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.
II.
III.
IV.
V.
VI.
Introduction
Theoretical Background
II.1 Educational Decision
5
II.2 Child labour and earnings
6
Literature Overview
9
III.1 Supply side of Child labour
9
III.2 Child labour and earnings
10
Methodology
Data
11
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
Results
VI.1 Robustness Check
17
20
VI.1.1 Potential Bias from migration
20
VI.1.2 Potential Bias from Different Age Group
21
VI.2 Discussion
VII.
1
3
Conclusion
References
22
23
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 determinants of child
1
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;
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.
2
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 Background
Everyone 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 nonphysical 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
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
3
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 ageearnings 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.
4
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 Decision
Every 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 ageearnings 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 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
5
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 earnings
Before 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).
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
6
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 wellbeing 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 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).
7
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 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).
8
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 Overview
In 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 labour
There 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 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
9
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 earnings
Previous 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 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.
10
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: Methodology
There 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 (𝑙𝑛 𝑌𝑖 ), for individual
i:
(1) 𝑆𝑖 = 𝑋𝑖 𝜕 + 𝑉𝑖
(2) 𝑙𝑛 𝑌𝑖 = 𝑋𝑖 𝛾 + 𝑆𝑖 𝛽 + 𝜗𝑖
Xi is a vector that observes attributes of the individual and 𝑉𝑖 and 𝜗𝑖 are the random error
terms that are assumed to be uncorrelated with 𝑋𝑖 . 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):
11
(3) 𝑆𝑖 = 𝑋𝑖 𝜕 + 𝑉𝑖
(4) 𝐶𝐿𝑖 = 𝑋𝑖 𝛼 + 𝜔𝑖
(5) 𝑙𝑛 𝑌𝑖 = 𝑋𝑖 𝛾 + 𝑆𝑖 𝛽 + 𝐶𝐿𝑖 ∅ + 𝜗𝑖
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 𝜗𝑖 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 𝑋𝑖 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
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
12
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 = 𝑋𝑖 |𝑍𝑖𝛿 + 𝑣𝑖
(7) 𝐶𝐿𝑖 = 𝑋𝑖 |𝑍𝑖𝛼 + 𝜔𝑖
(8) 𝑙𝑛 𝑌𝑖 = 𝑋𝑖 𝛾 + 𝑆𝑖 𝛽 + 𝐶𝐿𝑖 ∅ + 𝜗𝑖
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 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: Data
V.1 Data Description
The 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.
13
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
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.
14
Table 1: The Sample Selection
Variable
Income
Age Started to Work
Years of Schooling
Father’s Education
Mother’s Education
Instruments:
School Distance
School/Student Age=6
Teacher/student Age=12
Observation
9536
2556
2556
2556
2556
1830
1830
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
Income
Age Started to Work
Age
Dummy Gender (if Male=1)
Years of Schooling
Father's Year of Schooling
Mother's Years of Schooling
The Instruments
School-Student Ratio
School Location
Teacher-School Ratio
Std.
Obs
Mean
Dev.
Min
Max
1830
13.124
0.9036
8.9871
16.213
1830
19.081
3.620
7
30
1830
23.946
5.136
15
35
1830
0.628
0.483
0
1
1830
8.156
4.915
0
18
1830
3.910
4.613
0
18
1830
2.96
3.924
0
18
1830
1830
1830
5.338
11.318
7.911
1.064
8.421
1.1573
2.8663
1
5.183
8.713
90
13.432
V.2 Yearly Wage Log
The 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
15
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 age
The 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 Schooling
Education 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 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 Instruments
There 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),
16
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
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.
17
However, since we have no control for others variable yet, then we can’t take any conclusion
from the graphs.
Chapter VI: Result
In 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.
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
Years of Schooling
Age Started to Work
Age Started to work
squared
Age
Father Education
Coeff
0.15*
-0.003*
0.029*
0.023*
3.a
Std
Error
3.b
Std
Coeff
Error
0.014*
0.0032
0.029
0.15*
0.029
0.0007
0.0029
0.004
-0.003*
-0.029*
0.021*
0.0007
0.029
0.004
18
Mother's Education
Gender
Constant
No Obs
0.036*
0.005
-0.14*
0.017
10.77
0.307
2200
0.035*
0.035
-0.15*
0.017
10.8
0.306
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 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
19
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
4.a
4.b
Variables
Coeff Std Error Coeff Std Error
Years of Schooling
0.021
0.31
Age Started to Work
0.407
1.35
0.31
2.39
Age Started to work Squared
-0.02
0.027 -0.023
0.048
Age
0.288
0.463 0.287
0.435
Father Education
0.07
0.102 0.068
0.0927
Mother's Education
0.035
0.024 0.034
0.02
Gender
0.05
0.366 0.041
0.34
Constant
7.71
12.72
8.51
21.98
No Observation
1830
1830
Hansen 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. 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 Check
To examine whether our model is sensitive to changes in regression specification, we
performed several robustness check.
20
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 migration
There 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
Variables
Coeff
Years of Schooling
Age Started to Work
0.42
Age Started to work Squared
-0.024
Migration
0.18
Age
0.256
Father Education
0.063
Mother's Education
0.033***
Gender
0.031
Constant
7.56
Hansen J-Statistic Chi-Square
0.387
No. Observation
1830
Earnings is maximized at age at work
9.3
5.a
Std Error
1.22
0.025
0.178
0.398
0.081
0.021
0.317
11.5
5.b
Coeff
Std Error
0.029
0.29
0.29
2.234
-0.02
0.044
0.18
0.17
0.25
0.374
0.06
0.078
0.03***
0.017
0.01
0.28
8.7
20.53
0
1830
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
21
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 Group
From 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
6
Variables
Coeff
Std Error
Years of Schooling
0.22
0.3001
Dummy Age Started to Work
(D=1 if Age started to work>=18)
-0.22
2.674
Age
0.24
0.0623
Father Education
0.002
0.019
Mother's Education
0.011
0.021
Gender
0.528
0.22
Constant
10.5
1.97
Hansen 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
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 Discussion
The 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).
22
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
0.4
0.2
0
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
-0.2
-0.4
-0.6
-0.8
-1
-1.2
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.
1
Marginal 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
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
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. Conclusion
This 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.
24
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.
25
References
Akabayashi, H., & Psacharopoulos, G. (1999). The Trade-off between Child Labor and Human Capital
Formation: A Tanzanian Case Study. Journal of Development Studies.
Basu, K., & Van, P. H. (1998). The Economics of Child Labour. American Economic Review, 412-427.
Becker, G. (1962). A THEORETICAL AND EMPIRICAL ANALYSIS, WITH SPECIAL REFERENCE TO
EDUCATION. The University of Chicago Press Book.
Beegle, K., Dehejia, R., & Gatti, R. (2006). Child labour and agriculture shock. Journal of Development
economics.
Borjas, G. (2013). Labor Economics. New York: McGraw Hill Education.
Brown, D., Deardroff, A., & Stern, R. (2002). The Determinants of Child Labour: Theory and Evidence.
RSIE.
Card, D. (1993). Using Geographic Variation in Collage Proximity to Estimate The Return to Schooling.
NBER.
Dehejia, R., & Gatti, R. (2002). Child Labour: The role of income variability and credit constraint
accross countries. Working Paper no. 9018 (National Bureau of Economic Research,
Cambridge, MA).
Edmonds, E. (2007). Child Labour. Institute for the Study of Labour.
Emerson, P., & Souza, A. (2002). Birth Order, Child labor, and school attendance in Brazil. Working
Paper no. 212 (Vanderbilt University).
Emerson, P., & Souza, A. (2007). Is Child Labor Harmful? The Impact of Working Earlier in Life on
Adult Earnings. IZA.
Goldin, C., & Parson. (1981). Economic Well Being and Child Labor: The interaction of family and
industry. NBER.
Grootaert, C., & Kanbur, R. M. (1995). Child Labour: An Economic Perspective. International Labor
Rev.
Ilahi, N., Orazem, P., & Sedlacek, G. (2005). How Does Working as a Child Affect Wage, Income and
Poverty as an Adult? Social Protection Discussion Paper Series World Bank.
Jacoby, H., & Skoufias. (1996). Risk, Financial markets, and human capital in a developing country.
Review of Economic Studies.
Menon, M., Pareli, F., & Rosati, F. (2005). Estimation of the contribution of child labour to the
formation of rural incomes: An application to Nepal. Working Paper no.10 (Centre for
household income, labour, and demographic, Rome, Italy).
Murray, M. (2010). The Bad, the weak, and the ugly: Avoiding the pitfalls of instrument variable
estimation.
26
Organization, I. L. (2009). Working Childrean in Indonesia. Jakarta: ILO.
Patrick, E., & Souza, A. (2007). Is Child Labor Harmful? The Impact of Working Earlier in Life on Adult
Earnings. IZA.
Pitriyan, P. (2006). The Impact of Child Labor on Child's Education: The Case of Indonesia. Working
Paper in Economics and Development Studies.
Psacharopoulus, G. (1997). Child labor versus educational attainment: Some evidence from Latin
America. Journal of Population Economics.
Ravallion, M. &. (2000). Does Child Labor Displace Schooling? Evidence on Behavioral Responses to
an Enrollment Subsidy. Economic Journal.
Ray, R. (1999). How child labour and child schooling interact with adult labour. Working Paper 2179
World Bank.
Sim, A., & Suryahadi, A. (2012, September 19). East Asia Forum. Retrieved from East Asia Forum:
http://www.eastasiaforum.org/2012/09/19/the-effect-of-child-labour-on-skills-evidencefrom-indonesia/
Triningsih, N., & Ichihashi, M. (2010). The Impact of Poverty and Educational Policy on Child Labour in
Indonesia. IDEC.
27