Demographic Features of the Nigerian Household: Does …serialsjournals.com/serialjournalmanager/pdf/1468234675.pdf · Demographic Features of the Nigerian ... Central Bank of Nigeria

Demographic Features of the Nigerian Household:Does it Matter for Poverty?

A. O. Omotayo*, J. O. Saka**, I. A. Adenuga*** and A. A. Adebayo****

Abstract: This paper examined the relationship between selected demographic characteristicsof the household and poverty indicators across twelve states in the six geo - politics zones ofNigeria with a view to determining the extent to which such characteristics influence the povertystatus of the household. Given the underlying theory, the modeling frame work of a three-variable case ordinary least square regression estimation technique was adopted in carryingout the study. The poverty measures used are poverty incidence and poverty gap while thedemographic characteristics involved are household size and the proportion of householdmembers in a specific age group. The estimates show cross-state variations in the contributionsof the explanatory variables to each of poverty incidence and poverty gap. The first orderordinary least square test carried out shows that explanatory power is highest for Yobe (in theNorth - East) in the poverty incidence model while it is highest in Ebonyi (in the South - East)under the poverty gap model. In most cases, there is significant effect of the independent variablesfor the two models. It was thus suggested that government should ensure that intervention onpoverty alleviation or reduction should focus on developing appropriate policy that targetshousehold size such as through a better Planned Parenthood action.

Key words: Household, Demographic Characteristics, Poverty incidence, Poverty gap

JEL Classification Codes: F63, H31, I32

1. INTRODUCTION

In Nigeria, high incidence of poverty has emerged as one of the obstacles hindering theachievement of targeted growth and development over the last two decades. And whileconsiderable awareness exists on the poverty crisis, the effect of policy aimed at drasticallyreducing the menace has not been felt. Reduction of poverty is in fact one of the most difficultchallenges facing Nigeria today. And, it has been argued that this failure reflects the poorunderstanding and conceptualization of the diverse context in which poverty is taking place inthe country (Adebayo, 2009).

* Professor in the Department of Geography and Dean of the Faculty of Social Sciences, Lagos State University,Ojo, Lagos State, Nigeria. He is also a Research Consultant to many local and international organizations.

** Lecturer in the Department of Economics of the Lagos State University, Ojo, Lagos State, Nigeria. He is avisiting scholar to the International Monetary Fund (2012) and a research analyst.

*** Lecturer in the Department of Economics of the Lagos State University, Ojo, Lagos State, Nigeria.**** Principal Lecturer and Head of the Department of Economics, Michael Otedola College of Pry Education,

Noforija, Epe, Lagos State, Nigeria.

E-mail: [email protected]; [email protected]

IJE : Volume 10 • Number 1 • June 2016, pp. 49-62

50 � A. O. Omotayo, J. O. Saka, I. A. Adenuga and A. A. Adebayo

With a population of about 150 million, the largest in Africa, a large expanse of agriculturallyfertile land, and numerous natural resources including large deposit of crude oil, the resourceendowment status of the Nigerian economy often tends to distract attention from the fact thatpoverty exists in the nation at a relatively high level. Agriculture was the main stay of theNigerian economy from independence in 1960 before the advent of oil changed the situation.During this time, the agricultural sector employed close to two-third of the country’s labourforce and provided livelihood for about 90% of the rural population. Till today, the Nigerianeconomy has remained the world’s largest producer of cassava, yam and cowpea which are allstaple food in Sub-Saharan Africa (SSA) and the country is also a major producer of fish, all ofwhich points to high capacity for potential growth within the sector (Olawuyi and Adetunji,2013). Ironically however, the Nigerian economy remains food-deficient and the country is animporter of large amount of grains and livestock products including fish. The huge agriculturalresources and oil wealth of the country has largely been a curse rather than a blessing hencepoverty is not only widespread but also increasing over time (see Table I).

Over the past two decades, the declining quality of life in Nigeria has received considerableattention in the literature. Such studies as Ogwumike (1991), World Bank (1996), Obadan(1997), Odusola (1997), Central Bank of Nigeria and World Bank (1999), Kayode and Odusola(2000), Osinubi (2003), Oyeranti and Olayiwola (2005), Adebiyi and Isola (2006) and Anyanwu(2010, 2011, 2012, 2013) have examined the incidence and dimension of poverty in Nigeria. Themajor conclusion from these studies is that poverty is intense and widespread in the country. Forexample, World Bank (2001) using the 1997 survey conducted on Nigeria and internationalpoverty lines, calculated the population surviving on less than $1 per day to be 70.2 per cent, witha poverty gap of 34.9 per cent. When the scope was extended to less than $2 a day, 90.8 per centof the population was categorized as being poor, with a poverty gap of 59.0 per cent. In a similarresult, Anyanwu (2013) relying on the Harmonized Nigeria Living Standard Survey (HNLSS)data of 2009/2010 by the National Bureau of Statisitcs (NBS) (NBS, 2009) found that more than70% of Nigerians are poor with 60.9% living in absolute poverty. The situation is worse in therural areas where social services and infrastructure are limited and as much as 80% of the ruralpopulation are classified as living below the poverty line (Olawuyi and Adetunji, 2013).

Table IIndicators of Income Poverty in Nigeria, Selected years, 1960 – 2004

Year Estimated Total Population in Poverty Incidence Poverty Depth Poverty SeverityPopulation Poverty (% of Population) (%) (%)

(million) (million)(1) (2) (3) (4) (5)

1980 64.6 18.1 28.1 9.0 4.31985 75.4 34.9 46.3 16.3 7.81990 86.6 38.0 44.0 n.a n.a1996 102.3 67.1 65.6 30.4 17.42000 115.2 80.6 70.0 n.a n.a2004 129.9 70.7 54.4 21.8 11.92010 163.0 112.5 69.0 36.1 23.0

Sources:- 1, 2 and 3 were compiled from Oyeranti and Olayiwola, 2005; CBN Annual Reports, 2006 and Anyanwu, 2013.- 4 and 5 were extracted from Anyanwu 2013.

Demographic Features of the Nigerian Household... � 51

This paper examines the relationship between selected household characteristics and basicindices of poverty with a view to determining the extent to which such characteristics determinethe poverty status of households within selected states of Nigeria. Many issues relating topoverty have been extensively discussed in both policy and academic circles. Indeed, scholarshave over the years carried out extensive studies in the areas of poverty at various levels;country, regional and global. This paper is thus a re-examination of the issue. Apart from thishowever, this study is also an extension of poverty related matters previously discussed in theliterature. The study analysed poverty at a much disaggregated level than is common in previousstudies. The poverty analysis carried out is extended to the situation in various zones across thecountry thereby providing evidence of a broader look into the country’s poverty profile. Ifstudies on poverty mapping are to translate into actionable outcomes, there is the need foranalysts to probe further into its types, nature and determinants within the various socio-economic profiles of the poor. Consequently, the objectives of this paper are as follows:

(i) To evaluate the relationship between selected household variables and povertymeasures across the various geo-political zones of Nigeria.

(ii) To provide some comparisons among these zones based on poverty - householdfeatures interaction for relevant policy implementation.

2. CONCEPTUAL ISSUES AND LITERATURE REVIEW

2.1. The Nature of Poverty

There is no uniform approach to defining, identifying and measuring poverty; rather severalapproaches exist. This is because poverty is multidimensional in nature. Indeed, it is analogousto the body of an elephant; it is the part seen by an analyst that can be explained by him.Among the approaches that has been adopted are the monetary approach, the human rightsapproach, the basic needs approach and the capability approach. However, there is a generalconsensus that poverty implies pronounced deprivation. There are several dimensions to thisincluding material deprivation as measured by lack of income and consumption, and non materialdeprivation such as lack of health care, lack of access to education and lack of basic humanrights.

Hence, according to Anyanwu (1997) the poor can be categorized within the Nigeriancontext as: (i) those households or individuals below the poverty level and whose incomeare insufficient to provide for basic needs and services; (ii) households or individuals lackingpolitical contacts and other forms of support; (iii) people in isolated rural areas lackingessential infrastructure; (iv) female - headed households whose nutritional needs are notbeing adequately met; (v) persons who have lost their jobs and the unemployed; and (vi)ethnic minorities who are marginalized, deprived and persecuted economically, socially,culturally and politically.

In developed and underdeveloped countries, income and consumption are some of themost common indices used as measures of poverty to identify the extent of deprivation in thesociety (Wagle 2006:74). Thus, the economic definition of poverty relates it to income andconsumption along side other social indicators including nutrition, literacy, infant mortalityand life expectancy. This surmises that not only can poverty be seen from the perspective of


lack of income but also within exclusion of people who live in poverty as a result of vulnerableconditions, customs and patterns.

Poverty is often described as either absolute or relative. Absolute poverty describes thestate in which the basic needs are unattainable. Thus, it connotes acute state of lack ordeprivation. Relative poverty refers to the unequal distribution of resources within a societyand is associated with matters of social equity. It relates to average income of the society andsocial exclusion.

2.2. Indicators and Determinants of Poverty

Purchasing Power Parity (PPP) is a common measure of poverty. This includes poverty linesbeing drawn on the basis of goods that satisfy an individual and whose units are converted toPPP. Incidence of poverty can be measured from the head count ratio and is calculated as thepercentage of the poor in total population (Dike, 2003; Anyanwu, 1997). Although this iseasily measured but the intensity and depth of poverty can be sufficiently tracked (Siddiqui2006). Poverty gap index is the extent of poverty to which an individual falls under a giventhreshold. This measure explains the depth of poverty and deficit between income andpoverty and computes the mean distance between the poverty thresholds (Afonja and Ogwumike,2003).

The use of socio-economic indicator like per capita income, life expectancy at birth, accessto health care services, safe water, education, sanitation facilities also show the extent of povertyin SSA. The rate of poverty has not shown any remarkable reduction when viewed from theseindicators and compared with other continents (Adeyemi, Raheem and Ijaiya, 2009).

According to Yahie (1993), the factors that result in poverty include structural causes andtransitional causes. The structural causes are dependent on a host of exogenous factors includinglimited resources, lack of skills, locational disadvantage and other factors inherent in the socialand political set-up. Transitional causes are mainly due to structural adjustment reforms anddomestic economic policy dynamics resulting in price changes, unemployment etc. Embeddedin transitional causes are natural calamities such as drought and man-made disasters includingwars, environmental degradation, etc. (Narayan, Patel, Schafft, Rademacher and Koch-Schutte,2000). Obadan (1997) observes that in SSA, poverty is mainly caused by inadequate access toemployment opportunities, inadequate physical assets such as land, capital and poor access tocredit at even a small scale.

Many studies (e.g. World Bank, 1996; Central Bank of Nigeria and World Bank, 1999;Osinubi, 2003; Adebiyi and Isola, 2006 and Anyanwu, 2012) have identified various factors asbeing responsible for the high incidence of poverty in Nigeria. Critical among these are poorgrowth prospect arising from poor economic management, high population growth, weakemployment capacity, poor access to market, poor agricultural development, limited participationof women in development process, disadvantaged ethnic region, degraded environment due toresource exploitation, political instability and a host of household characteristics. A number ofstudies that examined the impacts of household features on poverty (Anyanwu, 2013; Oni andYusuf, 2007) identified such variables as household size, gender, age structure within thehousehold, household head’s characteristics, marital status e.g monogamous marriage, divorce/separation, widowhood etc as important factors.


3. METHODOLOGY

3.1. The Model

There are various approaches to modeling the determinants of poverty. Each approach is howeversubject to data availability. According to Ravallion (1996) cited in Omononia (2009), anincreasingly common practice in poverty analysis is to construct the poverty profile in the formof a regression of poverty measured against a variety of household characteristics. With referenceto the work of Datt and Jolliffe (1999), Okurut, Odwee, and Adebau (2002), and Similer,Mukherjee, Dava and Datt (2004) which were based on household data, the most commonmethods are the household’s consumption model and poverty measures. The house holdconsumption measure is modeled as

ln cj = �x

j + µ

j(1)

where ln cj = natural log of household per capita consumption, x

j = a given set of household

characteristics, µj = the random process and ��= a vector of parameter estimates

The simplest and most common measure of poverty is the headcount ratio or the “incidenceof poverty.” The poverty headcount is the number of people in a population who are poor,while the poverty headcount ratio (H) is the fraction of the population that are poor. That is:

H = (q / n)

where:

q = the number of people below the poverty line;

n = the population size

The poverty headcount and the headcount ratio are only concerned with the number ofpeople below the poverty line. They are insensitive to the depth or severity of poverty and tochanges below the poverty line. That is, they do not satisfy the axioms of “strong monotonicity”or “distributional sensitivity.” However, the headcount ratio is the most commonly used measureof poverty because of its simplicity and ease of calculation (Bourguignon and Fields, 1997).

The P� index proposed by Foster, Greer and Thorhecke (1984) incorporates some degreeof concern about poverty through a “poverty aversion” parameter �. The P� index measurescan be used to generate the headcount ratio (� = 0), as well as the depth (� = 1), and severity(� = 2) of poverty. Based on the model of Foster, Greer and Thorhecke (1984), the householdmeasure is

max(1 ),0 , 0jj

cP

z

�

� ��

� � ��

2

where cj = household j per capita consumption, z = the poverty line, ��= a non-negative parameter

that takes the value 0, 1, 2 depending on the degree of concern about poverty. By increasingthe value of �, the “aversion” to poverty can be measured (Anyanwu, 2013). Suppose that��= 0, 1, 2 then the corresponding equations can be generated from 2 as follows:

0

0max(1 ),0� �

� � � � � ��

jj j

cP P P

z(3)


1

max(1 ),0 max(1 ),0� � � �

� � � � ��

j jj

c cP

z z = P

1(4)

2

max(1 ),0� �

� � ��

jj

cP

z = P

2(5)

Equations 3, 4 and 5 respectively correspond to poverty incidence (P0), poverty gap index

(P1,

a measure of the depth of poverty) and the square poverty gap index (P2 , which is a measure

of the severity of poverty).

In this study we focus on making inferences between selected set of variables and twopoverty indicators namely; incidence and gap. The objective is to set variables from three maindomains of socio-economic profiles against the poverty measures. This will help us to determinethe contribution of the variables to the poverty measures adopted. Following the aforementionedtheoretical base and considering data availability we specify our model as follows though withsome modifications

[ min ( )]�Pov f Deter ants Pov (6)

where Pov = measures of poverty including incidence and gap. Thus, the poverty incidencemodel specification is

0 1 3 1� � � � � � � �ipov ahs agh (7)

0 1 2 2� � � � � � ��gpov ahs agh (8)

Where povi = poverty incidence, pov

g = poverty gap, ahs = average household size, agh =

proportion of household in a specific age group, �0.......... �

2, �

0..........�

2 = vector of parameter

estimates and �1 = �

2 ��= error process.

3.2. Data and Estimation Technique

The data used for this study were gathered in year 2012 through the administration of structuredquestionnaires on households in twelve states of Nigeria with two states representing each ofthe six geo - political zones of the country. Relevant questions relating to householdcharacteristics including average size of household and proportion of household in specificage groups were included in the instrument. Within each state, samples were drawn followinga random sampling procedure and the data gathered was then subjected to quantitative analyticaltechniques.

A three variable case linear regression technique was adopted to show the effect ofeach demographic variable on the measure of poverty under consideration. Using themultiple linear regression technique enable the determination of the variables with themost significant contribution to poverty and is supported by the Analysis of Variance(ANOVA). Such an information provides scientific evidence that enables policy makers anddecision takers focus efforts on the most important variables relevant to eliminating the problemof poverty.

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4. DATA ANALYSIS AND DISCUSSION

4.1. General Comments on the Zones

The analysis takes into cognizance the interactions between a set of socio-economic indicatorsand poverty measures among the six geo-political zones of Nigeria namely North Central,North East, North West, South-South, South East and South West. Included in the list ofstates are Nasarawa (North-Central), Niger (North-Central), Borno(North-East), Yobe(North-East), Jigawa(North-West) and Kano (North-West). Others are Ebonyi (South-East), Enugu (South-East), Bayelsa (South-South), Cross River (South-South), Ogun(South-West) and Oyo(South-West). These represent two states from each of the six geo-political zones.

4.2. Poverty Incidence Model

The first case is on the effect of the socio-economic indicators on poverty incidence across thezones under consideration. The average household size in each zone tends to influence povertyincidence positively except in Oyo, South-western zone. By implication, increasing householdsize is a reflection of increasing incidence of poverty across almost all the zones. This is in facta reality especially in households engulfed with increasing unemployment crisis in a typicalNigerian environment. Increasing poverty incidence is most pronounced in Bayelsa in theSouth-South with about 19.04 units increase in the incidence upon a unit increase in the averagesize of household. Next is Ogun with about 18.78 units rise in poverty incidence for a unit risein average household size. It can be seen that the two states in the South-South and South-Westare very sensitive to household size compared to other zones.

However, the lowest incidence of poverty of about 0.90 units for a unit increase in theaverage household size is recorded for Ebonyi. The only exception (Oyo) is a case where a unitincrease in the average household size results in about a decrease of about 25.37units in povertyincidence. This must however be interpreted with caution. Oyo is one of the zones with risingunemployment crisis, low income, low aggregate demand and poor trade performance whichoften results in rural-urban drift. One unique feature is that the zone is known for practicalskills and active involvement in agricultural activities for which an average household reliesupon. The result for Oyo State perhaps conforms to the age long tradition where family pullstogether to farm in order to increase average farm output. If this holds true it then means thatintervention in a place like Oyo State must be played out in a tactically different strategicmanner. For example, a social protection programme (Social Safety Net) targeting farm inputsand encouraging cooperative farming schemes may be more appropriate. The significant effectsof average household size at conventional levels are noticeable in North-East, North-West andSouth-West zones comprising Borno, Yobe, Jigawa, Kano, Ogun and Oyo respectively asindicated by the first order ordinary least square test.

Effects of proportion of household in a specific age category seem not to contributepositively to increasing poverty incidence except in Nasarawa(North-Central), Enugu(South-East), and Ogun (South-West) with Ogun recording the highest positive impact of proportionof household (1.69) across the zones. In 8 out of the 12 states considered for the zones, increasingproportion of households in a specific age category is not a major factor towards rising poverty


incidence. This however is workable under some assumptions including households havingstrong financial background, greater number of economically active population of householdinvolved in gainful employment, among others. The significant effects of the proportion underdiscussion are not noticeable in most of the zones. A critical look at the analysis above throwsmore light on the explanatory power of household size. This variable has the lowest explanatorypower on poverty incidence in Oyo and is as low as 23%.

4.3. Poverty Gap Model

The effect of household size on poverty gap is positive except in Oyo. This is similar to theresults obtained for poverty incidence. The highest significant positive impact of householdsize on poverty gap is recorded in Cross River and is about 12.24 units. This is followed byYobe with significant positive impact (8.20 units) of average household size on poverty gap. Itfollows that household size has a significant role to play on poverty gap in the South-Southand North-Eastern zones. The only exception again is Oyo with a decline of about 16.91 unitsin poverty gap for a 1 unit rise in average household size. This needs caution in terms ofinterpretation and may be based on the reason provided earlier for Oyo. The proportion ofhousehold in a specific age category has significant positive impact (0.57) on Ogun’s povertygap while negative on other zones’ poverty gap. A perceived implication of this is that a highproportion of households in a specific age category may result in a wide gap between the richand the poor in Ogun while this gap closes for other zones. However, the explanatory powersof these socio-economic indicators on poverty gap are highest for Ebonyi (75%) followed byYobe (72%) but is low for Ogun (32%). Hence other factors not accounted for could explainpoverty gap in Ogun.

On the final note, the analysis of variance (ANOVA) shows that the socio-economicindicator of average household size is significant on the average in influencing both povertyincidence and gap.

4.4. A State by State Analysis

The analysis takes into cognizance the interactions between the set of socio-economic indicatorsand poverty measures among the states within the selected zones.

(i) Nassarawa: The average house hold size in Nassarawa tends to influence povertyincidence positively. It follows that a unit increase in the household size results inabout 7.29 units increase in poverty incidence. Hence, the number of household tosome extent determine incidence of poverty especially with increasing level ofunemployment and low level of economic activities. Nassarawa is known as one ofthe low income states with possible increasing level of unemployment. In the samevein, the proportion of household in a specific age group affects the incidence ofpoverty positively. A unit increase in the proportion of household in a specific agegroup generates about 0.84 unit increase in poverty incidence. Comparing the effectof these socio-economic indicators, it appears that the household size much moreinfluences poverty than the proportion of household in a specific age group inNassarawa even though the explanatory power of the socio-economic indicators isstill low (0.49). Poverty gap is however missing for this zone.


(ii) Niger: In Niger, household size equally affects poverty incidence positively but witha stronger influence than in Nassarawa above. A unit increase in the household sizebrings about 9.27 units increase in poverty incidence. Nassarawa and Niger are bothNorth-Central geopolitical zone and therefore may possess similar povertycharacteristics. However, increase in the proportion of household in a specific groupis not a major factor driving poverty in this zone unlike the former. The explanatorypower of the socio-economic indicator is weaker compared to Nassarawa.

(iii) Borno: In Borno, the average household size is significantly positively related topoverty incidence. Infact, about 11.12 units increase in poverty incidence is observedfor a unit increase in the average household size. Hence, it appears that the averagehousehold size in this zone makes the strongest influence on poverty compared withthe first three zones. Increasing proportion of household in a specific age categoryseems to also influence poverty in this zone but in the reversed order. As low as 0.78unit decline in poverty incidence results for a one unit increase in the householdproportion in a specific age group.

(iv) Yobe: Yobe zone has demonstrated a high level of influence of average householdsize on poverty. A unit increase in average household size would result in about16.04 units increase in poverty incidence and this is significant while the proportionof household in a specific group influences poverty incidence negatively (-1.89).The high explanatory power of these socio-economic indicators on poverty is areflection of the positive contribution of average household size.

(v) Jigawa: Analysis of Jigawa is similar to that of Yobe except that there is a lowerinfluence of average household size on poverty compared with that of Yobe. In Jigawa,a unit increase in average household size results in about 13.96 units increase inpoverty incidence while a unit increase in the proportion of household in a specificage group reduces poverty by 1.22 units. The explanatory power in this case is fairlyhigh.

(vi) Kano: Kano demonstrated the least influence of average household size comparedwith Nasarawa, Niger, Borno, Yobe, and Jigawa. A unit increase in the averagehousehold size brings about 5.5 units increase in poverty incidence and is significant.Meanwhile, a unit increase in the proportion of household in a specific age groupreduces poverty by 0.46 unit. Given the small influence of household size on povertyin Kano, it can be inferred that some other major factors also influence poverty to agreater extent in the state. Kano is a state known for frequent social upheavals suchas ethnic, religious and political disturbances. Frequent instability in a state impedeseconomic activities which may be an inducement for increasing poverty.

(vii) Ebonyi: In Ebonyi, though household size influences poverty positively but as lowas 0.9 unit increase in poverty results from a unit increase in household size. However,the proportion of household in a specific age group influences poverty negatively.The indicators in this case have a fairly better explanatory power.

(viii) Enugu: Both household size and proportion of household in a specific age grouphave positive influence on poverty in Enugu and this is similar to that of Nassarawa


except for the degree of influence. In Enugu, a unit increase in the household sizeand the proportion of household in a specific age group bring about 13.04 and 0.12units increase in poverty respectively. These two indicators therefore explain povertystatus in this zone.

(ix) Bayelsa: A unit rise in the average household size results in about 19.04 units rise inpoverty in the case of Bayelsa. This coefficient demonstrates the highest influence ofhousehold size on poverty though not significant. By implication, household size inBayelsa is a major indicator for determining poverty extent. However, increasingproportion of house hold in a specific age group reduces poverty.

(x) Cross River: In cross river, a unit increase in the household size results in about10.81 units increase in poverty while increasing proportion of household in a specificage group reduces poverty by 0.34 unit. The explanatory power of these indicators islow demonstrating that other key factors may be cogent in determining extent ofpoverty in this state.

(xi) Ogun: Next to Nassarawa zone is Ogun with about 18.78 units rise in povertyincidence for a unit rise in average household size. Effects of proportion of householdin a specific age category seem to contribute positively also to increasing povertyincidence recording the highest positive impact of 1.69 units.

(xii) Oyo: The only exception is Oyo where a unit increase in the average household sizeresults in about a decrease of about 25.37units in poverty incidence. This must howeverbe interpreted with caution. Oyo is one of the zones with rising unemployment crisis,low income, low aggregate demand and poor trade performance and this has oftenresulted in rural-urban drift. One unique feature is that the zone is known for practicalskills and active involvement in agricultural activities for which an average householdrelies upon.

Summarizing the joint effect of household size and proportion of household in a specificage group on poverty incidence, the analysis of variance shows that the joint effect is significantin all the zones except Kano, Enugu and Bayelsa. For the poverty gap, the joint effect ofhousehold size and proportion of household in a specific age group is significant in all zonesexcept for Niger, Borno, Enugu, Bayelsa and Oyo.

In 8 out of the 12 states considered, increasing proportion of household in a specific agecategory is not a major factor towards rising poverty incidence. This however is workableunder some assumptions including households having strong financial background, greaternumber of physically active population of household involving in gainful employment, amongothers. The significant effects of the proportion under discussion are not noticeable in most ofthe zones.

Meanwhile, the estimation of the poverty gap model gave quite interesting results. First,average household size appears to have impacted positively on poverty gap in all the selectedstates except Oyo, with largest impact occurring on Cross River poverty gap of 12.24 units andlowest from Borno with about 1.77 units. Second, the proportion of household in a specificage group impacts negatively across all the selected states’ poverty gap except Ogun. However,the largest decline in poverty gap following a unit increase in the proportion of household in a


specific age group occurs in Ebonyi(-1.996). The socio-economic indicators have the highestexplanatory power of about 72% in Yobe.

On a general note caution needs to be taken in drawing statistical inference from the analysispresented for Kano, Enugu and Bayelsa with respect to poverty incidence and that of Niger,Borno, Enugu, Oyo and Bayelsa with regard to poverty gap respectively as the ANOVA’sprobability values of those states are very high and are beyond the universally acceptedsignificant ceiling, which range between 1 to 10 percent. Hence, using it for autoregressivecases might generate coefficient outside the interval range. However, this situation does notactually render the results useless or irrelevant since they still indicate the direction of relationshipestablished between the exogenous and endogenous variable.

5. CONCLUSION AND RECOMMENDATIONS

This paper analyzes the effect of some selected socio-economic characteristics (namely householdsize and proportion of household members in specific age groups) on poverty in 12 states underdifferent sub-regions in Nigeria. The estimations carried out show different variations in thecontribution of the socio-economic characteristics on poverty. It was found that increasing povertyincidence is most pronounced in Bayelsa (South-South) while Ebonyi (South-East) recorded thelowest incidence of poverty for a unit increase in the average household size. Effects of proportionof household in a specific age category seem not to contribute positively to increasing povertyincidence except in Nasarawa, Enugu and Ogun with Ogun recording the highest positive impact.The highest significant positive impact of household size on poverty gap is recorded in CrossRiver while the proportion of household in a specific age category has significant positive impacton Ogun’s poverty gap while negative on other zones’ poverty gap.

From these discussions, government should ensure that intervention on poverty alleviationor reduction should target a reduction in household size through more Planned Parenthood andincrease employment to employable individuals who still cannot find jobs in order to establishtheir own households.

References

Adebayo A. A. (2009), Theoretical Perspectives on Poverty Reduction: Challenges for Policy in Nigeria,International Journal of Economic and Development Issues, Vol. 8 (1 & 2).

Adebiyi A. and Isola W (2006), Poverty Rates and the Economic Conditions of Nigerians: A Comparisonof Pre and Post Reform Periods. In: Akano O. and Familoni K. A., (2006) The National EconomicEmpowerment and Development Strategy: Philosophy, Opportunities and Challenges, Lagos,University of Lagos Press.

Adeyemi S. L, G. T. Ijaiya and U. A. Raheem (2009), Determinants of Poverty in Sub-Saharan Africa,African Research Review, Vol. 3(2), January.

Afonja B and Ogwumike F. O. (2003), Poverty: Meaning, Measurement and Causes, In Obadan M. I.,Fajingbesi A. A. and Uga E. O. (Eds.) (2003) Integrating Poverty Alleviation Strategies Into Plansand Programmes in Nigeria, Ibadan, National Centre for Economic Management and Administration.

Anyanwu J. C. (1997), Poverty in Nigeria: Concepts, Measurement and Determinants, Proceedings ofthe Nigerian Economic Society Annual Conference on Poverty Alleviation in Nigeria 1997, Ibadan,Nigerian Economic Society.


Anyanwu, J. C. (2010), “Poverty in Nigeria: A Gendered Analysis”, African Statistical Journal, Vol. 11,November, 38-61.

Anyanwu, J. C (2011), “Towards reducing poverty in Nigeria: The case of Igboland”, Journal of Economicsand International Finance, Vol. 3, No.9, September, 513–528.

Anyanwu, J. C. (2012), Accounting for Poverty in Nigeria: Illustration with Survey Data from Nigeria,African Development Bank Working Paper, No. 149, May.

Anyanwu, J. C. (2013), Marital Status, Household size and Poverty in Nigeria: Evidence from the 2009/2010 Survey Data”, Working Papper Series No. 180, Tunis, Tunisia.

Bourguignon, F. and G. Fields (1997), Discontinuous losses from poverty, generalized Pá curves, andoptimal transfers to the poor, Journal of Public Economics, 63, 155-175.

Central Bank of Nigeria (1999), Nigeria’s Development Prospects: Poverty Assessment and AlleviationStudy, Central Bank of Nigeria in Collaboration with the World Bank.

Datt, G. and D. Jolliffe (1999), Determinants of Poverty in Egypt: 1997 FCND Discussion paper 75International Food Policy Research Institute, Washington D.C.

Dike E. (2003), Meaning and Dimensions of Poverty, In Obadan M. I., Fajingbesi A. A. and Uga E. O.(Eds.) (2003) Integrating Poverty Alleviation Strategies Into Plans and Programmes in Nigeria,Ibadan, National Centre for Economic Management and Administration.

Federal Office of Statistics (1999), Poverty Profile for Nigeria (1980-1996). Federal Office of Statistics,Abuja.

Foster, J; Greer, J. and Thorbecke, E (1984), A Class of Decomposable Poverty Measures. Econometrica.Vol. 52 pp. 761-6.

Kayode, M. O. and A. F. Odusola (2000), “Poverty in the Midst of Plenty”, Paper Delivered at theAnnual Dialogue of the Obafemi Awolowo Foundation, Nigerian Institute of International Affairs,Lagos, 4 - 5 December.

Narayan, D, Patel, R, Schafft, K, Rademacher, A and Koch-Schutte S. (2000), Voices of the poor, Cananyone Hear us? New York: Oxford University Press.

National Bureau of Statistics (NBS) (2005), Poverty Profile for Nigeria, National Bureau of Statistics,Lagos. Available on line: http://www.nigerianstat.gov.ng/Connections/poverty/POVPreliminary.pdf

National Bureau of Statistics (NBS) (2010), The Nigeria Poverty Profile 2010 Report, National Bureauof Statistics, Abuja.

Obadan M. (1997), Analytical framework for Poverty Reduction: Issues of Economic Growth Versusother strategies, Proceedings of the Nigerian Economic Society Annual Conference on PovertyAlleviation in Nigeria 1997, Ibadan, Nigerian Economic Society.

Obadan, M. I. and A. F. Odusola (1999), “Savings, Investment and Growth Connections in Nigeria:Empirical Evidence and Policy Implications”, NCEMA Policy Analysis Series, Vol. 5, No. 2.

Odusola, A. F. (1997), “Poverty in Nigeria: An Eclectic Appraisal”, Poverty Alleviation in Nigeria,Proceedings of the Nigerian Economic Society Annual Conference on Poverty Alleviation in Nigeria1997, Ibadan, Nigerian Economic Society.

Ogwumike F. O. and Ekpenyong, D. B. (1995), Impact of Structural Adjustment Policies on Poverty andIncome Distribution in Nigeria. Final Report to the African Economic Research Consortium (AERC),Kenya.


Okurut, F N, Odwee, J.A.O and Adebaua (2002), Determinants of Regional Poverty in Uganda. AfricanEconomic Research Consortium Research Paper No 122

Olaniyan O. and Bankole A. S. Human Capital, Capabilities and Poverty in Rural Nigeria, Being anInterim Research Report Submitted to the African Economic Research Consortium (AERC), Nairobifor the Second Phase Collaborative Poverty Research Project

Olawuyi, S. O. and Adetunji, M. O. (2013), Assessment of Rural Households Poverty in Nigeria: Evidencefrom Ogbomoso Agricultural Zone of Oyo State, Nigeria. Journal of Scientific Research & Reports2(1): 35-45.

Omonona B. T. (2009), Quantitative Analysis of Rural Poverty in Nigeria, Nigeria Strategy SupportProgram (NSSP), Background Paper No. NSSP 009, International Food Policy Research Institute,November.

Oni, O. A., & Fashogbon, A. E. (2013), Food Poverty and livelihood Issues in Rural Nigeria. AfricanJournal of Agricultural and Resource Economics, 8(2), 107-128.

Osinubi T.S (2003), Macroeconomic Policies and Pro-Poor Growth In Nigeria Paper Submitted forPresentation at the World Institute for Development Economics Research (WIDER) Conference onInequality, Poverty and Human Well-Being, Helsinki, Finland 30-31 May 2003.

Oyeranti O and K Olayiwola (2005), Policies and Programmes for Poverty Reduction in Rural Nigeria.Interim Research Report Submitted to the African Economic Research Consortium (AERC) Nairobifor the second phase collaborative poverty research project, October.

Ravallion M. (1996), “How well can method substitute for data?: Five experiments in poverty analysis”World Bank Research Observer, Volume 11, Issue 2.

Siddiqui A (2006), India and South Asia: Economic Developments in the age of Globalization, NewDelhi : Pentagon Press.

Similer, K.R, Mukherjee S, Dava G.L and Datt G. (2004), Rebuilding After War: Micro-Level Determinantsof Poverty Reduction in Mozambique. International Food Policy Research Institute Research ReportNo 132.

Wagle U. R. (2006), Poverty in Kathmandu, What do subjective and objective Economic Welfare Conceptssuggest?

World Bank (1996), “Nigeria: Poverty in the midst of Poverty”, A World Bank Poverty AssessmentReport No. 14733 – UNI.

Yahie (1993), The Design and Management of Poverty Alleviation Projects in Africa: Evolving GuidelinesBased on Experience. World Bank EDI Human Resource Division.

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