BACKGROUND
TO THE STUDY
In Nigeria, the
agricultural sector dominates the general sector of the economy (Kaine and Ume
2017). It has been documented that the agricultural sector is the most
important sector of the economy that ensure food security and enhance economic
growth and development (Audu, 2017; Adeoye et
al 2017and Kaine, 2018a). Adeoye et
al (2017) reported that the Nigerian agricultural sector is characterized
by small land holdings and use of child labour. They further reported that the
use of child labour in agricultural production was adopted as a means of
reducing the cost of production.
Child labour in this
context is used to refer to all activities carried out in the farm by children
of school age that interferes with schooling. However, the International Labour
Organization (ILO) reports (2013) noted that where child labour does not have
negative effect(s) on the schooling of the child, health status, physical and
mental development, it is generally not regarded as child labour. The claim was
further exemplified and stated that where a child is used to look after the
siblings at home, working to earn pock money after school hours and during
holiday cannot be regarded as child labour.
Alao et al (2013) reported that about 186
million child labourers are used all over the World. From the estimated
population it was stated that about 111.3 million of the children used, work in
harmful environments. It was also reported that at least 120 million of the
worlds children labourers who are engaged on full time basis ranges between
the ages of 5 and 14 years and work for more than 10 hours a day.
The effect(s) of
child labour has been documented to include but not limited to: fatigue, waste
of talent and energy, academic waste, high rate of school dropout among others
(Ofuku et al 2014). Determinants of
child labour have been identified to include: poverty, high fertility rate with
a consequent increase in household size, cultural and family tradition as well
as lack of access to affordable and quality education (Etim and Udofia, 2013
and Glory and Nsikak Abasi, 2013).
Kaine and Ume (2019)
and Kaine and Ume (2017) regarded household size as individuals or people
occupying the same building or a house and its occupants. The authors further
opined that household size is the number of people living under the same roof
and feeding from the same pot. The household is either headed by a male or a
female (adult or youth) who provides the needs of the family. Where the female
takes charge of the entire needs of the family, it is regarded as female headed
household.
It has been
documented that Nigerian farmers make use of family labour majorly made of
children and do not often hire children outside their households (Ofuoku et al 2020). Although, various studies
have been conducted to examine various level and nature of childrens
involvement in farming activities, it is not certain that studies have been
carried out with respect to determinants of child labour among female micro cassava farming
households in South South agro ecological zone in Nigeria. It is against
this background that this study was carried out to examine the socio economic
characteristics of the farmers, ascertain the factors that determine the supply
of labour and estimate the
socio-economic determinants of child labour.
MATERIALS
AND METHODS
The
Study Area
The study was
conducted in South South agro ecological Zone of Nigeria. Three (3)
randomly selected states was used for the study. South South comprises of six
States which include: Akwa Ibon, Balyasa, Cross River, Edo, Delta and River
States. The zone has a total population
of twenty one million, and forty four thousand and eighty one (21,044, 081)
people. This population is made up of ten million, seven hundred and five
thousand, five hundred and thirty three (10,705,533) male. The female population
was recorded to be ten million, three hundred thirty eight thousand, five
hundred and forty eight (10,338,548) (National Population Commission, 2006).
Total projected population figure with a growth rate of 3.2 percent as at 2022
was estimated to be thirty four million, nine hundred thirty three thousand,
one hundred and seventy four (34,933,174) people with seventeen million, seven
hundred and seventy one thousand one hundred and eighty five (17,771,185) male.
The female projected population was estimated to be seventeen million, one
hundred and sixty one thousand, nine hundred and ninety (17,161,990). Akwa
Ibom, Delta and Edo were the three (3) randomly selected States that were used
for the study. Total population of these States was eleven million, two hundred
and forty seven thousand, seven hundred and sixty two (11,247,762) people
(Census, 2006). The total projected estimated figure was eighteen million, six
hundred and seventy one thousand, two hundred and eighty five people.
Sources
of Data
Data required for the
study was collected using both primary and secondary sources of data. A
well-structured questionnaire (primary source) was used to collect the required
data for the study. Secondary source of data was generated from review of previous
work.
Sampling
Procedure
Multi stage random
sampling technique was used for the study. The first stage involved selection
of three (3) States out of the six (6) States in the zone. The second stage
involved the selection of two (2) agricultural zones from each of the selected States.
Thirdly, three (3) Local Government Areas was randomly selected from the three
(3) selected States. Thereafter, four (4) communities each was selected from
the randomly selected Local Government Areas. The sample frame consist of five
(5) female headed household cassava farmers selected from the thirty six (36)
communities given a total sample size of one hundred and eighty (180)
respondents.
Enumerators that
could read and write in English and Local Language of the selected communities
were recruited, trained and assigned to the selected communities with the
assistance of the Assistant Chief Agricultural Officers (ACOA), Local
Government Agricultural officers and resident Agricultural Extension Officers
in the selected Local Government Areas as well as the village heads and chiefs
were used for effective data collection exercise.
Data
analytical technique
Descriptive statistics, and the logistic
model were used for data analysis.
Model
Specification
Logistic
Model
The model is expressed as
Yi = β0 + β1X1
+ β2X2+ β3X3 +β4X4
+β5X5 +β6X6 +β7X7
+β8X8 +Ui
Where Yi = Dummy variable which takes the
value of unity 1 If the household engages in child labour and zero 0 if
otherwise.
The independent variables include:
X1 = Age of the household head
(years),
X2 = Household size (Number)
X3 = Educational level of the
household head (years),
X4 = Farm size (ha),
X5 = Household income (N),
X6 = Number of male children,
X7 = Number of Female Children,
X8 = Number of Female Children
Ui = Error term, β 1 β
8 =Parameters.
RESULTS AND DISCUSSIONS
Socio
economic characteristics of female headed household cassava farmers
Socio economic
characteristics of female headed household cassava farmers in the study area
was studied, determined and presented in Table 1. The result of the variable:
age indicated that the farmers were relatively young. Thirty (30) (20.68%) of
the farmers were within the age bracket of 20 30 years, thirty nine (39)
(26.89%) were within the age bracket of 31 40 while seventy six (76) (54.40%)
were within the age bracket of 41 and above. The result of the marital status
studied showed that one hundred and six (106) (73.10%) of the farmers were
single while only thirty nine (39) ((26.909%) of them were married.
Household size
examined revealed that majority (108) (74.48%) of the respondents in the study
area had household size within the range of 0 4. The result indicated that
majority of the farmers had small household size. Analysis of the educational
attainment studied showed that literacy level of the farmers in the study area
was high. The result revealed that one hundred and fourteen (114) (74.48%) of
the farmers had formal education while thirty one (31) (21.38%) had informal
education.
The variable farming
experience determined showed that the farmers were well experienced in cassava
production. Majority of the farmers (124) (85.52%) had a farming experience of
over ten (10) years. The result of the farm size revealed that farmers in the
study area were small holder farmers. A detailed analysis of the farm size
indicated that sixty eight (68) (46.90%) of the farmers had a farm size range
of 0 1 hectare, forty nine (49) (33.79%) had a farm size range of 2 4
hectares while twenty three (23) (15.86%) and twenty three (15.86%) had a farm
size range of 4 5 and 6 and above respectively.
Table 1. Socio economic characteristics of the
farmers ∑n = 145
|
Characteristics Frequency Percentage
|
|
Age (Categories)
20
30 30 20.68
31
40 39 26.89
41
50 63 43.44
50
and above 13 8.96
Marital Status
Married 39 26.90
Single 106 73.10
Household Size
0
4 108 74.48
5
9
21
14.48
10
14 9 6. 21
15
and above 7 4.83
Education
attainment
Informal 31 21.38
Formal 114 78.62
Farming Experience
1
5
11
7.58
6
10 10 6.90
11
15 63 43.45
16
20 18 12.41
20
and above 43 29.66
Farm Size
0
1 68 46.90
2
3
49
33.79
4
5
23 15.86
6
and above 05 3.45
|
Source: Computed from field
survey, 2023
Factors that determine
of supply of child labour in the study area
The result of the
determinants of supply of child labour in the study area was determined and
presented in Table 2. The result revealed that majority: one hundred and seven
(107) (73.79%) of the respondents indicated wage rate as a determinant of
supply of child labour. Rural urban migration was established as a determinant
of supply of child labour. This was
indicated by ninety eight (98) (67.59%) of the respondents. Population (63)
(43.45%), substitution effect (31) (21.38%), efficiency (93) (64.14%) and
change in income (51) (35.17%) were proven by the farmers to be determinants of
child labour in the study area.
Table 2. Determinants
of supply of labour
|
Variables
|
Frequency
|
Percentage
|
|
Rural
urban migration of adults
|
98
|
67.59
|
|
Wage
rate
|
107
|
73.79
|
|
Population
|
63
|
43.45
|
|
Substitution
effect
|
31
|
21.38
|
|
Efficiency
|
93
|
64.14
|
|
Changes
in income
|
51
|
35.17
|
Source: Computed from field
survey, 2023
Socio-economic
determinants of child labour use among
respondents (logistic regression)
The logistic regression model
was used to decide or estimate the social economic determinants of child
labour in the study area and the result was presented in Table 3. It was
revealed that the coefficient of age (0.039) was a positive and significant determinants
of child involvement in cassava farming at 0.202 probability level. This result
was in consonance with the result of Alao et
al (2013) who found a positive relationship between age of farmer and the
use of children in the farm. A positive relationship between age and child
labour that was observed in this study implied that as the household head is
aging, the higher the likelihood of using child labour in the farm. This is in
line with the a priori expectations that aged farmers are associated with more
lassitude that make them to be exhausted easily. Oladimeji and Edun (2018) who
observed a positive relationship between age and child labour also reported
that older farmers were more likely to involve more children in the farm than
the younger farmers. This the authors stated that it is expected because the
older people tend to be less productive. Iheke (2010) on the other hand
observed a negative relationship between age and child labour.
The variable marital status was
considered, determined and presented in Table 3. It was revealed that the
coefficient (-0.151) of marital status was negative but significant determinant
of child labour at probability level of 0.878. A similar result was obtained by
Oladukun et al (2017). The authors
reported that a negative result coefficient found implied that single headed
household were less likely to employ the use of child labour in farming activities.
However, this result was not in consonance with that of Adeoye et al (2017) who reported a positive
relationship between marital status and use of child labour.
Education, another important
variable that was determined in this study. The result showed that the
coefficient of education (0.068) had a positive relationship with the
engagement of child labour among the farmers. It implied that education was a
positive and significant determinants of child labour in the study area at a
probability level 0.837. Empirical studies carried out by Ume et al (2018), Iheke, (2010) and Ume and
Okoye (2007) revealed that literacy level of farmers affect the use of child
labour in the farm. Okpukpara and Chukkwuone (2007) also established a positive
relationship between educational level of farmers and the use of child labour.
This is in line with the findings of this study. The authors opined that the
more educated the household head is, the lower the likelihood of involving
children in the farm. The positive coefficient of education found in this study
is not in line with the result of the study carried out by Oladokun et al (2020), Adeoye et al (2017) and Aloe et al (2013). A negative relationship
between education and child labour use was reported by the authors. According to
the authors, a negative coefficient of education influences farmers to the use
child labour in the farm. The authors further stated that the less educated the
farmers, the higher the propensity to employ child labour in the farm. This is
in consonance with the a priori
expectation that illiterate farmers often use children and household labour to
work in the farm without considering the benefits of education on human capital
development.
Household size was another important variable that was
studied and discussed. The result established that household size (-0.413) was
a negative but statistically significant determinant of child labour use among
the farmers. The variable was statistically significant at 5% level of
significance. A negative coefficient of household size according Oladokun et al (2020), implied that an inverse
relationship exist between household size and use of child labour. This result is in line with a priori expectation that with a lower number of household size,
it is anticipated that family expenditure on feeding, clothing and other social
services will be lesser. Hence the higher propensity to use child labour in the
farm. On the other hand, Omeje et al (2020) and Alao et al (2013), found a positive
relationship to exit between household size and use of child labour.
A further analysis of the
social economic determinant of child labour use with respect to the variable
experience established that coefficient of experience (-0.457) had a
negative sign. This implied an inverse relationship with the variable and this
might predispose the farmers to the use of child labour.
Farm size was studied and
determined. The result showed a negative coefficient (-1.264) that was statistically significant at 5% level of
significance. This implied that the variable was a significant determinant of
child labour. A negative coefficient of farm size observed in this study is not
in consonance with that observed by Alao et
al (2013). The authors established a positive relationship between farm
size and use of child labour. The authors stated that the larger the farm size
the more the farming activities that will be required and will predispose the
household heads to use children on the farm to cultivate the large expanse of
land.
Age of child
also showed a negative coefficient (-0.779). This result is in line with that observed by Ogunwande et al (2016).
Dichotomous response variable (use of child labour):
Don't use = 63.9%; Use = 36.4%
CONCLUSION
AND RECOMMENDATION
The study revealed
that farmers in the study area were relatively young and well experienced in
farming. Literacy level was recorded to be high, household size was small and
the result also indicated that farmers in the study were small holder farmers.
The result of the determinants of supply of child labour was established to
include but not limited to rural urban migration of adults, population,
substitution effect, wage rate and change in income among others. The logistic regression model was
used to estimate the social economic determinants of child labour in the
study area. Analysis of the
result indicated that coefficient of age and education were positive
determinants of child labour. It also established that the coefficients of the
variables marital status, number of children, farming experience, household
size and age of child were negatively and inversely related to use of child
labour.
Since the study
established that rural urban migration of adult was a determinant of supply of
child labour in the study area, it was recommended that government at all level
should embark on rural and agricultural developmental policies, projects and
programmes. This will not only make rural life meaningful but will discourage
rural urban migration. Wage rate and changes in income were also identified as
determinants of supply of child labour in the study area. It was recommended
that the government should put in place policies that will provide social
security programmes and services. This should be considered as strategic
caution against adverse effects of wage rate and changes in income. Use of
conditional cash transfer should be expanded. Education was also reported to be
a determinant of child labour use in the study area. Since access to education
is one of the basic right of a child, it is imperative for Federal, States and
Local Governments to formulate policies that will discourage child labour but
improve access to qualitative education in the study area. Intensification of
sensitization campaign against child labour should be carried out. This will
help to increase awareness and change attitude of people towards supply of
child labour.
ACKNOWLEDGEMENT
The
Tertiary Education Trust Fund (TETFund) sponsored this research work Socio
economic determinants of child labour among female headed household cassava
farmers in some selected States of South - South, Nigeria: A Post Covid-19
Experience by providing the much needed fund that was used to carry out the
study. Much appreciation to this great institution for their kind gesture. The
contributions of other scientist are acknowledged.
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