Multidimensional Poverty: Complementary Measure to Absolute and Relative Poverty

Poverty is rather high in contemporary Russia: every eighth Russian was poor according to the official statistics in 2018. Fighting poverty is among the top strategic development goals for the period till 2024. The paper studies gender disparities in poverty among Russian adults. Better understanding of gender structure of poverty in Russia would facilitate poverty reduction via better targeting and better tailoring of policy instruments. We utilize micro data of the Survey of income and social program participation by Rosstat as of 2017 for our statistical analysis. Two alternative poverty concepts are used: absolute income poverty (the official methodology of poverty measurement in Russia) and poverty risk and social exclusion index (the key component of poverty monitoring indicator in the EU). The latter index is a composition of relative poverty and deprivation poverty measures. Our results indicate that, on average, there is no gender poverty gap in Russia when absolute poverty concept is used: poverty rates among males and females are the same on average. Hence, we find no statistical support to the widely used by the media thesis of poverty feminization in Russia. Absolute poverty rate, however, is found to vary significantly across different gender-age groups: absolute income poverty among females is higher than among males in young and senior ages, while males are poorer in mid-age groups. When multiple criteria definition of poverty and social exclusion (AROPE) is applied, we find clear signs of feminization of poverty in Russia. Multidimensional poverty is much higher among females, and this is observed in all age groups. The highest gender poverty gap is observed in senior ages. The age-gender poverty structure changes dramatically when we move from absolute poverty concept to the multidimensional one: we observe higher share of females in poverty, and higher share of senior people in poverty. The key determinants of higher multidimensional poverty of females is their relatively (to males) lower wages, and relatively lower pensions.

This paper has a twofold aim. On the one side, without the presumption of being exhaustive, it tries to give a general overview of the different approaches and measures of poverty, and, on the other side, it focusses the attention on the absolute poverty indicators, describing the most important innovations introduced in the Italian methodology by the Scientific Commission established at beginning of 2022. Therefore, after a general introduction, the second paragraph mainly discusses the multiple dimensions of poverty and the approaches to provide a measure of these dimensions (relative and absolute poverty, income and consumption-based measures, the role of wealth, the importance of investigating hard to reach population and the subjective poverty). In paragraph three, an overview of the Italian measures of poverty in the European context is given, by describing the European Union Statistics on Living Conditions (EU-SILC) indicators and those compiled relying on national Household Budget Survey (thereof those about absolute poverty). In the fourth paragraph the attention is addressed to describe the Italian methodology to estimate absolute poverty, focussing on the recent innovations introduced. Paragraph five discusses the use of income instead of consumption in the context of Italian methodology. Finally, some concluding remarks are traced, raising specifically the issue of the nonmonetary components in the estimation of absolute poverty and the related challenges for the future research work in the Italian context.

In the post-2020 era, China’s poverty governance has pivoted from eliminating absolute poverty to addressing relative poverty, creating a pressing need for effective financial instruments. Constructing a composite index of financial inclusion and employing a panel Probit framework, we find that financial inclusion significantly mitigates household multidimensional relative poverty (MRP). This poverty-reducing effect exhibits a clear pro-poor characteristic, with substantially stronger impacts among households experiencing deeper deprivation. Heterogeneity analyses further reveal a nuanced mechanism: digital capability and financial literacy act as negative moderators, indicating that the poverty-alleviating influence of financial inclusion is most pronounced for households with lower levels of these capabilities. These findings provide timely evidence for designing China’s next-stage anti-poverty strategies and enrich the broader literature by unveiling how financial inclusion interacts with multidimensional poverty through capability-based heterogeneity.

Although poverty statistics serve as crucial economic and social indicators, their measurement varies depending on the chosen definition of poverty. This study examines whether poverty estimations in Türkiye significantly differ based on the definition used and explores which concept of poverty is most appropriate. Utilizing the SILC micro datasets from 2014 to 2021, the study estimates two absolute poverty measures, a subjective poverty measure, and a multidimensional poverty measure. The findings reveal that poverty rates are highly sensitive to the definition applied. For instance, while the updated absolute poverty line from TurkStat identifies less than 20% of the population as poor, the subjective poverty line and the absolute poverty line defined by TÜRK-İŞ classify approximately 60% and 70% of the population as poor, respectively. Furthermore, while 43% of the population was considered multidimensionally poor in 2014, this figure declined to 31% in 2021. A significant concentration of poverty is observed in the Southeastern and Eastern regions across all measurement approaches. Finally, the study conducts a comparative analysis of various poverty definitions by examining trends in poverty rates, the overlap of poor households, regional rankings, pairwise correlations, and the demographic profile of individuals living in poverty.

According to official statistics, the incidence of absolute poverty in Italy has sharply risen from 3.6 in 2005 to 7.7 percent in 2020, and this number is likely to rise in the post-pandemic period. However, standard poverty measures only consider the monetary aspect of poverty, neglecting the multidimensional nature of the phenomenon. The last decades have offered solid empirical evidence to guide political efforts to tackle both multidimensional wellbeing and poverty. In these times of rising inequalities, these efforts seem particularly valuable for identifying the most vulnerable groups. While Italy has made significant progress in defining multidimensional wellbeing, less attention has been given to multidimensional poverty. Hence, the goal of this paper is to offer a means for measuring multidimensional poverty (MPI) in Italy based on the Alkire-Foster method by using a widely recognized national framework for wellbeing (BES equitable and sustainable wellbeing) as the normative basis for the construction of the index. The contribution of the paper is twofold: (1) on the theoretical side, it seeks to increase the usability of national and international frameworks for wellbeing by proposing a national assessment scheme as a normative base for defining the dimensions of poverty; (2) on the empirical side, it provides evidence on the frequency and composition of multidimensional poverty in Italian regions. The results show that multidimensional poverty in Italy has increased over time, with the percentage of individuals considered as multidimensionally poor rising from 9.5 percent in 2005 to 17.5 percent in 2015. Moreover, a dimensional breakdown across regions and logistic regression shows that being older, female, from the South and married or widowed increases the probability of facing multidimensional poverty.

This paper presents a methodology for the incorporation of poverty related concepts into the goal programming paradigm. The concepts of poverty line, absolute poverty and relative poverty are discussed in the context of their potential usage in the field of Operations Research. The synergy between goal programming and poverty lines is examined. Two additional meta-goals are proposed for inclusion in a meta-goal programming framework based around absolute and relative poverty respectively. The resulting poverty incorporating meta-goal programming model is compared against other goal programming variants over a test set of school budget allocation models. The results demonstrate how the incorporation of poverty principles allows for greater modelling flexibility in the goal programming framework. This in turn allows decision makers to avoid solutions that are below threshold levels for a subset of stakeholders in either a relative or absolute sense. Appropriate conclusions and suggestions for future research are given.

Multidimensional poverty measurement and analysis is evolving rapidly. Notably, it has informed the publication of the Multidimensional Poverty Index (MPI) estimates in the Human Development Reports of the United Nations Development Programme since 2010, and the release of national poverty measures in Mexico, Colombia, Bhutan, the Philippines and Chile. The academic response has been similarly swift, with related articles published in both theoretical and applied journals. The high and insistent demand for in-depth and precise accounts of multidimensional poverty measurement motivates this book, which is aimed at graduate students in quantitative social sciences, researchers of poverty measurement, and technical staff in governments and international agencies who create multidimensional poverty measures. The book is organized into four elements. The first introduces the framework for multidimensional measurement and provides a lucid overview of a range of multidimensional techniques and the problems each can address. The second part gives a synthetic introduction of 'counting' approaches to multidimensional poverty measurement and provides an in-depth account of the counting multidimensional poverty measurement methodology developed by Alkire and Foster, which is a straightforward extension of the well-known Foster-Greer-Thorbecke poverty measures that had a significant and lasting impact on income poverty measurement. The final two parts deal with the pre-estimation issues such as normative choices and distinctive empirical techniques used in measure design, and the post-estimation issues such as robustness tests, statistical inferences, comparisons over time, and assessments of inequality among the poor.

Assessments of poverty among agricultural households in Sub-Saharan Africa often rely on either monetary or multidimensional indicators considered separately, overlooking key structural constraints. This study investigates the determinants of both monetary and multidimensional poverty among agricultural households in Togo. Using nationally representative EHCVM 2021/22 data from 2893 households, monetary poverty is measured using the Foster–Greer–Thorbecke Index, while multidimensional poverty is assessed with the Alkire–Foster method. A survey-weighted logit model is employed to identify the drivers associated with each poverty dimension. Results show that multidimensional poverty (59.40%) is more widespread than monetary poverty (51.50%). Education substantially reduces poverty risk, whereas larger household size, limited market access, and residence in the Savannah region increase it. Economic and natural shocks are negatively associated with monetary and absolute poverty, while cooperative membership raises the likelihood of being poor. Investment in livestock (TLU) reduces monetary poverty but increases multidimensional deprivation. These findings highlight that poverty among agricultural households in Togo is shaped by interconnected socioeconomic and institutional constraints rather than income deprivation alone. Therefore, integrated strategies aligned with the Sustainable Development Goals, particularly those promoting education, rural credit access, market integration, and resilience-building, are essential for achieving effective and context-specific poverty reduction.

ABSTRACT: As an essential way to enhance farmers’ self-development ability, off-farm employment plays an indispensable role in farmers’ multidimensional poverty reduction in many countries. Employing a survey of 1926 farmers in five provinces of the Yellow River Basin in China, this paper examined the multi-dimensional poverty reduction effect of off-farm employment and the heterogeneous influence of different dimensions of off-farm employment (modes, levels, distances and frequency). The results showed that (1) although absolute poverty in the income dimension was largely eliminated in the Yellow River Basin, the poverty in social resources, transportation facilities, employment security were the key bottlenecks restricting farmers’ self-development. (2) The province with the best multidimensional poverty reduction effect for non-farm employment was Shaanxi, with the largest contribution to employment security. (3) Improving off-farm employment level, distance and time can significantly alleviate the multi-dimensional poverty of farmers. Therefore, to lessen the multi-dimensional poverty of farmers in the Yellow River Basin, it is necessary to focus on the governance of multi-dimensional key poverty-stricken areas, such as the middle and upper courses of the Yellow River, adopting multidimensional poverty alleviation strategy of off-farm employment according to local conditions, working on the farmers’ deficiencies in social resources, mobility, employment security, and deepening the effect of off-farm employment on benefiting farmers and helping the poor.

Multidimensional Poverty and Its Discontents

Poverty is a multifaceted phenomenon that has always existed historically. In addition to discussions of poverty issues, aspects of poverty measurement are essential topics. A prominent poverty measurement system is the unidimensional measurement based on poverty line income (PLI). Therefore, the use of multidimensional poverty measurements is proposed, specifically the multidimensional poverty index (MPI), in assessing poverty, including non-monetary aspects. This study discusses the concept of unidimensional and multidimensional poverty measurement and the implementation of these concepts in Malaysia during the COVID-19 outbreak. Furthermore, it aims to contribute to the public debate on COVID-19 policy responses by quantifying the potential impact on global multidimensional poverty using the Global Multidimensional Poverty Index (MPI), which captures concurrent or overlapping deprivation at the household level. This study recommends policies by which members of the community and industry experts would be included in the poverty reduction program, in line with Maslow’s requirements. Overall, this research focuses on planning and orienting policy responses from a multidimensional perspective that integrates health, social and economic goals.

The article presents an analytical study of the problems of poverty and quality of life of the poor and the poorest of our planet. Basic information database of the research are materials of the UN and its constituent units. This article discusses and clarifies the concept of absolute and relative poverty, and presents an approach to the definition of deprivation poverty. The main indicators of poverty, according to the report of the UN World Development, are highlighted. The author links the indicators of availability of services with the provision of basic human needs according to Maslow´s theory. The article also examines the main economic causes of poverty, including in a number of Third World countries. Further, the author shows the contribution of the United Nations and the developed countries in addressing the problem of poverty and absolute poverty. In this regard, suggested is the poverty reduction strategy, the implementation of which should lead to the achievement of the goals by 2015, that is, to the present time. Almost all of the goals are related to the availability of socially important services for the poor and the poorest. It is proved that a regulatory role in this process should be undertaken by the state, and the process should be based on the mechanism of social partnership between government, service providers and major consumer groups. In the final part of the article highlighted are criteria for selecting suppliers and funding mechanisms for services for the populations concerned.

This research established a link that exists between climate smart agricultural practices and poverty in North-West geopolitical zone of Nigeria. The study was motivated by the increasing consequence of climate change and its impact on poverty status among farmers in the study area. Farming households changing agricultural practices as a result of global observation of climatic and environmental changes. It was based on this that the study examines the impact of climate smart agricultural practices on poverty status among farmers in North West Nigeria. A multi-stage sampling techniques was used to select two hundred and ninety four (294) farming households in the study area who provide the relevant primary data information for the study through a set of pre-tested structured questionnaires. The objective was to decompose poverty status for high-users and low-users of climate smart agricultural techniques in the study area. Foster Greer and Thorberk model, Watt’s index, Sen, Shorrocks and Thon index were used to ascertain the objective. Poverty head count according to the FGT index for the total population is 35.89% for absolute poverty and 9.12% for relative poverty. This means that the average climate smart agriculture farmers had about 36% deprivation of basic human needs such as food, safe drinking water, health, shelter, education and information. On the other hand, for the absolute poverty of 9%. It means the average climate smart agricultural farmers had 9% deprivation to maintain the average standard of living. It connotes that the average climate smart agricultural farmers had 33% deprivation of food and 13% deprivation of average standard of living. Analysis of health poverty reveals that the absolute poverty is 42.38% and relative poverty 27.64%. It implies that the average climate smart agricultural farmers were deprived of health by 42% and by average standard of living by 28%. Further, analysis on education poverty reveals the absolute poverty and relative poverty value of 47.10% and 28.26%. This signifies that about 47% of the climate smart agricultural farmers were deprived of basic education and about 28% of climate smart agriculture farmers were deprived of average standard of living. The study concludes that poverty is evident in the study area. It therefore reccommends that Government, Non-Governmental Organizations and farmer associations should create a conducive knowledge exchang enviroment to encourage the low-users of climate smart agriculture to improve on their performance. Spouses especially should develop interest in climate smart agricultural farming. women empowerment programme can be embarked upoun by government and private individual. Policy on formal education should be enriched and developed in the curriculm to meet the climate smart agricultural challenges.

In rich countries there is relative poverty — some are poor relative to others who are affluent. In poor countries there is absolute poverty — people do not have enough income to meet their basic needs for food, clean water, clothing, shelter, sanitation, health services and education. Millions of people in poor countries are absolutely poor. As a result, they suffer hunger and malnutrition, which cause vulnerability to disease, from which millions, especially children, die. Thus, poverty causes high infant mortality, shorter life expectancy, stunted child development, misery and premature death for millions of people in the world today. ‘Absolute poverty is probably the principal cause of human misery today.’2KeywordsPoor CountryRich CountryPoor FarmerRich StateAbsolute PovertyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The accurate accounting of where and for whom deprivations occur is fundamental to addressing poverty. In the United States, the official poverty measure considers only a person’s income, although poverty is increasingly understood internationally as a set of multiple, interlinked deprivations. This article introduces a decomposable multidimensional poverty (MDP) measure that addresses these shortcomings by using thirteen American Community Survey microdata indicators to identify education, health, housing, and economic security deprivations for individuals. In 2017, the national poverty rate was 13.7 percent when measured using MDP and 13.1 percent using official poverty. Although similar at the national scale, Hispanic, Asian, and older persons had higher poverty rates using the multidimensional measure, whereas Black and young persons had higher rates when using official poverty. MDP tended to be higher than official poverty in dense urban areas, whereas official poverty tended to be higher in rural areas. Further, MDP was a stronger correlate with COVID-19 death rates than official poverty through the first three waves of the pandemic. The design of MDP recognizes that individuals can experience poverty in different ways and provides a more holistic view of people and places.