Household Poverty Status Research Articles

Factors Influencing The Food Security And Nutrition Expenditures In Farm Households In Punjab, Pakistan

The study was designed to explore the preventive side of a healthy farming community which depends on better food intake and the patterns of food and its linkages with socio-economic status of the farmers in the province of Punjab. The aim of the study was to identify the important factors that contribute to poverty amongst the farm households. The study also determines the factors responsible for the nutritious food consumption patterns amongst the farm households. In order to find the important determinants of poverty amongst farmers, we have used the binary logistic regression. The results of the study revealed that education, family size, crops diversification, time given to farm activity and farm size are significant factors in determining the poverty status of farming households. The results also depicted that small farmers spend fewer amounts on nutritious food items than big farmers. This study found a positive impact of diversified cropping patterns on food consumption amongst farmers. The level of education was also found to be significantly affecting the food consumption expenditures. The study recommended that pro-poor policies be implemented to increase labour productivity and improve food consumption among farmers. Educating farm families about the value of a healthy diet will help them make better food choices. Furthermore, the value of crop diversification must be recognized in order to lift farm households out of poverty and ensure their food security.

DETERMINAN TINGKAT KEMISKINAN DI KOTA BANDUNG

This study aims to find the determinants of household poverty status in the city of Bandung by using the National Socioeconomic Survey (Susenas) of March 2018 Expenditure Consumption in Bandung. The methodology used is an ordered logit regression model. The results showed that the demographic characteristics variables that had a significant and positive effect on the poverty status of households in the city of Bandung were female household heads, marital head households and household size and dependency burden. Meanwhile, economic characteristics, namely household access to the internet, ownership of mobile phones (HP), ownership of one's own residence, and access to business credit have a negative and significant effect on household poverty status in the city of Bandung. Furthermore, social characteristics that negatively affect the status of household poverty in the city of Bandung are variable levels of school education as measured by the years of schooling.

Analysis of Poverty Status of Ebonyi State Farming Households

Poverty is a major menace in Nigeria. Therefore, the research centered on the analysis of poverty status of farming households in Ebonyi State. Multi stage and simple random sampling techniques were used to select 450 respondents for the study. Primary data were collected using electronic data capturing instrument containing the questionnaire and analyzed using descriptive statistics, Foster, Greer and Thorbeecke (FGT), and Logit regression model. The result from the study shows that 54% of the farming households in Ebonyi state were poor while 46% of them were not poor. The result further reveals that household size, dependency ratio, sex, monthly household expenditure, and farm size were the significant factors that influenced poverty status of Ebonyi state farming households. Therefore, government at the federal, state and local levels should consider socioeconomic characteristics of the farming households in the design and implementation of any poverty driven projects in order to improve their standard of living. Also, government at the various level, should consider embarking on programs to address the youth unemployment and aged members of the Nigerian society for these will reduce the dependency burden which have continued to increase the poverty level in farming households in Ebonyi state.

KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN ALGORITMA C5.0 DI KABUPATEN PEMALANG

Pemalang regency is a district which has amount of poverty around 16.04%. One of the effort that must be improved in tackling poverty is increasing the accuracy of the government program’s target. The improvement of target accuracy is expected to give the better impact on the welfare of the population. This study classified the poverty status of households in Pemalang regency using C5.0 Algorithm. The poverty status of households is divided into two classes, namely poor and non-poor. There was an imbalance of data in both classes. Data imbalances were handled by using Synthetic Minority Oversampling Technique (SMOTE). From the research that has been done, SMOTE application in classification of household poverty status affected the evaluation value of the model. Previously the model could not classify the minority class and after using SMOTE the model produced an average value of sensitivity 25.80%. SMOTE application increased the average value of specificity from 91.16% to 94.91%. However, SMOTE application decreased the average value of accuracy which originally 91.16% down to 82.2%.Keywords : C5.0, Household poverty, Classification, SMOTE

Community Driven Development: The Case of Fadama II Cooperatives in Alleviating Poverty in a Developing Country

Summary This study investigated the effects of Fadama II cooperatives on poverty status of farming households in Oyo State, Nigeria. Specifically, the study assessed the level of participation in Fadama II cooperatives among the participants; determined the factors influencing the level of participation in Fadama II cooperatives; examined the poverty status of the participants in Fadama II cooperatives; and determined the effects of Fadama II cooperatives on the poverty status of the participants. The study was conducted in Oyo State, south-western Nigeria. A multi-stage sampling procedure was used to select respondents for this study. A total number of 150 farmers were selected for the study. Data were analyzed using descriptive statistics, participation index, multinomial logit regression model, Foster, Greer and Thorbecke (FGT) index, and Probit regression model. The result showed that the respondents participated in the designated activities carried out within their respective Fadama II cooperatives, with the maximum participation in payment of counterpart fund (100%), followed by participatory rural appraisal process (86.7%). Participation index revealed that the majority (46.7%) of the respondents had their level of participation above the grand mean. Interestingly, gender, farming experience and income from Fadama II cooperative positively and significantly influenced both partial and full participation in Fadama II cooperatives. The poverty line is observed to be ₦44300 per month ($115.36 per month). The incidence, depth and severity of poverty are estimated to be 0.28, 0.067 and 0.013, respectively. Probit estimates revealed that Fadama II cooperatives had significant and positive effect on alleviating the poverty status of the participants. Following the findings of the study, government and non-governmental organizations are encouraged to ensure that more farmers participate in Fadama II cooperatives. In addition, the approach of Fadama II cooperatives should be adopted for intervention programmes in developing countries that depend on agriculture not only for feeding their population but also as a business venture.

Short-run effects of poverty on asthma, ear infections and health service use: analysis of the Longitudinal Study of Australian Children.

Many studies have reported an inferred causal association of income poverty with physical health among children; but making causal inference is challenging due to multiple potential sources of systematic error. We quantified the short-run effect of changes in household poverty status on children's health (asthma and ear infections) and service use (visits to the doctor and parent-reported hospital admissions), using a national longitudinal study of Australian children, with particular attention to potential residual confounding and selection bias due to study attrition. We use four modelling approaches differing in their capacity to reduce residual confounding (generalized linear, random effects (RE), hybrid and fixed effects (FE) regression modelling) to model the effect of income poverty (<60% of median income) on health for 10090 children surveyed every 2nd year since 2004. For each method, we simulate the potential impact of selection bias arising due to attrition related to children's health status. Of the 10090 children included, 20% were in families in poverty at survey baseline (2004). Across subsequent years, ∼25% experienced intermittent and <2% persistent poverty. No substantial associations between poverty and child physical health and service use were observed in the FE models least prone to residual confounding [odds ratio (OR) 0.94, 95% confidence interval (CI) 0.81-1.10 for wheeze], in contrast to RE models that were positive (consistent with previous studies). Selection bias causing null findings was unlikely. While poverty has deleterious causal effects on children's socio-behavioural and educational outcomes, we find little evidence of a short-run causal effect of poverty on asthma, ear infections and health service use in Australia.

The impact of the Vietnam Bank for Social Policies preferential credit on household welfare in Vietnam: a panel data analysis

PurposeThe study examines the impact of the preferential credit provided by the Vietnam Bank for Social Policies on poverty reduction in Ninh Binh province, Vietnam. It also identifies and ranks the barriers of accessing the credit.Design/methodology/approachThe study applies fixed-effects method to handle the panel data to examine the impact of the credit on poverty reduction. It also uses face-to-face interviews and group discussions to identify and rank the barriers of accessing to the credit.FindingsThe results show that the credit (represented by loan volume) positively and significantly helps improve household income, but does not help to improve household consumption. The major barriers include the time spent to get to the nearest bank branch, banking support services provided to clients and the transparency of household poverty status assessment.Research limitations/implicationsData are collected in three years and the number of the observations is limited to 300 households.Practical implicationsThe VBSP preferential credit may need to be modified to significantly help reduce poverty and the VBSP and involved parties may need to eliminate the barriers so that the poor can have a better access to the credit.Social implicationsThe VBSP preferential credit is one of the reasonable sources that can help eliminate poverty though increasing household income.Originality/valueThe VBSP preferential credit can help increase household income, but does not really help improve household consumption due to the small volume of loans. In addition, banking support services and the household poverty assessment are seen as barriers to the access of the poor.

Impact of climate-smart agricultural technology on multidimensional poverty in rural Ethiopia

A large body of empirical literature showed that the adoption of agricultural technologies reduces poverty. However, dominants of those studies so far used one-dimensional income or expenditure-based measures of poverty which may not reflect other types of deprivations. Therefore, the major objective of this study is to examine the impact of adopting climate-smart agricultural technology, which refers to a joint application of row planting methods and the use of chemical fertilizers, on the multidimensional poverty status of rural households in Ethiopia. To estimate the impact of the stated technologies, this study employs propensity score matching and endogenous switching regression methods. To measure the multidimensional poverty index, the study also uses the Alkire and Foster counting approach. Using the Ethiopian Socioeconomic Survey (ESS), collected in 2015-Wave 3, the results of the study show that the adoption of these technologies reduces deprivation score and one of its component, the standard of living part. Regionally, a high reduction in deprivation is observed in Amhara and Oromiya regions. The results also show that the impact is significantly higher in severely deprived households. It is also observed that the reduction in multidimensional poverty due to the technology is through an increase in income/consumption via improvement in production gain. The impact channels more through the non-food expenditure pathway. Finally, this study also sheds light on the effects that technology adoption has on multidimensional poverty reduction.

Effects of cuniculture commercialization on household poverty status in south western Nigeria

This study evaluated the effect of Cuniculture commercialization on household poverty in Osun State, Nigeria. Descriptive and inferential statistics of frequency counts, Foster-Greer-Thorbecke (FGT), Household Commercialization Index (HCI) and Tobit Regression was used to analyze data collected from 120 respondents randomly selected from the register of Rabbit Farmers and Breeders Association of Nigeria (RFABAN) of Ife/Ijesha Agricultural development Project (ADP). The results of the study show that Cuniculture is a male dominated enterprise. The modal age of respondents was 18-60 with 87.5% of respondents educated. Majority of respondents earned between NGN20000 - NGN100000 monthly from Cuniculture. The study found that 16% of respondents were poor and living below the poverty line. The HCI of Cuniculture indicates that 54.2% of production was done mainly for household consumption. Access to credit and unavailability of markets were the major constraints with Household size and access to credit influencing the commercialization of Cuniculture. The study recommends that more people be encouraged to go into Cuniculture to serve as additional income to household; rabbit farmers become more market oriented beyond their present level; they organize themselves into cooperative for access to credits and market creation and that appropriate solutions be sought for the challenges encountered.

Energy poverty measures and the identification of the energy poor: A comparison between the utilitarian and capability-based approaches in Chile

This work explores the consequences that different energy poverty definitions and measures might have for the identification of the energy poor. Using the 2017 National Survey of Public Energy Perception applied to a sample of 3,500 households in Chile, we compare the respective identification outcomes of applying the ten percent rule index (TPRI) and our proposed Perception-based Multidimensional Energy Poverty Index (PMEPI) against the monetary poverty identification outcome. Based on the findings of this comparation, we propose a classification system of first- and second-order energy poverty measures depending on their degree of association with income poverty (as indicated by the distribution of the income poverty status of households). A first-order energy poverty measure exhibits a high association level with income poverty. By contrast, a second-order energy poverty measure shows a low level of association. Coincidentally, our TPRI (first-order) and PMEPI (second-order) estimates each classify 15.5% of the population as energy poor. However, the adoption of any particular definition necessarily narrows the resulting set of energy-poor households in a way that is distinct from other definitions, meaning that the use of multiple definitions produces diverging energy poverty rankings across the territory. Moreover, the TPRI neglects supply-side constraints captured by the PMEPI. Consequently, when identifying and targeting the energy poor, first- and second-order definitions should not be used as substitutes but rather as complements. This fact needs to be considered in the energy policy debate on the implementation of energy poverty alleviation actions.

A Distributional Analysis of Uni-and Multidimensional Poverty and Inequalities in Ethiopia

This study analyzes uni-and multidimensional poverty and inequalities in rural and small towns in Ethiopia. Unlike the unidimensional measure, the multidimensional measure of poverty shows all the channels through which poverty may manifest itself; it also shows the extent of deprivation. The analysis uses 6 dimensions with 14 indicators to construct a multidimensional index of poverty and inequalities using Ethiopian Households’ Socioeconomic Survey dataset. The study also uses multiple correspondence analyses for determining relative weights in computing a multidimensional index and conducts a stochastic dominance analysis of distribution of poverty for different population segments. The paper sheds light on the degree of inequalities in consumption expenditure and multidimensional deprivations. In addition, it also compares the degree of poverty using the conventional measure of poverty and the multidimensional approach. It also examines the determinants of household poverty status using both unidimensional and multidimensional measures using the logit model. The results show that the intensity, severity, and depth of poverty varies substantially across the 2 measures. The unidimensional measure of poverty shows that 36 percent of the households were poor as compared to 46 percent multidimensionally poor households. Moreover, demographic, regional, and household heads’ characteristics also affect households’ poverty status.

Poverty Classification Using Machine Learning: The Case of Jordan

The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.

Neighbourhood maternal socioeconomic status indicators and risk of congenital heart disease

BackgroundThis study aimed to examine the relationships between various maternal socioeconomic status (SES) indicators and the risk of congenital heart disease (CHD).MethodsThis was a population-based retrospective cohort study, including all singleton stillbirths and live births in Ontario hospitals from April 1, 2012 to March 31, 2018. Multivariable logistic regression models were performed to examine the relationships between maternal neighbourhood household income, poverty, education level, employment and unemployment status, immigration and minority status, and population density and the risk of CHD. All SES variables were estimated at a dissemination area level and categorized into quintiles. Adjustments were made for maternal age at birth, assisted reproductive technology, obesity, pre-existing maternal health conditions, substance use during pregnancy, rural or urban residence, and infant’s sex.ResultsOf 804,292 singletons, 9731 (1.21%) infants with CHD were identified. Compared to infants whose mothers lived in the highest income neighbourhoods, infants whose mothers lived in the lowest income neighbourhoods had higher likelihood of developing CHD (adjusted OR: 1.29, 95% CI: 1.20–1.38). Compared to infants whose mothers lived in the neighbourhoods with the highest percentage of people with a university or higher degree, infants whose mothers lived in the neighbourhoods with the lowest percentage of people with university or higher degree had higher chance of CHD (adjusted OR: 1.34, 95% CI: 1.24–1.44). Compared to infants whose mothers lived in the neighbourhoods with the highest employment rate, the odds of infants whose mothers resided in areas with the lowest employment having CHD was 18% higher (adjusted OR: 1.18, 95% CI: 1.10–1.26). Compared to infants whose mothers lived in the neighbourhoods with the lowest proportion of immigrants or minorities, infants whose mothers resided in areas with the highest proportions of immigrants or minorities had 18% lower odds (adjusted OR: 0.82, 95% CI: 0.77–0.88) and 16% lower odds (adjusted OR: 0.84, 95% CI: 0.78–0.91) of CHD, respectively.ConclusionLower maternal neighbourhood household income, poverty, lower educational level and unemployment status had positive associations with CHD, highlighting a significant social inequity in Ontario. The findings of lower CHD risk in immigrant and minority neighbourhoods require further investigation.

DETERMINAN PEMBERIAN ASI EKSKLUSIF OLEH IBU YANG MEMILIKI BADUTA

Dalam menentukan derajat kesehatan, terdapat beberapa indikator yang dapat digunakan salah satunya Angka Kematian Bayi (AKB). Pada tahun 2017, AKB di Indonesia masih ada sekitar 24 kasus dari 1000 kelahiran hidup. Padahal, Sustainable Development Goals (SDGs) menargetkan pada tahun 2030 AKB pada seluruh negara di dunia turun menjadi 12 kasus dari 1000 kelahiran hidup. Salah satu upaya untuk menurunkan AKB adalah dengan memberikan ASI eksklusif. Selama periode 2015-2017, persentase bayi yang mendapat ASI eksklusif di Kalimantan Barat selalu berada dibawah angka nasional dan bahkan menjadi yang terendah di Pulau Kalimantan. Penelitian ini bertujuan untuk mendeskripsikan karakteristik ibu yang memiliki baduta untuk memberikan ASI eksklusif, mengetahui variabel-variabel yang berpengaruh serta menganalisis kecenderungannya terhadap pemberian ASI eksklusif oleh ibu yang memiliki baduta di Provinsi Kalimantan Barat tahun 2019. Hasil dari penelitian ini adalah secara umum, pemberian ASI eksklusif di Provinsi Kalimantan Barat banyak ditemukan pada ibu yang berusia 25-34 tahun, berpendidikan sekolah menengah ke atas, tinggal di perdesaan, berstatus tidak bekerja, bukan rumah tangga miskin, dan penolong saat melahirkan adalah tenaga medis. Selanjutnya, variabel-variabel yang berpengaruh signifikan terhadap pemberian ASI eksklusif oleh ibu yang memiliki baduta di Provinsi Kalimantan Barat tahun 2019 yaitu tingkat pendidikan, status bekerja, dan status kemiskinan rumah tangga.

Determinants of Household Poverty Status in Kediri City

Analyses of the causes and the characteristics of poverty at micro levels provide more efficient strategies for the attainment of main Sustainable Development Goals. This study aimed to analyze the extent to which the characteristics of individuals, households, and communities influence the probability of household poverty status. The 2019 Social Welfare Integrated Data and Village Potential Data of Kediri City were analyzed using an ordered logit regression model and then interpreted based on marginal effect calculation. The study found that household heads’ squared-age, household members’ education, household members’ occupation, household head gender (female), ownership of assets, access to the internet, access to proper sanitation, and access to financial institutions reduced the probability of households being categorized as very poor and poor. This finding indicated that household productivity influenced by the household head’s characteristics in managing productive assets, supported by access to infrastructure, could increase the household's welfare. However, the household head’s age and marital status, dependency ratio, and access to health facilities increased household’s probability of being very poor and poor. Policies regarding poverty must be adjusted to the poverty characteristics and status. Improving access, equalizing education, and improving job opportunity and infrastructure management that ensure accessibility and enhancement in service quality need to be made to increase the status of households with the lowest 40% welfare in Kediri City. Policies regarding poverty should be focused more on social programs for very poor and poor households. Meanwhile, those near-poor and vulnerable-to-poor need more empowering programs.

Poverty status and its determinants in rural households of Enda-mohoni woreda, Northern Ethiopia

This research generates specific, contextualized identification of existing poverty status and poverty causing factors in Enda-mohoni woreda in Tigray Region, Ethiopia. Agroecology based cluster sampling technique was employed to select 154 household heads. Logit model was used to analyze household poverty status and FGT poverty index estimation model for poverty incidence analysis. The poverty analysis found a 30.9% headcount ratio, 4.4% poverty gap ratio, and 1% poverty severity. Furthermore, the result of the logistic regression revealed that among the explanatory variables used in the model, family size and agroecological location of the household head were found to positively influence HHs' poverty status at (P&lt;0.01) and (P&lt;0.05) respectively. Whereas, owning livestock and marital status of the HHH were found to negatively influence HHs' poverty status at (P&lt;0.05) and (P&lt;0.1) respectively. It is with appropriate policies that recognize the importance of poverty features and trends would it be possible for more people to make positive exits from poverty risk.

Can Credit Recipient Household Escape from Poverty?

This study aims to answer whether or notfinancial distribution can play a role in development and poverty alleviation in Indonesia. Households who receive credit are treated as the analysis level and research object. Credit is an instrument to help households escaping from poverty. Some opinions believe that by being given credit, households will be able to boost their economic capacity both in terms of purchasing power or business development capacity. However, to prove this opinion as well as to answer the question, using panel data from the Indonesian Family Live Survey (IFLS) in 2007 and 2014 will attempt to estimate the effect of the probability of households receiving credit on their poverty status. The probability of a credit recipient householdwill be calculated using Propensity Score Matching so that a similarity score of household characteristics will be obtained between those who get credit and those who do not. Using Double Differences, this study will address the description of changes in household poverty status after receiving credit from financial institutions. The PSM calculations results show that there are four variables as credit recipient household’s characteristics, namely collateral ownership, the status of property ownership, history of natural disasters, and gender. Meanwhile, the estimation results on poverty status indicate that credit recipient households have a greater probability of escaping poverty than those who do not receive credit. Therefore, the anti-poverty policy through the transmission of financial institutions is relevant to be prepared. The anti-poverty policy is related to low credit interest rates through government subsidies, public fund placement with low cost of fund, increasing financial literacyand knowledge of the society, and adjusting credit approval based on regional economic conditions.

Social capital networks, microcredit and poverty status of rural households in Nigeria

This study examined how social capital networks contribute to rural households’ poverty status in Southwestern Nigeria. A multistage sampling procedure was used to select a total of 300 households for this study. A structured questionnaire was used to obtain information and data were analyzed using descriptive statistics, Foster, Greer and Thorbecke (FGT) poverty measure and Two-Stage Least Square model (2SLS). Results showed that poverty incidence, depth and severity were 60%, 46.70% and 20.10% respectively among the sampled households. The results indicated that forms of social capital networks in the study area include cooperative societies, family and friends, farmers’, professional career, religious, and microfinance groups. The results further showed that 66.00% of the households in the study area sourced microcredit from cooperative societies. The 2SLS estimate showed that the coefficient of the aggregate social capital index (β =730.83, p &lt; 0.05) also showed a positive, significant relationship with household per capital expenditure. The result indicated that a unit increase in social capital network index of the household would increase household per capita expenditure in the study area by N730.83. The study concluded that membership of social capital networks positive influence households’ access to access to microcredit and poverty reduction.

Analysing the Determinants of Poverty among Female-Headed Households: The Case of South Africa

Poverty remains a critical and complex phenomenon in developing countries, South Africa included. Over the years, poverty has escalated significantly, particularly in Africa, while it has declined in most developed regions. The worsening statistics in Africa have raised concerns of a lack of understanding of poverty among policymakers and governments. In South Africa, poverty has been increasing, with more than 50 percent of households living in poverty. Additionally, approximately 26 percent of females live in extreme poverty, while at the same time the country is experiencing a growing trend of female-headed households continually identified as vulnerable to the indignities of poverty compared to male-headed households. Although poverty in South Africa has been studied extensively, existing literature shows that it is still a debatable topic, since the dynamics have not improved much, but rather worsened in recent years particularly post-apartheid. Using the 2018 general household survey data with a sample of 19 219 observations collected by Statistics South Africa, the primary objective of the study is to examine the determinants of poverty among female-headed households in South Africa. The study uses the following statistical methods: descriptive statistics, cross-tabulations and a binary logistic regression. The estimated results show that factors such as education level, employment status, age, gender, race, household size and marital status determine the poverty status of female-headed households. Females experience higher levels of poverty when compared to their male counterpart using the upper bound poverty line. Keywords: Poverty, household, female-headed household, male-headed households, South Africa

IMPACT OF ADOPTION OF QUALITY PROTEIN MAIZE ON POVERTY STATUS OF RURAL FARMING HOUSEHOLDS IN NORTHWEST, NIGERIA

The study assessed impact of adoption of quality protein maize (QPM) on rural poverty status of households in Northwest, Nigeria. The study collected data from 652 households using descriptive statistics; t-test, poverty decomposition, propensity score matching, average treatment effect, and potential outcome mean to analyze the data. The study found that about 29% - 31% (N351, 894 - N401, 658) of household expenditure goes to the purchase of food. It was equally established that 32% - 37% (N111, 878 - N141, 783) of household’s non-food expenditure was on cloth, transport, school fees and school materials. The average per capita expenditure poverty for farmers was N90.5 to N2863.60 per head per day. For farmers that do not adopt QPM, their current multidimensional poverty was 38% and had they choose to produce QPM, their multidimensional poverty index (MDPI) would be 34.7%. The current expenditure poverty status was 33% and had they choose to produced QPM, their expenditure poverty would be 61%. The study recommended increase in the publicity of QPM through bulletin, guide, and radio jingle in the study area.