Multidimensional Poverty Measurement Research Articles

Children with disabilities: Left behind or forgotten? Empirical evidence from Botswana using multidimensional poverty measures

The 2030 Agenda for Sustainable Development recognises the importance of multidimensional poverty and disability. Although there is compelling evidence suggesting that people with disabilities have a higher probability of being poor, studies that compare children with disabilities with those without disabilities remain scarce and limited to developed countries. Using the 2015/16 Botswana multi-topic household survey, we examined the link between childhood disability and multidimensional child poverty in Botswana. The study employs the conceptual framing of the capability and human rights-based approaches. Results reveal significant disparities in poverty levels between children with and those without disabilities across demographic, economic and geographical variables, with children with disabilities experiencing higher poverty levels than the rest of the children population. We conclude that children with disabilities in Botswana are left out and argue that deliberate efforts should be put in place to implement existing and develop new inclusive policies that ensure equal opportunities for children with disabilities in order not to be left behind. The paper provides policy implications for childhood disability and multidimensional child poverty research in Botswana.

Impact of the Information and Communications Technologies (ICT) infrastructure on the returns to higher education in Mexico: A gender analysis

This paper aims to analyze the impact of access to Information and Communication Technologies (ICT) on the private returns to higher education (HE) focusing on gender inequality in 2020. Methodology: To evaluate the above impact a set of Mincerian equations will be estimated. The proposed approach mitigates biases associated with self-selection and individual heterogeneity. Data: The database comes from the National Household Income and Expenditure Survey (Encuesta Nacional de Ingresos y Gastos de los Hogares, ENIGH) from 2020. Results: Empirical evidence suggests that individuals that have HE have a positive and greater impact on their salary income compared to those with a lower educational level, being women that do not have access to ICT those with the lowest wage return. Policy: Access to ICT should be considered as one of the criteria that integrate social deprivation in the measurement of multidimensional poverty. Likewise, it is necessary to design public policies that promote the strengthening and creation of educational and/or training systems in technological matters for women. Limitations: No distinction was made between individuals that graduated from public or private schools, nor was income from sources other than work considered. Originality: This investigation evaluates the impact of access to ICT on the returns to higher education in Mexico, in 2020, addressing gender disparity.

Evaluating the effects of digital finance on urban poverty

This study examines the impact of digital financial services development on urban poverty. Utilizing the vicious cycle of poverty theory, we established a multidimensional poverty measurement index to assess the poverty status of urban households and propose strategies for leveraging digital finance to address urban poverty. Through empirical research based on a comprehensive dataset from China covering the period from 2012 to 2020, we evaluated the moderating effect of digital financial growth on urban poverty. Our analysis, which categorized poor urban households into five groups—laid-off workers, unemployed individuals, migrants, the elderly, and disabled persons—revealed that only the unemployed and migrants experience significant benefits from digital financial development. Lastly, we explore the underlying mechanisms and emphasize the role of investment, financing, and entrepreneurship in utilizing digital finance for poverty alleviation.

Exploring the association between multidimensional poverty and antenatal care utilization in two provinces of Papua New Guinea: a cross-sectional study

BackgroundAlthough global poverty rates have declined in the last decade, the fall in the Asia-Pacific region has been slow relative to the rest of the world. Poverty continues to be a major cause of poor maternal and newborn health, and a barrier to accessing timely antenatal care. Papua New Guinea has one of the highest poverty rates and some of the worst maternal and neonatal outcomes in the Asia-Pacific region. Few studies have investigated equity in antenatal care utilization in this setting. We explored equity in antenatal care utilization and the determinants of service utilization, which include a measure of multidimensional poverty in Papua New Guinea.MethodsTo explore the association between poverty and antenatal care utilization this study uses data from a ten-cluster randomized controlled trial. The poverty headcount, average poverty gap, adjusted poverty headcount, and multidimensional poverty index of antenatal clinic attendees are derived using the Alkire-Foster method. The distribution of service utilization is explored using the multidimensional poverty index, followed by multivariate regression analyses to evaluate the determinants of service utilization.ResultsThe poverty headcount was 61.06%, the average poverty gap 47.71%, the adjusted poverty headcount 29.13% and the average multidimensional poverty index was 0.363. Further, antenatal care utilization was regressive with respect to poverty. The regression analyses indicated that older women; being a widow (small number of widows (n = 3) asserts interpreting result with caution); or formally employed increase the likelihood of accessing antenatal care more often in pregnancy. Travelling for over an hour to receive care was negatively associated with utilization.ConclusionThis study indicated high levels of multidimensional poverty in PNG and that ANC utilization was regressive; highlighting the need to encourage pregnant women, especially those who are economically more vulnerable to visit clinics regularly throughout pregnancy.

Measurement and Comparison of Poverty in Abhujmadiya Tribe: Income-based and Deprivation-based Poverty Approach

The present paper is attempted to measure and compare the difference and discrepancy between the income-based and deprivation-based poverty measurement in poverty of Abhujmadiya tribes of Chhattisgarh. Two international poverty measurement approach is considered for poverty measurement of Abhujmadiya tribes, first income-based measurement developed by World Bank and second, Multidimensional Poverty Index developed by United Nation Development Program (UNDP). Research study is based on primary data of 80 household collected from 8 village of 2 block of Narayanpur district of Chhattisgarh State. Results shows that 90% Abhujmadiya peoples are poor in income-based poverty whereas, 48.43% Abhujmadiya peoples are poor in deprivation-based poverty measurement. A difference of 41.57% is found in the measurement of poverty by both the poverty measurement approach and this discrepancy is more in income-based poverty as compare to multidimensional poverty measurement. The study also reveals that health facilities, year of schooling, cooking fuel, sanitation and housing conditions are the major contributor in the poverty of Abhujmadiya Tribes.

A data-driven approach to mapping multidimensional poverty at residential block level in Mexico

AbstractAccurate, inexpensive and granular human poverty assessments are critical for data-driven policy decision-making. This research proposes a novel approach to computing poverty scores utilizing multispectral satellite images and indices calculated from census reference values. We show how this approach can leverage standard and sparse survey-based multidimensional poverty assessments at the municipal level to develop a deep learning architecture to obtain poverty scores at the residential block level. This method has the distinctive feature that the obtained inference corresponds to Multidimensional Measurement of Poverty generated by CONEVAL, the Mexican agency responsible for measuring poverty. We provide a reliable alternative to survey-based approaches with an $$R^2$$ R 2 of $$0.802\pm 0.022$$ 0.802 ± 0.022 for the lack of housing quality and spaces dimension. A convolutional neural network trained on multispectral satellite images and the lack of housing quality and spaces dimension, which is regressed from census reference variables corresponding to lack of water, electricity, sewage, concrete floor, toilet and occupancy level obtains an $$R^2$$ R 2 of 0.753. These results represent a significant step forward in including machine learning techniques to provide reliable information at reduced costs and a higher spatiotemporal frequency than traditional person-to-person surveys.

Multi-Dimensional Decomposition, Measurement, and Governance Mechanism of Relative Poverty in Chinese Households under the Goal of Common Prosperity: Empirical Analysis Based on CFPS2020 Data

From the perspective of household productivity, this paper analyzes the capital composition of the household and the formation mechanism of relative poverty. Based on the CFPS data in 2020, it uses the Alkire–Foster (AF) index to measure the breadth and depth of relative poverty in different regions and puts forward its governance mechanism. The results show that there are significant differences between urban and rural areas and among different regions. The relative poverty incidence rate is mainly concentrated in four indicators, accounting for 30.8% of the total number of indicators. From high to low, the incidences of relative poverty in a single dimension include financial assets, livelihood assets, health, and employment levels. The breadth is mainly reflected in economic capital, health, employment, and education level. The depth is reflected in financial assets, health level, and social network indicators. Except for the social capital dimension, the poverty in rural areas is higher than urban areas, and the central and western regions are higher than the eastern regions, showing a distinct characteristic of imbalanced urban–rural and regional development. This paper proposes the relative poverty governance mechanism of households’ capital accumulation, urban–rural integrated development and regional coordinated development.

Multidimensional child poverty in South Korea: developing measures to assess progress towards the sustainable development goals

This article presents the development of a measure to assess the prevalence and patterning of multidimensional child poverty in South Korea. The first goal of UN’s Sustainable Development Goals (SDGs) is to reduce poverty in all its dimensions, and countries are increasingly developing their own measures of multidimensional poverty. This flourishing of different measures presents challenges for international comparisons. The article applies an internationally-validated method of assessing multidimensional poverty to demonstrate its suitability for use in a high-income Asian economy. Multidimensional child poverty is assessed by combining data on child material deprivation with their household income. Using data from the 2018 Korean National Children Survey, we show that child material deprivation is higher (15%) than income poverty (12%). When measured using a combined measure of material deprivation and income, around one in every three children in Korea were found to be either poor or vulnerable to poverty. These findings show that the official monetary poverty measure on its own may understate the percentage of children unable to afford necessities in Korea, as envisioned by international targets like the SDGs. In terms of policy, analysis of individual deprivations suggests that a combination of in-kind benefits such as vouchers for leisure activities or education and asset-building programmes as well as cash transfers are needed for tackling child poverty.

Measurement and determinants of multidimensional urban poverty: Evidence from Shandong Province, China.

China eliminated rural poverty under current poverty standards in 2020. However, compared with rural poverty, urban poverty in China has been somewhat neglected. This paper aims to discover the changes and determinants of multidimensional urban poverty in Shandong Province, a representative province in Eastern China. Using a nationally representative panel dataset, the China Family Panel Studies, and the Dual Cutoff method, this study creates a multidimensional poverty index with four dimensions and 11 indicators to measure urban poverty in Shandong Province. This paper discovers that while the incidence of multidimensional urban poverty in Shandong Province decreased from 47.62% in 2010 to 36.45% in 2018, the intensity of multidimensional poverty only decreased from 41.27% to 37.25%, which indicates the inadequacy of urban anti-poverty efforts in Shandong Province. This paper also uses logistic regression to identify the determinants of multidimensional urban poverty. The findings suggest that income, health, drinking water, and durable goods are the main determinants of multidimensional urban poverty in Shandong Province. Based on these findings, this study provides targeted recommendations for future urban anti-poverty policies in Shandong Province.

Multidimensional Poverty and Risk of Atherosclerotic Cardiovascular Disease: A U.S. National Study

BackgroundPoverty is associated with atherosclerotic cardiovascular disease (ASCVD). While poverty can be evaluated using income, a unidimensional poverty metric inadequately captures socioeconomic adversity. ObjectivesThe aim of the study was to examine the association between a multidimensional poverty measure and ASCVD. MethodsSurvey data from the National Health Interview Survey was analyzed. Four poverty dimensions were used: income, education, self-reported health, and health insurance status. A weighted deprivation score (ci) was calculated for each person. The multidimensional poverty index was computed for various cutoffs, k, for total population, and by ASCVD status. The association between multidimensional poverty and ASCVD was examined using Poisson regression. Area under receiver operator characteristics curve analysis was performed to compare the multidimensional poverty measure with the income poverty measure as a classification tool for ASCVD. ResultsAmong the 328,164 participants, 55.0% were females, the mean age was 46.3 years, 63.1% were non-Hispanic Whites, and 14.1% were non-Hispanic Blacks. Participants with ASCVD (7.95%) experienced greater deprivation at each multidimensional poverty cutoff, k, compared to those without ASCVD. In adjusted models, higher burden of multidimensional poverty was associated with up to 2.4-fold increased prevalence of ASCVD (ci = 0.25, adjusted prevalence ratio [aPR] = 1.66, P < 0.001; ci = 0.50, aPR = 1.99; ci = 0.75, aPR = 2.29; P < 0.001; ci = 1.00, aPR = 2.38, P < 0.001). Multidimensional poverty exhibited modestly higher discriminant validity, compared to income poverty (area under receiver operator characteristics = 0.62 vs 0.58). ConclusionsThere is an association between the multidimensional poverty and ASCVD. Multidimensional poverty index demonstrates slightly better discriminatory power than income alone. Future validation studies are warranted to redefine poverty's role in health outcomes.

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.

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

Multidimensional Poverty Among B40 in Kuala Krai, Kelantan

Poverty is a phenomenon faced by an individual or a family without their own will. The poor can be identified through poverty measurement as a first step. The objective of this study is to identify the indicators that define the multidimensional poverty of the B40 group in Kuala Krai and examine the needs of the population in helping them to eradicate poverty. Poverty measurement is divided into two types: unidimensional poverty measurement, which shows individual poverty in terms of income only, and multidimensional poverty measurement, which is related to poverty in the holistic aspect of humanity. Usually, multidimensional poverty is experienced by B40 households. B40 households are low-income households, and a total of 2.7 million households still belong to the B40 group. This study was conducted on 153 households in Kampung Lubuk Simpul, Kuala Krai, by using questionnaires, indirect interviews, and observations. Quantitative methods such as factor analysis, the crosstab method, and frequency. The results of the study found that, in addition to economic factors, social and spatial factors are also the cause of B40 poverty. Therefore, in a situation where Malaysia is developing in line with other countries, the B40 household should not be marginalized to enjoy the opportunities resulting from the country's prosperity. To eradicate poverty, all parties need to look at poverty from a multi-dimensional perspective to identify the poor B40 group. Therefore, this study uses multidimensional poverty measurement because it can find out the indicators that cause the population to experience multidimensional poverty. The results of this study are not only to identify indicators that define multidimensional poverty among the population but also to study the needs of the population to eradicate multidimensional poverty. Therefore, this study can help the authorities improve the existing implementation methods to achieve the goals of work and the SDG to achieve zero poverty

From Nutritional Capability to Food Capability: Measurement of Multidimensional Food Poverty in Japan

Amartya Sen’s work has contributed to shifting our focus from food availability to food access and utilisation, together called ‘nutritional capability’. Existing food insecurity instruments have been informed partially by the capability approach, but remain focused on material deprivation and its economic sub-dimensions. This narrow focus has become problematic, particularly in high-income countries, where material deprivation is largely overcome and food poverty manifests itself differently from that in low-income countries. Food poverty in high-income countries should thus be approached from a ‘food capability’ perspective that fully recognises the multidimensionality of eating lives and the multiple factors that affect their dietary standards. To demonstrate the usefulness of this perspective, the Alkire-Foster method for multidimensional food poverty was utilised to analyse food poverty in Japan. The Alkire-Foster measure has a dual cut-off approach and its adapted method can produce a multidimensional food poverty index by setting reasonable food deprivation and poverty thresholds. The national data used for empirical demonstration was obtained from a web-based questionnaire conducted in Japan, to which 973 participants (aged 20–60 years) responded. The measurement identified a 20.6% food-poor population and inequalities in food capabilities among subgroups. Although economic deprivation was tightly linked to food poverty, the results suggested that gender- and age-based inequalities were larger than socioeconomic status-based ones, which reflected much wider societal problems in Japan than growing economic poverty, such as gender inequality and population ageing. The proposed measurement is effective for monitoring food policy impacts and complementing existing food insecurity assessment tools. The results will be useful for promoting social debate about what the minimal dietary standard should be in high-income societies.

Gender Differences in Multidimensional Poverty in Brazil: A Fuzzy Approach

AbstractThis study aims to improve the measurement of multidimensional poverty for the purpose of analyzing gender differences while considering the limitations of household surveys. To effectively analyze gender disparities, it addresses three issues that are often overlooked in the literature: disregard for within-household inequalities in household-level indicators; disregard for ineligible populations in indicators that represent only a specific group; and disregard for intermediate deprivation situations in cutoff-based poverty estimations. Using data from the Brazilian Consumer Expenditure Survey 2017–2018, we create two indexes with indicators that are key aspects in gender and feminist analyses. Applying a fuzzy approach and the Alkire–Foster method, we estimate multidimensional poverty and gender differences from three perspectives: intrahousehold, interhousehold, and intracouple. We also calculate inequality among the poor and intracouple gender gaps proposing fuzzy versions for these analyses. The main findings suggest that women are disadvantaged in terms of work and time quality, economic security, and access to resources—all of which are crucial components of agency or degree of empowerment.

Is Multidimensional Poverty Associated to Dementia Risk? The Case of Older Adults in Pakistan.

Multidimensional poverty is associated with dementia. We aimed at establishing this association in Pakistan. A cross-sectional study was conducted in Punjab and Sindh, Pakistan, between March 30, 2002, and August 22, 2022, among adults aged 50 and older. Multidimensional poverty measures were composed of 6 dimensions and 15 indicators. Poverty was compared between adults with and without dementia using the Rowland Universal Dementia Assessment Scale, adjusting for sex, age, marital status, and household size. Associations between dementia and poverty were investigated using a multivariate logistic regression model. We found that 594 (72.7%), 171 (20.9%), and 52 (6.4%) had no, mild, and moderate-to-severe dementia, respectively. More women than men had dementia (11.4% vs 2.9%). Approximately 40.4% of adults with dementia were found to be deprived in 4 or more dimensions compared to 8.9% without dementia, and the difference in multidimensional poverty between them was 348.6%. Education, health, living conditions, and psychological well-being were the main contributors to poverty. Poverty in 4 or more dimensions was strongly associated with dementia (odds ratio [OR], 5.02; 95% confidence interval [CI], 2.07-12.16) after adjusting for sex, marital status, age, and household size, with greater odds for older women (OR, 2.02; 95% CI, 1.41-2.90). Our findings suggest that early improvement in social determinants of health through targeted structural policies may prevent dementia later in life. Improving access to free, quality education, health care including mental health care and basic living standards, and employment should reduce the collective risk of dementia.

Multidimensional Poverty: Complementary Measure to Absolute and Relative Poverty

Poverty is a complex issue that involves more than just income levels, affecting various aspects of life such as health, education, and living conditions. The ongoing discussion about how to measure poverty, whether through absolute or relative terms, reflects the challenges in assessing it. Absolute poverty is based on a set standard that focuses on meeting basic needs for survival, while relative poverty looks at income disparities within a society. This study explores existing research on multidimensional poverty and examines the debate between absolute and relative poverty measures, considering their respective advantages and disadvantages. A notable conclusion from the research is that multidimensional poverty indices (MPIs), like the Multidimensional Poverty Index, provide a broad view of deprivation by including multiple factors. MPIs help policymakers design more targeted interventions in areas such as education, healthcare, and living standards. However, absolute poverty measures, such as the World Bank’s $1.90-per-day threshold, are still essential for tracking extreme poverty in developing nations, offering a clear benchmark for international comparisons. In contrast, relative poverty measures are particularly relevant in wealthier nations, where inequality and social exclusion are more prominent factors in poverty. This study highlights gaps in how these different approaches are integrated and calls for further research to improve models that address both absolute and relative poverty. The findings suggest that combining multidimensional, absolute, and relative poverty measures is important for fully understanding poverty and creating effective strategies to reduce it, adapted to the needs of specific regions and contexts.

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

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

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

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