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

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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

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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

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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

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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)

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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

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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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