The direct and indirect impact of child labor on educational achievement - evidence from Vietnam.pdf

1. UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM
HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES
VIETNAM THE NETHERLANDS
VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE DIRECT AND INDIRECT IMPACT OF
CHILD LABOR ON EDUCATIONAL
ACHIEVEMENT: EVIDENCE FROM VIETNAM
BY
NGUYEN TAN PHUC
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
HO CHI MINH CITY, November 2017

2. UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE DIRECT AND INDIRECT IMPACT OF
CHILD LABOR ON EDUCATIONAL
ACHIEVEMENT: EVIDENCE FROM VIETNAM
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
NGUYEN TAN PHUC
Academic Supervisor:
Assoc. Prof. Nguyen Huu Dung Ph.D.
HO CHI MINH CITY, November 2017

3. Contents
CHAPTER I: INTRODUCTION.................................................................................................... 3
1. Problem statement............................................................................................................... 3
2. Research objectives and methods ....................................................................................... 5
3. Structure of thesis ............................................................................................................... 6
CHAPTER II: LITERATURE REVIEW ....................................................................................... 7
1. Theoretical framework........................................................................................................ 7
2. Review of empirical studies................................................................................................ 8
CHAPTER III: RESEARCH METHODOLOGY ........................................................................ 11
1. Empirical models .............................................................................................................. 11
2. The data: Young Lives Round 4 – 2013........................................................................... 15
3. Data description ................................................................................................................ 16
CHAPTER IV: RESEARCH RESULTS...................................................................................... 19
1. Overview of child labor and education in Vietnam.......................................................... 19
2. Summarize the data........................................................................................................... 21
3. The estimation results for Whole sample.......................................................................... 24
4. Further estimation............................................................................................................. 26
5. Results of two – stage least squares regression ................................................................ 29
CHAPTER V: CONCLUSION..................................................................................................... 32
REFERENCE................................................................................................................................ 35
APPENDICES .............................................................................................................................. 38

4. 2
Abstract
This study explores the direct and indirect impact of child work on educational
achievement of children at the age from 11 to 20 across rural and urban areas in Vietnam,
using the data of Young Lives Round 4 complemented in 2013. Given the characteristics
of individual, household and schooling which are controlled in estimation, the results
indicate that there is negative relationship between hours worked and math scores of
children, but the impact in the rural areas is different from that in the urban. In urban,
exhaustion while working or doing other activities besides learning is responsible for
weak performance in schools. Meanwhile, school dropouts and delays because of
working is the main reason of low educational outcomes of children in rural. Further,
schooling attributes contribute to the increase in math scores of children, especially those
in rural, raising the necessities of improving qualities of education in those regions. This
study also uses a set of factors including income earned from crops, household shocks,
and community – level rice price as instruments of hours worked variable. But after
Hausman examination, the Ordinal Least Square (OLS) results are preferred due to weak
instruments.
JEL Classification: I21, J13, J22, O15
Key words: child work, educational achievement, Vietnam

5. 3
CHAPTER I: INTRODUCTION
1. Problem statement
Child labor is described as the engagement of children in various activities (paid or
unpaid) that keep them from their childhood. According to Global Child Labor Trends, there are
approximately 10.6 percent of children at the age of 5 – 17 in the world participate in workforce.
Following the first National Child Labor Survey in 2014, this number in Vietnam is about 9.6
percent. Most of them living in the countryside, being involve in agricultural activities or their
family businesses over 42 hours per week. Consequently, around 96.2 percent of them were not
going to school.
Many argue that working of children would bring many disadvantages for the
development of them following reasons. One disadvantage concerns the increase in the risks of
issues which could harm seriously physical and mental health of children when they participate
in workforce. It is obviously to see that jobs employing children as workforce normally are
described as unskilled types of work, together with poor quality in working conditions. The
occupational injuries potentially appear from operations with dangerous equipment, heavy loads
or poison exposure. Moreover, throughout history of the world, child labor often relates to illegal
activities such as slavery, drug trade, child prostitution and human trafficking. These kinds of
abuses cause both physically and mentally traumas for the whole life of children involved.
According to UNICEF, Sub – Saharan Africa has the highest percentage of young
workers around the world. At the group of 5 – 14 ages, child labor in this country accounts for 28
percent, while countries such as Middle East and North Africa and East Asia and the Pacific are
about 10 percent. Following International Labor Organization, using the sample of 26 countries,
there is a quarter of children who suffer injuries while working. In United States, industries using
children as employment force accounts for the higher injury rates than the average level every
year. In term of Vietnam, according to Hanoi school of Public Health report, about 23000
children were injured by sharp objectives, and half of these children got damaged when they
were working. Further, more than 60 percent of injuries caused by machines in young workers,
mainly at the age of older 14. There was investigation in 2001 conducted by the Ho Chi Minh
City Department of Labor, Invalids, and Social Affairs, found that child labor problem still

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remains at least 7 of 24 districts throughout Vietnam, especially in rural areas. More than 90
percent of companies that employ children as workforce without legal license, and children have
to work under hazardous conditions, particularly in environments of gold mines, timber
operations or cargo transport. In addition, the survey implemented by Statistical Information and
Monitoring Programme which disentangle the state of child labor in four countries Cambodia,
Laos, Mongolia, and Vietnam reports that 43 percent of children aged 5 – 14 and 51 percent of
children aged 15 – 17 are suffering dangerous conditions at their work. In 2015, The Guardian
documented that about 3000 children in Vietnam were trafficked to the UK for working and debt
paying purpose, with regard to illegal businesses such as brothels or cannabis farms.
The second drawback of child employment is to prevent children from their childhood
that they should have. The fact is, children will miss the opportunity to attend school and to
acquire academic education. They also have no time playing outside with those at the same age.
In turn these factors lead to the statement that children are limited for developing fully their
understandings, awareness and knowledge.
Education plays a vital role for the long – term growth of people, especially children. It is
believed that, living in a competitive world, candidates with a high education and knowledge will
have more choices in labor market than others. The reason for this could be explained that
children acquire from basic to advanced level of accumulated understandings of a field through
learning. From that, they will be able to have a general perspective for any situations,
circumstances, and abilities. Knowledge also helps them avoid faults and build on achievements
from the past. Moreover, spending more time in school improves their soft skills as well,
including responsibility, time management, disciplines, organizational and social skills. These
strengths contribute significantly to their success and quality of life in future. In term of country
level, education is described as a main tool for sustainability, economic development and social
welfare. One of the prior goals of both developed and developing nations in the world is to
improve the total capacity and quality of their human resources from their own country or from
others. Every year, they invest large amount of money in human capital as well as enhance the
awareness of residents about benefits of acquiring knowledge.
Educational achievement is considered as the instructional goals or learning objectives of
education. It is described as outcomes that an individual obtains from learning activities in

7. 5
academic institutions, such as in schools, colleges, universities, etc. Students reveal educational
performance by their ability of knowledge, understanding and skills acquisitions in a particular
field (numeracy, literacy, science, art, computing, etc.) through distinct measurements like scores
or grades on tests, level of academic degrees, and number of educational certificates. Educational
achievement is normally employed to measure ability of one person. It shows the outcome that
an individual obtains from his or her engagement of education, focusing on what he or she could
actually do, rather than the level of participation in education. Educational achievement also
helps to setting standardized assessments for distinguishing capacities of students in schools,
contributing to household decisions whether a child should continue education or not, as well as
enhancing the motivation of education engagement of children.
2. Research objectives and methods
This study mainly aims to investigate the direct and indirect effect of child labor on
educational achievement which measured by the cognitive achievement such as mathematics
performance for children in Vietnam. The exploration from this research could solve some
following problems. At first, educational achievement (or educational performance) is seen as
one of important factor which highly influence long – term development as well as enhance the
quality of life of an individual, household, society or even a country. Therefore, it is necessary
and reasonable for exploring the effect of a historical and prevalent problem like employment of
children at work, on their educational achievement. Second, previous studies primarily
concentrate on school attendance or school enrollment, and use this measurement as the
indicators of learning achievement. However, this approach is unable to estimate truly the
harmful which is caused by child labor. For example, working could harm potential achievement
or gains acquired from education even attaining school or not. According to Christopher (2000),
it could over – estimate the effect of working at the early age in case children enroll in poor
education or school, but they can improve their knowledge through their job. Besides, it also
would under – estimate the child labor due to the scenario in which student in spite of going to
school but having no time learning or completing homework after working. Consequently, it is
obviously necessary to employ other indicators for educational achievement than simply using
school enrollment rates. Unlike previous studies, this study employs new measurement of
learning achievement, the mathematics performance of children. Third, by employing rich set of

8. 6
control variable, including individual characteristics, household characteristics, and schooling
characteristics, the results suggest which factor of an individual could potentially affect
educational achievement, together with child work. For example, it is considered that older
children are likely to outperform younger children in term of both working and learning
achievement due to their higher physical health. As a result, the intensity of work will not affect
performance of older children as much in comparison to younger ones. Finally, it will show the
general picture as well as support the factual evidence about consequences of using children as
labor force and its effect (both direct and indirect) on human growth and learning, of one typical
developing country like Vietnam. These results will help policy makers in taking child labor into
consideration, then building appropriate and effective policies, also contribute to academic field
of employment of children at work as well as learning achievement for later related studies.
Following this research objective, the chosen empirical method in this study is based on
the model set by Heady (2000), using the Young Lives Round 4 data in Vietnam which covers
839 children at the age from 11 to 20. In which, the total effect is measured when regressing
educational achievement on child labor while excluding schooling attributes out of estimated
equations, given the control of individual characteristics and household characteristics of
children. The direct effect, however, is exposed in the same analysis but keep the schooling
attributes constant. The estimations in this study are Ordinal Least Square (OLS) and Two –
stage Least Square with a set of instrumental variables for robustness check. In my expectation,
weak performance in education is driven by the incidence of child work, and this negative impact
is different across urban and rural areas.
3. Structure of thesis
This study is divided into five main sectors followed: The literature review related to the
research problems and methodologies will be shown in section II. Section III describes the
theory, chosen empirical model, data sample and data requirements used in this study, plus the
suggestion of potential problems and solutions. Section IV reports the results of regressions as
well as tests. Finally, section V conclude some remarks, including main findings, policy
implications and limitations appeared in this study.

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CHAPTER II: LITERATURE REVIEW
This chapter provide the theoretical framework about the decision in whether children
take part in labor force or continue their education is made within household as well as the
correlation between child work and educational achievement of children. After that, some related
studies and researches are discussed for the purpose of further understandings about this
academic field.
1. Theoretical framework
Following the definition of ILO, child labor is “work that deprives children of their
childhood, their potential and their dignity, and that is harmful to physical and mental
development” (ILO 2004, p.16). Heady (2000) described child labor as paid or unpaid activities
which provided on the labor market as well as on household farms or companies. This approach
of description of child work excludes the domestic works in the households (including taking
care of ill members or younger children, cleaning, washing, cooking, etc). He employed a group
of questions to ask children about their economic activities for measuring the intensity of child
work, such as whether a child had worked in the past twelve months, how many weeks and how
many hours per week they had worked in the past twelve months. Mavrokonstantis (2011)
pointed out that child labor should be considered as the economic works, including paid
activities outside the household and unpaid activities inside the household.
The educational achievement of a child is the indicator of the school output of one
individual, which is derived from an educational production function, given the student inputs.
An educational production function is determined as follows:
E = f (X1, X2, X3) (1)
Where E represents the school output of a student, in other says, the educational
achievement of a student. X1 includes factors around the school environment, such as the
teaching methods and materials, the school infrastructures, the length of time that student use for
schooling. X2 comprises the environmental influences on education outside the school, like
educational backgrounds of parents, or motivation for education of a student. X3 represents
factors which measure the initial level oriented towards learning of student.

10. 8
The intensity of child labor, which is measured by the time allocated on economic works,
theoretically affect educational achievement following numerous ways. On the one hand, child
labor will reduce educational outcome of a child due to the fact that time allocation is scarce
resource, thus the increase in hours worked will lead to the decrease in input factors of
educational production function, such as hours spent on attending schools or extra classes, and
hours spent on accomplishing homework. Additionally, working could cause exhaustion, lack of
energy and ability to learn academic knowledge.
On the other hand, child labor is considered to enhance the educational performance of
children by allowing them to apply academic knowledge they have learnt at school in real life.
More than this, working could provide children not only specific experiences related to jobs, but
also a number of soft skills such as time management, responsibility, communication,
confidence, problem solving, etc. In turn, these elements will help to increase the educational
outcome of children in their schools.
Alternatively, child labor could have no effect on educational achievement if the
incidence of child work is substantial low, or if children know how to arrange efficiently their
time between schooling and working.
Educational achievement (or academic performance) is the short or long – term
educational goals that one individual has obtained from their engagement of education.
Obviously, the level of educational performance of a child could highly determine his or her
future income as well as living conditions, rather than his or her years of schooling. Educational
achievement is normally measured through scores from examinations or tests of cognitive skills,
including verbal skills and mathematics skills. For example, Heady (2000) employed results
from an easy reading test, an easy mathematics test, an advanced reading test, and an advanced
mathematics test in his survey as the measurement of educational achievement of children in
Ghana. Gunnarsson et al. (2006), similarly, used the mathematics and language test scores of
children on third and fourth year primary schools in nine Latin American countries. Bezerra et al.
(2009) employed the school achievement tests in Portuguese and mathematics for students in
Brazil.
2. Review of empirical studies

11. 9
Existing studies find that there is a board category of determinants which could
simultaneously affect child work and their schooling. Jensen and Nielsen (1997) point out that
both economic factors and sociological elements affect the decision between school enrollment
and working of children among families in Zambia. For instance, poor families tend to keep their
children away from school. Moreover, an imperfect capital market and a household heads work
also have an impact on school participation of children. Also, Canagarajah and Coulombe (1997)
suggest that there is a highly positive correlation between welfare of household and school
attendance. According to their estimation, attitude to education of parents increases school
engagement of children as well as decreases their child work. The same exploration about the
impact of parental education is confirmed by Khanam (2004), using the sample of 1628
Bangladeshi children aged 5 – 17 years in rural area. Ray’s (2000) support the evidence that the
difference in individual characteristics, such as gender, could explain the difference in schooling
decision in case of Pakistan and Peru. For example, the 10 – 14 years old girls in Pakistan seem
to leave their school and take part in labor force.
The debate about working at the early age affect positively or negatively on schooling is
discussed and supported by a lot of studies. Some of studies conclude that child labor contributes
to the increase in school enrollment. For example, Mortimer and Johnson (1997) argue that
children enhance their psychological wealth while working, regarding to self-esteem,
competence, responsibility, and confidence. As a result, these skills would contribute to the
increase in their performance at school. The studies of Ravallion and Wodon (2000), Binder and
Scrogin (1999), and Patrinos and Psacharopoulos's (1997) also support the evidence that child
labor is not harmful the schooling, and the adverse effect between two factors is very small.
On the other hands, other studies document the negative consequences between intensity
of child work and school attendance. Further, working also hurt child physical as well as mental
development following many ways. For instance, Boozer and Suri (2001) suggest that one hour
of working reduces 0.38 hours of school attainment in case of Ghana students in both Northern
and Southern regions. Lavy (1985) and Rosenzweig and Evenson (1977) point out that child
labor would lead to the low level of school enrollment. Similarly, Amin, Quayes and Rives
(2006), based on the substitutes or complements hypothesis in the family labor supply decision,
report in their analysis that children in Bangladesh who engage in labor market will decrease
their schooling attendance. The same exploration documented by Khanam and Ross (2008) for

12. 10
children in rural Bangladesh. Using logistics regression models, they disentangle linkages
between child work and school attainment by looking at the level of school attainment as the
proxy for learning achievement of children. They find that children who are working perform
lower school attendance and grade attainment at school, given control factors of education of
parents and household income.
Beside the estimation of correlation between child labor and school enrollment, some
other studies concentrate on the consequences of working on educational achievement at the
early age. In term of school participation, the effect of working is reflected clearly because this is
the allocation and trade off of daily time. But with respect to the outcome factor such as learning
achievements, the estimations are quite complicated. Using the sample of children in Ghana
through Ghana Living Standards Survey (GLSS2), Heady (2000) estimates the direct and
indirect impact of working activities of children on their level of academic achievement,
including reading and mathematics. In his statistical approach, he employs a board range of
exogenous factors such as individual characteristics of children, attitudes to education in local
and household, quality and characteristics of schooling to resolve the causality problem between
working and school attendance. He concludes that there is significant effect of working on
learning achievement of children in Ghana at both basic and advanced level. He suggests that
those working find themselves unable to learn or complete homework due to exhaustion and
lacking of time. Later, Rosati and Rossi (2003) also report the same results by using the data for
Pakistan and Nicaragua.
Some studies continue to examine this correlation following different approaches.
Admassie and Bedi's (2003) explore that the higher the working hours, the lower the ability of
reading and writing ability (RWA) of children in Ethiopia rural. Alternatively, Bezerra, Kassouf,
and Mary (2009) disentangle the relationship between child labor and school performance by
employing the results of achievement tests, including the standardized scores of language and
mathematic tests from 2003 Sistema Nacional de Avaliação da Educação Básica (SAEB) in
Brazilian school. The impact of child labor is analyzed by separating whether children work
inside or outside. In their empirical research, they use instrumental variable such as the average
wage for unskilled labor, especially male, in the state and run the two – stage least squares
approach to resolve the problem of endogeneity of child labor. Further, the authors employ
characteristics factors of school, family and individual as the control variables for their

13. 11
estimation. They conclude that children who do not spend time working outperform those
working. In addition to this, there are also differences in work conditions (inside or outside their
house) that affect school achievement by varying ways.
CHAPTER III: RESEARCH METHODOLOGY
1. Empirical models
Based on the model set by Heady (2000), this study aims to explore both direct and
indirect impact of child labor on educational achievement using data from Young Lives round 4
for children in Vietnam in 2013. This empirical model is chosen because it is efficient to
investigate different aspects of effect of child work. Moreover, the available data of Young Lives
also contribute to capture almost factors needed for estimation.
The child work affects educational achievement following two ways: direct and indirect.
The direct effect results in the consequences such as exhaustion, or tiredness after children
engage in working. Alternatively, the indirect effect appears via schooling attributes: working
may oblige children to drop or delay their education, or reduce their time on learning or doing
homework, or lose their motivation about obtaining academic knowledge. These factors lead to
the decrease in their educational performance at school.
In practice, the direct effect is described when the analysis shows the direct correlation
between working and education performance while keeping the schooling factor of children
constant, in other say, the model takes schooling variables into account. The indirect effect
indicates the analyzed results of work on educational achievement via schooling, which means
working affects schooling, and then learning performance. To obtain this goal, three estimation
models are applied:
Ai = αi + Ii + βWi + Fi + ei (2)
where A denotes the mathematics test scores variable of individual i. The main
explanatory variable W represents the intensity of child labor of individual i. In addition, the
model also picks up the rich set of characteristics as control variables such as individual
characteristics I (age, gender, body mass index, number of siblings, innate ability, and time spent
doing housework) and household characteristics F (region, area, household size, consumption per
capita, household quality index, and access to service index) of individual i. These factors are

14. 12
claimed that they can potentially affect working status and educational achievement of children.
By excluding schooling factors, the estimated equation (2) allows to measure the total effect
(both direct and indirect) of child labor on educational achievement. Indeed, the coefficient of
the working variable will reflect the direct impact of employment on learning performance by
itself, as well as the indirect impact via schooling.
To analyze the direct effect of working, as mentioned above, the estimation model will be
added by schooling variables S of individuals as follows:
Ai = αi + Ii + βWi + Fi + Si + ei (3)
The schooling factors S consist of education background of children, schooling costs of
households, parental education years, and their motivation about education. In previous papers,
authors normally employ the school enrollment or school attendance as the measurement of
schooling factor, but it would lead to the existence of causality problem between working and
school attainment. For example, whether the choice in which children engage in labor market
will cause them to drop out of school, or the decision of start working and leaving school is made
simultaneously. Bezerra, Kassouf, and Mary (2009) report that factors lead to the increase in
child labor will result in the decrease in school attainment. To avoid this problem, school
attainment is not chosen for the indicator of schooling level in this paper. The coefficient of
working variable in equation (3) will indicate its direct effect on education performance, given
the level of schooling characteristics of children.
Finally, to test whether control variables and schooling factors truly affect educational
achievement without taking working into consideration following literature of educational
achievement, I exclude the child work variable from equation (4) as follow:
Ai = αi + Ii + Fi + Si + ei (3)
The dependent variable is the scores of mathematic test obtained from Young Lives data
(2013) will be regressed in three estimation models. These scores are described as continuous
variables. As a result, three equations (2), (3) and (4) are analyzed by using ordinary least
squares (OLS) method.
It is expected that the correlation between child work and educational achievement
operates negatively, in other say, the higher level the intensity of working, the lower the

15. 13
education performance of children. The estimated coefficient of child work indicator is expected
to be negative β < 0. In addition, the individual characteristics and household characteristics have
impact on both child work and educational achievement of children following different ways.
Consequently, in expectation, by adding as much as these factors into equations, the effects of
error term will be restricted.
Normally in Vietnam, the choice of working of children is made by their parents
following ambiguous reasons such as household wealth or innate ability of their children. For
example, with respect to level of poverty, poor families may send their children to labor market
as a supplemental resource for the household basic needs. Otherwise, some families find that
their children have lower innate ability, thus educational achievement could not be obtained even
they are working or not. As a result, parents might decide that their child should work rather than
go to school. These omitted factors could lead to bias in the results of OLS estimation. To
resolve this problem, this study employs the consumption per capita, the housing quality index,
the access to services index for measuring the welfare of household, and the results from
Peabody Picture Vocabulary Test (PPVT) for capturing the innate ability of children.
See this picture below for the analytical framework of this study:

16. 14
Mavrokonstantis (2011) defined some potential problems occurred when using OLS
regression for estimating the effect of child labor on education performance. These problems
comprise measurement error, simultaneity, non – normal distribution of the residuals, and
heteroskedasticity.
The measurement error appears since children, through questionnaire, are asked to report
the number of hours they have spent on different activities in typical day. Obviously, the data
collected by this approach is retrospective valuation of children. This type of collecting
information does not allow us to track exactly the intensity of child work, thus the OLS results
could be potentially biased.
Bezerra, Kassouf, and Mary (2009) determine the possibility of simultaneity problem
which potentially caused by some omitted factors that affect child work as well as their
educational achievement, such as hours spent on learning or doing homework, or the quality of
education of children. These factors are not captured in these models, and are seen to become a
component of the error term. For example, time spent on working reduces hours they used for
learning or completing homework, in turn decreases their educational performance in school. In
a different way, given the lower quality in infrastructures or teaching at local schools, families
could tend to discourage their children from attaining school and encourage them to work.
Unfortunately, Young Lives data is not able to provide the measurement for school quality of
children.
Measurement error and simultaneity together would result in the endogeneity problem of
child work in this empirical estimation. This issue will be solved by using the instrumental
method. The method of instrumental variables is applied, with the number of hours worked is
treated as endogenous. Mavrokonstantis (2011) and Beegle (2009) suggested employing the
community – level rice price to be an instrumental variable for child work measurement. In this
study, a set of factors including the income earned from crops in the last agricultural years of
household, the shocks happened from 2011 to 2013 which affect household wealth, and the
community – level price of rice are used as instruments for hours worked variable. These
instrumental variables are expected to affect the intensity of child work following different ways,
but not to affect directly math scores of children. Specifically, the increase in income from crops
is expected to decrease the child work following income effect. On the other hand, one concerns

17. 15
for agricultural households, the rise in revenue earned from crops this year requires the
expansion of scales of production, leading to the increase in demand for child work. Shocks
affect household wealth, thus expectedly raise the possibility of labor force participation of
children in family. According to Mavrokonstantis (2011) and Beegle (2009), for children in
urban, rice price and child work have a positive correlation following income effect. But for
those in rural, rice price expectedly performs both negative impact following income effect and
positive impact due to the increase in cultivation of rice.
In practices, the first stage in two – stage least squares estimation is applied as follows:
Wi = αi + Li + Ii + Fi + Si + ei (5)
where Li represents instrumental variables, then the second stage is estimated after
predicting the hours worked of children :
Ai = αi + Ii + β i + Fi + Si + ei (6)
Other problem exists in this regression is whether the residuals derived from estimation
models are normally distributed and homoscedasticity. For these issues, White test and Shapiro-
Wilk test are employed to check the null hypothesis of normal distribution and homoscedastic
assumption of residuals obtained from estimation models respectively.
2. The data: Young Lives Round 4 – 2013
Young Lives is known as an international research project on childhood and their
changing lives over specific period of time. Using interview, group work and case studies with
subjects of children, their family, their school and their community, researchers try to collect
background information about lives, physical and mental health, and future prospects of children
following different contexts. These subjects have a long – term commitment when they agree to
take part in this project. This longitudinal database gathers information about 12000 children in
four developing countries, namely Ethiopia, India, Peru and Vietnam during 15 years. In each
country, sample is divided into two age cohorts: 1000 children who were born in 1994 – 1995
and 2000 children who were born 2001 – 2002. The main goals of this project is to find out
commonalities and differences in lives of children in four typical and different developing
countries, then to build patterns and understandings about poverty transfer and poverty reduction
policies.

18. 16
Data used in this study is from Young Lives Round 4 implemented in 2013 for Vietnam.
The Round 1 was conducted in 2002, followed by Round 2 in 2006, and Round 3 in 2009. The
chosen sample is order cohort children who have age from 18 to 20 years old and younger cohort
children at the age from 11 to 14. Each cohort includes two sub-samples. First, the household
data which covers household education, livelihoods and asset framework, household food and
non – food consumption and expenditure, social capital, economic changes and recent life
history, and socio – economic status. Second, the child data which shows education,
employment, earnings, and time – use, feelings and attitudes, anthropometry, health and nutrition
of children in each cohort.
3. Data description
Educational achievement: a dependent variable of study. This factor is reflected by the score of
cognitive development test: the mathematics test for each child in the sample.
Mathematic achievement test scores: children have to answer 27 exercises for older cohort and
34 exercises for younger cohort which containing additions, subtractions, divisions,
multiplications, and problems related to math. One point will be recorded for each correct
answer. The refused – to – answer and blank answers will be seen as incorrect ones. As a result,
the Math scores variable, pointing out mathematics performance of children, is counted by the
proportion of correct answers.
Working state: is the main explanatory variable in this study. This factor indicates the child
work, and is measured by a number of hours that children spent their time on working on a
typical day last week (typical weekday, not weekends or holidays). The definition of work, even
children is paid or not, covers not only activities inside household such as tasks on family farm,
cattle herding, other family business, shepherding, piecework or handicrafts done at home but
also different activities outside their household. If children do both kinds of activity, the Hours
of economic work variable are the sum of hours they spent on both locations. Following my
hypothesis, the correlation between working and education performance would be expectedly
negative. The number of working hours is drawn from Child employment, earnings and time –
use sector in Young Lives.
Schooling characteristics: This factor attempts to catch up the education background and
attitude of children about their school, such as school years, attitude to education of household,

19. 17
education background of parents, and motivation to learning of children. These variables are
expected to be positively related to educational achievement of children.
School years: is the number of years of education that children have completed, denoted by
Education years of child variable. This data is obtained from household and child education
sector.
Attitude to education of family: is the amount paid for educational expenditure in household,
including school uniforms, schooling fees (registration and examination), donations to school,
extra tuition, school books and stationary, and transport to school. The sum of these expenses is
denoted by Schooling costs variable.
Education background of parents: the parental education years of parent (father or mother) of
children, represented by Parental education years variable. This data is also contained in
household and child education sector.
Educational motivation: there are six questions about feelings as well as motivation of children
about their school. These questions are built based on Likert scale from 1 – strongly disagree to 5
– strongly agree, including (1) being proud of clothes, (2) having the right books, pencils and
other equipment for school, (3) being proud of shoes or of having shoes, (4) having correct
uniform, (5) making plans for future studies and work, and (6) will be rewarded by a better job in
future if study hard. The Motivation about school variable is the average of recorded answer of
six questions, which are extracted from feelings and attitudes section.
Individual characteristics: includes background information of children (age, gender, body
mass index, number of siblings), innate ability of children (PPVT score), ant number of hours
spent in housework.
Age: age of children, with the expectation that older children outperform younger ones.
Gender: is dummy variable whether children is female. According to the definition of working,
boys will be expected to outperform girls.
BMI: the body mass index of children, calculated by using weight in kilograms (kg) divided by
height in meters squared. This index defines a healthy body weight, including underweight
(<18.5), normal weight (18.5 – 24.9), overweight (25 – 29.9), and obese (>= 30).
Number of siblings: is the number of siblings in family of children.

20. 18
Innate ability: this factor is measured by using the score from The Peabody Picture Vocabulary
Test (PPVT).
PPVT is described as a measurement for receptive vocabulary ability of children which is not
affected by working or by education. In this test, based on group of age, children are shown a
series of four pictures that are numbered. After hearing the “one – word” description of one
picture, children have to say or to point to a number of described picture as their answer.
Expectedly, children with high innate ability will perform well in their learning achievement.
Time spent in housework: regarding the number of hours used in domestic activities within
household like caring for younger or ill members, cleaning, cooking, washing, etc. The Hours of
domestic tasks variable is expected to harm the learning performance of children due to lacking
of time for them to complete their homework.
Household characteristics: control for differences in background information of household
lives, comprising region, area, household size, food consumption per capita, housing quality
index, and access to services index.
Region: there are four regions in this sample, including Northwest, Red River Delta, South
Central Coast, and Mekong River Delta.
Area: a dummy variable indicating household of children stays at urban. It is expected that
children in urban perform better than those in rural due to quality of education, convenient
transportation, good standard of living, etc.
Household size: the amount of members in family.
Food consumption per capita: measures household welfare following Moratti and Natali (2012).
The household spending is the total value in VND of expenditure of food consumed by family in
the last 15 days, such as beans/ rice/ bread/ cereals, meat products, milk or milk products, fish
and sea products, eggs, vegetables, fruit, spices, drinks, etc. The food consumption per capita
variable is obtained by dividing total spending by the amount of members in family.
Housing quality index: is defined as the average of rooms per person, floor, roof and wall.
Access to service index: is defined as the average of a set of dummies which indicating
households having drinking water, electricity, toilet and fuel.

21. 19
CHAPTER IV: RESEARCH RESULTS
This chapter discuss the general picture of child labor and education in Vietnam, then
report all the results from empirical estimations: OLS and Two – stage Least Square, followed by
the discussion of main findings.
1. Overview of child labor and education in Vietnam
In Vietnam, education is a system of public and private education which is administrated
by the Ministry of Education and Training. This ministry is responsible for designing a long –
term plan for education, following the requirements of labor market. The formal education is
twelve years begins at age 6 and is divided into three levels: primary school (five years),
intermediate school (four years), high school (three years). There are also pre-schools, vocational
education and higher education (university, college, or institute). The primary education is
compulsory, and students will learn typical subjects such as morals, Vietnamese language, math,
nature and society, arts and physical activities in school at this level of education. According to
the Resolution of the 4th Plenum of the Central Committee of the 7th Party Conference (1993),
the main target of education in Vietnam is "improving people’s general knowledge, training
quality human resources, and nurturing and fostering talent." Based on the strategy of education
reform, Vietnamese Government has continuously raised the pubic budget on education every
year. According to UNESCO, the share of GDP spent on education increased from 4.81 percent
in 2011 to 5.66 percent in 2013. In the international Pisa test organized in 2012, Vietnamese
students obtain impressive successes. They achieve higher scores in reading, maths and science
tests than other developed countries, such as United Kingdom and United States. Following
global ranking published by the OECD in 2015, the rank of Vietnam was 12th
, compared to the
United States of 28th
.
However, following the report of World bank, although there is a remarkably increase in
school enrollment rates in recent years, the quality and effectiveness of education, which are
represented by educational achievement of students, continue to be low, especially of poor
regions and provinces. The quality in Vietnam education is still measured below the international
standards because of poor teaching materials and methods, lacks of discussion and interaction
between teachers and students, or interferences of the Vietnamese Ministry of Education and

22. 20
Training. As the results, many graduated students, who achieve high scores in their schools and
universities, find themselves difficult to get a well – paid job, and needed to be retrained since
they start working. Meanwhile, the drop – out and repetition rates are also reported as high level,
leading to the fact in which child labor still exists.
Although Vietnam Government attempts to reduce the incidence of child work by
releasing many laws in which the employment of children under the age of 15 is prohibited, but
they are not aggressively applied and enforced, thus the protection for children is still weak.
There was the survey implemented in 2012 by the General Statistics Office (GSO) of Vietnam,
they report that child labor accounts for one – sixth (approximately 2.83 million children) of the
whole child population, with about 42.6 percent of them are girls. They engaged mainly in
unskilled jobs including agriculture, construction, garments and restaurant services. Among
working children, there were about 32.4 percent of children which worked over 42 hours per
week, and experienced decrease in time spent on schooling. As a result, 96.2 percent of them
drop their school because of work.
Child labor remains to be a serious problem in Vietnam due to some factual concerns
which are discussed in turn. Firstly, many families who have their traditional job across
generations think that education is time – wasting and irrelevant. They argue that their children
only need to learn specific field enough in order to take over his or her family business, not
spend almost time on enrolling school and get useless things. As a result, they send their children
to labor force after withdrawing them from their school. In fact, families with farming tradition,
children have a trend to take over and maintain the agricultural assets from their relatives.
Secondly, some underestimate the negative impact of working at the early ages on education as
well as educational achievement. On the one hand, they claim that for a particular job market,
children who work and improve the required skills will have more competitive advantages
compared to those without working background. One the other hand, in case of households
which are in poor conditions, children should take part in employment for sharing workload and
supporting their families. As a consequence, they have to spend their daily time on both learning
and working, or drop out of school in a specific time for working. This situation is popular and
conventional in rural areas in which children typically work on farms or agricultural lands,
therefore, they were not permitted to attend school by their parents during the seasons of harvest
and planting. Lastly, poor families found themselves unable to pay for school charges and fees,

23. 21
including registration, uniform, books and stationery, or extra tuition. In case of private schools,
these costs are generally much higher and beyond the affordability of their conditions. In
addition to this, they are inaccessible to any support or aids due to the failures of policies and
campaigns of education reforms of Vietnamese government. Consequently, many of children
have no other option than dropping out of schools.
2. Summarize the data
This study covers data for 839 children at the age from 11 to 20 which are separated in
two sub samples, including 449 (53.52 percent) that stay in rural areas and 390 (46.48 percent)
that lives in urban areas. Table 1 shows the descriptive statistic for all variables of two samples.
For individual characteristics, it is surprisingly that children in rural sample achieve the
higher average ppvt scores than those in urban sample (111.47 compared to 85.41). The average
body mass index (BMI) of children in both areas are within the normal range of healthy (from
18.5 to 24.9). The gender distribution is balanced adequately, especially the percentage of female
is about 50 in rural sample and 50.8 in urban sample. Children in rural households spend more
time on doing domestic activities than children in urban households, the average number of
hours per typical day for these kinds of tasks for two sample are approximately 1.39 and 0.82
respectively.
Regarding household section, the comparison between two samples is fairly complicated.
Urban households predictably spend more money for food consumption than rural households. It
is also no doubt that households in urban area have more opportunities to access services rather
than those in rural area. However, the average housing quality index of rural households is
surprisingly higher than of urban households. There is no difference between the number of
members per household in each area (from 5 to 6 people in a family). About region
characteristics, the full sample covers children from four regions: Mekong River Delta (10.3
percent), Northwest (6.2 percent), Red River Delta (29.6 percent), and South Central Coast (54
percent).
In term of schooling attributes, the level of parental education in rural households is
lower than in urban households due to difficulties in living conditions in the past, specifically the
average education years of parents in both areas are 8.3 and 9.7 respectively. The scenario is
contrastive for the level of education of children, the average education years of children are 8.9

24. 22
in rural and 7.5 in urban. However, it is obvious that households in urban spend more funds on
schooling than those in rural. For the educational achievement, children in urban areas perform
predictably better than those in rural areas, but not much. Specifically, the average percentages
of correct answers of math test which children in rural and urban households achieved are about
45.5 and 50 percent respectively.
For the level of working, it is worth noting that children residing in rural areas spend
approximately 4.6 hours per typical day on engaging in economic work, compared to about 2.3
hours of those staying in urban households on these kinds of activities. Further, activities outside
household account for a high proportion in economic work in both areas.

25. 23
Table 1: Descriptive statistics of the sample
Variable
Full sample (839 obs) Rural (449 obs) Urban (390 obs)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Individual characteristics
PPVT score 99.356 46.840 111.472 45.779 85.408 44.140
Age (months) 184.670 41.555 197.071 40.418 170.392 38.166
Gender (Female = 1) 0.503 0.500 0.499 0.501 0.508 0.501
BMI 18.760 3.069 18.452 2.715 19.113 3.400
Domestic tasks (hours per
typical day)
1.125 1.042 1.390 1.054 0.820 0.941
Number of siblings in
household
1.327 0.965 1.232 0.894 1.436 1.032
Child labor measure (hours per typical day)
Activities inside household 0.816 2.063 1.069 2.316 0.524 1.683
Activities outside household 2.705 3.984 3.523 4.185 1.765 3.517
Economic work (total) 3.521 4.269 4.592 4.318 2.289 3.867
Household characteristics
Ln(consumption/capita) 6.895 0.878 6.457 0.747 7.400 0.737
Housing quality index 0.629 0.118 0.650 0.138 0.605 0.083
Access to services index 0.721 0.245 0.553 0.181 0.915 0.148
Household size 5.253 2.123 4.675 1.609 5.918 2.429
Area (Urban = 1) 0.465 0.499 - - - -
Region: Mekong River Delta 0.103 0.303 0.185 0.389 0.008 0.087
Region: Northwest 0.062 0.241 0.109 0.312 0.008 0.087
Region: Red River Delta 0.296 0.457 0.530 0.500 0.026 0.158
Region: South Central Coast 0.540 0.499 0.176 0.381 0.959 0.199
Schooling characteristics
Education years of child 8.308 2.914 8.967 2.860 7.549 2.792
Parental education years 8.971 3.366 8.334 3.116 9.705 3.496
Motivation about school 3.808 0.471 3.758 0.463 3.866 0.474
Schooling costs (million
VND)
12.950 22.599 8.024 18.962 18.620 25.017
Educational achievement measure
Mathematics test scores 47.620 17.492 45.541 17.983 50.013 16.613
Instrumental variables
Income from crops (million
VND)
11448.560 41899.220 20010.870 54971.310 1590.923 10938.560
Household shocks 0.524 0.500 0.494 0.501 0.559 0.497
Community - level rice price
(thousands VND)
11.572 1.296 11.105 1.192 12.110 1.200

26. 24
3. The estimation results for Whole sample
Table 2 presents discretely the OLS results arrived at three estimation models in different
columns for the mathematics performance. To summarize, column I indicates the relationship
between educational achievement and working state without taking schooling attributes into
consideration (the total effect) according to model (1). Column II reports the results which add
the schooling characteristics (direct effect) following model (2). Column III describes the results
which exclude the working state according to model (3). The estimated coefficients are
significant at 10 per cent or less will be marked *. Similarly, they will be marked ** if they are
significant at 5 per cent or less, and will be marked *** in case they are statistically significant at
1 per cent or less. The values in parentheses show the standard errors of each coefficients
reported.
As column I of table shows, there is negative correlation between hours worked and
mathematics scores of children following expected sign. With significance level of 1%, the
significant coefficient of working state variable represents the direct effect of working on
mathematics achievement by itself plus the indirect effect via schooling. Moreover, the set of
characteristics comprising ppvt score, age, body mass index, food consumption per capita, and
access to services index together appear to be determinants that affect the mathematics
performance of children in this sample. It is interesting to note that these factors affect
mathematics scores following expected signs, except age variable. Specifically, the coefficient of
age turns out to be negative, leading to the statement in which younger children surpass older
children in the math test.
Column II indicates the results after adding schooling characteristics into regression
model, the score of mathematics test remains to be affected by the intensity of working, this
negative impact is lower than the total effect (in absolute value) at column I because in this case,
the coefficient of hours worked no longer capture the effect of working on mathematics
achievement through schooling. The difference implies that the indirect impact of child work on
educational achievement exists. To explain, with significance level of 10%, when the number of
working hours increases by 1 hour, the scores of the mathematics test of children will decrease
directly by 0.72 percentage points, other things constant, together with the reduction by 0.28 (1 –
0.72) percentage points in math scores caused by the effect of working on schooling attributes.

27. 25
However, the schooling attributes including parental education years and education years of
children predictably increase the outcome of math test at the level of 1%. Body mass index
(BMI) factor is no longer affect the mathematics performance, meanwhile the other significant
determinants still statistically relate to math scores of children, following same signs as column I.
Column III reports the estimation model which drops the working state of children. The
results confirm the positive impact of schooling attributes, measured by education years of
parents and children, on the mathematics achievement, not be biased by the omission of child
work from the estimation model.
Table 2: OLS estimation results for mathematics test scores (whole sample)
Variables (I) (II) (III)
Hours of economic work -1.006*** -0.723***
(0.26) (0.24)
PPVT score 0.337*** 0.231*** 0.223***
(0.04) (0.04) (0.04)
Age (months) -0.325*** -0.425*** -0.487***
(0.05) (0.05) (0.04)
Gender (Female = 1) 1.017 -0.382 -0.508
(1.09) (1.04) (1.05)
BMI 0.352* 0.313 0.316
(0.20) (0.19) (0.19)
Hours of domestic tasks 0.044 0.066 0.392
(0.56) (0.53) (0.52)
Region: Northwest -0.863 0.645 1.474
(2.85) (2.69) (2.69)
Region: Red River Delta 4.439** 4.409** 4.927**
(2.21) (2.09) (2.10)
Region: South Central Coast -0.161 1.073 1.647
(2.47) (2.33) (2.34)
Ln(consumption/capita) 2.781*** 2.053** 2.190***
(0.85) (0.82) (0.82)
Area (Urban = 1) -1.737 -1.356 -1.614
(2.13) (2.02) (2.03)
Housing quality index 6.757 4.160 3.722
(4.83) (4.57) (4.59)
Access to services index 12.170*** 6.076* 6.432*
(3.48) (3.38) (3.39)
Household size -0.488 -0.167 -0.158
(0.32) (0.31) (0.31)

28. 26
Table 2 continued
Number of siblings -0.307 -0.646 -0.657
(0.66) (0.62) (0.62)
Education years of child 3.221*** 3.338***
(0.37) (0.36)
Parental education years 0.582*** 0.586***
(0.17) (0.18)
Motivation for education 1.029 1.028
(1.13) (1.14)
Schooling costs (million VND) 0.018 0.019
(0.02) (0.02)
Constant 41.043*** 43.038*** 50.044***
(10.02) (10.66) (10.44)
Observations 839 839 839
R2 0.215 0.307 0.300
Joint F test 14.984 19.125 19.509
White test 117.650 209.250 187.770
p-value 0.712 0.330 0.350
Shapiro-Wilk W test 0.9972 0.998 0.997
p-value 0.157 0.375 0.127
Note: Values in parentheses represent standard errors
*** Notes significance at the 1% level
** Notes significance at the 5% level
* Notes significance at the 10% level
4. Further estimation
Due to the large differences between rural and urban areas in types of work, perception of
parents, quality of school which cannot be captured in the model, the further estimation in which
mathematics achievement are regressed for rural sample and urban sample respectively is
suggested. Table 3 presents distinctly the OLS results for math scores following three empirical
models in each column: I, II, III for rural sample, and IV, V, VI for urban sample with the same
structure.
The work intensity still reduces the score of math tests of children in both areas after
controlling the effect of schooling characteristics, but the direct impact of working in urban is
higher than in rural. Specifically, as column II and column V point out, the math score decreases
directly by 0.55 percentage points for children in rural at the 10% level and by 1.03 percentage
points at the 1% level for children in urban since the number of hours that they participate in
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29. 27
working increases by 1 hour. This result could be explained by the fact that as characteristics of
rural areas, children tend to be familiar with working, especially they often engage in simple jobs
such as farmer, construction worker, or factory worker. Consequently, children in rural suffer
less damage induced directly by working than those in urban. Further, it is possibly that there are
some outside activities children in urban spend on which could not be captured in this study
besides working and learning, like entertainment or sports. As a result, they are easy to be
exhausted after working, thus affect directly to their educational achievement.
However, it is interesting to note that the coefficients which represent indirect impact of
child work on math scores in urban is 0.16 (1.19 – 1.03) and in rural is 0.34 (0.89 – 0.55). The
higher indirect impact indicates that work engagement affect schooling attributes (including
education years of children and their parents, schooling costs, motivation about school), then
educational achievement of children in rural rather than those in urban. It is possibly explained
that participation in working force obliges children in rural area to drop their school, or lose their
motivation of attending school and learning academic knowledge. The other characteristics
including ppvt score, age, and food consumption per capita still remain statistically significant
with mathematics performance of children in both rural and urban areas following same sign as
estimation for the whole sample. It is exciting to figure out that in the mathematics test, boys
outperform girls in rural area while girls achieve higher scores than boys in urban area.
The results continue confirm the strong positive influence of schooling attributes, such as
education years of children, on mathematics achievement in both sub samples, even controlling
the effect of child work or not. To be more specific, according to the significant level of
coefficients shown in column II and column V, the increase in one education year will rise the
math scores by 3.42 percentage points of the children in rural and by 2.88 percentage points of
those in urban. Additionally, it is noted that the magnitude of coefficient in rural sample is
unexpectedly larger than in urban sample although children in urban are provided higher
education quality (in learning materials, teaching methods, physical infrastructure and facilities,
human resources) than children in rural. This finding contribute to the evidence that school
engagement is highly influential factor which would enhance much the level of educational
outcome of children, especially those in rural area. Along with the education years of children,
the number of years that parents of them have completed also relate to math scores in urban
households at the level of 1% following expected sign. Similarly, the schooling costs operate

30. 28
positively with the scores of math test of children in rural households at 5% level. On the other
hand, it is reasonable to observe that the index of accessing to services shows the significantly
positive relationship with the math score for only children on urban area, meanwhile the number
of siblings in household turns out to be statistically significant for those in this sample because of
large education costs in urban area, consequently the budget for education is allocated among
children in households.
Finally, column III and column VI suggest that the omission of child work does not
change the statistically significance of schooling attributes as well as other determinants,
indicating that the estimated returns to schooling remain constant without adding working into
the equation.
Table 3: OLS estimation results for mathematics test scores (sub - samples)
Variables
Rural Urban
(I) (II) (III) (IV) (V) (VI)
Hours of economic work -0.886*** -0.554* -1.191*** -1.025***
(0.34) (0.32) (0.39) (0.37)
PPVT score 0.320*** 0.210*** 0.204*** 0.358*** 0.236*** 0.232***
(0.05) (0.05) (0.05) (0.06) (0.06) (0.06)
Age (months) -0.300*** -0.407*** -0.454*** -0.332*** -0.397*** -0.490***
(0.07) (0.07) (0.06) (0.07) (0.08) (0.07)
Gender (Female = 1) 3.722** 1.340 1.259 -2.130 -2.760* -2.978**
(1.57) (1.52) (1.52) (1.52) (1.45) (1.46)
BMI 0.234 0.125 0.147 0.348 0.320 0.306
(0.36) (0.33) (0.33) (0.24) (0.23) (0.23)
Hours of domestic tasks 0.091 0.250 0.542 -0.527 -0.634 -0.274
(0.76) (0.72) (0.70) (0.85) (0.82) (0.81)
Region: Northwest -2.263 -0.539 0.132 10.174 13.519 12.129
(3.09) (2.90) (2.88) (12.32) (11.73) (11.82)
Region: Red River Delta 5.932** 6.022** 6.474*** -12.939 -9.717 -12.234
(2.55) (2.41) (2.40) (10.14) (9.69) (9.73)
Region: South Central Coast -0.045 2.069 2.527 -13.558 -14.942* -16.238*
(2.74) (2.60) (2.59) (9.51) (9.04) (9.11)
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Table 3 continued
Ln(consumption/capita) 2.329* 1.950* 2.014* 3.362*** 2.069* 2.313*
(1.20) (1.14) (1.14) (1.21) (1.21) (1.21)
Housing quality index 4.001 1.246 0.751 9.810 8.477 9.765
(5.79) (5.44) (5.44) (9.54) (9.28) (9.35)
Access to services index 7.354 0.867 1.370 22.983*** 19.157*** 18.681***
(4.53) (4.39) (4.39) (6.02) (5.83) (5.88)
Household size -1.083 -0.622 -0.581 -0.306 0.012 0.005
(0.69) (0.66) (0.66) (0.35) (0.34) (0.34)
Number of siblings 1.333 0.671 0.586 -1.310 -1.405* -1.326*
(1.13) (1.07) (1.07) (0.82) (0.78) (0.79)
Education years of child 3.432*** 3.526*** 2.883*** 2.998***
(0.50) (0.49) (0.56) (0.56)
Parental education years 0.356 0.363 0.794*** 0.789***
(0.26) (0.26) (0.24) (0.25)
Motivation for education 1.047 1.023 1.086 1.179
(1.64) (1.65) (1.56) (1.58)
Schooling cost (million VND) 0.086** 0.088** -0.028 -0.028
(0.04) (0.04) (0.03) (0.03)
Constant 45.552*** 47.780*** 52.901*** 38.597** 41.030** 52.749***
(13.79) (14.92) (14.66) (17.22) (17.48) (17.09)
Observations 449 449 449 390 390 390
R2 0.209 0.318 0.313 0.251 0.335 0.321
Joint F test 8.205 11.139 11.566 8.976 10.364 10.353
White test 87.780 164.250 138.440 78.110 149.510 132.510
p-value 0.956 0.823 0.919 0.849 0.496 0.520
Shapiro-Wilk W test 0.998 0.998 0.998 0.997 0.998 0.998
p-value 0.310 0.652 0.314 0.179 0.610 0.253
Note: Values in parentheses represent standard errors
*** Notes significance at the 1% level
** Notes significance at the 5% level
* Notes significance at the 10% level
5. Results of two – stage least squares regression
Column II and V of table 4 show the results of first stage estimation with the dependent
variable of hours worked for children in two areas. The instruments perform well in predicting
child work. The income from crops reduce expectedly the number of hours worked of children in
both sample following income effect, but this factor is only statistically significant in rural
sample. Also, the community – level rice prices affects the intensity of work of children based on
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