Gildas Tiwang N.1, Manu Ibrahim2
1
Department of Agricultural Economics, Faculty of Agronomy and Agricultural Sciences,
University of Dschang, P.O Box 222 Dschang-Cameroon
E-mail: ttiwangg@gmail.com
2
Department of Agricultural Extension and Rural Sociology, Faculty of Agronomy and
Agricultural Sciences, University of Dschang, P.O Box 222 Dschang-Cameroon
Assessing the determinants of children’s work in
agribusinesses in Cameroon
Abstract
This study seeks to assess socioeconomic factors that influence significantly child labour in
agribusinesses in Cameroon. The methodology adopted for the study is the simple logit model.
Excel 2013, Latex, Stata version 13.0 and Statistical Package for Social Science (spss) version
20 software packages are used for analyses. The population of this study is made up of 51,190
individuals of both sex that was involved in the third Cameroon National Household Survey.
The sample drawn from this population is constituted of individuals of age from 5-17 years
old, making a total of 17,550 children. The main results of this study reveal that agribusiness
child labour is present everywhere in Cameroon and by both boys and girls. Children of all
ages of the sample are concerned by the phenomenon. It also demonstrates that an amelioration
of the condition of life of these children, of their feeding, their health, and their transportation
problems will help considerably to reduce this phenomenon. In addition, an increase in school
enrolment of these children and the care of their academic needs may diminish considerably
this child labour which is highly link to the level of poverty of these children.
Key words: Agribusiness, Logit model, Child labour, Cameroon
Introduction
A third of all children in West and Central Africa are estimated to work full- or parttime, paid
or unpaid. Many of these children are involved in hazardous and harmful economic activities,
such as working in mines. Some are also exploited for sex and trafficked. Even if the work
itself is not hazardous, many working children do not have access to education or drop out
of school due to the opportunity costs for parents of keeping children in school and out
of work. However, some children do combine work and school and earning an income
may enable children to continue their schooling. In some cases where the quality of education is
poor, or children are exposed to violence in schools, some children may prefer work to school.
In some contexts, children derive a sense of meaning and responsibility from their work and the
contributions they make to the family. To take them away from work without replacing the
meaning and status, they may receive can result in worse outcomes. In addition, raw farm
products and first transformation products businesses rely heavily on them as primary sources
of labour for agricultural activities. Where they cannot afford to hire additional labour and since
the labour from their adult members is grossly inadequate almost in all cases, these households
always resort to the use of child labour. It is in this context that child labour has become a
growing phenomenon in developing countries, including Cameroon. The study will enable to
examine this situation of child labour considering all the actions that are being taken and the
exponential growing population and agribusinesses in Cameroon
Nature and Sources of Data
The data used for the analysis in this study came from a secondary source, the 2007 Cameroon
Household Survey (CHS 3) cross-sectional dataset produced by the National Institute of
Statistics. Survey covered all ten regions of Cameroon and was conducted in strata
(both urban and rural residential areas) using a sample of 12000 households of the
CHS 3 which 11391 were actually visited. Also, it is organized into six agro-ecological zones
(Yaoundé, Douala, Other Towns, Rural Forests, Rural Highland Plateaus and Rural
Savannah). The dataset contain variables on child labour, the various sectors of child
labour, as well as individual, households and regional-level characteristics. One objective of
the survey was to provide datasets of a system that allows to follow up and to
evaluate households’ living conditions in general and the programme of poverty reduction in
particular. Another objective was to provide datasets, which could help to evaluate the impact
of projects, policies, and programmes implemented during the preceding five years on the living
conditions of households.
The CHS 3 concerns the household as well as individuals who belong to this household. This
survey differs investigations ECAM I and II in that, it includes sections on economic and
domestic activities of household members aged 5 years at least. The survey questionnaire
contains 17 sections covering the main theme of poverty (13 sections) and other related
topics. Section 3 which deals with items relating to education (the different household
members), section 4 that will focus on the economic activities of children over 5 years and
Section 5 which provides information on domestic activities of household members. section 13
is reserve for agriculture activites in the rural area(hunting, grazing, fishing…) The database
consists of a single file that provides each individual personal information, as well as,
those relating to his household.
But child labour phenomenon is analysed here on a sample that has two distinctive features: (i)
the sample is exclusively devoted to child labour in agribusinesses, and (ii) all the
participants in the survey are exclusively children themselves. No adult (parent, guardian,
elder, employer, etc.) was consulted and given a chance to answer on behalf of a child. This
process is extremely rare in child labour, since in general individuals other than children are
requested to testify and answer instead of children.
Sampling
The population of this study is made up of 51190 individuals of both sex that was concerned
by the CHS 3. From this study, we draw all the individuals of age from 5-17 years old making
a sample of 17550 individuals. From this sample, we make the distribution in the various
sectors of activities as shown on the figure 1.
Figure I: Distribution of children work (age 5-17 years) per sector of activity
From figure 1, we realize that our study on agribusinesses will include all the children in the
primary sector as well as children in the industry added to those in commerce. In the industry,
they are confined to most of the work does not require much muscles but they are however
submitted to long periods of work. These three sectors constitutes 91.9 per cent of the 17550
children
involve in child labour and justify the topic of this work of child labour in
agribusinesses. The study deals with children from 5-17 years old as can be shown in table I
which shows that all the ages of the sample are concerned by child labour.
Table I: Distribution of the sample per different ages of children
Age(years)
Frequency
Percent
Valid
Percent
Cumulative
Percent
5
6
7
8
9
10
11
12
13
14
15
16
17
Total
1363
1530
1629
1454
1205
1469
1146
1369
1251
1304
1321
1218
1291
17550
7,8
8,7
9,3
8,3
6,9
8,4
6,5
7,8
7,1
7,4
7,5
6,9
7,4
100,0
7,8
8,7
9,3
8,3
6,9
8,4
6,5
7,8
7,1
7,4
7,5
6,9
7,4
100,0
7,8
16,5
25,8
34,1
40,9
49,3
55,8
63,6
70,7
78,2
85,7
92,6
100,0
Source: Author’s estimates compiled using the 2007 CHS3 dataset and Stata 13.0
The analysis will further divide the variable age into three classes: the first class will be made
up of children from 5-9 years, the second will go from 10-13 years and the last lass of 14-17
years old. These classes can be better observed on figure 2 which reveals that the class of
children from 5-9 years is the most affected class.
Figure II: Distribution of the sample in three categories of age groups
The two first classes of children from 5-13 years represent more than 70per cent of the sample of
the children involve in agribusiness work.
Results
The objective of this study is to assess the determinants of child labour in agribusinesses in
Cameroon.
Table II: logistic regression of the involvement of child labour in Cameroon
Logistic regression
LR chi2(15)
Prob > chi2
Number of obs =
= 1951.46
= 0.0000
Log likelihood = -8937.2872
Child labour
15514
Pseudo R2
z
P>|z|
=
Coef.
Std. Err.
[95 per cent Conf. Interval]
enrol_
-.6663851
.0587087
-11.35
0.000
-.781452
-.5513181
nivie
-.3423808
.0481806
-7.11
0.000
-.4368131
-.2479485
culture_group .9635863
.1920958
5.02
0.000
.5870855
1.340087
0.0984
tailm
.0270306
.0090659
2.98
0.003
.0092618
.0447994
typmen
-.0158805
.0150061
-1.06
0.290
-.0452919
.0135309
nivins
.4051824
.0275167
0.000
.3512506
.4591143
dephab
-1.67e-07
1.36e-07
-1.23
0.221
-4.34e-07
1.00e-07
deploi
-1.33e-06
6.54e-07
-2.04
0.041
-2.62e-06
-5.33e-08
deplog
-2.71e-06
1.63e-07
-16.58
0.000
-3.02e-06
-2.39e-06
depsan
6.23e-07
1.55e-07
4.03
0.000
3.20e-07
9.26e-07
deptran
1.01e-07
8.83e-08
1.15
0.252
-7.19e-08
2.74e-07
depedu
-1.41e-06
2.28e-07
-6.21
0.000
-1.86e-06
-9.67e-07
depal
2.61e-08
5.26e-08
0.50
0.619
-7.70e-08
1.29e-07
depbien
5.07e-07
2.98e-07
1.71
0.088
-7.59e-08
1.09e-06
deptet
-6.37e-07
2.07e-07
-3.08
0.002
-1.04e-06
-2.32e-07
_cons
.4073063
.1252006
3.25
0.001
.1619177
.652695
14.72
Source: Author’s estimates compiled using the 2007 CHS3 dataset and Stata 13.0
Presentation of the treatment Variables
enrol_
School enrolment of the child actually
Nivie
Standard of living of the family where the child comes from
typmen
type of household where the child comes from
nivins
level of education of the child
dephab
Expenditure for clothes and of the child and others
deploy
Expenditure for leisure of the child and others
deplog
Expenditure for housing of the child and others
depsan
Expenditure for health of the child and it prevention
deptran
Expenditure for transports of the child to school and others
depedu
Expenditure for education fees and associated expenditures
depal
Expenditure for food and non-alcoholic drinks by the child
depbien
Expenditure for goods and services needed by the child
deptet
Expenditure per head in a household
Source: Compiled by the author from the 2007 Cameroon Household Survey (ECAM III) based on the literature and past empirical studies.
Table II shows the influence of some independent variables on the involvement of the child in
agribusinesses. From table II, the coefficients, on the first column, of the independent variables
are of both signs meaning that the exogenous variables have inverse effects on the dependent
variable which is child labour involvement in agribusiness. The second column shows the
standard error that can be attached to the coefficients of the first column and help to better
appreciate those coefficients. Column 3 of table II shows the results of the student statistics that
test the significance of the influence of a given independent variable on child labour
involvement. The next column of this same table help to make a decision of the previous
significance level from the student test by using the probability rule. Lastly, the column of
intervals help to better appreciate the precision of the influence of a given exogenous variable
on child labour involvement.
Interpretation of table II
From the 15 independent variables, seven of them have negative signs and thus explain the
reduction of child labour involvement if they are increased. From the analysis, an increase in
school enrolment, standard of living, type of household, clothing and shoes, housing,
education, leisure and lastly expenditure per individual in a household are variables that will
help to reduce child labour involvement in agribusinesses if they are ameliorated. Thus, a
quantitative analysis shows that an increase of one new child enrolment in school or of his
standard of living or an analysis of this counselling based on the type of household from which
this child comes from, will lead respectively to a reduction of 66.6 per cent, 34.2 per cent,
27.03 per cent of labour involvement of this given child.
On the other hand, The influence of foreign cultures through the medium and a globalizing
environment, the cultural believes that a child must learn how to go certain experiences to be
considered in the society as a good soon or as a grown up somebody are all some of the
cultural elements that still motivates parents to send their children to work in agribusinesses.
This is quite unfortunate because according MMYE (2007) however, in 85 per cent of cases
the decision for a child to live in another community for paid work was taken by parents, in
11.4 per cent by relatives and only in 3.4 per cent by the children.
The size of the household where the child comes from also affects the family decision to send
the child for labour. Barrientos et al., (2008), Bourdillon et al., (2010), De Lange(2006),
Hashim (2005), Imorou (2008), Ndao(2008) and Thorsen (2007a) say children’s wish to earn
an income to purchase commodities that enhance their social position at home. The level
of training of the child on how to do something with his hands is also one of the concerns that
parents have in sending their children to work. MMYE(2007) and SWAC/OECD (2009b)
seems to look in the same direction when they say given the difficulties for rural children to
find paid employment, parents are keen to provide their children with the necessary skills to
become good farmers.
Children’s work is explained as an outcome of the poor health state of the child. When the child
is ill and has no means to cure himself or when he has an illness that has no cure like
HIV/AIDS, he goes into work because he is desperate or to earn some money for his treatment.
On the other hand, some children working in cocoa consistently complain about pain in
the neck, back, shoulders and arms (ILO, 2007c; Zdunnek et al., 2008) and maybe these are
the concerns that will inhibit parents on sending their children to work in activities with a very
high level of risks. ILO(2007c), MMYE (2008b) and Swedwatch (2006) enumerate some
health hazards frequently discussed like work overload, children’s use of machetes1 , their
role in transporting cocoa pods and other crops, and their participation in spraying pesticides
and other agro-chemicals.
The distance the child has to cover to be to school and others services, the difficulty of the child
to feed as well as the difficulty of the child to satisfy basic needs are some of the variables
studied that may have an increasing effect on the involvement of the child in agribusiness
labour. Thus, a quantitative analysis shows that an increase of the child in the his cultural
influence, or his level of training or an analysis of the size of the household from which this
child comes from, will lead respectively to an increase of 96.35 per cent, 40.51 per cent, 2.7 per
cent of labour involvement of this given child. These mostly economic reasons highlight a
number of consequences of poverty, namely the need of school-going children to
contribute to their own school fees (Barrientos,et al., 2008; Bourdillon et al.,2010; Hashim,
2007) and to contribute to the household budget (Bourdillon, 2010; Hashim, 2007; Thorsen,
2009a) or to migrate to destinations further away and with higher earning
1
In Cameroon, a machete may also be called a cutlass.
potentials(Abdul, 2007; Imorou, 2008; Thorsen, 2007b). Table III examines the significance
level of the variables analysed in table II whose effects are positive and negatives.
Table III: Analysis of the level of significance of Determinants of child labour
involvement in agribusinesses
Logistic regression
Number of obs =
LR chi2(15)
Prob > chi2
Log likelihood = -8937.2872
School enrolment
15514
= 1951.46
= 0.0000
Pseudo R2
=
0.0984
-0.666
(11.35)**
Standard of living
-0.342
(7.11)**
Cultural group
0.964
(5.02)**
Size of the household
0.027
(2.98)**
type of household
-0.016
(1.06)
level of education of the child
0.405
(14.72)**
Expenditure for clothes and shoes
-0.000
(1.23)
Expenditure for leisure
-0.000
(2.04)*
Expenditure for housing
-0.000
(16.58)**
Expenditure for health
0.000
(4.03)**
Expenditure for transports
0.000
(1.15)
Expenditure for education
-0.000
(6.21)**
Expenditure for food and non-alcoholic drinks
0.000
(0.50)
Expenditure for goods and services
0.000
(1.71)
Expenditure per head
-0.000
(3.08)**
Constant
0.407
(3.25)**
* p<0.05; ** p<0.01
Source: Author’s estimates compiled using the 2007 CHS3 dataset and Stata 13.0
This table III examines the significant influence of the variables on the involvement of
children in agribusiness labour. It uses both the student test and the probability law to show the
level of significance of the variables which are 5 per cent and 1 per cent for this study.
Interpretation of table III
From table III, expenditure for leisure affects significantly the involvement of the child in
labour at a 5 per cent level of significance. This means that for every sample of 100 children
involve in labour, 5 among the 100 involved come into labour not because they need money
for leisure. In other words, 95 children from a sample of 100 who are in agribusiness work
come in because they need means for their leisure. The other variables with 2 stars have a
strong significance level on the dependent variable. Thus for a sample of 100 children
working, 99 may come in because of the fact that:
They are not currently enrolled in a School,
Their standard of living is very bad,
They are culturally interested in the way things are done elsewhere and wants to copy,
The Size of the household is not favouring the consideration of all its members. Olayide et
al (1982). supports this point when he reports that direct costs are often a heavy financial
burden for households in developing countries. Dottridge (2011) and Ibrahim et al (2005) go
further to present evidence from Uganda and Pakistan on direct costs discouraging
household investment in schooling. Grootaert (1999a), however, finds no association
between direct costs and household schooling and child labour decisions in rural Côte
d’Ivoire.
The level of education of the child that cannot permit him to have something better to do,
The invitation of the child to contribute to housing expenditure,
The expenditure for health are obliging the child to involve so as to survive,
The expenditure for transports due to the distance to cover in order to have access to mass
services,
The expenditure for the education of the child,
The expenditure for the head in the household, are some of the direct
costs that brings the child to the labour market.
The table also shows that the constant is significant at 1 per cent level of significance.
This means that there are variables that should be included for the analysis to be complete. This
is due to the fact that our sample is constituted only of children between 5 and 17 years. There
are variables that will come in when the sample will extend to parents, policy makers and
others. All the variables that affect child labour involvement in labour are related to lack of
means of these children but Nielsen (1998) as well as Grootaert (1999a) do not find a positive
relation between poverty and child labour, and thus raise doubts to the claim of poverty
being a main determinant of child labour.
Table IV: Odds ratio analyses of the involvement of children in agribusiness child labour
Logistic regression
likelihood = -8939.4556
Std. Err.
z
P>|z|
Number of obs = 15514
LR chi2(14) = 1947.12
Prob > chi2 = 0.0000 Log
Pseudo R2
= 0.0982
[95 per cent Conf. Interval]
Child variables
Odds Ratio
Std. Err.
z
P>|z|
[95 per cent Conf. Interval
enrol_
.5135301
.0301457
-11.35
0.000
.4577178
.5761478
nivie
.7117295
.0342931
-7.06
0.000
.6475923
.7822187
culture_group
2.603769
.4991793
4.99
0.000
1.788196
3.791315
tailm
1.02692
.0093216
2.93
0.003
1.008811
1.045353
typmen
.9837102
.0147634
-1.09
0.274
.9551959
1.013076
nivins
1.498366
.0411955
14.71
0.000
1.419761
1.581322
dephab
.9999998
1.36e-07
-1.30
0.195
.9999996
1
deplog
.9999972
1.62e-07
-17.06
0.000
.9999969
.9999976
depsan
1.000001
1.53e-07
3.93
0.000
1
1.000001
deptran
1
8.49e-08
0.62
0.532
.9999999
1
depedu
.9999985
2.27e-07
-6.50
0.000
.9999981
.999999
depal
1
5.27e-08
0.62
0.537
.9999999
1
depbien
1
2.96e-07
1.43
0.152
.9999998
1.000001
.9999993
2.06e-07
-3.39
0.001
.9999998
.9999997
1.53444
.1914379
3.43
0.001
1.201582
1.959505
deptet
_cons
Source: Author’s estimates compiled using the 2007 CHS3 dataset and Stata 13.0
Presentation of the treatment Variables
enrol_
School enrolment of the child actually
Nivie
Standard of living of the family where the child comes from
typmen
type of household where the child comes from
nivins
level of education of the child
dephab
Expenditure for clothes and of the child and others
deploy
Expenditure for leisure of the child and others
deplog
Expenditure for housing of the child and others
depsan
Expenditure for health of the child and it prevention
deptran
Expenditure for transports of the child to school and others
depedu
Expenditure for education fees and associated expenditures
depal
Expenditure for food and non-alcoholic drinks by the child
depbien
deptet
Expenditure for goods and services needed by the child
Expenditure per head in a household
Source: Compiled by the author from the 2007 Cameroon Household Survey (ECAM III) based on the literature and past empirical studies.
Interpretation of table IV
The value of the odds ratio of school enrolment is 51.35 per cent, that of the standard of living
is 71,17 per cent and that of the impact of culture is 2.6 respectively. These figures mean
respectively that the enrolment of one new child will lead to a reduction of child labour by
48.69 per cent independently of the consideration given to any other variable. In addition, the
amelioration of the standard of living of one new child will lead to a reduction of child labour
by 28.83 per cent independently of the consideration of any other variable. Lastly, the influence
of foreign culture on a child will lead to an increase of child labour by 1.6 independently of the
consideration of any other variable.
Discussion of Results
One of the principal factors in deciding to work for children is presumably the amount of
income this work generates directly or indirectly. Therefore, to understand and ultimately
influence child work and schooling decisions, it is important to have an accurate evaluation of
the magnitude of these child income contributions and their determinants. Protagonists of
banning children’s work to protect them against trafficking and slavery explain the
employment of child workers with the inability of poor farmers to pay adult wages and
thus their inability to attract young men over the age of 18 to work on the
farms(AntiSlavery, 2010; ILO, 2007c). Proponents of a child and family-centred
perspective emphasize the use of family labour and argue that farmers who lack money to hire
workers, need to balance the desire to increase production with the desire for their children
to concentrate on schooling (Barrientos, 2008; MMYE, 2007). Knowledge of children's
income contributions is also crucial to determine the level of compensation required in
income-based policies to reduce child work and encourage school attendance.
This information provided us with an idea of the total short-term cost for the economy of
reducing child work to be weighed against the long-term benefits of increased schooling.
Where children have the option of working on the labour market, the child wage rate provides
an accurate measure of children's income contribution and that this income contribution would
vary little between children. However, smoothly functioning child labour markets rarely exist.
In their absence, a child's income contribution depends on his labour productivity in
agribusiness activities, which likely vary substantially according to the household's asset
profile, demographic composition and other characteristics. In Cameroon, teachers often
require their students to work on their cocoa farm or hire out children to local farmers during
school time. The teachers earn money for this work. One school director threatened to retire if
authorities prohibited the practice (ILO, 2007c).
Duelling on some variables can help to reduce the involvement of children in agribusinesses.
An increase of the number of school enrolments, a better standard of living through access to
school, healthcare services can discourage the child involvement in labour. The
accompaniment of the child in some basic needs within the household like clothing, shelter,
education will also help the child not to think of going into these premature risky activities. On
the other hand, a better management of the cultural environment of the child, the standard of
live of the child, his expenditure for transport, for feeding and for others needs will help in
reducing seriously this phenomenon of child labour in agribusinesses.
Family size is another important factor that can cause a household to be involve in child
labour. In the first place, a large household size could lead to lower per capita income and
therefore the need for extra income, which may create an incentive for the parents to send their
children to work. Again, a large household size generates more labour within the farm
household. With fixed productive assets (land, tools, technology, animals, etc), the marginal
productivity of labour within the household would begin to diminish. Nwaru (2005) pointed
out that households would easily reallocate excess labour from its members to off farm
economic activities or alternatively hire such excess labour out as a strategy to optimize the
use of available labour, diversify household income and as a tool against poverty.
Conclusion
From the study, all the variables that affect significantly the involvement of children in
agribusinesses labour are all link to poverty, cultural deviations, the household
management policy as well as the national educational policy to a certain extend. The
amelioration of some of these variables will have a very good impact in the reduction of
this phenomenon. The creation of schools in enclave areas, the training of teachers, the
facilitation of the admission of children to schools are some of the variables that will help to
boost the enrolment rate and by so doing, it will reduce the involvement of children on the
labour market. This study convenes also the household responsibility in the reduction of the
child work. Providing a better care to the child at the household level will kept him away from
work to a certain extend. A better coordination between the house and the school will also
enable a better reorientation of the management of the free time of the child away from work.
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