Multidimensional Poverty Research Articles

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.

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.

Internet usage and household energy poverty

Abstract Based on the double critical value method and the data of the China Household Panel Survey from 2014 to 2023, this paper measures the multidimensional relative household energy poverty in China. After confirming the impact of income poverty on energy poverty, this paper explores the formation mechanism of energy poverty from the perspective of opportunity acquisition. It explores the occurrence path of “opportunity access-income poverty-energy poverty” of residents by combining the structural equation model. Finally, the impact of Internet use on energy poverty was investigated through multiple regression analysis. According to the study, China’s official poverty line is about 2.0 US dollars, and the energy poverty line has increased by 49.6% in 2022 compared with 197.2 yuan in 2018. In the empirical analysis of access level and energy poverty, the coefficient of Internet learning is 0.311. Further, adding the moderating effect model regression results of the interaction term shows that the Internet penetration rate has a significant positive impact on multidimensional energy poverty. When it is increased by 1%, the multidimensional energy poverty is reduced by 9.25%.

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

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

The Impact of IoT on the Performance of Vaccine Supply Chain Distribution in the COVID-19 Context

The Government of India has started the distribution of the Covishield and Covaxin vaccines across all the comprising states. In developing states like Bihar, the vaccine supply chain (VSC) is likely to face many challenges due to poor health infrastructure, multidimensional poverty, and poor literacy. Supply chain practitioners are experimenting with available technologies to deal with supply, demand, and behavioral challenges. The present research work seeks to inspect the potential impact of Internet of Things (IoT) on the performance of the VSC. This study draws literature on the impact of IoT on product management, demand management, supply management, social behavior, and government rules and regulations to develop and test the conceptual model in Bihar. Consequently, the study administrated a survey and used structural equation modeling to investigate the proposed hypothesis. The analysis illustrates the positive influence of IoT adoption on the performance of the VSC in distributing the COVID vaccine. The finding also shows the positive relationship between product, supply, demand, and social behavior in IoT adoption. Analysis displays that Indian politicians can substantially impact vaccine distribution because they have influence and awareness of their local districts. This research work has a significant theoretical and managerial contribution for government and practitioners, supporting regulatory officials and policymakers in improving vaccine distribution.

Compounding impacts of COVID-19, cyclone and price crash on vanilla farmers' food security and natural resource use.

The intersection of the COVID-19 pandemic with other crises can amplify vulnerabilities and push communities further into poverty. In low-income countries, the dual impacts of COVID-19 and extreme weather events, along with multidimensional poverty and structural vulnerabilities in agriculture can decimate farmer livelihoods. This study aims to understand the effects of individual and compounding crises (COVID-19, cyclones, and vanilla price collapse) on smallholder vanilla farmers and local coping strategies in Madagascar, one of the world's largest vanilla producers and poorest countries. We used semi-structured and scenario-based interviews across two case study villages with contrasting enforcement of forest regulations. We found that the impact of the pandemic, combined with the cyclone event, disrupted livelihoods, resulting in income losses and food security challenges that exacerbated farmer vulnerabilities. Sixty eight percent of households reported crop losses due to strong winds and heavy rainfall brought by cyclone Enawo in 2017. The COVID-19 outbreak struck the region just as the residents were recovering from the effects of the cyclone. COVID-19-related travel restrictions in the aftermath of the cyclone took a substantial economic toll, with 54.1% of respondents experiencing a decline in earnings, and 17% facing a total loss of income due to the imposed lockdown. The decline in vanilla prices at the onset of 2020 had a far-reaching additional impact, affecting not only farmers but also residents who rely on other sources of income. Local communities reported using the forest resources more frequently as a safety net during crises in the village with more lenient regulations. This study underscores the importance of understanding the interconnectedness and compounding impacts of cascading crises on food security and natural resource use. We highlight the need for a comprehensive approach to increasing farmer resilience, particularly for those reliant on global market crops such as vanilla.

Headship of Household and Multidimensional Poverty in Sudan: An Empirical Analysis for The Case of Al Manqil Locality, 2023

Headship of Household and Multidimensional Poverty in Sudan: An Empirical Analysis for The Case of Al Manqil Locality, 2023

Headship of Household and Multidimensional Poverty in Sudan: An Empirical Analysis for the Case of Al Manqil Locality, 2023

Headship of Household and Multidimensional Poverty in Sudan: An Empirical Analysis for the Case of Al Manqil Locality, 2023

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.