Poverty Index Research Articles

Prevalence of food insecurity among caregivers of young children during the COVID-19 pandemic in Ireland.

Our objectives were to explore the prevalence of food insecurity in primary caregivers of young children in Ireland and to compare two tools for measuring food insecurity during the COVID-19 crisis. A cross-sectional online survey was conducted among caregivers of children aged <2 years in Ireland in May/June 2020. Relevant survey questions were closed-ended using two established tools for measuring food poverty/insecurity; the Irish Food Poverty Indicator and the Food Insecurity Experience Scale, developed by the Food and Agriculture Organization. Descriptive statistics were used to calculate the prevalence of food poverty/insecurity. To explore agreement in the classification of food insecurity by the Food Poverty Indicator and the Food Insecurity Experience Scale, Cohen's κ was used. Analyses are based on 716 participants; most had a 3rd-level education and were married or in a partnership. Per the food poverty index, Ireland's national measure of food poverty, 3.9% (n = 28) of our sample were experiencing food poverty. This rose to 10.5% (n = 75) experiencing food insecurity when using the Food Insecurity Experience Scale, which also measures worry/anxiety around access to food. There was low agreement between the tools, with 11.3% of the sample classified as food secure by one tool and food insecure by the other. Our current measure of food poverty in Ireland may not be sufficient to describe the food-access struggles or worry/anxiety about food access, experienced by the population, particularly during an emergency like COVID-19.

Role of SHG Federations in Promoting Economic and Social Empowerment of Women: Empirical Evidence from a Project in Madhya Pradesh, India

Self-help group federations (SHGFs) in India have come to be recognized as catalytic drivers of women’s economic and social advancement. Over time, their role has expanded to promoting grassroots community leadership, village development and undertaking advocacy initiatives for women’s greater inclusion and participation in their households, communities, and economy. This paper expounds the role and impacts of SHGFs in promoting women’s economic and social empowerment in Madhya Pradesh, one of India’s most densely populated and poverty affected States. With over one third of its population (roughly 25 million people) living in poverty (as per Niti Aayog’s Multidimensional Poverty Index, 2021), the State lags behind in critical human development indicators such as malnutrition, infant mortality, education, health, lack of clean drinking water, skills and employment opportunities. Women disproportionately suffer the brunt of these challenges coupled with regressive social and cultural norms that subject them to myriad forms of marginalization and discrimination. Over the last decade, Hand in Hand India (HiHI), a leading NGO championing gender equality through job creation and financial inclusion has been actively working for women’s empowerment, livelihoods promotion and integrated community development in Madhya Pradesh. Under one of our projects in Madhya Pradesh, ‘UNITED: Bringing Unity to Improve Livelihoods and Women Empowerment,’ we worked with a network of 200 SHGFs representing over 20,400 women over 3 years, between 2019 and 2022, where a majority of the women came from backward communities and were supported with integrated training and capacity building programs for livelihoods promotion. Additionally, SHGFs were trained and mentored to become frontrunners for advancing enterprise creation, women’s empowerment and community development. This paper presents empirical evidence of positive transformations witnessed in women’s social and economic spheres that enabled them to expand their agency and improve their lives, livelihoods and communities in significant ways.

Towards closing socio-economic status disparities in COVID-19 premature mortality: a nationwide and trend analysis in Chile.

Socio-economic status (SES) disparities in coronavirus disease 2019 (COVID-19) mortality have been reported but complete information and time trends are scarce. In this study, we analysed the years of life lost (YLL) due to COVID-19 premature mortality during the pandemic in Chile and its evolution according to SES and sex compared with a counterfactual scenario [cerebrovascular accidents (stroke)]. We used Chile's national mortality databases from 2020 to 2022. YLL and age-standardized YLL and mortality rates by sex and by epidemic waves were determined. The 346 communes were stratified into SES groups according to their poverty index quintile. Negative binomial regression models were used to test trends. In >2 years of the pandemic, the COVID-19 YLL was 975 937, corresponding to 61 174 deaths. The YLL rate per 100 000 inhabitants was 1027 for males and 594 for females. There was a heterogeneous distribution of YLL rates and the regional level. Communes in the most advantaged SES quintile (Q5) had the highest YLL during the first wave compared with those in the lowest SES quintile (Q1) (P < 0.001) but the opposite was true during the second wave. COVID-19 YLL trends declined and differences between Q1 and Q2 vs Q5 converged from the second to the fourth waves (0.33 and 0.15, Ptrend < 0.001 and Ptrend = 0.024). YLL declined but differences persisted in stroke (-0.002, Ptrend = 0.979). COVID-19 deaths resulted in a higher impact on premature death in Chile, especially in men, with a heterogeneous geographic distribution along the territory. SES and sex disparities in COVID-19 premature mortality had narrowed by the end of the pandemic.

Estimation of Poverty Measures for Small Areas Under a Two-fold Nested Error Linear Regression Model: Comparison of Two Methods

Demand for reliable statistics at a local area (small area) level has greatly increased in recent years. Traditional area-specific estimators based on probability samples are not adequate because of small sample size or even zero sample size in a local area. As a result, methods based on models linking the areas are widely used. World Bank focused on estimating poverty measures, in particular poverty incidence and poverty gap called FGT measures, using a simulated census method, called ELL, based on a one-fold nested error model for a suitable transformation of the welfare variable. Modified ELL methods leading to significant gain in efficiency over ELL also have been proposed under the one-fold model. An advantage of ELL and modified ELL methods is that distributional assumptions on the random effects in the model are not needed. In this article, we extend ELL and modified ELL to two-fold nested error models to estimate poverty indicators for areas (say a state) and subareas (say counties within a state). Our simulation results indicate that the modified ELL estimators lead to large efficiency gains over ELL at the area level and subarea level. Further, modified ELL method retaining both area and subarea estimated effects in the model (called MELL2) performs significantly better in terms of mean squared error (MSE) for sampled subareas than the modified ELL retaining only estimated area effect in the model (called MELLI). AMS Subject Classification: 62D05, 62G09

Small Area Poverty Estimation Under Heteroskedasticity

Abstract Multilevel models with nested errors are widely used in poverty estimation. An important application in this context is estimating the distribution of poverty as defined by the distribution of income within a set of domains that cover the population of interest. Since unit-level values of income are usually heteroskedastic, the standard homoskedasticity assumptions implicit in popular multilevel models may not be appropriate and can lead to bias, particularly when used to estimate domain-specific income distributions. This article addresses this problem when the income values in the population of interest can be characterized by a two-level mixed linear model with independent and identically distributed domain effects and with independent but not identically distributed individual effects. Estimation of poverty indicators that are functionals of domain-level income distributions is also addressed, and a nonparametric bootstrap procedure is used to estimate mean squared errors and confidence intervals. The proposed methodology is compared with the well-known World Bank poverty mapping methodology for this situation, using model-based simulation experiments as well as an empirical study based on Bangladesh poverty data.

Households’ Factors Influencing Choice of Waste Management Strategies in Lagos State, Nigeria

The study examined the factors influencing Households' choice of waste management strategies in Lagos State, Nigeria. A multi-stage sampling procedure was used to select respondents for the study. In the first stage, two states (Lagos state and Ogun state) were purposively selected based on their well-organized waste management systems, relatively advanced waste management arrangements and close proximity. In the second stage, three LGAs were purposively selected from Lagos state, while three LGAs were selected in Ogun state considering well-organized waste management systems. In the third stage, a stratified sampling technique was used to select four communities from each LGA into two different strata, namely: high-income and low-income stratum. In the fourth stage, fifteen respondents were selected using a systematic sampling technique. In all, 360 respondents were used for the study. &#x0D; Primary and secondary data collected from 360 respondents were analyzed using descriptive statistics, multidimensional poverty index, multinomial logit, regression model, contingent valuation and probit regression model. The average age of the household head was 47 years, which implies that the respondents were mostly middle-aged people who were still within their economically active age group. This is expected to positively influence the respondents' decision-making as they would be making sound decisions regarding waste management since they are likely to be more exposed to different health-related information. The result further proves that the majority (51.1%) of the household heads in Lagos and Ogun States were male. The higher percentage of male-headed households in the study area suggests that they were the breadwinners of their respective households. The Multinomial logistic regression result showed that years spent in school, household size, cost of waste disposal, membership of cooperative, biodegradable and transfer payment were the major factors that determine the acceptance of waste management strategies alternatives available in the study area.

Digital Health Literacy and Social Determinants of Health Affecting Telehealth Use by Athletic Trainers.

While increasing telehealth use throughout sports medicine has improved patients' access to health care, some communities may not have the same opportunities to connect with a provider. Barriers to telehealth implementation can be influenced by internal (eg,provider's digital health literacy and resources) and external (eg,community's social determinants of health or "SDOH") factors. This study aimed to assess the impact of internal and external factors on telehealth use by athletic trainers (ATs). Cross-sectional survey. In total, 767 ATs participated in the study. Participants (age = 39 [13]y) completed a survey containing the electronic health literacy scale and digital health literacy instrument, reported professional use of telehealth as a provider (yes/no), provided resources at their clinical site, and provided the zip code for the community they served. After data collection, the researchers extracted SDOH information using the zip code data from 2 US databases, including population density, median household income, poverty index, education level, and technology access. Chi-square or independent samples t tests were conducted to compare telehealth use by each SDOH factor. In total, 62.3% (n = 478/767) of ATs reported using telehealth, and 81.6% of ATs (n = 626) had a dedicated facility to offer health care services. We identified a significant difference in digital health literacy scores between users and nonusers of telehealth (P = .013). We did not identify any significant differences between telehealth users by community type (P = .957), population density (P = .053), income (P = .462), poverty index (P = .073), and computer (P = .211) or broadband internet access (P = .295). Our data suggest that internal factors such as digital health literacy and clinical site resources may have contributed to an AT's previous telehealth use in clinical practice. However, the SDOH data extracted from the community zip code where the AT provided clinical services were similar for those with and without previous telehealth use.

Transición sociotécnica en el sector de la palma aceitera: análisis desde una perspectiva multinivel en el municipio Colombiano de Tibú

Abstract Changes in the interactions between agents involved in the value chain of the palm oil sector in the municipality of Tibú (Colombia) over the period 1985-2020 have resulted in a reduction in the indices of poverty, social inequality, and unemployment for the inhabitants of the region, which can be explained by a process of socio-technical transition. The main objective of this study is to analyze the factors that determined the success of this process from a multilevel perspective. Exploratory research was developed based on a case study analysis using semi-structured interviews of commercial and non-commercial agents in the sector and information collected from secondary sources. The results show that changes in the legal and regulatory framework established by the government, landscape transformation, and the transition from small farmers to medium entrepreneurs of the farms in the region were important factors revolutionizing the system and are encouraging the emergence of new alternative practices in the palm oil sector.

POVERTY AND INEQUALITY IN NIGERIA: A HOUSEHOLD-LEVEL FOUR-WAVE COMPARATIVE STUDY

Poverty and income inequality have been a recurring challenge in the global space for more than five decades. While these challenges have been declining globally, they have remained unabated and rising in Nigeria; thus, making Nigeria tagged as their destination. Thus, the research set out to evaluate the poverty and inequality in Nigeria using a household four-wave of secondary panel data of 4,000 nationally representative households of the Nigeria General Household Survey GHS - Panel collected by the National Bureau of Statistics from 2010 to 2019. Analytical tools used were descriptive statistics, the Foster-Greer-Thorbecke (FGT) poverty index, Lorenz’s curve, and the Gini coefficient. The results of the Foster-Greer-Thorbecke poverty headcount showed that over 50% of all the respondents remained under the poverty line throughout the four waves, thus indicating the prevalence of chronic poverty rather than transient poverty during the period. The general poverty depth ranged from 0.21 to 0.23 and the poverty severity ranged from 0.11 to 0.12. The Gini coefficient of inequality for total expenditure ranged from 0.39 to 0.44 with higher sectoral inequality. The results showed that Nigeria had chronic poverty, moderate poverty depth, and severity; and moderate inequality. Therefore, with strong political will and consistently right policies, Nigeria can exit extreme poverty. This work unraveled the reason behind the persistently high poverty status and inequality of Nigeria and has revealed the path not taken, which if done would enable Nigeria to exit extreme poverty and hunger.

Poverty Analysis and Prediction in South Africa Using Remotely Sensed Data

This study aimed to analyse and predict the state of poverty in South Africa using remotely sensed data. In the developing world, trustworthy knowledge is still hard to come by for economic survival. One of the 17 sustainable development goals listed by the United Nations is to eradicate poverty. Ground‐level survey data, such as household consumption and wealth, were historically the primary sources for evaluating poverty. As technology has advanced, one method that is now used by both wealthy and developing nations is to estimate the poverty index of a region using remote sensing data, such as satellite images, and a machine learning algorithm. This method uses a highly structured, reasonably priced, and easily accessible data source. This machine learning method calculates the poverty index for a given year and determines how the index relates to other years. Inception NetV3, ResNet50 and convolutional neural network are trained beforehand to estimate the intensity of the night‐time illumination matching to the input daytime satellite images. The suggested model also forecasted the cluster wealth score and created a link between wealth scores derived from satellite images and data from the Demographic and Health Survey (DHS). The study investigates a possibly more affordable alternative technique for estimating poverty. The findings imply that satellite data and machine learning can be useful techniques for estimating poverty. By analysing the resilience of the generated estimates to user‐specified algorithmic parameters and model specifications, the work adds to the body of the current literature.

Examining approaches to material hardship classification: Cross-sectional and longitudinal perspectives

Material hardship is a significant indicator of poverty, offering direct insights into the inability to afford necessities. When examining material hardship, often a decision lies in determining the boundaries that distinguish those who experience hardship from those who do not. This decision holds significance given the threshold for differentiating hardship from non-hardship populations guides poverty identification, informs assessments of the impacts of material hardship, and directs poverty reduction initiatives. While there are several material hardship classification approaches, there is limited research investigating the effects of different approaches on outputted proportions of material hardship. This study examines three common methods for classifying families based on their material circumstances to assess the consistency and variability in who is classified as living in hardship. Data from four waves of the Growing Up in New Zealand longitudinal study (n = 6575), spanning early childhood through to early adolescence, were utilised to explore the outcomes of these classification approaches across the four waves. Findings reveal differences in the prevalence rates and interpretations of material hardship over time, depending on the adopted method. This research demonstrates the influence of a researcher's choice of classification method on the understanding of material hardship within a given population. Setting thresholds is not a ‘one-size-fits-all’ pursuit but a decision requiring careful consideration. Consequently, this study offers insights to equip researchers in selecting an appropriate method aligned with their specific research aims. Clear reporting of the chosen method is encouraged to ensure informative assessments.

Multidimensional Poverty: Complementary Measure to Absolute and Relative Poverty

Poverty is a complex issue that involves more than just income levels, affecting various aspects of life such as health, education, and living conditions. The ongoing discussion about how to measure poverty, whether through absolute or relative terms, reflects the challenges in assessing it. Absolute poverty is based on a set standard that focuses on meeting basic needs for survival, while relative poverty looks at income disparities within a society. This study explores existing research on multidimensional poverty and examines the debate between absolute and relative poverty measures, considering their respective advantages and disadvantages. A notable conclusion from the research is that multidimensional poverty indices (MPIs), like the Multidimensional Poverty Index, provide a broad view of deprivation by including multiple factors. MPIs help policymakers design more targeted interventions in areas such as education, healthcare, and living standards. However, absolute poverty measures, such as the World Bank’s $1.90-per-day threshold, are still essential for tracking extreme poverty in developing nations, offering a clear benchmark for international comparisons. In contrast, relative poverty measures are particularly relevant in wealthier nations, where inequality and social exclusion are more prominent factors in poverty. This study highlights gaps in how these different approaches are integrated and calls for further research to improve models that address both absolute and relative poverty. The findings suggest that combining multidimensional, absolute, and relative poverty measures is important for fully understanding poverty and creating effective strategies to reduce it, adapted to the needs of specific regions and contexts.

An Analytical Study of Multidimensional Poverty in Urban and Rural Areas of Iran

This study aims to measure and analyze multidimensional poverty in Iran using the Alkire-Foster (AF) index across five dimensions: education, employment, living standards, health, and housing. The study period covers the years 2017 to 2019. The results indicate that approximately 28% of individuals in rural areas and 25% in urban areas are poor. Additionally, about 51% of the poor live in rural areas, while 49% reside in urban areas. The highest deprivation in both regions pertains to employment, accounting for around 40%. In other dimensions, deprivation rates are 16% in education, 4% in living standards, 24.5% in health, and 15.5% in housing. The multidimensional poverty index (MPI) is calculated to be 10% for urban areas and 11.5% for rural areas. The findings show that women, particularly in the education dimension, experience higher levels of deprivation in both urban and rural areas. This study demonstrates that poverty is a multidimensional phenomenon, and effective policies for poverty reduction must address all its dimensions. Based on the results, improving employment conditions and enhancing living standards are primary priorities for reducing poverty. Additionally, special attention to improving health and education services, especially for women, is essential. The research indicates that targeted and precise policies can significantly reduce poverty and social inequalities.

The Impact of Poverty on Northern Nigeria’s Development: A Comprehensive Analysis

Poverty in Nigeria significantly hampers the nation’s developmental progress, with Northern Nigeria bearing the brunt of this socioeconomic issue. Despite the country’s rich agricultural and natural resources and a relatively significant industrial sector, Nigeria remains one of the world’s poorest countries, with up to 70% of its population living in poverty. This paper explores the multidimensional nature of poverty in Nigeria, utilizing data from the National Bureau of Statistics and other sources to provide an analysis and graphical representation of poverty rates. The 2022 National Multidimensional Poverty Index reveals that 63% of Nigerians, or over 133 million people, live in poverty, with a disproportionate impact on Northern Nigeria. Contributing factors include adverse economic policies, regional disparities, and demographic challenges. This analysis underscores the urgent need for targeted interventions to address the deep-rooted poverty in Northern Nigeria and foster sustainable development across the country. This paper examines the severe impact of poverty on Nigeria’s development, focusing on the acute disparities between Northern and Southern regions. Despite Nigeria’s abundant agricultural and natural resources and a relatively significant industrial sector, it remains one of the world’s poorest countries, with up to 70% of its population living in poverty. Using data from the National Bureau of Statistics and other sources, the paper provides a comprehensive analysis and graphical representation of poverty rates. The 2022 National Multidimensional Poverty Index reveals that 63% of Nigerians, or over 133 million people, live in poverty, with Northern Nigeria experiencing the highest rates. Factors contributing to this include adverse economic policies, regional disparities, and demographic challenges. By integrating multiple sociological theories—structural functionalism, conflict theory, dependency theory, social exclusion theory, and human capital theory—the paper offers a thorough understanding of poverty’s root causes in Northern Nigeria. It concludes with targeted recommendations for interventions to mitigate poverty and promote sustainable development across the country.

Pobreza multidimensional no Brasil: evidências para as áreas rurais e urbanas

Abstract This paper studied the behavior of multidimensional and income poverty, as well as its determinants in Brazil, in addition to rural and urban areas, based on information from the 2019 National Continuous Household Sample Survey (PNADC). The Alkire-Foster methodology was used in the construction of a Multidimensional Poverty Index (MPI), in addition to the logit model to estimate the impacts of determinants related to household characteristics on poverty. The health and sanitation dimension was the one that contributed the most to the MPI, followed by the education and housing dimensions. Unidimensional poverty was greater than multidimensional and rural poverty was also greater. Among the determinants of poverty, being in households with non-white heads, younger, unmarried, unemployed, less educated and in the North and Northeast regions increased the chances of poverty. In addition, while female heads reduced the chances of multidimensional poverty, it increased the chances of the unidimensional one.

Agricultural Water Poverty Index: A Tool for Agricultural Water Resources Management in District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Agricultural Water Poverty Index: A Tool for Agricultural Water Resources Management in District Dir Lower, Khyber Pakhtunkhwa, Pakistan

Multi-dimensional poverty and health outcomes in Ghana

There is evidence on the social gradient in health in multiple settings. Recent research is increasingly focused on linkages between multidimensional poverty and health to capture and ascertain the role of non-monetary factors on individuals' health. In this paper, using the 2018/19 wave of the Ghana Socioeconomic Panel Survey (GSEPS) and the country's newly introduced multidimensional poverty index, bivariate correlations and regression analyses are carried out to explore associations between household multidimensional poverty and a series of health outcomes, including physical and mental health. There is evidence of significant subgroup differences across the country by gender, rural/urban residence, education, and region. Regression analyses consistently indicate that poorer mental and physical health outcomes are positively and significantly correlated with greater multidimensional poverty. Robustness tests indicate that deprivations in electricity access, safe sanitation and education are particularly relevant. Findings suggest the importance of a more equal geographical distribution of social amenities and infrastructure, as well as adequate targeting of policies to reduce health inequalities across the country and achieve a more equitable and inclusive distribution of resources within the country.

Predictive Modeling of Energy Poverty with Machine Learning Ensembles: Strategic Insights from Socioeconomic Determinants for Effective Policy Implementation

This study aims to identify the key predictors of the multidimensional energy poverty index (MEPI) by employing advanced machine learning (ML) ensemble methods. Traditional energy poverty research often relies on conventional statistical techniques, which limits the understanding of complex socioeconomic factors. To address this gap, we propose an approach using three distinct ML ensemble models: extreme gradient boosting (XGBoost)‐random forest (RF), XGBoost‐multiple linear regression (MLR), and XGBoost‐artificial neural network (ANN). These models are applied to a comprehensive dataset encompassing various socioeconomic indicators. The findings demonstrate that the XGBoost‐RF ensemble achieves exceptional accuracy and reliability, with a root mean squared error (RMSE) of 0.041, an R‐squared (R2) of 0.975, and a Pearson correlation coefficient of 0.992. The XGBoost‐MLR ensemble shows superior generalizability, maintaining a consistent R2 of 0.845 across both the testing and training phases. The XGBoost‐ANN model balances complexity with predictive capability, achieving an RMSE of 0.056, an R2 of 0.954 in the testing phase, and an R2 of 0.799 in training. Significantly, the study identifies “Education,” “Food Consumption Score (FCS),” “Household Food Insecurity Access Scale (HFIA),” and “Dietary Diversity Score (DDS)” as critical predictors of MEPI. These results highlight the intricate relationship between energy poverty and factors related to food security and education. By integrating the insights from these ML models with policy initiatives, this study offers a promising new approach to addressing energy poverty. It highlights the importance of education, food security, and socioeconomic factors in crafting effective policy interventions.

Poverty line and multidimensional poverty index through Sustainable Development Goals in Indonesia

Poverty line and multidimensional poverty index through Sustainable Development Goals in Indonesia

DETERMINANTS OF BROILER PRODUCTION AND ITS EFFECTS ON POVERTY STATUS AMONG CIVIL SERVANTS IN JOS METROPOLIS, PLATEAU STATE, NIGERIA

The research was carried out to analyze broiler production as a sustainable tool in reducing poverty among civil servants in Plateau State, Nigeria. The specific objectives were to: determine the profitability of broiler production; evaluate the poverty status and the effect of broiler income on the poverty status of the civil servants; identify the socio-economic factors influencing the level of broiler production; and describe the constraints of broiler production. Data from 120 members of Poultry Association of Nigeria (PAN) who are civil servants, were analysed using descriptive and inferential statistics. Majority of the civil servants that engaged in broiler farmers were women. Findings revealed that the influx of new entrants into broiler production in recent times was due to the ban on importation of frozen poultry product by the Federal Government of Nigeria which create high demand-supply gap. The civil servants’ broiler producers earned a profit margin of 58% and return on investment of ₦1.67 per Naira invested. This resulted into reduced FGT poverty indices. The level of broiler production were significantly determined by sex, level of education and experience at different level of probability. Likewise, the poverty status of the civil servants were significantly affected by salary and income from broiler production at 1 and 5 % levels of probability. Major constraints in broiler production were high cost of feed and vaccines. Government ban on importation of poultry meat is plausible and should be further imbibed by the Nigerian citizens to consume home grown poultry meat.