Identification Of Poverty Research Articles

Identification and prediction of the degree of multidimensional returning to poverty risk for the household in China through the novel hybrid model: Based on the survey data of China Family Panel Studies (CFPS)

In China, absolute poverty has been effectively eliminated, but this does not signify the complete eradication of poverty. Instead, poverty persists in the forms of relative and secondary poverty. More concerningly, regions or households lifted out of poverty continue to face numerous risks of returning to poverty. In this context, measuring poverty solely based on monetary metrics is no longer adequate. Furthermore, the primary focus of grassroots governance has shifted from merely assessing poverty to accurately predicting the multifaceted risks associated with falling back into poverty. A multidimensional poverty indicator system is constructed to measure and predict the risk of multidimensional returning to poverty in China. Then, the Alkire–Forster counting method is applied to measure the risk of multidimensional poverty return and demonstrate the contribution of various indicators to multidimensional poverty return using tracking survey data from the China Family Panel Studies (CFPS). The results show that the multidimensional poverty return in China is mainly caused by the factors in two or three dimensions, where the social development capability dimension has the highest contribution to the multidimensional poverty return index with 43.12 %, followed by the health and education dimensions. Moreover, according to the finding that the identification of multidimensional poverty varies with the values of the poverty cut-off, a novel and practicable method is proposed to classify the risk into three levels, noted as the high, medium and low levels. Consequently, a hybrid model is constructed to predict the risk of multidimensional poverty return by integrating the Archimedes optimization algorithm (AOA), variational mode decomposition (VMD) and Bi-directional long short-term memory (BiLSTM) neural networks. Finally, the performance of the constructed model is validated with an accuracy up to 99.81 %. The constructed model's efforts outperform the traditional BiLSTM and several prevalent machine learning algorithms through extensive comparative experiments. These results illustrate that the proposed model can accurately and stably predict the potential risk of poverty return for multidimensional poverty groups and regions. In conclusion, drawing from the analysis of factors contributing to the risk of relapsing into poverty in this study, policy suggestions have been formulated focusing on education, social capacity enhancement, and healthcare system advancement. In summary, this paper provides new insights into the factors contributing to multidimensional poverty recurrence and the methods for assessing its risk levels, while also introducing a more precise approach to predicting these levels.

Mapping Poverty for Sustainable Development Using AI, A Review of Literature

Extreme poverty is among the challenges the United Nations seeks to eradicate by the year 2030 as outlined in its Sustainable Development Goals. However, governments and other stakeholders face challenges in accurately identifying poverty in households for evidence- based implementation of SDG programs. Current strategies are slow, inaccurate and costly to efficiently support efforts to identify poverty for sustainable development. Consequently, many strategies to map out poverty for intervention measures do not succeed which could be contributing to the global decline in the rate of reducing poverty. Artificial intelligence which has become widely available and has been used in many sectors, could be leveraged to improve poverty mapping for evidence-based interventions for sustainable development. Despite living in the era of AI, it has not been fully utilized in mapping poverty. This review seeks to explore the extent of research on the adoption of AI in mapping poverty so as to find the gap for further research. It aims to establish the extent of AI-based research on identification of poverty in respect to global distribution of research studies, methods, algorithms and sources of data which have been used in studies to identify poverty. The findings will help to identify gaps for research to help in designing evidence-based strategies for intervention measures. A systematic review was done for the period 2020 to 2024 using databases and snowballing hybrid search approach. A qualitative analysis was done on the extracted data to uncover new patterns and identify research gaps.

A Study on the Measurement of Relative Poverty in Developing Countries with Large Populations

Under the goal of global poverty eradication, it has become a forward-looking research aim to establish relative poverty criteria and identify people experiencing relative poverty in countries with different demographic characteristics. This paper introduces a new method to measure the relative poverty standard, which is to use the fuzzy decision tree algorithm to objectively estimate the relative poverty standard. The advantage of this algorithm lies in (1) it not only maintaining the regression idea of measuring absolute poverty, but also emphasizing the nonlinearity when the demand is increasing, which can reflect the change in human needs. (2) It overcomes the division of the traditional method which clearly distinguishes between those experiencing poverty and those who are not by means of a subjective threshold, and it also avoids the subjectivity of the selection of multidimensional indicators. (3) It overcomes the problems of data skewness and extreme value issues that traditional methods have, and can exhibit multi-dimensional characteristics. (4) Most importantly, this method can overcome the gap problem caused by the complex population structure in developing countries with huge populations, and is more adaptable under big data conditions than traditional methods. Taking China as an example, using data from the China Household Finance Survey for validation, the validation results show that the relative poverty standard in China in 2019 can be approximately delineated as 5288.5 RMB; this result is higher than the absolute poverty standard line delineated in China in that year, lower than the relative poverty standard line measured using the proportion method, and it can satisfy the average per capita food, tobacco, and alcohol consumption expenditure of Chinese residents in that year. Thus, compared with other methods, the fuzzy decision tree algorithm can better match the identification of relative poverty in developing countries with large populations.

Manajemen Badan Amil Zakat Nasional Sumatera Utara dalam Meningkatkan Kesejahteraan Sosial Mustahik

This study aims to determine the management of the Empowerment of the North Sumatra National Amil Zakat Agency in improving mustahik social welfare. This study uses a descriptive qualitative approach to observe the natural conditions of the North Sumatra National Amil Zakat Agency. The data source of this study refers to mustahik conditions through non-part participant observation and semi-structured interview results. This research data is also sourced from journal articles, and relevant literature. In analyzing research data, researchers refer to the concept of reduction, presentation, and drawing conclusions. Data validity techniques This study refers to triangulation. The results of this study indicate that the North Sumatra Baznas plays a central role in improving mustahik welfare with a focus on zakat. Through the identification of poverty, surveys, and collaboration with local stakeholders, Baznas compiled sharia -based programs such as North Sumatra Care and North Sumatra Makmur. They changed the paradigm of giving zakat from consumptive to productive, involving training skills, financial education, and business capital. With a holistic approach, Baznas ensured that zakat improved mustahik welfare, reflecting productive investment for sustainable social welfare.

A Hidden Cost: Estimating the Public Service Cost of Poverty in Ireland

Abstract Living life on a poverty income remains commonplace in most modern welfare states. A growing literature highlights the impact on individuals, families, and communities of poverty, costs that are both current to the experience and reflecting its scarring effects. A further cost, one that is frequently hidden, is the cumulative and recurring public expenditure associated with policy responses to poverty. These costs derive from the identification of poverty as a determining factor in the need for, and demand for, a wide range of public services. Estimating the nominal value of these costs, based on an assessment of public expenditure in one EU-15 state, Ireland, is the focus of this article. The findings establish annual costs of between €3bn-€7bn and highlight for all members of society, whether above or below the poverty line, the recurring public expenditure costs incurred by society as a result of poverty.

Multidimensional and monetary poverty in South Sulawesi Province

The objective of this study is calculate and analyze multidimensional poverty occurred in South Sulawesi in 2011 and 2015, and looking at the comparison calculation results multidimensional poverty and monetary (based on expenditure per capita per month) that occurred in South Sulawesi in 2011 and 2015. This study uses secondary data sources from the National Socio-Economic Survey (Survei Sosial Ekonomi Nasional-Susenas) in 2011 and 2015 which have been implemented by Statistics South Sulawesi province to the identification and aggregation of multidimensional poverty. Dimensions, indicators/variables, cuttoff and weights used is based upon the Global Multidimensional Poverty Index with some adjustments at the indicators used. While monetary poverty data source is the official poverty statistic released. The results of this study indicate that (1) multidimensional poverty in South Sulawesi Province is a rural phenomenon as indicated by the three indicators of multidimensional poverty is higher than urban; (2) The dimensions of health is the largest contributor of multidimensional poverty in South Sulawesi; (3) Monetary Poverty create a different description from non-monetary dimensions, the entire district/city in South Sulawesi have the results of multidimensional poverty which higher than the monetary poverty; (4) The decline in the percentage of poor monetary occurred during the years 2011-2015 in the province was followed by a decrease in the percentage of poor multidimensional with a faster decline; (5) Spatially recorded 22 districts/cities in South Sulawesi are in the poor category multidimensional high and very high in 2011, in 2015 only 14 districts were still at that level.

Definitions matter: investigating indicators for transport poverty using different measurement tools

Abstract Background An individual’s inability to fully participate in social life due to limited means of transport options, in short, transport poverty, is a common theme in contemporary transport planning. However, due to the lack of a universal definition, identification and measurement of transport poverty can be challenging. Purpose This paper aims to assess the implications of three widely used definitions and a newly developed measurement scale by comparing them in terms of scale levels, measurement dimensions and research contexts Methodology This study first systematically reviews the literature on transport poverty and summarizes aspects of the transport poverty concept emphasized in previous studies. Using survey data from two Dutch cities, the study then measures transport poverty by means of a scoring system and an indicator derived from a new measurement scale by factor analysis. Finally, by performing a series of linear regression models on the generated scores, the predictors of each definition (measurement scale) are compared to identify which aspects of transport poverty are prioritized by each definition. Findings Each transport poverty definition correlates with a different set of predictors, indicating that the used definition of transport poverty has an impact on how the concept is identified and implying in which contexts the definition (measurement scale) can be applied. The findings could help policymakers evaluate the applicability of different transport poverty definitions in specific contexts and help them select the optimal measurement tool for assessing the problem they are aiming to solve.

Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China

Spatial identification of poverty in mountainous cities based on the mountain poverty spatial index: A case study of Ganzhou city in 2018 in China

Technology Heterogeneity and Poverty Traps: A Latent Class Approach to Technology Gap Drivers of Chronic Poverty

The analysis of household wealth dynamic remains an important methodology in the identification of poverty traps. To overcome measurement issues in survey data, livelihoods-based approaches of the dynamics of poverty are typically examined using panel regressions of a livelihoods regression on household assets and other socio-economic factors over time. In this paper, we characterise the livelihoods regression as a ‘livelihoods technology’, and use a latent class-technology approach to account for heterogeneity in how households generate a livelihood. We use a detailed dataset from rural India covering 213 households across 2001–2014, and control for selection issues through a Heckman Selection model. Our results are the first in the wealth dynamics literature to show that substantial heterogeneity exists in the technologies with which households generate their livelihoods. Importantly, we show that accounting for heterogeneity in household livelihoods ‘technologies’ more readily identifies different equilibria in wealth levels and provides previously foregone information on who is poor and why they remain poor.

Identification and Measurement of Multidimensional Relative Poverty of Chinese Rural Adults Considering Climate Factors

The climate conditions in different regions of China are different, resulting in uneven climate resources owned by residents. It is important to design a comprehensive evaluation method to measure the multidimensional relative poverty (MRP) status and differences in rural areas considering climate factors from the micro-level. With adults as the research object, imitating multidimensional poverty index (MPI) and other indexes, and referring to the relative poverty lines in Britain, Australia, and other countries, this study considers the housing sunshine level and air quality of the living environment, which can reflect the superposition of economy and climate, in the dimension of human settlements environment, and establishes an indicator system of MRP in rural China. Using the Chinese General Social Survey data in 2018 and the A-F method to measure the indicator poverty rate, multidimensional relative poverty index (MRPI), and indicator contribution rate, this study evaluates the MRP in rural China including climate factors. The results show that the poverty rate of sunshine level and air quality indicator in North China is the highest in China, and the MRPI is the lowest. In North China, the sunshine level and air quality indicator poverty rate are 17.47% and 53.01%, respectively. MRPI under K = 1 standard is 0.1182. It shows that the indicator system can identify the typical phenomenon that highly industrialized economic development may negatively affect the environment. MRP alleviation should focus on coordinated governance of the economy, education, health, and the human settlements, we should establish a climate emergency plan for joint prevention and control with the meteorological department, set afforestation protection areas, set climate-related building standards such as sunshine times of rural houses, and improve and upgrade the energy use in rural areas to achieve the harmonious development of the society, economy, and environment and the high-quality life pursued by rural residents.

Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China

As absolute poverty in China, measured by the current standard, is being eliminated, the focus of future poverty reduction projects will necessarily shift to addressing relative poverty. Contiguous poverty areas have been identified in Hebei province around Beijing and Tianjin (HABT), and this is not conducive to the coordinated development of the Beijing-Tianjin-Hebei region. The dynamic identification of relative poverty at the county level within the region must be the basis for formulating scientific strategies for poverty reduction. Night light (NTL) data can reveal socio-economic information and reflect human activities, and has a wide range of other applications for evaluating and identifying poverty. For this reason, NPP/VIIRS (Visible Infrared Imaging Radiometer Suite equipped on the Suomi National Polar orbiting Partnership satellite) NTL data from 2012 to 2020 were corrected, and NTL data for HABT were obtained. A multidimensional relative poverty index (MRPI) that assesses being “free from worries over food and clothing and having access to compulsory education, basic medical services, and safe housing” using social statistical data was created with the analytic hierarchy process and entropy weight method. A panel regression model with fixed effects was established for MRPI and corrected NPP/VIIRS NTL data. The R2 of fitting was 0.6578 and confirmed a strong correlation between MRPI and corrected NPP/VIIRS NTL data. Based on this, the MRPI estimation model was constructed based on the MRPI and corrected NPP/VIIRS NTL data, and passed the accuracy test. Finally, using the national list of poverty counties, it was verified that, at the county scale, the corrected NPP/VIIRS NTL data could effectively identify areas of relative poverty. This study lays the foundation for the use of NPP/VIIRS NTL data in the identification of areas of relative poverty. It provides a feasible method and data reference for analyzing relative poverty at a smaller scale. The dynamic identification of areas of relative poverty can also provide a basis for formulating scientific poverty reduction strategies.

Where do Energy-Poor Households Live? Empirical Evidence from Indonesia

Empirical analysis on the links between geography and energy access in archipelago setting is still limited. In particular, the territorial identification of energy poverty in Indonesia is still missing. Our study maps geographical location and estimates factors that determines the probability of being energy poor household in relation to electricity. We used the OLS (Ordinary Least Square) estimation and utilized the socioeconomic survey (Susenas) combined with data on terrain elevation, presence of geographic features such as mountainside, topography characteristics, ocean and forest obtained from the village census (PODES). The results show that energy poverty in Eastern part of Indonesia is larger than in the Western. In Eastern Indonesia, we estimate that 13.5% of the total households are energy poor compared to the Western which only 7.21%. Households located in the forest area was the dominant factor to influence prevalence of energy poverty among geographic constraints, with magnitude of influence at 22-23 percentage point to non-forest residents. Secondly, the presence of steep-sloped terrain is the next meaningful geographical constraint with contribution effect to energy poverty prevalence at around 18 percentage point. The result highlighted priority of locations in which resource and policy to reduce energy deprivation need to be allocated.

A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion

This paper adopts the method of multisource big data fusion to conduct an in-depth study and analysis of precision poverty alleviation and uses big data statistical analysis model to explore and analyze it. Combining the characteristics of big data itself and the development of precision poverty alleviation, it focuses on the exploration of big data and introduces the background, development status, and achieved results of poverty alleviation with typical cases, followed by the analysis of the problems in the process of big data precision poverty alleviation and the study of the improvement path of big data technology precision poverty alleviation. Through the comparative analysis of the simulation accuracy of three models, the results show that the random forest model has the lowest error rate, after which the importance degree of indicators is derived using the model. In addition, the empirical analysis of the preprocessed sample data for multidimensional identification of poor households yields the contribution rate of each dimensional indicator that leads to multidimensional poverty of farm households, establishing scientific judging criteria to accurately judge whether farm households are poor on the one hand and selecting accurate identification methods to achieve accurate identification of poor households on the other hand. The tenfold crossover method is used to verify the errors in the test sample set. When the number of classification trees is greater than 100, it will gradually increase. Therefore, it is most appropriate to select the number of trees as 100. The multidimensional accurate identification model of farm household poverty constructed in this paper has an accuracy rate of 90.26% for the identification of poor households. By analyzing the accuracy rate of model identification and the contribution rate of multidimensional indicators leading to the poverty of farm households at the same time, the poverty degree of farm households under each dimensional indicator is derived, to accurately identify the poor households and their poverty status. The results show that the multidimensional accurate identification model of farm household poverty has the accurate identification ability and application value in the identification problem of poor households, and through the implementation of the model algorithm, a good application environment of accurate identification of poverty is created, which provides technical support to help poverty alleviation work and improve the accuracy of identification of poor households.

Tools used in nursing education to assess attitudes about poverty: An integrative review.

To analyze the assessment of attitudes about poverty used in nursing education considering the first Sustainable Development Goal targets. Tools assessing attitudes about poverty have been used in nursing education studies to examine the beliefs of nursing students. An integrative literature review was carried out in five international databases in the areas of health and nursing and in four Latin American and Caribbean databases. Four tools were identified, with Attitude Toward Poverty Short Form and Attitudes about Poverty and Poor People being the most used in the studies. Items from tools measured attitudes negative, prejudiced, and stigmatizing about the population living in poverty. In addition, items from tools measured a range of concepts from poverty to social inequality and disenfranchisement. The identification of poverty is a critical strategy for tackling poverty. In this sense, poverty assessment tools have the potential to recognize poverty as a multidimensional phenomenon.

Village-level poverty identification using machine learning, high-resolution images, and geospatial data

Tracking progress in poverty alleviation and promptly identifying the distribution of poor areas are critical for strategic policy interventions, especially for regions with poor statistical systems. The massive satellite imagery and geospatial data provide great opportunities for timely and cost-effective socioeconomic evaluations. However, existing research on poverty identification is mostly based on satellite images, and the potential of combined multi-source geospatial data on poverty identification has not been fully explored. Here, we propose an approach that evaluates how village-level poverty can be identified by integrating high-resolution imagery (HRI), point-of-interest (POI), OpenStreetMap (OSM), and digital surface model (DSM) data. The study area included 338 villages from Yunyang County, located in Hubei Province, central China. We extracted the explanatory variables indicating access to facilities and services, agricultural production conditions, village construction, and the spatial distribution of village settlements from the HRI, POI, OSM, and DSM data. The random forest algorithm was then used to model the relationship between village-level poverty and explanatory variables. The results demonstrated a 54% accuracy in the prediction of village-level poverty; the best prediction performance (72%) was observed for the villages categorized as poor. The built-up land proportion and the time cost to the facilities and services contributed the most to the identification of village-level poverty, while the proxy variables of agricultural production conditions contributed the least. This study provides an approach to village-level poverty identification using satellite imagery and geospatial data and proves that the data employed in this study could identify the poorest areas that are highly coupled with natural geographical conditions and backward public services.

Identification and alleviation pathways of multidimensional poverty and relative poverty in counties of China

Identification and alleviation pathways of multidimensional poverty and relative poverty in counties of China

A qualitative study of mothers' health literacy related to malnutrition in under 5-year-old children in southern Mozambique.

To explore mothers' perceptions of malnutrition and its causes in U-5's in Mozambique, as well as their ability to recognise, prevent and act on signs of malnutrition. A qualitative exploratory inquiry using focus group discussions and individual interviews analysed using Nutbeam's health literacy themes. Manhiça District Hospital in Manhiça, Mozambique. Mothers of U-5's (n 53) attending the in- and out-patient paediatric wards. Different malnutrition literacy levels were identified in mothers' responses. Mothers' reflections on the causes of malnutrition in U-5's were more elaborate compared to those of recognition, prevention and treatment strategies. Only severe forms of acute malnutrition were recognised by mothers, while early signs of undernutrition and stunting largely went undetected or unmentioned. Limited knowledge, time and financial resources were mentioned as contributors to suboptimal practices resulting in malnutrition. The district hospital, rather than community resources or local health posts, was indicated as the place mothers would go to seek advice and treatment for malnutrition. All mothers requested additional information on how to prevent and treat malnutrition. The varying literacy levels among mothers, the lack of references to community health workers as a resource in identifying and managing malnutrition, and the identification of poverty and sociocultural conditions as contributors to suboptimal practices indicate the need for in-depth research focused on the social determinants of malnutrition. A more comprehensive understanding of mothers' health literacy would contribute to the development of holistic programmes aiming to improve community management of malnutrition.

High-resolution remote sensing data can predict household poverty in pastoral areas, Inner Mongolia, China

The accurate prediction of poverty is critical to efforts of poverty reduction, and high-resolution remote sensing (HRRS) data have shown great promise for facilitating such prediction. Accordingly, the present study used HRRS with 1 m resolution and 238 households data to evaluate the utility and optimal scale of HRRS data for predicting household poverty in a grassland region of Inner Mongolia, China. The prediction of household poverty was improved by using remote sensing indicators at multiple scales, instead of indicators at a single scale, and a model that combined indicators from four scales (building land, household, neighborhood, and regional) provided the most accurate prediction of household poverty, with testing and training accuracies of 48.57% and 70.83%, respectively. Furthermore, building area was the most efficient indicator of household poverty. When compared to conducting household surveys, the analysis of HRRS data is a cheaper and more time-efficient method for predicting household poverty and, in this case study, it reduced study time and cost by about 75% and 90%, respectively. This study provides the first evaluation of HRRS data for the prediction of household poverty in pastoral areas and thus provides technical support for the identification of poverty in pastoral areas around the world.

Remote Sensing of Urban Poverty and Gentrification

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

Economic & Nutritional Status of Industrial Workers in Sikandrabad City of Bulandshahr District - A Review Study

The increasing rate of marginalisation due to the fast changing globalisation and mechanisation has accelerated the severity of exploitation of unskilled workers, particularly unorganised workers in the industries. There has been an increasing identification of poverty as evidenced by statistics pertaining to the informal industrial sector. Moreover, the nature of the work in the industrial sector is increasingly becoming subcontracting due to the opening up and also to withstand the pressure of labour market demand in the global setting. The increasing tendency of openness makes the illiterate and untrained labourers in the industrial sector more vulnerable to further exploitation. The working and living conditions are revealed to be highly detrimental to workers health. To overcome this situation, the government from time to time has enacted legislations and constituted welfare boards to improve the working and living conditions and wage regulations. But these bodies failed miserably in ensuring the said priorities. It is found that to tide over this situation, they have been introducing their own ways of welfare programs. But these programs in effect are found to be providing only cosmetic face lift to the workers in the industrial sector and are fully outside the ambit of the basic requirements of industrial workers particularly Sikandrabad city.