Effect of Child Economic Contribution to Farming Households’ Income in Akwa Ibom State. Nigeria

Effect of Child Economic Contribution to Farming Households’ Income in Akwa Ibom State. Nigeria

Nyong, I. E.¹, Udoh, E.J.^1*, Edet, G. E.¹ and Inyang E. B.²

¹Department of Agricultural Economics, University of Uyo, Uyo, Nigeria

²Department of Agricultural Extension and Rural Development, University of Uyo, Nigeria

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.806053

Received: 15 May 2024; Accepted: 29 May 2024; Published: 02 July 2024

ABSTRACT

This paper examines effect of child economic contribution to farming households’ income in Akwa Ibom State. The study adopted a non-experimental design (cross sectional Survey). Data were obtained using a multi-stage sampling procedure. The study used logistic regression and propensity score matching to assess the effect of child’s economic contribution to farming households’ income in the study area. Child’s economic contributions significantly related with sex of child and parent marital status, suggesting, gender and family structure play a role in child economic contributions to households’ income. Parental Educational status, Parental monthly expenditure, Household size, Household resource deprivation status, Family socio-economic well-being and drive for economic wealth creation were found to explain child work involvement in farming households. The PSM outcome indicated that the mean difference of respondents who involved children in work and those who did not, remained the same, (0.00018). And the T test result shows that there is no significant effect between child involvement in work and child economic contribution to households’ income. In conclusion, it was discovered that there was no positive effect of child involvement in work and households’ income. Therefore, the children should be trained through school, skill programmes to develop their human capital potentials. As this is going to be the only way they can impact positively on the households and the economy at large through expertise skill. And Businesses should be encouraged to adopt ethical business practices and eliminate child labour from their supply chain. Promote the right of children and ensure that policies and practices prioritize their well-being. And the children involvement in decision making that involves them should be encouraged.

Keywords: Child work involvement, Farming Households, Socio-economic conditions, Akwa Ibom State, Nigeria

INTRODUCTION

Many children in the world are involved in income generating activities, both through child labour and child work. Child Work imply Children’s or adolescents’ participation in work that does not affect their health and personal development or interfere with their schooling, and it’s generally regarded as being something positive (ILO, 2021). While Child Labour refers to work that is mentally, physically, socially or morally dangerous and harmful to children and interferes with their Schooling either by depriving them of the opportunity to attend school, obliging them to leave school prematurely or by requiring them to attempt to combine school attendance with excessively long and heavy work (ILO, 2022).

About 246 million children work in the world (ILO 2022). With about 179 million classified as child laborers in both industrialized and developing countries (UNICEF, 2022)). Working children contribute immensely to the incomes of their households by either working directly on household farms, non-farm enterprises or working outside the home and contributing to total household income. It has been widely theorized that households from which child workers emerge survive on incomes of these children and use them to meet subsistence needs and that poverty is the determinant factor that push children to work (Magaji and Tsauni, 2022). From a study in rural Ethiopia, Cockburn (2002) showed that child workers, on average, contribute 4 to 7 per cent of household income, with some children contributing up to 50 per cent. There are over 15 million working children under the age of 14 in Nigeria according to the estimates by International Labor Organization. Estimates determine that the current number of child workers in Nigeria is 15 million according to the International Labor Organization (ILO). At a staggering 43% of the total population of minors, it is the highest recorded rate in Western Africa. This environment of financial strife causes more and more families to expect their children to go out to work and contribute an income.Usually, these children are underfed and poorly paid. The International Labour Office also reports that children work the longest hour and are the worst paid. It is perceived that certain factors possibly do influence the decision of parents to put their children to work. These among others are; Age, sibling rank, sex, household income, household size education aspiration, presence of working children in the household and community, family trading, nature of employment and weak enforcement of child labour laws. Majority, poverty is that factor that predispose children to work. Poverty is a rural phenomenon in Nigeria and the rural household are predominantly engaged in Agriculture. According to the National Bureau of Statistics (NBS 2019), in a report about poverty and inequality from September 2018 to October 2019, 40% of people in Nigeria which is the world’s most populous country live below the poverty line of 137,430 Naira ($381.75) a year. That is about 82.9 million people. Poverty among farmers are reflected in such characteristics as Low income, Low level savings and low investments, generally low output levels resulting in a generally low living standards or wellbeing. Ekpo and Uwatt, (2005), noted that 27.51% of people live in core poverty in 2005 while 56.9% overall poverty level exist in Akwa Ibom State. Therefore these farmers believe that engaging children in work can make a difference between grinding poverty and economically secure life. Amidst policy operations to tackle children involvement in work, there seems to be no felt impact among poor families as their situations worsen over time and there is an upsurge of children involvements in various kind of economic activities to support their households, there has been copious study as mentioned above that has identified poverty as the main factor that affect the decision to put children to work. The growing situation begs the question; how substantial does the children economic earnings contributes to their households in beating subsistent struggles. That is, to what extent does a child’s economic contribution affects the households’ income? To assess this, The Household characteristics of respondents were identified, factors that influence child’s work involvement in the study Area was identified and the effect of child economic contribution to households’ income in the study area was accessed.

THEORETICAL FRAMEWORK.

Based on the theory of demand and supply, considering people’s behaviors, demand is higher for cheaper alternative and with easy availability. There are many explanations for the use of children in agriculture. The use of child labour is usually the result of a need for intensive labour coupled with a readily available supply of labour that is cheap and easily controlled. Employers hire children because they are available in large numbers and because, in the view of some employers, child workers are preferable to adult (ILO, 2007). While most agree that children should grow up in an environment that provide the best possible conditions of physical and mental growth, many factors compel the children to enter the workforce. As a result, a large supply of children is often available to meet the demand. The nimble fingers theory claims that children have a comparative advantage in some kinds of occupation, that is, children are more suitable labourers than adults for some occupations. This theory can then plausibly explain the existence of a large proportion of child labourers, and was the held view for a long time. More recently, however, important studies have refuted the theory and exposed new directions of causality explaining the demand for child labour.   Anker et al (1998.) gives an understanding of the economics of replacement of child labour with adult labour in the carpet, gems and diamond industries. The papers in this volume refute the nimble fingers theory and note that non-pecuniary and non-economic factors are often very important reasons why employers hire children. Among the non-pecuniary reasons given for hiring child labour are (i) awareness, subservience and innocence (that is, child workers are more docile and less troublesome, children show greater willingness to do repetitive monotonous work, have greater innocence, do less absenteeism, do not join trade unions or agitate for their rights, etc.); (ii) prevailing traditions in society (tradition of hiring child labour by employers, traditional occupations encourage the children to work alongside parent(s), the social and community status of the employer gets enhanced by providing jobs to children in the community, employers need workers and this assures availability of skilled labour in the future); and (iii) the physical characteristics of children.

Furthermore, Grimsrud (2001) says that one may also look at the total labour demand and supply in the economy. By so doing, it is noted that child labour supply is a result of decisions within the household influenced initially by the wealth of the household. Working children’s relationship to the labour market is generally closely linked to their parents’ labour market relations. With reduced income the household will respond by sending its children out into the labour.

Poverty is well recognized as an important supply side factor on the child labour issue, and may be viewed as an influential supply side factor at both the micro and macro level. At the macro level, it is seen that economically active children represent a decreasing proportion of the total labour force as gross domestic product (GDP) per capita increases. A World Bank report (1998) notes that the higher the share of agriculture in an economy’s GDP, the higher the incidence of child labour. These macro level observations do not however help us form a precise view of the dynamics influencing child labour. The micro level dynamics operate at the level of the household. The existing literature discussed below, makes two crucial assumptions, namely, the ‘luxury’ and ‘substitution’ axioms. These assumptions are made for the labour market in which children are workers. As an extension, the effects of adult labour supply and wages on those of children are examined. For the above line of argument, poverty is analyzed in relation to the structure of the labour market. In another line of argument, poverty is analyzed in relation to cultural and gender factors. Last, but not the least, some of the literature analyses poverty and risk factors together to conclude that child labour is a buffer mechanism for poor households.  The Basu and Van (1998) assume that parents are altruistic. They then proceed to model the supply of child labour under the two crucial assumptions mentioned above – the ‘luxury’ and the ‘substitution’ axioms. The luxury axiom asserts that households send their children to work only when driven to do so by poverty. The substitution axiom asserts that adult and child labour are substitutes that is that adults can. Galli 2001 notes that a lack of alternative opportunities for adults will tend to increase child labour supply in low-income households.    

According to Webbink et al, (2013), the socio-economic components of the household factor- education level of household heads, income level, occupation (work) classified as resources and the demographic factors – sex, age, number of biological children/ foster children birth order number of sisters etc., are of utmost relevance to household income sustainability, thus exerting a considerable level of pressure on or influences household income contribution option (participation) among its members.

Household factors serves as a veritable measuring indexes of child work participation/ income contribution, thus creating interlinkages or interactions with other variables in determining child economic participation to households, which forms the basis of the study conceptual framework. However, each of these household factors predisposes household members (child labourers) towards undertaking active economic or income contribution roles.

For instance, educational disadvantage disposition of the household head constrains higher income earning opportunities either within farming or non-farming spheres, thus implying or suggesting a possible likelihood of child labourer involvement or enlistment into economic contribution schemes in meeting the pressing economic needs of the family, at times to the detriment of the child’s social/economic prospect. Low income level / wealth size of households enhances the appeal for child participation in economic activity to meet subsistence living obligations, this agrees with (Basu and Van, 1998) assertion which attributes the emergence of child labour or child labourer in households as survival strategy in meeting subsistence needs. Empirical studies is replete with facts or evidences of socio-economic factors being a strong indicator for child economic engagement as a rationale for income earning, impacts or determines the dimensions of work performed by child labourers.

Ekpo and Uwatt, (2005), noted that 27.51% of people live in core poverty in 2005 while 56.9% overall poverty level exist in Akwa Ibom State. Therefore these farmers believe that engaging children in work can make a difference between grinding poverty and economically secure life. Agricultural child workers is three times what can be seen in other sector (FOS, 2001). These children workers virtually come from these households where people just manage to work out a living from subsistence Agriculture. The need of their poor rural households make them send their children to work rather than going to school. The implication is that these children may be able to provide extra income for their family and thus reduce their poverty level. However, these may only be in a short term. When one is interested in the long run effect or what happened to the children when they grow up, the opportunity cost of reduction in Human Capital Development must be considered.

METHODOLOGY

Data Source and Data Collection

This study adopted a non-experimental design (cross sectional survey) and utilized the logistic regression and propensity score matching technique to evaluate the relationships that exist among the variables. This research was carried out in Akwa Ibom State, which is one of the major crude oil producing Niger Delta States in Nigeria. Due to its natural location, over 80 percent of the households are engaged in agricultural related activities, especially in farming and fishing. Administratively, there are 31 local government areas (LGAs) with Uyo LGA being the capital city.  Based on the degree of rurality, cosmopolitan and the propensity for children to be engaged in household income generating farming activities, Uyo metropolis  and Oron LGA were purposively chosen. Uyo metropolis comprises of  Uyo LGA, Uruan LGA, Itu LGA, Ibiono LGA, and Nsit Ibom LGA. These LGA’s were purposively chosen based on the desire to cut across considerable aspects of farming, like the riverine farming and the non-riverine farming activities. Therefore, following a multi-stage sampling procedure, a sampling size of 240 children of farming households were collected from the the six LGAs.

Analytical method

A combination of analytical tools was employed to analyze  the objectives, these included the descriptive statistics, correlation, regression and propensity score matching. Specifically, Pearson product moment correlation was used to determine factor that influence decision to put children to work and  the Logistic regression model and propensity score matching were used to analyze the effect of child involvement in work and  their economic contribution to households income.

Limitations

It is worthy to note that in carrying out this research study, there were some limitations. Some included gaining access to children due to their irregular scheduled and potential reluctance of parents and guardians. Ethical considerations was also part of the limitations, which included obtaining informed consent and ensuring the well-being of participants was really complex. Another was that of reliability of data collected. Working children felt pressured to give socially desirable answers and at other times afraid to disclose their true working condition and experiences, this could have led to potential bias in data collected. Logistic challenge was also a limitation while carrying out the work. It was resource- intensive having to go to remote locations for data collection. Despite all these limitations, with careful planning, ethical considerations, and appropriate methodological approaches, meaningful research work was successfully carried out.

RESULTS AND DISCUSSION

Household’s characteristics of respondents

The result of findings show that most of the children were male, aged 14 to 16 years. This aligned with Koomson and Asongo, (2016), while studying Ghana Living Standard Survey

(GLSS6) stated that 84% of male were involved in Agricultural activities against 71.2% female, following the 13%, also 2% by non-farming activities – wholesale and retail trade.

This is in consonant with what ILO, 2010 reported, that agriculture employs about 60% of children within the ages of 5- 17 years globally. The findings also revealed that 41% of the respondent parents attended tertiary institution.  According to Webbink, Smith and Jong (2013), article on household and context determinant of child labour in developing conutries, Parental Education as a resource can significantly affect the decision to put children to work. This perhaps explain why the very young children, ages 5 to 14 were not predominantly put to work but only those ages who have either finished school or about to finish actually supported out.  Because the parents know the importance of education. Yinalabi and Francis (2022), observed that large poor households usually have more children involved in labour than children from smaller households. Which demonstrates that family size has an effect on working children population. This totally agrees with the findings of the study, as households with higher family size were observed as having more children working population.

53% of the farmers were into livestock farming in both subsistence and commercial capacity. Meaning that they derive their livelihood through Agriculture.

68.9% of the parents had other economic engagements other than farming. Some were civil servants, traders, Artisans, etc. and predominantly members of cooperative society who have access to credit facilities.

The result reveals that 99% of the respondent’s households were into both subsistence and commercial farming, and these farmers were engaged in all types of farming including Agro-processing/marketing. This agrees once again with Koomson and Asongu (2016), who observed that 91.2% of children are involved in all types of of Agriculture.

Table 1. The distribution of respondents based on household’s characteristics

From the chi square analysis carried out, 67% of the children combined school with work, a clear evidence of child work, since their economic engagement did not hinder their developmental processes, including schooling. While 13.7% where involved in child labour. Showing that the economic engagements is looked at from the angle of child labour and child work

Factors that Influence Child’s Involvement in work. (FICWI)

From the analysis, 65.8% of the respondents fall under poor category, which ranges between 0.26 – 0.5099, this suggest that  various factors out of the possible factors mentioned in table 2, contributes to a child’s economic involvement, with some factors having a moderate impacts and others potentially having a strong influence. 21% are of a very high influence while 12.8% had no influence at all to child’s economic contribution

 Level of Factors that Influence Child Work Involvement

In table two, 1^st – nth positions shows ranking positions in ascending order. Higher rank indicate stronger preference, favourable view, positive perception to the items considered and affirmation.

Underlying Factor 1= PFSEW – Poor Family Socio Economic Wellbeing, (captured in items No. 1, 2and 3)

Underlying Factor 2 = DFWC- Drive for Wealth Creation, (captured in items No. 6, 10 and 11)

Underlying factor 3 = HRD –    Household Resource Deprivation, (captured in items 4,5,7,8 and 9)

Table 2. Underlying factors influencing child involvement in Work

Figures in Bracket represent the frequency of outcome

Effect of Child Economic Contribution to Farming Households’ Income in Akwa Ibom State

In an attempt to evaluate effect of child economic contribution to farming households’ income, propensity score matching using the logistic regression model was deployed. Propensity score matching is an evaluation method that is often used to balance covariates between treatment and non-treatment group. It helps to obtain information on what would have happened to participants in the experiment group if there was no contribution.  By comparing the differences, the effect of the contribution is properly determined (Heinrich, et al, 2010).  In other words, propensity score analysis reduces the differences in the characteristics of participants into a single variable and balances the inequality of the characteristics of the control group in observational studies (Qin, Titler, Shever & K). The effect of the contribution is the mean difference between the experiment and the control group. This means that for the difference to be acceptable, the comparison or control group must be equivalent or identical to the experiment group except for the fact that one of them received the treatment (Heinrich, et al, 2010).  PSM is generated through logistic regression in which the experiment group is regressed on some demographics characteristics.

Table 3 Outcome of Logistic regression for PSM estimation

Source: Field Survey 2023

Where:

AoC = Age of children

FS = Family size

SoC = Sex of child

PEL = Educational level of parents

CPTW = Child perception towards work

HRD = Households Resource Deprivation

PFSEW= Poor Family socioeconomic wellbeing

DFWC= Drive for family wealth creation

ECCFI = Economic contribution of Child to family income.

Table 3 shows the unstandardized coefficients of each of the variable in the model as well as the measure of significance which is tested by Wald. The meaning of the abbreviations is explained below: B = unstandardized regression coefficient which represents the effect the independent variables (IV) has on dependent variables (DV). The independent variable here is not the independent variable of the study but the eight variables selected from the questionnaire which directly influence the selection and non-selection of participant. Therefore, the dependent variable in this case is Child involvement in work. S. E. = standard error of B, Wald = measure of significance for B and it represent the significance of each variable in its ability to contribute to the model.  Df and Sig = degree of freedom and significance level respectively for Wald statistics.

Exp (B) = calculated odds ratio for each variable. Odds ratio represent the increase or decrease (exp (b)= <1) in the odds of being classified in a category when the predictor variable increase by 1 (Mertler and Vannatta, 2005).

In table 3, the average age of children (Exp B = 0.887), Family size (Exp B = 1.250), sex of child (Exp B =0-308), educational level of parents (0.611), CPTW (1.083), HRD (1.224), PFSEW (0.958) and DFWC (0.818).), reliably predict Decision to involve child in work.

Looking at Family size with probability of 1.250, this suggest that as one unit of the family size increases, the decision to put a child to work increases by 1.250 which suggest positive association between family size and decision to involve children in work.

Looking at the variable SoC, with (Exp B) = 0.308, it suggest that for a one unit increase in either sex, (male or female) the odds of the outcome decrease by approximately 69,2% (1- 0.308), assuming that all other variables in the model are held constant. And each unit increase in the educational qualification of parents decreases the odds of decision to involve children in work by 0.611, while each unit increase in HRD increases the odds by 1.224.  This means that household resource deprivation, family size and child perception towards work, directly relates to involvement of children in work. While other variables in the model indirectly relates to the dependent variable.

Matching Agorithm

The next step in this process was to choose the matching algorithm and the algorithm used in this study was the nearest neighbour (NN) without replacement with a calliper of 0.001.  In using the nearest neighbour, individuals from the control group were used to match with those of the experiment group that has close propensity score. Furthermore, matching without replacement means that a propensity score was used once and could not be used again. The condition for matching with regards to the calliper specification is to match all individuals with differences of up to 0.001.

TABLE 4: Summary of PSM outcome

Table 4, it could be seen that the propensity score was generated from fuzzy matches of 36 farming households from the control group and these were successfully matched to those of the experiment group while 50 were left unmatched

Table 5 also shows the outcome of PSM but in the area of the number of times matches were attempted

Table 5 Summary of PMS Outcome (Tolerance analysis)

CONCLUDING REMARKS

The main focus of this study was to examine the effect of child economic contribution to farming households’ income in Akwa Ibom State. The economic contribution was looked at from the angle of all work and labour performed by children.

The result of the selected characteristics of the respondents, being children of farming households reveals that most of them were male (50.2%), aged 14 to 16 yaers and prominently involved in farming activities. Majority of the respondents (77%), lived with their parents who engaged in farming both in subsistent and commercial capacity, meaning that they derived their livelihood through Agriculture.. Parental educational level and households’ size had weak association with household’s income. And parental monthly expenditure showed an inverse association, this imply that, the richer the household, the less likely that they will put their children to work. The sex of the child, showed a significant relationship with Effect of child contributions to family income well as marital status and place of residence. This children engaged in a number of economic activities and farming activities with marketing giving the highest average weekly income of N10, 000.00. The study revealed that they are certain underlying factors that influence child involvement in work, from households’ resource deprivation, poor family socio-economic wellbeing to drive for family wealth creation. This was revealed from the questionnaire response to questions associating to this. The result for the differences in Mean shows that the mean difference of respondents who involved children in work and those who did not remain the same, (0.00018). And the T test result shows that there is no significant effect between child involvement in work and child economic contribution to households’ income.

Although work did not interfere in their developmental process, especially in the aspect of schooling, it could therefore be concluded that poor family socio-economic wellbeing and a drive for family wealth creation were actually factors influencing decision to involve children in work. Evident from the study supports that children contributions to family income is insignificant. No matter how much they are employed, if their contribution cannot have   any positive effect in the family’s income which is the smallest nucleus of the economy, then they cannot make any positive impact in the economy at large. Therefore, the children should be trained through school, skill programmes to develop their human capital potentials. As this is going to be the only way they can impact positively on the households and the economy at large through expertise skill. For those who are engage in work, they should have flexible education program that accommodate their work schedule. This might include evening classes, on-line learning or part time schooling. They must be provided access to health care services as they engage in various tasking activities that may have impact on physical and mental health. This includes regular check- ups, mental health support and nutrition programs for them. Policy makers should strengthen child labour laws to prevent exploitation and ensure safe working condition. The government should offer social services that support working children and their families, such as counselling and financial assistance. Support for fair wages and reasonable working hours to ensure children have time for education, rest and recreation.

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