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EUR · Web viewInternational Labour Organisation (ILO) estimates that 168 million children all...
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The Impact of Child Labour on Future Earnings: Indonesian Case
Erasmus University Rotterdam
Erasmus School of Economics
Department of Economics and Business
Master Thesis Policy Economics
Author : Muhammad Syarif Hidayatullah
Supervisor : Dr. Anne Gielen
Student Number : 379999
Date : December 2015
Table of Contents
I. Introduction 1II. Theoretical Background 3
II.1 Educational Decision 5
II.2 Child labour and earnings 6
III. Literature Overview 9
III.1 Supply side of Child labour 9
III.2 Child labour and earnings 10
IV. Methodology 11V. Data 13
V.1 Data description 13
V.2 Yearly Wage Log 15
V.3 Work Starting Age 15
V.5 Years of Schooling 16
V.6 The Instruments 16
VI. Results 17
VI.1 Robustness Check 20
VI.1.1 Potential Bias from migration 20
VI.1.2 Potential Bias from Different Age Group 21
VI.2 Discussion 22
VII. Conclusion 23
References
Table of Figures
Figure II.1: Wage Schooling Locus 5
Figure V.1: Box plot Graph of relationship between Income and Work Starting Age
Figure VI.1: Marginal Impacts on Work Starting Age 23
Table of Tables
Table 1 Sample selection 15
Table 2 Summary Statistic 15
Table 3 OLS Estimation 18
Table 4 IV Estimation 19
Table 5 IV Estimation with migration 21
Table 6 IV Estimation with Dummy Variable 22
Chapter I: Introduction
International Labour Organisation (ILO) estimates that 168 million children all around the
world are child labourers (between 5-17 years old), most of them living in developing
countries (ILO, 2012). Among these 168 million child labour, 120 million of them are below
14 years old, while further 30 million (mostly girls) perform unpaid household chores within
their own families (Unicef, 2015). Even though since 2000 there is a steady decline in
number of child labour, but the progress is still pretty slow. UNICEF estimates in 2020 there
will be 100 million children trapped in child labour. Some countries, started to discuss the
possibility of banning child labour. This type of policy responses have been widely debated
among economists (Emerson &Souza, 2007).
Indonesia is the fourth most populous country in the world, where almost 30 per cent of its
population are below 15 years old (ILO, 2014). Based on ILO estimation, there are 3.2 million
children between 10-17 years old who engaged in employment with some of them involved
in the worst form of child labour, for example, children worked in hazardous place or
commercial sexual exploitation. Moreover the labour’s participation rate of the children in
Indonesia is around 12.1 per cent (ILO, 2009).
We can classify a child labour is when the child is economically active (Ashagrie, 1993). A
person is economically active when he works for a regular basis and get remuneration (Basu,
1999). Child labour, based on International Labour Organization (ILO) definition, refers to
every children who; (1) aged 5-12 years old and working regardless their working hour; (2)
aged 13-14 who work more 15 hours per week, and (3) aged 15-17 who work more than 40
hours per week. In Indonesia, based on ILO convention 138 and ratified by Article No. 20 in
1999, stated that minimum age admission for employment is 15 years old. A little bit stricter
on Manpower’s Article no. 13/2003 stated that child is every person who is under 18 years
old (ILO, 2009).
There are many factors that contribute for rising number of child labour. As Rajan (1999)
suggested, credit constraints could raise the phenomenon of child labour, especially in
developing countries. There are also several factors that determined child labour in
Indonesia, Triningsih and Ichihashi (2010), found that poverty is one of the main
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determinants of child labour, and other factors are age, farming sector, and parent
education. Research on the effect of child labour in Indonesia has been done in several
topics. Some of them related to the adverse effect of child labour on health and education
(Sim&Asep, 2012) (Pitriyan, 2006), and some others evaluate the effect of government policy
on child labour.
From welfare perspective, it reflects that child labour can cause inefficiency. Even though
child labour could pushing down labour wage on market, thus benefited many firms, and
also child labour cause a major loss in social welfare. Baland and Robinson (2000) argued
that child labour is inefficient if it is misused by parents as substitute of negative incomes
and savings (to transfer income from child to parents) or, due to capital market
imperfections, it is being used to transfer income (of the children) from the future to the
present.
In general, researchers found adverse effect of child labour. For instance, in George
Pascharopoulus (1997) study, using survey data from Bolivia and Venezuela, found that
education attainment of working children is significantly lower than non-working children,
although working children significantly contribute to household income.
The effects of child labour on future earnings are still an empirical question. Some
researchers believe that child labour has adverse effect on future earnings, while some
others believe the opposite. Baland and Robinson (2000) thought that child labour is
inefficient if it adversely affects on child future earnings. Emerson and Souza (2007) stated
that the potential effects of child labour on adult earning are doubled up. On one hand, child
labour can be harmful through hindering the acquisition of formal education; on the other
hand there may be pecuniary benefit from vocational training, learning by doing (Emerson &
Souza, 2007). Furthermore, child labour could be a way to finance education, hence lead to
better outcomes for older child (Akabayashi and Psacharopoulus, 1999).
The central objective of this research is to empirically relate the effect of entering labour
market earlier with future income. The hypothesis of this study is that entering labour
market earlier leads to a decrease in the future income. The research question for this thesis
is: (1) is working during child age affecting individual current income;
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The result shows us that child labour has adverse effect on future earnings. Individual who
postpone entering the labour market has higher income than individual who work in earlier
age. However, the negative effect of child labour ceases at around ages 7-11.
This thesis is organized as follows: in section 2 provides theoretical background on what has
been established on the determinant of individual’s income and about human capital theory.
Section 3 provides some literature review on child labour. Section 4 elaborates dataset and
variables used for this analysis. Section 5 is about research methodology. Section 6
presented the results. Section 7 is the conclusion.
Chapter II: Theoretical BackgroundEveryone has a different well-being or income. Before 1960, many economists believe that a
difference is in a different physical capital, since rich individuals had more physical capital
than others (Becker, 1962). After 1960, there has been increasingly body of evidence that
shows non-physical capital also plays important role in creating that differences. One of
those non-physical capitals is human capital.
According to human capital theory, the increments in human capital or individual’s
knowledge stock raise his or her productivity in the economy where they can earn money
(Grossman, 2000). In order to raise the knowledge stock, individual have to choose particular
set of skills, how much investment on human capital he have to take. And basically, human
capital theory is about how those investments affect the evolution of earnings over the
working life (Borjas, 2013).
Lately, human capital theory becomes the dominant meaning of understanding how wage
are determined. Income determined by productivity and the productivity of labour is
determined by the labour’s skills or their human capital. Based on Becker’s view, Human
capital is directly useful in production process, explicitly it can increases workers productivity
(Acemoglu, 2005). Human capital has many sources. According to Acemoglu (2005), there
are several sources of human capital, such as schooling, innate ability, school quality,
training, and pre-labour market influence.
Human Capital Framework that used by Becker (1967), determined the optimal quantity of
human capital investment at any age. Based on Becker (1967), there are two types of human
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capital investment, first is on the job training, and second is in school. There is a specific
human capital investment on the job training. Skill that acquired from the job training
usually closely related to the individual’s current jobs, and more likely is not really
implemented in others jobs. This type of investment has an important effect on the relation
between earnings and age. Trained labour will receive lower earnings during training period
than untrained labour. But, after training period the earnings curve of trained labour will
much steeper than untrained labour. Becker also shows that after trainings period, the
earnings curve also become more concave, which means that the training has more effect on
younger age. Jobs training would be provided by the firm only if the marginal product of the
workers after training is equal to the initial wage of the workers.
Different from the job training, skill that being obtained from school is more general. It is not
specific to one type of jobs, but it can be used in numbers type of jobs. Hence, investment
on school is more transferable across job types than on the jobs trainings. Based on Becker
(1967), schooling has the same effect as on the job training. Schooling steepens the age-
earnings profile, mixing the income and capital accounts and allows depreciation on human
capital (Becker, 1967).
People are diverse on vast array of skill. The difference on skill comes from the differences
on individual’s endowment (genetics, parent’s investment) and individual’s human capital
investment. Parent’s education attainment and their education investment on their child
could affect individual’s skill. Children who have better educated parents are most likely to
have better education achievement.
Education is associated with higher earnings, yet not all workers want to get doctorates or
professional degrees. Education is valued only because they could increase income. Workers
would choose the level of education that maximizes the present value of earnings stream.
Workers earnings come from salary that employers are willing to pay for every level of
schooling.
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Figure II.1 Wage Schooling Locus
Source: Borjas (2013)
Figure II.1 shows the wage-schooling locus, the employer willingness to pay for every level of
schooling. From the graph above, we can see the wage-schooling is upward sloping, which
means that employers willing to pay higher wage for more educated workers. Moreover, as
we can see from the graph, the wage-schooling locus is concave; it means that monetary
growth from additional schooling is weakening as more schooling is acquired (Borjas, 2007).
II.1Educational DecisionEvery individual tries to maximize their own welfare. They are investing on human capital in
order to increase their future earnings. Basically every person follows the trajectory of age-
earnings profile or the wage path over the life cycle. For example, an individual who quit
school after getting high school diploma can earn some amount of wage from age 18 until
the age of retirement. But, if the individual choose to delay entering the labour market and
decides to go to college, he forgoes these earnings and incurs a cost for several years and
then earns higher wage until retirement age (Borjas, 2013). Therefore, many people are
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maximizing their welfare by choosing level of educations and trainings, such that the
marginal benefit of education and training is equal to its marginal cost.
Marginal benefits are both the material benefit (wage) and non-pecuniary benefit (academic
status, etc). On the other hand, marginal cost is such as direct cost (education cost, tuition
fee) and indirect cost (forgone earnings). Indirect cost or forgone earnings are differing
between what could have been and earned by individuals (Becker, 1962). If the marginal
benefit is lower than the marginal cost then people will cut their human capital investment
or even do not take any human capital investment.
There are two key factors that lead various workers to obtain different level of education or
human capital investment, thus to get different earnings, first, differences in the rate of
discount, second, differences in ability. First, workers who discount future earnings heavily
do not go to school because they are too present oriented (Borjas, 2013). Based on schooling
model, decision to continue to go to school is depends on present value of age earnings
profile. Higher education leads to higher future earnings. If one individual discounting
his/her future earnings too high, than the present value of future earnings would be low,
thus they will prefer not to take more education. Second, the difference in ability also effect
individual educational decision. Individual with better ability has relatively higher marginal
return on education.
II.3 Child labour and earningsBefore we discuss about the theoretical framework of child labour and earnings, we will
discuss the theory of supply side of child labour. To understand the supply side of child
labour, we need to consider the basic theory of household decision making. A generic
household decision model assumes that the household acts to maximize utility, which is
function of the number of children, children education, the leisure time per child, the leisure
time of the parents and a composite consumption goods (Brown, Deardorff, Stern, 2002).
Household income earned by selling goods that is produced in household enterprise or by
working. The husband allocates time between market work and leisure, the mother allocates
time among market work, leisure, child rearing and home production, and the children
allocate time among market work, leisure, education, and home production (Brown,
Deardorff, Stern, 2002).
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There are several uncompensated cross-elasticity in this model. For the father, an increase in
wage could raise the implicit price of leisure. Child education is substitute to father’s leisure.
In order to pay for his child’s education, father has to sacrifice some amount of leisure, and
takes more hours of works. If child’s education is more important than father’s leisure, and
later will be substituted, then this will lead to the change in child’s education. As for the
mother, an increase on her wage will increase the opportunity cost of each child, hence
lowering the family size. Decreasing family size will lead to raise education investment.
Moreover, the rise in mother’s wage will increase the demand on all normal goods, and also
education. For the children who works, the increase of (child) wages will step up the
opportunity cost of time that been spent on school. Moreover, the rise in the child wage will
increase the return to each birth, leads to larger family size and smaller education
investment.
From that basic model, Balad and Robinson (2000) developed a theoretical framework about
two period household decision model. BR assumes that household has a single decision
maker who decides child labour and schooling decision after making household income
decision. In the first period, parents choose the amount of savings and the fraction of
children working time. In the second period, parents receive saving income and gives
bequest to the child. Thus, parent’s utility comes from consumption in period 1, 2 and child
well-being, and the child well-being depends on the time they are not working and the
amount of bequest.
Balad and Robinson shows that if saving and bequest are not zero, then parents will choose
child labour so that the cost, in term of forgone consumption today of decreasing child
labour, is equals to the return of foregoing the child labour. On the other hands, if the saving
and bequest are zero, children cannot compensate parents for the forgone consumption
that comes from decreasing in child time spent to work.
The problems with inefficient child labour arise when families are credit constrained (Laitner,
1997), Parson and Goldin (1981), Jacoby and Skoufias (1996). In this situation, it’s very
difficult for parents to borrow money for their future needs, thus the parents have to rely on
internal assets. In child labour scenario, the parents prefer to send their children in labour
market rather than investing in human capital. This strategy will inefficient, because the
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present value of another hours of schooling is greater than the return of another hour of
work.
An increase in the child’s wage can affect education decision through several channels. First,
the increasing on the child’s wage could raise the opportunity cost of spent time in school;
second, increases in the child’s wage could also profit their family incomes. Based on this
phenomenon, many families try to enlarge their size or to have more children in order to
increase their income, but this will lead a decrease in educational attainment for children
(Brown, Deardorff, Stern, 2002).
There are several channels for child labour to affect the future earnings. First, child labour
can affect future earnings by changing the number years of schooling. Children who start to
work at very young age are more likely to attain less education, thus their earnings would be
lower than the other children who are delaying to enter the labour market. However,
working and having an education may even be complementary activities. In a household
with a low income and credit constrained, parents will force their children to work in order
to raise their household income. It is become the only way for the children to have an extra
education, whether it’s the working children or their siblings. Without extra income from
child labour, these household may be not able to send their children to school.
Second, child labour can affect working experience. Based on the Mincer model (1974), we
can see that working experiences will raise wages rate. Based on Mincer (1974) human
capital earnings function (HCEF), log of individual earnings particular time has two functions
in linear education and quadratic experience. From HCEF, we can see that working
experience will determine individual’s wage level, probably because human capital is
generated from learning by doing. Therefore, it is possible work experiences dominate the
length of school (Ilahi, Orazem&Sedlacek, 2005).
People who enter the labour market earlier have more working experience than people who
choose education over work. From Becker’s model, we can see that job training can also give
a raise to human capital, hence it also give a rise to individual’s earnings.
Many people would prefer to enter the labour market earlier than invest on extra education.
This can happened if the return to year of working experience is higher than the return to
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year of schooling. Thus, the decision to enter labour market at early stage could increase
lifetime earnings.
Child labour can affect work experience, length of education and human capital that based
on education level. The direct impact of child labour on future earnings is through physical
capital endowment inherited from parents or from work experience. Based on Ilahi’s model,
etc (2004), income determined by the direct effect of child labour plus the return on
education. Specifically, they also multiply the return on education with the effect of child
labour on education (Ilahi, Orazem & Sedlacek, 2005).
Educational cost can determine children’s decision to be a child labour. The higher
educational cost will cause the decrease on education investment. If the benefit of education
investment is lower than the benefit on having more working experience, many people
would enter labour market on earlier stage.
Chapter III: Literature OverviewIn this part we will discuss numbers of literature and empirical evidence that has been done
related to child labour issues. It will be divided in three parts. First is empirical evidence
about supply side of child labour. Second is the basic human capital model, specifically about
how education and experience affect individual’s wage. Third is recent empirical evidence
about the effect of child labour on future earnings.
III.1 Supply side of child labourThere is a lot of research that have tried to examine the supply side of child labour. In their
seminal work, Basu and Van (1997) stated that children only works if the family unable to
meet their basic needs. This statement has been proved by several empirical works. For
instance, Pscharopoulos (1997) found that income earned by age 13 Bolivian children is
equal to 13 per cent of total household income on average. An estimation made by Menon
et al (2005), found that 11 per cent of Nepal agricultural production comes from child
labour.
As we discussed in the previous chapter, child labour occur due to credit constrain. To test
this theory, Deheija and Gatti (2002) conducted a research using panel of 172 countries in
1950, 1960, 1970, and 1980, and used the share in GDP of private credit as a proxy of credit
constrained. Based on their estimation, one standard deviation increase in the share of
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credit is associated with 10 per cent of decreasing standard deviation on child labour, this
means that families with access to credit are less likely to put their children on work. Similar
estimation also has been done by Emerson and Souza (2002). They found that credit
constrained family will invest only in one children and let others children to work. By using
PNAD data (1998) and bivariate profit method, Emerson and Souza found that first born son
are less likely to work and first born daughter are less likely to attend school.
Other theories suggest that poverty is an important contributor to child labour. Vasquez and
Albar (2000), tried to prove this theory using Mexican household data dated from 1984 to
1996. They found that household income has little effect on child labour. Based on their
estimation, even if the household income is being doubled, it only increases the probability
of being fully-time student by 0.01 for rural girls and 0.03 for rural boys. In contrast, Ray
(1999) found that poverty will increase the child’s working hour. Based on his estimation, a
previously non-poor Pakistani household will increase their children’s working hour to
500/year if their family were below poverty line.
Some other research tried to find the effect on household income in child labour. A Study
that has been done by Kochar, Jacoby, and Skoufias (1997), found that child labour is an
important part of the household self-insurance. A small farm household adjusted their
children education and child labour in response to both predictable and unpredictable
variation in their family income. There were also a similar research that has been done in
Tanzania by Beegle, Dehejia and Gatti (2006). They correlated the crop shock as an
unpredictable variation in their income from child labour. They found a significance increase
of child labour supply in the household that report experiencing crop shock.
III.2 Child labour and earningsPrevious studies have shown that child’s school years may be increased or decreased, is they
need to work (Ilahi, Orazem & Sedlacek, 2005). Some studies also found evidence that child
labour have a lower grade and also a lower achievement in education every year
(Pscharopoulus, 1997) (Akabayashi and Pscharopoules, 1999). Ray (2003) found that
additional work hour in Ghana will caused children to have a shorter school year. Similar
with that finding, Pascharopoulus (1997) observed that children who worked in Bolivia
completed school nearly a year less than non-working children. On the other hands, based
on the fact that many working children also are supposed to be in school, some analyst has
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suggested that child labour and education are not mutually exclusive (Ravallion and Wodon,
2000) and may be complementary.
The issues of child labour are important because of two facts. First, child labour has
immediate effect on short term aspect of children who has to do physical work beyond their
capacity. Second, it has longer impact, for example, being labourer today, young person is
disinvesting in human capital formation (Pscharopoulus, 1997). As suggest by Grootaert and
Kanbur (1995), if there is a trade-off between child labour and education, then child labour is
inefficient as it has positive externalities with human capital formation.
Estimation made by Emerson and Souza (2007) found that child labour has a big negative
effect on adults earnings, and the negative impact started to reverse at around ages 12-14.
Similar with them, Ilahi, Orazem & Sedlacek (2005) found that child workers were 14% more
likely to be in the lowest two income quintiles as adults than children who did not enter
labour market until 12 years old.
Chapter IV: MethodologyThere are a lot of studies about the causes of child labour, but only few studied about the
consequences of child labour on their future earnings. The main reason of this study is the
confounding effect of potentially endogenous variables. There is a strong possibility that
unobserved variables (ability, ambition, etc) could affect both educational choice of a person
and his earnings in their adulthood. Many of the recent research has relied on the use of
instrument variable approach, but this approach have one main drawback, which is a
demand of a robust set of instrument for someone educational choice (Emerson & Souza,
2007).
In order to overcome these problems, I will replicate an empirical strategy that had been
used by Emerson and Souza (2007). Based on Emerson and Souza (2007), the discussion of
the empirical issues on the effect of child labour usually begins with a presentation of
standard two equation system that describes schooling (Si) and log current wages (lnY i), for
individual i:
(1) Si = X i ∂+V i
(2) lnY i=X i γ+S i β+ϑ i
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Xi is a vector that observes attributes of the individual and V i and ϑ i are the random error
terms that are assumed to be uncorrelated withX i. The β variable is a measure of the
educational benefit or average educational benefit. It is likely that education can have a
correlation with the unobserved component of the log earning equation, due to ability bias.
Hence, estimation of the β coefficient will be biased upwards. In the developing countries,
such as Indonesia, the decision to work as a child is likely correlated with the educational
decision and is also likely correlated with adults’ earnings. Therefore, where child labour is
widespread the educational and child labour decision are both likely to affect adults’
incomes and are likely to be correlated, the description of the process would involve a three
equation system (Emerson & Souza, 2007):
(3) Si = X i ∂+V i
(4) CLi=X iα+ωi
(5) lnY i=X i γ+S i β+CLi∅ +ϑ i
CL is age when a person starts to work, and ω is the unobserved random error term. In order
for ∅ to be measure of the effect on start working at a certain age, ωi and ϑ i must be
uncorrelated. But, these error terms are likely correlated because the same ability bias that
cause high ability individual in choosing educational over work at earlier stage and also
might choose to start working when they old enough.
To solve that problem, we need a set of regressor, Zi, that can be added to the vector X i
that will affect educational choice but will not affect the unexplained earnings component,
and this will affect the age level of someone who would start to work but not the
unexplained component of earnings Emerson and Souza (2007). In their research, Emerson
and Souza (2007) were using three instruments variables. First is regional GDP/capita for
children in 12 years old of age, second, school-student ratio and third teacher-school ratio.
One potential pitfall of Emerson and Souza estimation is the instruments could be correlated
with some omitted relevant variable. An instrument could be invalid if it is correlated with an
omitted relevant variable, even if the omitted variables does not correlated with the
endogenous variables (Murray, 2010). Emerson and Souza model has a lack of control in
parent’s characteristic. This model is controlling parent’s education but not controlling
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household’s income or parent’s income. Household income is correlated with the regional
GDP/capita.
In order to control the potential endogeneity, the instrument must be both relevant and
valid. It means that the instrument not only has to be well-correlated with the potentially
endogenous variables but also uncorrelated with the unexplained variation in earnings. In
this research, we used three instruments: distances between the house and primary school
sample; the school and student ratio; teacher and school ratio.
School distance as an instrument had been used by Card (1993). He argued that one would
expect a higher cost (live far away from college) to reduce investment in education, or at
least among the children from low-income families. It means that school distance is likely to
have correlation with both education and start working age. Meanwhile, this instrument is
more likely to be uncorrelated with future earnings.
Emerson and Souza (2007) used both school-student ratio and teacher-school ratio as
instruments in their estimation. Both instruments are well correlated with education and
start working age variables. The schools availability in one region could lower the
educational cost. Thus, the children are more likely to have more education than to enter
the labour market at earlier age. Same as with the teacher-school ratio that is basically could
affect the benefit and cost of education. These instruments are also more likely to be
uncorrelated with the unexplained variation of earnings. Furthermore, I will control family
background (parents’ education and income) and other cofounding effect in order to
manage the selectivity of the data.
In their study, Emerson and Souza (2007) were using the following instrumental variables
regression:
(6) Si=X i|Z iδ+v i
(7) CLi=X i|Z iα+ωi
(8) lnY i=X i γ+S i β+CLi∅ +ϑ i
They estimated the model both with and without the years of education variable to evaluate
the impact of the early entry in labour market and both also including the effect on schooling
and then. When schooling variable is included, it also has effect of early entry over and
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above the impact on schooling. Based on this model I will pull estimation. Similarly, I will also
estimate the model by both including and excluding the schooling variable. Furthermore, I
will also include one extra instrument variable, which is the school distance.
Chapter V: DataV.1 Data DescriptionThe main data sources utilized in this research are come from Indonesian Family Life Survey
(IFLS), a longitudinal household survey in Indonesia that has been conducted by RAND since
1993. Until now, there are 4 IFLS data waves (1993, 1997, 2000, and 2007). IFLS is a
comprehensive survey, collecting wide range of topics, including education, health, financial
assets, labour supply, nutrition, and child labour.
The first wave covered 13 of 27 provinces. This initial round interviewed roughly 7,200
households. By 2007, the number of households had grown to 13,000 as the survey
endeavored to re-interview many members of the original sample that form or join new
households. Household attrition is quite low; only around five percent of households were
lost in each wave. Overall, 87.6 percent of households that participated in IFLS1 were
interviewed in each of the subsequent three waves (Strauss et al., 2009).
To examine the effect of child labour on future earnings, I need two primaries information.
First is child labour status, and second is current income. To obtain the first information, we
used some information from IFLS related to working experience. In the very latest survey
(IFLS 2007), they obtain some information from the household head, spouse and family
member about their first jobs. In this section, this survey gathered information about the age
they entered the labour market, their occupation, employment status, how they can get the
job and about their salary. They also collected some detailed information such as jobs
category, whether it was self-employed, unpaid family worker, or private worker. From this
section, basically, I could have information about the group of people who had already
worked in their childhood.
We also can have some information about education history. Specifically, not only the
education history sample but also the parent education history sample. IFLS has also some
information about the school starting age, highest grade, and national test result. For parent
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education, IFLS has gathered some good information, such as highest education that been
attained by them.
Table 1 shows the number of observation that has been kept in our sample due to each
criteria of the selection process. The total number of group that is over 15 years old is
29,000. Only 9,536 of 29,000 have and know their own yearly salary or only around 27% of
this group knows their salary. Based on Indonesian Statistical Bureau, in 2007, there are 97
million workers in Indonesia, or about a half of Indonesia population at that time. After that,
we restrict the sample to the group of people who never migrated since they were born. We
also limited the sample by the availability of work starting age information. Doing so, we
ended up with 2,556 observations. As we can see in Table 1, number of observation is stay
the same, even after we restrict for years of schooling, father’s education, mother’s
education. But the number of observation dropped after we restrict for instrument.
Table 1: The Sample Selection
Variable ObservationIncome 9536Age Started to Work 2556Years of Schooling 2556Father’s Education 2556Mother’s Education 2556Instruments:School Distance 1830School/Student Age=6 1830Teacher/student Age=12 1830
After we have done our regression, the numbers of observation was 1830. As described in
Table 2, age of the working group is between 15-35 years old. They started to work since 7
to 30 years old. The interval of years of education in this group is 0-18 years. On average,
sample in years of education are much higher than the parents. Just like before, the father’s
year of education is slightly higher than the mother.
Table 2: Summary Statistic
Variable Obs MeanStd. Dev. Min Max
Income 1830 13.124 0.9036 8.9871 16.213Age Started to Work 1830 19.081 3.620 7 30
15
Age 1830 23.946 5.136 15 35Dummy Gender (if Male=1) 1830 0.628 0.483 0 1Years of Schooling 1830 8.156 4.915 0 18Father's Year of Schooling 1830 3.910 4.613 0 18Mother's Years of Schooling 1830 2.96 3.924 0 18The InstrumentsSchool-Student Ratio 1830 5.338 1.064 2.8663 8.713School Location 1830 11.318 8.421 1 90Teacher-School Ratio 1830 7.911 1.1573 5.183 13.432
V.2 Yearly Wage LogThe dependent variable is the log of yearly wage. The wage variables are obtained from the
2007 survey, specifically from IFLS Book 3A. Respondent were asked about their one year
salary including the value of benefit. 141 of 9536 people answered that they did not know
about it, and 3 respondent data is missing. Thus, the total number of sample that can be
used is 9,536.
V.3 Work starting ageThe main independent variable is legal working age and education attainment or school
starting age. This variable is gathered from IFLS 2007, Book 3a, section TK. They was asked
about when they started working full time for the first time. Full time work is when the job
was their primary activity. 5,856 of 6,951 of people answered that they know exactly the
year when they did start full time working. The rest answered that they either they didn’t
know or their job was never be their primary activity. To obtain this work starting age
variable is by simply subtracting birth year from starting year of full time working.
Before we do the regression variable work starting age, it is ranged between 0-62 years old. I
assume that 0-3 years old was caused by error on collecting the data, thus I dropped those
data. In this research, I limited the age variable only from 4-30 years old. Hence, the number
of this group that is left is 5,236.This number is reduced to 1830 after we are doing our
estimation.
V.4 Years of SchoolingEducation accomplishment is the total years of schooling of the group. 90.09% of the
respondent has 12 years of education or less. 22.91% of total respondent (29,057) have no
education, or zero years of schooling. Average year of education is 6.374 years. To control
the model, we will use several variables, such as father’s years of schooling, mother’s years
16
of schooling, age, and gender. Father’s and Mother’s years of schooling have the same
range, between 0-18, with father’s years education is slightly higher than mother’s.
V.5 the InstrumentsThere are three instruments that are used for this research, first the distance between house
and school, and second is the ratio between school and student, third is the ratio between
teacher and school.
First instrument that will be used in this research is the distance between house and school
(primary school). The data is measured in minute. This data is gathered from IFLS book 3a. In
that survey people were asked about how much time it takes to go from house to school. In
this research, we use the distance when they went to primary school. Based on Card (1993),
distance between house and school (college) is a good instrument for education. He argued
that people would expect this higher cost (live far away from college) to reduce investment
in education, or at least among the children from low-income families. This instrument is not
directly affect earnings, which make this variable can be a good instrument. School distance
can affect earnings through educational decision.
Second is the number of elementary schools in one region. The availability of school in
individual’s state could lower the cost of attending school by reducing the travel cost. Based
on human capital model, a lower education cost will increase an investment on education
and likely to cause a delaying to start working. This data is come from the Indonesian
Statistic Bureau. Due to the data limitation, we only have number of school data from 1978-
1998.
Third is number of teachers in elementary school, where the children started to have an
education at the age of 6. Similar with the number of school instruments, number of teacher
per school is source of exogenous variation in both cost and benefit of education. Hence,
with the same limitation as before, we only have the data from 1978-1998.
Figure V.1 Box plot Graph of relationship between Income and Work Starting Age
17
Based on figure V.1, there is a positive correlation between income and work starting age.
Based on the box plot graph above, the means is increases as age started to work increases.
However, since we have no control for others variable yet, then we can’t take any conclusion
from the graphs.
Chapter VI: ResultIn order to estimate the effect of being a child worker on income, we started this study by
estimating two types of earnings equations, the first type included the age variable when the
children started to work and its square, the age of the individual, the sex variables when one
for male and zero for female. The second type contained the same variables, but added with
year of schooling variable. All estimations are included the father’s and mother’s year of
education that control for family background. Controlling family background is important,
because if not properly controlled the estimation can be bias. For example, richer children
are more likely to attend school and enter labour market later and poorer children more
likely to abandon school and start to work early. Moreover, more educated parents may
choose to locate themselves near good school.
We begin by estimating the earnings model from OLS and then using the set of instrument
variable described above in IV framework. The first regression does not control years of
schooling. An individual who worked during childhood will likely to attend less education.
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Thus, the coefficient of age started to work variables when it is not controlled by education
(years of schooling), it could capture the expected forgone earnings of the young workers.
Then, when we controlling for education, it could capture the effect on adults’ earnings. In
order to get the Standard Error and statistics that are robust to the presence of arbitrary
heteroskedasticity and intra-group correlation, we are using robust standard error and
clustering standard error on region.
Table 3: OLS Estimation of Logarithm of Earnings
Variables
3.a 3.b
CoeffStd Error Coeff
Std Error
Years of Schooling 0.014* 0.0032Age Started to Work 0.15* 0.029 0.15* 0.029Age Started to work squared -0.003* 0.0007 -0.003* 0.0007Age 0.029* 0.0029 -0.029* 0.029Father Education 0.023* 0.004 0.021* 0.004Mother's Education 0.036* 0.005 0.035* 0.035Gender -0.14* 0.017 -0.15* 0.017Constant 10.77 0.307 10.8 0.306No Obs 2200 2200
*, **, and *** represent respectively statistically significance at the 1%, 5% and 10% level
Table 3 presents the OLS estimations, which include and exclude the education variables.
The first column (3a) shows the estimation without education variables. The main variable
(age started to work) is statistically significance at the 0.01 level. The coefficient is positive
which would indicate that the older someone enters the labour market, the higher earnings
they had. But, the negative effect of child labour will be diminishing after certain age. Using
the coefficient of age started to work and it’s squared, we calculated that the negative effect
of starting to work at younger age end at age 25. Columns 2.b present the estimation that
includes education attainment variable. The year of schooling variable is statistically
significance at the 0.1 level. The coefficient is positive which would indicate that there is 1.3
per cent increase in current earnings for each additional years of schooling.
Now we turn to the fourth estimation with and without school control. Inclusion of the
squared term of work starting age variables is to get the turning point of the relationship.
From it, we could know the age when working early started to have positive impact on
income. In order to get the Standard Error and statistics that are robust to the presence of
19
arbitrary heteroskedasticity and intra-group correlation, we are using robust standard error
and clustering standard error on region.
Table 4, column 4a, present the regression result of the first stage on this estimation. The F
test of the included instruments is all below 12; this indicates that they are not strongly
correlated with the endogenous variable. The Kleibergen-Paaprk LM statistic for under
identification test shows us that the p-Value is above 0.05. Thus we can’t reject the null
hypothesis, or it means that the model is not well identified, i.e., that the excluded
instrument are not strongly correlated with the endogenous regressors. The School Ratio
instrument is positively associated with the endogenous variable, it means the higher the
school ratio are the longer an individual delaying to enter the labour market. This is make
perfect sense, because higher school ratio means lower education cost. The school distance
instrument is negatively associated with the age of working. This finding also makes perfect
sense. If the school is far from home, than the cost of taking education become higher.
Hence the person is more likely to consume less education. Therefore, they will prefer to
enter the labour market earlier.
Table 4: IV Estimates – Second Stage Regression of Logarithm of Earnings
Variables4.a 4.b
Coeff Std Error Coeff Std ErrorYears of Schooling 0.021 0.31Age Started to Work 0.407 1.35 0.31 2.39Age Started to work Squared -0.02 0.027 -0.023 0.048Age 0.288 0.463 0.287 0.435Father Education 0.07 0.102 0.068 0.0927Mother's Education 0.035 0.024 0.034 0.02Gender 0.05 0.366 0.041 0.34Constant 7.71 12.72 8.51 21.98No Observation 1830 1830Hansen J-Statistic Chi-Square 0.946 0 Earnings is maximized at age at work 10.5 7.8
Robust standard error, clustered at regional level,. *, **, and *** represent respectively statistically significance at the 1%, 5% and 10% level
From the second stage (Table 4a) estimation we can see that work starting age variable
shows a positive relation, but the squared term has negative relation. However, we are
unable to rely on the result of the second stage due to the weak instruments. We can
calculate the turning point when working earlier started to give positive impact on income.
20
Based on the coefficient of age variable and its squared term, we can get the turning point at
age 10.5. But, once again, we unable rely on this result due to the weak instruments.
Table 4, column 4b, shows the IV estimation that include the year of education variable. The
result of first stage shows us that school ratio is statistically not significant to years of
schooling. On the other hand, both distance and teacher ratio instrument variable is
statistically significant to years of schooling. F test for the first stage estimation is below 12.
Even it is lower than the rule of thumb, but it is higher than the first IV estimation (which is
without years of schooling variable). Consistent with previous results, work starting age
variable is positive, and its square is negative. But no variables are statistically significant.
Based on the result, the turning point is at age7.8.
VI.1 Robustness CheckTo examine whether our model is sensitive to changes in regression specification, we
performed several robustness check.
First is to get the idea whether the results is robust or not to the inclusion of other
potentially relevant variables. We include the estimation migration, because we suspect that
the exclusion of migration will be the source of biasness. Second, we want to know whether
the results are differing by age group. We run the regression using dummy variable for work
starting age.
VI.1.1Potential Bias From migrationThere are several source of bias from this estimation. One is migration. Around 30 per cent
of our sample was migrated during their life time or living in a different state since birth. Bias
would occur if there is some underlying selection process where migration decision is
affected by some unobservable individual characteristic that correlated with child labour and
adult earnings (Emerson & Souza, 2007). For instance, the higher ability are more likely that
they would migrate to better place where they can get better education or job opportunity
or salary.
Table 5: IV Estimates- Second Stage Regression of Logarithm of Earnings with Migration variable
Variables5.a 5.b
Coeff Std Error Coeff Std ErrorYears of Schooling 0.029 0.29Age Started to Work 0.42 1.22 0.29 2.234
21
Age Started to work Squared -0.024 0.025 -0.02 0.044Migration 0.18 0.178 0.18 0.17Age 0.256 0.398 0.25 0.374Father Education 0.063 0.081 0.06 0.078Mother's Education 0.033*** 0.021 0.03*** 0.017Gender 0.031 0.317 0.01 0.28Constant 7.56 11.5 8.7 20.53Hansen J-Statistic Chi-Square 0.387 0 No. Observation 1830 1830Earnings is maximized at age at work 9.3 7.6
Robust standard error, clustered at regional level,. *, **, and *** represent respectively statistically significance at the 1%, 5% and 10% level
Table 5 is the result from both estimation (that include and exclude the years of schooling),
where we keep the migration as control variables. The result is basically similar with
previous estimation. The instrument variables do not really have an impact to the
immigration variables. The F test for the estimations is below 12. This indicates that we
cannot rely on to the IV estimation. Consistent with the previous estimation, the sign of the
age started to work variable is positive, and negative for its square. This means that entering
the labour market in earlier stage would lower the future income. The turning point in this
estimation is at age 9.3 if we do not include years of schooling variable.
VI.1.2 Potential Bias from Different Age GroupFrom previous result, we can see that child labour would have negative effect on future
earnings. But, this negative effect will be perished over time.
Table 6: IV Estimates- Second Stage Regression of Logarithm of Earnings Using Dummy Variable
Variables6
Coeff Std ErrorYears of Schooling 0.22 0.3001Dummy Age Started to Work(D=1 if Age started to work>=18) -0.22 2.674Age 0.24 0.0623Father Education 0.002 0.019Mother's Education 0.011 0.021Gender 0.528 0.22Constant 10.5 1.97Hansen J-Statistic Chi-Square 0.193 No. Observation 1830
Robust standard error, clustered at regional level,. *, **, and *** represent respectively statistically significance at the 1%, 5% and 10% level
22
Table 6 represents the estimation using dummy variable for work starting age. The dummy
variable is equal to child labour that is higher than 18 years old. The result is quite
interesting. Different from previous estimation, the dummy variable has negative sign, which
shows us negative correlation with income. That means delaying to enter the labour market
further will harm individual’s earnings. One explanation from this result is that the negative
effect of child labour on earnings already diminishes before 18 years old. This is also in line
with our previous estimation which showed us that the negative effect will be diminished at
around 8-11 years old. However, this result is slightly lower than Emerson and Souza’s (2007)
result; they found that the negative effect will be perished at 12-14 years old.
VI.2 DiscussionThe results suggest that there is a negative effect of being child labour on individual earning.
Based on this estimation, the effect would be ceases around ages 8-11. In compare with
Emerson and Souza result, this is slightly lower. The negative effect on child labour ceases
faster in our estimation than in Emerson and Souza (2007).
Figure V.1 shows us the marginal impact of age variable in 4a and 4b1. The declining trend of
the line means that the marginal effect of delaying to enter the labour market will keep go
downward as the age started to work increases and will be diminished in some certain age.
As we can see from the graph, based on this estimation as showed in 4a and 4b, the
marginal impact will be negative consecutively after age 8 and 10.
Figure V.1 Marginal Impact of Age Started to Work
1Marginal impact of age started to work was estimated by using the coefficient of age started to work and its square: (
(α (x2 )−β (x2 )2 )−(α ( x1 )−β (x1 )2)
23
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
4a 4b
In order to know the magnitude of the effect on entering the labour market earlier, we
compared the marginal impacts in this age variable on adults when we controlled education
and when we not controlled it. We are using the estimation from table 4, where 4a is
showing the uncontrolled education, and model 4b is when we were controlling for
schooling. From graph above, we can see a quite huge gap between the line, or we can say
that the negative effect of child labour diminish much faster when we control education.
This means that the negative effect of child labour mostly comes from education attainment.
The results show us that the IV estimation coefficient is always higher than the OLS
estimation. This might be counter intuitive, because some researcher believes that ability
bias biases the OLS estimates coefficient upward. However, we can argue that ability also
increase the opportunity cost of schooling, thus lead to downward bias on OLS estimation.
There are two main drawbacks in our estimation that can be improved in future research.
First is the weak instruments problem. All of the instruments are weak for every endogen
variables. This result quite surprising, because the same instrument has been used in others
research, and it shows strong result. For further research, it is better to replace the
instrument or maybe just add another instrument that might be good for this estimation.
Second, there is a possibility that the instrument is correlated with the omitted variables. An
instrument could be invalid if it is correlated with an omitted relevant variable, even if the
omitted variables does not correlated with the endogenous variables (Murray, 2010). This
could be the case because we have only used limited number of control variables. There is
24
possibility that our instrument is correlated with the omitted variables. For instances, we
used school distance as instrument variable. This can be correlated with the parent’s
income, which we were not control in our model. Parent’s income could be related to school
distance. The higher the income, parent’s will prefer or able to choose to live nearby the
school.
VII. ConclusionThis research investigated the effect of child labour on individual’s earnings. We find that
child labour is negatively correlated with individual’s earnings. We find that this negative
correlation happened, mostly due to the trade-off with education attainment, and the effect
of education attainment on earnings. We also find that the negative net effect reverse at
ages around 7-11.
Basically, it is hardly to conclude that it is optimal for contemporary Indonesian child to start
working at ages around 7-11. Considering the environment of the individuals in this research
grew up, maybe it is rational for them to started working earlier. Individuals in this research
were born between 1973 and 1992, 76% of them were born before 1988. As we mentioned
in theoretical part, credit constrained plays important role in household decision, especially
about investment in education and child labour. Before 1988, Indonesia has not liberalized
their banking sector. Access to the banking sector is very limited, because there were only
few bank exist. It is very hard for a household, especially the poor one, to get credit. This
could be the reason, why it is optimal for individuals in this sample to work earlier.
For further research, additional instruments are needed, because some instruments that
have been used in this research are not strong enough. For instance, some research used
regional GDP/Capita as instruments for this kind of estimation.
Other thing that can be done is using the newest IFLS, which might be available in 2016.
Children whose were 7-15 years old in 1993 will be 29-37 years old at 2015. By using rich
dataset from IFLS survey (there is special survey for children), we can have better research.
We can control more variables like children cognitive skill and parent’s income.
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