Child Labor & Liberia's Rubber Industry

David Koffa Jr. 3.01.2015

Topics in Development Economics Dartmouth College

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Child Labor & Liberia’s Rubber Industry: An Empirical Study of the Effects of Liberia’s Rubber Commodity Exports on

Child Labor & Education Attainment

Written by: David Koffa Jr.

Abstract

In this paper I analyzed the effects of the rubber industry in Liberia on child labor

and education. I tried to answer the important question of how does a production “boom”

in the rubber industry affect child labor and education in Liberia? I conducted empirical

research on whether rubber exports are related to low levels of schooling and high-levels

of child labor in Liberia using economic theory and econometric methodologies.

I did this by doing a comparative cohort study that analyzed whether children that

live within rubber producing districts attain low levels of education compared to areas

and districts that don’t have it. Using a difference-in-difference model to test the effects

of the rubber industry on children education in Liberia I found that children that lived in

rubber producing districts during periods of high rubber production output had a

significant decline in education attainment compared to their other cohorts that lived in

non-rubber producing regions.

Section 1 of this paper introduces Liberia’s rubber industry and its relation to

children education. Section 2 explores the data in the study and discusses the study

methodology. Section 3 discusses the study’s identification strategy and empirical

implementation. Section 4 analyzes the results of the study and explores robustness

checks and ancillary findings. Finally, section 5 discusses policy implications for the

study and concludes.



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I. Liberia’s Rubber Industry and Education System

Liberia, like most countries in Africa, is a land rich in natural resources, and one

of the resources that Liberia has that is highly coveted by the world are it’s rubber trees

and production. Since the building of it’s first major rubber plantation for the planting,

processing, and production of rubber in 1926 by the Firestone Plantation Company,

which owns the world’s largest industrial rubber plantation located in Harbel, Liberia

(UN 2006), rubber exports have played an integral role in the Liberian economy (Verite

2012). The rubber produced in Liberia plays a vital role in creating rubber products such

as latex gloves and, most importantly, tires for automobiles (Verite 2012). Since the

1920s, the rubber produced at the Firestone Rubber Plantation in Liberia was a major

supplier of car tires for the Ford Motor Company and many other automobile makers

(Greg & Paul 2010).

In 2010, rubber accounted for 61% of Liberia’s total exports, and plantation

companies are a major source of tax revenue for the country. Commercial rubber farms

employ an estimate of more than 20,000 people and up to 60,000 small holder households

are involved in planting rubber trees (Verite 2012). Rubber also serves as a major source

of formal, salaried employment in a country that has a largely subsistence-level

agricultural economy and country that has an unemployment rate of 85% (CIA: 2003).

In essence, rubber is the country’s most important cash crop and an important part of the

Liberian economy.

Although the rubber industry plays a vital role to the Liberian economy, it has

also been a source of high exploitation and blatant human rights violations through the



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prevalent use of child labor practices on all of Liberia’s seven major rubber plantations

(U.N. 2006).

The U.N. states that on the rubber plantations “child labor is indirectly

encouraged by work practices and lack of access to education” (U.N. 2006). This is due

primarily to the fact that rubber production is a highly labor intensive process that

requires workers to “tap” hundreds of rubber trees that create the sap that produces the

rubber. Due to the fact that workers are given excessive and unreasonable quotas to reach

a day, they often have to use their children to help them work (IRIN 2006). Due to the

high demand and deplorable conditions of the rubber plantations, child labor is frequently

used on the plantations. Many times children cannot benefit from free education and

health care that the major rubber plantations claim to provide because the children on the

plantations often are not registered at birth by plantation health facilities (UN 2006).

The educational facilities provided by the rubber corporations are inaccessible to

many children due to the excessive distance of the facilities from their settlements, and

many families cannot afford to send their children to private schools. Thus, the parents of

the children on rubber plantations often have no choice but to have their child work with

them on the plantations. The 2006 United Nations report states that the “lack of available

or accessible public schools or schools provided in concession areas, lead children to

assist parents in the plantation to increase the family income” (UN 2006: 43). This

reports supports known economic literature on the effects of school proximity on child

labor, the further the school is away from a child in a developing country, the higher the

prevalence of child labor (Florence & Manacorda 2012). The use of child labor to help

increase the family income of workers on the rubber plantations who are often paid less



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than 2 USD a day (UN 2006) also confirms known studies on how child labor is used by

impoverished families to help reduce the effects of poverty and smooth consumption

(Udry 2006) (Edmonds 2009).

A. Analysis of Liberia’s Rubber Production

Figure 1. Liberia’s rubbery production trends from 1972-2013 (Source FAOSTAT)

Figure 2. Liberia’s rubber yield from 1972 to 2013(Source: FAOSTAT)

Figures 1 and 2 show Liberia’s rubber production and rubber yield respectively

from 1972 to 2013. The figures show that Liberia averages around 80,000 tons of rubber

and a yield averaging about 7,000 kilograms per hectare between 1972 and 2013. The

data also depicts rubber boom years when there is both high production and yield in

rubber. During boom years Liberia averages around 100,000 tons of rubber production

0

50000

100000

150000

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Tons of Rubber Produced

Year

Liberia's Rubber Production

Liberia

0 5000 10000 15000

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Kilogram

s per Hectare

Year

Liberia Rubber Production Yield

Liberia



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and more than 10,000 kilograms per hectare. Figure 1 also shows that between the years

1990 to about 2000 there was a dramatic drop in rubber production in Liberia. This

decline is primarily due to the first Liberian Civil War, which raged on from 1989 to

1997. This is evident between the years 1990 and 1996, which has the lowest production

numbers, with the lowest being in 1994 where the total rubber production was about

10,000 tons (FAOSTAT). These low numbers most likely indicates the years of the

highest and most disruptive fighting among rebels. The data also shows that during the

2000s the number of rubber production increased due to the fact that the war came to an

end around that time and a new and more peaceful governance came into to power. The

data also shows that after 2007 there was a decline in rubber production; however, this

decline may be due to government policy changes and less on the effects of war as during

this time period the President of Liberia, President Ellen Johnson-Sirleaf, tried to make

rubber companies process rubber within the country instead of simply export the raw and

unprocessed rubber (Reuters 2013).

Figure 3. Comparison of rubber production of the top three rubber producers in Africa (Source: FAOSTAT)

0 50000 100000 150000 200000 250000 300000 350000

1960 1970 1980 1990 2000 2010 2020

Tons of Rubber Produced

Years

Rubber Production

Liberia

Cote d'Ivoire

Nigeria



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Figure 3 shows Liberia’s rubber production compared to the other two top rubber

producers in Africa, Cote d’Ivoire and Nigeria. Looking at the time trends over the years,

it is evident that Liberia was the leading rubber producer in Africa from 1972 to 1989,

during the time period of interest for my research and before the civil war, but after 1989

Nigeria became the leading rubber producer from 1990 to 1998 and they were ultimately

surpassed by Cote d’Ivoire from the year 2000 and onwards where Cote d’Ivoire

produced more than 200,000 tons of rubber increasingly every year, far more than Liberia

even during it’s rubber boom years (FAOSTAT 2013). This exponential rise in rubber

production and yield from Cote d’Ivoire over the years, as seen in Figures 3 and Figure 4,

may be due mostly to the fact that Cote d’Ivoire is a highly developed country and

agriculture exports oriented country due to the massive success and prevalence of it’s

cocoa producing industry.

It is very plausible that the methodology used to get high production and yields

from cocoa trees may be the same ones used in Cote d’Ivoire’s rubber industry. The

indication of Nigeria’s apparent decline and plateau of rubber production may be due to

the fact that Nigeria has switched to be more of an industry and oil exports based

economy than an agricultural one. Figure 4 shows the trends in rubber yield in each of the

three African countries. Although they are all quite different, with Cote d’Ivoire being the

only country showing a trend with an incline yield after the 2000s, the yield curves all

tend to have sharp dips at the same time in all three countries, with the sharpest decline

being in 1995. These similar declines in yield may be due to the fact all three countries



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share similar regions and climates and any change in climate, rainfall, plant disease, or

decrease in productivity of rubber trees will effect all of the countries.

Figure 4. Yield in rubber production between the top three rubber producers in Africa (Source: FAOSTAT) II. Empirical Implementation A. Data

For my research, I obtained survey census data for Liberia from the IPUMS-

International database. The data that I used was primarily taken from the year 2008 to

perform my analysis; however, I used the IPUMS-I 1974 data to locate specific districts

in Liberia that produces rubber. I then classified a district as a rubber-producing district if

thirty or more individuals within that district farmed rubber. I chose thirty or more

individuals farming rubber as my indicator of a rubber producing district as due to the

prevalence of small rubber farms, using lower number of individuals would have made

almost half of all districts in Liberia be classified as a rubber producing district compared

to about 38% of districts being classified as a rubber producing district when I used thirty

individuals. Although the estimated percentage of rubber producing districts is still

0 5000 10000 15000 20000 25000

1960 1970 1980 1990 2000 2010 2020

Kilogram

s per Hectare

Year

Top African Rubber Production Yield

Liberia

Cote d'Ivoire

Nigeria



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relatively high as there exists only seven major rubber plantations in the country, but due

to the lack of data on the percentage of rubber exports these individual plantations

produce I cannot limit the classification of rubber-producing district to only the districts

these plantations are located in as this may not be representative of the full effects of the

rubber industry on children in the country.

To classify a district as “rubber-producing”, I merged the 1974 data of districts

where rubber is produced and farmed with the 2008 IPUMS-I data. Both the households

and rubber districts where merged and matched with the data of the households that took

the IPUMS-I survey both in 1974 and 2008. By doing this I was able to evaluate the

effects of rubber booms on children education by combining the differences across

regions and districts with rubber factories with difference in education across cohorts not

within rubber producing regions during this time.

B. Methodologies:

To test my research question of analyzing how a production “boom” in the rubber

industry affect child labor and education in Liberia, I tested a null hypothesis H0 stating

that there is no difference and effect on educational attainment between school decision-

making aged children living in rubber producing districts in Liberia and children that do

not live in rubber producing districts during rubber “boom” years.

To test this null hypothesis, I created multiple classifications and established

multiple parameters. First, I classified a school decision making aged child as children

between the ages of 10 and 16. This is due to fact that children finish primary school in

Liberia around these ages and make the decision to continue their education in Junior



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High school as in Liberia it is mandatory for every child to complete primary school. I

also chose these age ranges because they are major age ranges where child labor is

prevalently used. I then classified educational attainment, my dependent variable, as

individuals completing primary education, grades 1-6, or higher.

Second, I classified “boom” years as years where there is very high rubber

production compared to other years. For my analysis, the “boom” years that I analyzed

were from 1984-1988 as shown in Figure 5. Children that were of school decision-

making age (ages 10-16) during these “boom” years were designated my treatment

cohort. My control cohorts and comparison group (ages 18-24) were people who were not

really impacted by child labor in the rubber industry. I created a treatment cohort and

comparison cohort to fully test impact of the rubber-industry on children by testing the

difference-in-difference in education attainment between the two groups

III. Identification strategy

My identification strategy uses the fact that the influence of the rubber industry on

children varies by region of birth (districts where rubber plantation exists) and date of

birth (children who were of school age during the years rubber production was very

high). I used a difference-in-difference analysis that controls for variation on education

across regions and across cohorts.

The cohort group that I used were individuals born between the years of 1968-

1974 in the 2008 IPUMS data as they were of school decision making age, 10-16

(UNICEF 2011), during what I identified as boom years in rubber production in Liberia



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from the data on the FAOSTAT data base, which was between 1984-1988, the years

before the Liberian civil war.

A. Empirical Specification To analyze the effects that living within a rubber-producing district has on children

educational attainment I used the following basic difference-in-difference regression

model to test my null hypothesis.

My regression model is: Schooljt = B0 + B1(treatment)jt + B2(rubber_district) + B3(treatment*rubber_district) + δ1dage + λj + e Equation 1.

Where school is a dummy variable for each individual’s education attainment, j is

each individual, t is the time, B0 a constant, treatment is the treatment cohort age 10-16

during “boom” years (1984-1988), rubber_district is a dummy variable that indicates if a

district produces rubber or not, treatment*rubber_district is an interaction variable

between the treatment cohort variable and rubber producing district variable, δ1dage is a

fixed effect for age, λj is a district of birth fixed effect, and e are potential error terms.

As shown in Figure 5, the difference-in-difference I calculated is:

[(Ed11-Ed01) - (Ed10-Ed00)] Equation 2. Where Ed# is the output of a control or treatment variable on a rubber producing district

or non-rubber producing district.



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Rubber Producing District Non-Rubber Producing District

Control Cohort (Ages 18-24) Ed01 Ed00

Treatment Cohort (Ages 10-16) Ed11 Ed10

Figure 5: Difference-in-difference model matrix for study

From my regression model, I can expand upon the difference-in-difference model

from Figure 5 and include actual coefficients I will be solving for within my regression as

shown in Figure 6. Using the same difference-in-difference calculation of Equation 1, I

can apply it to using coefficients from Equation 2 and Figure 6 to solve:

[((B0+B1+ B2+B3) – (B0+B1)) – ((B0+B2) – (B0))]= B2+B3 – B2 = B3 Equation 3. Equation 3 states that the coefficient that will give me the difference-in-difference result

within my main regression analysis is the coefficient of the treatment and rubber

producing interaction variable or B3.

Rubber Producing District Non-Rubber Producing District

Control Cohort (1976-1980) B0+B2 B0

Treatment Cohort (1984-1988)

B0+B1+ B2+B3 B0+B1

Figure 6: Regression model applied to difference-in-difference model matrix B. Data Summary Statistics

Based off Figure 7 and appendix A, in the data I used from IPUMS there was a

total of 46,982 observations or individuals surveyed. Male individuals were composed of

about 50.08% of those surveyed and there were a total of 49.92% females within the data.



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Based off my parameter and definition of what is considered a rubber-producing

district, where 30 or more individuals farm rubber, about 39% of districts within my data

are labeled a rubber-producing district. There were a total of 46,982 observations of

individual’s education attainment, the dependent variable, within my data.

[1] [2] [3]

Variable Non-Rubber District

Rubber District Total

Rubber "Boom" Years Cohort

Number of Individuals 8299 5542 13841 Average Years of Schooling 5.2 2.9 Percent Male 49% 48% Percent Female 51% 52% Completed Primary School or Higher 34% 43%

Control Cohort

Number of Individuals 4812 2731 7543 Average Years of Schooling 5.9 3.7 Percent Male 54% 56% Percent Female 46% 44% Completed Primary School or Higher 34% 43%

Figure 7. Means and statistics of cohorts(Source: STATA)



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Appendix A: Data Summary Statistics of Variables of Interest (Source: STATA)

Variables Observations Mean Std. Dev. Individuals Per Household 46982 2.059086 2.131418 Age 46982 40.01592 4.22508 Sex 46982 1.490848 0.4999215

Education Attainment 46982 1.640628 0.8685118 Literate 46982 1.483483 0.4997324 Years of School 46294 4.495334 5.420105

District 46982 2470.099 1096.652 Rubber Producing District 46982 0.3901707 0.4877936

Birthplace 46693 24.30274 17.57332

Male 46982 0.5091524 0.4999215 Individual Farms Rubber 46982 0.0916096 0.2884771 Complete Primary or Higher 46982 0.406262 0.4911398

Rural District 46982 0.7138053 0.4519863 Treatment Cohort 46982 0.6278575 0.4833813 Control Cohort 46982 0.3721425 0.4833813 Rubbery District*Treatment Cohort 46982 0.2467541 0.431127 Rubber District*Cohort 46982 0.1434166 0.3505009

IV. Results

From Figure 8, it is evident that there is an effect on the educational attainment of

children that live within rubber-producing districts, proving my null hypothesis H0

wrong. Figure 8 is a regression analysis and result of Equation 1, where Rubber

District*Cohort is the B3 coefficient of interest and value that shows the effect of living

in a rubber-producing district compared to a non-rubber producing district. The value of

the Rubber District*Cohort coefficient is -0.0254 or -2.54% with a t-value of -3.43 and a

p-value of 0.01 indicating the coefficient is statistically significant at the 1% level. The



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results from the regression model shows that children of school decision-making age who

lived in rubber-producing districts have a 2.54% decline in education attainment

compared to children who lived in a non-rubber producing district. This result shows that

children who live in rubber-producing districts have a significantly less attainment in

education compared to those do not live in a rubber-producing region.

(1) (2)

Explanatory Variable Education Attainment Education Attainment Rubbery District*Cohort -0.170*** -0.0254*** (0.00493) (0.00741) Constant 0.448*** 0.286*** (0.00264) (0.0163)

Controls No Yes Fixed Effects No Yes Observations 46,982 46,693 R-squared 0.022 0.223 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Figure 8. Effects of Education Attainment on treatment cohort living in rubber producing districts compared to control cohorts and those living in non-rubber producing districts (Source: STATA) A. Robustness Checks and Ancillary Findings

I tried multiple robustness checks to ensure the validity of my findings and test

against any potential factors that might cause bias in my results. Figure 9 shows a similar

regression analysis as in Figure 8 except that it analyzes every individual that was of

school eligible-age (ages 14-16) during the rubber “boom” years; however, the

comparison group that the regression is being tested against is all of the 346,179

individuals participating in the data. I performed this analysis to have more variations to

test against in the data. Figure 9 shows that there was a statistically significant decline of



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about 12.5% in education attainment of children of school decision-making age who

lived in rubber-producing districts compared to those who lived in a non-rubber

producing district. The decline in education attainment among every school eligible child

is higher than that of those who are only of school decision-making age. This is also

evident when a similar regression is computed for children below the school-decision

making age (6-13) as shown in Figure 10 as they had a 14.5% decline in education

compared to those who lived in non-rubber producing district.

I performed a test to ensure that urban factors in Liberia did not cause a bias of

the results I obtained. In Liberia the capital, Monrovia, have a disproportionate number of

the total schools in the country, accounting for almost 80% of all the academic

institutions in the entire country. Monrovia also tends to have the largest population

concentration in the country, due to this I tried to test to make sure that the capital was

not biasing my results. To test only the other regions without the potential bias of the

capital, I excluded Monrovia as a district in my test and ran the same regression as Figure

8 and with similar comparison groups. Figure 11 shows that with Monrovia excluded as a

district within the regression output there is a 0.9% decline in education attainment of

children within rubber-producing districts although this value is not statistically

significant. However, Figure 10 shows that when the treatment cohort is regressed

without Monrovia being included as a district and the comparison group is everyone that

participated the survey to increase variability within the regression, there is a statistically

significant decline in education attainment of 7.69%. Both result indicates that Liberia’s

capital plays a significant role on the education attainment of the country overall due to



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it’s high density of schools and also there is still an adverse affect of the educational

attainment of children living in rubber-producing districts.

Analyzing the data I also tried to analyze the differences in the education

attainment of male and female children living in rubber-producing regions. This research

question is very important as it analyzes the discrepancies between male and female

education and it may provide insight on which demographic is most affected by the

rubber-industry. Figure 10 shows that male children had a 2.73% decline in education

attainment compared the 2.69% decline of female children. Figure 13 shows a similar

regression but with greater observations within the dataset as the comparison group and

the results from Figure 13 shows that male children had a 14.2% decline in education

attainment compared to the 9.9% decline in education by female children. Both

regression output shows that male children are more affected by the rubber industry

within these districts than female districts. The potential cause of male children being

more effected is because harvesting rubber sap from rubber trees is a heavy labor task

that requires a lot of strength and young boys will most likely be used more by their

parents than young girls although the young girls will most likely be taken out of school

to perform household chores while their parents are working on the plantations.



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[1] [2] [3] [4]

Explanatory Variables

Education Attainment - 1976-1980

Education Attainment

Education Attainment-1984-1988 Difference

Rubber District*Cohort -0.0965*** -0.116*** -0.125*** -0.0285 (0.0221) (0.00831) (0.0317) Cohort 0.107*** 0.0496*** 0.0625* 0.0445 (0.0290) (0.00559) (0.0348) Rubber District -0.0818*** -0.0857*** -0.0792*** 0.0026 (0.0224) (0.00171) (0.0218)

Controls Yes No Yes Yes

Constant 0.457*** 0.427*** 0.458*** 0.001 (0.0418) (0.00109) (0.0415)

Observations 346,179 348,057 346,179 346,179 R-squared 0.023 0.009 0.023 0.023 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Figure 9: Effects of Education Attainment on treatment cohort living in rubber producing districts compared to control cohorts and those living in non-rubber producing districts except that the comparison group is expanded to every observable individual in the data to see the effects with a larger sample size.



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(1) (2) (3)


Education Attainment - Ages

6-18

Education Attainment - Ages

6-12

Education Attainment-

Rural

Rubber Distritct*Cohort -0.145*** -0.140*** -0.0769*** (0.0334) (0.0312) (0.0159) Cohort 0.0852* 0.0796* 0.00811 (0.0431) (0.0421) (0.00999) Rubber District -0.0597*** -0.0692*** -0.0218 (0.0171) (0.0194) (0.0142)

Controls Yes Yes Yes

Constant 0.444*** 0.451*** 0.378*** (0.0353) (0.0381) (0.0190)

Observations 346,179 346,179 249,886 R-squared 0.025 0.024 0.006 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Figure 10: Effects of Education Attainment on treatment cohort living in rubber

producing districts compared to control cohorts and those living in non-rubber producing districts. The comparison group is expanded to every observable individual in the data to see the effects with a larger sample size and two different treatment cohorts are used to test for possible change in results. Rural districts are also tested where Monrovia is excluded from the data analysis.



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(1) (2)

Explanatory Variable Education

Attainment- Rurual Education

Attainment- Ages 6-9 Rubber District*Cohort -0.00990 -0.0212** (0.00931) (0.00866) Constant 0.330*** 0.276*** (0.0222) (0.0176)

Observations 33,377 35,822 R-squared 0.056 0.211 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Figure 11: Effects of education attainment on treatment cohort living in rubber producing districts compared to control cohorts and those living in non-rubber producing districts except the data analyzes only rural regions (excludes Monrovia from data being tested) Also analyzes the effects of education attainment on a new treatment cohort between the ages of 6-9. (Source: STATA) (1) (2)

Explanatory Variable

Education Attainment -

Male


Female Difference Rubber District*Cohort -0.0273** -0.0269*** -0.0004 (0.0111) (0.00972) Constant 0.566*** 0.325*** (0.0237) (0.0219)

Observations 23,730 22,963 R-squared 0.132 0.150 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Figure 12. Ancillary Findings: Analyzes the Effects of education attainment between male and female children living in rubber producing districts compared to control cohorts and those living in non-rubber producing districts (Source: STATA)



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[1] [2] [3]



Males


Females Difference Rubber District*Cohort -0.142*** -0.0992*** -0.0428 (0.0123) (0.00979) Cohort 0.169*** -0.0403*** 0.2093 (0.00755) (0.00719) Rubber District -0.0988*** -0.0587*** -0.0401 (0.00314) (0.00299) Constant 0.545*** 0.370*** (0.00731) (0.00691)

Controls Yes Yes

Observations 173,271 172,908 R-squared 0.022 0.030 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Figure 13: Effects of Education Attainment on male and female treatment cohorts living in rubber producing districts compared to control cohorts and those living in non-rubber producing districts. The comparison group is expanded to every observable individual in the data to see the effects with a larger sample size.

V. Conclusion

In my research I analyzed the effects of the rubber industry in Liberia on child

labor and education. I empirically tested and tried to answer the important research

question of how does a production “boom” in the rubber industry affect child labor and

education in Liberia? My research found that children that lived in rubber producing

districts during periods of high rubber production output had a significant decline of

2.54% in education attainment compared to their other cohorts that lived in non-rubber

producing regions. My ancillary finding showed that male children are more affected in

rubber-producing districts than female children.



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These results show that children that live within rubber-producing regions are

negatively affected in education attainment compared to their peers in non-rubber

producing districts. Based on the robustness checks performed and literature concerning

these districts, the most likely reason for the decline in education for these children is

attributed to the rubber industry in these districts that play a major role in these children’s

family decisions to utilize child labor to increase their productivity and remain employed.

This research provides empirical evidence that there is a statistically significant

negative effect of the rubber industry in Liberia on children education and adds value to

the little empirical evidence of the impact of the rubber industry on the children in

Liberia. These results can help policies to be implemented that target specific rubber

producing districts to help promote education efforts to make schooling readily available

and also to put pressure on rubber industries to discourage their workers from using their

children. Policies can be enacted that requires rubber companies to have better and more

practical quotas per employee that will make them not forced to utilize their children to

help them fulfill these quotas. It is my hope that this research can help provide more

evidence to help create policies that reduces the prevalent use of child labor in Liberia

and other countries around the world.



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8. Joint Government of Liberia, and United Nations Rubber Plantations Task Force. Report Presented to H.E. Mrs. Ellen Johnson Sirleaf, President of the Republic of Liberia. May 23, 2006. http://www.laborrights.org/sites/default/files/publications-and-resources/Rubber_TF_Report.pdf

9. Joint Government of Liberia, and United Nations Rubber Plantations Task Force. Report Presented to H.E. Mrs. Ellen Johnson Sirleaf, President of the Republic of Liberia. May 23, 2006. http://www.laborrights.org/sites/default/files/publications-and-resources/Rubber_TF_Report.pdf

10. Kondylis, Florence and Marco Manacorda (2012), “School Proximity and Child Labor,” Journal of Human Resources 47(1): 32-63.

11. "Liberia Suspends Exports of Unprocessed Rubber." Reuters. Thomson Reuters, 19 Apr. 2013. Web. 6 Feb. 2015. http://www.reuters.com/article/2013/04/19/liberia-rubber-idUSL5N0D62RV20130419

12. "Liberia Suspends Exports of Unprocessed Rubber." Reuters. Thomson Reuters, 19 Apr. 2013. Web. 6 Feb. 2015. http://www.reuters.com/article/2013/04/19/liberia-rubber-idUSL5N0D62RV20130419

13. "Report on the Liberia Labour Force Survey 2010." 1 Feb. 2011. Web. 9 Jan. 2015. http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_156366.pdf



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14. "Refworld | Human Rights in Liberia's Rubber Plantations: Tapping into the Future." Refworld. United Nations, 1 May 2006. Web. 9 Mar. 2015. http://www.refworld.org/docid/473dade10.html

15. Udry, Christopher (2006), “Child Labor," in Abhijit Banerjee, Dilip Mookherjee, and Roland Benabou, eds. Understanding Poverty, Oxford University Press, pp. 269-285.pdf

16. U.S. Department of State. "Liberia." In Country Reports on Human Rights Practices- 2007. Washington, DC, March 11, 2008; available from http://www.state.gov/g/drl/rls/hrrpt/2007/100489.htm.

17. U.S. Department of State. "Liberia." In Country Reports on Human Rights Practices- 2004. Washington, DC, February 28, 2005; available from http://www.state.gov/g/drl/rls/hrrpt/2004/41611.htm.

18. UN Mission in Liberia. Human Rights in Liberia’s Rubber Plantations: Tapping into the Future. Monrovia, May 2006; available from UN Mission in Liberia. Human Rights in Liberia’s Rubber Plantations: Tapping into the Future. Monrovia, May 2006.

19. "UNICEF Liberia-The Children." UNICEF. UNICEF, 1 Jan. 2011. Web. 9 Feb. 2015. <http://www.unicef.org/liberia/children_7916.html>

20. "Liberia Rebuilds Education System after Years of Civil War." UNICEF. UNICEF, 1 Sept. 2011. Web. 9 Feb. 2015. http://www.unicef.org/education/liberia_59842.html

21. Verité, Inc. Rubber Production in Liberia: An Exploratory Assessment of Labor Conditions. 2012; available from http://www.verite.org/research/indicators_of_forced_labor

22. "World Fact Book." Central Intelligence Agency. Central Intelligence Agency, 4 Jan. 2013. Web. 9 Feb. 2015. <https://www.cia.gov/library/publications/the-world-factbook/geos/li.html>http://www.laborrights.org/stop-child-labor/stop-firestone/news/10929

23. "World Data on Education: Liberia." UNESCO-IBE, 1 Sept. 2010. Web. 9 Feb. 2015. <http://www.ibe.unesco.org/fileadmin/user_upload/Publications/WDE/2010/pdf-versions/Liberia.pdf>.

24. "2008 POPULATION AND HOUSING CENSUS." LIBERIA INSTITUTE OF STATISTICS AND GEO-INFORMATION SERVICES (LISGIS), 2 May 2009. Web. 9 Feb. 2015. https://www.umassmed.edu/PageFiles/46299/Liberia Household_Demo Report 08.pdf

Child Labor & Liberia's Rubber Industry

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