Multidimensional Poverty Research Articles

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.

Do languages matter? The impact of local dialect proficiency on multidimensional poverty alleviation among rural-to-urban migrants in China

Language plays a crucial role in poverty among migrants. However, the relationship between language skills and poverty among internal migrants in developing countries has received limited attention. Using data from 11,789 rural-to-urban (RTU) migrants in the China Migrants Dynamic Survey, this study empirically assesses the impact of local dialect proficiency (LDP) on multidimensional poverty alleviation among migrants and further investigates potential mediators of this association. The results indicate that enhanced LDP significantly reduces the prevalence of multidimensional poverty among RTU migrants, with speaking proficiency having a more significant effect than listening proficiency. Moreover, three key mechanisms contribute to multidimensional poverty alleviation through LDP: the expansion of social networks, enhancement of urban identity, and improvement of employment quality. Heterogeneity analyses reveal that the impact of LDP on multidimensional poverty alleviation is more significant in regions characterized by higher dialect diversity and lower popularity of Mandarin, as well as among males, older generations, and RTU migrants with lower levels of education. In conclusion, this study advances understanding of the role of language proficiency in poverty alleviation among migrants by constructing a cultural-linguistic theoretical framework.

Feminization of poverty: an analysis of multidimensional poverty among rural women in China

Few studies from an individual perspective have analyzed the multidimensional poverty of rural women in China. Therefore, based on the CFPS data from 2010 to 2020 and the Alkire-Foster approach, this study built a six-dimensional system to portray the status of multidimensional poverty among rural women. The overall comparisons found that rural women were more likely to be multidimensional poor than other subgroups. And the results of rural women showed significant demographic and spatio-temporal differences. That is, older rural women were more deprived than younger rural women. Rural women with spouses or confidence were less deprived than those without spouses or confidence, respectively. From the spatial perspective, the censored headcount ratios of rural women in descending order were Western Region, Central Region and Eastern Region. From the temporal perspective, the risk of rural women’s multidimensional poverty decreased significantly from 2010 to 2020. The importance of non-material indicators was gradually becoming prominent, including education, health and subjective wellbeing. The conclusions can contribute to the development of policies, even if some limitations need to be further improved.

Interdependence of rural household welfare measurement in the context of climate variability in Ethiopia

The Ethiopian government and various stakeholders have made significant efforts to improve household consumption, poverty, and food security. However, the results have not met expectations. To ensure that programs are effective, it is critical to have a robust measure of well-being that considers social, economic, institutional and environmental challenges. Previous studies on welfare have focused on a single measurement, but it is important to recognize the interdependence between various well-being indicators to formulate comprehensive interventions and initiatives for rural households in the context of climate change and variability. This paper examines the dynamic link between unidimensional poverty, multidimensional poverty, and food security using panel data from Ethiopia. The interdependency of welfare measurements was estimated using a seemingly unrelated multivariate probit model with a conditional mixed process estimator with full information, a simulated maximum likelihood approach, and the presumption that different metrics can have various policy implications. The chances of interdependence wellbeing status were low for households that were simultaneously food insecure and poor or nonpoor. Multidimensional poverty has a higher success rate than food insecurity or consumption poverty. Unlike unidimensional poverty and food insecurity, multidimensional poverty has a higher chance of to be deprived. Policymakers must consider the synergies and trade-offs of different types of welfare and the dynamic linkage between climate-induced shocks and other covariates with household welfare to minimize households' vulnerability to climate-induced shocks for effective intervention for rural households to obtain a whole picture of wellbeing.

Tác động của tài chính toàn diện đến nghèo đa chiều ở cấp độ hộ gia đình tại Việt Nam năm 2020

The relationship between financial inclusion and poverty is of interest to governments and researchers worldwide, including Vietnam. The research focuses on assessing the current status of financial inclusion at the household level in Vietnam in 2020 and its impact on multidimensional poverty. The study employs principal component analysis and a logistic regression model. Based on data from the Household Living Standards Survey, the study shows that the assessment of the financial inclusion differs between ecological regions, with households living in the Red River Delta and Southeast regions having the highest level of financial inclusion. An increase in financial inclusion at the household level has a statistically significant impact on reducing multidimensional poverty (relative risk reduces by 0.5 times). Based on research results, sustainable multidimensional poverty reduction policies can be implemented in combination with comprehensive financial inclusion development strategies. Furthermore, factors such as region, education, and occupation of the household head need to be considered when addressing poverty reduction and promoting financial inclusion.

Interrogating Covid-19 Protocols and Policing in Nigeria: Implications of Government Distrust and Service Failure (2019 – 2022)

The study examined covid-19 protocols and policing in Nigeria, with implications of government distrust and service failure, 2019 – 2022. Specifically, the study interrogated how government’s attitude to healthcare delivery undermined covid-19 protocols and policing in Nigeria; and how multidimensional poverty in the country undermined Nigeria’s lockdown protocols and policing in the fight against the virus. By adopting documentary method of data collection, expost facto research design and securitisation theory, the study found that government’s historical attitude towards healthcare delivery for its citizens led to the failure of its covid-19 protocols and policing, as result of the poor compliance of citizens. The study further found that multidimensional poverty among Nigerians led to the undermining of governments lockdown measures and guidelines during the pandemic. This were borne out of the citizens’ distrust of their government who have hoarded palliatives meant to cushion the effect of the pandemic. The study concluded and recommended amongst others, that the government must invest in quality healthcare by increasing its spending and budget allocations to the healthcare sector. It must also ensure that it desist from playing politics with palliatives meant to help cushion the effect of the pandemic on its citizens.

Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

Multidimensional poverty of ethnic minority households: A case study in A Luoi district, Thua Thien Hue province

“Poverty” is widely known as a multifaceted concept and could be evaluated using multidimensional measurements. In Vietnam, there is still too little research conducted at the micro level in villages, communes, and households. This study explored multidimensional poverty in ethnic minority communities in the Central region of Vietnam. Based on the result of the survey of 221 ethnic minority farmers in A Luoi district, Thua Thien Hue province, this study applied the Alkire-Foster method to measure the multidimensional poverty of farmers through 11 indicators of 6 aspects of poverty, including income, education, health care, housing, living standard, and information access. The results showed that about 39.82% of households are in multidimensional poverty. The degree of deprivation in the 6 poverty dimensions was not consistent, with a large proportion of shortages related to income (72.64%), housing area (6.04%), and adult education (6%). Using the Binary Logistic Regression model showed that 8 factors affect the multidimensional poverty of ethnic minority households, including family size, farm size, number of livestock, irrigation, diverse farm income sources, diverse non-farm income sources, distance to central markets, and number of adverse external influences. Critical recommendations from the study to reduce multidimensional poverty of ethnic minority households include: diversifying livelihood activities for households, both non-farm and on-farm, with a greater focus on livestock; maintaining and developing good agricultural production land, including investment in irrigation and mechanisation of production; strengthening support for farmers to access the market...

Analysis of incidence, intensity, and gender perspective of multidimensional urban poverty in Kenya

Poverty continues to be one of the biggest challenges facing many economies worldwide, and its incidences and intensities are very high in developing economies. This paper utilized the Alkire-Foster (AF) method to compute the multidimensional poverty index (MPI) and analyze the incidence and intensity of multidimensional poverty among urban households in Kenya. The findings indicated that 8.7 % of urban households are multidimensionally deprived in 33.3 % of the selected dimensional indicators. Also, the results showed that over 50 % of urban households are deprived of drinking water and sanitation services. In addition, the findings revealed that higher poverty incidence, intensity, and urban multidimensional poverty exist among female-headed households, old household headships, and households residing in peri-urban regions. The Probit regression analysis indicated that large household size, number of children under five years, household head age, gender, marital status, urban food insecurity, health status, and residing in Malaria endemic zone are significant positive predictors of urban multidimensional poverty. On the other hand, an increase in the number of educated women, owners of insurance coverage, agricultural land, and wealthy and home-owning households is linked to a decline in urban multidimensional poverty. The paper professes that policymakers should cautiously consider household socioeconomic differences while designing poverty alleviation policies.

Unlocking Sustainable Futures: Exploring Multidimensional Poverty in Belt & Road Initiative Countries Through Forest Plantation Perspectives

ABSTRACT Forest plantation initiatives hold significant prospects to impact various dimensions of poverty, highlighting the crucial need to understand this link in the context of sustainable development. In the 1990s, countries participating in the Belt and Road Initiative (BRI) witnessed a notable progress in forest plantations. While earlier research primarily focused on assessing the effects of these plantations on specific facets of rural poverty, this study aims to delve into the broader macroeconomic implications of forest plantations on the Multidimensional Poverty Index (MPI). From 2010 to 2020, the study used the Fully Modified Ordinary Least Square (FMOLS) and Driscoll-Kraay estimators to scrutinize the influence of forest plantations on the MPI. The research findings unveil a significant negative effect of various factors, including the rule of law (RL), agricultural expansion (AGR), and forest plantation (FP), on the MPI in BRI countries. Interestingly, political stability and the control of corruption exhibit a positive effect on the MPI. Furthermore, causality tests indicate a unidirectional link between MPI and political stability and a one-way connection between forest plantations and MPI. Suggestions for BRI countries involve promoting sustainable forest plantation initiatives through strategic encouragement and investment while highlighting responsible management practices. Also, it is advised to implement policies that encourage political stability and strengthen governance, thereby developing an enabling environment for effective poverty reduction.

Targeting the “poorest of the poor”: A multi-scale approach

Understanding that poverty presents multidimensional and multi-scale deployments, a methodology is proposed that allows to identify those poorest families among the poor in the State of São Paulo for cash transfer purposes. We propose a method to combine the vulnerability of the family with the other scales, resulting in a more refined classification of the situation of poverty, which is more difficult to be biased by the selfreported register. We added a georeferenced context-based component to differentiate among families with same vulnerability but living in different levels of intraurban and municipal conditions. At the municipal scale, we developed two sub-indexes through Principal Component Analysis (PCA): a) the Municipal Context Sub-Index, and b) the Municipal CadUnico Sub-Index. These sub-indexes were then integrated. On the scale of the Census enumeration area, we considered aspects that can aggravate the poverty condition, such as physical environmental risks and socioeconomic deprivation. On the family-scale, we consider the score defined by the São Paulo Multidimensional Poverty Index (MPI). The final multi-scale classification of families results from the sum of the scores obtained at the family level, the Census enumeration area and the municipality. The indexes were validated internally and externally. We simulated a scenario of hypothetical cash transfer programs using the current tool (MPI-S) and the proposed methodology to benefit up to 10,000 and 100,000 families in the State of São Paulo. We implemented the solution in Power BI tool to allow automatic updates of the indexes to be made in the developed “dashboard”. Results allow us to evaluate the impact of both approaches. The current approach, based only in the multidimensional poverty of the family is more distributed, with a wider geographic distribution, sometimes benefiting only one family in some municipalities or a great number of families in one municipality. The proposed approach tends to concentrate the assistance with greater percentages of families in the CadUnico by municipality. Besides aiding on cash transfer decision, the results obtained on the context and the intensity of municipal poverty can be used for programs aimed at leveraging economic development in the different administrative regions of the State of São Paulo.

Multidimensional relative poverty alleviation of the targeted microcredit in rural China: a gendered perspective

PurposeThis study explores whether targeted microcredit can effectively alleviate households’ multidimensional relative poverty (MdRP) in rural China in the new era following the poverty elimination campaign and discusses it from a gendered perspective.Design/methodology/approachThis study applies a fixed-effects model, propensity score matching (PSM) and two-stage instrumental variable method to two-period panel data collected from 611 households in rural western China in 2018 and 2021 to explore the effects, mechanisms and heterogenous performance of targeted microcredit on households’ MdRP in the new era.Findings(i) Targeted microcredit can alleviate MdRP among rural households in the new era, mainly by reducing income and opportunity inequality. (ii) Targeted microcredit can promote women’s empowerment, mainly by enhancing their social participation, thereby helping alleviate households’ MdRP. The effect of the targeted microcredit on MdRP is more significant in medium-educated women households and non-left-behind women households. (iii) The MdRP alleviation effect is stronger in villages with a high degree of digitalization.Research limitations/implicationsLearn from the experience of targeted microcredit. Accurately identify poor groups and integrate loan design into financial health and women empowerment. Particularly, pay attention to less-educated and left-behind women households and strengthen coordination between targeted microcredit and digital village strategies.Originality/valueThis study clarifies the effect of targeted microcredit on women’s empowerment and households’ MdRP alleviation in the new era. It also explores its various effects on households with different female characteristics and regional digitalization levels, providing ideas for optimizing microcredit.

Unveiling the Complex Facets of Poverty: Unidimensional and Multidimensional Insights from Rural Areas of Suri Sadar Sub-Division, Birbhum District, Eastern India

Poverty, particularly in developing regions, is a complex, multifaceted issue deeply embedded in various interrelated factors. It extends beyond mere financial insufficiency, encompassing limited access to essential services such as healthcare, education, and overall living standards. This study examines both the unidimensional and multidimensional aspects of rural poverty in Suri Sadar Sub-Division, located in Eastern India. For the unidimensional aspect, this study employs the poverty headcount ratio and the Poverty Gap Index to gauge the incidence and intensity of poverty. In contrast, the multidimensional approach utilized three dimensions and 12 indicators to assess the incidence, severity, and multidimensional poverty index utilizing the Alkire–Foster (AF) methodology. The unidimensional analysis, focusing on income and consumption, highlights significant economic disparities, particularly in the western Community Development Blocks, namely, Khoyrasole, Md. Bazar, and Rajnagar. The highest levels of multidimensional poverty are generally consistent with the unidimensional findings, particularly in the western blocks. These results underscore the need for comprehensive poverty reduction strategies that address both economic and broader aspects of poverty. In areas like the western blocks, where both income-based and multidimensional poverty rates are high, strategies should integrate economic development, improved healthcare access, enhanced educational quality, and living standards improvement. Therefore, this study serves not only as an academic endeavor but also as a vital tool for informed policymaking in poverty alleviation, providing planners, administrative officials, and researchers with essential insights to develop effective, localized, and sustainable poverty reduction strategies.

Widowhood and multidimensional poverty: Evidence from Nigeria

AbstractPoverty among widows has received little empirical attention in Africa despite women's severe vulnerability to death shock. We provided empirical evidence on widow households' transition in and out of poverty and factors influencing their probability of being in poverty. The Markov transition probabilities show moderate but increasing positive transitions for severely poor widows. Non‐poor widows are stayers who primarily sustain their non‐poor class. The ordered logit estimation shows that higher dependency ratio increases the chances of a widow being severely poor. Being an older widow and having literacy skills reduced the probability that a widow household will be severely poor. Household size and dependency ratio are noted to play important roles in the probability of transitions across poverty classes as shown by the estimated multinomial logit model. These findings are robust to alternative poverty measure, estimation method and different set of weights. Generally, the results echo the need for social safety nets to cushion widows' financial strains. Life insurance policy for spouses, increased sensitization of widows of their rights and adult education programmes targeted at widows could mitigate the negative impact of widowhood on women.

Investigation of multidimensional poverty in Pakistan at the national, regional, and provincial level

AbstractTo achieve the sustainable development goal of “no poverty”, many attempts have been made to measure poverty so that policy intervention can target the right people with the correct intensity. Since the traditional method of a unidimensional approach using monetary indicators, such as income and consumption, is now considered insufficient, a multidimensional approach has been employed using non-monetary indicators. The latter approach encompasses the different poverty aspects affecting an individual’s capabilities and functioning. This study aimed to calculate multidimensional poverty in Pakistan using the Alkire & Foster method and Pakistan Social and Living Standards Measurement (PSLM) published by the Pakistan Bureau of Statistics data for 2018–19. To further complete the research, a binary logistic regression has been run to measure the effects of a deviation in income status on multidimensional poverty to see the impact of monetary income on non-monetary measures. Results show that the national global Multidimensional Poverty Index (MPI) is 0.24, meaning that multidimensionally poor people in Pakistan experience 24% of the total deprivations. The most deprived dimension of the three is education (44.7%), which needs special attention. Furthermore, if a household’s total annual income increases and becomes more significant than the mean income of the sample, the household’s probability of being multidimensionally poor will decrease. This implies that with the increase in average national income, the national poverty will reduce not only in absolute terms but also multidimensionally. This study’s findings have several implications for policymakers, and the results of the MPI should align the allocation of public sector resources calculated to give a geographical and sectoral image of the poverty situation, which will guide policy designs and allocation of budget and resources.

Status of Poverty in India: A Study on State wise Analysis

Poverty in India is a multidimensional phenomenon involving the deprivation of basic needs. According to the Sustainable Development Goals 2030 Agenda, the goal was to end poverty and zero hunger. However, poverty statistics also vary between states in India with some states having the lowest number of poverty and some states having the highest number of poverty. According to the Headcount Ratio 2022-23 MPI index report, population poverty was 24.85% in 2015-16 and 14.96% in 2019-21 across India (NITI Aayog, MPI Index, India, 2023). This study aims to understand the existence and state-wise variations of poverty in India. This study uses data collected from the Planning Commission of India's report, India's Headcount Ratio Intensity and MPI, NITI Aayog National Multidimensional Poverty Index 2023, and (NSSO) HCES: 2022-23 factsheet report on poverty statistics of various states and Union Territories of India and per capita monthly income of various states.

Inequality in environmental risk exposure and procedural justice in the Matanza-Riachuelo River Basin

ABSTRACT Using a capabilities-based perspective on socioenvironmental justice, this paper aims to contribute to the understanding of the interconnection between social equity and care for the earth in the case of the Matanza-Riachuelo – the most industrialized and contaminated river basin in Argentina. First, it uses a spatial regression model to analyse the correlation between multidimensional poverty and potential exposure to three types of environmental risks: environmental hazards of productive establishments, open-air waste dumps, and proximity to contaminated rivers and streams. Second, it analyses inequities in the processes of resettlement of households living in conditions of extreme environmental risk in informal settlements. It shows that higher poverty areas face greater risk of exposure to open-air waste dumps but lower risk of exposure to industrial and commercial environmental hazards. It also demonstrates how enhanced participation of households in decision-making on resettlement contributed to improved outcomes.

Análisis de los patrones espaciales del desarrollo y pobreza en Costa Rica

This paper aims to analyze the relationships between the different dimensions of poverty and development from a multidimensional and spatial perspective. Methodologically, this research is composed of two phases. The first, a descriptive statistics analysis of the Multidimensional Poverty Index and the Human Development one, was carried out, departing from the definitions of territoriality used in Costa Rica (rural-urban and by planning regions) with county-level data. In second place, a cluster analysis, with K-means method, was developed with a database of 32 variables for development and poverty at the country level. About the results, we discovered that the typologies of rural and urban areas are significant but can be self-explanatory; the category of planning regions reveals relevant patterns in the center-periphery dynamics, particularly due to asymmetries between the center, the Pacific coast and, the Atlantic coast and borders. The cluster analysis confirms these patterns, showing that in terms of development, the country seems to be divided into 3 regions, while in terms of poverty is possible to identify 4. Concluding with some public and economic policy recommendations.

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas.

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

Hybrid measures of multidimensional poverty

Hybrid measures of multidimensional poverty