AN ASSESSMENT OF VULNERABILITY TO POVERTY IN RURAL NIGERIA

AN ASSESSMENT OF VULNERABILITY TO POVERTY IN RURAL NIGERIA M. A. Agbaje, F. Y. Okunmadewa, B. T. Omomona, and O. A. Oni Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria E-Mail: deolaagbaje2002@yaoo.com ABSTRACT Poverty is a serious callenge in Nigeria especially in te rural area and it as been intractable because policies and programmes in te country are based on static poverty analysis. For effectiveness of policies and programmes on poverty, a forward looking approac-vulnerability to poverty, sould be adopted. Tis study investigated te vulnerability to poverty across in rural Nigeria, using te 2004 NLSS data. Te result of te 3-Stage Feasible Generalized Least Squares sowed tat at te standard vulnerability tresold of 0.5, 62.2% of rural ouseolds were vulnerable to poverty. Vulnerability to poverty was iger in nortern zones and lower in te soutern zones. Male eaded ouseolds (59.8%) were more vulnerable to poverty tan teir female counterpart (43.9%). For every 100 ouseolds tat were poor, 12 more will be poor in future in sout, 11 more in ouseolds wit eads witout formal education, 8 more in farming ouseolds and 6 more in male eaded ouseolds. Consumption variance index was consistently iger among non-farming ouseolds (0.0301), female eaded ouseolds (0.0403) and tose wit primary education (0.0327). Mean consumption index was lower among farming ouseolds (0.0274), male eaded ouseolds (0.0274), tose wit no formal education (0.0237) and tose wit ouseold size greater tan 20 (0.0168). Keywords: poverty, vulnerability, rural Nigeria. INTRODUCTION Poverty remains a serious problem in sub- Saaran Africa. Its reduction in developing economy is te major callenge facing development stakeolders today. Te first UN Millennium Development Goal (MDG) agreed by world leaders in 2000, is te eradication of extreme poverty and unger by 2015. In quantitative terms, te associated target for tis MDG is te reduction by 50% of te proportion of people wose income is less tan a $1-a-day over te period 1990-2015. According to Alayande and Alayande (2004), Nigeria suffers from ig levels of poverty in spite of er enormous wealt of uman and material resources. Te 2004 Nigeria Living Standard Survey reports tat 54.7% of te total population is poor, wit 36.6% of te total population living in extreme poverty. Tere are marked differences in te incidence of poverty between urban and rural ouseolds. Te NLSS 2004 survey as revealed tat poverty in Nigeria is largely a rural penomenon. It reveals a 20 percentage point gap between te poverty incidence of te urban and rural ouseolds. Wile 43 percent of urban ouseolds are poor, 63.8 percent of te rural ouseolds are poor. Furter investigation of te poverty profile in te rural area reveals tat about 44.4 percent cannot meet te food expenditure requirements wile 19.38 percent of te ouseolds, altoug could meet te food expenditure requirements are unable to meet te minimum expenditure to cover oter basic needs. In te case of te urban ouseolds, only 26.7 percent of te ouseolds tat could not meet te required expenditure on food wile 16.4 percent of te ouseolds are moderately poor since tey could meet food expenditure but not oter non-food basic needs expenditure. Te starting point in a dynamic conceptualization of poverty is te recognition tat te welfare of ouseolds is not simply dependent upon teir income or consumption in any given period but also on teir exposure to risk. During te last few years te increasing recognition tat tere are considerable flows into and out of te poverty pool (Baulc and Hoddinott, 2000) as focused interest in ouseold vulnerability as te basis for a social protection strategy. Wile it as long been demonstrated (Rosenzweig and Binswanger, 1993; Banerjee and Newman, 1994) tat considerations of risk and uncertainty are key to understanding te dynamics leading to and perpetuating poverty, it is only recently tat policy makers ave taken a more active interest in trying to incorporate considerations of risk and vulnerability into teir strategies to reduce poverty. Tis concern wit risk and vulnerability among policy makers is ecoed by findings from te worldwide consultations of te poor wic reveal tat risk and uncertainty often preoccupy teir life and lie at te core of teir poverty. Houseolds face a lot of risks and tey often experience socks leading to a wide variability in teir consumption. To reduce poverty sustainably, owever, reducing ouseold vulnerability and increasing ouseold resilience are necessary. Vulnerability is a forward-looking ex-ante measure of ouseold s well-being wic sows tat a ouseold weter or not is poor today, will find itself poor tomorrow (Cauduri, 2001). In anoter dimension today s poor may or may not be tomorrow s poor. Currently non-poor ouseolds, wo face a ig probability of a large adverse sock, may, on experiencing te sock, become poor tomorrow. And te currently poor ouseolds may include some wo are only transitorily poor as well as oter wo will continue to be poor (or poorer) in te future. Tis definition was adopted for tis study. 60

Vulnerability is desirable terefore to ave a measure of ouseold welfare wic takes into account bot average expenditures and te risk tat ouseolds bear. Vulnerability assessments facilitate in designing better risk-management and anti-poverty policies by igligting te distinction between ex-ante poverty prevention interventions and ex-post poverty alleviation interventions. Vulnerability is a major obstacle to social and economic development. Poor people are especially vulnerable; tey ave few buffers or resources to cope wit azards or socks. Yet tey are significantly more likely to be affected by ill-ealt, unemployment, trade socks, climate cange, famine or conflict. Sustainable poverty reduction can only be acieved and managed effectively if vulnerability is better understood. Nigerians especially tose living in rural areas are prone to azards or socks because rural infrastructure in Nigeria as long been neglected, wile investments in ealt, education and water supply ave largely been focused on te cities. As a result, te rural population as very limited access to services suc as scools and ealt centres, and about alf of te population lacks access to safe drinking water. Limited education opportunities and poor ealt perpetuate te poverty cycle. Te neglect of rural infrastructure as also reduced te profitability of producing for te markets. Nigeria s rural road network is one of te least developed in sub-saaran Africa (IFAD, 2007). Te poor tend to live in isolated villages tat can become virtually inaccessible during te rainy seasons. During te periods of post - arvest marketable surplus, it is not always easy to reac te markets. Te large total land area of 923, 773Km 2 notwitstanding, Nigeria is bedeviled wit major environmental azards tat ave adversely affected te earning capacity of te people especially in te rural areas. Te major environmental problems tat ave made Nigerian especially te rural dwellers vulnerable to poverty are: drougt and desertification due to population pressure, over-grazing and continuous exploitation of marginal lands; agricultural land degradation wic as affected all te 36 states of te federation in an effort to increase agricultural production to meet te demand of te growing population; te ig incidence of gully, coastal and marine erosion are most noticeable in te sout-sout, sout east and sout-western parts of te country. Tere is also flooding (coastal river and urban) wic as become a national issue. Large industrial pollution and urban waste management are oter environmental problems tat ave seriously affected te ealt of te people and ence teir income earning capacity. Te current economic meltdown/global recession tat started in 2008 is likely to subject Nigerian ouseolds and organisations to price and economic socks. Tis may adversely affect consumption, tus leading to more people falling into poverty or become vulnerable to poverty. Already, te economic recession as ignited a cain of reactions in te nation s economy. Tere as been massive retrencment especially in te banking sector due to te collapse of some banks. Government as ad to increase pump price of petroleum products and te capital market is melting down. Government at all levels, ave embarked on several programs in order to alleviate te incidence and severity of poverty in Nigeria. Some of suc programs include National Directorate of Employment (NDE), te Family Support Program (FSP), te National Agricultural Land Development Autority (NALDA), te Directorate for Food, Roads, and Rural Infrastructure (DFRRI), Family Economic Advancement Program (FEAP) and National Poverty Eradication Program (NAPEP) (Osinubi, 2003). Tese programmes, approaces and strategies are aimed at improving te conditions of te rural poor, wile some of te efforts are still on course, many ave since gone moribund (Nwacukwu and Eze, 2007). Te failure of tese previous programmes and strategies to slow down te incidence of poverty in Nigeria bears a strong testimony to one major issue. Te country sould pay more attention to te issue of risk and uncertainty tat are important for te understanding of te dynamics leading to and perpetuating poverty. Given te importance of risk and uncertainty, policy makers are beginning to incorporate risk and vulnerability into teir strategies to reduce poverty (Alayande and Alayande, 2004). Te 2004 National Living Standard Survey (NLSS) data was used for te purpose of tis study. Due to ig incidence of poverty in te rural area in Nigeria and te fact tat poverty studies ave over te years used static rater tan dynamic concept in its analysis. Recent literature as observed (static) poverty status migt not be a good indicator of te ouseold s general poverty risk (its vulnerability to poverty). Tus, recent literature as empasized te need for poverty to be considered a forward looking approac-vulnerability to poverty. However empirical evidence of vulnerability is scarce in Nigeria, terefore, te vulnerability to poverty in Nigeria was investigated. Te specific objective of tis paper is to present te vulnerability to poverty profile of rural Nigeria. Teoretical framework for vulnerability Hoddinott and Quisumbing (2003) identified tree approaces to assessing vulnerability. Tese are vulnerability as expected poverty (VEP), vulnerability as low expected utility (VEU) and vulnerability as uninsured exposure to risk (VER). According to te autors, tese tree approaces sare a common caracteristic since eac of tem constructs a model tat predicts a measure of welfare. Furter VEP and VEU sares two caracteristics viz: tey make reference to bencmark for te welfare indicator and enunciate a probability of falling below tis bencmark. VEP and VEU approaces measure 61

vulnerability at individual level, summing over all individuals or ouseolds give a measure of aggregate vulnerability. However, VER according to Hoddinott and Quisumbing does not measure vulnerability because it does not construct probabilities. Instead, VER assesses weter observed socks generate welfare losses. VEP approac as advantage in terms of its capability to identify ouseolds at risks wo are not poor and te fact tat it can be estimated wit single crosssectional data, tis study adopts te VEP approac as its teoretical framework. Tis becomes imperative since only cross sectional data are presently available in Nigeria to carry out welfare studies. Following te exposition by Dercon (2001), te starting point for VEP is to consider te vulnerability of particular ouseold at time t as te probability tat te ouseold will find itself consumption poor at time t+1: V = Pr (c = z) (1) t, t+1 Were c is te ouseold s consumption level at t+1,t+1 and z is te poverty line. In situation were cross-section data are available, deriving a consumption prediction model is considered in general as: c c (X I δ α ε ) (2) t =,,,, t Were X is a vector of observable ouseold caracteristics, I is a vector of risk management instruments, δ is a vector of parameters describing te state of te economy at time t, α are unobserved but fixed ouseold caracteristics and ε stocastic errors. To derive te vulnerability measure at te ouseold level, substituting (2) in (1) gives: In words, te estimated probability of being poor in period t+1 using te prediction model (1), based on information available in period t, but including (possibly predicted) information about δ and ε t t+1. Te crucial elements in tis specification are δ t+1 and ε.bot variables are indexes and terefore include t+1 information about evolving state of te aggregate economy (constant across ouseolds) and aggregate socks. Typically, and in Cauduri et al., (2001), tere is no information on δ witin te data and so ignored, wile t + 1 similarly time dependence of errors and socks ε is,t+1 allowed for. In sort, te risk and te vulnerability to poverty considered are only idiosyncratic-i.e., no aggregate socks are allowed for. In order to address tis problem, Dercon (2001) suggests te use of additional independent variables to te ones suggested in te Cauduri approac. Using a useful caracteristic of tis linear model and from te normality assumption of te errors, and defining Φ as te standard normal distribution, ouseold vulnerability is estimated as: V Pr (lnc < ln zx = Φ ln z. X δ/ X δ (3) = ) Te step by step metod of analysis is as explained in te metodology. Te FGLS approac is terefore used in assessing te vulnerability of ouseolds to poverty. METHODOLOGY (Data source and analytical tecnique) Te study used merged data from te National Living Standard Survey (NLSS) and NBS Annual Abstract of Statistics for te same year 2004. Te National Living Standard Survey (2004) of ouseolds tat was carried out between September 2003 and August 2004. Te sample design used was te two-staged stratified sampling. Te first stage involves te Enumeration Areas (EAs), wile Housing Units constitute te 2 nd stage. Clusters of 120 ousing units called Enumeration Areas (EA) were randomly selected per state. Te second stage involved random selection of 5 ousing units from te selected EAs. A total of 600 ouseolds were randomly selected from eac state and 300 from te FCT, summing up to 21, 900 ouseolds in all. However data available for te rural areas was 14, 512 ouseolds. Data on consumption, expenditure, demograpic caracteristics, education attainment, type of ousing unit available, membersip of any social organization and oter socio-economic caracteristic will be got from NLSS. Wile state specific covariate data like price level, unemployment rate, volatility of government expenditure, rainfall, sunsine, radiation, reported cases of armed robbery, reported cases of AIDS, malaria, measles, river blindness and was taken from NBS Annual Abstract of Statistics. Analytical tecnique Determinants of rural ouseold consumption Following Cauduri (2000) te step by step metod of FGLS approac is explained below. Suppose tat a ouseold s consumption in period t is determined by a set of variables X. We can ence set up te equation: lnc = X β + e (4) were lnc is te log of per capita ouseold consumption, X a set of ouseold as well as community caracteristics, and e te part of ouseolds consumption tat cannot be explained. Cauduri, Jalan and Suryaadi (2002) suggest tat tis error term, or te variance in consumption of oterwise equal ouseolds, captures te impact of bot 62

idiosyncratic and community specific covariate socks on ouseolds consumption and tat tis variance is correlated wit observable ouseold and community caracteristics. In a second step, te variance of te error term was terefore regressed on te same (and oter) ouseold and community caracteristics. σ 2 e, = X θ = i j>i X i X θ ij (5) Standard regression analysis using ordinary least squares (OLS) estimation tecniques assume omoscedasticity, i.e., te same variance V(e i ) = σ 2 across all ouseolds i. Cauduri (2000) assumes tat te variance of te error term is not equal across ouseolds, reflecting te impact of socks on consumption; i.e., te error term is assumed to be eteroscedastic. Using OLS for an estimation of β and θ led to unbiased but inefficient coefficients. Following Amemiya (1977), te parameters of β and θ are estimated by using a tree- stage feasible generalized least square (FGLS) procedure. In te first stage, equation (4) was estimated wit ordinary least square (OLS) metod and te square of te generated error term are to be regressed against te explanatory variable in equation (6) below. ê 2 OLS, = X θ + η (6) Te predicted value of te error term in equation (6) was used to transform te same equation in a manner specified below: Equation (5) was estimated using OLS metod to obtain an asymptotically efficient FGLS estimate denoted ^ as θ FGLS. In tis case, X θ FGLS is a consistent estimate of σ 2 e, te variance of te idiosyncratic component of ouseolds consumption expenditures. Terefore equation (5) was transformed wit To obtain Equation (10) was estimated using OLS metod, and it yields a consistent and asymptotically efficient β. For eac ^ ouseold te ^ expected mean as well as variance of consumption can be estimated using te consistent and asymptotically efficient estimator s β FGLS and θ FGLS. (7) (8) (9) (10) (11) Te vulnerability level of ouseold wic is te probability tat ouseold wit caracteristics X will be poor in te future can be estimated by assuming tat ouseolds consumption expenditure are log normally distributed. Letting Ф (.) denote te cumulative density of te standard normal distribution, te estimated vulnerability probability is given by: were (12) c = Consumption level of ouseold X = Vector of independent variables including ouseold demograpic composition and uman capital β = Regression coefficients of idiosyncratic variables σ e,. = Variance of idiosyncratic and covariate variables = Error term e Te independent variables are as follows: Ageyears = Age of ouseold ead Ageyearsq = Age squared (years) Sex = Sex of ouseold ead (1=Male, 0= oterwise) Hsize = Houseold size Ss = Sout sout Se = Sout east Sw = Sout west Ne = Nort east Nw = Nort west Occ = Occupation (farming=1, 0= oterwise) Pri = Primary education of te ouseold ead (Yes = 1, 0 = oterwise) Secon = Secondary education of ouseold ead (Yes = 1, 0 = oterwise) Tert = Tertiary education of ouseold ead (Yes = 1, 0 = oterwise) Hytoilet = Hygienic toilet (Yes = 1, 0 = oterwise) Modernwall = modern wall (Yes = 1, 0 = oterwise) Safewater =Safe drinking water (Yes = 1, 0 = oterwise) Occs = House ownersip) (Yes = 1, 0 = oterwise reason 2 = (Yes = 1, 0 = oterwise) reason 3 = (Yes = 1, 0 = oterwise) reason 4 = (Yes = 1, 0 = oterwise) reason 5 = (Yes = 1, 0 = oterwise) 63

reason 6 = (Yes = 1, 0 = oterwise) reason 7 = (Yes = 1, 0 = oterwise) Unemploy = Unemployment rate Rain = Amount of rainfall Aids = Reported number of AIDS cases Malaria = Reported number of malaria cases Rivblind = Reported number of river blindness cases Tempera = Temperature Humidity = Humidity Armrob = Reported armed robbery cases Memberco = Membersip of local group/association (Yes = 1, 0 = oterwise) Credit = Access to credit (Yes = 1, 0 = oterwise) Electricity = Access to electricity (Yes = 1, 0 = oterwise) Farmsize = Farm size (ectares) Singleroom = dwell in singleroom (Yes = 1, 0 = oterwise) Apart = dwell in apartment (Yes = 1, 0 = oterwise) Duplex = dwell in duplex (Yes = 1, 0 = oterwise) Wbuilding = dwell in wole building (Yes = 1, 0 = oterwise) * Te regression for 3FGLS used te reasons given by respondents for being poor, te reasons tat te igest percentage were classified into Reasons 1, 2, 3, 4, 5, 6, and 7. reason 1 represents: ig agricultural inputs price, reason 2 represents: lack of agric input, reason 3 represents: low agricultural production, reason 4 represents: low agricultural prices, reason 5 represents: agricultural capital problem, reason 6 represents: no capital to expand own business and reason 7 represents: ard economic times. Decomposition of vulnerability sources In order to inform policy, following te literature of (e.g. Bidani and Ricter, 2001; Alayande and Alayande,2004; Oni and Yusuf, 2007), te pool of vulnerable ouseolds divided into two mutually exclusive groups namely (i) tose wo are vulnerable due to te ig volatility of teir consumption and (ii) tose wo are vulnerable due to teir low expected mean consumption. Te objective to decompose sources of vulnerability to poverty was acieved by adopting equation 13. First cose a reference ouseold wit an associated bundle of caracteristics, X r. Te vulnerability of oter ouseolds was ten assessed relative to tis reference ouseold. Te difference between te vulnerability level of a ouseold wit caracteristics X and tat of te reference ouseold was decomposed as follows: vˆ vˆ r = v c c c c ( ˆ µ, ˆ σ ) v( ˆ µ, ˆ σ ) r r c c c [ v( ˆ µ ˆ ) ( )] c σ v ˆ µ ˆ r, σ c c [ v( µ ) v( ˆ µ, ˆ σ )] c ˆ (13) r r Tis decomposition exercise is useful because it can be explained as follows. If two groups in te population are estimated to be equally vulnerable relative to te reference ouseold, but in one case it is due to low levels of mean consumption, and in te oter because of ig consumption variance. Te appropriate policies for mitigating te vulnerability of tese two groups will in general differ and it will terefore be important, for policy purposes, to be able to discriminate between te different sources of vulnerability. As in previous studies tat ave used tis metodology, tere was no issue of endogeneity. However, tis is not being ruled out. Hence, an appropriate diagnostic test of endogeneity and corrections was carried out. RESULTS AND DISCUSSIONS Table-1 reveals tat te mean age in te study area is 47.8 ±14.5 years. Most of te ouseold ead fell between 40 to 49 years of age, closely followed by 60 years and above, wit a percentage of 23.02, wic mean a lot of te elderly live in te rural areas. Te ouseold eads between 30 to 39 years are te tird largest group (22.81 percent). It is clear from te distribution tat te bulk of te people in te study area are in teir active years. Te mean ouseold size was 5.0 ±2.9. Te least family size is one wile te largest is twenty two. Te result sows tat ouseold size in Nigeria is still relatively large in rural areas. Tey constitute ouseold labour since tey are predominantly farmers. Te family members are needed in farming especially in operations like planting, weeding, arvesting and marketing. Houseold size is factor affecting te level of poverty incidence, dept and severity in rural farming ouseolds in Kogi State (Omonona, 2001) Most of te ouseold eads in te rural areas were males, wile a few were eaded by females. Te percentage of male ouseold eads was 86.49 percent, wile teir female counterpart was only 13.51 percent, a ratio of 1: 6. Te ousing unit type was classified based on weter tey are single rooms, flats apartments, duplex, wole building and oters. From Table-1, te respondents wit single rooms were 9, 573. Similarly, tose wit wole building were 4, 296. Tose living in apartment or flat were 372. Te respondents living in Duplex were te fewest 63 wile tose living in oter types of ousing units were 208. Sixty six percent (66%) of ouseold members live in single rooms; an average family in te study area ad five members. Living in a single room may ave implications for te ealt status of ouseold members. Asset is a key factor in reducing poverty. According to tis data 86.11 percent of te ouseolds 64

own te ouses tey live in, it is eiter owned by te usband or is spouse or by bot usband and spouse and only 13.86 percent live in rented ouses. Tis factor did not seem to ave an effect on te poverty status of te respondents Human capital development is very important for poverty reduction. Education was classified into nonformal education, primary scool education, secondary education and tertiary education. More tan alf of te ouseold eads in rural Nigeria ave no formal education. Tose wit no formal education are 52.89 percent followed by tose wit secondary education 29.87 percent, 5.38 percent ave primary education 3.34 percent ave tertiary education and 8.51 percent ave oter types of education. Inadequate resource of uman capital is a major cause of poverty wic is evident in tis study, wit more tan alf of te respondents not aving any formal education. Poverty decreases as te level of education increases (Omonona, 2001; World Bank, 2000). Tere were various types of occupation in te rural areas. Of tese occupations agriculture was still te main occupation of most rural ouseolds in Nigeria. Agriculture and forestry accounted for 76.7 percent of te occupation groups in te rural areas, followed by sales accounted for just 5.48 percent. Te data sows tat 84.14 percent of te ouseold eads were member of one or more associations in teir communities, wile only 15.86 percent were not members of any association. It is owever instructive to know tat 79 out of 100 of respondents in te rural areas ad access to credit outside te ome, wile 21 out of 100 ad no access to credit outside te ome. Houseold s ygienic condition is defined as bad if a ouseold does not ave sanitary latrine and safe drinking water. Safe drinking water is available to very few ouseolds in te rural areas. Six out of 100 respondents ad access to pipe borne water, sixteen out of 100 respondents use boreole, wile te oters use low quality sources of water, sources suc as unprotected wells, rainwater, river, lakes or ponds. Also sources like vendors or water trucks. Te types of toilets available in te rural areas are unygienic tis migt lead to ealt azard wic migt affect te wole community. 11.45 percent of te respondents ad no toilet, 42.85 percent ad covered pit latrine, 15.12 percent ad uncovered pit latrine, 3.96 percent use pail or buckets, only 1.69 percent use VIP latrine, flus to sewer and septic were 1.04 and 1.12 percent respectively. Bad ygiene as implications for te ealt status of ouseold members, rendering tem vulnerable to ealt azards. Respondents connected to electricity from te national grid were only 17.42 percent; oters use oter sources of ligting. On ousing conditions and facilities, 70 out of 100 respondents live in ouses wose outside wall were made of mud, wood or bamboo, iron seet and cardboard. Only 30 out of 100 of tem ad concrete, burnt bricks and stones as teir outside walls. Te mean per capita expenditure was N37, 767.97 per annum. Tis is an indication of very poor condition of living as it conforms to an income of less tan $1 per day wic is te widely accepted tresold of poverty below wic an individual will be considered poor. Te mean unemployment rate in rural Nigeria is 13.90 ± 9.72 percent. Te amount of rainfall is an important factor in te rural areas since agriculture is rain fed in most parts of Nigeria. Te mean rainfall in Nigeria is 84.96 ± 62.00 mm. Temperature and umidity in te nation will also affect agriculture. Diseases tat affect Nigerians can be key to making non vulnerable Nigerians vulnerable to poverty in te future. Diseases like HIV/AIDS, malaria and river blindness. Te mean HIV/AIDS cases in Nigeria are 91551.37 ± 36080.83 and tis is very alarming. Also te mean reported cases of malaria are 3799.35 ± 6103.83. Table-1. Selected caracteristics of respondents. Variables Frequency Percent Gender Male 12, 552 86.49 Female 1, 960 13.51 AGEGR10 14, 512 100.00 10 to 19 years 37 0.25 20 to 29 years 1, 204 8.54 30 to 39 years 3, 310 22.81 40 to 49 years 3, 690 25.43 50 to 59 years 2, 894 19.94 60 and above 3, 341 23.02 Houseold size 100.00 1-5 9, 445 65.08 6-10 4, 399 30.3 11-15 641 4.43 16-20 27 0.19 Educational status 14,512 100.00 No formal education 7, 676 52.89 Elementary 128 0.88 Primary 653 4.50 Secondary 4, 335 29.87 Tertiary 485 3.34 Oter 1, 235 8.51 Occupational status Farming Non Farming 14, 512 100.00 65

Types of dwelling Single room 9, 573 65.97 Apartment or flat 372 2.56 Duplex 63 0.43 Wole building 4, 296 29.60 Oters 208 1.43 Source of water 14, 512 100.00 Pipe borne 853 5.88 Untreated pipe 305 2.10 Boreole/and pump 2, 347 16.17 Protected well 2, 423 16.70 Unprotected well or rainwater 3, 491 24.06 River, lakes or pond 4, 321 29.78 Vendor or water truck 323 2.23 Oters 449 3.09 Credit 14, 512 100 Access to credit 8, 698 79.2 No access to credit 2, 303 20.8 11, 001 100 Main source of ligting Kerosine 10, 982 75.68 Gas 162 1.12 Main electricity 2, 528 17.42 Electric generator 73 0.50 Battery 9 0.06 Candle 20 0.14 Firewood 552 3.80 Oters 186 1.28 Types of toilet 14, 512 100 None 1, 661 11.45 Toilet on water 708 4.88 Flus to sewer 151 1.04 Flus to septic 162 1.12 Pail or bucket 574 3.96 Covered pit latrine 6, 219 42.85 Uncovered pit latrine 2, 194 15.12 VIP latrine 245 1.69 Oters 2, 598 17.90 Total 14, 512 100 Ownersip status House owned by ouseold 12,497 86.11 House not owned by ouseold 2, 015 13.89 Total 14, 512 100 Membersip of association Yes 12, 211 84.14 No 2, 301 15.86 Total 14, 512 100 Material of outside wall Mud 9, 043 62.31 Stone 84 0.58 Burnt bricks 223 1.54 Cement or concrete 4, 052 27.92 Wood or bamboo 133 0.92 Iron seet 71 0.49 Cardboard 37 0.25 Oters 869 5.99 Total 14, 512 100 Occupation groups Student, retired, unemployed/inact 559 3.85 Professional or tecnical 650 4.48 Administration 13 0.09 Clerical 419 2.89 Sales and related 795 5.48 Services and related 325 2.24 Agricultural and forestry 11, 131 76.70 Production and transport 197 1.36 Manufacturing and processing 123 0.85 Oters 300 2.07 Total 14, 512 100 66

Table-2. Descriptive statistics of selected covariate variables. Variables Mean Standard deviation Definitions Covariates Unemploy (%) 13.906 9.7214 Unemployment rate Rain (mm) 84.968 62.008 Rainfall Aids 91551.37 36080.83 HIV/AIDS (in number) malaria 3799.35 6103.83 Reported malaria cases(number) rivblind 1263.3 1724.20 River blindness (number) tempera 24.595 20.67 Temperature ( 0 C) umidity 36.606 26.34 Humidity armrob 80.85 51.60 Reported armed robbery cases DETERMINANTS OF RURAL HOUSEHOLD CONSUMPTION Table-3 presents te regression results for vulnerability estimations for NLSS (2004) and NBS Annual Abstract of statistics (2004). Te results for log mean per capita consumption expenditure (MPCE) was later used to assess te vulnerability to poverty of ouseolds. Te results sow good performance of te model, wit F value being statistically significant. Te adjusted coefficient of determination sows tat 57.12 percent of te variations in te values of log consumption expenditure was accounted for or explained by te explanatory variables used. Te age of te ouseold ead is negative and significant to explain per capita ouseold consumption wile te age squared a positive association wit log per capita consumption. As expected a large ouseold size will reduce per capita consumption. It is well known tat families wit many cildren are on te average poorer. Sex dummy of male eaded ouseold sowed positive significance to log per capita consumption. Higer levels of educational attainment are positively and significantly associated wit iger per capita consumption. No formal education and primary education dummies ad negative signs. Secondary and tertiary dummies on education are found to affect consumption per capita positively. Te relevant coefficients are all statistically significant as well. Tis basically conforms to oter studies concluding tat literacy and education attainment decrease poverty (e.g. World Bank, 2002). Access to electricity and availability of safe drinking water were found to affect per capita consumption positively. Weter ouseolds participate in social safety net programmes or not is very important. Tose ouseolds tat ad access to credit outside teir ome and tose wo belong to one or more associations affected consumption per capita negatively and were not significant. Te main occupation in rural Nigeria was farming. Houseold-eads engaged in farming activities were negatively associated wit log per capita consumption. Due to weater variations and oter environmental factors affecting agricultural production (especially if permanent irrigation is not available), teir income fluctuates from one season to anoter and tis is likely to affect future consumption in te rural areas. Wile te variables oter tan ousing conditions seem to be natural candidates for inclusion in te regression (Suryaadi and Sumarto, 2003), ousing condition defined by te type of te construction materials used in building ouses, is included in te model as tis is tougt to be a major and quite regular source of socks for rural ouseolds. Wen tere is a eavy rainfall, flooding and storm, wic is fairly common in Nigeria, ouseolds particularly in rural areas need to spend a significant amount of resources for te repair and reconstruction of teir ouses. Construction material suc as mud, wood or bamboo, iron seet common in te rural areas are considered not modern. Te use of modern material like cement, burnt bricks and stone in construction of outside wall of ouses in te rural areas affected per capital consumption positively. Of all te types of dwellings, single room and wole building was te only significant dwelling. Wole building was positively associated wit consumption per capita wile single room was negatively associated wit consumption per capita. A few results are surprisingly contrary to a prior expectation For example te dummy variable for ouse ownersip and farm ownersip were not significant. One would ave expected tat wat te ouseold eads considered as reasons for being poor, if significant, would ave a negative effect on consumption. All te reasons tat were significant ad positive signs. Te coefficient estimate for AIDS was significant and ad a positive sign. Tis means tat consumption will increase as AIDS cases increases. Tis migt be due to te special diet people living AIDS will ave to consume and migt be because of well organized AIDS programme in Nigeria wic reduces te progression of HIV infection to full blown AIDS. Malaria and river blindness (ealt azards) as expected were found to affect per capita consumption negatively. Oter covariate variables like rainfall and temperature affects consumption per capita positively. Tis is expected since agriculture in Nigeria is rain fed. Unemployment rate and armed robbery cases were negatively associated wit consumption per capita. 67

Table-3. Last stage of te 3FGLS estimates. Variable Coefficient Std Err. t P>(t) Ageyears -.0091082.002273-4.01 0.000*** ageyearsq.0001096.0000216 5.07 0.000*** Sex.0993339.0170943 5.81 0.000*** Hsize -.089125.0021599-41.26 0.000*** Ss.263703.0210322 12.54 0.000*** Se.4872546.0225974 21.56 0.000*** Sw.1890957.0252004 7.50 0.000*** Ne.144737.0211054 6.86 0.000*** Nw.3571821.0217412 16.43 0.000*** Occ -.1641345.0130198-12.61 0.000*** Noedu -.16071.0220478-7.29 0.000*** Pri -.0625954.0316733-1.98 0.048** Secon.0671281.0262219 2.56 0.045** Tert.3857917.0308326 12.51 0.000*** ytoilet.0628442.0512423-1.23 0.220 modernwall.1488824.073793 2.02 0.044** Occs.0066849.0145266 0.46 0.645 Reason2.0330573.0211287 1.56 0.118 Reason3.0227484.0210199 1.08 0.279 Reason4 -.0244963.0294989-0.83 0.406 Reason5 0.237191.0212721 1.12 0.265 Reason6 -.0087654.0279345-0.31 0.754 Reason7.0449101.03341 1.34 0.179 Unemploy -.0108074.0007368-14.67 0.000*** Rain.0003626.000192 1.89 0.059* Aids 3.46e-06 1.92e-07 18.08 0.000*** Malaria -5.76e-06 1.08e-06-5.31 0.000*** Rivblind -.0000145 3.99e-06-3.64 0.000*** Tempera.0015246.0006375 2.39 0.017** Humidity -.0028036.0004623-6.06 0.000*** Armrob -.0006003.0001349-4.45 0.000*** Memberco.018038.0161437 1.12 0.264 Credit.0041931.0138399 0.30 0.762 Electricity.0267325.0158765 1.68 0.092* Farmsize.0000812.0000551 1.47 0.141 Singleroom -.1074091.0440999-2.44 0.015** Apart -.051213.0537906 0.95 0.341 Duplex -.0195004.0989924-0.20 0.844 Wbuilding.076224.0445784 1.71 0.087* Cons 10.36616.0828305 125.15 0.000*** ***significant at 1% **significant at 5% *significant at 10% Observations 14512 R Squared = 5730 Adj R Squared = 5712 Joint significance F () = Prob> F = 0.000 68

Vulnerability/observed poverty profile A vulnerability profile by selected ouseold caracteristics can be seen on Table-4. Te results from te table sow tat poverty and vulnerability varied across demograpic, occupational and socio-economic caracteristics. Te predicted/ observed poverty ratio was used to estimate te expected poverty incidence based on te static poverty estimate. At a standard vulnerability tresold of 0.5, 62.2% of rural ouseolds in Nigeria were vulnerable to poverty. Also at te geo-political zones, poverty is expected to decline in all zones except sout. Te predicted/observed poverty ratios for bot zones were 1.12. Tis means tat for sout zone for every 100 ouseold ead witin tis zone tat are poor now, 12 more are expected to be poor in future. Te level of education and literacy of ead of ouseold are an important determinant of vulnerability (as well as poverty). As evident from te Table-4, educational attainment significantly affects te incidence of vulnerability. For instance, 47.7 percent of te population is vulnerable in ouseolds were te ead of te ouseold as secondary education, 23.6 percent were te ead of te ouseold as tertiary education. Tis is against a 69.1 percent incidence in case ead of ouseold wit no formal education. An assessment of gender of ouseold ead, poverty and vulnerability sows tat male-eaded ouseolds ad iger poverty incidence tan te femaleeaded ouseolds. Indeed, te male-eaded ouseolds ave a poverty incidence of 58.2% as against te femaleeaded ouseolds tat ave an incidence level of 43.8%. Te male-eaded ouseolds also contribute more to expected poverty tan teir female-eaded counterparts. Tis is estimated at 59.8% and 43.9%, respectively for male-eaded and female-eaded ouseolds respectively. Ja and Dang (2008) found for a group of selected countries in Central Asia (Azerbaijan, Kazakstan, Kyrgyzstan, and Tajikistan) tat female-eaded ouseolds were muc less vulnerable tan teir male counterparts. Houseold eads tat derive income principally from agriculture and forestry also sow iger incidence of poverty compared to non-farm worker. Expected poverty for farm workers is 66.9 per cent compared to rougly 40.9 per cent for non-farm workers. Tis indicates tat non farming liveliood reduces te level expected poverty in rural Nigeria. Observed and expected poverty are igest among ouseold eads wose age ranges between 40-49 years. A predicted/observed poverty ratio of 1.14 for age range 40-49 years, implies tat for every 100 ouseold eads witin tis age group tat are poor now, 14 more are expected to be poor in future. Te age group between 20-29 years ad te lowest observed poverty but te predicted/observed poverty ratio of 0.99. Te age groups tat is 60 years and above also ad predicted/observed poverty ratio of 0.91, tis implies tat tey ave ig likeliood of exiting poverty in te future. Te group 40-49 and 50-59 year migt get into more poverty because it is likely tat most of tem are not educated and migt ave large family size to cater for. Te Table depicts a positive correlation between vulnerability and ouseold size. Te least vulnerable ouseold size of 1-5 and tey are te only group tat will ave a ig probability of exiting poverty in future because te predicted/observed poverty ratio is 0.84. Te oter groups from 6-10 to above 20 ad te number of vulnerable ouseold increasing wit increased ouseold size. Te relationsip between vulnerability to poverty and te assets wic ouseolds ave is also important from policy perspectives. In general, people wo live wit ouseold eads wit some form of asset are likely to exit poverty in future because tey ave some form of buffer wen confronted wit any kind of sock. In tis study access to electricity was analysed. Houseolds tat ave access to electricity from te national grid ad an observed poverty of 50.89 percent, expected poverty of 47.86 percent and a predicted/observed poverty ratio of 0.94. Wic means tat ouseold wit electricity ave a ig probability of exiting poverty in future. Tose ouseolds tat do not ave access to electricity ad an observed poverty of 54.4 percent wit vulnerability index of 57.6 percent. Te unemployment rate in Nigeria is ig and it affects poverty and vulnerability to poverty. Table-5 reveals tat observed and predicted poverty was 57.5 percent and 51.2 percent wen unemployment rate is between 0-10 percent wit predicted/observed poverty ratio of 0.89. Tis group as ig probability of exit poverty wile te remaining unemployment groups are not likely to exit poverty. Te more rainfall in millimetres te lower te observed and expected poverty and ouseold are more likely to exit poverty in future. Malaria is prevalent in Nigeria and as been said to a source and cause of poverty in Nigeria. Houseolds wit malaria groups between 0-3000 and 3001-6000 ave a predicted/observed poverty ratio of 0.82 and 0.75, wic implies tat tey are likely to exit poverty in future. Tis migt be because of malaria control measures, suc as te roll back malaria effort of te government. Te ouseolds witin te oter malaria group ave a ig probability of not exit poverty. All te cases of river blindness in te pooled data increased poverty and vulnerability to poverty and none of te ouseolds aving cases river blindness is likely to remain poor in future. Tis migt be because river blindness causes irreversible blindness and te sufferers migt not be able to continue wit teir respective vocation. AIDS is prevalent in te Nort Central zone. In te pooled data, only te first group of AIDS cases as ig probability of exiting poverty. Te predicted/observed poverty ratio increased wit increase in te number of armed robbery cases. Te number of armed robbery cases between 0-50 will probability exit poverty in future wit a predicted/observed poverty ratio of 0.96. Table-4. Expected/ observed poverty profile of rural ouseolds in Nigeria by demograpic/ 69

socioeconomic caracteristics. Demograpic\socioeconomic caracteristics Expected Poverty Observed poverty Expected\observed poverty ratio Geopolitical zone Sout 0.5969 0.5329 1.12 Sout East 0.3643 0.4198 0.86 Sout West 0.2886 0.4677 0.61 Nort East 0.7107 0.7362 0.96 Nort West 0.6864 0.7095 0.98 Nort Central 0.6632 0.6953 0.95 Educational level No formal education 0.6916 0.6204 1.11 Primary education 0.5537 0.5202 1.06 Secondary education 0.4777 0.4975 0.96 Tertiary education 0.2360 0.3768 0.62 Farming/Non farming Farming 0.6909 0.6692 1.08 Non farming 0.3649 0.4092 0.96 Gender Male 0.5988 0.5829 1.02 Female 0.4392 0.4385 1.00 Age ouseold ead 10-19 years 0.5121 0.4543 1.12 20-29 years 0.4461 0.4478 0.99 30-39 years 0.5254 0.5357 0.98 40-49 years 0.6683 0.5813 1.14 50-59 years 0.6443 0.5729 1.12 60 and above 0.5193 0.5696 0.91 Houseold size 1-5 0.4114 0.4853 0.84 6-10 0.5587 0.5406 1.03 11-15 0.6834 0.7141 1.26 16-20 0.7642 0.7318 1.01 >20 1 0.8536 1.17 Access to electricity Yes 0.4786 0.5089 0.94 No 0.5766 0.5448 1.05 Access to drinking water Yes 0.4576 0.4818 0.95 No 0.5099 0.4126 1.11 Modern wall Yes 0.4047 0.4310 0.94 No 0.5088 0.4765 1.07 Unemployment (%) 0-10 0.5125 0.5733 0.89 70

11-20 0.6050 0.5842 1.03 21-30 0.6729 0.6507 1.03 31-40 0.7482 0.6405 1.16 Rainfall (mm) 0-50 0.6224 0.5348 1.16 51-100 0.4135 0.5280 0.78 101-150 0.4726 0.5725 0.82 151-200 0.5362 0.5437 0.98 AIDS cases 0-31000 0.3226 0.5981 0.53 31001-61000 0.4735 0.5042 0.93 61001-91000 0.6143 0.5921 1.03 91001-121000 0.6047 0.5425 1.11 121001-151000 0.7003 0.6538 1.07 Malaria cases 0-3000 0.4595 0.5564 0.82 3001-6000 0.3597 0.4745 0.75 6001-9000 0.5684 0.5572 1.02 9001-15000 0.5859 0.5023 1.16 >15000 0.6580 0.6424 1.02 River blindness cases 0-2000 0.5423 0.5496 0.98 2001-4000 0.6135 0.5604 1.09 4001-6000 0.5725 0.5125 1.11 >6000 0.6734 0.6265 1.07 Temperature 15-18 0.5679 0.5419 1.04 19-21 0.2867 0.5024 0.57 22-25 0.5900 0.5602 1.05 26-33 0.3919 0.4534 0.86 Armed robbery cases 0-50 0.5783 0.5467 1.05 51-100 0.5941 0.5605 1.05 101-150 0.5494 0.5294 1.03 151-200 0.6286 0.5704 1.10 >200 0.6724 0.5705 1.17 Source: Autors computation Decomposition of expected poverty by sources Understanding te source of vulnerability is a prerequisite for formulating a coerent social risk management strategy. Te decomposition of te expected poverty was arrived at by comparing te expected poverty of ouseold, wit a reference ouseold wic is te one wit te igest level of expected poverty in te population. Te decomposition was done based on te significant variables in te 3FGLS. Selection of variables relating to geograpical zonings, educational status, occupation, gender, age of ouseold ead and ouseold size based on its significance. Also selected, are electricity, safe drinking water and use of modern wall. Tis decomposition was done by estimating te relativity of te expected per capita consumption of a given ouseold to te ouseold wit te igest level of expected poverty keeping te variance constant. Te difference in te variance of expected consumption was estimated using te relativity of te variance of a given ouseold to te reference ouseold keeping te expected 71

log of consumption constant. Te results of te decomposition are presented in Table-5. Te decomposition by geograpical zones sows tat te Nort Central and Nort East ave te least mean consumption and te second least variance. Te key mitigating strategy against ig level of expected poverty among ouseolds in te Nort Central is raising per capita consumption and stabilizes consumption. Stabilizing consumption can be done by processing and preserving farm product wen tere is a glut so tat tere will be enoug during te dry season. Income diversification can also be an effective tool in stabilizing consumption. However te source of expected poverty indicates tat te variance of consumption explains te predicted poverty more in te Sout East and Sout West zones. Te appropriate policy for alleviating expected poverty is more of consumption smootening. Te Sout west zone as te lowest expected poverty and te second igest mean consumption and also one of te lowest consumption variance. Expected poverty is igest in te Nort East and it is caused by low mean consumption. Te decomposition of expected poverty wit respect to educational status of ouseold eads sows tat ouseold eads wit no formal education ave te lowest mean consumption level wile tose wit tertiary education ave te igest mean consumption. In general, mean consumption increases wit te level of educational status. On te oter and, te consumption variance is igest for ouseolds eaded by primary scool leavers. Tis is followed by tose wit no formal education. Te consumption variance for secondary education is te least followed by tertiary education. Cauduri, Jalan and Suryaadi (2002) determined for Indonesia a negative correlation between scooling and vulnerability. Interestingly, teir results also disclose tat vulnerability of rural ouseolds wit no formal scooling stems from low mean of consumption prospects. Houseold wose principal occupation is farming ave iger mean consumption and iger variability in consumption compared wit teir non-farming counterparts. Smootening consumption strategies are necessary to militate against expected poverty among farming ouseolds. Male eaded ouseolds ave lower mean consumption and lower consumption variance compared wit female-eaded ouseolds. Cristiaensen and Boisvert s (2000) study in te Nortern part of Mali also points to te same direction, in tat female-eaded ouseolds sow iger expected average consumption along wit a lower variance. Raising per-capita consumption for male-eaded ouseolds and consumption smootening strategies for female-eaded ouseolds are key to militating against expected poverty. Te decomposition of age of ouseold ead sows tat te age group between 10 and 19 as te igest mean consumption and one of te igest consumption variance. For tis group consumption smootening strategies is needed to guard against expected poverty. Te age group 40-49 years and 50-59 years as almost te same expected poverty and te lowest consumption variance and lowest per capita consumption. Te expected poverty of ouseold eads tat are 60 and above is caused by consumption variance because it as te igest consumption variance Te result of te decomposition of expected poverty wit respect to ouseold size sows tat mean consumption decreased wit increase in ouseold size. Terefore ouseold wit 1-5 members ad te igest mean consumption and ouseolds wit more tan 20 members ad te lowest mean consumption. Houseolds wit 16-20 members ad te igest consumption variance wile 1-5 members ad te lowest consumption variance. Te key mitigating strategies against ig level of predicted poverty among ouseolds wit more tan 20 members are raising per capita consumption and stabilizing consumption. Houseolds wit access to safe drinking water and tose witout ad te same mean consumption but different consumption variance, te consumption variance of ouseolds wit safe drinking water is lower tan teir counterpart witout access to safe drinking water. Houseolds eads tat ave access to electricity ave iger mean consumption and iger consumption variance wen compared wit teir counterparts tat do not ave access to electricity. Tose ouseolds wose ouses are built wit modern walls ave lower mean consumption and iger consumption variance tan tose witout ave modern walls. As te amount of rainfall increase te mean consumption of ouseold s increases and te consumption variance decreases. 72

Table-5. Decomposed different sources of expected poverty among rural ouseolds in Nigeria. Demograpic\socioeconomic caracteristics Geopolitical zone Expected poverty Mean consumption index Consumption variance Index Sout 0.6469 0.0518 0.0326 Sout East 0.3643 0.0732 0.0481 Sout West 0.2886 0.0576 0.0373 Nort East 0.6807 0.0195 0.0125 Nort West 0.6264 0.0028 0.0076 Nort Central 0.6532 0.0119 0.0246 Educational level No formal education 0.6916 0.0237 0.0327 Primary education 0.5537 0.0452 0.0289 Secondary education 0.4777 0.0511 0.0227 Tertiary education 0.2360 0.0830 0.0281 Farming/Non farming Farming 0.6909 0.0274 0.0274 Non farming 0.3649 0.0570 0.0301 Gender Male 0.5788 0.0340 0.0226 Female 0.4392 0.0653 0.0403 Age ouseold ead 10-19 years 0.5121 0.0713 0.0293 20-29 years 0.4461 0.0608 0.0171 30-39 years 0.5254 0.0426 0.0213 40-49 years 0.6683 0.0310 0.0277 50-59 years 0.6443 0.0306 0.0318 60 and above 0.5193 0.0430 0.0367 Houseold size 1-5 0.4114 0.0991 0.0207 6-10 0.5587 0.0699 0.0296 11-15 0.6834 0.0548 0.0396 16-20 0.7642 0.0345 0.0618 >20 1 0.0168 0.0468 Access to electricity Yes 0.4786 0.0385 0.0280 No 0.5766 0.0389 0.0285 Unemployment (%) 0-10 0.6125 0.0722 0.0242 11-20 0.6050 0.0609 0.0234 73