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An Investigation into the Determinants of Rural Poverty in Yobe State, Nigeria

The study investigates empirically the determinants of rural poverty in Yobe state, Nigeria. Questionnaires were distributed and the responses obtained were analyzed using log it regression model. The findings of the study show the coefficients for variables gender and availability of credit were not found to be statistically significant at 1or 10 percent level and hence do not influence the level of poverty in Yobe state. Whereas, the coefficients for Age, household size, educational level of the households, monthly income/expenditure, primary occupation were found to be statistically significant with odds ratio different from zero and hence were found to be determinants of rural poverty in the study area. Based on the findings, the study recommends among other; provision of social or basic infrastructures in the rural areas, boosting and supporting agriculture this will help create jobs and income thereby reducing the number of dependents, soft loans should be provided to farmers and businesses in the rural areas, large family size should be discouraged, health and educational facilities in the rural areas should be improved significantly.

Nigeria despite efforts made to reduce it, it thus becomes a problem not to the poor alone but a public problem that must be addressed through public policy matters in the public domain.
Nigerian rural areas are characterized by inadequate access to agricultural inputs like land, fertilizer, credit facilities and extension service. Similarly, lack of adequate security was identified as another dimension of poverty in urban areas; (World Bank, 2006).Some of these factors directly cause poverty. Others contribute indirectly by producing inequality, by stifling the political power of certain sectors of the population, for example or denying them their human rights (Chukwuemeka, 2008). Since the sources of poverty are diverse, it should be seen as a multi-dimensional problem that calls for a solution with a multi-pronged approach, especially as it affects rural households who face multiple disadvantages.
However, Rodgers (1989) referred to urban poverty, to a lesser or greater degree as a reflection of rural poverty in most developing countries. It should also be noted that poverty incidences are highest in northern states. Although national poverty incidence declined from 65.6 percent in 1996 to 54.4 percent in 2004, the number of people that were poor was on the increase (NBS,2009). Similarly, in some studies, poverty had been found to be strongly influenced by location (rural or urban), age, education of household heads, and size of household (World Bank 2006;Okunmadewa 1997;NBS 2009).
The spatial differences in the incidence of poverty in Nigeria, is a worrisome phenomenon. There are considerable differences between regions in the concentration of the poor and the non-poor in separate communities. There are also large differences between regions in their share of the poor and non-poor communities. Whereas nearly two-thirds of the non-poor communities are in the south, almost half of the poor communities are in the north. In the south, only 18% of the region's population resides in communities that have been classified as poor whereas in the north nearly half of the population of that region resides in such communities (World Bank, 2006). The spatial distribution of poverty in Nigeria in 2010 as presented by NBS (2010) shows that the North East region which Yobe State belong had the highest incidence of poverty, with 69.3% of the population in poverty while the South East region had the lowest incidence.
The North West region accounted for about 40% of the poor in Nigeria. This was followed by the South West, which contributed 18.7% to the national incidence of poverty. The North East region had the highest depth of poverty while the North Central had the highest severity of poverty. This decomposition of poverty by geo-political zones highlights two aspects of the poverty profile. One is that contribution to poverty tends to be higher in the northern part of the country. Thus, both measured poverty and contribution to poverty are higher in the north. The second aspect is that while contributions to poverty tend to decline with intensity of poverty in the south, they tend to rise in the north. Both aspects thus suggest that the north constitutes the bulk of the poverty problem in the country.
The foregoing implies that identifying the determinants of poverty in rural areas is very crucial to understanding not only the causes of poverty, but for formulating policies directed at its reduction. In Nigeria the rural area ironically, the level and incidence of poverty are most pronounced. Since poverty is a major constraining factor among rural households, it is important to investigate the trend, structure and determinants of poverty among rural households in Nigeria using Yobe State as a case of study.
In addition, to slow down the level of poverty especially amongst the rural households, there is need to gain a thorough understanding of poverty amongst rural households requires further knowledge about their characteristics, constraints and coping strategies against poverty. This information is crucial to formulating an effective strategy for reducing poverty and for designing social protection programmes. The objectives of this research are to:  Determine the level of poverty among rural household.  Identify the determinants of poverty among rural households

Literature Review
Poverty is multifaceted and has no single universally accepted definition. The World Bank (2001) defined poverty as a pronounced deprivation of human wellbeing; which include vulnerability to adverse events outside their control, being badly treated by the institutions of state and society and being excluded from having a voice and power. Any household or individual with insufficient income or expenditure to acquire the necessities of life is considered to be poor (Olayemi, 1995).
A number of empirical studies have been conducted in relation to rural poverty in some states in Nigeria but limited studies are available on rural poverty in Yobe State. A brief review of empirical literatures on the determinants of poverty revealed that, Fofack (2002), using a probit model with binary outcomes over two reference periods (1994 and 1998) indicated that age dependency ratio, education level of household head, household assets and female literacy are significant determinants of rural poverty in Burkina Faso. These determinants are found to be significant and relatively stable over time with low asymptotic standard errors. Age dependency ratio followed by asset ownership and level of education remains the strongest predictor of rural poverty, with the largest marginal effect on the probability of being poor. While the age dependency ratio is negatively associated with welfare, asset ownership structure and education are positively associated.
According to Oduro etal (2003), Polygamous marriage, household size and education are important determinants of rural poverty in Nigeria. Among male-headed households in rural areas, both depth and severity of poverty is greatest in polygamous households. Such households, while constitute 27 percent of all rural households, contribute to about 37 percent of the rural poor. The incidence of poverty was not lower for households headed by women. Of all rural households, 9 percent were female headed and represent 5.4 percent of the rural poverty. In rural areas, the average household size of poor households was higher 6.8 compared to 4.2 for non-poor households. In Nigeria, household heads with no education had a higher incidence of poverty 39.5 percent in 1992 compared to 22.8 percent of household heads with post secondary education. In the rural areas, 51.9 percent of the households whose heads are without education were found poor compared with 37.8 percent in the urban areas. Studies of chronic poverty in other West African countries by Oduro etal (2003) Olaniyan et al (2005) used the merged data from the 1996 General Household Survey (GHS) and the National Consumer Survey (NCS) to study the effect of human capital and capabilities on rural poverty in Nigeria. Both surveys had a national coverage and used the same sampling design in addition to covering all the federation's 30 states at the time and the Federal Capital Territory. The sample design for the study was a two-stage stratified sample design. Using the probit model to determine the probability of being poor as a result of a unit change in a variable, all the predictors of human capital variables represented by the educational level of the household head were statistically different. The results indicate that education reduces the probability of being poor in a household. Judging from the marginal effects, the largest impact is for those who have up to a post-secondary education, which is followed by those with a primary education. Human capital has a decreasing effect on the probability of being poor among all rural households, whether they are engaged in farm activities or non-farm activities. The marginal effects indicate that the coefficients are significant both in magnitude and in sign (positive or negative). Kedir and McKay (2005) applied multinomial logit model for urban chronic poverty in Ethiopia. The result their study revealed that poverty is strongly associated with high dependency rates, low levels of human capital, unemployment, and being homeless. Lawson et al. (2006) used logistic model in a study of poverty persistence and transition in Uganda to show that education attainment, engagement of members in non-agricultural activities and assets acquired through purchases or inheritances are often an important escape routes while losing productive assets is an entry into poverty. While market constraints, a feeling of exploitation, increased taxation and impacts of HIV/AIDS are also identified as factors that deteriorate living standards. Oni and Yusuf (2007) examined the determinants of expected poverty among rural households in Nigeria. The data for their study were obtained from the merged General Household Survey (GHS) and the National Consumer Survey (NCS) of 1996. The cross sectional data were augmented with certain covariate factors. The data were analyzed using three stage Feasible Generalized Least Squares (FGLS). Both idiosyncratic and covariate factors affected the expected log per-capita consumption of rural Nigerians. The overall expected poverty for the country was 0.535 and this was 1.02 times the observed poverty in 1996. Higher expected poverty was synonymous with living in the Northeast, having no formal education, farming, being an older or a male head of household, and having a large household. The Northeast region has both lower mean per-capita consumption and higher consumption variance compared with other regions of the country. Further, consumption variance is highest for households whose heads have a secondary education while households whose heads have no formal education have the lowest mean expected consumption.
Ibrahim etal (2008) assess the determinants of poverty as well the poverty coping strategies among farming households in Nasarawa State, Nigeria. The study employed simple random sampling to select 150 farming households and used Costs of Calorie method and Discriminate Analysis to determine the incidence of poverty as well as its determinants respectively. The incidence of poverty among the sampled households was found to be high and the major determinants of poverty include household size, number of income sources of the household head, number of household members employed outside agriculture and the number of literate adult males and females in the household. The major poverty coping strategies include skipping of meals, reduction in the quantity of meals served and engaging in wage labour. The study recommends that the farming households should be effectively involved in the formulation of strategies for imparting knowledge on family planning to the farming households. Sarwar (2010) examined the determinants of urban poverty in Sargoha, Pakistan. The result revealed employment in public sector, investment in human capital and access to public amenities strongly reduced poverty, while employment in informal sector, household size, and female dominated households increased poverty in their study area.
Akerele and Adewuyi (2011) investigated socio-economic determinants of welfare among urban households in Ekiti State, Nigeria. The result of the study revealed sex of the head of household, educational level, household size and dependency ratio are statistically significant in explaining poverty in the area. Adebayo (2013) analysed level of poverty among households in Osun State, Nigeria. Using Foster, Greer and Thorbecke (FGT) model, the study revealed incidence of poverty in the state as 36.36 percent, poverty depth of 7.2 percent, and severity of poverty as 2.79. In sum, it is importance to note that, although, these studies are intended to contribute to knowledge, the nature of analysis and methodology employed are meant to only identify the nature and determinants of poverty in urban setting, therefore, their application would be limited to the understanding of nature and characteristics of urban poverty. Kabuga and Adamu (2015), studied the determinants of poverty in rural area in Katsina State. A multistage sampling technique was used to randomly select 300 respondents for the study. The findings show that majority of households living in the rural part of the state are poor or wallowed in chronic poverty. It was also revealed in the study that the major factors that can strongly be associated with poverty status of the households are age of the head of household, gender of the respondents, household size, non-farm jobs, and years of schooling, while level of household income, asset ownership, dwelling unit type are reported to be insignificant in explaining probability of being poor In summary, all these studies revealed that poverty levels were inversely related to the level of formal education and the ownership of assets such as houses, buildings and businesses. In addition, the higher the household size, the worse the poverty, while households in the northern part of the country are poorer than those in the south. It appears that efforts to estimate the determinants of poverty focused on demographic/household variables only while excluding community variables. Hence, the research will try to see the impact of socio-economic variables have on poverty among rural households, which had been left out of most previous studies. In addition, this study will contribute to an understanding of the poverty of rural households and its determinants. On the analytical front, the research shall apply logit model that are popular in the literature.

Methodology
From the empirical literature reviewed, some scholars such as Fofack (2002), Ibrahim etal (2008), Oni et al, (2008), Olorunsanya et al, (2011), Kedir and McKay (2005), Kabuga and Adamu (2015),adopted the use of the Ordinary Least Square Multiple regression Model as well as Logit and Probit model to determine the factors responsible for rural poverty. However, this research study applied logit and probit model to analyse the determinants of rural poverty in Yobe state, Nigeria.
The logit specification is designed to analyse qualitative data reflecting a choice between two alternatives, which in this case are the poor and the non-poor. The choice of the logit model is premised on the fact that ordinary least squares assumes a continuous dependent variable while in the case of poverty, the response is a binomial process taking the values of 1 for poor and 0 for non-poor. The probability of being poor (POVSTA) is specified as the value of the cumulative distribution function Z which is specified as a function of the explanatory variables. Equation 3 is simply the odd ratio in favour of household falling below the poverty. This is the ratio of the probability that a household will be poor to the probability that it will not be poor. The natural log equation 3 results into: Li = Ln  HA=Household Age HG=Household Gender HS=Household Size HMIE=Household monthly Income/Expenditure HCA=Household Credit Availability HPO=Household Primary Occupation HEL=Household Educational Level ei=Random error term A prior expectation of this logit regression model is that: β0, β1, β2, β3, β4, β5, β6, β>0 In estimating the factors (determinants) associated with poverty among the respondent households in the study area, logit regression model was fitted to the study data. The poverty status of the household (1 = poor, 0= otherwise) served as dependent variable while a number of independent variables were employed. The results of the logit regression of the determinants of rural poverty in Yobe State is presented in table 1 The specified model is found to be statistically significant at 1 percent level, implying that the model produces a good fit for the research. The analysis shows that all the estimated parameters have the expected signs. It reveals that age, household size, and primary occupation of the households have positive relationship with the poverty status, that is the probability of the household becoming poor increases as these variables increase, only household size and primary occupation of the household heads are statistically significant at 1 percent and 10 percent test level respectively. However, household monthly Income/expenditure, credit availability, educational level, and the gender of the households have negative influence on the poverty status of the households, but only monthly Income/expenditure and educational level are statistically significant at 1 percent and 10 percent level respectively. This implies that as these variables increase, the probability of a household to be poor reduces.

Regression Results and Discussions
Age of the households (HA) was positively related to poverty status and statistically significant at 1 per cent level of significance. This is due to the fact that at the early stage of life there is always greater energy, which would probably have helped the households at that time to increase output and income. However, as individual in the household gets older, the energy begins to depreciate and output and income decline which increases the chances of the household falling into poverty. This supports the findings of Khalid et al (2005) Ahmed et al. (2008, that age of the household head is very important for reducing the poverty status of the household, but does not support the findings of Baulch and McCulloch (1998) who found no significant effect on the probability of the household being poor due to change in the age of the household.
The household size regression co-efficient has positive sign and statistically significant at 1 percent level, implying that as the household size increases by one individual, the probability of the household to be poor increases by 0.9378. This is expected because as common in rural areas, large family size is poverty enhancing, as it tends to reduce per capita expenditure of the households. This result is consistent with that obtained by Okurut (2002).This is because a larger household will likely have more children, who are unproductive but take a big proportion of the household income in terms of school requirements, medical attention, food and clothing.
Household size (HS) was a significant determinant of poverty among rural households. There are two opposing interpretations for the relationship between household size and poverty status. The larger the household, the greater will be the total consumption needs and thus, the higher the poverty status of the household. However, to the extent that a larger household size also augments the total labour supply of the farming household thereby enhancing its incomegenerating potential, the effect of a larger household size on poverty status may be neutralized. This study shows that household size is positively related to poverty status and is significant at 1 per cent. This implies that as the household size increases, the poverty status of the household also increases. This result is consistent with the findings of Olaniyan (2005), Etim (2007), Etim and Edet (2007) and Etim et al., 2008 who concluded that the size of the household is positively related to the poverty status of the household.
The primary occupation coefficient shows positive influence on household poverty status and significant at 10 percent level, meaning that those whose primary occupation are agric-based (farming, fishing, and lumbering) have 0.2835 probability of being poor. This is so, because farmers, fisher folks and timber contractors do not have enough funds to increase the scope of or establish firmly their businesses to the level that more income could be generated so as to enhance the needs of the large-size rural households for better standard of living and increased per capita expenditure. This finding is supported by Akinleye (2004).The incidence of poverty is higher in the rural areas where farming is the core occupation. This study shows that rural households have a higher probability of being in poor. This is may be due to the availability of diverse jobs in the urban areas, some of which are well paid relative to the jobs in the rural areas. Also, the lack of infrastructural facilities like good roads, electricity, and access to markets limits income earning opportunities in the rural areas.
Educational level of the households has statistically significant negative relationship with poverty status. The result reveals that the probability of the households to be poor declines by 8.82 percent as households move from one certified educational level to another, that is, probability of household's poverty reduces, as the year of schooling of the households increases. This is expected because adoption of technological innovations for higher productivity and higher income has higher probability with education. The result is consistent with the findings by Olaniyan (2005). Household monthly Income/expenditure (HMIE) tends to reduce poverty status. From the regression coefficient, poverty status of the household decline as monthly Income/ expenditure improves by about 67 percent per thousand-naira increase. Income from farming activities is inversely related to the poverty status of the household and significant at 1 per cent. This shows that as the income from farming activities increases, the probability of being poor decreases. This reveals that the likelihood of a farming households being poor is reduced if the income from farming activities increases. Moreover, the constant term (-2.9881) is the autonomous level of poverty of the researched households.

Conclusion and Recommendations
In line with the Millennium Development Goals (MDGs) of the United Nations (UN), poverty reducing efforts in developing countries must be based on adequate knowledge of which the poor are, where they dwell, their source of income, and production activities, if any tangible result is to be achieved. This research therefore analyzed the determinants of rural poverty and factors that are associated with this socio-economic phenomenon among the rural households in the three senatorial zones of Yobe State.
At the current level of national development, it does appear that certain fundamental microeconomic variables have not been properly and adequately attended to, for the enhancement of rural income and reduction in the rate of impoverishment. However, the case of rural households in Yobe State is not an exception. As a result, a critical level of development of productive forces and an enabling environment is necessary especially at the rural level, if a sustainable development is to be achieved in Yobe State in particular, Nigeria as whole.
From the result of the analysis, the following suggestions are thus relevant to boost the economic activities of the rural areas and thus alleviate poverty among households in the research area.
The government should embark on programmes that would encourage people to take up agriculture as the main occupation has their multidimensional poverty is higher than those in other sector in the rural area. The government should ensure that healthcare facility in the rural area is within the reach of the rural people. Also, ensure that the dilapidated health facility is rehabilitated. In addition, housing/sanitation poverty should be tackled in the rural areas.
The provision of basic infrastructure in the rural areas in particular is a necessary requirement for poverty alleviation. Soft loans should be provided to rural masses through local financial institutions, micro-finance banks, and cooperative societies. In line with this, State Agricultural and Multipurpose Credit Agency (SAMCA) can be established or fortified, and be more easily accessible by rural dwellers. All these will improve the income of rural households and consequently their standard of living and thus reduce poverty Educational advancement of the dwellers should be encouraged as a source of guaranteed minimum returns to the households. Improvement of human capital through investment in education, this involve training in life skills and vocations which would help stimulate the innate entrepreneurial potentials of the household and expand the frontier of their income generating capacities and become more productive.
Policy efforts that would facilitate employment opportunities would help reduce the number of dependents(who could be potentially employed) and enable them to contribute to the general house expenditure. This would be a welcome development towards reducing poverty in the research area.
Measures at discouraging excessively large family sizes should be fortified and encouraged. This could be achieved through intense orientation campaigns on family planning and its attendant benefits so as to reduce dependency ratio and encourage child spacing.