial periods of the year despite underemployment in many other periods. These examples suggest
that the concept of time poverty is a potentially important one. In the rest of this paper,
after outlining our analytical framework in the next section, we present empirical results
obtained with a recent survey for Guinea on the extent of time poverty in that country.
A brief conclusion follows.
Analytical Framework
This paper provides measures of time poverty in Guinea using the latest nationally representative household survey for the period 2002–2003. Our framework is straightforward as
we simply apply the traditional concepts and techniques used for the analysis of income or
consumption poverty to time poverty. For the reader who may not be familiar with these
concepts, we follow their presentation as provided by Coudouel, Hentschel, and Wodon
(2002), and simply adapt this presentation to the measurement of time poverty.
In most empirical research on poverty, poverty measures of the so-called FGT class
(Foster, Greer, and Thorbecke 1984) are used. The first three measures of this class are the
headcount index of poverty, the poverty gap, and the squared poverty gap. In a time
poverty framework, the headcount index is the share of the population which is time poor,
that is, the proportion of the population that works a number of hours y that is above a
certain time poverty line z. Suppose we have a population of size n in which q individuals
are time poor. Then the headcount index of time poverty is defined as:
H=
q
n
(1)
The time poverty gap represents the mean distance separating the population from the
time poverty line, with the non-time poor being given a distance of zero. This measures
the time deficit of the entire population, in effect, the amount of time that would be
needed to shift all individuals who are time poor below a given time poverty line through
perfectly targeted “time transfers.” Such transfers are actually provided to some households in some developed countries, for example through the provision of subsidies for
taking care of children in working families (or simply of large families—in Belgium,
Gender, Time Use, and Poverty in Sub-Saharan Africa
79
households having three very young children may benefit from the help of a social worker
at home.) Mathematically, the time poverty gap is defined as follows:
1
PG =
n
q
yi − z
z
∑
i =1
(2)
where yi is total working hours of individual i, and the sum is taken only among those individuals who are time poor. Consider for example a situation in which the time poverty gap
is equal to 0.20. This means that the transfer of time needed to enable all time-poor individuals to escape time poverty represents 20 percent of the time poverty line on average. If
the total time available (say, after accounting for a minimum amount of time devoted to
rest) is equal to twice the time poverty line, the time transfer that would be needed to eradicate time poverty would represent 10 percent of the total time available. Such simple calculations can be used to communicate in an intuitive manner the meaning of the time
poverty gap and the magnitude of the time reallocation that would be needed in order to
eradicate time poverty. In practice however, given that perfectly-targeted time transfers to
eradicate time poverty are neither feasible nor necessarily a good thing, one must be careful in their use. Note also that the time poverty gap can be written as being equal to the
product of the headcount index of time poverty by the time gap ratio I, i.e. PG = H ∗ I, with
I itself defined as:
I=
yq − z
z
1
where yq = q
q
∑ y is the mean working hours of the time poor.
i
(3)
i =1
As is well known in the poverty literature, the time gap ratio I is not a good measure of
poverty in itself, because there may be situations where the time gap ratio is reduced over
time. For example, if some individuals who are close to the time poverty line reduce their
working hours, they may escape time poverty, so that aggregate time poverty as measured
by the time poverty gap would be reduced, but with an increase in the time gap ratio computed among those individuals who remain time poor.
While the time poverty gap takes into account the distance separating the time poor
from the time poverty line, the squared time poverty gap takes the square of that distance
into account. When using the squared time poverty gap, more weight is given to those who
have extra long working hours. Said differently, the squared poverty gap takes into account
the inequality among the time poor. It is defined as:
1
SPG =
n
q
∑
i =1
yi − z
z
2
(4)
The headcount, poverty gap, and squared poverty gap are the FGT class of poverty measures whose formula includes a parameter α taking a value of zero for the headcount, one
for the poverty gap, and two for the squared poverty gap in the following expression:
1
Pα =
n
q
∑
i =1
α
yi − z
z
(5)
In terms of interpretation, it is worth noting that contrary to what happens with monetary
poverty measures, the (normalized) time poverty gap need not always be smaller than the
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World Bank Working Paper
time headcount index, and the squared time poverty gap need not be smaller than the time
poverty gap. When using (z − y)/z as the household level indicator for consumption or
income poverty, the normalization of (z − y) by z implies that we always have values that
are between zero and one. For time poverty by contrast, because the definition in (5) relies
instead on the value of (y − z)/z, we may have relatively large values for y − z, so that some
values at the individual level may be larger than one, and the poverty gap may itself have a
higher value than the headcount index in the aggregate, especially if the time poverty line
is set at a relatively low value. However, as long as one remembers that the division by z is
only used for normalization purpose, so that it does not affect the key properties that
poverty measures must observe, this should not lead to confusion. In case of confusion, it
would suffice to use an alternative normalization, such as (y − z)/168 if we are using weekly
hours as the benchmark (because there are 168 hours in a week), in order to make sure that
all the time poverty measures are between zero and one.
A few more comments may be useful before presenting an empirical illustration.
Firstly, when measuring time poverty, we have data at the individual level, while in most
cases, when measuring income or consumption poverty, we only have aggregate data at the
household level. This means that for time poverty, we can look at intra-household allocations and at the impact of intra-household time inequality on time poverty.
Secondly, there is always a difficulty in traditional poverty measurement in comparing the welfare of households of different sizes and composition, because of differences in
needs between individuals, as well as economies of scale in consumption. To some extent,
these difficulties persist for the measurement of time poverty, as there may be differences
in needs for time poverty measurement, for example if children need more rest and leisure
time than adults. By contrast, even though there are clearly economies of scale at the household level in terms of the amount of time required to perform some domestic tasks that
benefit all household members at once, this is not problematic for the measurement of time
use because we observe the hours of work of each individual.
Thirdly, although (1) to (3) above are written by considering the amount of work
above a certain time poverty threshold, they could be modified to consider instead as time
poor those individuals who have less than a certain amount of time for leisure and rest.
This can be done because the amount of time available in one day is fixed, so that there is
a perfect correspondence between the two approaches. If the amount of time available in
a day were not bounded, we would need to use the “above the line” approach both for measurement and for assessments of the robustness time poverty comparisons, as done in the
case of pollution and CO2 emissions by Makdissi and Wodon (2004).
Fourthly, what is perhaps more arbitrary when analyzing time poverty as compared to
consumption poverty is the choice of the time poverty line above which individuals are
considered as overworked or time poor, and thereby lacking enough time for leisure and
rest. In the income/consumption poverty literature, we often have clear nutritional-based
“cost of basic needs” approaches to estimating poverty lines. When dealing with time
poverty, the correct level for the time poverty line is less clear, at least if one wants to consider an allocation of time for leisure on top of what is strictly needed for rest from a health
point of view. In practice, depending on the social context of the country for which the
analysis is conducted, we may want to use relative as opposed to absolute time poverty lines
together with some tests for the robustness of comparisons of time poverty obtained over
time or across households groups to the choice of the time poverty line.
Gender, Time Use, and Poverty in Sub-Saharan Africa
81
Data and Results
Time Use Statistics
To illustrate time poverty measurement and comparisons, we use data from Guinea for the
year 2002–2003. The data are from the EIBEP (Enquête Intégrale de Base pour l’Evaluation
de la Pauvreté) survey implemented between October 2002 and October 2003 by the Direction nationale de la statistique (DNS) of the Ministry of Planning. The individual-level indicator that we use to determine who is time poor is the total amount of time spent by
individuals working, whether in the labor market, in domestic chores or in collecting water
and wood. Note that we have no information about the time spent caring for children, sick
household members and disabled people. This probably leads us to underestimate the
workload of individuals, even if we could argue that this activity is often performed as a
“secondary activity” in combination with one of the other productive or domestic activities
recorded in the questionnaire and included in our estimates of the total time devoted to
work. We also create a second definition of the total time allocated to work by adding to
the components of the first definition the amount of time spent helping other households
and in community services (this is done because it is unclear whether these activities are
more for work than for leisure).
Figure 4.1 shows the distribution of the total individual working hours per week for
adult individuals (aged 15+), separately for men and women, as well as for urban and
rural areas. Women work a much higher number of hours than men, and a larger proportion of men than women do not work at all (9.9 percent of men versus 6.4 percent of
women). Similarly, individual working hours are much higher in rural than in urban
areas, and the hours worked distribution in urban areas is highly skewed with a large proportion of low values. For example, while in urban areas 10.4 percent of individuals do
not work any hour at all, this percentage is 6.8 in rural areas. Table 4.1 provides data on
the main uses of working time (more details on the distribution of time worked are provided in appendix Tables 4.A1 and 4.A2). For example, under the first definition of
working time, the mean working time in urban areas is 36.2 hours for the adult population (above 15 years of age), 38.8 hours for women, and 33.6 hours for men. While men
spend more time on the labor market, the amount of time spent by women on domestic chores is much higher than for men. Girls also work longer hours than boys, again
mainly due to a higher burden from domestic work, but the amount of work remains
fairly reasonable, at an average of 5.5 hours per week. In rural areas by contrast, children
work substantially more, for an average of 19.6 hours according to the first definition of
working time. For adults, the average working time is 48.6 hours, again with a higher
level for women than for men.
The average number of total working hours, the median, and the 25th and the 75th
percentiles in the distribution of working hours are provided in Table 4.2 at the national
level and for various groups of individuals. Clearly, throughout the distribution of time
use, there are large differences between men and women, and between urban and rural
areas. Using the second definition of total time worked (which includes also the time spent
helping other households and in community services) slightly decreases the gender gap
because men are relatively more likely than women to spend time in community services),
but the qualitative results do not change. As for comparisons across urban and rural areas,
the median total individual working time in rural areas is more than twice the median in
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World Bank Working Paper
Figure 4.1. Distribution of Individual Working Time by Sex and Area
(Individuals aged 15+)
Women
.07
.06
.06
.05
.05
Density
Density
Men
.07
.04
.03
.04
.03
.02
.02
.01
.01
0
0
0
50
100
0
Total individual time, definition 1 (hours/week)
Urban areas
100
Rural areas
.07
.07
.06
.06
.05
.05
Density
Density
50
Total individual time, definition 1 (hours/week)
.04
.03
.04
.03
.02
.02
.01
.01
0
0
0
50
100
Total individual time, definition 1 (hours/week)
0
50
100
Total individual time, definition 1 (hours/week)
Source: Authors’ estimation using EIBEP 2002–2003.
urban areas. Interestingly, the gap between urban and rural areas in total individual working
time according to definition 2 is larger than the gap according to definition 1 because individuals living in rural areas spend relatively more hours helping other households and in
community services than urban individuals, despite their already higher total time spent
in work and household activities.
Table 4.2 also provides data on time use for children. On average, children spend about
16 hours a week working in paid and unpaid tasks. The large difference between the mean
and the median and the 25th and the 75th percentile suggests that these working hours are
very unequally distributed. Working hours are much higher for children that do not go to
school (about 25 hours/week on average) than for children that are currently in school (about
7 hours/week). Although this is not shown in the table, it is worth noting that children that
Gender, Time Use, and Poverty in Sub-Saharan Africa
83
Table 4.1. Average Number of Weekly Hours Spent for Various Activities,
by Sex and Age
Age 6–14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Cooking
Cleaning
Washing
Ironing
Market
All domestic chores (1 to 5)
Collection of wood
Collection of water
Aid to other households
Community activities
Work for a wage
Work in a farm of family business
Work in labor market (11 + 12)
Total time (definition 1)
Total time (definition 2)
Male
Female
0.2
0.4
0.9
0.2
0.3
1.9
1.9
1.3
0.2
0.2
0.4
8.0
8.4
13.4
13.8
2.3
1.6
1.7
0.2
0.8
6.5
1.1
2.1
0.3
0.1
0.5
7.8
8.3
18.0
18.4
Age 15+
All
Male
National level
1.2
1.0
1.3
0.2
0.5
4.2
1.5
1.7
0.2
0.1
0.5
7.9
8.4
15.7
16.1
0.2
0.4
0.8
0.5
0.6
2.5
1.1
0.6
0.8
0.9
17.8
16.9
34.7
38.8
40.5
Female
All
8.5
2.6
2.9
0.7
2.9
17.5
1.8
2.7
0.8
0.5
11.6
15.8
27.4
49.3
50.6
4.8
1.6
1.9
0.6
1.9
10.8
1.5
1.7
0.8
0.7
14.4
16.3
30.7
44.6
46.1
6.8
2.3
2.4
1.1
3.0
15.5
0.2
1.2
0.4
0.2
18.7
3.2
21.9
38.8
39.4
3.4
1.4
1.6
0.9
1.6
8.9
0.2
0.8
0.3
0.3
22.3
4.0
26.3
36.2
36.7
9.2
2.8
3.1
0.5
5.4
1.8
2.1
0.4
Urban areas
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Cooking
Cleaning
Washing
Ironing
Market
All domestic chores (1 to 5)
Collection of wood
Collection of water
Aid to other households
Community activities
Work for a wage
Work in a farm of family business
Work in labor market (11 + 12)
Total time (definition 1)
Total time (definition 2)
0.1
0.4
0.8
0.2
0.2
1.7
0.3
0.6
0.1
0.1
0.4
1.0
1.3
3.9
4.0
1.2
1.4
1.3
0.2
0.5
4.6
0.1
0.9
0.1
0.1
0.5
0.9
1.4
7.1
7.2
0.6
0.9
1.0
0.2
0.4
3.2
0.2
0.8
0.1
0.1
0.5
0.9
1.4
5.5
5.6
0.2
0.5
0.8
0.7
0.2
2.4
0.2
0.4
0.2
0.3
25.9
4.8
30.7
33.6
34.1
Rural areas
1
2
3
4
Cooking
Cleaning
Washing
Ironing
0.2
0.4
0.9
0.1
2.7
1.7
1.8
0.2
1.4
1.0
1.3
0.2
0.3
0.4
0.7
0.3
(continued)
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World Bank Working Paper
Table 4.1. Average Number of Weekly Hours Spent for Various Activities,
by Sex and Age (Continued)
Age 6–14
5
6
7
8
9
10
11
12
13
14
15
Market
All domestic chores (1 to 5)
Collection of wood
Collection of water
Aid to other households
Community activities
Work for a wage
Work in a farm of family business
Work in labor market (11 + 12)
Total time (definition 1)
Total time (definition 2)
Male
Female
0.3
1.9
2.5
1.5
0.2
0.2
0.4
10.6
11.0
16.9
17.3
0.8
7.3
1.5
2.6
0.3
0.1
0.5
10.6
11.0
22.4
22.9
Age 15+
All
Male
Rural areas
0.6
4.5
2.0
2.0
0.3
0.2
0.5
10.6
11.0
19.6
20.0
0.9
2.6
1.6
0.7
1.1
1.2
13.1
23.9
37.0
41.8
44.2
Female
All
2.8
18.3
2.4
3.3
1.0
0.6
8.6
21.0
29.7
53.7
55.2
2.0
11.7
2.1
2.2
1.1
0.9
10.5
22.2
32.7
48.7
50.6
Note: Zeros are included. Total time (definition 1) is the sum of 6 (all domestic chores), 7 (collection
of wood), 8 (collection of water), and 13 (work in labor market). Total time (definition 2) is the sum of
total time (definition 1), 9 (aid to other households), and 10 (community activities).
Source: Authors’ estimation using EIBEP 2002–2003.
are out of school spend about 17 hours/week on average in paid work (or farm or family
business), while the median of their hours of paid work is zero. The time spent in paid work
by children who go to school is by contrast negligible (0.5 hours/week on average). Therefore, while almost all children who work in the labor market (or family farm or business)
are out of school, the opposite is not true; moreover, a large part of child labor is spent in
domestic tasks and in fetching water and wood, among children both in and out of school.
Finally, as is the case for adults, girls spend more time than boys in paid and particularly
unpaid work (as previous tables had suggested). The gap at the mean is 34 percent—even
higher than the one existing between adult men and women (25 percent). This gap is larger
for children who are enrolled in school, suggesting that it may be more difficult for girls to
find the time to study, especially in rural areas.
Time Poverty
Because we have data at the individual level, we focus on individual-level measures of time
poverty, although we could compute household time poverty measures as well through
some aggregation procedure. In the absence of well-established practices to measure time
poverty, we use two alternative relative poverty lines, a lower threshold equal to 1.5 times
the median of the total individual working hours distribution and a higher threshold equal
to 2 times the median. We have calculated the threshold separately for children aged 6–14
Gender, Time Use, and Poverty in Sub-Saharan Africa
85
Table 4.2. Selected Values in the Cumulative Distribution of Working Time
for Various Groups
Mean
Median
25th Percentile
75th Percentile
Adult population (15 years of age and older), definition 1
All
Men
Women
Gender gap (%)
Urban
44.6
38.8
49.3
+27.1
36.2
47.0
44.0
51.0
+15.9
31.0
19.0
8.0
25.0
+212.5
5.0
64.0
57.0
70.0
+22.8
61.0
Rural
Area gap (%)
48.7
+34.5
49.0
+58.1
32.0
+540.0
65.0
+6.6
All
Men
Women
Gender gap (%)
Urban
Rural
Area gap (%)
46.1
40.5
50.6
+24.9
36.7
50.6
+37.9
Adult population (15 years of age and older), definition 2
48.0
46.0
52.0
+13.0
32.0
51.0
+59.4
20.0
9.0
26.0
+188.9
5.0
34.0
+580.0
66.0
60.0
72.0
+20.0
62.0
68.0
+9.7
Children (below 14 years of age), definition 1
All
Boys
Girls
Gender gap (%)
Urban
Rural
Area gap (%)
15.7
13.4
18.0
+34.3
5.5
19.6
+256.4
6.0
4.0
8.0
+100.0
2.0
9.0
+350.0
1.0
1.0
2.0
+100.0
0.0
3.0
n.d.
22.0
15.0
28.0
+86.7
6.0
35.0
+483.3
Children (below 14 years of age), definition 1, by school
enrollment status
Not enrolled all
Not enrolled boys
Not enrolled girls
Gender gap (%)
Enrolled all
Enrolled boys
Enrolled girls
Gender gap (%)
25.4
24.1
26.5
+10.0
6.8
5.5
8.4
+52.7
16.0
13.0
18.0
+38.5
4.0
3.0
5.0
+66.7
2.0
1.0
4.0
+300.0
1.0
0.0
1.0
n.d.
45.0
45.0
45.0
0.0
9.0
7.0
11.0
+57.1
Note: Zeros are included. Total time (definition 1) is the sum of 6 (all domestic chores), 7 (collection of wood),
8 (collection of water), and 13 (work in labor market). Total time (definition 2) is the sum of total time (definition 1), 9 (aid to other households), and 10 (community activities). The “area gap” in total hours is expressed
as the higher percent of total hours of rural with respect to urban area. The gender gap in total hours is
expressed as the higher percent of total hours of women with respect to men, or girls with respect to boys.
Source: Authors’ estimation using EIBEP 2002–2003.
86
World Bank Working Paper
Table 4.3. Time Poverty Rates (Share of individuals in the group that are time poor)
Adult population
Men
Women
All
Time poverty line 70.5 hours/week
Time poverty line 94 hours/week
Urban
Rural
All
Urban
Rural
All
11.7
18.6
15.1
8.3
26.5
18.8
9.5
24.2
17.6
2.7
4.7
3.7
1.8
7.9
5.3
2.1
7.0
4.8
Children
Time poverty line 9 hours/week
Boys
Girls
All
Time poverty line 12 hours/week
Urban
Rural
All
Urban
Rural
All
7.7
20.4
14.2
40.9
56.9
48.7
32.0
46.5
39.2
5.4
14.4
10.0
36.1
49.9
42.8
27.9
39.8
33.8
Note: For adults, the “time poverty line” of 70.5 hours/week corresponds to 1.5 times the median
number of hours of all adults aged 15+ (47 hours/week). The “time poverty line” of 94 hours/week
corresponds to 2 times the median. For children, the “time poverty line” of 9 hours/week corresponds
to 1.5 times the median number of hours of work among children and the “time poverty line” of
12 hours/week corresponds to 2 times the median among children.
Source: Authors’ estimation using EIBEP 2002–2003.
and adults aged based on their own respective distribution. The resulting poverty lines are
9 hours and 70.5 hours per week for the lower threshold for children and adults respectively, and 12 hours and 94 hours for the higher threshold.
Table 4.3 shows the time poverty rates based on the two alternative poverty lines for
men and women living in urban and rural areas. According to the lower threshold about
18 percent of all individuals are time poor. This rate is much higher for women (24.2 percent) than men (9.5 percent), and in rural areas (18.8 percent) as compared to urban areas
(15.1 percent). More women living in rural areas are time poor (26.5 percent) than women
living in urban area (18.6 percent). For men, it is the reverse, with urban men more likely
to be time poor than rural men (11.7 vs. 8.3 percent). When we adopt a higher threshold
the time poverty rates are lower, with he overall time poverty rate dropping to 4.8 percent,
but the patterns in terms of comparisons between groups are very similar. The differences
between men and women are in this case even larger—moving from the lower to the higher
threshold makes time poverty rates for women decrease by a factor of 3, while time poverty
rates for men decrease by a factor of almost 5.
Table 4.3 also shows the child time poverty rates. Given that the time poverty lines have
been computed separately for children and adults, in each case with reference to their own
hour distribution, we may very well have higher relative rates of time poverty among children than among adults since both the lower and the higher time poverty lines turn out to
be significantly lower than for adults, at 9 and 12 hours/week respectively. Looking first at
the results obtained with the lower threshold, we notice that the time poverty rates are
again much higher in rural (49 percent) than in urban areas (14 percent); they are also
Gender, Time Use, and Poverty in Sub-Saharan Africa
87
Table 4.4. Time Poverty Gap and Squared Time Poverty Gap
Time poverty gap, adult population
Time poverty line 70.5 hours/week
Men
Women
All
Time poverty line 94 hours/week
Urban
Rural
All
Urban
Rural
All
2.8
4.4
3.6
1.9
7.1
4.9
2.2
6.3
4.5
0.6
0.7
0.7
0.4
1.4
1.0
0.5
1.2
0.9
Squared time poverty gap, adult population
Time poverty line 70.5 hours/week
Men
Women
All
Time poverty line 94 hours/week
Urban
Rural
All
Urban
Rural
All
1.4
1.9
1.6
0.9
3.3
2.3
1.1
2.9
2.1
0.2
0.2
0.2
0.1
0.4
0.3
0.2
0.3
0.3
Note: For adults, the “time poverty line” of 70.5 hours/week corresponds to 1.5 times the median
number of hours of all adults aged 15+ (47 hours/week). The “time poverty line” of 94 hours/week
corresponds to 2 times the median.
Source: Authors’ estimation using EIBEP 2002–2003.
higher for girls (47 percent) than for boys (32 percent). Using the higher threshold
decreases the time poverty rates somewhat, but the same pattern arises.
In order to illustrate the use of higher poverty measures, we provide time poverty gap
and squared time poverty gaps for the adult population in Table 4.4, using the time poverty
line for the normalization. As for Table 4.3, all values have been multiplied by 100. The key
conclusions in terms of comparing urban and rural areas, as well as men and women, are
the same with these measures as what was observed with the headcount index.
Correlates of Time Poverty
What are the determinants or correlates of time poverty? To answer this question we ran
probit regressions to explain the probability of being time poor as a function of personal,
household and area variables. The analysis is again carried out at the individual level, that
is, each individual is classified as time poor or not depending on his or her own individual
total time worked. Among the regressors we included, beside the usual demographic variables (age, sex, and marital status), the educational qualifications, religion, the consumption quintile of the household, the number of infants (aged 0–5) and children (aged 6–14),
adults (aged 15–64) and senior people (aged over 65), and their square values. We also
included dummy variables for the presence of disabled people, and for households with
only women.12 Finally, we included geographical dummies for rural/urban areas and for
the region of residence. Separate regressions were estimated for men and women, as well
as for rural and urban areas. The results are reported in Table 4.5.
12. We preferred this variable to the alternative “female headed household,” because many female
headed households include several adult men.
88
World Bank Working Paper
Table 4.5. Probit Regression for the Probability of Being Time Poor
(Lower time poverty line)
Men
All
Age
Age squared
Female
Rural
Female*rural
Disabled
Monogamous
Poligamous
Divorced
Widow/er
Christian
Other religion
Primary
Secondary 1st
Secondary 2nd
Technical
University
Unknown ed.
0.012
Men
***
(0.001)
−0.000***
(0.000)
0.033***
(0.006)
−0.067***
(0.007)
0.101***
(0.010)
−0.087***
(0.007)
0.077***
(0.008)
0.079***
(0.009)
0.088***
(0.019)
0.023*
(0.014)
0.023***
(0.009)
0.008
(0.013)
−0.068***
(0.005)
−0.077***
(0.006)
−0.078***
(0.014)
−0.078***
(0.008)
−0.093***
(0.007)
−0.013
(0.033)
Women
Women
Urban
0.010
(0.001)
−0.000***
(0.000)
0.015
(0.001)
−0.000***
(0.000)
0.015
0.003
(0.001)
(0.001)
−0.000*** −0.000***
(0.000)
(0.000)
−0.037***
(0.006)
0.014*
(0.008)
−0.045***
(0.009)
0.015*
(0.009)
0.013
(0.011)
0.051*
(0.028)
0.030
(0.036)
0.002
(0.009)
0.009
(0.016)
−0.049***
(0.005)
−0.054***
(0.005)
−0.050***
(0.010)
−0.050***
(0.007)
−0.065***
(0.005)
−0.047*
(0.029)
−0.139***
(0.011)
0.138***
(0.014)
0.132***
(0.014)
0.140***
(0.027)
0.070***
(0.021)
0.049***
(0.014)
0.014
(0.020)
−0.078***
(0.010)
−0.095***
(0.012)
−0.122***
(0.036)
−0.108***
(0.016)
−0.089***
(0.033)
0.021
(0.054)
***
***
Rural
***
−0.043***
(0.013)
0.021*
(0.011)
0.023
(0.016)
0.029
(0.034)
0.029
(0.052)
−0.010
(0.011)
0.130**
(0.065)
−0.048***
(0.006)
−0.058***
(0.006)
−0.056***
(0.010)
−0.065***
(0.007)
−0.073***
(0.005)
−0.041
(0.037)
Urban
Rural
***
0.019
(0.002)
−0.000***
(0.000)
0.012***
(0.002)
−0.000***
(0.000)
−0.044*** −0.122***
(0.012)
(0.013)
−0.001
0.108***
(0.013)
(0.016)
0.005
0.100***
(0.015)
(0.018)
0.071
0.106***
(0.044)
(0.031)
0.022
0.066**
(0.044)
(0.026)
0.025
0.006
(0.018)
(0.015)
0.009
−0.074*
(0.019)
(0.040)
−0.043*** −0.055***
(0.008)
(0.011)
*
−0.029
−0.083***
(0.017)
(0.011)
0.008
−0.097***
(0.084)
(0.035)
0.086
−0.095***
(0.056)
(0.014)
−0.048*
−0.086***
(0.027)
(0.026)
−0.045
(0.051)
−0.159***
(0.018)
0.130***
(0.024)
0.126***
(0.022)
0.128***
(0.045)
0.028
(0.032)
0.187***
(0.030)
0.175***
(0.035)
−0.142***
(0.023)
0.026
(0.119)
**
0.140
(0.107)
(continued)
Gender, Time Use, and Poverty in Sub-Saharan Africa
89
Table 4.5. Probit Regression for the Probability of Being Time Poor
(Lower time poverty line) (Continued)
Men
Infants (0–5)
Infants squared
All
−0.003
(0.003)
0.001**
(0.000)
−0.005**
(0.002)
Children squared
0.001***
(0.000)
Adults (15–64)
−0.013***
(0.001)
Adults squared
0.000***
(0.000)
Seniors (65+)
0.011
(0.007)
Seniors squared −0.007*
(0.004)
Disabled ind.
0.003
(0.005)
Only women
0.021*
(0.012)
2nd quintile
0.011
(0.008)
3rd quintile
0.027***
(0.008)
4th quintile
0.025***
(0.008)
5th quintile
0.036***
(0.008)
Conakry
−0.005
(0.008)
Faranah
0.010
(0.008)
Kankan
−0.005
(0.008)
Kindia
0.022***
(0.008)
Children (6–14)
Men
0.005
(0.003)
Women
−0.010**
(0.004)
0.000
0.001**
(0.000)
(0.001)
−0.007*** −0.003
(0.002)
(0.003)
0.000**
0.001***
(0.000)
(0.000)
−0.003*
−0.025***
(0.002)
(0.002)
0.000
0.001***
(0.000)
(0.000)
0.005
0.012
(0.009)
(0.011)
−0.002
−0.010*
(0.005)
(0.006)
−0.007
0.012
(0.006)
(0.009)
0.015
(0.015)
0.026** −0.000
(0.011)
(0.011)
0.026**
0.031***
(0.010)
(0.012)
0.037*** 0.016
(0.011)
(0.012)
***
0.044
0.032***
(0.011)
(0.012)
0.032*** −0.046***
(0.010)
(0.011)
0.001
0.015
(0.010)
(0.013)
0.028** −0.032***
(0.011)
(0.011)
*
0.021
0.023*
(0.011)
(0.013)
Urban
0.006
(0.006)
Rural
0.010**
(0.004)
Women
Urban
−0.010
(0.007)
−0.001
−0.000
0.001
(0.001)
(0.001)
(0.001)
−0.012*** −0.001
0.001
(0.003)
(0.003)
(0.004)
0.001**
0.000
0.000
(0.000)
(0.000)
(0.000)
−0.000
−0.015*** −0.020***
(0.002)
(0.005)
(0.003)
**
−0.000
0.001
0.001***
(0.000)
(0.000)
(0.000)
0.015
−0.005
0.013
(0.012)
(0.012)
(0.015)
−0.005
−0.002
−0.003
(0.006)
(0.006)
(0.007)
−0.019**
0.013
−0.005
(0.008)
(0.010)
(0.011)
0.021
(0.022)
0.023
0.029**
0.015
(0.020)
(0.012)
(0.021)
0.027
0.021
0.015
(0.018)
(0.013)
(0.020)
0.027
0.056***
0.012
(0.018)
(0.015)
(0.019)
0.037**
0.057*** 0.028
(0.018)
(0.017)
(0.020)
0.032***
−0.005
(0.011)
(0.014)
0.018
−0.022*
0.057***
(0.015)
(0.013)
(0.019)
0.003
0.048*** 0.016
(0.014)
(0.017)
(0.018)
0.019
0.023
0.055***
(0.015)
(0.015)
(0.019)
Rural
−0.009
(0.006)
0.001*
(0.001)
−0.004
(0.005)
0.001***
(0.000)
−0.041***
(0.007)
0.001**
(0.001)
0.005
(0.018)
−0.017*
(0.009)
0.029**
(0.014)
−0.003
(0.021)
−0.015
(0.014)
0.043***
(0.016)
0.013
(0.017)
0.029
(0.018)
−0.036**
(0.018)
−0.077***
(0.015)
−0.010
(0.018)
(continued)
90
World Bank Working Paper
Table 4.5. Probit Regression for the Probability of Being Time Poor
(Lower time poverty line) (Continued)
Men
Labe
Mamou
Nzerekore
Observed probability
Predicted probability
Pseudo R2
Log likelihood
Number of
observations
Women
−0.054***
Urban
0.006
Women
All
−0.021***
Men
0.018
Rural Urban
0.032* −0.002
(0.008)
0.034***
(0.010)
−0.044***
(0.012)
(0.011)
0.037*** 0.031**
(0.014)
(0.014)
0.011
−0.094***
(0.015)
(0.019) (0.018) (0.015)
0.064*** −0.004
0.050** 0.008
(0.020)
(0.016) (0.021) (0.020)
0.010
−0.000 0.008 −0.230***
(0.007)
(0.011)
(0.010)
(0.014)
(0.016) (0.017)
(0.014)
0.161
0.130
0.110
−11711
29793
0.096
0.081
0.066
−4074
13761
0.217
0.183
0.103
−7527
16032
0.106
0.084
0.091
−2583
8419
0.082
0.069
0.056
−1424
5334
0.255
0.215
0.115
−3681
7325
0.185
0.154
0.101
−3747
8699
Rural
−0.106***
Notes: Marginal effects (rather than coefficients) shown in the table. The marginal effect is computed at
the mean of regressors. For dummy variables it is given for a discrete change from 0 to 1. Standard errors
in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. Sample is restricted to individuals aged 15+. “Adults” are individuals aged 15–64; “seniors” are individuals aged 65+. The ‘time poverty
line’ is 70.5 hours/week. The reference categories are: male, not disabled, urban, single never married,
muslin, no education level (or never in school), no children aged 0–5 in the household, no children aged
6–14 in the household, no disabled people in the household, household with also men, first consumption quintile, and living in Boke. Predicted probability computed at the mean of the regressors.
Source: Authors’ estimation using EIBEP 2002–2003.
Table 4.5 gives the marginal effects estimated at the mean of the variables rather than
the coefficients—for dummy variables the marginal effect represents the change in probability when the dummy variable changes from 0 to 1. For example, the first column
(pooled regression) indicates that women are 3 percentage points more likely to be time
poor than men; for women living in rural area this probability increases by an additional
10 percentage points. The coefficient of living in rural areas is estimated to be negative
(–7 percentage points), but this is driven by the male sample; by comparing the marginal
effect of the rural dummy reported in columns 2 and 3, where different regressions are estimated for men and women, we notice that men living in rural area are less likely to be time
poor, while for women the opposite is true. Obviously, being disabled significantly and
substantially decreases the probability of being time poor, given that disabled people are
less able to work in paid and unpaid tasks. Marital status is also associated with variations
in the probability of being time poor, but this effect is significant (and substantial) only for
women. Married women (either in monogamous or polygamous union) are more likely to
be time poor than single never married women (about 10–11 percentage points more in
urban area and 13 percentage points in rural area; see columns 6 and 7). A similar effect is
estimated for divorced women. Interestingly, women living in rural areas who are Christian
Gender, Time Use, and Poverty in Sub-Saharan Africa
91
or belong to a religion other than Muslim are more likely to be time poor, about
18–19 percentage points more than Muslim rural women.
The educational qualification is also a powerful predictor of time poverty, for both
men and women, and especially in urban areas. Increasing education is associated with
lower probabilities of being time poor; in rural areas where people with qualifications
above primary education are extremely rare, especially among women, having completed
primary education makes individual less likely to be time poor compared to those with no
educational qualifications (–4 percentage points for men and –14 percentage points for
women). By contrast, well-being measured by the consumption quintile appears to be
more weakly associated with time poverty. A significant effect exists for men living in rural
area—those in the top 4th and 5th quintile are about 6 percentage points more likely to
be time poor than the poorer men. For men living in urban areas, those in the 5th quintile are 4 percentage points more likely to be time poor. However, no significant effect is
estimated for women (except that women living in rural area who are in the 3rd quintile
are 4 percentage points more likely to be time poor than the remaining women).
The coefficients for the number of infants and children do not provide a clear story.
We included these variables among the regressors to test the idea that the presence of young
children may require more time from adult members (but recall that time spent in childcare is not explicitly collected in the survey), while older children may provide substitute
labor and therefore make adult members save time. In fact, a positive coefficient is estimated only for men living in rural areas—indicating that only for this group each extra
child increases the probability of being time poor (1 percentage point for each additional
child). On the other hand, a negative coefficient for the number of older children is estimated for men living in urban areas—for them each extra child aged 6–14 decreases the
probability of being time poor, at a decreasing rate (so that one child decreases this probability by 1 percent, while at six children the change in probability is zero and after that the
variation becomes slightly positive). Women’s time poverty, by contrast, does not seem to
be affected by the number of either young or older children living in the household. More
adults in the household, on the other hand, make everybody less likely to be time poor,
indicating that the workload will be more equally distributed across members. This effect
is stronger for women living in rural area (the first adult decreases the time poverty probability by about 4 percentage points, and each subsequent adult slightly less than that);
smaller marginal effects are estimated for women living in urban area and men living in
rural area. The presence of disabled people in the household increase the probability of being
time poor for women living in rural areas (about 3 percentage points), while it decreases the
probability of being time poor for men living in urban areas by about 2 percentage points.
Finally, there are also geographical differences in the probability of being time poor according to Guinea’s main natural regions.
Conclusion
Time poverty has long been recognized as a constraint to development in Sub-Saharan
Africa, with women working especially long hours due in part to a lack of access to basic
infrastructure services such as water and electricity, but also due to the rising demands
from the “care economy.” The very concept of time poverty and the evidence on high
92
World Bank Working Paper
workloads for women could be of use for policymakers. However, when combined with
other dimensions of welfare, such as consumption or income poverty, the analysis of time
poverty can be even more revealing. Other papers in this volume provide simulations of
the impact that increases in hours of work (working up to a certain time poverty line or
norm) could have on monetary poverty. The gains from what could be referred to as full
employment can be compared to gains that would be achieved from higher pay per hour
working.
Apart from looking at the link between time poverty and consumption or income
poverty, work also needs to be carried out on the relationship between time poverty and
other development outcomes. When looking at the targets set out in the Millennium
Development Goals, it is clear that the time spent by children working may have a detrimental impact on their enrollment in school. Yet, time poverty may also affect other outcomes, such as the nutritional status of children. Conversely, conditions related to health
(such as the HIV/AIDS crisis) may increase time poverty and thereby reduce the amount
of time that households and individuals may allocate to work.
Despite a growing number of studies on time use in Africa and elsewhere, time poverty
has remained loosely defined. In this paper, we have argued that the techniques used for
the measurement and analysis of the determinants of poverty can be applied readily to the
issue of time poverty. While the concepts and examples presented in this paper have not
dealt with the issue of the impact of time poverty on development outcomes, we hope that
they have provided some ideas on how to use the measurement and analysis techniques
that have been developed for the analysis of monetary poverty in this new and exciting area
of work that time poverty represents.
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Appendix Table 4A.1. Number of Weekly Hours Spent for Various Activities, by Sex,
Time Spent Collecting Water, and Urban/Rural Area
Men 15ⴙ
Urban
1
2
3
4
5
6
7
8
9
10
11
12
0 Hrs 1–4 Hrs 4ⴙ Hrs
14
Cooking
0.1
Cleaning
0.3
Washing
0.6
Ironing
0.5
Market
0.1
All domestic chores (1–5) 1.6
Collection of wood
0.1
Collection of water
0.0
Aid to other households
0.2
Community activities
0.3
Work for a wage
28.3
Work in a farm of family
4.7
business
Work in labor market
33.0
(1 ⴙ12)
Total time (definition 1) 34.7
15
Total time (definition 2)
35.2
Cooking
0.1
Cleaning
0.2
Washing
0.3
Ironing
0.2
Market
0.8
All domestic chores (1–5) 1.6
Collection of wood
1.2
Collection of water
0.0
Aid to other households
1.1
Community activities
1.3
Work for a wage
13.4
Work in a farm of
25.1
family business
Work in labor market
38.5
(11 ⴙ 12)
13
Women 15ⴙ
All
0 Hrs 1–4 Hrs 4ⴙ Hrs
All
0.5
1.2
1.7
1.3
0.4
5.1
0.4
1.6
0.4
0.3
16.9
5.1
0.4
2.3
3.7
2.6
0.7
9.6
0.8
7.5
0.3
0.2
8.7
6.0
0.2
5.6
0.5
1.8
0.8
1.8
0.7
0.8
0.2
2.4
2.4 12.5
0.2
0.1
0.4
0.0
0.2
0.2
0.3
0.2
25.9 18.4
4.8
2.6
7.8
2.6
2.8
1.3
3.4
17.8
0.3
1.9
0.5
0.3
18.9
4.0
10.8
4.5
4.6
2.1
5.9
27.7
0.4
7.5
0.9
0.4
20.4
3.9
6.8
2.3
2.4
1.1
3.0
15.5
0.2
1.2
0.4
0.2
18.7
3.2
21.9
14.8
30.7 21.0
22.9
24.3
21.9
29.0
32.6
33.6 33.5
42.9
60.0
38.8
29.7
33.1
34.1 33.9
43.7
61.3
39.4
0.8
0.9
1.5
0.8
0.9
4.9
2.4
1.7
1.1
0.9
12.3
20.1
1.4
1.3
4.2
0.8
2.2
9.9
7.1
8.8
1.3
1.1
10.8
21.8
0.3
0.4
0.7
0.3
0.9
2.6
1.6
0.7
1.1
1.2
13.1
23.9
4.8
1.3
1.3
0.2
1.3
8.9
0.9
0.0
0.5
0.3
6.5
15.8
9.4
2.4
2.8
0.5
2.7
17.9
2.1
2.2
1.0
0.6
8.9
22.6
13.2
4.9
5.5
0.7
4.5
28.9
4.6
9.0
1.5
0.8
10.2
23.2
9.2
2.8
3.1
0.5
2.8
18.3
2.4
3.3
1.0
0.6
8.6
21.0
32.4
32.6
37.0 22.3
31.5
33.4
29.7
Rural
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Total time (definition 1)
41.2
41.4
58.4
41.8 32.1
53.6
75.9
53.7
15
Total time (definition 2)
43.7
43.4
60.8
44.2 32.9
55.2
78.2
55.2
Note: Zeros are included. Total time (definition 1) is the sum of 6 (all domestic chores), 7 (collection
of wood), 8 (collection of water), and 13 (work in labor market). Total time (definition 2) is the sum of
total time (definition 1), 9 (aid to other households), and 10 (community activities).
Source: Authors’ estimation using EIBEP 2002–2003.
Gender, Time Use, and Poverty in Sub-Saharan Africa
95
Appendix Table 4A.2. Number of Weekly Hours Spent for Various Activities, by Sex,
Time Spent Collecting Wood, and Urban/Rural Area
Men 15ⴙ
Urban
1
2
3
4
5
6
7
8
9
10
11
12
0 Hrs 1–4 Hrs 4ⴙ Hrs
14
Cooking
0.1
Cleaning
0.5
Washing
0.8
Ironing
0.6
Market
0.2
All domestic chores (1–5) 2.2
Collection of wood
0.0
Collection of water
0.3
Aid to other households
0.2
Community activities
0.3
Work for a wage
26.3
Work in a farm of family
4.7
business
Work in labor market
31.0
(11ⴙ12)
Total time (definition 1) 33.6
15
Total time (definition 2)
13
Women 15ⴙ
All
0 Hrs 1–4 Hrs 4ⴙ Hrs
All
0.7
0.9
1.2
1.0
0.7
4.6
1.6
1.0
0.7
0.7
20.2
4.9
0.3
1.3
1.9
1.5
0.2
5.2
9.1
1.6
1.1
1.1
12.5
15.7
0.2
6.7
0.5
2.2
0.8
2.3
0.7
1.0
0.2
3.0
2.4 15.3
0.2
0.0
0.4
1.1
0.2
0.3
0.3
0.2
25.9 18.6
4.8
3.1
7.5
2.5
2.5
1.3
2.9
16.8
1.6
1.9
0.7
0.7
20.0
3.7
11.7
5.0
5.5
2.6
6.0
30.7
8.0
3.1
1.3
1.4
22.6
8.1
6.8
2.3
2.4
1.1
3.0
15.5
0.2
1.2
0.4
0.2
18.7
3.2
25.1
28.2
30.7 21.7
23.7
30.7
21.9
32.2
44.1
33.6 38.1
43.9
72.4
38.8
34.1
33.6
46.2
34.1 38.6
45.3
75.1
39.4
0.1
0.2
0.2
0.1
0.7
1.3
0.0
0.2
1.0
1.2
14.8
23.0
0.5
0.6
1.1
0.5
1.0
3.7
2.1
0.9
1.2
1.1
11.3
24.4
0.5
0.8
2.2
0.6
1.6
5.6
8.8
2.4
1.4
1.6
10.2
26.7
0.3
7.1
0.4
2.2
0.7
2.1
0.3
0.2
0.9
2.0
2.6 13.6
1.6
0.0
0.7
2.0
1.1
0.6
1.2
0.3
13.1
6.6
23.9 18.6
9.2
2.6
2.9
0.6
2.6
17.9
2.3
3.1
1.1
0.7
9.0
22.3
13.9
4.7
6.1
0.9
5.0
30.5
8.4
7.0
1.7
0.9
12.1
23.0
9.2
2.8
3.1
0.5
2.8
18.3
2.4
3.3
1.0
0.6
8.6
21.0
37.8
35.7
36.9
37.0 25.2
31.3
35.1
29.7
Rural
1
2
3
4
5
6
7
8
9
10
11
12
Cooking
Cleaning
Washing
Ironing
Market
All domestic chores (1–5)
Collection of wood
Collection of water
Aid to other households
Community activities
Work for a wage
Work in a farm of family
business
13 Work in labor market
(11ⴙ 12)
14 Total time (definition 1)
15 Total time (definition 2)
39.2
42.4
53.7
41.8 40.8
54.4
81.0
53.7
41.4
44.7
56.7
44.2 41.7
56.2
83.7
55.2
Note: Zeros are included. Total time (definition 1) is the sum of 6 (all domestic chores), 7 (collection
of wood), 8 (collection of water), and 13 (work in labor market). Total time (definition 2) is the sum of
total time (definition 1), 9 (aid to other households), and 10 (community activities).
Source: Authors’ estimation using EIBEP 2002–2003.
CHAPTER 5
Labor Shortages Despite
Underemployment? Seasonality
in Time Use in Malawi
Quentin Wodon and Kathleen Beegle13
Evidence for Malawi and other developing countries suggests the existence of labor shortages
at the peak of the cropping season, with negative impacts on the ability of households to make
the most of their endowments such as land. At the same time, for most of the year, there is substantial underemployment, especially in rural areas. It could therefore be argued that seasonality in the demand for labor is leading to both underemployment and labor shortages. This paper
provides basic descriptive data from a 2004 nationally representative household survey to assess
the typical workload of the population. The data confirm the presence of strong seasonality effects
in the supply of labor, as well as substantial differences in workload between men and women due
to the burden of domestic work, including the time spent for collecting water and wood.
T
he issue of seasonality in labor demand and supply in developing countries has
been discussed extensively in the literature. For example, using household panel
data from India, Skoufias (1993, 1994) suggests the presence of significant
intertemporal substitution in the labor supply of women, but not of men. Dercon and
Krishnan (2000) use data from rural Ethiopia to show high levels of seasonal and year-toyear variability in consumption and poverty, with households also responding to changes
in labor demand and prices. Pitt and Khandker (2002) show how group-based credit mechanisms used to fund self-employment by landless households in Bangladesh help to smooth
13. The authors are with the World Bank. This work was prepared as a contribution to the Poverty
Assessment for Malawi prepared at the World Bank. The authors acknowledge support from the GENFUND
as well as the Belgian Poverty Reduction Partnership for research on this issue as part of a small research program on gender, time use and poverty in Sub-Saharan Africa. Preliminary results from the paper were presented at a World Bank workshop on the topic in November 2005. The views expressed here are those of
the authors and need not reflect those of the World Bank, its Executive Directors or the countries they
represent.
97
98
World Bank Working Paper
seasonal patterns of consumption and even out male labor supply. Ellis (2000) suggests
that households adopt multiple livelihood strategies in part to deal with seasonality, with
diversified rural livelihoods leading to a reduction in vulnerability. Finally, using data
from India, Kanwar (2004) analyzes how labor supply and demand respond to wages in
the agricultural market for daily-rated labor. While the agricultural labor market is in equilibrium during the rainy season, it experiences excess supply in the post-rainy season.
The importance of seasonality in the allocation of rural farm labor in Malawi is also
relatively well documented. For example, Kamanga (2002) provides seasonal cropping and
labor calendars for two villages. The first village, Chisepo, is located in the Kasungu area.
The village has a semi-arid to sub-humid climate with unimodal rainfall from November
to April (the annual rainfall is estimated at 845 mm with a mean temperature of 25°C).
Farmers cultivate tobacco, maize and groundnut on soils of low to moderate fertility. The
second village is Songani, in the Zomba area. Rainfalls are concentrated between October
and April, with Chiperoni rains from May to July. The total annual rainfalls vary from 800
to 1,200 mm, and the mean temperature is 22.5°C. Apart from maize, farmers also cultivate cassava, pigeon peas, groundnuts, beans, and pumpkins. Farming is seasonally driven,
with few differences between the two villages. In both villages the periods of highest intensity of labor are concentrated in December–January, as shown in Tables 5.1 and 5.2 where
the dark shaded areas represent high labor intensity.
As explained by Brummett (2002), the fact that labor is scarce at some periods of the
year has implications for the ability of farmers to diversify and enter into new activities. In
the case of aquaculture, apart from the seasonal availability of inputs for the ponds, the
availability of water and labor are constraining aquaculture adoption and production. That
is, household labor is required for the production of staple crops precisely when inputs for
aquaculture are available. Brummett argues that such constraints to the development of
aquaculture are seldom recognized in analytical work and programs.
A large sample study for Malawi by Tango International (2003) based on a household
survey conducted in 2003–2004 with data on 2030 households identified the scarcity of
labor as an important constraint to the development of rural farming. The most common
reason cited by households for not cultivating all of their land was a lack of inputs such as
fertilizer and pesticides (cited by 62.7 percent of households). This was followed by the lack
of labor (44.5 percent), and the lack of seeds (21.1 percent). Other reasons cited for not
cultivating all the land available were the lack of rainfall (5 percent), the need to leave land
as fallow in order to conserve soil fertility (2.6 percent), and other reasons (13.5 percent).
When combined with an analysis of the level of vulnerability of the households in the sample, it appeared that more vulnerable households were more likely to cite the lack of labor
as the main constraint to farming all their available land.
Another interesting finding from the Tango International study relates to the relationship between labor availability and food security. Households were asked why their
food stock expectations had decreased for the current harvest as compared to a normal harvest, which led to a lack of food for many. Most households associated the insufficient
availability of food to a lack of inputs, an issue likely to be related to the recent reduction
in input subsidies provided by the government (Starter Packs which contain, among other
items, fertilizer). The impacts of droughts and “other reasons” came in as the second and
third most important reasons for a lack of sufficient food. The lack of labor ranked fourth,
before the lack of land, poor soils, not enough seeds, and draught power. There are signs
Table 5.1. Seasonality in Cropping Activities, Kasungu, Northern Malawi
Crops
June
Maize
Harvesting
Sweet
Potatoes
Chickpeas
Beans
August
September
October
Clearing
Planting dimba
and
clearing and ridging
ridging
Harvesting and clearing
Nursery activities
November
December
Planting, weeding,
and fertilizing (1)
Fertilizing (2)
and weeding (2)
Planting
Planting, fertilizing (1)
and (2), weeding (1),
and bunding
Weeding
Harvesting
Note: Dark shaded areas represent high labor intensity.
Source: Kamanga (2002).
January
February
March
April
May
Weeding (2) and
bunding
Harvesting
Picking, processing,
and uprooting stems
Clearing
Planting
Planting
Planting
Harvesting
Harvesting
Gender, Time Use, and Poverty in Sub-Saharan Africa
Groundnuts
Tobacco
July
99
100
Crops
June
Maize
Incorporation
Incorporation
of residues
of residues
(clearing)
and ridging
Harvesting
and clearing
Harvesting
Groundnut
Pigeons
peas
Cassava
Sweet
potatoes
Mucuna
July
August September
October
November
December
Ridging, planting,
weeding (1) and
fertilizing (1)
Planting
Planting and weeding
January
February
Weeding (2) Weedubg
and fertilizing (2) and
(2)
bunding
Weeding
March
April
May
Harvesting
Harvesting
Weeding
Planting and
ridging
Harvesting
Chick peas
Beans
Note: Dark shaded areas represent high labor intensity.
Source: Kamanga (2002).
Planting
Planting at low
population densities
Planting
Planting
Harvesting
Harvesting
Harvesting
World Bank Working Paper
Table 5.2. Seasonality in Cropping Activities, Zomba, Southern Malawi
Gender, Time Use, and Poverty in Sub-Saharan Africa
101
that the problem of a lack of labor is being exacerbated by the HIV/AIDS crisis. Apart from the
direct impact of death itself, caring for the sick, and burying the dead has led to a reduction in
the time available for productive activities (Shah and others 2001).14
The above evidence for Malawi on labor shortages suggests that such shortages are
temporary, but that they do have a negative effect on the ability of households to make the
most of their endowments. It could be argued that seasonality in the demand for labor is
leading to both underemployment and labor shortages. For most of the year, household
members have extra time available to undertake productive ventures, but many do not
because of the limited opportunities available to them. At the peak of the cropping season,
around December–January, the demands in the agriculture sector make it difficult to find
the labor necessary to perform all the work that has to be done. In addition, time constraints may force household to conduct necessary tasks (such as planting and weeding) at
suboptimal times, thereby reducing yields.
The contribution of this paper is to provide basic yet detailed statistics from recent
household survey data on time use patterns in Malawi. This is done using a 2004 nationally representative household survey that includes questions on time use. Because the survey was implemented over a 13-month period, we can analyze changes in the patterns of
time use between households who were interviewed at different periods of the year. We
limit the analysis to providing basic statistics on the allocation of time by individuals to different tasks at different periods of the year, with breakdowns according to age, gender, and
the status of the household in the distribution of consumption per capita. The results in
the next section confirm the presence of strong seasonality in time use. The brief conclusion that follows suggests that seasonality leads to different policy implications as compared to a situation without such seasonality.
Data and Empirical Results
This paper provides measures of time use in Malawi using the 2004 Second Integrated
Household Survey. Data are available for all household members in the sample of 11,280
households (a total of over 52,000 individuals). The questions on time use are asked to all
individuals above 4 years of age. More specifically, the employment and time use model in
the survey asks the following questions to all household members above 4 years of age:
(a) How many hours did you spend yesterday cooking, doing laundry, cleaning your
house, and the like?
(b) How many hours did you spend yesterday collecting firewood (or other fuel
materials)?
(c) How many hours in the last seven days did you spend on household agricultural
activities (including livestock) or fishing, whether for sale or for household food?
14. In contrast, in labor surplus areas, on average there may be no observable impact of a prime-age
death on the labor supply of surviving household members, as suggested in a study of northwest Tanzania
by Beegle (2005).
102
World Bank Working Paper
(d) How many hours in the last seven days did you do any work for a wage, salary, commission, or any payment in kind, excluding ganyu?15
(e) How many hours in the last seven days did you engage in casual, part-time or ganyu
labor?
(f) How many hours in the last seven days did you help in any of the household’s
nonagricultural or non-fishing household businesses, if any?
(g) How many hours in the last seven days did you run or do any kind of nonagricultural or non-fishing household business, big or small, for yourself?
When computing the total time of work, the individual-level indicator is the sum of the time
spent by individuals in the various categories of work identified in the survey, whether this
time is spent in the labor market, for domestic chores or for collecting water and wood. The
absence of questions in the survey on the time spent by individuals caring for children, sick
household members and disabled people makes it likely that our estimates of the total
workload of individuals are too low, but this bias need not be very large if it many activities related to care are carried on as secondary activities in combination with other activities (such as cooking, cleaning, or making laundry) that are recorded in the survey.16
Another limitation of the data is that there is a single question for all domestic chores
(cooking, laundry, and cleaning) apart from water and wood collection, which is likely to
lead to some noise in the data. Yet, given that we are focusing in this paper on the seasonality of time use, and that domestic chores are not likely to have the same degree of seasonality as labor-related activities, the potential errors of measurement for domestic work
time are less serious.
Figure 5.1 provides the distribution of total individual working hours per week for
adults (individuals aged 15 and above). Hours have been aggregated into hour worked in
the last seven days (where daily hour non-income generating work is multiplied by seven).
The four graphs account respectively for men and women, as well as urban and rural areas.
Clearly, rural individuals work longer hours than urban individuals, and women work
more than men. Mean values for the number of hours worked are given at the national
level and in rural areas by quintile of consumption per equivalent adult and by month in
Tables 5.3 and 5.4 for both the adult population and children. The mean working time
year-round nationally is 36.4 hours per week for the adult population (above 15 years of
age) and a much lower 8.5 hours for children. In rural areas, where 88 percent of the population lives, the mean values are slightly higher.
What is most important for our purpose is the seasonality evident in Tables 5.5 and 5.6.
For the adult population, the average level of working hours is peaking in December–
January, which is as discussed earlier, the busy part of the cropping season. At that time,
the adult population works on average more than five hours more per week than the annual
mean. The seasonal differential in working hours is largest for the individuals who belong
to the poorest quintile of the distribution of consumption per capita. In rural areas, the
15. Ganyu refers to short-term, temporary rural daily labor.
16. Malawi is facing one of the world’s most severe HIV/AIDS Pandemics. With an estimated prevalence rate of 14.2 percent, it ranks eighth in the world (Population Reference Bureau 2004).
103
Gender, Time Use, and Poverty in Sub-Saharan Africa
Figure 5.1. Distribution of Individual Working Time by Sex and Area
(Individuals aged 15ⴙ)
Women
.04
.02
.03
.015
Density
Density
Men
.02
.01
.005
.01
0
0
0
50
100
150
0
50
100
150
Total hours (water, firewood, ag, non-ag,
salary/wage)
Total hours (water, firewood, ag, non-ag,
salary/wage)
Urban areas
Rural areas
.04
.02
.03
.02
Density
Density
.015
.01
.01
.005
0
0
0
50
100
150
Total hours (water, firewood, ag, non-ag,
salary/wage)
0
50
100
150
Total hours (water, firewood, ag, non-ag,
salary/wage)
Source: Authors’ estimation using IHS2.
additional workload in December compared to the annual average amounts to close to
10 hours in the first quintile. December is also the busiest month of the year for children.
Tables 5.5 and 5.6 provide additional information by showing the distribution of
hours of work according to the type of work performed and the gender of the individual.
As expected, adult men spend more time in the labor market than adult women, essentially
because of a larger average amount of time given to salaried work, as well as casual, parttime and ganyu work and non-agricultural business-related work. On the other hand, the
differences between adult men and women in terms of the time spent on agricultural work
104
World Bank Working Paper
Table 5.3. Total Time Spent Working by Area and Consumption Quintile,
National Sample
Poorest
Quintile
2nd Quintile 3rd Quintile
4th Quintile
Richest
Quintile
Total
National, adults (Age 15 and over)
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
September 2004
October 2004
November 2004
December 2004
January 2005
February 2005
March 2005
34.0
34.1
28.3
34.0
28.4
33.3
34.5
34.9
39.8
44.5
38.9
35.1
33.8
32.6
34.7
31.7
33.2
33.1
34.9
35.5
39.2
40.2
42.1
41.1
36.1
38.5
36.0
35.6
33.7
35.6
32.2
33.2
35.4
38.5
40.3
42.1
43.1
37.6
36.7
35.6
35.7
34.5
36.0
34.4
32.9
38.3
38.6
38.1
37.9
41.1
38.8
36.2
38.4
37.8
38.3
35.4
35.2
36.3
34.5
37.5
40.3
41.3
42.1
35.3
41.2
35.7
35.8
33.8
35.1
33.5
34.3
35.7
37.6
39.8
41.7
41.2
36.5
37.0
Annual average
35.0
36.2
36.5
36.3
37.5
36.4
8.5
10.1
6.4
7.4
7.8
10.8
7.2
7.6
8.1
11.3
7.6
7.3
12.4
8.6
7.9
10.2
7.3
7.2
7.6
9.6
7.5
7.8
9.0
12.9
8.1
8.1
8.5
8.5
National, children (Below 15 years old)
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
September 2004
October 2004
November 2004
December 2004
January 2005
February 2005
March 2005
Annual average
8.0
9.5
5.6
5.3
5.4
8.3
6.8
7.4
8.3
12.8
8.8
7.4
6.5
7.7
7.7
10.7
7.8
6.8
7.2
9.7
8.2
8.4
9.6
12.3
7.7
8.2
10.1
8.9
6.9
9.9
7.3
8.3
7.5
9.8
7.3
8.6
8.6
13.2
7.9
9.0
7.6
8.6
8.5
10.4
9.8
7.9
8.6
9.3
8.2
6.9
10.9
15.4
7.8
9.4
10.4
9.3
Source: Authors’ estimation using 2004 HIS.
are more limited on average (all values in the tables include zero values). As for domestic
work, it is performed mostly by women, and the same holds for the collection of wood and
water. In total, the mean and median working hours for women are about 10 hours above
the corresponding values for men at the national level.
Gender, Time Use, and Poverty in Sub-Saharan Africa
105
Table 5.4. Total Time Spent Working by Area and Consumption Quintile,
Rural Areas
Poorest
Quintile
2nd Quintile 3rd Quintile
4th Quintile
Richest
Quintile
Total
Rural areas, adults (Age 15 and over)
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
September 2004
October 2004
November 2004
December 2004
January 2005
February 2005
March 2005
34.3
34.1
29.0
34.0
27.4
33.7
33.7
35.1
40.1
45.0
39.1
35.5
34.3
32.9
34.8
31.8
33.3
33.1
34.6
35.0
39.3
40.7
42.9
40.2
36.6
38.2
36.4
36.0
35.0
36.1
31.9
32.9
35.0
38.7
41.2
43.1
41.5
38.2
37.5
37.3
36.2
34.8
36.1
34.8
32.7
37.9
37.8
39.4
38.5
41.4
38.8
35.9
39.5
38.4
38.2
35.7
35.6
35.4
35.7
38.8
41.5
42.0
44.2
35.1
40.2
36.3
36.1
33.8
35.2
33.6
33.9
35.6
37.8
40.6
42.5
41.1
36.8
36.9
Annual average
35.2
36.2
36.8
36.5
37.8
36.5
Rural areas, children (Below 15 years old)
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
September 2004
October 2004
November 2004
December 2004
January 2005
February 2005
March 2005
Annual average
8.1
9.6
5.7
5.3
5.2
8.5
6.8
7.5
8.3
12.9
7.9
7.6
6.8
7.8
7.9
11.0
7.9
6.4
7.0
9.6
7.7
8.5
9.5
12.3
7.7
8.3
10.0
8.8
7.2
10.0
7.4
8.2
7.5
9.8
6.4
8.7
8.9
15.0
7.8
8.4
7.3
8.5
9.4
10.8
9.8
7.7
9.0
8.8
8.1
6.5
12.3
15.5
7.5
6.2
7.4
9.1
9.8
10.9
6.9
7.9
8.0
11.2
7.5
9.2
6.9
8.7
9.5
6.5
7.5
8.9
8.4
10.4
7.4
7.1
7.7
9.5
7.2
8.0
9.2
13.2
7.9
7.8
8.0
8.5
Source: Authors’ estimation using 2004 HIS.
As we expect gender and seasonality issues to be more pronounced in rural households, Tables 5.7 and 5.8 focus on the population residing in rural areas. The gender differences are even larger, at 11.0 hours for the median, and 11.6 hours for the mean. The
workloads for children are much lower, but girls do work longer hours than boys, again
mainly due to a higher burden from domestic work as well as water collection.
106
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
Cooking
laundry,
and
cleaning
Collecting
water
Collecting
firewood
1.6
1.9
1.9
1.9
2.2
2.4
2.7
2.2
2.6
1.8
2.4
1.8
2.4
0.5
0.6
0.6
0.9
0.9
0.7
0.8
0.6
0.7
0.6
1.0
0.7
0.7
0.5
0.5
0.3
0.3
0.6
0.3
0.4
0.3
0.4
0.3
0.3
0.3
0.3
Helping
Casual,
Running
for
part-time
Agricultural non-ag. non-ag.
& ganyu
work
business business
work
Adult males (age 15 and over), national
13.9
13.5
11.8
10.9
10.5
10.7
10.8
13.8
15.8
20.6
18.5
15.6
14.2
3.4
5.4
4.5
3.8
5.4
5.7
3.9
4.1
3.0
4.2
3.4
3.1
3.9
0.7
0.7
0.8
0.7
0.5
0.4
0.4
0.2
0.2
0.3
0.1
0.2
0.5
2.5
2.5
1.8
2.5
3.2
2.5
3.0
3.4
2.7
3.2
2.5
2.2
2.6
Salaried
work
Total
work
(mean)
Total
work
(median)
Working
less
than
10 hours
Working
more
than
70 hours
6.0
6.3
6.6
8.5
5.7
6.0
9.0
6.4
8.9
5.7
7.6
6.7
7.5
29.1
31.5
28.3
29.6
29.0
28.6
31.0
31.1
34.4
36.7
35.9
30.6
32.2
24.5
30.0
26.0
30.0
27.0
25.0
28.0
30.0
34.0
36.0
35.0
30.0
30.0
27.4
18.0
24.3
22.0
19.7
21.8
19.6
17.7
12.3
6.8
8.8
16.1
16.3
8.0
7.3
5.4
4.7
5.3
5.4
7.7
6.5
7.6
6.5
9.2
4.5
7.1
1.4
1.2
1.3
1.5
41.9
39.8
39.0
40.3
38.0
37.0
38.0
38.0
11.8
11.8
10.9
10.7
17.4
12.9
10.9
14.4
Adult females (age 15 and over), national
March 2004
April 2004
May 2004
June 2004
14.8
14.0
13.6
15.0
5.3
5.1
5.4
6.1
3.0
2.1
2.3
2.3
12.5
13.0
13.2
11.0
2.2
2.5
1.6
2.3
1.1
0.6
0.6
0.5
1.7
1.2
1.0
1.7
World Bank Working Paper
Table 5.5. Work Time by Gender, Month, and Age According to the Categories of Time Recorded in the Survey,
Malawi–National, 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
14.5
15.3
15.2
15.3
14.6
13.6
14.2
14.1
14.7
6.4
7.0
6.9
6.9
6.7
5.9
7.8
6.4
6.8
2.4
2.3
2.1
2.1
2.0
1.6
2.0
1.7
2.1
8.6
9.6
11.0
14.6
16.4
20.2
17.4
15.1
13.2
2.6
3.0
2.5
2.0
1.6
1.6
1.0
1.3
1.5
0.3
0.2
0.4
0.3
0.3
0.2
0.5
0.2
0.3
1.6
1.0
0.9
1.0
1.3
1.7
1.2
1.6
1.3
1.2
1.1
1.2
1.6
2.3
1.0
1.8
1.2
1.6
37.7
39.6
40.3
43.7
45.2
45.9
45.9
41.9
41.5
35.0
37.0
38.5
43.0
45.0
45.5
46.0
41.5
40.5
11.5
8.1
9.6
7.7
7.1
6.3
7.4
8.5
6.9
10.9
11.6
12.8
12.1
14.6
12.2
13.1
11.3
10.1
Source: Authors’ estimation using 2004 HIS.
Gender, Time Use, and Poverty in Sub-Saharan Africa
107
108
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
Cooking
laundry,
and
cleaning
Collecting
water
Collecting
firewood
1.0
1.3
1.2
1.6
1.0
1.9
2.1
1.1
1.8
1.6
1.3
1.5
2.0
0.8
1.0
0.9
1.2
1.3
1.4
1.2
1.0
0.9
1.1
1.1
1.1
1.0
0.1
0.3
0.5
0.4
0.2
0.3
0.2
0.2
0.3
0.4
0.1
0.2
0.3
Helping
Casual,
Running
for
part-time
Agricultural non-ag. non-ag.
& ganyu
work
business business
work
Boys (age 5 to 14), national
2.4
4.5
2.4
2.3
1.8
3.0
1.9
2.8
4.1
6.8
2.7
3.3
2.5
0.2
0.1
0.2
0.1
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.1
0.2
0.2
0.1
0.1
0.3
0.2
0.0
0.1
0.0
0.0
0.1
0.0
0.0
Salaried
work
Total
work
(mean)
Total
work
(median)
Working
less
than
10 hours
Working
more
than
70 hours
0.2
0.3
0.3
0.2
0.4
0.2
0.2
0.3
0.3
0.8
0.2
0.3
0.2
0.2
0.1
0.1
0.1
0.2
0.2
0.1
0.0
0.1
0.1
0.0
0.1
0.2
5.3
7.8
5.6
6.0
5.3
7.2
5.8
5.5
7.5
10.9
5.5
6.6
6.4
0.0
3.0
0.5
0.5
0.0
0.0
0.0
0.0
1.0
3.5
0.0
0.0
0.0
77.9
71.0
77.8
76.9
79.5
68.7
77.6
79.6
72.5
60.0
74.2
77.4
78.5
2.9
2.6
2.0
2.2
1.0
2.2
2.0
1.1
2.3
1.1
0.6
1.3
1.3
0.3
0.2
0.2
0.3
0.3
0.0
0.0
0.2
10.7
12.5
9.2
8.4
4.5
7.0
7.0
3.5
61.3
58.2
61.6
67.7
3.0
2.7
1.3
1.4
Girls (age 5 to 14), national
March 2004
April 2004
May 2004
June 2004
3.4
3.8
3.2
3.1
3.0
3.4
3.0
2.6
0.8
1.1
0.7
0.7
2.1
3.3
1.9
1.3
0.3
0.2
0.1
0.1
0.6
0.4
0.1
0.2
World Bank Working Paper
Table 5.6. Work Time by Gender, Month, and Age According to the Categories of Time Recorded in the Survey,
Malawi–National, 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
3.4
4.4
3.5
3.4
3.5
4.3
2.9
2.9
4.2
3.5
4.1
3.4
3.3
3.1
3.3
3.6
3.3
2.8
1.0
0.8
0.8
1.0
0.6
1.0
0.4
0.5
0.7
1.0
1.9
1.0
2.1
2.9
5.9
3.3
2.8
2.6
0.1
0.0
0.0
0.1
0.0
0.1
0.1
0.0
0.0
0.4
0.3
0.2
0.1
0.1
0.0
0.0
0.0
0.0
0.4
0.1
0.0
0.1
0.0
0.6
0.2
0.1
0.1
0.0
0.1
0.1
0.0
0.5
0.0
0.0
0.0
0.3
9.7
11.8
9.1
10.0
10.6
15.2
10.6
9.7
10.7
4.5
7.0
7.0
7.0
7.0
10.0
7.0
3.5
3.5
64.9
56.1
62.1
62.3
59.9
49.5
58.6
67.1
67.3
0.9
2.6
2.0
1.4
2.1
0.9
0.6
1.1
3.2
Source: Authors’ estimation using 2004 HIS.
Gender, Time Use, and Poverty in Sub-Saharan Africa
109
110
Cooking
laundry,
and
cleaning
Collecting
water
Collecting
firewood
Helping
Casual,
Running
for
part-time
Agricultural non-ag. non-ag.
& ganyu
work
business business
work
Adult males (age 15 and over), rural
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
1.5
1.7
1.5
1.6
2.1
2.2
2.4
1.9
2.1
1.3
1.5
1.6
0.5
0.6
0.6
0.9
0.8
0.7
0.8
0.6
0.7
0.6
0.9
0.7
0.6
0.5
0.4
0.4
0.6
0.3
0.4
0.3
0.4
0.3
0.3
0.3
15.6
15.2
13.9
12.6
11.2
12.2
12.8
15.5
18.8
24.7
19.4
16.7
2.6
5.9
3.6
3.3
4.9
4.4
3.6
4.0
2.2
2.8
3.4
3.0
0.7
0.7
0.5
0.7
0.6
0.3
0.2
0.2
0.2
0.3
0.1
0.2
March 2005
1.9
0.8
0.3
16.3
3.3
0.2
Salaried
work
Total
work
(mean)
Total
work
(median)
Working
less
than
10 hours
Working
more
than
70 hours
2.6
2.5
1.9
2.6
2.9
2.4
2.7
3.4
3.1
3.1
2.4
2.3
4.9
3.6
4.7
6.1
5.1
4.4
6.7
4.5
6.3
3.0
7.1
5.2
29.0
30.9
27.0
28.3
28.3
26.9
29.4
30.3
33.9
36.2
35.2
29.9
25.0
29.0
25.0
28.0
26.0
24.0
26.0
30.0
32.0
34.5
34.5
28.0
26.4
17.2
24.2
22.0
19.1
22.2
18.4
16.5
10.9
4.0
7.4
15.7
8.1
6.9
4.8
4.7
4.9
4.6
7.0
5.8
6.8
5.5
7.4
4.3
2.8
5.2
30.8
30.0
15.6
5.2
0.8
0.9
0.4
0.4
43.0
40.7
39.9
41.7
38.5
38.5
38.5
39.5
10.5
10.8
8.6
9.5
18.1
13.2
10.4
14.6
Adult females (age 15 and over), rural
March 2004
April 2004
May 2004
June 2004
14.7
14.0
13.6
15.1
5.6
5.4
5.8
6.7
3.3
2.2
2.6
2.6
13.6
14.1
14.8
12.4
1.9
2.4
1.3
2.0
1.2
0.6
0.3
0.5
1.8
1.1
1.1
1.9
World Bank Working Paper
Table 5.7. Work Time by Gender, Month, and Age According to the Categories of Time Recorded in the Survey,
Malawi–Rural, 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
14.5
15.4
15.4
15.3
14.3
12.6
13.8
14.0
14.0
6.7
7.3
7.4
7.4
7.4
6.4
8.2
6.8
7.3
2.5
2.5
2.4
2.3
2.3
1.8
1.9
1.8
2.2
9.3
10.6
12.4
15.8
19.0
23.3
18.2
15.9
14.8
2.4
2.5
2.1
1.7
1.2
1.0
0.9
1.3
1.4
0.4
0.2
0.4
0.3
0.3
0.2
0.5
0.3
0.2
1.6
1.0
0.8
1.1
1.5
2.0
1.0
1.7
1.5
1.2
0.6
0.5
0.7
1.1
0.4
1.7
1.2
1.0
38.5
40.2
41.4
44.6
47.2
47.7
46.4
43.1
42.4
35.0
38.0
40.0
44.0
46.0
47.0
47.0
43.0
41.0
11.1
7.6
8.9
6.9
5.2
4.5
5.5
7.4
6.8
11.1
11.3
13.0
12.5
15.0
12.1
12.9
11.6
10.3
Source: Authors’ estimation using 2004 HIS.
Gender, Time Use, and Poverty in Sub-Saharan Africa
111
Collecting
water
Collecting
firewood
0.9
1.2
0.9
1.1
1.0
1.3
1.3
0.9
1.5
0.9
0.8
0.8
1.2
0.9
1.0
0.9
1.3
1.4
1.4
1.2
1.0
0.9
1.2
1.2
1.2
1.0
0.1
0.3
0.5
0.4
0.2
0.3
0.2
0.2
0.3
0.4
0.1
0.2
0.4
Helping
Casual,
Running
for
part-time
Agricultural non-ag. non-ag.
& ganyu
work
business business
work
Boys (age 5 to 15), rural
2.7
4.7
2.6
2.5
1.9
3.2
2.1
3.0
4.5
7.7
2.7
3.5
2.7
0.2
0.1
0.3
0.1
0.1
0.0
0.1
0.0
0.0
0.1
0.0
0.0
0.1
0.3
0.3
0.1
0.1
0.2
0.2
0.1
0.1
0.0
0.0
0.2
0.1
0.0
Salaried
work
Total
work
(mean)
Total
work
(median)
Working
less
than
10 hours
Working
more
than
70 hours
0.3
0.3
0.3
0.2
0.5
0.2
0.2
0.3
0.3
0.9
0.1
0.3
0.2
0.3
0.1
0.1
0.1
0.2
0.2
0.2
0.0
0.1
0.1
0.0
0.0
0.2
5.6
8.0
5.6
5.7
5.6
6.9
5.3
5.6
7.6
11.3
5.0
6.1
6.0
0.0
3.0
0.0
0.0
0.0
0.0
0.0
0.0
2.0
4.0
0.0
0.0
0.0
76.6
70.5
78.0
77.9
78.8
70.0
79.3
79.8
72.4
58.5
75.8
78.0
78.9
3.2
2.6
2.3
2.1
0.7
2.1
2.3
0.7
2.4
1.1
0.7
1.4
1.2
0.3
0.2
0.2
0.4
0.3
0.0
0.0
0.1
11.3
13.0
9.4
8.4
6.0
7.0
7.0
3.5
58.9
57.5
60.7
67.5
3.1
2.4
1.4
1.3
Girls (age 5 to 15), rural
March 2004
April 2004
May 2004
June 2004
3.6
3.8
3.0
2.7
3.2
3.5
3.2
2.9
0.9
1.2
0.8
0.8
2.2
3.6
2.1
1.4
0.3
0.2
0.1
0.1
0.6
0.5
0.1
0.1
World Bank Working Paper
March 2004
April 2004
May 2004
June 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
Cooking
laundry,
and
cleaning
112
Table 5.8. Work Time by Gender, Month, and Age According to the Categories of Time Recorded in the Survey, Malawi – Rural, 2004
July 2004
August 2004
Sept. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Jan. 2005
Feb. 2005
March 2005
3.3
4.0
2.9
3.3
3.2
3.0
2.5
2.3
3.2
3.5
4.4
3.7
3.4
3.4
3.6
3.8
3.5
3.0
1.0
0.9
0.9
1.1
0.7
1.1
0.4
0.6
0.9
1.0
2.1
1.2
2.2
3.3
6.9
3.2
2.9
2.8
0.1
0.0
0.0
0.2
0.0
0.1
0.1
0.0
0.0
0.2
0.3
0.2
0.1
0.1
0.0
0.0
0.0
0.0
0.5
0.1
0.0
0.1
0.0
0.7
0.1
0.1
0.1
0.0
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.1
9.7
12.1
9.0
10.3
10.8
15.4
10.3
9.4
10.1
4.5
7.0
7.0
7.0
7.0
10.5
7.0
3.5
3.5
65.1
56.0
62.9
61.2
57.4
49.0
58.9
67.1
66.7
1.0
2.7
1.9
1.4
1.7
0.7
0.7
1.2
3.1
Source: Authors’ estimation using 2004 HIS.
Gender, Time Use, and Poverty in Sub-Saharan Africa
113
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As noted by Bardasi and Wodon (2005), the concept of time poverty can be used to
measure the share of the population that works very long hours, and can therefore be considered as time-poor. In their paper on Guinea, Bardasi and Wodon consider a time
poverty line of about 70 hours per week. A similar threshold has been used in Tables 5.5–5.8
to measure the share of the population working at least 70 hours per week. In rural areas,
as shown in Table 5.7, on an annual basis 5.2 percent of the adult male population works
more than 70 hours per week, while the proportion is 10.3 percent for women. Interestingly, there is no clear seasonal pattern in the share of the population working more than
70 hours per week, suggesting that the overall increase in working hours observed around
December–January is likely to be provided by those household members that have a reserve
of time at their disposal rather than by those who already work the most.
While a small share of the population in Malawi can be considered as time poor
according to the data in Tables 5.5–5.8, a larger share can be considered as underemployed,
at least in the case of men. On an annual basis, 15.6 percent of adult males work less than
10 hours per week, and this proportion peaks to more than 20 percent in some months.
For women, the proportion working less than 10 hours per week is much smaller. Importantly, we do see the impact of seasonality in this measure of underemployment, since the
proportion of adults working less than 10 hours per week is lowest again in December. The
corresponding data for children suggest a much larger share with a small burden of work,
but also some cases apparently of very high workload.
Looking more closely at rural households, we examine to what extent land holdings
per adult are associated with seasonal labor constraints. The indicator of the seasonality of
labor is the ratio of mean adult hours in the peak months (December–January) to the surplus months (May–July). This is a crude measure, as peak and surplus months will vary
across regions (as described in the introduction and shown in Tables 5.1 and 5.2).
Nonetheless, even with this imprecise measure we find evidence that seasonality affects
small land holders the most. Figure 5.2 shows that seasonal labor issues are most pronounced for the smaller holders with less than 0.15 hectares of land per adult. Among these
small land holding households, mean hours in December–January are more than 35 percent
higher than the corresponding measure during the surplus labor season. For other land
categories, including households with no land holdings and those with large holdings, we
also see seasonality. For landless households, this will reflect land demand for ganyu workers during planting seasons. In turn, it is the larger land holders who hire such labor,17
which explains the lower ratio of peak-to-surplus season hours for the large holders.
Conclusion
With a population density of 112 people per square kilometer, Malawi has the highest population density among neighboring countries. Generally, labor in Malawi is assumed to be
17. While the prevalence of hiring labor at least for one day on rain-fed plots is even across the land
categories in Figure 5.2, the intensity of such labor is not even. The number of days of hired labor increases
significantly as land holdings increase.
Gender, Time Use, and Poverty in Sub-Saharan Africa
115
Figure 5.2. Seasonality of Labor Hours among Rural Households by Land Holdings
Ratio of hours in Dec–Jan to
May–July (15+ years)
1.40
1.35
1.30
1.25
1.20
1.15
1.10
1.05
1.00
0
<=.15 hec
pc
<=.30 hec
pc
<=.50 hec
pc
<.75 hec
pc
>.75 hec
pc
Land holding categories
Source: Authors’ estimation using 2004 HIS.
in surplus supply, with extensive underemployment. However, low mean hours in incomegenerating activities mask the existence of labor shortages at the peak of the cropping season. This seasonality in labor supply can have potentially large negative impacts on the
ability of households to make the most of their endowments such as land as well as their
labor. Using data from 2004 collected from households over a 13-month period, this paper
has documented the extent to which the seasonality in the demand for labor is leading to
both underemployment and labor shortages.
Defining work broadly to include income-generating activities (including work on the
household farm) as well as main household chores (including fetching firewood and
water), we find typical labor supply patterns. The population in rural areas works longer
hours than urban individuals, and women work more than men. Across activities, while
men have higher hours in income-generating work, chores (including firewood and water
collection) are more extensively done by women such that their total hours are higher. The
seasonal differential in working hours is largest for individuals who belong to the poorest
quintile of the distribution of consumption per capita. As alternative to consumption
wealth, the paper also examined seasonal differences by household landholdings. Small
holders had the largest seasonal differences, with mean hours in peak month 35 percent
higher than surplus months.
Understanding the implications of these patterns will require additional analysis, but
the results suggest that the precious few endowments of poor households (labor and land)
may not be utilized in the most efficient way, or at least, it can be argued that there are serious constraints to the generation of higher earnings for households, despite the presence
of underemployment for most of the year. Poverty reduction strategies would need to take
into account the strong seasonal dimensions to labor supply to be effective.
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World Bank Working Paper
References
Bardasi, E., and Q. Wodon. 2005. “Measuring Time Poverty and Analyzing its Determinants: Concepts and Application to Guinea.” (Chapter 4 in this volume.)
Beegle, K. 2005. “Labor Effects of Adult Mortality in Tanzanian Households.” Economic
Development and Cultural Change 53:655–684.
Brummett, R.E. 2002. “Seasonality, Labor and Integration of Aquaculture into Southern
African Smallhold Farming Systems.” Naga - The ICLARM Quarterly, 25(1).
Dercon, S., and P. Krishnan. 2000. “Vulnerability, Seasonality and Poverty in Ethiopia.”
Journal of Development Studies 36:25–53.
Ellis, F. 2000. “The Determinants of Rural Livelihood Diversification in Developing Countries.”
Journal of Agricultural Economics 51:289–302.
Kamanga, B.C.G. 2002. Understanding the Farmer’s Agricultural Environment in Malawi.
Risk Management Projects Working Paper Series 02-01, International Maize and
Wheat Improvement Center, Mexico.
Kanwar, S. 2004. “Seasonality and Wage Responsiveness in a Developing Agrarian Economy.”
Oxford Bulletin of Economics and Statistics 66:189–204.
Pitt, M.M., and S. Khandker. 2002. “Credit Programmes for the Poor and Seasonality in
Rural Bangladesh.” Journal of Development Studies 39:1–24.
Population Reference Bureau. 2004. “Top 15 HIV/AIDS Prevalence Countries (end
2003).”
Shah, M.K., N. Osborne, T. Mbilizi, and G. Vilili. 2001. Impact of HIV/AIDS on Agricultural Productivity and Rural Livelihoods in the Central Region of Malawi. CARE International, Malawi.
Skoufias, E. 1993. “Seasonal Labor Utilization in Agriculture: Theory and Evidence from
Agrarian Households in India.” American Journal of Agricultural Economics 75:20–32.
Skoufias, E. 1994. “Risk and Seasonality in an Empirical Model of the Farm Household.”
Journal of Economic Development 19:93–116.
Tango International. 2003. Malawi Baseline Survey: Report of Findings. C-Safe.
PART III
Time Use and Development
Outcomes
117
CHAPTER 6
Poverty Reduction
from Full Employment:
A Time Use Approach
Elena Bardasi and Quentin Wodon18
Despite long working hours, for many household members, and especially women, underemployment is nevertheless affecting a large share of the population in many developing countries.
Using data on time use, wages, and consumption levels from a recent household survey for
Guinea, this paper provides a simple framework for assessing the potential impact on poverty
and inequality of an increase in the working hours of the population up to what is referred to
as a full employment workload. The framework provides for a decomposition of the contribution to higher household consumption of an increase in working hours for both men and
women. The key message is that job creation and full employment would lead to a significant
reduction in poverty, even at the relatively low current levels of wages and earnings enjoyed by
the population. However, even at full employment levels, poverty would remain massive, and
the higher workload that the full employment scenario would entail would be significant.
A
ccording to economic theory, individuals spend more time in work to achieve a
higher level of utility, based on the budget constraint they face and their preferences for work and leisure. By extension, they allocate time between labor market
and household production based on the returns they can obtain in the two domains. However, because markets are far from perfect, and because various individuals and households
18. The authors are with the World Bank. This work was prepared as a contribution to the Poverty
Assessment for Guinea prepared at the World Bank. The authors acknowledge support from the Trust
Fund ESSDD as well as the Belgian Poverty Reduction Partnership for research on this issue as part of a
small research program on gender, time use and poverty in Sub-Saharan Africa which also benefited from
funding from the GENFUND. Preliminary results from the paper were presented at a three-day workshop
organized in Guinea in October 2005 in collaboration with the country’s National Statistical Office (Direction Nationale de la Statistique), and at a World Bank workshop in November 2005. We are grateful to
Kathleen Beegle and Mark Blackden for comments. The views expressed here are those of the authors and
need not reflect those of the World Bank, its Executive Directors or the countries they represent.
119
120
World Bank Working Paper
have different endowments, reality is different. Although we may expect more time in
work (especially more time spent in the labor market) to be associated with higher consumption, empirical evidence indicates that “vulnerable” categories, such as women and
low educated people, often work very long hours for very little output and that for these
groups a lack of time to perform any additional work and poverty itself may go together.
This occurs when the available technology is so poor that very labor-intensive activities
are required to reach a minimum subsistence level. The consequence is that not only are
long hours spent to achieve little output (as measured through production, income, or
consumption)—in effect, the productivity of one working hour is low—but, because of
the long hours already worked, few additional time resources are available to increase consumption (or income) further.
The difficult situation of labor markets for the poor in Sub-Saharan Africa and many
other developing areas in terms of both low productivity (low earnings per hour of work)
and limited time available for productive work are due to a complex range of factors. On the
time constraint side, the lack of access to basic infrastructure services means that households
spend a lot of time in domestic chores and for fetching wood and water (see Chapter 3 for a
review of the empirical evidence on time use in Africa). On the productivity or earnings
potential side, there has been a process of in formalization in many countries, with in some
cases a gap arising between the education received by young adults and the requirements
of the job opportunities available to them (see for example Calves and Schoumaker 2004
on urban Burkina Faso). Furthermore, given that many African countries have suffered
from low rates of economic growth, the economic opportunities for emerging from
poverty through hard work have been limited.
If many among the poor already work long hours and if their productivity is limited,
is it correct to state that the main asset of the poor to fight poverty is their labor? Not necessarily, or at least not in a necessarily straightforward way. It is clear that the poor in Africa
derive their livelihood from their labor, and in that sense, it is indeed correct to state that
their main asset to emerge from poverty is their labor. However, it is not as clear that labor
is abundant and systematically underused (Blackden and Bhanu 1999), and it is also not
fully clear whether an increase in the supply of labor by the poor would actually help in a
significant way to reduce poverty since the productivity of the poor is constrained in many
ways, especially among female headed households (Buvinic and Rao Gupta 1997), but also
more generally.19 The answer to these two questions must essentially be settled empirically
as conditions may differ between countries.
In practice, in order to analyze the potential for poverty reduction from full employment, it is useful to rely on a time use approach in order to estimate for the individual what
would be the level of a reasonable increase in labor supply that could be provided by household members without reaching such high levels of work as to become time poor (this idea
follows Bardasi and Wodon 2005). The objective of this paper is thus to provide a simple
framework for analyzing these questions, and apply the framework to recent household
survey data from Guinea. Although we do recognize that substantial long-term increases
19. Buvinic and Rao Gupta (1997) argue that poverty is often higher among female-headed households not only because of higher dependency ratios, but also because of lack of economic opportunities
and low wages for women.
Gender, Time Use, and Poverty in Sub-Saharan Africa
121
in standards of living in countries such as Guinea will probably need to come first from
higher productivity that would lead to higher wages and earnings per hour of work, we
focus in this paper solely on the potential for poverty reduction from full employment at
current wages and productivity levels. That is, we answer the question: by what magnitude
would poverty be reduced under full employment, assuming that higher working hours
would be remunerated at their current level?
The basic idea of the paper is to measure how much additional income or consumption could be obtained by households if all their members who are currently working fewer
hours than a certain threshold were working a number of hours corresponding to that
threshold. Although we are aware of the issue of seasonality in time use and work patterns, we
do not discuss here the question of the impact of seasonality on labor demand and supply.20
We also do not consider the issue of what exactly individuals would do if they worked
more—although it is clear that households tend to adopt multiple livelihood strategies with
diversified rural livelihoods leading to a reduction in vulnerability (Ellis 2000), we simply
assume here that additional hours of work are paid at the same wage or productivity rate
as the hours currently worked by individual household members. Finally, we do not look at
whether there is in fact a labor demand out there that could absorb the additional working hours that individuals would be willing to work (in India, Kanwar [2004] analyzes how
labor supply and demand respond to wages in the agricultural market for daily-rated labor,
suggesting excess supply in the post-rainy season.)
The very simple framework provided in this paper is limited, but it does enable the
analysis of the potential impact of full employment at current wages and productivity on
poverty to be conducted for the population as a whole as well as by gender, with a number
of tests for the robustness of the results to the assumptions made. The next section presents
the data we use. The two sections after that present the framework and the empirical
results. The final section draws conclusions.
Data
The data we use come from the Enquête Intégrée de base pour l’évaluation de la pauvreté
(EIBEP) of Guinea, year 2002–2003. Section 4 of the questionnaire includes a section where
each individual aged 6 and over is requested to report the time spent in the week before the
interview for a set of domestic tasks (cooking, cleaning, laundry, ironing, going to the market), fetching water, fetching wood, helping other households and being involved in community activities. In the same section other questions aim to record the amount of time
the individual spent working in the labor market, for a wage (as an employee) or in a farm
or family business (as a self-employed or contributing family member). We used these data
to compute the total time spent by individuals in work of any type (domestic work and
work in the labor market, whether paid or unpaid).
Three caveats are in order regarding the data used. The data were collected retrospectively for the week before the interview. Such data are, according to many researchers, not
20. On seasonality, see for example Skoufias (1993); Dercon and Krishnan (2000); and Wodon and
Beegle (2005).
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World Bank Working Paper
the best quality data to study time use—diaries generate more accurate data. Moreover
(and related to this), simultaneous activities are not counted; however this is perhaps not
a major problem when the interest is—as in our case—in the total time spent in work.
Finally, there is no information in the questionnaire about caring activities (time spent caring for children, old, sick, and disabled people); however, we can probably assume that
these activities are in large part usually performed as a “secondary activity” in combination with one of the other activities recorded in the questionnaire.
As discussed in the companion paper in this volume by Bardasi and Wodon (2005),
we have created two definitions of the total time spent in work. The first definition includes
the total amount of time spent by the individual in the labor market, in domestic chores
and in collecting water and wood. The second definition adds to the first the amount of
time spent helping other households and in community activities. One may argue that
spending time helping other households and in community activities has more of a
“choice” than of a “duty” connotation—it could be seen as a use of leisure rather than
“work.” For this reason, we excluded this use of time helping other households and in community activities from the total time spent “in work” here.
Table 6.1 shows the average amount of time spent by adult individuals (15 years of age
and above) in various activities, by quintile of consumption per person and in rural and
urban areas. First, individuals in the top quintiles spend slightly more time in all type of
work than poorer individuals. This is true in both urban and rural areas. The only exception is represented by work in a farm or family business, in which poor people spend longer
hours than rich people. However, this trend is more than compensated by the pattern of
hours spent working for a wage —in this case hours are much longer in the top than in the
bottom of the consumption distribution, so that the overall time spent in the labor market tends to be higher in the top quintiles (with the exception of the rural areas, where it is
almost the same in every quintile). Second, the differences between urban and rural areas
tend to be larger than across consumption quintiles; in particular, the time spent in the labor
market and the time spent fetching water and wood is higher in rural than in urban areas.
Third, the differences across quintiles are more pronounced in urban than in rural areas.
Looking at the differences in total time (according to our first definition), the average adult
individual in the top quintile spent about 39 hours in employment in urban area and
49 in rural area, while in the bottom of the distribution the average time in employment
was 31 and 48 hours respectively (these figures include the zeros).
Analytical Framework
Table 6.1 above hides a lot of heterogeneity. While many individuals work very long hours,
others are clearly underemployed and could potentially increase the amount of time they
work to increase the well-being of their household. In what follows, we conduct simulations to try to measure the loss in consumption or income associated with underemployment for the individuals in our sample. We assume that each adult individual who is
working less than a certain number of hours per week could increase his or her working time
up to that level in order to increase the level of consumption of the household members,
while all the other members who are at or above the time poverty line continue working the
123
Gender, Time Use, and Poverty in Sub-Saharan Africa
Table 6.1. Working Time per Week, Adult Population by Consumption Quintile
and Location
1 Cooking
2 Cleaning
3 Washing
4 Ironing
5 Market
6 All domestic chores (1–5)
7 Collection of wood
8 Collection of water
9 Aid to other households
10 Community activities
11 Work for a wage
12 Work in a farm or family business
13 Work in labor market (11 + 12)
14 Total working time (definition 1)
15 Total working time (definition 2)
1
2
3
Urban
4
5
2.6
1.2
1.2
0.6
1.3
6.8
0.4
0.8
0.2
0.2
17.2
6.2
23.4
31.4
31.9
3.0
1.3
1.5
0.6
1.7
8.1
0.2
0.9
0.3
0.3
19.1
5.6
24.7
33.9
34.4
3.0
1.2
1.5
0.8
1.4
8.0
0.2
0.9
0.3
0.3
20.3
4.7
25.1
34.1
34.7
3.5
1.3
1.6
0.9
1.6
8.9
0.2
0.7
0.2
0.2
21.5
4.2
25.7
35.4
35.9
3.8
1.5
1.7
1.0
1.7
9.8
0.1
0.8
0.4
0.4
25.5
2.8
28.3
39.0
39.7
5.5
1.8
2.2
0.5
1.9
11.9
2.1
2.2
1.1
0.9
11.7
20.5
32.2
48.3
50.3
5.7
1.8
2.1
0.6
2.2
12.4
2.1
2.3
1.1
1.2
15.9
16.4
32.3
49.2
51.5
Rural
1 Cooking
2 Cleaning
3 Washing
4 Ironing
5 Market
6 All domestic chores (1–5)
7 Collection of wood
8 Collection of water
9 Aid to other households
10 Community activities
11 Work for a wage
12 Work in a farm of family business
13 Work in labor market (11 + 12)
14 Total time (definition 1)
15 Total time (definition 2)
5.2
1.5
2.0
0.3
1.9
10.9
2.0
1.9
1.0
0.8
7.2
25.9
33.1
47.9
49.7
5.2
1.8
2.0
0.3
1.9
11.3
2.1
2.3
1.0
0.7
9.3
23.8
33.1
48.7
50.5
5.7
1.9
2.2
0.4
2.2
12.4
2.2
2.2
1.1
0.8
10.6
22.1
32.7
49.4
51.3
Note: Zeros are included. Total time (definition 1) is the sum of 6 (all domestic chores), 7 (collection
of wood), 8 (collection of water), and 13 (work in labor market). Total time (definition 2) is the sum of
total time (definition 1), 9 (aid to other households), and 10 (community activities).
Source: Authors’ estimates using EIBEP 2002–03.
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World Bank Working Paper
same amount of time. The increase in the total consumption of household j that would follow is therefore:
M
∆C j =
∑[[(T
max
(1)
− Ti ) ⋅ mi ] ⋅ ω i ]
i =1
where Tmax is the time poverty line or, in this context, a threshold of full employment in terms
of the total number of hours worked (whether paid or unpaid), Ti is the time currently worked
by individual i, mi is an indicator equal to 1 if the individual is working a number of hours
below the time poverty line, w i is the value of the time of individual i, and M is the total number of individuals in household j that can increase the total time worked. In order to run the
simulations, we need to define Tmax. Two standards were used for this purpose. The first one is
a full employment work level defined arbitrarily at 50 hours a week; and the second one is a relative workload threshold set at 1.5 times the median of the total individual hour distribution,
which turns out to be 70.5 hours.
The increase in per capita consumption of each member of household j can be re-written:
M
M
∆C j
M
=
⋅
N
N
∑[(T
max
∑[[(T
− Ti ) ⋅ mi ]
i =1
M
max
⋅
− Ti ) ⋅ mi ] ⋅ ω i ]
i =1
=
M
∑[(T
max
− Ti ) ⋅ mi ]
M
⋅ HM ⋅ ωM
N
(2)
i =1
where N is the household size (N ≥ M). The above formulation is helpful because it highlights
three possible sources of increase in per capita consumption: the ratio of non-time poor individuals with respect to the total (first term on the righthand side), the average number of extrahours that each of the non-time poor individuals can work (second term) and the average
value that each of these extra-hours can obtain (third term). When the calculation is made at
the quintile level (the subscript j indicates the quintile rather than the household), the above
decomposition gives us the average of each term for each quintile and their product gives the
exact average of the increase in per capita consumption for all households in that quintile.
An empirical question is what value to assign to w i , the value of time of individual i. Here
we have adopted three measures. A first candidate is the “potential wage” that each individual could earn in the labor market based on their personal and household characteristics.
After estimating wage regressions separately for men and women (including the usual
explanatory variables) we have predicted a wage for everybody in the sample. The estimates
for the wage regressions are presented in Appendix Table 6.A1.21 However, because the size
of the formal labor market is small in Guinea, one can argue that few are the individuals who
can increase their employment and be paid a wage for those extra hours. For this reason, we
have created two additional measures of the value of one hour of work. First, we have divided
21. At this stage, the wage regressions have been estimated using the sample of individuals working for a
wage, without correcting for selection for being in or out of the labor market. While other studies indicate that
not correcting for selection is not likely to bias the coefficient in any substantial way, we face the problem of
predicting the wage for individuals who are not working because many regressors are missing for them (for
example, industry, type of employer, type of contract, and so forth). We have assigned to these individuals the
median predicted wage of the groups defined by age, sex, maximum education level and urban and rural area.
Gender, Time Use, and Poverty in Sub-Saharan Africa
125
the total household consumption by the total working time of all its members. This ratio can
be considered a sort of “household consumption productivity” because it represents the efficiency of the household in translating each hour of work by any of its member into consumption. While this measure considers all household activities as “productive” and
therefore able to generate consumption, it is true that extra employment aimed at increasing
consumption would be mostly directed at the labor market and/or in farm or family business. Therefore we have also computed an alternative measure of “household consumption
productivity” by dividing the total household consumption by the total number of hours
spent by household members in the labor market (for a wage, in the informal labor market,
or as contributing family members). In any case, it is clear from equation (2) that the choice
of the threshold Tmax and of the estimation of w i are crucial for the results we obtain.
Results
Impact on Consumption
We first calculated the impact on consumption, based on (1). The results are presented in
Table 6.2, using both the predicted wage rate and the household productivity (definitions A
and B) as the value of one hour of extra time in employment, and a full employment work
threshold of 50 hours/week. When the predicted wage rate is used for wi (columns (3) and (4)
in Table 6.2) the increase in consumption would be higher in the top than in the bottom of
the distribution in absolute level, but larger in the bottom (and in the middle) of the distribution in relative terms. In this case the increase in employment would be essentially pro-poor.22
However, when each hour of extra-employment is evaluated using the household productivity (columns (5)–(6) and (7)–(8)), the increase in consumption is substantially
higher in the top than in the bottom of the distribution and the increase in employment
would result in a strong increase in inequality. In fact when the household productivity is
used as a measure of the value of time, the increase in consumption is large at the upper
tail of the distribution, especially when in the calculation of the household productivity
only the time spent in the labor market is taken into account (definition B). Notice that
when this latter measure is adopted, the increase in consumption in absolute terms in the
bottom of the distribution is the largest (and therefore so is the reduction in poverty).
The fact that for the upper quintiles, we have a substantial divergence in the estimated
values for wi implies that the impact on inequality of full employment will differ depending on the method used to evaluate the value of time. On the other hand, for the poverty
simulations, what matters is the range of estimates in the bottom quintiles. Then, the magnitudes of the estimates from the two methods of estimation are fairly similar (when using
the definition A of household productivity), so that the results are likely to be robust.
Table 6.3 gives us some clues about the main sources of the increase in consumption and the differences across quintiles. The bottom quintiles have the lowest amount
22. The increase in average consumption would be larger in the third than in the second quintile,
though. As it will be shown later the interpretation of the impact on inequality differs somewhat depending on whether one looks at changes in the Gini coefficient or the Theil index, given that the two measures are more sensitive to changes in different parts of the distribution.
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World Bank Working Paper
Table 6.2. Average Increase in per Capita Consumption Following an Increase
in Individual Working Time, by Quintiles of per Capita Consumption
(Full employment ⴝ 50 hours/week)
Increase in per capita consumption
Evaluated
at the
wage rate
Evaluated at the
household cons.
productivity (A)
Evaluated at the
household cons.
productivity (B)
Quintile
of Cons.
(1)
Weekly
Average per
Capita Cons.
(2)
Average
(3)
%
(4)
Average
(5)
%
(6)
Average
(7)
%
(8)
1
2
3
4
5
3355
5465
7642
10801
23288
1010
1532
2617
3111
3995
30.1
28.0
34.3
28.8
17.2
959
1931
3310
5823
17045
28.6
35.3
43.3
53.9
73.2
1767
3489
5611
9482
22113
52.7
63.8
73.4
87.8
95.0
Note: See text in the “Analytical Framework” section for the definition of househld consumption
productivity (A) and (B).
Source: Authors’ estimates using EIBEP 2002–03.
of resources. The proportion of people that can increase their working time within a household as a proportion of household size is lower in the bottom than in the top of the distribution (26 percent in the bottom quintile versus 35 percent in the top quintile). Also, the
average number of extra-hours that each individual below the full employment working
hour threshold can work is lower in the bottom than in the top of the distribution (23 hours
versus 30 hours). Finally, the value of one hour of time is higher for richer as compared to
poorer individuals—twice as much for the top quintile with respect to the bottom in the
case of the wage rate and as much as ten times in the case of the household productivity.
Table 6.3. Results of Decomposition for Full Sample
(Full employment ⴝ 50 hours/week)
Quintile of
Consumption
M/N
1
2
3
4
5
0.258
0.276
0.297
0.331
0.353
HM
wM
(Wage)
w M (Household
Consumption
Productivity A)
w M (Household
Consumption
Productivity B)
22.8
25.5
27.9
28.7
29.6
172
217
316
328
383
163
274
399
614
1634
300
494
677
999
2120
Note: See text in the “Analytical Framework” section for the Definition of househld consumption
productivity (A) and (B).
Source: Authors’ estimates using EIBEP 2002–03.
127
Gender, Time Use, and Poverty in Sub-Saharan Africa
Table 6.4. Contribution of Men and Women to Average Increase in per Capita
Consumption, by Quintiles of per Capita Consumption
(At wage rate; full employment ⴝ 50 hours/week)
Increase in per capita consumption
Quintile of
Consumption
Weekly Average
per Capita
Consumption
Average
%
Average
%
1
2
3
4
5
3355
5465
7642
10801
23288
631
949
1654
1863
2403
18.8
17.4
21.6
17.2
10.3
380
584
963
1248
1592
11.3
10.7
12.6
11.6
6.8
Men
Women
Source: Authors’ estimates using EIBEP 2002–03.
All these factors contribute to the lowest increase in consumption (in absolute terms) in
the bottom of the distribution. Note again that the average value of one hour calculated
using the predicted wage and the household productivity (definition A) is very similar in
the three bottom quintiles, even if the two values have been derived in two different and
unrelated ways. The average value of one hour calculated as the household productivity
definition B is higher than definition A, given that in the former case only the hours spent
in the labor market are included in the denominator.
Tables 6.4 and 6.5 present the same exercise separately for men and women (using
their respective wage rates).23 As we could expect, the increase in consumption due to an
increase in working hours by women would be substantially lower (30 to 40 per cent lower)
than what an increase in employment by men could produce. What is surprising, though,
is the sources of this difference as revealed by Table 6.4. Contrary to our expectations, the
average number of individuals who can increase their hours of employment is almost the
same for men and women in all quintiles. This result may seem in contrast with the “time
poverty” estimates presented in Bardasi and Wodon (2005) in this volume. However, in
part because there are more women than men in the total population of Guinea, the percentage of women who are not time poor over the whole population is almost the same as
men’s. Also, the average number of extra-hours that non-time poor individuals can add to
what they work already does not differ much between men and women (we may overestimate however the ability of women to increase their working hours due to the fact that time
spent for providing care is not recorder in the survey). Therefore, the differences between
the sexes in the impact of higher working hours is driven almost entirely by the difference
in their average wages, ranging from 20 to 44 percent less for women depending on the
quintile (and much larger in the bottom than in the top of the distribution).24
23. The evaluation of the increase in consumption at the household productivity is less interesting in
this case because this would be the same for both sexes.
24. Notice that this differential is not adjusted for characteristics, i.e. the average wage reflects both
gender “adjusted” differentials and compositional effects.
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World Bank Working Paper
Table 6.5. Results of Decomposition for Men and Women
(Full employment ⴝ 50 hours/week)
Men
Women
Quintile of
Consumption
M/N
HM
wM
M/N
HM
wM
1
2
3
4
5
0.129
0.140
0.156
0.173
0.182
22.1
25.2
28.3
29.7
30.9
222
270
374
362
427
0.129
0.137
0.141
0.158
0.171
23.6
25.9
27.5
27.5
28.1
125
165
249
288
331
Source: Authors’ estimates using EIBEP 2002–03.
As a robustness test, we report below (Tables 6.6 to 6.9) the same tables calculated
using a much higher full employment workload threshold of 70.5 hours/week or 1.5 times
the median of the individual total time distribution. Although the magnitude of the results
changes (larger gains in consumption due to higher level of working hours), the conclusions are qualitatively very similar when comparing quintiles or sexes. For example, the
difference in the contribution of men and women to the increase in consumption remains
substantial, and also in this case it is mostly driven by differences in average wages.
Impact on Poverty and Inequality
Finally, we have computed the impact of the increase in consumption on poverty and
inequality. The results are presented in Table 6.10. In the columns, the average total annual
Table 6.6 Average Increase in per Capita Consumption Following an Increase
in Individual Working Time, by Quintiles of Current per Capita
Consumption (Full employment ⴝ 70.5 hours/week)
Increase in per capita consumption
Evaluated at
the wage rate
Evaluated at the
household cons.
productivity (A)
Evaluated at the
household cons.
productivity (B)
Quintile
of Cons.
(1)
Weekly
Average Per
Capita Cons.
(2)
Average
(3)
%
(4)
Average
(5)
%
(6)
Average
(7)
%
(8)
1
2
3
4
5
3355
5465
7642
10801
23288
2546
3542
5566
6741
8938
75.9
64.8
72.8
62.4
38.4
2197
4102
6565
11059
30593
65.5
75.1
85.9
102.4
131.4
3835
7197
11085
18132
41731
114.3
131.7
145.1
167.9
179.2
Note: See text in the “Analytical Framework” section for the Definition of househld consumption
productivity (A) and (B).
Source: Authors’ estimates using EIBEP 2002–03.
129
Gender, Time Use, and Poverty in Sub-Saharan Africa
Table 6.7. Results of the Decomposition for Full Sample
(Full employment ⴝ 70.5 hours/week)
Quintile of
Consumption
M/N
1
2
3
4
5
0.409
0.429
0.450
0.485
0.509
HM
wM
(Wage)
w M (Household
consumption
productivity A)
w M (Household
consumption
productivity B)
31.5
33.6
35.7
37.1
38.0
198
246
347
374
463
171
284
409
614
1583
298
499
691
1007
2159
Note: See text in the “Analytical Framework” section for the Definition of househld consumption
productivity (A) and (B).
Source: Authors’ estimates using EIBEP 2002–03.
consumption has been computed for each quintile after the simulated increase, under the different assumptions about time poverty lines and values of ωi. In the bottom of the Table, the
“new” consumption poverty rate (headcount), Gini coefficient and Theil index are shown.
Clearly, the largest increase in consumption and decrease in poverty would be
obtained when using a higher threshold for the level of working hours. However, even with
the lower workload for full employment at 50 hours a week, the increase in consumption
and reduction in poverty would be substantial (reduction in the share of the population in
poverty by 10 to 15 percentage points). This shows that, even in the bottom of the distribution there are “unused” time resources that can be used to increase employment and the
well-being of the household. At the same time, it is clear that poverty would remain massive in Guinea even if all individuals were working at the full employment level of 50 hours,
Table 6.8. Contribution of Men and Women to the Average Increase in per Capita
Consumption, by Quintiles of Current per Capita Consumption
(At wage rate; full employment ⴝ 70.5 hours/week)
Increase in per capita consumption
Quintile of
Consumption
Weekly Average
per Capita
Consumption
Average
%
Average
%
1
2
3
4
5
3355
5465
7642
10801
23288
1517
2108
3410
4083
5497
45.2
38.6
44.6
37.8
23.6
1029
1434
2156
2658
3442
30.7
26.2
28.2
24.6
14.8
Source: Authors’ estimates using EIBEP 2002–03.
Men
Women
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World Bank Working Paper
Table 6.9. Results of Decomposition for Men and Women
(Full employment ⴝ 75.0 hours/week)
Men
Women
Quintile of
Consumption
M/N
HM
wM
M/N
HM
wM
1
2
3
4
5
0.189
0.201
0.225
0.248
0.262
32.8
35.5
37.4
38.4
38.9
245
296
407
428
538
0.220
0.228
0.225
0.237
0.247
30.4
32.0
34.0
35.7
36.9
154
197
282
314
378
Source: Authors’ estimates using EIBEP 2002–03.
or even 70.5 hours, so that an increase in labor at current wages and productivity level does
not represent a magic bullet for the fight against poverty.
Note also that the monetary value of the extra hours is typically lower in the bottom
than in the top of the consumption distribution, so that inequality tends to increase when
approaching full employment. The exception is represented by the simulation that uses the
predicted wage to evaluate one extra hour of employment (but even in this case, the Gini
coefficient and the Theil index give opposite conclusions because consumption is increasing
more in the third than in the second quintile in relative terms). When using the “household
productivity” measure to value the additional hours of work assumed in the simulations,
inequality is increasing substantially.
Table 6.10. Increase in Average Consumption and Changes in Poverty Rate
and Inequality Following an Increase in Individual Working
Time Under Various Hypotheses
Full employment
at 50 hrs/week
Consumption
Quintiles
1
2
3
4
5
Poverty rate
Gini coefficient
Theil index
Average per
Capita Current (At wage (At HH
Consumption
rate)
prod. A)
171316
284150
394745
559642
1272735
48.9
0.405
0.331
Full employment
at 70.5 hrs/week
(At HH (At wage (At HH
prod. B)
rate)
prod. A)
(At HH
prod. B)
223858 221181 262218 303695 285543 370737
363839 384581 465580 468332 497455 658378
530851 566864 686537 684162 736114 971154
721402 862438 1052722 910172 1134718 1502527
1480456 2159070 2422609 1737529 2863585 3442761
39.4
37.1
34.1
29.2
26.4
21.5
0.418
0.510
0.536
0.412
0.527
0.552
0.321
0.612
0.592
0.299
0.657
0.631
Source: Authors’ estimates using EIBEP 2002–03.
Gender, Time Use, and Poverty in Sub-Saharan Africa
131
Conclusions
Conceptually, there could be two ways to rely on the labor of the poor to reduce poverty.
One possibility would be to increase the productivity of that labor, so that the poor obtain
higher wages or earnings from the effort they already put in. The second possibility is to
increase the working hours of the poor, taking note of the fact that underemployment is
pervasive in many countries. While it is true that many men and especially women already
work long hours in Sub-Saharan Africa, in large part due to domestic chores and other
household tasks, underemployment is nevertheless affecting a large share of the population. In addition, in most countries, because standards of living are so low and a large share
of the population is poor, many individuals would like to work more in order to be able to
improve their condition, even at low wage levels.
In this paper, we have not discussed what could actually be done in Guinea to improve
employment prospects, both in terms of the availability of jobs and work, and in terms of
the quality of those jobs. We have also not simulated how poverty could be reduced thanks
to an increase in productivity that would lead to higher earnings or wages per hour of work
for the population. Our aim has been rather modest, namely to estimate the reduction in
consumption poverty that could be achieved if the adult population were working full
time. Different thresholds were considered for what a full employment workload would
be, and the magnitude of the reduction in poverty clearly depends on such thresholds. One
key message is that job creation and full employment would lead to a significant reduction
in poverty, even at the relatively low current levels of wages and earnings enjoyed by the
population. Yet at the same time, poverty would remain massive even if all working age
individuals would work full time.
In future work, the results obtained here could be compared to other results, such as
the impact of an increase in productivity that would lead to higher hourly wages and earnings, or a shift in working hours within households to relieve the high burden placed on
some members. What we hope to have demonstrated is that a time use approach to the
analysis of employment is an attractive way to make the link between time use and consumption poverty, and that this type of simulation and results can be useful in thinking
about the employment aspects of the poverty reduction strategies that many countries are
now preparing, implementing, or revising.
References
Bardasi, E., and Q. Wodon. 2005. “Measuring Time Poverty and Analyzing its Determinants: Concepts and Application to Guinea.” (Chapter 4 in this volume.)
Blackden, C.M., and C. Bhanu. 1999. Gender, Growth, and Poverty Reduction. Special Program of Assistance for Africa 1998 Status Report on Poverty, World Bank Technical
Paper No. 428, Washington, D.C.
Buvinic, M., and G. Rao Gupta. 1997. “Female-Headed Households and Female-Maintained
Families: Are They Worth Targeting to Reduce Poverty in Developing Countries.” Economic Development and Cultural Change 45:2, 259–280.
Calvès, A.-E., and B. Schoumaker. 2004. “Deteriorating economic context and changing
patterns of youth employment in urban Burkina Faso: 1980–2000.” World Development 32:1341–1354.
132
World Bank Working Paper
Charmes, J. 2005. “A Review of Empirical Evidence on Time Use in Africa from UN-sponsored
Surveys.” (Chapter 3 of this volume.)
Dercon, S., and P. Krishnan. 2000. “Vulnerability, Seasonality and Poverty in Ethiopia.”
Journal of Development Studies 36:25–53.
Ellis, F. 2000. “The Determinants of Rural Livelihood Diversification in Developing Countries.” Journal of Agricultural Economics 51:289–302.
Kanwar, S. 2004. “Seasonality and Wage Responsiveness in a Developing Agrarian Economy.” Oxford Bulletin of Economics and Statistics 66:189–204.
Skoufias, E. 1993. “Seasonal Labor Utilization in Agriculture: Theory and Evidence from
Agrarian Households in India.” American Journal of Agricultural Economics 75:20–32.
Wodon, Q., and K. Beegle. 2005. “Labor Shortages Despite Underemployment? Seasonality in Time Use in Malawi.” (Chapter 5 in this volume.)
World Bank. 2001. Engendering Development: Through Gender Equality in Rights, Resources,
and Voice. World Bank Policy Research Report, Washington, D.C.
Gender, Time Use, and Poverty in Sub-Saharan Africa
133
Appendix Table 6.A1. Wage Regressions, by Gender (Individuals aged 10+,
not in school, who earn a wage or profit)
Age
Age squared
Disabled (Base not disabled)
Marital Status (Base single)
Monogamous
Poligamous
Divorced
Widow/widower
Education Completed (Base none)
Primary
Secondary 1st
Secondary 2nd
Technical
University
Industrial Sector (Base manufacturing)
Agriculture
Mines
Energy
Construction
Trade
Transport
Finance, IT
Men
Women
0.039***
(3.976)
−0.000***
(3.650)
0.045
(0.334)
0.037***
(4.297)
−0.000***
(4.147)
−0.180
(0.994)
0.157**
(2.205)
0.373***
(4.379)
−0.111
(0.616)
0.252
(0.903)
0.183**
(2.256)
0.135
(1.597)
0.216*
(1.878)
0.057
(0.514)
0.211***
(3.131)
0.333***
(3.967)
0.354**
(2.016)
0.615***
(5.998)
0.742***
(7.759)
0.189**
(2.232)
0.288**
(2.351)
0.268
(0.666)
0.656***
(4.275)
0.994***
(4.432)
−0.801***
(9.116)
0.727***
(5.299)
0.266
(0.838)
0.174
(1.624)
0.301***
(4.082)
0.266***
(2.746)
−0.089
(0.544)
−1.112***
(10.617)
−0.039
(0.185)
−1.742
(1.326)
−0.415
(0.703)
−0.014
(0.167)
−0.011
(0.032)
−0.286
(0.929)
(continued)
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World Bank Working Paper
Appendix Table 6.A1. Wage Regressions, By Gender (Individuals aged 10+,
not in school, who earn a wage or profit) (Continued)
Men
Public admin, educ., health
Status in Employment (Base employee
priv. sect., formal)
Public employee
Employee priv. sect., inform.
Self-employed
Type of Contract (Base permanent)
Seasonal
Daily and piece work
Rural (base urban)
Geographical Area (Base Conakry)
Boke
Faranah
Kankan
Kindia
Labe
Mamou
Nzerekore
Constant
Observations
R-squared
−0.067
(0.745)
Women
−0.399***
(3.106)
0.338***
(3.395)
0.219
(1.172)
−0.361***
(3.212)
0.218**
(2.259)
−0.423**
(2.127)
−0.082
(0.441)
−0.309***
(4.435)
−0.048
(0.722)
−0.344***
(5.326)
−0.165***
(2.646)
−0.066
(1.035)
−0.198***
(3.032)
−0.005
(0.071)
0.124
(1.575)
0.015
(0.174)
0.041
(0.503)
0.153*
(1.704)
0.376***
(3.835)
−0.010
(0.137)
5.210***
(23.790)
4350
0.276
−0.025
(0.340)
0.139*
(1.832)
0.068
(0.799)
−0.282***
(3.513)
0.105
(1.088)
0.311***
(3.303)
0.012
(0.162)
5.541***
(22.038)
4356
0.239
Note: The dependent variable is the logarithm of the hourly wage, spatially adjusted (using poverty
lines) for differences in purchasing power across regions; * significant at the 10% level, ** significant at
the 5% level, ***significant at the 1% level.
Source: Authors’ estimates using EIBEP 2002–03.
CHAPTER 7
Assessing the Welfare of
Orphans in Rwanda:Poverty,
Work, Schooling, and Health
Corinne Siaens, K. Subbarao and Quentin Wodon25
One of the aspects of the orphan crisis in Sub-Saharan Africa relates to time use, namely where
orphans end up living and what they spend their time doing in their new household of adoption. While some orphans are welcomed in centres and institutions, many live with relatives or
other members of their communities, and others are welcomed by families which are not
directly related to them. Orphans are in many ways better off when welcomed by relatives or
other families than when living by themselves or in institutions, but there are also concerns that
the orphans (and especially girls) that are welcomed in some families may be required to provide more help for the domestic tasks to be performed, with the resulting time pressure in terms
of workload preventing them from benefitting from the same opportunities in education and
other aspects of their development as other children. The objective of this paper is to conduct
preliminary work to test this assumption using recent household survey data from Rwanda,
with an attention not only to traditional variables of interest such as school enrollment, child
labor and time use, but also with an eye to assessing other dimensions of the children’s welfare.
W
hile there have been orphans in much of Africa for a long time in part due to a
comparatively high incidence of conflicts, AIDS has swelled their number in
many countries. According to a communiqué by UNICEF and UNAIDS (2003),
the share orphans in Africa specifically due to HIV/AIDS has increased from 3.5 percent
in 1990 to 32 percent in 2001. By 2010, the two agencies estimate that some 20 million
25. The authors are with the World Bank. This work was prepared as a contribution to the Poverty
Assessment for Rwanda prepared at the World Bank. The authors acknowledge support from the Belgian
Poverty Reduction Partnership for preparing this paper. Results from the paper were presented at a workshop organized in Kigali in March 2005 in collaboration with the government unit in charge of the country’s Poverty Reduction Strategy. The views expressed here are those of the authors and need not reflect
those of the World Bank, its Executive Directors or the countries they represent.
135
136
World Bank Working Paper
African children will have lost one or both parents to AIDS. According to UNICEF’s Executive Director Carol Bellamy, “the crisis of orphans and other children made vulnerable
by HIV/AIDS is massive, growing and long-term. But two-thirds of countries hard-hit by
the disease do not have strategies to ensure the children affected grow up with even the
bare minimum of protection and care.”
Because of the legacy of the Genocide, the situation of orphans is perhaps more dramatic in Rwanda than in other countries. Even as the country has emerged out of conflict,
the AIDS pandemic has begun to take a heavy toll of human lives, contributing significantly to
adult mortality. How serious is the problem of orphans in Rwanda? Is it threatening the
traditionally strong care-giving capacity of households and communities? Are orphans
placed in fostering households well-protected, for example in terms of what is required to
them for domestic work? Will the crisis of orphans in Rwanda threaten the attainment of
human development goals especially the goals set for education, nutrition and poverty
reduction? Finally, what is the role of public action to mitigate the crisis of orphans? While
qualitative work has been done on the situation of orphans in Rwanda (Dona 2003), good
quantitative evidence is still lacking to assess the situation. This paper aims to start to fill
the gaps by providing partial answers to the above questions. These questions, in turn, are
important for the broader purpose of this volume devoted to gender, time use, and poverty,
because of the differences in the treatment of orphan girls and boys especially as it relates
to time use, for example in the area of domestic work.
There are several reasons why orphans constitute an important development issue in
Africa, and especially in Rwanda. We outline four such reasons here. First, the sheer numbers and the size of the problem threatens the traditional care-giving capacity of communities and households, in part because of the pressure that care-giving puts on the time
available for other productive activities. This is already evident from both quantitative
studies based on longitudinal data sets for Uganda (Deininger, Garcia, and Subbarao,
2003), and from a number of qualitative studies or situation analyses for various countries
documented in Subbarao and Coury (2003).
Second, true to the African tradition, most orphans are placed either in extended families or in fostering households. Yet this communal arrangement, laudable as it is, may
come at the cost of consumption shock to households who have taken in orphans. If the
households that have absorbed orphans are already poor to begin with—and there is evidence to suggest that on average orphans in Africa live in poorer households compared
with non-orphans (Case, Paxson, and Ableidinger 2002)—the consumption shock may
translate into deeper poverty. Even if orphans are housed in relatively non-poor households as is the case in Rwanda, the consumption shock and consequential welfare loss may
persist.
Third, faced with limited resources, one may expect fostering households to favor their
biological children over fostered ones, denying orphans proper access to basic needs such
as education, health care and nutrition. In Kampala, Uganda, 47 percent of households
assisting orphans lacked money for education compared with 10 percent of apparently
similar households not charged with the responsibility of caring for orphans (Muller and
Abbas 1990). One out of seven children face this risk in Rwanda, with the potential of an
erosion of the country’s human capital, thereby jeopardizing the realization of millennium
development goals.
Gender, Time Use, and Poverty in Sub-Saharan Africa
137
Fourth, orphaned children face other related risks including child labor. Children living with sick parents, even before they are orphaned, may be pulled out of school to engage
in household chores or economic activities. This risk may be particularly the case for
orphaned girls. Evidence also suggests that the lack of parental protection and supervision
may leave an open door for abuse, neglect and exploitation, and even violation of rights
such as property grabbing (Subbarao and Coury 2003). Moreover, following parental
deaths, some children may become household heads often with little skills to conduct the
activities of a household head.
The implication of the above is that parental loss can have negative consequences for
a household, the orphans, and the community at large. Figure 7.1 provides a simple diagrammatic representation of the key short- and longer-term impacts of parental loss on
orphans themselves, the community, the host household as well as the broader economy.
The costs to children include the strong possibility of dropping out of school, a decline in
nutritional status, possible increase in child labor, potential loss of assets including land,
Figure 7.1. Impacts of Parental Loss
Impact on orphans
Immediate:
• Dropping out of school
• Increase in Child Labor
Longer term:
• Loss of assets/land grabbing
• Decline in health/nutrition status
• Discrimination/Exploitation
Impact on communities
Parental loss
Immediate:
• Direct loss of productive labor
• Increase in working day
Longer term:
• Increase in care-giving activity
• Stress on informal coping capacity
Impact on households and the economy
Immediate:
• Reduced savings and investment
• Potential Decline in GDP
Longer term:
• Increase public expenditure on welfare,
health and education
• Increase in crime, social disruption
Source: Subbarao and Coury (2003).
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World Bank Working Paper
and discrimination and exploitation. The costs to households and communities include
the extra burden associated with the care-giving activity, a potential decline in available
productive labor, and a general weakening of informal coping capacity. Few studies have
set out to describe and quantify these impacts, especially the ones that arise in the short
term (for example, the adverse schooling outcomes).
Full quantification of the different outcomes and channels through which the presence of orphans may affect welfare would require panel data that are not available for
Rwanda. However, with the available data, namely a recent nationally-representative living standard measurement-type household survey, we are able to quantify the impacts of
welcoming orphans on household consumption of fostering families, and the impact of
being an orphan on schooling outcomes and work burden. The medium and longer term
impacts on growth of orphans in Rwanda are beyond the scope of this paper.
The paper is structured as follows. The first section presents a broad quantitative picture of orphans in Rwanda, including a profile of orphans by age, gender and other characteristics. The second section assesses the impact of fostering orphans on the household
consumption (and thereby on poverty) of foster families, and the impact on the child’s
education and nutrition outcomes of being an orphan. Conclusions and policy options are
briefly discussed in the last section.
Number of Orphans and Qualitative Findings
Number of Orphans
As mentioned earlier, there are two main reasons explaining the high incidence of orphans
in Rwanda. First, at least 800,000 people (10 percent of the population) died in the Genocide
of 1994. While many of those who were left orphaned by the war have now reached adulthood, some are still under 15 years of age today, and since we use survey data for 1999–2001
for our analysis, the number of orphans from these events probably26 remains large in our
data. Second, AIDS in Rwanda as in much of Africa is also contributing to a high incidence
of orphans.
Our empirical work is based on an analysis of the unit level data of Rwanda’s Enquête
Intégrale sur les Conditions de Vie des ménages. This is an Integrated Household Living
Conditions Survey conducted between October 1999 and July 2001. Data collection in
urban areas was carried out between October 1999 and December 2000. In rural areas,
where 90 percent of the population lives, the survey was implemented from July 2000 to
July 2001. When reporting results, we will consider the survey as representative of conditions as they stood in 2000–2001.
We will consider as orphans children who do not live with their mother, nor with their
father. While this group may include some children who are not orphans, qualitative
knowledge from the situation on the ground and a few simple data tests make us confident
that this is a relatively good proxy. For example, although still very low overall, the share
26. Although we have a good handle on how to identify orphans in our survey data, we do not know
why they are orphans, hence the use of “probably” in the above sentence.
Gender, Time Use, and Poverty in Sub-Saharan Africa
139
Table 7.1. Incidence of Orphanhood by Age, Area, and Poverty Status,
Rwanda 2000–01
Double orphan
Father is not in household
Mother is not in household
Both parents are in the household
All children
All
Urban
Rural
Age 0 to 6
7.2%
19.3%
1.6%
71.9%
100.0%
7.3%
21.9%
2.5%
68.3%
100.0%
7.1%
19.0%
1.5%
72.3%
100.0%
Poor
Non poor
7.2%
23.2%
1.5%
68.2%
100.0%
7.1%
16.7%
1.8%
74.5%
100.0%
13.4%
31.9%
4.1%
50.6%
100.0%
23.1%
25.1%
5.4%
46.5%
100.0%
Age 7 to 15
Double orphan
Father is not in household
Mother is not in household
Both parents are in the household
All children
18.4%
28.4%
4.8%
48.5%
100.0%
32.6%
25.7%
4.7%
37.0%
100.0%
16.9%
28.6%
4.8%
49.7%
100.0%
Note: A child is defined as a double orphan when neither his father or his mother live in the same
household.
Source: Authors’ estimation using EICV 2000/01.
of so-defined orphans who benefit from a grant from Rwanda’s Genocide Fund, a fund set
up in the late 1990s to help the victims of the Genocide, is much higher among that group
than among children who live with their mother, their father, or both. In any case, our definition implies that we are focusing our analysis on “double” orphans, that is, those that
are likely to have lost both parents.27
In Table 7.1, the proportion of double orphans, as well as of orphans who are assumed
to have lost only one parent (living with either their mother or their father, but not both)
are shown in two age groups: 0–6 and 7–15. In these two age groups, respectively 7.2 percent
and 18.4 percent are orphans. Thus, as in other countries, a large majority of orphans in
our data fall in the age group 7–15. As mentioned earlier, this is due to both adult mortality due to AIDS and to the impact of the Genocide which was also felt at the time of the
survey mostly in that age group.
A much higher percentage of children (19.3 percent and 28.4 percent respectively for
the two age groups) have lost their father but not their mother, whereas the proportion of
maternal orphans appears to be smaller (1.6 percent and 4.8 percent respectively). The reason
27. This does not mean that we minimize the adverse consequences on the child of loss of a single parent. A recent study for Zimbabwe had shown that children in the age group 13–15 who had lost their
mothers were less likely to have completed primary school than children who lost their fathers, after controlling for other factors that influence primary school completion (Nyamukapa and Gregson 2003).
140
World Bank Working Paper
for a much higher percentage of paternal orphans is clearly the result of conflict which typically leads to higher adult male mortality in much of Africa, including in Rwanda. There
are also rural-urban differences in the location of the 7 to 15 years orphans. In that age
groups, a much higher percentage of orphans happen to be in urban areas than in rural
areas, whereas there are no significant rural-urban differences in the proportion of children who have lost either parent under both age groups.
How do these estimates of the share of orphans compare with other estimates?
According to UNAIDS, there could be up to 613,000 orphans due to AIDS only in the age
group 0 to 14, or 17.5 percent of the child population. These estimates, which are very high,
take into account both double and single orphans, and they would need to be increased
further to take into account other orphans, mainly due to the Genocide. Using data from
UNICEF’s Multiple Indicator Cluster Survey for the year 2000, a recent World Bank report
(2002) on education in Rwanda estimates that 28.5 percent of children were orphans, a
proportion slightly below that of the UNAIDS estimate when Genocide orphans are taken
into account.
Our own estimates in Table 7.1 are broadly similar to the estimates provided by
UNAIDS and the World Bank education report, but because we will concentrate on double orphans in this paper, we will focus on a subset of the orphan population. Also, it is
worth emphasizing that the AIDS prevalence may not have reached the high rates that were
used until recently associated with Rwanda. Preliminary data from the 2004/05 Demographic and Health Survey suggests much lower rates of HIV prevalence than previously
expected. This reduction in prevalence may reflect both an improvement in the quality of
information and an indication that infection rates may actually have declined over time,
especially in urban areas.
The bottom line is that the number of orphans in Rwanda is subject to debate, and the
above estimates may actually be on the high side, essentially because the way to capture
orphans in the survey used here relies on identifying children who do not live with any of
their parents, but clearly some of these children may very well have one or both parents
alive. The rest of the paper, which compares indicators of well-being between orphans and
non-orphans and the key arguments made regarding these differences do not hinge on the
actual number of orphans.
Qualitative Evidence on Living Conditions
A qualitative study of orphans was recently prepared for the Government of Rwanda,
UNICEF and Save the Children Alliance (Dona 2003). According to this study, fostering a
child can be a very spontaneous and informal decision but it can also take place through
official placement networks. The likelihood of success is possibly higher in the case of organized fostering because it offers higher visibility and foster parents may have a longer-term
vision for the child. Nevertheless, motivations and obligations are the same in both cases
and, eventually, the impact for the parents will depend on their personal attitudes toward
the child, on the child’s integration with the siblings and on the child’s own attitude.
Among the reasons why parents decide to foster, pity, social responsibility, loss of their
own children, a desire to have children, and loneliness are frequently reported. After so
much terror and pain in the country, people feel a common responsibility for each other.
Gender, Time Use, and Poverty in Sub-Saharan Africa
141
“Children belong to us because all Rwandans have lost their own,” said a parent. Apart
from cultural, humanistic, and personal reasons, the need for assistance is also mentioned
as a key reason for fostering. As a woman explained it, “As a widow, and only with boys,
I needed a young girl that helped me in small domestic chores; you know, at a certain age,
boys wander around [and] I was alone at home.” While it is likely that the impact of fostering on the household will depend on the original motivations for fostering, the study
suggests that “the fact that parents want to foster a child for help does not necessarily mean
that the child was abused or exploited.”
Fostering a child also has implications for household dynamics. The relationship with
the siblings is most of the time perceived as good. Generally speaking, if there are adjustment difficulties, they are most prevalent at the beginning of the fostering process. Parents
complain about the financial burden caused by fostering and about the lack of external
assistance, but they seem to be generally happy and positive about the experience. They
insist that the child is much better off with them than within a center. Still, foster parents
are concerned about education and health, issues of identity, and the long-term future of
the children they adopt, with some concerns about the financial resources needed to bring
a child to maturity.
Overall, the study is rather positive regarding the ability of the fostering system to protect orphans. The study concludes that “the introduction of organized fostering programs
has proved to be an appropriate means of providing family care for separated children
unable to return to their own families,” and adds that the “general impression [is] of fostered children being happy and well-integrated into their families.” As we will see in the
next section, the results of our own quantitative analysis are somewhat less optimistic, but
this does not mean that they contradict the qualitative findings reported in Dona (2003).
While orphans in foster homes may be at a disadvantage versus other children, they may
still be much better off within foster homes than in orphanages. Interestingly, while spontaneous fostering was most prominent immediately after the Genocide, it gradually became
less important than organized fostering. In the case of organized fostering, children who
had been placed in a center are chosen by parents and must follow them and integrate a
new family. Children in centers are waiting to be chosen, hoping to be well treated, to continue their studies, and to not be exploited. Dona’s study thus concludes that in general
children “find household chores a pleasant and rewarding activity.” It helps them to be
integrated in their new family. Of course, “Problems arise when children indicate that they
work hard and when they say that they feel treated as unpaid servants.” In other words, in
some cases, foster children are clearly exploited or abused.
Living Conditions of Orphans: Quantitative Empirical Results
Household Consumption
An interesting aspect of the profile of orphans in Rwanda is that, no matter which age
group one considers, a higher proportion lives in relatively non-poor households. This can
be seen in Table 7.2. In fostering households as compared to households without orphans,
consumption per equivalent adult, as well as the number of years of education of the head
and spouse are all higher, while the unemployment rate for the household head is lower.
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World Bank Working Paper
Table 7.2 Selected Characteristics of Households with and Without Orphans,
Rwanda 2000–01
Average yearly consumption per equivalent
adult (Francs)
Population share in extreme poverty
Population share in poverty
Average size of land holdings (hectares)
Average number of infants (aged 0–4)
Average number of children (aged 4–14)
Average number of adults (aged 15 and above)
Share of households with female heads
Share of households without a spouse
Average number of years of education of
household head
Average number of years of education of spouse
Share of household heads searching for employment
Population share living in urban areas
Households with
double orphans
Households without
double orphans
99,452
67,850
32.0%
45.8%
0.8
1.2
2.0
3.8
28.2%
32.4%
4.3
47.8%
67.1%
0.7
1.3
2.0
3.4
20.6%
24.1%
2.9
2.3
1.4%
19.3%
1.9
2.8%
8.4%
Note: A child is defined as a double orphan when neither his father or his mother live in the same
household.
Source: Authors’ estimation using EICV 2000/01.
Households with orphans are more often urban, female headed or more generally without
a spouse for the household head. In fact, many double orphans are living in female-headed
households where the female head is self-employed. This means that “self-selection” is
going on, namely female-headed households working in informal sectors are probably the
ones who are volunteering the most to take in orphans, presumably to get some help in
domestic and economic work.
The fact that consumption is higher in households with orphans means that the probability of being poor is lower among those households. The poverty estimates used in
Rwanda follow the measurement method adopted by the Government of Rwanda for the
preparation of its Poverty Reduction Strategy. The method is explained in details in Ministry of Finance (2002). The share of the population in extreme poverty among households
with orphans was 32.0 percent, versus a much higher 47.8 percent among households without orphans. Similarly, the respective shares of the population in poverty among the two
groups are 45.8 percent and 67.1 percent. Addition all comparisons are given in the table
in terms of landholdings and family size.
While households with orphans tend to be richer, welcoming an orphan is still likely
to induce a loss in consumption for a household. According to preliminary estimates by
Siaens and Wodon (2003), the marginal impact of having one orphan in the household on
consumption is negative—estimated at the sample mean, there is a net reduction in per
Gender, Time Use, and Poverty in Sub-Saharan Africa
143
capita consumption of 5.2 percent and 11.5 percent in urban and rural areas respectively.
Yet, some fostering households are fostering more than one orphan. When estimated for
all orphans rather than for the addition of one orphan, the consumption shock is more severe:
the net reductions in per capita adult equivalent consumption are 9.1 and 18.6 percent
respectively for urban and rural areas. While these results should be considered as preliminary only,28 they are in line with findings for Uganda, where Deininger, Garcia, and Subbarao
(2003) also find a significant decrease in per capita consumption of fostering households
in comparison with similar households not fostering orphans.
Thus, while fostering by households is an extremely important traditional safety net
pervasive in Rwanda as in most other most African countries, its immediate consumption
shock for the households who agree to foster cannot be ignored. Rwanda’s Genocide Fund
which provides grants to victims of the Genocide, including orphans, in order to help them
with housing, education, and relocation expenditure may be a source of relief for fostering households, but unfortunately the data on such grants in the survey is weak, so that it
cannot be used at this stage to assess the impact of the Fund on the fostering families and
on the orphans’ well-being.
Education and Child Labor
Being an orphan is associated with a lower probability of school enrollment. For the country as a whole, 76.4 percent of boys and 73.8 percent of girls in urban areas, and 67.7 percent
and 67.2 percent in rural areas, are enrolled in school. The proportions for orphans are lower:
62.7 percent and 55.8 percent for boys and girls respectively in urban areas, and 61.5 percent
and 62 percent in rural areas. Both male and female orphans have a lower probability of
being enrolled in school, but the gap between orphans and non-orphans is larger for girls
than for boys. Also, although present in rural areas, the gap in schooling for orphans is
larger in urban areas, for both boys and girls. Table 7.3 also shows that a much higher proportion of both boys and girls are engaged in some form of non-domestic work, paid or
unpaid, if they are orphans. In urban areas, the proportion of orphans engaged in work
is twice as large for girls (31.6 percent) than for boys (18.4 percent). Orphans work also
more at home in terms of hours per week than non-orphans. The difference between both
groups of children is again higher in urban than in rural areas. Overall, it seems that some
orphans, especially girls, are being fostered by female-headed households to share their
work burden.
The fact that school enrollment is lower and the probability of working higher for
orphans does not necessarily means that orphans are discriminated against in their foster
28. The results in Siaens and Wodon (2003) are based on regressions for the logarithm of consumption per equivalent adult on a wide range of household characteristics, including the presence of orphans.
However, the number of orphans fostered by a household may itself depend on the level of well-being of
the household before fostering, in which case we would have bias due to endogeneity. Nonetheless, controlling for other variables (education, age and gender of head, employment, location, and so forth), welcoming an orphan is still very likely indeed to reduce consumption per equivalent adult in a household
because most of the impact on consumption comes through the increase in the number of equivalent
adults due to fostering (that is, the number of infants and children increase).
144
Head female
All
School enrollment rate
Working, paid or unpaid
(except domestic work)
Domestic work (Hours/week)
Orphans
Non orphans
All kids
Orphans
Head male
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Urban areas
Girls
Boys
Girls
Boys
Girls
76.4%
73.8%
62.7%
55.8%
81.8%
84.1%
79.5%
74.5%
71.8%
58.8%
74.9%
73.5%
6.2%
12.3%
18.4%
31.6%
1.4%
1.3%
3.4%
11.5%
5.2%
25.3%
7.6%
12.7%
6.38
14.80
10.03
22.76
4.94
10.27
5.90
15.14
6.85
20.56
6.61
14.62
Rural areas
School enrollment rate
Working, paid or unpaid
(except domestic work)
Domestic work (Hours/week)
67.7%
67.2%
61.5%
62.0%
68.9%
68.2%
68.3%
67.9%
62.4%
64.3%
67.3%
66.7%
7.5%
7.2%
14.4%
10.6%
6.1%
6.5%
8.4%
7.5%
10.3%
10.4%
7.0%
7.1%
6.84
10.34
7.48
11.46
6.71
10.11
6.71
10.36
7.14
11.17
6.91
10.32
Note: A child is defined as a double orphan when neither his father or his mother live in the same household.
Source: Authors’ estimation using EICV 2000/01.
World Bank Working Paper
Table 7.3. School Enrollment and Child Labor for Children Aged 7–15, Rwanda 2000–01
Gender, Time Use, and Poverty in Sub-Saharan Africa
145
family. For example, orphans are on average older than other children, and this may
explain part of the observed differentials in schooling and work. In order to assess whether
orphans are less likely to be enrolled in school than other similar children who are not
orphans, regression analysis is needed. Table 7.4 provides the results of probit regressions
for the probability of enrollment in urban and rural separately, for boys and for girls. Controlling for a variety of child, household and community characteristics together with the
education level and activity of the biological father and mother, the negative impact of
being a double orphan is still strong.
Thus, with the important caveat that we cannot control for the orphan’s life conditions just before fostering (for example, at the time of the parental loss, orphans may have
dropped out of school and start working out of necessity, and it might be very difficult for
these children to return to school even once they have found a foster family), the results in
Table 7.4 are an indication that there is indeed some level of discrimination against the
schooling of orphans in foster families.
Nutrition
Table 7.5 provides comparisons between orphans and non-orphans for selected health
indicators, with a focus on children below five years of age. There are few differences in the
probabilities of being sick, or to have had diarrhea over the last two weeks. However,
orphans are less likely to have been vaccinated than any of the other groups identified in
the table, and they are also less likely to benefit from a nutrition program. They are also less
likely to have benefited from a postnatal consultation, or to have received vitamins A, than
non-orphans children in the same households. Finally, the incidence of malnutrition (the
probability of being stunted, wasted, or underweight) is also higher among orphans than
among other children in the same households, but the measures are on par with the two
other groups identified in the table.
The fact that many health indicators for young orphans are below those observed for
other groups, especially other (biological) children living in foster families, again does not
necessarily mean that there is a systematic discrimination against orphans in terms of
healthcare and nutrition. It could be that orphans faced harsher situations before being
welcomed in foster families. Malnutrition indicators often result from events early in life,
which may have occurred before fostering. Still, the fact that orphans have lower rates of
participation in nutrition programs than biological children in the same households, and
that they have a lower probability of receiving vitamins A, begs questions as to whether they
indeed receive equal treatment.
Conclusion
Because of the combined impact of the Genocide and the AIDS pandemic, the number of
orphans (defined here as the children who live with neither their father nor their mother)
is high in Rwanda. The results presented in this paper suggest that although orphans tend
to live in foster households that are comparatively richer than the rest of the population,
they are also less likely to go to school, more likely to work both at home and outside of the
146
Table 7.4. Determinants of School Enrollment among Children Aged 7–15, Rwanda 2000–01
Boys
Rural areas
Girls
Boys
Girls
Coeff.
St. Er.
Coeff.
St. Er.
Coeff.
St. Er.
Coeff.
St. Er.
Characteristics of the Child
Age
Age squared
Double orphan (no father and no mother)
No father only
No mother only
0.330*
−0.016*
−0.318*
−0.259*
−0.208*
0.056
0.003
0.096
0.107
0.126
0.341*
−0.016*
−0.165*
−0.002
−0.159
0.062
0.003
0.079
0.079
0.152
0.510*
−0.024*
−0.175*
−0.015
−0.134*
0.032
0.001
0.063
0.061
0.053
0.535*
−0.025*
−0.243*
−0.149*
−0.119*
0.031
0.001
0.061
0.060
0.056
Characteristics of the Household
Migration (by the head, 5 years ago or more)
Number of infants
Number of infants squared
Number of children
Number of children squared
Number of adults
Number of adults squared
Household head female
No spouse in household
0.020
0.041
−0.015
−0.048
0.013
0.026
−0.002
0.153*
0.012
0.033
0.047
0.016
0.042
0.007
0.024
0.002
0.052
0.070
0.040
−0.071
0.017
0.044
−0.002
−0.012
0.002
−0.004
0.029
0.034
0.047
0.015
0.036
0.006
0.031
0.003
0.079
0.091
−0.003
−0.048
0.013
−0.080*
0.010*
−0.023
0.004
0.138*
−0.103*
0.018
0.026
0.010
0.027
0.005
0.020
0.003
0.047
0.051
0.014
−0.072*
0.022*
−0.045
0.006
−0.024
0.007*
0.100*
0.042
0.017
0.024
0.009
0.026
0.005
0.020
0.003
0.048
0.049
0.088*
0.093*
0.121*
0.131*
0.035
0.035
0.038
0.035
0.043
0.047
0.046
0.054
0.042*
0.086*
0.199*
0.196
0.019
0.025
0.032
0.059*
0.058*
0.104*
0.018
0.025
0.040
0.075
0.154
0.079
Education of Household Head
Primary, not completed
Primary completed
Secondary, not completed
Secondary completed or superior
0.076
0.067
0.110*
0.092
World Bank Working Paper
Urban areas
−0.032
−0.040
0.072
0.063
0.075
0.053
−0.015
0.046
0.111
0.066
0.061
0.021
0.077*
0.023
0.034
0.060*
0.100*
0.022
0.032
0.051
0.182*
0.043
0.103*
0.044
Employment of Household Head
Does not work
Works in industry/transport
Works in banking sector, or as professional
Works in commerce
0.067
0.044
−0.013
0.031
0.041
0.049
0.061
0.048
−0.035
−0.062
−0.091
−0.135*
0.066
0.073
0.078
0.070
0.003
−0.075
−0.033
0.080
0.021
0.070
0.077
0.066
0.027
0.047
0.098
0.133
0.020
0.062
0.057
Works in other sectors, but not agriculture
−0.020
0.082
−0.156
0.106
−0.025
0.083
0.052
0.057
0.082
0.045
0.100*
0.058
0.035
0.054
0.095
0.057
0.087*
0.029
0.087*
0.028
Biological father, secondary or superior
Biological father, unstated education level
Biological mother, primary not completed
Biological mother, primary completed or more
Biological mother, unstated education level
Biological father was in agriculture
0.177*
−0.031
0.054
0.011
0.059
−0.117*
0.029
0.080
0.056
0.056
0.069
0.059
0.117
−0.080
0.041
0.130*
0.051
−0.227*
0.042
0.049
0.093
0.068
0.040
0.075
0.056
0.161*
0.175*
0.049
0.095*
0.154*
0.039
−0.074
0.026
0.052
0.041
0.040
0.037
0.053
0.048
0.118*
0.145*
0.042
0.077
0.122*
−0.029
−0.016
0.028
0.052
0.038
0.040
0.039
0.063
0.044
Other Household Characteristics
Number of hectares of exploited land
Number of hectares squared
Head has health problems
Spouse has health problems
0.024
−0.001
−0.257*
0.119
0.025
0.002
0.159
0.062
0.071*
−0.007*
0.153
−0.001
0.032
0.003
0.052
0.157
0.017
−0.001
0.007
0.057
0.012
0.001
0.039
0.066
0.035*
−0.002
−0.085*
−0.019
0.012
0.001
0.041
0.065
Education/Work of Biological Parents
Biological father, primary not completed
Biological father, primary completed
147
(continued)
Gender, Time Use, and Poverty in Sub-Saharan Africa
Education of Spouse
Primary, not completed
Primary completed
Secondary completed/superior
148
Urban areas
Boys
Geographic Characteristics
Kigali geographic dummy variable
Population in locality (in millions)
Access to water in community
Access to electricity in community
Distance to market (in 100 km)
Distance to road (in 100 km)
Distance to primary school (in 100 km)
Distance to health center (in 100 km)
Rural areas
Girls
Boys
Girls
Coeff.
St. Er.
Coeff.
St. Er.
Coeff.
St. Er.
Coeff.
−0.022
0.033
−0.048
0.033
0.015
0.000*
0.029
0.046
0.029
0.000
0.018
0.031
0.062*
0.000*
0.018
0.051
−0.002
−0.016
−0.018*
0.005*
0.002
0.009
0.005
0.002
0.000
−0.023*
−0.017*
−0.002
St. Er.
0.027
0.000
0.018
0.028
0.002
0.009
0.005
0.002
Note: A child is defined as a double orphan when neither his father or his mother live in the same household. Coefficients with * are significant at the 5 percent
level. Coefficients underlined are significant at the 10 percent level. Omitted variables are: no education, agriculture, other regions than Kigali. Specification:
probits.
Source: Authors’ estimation using EICV 2000/01.
World Bank Working Paper
Table 7.4. Determinants of School Enrollment Among Children Aged 7–15, Rwanda 2000–01 (Continued)
Gender, Time Use, and Poverty in Sub-Saharan Africa
149
Table 7.5. Selected Health Indicators for Children Below 5 Years of Age,
Rwanda 2000–01
Single
parent
Double
orphan
Biparental child
in fostering family
Biparental child
in other families
0–5 Years old
Was sick in last 2 weeks
Received DTC vaccine
Received polio vaccine
Received rougeole vaccine
Received BCG vaccine
Received postnatal consultation
Had diarrhea in last 2 weeks
Receives A vitamins
Participates in nutrition program
33.9%
19.3%
24.0%
24.2%
27.0%
8.0%
20.3%
9.4%
19.9%
30.1%
13.9%
18.2%
19.8%
15.1%
8.8%
19.1%
10.1%
18.6%
35.7%
21.0%
25.4%
23.3%
36.7%
12.0%
20.5%
13.2%
28.8%
33.3%
16.9%
21.7%
24.4%
31.0%
8.1%
20.5%
10.8%
22.8%
3–59 Months old
Stunted (height for age)
Wasted (weight for height)
Underweighted (weight for age)
38.4%
8.8%
24.1%
40.4%
6.8%
23.5%
26.0%
5.2%
16.2%
40.4%
6.6%
26.6%
Note: A child is defined as a double orphan when neither his father or his mother live in the same
household.
Source: Authors’ estimation using EICV 2000/01.
home, less likely to be vaccinated, and more likely to suffer from health deficiencies. Thus,
there is clear evidence that orphans are an especially vulnerable group of children in
Rwanda.
The Government of Rwanda is aware of the plight of orphans, and policy interventions
have been set up to help them. Funding for the Genocide Fund, which was created to benefit orphans from the Genocide as well as other victims from the conflict, is substantial, but
it is unclear whether it reaches those who need help the most. The amounts in principle
disbursed by the Fund are high, at about 10 percent of total recurrent spending for primary
education, an amount also roughly similar to the total private spending on primary education in the country, including school fees. Yet, while some of this funding is supposed to
provide schooling grants for orphan children, we do not find much evidence in the data
that coverage is high.
The Government as well as NGOs are also aware that not all vulnerable children share
the same history and face the same problems, and that this calls for differentiated policy
responses. As noted in a recent Government report (MINALOC 2003), the war, the Genocide,
poverty, and HIV/AIDS have created different forms of vulnerability. Some children lost their
family and live in another household, or in special institutions or centers, or in the street.
Others are disabled or affected by HIV/AIDS, and still others have problems with the justice, are mistreated, or are victims of sexual abuse. Some vulnerable children are working,
live in an extremely poor household or are refugees. Each group faces specific problems
150
World Bank Working Paper
and programs must be designed accordingly. General strategies to help meeting the needs
of these various groups of children should also be implemented, but they are not enough
by themselves. Such general strategies include actions for sensitization of the children, their
parents and tutors, for example by promoting children’s rights and informing on the existing policies and laws. Information campaigns can also help to show the impact of
HIV/AIDS on the children. General strategies also involve building the necessary structures and human capacity to provide social protection and quality services to vulnerable
children, with good coordination mechanisms between the different actors, in order to
facilitate access for vulnerable children to basic services such as education, health, housing, income generating activities and credit (MINALOC 2003). In addition, inclusive sectoral level policy changes such as abolition of school fees may go a long way to promote
enrollment of all children including orphans.
International experience can help in designing appropriate social protection mechanisms for orphans. Given the identified risk patterns, how can further changes in policy or
programs ameliorate the observed vulnerabilities of orphans? Many questions regarding
the appropriate type of assistance and the way it should be channeled remain open. Who
should be targeted: the orphan, the fostering household, or communities? On what basis:
the level of poverty, or risks of unmet basic needs including schooling? How should the
transfer be channeled: cash or in-kind, and what would be an appropriate amount of transfer, and should transfer amount be uniform or adjusted to the needs? International experience especially in post-conflict countries such as Burundi and Eritrea suggest that
publicly funded cash transfer program should be carefully designed to avoid stigma and
adverse incentives (Subbarao and Coury 2003).
Based on this experience, and on Rwanda’s own circumstances, at least four options
seem to merit the attention of policymakers: (a) consider modifying the prevailing grant
program into a conditional cash transfer program; (b) consider the scope for geographic
targeting, using the school as the focal point for identification of eligible beneficiaries and
transfer of assistance; (c) consider the scope for fostering grants to communities rather
than directly to households; and (d) remove potential school-level barriers such as school
fees and uniforms.
One way to improve the grant program would be to make it a conditional upon all
children in the household, including fostered children, attending the school. There is now
ample evidence from both low and middle income countries that transferring small
amounts of cash to households conditional upon school attendance work, with small errors
of exclusion and inclusion and cost-effective impacts. For a review of Mexico’s PROGRESA,
see for example Wodon and others (2003).
The risk of orphans dropping out of school or engaging in paid and unpaid work is
more prevalent in urban areas than in rural areas, and in some provinces in rural areas.
Given regional variations in the risks of orphanhood, another policy option could be to
adopt a geographic targeting, or other forms of targeting. Resources could for example be
transferred to schools located in the region/area in which orphans are at most risk of dropping out of school, with the responsibility to administer the grant program. Identification
of eligible beneficiaries could then be done by a committee comprising of community leaders, school authorities, and the local government. This is along the lines of a program currently being administered in Zimbabwe. Information requirements for such a regional
approach are reasonable.
Gender, Time Use, and Poverty in Sub-Saharan Africa
151
Targeting “needy” orphans could be done based on (a) an enumeration of all needy
children within a community, and (b) a devolution to the community of the selection of
vulnerable children through some transparent process. Selection of needy children can be
done through workshops and home visits by grassroots actors with the help of external
support including prominent non-governmental agencies. In Burundi, for example, after
a census of all needy children, communities came up with four categories of children: (a)
double orphans who do not have any external support, (b) children separated from their
parents and currently living in refugee camps or camps for displaced children, (c) single
orphans that received no support from their surviving parent, and (d) double orphans living in very poor fostering households. Communities then began to prioritize and channel
assistance to the above categories ranked by the degree of vulnerability. The main advantage of this type of channeling for assistance is that it avoids stigmatization; it does not, for
instance, identify orphans by the nature of death of their parents (AIDS orphans are often
stigmatized). Often the needy children need not necessarily be orphans; in South Africa
“needy” children identified by communities turned out to be children of one important
stigmatized group: teenage mothers. This method of channeling assistance may not work
however where communities are divided along ethnic lines or if there is no community
cohesion.
In a situation where the average access to education and other services is high, but
there are differences in access between the poor and the non-poor, measures are needed at
the sectoral/school level to improve access to services. Waiving school fees and uniform
obligations has proven extremely helpful in Uganda; following this policy change, the discrimination against orphans in school enrollment has been completely wiped out in a
period of five years. Similarly in the health sector, vaccination campaigns and nutrition
supplementation programs would improve the general health of all orphans and vulnerable children.
Finally, beyond actions directly targeting orphans, it is also possible to think about the
issues in a very different way, alongside the time use approach used in this volume. It has
been argued that in at least some dimensions orphans may be better off when welcomed
by relatives or other families than when living by themselves or in institutions. However,
there are also concerns that the orphans (and especially girls) that are welcomed in some
families may be required to provide a lot of help for the domestic tasks to be performed,
with the resulting time pressure in terms of workload preventing them to benefit from the
same opportunities in education and other aspects of their development as other children.
If time is a key constraint in some of the households welcoming orphans, then policies aiming to reduce the time constraint may indirectly help orphans as well. The idea would be
to investment in programs that would reduce the burden of domestic tasks, for example
through the provision of infrastructure services (access to water and electricity) as well as
labor-saving technology, among others for food processing. Policies reducing the transport time faced by households could also help to relax their time constraint.
All these suggestions should not be construed as recommendations for the Government of Rwanda. More detailed work would be needed before making such recommendations. The above suggestions are merely options among others, but the findings from this
paper clearly suggest that something more should be done in order to better protect
orphans in Rwanda, and part of this effort could deal with the time constraints faced by
households welcoming orphans.
152
World Bank Working Paper
References
Case A., C. Paxson, and J. Ableidinger. 2002. “Orphans in Africa.” Center for Health and Wellbeing, Research Program in Development Studies, Princeton University. Processed.
Dona, G., with C. Kalinganire and F. Muramutsa. 2003. “The Rwandan Experience of
Fostering Separated Children.” Kigali. Processed.
Muller O., and N. Abbas. 1990. “The Impact of AIDS Mortality on Children’s Education
in Kampala, Uganda.” AIDS Care 2(1):77–80.
Deininger, K., M. Garcia, and K. Subbarao. 2003. “AIDS-Induced Orphanhood as a Systemic Shock: Magnitude, Impact and Program Interventions in Africa.” World Development 31(7):1201–1220.
MINALOC (Ministère de l’Administration Locale, de l’Information et des Affaires Sociales).
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Ministry of Finance. 2002. A Profile of Poverty in Rwanda. Kigali.
Nyamukapa, C., and S. Gregson. 2003. “Contrasting Primary School Outcomes of Paternal and Maternal Orphans in Manicaland, Zimbabwe: HIV/AIDS and Weaknesses in
the Extended Family System.” University of Zimbabwe. Processed.
Siaens, C., and Q. Wodon. 2003. “Determinants of Poverty in Rwanda.” The World Bank,
Washington, D.C. Processed.
Subbarao, K., and D. Coury. 2003. “Orphans in Sub Saharan Countries: A Framework for
Public Action.” The World Bank, Washington, D.C. Processed.
UNICEF and UNAIDS. 2003. “UNICEF and UNAIDS Applaud Milestone in Forging
Coordinated Global Response to Growing Crisis of Children Orphaned Due to AIDS.”
Press Release of October 21. Geneva.
Wodon, Q., B. de la Briere, C. Siaens, and S. Yitzhaki. 2003. “The Impact of Public Transfers on Inequality and Social Welfare: Comparing Mexico’s PROGRESA to Other Government Programs.” Research on Economic Inequality 10:147–171.
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Gender, Time Use, and Poverty in Sub-Saharan Africa is part of the
World Bank Working Paper series. These papers are published to
communicate the results of the Bank’s ongoing research and to
stimulate public discussion.
The papers in this volume examine the links between gender, time
use, and poverty in Sub-Saharan Africa. They contribute to a
broader definition of poverty to include “time poverty,” and to a
broader definition of work to include household work. The papers
present a conceptual framework linking both market and household work, review some of the available literature and surveys on
time use in Africa, and use tools and approaches drawn from analysis of consumption-based poverty to develop the concept of a time
poverty line and to examine linkages between time poverty, consumption poverty, and other dimensions of development in Africa
such as education and child labor.
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