Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org
A. O. Ejiogu and E.C. Onyenoneke
Department of Agricultural Economics,
Imo State University, Owerri
Corresponding author email: [email protected]
Abstract child labour use in Imo State of Nigeria. Specifically, the study determined the effect of the socioeconomic factors that -stage random sampling was employed in choosing the respondents for this study. One local government area (LGA) in each of the three agricultural zones was purposively selected for the study based on suitability for rice production. Communities which are known for rice production were purposively selected from the selected LGAs. The list of rice farmers in each chosen community was obtained from Imo ADP. The list formed the sampling frame from which the respondent rice farmers were selected using random sampling procedure. Thirty rice farmers were selected from each community, making a sample size of 90.Data was analyzed with binary logistic regression technique. The variables namely gender, marital ge and educational attainment were significant at 1% level. Furthermore, gender, marital status, family size, farm size, educational attainment and rice farm experience were statistically significant variables with negatively effect on the use of child labour. Age though statistically significant, had positive effect on the use of child labour. Efforts aimed at curbing the adverse effects of child labour should to all intents and purposes be fundamentally targeted at the predisposing socioeconomic factors.
Key Words: Rice farmers, labour use, socioeconomic, Imo State
1. Introduction Rice is a staple food for many people in Imo rice production in Nigeria has been identified to State, Nigeria. It is the most rapidly growing consist of lack of competiveness resulting from food source in the state and is of significant low and uneconomic productivity, poor access importance to food security for a large number to expensive inputs (especially fertilizer and of low- income food deficit countries including credit), low capacity to meet quality standard. It Nigeria (FAO, 2004). Rice-based production should be added that labour is one of the system and the associated post-harvest inputs on which lots of money is expended. operations employ many hands in rural areas Labour is an important factor of production. It of West Africa including Imo State. According means the physical and mental effort provided to Imo ADP (2009), the key problems facing by an individual worker towards production. Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org
In most cultures children help their immediate rice seedling, weeding, providing fire wood for families through work in manner that is neither parboiling before milling, applying fertilizer and hazardous nor exploitative. However, child pesticides. Togunde and Carter (2008) stated labour effectively deprives children of their that children engage in work in order to humanity, their childhood, their self-worth, their contribute financially to the sustenance of their potential and their general well being. Child families, and to acquire training needed in labour can equally interfere with the education future occupations. of the child. This comes about when children According to Ezedinma (2005), there is are forced to combine their education with scarcity of labourers in the rural areas; hence hazardously long and tedious work. It may also child labour becomes the alternative. Dillon find expression in children leaving school (2009) noted that illiteracy of the parents leads earlier than is expected; in extreme forms child to child labour. Basu and Van (1998) stated labour entails children not attending school that child labour is as a result of poverty and and are left to cater for themselves (UNDP, occurs when family or the household income is 2015). Child labour is an economic activity below subsistence level. There is still gap in performed by a person under the age of 18 understanding the effect of socioeconomic (Tiaji et. al., 2005). Whether these activities characteristics of rice farmers on the use of are paid or unpaid, they constitute child labour child labour in rice production. This study is an to the extent that they prevent the child within attempt to fill that gap in knowledge by this age limit from attending to school investigating the sign and size of the effect of activities. socioeconomic characteristics of the farmers According to Sartyarthi (2015) children are on the use of child labour in their farm preferred by way of child labour because they operations. The overall objective of the study is are the cheapest or even free, if bonded- to highlight the sign and size of the effect of labour. Sartyarthi (2015) further stated that the socioeconomic characteristics of the rice children are physically and mentally docile; do farmers on the use of child labour in their farm not form unions; do not go to court and so operations. Specifically, the objective is to pose no challenge to the employer. The determine the effect of the socioeconomic highest prevalence of child labour is in Sub- Saharan Africa, at one child in five (ILO, 2013). child labour. The hypothesis of the study is Nigeria is one of the countries that make up Sub-Saharan Africa. Imo ADP (2009) noted characteristics do not significantly affect the that children are involved in labour use in rice use of child labour in rice production. The production in Imo State and that the findings of the study will bring to light the contributions of children in rice production in the area include scaring birds, transplanting Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org socioeconomic characteristics on child labour variable can be examined with that of the use in rice production. independent variables. Such models cannot be estimated by either multiple regression or the Conceptual Framework Ordinary Least Square (OLS) techniques According to Imo ADP (2009), in Imo State, which may result in invalid parameter children are employed to scare birds from the estimates and wrong magnitude of the effects rice farm mostly with their catapults at the early of the independent variables on the dependent morning and evening hours. Togunde and variable, the OLS assumptions that the Carter (2008), noted that globalization of the variance of the error terms are constant and economy has led to the desire for cheap labour not correlated with the level of independent and profit maximization. However, one major variables are violated. backlash of the global development and Nonetheless, four commonly used approaches spread of industries has been the exploitation to estimate such models include: the linear of children in terms of low wages and their probability Model (LPM), logit model, probit deplorable working conditions. model and the tobit model (Gujarati, 2000). According to Ezedinma (2005), there is The LPM is not generally recommended scarcity of labourers in the rural areas; hence, because it provides predicted values that may the child labour becomes the alternative. In fall outside the 0-1 interval, thus violating the Nigeria, child labour is often taken as a means assumptions of the probability. Logit, probit of teaching the children survival skills and as a and tobit models give maximum likelihood means of social integration. This is clear when estimates and overcome most of the short the farmers go to the farm with their children, comings of LPM by providing consistent and showing them one skill or the other. efficient estimates. The logit model framework In analyzing the effect of socio-economic is however preferred among the other three characteristics of the respondents in the model framework because it has been found to involvement of children in rice production, this be efficient in explaining such dichotomous study employs a model that deals with child decision variables (Gujarati, 2000). labour use or not as the dichotomous dependent variable. That is the theoretical Binary Logistic Regression basis for the choice of Logit model for the Binary logistic regression is a form of regression which is used when the dependent from which the technique derives name is the variable is dichotomous and independent variables are of any type. They are used to The odds indicate the relative probability of predict a dependent variable on the basis of interest (Allan, 1986; Eboh, 2009). The independent variables, to determine the relationship of this behavioural dependent percentage of variance in the dependent Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org variable explained by the independent Li = logistic model, Ln = log, Pi = probability of variables, to assess interaction effects and the event occurring, Bo = constant or intercept, understanding the impact of the covariate B1X1 = coefficient of the independent variable. variables. It applies maximum likelihood estimation after 2. Research Methods transforming the dependent into logit variable Study Area:The study was carried out in Imo (the natural log of odds of the dependent State, Nigeria. The major economic activity of occurring or not). Logistic regression estimates the people of Imo State is farming. Among the the probability of a certain events, that is, major crops grown in the area are cassava, change in the log of odds of the dependent maize, yam, tree crops, vegetables and rice. and not changes in the dependent variable The crops are typically grown on small holder itself as the OLS regression does. plots. Imo State has suitable ecologies for The Probability of the event occurring is given production of different varieties of rice (Imo by the relationship ADP, 2009). This consists of temporary Pi = 1/1+e-z------(equation 1.1) flooded area and river banks. The shallow Where Pi = the probability of the event swamps have areas such as Imo River Basin occurring which ranges between 0-1 at Ihitte Uboma, Nzerem, Umuna (all in Z = Bo + B1x1 (which ranges - Okigwe agricultural zone); Urashi River basin e = the base of the natural logarithm (approx. at Oguta and Egbema, Ideato/Arondizuogu 2.72). flood plains, Otamiri/Nworie/Uramiriukwa flood The probability of the event not occurring is plains and isolated flood areas. given by the relationship 1 - Pi odds ratio = Pi = 1+ezi = ezi ------Sampling technique ------(equation 1.2) Rice farmers in the three agricultural zones in 1 - Pi 1+ezi Imo State served as the study population. The Note Pi three zones are Okigwe, Owerri and Orlu. 1 - Pi converts the probability Multi-stage random sampling was employed in into odds of the event occurring (i.e. the ratio choosing the respondents for this study. Stage of the event occurring to the probability of 1: One Local Government Area in each of the event not occurring. Log of odds is therefore three agricultural zones was purposively given by selected for the study based on suitability for rice production. The Local Government Areas Li = Ln(Pi ) were Ihitte Uboma in Okigwe Zone, Ohaji 1 - Pi Egbema in Owerri Zone and Ideato North in Orlu Zone. Stage II: Communities which are known for rice production were purposively Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org selected from the selected LGAs for the Study. The objective was realized with binary logistic Onicha Uboma from Ihitte Uboma LGA, regression. The model is
Etekwuru from Ohaji Egbema LGA and 1, X2, X3, X4, X5, X6, X7 ) + e
Ndiakueme Ikpa-Okorie of Arondizuogu from 1, X2, X3, X4, X5, X6, X7 ) + e
Ideato North LGA were the selected Y =InY=In (p/1- 1, X2, X3, X4, X5, X6, X7 ) communities. The list of rice farmers in each + e. chosen community was obtained from Imo Where Y = Child Labour use (Dummy variable: ADP. The list formed the sampling frame from child labour use =1; no child labour use =0), P which sample of rice farmers was selected = probability of child labour use, 1-P = using random sampling procedure. Thirty (30) probability of no child labour use, X1= Gender rice farmers were selected from each (dummy variable: male=1; female =0), X2= Age community, making a sample size of 90. At this of rice farmer (years), X3= Marital status
Child Labour Use in Rice Production Variable Coefficients S.B. Wald df Sig. (B) X1 -19.759 11363.014 3.842 1 .049*
X2 0.017 .047 12.765 1 .000 **
X3 - 0.506 1.369 5.037 1 .023 *
X4 -0.023 .150 5.029 1 .024 *
X5 -0.034 .079 8.629 1 .001 **
X6 -0.068 .049 5.666 1 .022 *
X7 -0.043 .119 3.893 1 .047 * Constan 19.115 11399.288 .000 1 .999 t **=Significant at 1% level, *= Significant at 5% level.Source: Field Survey, 2010
stage, three enumerators were recruited, (Dummy variable: married =1; single =0), X4= trained and assigned to collect data from the family size (number of persons), X5= level of communities selected. Education attainment (number of years in
school), X6
Method of Data Collection X7= Rice farm size (hectare), e = error term. Data for this study was collected using structured questionnaire. The questionnaire was administered to the rice farmers in the 2. Results and Discussion area for the collection of relevant data used to achieve the objective of the study. and Child Labour Usage in Rice Production Method of Data Analysis characteristics on child labour use in rice Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org production in Imo State are discussed in this feminization of poverty in which women bear a section. disproportionate amount of the burden of Table 1 shows the binary logit estimates of the poverty linked characteristics. This finding is similar to that of Ikenwilo et.al. (2016). Ikenwilo characteristics on child labour use in rice et.al. (2016) in a study examined the impact production.The estimation of the binary logit that a government-sponsored microcredit model was undertaken to ascertain the effect project targeting women in rural areas may have on vulnerability and empowerment of the on child labour use. The Omnibus tests of beneficiaries and members of their model coefficients and the likelihood ratio households. The two major research questions statistics as indicated by Chi-Square (X2) addressed were whether microcredit improves statistics are significant (P = 0.049) and (P = indicators of household vulnerability and 0.000) respectively, suggesting that the model women empowerment; and interrogating the has strong explanatory power. The extent to which family members of significance of this likelihood ratio statistic test beneficiaries were affected. The findings rejected the null hypothesis of this study which indicated that the beneficiaries of the microcredit were significantly less vulnerable characteristics do not significantly affect child than non-beneficiaries. This was attributed to labour use in rice production. This study significant reductions in both frequency of child accepted the alternate hypothesis that labour and food shortage in the household. socioeconomic characteristics do significantly Employment of cheap children labour in rice affect child labour use in rice production. The farming in Imo State could be on account of Pseudo R-Sqaure (Nagelkerke) which limited access to financial services at represents the coefficient of multiple affordable costs, to such sections of determination has a value of 0.735 (73.5%), disadvantaged and low income segments of implying that the explanatory variables jointly the society as the women. explained 73.5% of the variation in child labour use in rice production (dependent variable). Furthermore, age of the rice farmer (X2) had a Consequently, the interpretation of the binary positive and statistically significant (P = 0.000) logit result indicates that:Gender of the rice impact on the probability of using child labour farmers(X1) had negative and statistically in rice production. It means that the older rice significant effect (p=0.049) on the likelihood of farmers employ child labour more than the using child labour in rice production. It means younger rice farmers. The likelihood to use that female rice farmers are more likely to use child labour increased with increase in age of child labour more than the male rice farmer. the rice farmers. This finding is in line with This is a pointer to the phenomenon of Nwaru, (2004) who stated that the ability to do Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org manual work decreases with advancing age. also tends to confer on them the relative ease Thus with increase in age, the average rice to be able to successfully utilize external farmer tends to employ more child labour to sources of financing from which funds are augment the labour needs in the rice farm. generated for the hiring of more suitable farm Additionally, marital status of the rice farmers labour. (X3) had a negative and statistical significant Findings further indicated that rice farm (P = 0.023) influence on the probability of child experience (X6) had negative relationship with labour use in rice production. This implies that child labour use in rice production. Rice unmarried rice farmers are more likely to farming experience of the farmer significantly employ more labour (child labour) in rice farm (P = 0.022) decreases the probability of child production than the married rice farmers. labour use in rice production. This tends to Following the information from the study, buttress the fact that the more the experience family size of the rice farmers (X4) had garnered in rice farming the more the negative and statistically significant (P = 0.24) realization of the limitations of use of child relationship with the likelihood of child labour labour and hence the less the likelihood to use in rice production. This implies that the employ child labour in farm operations. smaller the family size of rice farmers, the Rice Farm size (X7) had a negative and more the likelihood of employment of child significant effect (P=0.047) on the use of child labour in rice farms. labour in rice production. This implies that rice Equally from the study, educational attainment farmers with small rice farm size use more of the rice farmers (X5) Educational had child labour in rice production than the farmers negative and statistical significant (P = 0.001) whose farm size is large. This tends to show effect on the probability of using child labour in that with the increase in rice farm size, the rice rice production. This finding is in line with farmer employs mechanization and to that Dillion (2009), who noted that parental extent is less dependent on the use of child education has inverse effect on child labour. It labour. should be added that the higher the educational attainment of the rice farmer, the 4. Conclusion less likely for the rice farmer to employ either The socioeconomic characteristics of the rice ild as a farmers such as gender, marital status, family labourer. This implies that rice farmers with size, farm size, educational attainment and rice higher educational attainment are less likely to farm experience are statistically significant employ child labour in their farms on account variables which negatively affect the use of of the dire consequences as a deprivation for child labour in the study area. Age though the child. In addition to the awareness that statistically significant, had positive effect on education gives to the rice farmers, education the use of child labour. Efforts aimed at Nigerian Agricultural Policy Research Journal. Volume 1, Issue 1, 2016. http://aprnetworkng.org curbing the adverse effects of child labour desist from engaging child labour in their should to all intents and purposes be activities. fundamentally targeted at the predisposing socioeconomic factors for if we cannot protect References our children from child labour in rice farming or Allan, A. B. (1986) Statistical methods for any other enterprise, we cannot protect our social science. Dellen Publishing agricultural enterprise. Company, San Francisco California. Based on the findings of the study, the following recommendations are made: Basu, K. & Van, P.H. (1998) The Economics of It is important that rice farmers in general and child labour. American Economics women rice farmers in particular should have Review, 88 (3): 412 427. access to formal financial services and products that are designed according to their Dillon, A. 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