Greener Journal of Social Sciences

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

ISSN: 2276-7800

Copyright ©2020, the copyright of this article is retained by the author(s)

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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.  yi = 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


 

 

L =

 

= ……………………………………………viii

 


 

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

 

  08

  29

342

  45

 

  38

183

160

  43

 

245

  80

  81

  18

 

  86

131

155

  52

 

308

   95

   21

 

 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) 

 

 


Table 2 presents the profile of the various household assets owned by FHH in rural Nigeria. More than 45% of the rural FHH in rural Nigeria did not own physical and human assets. Owning these assets could enhance good health, peace of mind and high mental development that can enhance proper planning and improving household welfare (Awotide et al.,2011). Productive assets play an important role in reducing poverty; in other words, greater access to productive assets can increase women’s productivity in their various activities and translate to higher returns in the form of income and other measures of well-being (Shambe, 2012).  Table 2 further reveals that, on average, more than 80% of the FHH did not own productive and financial assets. The result is similar to Shambel’s (2012) claimed that women’s access to and control of productive assets are seriously constrained by various social, cultural, economic, political and psychological factors in a household. According to Adepoju et al,. (2012), access to credit (financial assets) may enable farmers to purchase inputs or acquire physical assets, thus contributing to increased income. In summary, households with assets in various forms could have an edge over others in the provision of basic needs and make investments in future generations through health care, education, and training, while those lacking assets are more vulnerable to poverty and less able to recover from periodic disasters.


 

Table 2: Assets ownership profile by female-headed households in rural Nigeria

Assets

Frequency

Percentage

Physical

214

50.47

Productive

67

15.80

Financial

73

17.22

Human

179

42.22

Social

81

19.20

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

 

 


Analysis of household poverty

 

Mean per capita expenditure of the FHH was estimated as N30, 258.57 per annum with the poverty line of N 20,172.39 per annum. Also, 21.9 % of the FHH in the study areas were non-poor, 17.5% were moderately poor and 60.6% were core poor. Result in table 3 captures the comparison between the poverty statuses of the female headed households with their asset index, non- poor FHH had the highest assets index (78.5%) and the core poor FHH had the least (22.2%). This implies that possession or ownership of asset by these households could have help them to have an edge over other households (poor households) in terms of provision of basic needs and investments that can generate income and reduce poverty while those lacking assets (moderately poor and core poor household) are more vulnerable to poverty. The result was similar to Balogun, (2013) findings that the value of household assets measured the ability of the household to withstand economic shocks and income shortfalls and to finance the purchase of household needs.  Shambe (2012) also noted that women’s ownership of assets 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; and result in better outcomes for women in case of separation, divorce or widowhood.


 

Table 3: Poverty status and assets index

Poverty status

Percentage

Average assets index (%)

Core poor

Moderately poor

Non- poor

60.60

17.50

29.25

         0.2222    

                            0.4199

                            0.7852

 

 

 

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

>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

No

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.

 

 

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Cite this Article: Adeoye A; Salman KK; Balogun OL; Rufai M; Oguniyi B (2020). Effect of Asset Ownership on Poverty Status in Rural Geo-Political Zones of Nigeria. Greener Journal of Social Sciences, 10(1): 1-8.