Where child labor supply finds its demand

Krisztina Kis–Katos and Gunther G. Schulze ∗

May 17, 2006

Abstract

We analyze child labor incidence in small industries in Indonesia at the village level.

Using a data set of all Indonesian villages and urban neighborhoods we find that child

labor is significantly associated with poverty, natural and epidemic disasters, and with

unemployment. It is negatively associated with credit and school availability, only if we

correct for the existence of small industries in that village. Our results thus confirm the

importance of school availability and credit provision as policy instruments to reduce

child labor. They also show that it is imperative to look at the interplay of demand

and supply determinants of child labor to arrive at unbiased results and correct policy

conclusions.

JEL Classification: J8, I3

Keywords: Child labor, demand effects, SMEs, credit constraints, Asia, Indonesia

∗Institute for Economic Research, Department of International Economics, University of Freiburg, Germany.

Contact: krisztina.kis-katos@vwl.uni-freiburg.de. We are grateful to the Volkswagen Foundation for financial

support and Fitria Fitrani for excellent research assistance. All remaining errors are ours.

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1 Introduction

In 2000 186 million children worked as laborers worldwide, 110 million were under the

age of 12 (ILO 2002); in Indonesia approximately 1,7 million children were part of the

labor force (ILO 1985-2005). This is reason for great concern, not only because of negative

consequences for the development of the affected children; child labor also diminishes

human capital formation, which may have high rates of return (see e.g., Duflo 2001).1

Empirical evidence suggests that child labor is associated with poverty. In cross country

analyzes GDP per capita turns out as a very powerful determinant of child labor (e.g.,

Krueger 1996, Cigno, Rosati and Guarcello 2002); Edmonds and Pavcnik (2004) show that

open economies have less child labor due to the income gains from trade.

At the household level, direct evidence on the effect of income or wealth on child labor is

less conclusive, partly because of the endogeneity of income or expenditure measures to

the child labor decision (Bhalotra and Tzannatos 2003). Edmonds (2005) shows in a panel

approach for Vietnam that the favorable income effects of a trade liberalization reduced

child labor. Bhalotra (2003) estimates a negative own wage elasticity of child labor supply

for Pakistani boys (though not for girls); apparently, the income effect from rising child

wages outweighs any substitution effect.2 These findings support the notion that parents

send their children to work only if they are forced to do so in order to meet subsistence

1Even if most children who work attend school, their educational attainment suffers from child labor

(Beegle, Dehejia and Gatti 2004, Heady 2003, Orazem and Gunnarsson 2004). Out of the thirty percent

of children worldwide aged 5-14 who do not attend school almost 60 percent are working (Edmonds and

Pavcnik 2005).2Another possibility to overcome the endogeneity problem for income is to examine exogenous income

transfers provided by different school incentive programs like the Food for Education Program in Bangladesh

(Ravallion and Wodon 2000), the Mexican PROGRESA (Schultz 2004), or Brazil’s Bolsa Escola program

(Bourguignon, Ferreira and Leite 2003).

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needs.3 Yet, a large number of studies fails to find a positive relationship between poverty

and child labor (e.g., Ray (2000, Pakistan), Canagarajah and Coulombe (1999, India),

Psacharopoulos and Patrinos (1997, Peru)). Bhalotra and Heady (2003) and Kanbargi and

Kulkarni (1991) even find a positive relationship between household wealth and child labor

for agricultural households in Pakistan and Ghana, and in Karnataka/ India, respectively,

which they explain by imperfect agricultural markets for land and labor. Thus, at the

microeconomic level the role of wealth and income for child labor is still not entirely clear.

If investment in human capital is profitable, the poor could, in principle, borrow against

future earnings and send their children to school.4 Yet, in many developing countries the

poor are thought to have insufficient access to credit, which gives rise to child labor and

a misallocation of resources (Baland and Robinson 2000, Ranjan 2001). The empirical

problem in testing this hypothesis is that (insufficient) access to credit is typically not

directly observable and therefore has to be inferred from reactions to income shocks.

Dehejia and Gatti (2002) show that income volatility affects child labor more strongly

in countries with low financial development. Jacoby and Skoufias (1997) show for rural

India that school attendance declines in response to unanticipated seasonal fluctuations

in household income. Beegle, Dehejia and Gatti (2003) find that Tanzanian farmers react

to transitory income shocks with increased child labor, but that this increase is lower for

farmers with durable assets, who can be assumed to have better access to credit due to their

collateral. Edmonds (2004) shows that the introduction of a large pension scheme for black

South Africans reduced work of those children living with recipients of the pension, but not

of those living with future pensioners. These findings suggest that credit constraints are

binding.

3This has been coined the ’luxury axiom’ (Basu and Van 1998).4Child labor and school attendance need not be mutually exclusive; still credit would allow households to

optimize the trade off between school attainment and child labor income.

2

Contrary to this, Manning (2000), Cameron (2001), and Suryahadi, Priyambada and

Sumarto (2005) do not find any significant increase in Indonesian child labor in response

to the sharp decline in income during the Asian crisis 1997-1998. Manning (2000) argues

(but does not show explicitly) that high unemployment has made it hard for children to

find jobs; in other words, negative income shocks may increase the supply of child labor

and decrease the demand for it at the same time. We observe only the net effect.

Although these findings are suggestive they are necessarily indirect. An ideal empirical

strategy would seek to measure credit constraints directly, but data limitations typically do

not allow for that.5 Moreover, it would distinguish between supply and demand factors in

explaining child labor incidence and explicitly analyze the role of unemployment for child

labor.

Yet, most microeconometric studies have focused on the determinants of child labor sup-

ply, given by household characteristics (income and/or wealth, parental education, fam-

ily structure, etc.), but did not systematically control for demand factors (c.f. Bhalotra

and Tzannatos 2003). They also did not condition on (local) unemployment, presumably

because geographical coverage was too small to allow for cross-section variation in unem-

ployment. Changes in unemployment over time are correlated with income shocks, thereby

making an identification strategy difficult, if not impossible. Only Kambhampati and Ra-

jan (2005) analyze supply and demand determinants of child labor. They use the rate of

growth of regional GDP and the proportion of agriculture in state’s GDP as proxies for child

labor demand and consider the level of state GDP (as well as traditional household vari-

ables) as supply determinants. It is not entirely clear though that the level of regional GDP

5The only exception is Guarcello, Mealli and Rosati (2003) who measure credit restriction directly through

a survey recording credit history and find that credit constrained families in Guatemala are less likely to send

their children to school and more likely to increase child labor in response to income shocks.

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proxies supply factors and its change proxies demand factors.6 In other words, although

their approach is a major improvement over existing studies, their proxies for child labor

demand may be less than ideal. Our data set contains more direct measures.

Thanks to a unique data set we are able to directly and separately measure the effect of

credit constraints, (local) unemployment, income shocks, and poverty. Our data set is

the Village Potential Statistics (PODES, Potensi Desa), which covers all villages and urban

neighborhoods in Indonesia (more than 68 thousand). It provides information about child

labor incidence in small industry at the village level, credit availability, income shocks, a

wide range of poverty-related variables, local unemployment, and the economic structure

of villages and urban neighborhoods. This allows us a richer specification of supply and

demand factors such as size and structure of the small industries, and infrastructure vari-

ables, including the availability of schools. Unlike most other data sets, it also allows to

identify the determining factors of child labor separately and more comprehensively.7 Our

paper thus contributes to the understanding of the role of poverty, credit constraints, in-

come shocks, and unemployment on child labor and underlines the importance of demand

factors for child labor.

We find that child labor in small and medium industry is significantly associated with

poverty, negative income shocks, and unemployment. Credit availability increases child

labor, but only through a demand side effect: It increases the likelihood of small industry

locating in a village, and if so, the number of small firms in that village and thereby the

6Other things being equal, the higher the level of economic activity, the higher the demand for labor,

including child labor. In addition, the level of economic activity may be endogenous to child labor.7For instance, credit constraints may increase child labor also in the absence of income shocks, but have

been measured only in connection with income shocks due to the identification problem described above.

Likewise, credit constraints will undoubtedly exacerbate the impact of a shock, but shocks may have an impact

on child labor also in the absence of credit constraints. Furthermore, the omission of unemployment may lead

to an omitted variable bias if it is correlated with the error term.

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demand for child labor. In other words, the set of villages/urban neighborhoods that have

small firms is a biased sample of all Indonesian villages/neighborhoods with some of the

determinants of child labor also being responsible for the sample selection. If we correct

for the sample selection bias in a two stage Heckman procedure, credit availability signif-

icantly reduces child labor. This signifies the supply side effect of credit availability. Put

differently, if small firms were to locate randomly, credit availability would clearly reduce

child labor; since the probability of firms locating in a village and, if they do, their number

is endogenous to credits being available, credit availability increases child labor incidence.

On the local level, the demand effects outweighs the supply effect. By a similar argument,

school availability significantly reduces child labor only after correcting for location deci-

sions of small firms. Again, the likelihood of small firms locating in a village increases with

school availability. Our findings underline the notion that in order to understand the cross-

regional pattern of child labor incidence we have to analyze the interaction of demand and

supply factors.

We proceed as follows. In the next section we lay out the regional and sectoral incidence of

child labor in small industries throughout Indonesia, describe our data and introduce our

hypotheses about the determinants of child labor. Section 3 sets out the empirical model

and reports the results. Section 4 concludes.

2 Data and Hypotheses

2.1 Regional and Sector Incidence of Child Labor in Indonesia

Indonesia has experienced a decline in labor force participation rates of children aged

10-14 from 22.1% in 1960 to 7.1% in 2002. (During that period worldwide labor force

participation rates of children have declined from 24.9% to 10.6%.) This reduction is due

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to rising living standards, falling family sizes and structural change that reduced the labor

force in agriculture and the cottage and small scale industry, the main sectors in which

children worked. Children still work predominantly in agriculture: in 1998, 70% of the

children between 10-14 years worked in agriculture, 14% in trade, and 11% in manufac-

turing. For children aged 15-19 the respective figures were 51% for agriculture, 15% for

trade, and 17% for manufacturing. Thus, after agriculture and trade, manufacturing was

the third most significant sector where children were employed (cf. Manning 2000). An-

other important reason for the decline in child labor is a large school construction program

(Sekolah Dasar INPRES program) that was launched in 1973.8

There is a strong rural–urban divide in overall child labor incidence: In 1998 3.5% of

children aged 10-14 living in urban areas were part of the labor force, whereas the corre-

sponding figure was 11.1% for rural areas. In the age group 15-19 the respective figures

were 27.3% (urban) and 46.9% (rural).9 We analyze child labor in manufacturing only,

both in rural and urban areas.

2.2 Child Labor in Small Industries

Our data source is the Village Potential Statistics, (Potensi Desa; PODES) of 2003; it is

compiled by Statistics Indonesia (Badan Pusat Statistik, BPS) in the context of the periodic

census and covers 69125 villages and urban neighborhoods, all villages/neighborhoods in

Indonesia.10 It consists of a rich set of data at the village level; in particular it reports for

8From 1974 to 1978 more than 61,000 primary schools were built. This has led to a significant increase

in school enrollment rates for children aged 7-12 from 69% in 1973 to 83% in 1978 and had significant labor

market impacts; cf. Duflo (2001, 2004).9These data are based on the National Labor Force Survey (SAKERNAS) 1998, which discontinued to

report labor incidence of children aged 10-14 in 1998. Comparisons with regional in-depth studies suggest

that SAKERNAS significantly underestimates the extent of child labor (Manning (2000:15)).10Due to data encoding errors, our main regressions will be using observations on 68472 to 68702 villages.

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each village the number of firms in the small industries and handicraft sectors (leather,

wood, metal, ceramic, waving, food, and other) and whether children are employed in

these sectors. Thus, we do not have the number of child laborers, but we know in which

sectors and villages child labor occurs. The sectoral pattern of child labor incidence in

manufacturing is given in Table 1.

Child labor incidence % Small firmsBy industry (unc. mean) (cond. m.) in % of vill. Nr.(cond. m.)

Leather 0.4 17.5 2.3 7.2

Wood 4.4 19.9 22.3 7.9

Metal 0.8 18.1 4.2 9.0

Ceramic 4.1 24.7 16.7 29.1

Waving 1.8 18.9 9.6 26.1

Food 7.3 24.2 30.3 15.0

Other 2.8 23.0 12.0 25.3

Total 15.1 29.5 51.3 17.3

Notes: The unconditional mean of child labor incidence gives the average prevalence rate of child

labor over all villages. Conditional means are calculated for the subsample of those villages where

SME-s operate in the given industry (for Total: in any industry).

Table 1: Incidence of child labor in different small industries

The first column gives child labor incidence per sector, whereas the second column gives

child labor incidence conditional on that at least one small firm in that sector operates in

the village. The third column states the percentage of villages that have firms in that sector,

the fourth states the average number of firms for villages that have firms in that sector at

all.11 Child labor incidence is strongest in the food, ceramic and wood sectors, which have

highest prevalence in villages. Conditional incidence is highest in food and ceramic sectors

– a quarter of villages that have small firms in that sector have children working there.

Child labor incidence by province is given in Appendix A.1 (Table 6). Child labor incidence—

measured as percentage of villages in which children work in small industries—is highest

11For instance, in 4% of all villages children work in the ceramic industry; yet only 17% of all villages have

ceramic industry at all (with 29 firms on average). In that subgroup one in four villages employs children.

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in Java (20%) and Bali/Nusa Tenggara (20%) and lowest in Irian Jaya (3%) and Maluku

(7%). A very different picture emerges for child labor incidence conditioned on the exis-

tence of small firms: It is highest in Kalimantan (39%) and Iran Jaya (33%) and lowest

in Maluku (23%) and Java (27%). Regional incidence of child labor by sector is given in

Tables 4 and 5 in Appendix A.1.

2.3 Explanatory Variables and Hypotheses on Child Labor

Our endogenous variable is a binary variable that is one if child labor takes place in small

industries in that village at all and zero otherwise. Thus we do not capture child labor in

other sectors, notably not in agriculture.

In accordance with the literature we hypothesize that the supply of child labor depends

positively on the degree of poverty in a village, thus the larger the share of poor people

the more likely it will be that children work in small industries. As proxies for poverty

we use the share of families that live in a slum area and the share of families that live

in non-permanent houses. To control for size of the village, we use its population and its

population squared.

The importance of credit constraints for child labor has been discussed extensively in the

literature (cf. Sect. 1). Poor families without access to credits may have to use child labor

as consumption smoothing device. We thus hypothesize that villages that have access to

credit are less likely to experience child labor, other things being equal. PODES collects

data on various forms of small credits available to the village. We code a binary variable

that is one if the village has access to any form of credit and zero otherwise.

Nontransitory negative shocks create the need to smooth consumption through credit or

child labor. Even if people may have access to credit, credit facilities may be insufficient

or people may opt to compensate the loss of income by a combination of credit and child

8

labor. If the shock is permanent, for instance because the main income earner dies sud-

denly, children may have to work in order to compensate for the loss in income. Our third

hypothesis is that negative shocks increase the likelihood of child labor in a village. We

measure negative shocks in two dimensions. DISASTERS is a binary variable that is one

if the village has been hit by a natural disaster (flood, earthquake, landslide, etc.) in the

previous year. In addition we include the number of deaths by epidemics as a share of total

population that occurred in the previous year.

The decision to send children to work rather than to school depends on the opportunity

costs of work. These are higher, if schools are nearby and thus easy to reach.12 Obviously,

children could work and go to school, but the likelihood that children work can be expected

to be lower if they go to school (cf. Bhalotra and Tzannatos 2003). We thus hypothesize

that the likelihood of child labor goes down with the number of ’relevant’ schools in the

village. We include the number of primary schools and separately the number of junior-

high schools in the regression.

The demand for child labor in small industries is proxied by the size and the structure of the

village economy. Since we capture only child labor incidence in small industries we use the

number of small firms in the village and include a squared term to allow for non-linearities.

We hypothesize that the larger the number of firms in a village the higher the demand for

labor and thus the more likely child labor is to occur.

To account for alternative job possibilities for children, notably in agriculture and trade, we

include a dummy variable that is one, if the small industries are the main income source

of the village. We expect this variable to increase the likelihood of child labor in small

industries.

12Pradhan (1998) shows for Indonesia that region characteristics have a strong effect on the demand for

schooling, Alisjahbana (1994) finds that travel distance to school is an important variable in explaining enroll-

ment in primary and secondary schools in Indonesia.

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Variable Description

Child labor (0–1) variable indicating whether child labor is used in any small industry

Nr. of SMEs Nr. of SMEs (in .00) located in the village (in any industry)

Nr. of SMEs squared Nr. of SMEs (in .00) squared

Credit (0–1) variable indicating whether the village has access to any form of

credit

Credit to SME-s (0–1) variable indicating whether there are credits available for SMEs

Slum families Measures the share of families in the village who live in a slum area

Bad housing The share of families who live in a non–permanent house

Unemployment Village level unemployment rate, ratio of the unemployed to village

population

Epidemic deaths Incidence rate of deaths bc. of an epidemic in previous year (in % points)

Disasters (0–1) variable indicating whether the village has been hit by any kind of

disaster during the last year

Urban (0–1) variable indicating urban villages

Remote Geometrical average of the distance from own district office and the near-

est neighboring district office

Coastal (0–1) variable indicating coastal villages

Flat (0–1) variable indicating villages situated on flatland

Altitude Altitude of the village above sea level (in .000 meters)

Electricity Share of families with electricity

Nonfertile land (0–1) variable indicating the presence of nonfertile land in the village

Population Number of inhabitants (in .000)

Pop. squared Number of inhabitants (in .000) squared

Mainly industrial (0–1) variable indicating whether industry is the main income source

Primary schools Number of primary schools

Junior-high schools Number of junior high schools

Table 2: Definitions of variables used

As infrastructure variable we include the share of families with electricity. As the number of

firms may not fully capture the extent of economic activity (firms may have different sizes)

we assume that the level of economic activity per firm increases with infrastructure quality.

Thus we could expect demand for labor and therefore child labor incidence to increase

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with this variable. However, the share of families with electricity may be also indicative

of village wealth and thus proxy the degree of poverty (even though it captures the upper

end of the wealth distribution). The effect of this variable is thus a priori ambiguous.

The level of unemployment may affect demand and supply side. If the main income earner

becomes unemployed, labor supply by children may increase in order to (partly) compen-

sate the loss in income. At the same time the demand for child labor may go down as there

is an excess supply of adult labor. Children may find it harder to find a job. The net effect

is a priori undetermined.

To account for the rural urban divide in child labor incidence (Manning 2000) we include

a binary variable URBAN. We also include a binary variable for villages in the flatland as

economic activity is mostly concentrated in flat areas. The variable REMOTE measures the

geometrical mean of the distance to the district office and the nearest neighboring district

office. To control for unobservable characteristics of various parts of the archipelago such

as differences in culture, institutions, and mentality we included province fixed effects.13

In the regression determining the probability of SMEs locating in a village we include

additional locational factors. Credit to SMEs indicates credit availability specifically for

small businesses which can be expected to raise the probability of firms to locate in that

village. Further controls pertain to geographical concentration of economic activity such

as altitude and coastal location, and to the suitability for agriculture (NONFERTILE LAND).

All variables are taken from PODES 2003. They are summarized in Table 2.3. Descriptive

statistics can be found in the Appendix A.2 in Table 7.

13We have also experimented with religion as explanatory variable by including a dummy for villages with

muslim majority. Although this variable was positively correlated with economic activity, it had no additional

effect on child labor, and has been dropped from our final analysis.

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3 Empirical Model and Results

3.1 Empirical Strategy

We are interested in the supply and demand–side determinants of child labor incidence

in small industries at village level. As we observe only the existence, but not the extent

of child labor in a village, we estimate the probability of child labor occurring by a probit

model. However, as we can observe child labor only in those villages where SMEs operate,

the probit model leads to biased estimates on those variables that affect not only child

labor but also industrial location. For example, we expect school availability (measured by

the number of primary schools) to raise the opportunity costs of child labor and hence to

reduce child labor incidence. At the same time, if small firms locate preferably in villages

with a population with higher basic skills, the effect of school availability on child labor will

be biased downwards and may even change its sign. In other words, for any given number

of firms, school availability reduces child labor; but since the number of firms depends

positively on school availability and more firms increase the likelihood of child labor we

have two countervailing effects of school availability on local child labor incidence – the

opportunity cost effect and the location effect. The former refers to the supply side, the

latter to the demand side for child labor. In a simple probit estimation we explain only the

net effect for the restricted sample of villages that have SMEs. It may have either sign. If

location were random, we would still observe the effect of school availability on the supply

of child labor. A possible demand side effect of increased schooling would be limited to

overall higher economic activity, if at all, but no longer lead to a sample selection bias with

firms locating in villages with more schools.14 A similar argument can be made for credit

14Strictly speaking, the demand effect at the village level consists of a sample selection effect – villages with

more schools tend to have a larger share of small firms – and a level effect: more schools lead to overall higher

economic activity. We can control for the former, the latter effect will be observed jointly with the supply side

effect.

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availability.

We address the sample selection problem by estimating a Heckman probit model with

sample selection correction (see e.g., Greene 2003). We condition at the selection stage on

a binary variable that indicates whether small firms operate in a given village. Firm location

is assumed to depend on a set of location factors such as geographical characteristics,

nonfertile land, credit availability, school availability, and population.

At the outcome stage we explain the existence of child labor by a set of variables that

portray important supply and demand side determinants as detailed in section 2.3.

3.2 Results

To demonstrate the difference in results and the systematic biases involved, we first run a

probit model to explain child labor incidence at the village level for those villages/urban

neighborhoods that have at least one small firm. Results are reported in Table 3, columns

(1) to (3).

As expected, the likelihood of child labor incidence increases with poverty: a 1% point

rise in the share of families that live in a slum area raises the probability of child labor

incidence by 0.1% points (evaluated at the sample mean). Similarly, the likelihood of child

labor incidence rises with the share of families living in non–permanent houses. Negative

shocks that the village experienced in the previous year are associated with higher likeli-

hood of child labor. This is true for natural disasters and for epidemics (measured by the

number of deaths in percentage points of total population). While natural disasters are

truly exogenous, deaths in an epidemic may partly reflect prior health status (vulnerability

to diseases) which is influenced by economic development and health care provision.

A rise in the village level unemployment rate of 10% points raises the likelihood of child

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Dependent variable: Child labor incidence in SMEs

Probit model Probit with sample selection

(1) (2) (3) (4) (5) (6)

Explanatory variables Coeff. t-value dy/dx Coeff. t-value dy/dx

Credit* 0.030 1.68 0.010 −0.076 −1.87 −0.029

Slum families 0.367 3.03 0.125 0.335 2.83 0.128

Bad housing 0.086 3.14 0.029 0.082 3.09 0.031

Unemployment 0.901 7.45 0.308 0.871 7.32 0.332

Epidemic deaths 0.265 4.16 0.091 0.259 4.20 0.099

Disasters* 0.066 3.77 0.023 0.058 3.32 0.022

Urban* −0.035 −1.66 −0.012 −0.055 −2.54 −0.021

Remote −0.111 −7.24 −0.038 −0.103 −6.54 −0.039

Flat* 0.052 2.96 0.018 0.038 2.06 0.014

Population 0.026 5.20 0.009 0.019 3.42 0.007

Pop. squared −0.0005 −3.85 −0.0002 −0.0002 −1.61 −0.0001

Electricity 0.114 3.72 0.039 0.073 2.21 0.028

Mainly industrial* 0.160 3.52 0.057 0.131 2.84 0.051

Primary schools −0.002 −0.37 −0.001 −0.009 −1.68 −0.003

Junior high sch. −0.011 −1.11 −0.004 −0.015 −1.62 −0.006

Nr. of SMEs 0.292 12.60 0.100 0.283 12.14 0.108

Nr. of SMEs squared −0.022 −5.54 −0.007 −0.021 −5.47 −0.008

Province controls yes yes yes yes yes yes

N (observations) 35037 35037 35037 68472 68472 68472

n (uncensored obs.) 35037 35037 35037 34996 34996 34996

y predicted 0.289 0.382

Wald-test of model (χ1(46)) 1326.9 p = 0.000 1421.0 p = 0.000

Wald-test of indep. (χ2(1)) 7.71 p = 0.006

Notes: All regressions include a constant and dummies for 29 provinces. The first model is estimated

as a conditional probit for the villages where small firms operate. The second regression is based

on a probit model with Heckman selection correction, with presence of SMEs being the endogenous

variable at the selection stage. The reported t–values are based on robust standard errors. Marginal

effects are evaluated at the sample mean; for dummy variables (*), dy/dx is calculated for a discrete

change from 0 to 1.

Table 3: Determinants of child labor in small industries: Probit models with and without

sample selection correction

labor in SMEs by 3% points. This seems to indicate that the labor supply effect of unem-

ployment on child labor outweighs the effect of weaker labor market opportunities.15

15This finding is in contrast to the results by Bhalotra (2003) for Pakistani boys, who shows that community-

level unemployment rate lowers their equilibrium labor supply. As there is no theoretical conjecture about the

direction of the net effect of unemployment on child labor, these findings are not contradictory.

14

We find that child labor is less likely in urban settings; this is in line with the stylized

fact that child labor is more prevalent in rural villages both in Indonesia and around the

world (cf. Manning 2000, Neumayer and de Soysa 2005). Child labor is less likely in

more remote villages of Indonesia (measured by the distance from the own and/or the

neighboring center). This captures two types of villages: those that are remote in a relative

sense (farther from political and economic center), and those in less densely populated

outer islands. Child labor is more likely to occur in flat regions where economic activity is

higher.

Our controls for size are likewise strongly significant: Child labor incidence increases—

underproportionally—with population size and with the number of firms. This is to be

expected: as the supply of potential child workers (proxied by population) and possible

demand for child labor (proxied by job opportunities for child workers) increase, child

labor incidence becomes more frequent. Child labor in small industries is more likely in

villages where the primary income source is industry (as opposed to agriculture, trade,

services, or others). The economic structure must have an influence on sector incidence

of child labor: Our finding reflects the fact that children have less opportunity to work in

other sectors and therefore tend to be more likely to work in the dominating manufacturing

sector.

Larger shares of families with electricity raise the likelihood of child labor. This variable is

a proxy for infrastructure quality, but possibly also an indicator of the share of relatively

well-off population. Obviously, the demand side effect dominates a possible supply side

effect.

Neither the number of primary, nor of junior high schools has a significant effect on child

labor. Most surprisingly, the availability of credit in the village is positively associated with

child labor incidence!

15

The reason for these last two counterintuitive results is that both school and credit avail-

ability are positively associated with the existence of small industries and their number.

The variables thus reflect an increase in the demand for child labor. At the same time they

may reduce the supply of child labor. In order to single out the demand effect for these

and potentially other variables, we analyzed separately the location decision of small firms,

which are the main demand factor for child labor. We ran a Heckman selection model on

the number of small firms correcting for the existence of small firms in the village. Re-

sults are reported in Table 8 in Appendix A.3. It turns out that the number of schools in

a village/neighborhood and the availability of credit strongly increase the likelihood that

there are small firms, and if there are, the number of these firms. Credit availablity makes

it 17 percentage points more likely that small firms locate in that village/neighborhood,

each primary school raises this probability by two, each junior high school by one percent-

age points. Access to credit and primary schools are significantly associated with a larger

number of SMEs in those locations that have SMEs. We find parallel results for popula-

tion and geographical control variables, and at the selection stage for the share of families

with electricity. We thus corroborate conventional wisdom on location choice variables:

Closeness to markets, availability of skilled labor, infrastructure and credit availability all

increase the attractiveness of a location.

To account for the endogenous location choice and the resulting sample selection bias we

used a Heckman sample selection correction for the presence of small firms in that village.

Thus the endogenous variable of the selection stage is a binary variable that is one if the

village has small firms and zero otherwise. The endogenous variable at the outcome stage

is again the binary variable denoting child labor incidence in that village. The estimates of

the probability of child labor in a village, after correcting for sample selection bias due to

the small firms’ locational decisions, are reported in columns (4) to (6) in Table 3; the full

16

Heckman selection model is given in Table 9 in Appendix A.3.

Now credit availability reduces child labor incidence significantly! In other words, access

to credit increases the likelihood of small firms locating in that village (which makes child

labor in that sector only possible), and if there are firms in the village, the number of

firms. This tends to increase the demand for child labor. If we control for this endogenous

location effect of credit availability, however, available credits reduce child labor (as they

offer an alternative instrument for intertemporal consumption smoothing): Access to credit

attracts firms and thereby potential demand for child labor, it reduces child labor supply at

the same time.

The same logic applies to the number of schools. A better school availability is associated

with a higher probability of child labor incidence for those villages that have SMEs, but if

we control for the endogeneity of location decisions of small firms to school availability, a

larger number of schools reduces child labor significantly. By the same token, the impact

of electricity on child labor halves when correcting for the endogenous location of small

firms – electricity is clearly a location factor.

The results for the other variables remain largely the same in magnitude, direction, and

significance. In particular, unemployment, income shocks, and poverty raise child labor

incidence significantly and to almost the same extent as before. Controls for size – popula-

tion as a proxy for supply and the number of SMEs as proxy for demand – exert a strongly

positive, but diminishing influence on child labor incidence, as before.

4 Conclusion

In this paper we have analyzed child labor incidence in Indonesia in small industries. We

have used a unique data set that covers all Indonesian villages and urban neighborhoods

17

and collects data on incidence of child labor and a rich set of economic, social and geo-

graphical village data. We find that child labor becomes more likely the larger the share

of poor people in a village. Its incidence is higher in villages that were hit by natural

disasters (earthquakes, landslides, floods etc.) or epidemics in the last year, and it varies

positively with the village unemployment rate. It is lower in urban and in remote areas.

The likelihood of child labor increases strongly but with diminishing marginal increments

with the economic village size; i.e., with population as a proxy for size of the potential

supply of child labor and with the number of small firms as a proxy for demand. In a

simple probit model the availability of schools turns out insignificant and credit availability

increases rather than decreases child labor incidence in small-scale manufacturing sector.

This result, however, is due to the fact that these variables are not only determinants of

child labor supply, but at the same time location factors for the small firms. They increase

significantly the likelihood of small firms locating in a village, and if they are present, their

number. Through this channel they increase the demand for child labor as well. Thus,

simple probit or logit regressions lead to biased results. The set of villages that have small

firms (and thus in which child labor in these firms can occur) is a biased sample of all

Indonesian firms.

If we correct for this selection bias in a two stage Heckman procedure, credit availability in

a village turns out to be significantly negatively associated with child labor. The same holds

for the number of primary and junior high schools. Were small firms locating randomly

or were location factors orthogonal to the determinants of child labor, school and credit

availability would exert significantly negative influences on child labor incidence. These

findings point out the importance of opportunity costs (travel time to school) for the school-

labor decision and the importance of credit as an instrument for intertemporal optimization

of the labor-education trade off for children.

18

Our findings underline the role of endogenous location decision of firms. We largely corrob-

orate the determinants of child labor supply found in many microeconomic household stud-

ies, such as the role of poverty, credit constraints, school availability, and income shocks,

partly in a more direct approach as we have direct measures for credit availability and

shocks. At the same time we clearly show that these determinants are not sufficient to

predict local child labor incidence. Child labor supply needs to find its demand. Thus

ignoring the demand for child labor, which is party endogenous to the same variables as

the supply, yet with different sign, will lead to a biased picture. This may explain why

some microeconometric studies fail to find significant influence of presumed determinants

of child labor such as poverty. If these variables affect the supply of child labor positively,

but reduce the demand for child labor as economic activity is lower in poor areas, the

observed net effect of poverty may be insigificant. As a consequence, microeconometric

studies at the household level need to incorporate characteristics of the regional economy

when seeking to establish unbiased estimates for the microeconomic determinants of child

labor. In that sense we corroborate (and specify further) the findings by Kambhampati and

Rajan (2005).

We note one caveat. In one dimension our data set is much more comprehensive than those

of existing microeconometric studies which are typically confined to small geographical

areas: we have data on virtually every village in Indonesia.16 Because Indonesia is so vast

and so heterogeneous in terms of economic structure, income level, poverty incidence,

and geography, it allows to conveniently identify the roles that these variables play in

determining child labor incidence. Yet, in another dimension our focus is more narrow:

16Cameron (2001) and Suryahadi et al. (2005) analyze the effect of the Asian crisis 1997/8 on child labor

in Indonesia and use the ‘100 village survey’ covering only 10 out of 292 districts. [For the changing structure

of subnational governments see (Fitrani, Hofman and Kaiser 2005).] Although this is a large household data

set, it is still not representative as poor and rural districts are oversampled and the number of districts covered

is low.

19

We analyze child labor in the small scale manufacturing sector only. We consider this

an advantage since this sector has not been given systematic treatment, existing reports

on child labor in specific manufacturing branches notwithstanding.17 This homogeneity

allows us to identify the determinants of child labor in the manufacturing sector and not

an ’average’ response. There is however no guarantee that our estimates carry over to

agriculture or other sectors. In particular, studies that found a counterintuitive wealth

effect on child labor were mostly on agricultural child labor. Still, we believe that our

methodological remarks, that child labor incidence needs to be conditioned on the level of

economic activity, carries over to other sectors as well. We also expect the role of access

to credit, school availability, shocks and unemployment to be similar in principle, but not

necessarily in magnitude and level of significance.

Our findings demonstrate that providing credit and schools are – eventually – powerful

policy instruments for reducing child labor, but we cannot necessarily expect them to re-

duce local child labor incidence in the short run. In the short run they will increase local

economic activity and thereby possibly raise the demand for child labor. They will reduce

child labor supply through increased opportunity costs of child labor and the availability of

credit as alternative instrument for intertemporal consumption smoothing. The observable

net effect is a priori undetermined. In the longer run, enhanced economic activity will re-

duce poverty and thus the need for child labor. It will typically also increase the returns to

education, which strengthens incentives to increase school participation and reduce child

labor. Even though we cannot exclude the possibility of a J-curve effect for child labor in

the course of improved provision of credit and schools, it is the right cure for the disease.

Accompanying measures that reinforce the substitution of school attendance for child la-

bor, such as free school meals or free transportation to the school, will make a possible

17See e.g., BBS (2003) or USDOL (1994). These studies are mostly only descriptive and focus on a small

regional or industry segment of this kind of child labor only.

20

J-curve effect more short lived or avoid it altogether. These steps should be accompanied

by labor market reforms to make employers focus on adult labor rather than on child labor.

The really troubling result that we obtain is that child labor incidence increases with the

unemployment rate, that is the likelihood that children work increases with the amount

of idle adult laborers. A substitution away from child labor towards hitherto unemployed

adult labor would be clearly efficiency enhancing.

21

A Appendix

A.1 Descriptive Tables on Regional and Sector Incidence of Child Labor

Isle (Nr. of villages) Child labor incidence % Small firmsBy industry (unc. mean) (cond. m.) in % of vill. Nr.(cond. m.)

Sumatera (21113) 11.0 31.3 35.2 5.6

Leather 0.2 17.9 1.0 4.0

Wood 3.2 25.2 12.6 3.9

Metal 0.5 21.0 2.5 4.2

Ceramic 2.2 23.2 9.6 14.0

Waving 1.3 19.7 6.7 10.8

Food 5.7 27.4 20.7 8.7

Other 2.1 27.4 7.5 14.2

Jawa (24952) 19.5 27.3 71.5 30.2

Leather 0.8 18.3 4.5 8.3

Wood 5.1 15.4 33.0 8.4

Metal 0.9 15.9 5.7 11.0

Ceramic 5.6 21.9 25.4 37.9

Waving 1.7 17.6 9.8 19.1

Food 10.6 21.9 48.4 18.4

Other 3.6 19.1 18.9 32.0

Bali/Nusa Tengg. (3974) 20.6 30.7 66.9 46.4

Leather 0.2 11.7 1.9 2.7

Wood 7.4 30.6 24.3 23.7

Metal 1.3 19.6 6.8 18.2

Ceramic 5.4 29.5 18.1 44.3

Waving 7.2 18.1 39.7 53.4

Food 6.6 27.4 24.3 19.9

Other 3.7 25.7 14.3 37.0

Kalimantan (6014) 15.4 39.3 39.2 7.7

Leather 0.1 12.9 0.5 2.7

Wood 3.8 31.7 12.0 6.7

Metal 0.9 23.5 3.7 4.9

Ceramic 7.1 44.4 16.0 21.8

Waving 1.3 35.3 3.7 17.4

Food 5.6 26.5 21.0 8.6

Other 2.9 35.5 8.0 8.9

Notes: The unconditional mean of child labor gives the average prevalence rate of child labor over

all villages. Conditional means are calculated for the subsample of those villages where SMEs

operate in the given industry.

Table 4: Incidence of child labor in different industries by island (Part 1)

22

Isle (Nr. of villages) Child labor incidence % Small firmsBy industry (unc. mean) (cond. m.) in % of vill. Nr.(cond. m.)

Sulawesi (7659) 16.2 29.6 54.6 9.7

Leather 0.1 19.2 0.7 3.2

Wood 6.7 20.6 32.3 4.5

Metal 0.8 16.0 4.9 5.1

Ceramic 3.4 22.7 15.2 10.0

Waving 1.9 18.0 10.7 25.2

Food 6.5 27.1 23.9 10.1

Other 2.8 27.0 10.5 13.0

Maluku (1577) 7.4 23.2 31.7 4.4

Leather 0.3 23.5 1.1 2.1

Wood 2.6 35.3 7.4 3.9

Metal 0.6 35.7 1.8 2.1

Ceramic 2.8 20.0 14.0 8.5

Waving 1.3 17.7 7.2 13.6

Food 3.7 28.4 12.9 12.2

Other 1.0 20.0 4.8 6.1

Irian Jaya (3507) 3.2 35.1 9.1 1.6

Leather 0.1 7.5 1.5 11.3

Wood 1.2 28.6 4.2 15.5

Metal 0.2 31.8 0.6 2.3

Ceramic 0.9 40.0 2.3 5.7

Waving 0.2 37.5 0.5 1.9

Food 1.4 44.1 3.2 19.3

Other 0.5 50.0 0.9 6.8

Notes: The unconditional mean of child labor gives the average prevalence rate of child labor over

all villages. Conditional means are calculated for the subsample of those villages where SMEs

operate in the given industry.

Table 5: Incidence of child labor in different industries by island (Part 2)

23

Nr. of Child labor incidence % Small firmsProvince villages (unc. mean) (cond. m.) in % of vill. Nr. (cond. m.)

Sumatera 21113 11.0 31.3 35.2 5.6

N. Aceh D. 5773 9.8 28.8 34.0 5.0

Sumatera Utara 5377 8.0 30.1 26.7 4.2

Sumatera Barat 874 19.0 31.5 60.3 18.7

Riau 1625 9.8 28.8 34.0 3.8

Jambi 1189 12.1 31.0 39.0 4.5

Sumatera Selatan 2707 9.4 27.6 34.2 4.6

Bengkulu 1163 12.1 48.0 25.3 2.4

Lampung 2128 19.6 39.1 50.2 9.3

K. Bangka Belitung 317 14.8 22.7 65.3 11.2

Jawa 24952 19.5 27.3 71.5 30.2

DKI Jakarta 267 15.4 24.6 62.5 13.8

Jawa Barat 5753 21.2 30.2 70.1 24.4

Jawa Tengah 8554 21.3 27.1 78.6 40.9

DI Yogyakarta 436 28.4 30.5 93.3 84.2

Jawa Timur 8463 16.2 24.4 66.4 23.6

Banten 1479 19.2 32.3 59.5 15.6

Bali/Nusa Tenggara 3974 20.6 30.7 66.9 46.4

Bali 686 38.3 45.6 84.1 69.3

Nusa Tengg. Barat 738 34.0 44.3 76.8 63.4

Nusa Tengg. Timur 2550 11.9 20.0 59.3 35.3

Kalimantan 6014 15.4 39.3 39.2 7.7

Kalimantan Barat 1438 9.1 25.3 36.0 5.0

Kalimantan Tengah 1328 11.9 41.6 28.6 3.7

Kalimantan Selatan 1949 23.9 43.4 55.0 15.2

Kalimantan Timur 1299 13.2 44.2 29.8 3.5

Sulawesi 7659 16.2 29.6 54.6 9.7

Sulawesi Utara 1196 14.0 28.7 49.0 7.2

Sulawesi Tengah 1440 11.8 24.4 48.5 4.7

Sulawesi Selatan 3084 17.9 32.7 54.7 12.0

Sulawesi Tenggara 1563 18.7 29.8 62.8 11.9

Gorontalo 376 15.2 25.1 60.4 8.4

Maluku 1577 7.4 23.2 31.7 4.4

Maluku 836 5.6 20.4 27.5 4.0

Maluku Utara 741 9.3 25.1 36.6 5.3

Irian Jaya (Papua) 3507 3.2 35.1 9.1 1.6

Total 68796 15.1 29.5 51.3 17.3

Notes: The unconditional mean of child labor gives the average prevalence rate of child labor over

all villages. Conditional means are calculated for those villages where SME-s operate.

Table 6: Child labor incidence in SMEs by province, conditional on economic activity

24

A.2 Descriptive Statistics

Variable Nr. obs. Mean Stand. dev. Min. Max.

Child labor 68472 0.150 0.358 0 1

Nr. of SMEs (.00) 68472 0.173 0.636 0 19.94

Nr. of SMEs squared (.00) 68472 0.434 4.389 0 397.6

Credit 68472 0.536 0.499 0 1

Credit to SME-s 68472 0.318 0.466 0 1

Slum families (share) 68472 0.009 0.061 0 1

Bad housing (share) 68007 0.467 0.337 0 1

Unemployment (rate) 68472 0.039 0.060 0 1

Epidemic deaths (%) 68472 0.029 0.261 0 28.9

Disasters 68472 0.195 0.396 0 1

Urban 68472 0.173 0.378 0 1

Remote (.00 km) 68472 0.650 0.977 0 10

Coastal 68472 0.131 0.337 0 1

Flat 68472 0.694 0.461 0 1

Altitude (.000 m) 68472 0.279 0.458 0 6

Electricity 68472 0.562 0.335 0 1

Nonfertile land 68472 0.151 0.358 0 1

Population (.000) 68472 2.996 3.755 0.01 82

Pop. squared (.000) 68472 24.08 122.7 0.00 6667

Mainly industrial 68472 0.017 0.131 0 1

Primary schools 68472 2.419 2.355 0 43

Junior-high schools 68472 0.456 0.870 0 12

Table 7: Description of variables used in the main regressions

25

A.3 Further Results

Selection stage Outcome stage

Presence of SMEs Number of SMEs

Coeff. t-value dy/dx Coeff. t-value

Credit 0.416 29.61 0.165 0.214 7.12

Credit to SMEs 0.152 10.52 0.060 0.110 7.65

Urban 0.140 8.16 0.056 −0.068 −4.16

Remote −0.017 −2.16 −0.007 −0.030 −3.25

Coastal 0.066 3.77 0.026 0.039 2.29

Altitude −0.112 −8.01 −0.045 −0.028 −1.70

Population 0.031 8.08 0.013 0.040 10.25

Population sq. −0.001 −12.47 −0.0005 −0.001 −11.62

Mainly industrial 0.186 4.26 0.074 0.908 27.30

Nonfertile land 0.234 15.22 0.093 0.149 8.30

Primary schools 0.044 11.36 0.017 0.043 11.28

Junior high sch. 0.029 3.69 0.012 −0.005 −0.66

Flat 0.070 5.43 0.028

Electricity 0.174 8.94 0.069

Province controls yes yes yes yes yes

N (observations) 68702

n (uncensored obs.) 33476

Wald test of model (χ2(82)) 14007.4 p = 0.0000

Mill’s lambda 0.751 7.69

Notes: The model is estimated by a two-step Heckman procedure. Both regressions include a con-

stant and dummies for 29 provinces. The dependent variable in the selection stage is an indicator

whether any small firms are present in the village. Dependent variable in the outcome stage is the

number of SMEs. The reported t–values are based on robust standard errors.

Table 8: Determinants of the number of SMEs with correction for the location decision

26

Explanatory variables Child labor in SMEs

Outcome stage Coeff. t-value dy/dx

Credit* −0.076 −1.87 −0.029

Urban* −0.055 −2.54 −0.021

Remote −0.103 −6.54 −0.039

Flat* 0.038 2.06 0.014

Population 0.019 3.42 0.007

Pop. squared −0.0002 −1.61 −0.0001

Electricity 0.073 2.21 0.028

Mainly industrial* 0.131 2.84 0.051

Primary schools −0.009 −1.68 −0.003

Junior high sch. −0.015 −1.62 −0.006

Slum families 0.335 2.83 0.128

Bad housing 0.082 3.09 0.031

Unemployment 0.871 7.32 0.332

Epidemic deaths 0.259 4.20 0.099

Disasters* 0.058 3.32 0.022

Nr. of SMEs 0.283 12.14 0.108

Nr. of SMEs squared −0.021 −5.47 −0.008

Presence of SMEs

Selection stage Coeff. t-value dy/dx

Credit* 0.414 29.48 0.164

Urban* 0.139 8.04 0.055

Remote −0.016 −1.85 −0.006

Flat* 0.072 5.61 0.029

Population 0.031 7.25 0.012

Pop. squared −0.001 −10.57 −0.0004

Electricity 0.178 9.17 0.071

Mainly industrial* 0.190 4.31 0.075

Primary schools 0.044 10.45 0.018

Junior high sch. 0.029 3.48 0.011

Credit to SMEs* 0.156 10.86 0.062

Nonfertile land* 0.233 14.88 0.092

Altitude −0.108 −7.58 −0.043

Coastal* 0.063 3.52 0.025

Wald-test of model (χ1(46)) 1421.0 p = 0.000

Wald-test of indep. (χ2(1)) 7.71 p = 0.006

Notes: The regression includes a constant and dummies for 29 provinces. It uses 68472 observa-

tions, 34996 of which are uncensored. The model is estimated as a probit model with Heckman

sample selection correction. The reported t–values are based on robust standard errors. Marginal

effects are evaluated at the sample mean; for dummy variables (*), dy/dx is calculated for a discrete

change from 0 to 1.

Table 9: Determinants of child labor in SMEs: The full Heckman’s probit model

27

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