Multidimensional Poverty Research Articles

Analysis of the influence of regional spending on obligatory basic services on multidimensional poverty in Riau Province

Analysis of the influence of regional spending on obligatory basic services on multidimensional poverty in Riau Province

To UBI or Not to UBI? Universal Basic Income Creating Value in Rural Areas of Romania

People below the poverty line need financial support to meet their basic needs. One type of support is Universal Basic Income (UBI), which is paid to all adults individually, without means-test or work requirement. This paper investigates the effectiveness of basic-income social policies in rural areas of Romania. The author has analyzed possibly the longest-running Universal Basic Income program created by a non-governmental organization. This social experiment has been running for more than fifteen years and counting. Once a thriving bulwark of socio-economic stability in the country, rural areas grapple for decades with the tsunami effect of ill-fated socio-economic reforms of the ’90s, which dragged the rural economy and its people into a vicious cycle of poverty. Constant economic migration, depopulation, lack of basic needs, and a generation of orphans – the byproduct of economic migration and lack of education – constitute the new dire reality of a once prosperous blend of small economies. Under these circumstances, providing unconditional guaranteed income, independent of any other revenue streams, seems a natural way to alleviate the effects of systemic, multidimensional poverty. With mostly non-existent government programs and a weak business sector, the responsibility for such targeted interventions rests on the shoulders of third-sector organizations. The study’s results prove that such programs’ impact and benefits far outweigh their monetary costs and the stigma associated with “social welfare” proposals. Reframing the concept of UBI allows for creating more perfect baselines, a better way of measuring the needs, and means to fulfil these needs more effectively. This, in turn, drives the creation of a more powerful (and meaningful) set of tools for assessing these needs to meet the demand adequately.

Multidimensional poverty in Brazil: evidences for rural and urban areas

Resumo Este trabalho estudou o comportamento da pobreza multidimensional e de renda, bem como seus determinantes no Brasil, evidenciando as diferenças entre as áreas rurais e urbanas com base nas informações das Pesquisas Nacionais por Amostra de Domicílios Contínua (PNADC) de 2019. Foi utilizada a metodologia Alkire-Foster na construção de um Índice de Pobreza Multidimensional (IPM), além do modelo logit para estimar os impactos na pobreza de determinantes relacionados a características domiciliares. A dimensão saúde e saneamento foi a que mais contribuiu com o IPM, seguida da dimensão educação e condições habitacionais. A pobreza unidimensional foi maior do que a multidimensional, além disso, a pobreza nas áreas rurais foi também maior. Entre os determinantes da pobreza, estar em domicílios com chefes não brancos, mais jovens, não casados, desempregados, menos escolarizados e ser das regiões Norte e Nordeste aumentaram as chances de pobreza. Além disso, domicílios com chefes mulheres tiveram menores chances de pobreza multidimensional, embora mais chances de pobreza unidimensional.

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.

Multidimensional energy poverty and its determinants

Multidimensional energy poverty and its determinants

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.

Analysis of Child Poverty Using a Multidimensional Approach in Indonesia

Poverty prevents children from being able to fulfill their basic rights such as their need to learn, access to basic services, obtain a better education and participate in society. This research was conducted to examine the multidimensional child poverty. The results showed that the percentage of children living in multidimensional poverty was 35,54 percent. In the age group of 0-6 years, the dimension that was most deprived was health, while in the age group of 7-17 years, the dimension that was most deprived was food. On average, 32,9 percent of children aged 0-6 years are deprived in two or more dimensions, and 33.9 percent of children aged 7-17 years are deprived in two or more dimensions of basic needs. This study used logistic regression as a research method to identified the probability of child being poor. The result showed that the number of children, occupation of the household head, region, education of the household head and gender of the household head were significant variables affecting children's likelihood of being poor, as well as the interaction variables between the number of children*occupation of household head, the number of children*region and the number of children*education of household head. However, the gender of the kids variables did not have a significant effect.

MULTIDIMENSIONAL POVERTY AND EDUCATIONAL INEQUALITY: ASSESSMENT OF GOVERNMENT POLICY INTERVENTION IN NIGERIA

Aim of the study: Nigeria has a high incidence of multidimensional poverty and educational inequality. The government has implemented several policies to address these challenges, but the results have been mixed. Given this, the study assesses a comprehensive analysis of Nigeria's government's policy intervention regarding multidimensional poverty and educational inequality based on the country's current situation, policy initiatives, effectiveness, and the way forward. Design/Methodology: The study employed the interpretivism philosophy, and a qualitative approach was adopted in data collection and analysis. Secondary data were sourced from the current reports of the World Bank, UNDP, UNICEF, UNESCO, and NBS for a more detailed and accurate analysis. Findings: The findings of the study reveal that addressing multidimensional poverty and educational inequality is a complex and long-term process that requires sustainable efforts through investing in rural development, as 80% of all multidimensionally poor people live in rural areas. Practical Implications: The research was able to bring to light the need for governments to invest massively in formal and informal education, industrialisation, proper planning and policies, inter-ministerial collaboration, database unification, and timely population census. Originality/value: This empirical study will provide more in-depth knowledge of SDGs 1, 2, 3, 4, and 5 through practical implementation for Nigerian governments at all levels and other stakeholders. The study distinguished itself from prior similar investigations. To summarise, this empirical study will provide more knowledge through real-world application, primarily for addressing multidimensional poverty and educational inequality in Nigeria. Keywords: Government policy interventions, Multidimensional poverty, educational inequality, intersectionality Theory, Gender inequality Paper Type: Review Article

CONFRONTING PAKISTAN'S OVERPOPULATION ISSUE FOR LONG-TERM SOLUTIONS

CONFRONTING PAKISTAN'S OVERPOPULATION ISSUE FOR LONG-TERM SOLUTIONS

Small Area Estimation of Multidimensional Poverty in East Java Province Using Satellite Imagery

The government has so far focused on a monetary approach to overcoming poverty, while poverty is multidimensional. Holistic and accurate poverty indicators are needed as material for policy formulation, such as the Multidimensional Poverty Index (IKM), which is calculated from raw data from the National Socioeconomic Survey (SUSENAS). However, the direct estimation of the multidimensional poverty headcount (AKM) is only accurate at the provincial level, as seen from the relative standard error (RSE) of several districts and cities, which is still above 25 percent. Increasing the sample size requires time, effort, and cost, so the Small Area Estimation (SAE) method can be an alternative. Apart from using official statistics for accompanying variables, satellite imagery has the advantage of being up-to-date and available up to a granular level. This study aims to estimate the AKM at the district/city level in East Java Province by utilizing satellite imagery and official statistics in SAE. The results showed that SAE HB Beta-logistics, with the accompanying variables combined with satellite imagery and official statistics, has a higher accuracy than direct estimation.

Spatial and temporal patterns of energy aid and poverty in four African countries: Focusing on distributive and recognition justice

Energy poverty and unequal access to electricity are critical issues in African countries, underscoring the importance of understanding energy aid, a significant determinant, and its allocation. This study employs a framework of energy justice to investigate the spatial-temporal patterns of energy aid and the Multidimensional Energy Poverty Index (MEPI) over two decades (2001–2020) in four African countries: Nigeria, Tanzania, Ethiopia, and the Democratic Republic of Congo. We first explore the realm of distributive justice by scrutinizing where energy aid concentrates. Then, to tackle the domain of recognition justice, we analyze whether regions in need have benefited from energy aid through a comparison of regional MEPI and aid spending. The results indicate that energy aid is focused toward capital cities or urbanized areas, and analogous patterns are evident in electricity distribution and renewable energy sectors. The persistence of such patterns results in a temporal continuum of regional energy imbalances, with many marginalized groups not receiving sufficient aid. These findings highlight the importance of considering spatial and temporal aspects in pursuing energy justice while advocating for an equitable aid allocation to ensure an energy-just world.

Exploring the research on the follow-up industrial development of relocation for poverty alleviation based on the background of big data

Abstract Relocation for poverty alleviation is an important part of the implementation of the “five batches” project for poverty alleviation and has now entered the stage of consolidating the relocation results and promoting industrial development and economic growth. Based on this, this paper firstly explains the techniques related to the subsequent industrial development of relocation for poverty alleviation, including the double difference model, partial least squares regression algorithm and forward score matching algorithm, which provides the basis for the relevant analysis in the following paper. Secondly, the poverty reduction mechanism of industrial development in relocation for poverty alleviation is discussed, and the formation mechanism of poverty in relocation for poverty alleviation is explored. Finally, the impact of livelihood capital of relocated farmers was analyzed by using the double difference model, the partial least squares regression model analyzed the impact of industrial development, and the ATT effect of industrial development was analyzed by the PSM method. The results show that industrial development has a significant effect on reducing the multidimensional poverty index, multidimensional poverty order, income, education, health, and life-related poverty of relocated farmers, and their R2 test results are all above 0.6. The livelihood capital of relocated farmers is more affected by concentrated industrial development than decentralized development, and all of them have significant differences at the 5% level. The ATT effect based on PSM reflects that industrial development will increase the per capita total consumption level of relocated farmers, which can improve the living standard of relocated farmers to a certain extent. The subsequent industrial development of relocation for poverty alleviation should further optimize its industrial structure to provide an industrial foundation for the implementation of rural revitalization strategies.

The Relationship Between Poverty and Violence Against Women: Causality Contrary to Expectations

In this research, a field study and analysis are carried out to test the argument that violence against women can lead to women’s poverty. The theoretical background and applied methodology are referred to as a “multidimensional poverty approach.” The field study was carried out in districts representing the urban part of Antalya province in Türkiye. Findings suggest that key reasons pushing women into poverty stem from the “empowerment and security” dimension. The rate of women experiencing deprivation in the indicators of regional danger and violence; being treated fairly; ethnic, religious, and cultural differences preventing access to public services; excessive criticism; and exposure to violence is quite high.

Unveiling Multidimensional Poverty in Jammu and Kashmir: Insights from Alkire Foster Method and NFHS-5 Data

This study seeks to examine the multidimensional poverty in Jammu and Kashmir, aiming to illuminate its nature and extent beyond traditional monetary measures. Utilizing the Alkire Foster method and drawing on data from NFHS-5, the research uncovers a prevalence of multidimensional poverty surpassing that of monetary poverty in the region. Within surveyed districts, the study identifies Ramban as experiencing the highest incidence, contrasting with Srinagar, which exhibits the lowest headcount. Anantnag emerges as the district facing the most intense multidimensional poverty. In exploring the factors influencing multidimensional poverty, binary logistic regression reveals the significant roles of education, occupation, and land ownership. Conflict is identified as an amplifier of multidimensional poverty, and a predictive model achieves an accuracy rate of approximately 82.80%. The study underscores the critical importance of local factors in shaping poverty experiences, emphasizing the roles of nutrition, sanitation, housing, and education. Gender, age, and conflict are highlighted as pivotal determinants in the latter stages of the study. The research concludes with a managerial perspective, offering actionable insights for policy recommendations, development initiatives, and specific steps for government agencies and stakeholders. These proposed interventions aim to address identified determinants and contribute to the overarching goal of reducing poverty in Jammu and Kashmir.

How can energy poverty affect farmers’ health? Evidence from mountainous areas in China

The two important goals of sustainable development, affordable and clean energy, and good health and well-being have attracted global attention. This study explores the nexus and impact pathways between energy poverty and farmers' health, defining energy poverty from a multidimensional perspective that includes cooking, washing, warming, cooling, showering, traveling, entertainment, education, and communication. A mixed approach is deployed by using 1016 survey data collected from the rural mountainous areas of China, where the probit model, ordinary least squares, and conditional mixed process estimation methods are used for model estimation. Results indicate that farmers' physical and mental health is significantly negatively affected by multidimensional energy poverty. Regards to the different dimensions of energy poverty, cooking energy and traveling energy have considerable negative impacts on farmers' health. In terms of population heterogeneity, multidimensional energy poverty has a worse effect on farmers' health in the empty-nest stage and on males. To alleviate multidimensional energy poverty and improve health, this study proposes targeted policy recommendations for domestic policymakers and can shed light on other developing economies facing similar challenges.

Exploratory Analyses of Existing Nexuses Between Cooperative Membership, Production, Wellbeing, And Determinants Among Layer Egg Production Enterprises: Evidences from South West Nigeria

Cooperatives have been evidently solicited to play a positively significant multifaceted role in the promotion of wellbeing among members hence, this study was conducted to examine the existing interrelationships between cooperative membership, farmers’ welfare, and farm production level along its determinants among layer egg producers in southwest Nigeria, using a multidimensional approach. A multistage sampling technique was employed in the collection of data from 185 poultry farmers; 94 Cooperators, and 91 Noncooperators, randomly from four local government areas, using well-structured questionnaires. Descriptive statistics, T-test, Variance inflation factor, Alkire-Foster multidimensional poverty measures, and multiple regression are employed in data analysis.: Many (54%) of the poultry farmers are medium scaled, within which a larger proportion of about 61% are cooperators. Also, the output of the cooperator category was found significantly higher than their non-cooperator counterpart by 128.1 % crates per day at 5 % probability level. Furthermore, there exists a veritable positive relationship between farmers’ multidimensional welfare and increased production scale, while years of farming experience, farming as a primary occupation, farm size, and cooperative membership, significantly influence increased output. The finding showed that farmers’ multidimensional poverty headcount indices are inversely proportional to their scale of production. There also exists a significant positive relationship between increased production scales, cooperative membership, and farmers’ welfare statuses.

Construction of Zakat Multidimensional Poverty Index (ZMPI) in Malaysia based on Maqasid al-Shariah

Construction of Zakat Multidimensional Poverty Index (ZMPI) in Malaysia based on Maqasid al-Shariah

An Econometric Approach to Design a Multidimensional Poverty Measure for Rural Himalayan Areas

This study is an effort to design a multidimensional poverty measure for rural Himalayan areas. It investigated the level of multidimensional poverty and the underlying causes. The study aimed to identify the major causes of multiple deprivations using indicators of Global MPI. Then, an econometric technique was used to identify the dominant causes for designing a robust area-specific Multidimensional Poverty Index to offer critical policy insights. The findings revealed that 13 per cent of the sampled households were multi-dimensionally poor. The prominent causes were income-related, infrastructure and facilities-related, household/domestic problems, health problems, education, awareness and expense problems. However, income-related problems had dominantly affected households in most of the indicators. The study investigated the antecedents using NLPCA and found that only six dimensions can be retained using these causes. Based on dominant grounds, it is recommended to use these retained indicators/dimensions for designing MPI to address the actual causes of deprivation for sustained poverty alleviation.

Exploring the Association between Multidimensional Poverty and Depression Using Structural Equation Models

Exploring the Association between Multidimensional Poverty and Depression Using Structural Equation Models

Highway networks and regional poverty: Evidence from Chinese counties

This paper establishes a staggered difference-in-differences (DID) model to estimate the impact of highways on regional poverty using county-level panel data from China. We construct a multidimensional poverty index and use satellite-monitored night light brightness as a proxy indicator. The results demonstrate that highways significantly reduce county poverty and that the poverty reduction effect becomes increasingly pronounced over time. This result remains robust after controlling for non-random highway route selection. Furthermore, the poverty reduction effect of highways is conditional, with a significant poverty reduction effect in the less economically-developed western regions and non-municipal counties, and no significant poverty reduction effect in the economically-developed eastern and central regions and municipal districts. Moreover, the poverty reduction effect of highways gradually decreases as the average altitude of counties increases. Our tests provide empirical evidence for effective road investments in developing countries that incorporate poverty alleviation targets.