Analysis Of Poverty Research Articles

Social Economic Analysis of Financial Inclusion and Poverty in Indonesia

This article analyzes the impact of financial inclusion on poverty in Indonesia using data from the 2020 National Socioeconomic Survey (Susenas), with household heads as the sample. The study posits the hypothesis that financial inclusion significantly contributes to poverty reduction. The analytical method applied is the probit logit model, considering that the dependent variable is binary. The results indicate that financial inclusion has a negative and significant effect on poverty, with a probability of 0.000 at a 5% significance level. This suggests that increasing financial inclusion can reduce the risk of poverty by up to 8.2%. These findings underscore the importance of access to and use of financial services for households in the effort to alleviate poverty. Therefore, policies are needed to support the enhancement of financial inclusion through the expansion of access to financial services, improvement of financial literacy, adjustment of financial products and services, and cross-sector coordination among the government, private sector, and financial institutions.

A cross-comparative analysis of child poverty across sub-Saharan Africa: the case of Francophone and Anglophone African states

PurposeThe institutional structures established by French and British colonists continue to shape the socio-economic landscape of sub-Saharan Africa, potentially contributing to the unequal distribution of child poverty. This study, therefore, has significant relevance as it aims to estimate child poverty disparities within the context of colonial origins in Francophone and Anglophone African countries.Design/methodology/approachThis study utilised data from the Demographic and Health Surveys (DHS) conducted between 2000 and 2019 involving 22 sub-Saharan African states. A rights-based framework was applied based on the SDG-updated Gordon et al. (2003) methodology to measure child poverty. We then compared child poverty disparities between colonial origins using one-way ANOVA tests and risk ratios to quantify the magnitude of these differences.FindingsCritically high estimates of sanitation and housing poverty (above 70% prevalence) were observed in the two colonial settings. Children in Francophone states were 1.4 times (RR = 1.4; 95% CI: 1.1–1.8) more likely to experience higher risks of low medical access, almost twice as likely to experience low vaccine uptake (risk ratios-RR = 1.8; 95% CI: 1.2–2.8) or no formal education (RR = 1.7; 95% CI: 1.1–2.5). All other dimensions were equal between colonial origins.Research limitations/implicationsDue to its cross-comparative approach, this study is restricted in making conclusive inferences regarding the causal relationship between colonialism and health/education poverty. Also, given the limited number of countries, particularly Anglophone countries, our sample may not fully represent all French and British ex-colonies. As a follow-up to this study, we intend to repeat this analysis using the harmonised Multiple Indicator Cluster (MIC) surveys, providing a larger number of countries.Originality/valueDespite similar household poverty levels in both colonial settings, Francophone states had lower access to health and education services than Anglophone states. These findings indicate deeper systemic issues that may be rooted in the historical governance styles of centralisation. More effective decentralisation of healthcare and education services in Francophone countries and improved government commitment to addressing household poverty throughout sub-Saharan Africa are imperative.

Rural–urban analysis of parental time poverty and child over-nutrition in ghana

ABSTRACT This study examines the link between parental time poverty and child body mass index, prevalence of overweight and obsesity. The study relied on data from the seventh round of the Ghana Living Standards Survey (GLSS), a nationally representative household level data. Employing the standard instrumental variable (IV) to control for endogeneity indicates that parental time poverty is associated with a higher BMI z-score and higher likelihood of children being overweight and obese, particularly in rural Ghana. Additionally, there is evidence of heterogeneity in the effect of parental time poverty on the prevalence of children being overweight and obese based on income classification and gender. The implications of these findings suggest that interventions aimed at providing labor-saving technologies and improving social and physical environments in rural areas may help reduce parental time poverty and improve child nutrition outcomes.

Poverty Analysis of Children's Education Level in Paokmotong Village, Masbagik Sub-District

This study aims to determine the Poverty Analysis of Children's Education Level in Paokmotong Village. This research uses a descriptive type of qualitative approach. The data collection techniques used by researchers are observation, interviews and documentation with 80 respondents to poor people in Paokmotong village. While the data analysis technique used is descriptive analysis technique through qualitative research, which uses data and information obtained directly from information then analyzed using the existing theoretical basis and obtained systematically based on facts in the field. The poverty experienced by the poor in Paokmotng Village has a wide impact on the level of education in the community. This can be seen in various aspects, namely employment or income, human resources, natural resources and government policies. From these various aspects, the impact of poverty is relative because each family has and feels a different impact from other families in the level of education, namely a low level of education

Socio-Spatial Analysis of Poverty: A Comprehensive Study on Integrating Multidimensional Poverty Indices with Geographic Conditions in Krucil District, Probolinggo, Indonesia

Socio-Spatial Analysis of Poverty: A Comprehensive Study on Integrating Multidimensional Poverty Indices with Geographic Conditions in Krucil District, Probolinggo, Indonesia

Human bias and CNNs’ superior insights in satellite based poverty mapping

Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare.

A simple classification and clustering of poverty in rural areas using machine learning

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Consumption Quintile Groups in the Analysis of Poverty and Development Issues

Background: This paper provides computer program which facilitates to create consumption quintile groups (CQGs), and demonstrates some benefits of the created CQGs in the analysis of poverty and development issues. Methods: The proposed computer program is tested by creating CQGs using the data of 28,670 individuals enumerated in a sample of 5,988 households of Nepal Living Standard Survey (NLSS) III instigated by the Central Bureau of Statistics in 2010/11, and identifies some disadvantages of the lowest 20% of population compared to the highest 20% of population. Results: Some identified disadvantages of the lowest population are - excessive number of children, low literacy rate among the working-age population and low expenditure on non-food items, including education and health of children. Conclusion: This paper illustrates how to compute income inequality, an important issue of sustainable development, by Gini coefficient when the income distribution is across the CQGs and the computed value was 0.46. Novelty: This paper tries to draw attention of scholars on the use of CQGs in the analysis of poverty and development issues.

Energy poverty and health in Turkey: Evidence from Longitudinal data

This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.

Monitoring Poverty in a Data‐Deprived Environment: The Case of Lebanon

AbstractThis paper addresses the lack of data and limited statistical capacity in the Middle East and North Africa, particularly amid Lebanon's economic collapse. We apply a novel data augmentation technique to analyze poverty when traditional income data are limited or unavailable. By adapting existing methods, we recover continuous income distributions from interval data and derive dominance conditions for such data, accounting for non‐response. The proposed approach enables robustness checks by estimating the bounds of admissible cumulative distribution functions. Our empirical analysis uses Lebanese data to perform first‐order dominance tests on these bounds, highlighting the importance of the approach. We demonstrate how alternative data sources can be leveraged for essential poverty analysis.

Ecological shock and time poverty: Evidence from northern Ghana

Despite the importance of time poverty in economic decision-making and impact on welfare outcomes, empirical studies linking ecological shocks and time poverty is limited. This paper documents the relationship between an ecological shock and time poverty in northern Ghana using data from the seventh round of the Ghana Living Standards Survey (GLSS). We employ the standard instrumental variable (IV) method and conduct several robustness checks to ensure a robust estimate of ecological shock on time poverty. The results show that ecological shocks increases time poverty by 15 percentage points. The decomposition analysis of time poverty indicates that ecological shock is postively associated with time poverty based on paid work with no significant effect on time poverty based on unpaid work. Our results further show that the effect of ecological shocks on time poverty is a rural phenomenon. In addition, we note that the effect of ecological shock on time poverty is more prominent among the youth, large farm holders, and high-income earning household heads. The main implication of the findings is that development interventions that enhance access and adoption of gender-sensitive labour-saving technologies must be scaled up to reduce farm committed time, increase crop productivity and reduce the potential intergenerational transfer of time poverty. Provision of social protection measures such as the support through the Livelihood Empowerment Against Poverty to poor farm households would reduce the burden of ecological shocks on their wellbeing.

Analysis of Seasonal Time Poverty and Aspirations in the Upper East Region of Ghana

While studies on aspiration formation have progressively gained the interest of development practitioners and policymakers, little is known about the link between seasonal time poverty and aspirations. This study uses primary data on 337 smallholder households in rural northern Ghana to examine the relationship between seasonal time poverty and aspirations about income, assets, child education, and social status from household headship and age perspectives. The results indicate that seasonal time poverty is correlated with higher aspirations of income and assets but a lower social status. The effect is more significant in dry than wet seasons, and more evident in female- and adult-headed households. The findings are robust to alternative estimation methods for addressing endogeneity. Income is identified as the main potential channel through which seasonal time poverty influences aspirations formation and the implications of the findings are discussed. HIGHLIGHTS In Ghana, seasonal time poverty is positively associated with income and asset aspirations. Seasonal time poverty reduces social status aspiration. The effect of seasonal time poverty on aspiration is higher among women and adults. Programs that promote rural household income must be targeted, pursued, and sustained to improve aspirations.

Analysis of energy poverty in the Republic of Croatia

For a long time, energy has been a fundamental element of human growth and development. However, despite all technological progress and information development, many people in the world still do not have consistent access to at least one form of energy. This is where the concept of energy poverty appears. Energy poverty is generally defined as a situation in which households do not have enough disposable income to pay the bills for the energy needed in their household. The main factors that influence the appearance of energy poverty are primarily insufficient household incomes, the energy prices and their availability in a particular area, and the quality of housing units. This paper tries to show what the situation is in the Republic of Croatia and how to approach solving this problem. In the Republic of Croatia, this problem is tried to be solved by defining the vulnerable energy buyers, which places energy poverty in social policy, and accordingly, measures are most often implemented in the form of financial benefits (it is equated with energy poverty). What is missing here is the important connection between energy and social policy.

FeliX 2.0: An integrated model of climate, economy, environment, and society interactions

The Full of Economic-Environment Linkages and Integration dX/dt (FeliX) model is a System Dynamics-based Integrated Assessment Model (IAM), explicitly incorporating human behaviors and their dynamic interactions among global systems. This paper presents FeliX 2.0, describing the detailed framework and key interactions among nine integrated modules. FeliX 2.0 refined its original version in population dynamics, food and land use systems, and socioeconomic settings for poverty analysis. Robust calibration is applied to key variables against their historical data since 1950. Future projections of multiple variables up to 2100 demonstrate coherences between FeliX 2.0 and the IAMs used in IPCC assessments. Both outputs (the robust calibration results and future projections) underscore the efficacy of FeliX 2.0 in capturing complex interdependencies within global systems. FeliX 2.0 stands as an informative tool and offers insights into interactions within the human-Earth system and the analysis of complex economic-environmental-social challenges in short- and long-term future.

Poverty dynamics for Ghana during 2005/06–2016/17: An investigation using synthetic panels

Ghana has consistently kept its poverty rate lower than the regional average over the past 25 years, but this positive trend has slowed down recently. Without actual panel data, it would be next to impossible to probe into the dynamic patterns beneath this trend. We offer the first study that examines the dynamics of overall, moderate, and extreme poverty in Ghana during 2005/06–2016/17, addressing the lack of actual panel data by constructing synthetic panel data from three nationally representative cross-sectional rounds of the Ghana Living Standards Surveys (GLSSs).We present a well-defined decomposition framework, which allows us to examine a rich profile of the dynamics among different population groups. Beyond poverty analysis, we further investigate the dynamics of the full consumption expenditure distribution. Although we find considerable conditional chronic (extreme) poverty rates hovering around 50–60%, there was more upward mobility than downward mobility during this period. Poor households were also more likely to have stronger consumption expenditure growth. The findings suggest that factors such as educational attainment, female household headship, urban residence, and nonagricultural work are positively correlated with poverty reduction. Compared to all other correlates, educational attainment appears to be the most effective in pushing households out of poverty and keeping them from falling into poverty. These results contribute to the growing literature on poverty dynamics in developing countries in general and provide new and relevant inputs for policy advice in Ghana in particular, especially since policies that address chronic poverty typically differ from those that tackle transient poverty.

Comparison of Methods for Applying the Data Mining Clustering Concept to Banten Provincial Government Poverty Data: Systematic Review

An in-depth understanding of the phenomenon of poverty in Banten Province is the key to formulating effective strategies in overcoming this problem. In general, poverty in this area is caused by several interrelated and complex factors. This research provides a comparative analysis of the application of clustering data mining methods in calculating population poverty data in certain regions or provinces, especially Banten. This research aims to assess the consistency and effectiveness of various clustering methods used in previous research. Using a qualitative approach with a literature review, secondary data from relevant research was collected through systematic searching, reading, and note-taking. The data collected is then carried out through a process of data collection, filtering, presentation and drawing conclusions. The results show that most of the literature reviews show similar results regarding the effectiveness of various clustering methods in analyzing poverty data in Banten. This shows that there is a consensus among previous studies regarding the efficacy of clustering data mining techniques in overcoming problems related to poverty in the region. These findings contribute to a deeper understanding of the methodology used in poverty analysis and provide insights for policy makers and researchers to develop more effective strategies in poverty alleviation efforts. The conclusion of this research is that clustering data mining in analyzing poverty data in Banten Province has shown quite high consistency and effectiveness. Various literature studies that have been reviewed show that various clustering methods that have been applied previously provide similar results in identifying poverty patterns, understanding socio-economic structures, and providing valuable insights for developing more targeted policies.

Geospatial Analysis of Energy Poverty and Accessibility to District Heating Systems

This research paper undertakes a comprehensive geospatial analysis to investigate the relationship between energy poverty and district heating accessibility in Estonia. Utilizing ArcGIS Pro and statistical software, the study maps district heating systems, evaluates the energy efficiency of apartment buildings, and examines socioeconomic factors influencing energy poverty. By integrating GIS data with socioeconomic and building energy performance indicators, the research identifies areas where district heating is either absent or inefficient and correlates these findings with instances of energy poverty. Preliminary data may indicate a significant correlation between the lack of accessible district heating and increased energy expenses among lower-income households. The study also evaluates the impact of building energy efficiency on heating needs, revealing that older, less efficient buildings contribute disproportionately to energy poverty. Recommendations include policy interventions to expand and optimize district heating networks, alongside building renovation programs to enhance energy efficiency. This research contributes to the broader understanding of sustainable urban heating solutions and their role in mitigating energy poverty.

Empirical analysis of child labour, household poverty, and child health in Ghana

AbstractThe paper studies the effect of household poverty on child labour (CL) and explores how CL affects working children's health using an instrumental variable strategy. After correcting for endogeneity, we find that household poverty significantly contributes to the incidence of CL whilst increasing the working hour of children increases the likelihood that working children would sustain wounds/cuts, get skin diseases, or get burned by fire, signifying the adverse effect of CL on child health in Ghana. These findings are robust to different approaches to addressing endogeneity. The study recommends that while it is imperative to implement policies to reduce children's engagement in all hazardous forms of child work and to protect the health of working children, government policies that address poverty might also be necessary.

Spatial analysis and predictive modeling of energy poverty: insights for policy implementation

Spatial analysis and predictive modeling of energy poverty: insights for policy implementation

Coupling Coordination Analysis of County Tourism Development and Multidimensional Poverty Based on Nighttime Light Data

In China, tourism development is a crucial approach to poverty alleviation. With the consolidation of poverty alleviation achievements and the promotion of rural revitalization, it is of great significance to explore the relationship between tourism development and poverty alleviation from the perspective of multidimensional poverty. Therefore, this study took 28 key assistance counties for rural revitalization in the Sichuan–Chongqing region (hereinafter referred to as “key counties”) as the research objects, introduced NPP-VIIRS nighttime light (NTL) data, and a coupling coordination degree (CCD) model to explore the coordination relationship and mechanism between them. The results showed that from 2015 to 2020, the tourism development index (TDI) and estimated comprehensive development index (ECDI) of the key counties increased by 112.57% and 115.12%, respectively. In addition, the spatial differences in tourism development and multidimensional poverty both showed a narrowing trend. According to the results of the CCD model, the key counties basically faced coordination obstacles in the early stage, which were mainly transformed into reluctant coordination and moderate coordination in the later stage. This indicated that tourism poverty alleviation showed a coordinated development trend overall. However, the study also found that there may not be synchronicity between tourism development and poverty alleviation and analyzed the mechanism of their interaction. Overall, the study confirmed the positive impact of tourism development on alleviating multidimensional poverty. In addition, the study found that measuring multidimensional poverty based on NTL data has a high accuracy and can provide support for poverty research. These research results have an important reference value for China to carry out sustainable tourism poverty alleviation and comprehensively promote rural revitalization.