Effect of Asset Ownership on Poverty Status in Rural Geo-Political Zones of Nigeria

Greener Journal of Social Sciences

Vol. 10(1), pp. 1-8, 2020

ISSN: 2276-7800

https://gjournals.org/GJSC

$Description: C:\Users\user\Pictures\Journal Logos\GJSS Logo.jpg$

Effect of Asset Ownership on Poverty Status in Rural Geo-Political Zones of Nigeria

¹Adeoye A, ²Salman K. K., ³Balogun O.L, ²Rufai M., ²Oguniyi B.

¹Oyo State College of Agriculture and Technology, Igboora

² Department of Agricultural Economics, University of Ibadan, Nigeria

³Department of Agricultural Economics and Extension, Babcock University, Ilishan-Remo

ARTICLE INFO	ABSTRACT
*Article No.:* 01102005 *Type: Research*	Poverty is prevalent in rural Nigeria and women are more affected. Accumulation of assets is an important means by which people can improve their livelihood and come out of poverty. This study investigated the aggregate effect of asset ownership on poverty reduction among female headed household (FHH) in rural Nigeria, using the 2013 GHS data. Descriptive statistics, principal component analysis, Foster, Greer and Thorbeeke and Ordered Probit models at α0.05 were used to analyse 424 FHH. Majority (80.7%) of the FHH were widows. Age and household size were 58±13.7 years and 6±3.4 persons per household, respectively, while 57.8% did not have formal education. The index of asset ownership among the FHH was low. Mean per capita expenditure was N30, 258.6 while the poverty line was N20, 172.4 per annum. Sixty-one percent of the FHH were core-poor, 17.5% were moderately poor and 21.9% were non-poor. Poverty incidence, depth and severity increased as household size increased and decreased with the level of education of the households. Aggregate asset ownership, educational level and membership of a cooperative society were the major poverty-reducing variables among the FHH in rural Nigeria.
*Accepted:* 12/01/2020 *Published:* 31/01/2020
Corresponding Author Adeoye* A. *E-mail:* adeoyeadelayo2017@ yahoo.com
*Keywords: Asset ownership; Poverty incidence; Female-headed household; Rural Nigeria*.

INTRODUCTION

The economic situation of the rural population in Nigeria is characterized by a high degree of vulnerability and poverty (National Bureau of Statistics (NBS), 2010). Poverty is a multifaceted phenomenon that encompasses different dimensions of deprivation which relate to human capabilities, including consumption and food security, health, education, rights, voice, security, dignity, and decent work. (Samuel et al., 2014). The high poverty levels also promote continued landlessness, few asset acquisition and poor health, which prevent asset transfer across generations and promote a cycle of chronic poverty from one generation to the other.

There have been different approaches to reducing rural poverty in Nigeria, but their focus has been on certain aspects or manifestations of poverty, such as low income, unemployment, economic growth and poor nutrition; only few have considered asset ownership (Innocent et al., 2014). They attribute failure of many poverty-alleviation programmes to absence of good governance and inappropriate approaches, Omonona et al., (2009) argue that poverty reduction should be addressed with a multi-pronged approach in order to achieve more marginal improvement in the standard of living of poor households. Therefore, reducing poverty requires not only economic growth, good nutrition, income distribution e.t.c. but also investment in asset ownership so as to improve the productive capacity of the households (World Bank, 2014). IFAD (2012) also states that owning assets is crucial for broad-based growth and poverty reduction. Assets are stock of financial, human, natural or social resources that can be acquired, developed, improved and transferred across generations (The Ford Foundation, 2004). Ownership and control of assets such as land and housing provide multiple benefits to individuals and households, including a secure place to live, livelihoods, protection during emergencies and collateral. (Deere and Unidos, 2010).

The importance of asset ownership for women cannot be ignored. Dim et al. (2014) assert that owning assets empowers women socially, economically and politically. Owning assets give women additional bargaining power not just in the household, but also in their communities and other public arenas (Angahar, 2012). Women’s ownership of assets also keeps them out of poverty or saves them from destitution; leads to better outcomes for children, such as increased school retention or higher expenditures on education and health; or results in better outcomes for women in case of separation, divorce or widowhood (Deere and Doss, 2006).

This study focused on female asset ownership and poverty reduction in rural Nigeria. The findings of this study will aid policy makers and NGOs in effective formulation of poverty-reduction strategies that will focus on increasing the asset ownership of rural women in ways that will translate to improvement on their standard of living and productivity. Therefore, the main objective of this study was to assess the effect of asset ownership by female-headed households on their poverty status in rural Nigeria.

METHODOLOGY: (Scope and Source of data)

Nigeria is the most populous country in Africa and the ninth most populous country in the world. Nigeria covers a land area of 923,768km² with 1.4% covered by water. Nigeria is made up of 36 states and a Federal Capital Territory (FCT), grouped into six geopolitical Zones. The data for the study was sourced from the General Household Survey (GHS) data of 2013, collected by National Bureau of Statistics (NBS). Rural female selected from the data were 10983, however, due to the fact that only female headed households were considered for the study four hundred and twenty four female headed households were analyzed.

Methodological procedures for constructing asset index

The asset index was constructed using Principal Component Analysis (PCA) model. This involves resolution of a set of variables into a new set of composite variables or principal components that are uncorrelated with one another. (Filmer and Pritchett, 2001). The asset index derived from PCA for each household asset can be written as follows:

_{= …………………………………(}ii)

Where

A j is an asset index for each household (j =1,…….,n)

fi is the scoring factor for each asset of household (i =1,……,n)

aji is the i th asset of j th household (i ,j =1,……,n)

ai is the mean of i th asset of household (i =1,……,n)

si is the standard deviation of i th asset of household (i =1,……,n)

Construction of the Poverty Line:

In line with previous poverty studies (such as Adepoju et al., 2012) per capita household consumption expenditure was used as a proxy for per capita household income, and this relative poverty measure was used to categorize the rural households into core poor, moderately poor and non poor in this study (World Bank, 2010).

The standard FGT (Foster Greer and Thorbeeke, 1984) was used to examine the poverty status of the rural women. FGT measure involves, the head count index (P₀) poverty gap index (P₁) and poverty severity index (P₂). These measures respectively relate to different dimensions of the incidence of poverty. i.e the occurrence of poverty (P₀), the depth of poverty (P₁) and the severity of poverty (P₂) at a point in time in the study area.

---------------------- (ii)

Where:

Z = the poverty line defined as 2/3 of Mean per capita expenditure. y_i= the annual per capita expenditure q = the number of poor households in the population. n = the total number of households α = the degree of poverty aversion parameter or the FGT index, which takes value of 0, 1 and 2.

Ordered probit model

The ordered probit model is for variables with ordered, discrete values. This is a regression model which generalises probit regression by allowing more than two discrete outcomes that are ordered. Using the poverty line above, the poverty level of women households was categorized into nor poor, moderately poor and core poor which corresponds to censoring values 2, 1, and 0 respectively.

y* = x′β + ε ……………..(iii)

where x and β are standard variable and parameter matrices, and ε is a vector matrix of normally distributed error terms, Obviously predicted grades (y*) are unobserved. Given the classification, the study derives the probabilities of being poor of different degrees as follows:

y = 0 if y* ≤ 0 …….(iv)

y = 1 if 0 < y* ≤ μ1 …….(v)

y = 2 if μ1 < y* ≤ μ2 . …….(vi)

where μ1 and μ2, are the cut points i.e. the threshold variables in the probit model.

The likelihood for poverty level by a household is

where for the ith household, yi is the observed outcome and Xi is a vector of explanatory variables and is the cumulative logistic distribution. The unknown parameters βj are typically estimated by maximum likelihood and Z is the poverty level.

y =poverty status of rural women, (2 = non poor, 1 =moderately poor and 0=core poor).

X₁ = age (years), X₂ = Highest educational level (years of formal schooling) X₃= Marital status X₄ =Occupation, X₅ = Household size, X₆ = Membership of cooperative, X ₇ = Access to Credit , X₉ = Asset index, X₁₀= farm size (hectares)

RESULTS AND DISCUSSIONS

Table 1 showed the result of socio-economic characteristics of the rural FHH, the result shown that majority (80.7%) of the FHH was widows and 10.6% were divorced. This indicate that most household with female heads were previously had male heads who were no longer alive. The result was similar to Horrell and Krishnan’s (2006) findings, that a large proportion of rural female headed household were either widowed or divorced and were directly in charge of their family management. Almost half of the FHH were older than 60 years. The mean age of the FHH was 58 ±13.7 years. With regard to the educational status of the respondents, the result shows that more than half of the FHH had no formal education, while 4.3%, 19.1 % and 18.9% of the respondents had tertiary education, secondary and primary education, respectively.

Table 1: Socio-economic Characteristics of the Respondents

Socio-economic Characteristics

Frequency

Percentage

Mean

Marital status

Single

Married

Widowed

Divorced

Age Range(Years)

20-40

41-60

61-80

˃80

Educational level

No Formal

Primary

Secondary

Tertiary

Household Size

1-3

4-6

7-9

> 9

Occupation Status

Farming

Trading

Civil Servant

Membership of coop

Members

Non members

342

183

160

245

131

155

308

120

304

1.9

6.5

80.7

10.9

8.9

43.2

37.7

10.1

57.9

18.9

19.1

4.3

20.3

30.9

36.6

12.3

72.6

22.3

4.9

28.3

71.7

58 (years)

6 persons

Source: General Household Survey (GHS) Data (2013)

Based on the poverty line estimated earlier, the analysis undertaken for the whole sampled household yielded a poverty (incidence) head count ratio of 0.781, that is, 78.1% of the total population spent less than what they would need to meet minimum living standard requirements. Table 4 also indicates poverty depth as 0.5145, implying 51.45% whose average consumption expenditure was below the poverty line. The severity of the poverty index was 0.3922; that is, 39.22% represents the poorest among the FHH. All these imply that to escape from poverty female headed households has to mobilize financial resources to be able to meet 51.45percent of N 30,258.57 household per capita expenditure per annum and the core poor has to mobilize financial resources of 39.22percent more than is required for them to achieve the same feat.

The FGT result in table 4 revealed that the incidence, depth and severity of poverty increased as household size increased. This implies that the FHH with large household size tend to be poorer. In addition, the incidence, depth and severity of poverty were lower among rural FHH of aged 41-80 years. This age range belongs to the active population, which connotes that they may engage in some other secondary occupations, which tend to generate additional income for the household consumption expenditure.

Furthermore, the incidence of poverty, its depth and severity decreased with the level of education of the FHH. This is because education tends to open more opportunities for income generation through various means, such as participation in alternative livelihood activities. This result is similar to the claim of Adenegan et al. (2013) that education is a key factor in the reduction of rural poverty and that households with formal education have higher welfare level and lower poverty rate than households without formal education.

Table 4: Poverty profile of female headed household in rural Nigeria

Household characteristics

Poverty measures

All

Households size

Incidence of

Poverty (%)

P₀

0.78

Poverty

depth (%)

P₁

0.51

Severity of

poverty (%)

P₂

0.39

1-3

4-6

7-9

0.53

0.79

0.92

1.00

0.24

0.50

0.66

0.77

0.16

0.36

0.56

0.63

Age (years)

20-40

41-60

61-80

>80

0.76

0.63

0.62

0.67

0.40

0.34

0.33

0.43

0.29

0.24

0.23

0.34

Marital status

Single

Married

Widowed

Divorced

0.63

0.52

0.65

0.62

0.18

0.69

0.36

0.65

0.23

0.73

0.26

`0.47

Education

No Formal

Primary

Secondary

Tertiary

0.73

0.66

0.56

0.42

0.37

0.36

0.35

0.13

0.26

0.25

0.26

0.24

Occupation

Farming

Trading

Labourer

Civil Servant

0. 71

0.64

0.33

0.38

0.41

0. 35

0.13

0.18

0.24

0. 24

0.06

0.27

Access to credit

Yes

0.52

0.65

0.21

0.35

0.14

0.25

Source: General Household Survey (GHS) Data (2013)

The marginal effect of asset ownership on poverty status of rural women in Nigeria

Table 5 reveals the marginal effects of the explanatory variables on poverty. The asset index was negatively significant (P< 0.05), which implies that a unit increase in asset variable owned by the FHH lower the poverty level. That is, additional assets possessed by the FHH raises the household from poor to non-poor by 0.97% when compared to households without asset; and lower the likelihood that the household will fall under the categories of moderately poor and core poor by 0.95% and 0.81%, respectively. The view of Shambe (2012) noted that women’s ownership of assets keeps them out of poverty or saves them from destitution which leads to better outcomes for children. Table 5 also shows that household size was significant at 1% level (P< 0.01) and had a positive effect on poverty status in order of category. This means that increase in household size by one adult would increase the probability of being core poor and moderately poor by 12% and 14%, respectively, while it lowers the likelihood that a household will fall under the category non-poor by 26%. The result is in consonance with Okurut and Adebua (2002)and Awotide et al. (2011) , who argue that the larger the household size, the higher the dependency ratio is, and hence, the tendency to fall into poverty in the long run.

Age is expected to be associated with skill enhancement (experience), accumulation of resources, extensive social capital and others that ought to contribute positively to well-being (Bashaasha et al., 2006). The results seem also to confirm the statement: where age of the household head is found to be negative and statistically significant (p < 0.10), this implies that older households have greater likelihood of being non-poor. The educational attainment of FHH was negatively related to poverty level in the order of category. This shows that an additional year/level of education gained by the household head decreases the probability of the household being poor. That is, it will decrease the probability of being core poor and moderately poor by 2.75% and 3.22%, respectively, while it increases the likelihood that a household will fall under the category non-poor by 5.22%. The implication of this result is that, despite the fact that more than half (57.78%) of the respondents had no formal education (as revealed by the descriptive analysis result in Table 1) poverty was lower among the few that were educated. This is similar to the findings of Akerele and Adewuyi (2011), Bogale, (2013) and Adekoya (2014) that education attainment enhances human capital and participation in the labour market and has been widely accepted as a veritable tool for poverty reduction and improving peoples’ welfare.

Occupation was positively significant with farming at 1% level (p< 0.01). This implies that the poverty level increases with FHH engaging in farming activities. This is in line with the claim of Awotide et al., (2011) and Olawuyi et al. (2013), that poverty incidence, depth and severity were highest among households that had farming as a main occupation. Lawal et al. (2011) also reported that the poor households participated more in agriculture than non-agriculture. Engaging in farming activities will decrease the probability of being non-poor by 16.2%, while it raises the likelihood that a household will fall under the category moderately poor and core poor by 17.7% and 18.0%, respectively. Being a member of a cooperative society was significant (p< 0.05) and negatively related to household poverty. This implies that more involvement of the FHH in cooperative societies led to an increase in the probability of being non-poor by 7.3%, while it lowered the likelihood that the household would fall under the category moderately poor and core poor by 5.7% and 3.6%, respectively. This result corroborates the finding of Adepoju et al. (2011) and Ibitoye (2013), who found that agricultural cooperative societies performed moderately well towards agricultural development, economic improvement and capital formation of the rural dwellers.

Table 5: Marginal effect result of the ordered probit for categories of poverty status

Variables	Coefficient	Standard error	Z	Marginal effect for Y = core poor	Marginal effect for Y = moderately poor	Marginal effect for Y = non- poor
Asset index	- 0.6533**	0.0288	-2.27	-0.0081	-0.0095	-0.0097
Age Occupation	- 0.0470*	0.0281	- 1.67	0.0916	0.1087	0.1087
Farming	1.7983***	0.6745	2.67	0.1621	0.1772	0.1800
Trading	-0.9110**	0.4229	-2.15	-0.0519	-0.0596	-0.0596
Civil Servant	-0.1710	0.1825	-0.94	-0.0593	-0.0679	-0.0680
Household size
1-3	0.2664	0.2409	1.11	0.0452	0.0560	0.0741
4-6	0.8928***	0.1499	5.96	0.1181	0 .1444	0.2554
7-9	2.1385***	0.4408	4.85	0.0997	0.1241	0.1419
Education
Primary	- 0.0624	0.1933	-0.32	-0.2689	-0.2853	-0.2854
Secondary	-0.1962	0.1791	-1.10	-0.2361	-0.2534	-0.2537
Tertiary	-0.8041 **	0.3667	-2.19	-0.0275	-0.0322	- 0.0522
Cooperative	-0.6995 **	0.3284	-2.31	-0.0363	-0. 0574	-0.0734
LR chi2(18) = 128.39, Pseudo R² = 0.1863 Log likelihood = -199.16195 Number of Observation = 424 Prob > chi2 = 0.0000

Source: General Household Survey (GHS) Data (2013)

CONCLUSION

The effect of asset ownership on household poverty was examined in this study. Majority of the FHH were widows without formal education and had no access to credit. More than half of the FHH were core poor, living below the poverty line. Poverty incidence, depth and severity increased as household size increased and decreased with the level of education. Aggregate asset ownership, educational level and membership of a cooperative were the major poverty- reducing variables among the FHH in rural Nigeria.

RECOMMENDATION

Findings of this study underscore the need for appropriate policy intervention to encourage the ownership of certain assets. Owning assets will not only provide economic growth and income but a very critical determinant of poverty reduction. Also educational interventions that will encourage the acquisition of knowledge by the female folk should be designed.

REFERENCES

Adenegan, O.K, Yusuf, T. O. Balogun, O. L. (2013) Effects of Households Assets Endowment on Participation in Urban Farming in Ibadan Metropolis. Journal of Sustainable Development in Africa, 15 (6): 183-197.

Adepoju, A. A, Oni, O. A, Omonona, B. T, and Oyekale, A. S (2011), Social Capital And Rural Farming Households’ Welfare In Southwest Nigeria. World Rural Observations 1(3): 150-161.

Awotide, B.A.,Diagne, A, Awoyemi, T.T and Ebiohomon, V. (2011) Household Endowments and Poverty Reduction in Rural Nigeria: Evidence from Rice Farming Households. Medwell Journals of Agriculture. 6(1) :274-284.

Barrett C.B and Reardon T. (2000): Assets, Activity and Income Diversification Among African Agriculturist: Some Practical Issues, project report to USAID BASIS CRSP. University of Wisconsin-Madison Land Tenure Center.

Deere, C. D. and Unidos, E. (2010). “ Poverty, Headship And Gender Inequality In Asset Ownership In Latin America. Gender, Development And Globalization Program. Michigan State University.

Dim, E.M., Nwakoby, N. and Akpunonu, U., (2014) Real Economic Empowerment and Poverty Reduction: A Study Of Agricultural Development Bank In Akwa. African Journal of Social Sciences. 4 (4): 54-62.

Edoumiekumo, S.G., Karimo, T.M and Tombofa,S.S.,2014. Determinants of households’

income poverty in the south-south geopolitical zone of Nigeria. Journal of Studies in Social Sciences. 9.1: 101-115.

Filmer, D. and Pritchett, L. (2001) “Estimating Wealth Effects without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India”, Demography, 38(1):115-132.

Foster, J.E., J. Greer and E. Thorbeck, 1984. A Class Of Decomposable Poverty Measures. Econometrica, 52(1): 761-766.

Horrell. S. and Krishnan, P (2006) Poverty and Productivity in Female-Headed Households in Zimbabwe. Journal of Development Studies, July 2006

Innocent, A. P., Eikojonwa, O. and Enojo, A (2014): Poverty Alleviation Strategies and Governance in Nigeria. International Journal of Public Administration and Management Research (IJPAMR). 2 (2): 98-105

Kumar and Agnes, (2014), Gender, Shocks, And Resilience. Evidence from Building on Resilience for Food and Nutrition Security. 2020 Conference Brief, 2014

Kolev A. and Nichlas S. (2011) ‘Gender Disparities in Africa’s Labour Markets. A cross Country Comparison using Standardize Survey Data in ‘ Gender Disparities in Africa’s Labour Markets‘ Gender Disparities in Africa’s Labour Markets.

Lawal, J.O, Omonona B.T. and Oyinleye, O.D.,(2011) Effects of Livelihood Assets on Poverty Status of Farming Households’ in Southwestern, Nigeria. Paper Prepared for Presentation at the EAAE 2011Congress Change and Uncertainty Challenges for Agriculture.

Njuki and Mburu. S. (2013) , “Gender, Livestock and Asset ownership”. International Development Research Centre (www.idrc.ca) published by Routledge 2 Park Square, Milton park, Abingdon, Oxon OX144RN.

Obayelu, O. A. and Awoyemi, T. T. (2010) “Spatial Dimension Of Poverty In Rural Nigeria” Journal Of Development And Agricultural Economics 2(6): 231-244 .

Okonkwo, O, N. 2015. Poverty reduction and leadership in Nigeria: Evidence From South-East

Geo-Political Zone. International Journal of Innovative Development & Policy Studies 3.3:1-11.

Okurut, F.N., J.O. Odwee and A. Adebua, 2002. Determinants of regional poverty in Uganda. Nairobi: African Economic Research Consortium.

Omonona B.T., (2009) Quantitative Analysis of Rural Poverty in Nigeria. IFPRI-ABUJA. International Food Policy Research Institute c/o International Center for Soil Fertility and Agriculture Development No.6/ Plot 1413 Ogbagi Street. Nigeria Strategy Support Program (NSSP) Background Paper No. NSSP 009.

The Ford Foundation, 2004. Building Assets To Reduce Poverty And Injustice. New York: Ford Foundation.

World Bank, (2012) An Update to the World Bank’s Estimate of Consumption Poverty in the Developing World.

Assets	Frequency	Percentage
Physical	214	50.47
Productive	67	15.80
Financial	73	17.22
Human	179	42.22
Social	81	19.20