Determinants of Expected Poverty Among Rural Households in Nigeria

Vulnerability measures are becoming tools for evolving proactive steps to alleviate poverty. Against this backdrop, this study examined the determinants of expected poverty (a measure of vulnerability) among rural households in Nigeria. The data for the 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 analysed using three-stage feasible generalized least squares (3FGLS). Both idiosyncratic and covariate factors affect the expected log per capita consumption of rural Nigerians. The overall expected poverty for the country at 0.535 is 1.02 times the observed poverty in 1996. Higher expected poverty is correlated with living in the North East, no formal education, farming, older head of household, large household size and male-headed household. The North East region has both lower mean per capita consumption and higher variance compared with other regions of the country. Consumption variance is highest for households whose heads have secondary education, while households whose heads have no formal education have the lowest mean expected consumption. Farming households have lower mean per capita consumption than nonfarming households. Male-headed households have both lower mean consumption and higher consumption variance relative to their female-headed counterparts. Further, household heads below age 20 have the lowest mean consumption and the highest consumption variance. Households with more than ten members have very low mean consumption and very high consumption variance. Depending on whether there is low mean consumption or higher consumption variance or both, policy strategies suitable for the different groups will vary from increased mean per capita consumption to consumption smoothening or both. 2 R E S E A R C H P A P E R 1 8 3 Past studies (e.g., FOS, 1999; World Bank, 1996) have established that most of Nigeria's poor live in rural areas and that most rural households in Nigeria are poor. FOS (1999) and Omonona (2001) also took the step of identifying sources of poverty among rural farming households in Nigeria. A vulnerability assessment of Nigeria by Alayande (2003) found, again, that rural Nigerians are the most vulnerable to poverty, but did not provide information on the expected poverty profile of rural Nigerians using idiosyncratic and covariate variables or shocks. It therefore follows that it is necessary to probe into what makes rural households in Nigeria vulnerable to poverty. Granted that these households have different segments in terms of demographic and occupational compositions and the characteristics of the community in which the household resides, in this study we are interested in generating a vulnerability to poverty profile of the different segments of rural households of Nigeria. Vulnerability profiles of this type can be useful illustrative devices in the discussions of policy priorities among such segments of Nigerian rural population. For the purpose of this study, vulnerability is defined as expected poverty (VEP). This is ex-ante information that measures vulnerability to poverty using cross sectional data. It is one of three approaches for measuring vulnerability to poverty. Others are vulnerability as low expected utility and vulnerability as uninsured exposure to risk (Hoddinott and Quisumbing, 2003b). Nonetheless, the VEP adopted for this study is not without its own limitations, which are clearly underlined by the inconsistency between the uses of cross sectional data for analysis of dynamic concepts such as vulnerability. Hoddinott and Quisumbing (2003b) and Dercon (2001) highlight some of these drawbacks, which include the exclusive reliance of the approach on the strong assumption of the ability of cross sectional variability to capture temporal variability. Thus, any policy recommendation emanating from such results may be perverse. It is worth noting that one of the key advantages of VEP that allows for use of single cross sectional data in the analysis of vulnerability gives impetus to the use of VEP in this study. This is so since there exist no reliable panel data collected to date in Nigeria. Meanwhile, Dercon (2001) has shown that the VEP can be improved through the incorporation of covariate risks which will not necessarily be the same across regions and states. In this instance, this study extends the empirical application of VEP by Chaudhuri (2000) by including some covariate risks (regional specific variables) for which data are available in the country and in line with the suggestions by Dercon. Another key task of this study is its ability to discriminate between different sources of vulnerability as measured by expected poverty. Given that two groups in the population are estimated to be equally vulnerable, these two groups of population may have different household characteristics. The appropriate policies for mitigating the vulnerability of the two groups will differ, thus calling for discrimination between different sources of vulnerability. The dearth of knowledge on generating vulnerability to poverty profiles among different segments of rural populations and discriminating between different sources of vulnerability to poverty is a major policy challenge in Nigeria. Therefore, the study is interested in supplying the information lacking on these vulnerability to poverty issues. 2. Objectives, hypotheses and justification of the study The main objective of this study is to assess rural Nigerian households' expected poverty. The specific objectives are to determine household characteristics and regional specific risks that affect consumption of rural Nigerians; to generate a vulnerability profile using expected poverty measure of different segments of rural population in Nigeria; to discriminate between the different sources of expected poverty among rural households in Nigeria; and to draw policy implications regarding the issue of vulnerability to poverty among rural households in Nigeria.