Determinants Of Household Poverty Research Articles

Determinants of household poverty in Arsi Nagelle town, West Arsi Zone, Ethiopia

The struggle to achieve above the poverty line at the household level in the urban areas of Ethiopia dates back an extended period. In this regard, this study was conducted to identify and analyze the determinants of urban household poverty in the case of Arsi Nagele town. The study employed both primary and secondary data. Preliminary data was collected from 337 randomly selected households in two sub-cities by questionnaire. Both descriptive and Logit regression model was used for data analysis. From the three schools of thought, the basic needs approach is employed to identify households as poor and non-poor. Sixteen (14) explanatory variables, four continuous and ten discrete, were included in the logit regression model. Out of these variables, the sex of the household head, age of the household head, marital status of the household head, family size of household head, education level of household head, household head house tenure, household head access to electricity, household head access to the market center, household head access to credit, household head access to clear and sufficient water, the existence of solid institution was among the significant variables affecting the level of household's poverty. The research found that the area's food poverty line and the total poverty line were 13,788 Birr and 14,436 Birr per adult per year, respectively. Accordingly, the incidence of poverty, poverty gap, and severity was found to be 40 percent, 26.5 percent, and 7 percent, respectively. The research found that the area's food poverty line and the total poverty line are 13,788 Birr and 14,436 Birr per adult per year, respectively. Keywords: poverty, Logit, Poverty line, poverty gap, and severity

Determinants of Household Poverty: Empirical Evidence from Pakistan

The Millennium Development Goals (MDGs) aim at halving the percentage of world population in 1990 with income less than US $ 1 a day and halving the share of people who suffer from hunger by 2015. Being a developing nation, poverty reduction should be our foremost obligation. An appreciable decline has occurred recently, headcount decreased from 34.46 percent in 2000-01 to 23.94 in 2004-05 [Pakistan (2006-07)]. However, seeing only the statistics and the trends in poverty we can just observe that what happened to poverty in different periods and also the decomposition of poverty in different years gives us a more appropriate picture of the incidence of poverty. This knowledge is useful because it informs us whether poverty is increasing or decreasing overtime. However, this information does not provide us the details of the causes of poverty. For instance, is poverty high due to low education attainment or large family size or due to any other reason? Here is a need of research about the determinants of poverty that are positively or negatively linked with the poverty status. This is the area where research can be most useful because firstly we have to understand the main determinants of poverty before designing the most efficient policy to reduce poverty in the country.

The determinants of household poverty: the case of berehet woreda, amhara regional state, Ethiopia

Nowadays, poverty is one of the most important issues that needs due attention in many developing countries like Ethiopia. Nonetheless, poverty in Ethiopia remains widespread in both rural and urban areas. Therefore, this study aimed to examine the determinants and status of poverty in Berehet Woreda. The study was conducted using a cross-sectional survey. To conduct the study, a sample of 384 households was selected using a stratified simple random sampling technique. Foster Greer Thorbecke’s Poverty Index was used to examine the extent and severity of poverty in the Woreda. Accordingly, about 36% of households in Woreda live below the poverty line, with an average poverty gap of 12% and poverty severity of about 7%. The binary logit model showed that household education status, dependency ratio, residential area, and access to credit were statistically significant in determining household poverty status. Since the poverty situation in Woreda was worse than the national average, the regional government should prioritize this Woreda and develop a special type of projects especially in rural areas that can lift the majority of the poor out of poverty.

Determinants of Household Poverty: The Case of Shashemene Town, Oromia, Ethiopia

Ethiopia is one of the world’s poorest countries by any standards. As a result government, multilateral agencies and other stakeholders consider poverty as their priority issues. This study is the study on determinants of urban poverty with the objective of identifying the poor from non poor households and main factors those push them to the poverty trap. Cost of Basic Needs (CBN) approach is used to calculate food poverty line and total poverty line respectively. Out of the 381 surveyed household heads, 170(44.62%) of them were found poor. A total of 381 respondents were included in the survey and the main data for the study was collected through structured questionnaire. Binary Logit model was employed to analyze the determinants of households’ poverty. Fourteen explanatory variables were taken in to account in the model’s equation. The result of logit regression using STATA13.1 software confirmed that nine variables were found statistically significant; age of households (1%), income of house hold head (1%), marital status of households(1%), house tenure of households(1%), household family size (1%), households employment status(1%),educational level of households(5%),access to credit(5%) and saving(10%). From these significant variables; age of households, households’ family size, households’ marital status and households housing tenure were positively correlated with probability of being poor. While households education, households income, households saving, households employment status and households access to credits were negatively correlated with probability of being poor. Two sample t-test and chi-squared test was employed to compare non-poor and poor. The poverty head count ratio is extensive among the surveyed households that calls for urgent interventions aimed at solving the problems of the poor. Hence efforts should be made to increase the real income of households through well paying and better job creation by the set up of micro and small scale enterprises, with the increased provision of economic and social infrastructure. Thus, stakeholder interventions to tackle the determinant factors are important measures to be undertaken. Keywords: Poverty, Household, Logit Model, Shashemene, Ethiopia DOI: 10.7176/JESD/12-7-01 Publication date: April 30 th 2021

Asset Ownership and Income as Drivers of Household Poverty In South Africa

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.

The Determinants of Household Poverty in South Africa

South Africa was privileged to be part of the MDGs agenda which was adopted in 2000. One of the aims of MDGs was to reduce extreme poverty by half in 2015. For that reason, South Africa integrated policies and strategies to rid poverty by half to that of United Nations (UN). Through all the combined policy approaches, South Africa has successfully achieved the target of halving the population living below PPP$1.25c per person per day. Whichever threshold used, the results showed that the percentage share of people living below poverty line has now decreased from 11.3 per cent in 2000 to 4.0 per cent in 2011. However, these reports are not reflecting the exact poor’s experiences because at household level there is still an outright poverty. Therefore, if the national poverty report gives a good picture about South African poverty status whereas there is still prevalence of poverty at household level, there are high chances that wrong policies in regard to poverty reduction strategies will be wrought. Hence this paper focuses on the determinants of household poverty in South Africa. The sole aim of this paper is to assess the determinants of household poverty in South Africa. The reviewed literature on determinants of poverty in South Africa would enable policy makers to see the effect of demographic characteristics on poverty in South Africa. Thus, strategies and policies aimed at alleviating poverty in South Africa can be directed to the discussed factors.

Poverty and its Determinants in Bikaner District of Rajasthan State, India

The study used primary data from 100 households following multistage random sampling process. Minimum consumption expenditure per person was used as standard of measurement to categorize households into poor and non-poor. To address principal determinants of household poverty in the study area, the study used, logit model. In Bikaner poverty headcount was 17 percent. The poverty gap index and the squared poverty gap index were 0.2 and 0.011 respectively. Descriptive analysis results showed that,82 percent poor, 46 percent non-poor and 52 percent overall sampled households responded their own production is not enough year round. In Bikaner farm generates nearly 63 percent and non-farm incomes has 37 percent share. Out of monthly total expenditure 64 percent goes to food, 17 percent to non-food, 15 percent to education and the remaining 4 percept to medical expenditure monthly. Logit analysis found that, as family size increases by one member in adult equivalent, citrus paribus, the probability of that household to falls into poverty increase by 5.8 percent. As farm size increases by one hectare, the probability that a household falls into poverty decrease by 8.5 percent and as irrigated land increases by one hectare, the probability that a household falls into poverty decrease by 16 percent. As livestock ownership increases by one TLU, the probability that a household falls into poverty will decrease by 14.4 percent. The probability a household falls into poverty decreases by 0.03 percent in the district, as family income increases by one lakh rupees. As decision maker education increases by one unit, the probability that a household falls into poverty decrease by 5 percent and also as wife education increases by one unit, the probability that a household falls into poverty decrease by 2.5 percent.

Determinants of Household Poverty in South Korea : Using Multi-Level analysis

본 연구는 빈곤 결정의 다차원성에 주목하여 한국사회 가구빈곤의 결정요인을 미시와 거시수준으로 구분한 통합모델을 구축하고, 이들 각각의 영향을 체계적으로 규명하고자 하였다. 이를 위해 미시수준 요인으로는 가구주의 성별과 학력, 취업상태, 가구유형 등 개인 및 가구 단위의 특성들을, 거시수준 요인들로는 한국사회 전반의 경제와 분배, 노동시장 상황과 인구구조 등을 반영하는 지표들을 상정하여 각각의 독립된 영향을 실증 분석하였다. 분석에는 1990년부터 2011년까지의 『가계동향조사』와 국가통계포털에서 제공하는 제반 사회지표들의 시계열 자료를 병합하여 활용하였고, 분석방법으로는 위계적일반화선형모형을 이용하였다.BR 분석결과는 다음과 같다. 첫째, 경제위기 이후 한국의 상대빈곤이 상당히 심화되었음을 확인하였다. 이러한 빈곤의 심화에는 경제성장의 둔화, 소득불평등의 심화, 노동시장 상황의 악화 등 제반 거시적 사회환경의 변화가 일련의 배경이 된 것으로 파악되었다. 둘째, 미시자료의 분석결과에서는 선행연구들에서와 마찬가지로 빈곤층에서 개인 및 가구의 취약한 특성의 보유율이 상대적으로 높은 것으로 분석되었다. 마지막으로 다층분석결과 한국사회의 가구빈곤에는 취약한 개인 및 가구특성 뿐 아니라 개인들이 통제하기 어려운 제반 거시적 사회환경 측면들에 의해 주요하게 결정되고 있음을 경험적 분석을 통해 확인하였다. 이상의 결과를 바탕으로 우리사회의 빈곤문제 완화를 위해 미시적 측면에만 초점을 둔 정책적 접근에서 탈피하여 사회환경 및 구조 개선과 같은 거시적 측면을 포괄하는 적극적 대응이 요청됨을 정책적 함의로 제시하였다.BR

Assessment of Rural Households Poverty in Nigeria: Evidence from Ogbomoso Agricultural Zone of Oyo State, Nigeria

Poverty is a topical issue in developing countries especially Africa and Nigeria in particular. The dearth of studies on quantitative determinants of poverty in Nigeria is a major weak point in the countrys p overty reduction policy and strategy formulation. This study analyzed the incidence, severity and the determinants of household poverty in Ogbomoso Agricultural Zone of Oyo State, Nigeria using the data collected through well - structured questionnaire from120 respondents who were selected through a multistage sampling technique.Analytical techniques used include descriptive statistics (frequency counts, percentages and mean values),poverty Indicesand ordinary least square technique (OLS); this was used t o assess the determinants of poverty among the households. The findings revealed that poverty is higher among households that have old heads, female heads, and large household size, engage in farming as the only occupation and have no formal education; poverty therefore rises with the increase in household size while it reduces with increase in level of education, farm size and participation non -farm jobs as alternative livelihood source (livelihood diversification). Gender, household size, years spent in school, farm size and non-farm jobs were found to be important and significant factors determining poverty in the study area. The study recommends that,

Determinants of Poverty During Transition: Household Survey Evidence from Ukraine

The paper analyzes the incidence, the severity and the determinants of household poverty in Ukraine during transition using two comparable surveys from 1996 and 2004. We measure poverty using income and consumption and contrast the effects of various poverty lines. Poverty in both periods follows some of the determinants commonly identified in the literature, including greater poverty among households with children and with less education. We also identify specific features of poverty in transition, including the relatively low importance of unemployment and the existence of poverty even among households with employment. Poverty determinants change over time in line with the experience of transition and restructuring.

Adoption of Improved Cassava Varieties and Its Welfare Impact on Rural Farming Households in Edo State, Nigeria

ABSTRACT Poverty in Nigeria is an overwhelmingly grave problem and has been on the increase for many decades, being endemic to rural areas where the main occupation is farming. In recent times, technological advancement-especially in agriculture-has been very instrumental in reducing the poverty problem through the development of genetically modified crop varieties such as cassava. It is a crop of long history and has been acknowledged as a food security crop. This study examines the various factors influencing the adoption of improved cassava varieties and its impact on the welfare of rural farmers in Edo State, Nigeria. The study utilizes cross-sectional data collected through personal interviews of 150 farmers from the three territorial districts in Edo State. The personal interviews were conducted with the aid of a structured questionnaire. The Foster, Greer, and Thorbecke (FGT) class of measures was used to determine the incidence, depth, and severity of poverty among farming households who are adopters and non-adopters of improved cassava varieties. The results showed that the incidence, depth, and severity of poverty were higher amongst households who are non-adopters of improved cassava varieties. The Tobit regression model was used to determine the factors that affect adoption and poverty. The results showed that sex, age, access to extension agents, access to inputs, and crop yield were significant variables positively influencing adoption of improved cassava varieties. The results of the determinants of household poverty revealed that age, household size, years of education, and extent of commercialization were significant variables. Age, years of education, and extent of commercialization influenced household poverty in the negative direction, implying that a unit increase in any of the variables will lead to a decrease in household poverty. Household size, on the other hand, moved in a positive direction, implying that a unit increase in that factor will lead to an increase in household poverty. Implicit in these results is that, in order for poverty alleviation to be effective, human capital such as education should be emphasized. Extension services should also reach greater depths in which campaigns are staged to promote the relevance of new innovations, which are labour-saving and cost-effective.