A cross-national study of child labor and its determinants By: Joelle ...

A cross-national study of child labor and its determinants
By: Joelle Saad-Lessler
Child labor is a global phenomenon that has received much attention from
humanitarian concerns and from international organizations (ILO, UNICEF). It has been
blamed on poverty, corporate greed, parental neglect, and most recently on increased
globalization. Yet, for all the attention it has received, few of the claims made about child
labor are backed by empirical research. In this paper, I conduct a cross-national study of
child labor in an attempt to isolate the factors that determine child labor rates across
countries and bring about a better understanding of global child labor. The results of this
study will also reveal effective ways for reducing child labor rates all over the world.
Due to the long history of child labor, there is an extensive literature on the
subject 1 . Part of this literature is theoretical, focusing on modeling the demand and
supply of child labor and determining its welfare implications, while the other part is
empirical, with most studies using country specific micro data on the child laborers and
their families 2 . Because of their country specific scope, the latter offer a limited
understanding of global child labor. In an attempt to better understand global child labor,
a study by Grootaert and Patrinos (1999) tests an identical model of child labor on four
countries, using country specific micro data. This approach allows them to study the
effects of micro variables, like child’s age and gender, on child labor in the four
countries, but because they use different data sets, they cannot control for country
specific effects, like culture and social attitude toward child labor in the data. More
recently, a study by Swinnerton and Rogers (1999) used cross-country macro data to
study child labor in 1990. Since their dataset contains information on a number of
countries, it is well suited for investigating global child labor. However, because their
1 Basu 1999 provides a thorough review of the literature.
1

analysis is limited to 1990, they also do not control for country specific effects in the
data. Another recent study by Dehejia and Gatti (2002) uses time series cross country
macro data to investigate global child labor, but the focus of their study is on the effect of
access to credit on child labor.
In this study, I develop a list of macro variables that determine the child labor rate
in each country by building on the findings of the country specific literature. Then I
estimate an equation that includes all of these variables using data from the World Bank
Development Indicators on 86 countries with non-zero child labor rates from 1960 to
1999. The use of time series cross country macro data allows me to measure the effects of
the various variables on the child labor rate, while netting out country specific effects.
The results suggest that the average child labor rate for a country rises with the size of the
rural population, female labor force participation and fertility, whereas it falls with
increases in GDP per capita, the share of public educational expenditures in gross
national income, life expectancy and the share of the labor force in industry or agriculture
(as opposed to services). Looking at changes over time, as GDP per capita rises and as
trade expands, the child labor rate falls, whereas increases in the size of the rural
population and in the female participation rate lead to increases in the child labor rate.
The next section lists the variables and discusses their expected effect on child
labor, section 2 describes the data, section 3 implements the model and analyses the
results, section 4 evaluates the economic significance of the results and section 5
concludes.
1. Theory
The incidence of child labor is determined at the intersection of demand and
supply for child labor. The demand for child labor comes from employers who hire the
2 The empirical literature tests household choice models on micro data and finds that the supply of child
labor is associated with large family size, low levels of parental education (father's education matters more
than that of the mother), low income levels, high variability of income, higher costs of schooling,
geographic distance from school, parental employment, the child's gender (boys are more likely to work,
while girls usually stay home to care for younger siblings and conduct household chores), and birth order
(younger children are less likely to work if their older siblings are currently working). The demand for
child labor depends on the country's industrial distribution, with greater demand in the agriculture sector.
Finally, a study of child labor in India by Weiner (1991) finds that culture and attitude have an enormous
influence on child labor, beyond the effects of the typical cost and income variables.
2

child laborers. These employers use child labor as long as doing so is cost effective, and
therefore, they compare the cost of hiring a child with that of hiring an adult to do the
same job, or of using a machine (if that is possible). Anything that impacts the cost of
child labor relative to other alternatives affects the demand for child labor. For example,
the existence of child labor laws increases the relative cost of using child labor because of
the penalties involved in breaking the law. In addition, a country’s industrial distribution
affects the demand for child labor, since children are limited in the types of jobs they can
perform. Finally, current economic situation affects demand for child labor. Therefore,
when we look at what determines the level of child labor, we must include variables that
capture the relative cost of using adult labor, the level of mechanization in the economy
and the relative cost of running those machines (i.e. the cost of energy), whether or not
the country has laws against child labor, the industrial distribution of the country, and
whether the economy is in a recession or expansion.
The supply of child labor is determined at the household level, with each family
comparing the utility of sending their child to school with the utility of the child’s wage
income if he/she went to work instead. Households make this decision by comparing the
expected payoff to attending school with the payoff to working, and therefore, they take
into account the education wage premium, life expectancy, the cost of schooling, as well
as the immediate need for extra income. In addition, the supply of child labor is affected
by a country's cultural attitude toward child labor, because if child labor carries a
negative stigma, that reduces the utility of the child’s wage for the household. Therefore,
we must include among the determining variables for child labor a proxy for the quality
of schooling, its costs, the life expectancy of people, a measure of the household’s
income, and a control for the country’s cultural view of child labor.
2. The data
I use data from the World Bank Indicator Survey, which offers information on
210 countries from 1960 through 1999. Data on the child labor rate is available in years
1960, 1970, 1980, 1990, 1991-1999, and data on other variables fluctuates in availability
over the years. I limit my analysis to countries with a positive child labor rate since these
are the countries we really care about, and since the structural effects I estimate probably
3

differ for countries with no child labor. The child labor rate is defined as the number of
working children aged 10-14 divided by the total number of children in that age group.
The data does not contain information on how much a child works, and thus I have no
way of knowing how many of the working children work and attend school at the same
time. Moreover, I have no information on how strenuous the work performed is. Thus the
results from the estimation should be interpreted keeping in mind these shortcomings in
data availability.
The loose theory discussed in the previous section leads us to search among the
variables available in the data set for those that most closely satisfy our needs. Among the
demand variables, I use a variable that indicates whether or not the country has ratified
the 1973 Geneva Convention on child labor 3 as a proxy for the existence of laws against
child labor 4 . I also include the growth rate of GDP to control for current economic
conditions. Finally, I use data on the share of employment in the industrial and
agricultural sectors, which contains information on the industrial distribution as well as
the degree of mechanization within industry, since highly mechanized sectors use
relatively less labor 5 . I have no information on the wages of adults and children, and I do
not have data on the cost of energy, so I leave these variables out of the estimation 6 .
As for the supply variables, I use the log of real GDP per capita to capture the
effect of household income, total public educational expenditures measured as a
percentage of Gross National Income (GNI) to capture the quality of schooling 7 , and life
expectancy at birth. GDP per capita is allowed to affect the child labor rate in a nonlinear
fashion 8 . There are no variables in the data that could proxy for the cost of schooling, so I
rely on the percentage of the population that is rural to absorb this effect, since studies
3 This is the minimum age convention which sets a minimum age of 15 for children to work.
4 This is an imperfect measure of the existence of laws against child labor because many countries have
laws against child labor but have not ratified this convention. Moreover, the existence of child labor laws
does not imply that they are enforced.
5 I attempted to control for the degree of mechanization in the economy using data on capital per worker,
but that is highly correlated with the size of the industrial sector. Thus it does not allow us to tease out the
effect of increased mechanization within each sector.
6 If the cost of using adult labor relative to child labor, and the cost of using labor relative to machines is
constant for a country over time, that effect is absorbed in the country fixed effect.
7 Other potential measures of school quality are: the educational coefficient, persistence to grade 5, the ratio
of pupils to teachers, expenditure per student for each level of schooling and the repetition rate. However,
the availability of all these variables is low, making it impractical to use them.
4

have shown that the cost of schooling rises significantly in rural areas because the schools
are far and transport is more costly because there are fewer usable roads. As for cultural
attitudes toward child labor, I assume that those are country specific and that they do not
change much over time, and therefore their effects are netted out along with the country
specific fixed effects 9 .
In addition to these variables, I use the fertility rate, the female labor force
participation rate, the share of trade in total GDP and credit to the private sector as a
percentage of GDP to test the robustness of the results. I also add in continent effects as
an additional test of robustness.
3. Empirical Implementation:
Table 1 lists the mean, standard deviation, minimum and maximum values by
year for the variables mentioned above. These statistics are calculated for a constant set
of countries across the years, but the set of countries differs for each variable.
Looking at these statistics, we see that the mean child labor rate is declining over
time, while mean GDP per capita is increasing. The growth rate of GDP is slowing, the
size of the rural population is shrinking, public educational expenditure as a share of GNI
is increasing, and life expectancy is rising. Moreover, fertility is falling while the labor
force participation rate of women is on the rise, along with trade as a share of GDP, credit
to the private sector and ratification of the minimum age convention. The mean GINI
coefficient is unchanged over the years.
The baseline specification I work with is:
Child labor it = f{time trend, log real GDP per capita it , log real GDP per capita it - 6.2 ,
log real GDP per capita it – 7.3, growth rate of GDP, the percentage of the population that
is rural it, public educational expenditures as a percentage of GNI it, life expectancy it ,
8 I use a spline regression to allow the effect of GDP per capita to differ for different ranges of GDP per
capita.
9 I make the same assumptions about errors in data collections and processing, since the World Bank data is
collected from country surveys and censuses. As long as the errors in measurement and collection are time
invariant and country specific, the fixed effects net them out.
5

share of labor force in industry it , share of labor force in agriculture it , a dummy variable
for whether the country ratified the minimum age convention} 10 , 11
where i refers to the country and t refers to the time period.
I gradually add in variables like the female labor force participation rate 12 ,
fertility, trade, and credit to the basic specification in order to check the robustness of the
estimates obtained. I also add continent dummies to all the specifications, as an additional
robustness check.
Results are shown for the between, and within regressions. A Hausman test is
conducted to test the validity of the hypothesis that the fixed effects are uncorrelated with
the independent variables, and results are included with the fixed effects regression.
Results:
Regression results are in tables 2-3. Table 4 shows results when we add continent
dummies to the specifications. Table 2 displays the between regression results. This
version of the model focuses on how each country’s average child labor rate over time
varies with the country’s average characteristics over time. Thus, these results reveal
what factors determine why different countries have different levels of child labor on
average. Looking at the baseline specification (1), we find that the log of GDP per capita
has a significant negative effect on a country’s child labor rate, and that effect is largest (-
7.62) for countries in the 0-25 percent range of the GDP per capita distribution. The
effect is negative but much smaller (-7.62+7.11=-0.51) for countries in the 25-50 percent
range of the distribution, and it is insignificant for countries whose GDP per capita falls
in the 50-100 percent range of the GDP per capita distribution. An increase in the relative
size of the rural population raises the child labor rate, while an increase in life expectancy
lowers it significantly. Moreover, the GDP growth rate negatively impacts the child
labor rate, and so does an increase in public spending on education as a percentage of
GNI, a rise in the share of employment in industry relative to services, and a rise in the
10 I allow the effect of log GDP per capita to differ for countries in the bottom 25%, in the 25-50% range,
and in the 50-100% range of the GDP per capita distribution. The associated cutoff points are 1995US$ 493
and 1995US$ 1478, or 6.2 and 7.3 in logs.
11 The share of the labor force in the service sector is omitted.
12 A study by Bhalotra (2001) shows that female labor force participation is positively correlated with
higher rates of child labor in Pakistan.
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share of agricultural employment relative to services, though none of these estimated
effects are significant. This implies that the service sector, which is omitted, is the biggest
employer of children, followed by the agricultural sector and the industrial sector.
As we add more variables to the baseline specification, we find that female labor
force participation and the fertility rate increase the child labor rate significantly. The
positive effect of female labor force participation seems counterintuitive, but it is also
found by Bhalotra (2001) in data on Pakistan 13 . Depending on the type of work the
mothers do, it is possible that their children go along and help out, as they do when the
mother works in a home based industry.
The estimated coefficients are stable across the various specifications implying
that they are relatively robust. The R-squared in these regressions ranges from 0.80 to
0.83 with the highest achieved by specification (4). This means that we can explain 83%
of the variation in child labor rates across countries using the variables we included.
Table 3 shows results from the within regression. This is the fixed effect model,
and it tries to explain the variation in a country’s child labor rate over time using the
variation of the independent variables over time. Results for the baseline specification
show that as GDP per capita rises, the child labor rate falls. Again, this effect is largest
for countries in the 0-25% range of the GDP per capita distribution (-9.83), it is negative
but much smaller for countries in the 25-50% range (-9.83+6.8=-3.03), and it is
insignificant for countries in the 50-100% range of the GDP per capita distribution. The
only other variable that significantly affects the child labor rate over time is the size of
the rural population: as the relative size of the rural population rises, the child labor rate
also rises. As we add more variables to the baseline, the coefficients remain mostly
stable, and we find that female labor force participation and increases in credit to the
private sector increase the child labor rate while trade lowers it. However, of the four
variables added to the baseline, only the credit variable’s effect is significant. The impact
of a rise in the credit available to the private sector on child labor differs from what was
13 Bhalotra (2003) finds that when mothers work, their daughters aged 10-14 are also more likely to work,
and she argues that the reason for this effect is cultural, because if the head of the household is favorable to
women’s work, he would also not be opposed to female children working.
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found by Dehejia and Gatti 14 , and it contradicts the theory’s prediction that an
improvement in the availability of credit to the private sector removes households’ credit
constraints, allowing them to invest in schooling for their children.
The R-squared achieved for the various specifications range from 0.77 to 0.97
with the highest R-squared in specification (5). However, the improvement in R-squared
from (4) to (5) is suspicious since it involves a huge reduction in the number of
observations (38%), and some of the estimated coefficients do not make sense (life
expectancy significantly increases the child labor rate while fertility significantly
decreases it. Also, the effect of GDP per capita is significantly positive for countries in
the top range of the GDP per capita distribution). Given these reservations, I consider
specification (4) to be the best of the bunch. These results imply that the best hope for
reducing a country’s child labor rate over time is to increase GDP per capita and to
expand trade.
At the bottom of table 3 are results of a Hausman specification test with the null
hypothesis that the difference between coefficients of the fixed effects model and those
from a random effects model is insignificant. Across all specifications, the null
hypothesis is rejected, implying that the fixed effects are correlated with the independent
variables and that a random effects model is inappropriate. As such, I do not present
results from the random effects regressions.
Table 4 lists results from the between regression, adding in continent effects. The
estimated coefficients are very similar to those in table 2. Moreover, in all specifications,
a test of the hypothesis that the continent effects are jointly insignificant cannot be
rejected. Thus the estimates are robust to controls for continent effects.
At the bottom of table 3 are estimates of rho, which measures the share of
unexplained variation in child labor rates that is attributable to the fixed effects. The
estimated rho values range between 0.97-0.99, implying that most of the unexplained
variation in child labor rates comes from these fixed effects. These fixed effects include
any variables that did not vary over time, such as time invariant country specific cultural
effects and measurement errors. Using the coefficients from the fixed effect model I
14 Dehejia and Gatti find a positive effect of credit when they run a fixed effects model on their total
sample, but the effect becomes negative and significant when they restrict their sample to low income
countries.
8

predict every country’s child labor rate for each year, and I find that the variables
included in the model explain 63% of the variation in child labor rates over time. Thus
the share of the variation that is attributable to the fixed effects is 36%.
In an attempt to better understand these fixed effects, I regress estimates of the
fixed effects on all the variables in the model, and on GINI coefficients and the share of
the labor force in the military. I use specification (4) to construct the fixed effects. Since
the fixed effects are constant for each country over time, the independent variables are
averaged over time for each country. Results from this regression are in table 5. They
show that countries in East Asia had lower child labor rates than would be predicted
based on their characteristics. Moreover, countries with high GDP growth rates and high
fertility had higher child labor rates than would be expected, while countries with high
public educational expenditures and a large share of their labor force in the military
showed lower than expected child labor rates.
The variables included in this regression explain 58% of the variation in the
estimated fixed effects. Thus, of the 36% variation in child labor rates that was due to the
fixed effects, 58% is attributable to the variables included in the model, leaving 15%
(0.42*0.36=0.15) unexplained. This means that the variables included in the empirical
specification can account for 85% of the total variation in child labor rates. The left over
15% is plotted against the child labor rate in figure 1, and the plot reveals that there is no
clear link between the size of the residual and the child labor rate.
Economic Significance
To get a better understanding of the estimated effects, I look at the three largest
employers of child labor in the world: China, India and Bangladesh.
For these three countries, table 6 lists the predicted values of the average child labor rate
over time using the coefficients from the between regression, as well as predicted values
of the deviation of child labor rates from the country average using the coefficients from
the within regression. All predictions assume the fixed effects=0. I also break up the
predictions by each variable’s share, to get a sense of which variables played the largest
roles in determining the mean child labor rate as well as the deviations of the child labor
rate from the mean. The most important determinant of each country’s average child
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labor rate is its GDP per capita, followed by its life expectancy, the size of the rural
population, the female labor force participation rate, the share of the labor force in
agriculture, the fertility rate, the share of the labor force in industry, the share of public
educational expenditures in GNI and the GDP growth rate (the effect of trade is
insignificantly different from zero). From this angle, the most effective way to reduce a
country’s average child labor rate is to raise its GDP per capita and to raise its life
expectancy through improvements in public health. Less effective routes include
expanding educational expenditures, increasing trade and raising the GDP growth rate.
The most important determinant of deviations in each country’s child labor rate from its
average is the GDP per capita. The next biggest effect is due to the size of the rural
population, followed by the female labor force participation rate and trade (all other
effects are insignificantly different from zero). According to these numbers, the best way
to combat child labor is to improve GDP per capita and to expand trade.
5. Conclusion
This paper set out to gain a better understanding of what determines child labor
rates among countries that have non-zero rates of child labor. I looked at the effects of
real GDP per capita, the growth rate of GDP, the percentage of the population that is
rural, public educational expenditures as a share of gross national income, life
expectancy, the share of the labor force engaged in industry and agriculture, ratification
of the minimum age convention, female labor force participation, fertility, trade and
credit as possible determinants of child labor. Using data from the World Bank
Development Indicators, I found that the variables I include in the empirical specification
can account for 83% of the variation in child labor rates across countries, and for 79% of
the variation over time. Moreover, country specific fixed effects absorb 36% of the
unexplained variation in the data, but 58% of the variation of fixed effects across
countries can be traced to the variables included in the model, so only 15% of the total
variation in child labor rates is left unexplained.
Results show that the average child labor rate for a country rises with the size of
the rural population, female labor force participation and fertility, whereas it falls with
increases in GDP per capita, the share of public educational expenditures in gross
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national income, life expectancy and the share of the labor force in industry or agriculture
(as opposed to services). Looking at changes over time, as GDP per capita rises and as
trade expands, the child labor rate falls, whereas increases in the size of the rural
population and in the female participation rate lead to increases in the child labor rate.
I evaluate the importance of these effects for the top three employers of child
labor in the world, and I find that the biggest determinants of each country’s average
child labor rate were GDP per capita, followed by life expectancy, the size of the rural
population, the female labor force participation rate, the share of the labor force in
agriculture, the fertility rate, the share of the labor force in industry, the share of public
educational expenditures in GNI and the GDP growth rate. The most important
determinant of deviations in each country’s child labor rate from its average is the GDP
per capita. The next biggest effect is due to the size of the rural population, followed by
the female labor force participation rate and trade. These results suggest that the most
effective ways to combat child labor are to increase GDP per capita, improve life
expectancy, expand trade, increase spending on education and raise the GDP growth rate.
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Table 1
Variable Year # Countries Mean Std. Min Max
Child Labor 1960 101 31 17 1 79
Rate
1980 101 25 16 0 71
Ages 10-14 1999 101 18 15 0 52
GDP per
Capita
GDP Growth
Rate
(% annual)
Rural
Population
(% total)
Educational
Expenditure
(% GNI)
Life
Expectancy
(at birth)
Labor force in
industry
(% of total)
Labor Force in
Agriculture
(% of total)
Female
Labor Force
Participation
Fertility
Ratification of
Minimum Age
Convention
Trade
(% GDP)
Credit to
private sector
(% of GDP)
Gini
Coefficient
1960 41 392 202 98 827
1980 41 584 385 148 1678
1999 41 707 683 138 3711
1970 71 7 6 -5 31
1980 71 3 6 -12 18
1999 71 2 4 -8 8
1960 118 76 16 20 98
1980 118 66 18 15 96
1999 118 56 20 9 94
1960 34 2.2 1.0 0.4 4.1
1980 34 3.9 1.8 1.3 7.8
1995 34 4.2 1.8 1.4 8.6
1960 101 46 10 32 70
1980 101 55 10 35 74
1999 101 59 12 37 78
1980 38 22 11 1 49
1990 38 24 10 2 44
1995 38 22 9 2 41
1960 38 39 24 0 94
1980 38 31 25 1 86
1995 38 29 24 1 89
1960 102 32 16 3 62
1980 102 32 13 4 56
1999 102 35 11 9 56
1960 93 6.5 0.9 2.9 8.0
1980 93 5.8 1.4 2.1 8.3
1999 93 4.3 1.4 1.6 7.3
1960 138 0% 0% 0% 0%
1980 138 5% 22% 0% 100%
1999 138 31% 46% 0% 100%
1960 52 46 24 14 106
1980 52 62 30 12 163
1999 52 69 40 21 218
1970 19 16 15 2 69
1980 19 25 15 3 56
1990 19 25 20 2 72
1960 29 47.0 9.7 25.5 61.9
1980 29 46.8 9.5 25.5 61.9
1999 29 46.7 9.5 25.5 61.9
Educational expenditure=total public spending on education (% of GNI UNESCO)
GDP per capita measured in constant 1995 US$

Table 2
Between Regression
Log GDP per
Capita 1
Log GDP per
Capita 2
Log GDP per
Capita 3
GDP growth rate
(1) (2) (3)
(4) (5)
Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat
-7.62 -2.01 -5.85 -1.62 -6.61 -1.87 -6.50 -1.81 -8.19 -2.05
7.11 1.24 7.77 1.44 8.51 1.59 8.32 1.52 13.47 2.06
2.81 0.68 -0.82 -0.20 -0.07 -0.02 0.16 0.04 -5.07 -0.96
-0.19 -0.94 -0.17 -0.89 -0.25 -1.30 -0.23 -1.15 -0.14 -0.79
Rural Population 0.21 2.81 0.19 2.66 0.19 2.66 0.19 2.61 0.21 2.32
(% Total)
Public Educ. Exp. -0.81 -1.48 -1.03 -1.99 -1.11 -2.18 -1.08 -2.05 -1.33 -2.01
(%GNI)
Life Expectancy -0.96 -6.93 -0.99 -7.59 -0.67 -3.42 -0.67 -3.40 -0.67 -2.85
% Labor force in -0.26 -1.64 -0.34 -2.30 -0.33 -2.30 -0.33 -2.23 -0.35 -1.55
Industry
% Labor force in -0.11 -1.24 -0.17 -2.07 -0.19 -2.31 -0.18 -2.28 -0.20 -1.68
Agriculture
Ratification of 1.39 0.49 1.76 0.66 0.94 0.34 0.91 0.33 3.03 0.81
Min. Age Conv.
Female LFP 0.28 3.30 0.36 3.66 0.36 3.64 0.40 3.43
Fertility 2.06 1.99 2.03 1.95 2.63 2.36
Trade (% GDP) -0.004 -0.24 -0.01 -0.36
Credit to Private
0.10 1.35
Sector (% GDP)
Intercept 122.43 5.04 110.44 4.78 84.70 3.25 84.64 3.22 86.39 2.75
R-Squared 0.80 0.83 0.83 0.83 0.83
# Countries 86 86 85 85 72
Log GDP per Capita 1: for countries whose GDP per capita falls in the 0-25% range of the GDP per capita
distribution. Log GDP per Capita 2: for countries whose GDP per capita falls in the 25-50% range of the
GDP per capita distribution. Log GDP per Capita 3: for countries whose GDP per capita falls in the 50-
100% range of the GDP per capita distribution.

Table 3
Within (Fixed Effects) Regression
(1) (2)
(3) (4) (5)
Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat
year 1980 3.59 3.65 3.85 3.79 -- -- -- -- 5.81 2.16
year 1990 0.94 1.75 1.14 2.00 -3.21 -3.21 -3.18 -3.23 0.82 0.59
year 1995 -- -- -- -- -4.53 -3.03 -4.16 -2.80 -- --
Log GDP per -9.83 -5.34 -9.74 -5.29 -8.63 -3.81 -8.18 -3.63 -16.69 -2.61
Capita 1
Log GDP per 6.80 2.02 6.38 1.88 5.58 1.57 4.54 1.27 -0.81 -0.14
Capita 2
Log GDP per 2.41 0.68 2.51 0.71 1.21 0.31 3.14 0.77 22.09 2.17
Capita 3
GDP growth rate -0.01 -0.16 -0.01 -0.23 0.00 -0.01 -0.02 -0.36 -0.04 -0.34
Rural Population 0.13 2.24 0.13 2.20 0.11 1.41 0.13 1.62 0.21 0.80
(% Total)
Public Educ. Exp. 0.03 0.12 0.05 0.24 -0.05 -0.19 -0.20 -0.80 0.33 0.81
(%GNI)
Life Expectancy 0.01 0.11 0.00 -0.03 -0.03 -0.21 0.03 0.22 0.53 2.74
% Labor force in 0.01 0.17 0.01 0.13 0.04 0.45 0.07 0.80 0.11 0.89
Industry
% Labor force in -0.01 -0.19 0.00 -0.05 0.01 0.19 0.02 0.47 0.19 1.79
Agriculture
Ratification of -0.50 -0.38 -0.60 -0.45 -0.30 -0.21 -1.01 -0.68 -2.96 -1.26
Min. Age Conv.
Female LFP 0.11 1.02 0.19 1.61 0.18 1.51 -0.18 -1.30
Fertility -0.10 -0.15 -0.02 -0.03 -1.42 -1.24
Trade (% GDP) -0.03 -1.62 -0.01 -0.27
Credit to Private
0.20 2.08
Sector (% GDP)
Intercept 68.69 4.94 65.77 4.63 63.69 3.96 58.02 3.57 77.80 1.35
rho 0.98 0.97 0.97 0.97 0.99
R-squared 0.77 0.78 0.78 0.79 0.97
# Obs 163 163 152 152 94
Hausman Specification Test: H null: β random effects - β fixed effects=0
Chi2: 323.2 50.49 66.96 63.96 349.7
Prob>Chi2: 0 0 0 0 0
Log GDP per Capita 1: for countries whose GDP per capita falls in the 0-25% range of the
GDP per capita distribution. Log GDP per Capita 2: for countries whose GDP per capita
falls in the 25-50% range of the GDP per capita distribution. Log GDP per Capita 3: for
countries whose GDP per capita falls in the 50-100% range of the GDP per capita
distribution.

Table 4
Between Regression: Including Continent Effects
(1) (2)
(3) (4) (5)
Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat
Africa 7.09 1.30 5.59 1.08 3.33 0.62 3.48 0.64 -0.35 -0.05
East Europe -0.14 -0.02 -3.13 -0.52 -5.03 -0.83 -4.92 -0.81 -6.73 -0.72
East Asia 2.75 0.56 1.47 0.32 0.29 0.06 0.45 0.09 -4.98 -0.79
Oceana -1.76 -0.19 1.76 0.20 0.01 0.00 0.20 0.02 -1.11 -0.11
South+Central America 4.79 0.94 4.70 0.97 2.16 0.41 2.10 0.40 -1.21 -0.18
West Asia -0.87 -0.14 3.47 0.57 -0.57 -0.08 -0.73 -0.11 -1.64 -0.21
Log GDP per Capita 1
Log GDP per Capita 2
Log GDP per Capita 3
GDP growth rate
-7.18 -1.84 -5.53 -1.49 -6.28 -1.71 -6.19 -1.66 -8.21 -1.96
5.82 0.96 5.97 1.04 7.38 1.28 7.25 1.25 12.52 1.80
5.18 1.07 1.63 0.35 1.22 0.26 1.42 0.29 -4.60 -0.75
-0.23 -1.02 -0.21 -0.99 -0.27 -1.27 -0.26 -1.15 -0.10 -0.51
Rural Population 0.24 2.91 0.21 2.69 0.20 2.53 0.19 2.45 0.22 2.14
(% Total)
Public Educ. Exp. -0.98 -1.62 -1.09 -1.90 -1.20 -2.12 -1.18 -2.05 -1.59 -2.01
(%GNI)
Life Expectancy -0.75 -3.76 -0.78 -4.12 -0.51 -2.12 -0.51 -2.11 -0.56 -1.99
% Labor force in -0.18 -0.92 -0.21 -1.12 -0.24 -1.32 -0.24 -1.30 -0.34 -1.29
Industry
% Labor force in -0.07 -0.62 -0.11 -1.04 -0.13 -1.30 -0.14 -1.30 -0.18 -1.29
Agriculture
Ratification of Min. Age 0.28 0.09 0.79 0.28 0.48 0.16 0.47 0.16 2.30 0.57
Conv.
Female LFP 0.29 3.06 0.36 3.39 0.36 3.36 0.39 3.00
Fertility 2.00 1.67 2.01 1.66 2.41 1.84
Trade (% GDP) -0.005 -0.24 -0.004 -0.19
Credit to Private Sector
0.12 1.49
(% GDP)
Intercept 99.20 3.44 86.35 3.13 67.82 2.27 67.69 2.25 82.35 2.29
R-Squared 0.814 0.837 0.841 0.841 0.839
# Countries 86 86 85 85 72
Test: Continent effects jointly=0
Prob>F 0.66 0.75 0.85 0.85 0.88
* Omitted continent: Western Europe

Table 5
Dependent Variable: Estimated Fixed Effect from Specification (4)
Coeff. T-stat
East Europe 8.86 0.75
East Asia -9.80 -1.92
Oceana 9.23 1.21
South+Central America -0.56 -0.10
West Asia 4.97 0.52
West Europe 3.06 0.43
Log GDP per Capita
GDP growth rate
Rural Population (% Total)
Public Educ. Exp. (%GNI)
2.46 0.87
3.60 3.46
-0.11 -0.82
-2.76 -2.05
Life Expectancy 0.15 0.33
% Labor force in Industry
% Labor force in Agriculture
-0.26 -0.67
0.15 0.85
Female LFP 0.09 0.47
Ratification of Min. Age -6.17 -0.78
Conv.
Fertility 4.51 1.75
Trade (% GDP) 0.02 0.72
Credit to Private Sector (% 0.03 0.27
GDP)
GINI Coefficient -0.03 -0.18
% Labor Force in Military -3.04 -1.56
Intercept -46.92 -1.04
R-squared 0.85
Adj. R-squared 0.59
# Obs 33
* Omitted continent: Africa

Table 6
A look at China, India and Bangladesh in 1990
Child Labor rate: Yit
China India Bangladesh
15 17 32
Mean Child Labor Rate: Yi. 22 19 35
Predicted Mean Child Labor
Rate: b(Xi.)
Deviation of Child Labor
Rate from Country Average:
(Yit - Yi. + Y..)
Predicted Deviation from
Country Average:
b(Yit - Yi. + Y..)
30 26 40
8 13 13
7 8 9
Breakdown into effects from various sources:
b(Xit - Xi. + X..)
b(Xi.)
China India Bangladesh China India Bangladesh
Intercept 58.02 58.02 58.02 86.64 86.64 86.64
Log GDP per Capita -64.98 -62.56 -61.39 -34.53 -35.96 -35.89
GDP growth rate -0.01 -0.09 -0.12 -1.63 -1.05 -0.87
Rural Population
(as % of total)
Public Educational
Expenditures (% GNI)
6.24 6.60 6.23 14.85 14.68 16.41
-0.91 -1.04 -0.93 -2.44 -3.51 -1.43
Life Expectancy 2.06 2.01 2.00 -41.32 -36.39 -33.11
Share of Labor Force in
Industry
Share of Labor Force in
Agriculture
1.42 1.51 1.62 -6.57 -4.36 -3.61
0.73 0.74 0.75 -9.58 -12.32 -11.67
Ratification of Minimum Age C-0.07 -0.10 -0.10 0.02 0.00 0.00
Female Labor Force
Participation 6.81 5.33 5.38 17.78 10.79 15.02
Fertility -0.05 -0.07 -0.06 8.40 9.89 10.77
Trade -2.55 -2.31 -2.28 -0.09 -0.06 -0.08
All predictions assume the fixed effects=0

Figure 1
Child Labor Rate Vs. Unexplained Residual
40
35
30
Child Labor Rate
25
20
15
10
5
0
-10 -8 -6 -4 -2 0 2 4 6 8 10
Unexplained Residual