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Children’s Work, Study and Leisure Time in Five Countries:
Implications for Human Capital Accumulation April 2010 version
Cem Mete1
I. Introduction
In this study, time diary data are used to document and interpret cross-country differences in time-use
trends of children in a high-income country (UK), three middle-income countries (Estonia, Hungary and
Romania) and one low-income country (Pakistan). There are many reasons one might be interested in
studying time-use data, for example to better account for the value of home production, non-market
activities and the role of informal sector in the economy.2 The focus of this particular paper, however, is
the potential human capital implications of children’s time use patterns.
The development economics literature has paid ample attention to the fact that many developing countries
do not invest adequately on education and the existing public investments are not always put to good use.
In turn, low school enrollments/attendance as well as subpar learning outcomes in developing countries
are linked to the shortcomings in the schooling environment, while the household environment is treated
as a blackbox without much knowledge of its internal workings.
While the ongoing emphasis on schooling inputs is certainly well-deserved, it is also important to analyze
the ways in which children in developing countries are different from their industrialized country
counterparts when it comes to allocating their time among various activities, since there might be
opportunities to improve children’s human capital accumulation by improving the targeting of most
vulnerable children and also by providing the right incentive structures in programs that aim to increase
the educational attainment of children.
The activities that are worth considering in this context include learning as well as leisure and work.
Time spent on learning is best viewed as a long term investment in human capital, although the efficiency
of time spent on learning would depend on, among other things, the quality of the supply of education and
parental-support/adult-supervision at home. Some of the activities that are included under the leisure
category can be viewed as complements to more standard types of learning, which include time spent on
sports and listening music. For example, using U.S. data Hofferth and Sandberg (2001) find that learning
activities such as reading for pleasure as well as structured time spent on sports and social activities are
associated with higher achievement. Yet other components of leisure time — such as dance/party,
visiting friends, relaxing, conversation, entertaining friends — emerge as a gray area. Socializing is
1 The findings, interpretations and conclusions expressed in this paper are entirely those of the author and do not necessarily
represent the views of The World Bank, its Executive Directors or the countries they represent. The author thanks Stefania
Cnobloch and Denis Nikitin for data analysis support. 2 Juster and Stafford 1991 provides a good outline of research questions of interest. Even though this particular review is dated,
only recently large-scale diary based time use survey data are becoming available for developing countries and thus many of the
research questions raised remain relevant.
2
certainly desirable in itself and it is an important part of a child’s personality development but some of the
time categorized as leisure may also reflect idle time. To the extent that excessive amounts of child/youth
idle time leads to undesirable behavior, it can be a concern for policy makers that go beyond the
opportunity costs of not effectively using a scarce resource to build human capital. Here we present
empirical trends for time allocated to leisure, but the main focus remains on learning and child work.
In this paper the definition of child-work is quite comprehensive in the sense that it not only includes
formal work or employment in the manufacturing sector but also agriculture work and other work at
home, as described by section 3 below. It is true that even if one adopts more restrictive definitions, the
prevalence of child-labor is widespread especially in low-income countries. ILO estimates that roughly
2.5 million children aged 5-17 are economically active in the developed economies, 2.4 million in the
transition countries, 127.3 million in Asia and Pacific, 17.4 million in Latin America and the Caribbean,
48 million in Sub-Saharan Africa and 13.4 million in the Middle East and North Africa. In Pakistan
agriculture sector, carpet and various manufacturing industries employ many children (UNICEF 1992a,
1992b). Similarly, although to a lesser extent, child labor is a serious concern for transition countries,
with most attention focused on rural child labor and street children (ILO 2005). Focusing on specific
sectors and pre-defined occupations would result in significant underestimation of the prevalence of child
labor, however. As Edmonds (2007) observes, academic studies of child labor are better viewed as child
time allocation studies and that research must consider a wide scope of activities.
This paper’s key objectives are descriptive in nature. The questions of interest include (i) how do
children’s time use patterns vary among countries with different GDP per capita?; (ii) are there groups of
children where the time-use differences are particularly large across countries?; (iii) to what extent do
children who are more likely to undertake one type of activity (e.g., market or domestic work) are also
more likely to undertake another (e.g., learning). The next section provides the context by briefly
summarizing selected economic and social indicators for the five countries selected for this study.
Section 3 describes the main features of the data sets used. Section 4 outlines the empirical model and the
results. The last section concludes by highlighting the areas in which selected education and social safety
net programs might be strengthened to better target the most vulnerable children.
II. Cross-Country Trends in Time Use
Even though availability of comparable raw time use survey data files is the main inclusion criteria for
countries considered by this paper, this group of countries also happens to cover a wide range in terms of
GDP per capita and expected years of schooling. At the higher end of spectrum, UK’s GDP per capita is
$32,690 and expected years of schooling is 17 for females and 16 for males. Hungary and Estonia come
next, with a GDP per capita of $16,955 for Hungary ($16,548 for Estonia), 16 (17 for Estonia) years of
expected schooling for females and 15 years of expected schooling for males in both countries.
Romania’s GDP per capita is $8,789 and expected years of schooling is 14 for females and 13 for males.
Finally, at the other end of the spectrum, Pakistan’s GDP per capita is $2,184 and expected years of
schooling is only 6 for females and 7 for males.3
Certain time use trends for children and youth are quite robust across countries. These include boys
spending more time in market work and girls spending more time in domestic work, and increased time
3 Source is the World Development Indicators Database. GDP per capita figures are PPP 2005 International Dollars. Expected
years of schooling is the number of years a child of school entrance age is expect to spend at school, or university, including
years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary
and tertiary education.
3
spent on work as a child ages (Hsin 2009). However, it is difficult to make sweeping hypotheses on, for
example, the gender differences in time use without taking into account the country context.
Table 1 shows that time spent on domestic work is highest and time spent on learning is lowest in the two
countries with lowest GDP per capita in our sample: Romania and Pakistan. In Pakistan, the amount of
time boys spend on market work (an average of 155 minutes per day) stands out as being considerably
higher than the corresponding statistics for other countries. In UK too the time spent on market work is
quite high at an average of 91 minutes per day, but the same table reveals that this is driven by the
employment trends of older children (ages 15-19). Table 1 also shows that boys between ages 10 to 14 in
Pakistan allocate three times more time to market work (at 63 minutes on average) compared to the
closest follower, Romania. In all five countries girls spend more time on domestic work than boys, but
the gender differences are more significant for the two lower GDP per capita countries. Furthermore,
Appendix Tables A1 to A4 provide further descriptives, among other things revealing that the differences
in the time use of rural girls and urban girls are more pronounced in Romania and Pakistan.4
Table 1. Average time spent on various activities (minutes) by 10 to 19 year olds.
Market Work
Domestic Work Learn Leisure
Girls Estonia 33 92 363 285
Hungary 34 68 446 210
Pakistan 63 167 181 212
Romania 46 141 236 287
UK 57 70 325 312
Boys Estonia 35 74 359 315
Hungary 32 52 438 242
Pakistan 155 17 238 264
Romania 75 92 227 322
UK 91 46 304 339
Rural Estonia 33 94 341 313
Hungary 35 84 404 239
Pakistan 130 101 184 229
Romania 107 162 193 264
Urban Estonia 35 76 372 295
Hungary 32 48 462 219
Pakistan 78 74 252 254
Romania 26 80 260 337
4 Since in Pakistan prevalence of child labor in market and domestic work is quite high, it is useful to briefly mention the main
laws that regulate the employment of children: The legal minimum age for employment is 14 for shops and commerce, industry,
and work at sea, and 15 for mines and on railways. The constitution prohibits slavery, forced labor, the trafficking in human
beings, and employment of children below the age of 14 years in any factory or mine or any hazardous employment. The Bonded
Labor (Abolition) Act declares all customs, traditions, practices, contracts or agreements concerning bonded labor, whether
entered into or in operation before or after the effective date of the legislation, void and inoperative. The Employment of Children
Act 1991 prohibits the employment of children in certain occupations and regulates their conditions of work. No child is allowed
to work over-time or during the night. An earlier law prohibited the employment of children in the following industries: bidi
(cigarette) making; carpet making; cement manufacturing (including bagging of cement); cloth dyeing, printing, and weaving;
manufacturing of matches, explosives, and fireworks; mica cutting and splitting; shellac manufacture; soap manufacture; tanning;
and wood cleaning. The 1991 law added the following industries: shoe-making, leather, power looms, fishing, glass, garments, precious stones, metal and wood handicrafts, furniture, and paper.
4
Age 10-14 Estonia 5 66 383 303
Hungary 18 52 432 223
Pakistan 63 57 275 247
Romania 21 79 277 298
UK 14 52 368 319
Age 15-19 Estonia 58 97 341 300
Hungary 44 66 449 228
Pakistan 161 127 139 229
Romania 99 149 189 312
UK 144 65 251 333
III. Data
The analysis of time use trends to date have focused disproportionately on industrialized countries
primarily because in developing countries few nationally representative time use surveys have been
implemented using a methodology that is similar to the harmonization approach advocated by EuroStat
and others (EuroStat 2004). Restrictions on the distribution of raw data files compound the problem. As a
result, to our knowledge this is the first time TUS data are used to model the time allocation of children in
Romania and Pakistan. For comparison purposes, this paper also relies on three higher-income country
TUS data: UK, Estonia and Hungary.
The 2007 Pakistan Time Use Survey is the first national representative, time-diary based data collection
effort, implemented by the Federal Bureau of Statistics. The sample size is 19,600 households. The
survey covers all urban and rural areas of the four provinces of Pakistan defined as such by 1998
Population Census excluding Federally Administered Tribal Areas (FATA) and certain administrative
areas of North West Frontier Province (NWFP). The population of geographic areas excluded from the
survey constitutes about 2 percent of the total population as enumerated in 1998 Population Census. 24-
hour diaries, divided into 30 minute slots, were used. The main respondent for the household part of the
questionnaire is an adult member of the household who is likely to know the answers to all the questions.
For the demographic questions and time use diaries, two persons aged ten years or over are selected per
household. Female enumerators were hired and deployed for data collection throughout the country to
interview females. Also, due to socio-cultural characteristics of NWFP and because of the prevailing
situation of law and order, in order to ensure successful interviewing of females in most of the cases the
interviews with young females were conducted in the presence of elders and well informed person(s) of
the household.
The 2000 Romania Time Use Survey was also the first of its kind, implemented by the National
Commission for Statistics. The sample size is 7,607 households. The household head, spouse or another
adult member is interviewed for the household questionnaire, while the individual questionnaire and the
diaries are completed by all the household members aged 10 or older. A nationally representative sample
was drawn from the 1992 Population and Housing Census data.
The 1999/2000 Estonia Time Use Survey has a sample size of 2,581 households. The household head,
spouse or other adult member are interviewed for the household questionnaire, while the individual
questionnaire and the diaries are completed by all the household members aged 10 or older. A nationally
representative sample was drawn from the 1999 Population Database.
5
The 1999/2000 Hungary Time Use Survey has a sample size of 3,227 households. Each household was
visited 4 times a year, although the complete time-use diary was collected for those who are 10+ only in
round 3 (otherwise the information is collected for those who are 15 years old or more) and thus this
paper uses the second round data of the Hungary TUS. The household head, spouse or another adult
member is interviewed for the household questionnaire, while the individual questionnaire and the diaries
are completed by all the household members aged 10 or older. A nationally representative sample was
drawn from the 1996 Microcensus.
The 2001 UK Time Use Survey has a sample size of 6,414 households. The household questionnaire was
completed by the household head or his/her spouse, while individual questionnaire and the diaries are
completed by all household members aged 8 and older. A nationally representative sample was drawn
from the 2001 Census.
For the empirical analysis that follows, four mutually exclusive categories of time use are constructed:
market work, domestic work, learning and leisure. Market work includes formal work, paid work at
home, second job and time spent traveling to work (MTUS categories a1,a2, a3 plus time spent traveling
to work). Domestic work includes cooking/washing-up, housework, odd jobs, gardening, shopping and
childcare (MTUS categories av6 to av12). Learning includes time spent on school/classes, study, reading
books, reading papers/magazines and travel to school/study (MTUS categories av4, av33, av34, av35 and
travel to school/study). Leisure includes time spent on leisure travel, excursions/trips, playing sport,
watching sport, walks, church/mosque, civic organizations, cinema/theatre, dance/party, social clubs,
visiting friends, listening to radio, watching TV, listening to music, relaxing, conversation, entertaining
friends, knitting/sewing and pastimes/hobbies (MTUS categories av17 to av26, av29 to av32, av36 to
av40). Sleep time is not included here to facilitate comparisons to other papers that estimate similar
models to analyze industrialized country data.
IV. Empirical Analysis
A useful heuristic model to illustrate the key concepts of interest is offered by Edmonds (2007), which is
briefly summarized below.
maxE,P,M,H
𝑢(𝐹 𝑌 + 𝑤𝑀 − 𝑒𝐸, 𝐻 , 𝑅 𝐸, 𝑃 )
𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐸 + 𝑃 + 𝑀 + 𝐻 = 1, 𝐸 ≥ 0, 𝑃 ≥ 0, 𝑀 ≥ 0, 𝐻 ≥ 0
where Y is income from parents’ labor supply, E is education and e is direct schooling costs, P is leisure
and play, M is work outside of household at wage w, H is value obtained from the input of child’s time.
Thus the first component of the utility function considers purchased inputs and also input of child’s time,
while the second component of the utility function captures the value attached to child’s future welfare
which is a function of time allocated to education and play/leisure.
It is useful to highlight two implications of this framework here. Labor market conditions (through
parental earnings and wage rates that apply to child work) are explicitly part of the model. Indeed, not
6
only adult wages are likely to have an impact on child labor but also the sectoral distribution of labor, the
skilled versus unskilled labor supply mix, unemployment and underemployment rates etc. It is possible to
further model such relationships, for example allowing child labor to be a substitute to unskilled adult
labor (Doepke and Zilibotti 2005).
Also, education quality is implicitly included since the value that parents attach to children’s time spent
on learning will depend on the quality of schools. However, as we estimate reduced form time-use
models for five countries, we opt to work with comparable specifications and since this information is
available for only Romania and Pakistan (through merging of TUS data files with locality data for
Romania; and merging of TUS data with school census and LFS data for Pakistan) labor market and
school quality indicators are not included in the models presented next. However, note that even though
these indicators have statistically significant and sizable effects at the expected direction, their inclusion
in the model does not change the main results for the remaining explanatory variables for Romania and
Pakistan.
The reduced form equations that we estimate are:
𝑇𝑖 = 𝛽𝑖𝑋 + 𝜀𝑖
where T1, T2, T3 and T4 are time spent on market work, domestic work, learning and leisure respectively.
The vector of explanatory variables, X, include household head’s age and its square, household head’s
gender, an asset index as a proxy for household wealth, urban residence dummy, percentage of children in
the household, child’s gender, age and its square.
Multivariate tobit models are used for the estimation, because not all children report spending time on
three of the four categories of interest (all observations have non-zero time values for the leisure
category). This approach also allows each equation’s error term to be correlated with other error terms,
which provides useful information on the extent to which children who spend time on one activity are
more (or less) likely to spend time on another activity, after taking into account the effects of explanatory
variables X. Table 2 presents the results. Separate models for females and males are also presented in
Appendix tables A5 and A6 respectively.
7
Table 2. Multivariate tobit estimates of the determinants of time spent on market work, domestic work, learning and leisure.
Variable Estonia Hungary Romania UK Pakistan
MARKET WORK HH age -63.143*** 13.818*** -15.199*** 1.36 1.605
HH agesq 0.587*** -0.155*** 0.142** -0.075 -0.005
HH Head Female -80.63 -10.687 -40.987
-75.927***
Gender (male=1) -31.908 9.084 82.009*** 47.917*** 199.240***
Percent Children 0.895 -0.024 1.059 -0.119 1.250***
Asset Index 729.643** -92.904** 29.682 50.977 -770.508***
Residence (urban=1) -39.051 18.299 -190.855***
-102.785***
Age -29.18 -90.079*** -166.945*** -184.690*** 94.505***
Agesq 5.851 3.354*** 7.093*** 8.463*** -1.228
_cons -674.881 267.648 1075.991*** 779.751*** -1201.777***
DOMESTIC WORK HH age -4.150* -11.330** -0.861 -0.793 -4.523***
HH agesq 0.072*** 0.114** 0.008 0.01 0.036**
HH Head Female 11.192 -8.675 11.998
-9.064
Gender (male=1) -36.271*** -33.605*** -77.895*** -37.335*** -298.123***
Percent Children 0.700** 0.642*** -0.091 -0.589** 0.520***
Asset Index -55.501* -16.327 -160.168*** 20.906 -143.792***
Residence (urban=1) -15.882 -47.795*** -97.777***
-20.683***
Age -46.606* -50.262** 33.926 38.140** 65.855***
Agesq 1.963** 1.910*** -0.556 -1.274** -1.370***
_cons 387.604** 644.175*** -119.631 -190.167 -363.397***
LEARNING HH age 21.033*** 14.371** -2.433 -10.38 3.786
HH agesq -0.211*** -0.141* 0.02 0.18 -0.028
HH Head Female -4.467 -88.794*** 16.894
76.102***
Gender (male=1) -7.312 -14.566 -15.187 -28.597** 94.158***
Percent Children -1.285* -0.235 0.417 0.282 -0.588*
Asset Index 25.153 108.441** 414.940*** 345.648*** 638.946***
Residence (urban=1) 23.67 64.406*** 94.408***
50.030***
Age 229.830*** 125.957*** 137.606*** 203.507*** 55.979***
Agesq -8.297*** -4.340*** -5.707*** -8.316*** -3.977***
_cons -1644.736*** -857.391*** -688.666*** -1016.202*** -181.217
LEISURE--------------- HH age 5.120* -5.588 8.037*** 12.330** 1.139
HH agesq -0.070** 0.068 -0.061** -0.143** -0.014*
HH Head Female 4.85 53.138** -0.061
-5.431
Gender (male=1) 31.097*** 36.515*** 33.588*** 28.155*** 49.461***
Percent Children -0.153 0.011 -0.706** 0.066 -0.269**
Asset Index -43.693 -30.932 -92.917*** -175.313*** 84.923***
Residence (urban=1) -18.123 -20.764* 79.623***
10.604**
Age -50.784* 38.629 18.087 16.63 -27.336***
Agesq 1.668* -1.281 -0.701 -0.476 0.768**
_cons 595.636*** 59.614 -29.228 56.789 406.647***
8
Household head’s characteristics are potentially important determinants of children’s time use due to
his/her role in household decision making. Older household heads may have different perceptions and
preferences regarding the value of children’s work, play and learning. Female headed households are
often considered “vulnerable” for the design of social safety net programs, even though in many countries
there is no correlation between household head’s gender and poverty (see, for example, World Bank
2003).5 Still, it could be that such households are more likely to fall into poverty when faced with an
unexpected economic or other shock (e.g. sickness in the family). Or, it could be that children in female
headed households have to assume additional responsibilities and thus their education suffers. Yet this
hypothesis is far from certain, since when females’ decision making power increases households tend to
invest more in children’s education. Which effect dominates?
While there are no robust effects of household head’s age on children’s time use allocation, children in
female headed households are much less involved in market work in all five countries considered. The
positive impact on time devoted to learning is only visible in the two lower GDP per-capita countries.
Children in households with a female head spend on average 76 minutes more on learning in Pakistan
(statistically significant at 1 percent level) and 16 minutes more in Romania although the latter effect is
not statistically significant at 10 percent level. The learning-time effect of a female household head is
even more significant for girls in Pakistan, estimated to be 95 minutes (Appendix Table A5).
Household wealth is expected to be positively correlated with learning and negatively correlated with
child labor. Basu and Kan (1998) argue that sending children out to work tends to be an act of
desperation by parents and thus child labor is rare in well-off households.6 Using panel data from
Vietnam, Edmons (2005) finds that improvements in per capita expenditure explain 80 percent of the
decline in child labor that occurs in households whose expenditures improve enough to move out of
poverty. Pakistan case fits perfectly to this storyline: the effect is statistically significant at 1 percent level
and very large in magnitude in that basically children of wealthier households do not carry out market
work, they are also much less likely to do domestic work and tend to spend more time on learning.
Furthermore, the impact of household wealth on learning-time is even larger for girls (Appendix Table
A5). For other countries the estimated coefficients are not always statistically significant at 10 percent
level and the signs of the coefficients are mixed, with one exception: the children coming from wealthier
households in Estonia are more likely to spend time on market work. Referring to descriptive tables in the
previous section, we see that only those between ages 15 to 19 tend to carry out market work in Estonia
(with 4 minutes per day average for children between ages 10 and 14). Thus what is captured here for
Estonia seems to be school to work transition for the youth.
A priori, children residing in rural areas can be expected to spend more time working, because
agriculture work often relies on their contributions (Grootaert and Kanbur 1995). Lack of domestic
5 Two possible reasons for the lack of a correlation between household head’s gender and poverty are remittances (e.g., husband
may work at another city at a job that pays better than what he would earn in the local labor market); and household formation
decisions (more specifically, females who are truly destitute may not be able to maintain a separate household, instead they may
live with relatives in an extended family co-residence arrangement). 6 The authors go on to demonstrate the conditions for a multiple equilibria that would justify a total ban on child labor, which
would lead to labor shortages at first followed by increased adult wages ---- which in turn could make the ban unnecessary due to
the positive impact of increased adult wages on household welfare.
9
infrastructure in low-income country setup might also create a disproportionate burden on girls’ time use
especially in rural areas (Desai and Jain 1994). For Pakistan, security concerns and unwillingness of
conservative parents to send their daughters to schools that are not nearby or to schools that do not have
female teaching staff may also lead to less time spent on learning for girls (and more time spent on other
time use categories including, in theory, leisure). Indeed, all statistically significant estimates in work-
equations reveal that children work more in rural areas with particularly large effects seen in the two
lower GDP per-capita countries in our sample. After controlling for other explanatory variables, rural
children spend on average 190 minutes more per day on market work in Romania compared to urban
children. The corresponding estimate for Pakistan is 102 minutes. Appendix Tables A5 and A6 reveal
that the rural residence effect is more pronounced and robust for the female sample, with significantly
more time allocated to market and domestic work by rural females in Pakistan, Romania and Estonia.
Percentage of children in a household is likely to be correlated positively with work and negatively with
learning.7 Indeed, using panel data from Pakistan, Lloyd, Mete and Grant (2009) show that the single
most important “shock” that negatively influences female children’s schooling prospects is the birth of a
sibling in households that had revealed “no other child” preference during the wave-1 survey.8 The
estimation results in Table 2 reveal that this indicator is not a very strong predictor of children’s time use
in countries that are well advanced in terms of demographic transition: all countries other than Pakistan in
our case. For Pakistan, however, increases in number of children are associated with more market and
domestic work, less learning and less leisure. Models that are separately estimated for females and males
(Appendix Tables A5 and A6) reveal that, as expected, this trend is driven by the girls sample. For boys
too, increased number of children in the household results in more time spent on market work, but none of
the other effects are statistically significant at 10 percent level.
Since the “percentage of children” effect is likely to capture in part child-care demands in Pakistan, it is
useful to focus explicitly on children’s time spent on childcare to highlight the gender differences. Only
3.5 percent of male children reported non-zero child-care hours, while 17.4 percent of female children did
so. Furthermore, Figure 1 reveals that male children who did take care of other children put in much less
minutes compared to females.
7 Montgomery, Arends and Mete (2000) provide a cross-country empirical analysis of the implications of the quantity-quality
transition. 8 The authors also show that an unexpected discontinuation of remittances from abroad has a negative impact on boys’ schooling
prospects, increasing the likelihood that they will drop out of school and work. Similarly Mete, Ni and Scott (2008) use panel
data to show that unexpected shocks to household head’s health in Bosnia increases male children’s school dropout chances by
14 percentage points (with no impact on female children’s schooling).
10
Figure 1. Children’s time spent on childcare in Pakistan (conditional on reporting non-zero hours).
Kernel density estimates using Epanechnikov function.
Children’s age and gender are typically key predictors of time use, with gender differences becoming
more pronounced by age in industrialized countries (Hofferth and Sandberg 2001). While such trends
are not well documented in developing countries due to scarcity of nationally representative time-diary
data, the same trend is likely to prevail especially in rural areas and for conservative societies where
females have less access to employment and schooling opportunities as they become teenagers. Table 2
reveals that males in UK and Pakistan more likely to spend time on market work with estimated
coefficients being statistically significant at 1 percent level. However, the magnitude of the coefficient is
much larger for Pakistan and the UK trend is driven by the 15 to 19 years group. Male children are less
likely to spend time carrying out domestic activities in all five countries considered here, once again the
gender difference is much more pronounced in Pakistan. In Estonia, Hungary, Romania and UK females
spend more time on learning but in Pakistan males spend more time on learning. This trend is consistent
with the gender specific expected-schooling statistics for each country that has been discussed previously.
Finally, in all five countries considered males spend more time on leisure activities: all coefficient
estimates are statistically significant at 1 percent level and the gender effect ranges from an average of 28
minutes per day (in UK) to 49 minutes per day (in Pakistan).
In order to clarify the age effects, Figures 2 plots estimated probability of participating in market work,
domestic work and learning by gender and age, when all other explanatory variables are at their sample
means. Similarly, Figure 3 plots estimated time spent on the same categories. The estimates for leisure
are not included here, since they tend to be quite flat and clutter the presentation. We do not dwell here
on the country-by-country details. Among the more interesting trends by age are how soon the decrease
in learning-hours starts in Pakistan (right after age 10) and the robust sharp decrease that follows. Time
spent on learning remains consistently high in Hungary, also pretty high in Estonia and UK up until age
17 — after age 17 time spent on market work sharply increases in UK.
0
.00
5.0
1.0
15
0 200 400 600 0 200 400 600
Male Children (10 to 19) Female Children (10 to 19)
Tim
e S
pen
t on
Child
Care
Minutes per Day
11
Figure 2. Estimated probability of participation in market work, domestic work and learning.
Figure 3. Estimated time spent participating in market work, domestic work and learning.
0.1
.2.3
.4.5
.6.7
.8.9
0.1
.2.3
.4.5
.6.7
.8.9
10 11 12 13 14 15 16 17 18 19
10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19
Estonia Hungary Pakistan
Romania UK
Girls Econ Girls Non-Econ
Girls Learn Boys Econ
Boys Non-econ Boys Learn
Pre
dic
ted
pro
bab
ility
Age in years
01
23
45
67
89
10
01
23
45
67
89
10
10 11 12 13 14 15 16 17 18 19
10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19
Estonia Hungary Pakistan
Romania UK
Girls Econ Girls Non-Econ
Girls Learn Boys Econ
Boys Non-econ Boys Learn
Pre
dic
ted
tim
e s
pen
t
Age in years
12
Household head’s education is not included as an explanatory variable in the models presented here,
because the Pakistan TUS does not contain this information. However, for the remainder of the countries,
children who live in households with a more educated household head are less involved in market work as
well as domestic work. At the same time, household head’s education significantly increases children’s
time spent on learning. The impact of household head’s education on leisure is mixed. The inclusion of
the household head’s education variable does not invalidate the effects of other explanatory variables
discussed previously (results of alternative specifications are available from the author upon request).
Finally, Table 3 provides the correlation coefficients among the error terms of the four tobit time use
equations that are estimated, to illustrate the extent to which children who spend time on one activity
(e.g., market work) are more likely to spend time on another activity (e.g., learning), after taking into
account the effects of explanatory variables. A robust finding that emerges from this analysis is that
other than the relationship between domestic work and leisure, a child who spends more time on any one
of the activities is less likely to spend time on another activity. In particular, those who work (either
market or domestic) are significantly less likely to spend time on learning.
Table 3. Correlation matrix of the multivariate tobit estimation error terms.
Correlation coefficient Estonia Hungary Romania UK Pakistan
Ρmarketw_domesticw -0.036 -0.132*** -0.223*** -0.162*** -0.082***
Ρmarketw_learning -0.174*** -0.274*** -0.205*** -0.278*** -0.557***
Ρmarketw_leisure -0.083 -0.114 -0.231 -0.231 -0.236
Ρdomesticw_learning -0.594*** -0.470*** -0.410*** -0.331*** -0.379***
Ρdomesticw_leisure 0.071** 0.042 -0.052* 0.052* -0.064***
Ρlearning_leisure -0.785*** -0.799*** -0.737*** -0.769*** -0.361***
*** Statistically significant at 1 percent level; ** Statistically significant at 5 percent level; * Statistically significant at 10 percent level;
13
V. Conclusions
The cross-country comparative time use analysis reported here does not attempt to single out causal
relationships that might be relevant for the design of poverty reduction programs. However, when jointly
interpreted with the insights from other studies that do reveal certain causal effects but do not benefit
from time use survey data for a detailed look inside the household, some policy relevant insights emerge.
We find that gender is a more important predictor of a child’s time use patterns in lower-income
countries. This issue is important because of its implications for public policies that aim to provide equal
education opportunities to all children. More specifically, in countries where female children face
significant disadvantages in their home environment, the policy makers may aim to compensate for such
disadvantages by investing more in females’ schooling. For example in Pakistan where single-sex
schooling is the norm, the implication would be for the Government to invest more (and not less, as is
currently the case) to schools that serve females. This would be a complement to the existing/better-
documented arguments for investing more in female children’s schooling, which have to do with marginal
returns to girls’ schooling, the correlations between mother’s education and the health and schooling of
children, and the significant impact of schooling on labor force participation of women (Schultz 2001).
Poor families, especially those who reside in rural areas, rely much more on children’s market and
domestic work than previously captured by direct employment questions in standard LSMS type
household surveys. This trend seems to be much more visible in countries with low GDP per capita,
although this assessment of five country cases can only be suggestive. Even though child labor might be
prohibited by legislation, in practice the impact of legislation tends to be limited to only certain industries
(Tazeen 2007). Such high reliance on child labor in developing countries — combined credit constraints
and uncertain or underestimated returns to schooling by poor parents— implies that increasing the
quality and availability of schools may not be sufficient to ensure that poor children actually attend
school. Instead, demand for education may also be strengthened by conditional cash transfers programs
that provide different benefits to different groups of children. This research suggests it would be
particularly important to differentiate between not only males and females but also urban and rural
children while designing such demand-side policy interventions.
More broadly, this analysis provided additional information to better target vulnerable children in addition
to consumption or asset index based household wealth proxies. Among the more interesting findings is
that children from female-headed households do not seem to be disadvantaged in terms of time devoted to
learning. Since the empirical models already take into account the impact of household wealth on time
use allocation, the estimated female-headed household coefficients may be capturing increased value
attached to children’s education by female adults who are in a decision making role. If so, innovative
interventions that empower adult females (including the increasingly popular approach of providing social
safety net cash benefits to females) can have significant positive “side-effects”. We also find that
household size is a key predictor of children’s (especially female children’s) time use trends in Pakistan
but not in any of the other four countries that are more advanced in terms of demographic transition.
This is consistent with the panel data evidence that links unwanted fertility to lower schooling outcomes
of female children in Pakistan (Lloyd, Mete and Grant 2009), and provides yet another line of argument
for the potential payoffs to actively facilitating the developing countries’ demographic journey to less but
14
better educated children and smaller household sizes — rather than waiting for economic growth alone to
resolve the issue through its impact on fertility desires.
Even though the increasing availability of nationally representative time use survey data in developing
countries provide valuable insights to our understanding of the way households function, there are clear
ways in which future data collection efforts can be improved. There might be payoffs to the collection of
time use information in a panel survey setup, which is likely to reveal how different households cope with
unexpected economic and other shocks. Even at a more basic level, one might try to design Time Use
Surveys so that can be linked to other survey data (perhaps at the community level) that contain
information on, among other things, education environment (e.g., school characteristics, cognitive
assessments of children) and labor environment (prevailing unemployment rates, job search patterns,
employer surveys etc).
15
References
Basu, Kaushik and Pham Hoang Van. 1998. The Economics of Child Labor. The American Economic
Review, Vol.88(3), pp. 412-427.
Cardoso, Ana Rute, Elsa Fontainha and Chiara Monfardini. 2008. Children and Parents Time Use:
Empirical Evidence on Investments in Human Capital in France, Italy and Germany. IZA Discussion
Paper No. 3815.
Desai, S. and Jain, D. 1994. Maternal Employment and Changes in the Family Dynamics: The Social
Context of Women’s Work in Rural South India. Population and Development Review, 20, 115-136.
Doepke, Matthias and Fabrizio Zilibotti. 2005. The Macroeconomics of Child Labor Regulation. The
American Economic Review, Vol. 95(5), pp. 1492-1524.
Edmons, Eric V. 2005. Does Child Labor Decline with Improving Economic Status? The Journal of
Human Resources Vol20(1), pp. 77-99.
Edmons, Eric V. 2007. Child Labor. In T.P. Schultz and J. Strauss, eds., Handbook of Development
Economics Vol(4), Elsevier Science, Amsterdam, Holland.
EUROSTAT. 2004. Guidelines on Harmonized European Time Use Surveys. Office for Official
Publications of the European Communities, Luxemburg.
Fasih, Tazeen. 2007. Analyzing the Impact of Legislation on Child Labor in Pakistan. World Bank
Policy Research Working Paper No. 4399.
Grootaert, C. and Kanbur, R. 1995. Child Labour: An Economic Perspective. International Labor Review,
134.
Hofferth, Sandra L. and John F. Sandberg. 2001. How American Children Spend Their Time. Journal of
Marriage and Family Vol. 63(2), pp. 295-308.
Hsin, Amy. 2009. Children’s Time Use: Labor Divisions and Schooling in Indonesia. Unpublished
manuscript, UCLA.
ILO. 2005. Child Labour in Europe. International Labour Organization, paper posted at
http://digitalcommons.ilr.cornell.edu/child/11.
Juster and Stafford. 1991. The Allocation of Time: Empirical Findings, Behavioral Models, and Problems
of Measurement. Journal of Economic Literature Vol.29, pp. 471-522.
16
Lloyd, Cynthia; Cem Mete and Monica Grant. 2009. The Implications of Changing Educational and
Family Circumstances for Children’s Grade Progression in Rural Pakistan. Economics of Education
Review Vol 28(1).
Lloyd, Cynthia; Cem Mete and Zeba Sathar. 2005. The Effects of Gender Differences in Primary School
Access, Type and Quality on the Decision to Enroll in Rural Pakistan. Economic Development and
Cultural Change Vol 53(3).
Mete, Cem, Huan Ni and Kinnon Scott. 2008. The Impact of Health Shocks on Employment, Earnings
and Household Consumption in Bosnia and Herzegovina. In Cem Mete eds. Economic Implications of
Chronic Illness and Disability. World Bank, Washington DC.
Mete, Cem. 2004. The Inequality Implications of Highly Selective Promotion Practices. Economics of
Education Review Vol 23(3), 301-314.
Montgomery, Mark R. Mary Arends and Cem Mete. 2000. The Quantity Quality Transition in Asia.
Population and Development Review sV(26).
Sathar, Zeba; Cynthia Lloyd and Cem Mete. 2003. Schooling Opportunities for Girls as a Stimulus for
Fertility Change in Rural Pakistan. Economic Development and Cultural Change Vol 51(3), 677-698.
Schultz, Paul T. 2002. Why Governments Should Invest More to Educate Girls. World Development Vol.
30(2), pp. 207-225.
UNICEF. 1992a. Situation Analysis of Children and Women in Pakistan, UNICEF, Islamabad, Pakistan.
UNICEF. 1992b. Child Labor in the Carpet Weaving Industry in Punjab, UNICEF, Islamabad, Pakistan.
World Bank. 2003. Romania Poverty Assessment. Washington, DC.
17
Appendix
Table A1. Average time spent in each type of activity (minutes) by 10 to 19 year olds
Market
Work
Domestic
Work Learn Leisure
Rural Girls Estonia 34 109 345 293
Hungary 31 96 408 217
Pakistan 85 187 146 202
Romania 79 187 210 249
Urban Girls Estonia 33 83 374 280
Hungary 36 53 467 206
Pakistan 26 134 237 229
Romania 22 106 255 315
Rural Boys Estonia 32 81 337 330
Hungary 39 71 400 261
Pakistan 173 16 221 255
Romania 133 140 178 277
Urban Boys Estonia 36 71 371 307
Hungary 28 42 458 232
Pakistan 128 17 266 278
Romania 30 55 265 357
Table A2. Average time spent in each type of activity (minutes) by 10 to 19 year olds
Market
Work
Domestic
Work Learn Leisure
Girls 10-14 Estonia 7 71 387 288
Hungary 20 55 441 202
Pakistan 43 106 248 223
Romania 16 91 282 287
UK 14 59 386 298
Girls 15-19 Estonia 56 111 342 281
Hungary 43 77 449 214
Pakistan 83 228 112 201
Romania 76 190 191 288
UK 109 84 252 328
Boys 10-14 Estonia 3 62 380 316
Hungary 16 49 424 241
Pakistan 81 13 299 269
Romania 26 67 271 310
UK 15 46 350 340
Girls 15-19 Estonia 60 84 341 315
Hungary 45 55 449 242
Pakistan 242 21 167 257
Romania 118 114 188 333
UK 178 46 250 337
18
Table A3. Average time spent in each type of activity (minutes) by 10 to 19 year olds
Market
Work
Domestic
Work Learn Leisure
Rural 10-14 Estonia 1 87 343 331
Hungary 14 67 381 252
Pakistan 81 66 248 243
Romania 34 114 252 282
Rural 15-19 Estonia 61 101 339 296
Hungary 49 95 420 229
Pakistan 187 142 108 212
Romania 176 207 137 247
Urban 10-14 Estonia 7 54 407 286
Hungary 20 44 458 208
Pakistan 32 41 323 255
Romania 10 51 295 311
Urban 15-19 Estonia 57 94 343 302
Hungary 41 50 465 227
Pakistan 122 106 183 252
Romania 41 106 228 360
Table A4. Average time spent in each type of activity (minutes) by 10 to 19 year olds
Market
Work
Domestic
Work Learn Leisure
Rural Girls 10-14 Estonia 1 99 356 314
Hungary 16 70 378 224
Pakistan 61 127 213 219
Romania 24 123 271 266
Rural Girls 15-19 Estonia 67 119 332 271
Hungary 39 111 424 213
Pakistan 111 251 75 185
Romania 136 253 148 232
Urban Girls 10-14 Estonia 11 52 407 272
Hungary 22 48 471 192
Pakistan 13 71 309 231
Romania 9 66 290 303
Urban Girls 15-19 Estonia 51 108 346 287
Hungary 46 57 464 215
Pakistan 39 193 169 226
Romania 34 145 222 326
Rural Boys 10-14 Estonia 1 74 330 348
Hungary 13 65 384 273
Pakistan 98 12 279 264
Romania 45 106 235 297
Rural Boys 15-19 Estonia 55 87 343 315
Hungary 61 76 414 249
Pakistan 270 22 145 242
Romania 210 170 128 259
19
Urban Boys 10-14 Estonia 5 56 407 298
Hungary 18 39 446 223
Pakistan 50 13 336 278
Romania 12 37 300 319
Urban Boys 15-19 Estonia 63 83 340 315
Hungary 37 44 466 239
Pakistan 204 21 197 278
Romania 47 71 233 390
20
Appendix Table A5. Multivariate tobit estimates of the determinants of time spent on market work, domestic work, learning
and leisure. Girls sample.
Variable Estonia Hungary Romania UK Pakistan
MARKET WORK
HH age -98.952*** 27.925 -11.256 -6.613 6.941***
HH agesq 0.997*** -0.292 0.115 0.05 -0.055**
HH Head Female -39.033 -15.987 -79.687*
-47.874**
Gender (male=1)
Percent Children 5.764 0.366 1.665 -0.214 0.880**
Asset Index 888.576** -88.819 82.27 114.267 -579.280***
Residence (urban=1) -16.385 36.162 -153.873***
-140.996***
Age -253.504 -129.53 -160.195** -110.256** 119.885***
Agesq 13.041 4.649 6.582*** 5.548*** -2.994***
_cons 1561.516 159.059 922.199* 439.875 -1257.535***
DOMESTIC WORK
HH age 0.29 -18.214 -6.128 2.307 -6.229***
HH agesq 0.016 0.196 0.056 -0.03 0.054**
HH Head Female 19.797 -1.511 9.874
-12.074
Gender (male=1)
Percent Children 0.232 0.699 -0.187 -0.778** 1.005***
Asset Index -41.616 -23.167 -208.862*** -5.404 -242.146***
Residence (urban=1) -31.502** -55.733 -84.897***
-31.500***
Age -80.636** -34.336 8.519 22.904 80.759***
Agesq 3.131** 1.47 0.505 -0.689 -1.485***
_cons 563.433** 654.827 160.555 -121.418 -515.677***
LEARNING
HH age 22.291*** 21.605 5.039 -36.431*** 5.651
HH agesq -0.202** -0.246 -0.041 0.489*** -0.049
HH Head Female 4.278 -62.093 41.152
94.514***
Gender (male=1)
Percent Children 0.391 -0.172 0.505 0.332 -1.462***
Asset Index 61.089 151.916 437.071*** 306.343*** 763.542***
Residence (urban=1) 31.492 67.963 61.676***
87.167***
Age 193.325*** 105.547 135.097*** 226.838*** 12.564
Agesq -6.932*** -3.687 -5.667*** -9.244*** -2.821**
_cons -1570.736*** -853.237 -861.784** -588.052 142.529
LEISURE---------------------------------------------
HH age 6.920* -7.416 6.956** 37.197*** 1.181
HH agesq -0.089* 0.103 -0.070** -0.437*** -0.013
HH Head Female -14.619 42.776 -9.426
-20.256***
Gender (male=1)
Percent Children -0.662 -0.006 -1.113*** 0.445 -0.324*
Asset Index -125.293** 10.787 -100.250** -187.208*** 93.333***
Residence (urban=1) -15.849 -19.69 69.526***
10.038
Age -38.333 37.052 -0.189 1.706 -43.527***
Agesq 1.134 -1.209 -0.122 0.174 1.261***
_cons 553.779** 61.583 193.374 -401.48 539.060***
21
Appendix Table A6. Multivariate tobit estimates of the determinants of time spent on market work, domestic work, learning
and leisure. Boys sample.
Variable Estonia Hungary Romania UK Pakistan
MARKET WORK
HH age -32.43 5.53 -20.706*** 3.761 -4.898
HH agesq 0.269 -0.08 0.188** -0.114 0.067*
HH Head Female -162.288 -12.469 -1.443
-95.298***
Gender (male=1)
Percent Children -3.73 -0.257 0.611 -0.017 1.370***
Asset Index 669.14 -94.615** -4.072 -12.232 -894.256***
Residence (urban=1) -73.886 4.448 -217.599***
-59.914***
Age 476.578 -67.668** -163.482*** -250.345*** 34.047
Agesq -10.065 2.615** 7.217*** 10.970*** 1.52
_cons -5162.708 353.463 1267.204*** 1230.595*** -580.639**
DOMESTIC WORK
HH age -6.737** -5.843 5.144 -2.909 1.081
HH agesq 0.104*** 0.048 -0.048 0.039 -0.023
HH Head Female -0.582 -16.22 7.702
-10.405
Gender (male=1)
Percent Children 0.935* 0.554** -0.01 -0.397 -0.03
Asset Index -68.084 0.116 -121.150*** 51.694 20.68
Residence (urban=1) -1.84 -41.268*** -111.392***
2.345
Age -17.795 -55.858* 58.005* 50.305** 40.375**
Agesq 0.952 1.977* -1.598 -1.748** -1.215*
_cons 196.284 559.097** -478.317** -299.321 -458.778***
LEARNING
HH age 20.621*** 9.406 -9.226 4.291 1.2
HH agesq -0.222*** -0.065 0.074 0.008 -0.009
HH Head Female -10.356 -119.590*** -4.665
65.449***
Gender (male=1)
Percent Children -2.363** -0.265 0.276 0.305 0.006
Asset Index -16.723 53.782 397.074*** 397.278*** 546.469***
Residence (urban=1) 20.552 60.015** 126.319***
16.779
Age 270.198*** 135.088** 148.393*** 183.913*** 89.049***
Agesq -9.732*** -4.598** -6.023*** -7.534*** -4.849***
_cons -1824.532*** -840.641* -608.427* -1266.448*** -295.457
LEISURE-------------------------
HH age 3.551 -4.935 8.956** -1.244 0.916
HH agesq -0.055 0.049 -0.056 0.01 -0.011
HH Head Female 23.485 62.461* 11.511
8.52
Gender (male=1)
Percent Children 0.257 0.006 -0.295 -0.265 -0.217
Asset Index 31.972 -72.326 -82.712* -171.702** 77.391***
Residence (urban=1) -19.976 -20.026 88.032***
10.77
Age -69.345 44.928 30.662 30.582 -14.022
Agesq 2.394 -1.519 -1.081 -1.089 0.368
_cons 719.480** 87.164 -170.465 317.074 350.535***
22
