The Journal of Developing Areas

Volume 54 No. 3 Summer 2020

ASSET OWNERSHIP AND INCOME AS

DRIVERS OF HOUSEHOLD POVERTY IN
SOUTH AFRICA
Isaac B Oluwatayo
Moyosoore A Babalola
University of Limpopo, South Africa

ABSTRACT
The study was carried out to examine asset ownership and income as determinants of household
poverty in South Africa. The specific objectives were to determine the poverty status of the
households and, investigate the influence of asset ownership and income on household poverty in
South Africa. Secondary data was sourced from the National Income Dynamics Study (NIDS)
2014/2015 data. A sample size of 9619 households was selected for this study. The poverty status of
the households was determined using the poverty line. The poverty line was calculated as two-third
of the mean-per-capita household expenditure. Households with expenditure above the poverty line
were considered to be “non-poor” and those with expenditure below the poverty line were considered
“poor”. For the purpose of this study, assets were divided into monetary and non-monetary assets.
Binary logistic technique was further used to analyse the influence of ownership of monetary and
non-monetary assets, household income, household size (as a control variable), and provincial
locations on the household poverty status. An investigation into the assets owned by households
indicated that real estate assets, business assets, vehicle assets, financial assets, superannuation assets,
livestock assets and possessions, were assets owned by South African households. The results
showed that 59.49% of South African households experience poverty, particularly in Kwazulu-Natal,
Eastern Cape and Limpopo provinces. The results of the logistic regression further revealed that
ownership of non-monetary assets, income and household size had a positive influence on the
household poverty status. The Western Cape, Northern Cape, Free State, and Gauteng provinces had
high probability for improving the household poverty status, while Kwazulu-Natal province showed
a negative influence on the poverty status. It was recommended that policies should focus on income
redistribution through employment generation, which will lead to enhanced income. This can, in turn,
be used to acquire assets, especially in the most affected provinces like Kwazulu-Natal, Eastern Cape
and Limpopo province.

JEL Classifications: I32, O15, R2

Keywords: Asset ownership, Income, Household, Poverty, South Africa
Corresponding Author’s Email Address: isaacoluwatayo@yahoo.com

INTRODUCTION
The number of people living in extreme poverty in the world has dropped by more than
half – from 1.9 billion in 1990, to 836 million in 2015. However, a sizeable number of
people are still struggling to meet the most basic human needs; mostly living on less than
$1.25 a day (SDGFund, 2015). In South Africa, the proportion of the population living in
poverty declined from 66,6% (31,6 million persons) in 2006 to 53,2% (27,3 million) in
2011, but increased to 55,5% (30,4 million) in 2015. The number of persons living in
extreme poverty (i.e. persons living below the 2015 Food Poverty Line of R441 per person
per month) in South Africa increased by 2,7 million, from 11 million in 2011 to 13,8
 120

million in 2015 (Stats SA, 2017). Poverty is therefore still of serious concern in South
Africa, especially among households living in the rural areas (Bird, et al., 2002).
According to FAO, food production will need to double to feed an additional two
billion people by 2050 and growing demand for agricultural products will increase pressure
on already severely degraded natural resources (2017). This shows the significance of
studying poverty by agricultural institutions. The sheer weight of numbers, with the
majority of poor people living in rural areas, depending on agriculture would suggest that
they will benefit more from growth originating in agriculture. They may also benefit
indirectly through the labour market and employment expansion in non-traditional agro-
export sectors (Christiaensen, et al., 2011). Most importantly, understanding household
poverty and its determinants will help government to better focus policy and
implementation in the agricultural sectors, as well as others.
To promote rural development and inclusion, countries must take specific policy
and create programs that reach the poor directly. A significant change in the set of
development policies over the years (since the 1990s) have led to the adoption of a range
of direct interventions, variously called “antipoverty programs,” “social safety nets,” and
“social assistance.” Their common feature is the use of direct income transfers to poor
families (Ravallion, 2016). Furthermore, these types of social assistance highlight the
import of income to the household poverty discussion. In major surveys, income is often
used (in the absence of consumption) for measuring poverty (Ravallion, 2016). Household
assets, which help households to diversify their sources of income and consequently reduce
the risk of income failure have also been identified as important determinants of poverty
(Omotesho, et al., 2010).
This study is significant because it will contribute to knowledge in achieving Goal
1 of the 2030 Agenda for Sustainable Development; no poverty. This information will help
advice government in policy formulation with regards to poverty issues. It will also
contribute to knowledge base for further study into asset ownership, income and household
poverty issues in the country.
This paper therefore examined asset ownership and income as determinants of
household poverty in South Africa.

LITERATURE REVIEW
This section reviews existing literature on research that has been carried out, around Africa
and the World, on the influence of income and asset ownership on poverty.
A research carried out in Nigeria, to examine the levels and the major
determinants of food security and poverty among the rural households, observed that non-
farm income and ownership of physical assets were important determinants of rural poverty
(Omotesho, et al., 2010). According to that study, households with physical assets received
some rents from these assets and they did not pay for such asset, thus reducing cash
outflow. This supported the school of thought that asset ownership should lead to reduced
poverty.
According to Vijaya, et al. (2014), a multidimensional poverty study carried out
in India included an asset inventory to capture ownership details, and valuation data. It
considered assets as one of the most important poverty indicators, alongside education and
standard of living. The study also showed that assets provide insights into a household’s
economic activity and security in a way that is not possible using income or consumption
 121

data. Asset portfolios reflect both past and future income-generation opportunities through
their contribution to livelihood choices, and the potential for participating in financial
markets, generating rents, interests on savings, and profits from business. The
characteristics of assets impact the experience of poverty by providing a safety net during
times of economic crises, through their sale or pawning to cope with an income shortfall
(Vijaya, et al., 2014).
Akinbode (2017) carried out a study on “Women Asset Ownership and Household
Poverty in Rural Nigeria”. Data was collected from 363 respondents. The results of the
logit regression showed that income was one of the significant variables that determined
poverty status. It also highlighted that the personal possessions that was ranked as the most
valuable asset was the mobile phone, while the least valuable was the black-and-white
television.
Another research carried out in America in 2006, titled “Saving and asset
accumulation among low-income families with children in IDAs”, looked at asset
ownership as one of the important factors affecting low-income families. Assets ownership
was specifically defined as home ownership, car ownership, and/or being banked. These
seemed to be an important factor of savings among families with children. Home
ownership and car ownership were used as a proxy to the fact that participants already had
some experience with saving (Grinstein-Weiss, et al., 2006).
Lawal et al. (2011) prepared a paper for the European Association of Agricultural
Economists (EAAE) 2011 congress on “Effects of Livelihood Assets on Poverty Status of
Farming Households’ in Southwestern, Nigeria”. One hundred and thirty-five farming
households were examined. The results showed that 31.9 percent of the farming households
fell below the poverty line. Lawal et al. classified the socio-economic characteristics of the
respondents into human assets, physical assets and financial assets, and these assets proved
significant to improving the poverty status of farming households.
According to another study also carried out in Nigeria in 2017, 65% of Nigerians
live below the poverty line. The study aimed to examine the multidimensional welfare
deprivation of women in rural and urban South-South (SS) Nigeria. In ascending order of
contribution to well-being, the six dimensions considered as follows in rural SS were:
employment, information access, health and nutrition, education, autonomy, housing and
sanitation. While in the urban in ascending order the dimensions were arranged thus;
employment, health and nutrition, information access, autonomy, education, housing and
sanitation (Oladokun, et al., 2017).
These studies show that income and asset ownership are significant variables to
consider when carrying out poverty analysis. In this study, the influence of these variables
will be considered in the South African context.

METHODOLOGY AND ANALYTICAL PROCEDURES

STUDY AREA

The Republic of South Africa (RSA), is the southernmost country in Africa. It is bounded
on the south by 2,798 kilometres (1,739 mi) of coastline of Southern Africa stretching
along the South Atlantic and Indian Oceans; on the north by the neighbouring countries
of Namibia, Botswana, and Zimbabwe; and on the east and northeast
 122

by Mozambique and Swaziland; and surrounds the kingdom of Lesotho. South Africa is
the largest country in Southern Africa and the 25th-largest country in the world by land
area and, with close to 56 million people, is the world's 24th-most populous nation.

DATA SOURCES AND TYPE

Secondary data will be sourced from National Income Dynamics Study (NIDS) 2014/2015
data. A stratified, two-stage cluster sample design was employed by NIDS in sampling the
households to be included in 2008, when the data was first collected. In the first stage, 400
Primary Sampling Units (PSUs) were selected from Stats SA's 2003 Master Sample of
3000 PSUs. Each of these surveys was conducted on non-overlapping samples drawn
within each PSU. Over the combined field work periods NIDS fieldworkers knocked on
10,642 household doors. Of these households, 7305 agreed to participate and the interview
was completed. This equates to a 69% response rate. By the 2014/2015 data collection, the
household size had increased to 11895. The sample size for this study was 9,619
households.

ANALYTICAL TECHNIQUES

DESCRIPTIVE STATISTICS

Descriptive statistics such as tables, frequencies, mean, and charts will be used to describe
the assets owned by the respondents within the households in the sample.

POVERTY MEASURE

Poverty line was calculated as two-third of the mean-per-capita household expenditure.

Mean-per-capita household expenditure was calculated as the total amount that the
households spent for a month divided by the total number of households (Rose & Charlton,
2002). Households with expenditure above the poverty line were considered to be “non-
poor” and those with expenditure below the poverty line were considered “poor”.
𝑃𝑜𝑣𝑒𝑟𝑡𝑦 𝐿𝑖𝑛𝑒 = 2⁄3 𝑋 𝑀𝑒𝑎𝑛 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒……………1

THE BINARY LOGISTIC MODEL

Binary logistic regression estimates the probability that a characteristic is present. It is a

procedure used to analyse the effects of categorical and continuous explanatory measures
on a dichotomous response variable. Binary logistic technique was used to analyse the
influence of assets ownership, household income and provincial locations on household
poverty status. The general regression equation is expressed below:

𝑃𝑖
log ( ) = log 𝑖𝑡 𝑃𝑖 = 𝛽0 + 𝛽1 𝑥1 + 𝛽2 𝑥2 + 𝛽3 𝑥3 + ⋯ + 𝛽𝑛 𝑥𝑛 ……………...….2
1−𝑃𝑖
 123

TABLE 1: LIST OF VARIABLES AND DESCRIPTION

Variables Description of variables Unit of measurements

Y Poverty Status (0 = poor, 1 = not poor) Dummy
X1 Total Household Income Rands
X2 Monetary Assets Rands
X3 Non-monetary assets Rands
X4 Household size (control variable) Number
X5-X13 Provinces; Western Cape, Eastern Cape, Northern Dummy
Cape, Free State, Kwazulu-Natal, North West,
Gauteng, Mpumalanga, Limpopo

Assets provided in the dataset were divided into monetary and non-monetary
assets based on Mason and, Barnett and Su’s definitions. Monetary assets are assets that
provide monetary services and investment rate of return, while non-monetary assets
provide only their investment rate of return (Mason, 1976; Barnett & Wu, 2005).
RESULTS AND DISCUSSIONS
In Table 2, the highest population group was the African group with 84.58% of the
respondents in this category. The population group with the least respondents were the
Asian/Indian group with 0.77% represented in the study. Females were more represented
in the study at 54.25%, while males constituted 45.75% of the respondents. The
respondents were categorized into three age groups; under 15 years (32.99%), 15 to 64
years (60.87%) and over 64 years (6.14%). The highest number of the respondents studied
up to Grade 7-11 (37.20%). This was followed by 22.32% of the respondents that attended
Grade 1-6. The percentage of respondents that completed matric were 18.34%. While 18%
of the respondents had no schooling at all. Among the respondents, 71.69% of the
respondents were unemployed at the time of data collection. The 28.31% that had jobs were
employed in the following sectors; Private households (8.95%), Agriculture, hunting,
forestry and fishing (10.00%), Mining and Quarrying (3.37%), Manufacturing (10.82%),
Electricity, gas and water supply (1.03%), Construction (6.64%), Wholesale and Retail
trade; repair etc. (17.38%), Transport, storage and communication (4.51%), Financial
intermediation, insurance, real (9.28%), and Community, social and personal services
(28.02%).
 124

TABLE 2: SOCIO-ECONOMIC CHARACTERISTICS OF THE HOUSEHOLD

MEMBERS

Population Group
African 84.58%
Coloured 12.89%
Asian/Indian 0.77%
White 1.75%
Gender
Male 45.75%
Female 54.25%
Education
Grade R/0 3.90%
Grade 1 - 6 22.32%
Grade 7 - 11 37.20%
Grade 12 (Matric) 18.34%
NTC /NCV Qualification 0.0005%
Certificate not requiring Grade 12/Std. 0.0003%
Honours Degree 0.00002%
Others 0.0015%
No Schooling 18.00%
Employment Status
Employed 28.31%
Unemployed 71.69%
Sector code for occupation of employed
Private households 8.95%
Agriculture, hunting, forestry and fishing 10.00%
Mining and Quarrying 3.37%
Manufacturing 10.82%
Electricity, gas and water supply 1.03%
Construction 6.64%
Wholesale and Retail trade; repair etc; 17.38%
Transport, storage and communication 4.51%
Financial intermediation, insurance, real 9.28%
Community, social and personal services 28.02%
Age Groups
Under 15 32.99%
15 to 64 60.87%
Over 64 6.14%
Source: Authors’ computation from data

PROFILE OF ASSETS OWNED BY HOUSEHOLDS

Households in South Africa acquire various types of assets to improve their standard of
living. Table 3 below shows a profile of the assets owned by households in South Africa.
The table highlights the type of asset owned, the mean value of the asset to an average
 125

household in South Africa, and the number of households that owned the given asset. The
assets were categorized into two; real estate, vehicle, livestock and possessions assets were
classified as non-monetary assets, while business, financial and superannuation assets were
classified as monetary assets.
The results show that all households had personal possessions that they considered
assets. Personal possessions were valued at an average of R60,231.63 per household. Real
estate assets were the second popular asset acquisition of South African households. Of the
9619 households, 7895 households had real estate assets with a mean value of
R200,239.70. Financial assets had a mean value of R21,232.86 per household with 4908
households owning that asset type. Vehicle assets ranked fourth among the type of assets
owned. There were 1270 households that had acquired this asset, and the average value per
household was R108,500.30. Superannuation assets (also referred to as a “company
pension plan”) ranked fifth among the seven asset types. Only 769 households had access
to Superannuation assets, with each household having an average value of R1,224,168.00
on that asset. Livestock assets rank a little lower than superannuation assets with 754
households possessing the asset. The mean value of livestock assets per household was
R43,000.24. Business assets had the lowest ranking in the profile, as only 344 households
acquired this assets type. However, it proved more valuable than all the other assets (save
for superannuation assets) at R427,666.70 per household. Research has shown that a strong
inverse relationship exists between the incidence of poverty and small business
(Gebremariam, et al., 2004). This might explain this occurrence. Policies should include
financial education in poverty alleviation schemes as this would equip the population make
better financial decisions.

TABLE 3: PROFILE OF ASSETS OWNED BY HOUSEHOLDS

Type of Asset Mean Value (in Rands) Households that own asset
Monetary Assets
Business Assets 427666.70 344
Financial Assets 21232.86 4908
Superannuation Assets 1224168.00 769
Non-Monetary Assets
Real Estate Assets 200239.70 7895
Vehicle Assets 108500.30 1270
Livestock Assets 43000.24 754
Possessions 60231.63 9619
Source: Authors’ computation from data

POVERTY STATUS OF HOUSEHOLDS

As explained in the methodology, the poverty line was calculated as two-third of the mean-
per-capita household expenditure. The mean-per-capita of the households in the data was
R4874.97. Two-thirds of this was R3249.98. That is, the poverty line for South Africa
households per month was set at R3249.98. ArcGIS sets the average household size for
South Africa at 3.3 people per household (ArcGIS, 2017). Comparing the poverty line in
this study to the 2015 national poverty line by Stats SA, the lower bound poverty line
(LBPL) is R647 and the upper bound poverty line (UBPL) is R992, per person per month.
 126

Dividing the poverty line per household by the average household size, gives R985 poverty
line per person per month. This falls closely to the UBPL.
Households above the poverty line per household were coded as 1 (that is, non-
poor). While households below the poverty line were coded as 0 (that is, poor). Table 3
shows the results of coding 0 and 1 according to provinces.
Table 4 shows the provinces in South Africa and their poverty statuses. It reveals
that 59.49% of South African households are experiencing poverty. This figure is 4 percent
higher than that given by the Poverty Trends report (Stats SA, 2017). According to Stats
SA (2017), 55.5% of South Africans were experiencing poverty.
Kwazulu-Natal had the highest proportion of poor households (74.58%) in its
province, followed by Eastern Cape which had 68.37% of its households experiencing
poverty and Limpopo at 63.18%. The Poverty Trends report also recorded these three
provinces as the 3 poorest regions. A paper presented at the Centre for Social and
Development Studies (May, 2016), University of Natal suggested the following as the
causes of the poverty status in these provinces:

- “The impact of apartheid which stripped people of their assets, especially land,
distorted economic markets and social institutions through racial discrimination,
and resulted in violence and destabilisation;
- Under-mining the asset base of individuals, households and communities through
ill health, over-crowding, environmental degradation, the mismatch of resources
and opportunities, race and gender discrimination and social isolation;
- The impact of a disabling state, which included the behaviour and attitudes of
government officials, the absence of information concerning rights, roles and
responsibilities, and the lack of accountability by all levels of government (May,
2016).”

Western Cape, Northern Cape and Gauteng experienced the lowest proportions of
household poverty in their provinces at 39.85%, 48.17% and 48.21% respectively. The
Poverty Trends report also listed Western Cape and Gauteng ad having the least poverty.
However, Free State province ranked 3rd among the provinces with the lowest poverty
status, and Northern Cape ranked fourth.
 127

TABLE 4: PROVINCES AND POVERTY STATUSES

Provinces Poverty Status

Poor (0) Not Poor (1)
Western Cape 444 (39.85%) 670 (60.15%)
Eastern Cape 802 (68.37%) 371 (31.63%)
Northern Cape 343 (48.17%) 369 (51.83%)
Free State 312 (52.17%) 286 (47.83%)
KwaZulu-Natal 1,872 (74.58%) 638 (25.42%)
North West 354 (56.91%) 268 (43.09%)
Gauteng 675 (48.21%) 725 (51.79%)
Mpumalanga 412 (60.06%) 274 (39.94%)
Limpopo 508 (63.18%) 296 (36.82%)
Total 5,722 3,897
Percentage 59.49 40.51
Source: Authors’ computation from data

POVERTY DETERMINANTS AMONG HOUSEHOLDS IN SOUTH AFRICA

Binary logistic regression was also carried out regressing total household income,
monetary and non-monetary household assets, household size (as a control variable), and
provincial locations, against the poverty status of the households. The results of the analysis
can be seen in Table 5.
According to Table 5 below, the variables household income, non-monetary
assets, household size, Western Cape, Northern Cape, Free State, Kwazulu-Natal and
Gauteng provinces were significant at 1 percent level, with residence in Kwazulu-Natal
having a negative impact on the household poverty status, while the other significant
variables were positive. Household income had a positive impact on the household poverty
status. The marginal effect was 0.0000794. This represented the amount of change
expected in the status when there was a one unit change in the household income with all
other variables in the model being held constant. Akinbode (2017) got a similar result while
studying “Women Asset Ownership and Household Poverty in Rural Nigeria”. The results
of the study showed that as income increased, the likelihood of the household being
considered as poor reduced.
Household non-monetary assets also had a positive effect on household poverty
status. The marginal effect was interpreted as a 5.50e-07 change in the poverty status,
resulting from a one-unit change in non-monetary assets. In other words, as the non-
monetary assets increased, the the probability of the household not being poor increased.
A paper, prepared for presentation at the European Association of Agricultural Economists
(EAAE) 2011 Congress, showed that human capital, financial, physical and social capital
assets are important to reduce the poverty status of farming households in Southwestern,
Nigeria (Lawal, et al., 2011). The monetary assets had a positive impact on the poverty
status but was it was not a significant impact. This could be explained by the fact that
majority of the households had fewer monetary assets, compared to non-monetary assets.
Household size was another positively significant variable. It had a marginal
effect of 0.0129568, that is, with a unit increase in household size, the probability of the
household not being poor increased. Majority of existing literature (Meenakshi, 2002;
 128

White & Masset, 2003; Anyanwu, 2014) records a negative relationship between
household size and non-poverty. This divergence could be explained by multiple streams
of income increasing the total household income, thereby improving the poverty status.
In Western Cape province, the marginal effect was 0.1607243. In other words,
households in Western Cape had a 16.07% probability of not living in poverty. In Northern
Cape province, the households had a marginal effect of 0.1148131. That is, a probability
of 11.48% of not being poor. While the marginal effect in Free State was 0.1076662, with
a 10.77% probability of not being poor. Kwazulu-Natal province varied from the other
provinces with a negative significance. The households in that province 14.11% probability
of being poor. Gauteng province had a marginal effect of 0.0919634, with a 9.2%
probability of the household not being poor.
These results are significant when compared with the poverty status of provinces
discussed earlier in Table 4. Western Cape, Northern Cape, Free State and Gauteng are
four provinces with the lowest poverty statuses. Kwazulu-Natal had the highest percentage
of poor households, and this explains why households in this province had a negative
relationship the poverty status. These results corroborated the results of the Poverty Trends
report (Stats SA, 2017).
The above showed that income, assets, household size and the particular provinces
that a household resided in all had an impact on the poverty status of that household.

TABLE 5: LOGISTIC REGRESSION ANALYSIS (MARGINAL EFFECTS

AFTER LOGISTIC)

Explanatory variables dy/dx Std. Err. z P>z X

Household income 0.0000794 0.00000 35.78 0.000*** 6986.8
Non-monetary assets 5.50e-07 0.00000 14.20 0.000*** 242279
Monetary assets 5.42e-08 0.00000 0.90 0.369 123996
Household size (control) 0.0129568 0.0025 5.18 0.000*** 3.94792
Western cape* 0.1607243 0.0249 6.46 0.000*** 0.115812
Eastern cape* 0.0243929 0.02862 0.85 0.394 0.121946
Northern cape* 0.1148131 0.02856 4.02 0.000*** 0.07402
Free state* 0.1076662 0.0299 3.60 0.000*** 0.062169
Kwazulu-natal* 0.1411497 0.02677 -5.27 0.000*** 0.260942
North west* 0.04247 0.03278 1.30 0.195 0.064664
Gauteng* 0.0919634 0.02653 3.47 0.001*** 0.145545
Mpumalanga* 0.0332089 0.03371 -0.99 0.325 0.071317
(*) dy/dx is for discrete change of dummy variable from 0 to 1
*** indicates that variables are significant at 1 percent level.
Source: Authors’ computation from data

Likelihood-ratio test LR chi2(10) = 2388.33

(Assumption: m1 nested in m12) Prob > chi2 = 0.0000
 129

CONCLUSIONS AND RECOMMENDATIONS

The study was carried out on 9619 South African households to determine the influence of
asset ownership and income on household poverty. The results showed that assets owned
by households included real estate assets, business assets, vehicle assets, financial assets,
superannuation assets, livestock assets and possession. It also showed that 59.49% of South
African households were experiencing poverty. The results of the logistic regression
revealed that non-monetary asset ownership, household size and income had a significant,
positive influence on household poverty status. The provinces, Western cape, Northern
Cape, Free State, and Gauteng showed high probability of a non-poor poverty status, while
Kwazulu-Natal province showed a probability of a poor poverty status for households in
that province.
More jobs lead to increased income in rural households, which could be further
used in acquiring assets. Therefore, it was recommended that policies should focus on
income redistribution through employment generation. It was also recommended that
South African households should acquire business assets as they had higher average value
compared to other assets. This would increase the future income potential of these
households.

ACKNOWLEDGEMENT

The authors would like to thank the University of Cape Town for making the NIDS data
available for us to use in this study.

REFERENCES

Akinbode, S., 2017. Women Asset Ownership and Household Poverty in Rural
Nigeria. Journal of Studies in Social Sciences, 16(1), pp. 45-64.
Anyanwu, J. C., 2014. Marital status, household size and poverty in Nigeria:
Evidence form the 2009/2010 survey data. African Development Review, 26(1), pp. 118-
137.
ArcGIS, 2017. Average Household Size in South Africa. [Online]
Available at: https://www.arcgis.com/home/item.html?id=582208ececa2424ab6e 387d9cd
cf01e3 [Accessed 23 June 2018].
Barnett, W. A. & Wu, S., 2005. On user costs of risky monetary assets. Annals of
Finance, pp. 35-50.
Bird, K., Hulme, D., Moore, K. & Shepherd, A., 2002. Chronic poverty and
remote rural areas. Working Paper - Chronic Poverty Research Centre Working Paper No
13, pp. 1-57.
Christiaensen, L., Demery, L. & Kuhl, J., 2011. The (Evolving) Role of
Agriculture in Poverty Reduction. Journal of Development Economics, 96(2), pp. 239-254.
FAO, 2017. Ending poverty and hunger by investing in agriculture and rural
areas, Rome: Food and Agricultural Organization.
Gebremariam, G. H., Gebremedhin, T. G. & Jackson, R. W., 2004. The Role Of
Small Business In Economic Growth And Poverty Alleviation In West Virginia: An
Empirica lAnalysis.[Online] Available at: https://www.researchgate.net/publication/23
 130

505715_The_Role_Of_Small_Business_In_Economic_Growth_And_Poverty_Al leviatio
n_In_West_Virginia_An_Empirical_Analysis [Accessed 23 June 2018].
Grinstein-Weiss, M., Wagner, K. & Ssewamala, F. M., 2006. Saving and asset
accumulation among low-income families with children in IDAs. Children and Youth
Services Review, 28(2), pp. 193-211.
Lawal, J., Omonona, B. & Oyinleye, O., 2011. Effects of Livelihood Assets on
Poverty Status of Farming Households’ in Southwestern Nigeria, Zurich: EAAE.
Mason, W. E., 1976. The Empirical Definition of Money: A Critique. Economic
Inquiry, 14(4), pp. 525-538.
May, J., 2016. Poverty and Inequality in South Africa. Kwazulu-Natal: Research
Gate.
Meenakshi, J. V., 2002. Impact of household size and family composition on
poverty in rural India. Journal of Policy Modeling, pp. 539-559.
Oladokun, Y. O. M., Adenegan, K. O. & Oluwatayo, I. B., 2017. Comparative
Analysis of Multidimensional Welfare Deprivation among Women in Rural and Urban
South-South Nigeria. Open Access Library Journal, 4(12), pp. 1-16.
Omotesho, O., Adewumi, M. & Fadimula, K., 2010. Food Security and Poverty
of the Rural Households in Kwara State, Nigeria. Libyan Agriculture Research Center
Journal International, 1(1), pp. 56-59.
Ravallion, M., 2016. Are the World’s Poorest Being Left Behind? Reconciling
Conflicting Views on Poverty and Growth. Journal of Economic Growth, 21(2), pp. 1-34.
Rose, D. & Charlton, K., 2002. Prevalence of household food poverty in South
Africa: Results from large representative. Public Health Nutrition, 5(3), p. 383–389.
SDGFund, 2015. Goal 1: No Poverty. [Online] Available at: http://www.sdgfund.
org/goal-1 -no-poverty [Accessed 05 June 2018].
Stats SA, 2017. Poverty Trends in South Africa, Pretoria: Statistics South Africa.
Stats SA, 2017. Poverty Trends in South Africa: An examination of absolute
poverty between 2006 & 2015. [Online] Available at: http://www.statssa.gov.za/?p
=10341 [Accessed 17 April 2018].
Vijaya, R. M., Lahoti, R. & Swaminathan, H., 2014. Moving from the Household
to the Individual: Multidimensional Poverty Analysis. World Development, 59(1), pp. 70-
81.
White, H. & Masset, E., 2003. The importance of household size and composition
in constructing poverty profiles: An illustration from Vietnam. Development and Change,
34(1), pp. 105-126.
Wave 4 Data: Southern Africa Labour and Development Research Unit. National
Income Dynamics Study 2014 - 2015, Wave 4 [dataset]. Version 1.1. Cape Town: Southern
Africa
Labour and Development Research Unit [producer], 2016. Cape Town: DataFirst
[distributor], 2016. Pretoria: Department of Planning Monitoring and Evaluation
[commissioner], 2014
Copyright of Journal of Developing Areas is the property of Tennessee State University,
College of Business and its content may not be copied or emailed to multiple sites or posted
to a listserv without the copyright holder's express written permission. However, users may
print, download, or email articles for individual use.