Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 337 Analysis of Child Labour among Rural Household of Oyo State, Nigeria Alao Bashir Idowu Management Development Officer I, Agricultural and Rural Management Training Institute, Ilorin, Kwara State Nigeria Olasore Abiodun Amos Principal Management Development Officer, Agricultural and Rural Management Training Institute, Ilorin, Kwara State Nigeria Aremu Adeola Olabisi (Mrs.) Management Development Officer I, Agricultural and Rural Management Training Institute, Ilorin, Kwara State Nigeria Abstract 1 This study is on analysis of child labour determinants among the farming households of Oyo State, Nigeria. Data for the study were obtained from 120 respondents from the target group using a three-stage sampling procedure and analyzed using descriptive statistics and logistic model. Results showed that 85% of the respondents are male. The mean age of the respondent is 58 years 74.16% of them are married. Religions of the respondent are 40%, 53.33%, and 6.67% for Christianity, Islam and traditional religions respectively. 43.33% of the households have no formal education. The average household size is 11. The average farm size of the respondent is 9.12ha. The mean income per household is less than N150 ($1) per day. Age of the household head and the farm size were significant at 5%. Age of household head, household size and number of male child in the house respectively have positive coefficients. Gender of household head, educational level, number of female child and household income has negative coefficients. This implies that as these variables increase child labour will reduce. Keywords: Analysis, Child labour, Rural, Household, Oyo State 1 Corresponding author‟s details: Olasore Abiodun Amos Email: aaolasore@yahoo.com Asian Journal of Agriculture and Rural Development journal homepage: http://aessweb.com/journal-detail.php?id=5005 mailto:aaolasore@yahoo.com Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 338 Introduction Child labour is a widespread phenomenon in developing countries. In recent times, the issue of child labour has continued to attract attention among policy makers and researchers. Child labour is a persistent problem found almost in all the developing countries and to a lesser extent in the developed countries. Africa and Asia together account for over 90 percent of the total child employment. This is especially prevalent in the rural areas where the capacity to enforce minimum age requirement for schooling and farming is lacking. According to International Labour Organization (ILO, 2002), there are approximately 186 million child labourers in the World, among which about 111.3 million children work in hazardous conditions. At least 120 million of the world‟s children between the ages of 5 and 14 years did full- time, paid work. Many of them worked under hazardous and unhygienic conditions and work for more than 10 hours a day” In Nigeria, the most populous black nation with 140 million people, there exist high incidences of child Labour (World Bank, 2006). In the Nigeria context, child labour is defined as work done by children under the age of fifteen that is mentally, physically, socially and morally dangerous and harmful to them. It refers to work that interferes with their schooling by depriving them the opportunity to attend school thereby obliging them to leave school prematurely or requiring them to attempt to combine schooling with working at times on the farm (Olujide, 2007). While children have always worked in Nigeria, the incidence has increased significantly over the years. The end of oil boom in the late 1970s coupled with the mounting poverty has driven millions of children into labour especially farm work. Traditionally, children have worked with their families, learning skills they would need as adults but today they are forced to work on the family farm and even as labourers in some commercial farms. The situation in rural Nigeria is worst as children are either not enrolled in school or drop out of school thereby constituting the pool of child Labour (Obasi, 1991). The Global phenomenon of child labour is attributed to several factors. The rapid population growth of many less developed countries, high rates of unemployment, inflation and low wage rate have contributed to the occurrence and necessity for children to be engaged in economic activities (Grootaert, 1999). Child labour is found predominantly in the informal sectors in Nigeria. In rural areas of the country, children are found working on farms and herding animals. They are mostly employed by state-owned commercial agriculture plantations, which are responsible for much of the agricultural production (Aredo, 1995). In urban areas and towns, children work on the streets as vendors, shoe-shine boy, car washers, scavengers, beggars, head-load carriers, feet-washers and bus conductors (Aderinto, 2000). Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 339 According to Olujide (2007), the Child Welfare League reported that in Lagos alone there are 100,000 boys and girls living and working on the streets. In northern Nigeria, children, known as the almajirai are atimes employed in private farms and in commercial farms. Some of the children are even trafficked and used as farm labourers. Robinson (2004) stated that National Child Labour Survey estimates that there are 15 million children engaged in child labour in Nigeria. These children are also vulnerable to being forced to farm work and in many instances they are being deprived of access to education. Methodology The target respondents for this study were the rural households in Oyo state, Nigeria. Oyo State lies within latitudes 7 0◦ 5‟N and 9 0 10‟N of the equator and longitudes 2 0 30‟E and 4 0 35‟E of the Greenwich Meridian; the rural households were targeted because of low income status of the households that make it necessary for them to use children in their farming activities. Shaki, Ogbomoso, Oyo and Ibadan are four Agricultural Development Zones in Oyo State. Two Agricultural Development Zones namely Ibadan and Saki were randomly selected. With the guide of list of villages from Ministry of Local Government, Oyo State, ten villages each were selected from the two agricultural zones out of which a total of 120 farming households were selected for the study. The tools of analysis for this study were the descriptive statistics such as measure of central tendencies which involves the use of means, coefficient of variation and percentages. The Logistic model was used to measure the determinants of child labour among the rural households. 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 = Gender of the household head (1 for male, „0 if otherwise), X3 = Household size (Number) X4 = Educational level of the household head (years), X5 = Farm size (ha), X6 = Household income (N), X7 = Number of male children, X8 = Number of Female Children, Ui = Error term, β 1 – β 8 =Parameters. Results and Discussion Socio Economic Characteristics of Respondents Table 1 shows the socio-economic characteristics of the target group. About 85% of the respondents are headed by male. The rural communities are mostly male headed so that they will be able to take full Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 340 responsibility of the overall household members as well as providing for their basic needs. The age of the household head to a larger extent affect the rate at which child labour use is seen. The age distribution of the household head indicates that the modal age of the respondents fall between the age group of 56-65years which accounts for more than one-third. The mean age of the respondent is 58 years and the coefficient of variation is 11.32. This shows that as household-head gets older it becomes increasingly hard to sustain the household which force them to involve children in farm labour. Household-head less than 35years are not involved in child labor practice in the area. The marital status of the respondent indicates that 74.16% of them are married. The implication of this is that more children are expected to be living in the households. This high number of married respondents will lead to more child labour. The results showed that 40%, 53.33%, and 6,67% respectively practice Christianity, Islam and traditional religions. Islam and traditional religions support polygamy. This means 60% of the respondent can practice polygamy which enables them to have many wives and children. Education is known to facilitate greater understanding about the menace of using child labour in farming activities. The educational level of the household indicates that about 43.33% of the households have no formal education at all and only 19.1% had above primary education. This percentage is too low to make appreciable impact in reducing child labour in the target area. The household size is an important factor that determines the number of children in the households. Family size is an important source of labour in the rural areas. The average household size is 11 and the coefficient of variance is 3.72. The size of the household indicates that majority of the household members fall within the range of 11-15. These larger members and the number of dependents may be the more reason why the household head engages the children in farm work. The high average size of the household may be as a result of low literacy and religion practiced by the respondents that permit polygamy. The area of land cultivated by the respondents is very important as it determines to some extent the number of children engaged in farm work. The average farm size of the respondent is 9.12ha and the coefficient of variance is 5.87. The implication is, since farming is done manually children will be engaged to meet the farming labour needs. The Income level of the household head indicates that about 31.67% earns between N110,000 to N 150,000 per annum. The mean income of the respondents is 184,820 and the coefficient of variance is 175,561,52. The mean income per household is less than $1 per day. The meager amount might be the reason why household use children on the Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 341 farm in order to augment the family income and reduce cost of labour. Table 1: Socio-Economic Characteristics of Respondents Characteristics Frequency Percentage % Gender Male 103 85.83 Female 17 14.17 Total 120 100 Age  35 6 5 35-45 17 14.16 46-55 20 16.67 56-65 50 41.67  65 27 22.5 Total 120 100 Mean 58 Co-efficient of Variation 11.32 Marital -Status Married 89 74.16 Widowed 15 12.5 Widower 15 12.5 Single 1 0.83 Total 120 100 Religion Christianity 48 40 Islam 64 53.33 Traditional 8 6.67 Total 120 100 Educational Status No Formal Education 52 43.33 Adult Literacy Training 20 16.67 Primary Education 25 20.83 Secondary Education 17 14.1 Post Secondary Education 6 5 Total 120 100 Household Size 1-5 21 17.5 6-10 35 29.16 11-15 48 40 16 16 13.33 Total 120 100 Mean 11 Co-efficient of Variation 3.72 Farm Size (ha) 1-5 29 24.18 6-10 55 45.83 11-15 13 10.83 16-20 13 10.83 Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 342  21 10 8.33 Total 120 100 Mean 9.12 Co-efficient of Variation 5.87 Income (N)  25,000 13 10.83 26,000-50,000 18 15 51,000-100,000 10 8.33 110,000-150,000 38 31.67 160,000-200,000 14 11.67 250,000-300,000 12 10  300,000 15 12.5 Total 120 100 Mean income 184,820.10 Co-efficient of Variation 175,561,52 Source: Field Survey, (2010) Table 2 indicates that age of the household head X1 and farm size X7 were significant at 5% level, implying that these factors predispose the households in the study area to engage children in their farming activities. The age of the household head X1, has a positive coefficient. This means that as the household head becomes more aged, the higher will be the likelihood that the children will be used on the farm. This is probably, because of the fatigue that may be experienced by the aged household head. This is also expected because there is no social insurance that takes care of the aged in Nigeria. The aged most often maintain their farm land especially the plantations until they die. Since the aged cannot take care of their farms themselves, they depend on the children around (who many atimes are their grand children) that stay with them to take care of their farm lands. The household size X3 has a positive co- efficient. This implies that as the household size become larger, there will be more number of dependents in the household; the more the likelihood that more children will be engaged in farm work. That is, the higher the number of dependents in the household, the greater will be the likelihood of them taking children to work on farm. In most rural communities, people in their old ages still give birth to children. It is not uncommon to see men in their 70s and 80s still giving birth through their younger wives since most men in the rural areas of the study area practice polygamy. This scenario gives rise to an increasing family size coupled with grand children who may be asked to live with their grandparents. The number of male child in the household X5, has a positive co-efficient, implying that the higher the number of male children, the easier it is for the household head to engage them to farm work and the more will be likelihood of engaging them on the farm. There is gender division of labour in farming activities in Nigeria. In South-West Nigeria Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 343 in which the people of the study area belong, most tedious farm works which are carried out manually such as land preparation, weeding and harvesting are performed by men. These tedious farming activities require more man-days to do. In this case, more adult male and male children are engaged to do them. The farm size X7 co-efficient is also positive and significant because the larger the farm size, the more will be the form of different farming activities that would be required in the farm and this will predispose the household to use children on the farm in order to cultivate the large expanse of land. The children might be forced to engage in heavy farm work at the rate that will jeopardize their passion for learning. The gender of the household head X2 has a negative significance. The sex of the household head has no effect on the likelihood of using child labour on the farm because most of the sample households were male headed and female headed household are not prevalent especially in the study area and in Nigeria at large. The educational level of the household head X4 is not significant at 5 percent and therefore will not predispose them to use children on the farm; this is because as the household head become more educated, the less will be the likelihood of using children on the farm. It is only the illiterate household heads that will put children to farm work not minding the benefit of education on human capital development of the child. The higher the literacy level of the household and the less will be the need to use children on the farm. This is so because educated families send their children to school very early. This situation makes such children escape being used as labour on the farm. The number of female child X6 in the households has a negative significance on the predisposing factors that determine household to use child labour on farm, the female children by nature are created weak and will not be able to do heavy farm work like their male counterpart. The higher the number of female children in the household, the lower the likelihood that such household head engage them on the farm. This is contrary to the result obtained for number of male child. As said, there is gender division of labour in farming activities of the study area in which female are less engaged in tedious farm works. The income level of the household head X8,is not significant at all because the higher the income earned by the household head from the farm output, the lower will be the likelihood of using child labour on the farm. Instead of using children, the household head will prefer to employ hired labourers to work on the farm. The income level may also influence use of modern technology in farming activities. When farming households earn more income they may engage herbicides and sprayers in control of weeds and hire tractor for land preparation. These two activities will reduce use of human labour in farming activities. Asian Journal of Agriculture and Rural Development, 3(5) 2013: 337- 345 344 Table 2: Logistic Regression Estimates of Factors Determining Household Use of Child Labour Factors Coefficient Standard error t –value Age of household head X1 Gender of household head X2 Household size X3 Educational level X4 No. of male child X5 No. of female child X6 Farm size X7 Household Income X8 0.0236637 0.4821543 -0.0606467 -0.4217471 0.281733 -0.1755933 0.0638322 -0.3884857 0.0234575 0.7440525 0.0798968 0.1876571 0.168406 0.1669436 0.0510947 0.2332468 2.01* -0.65 1.76 -0.23 1.67 -1.05 1.75* -1.27 Field Survey; 2010 * Coefficient significant at 5 percent Conclusion and Recommendation Based on this study, the more the age of the household head and the farm size the higher the incidence of child labour. In view of this, the government could initiate social insurance for the aged to take care of them. Family planning education should be promoted among the people of the rural areas to reduce household size. There may be special micro credit for the rural household which will help them acquire modern technology that will reduce use of human labour in carrying out farming activities. . Example of such technology is use of sprayer and herbicides. Also, it is concluded from the study that the higher the educational level, number of female children and household income the lesser the child labour. The target group should be educated on the menace of child labour. This could be done through informal education. There is evidence of poverty among the target group in which they spend less than N150 ($1) per day. Income generation projects should be initiated for the target group. References Aderinto, A. A. (2000). Social Correlates and Coping Measures of Street Children: A Comparative Studies of Street and Non-street Children in South- Western Nigeria. Child Abuse and Neglect, 24(9):199-213. 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