scholarly journals Multidimensional Deprivation Spectrum: A Step Forward from Alkire–Foster Methodology

2019 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
Taseer Salahuddin ◽  
Alia Ahmed
Keyword(s):  
Social Stigma ◽  
Multidimensional Poverty ◽  
Multidimensional Poverty Index ◽  
Working Paper ◽  
Units Of Analysis ◽  
Poverty Measures ◽  
Multidimensional Deprivation ◽  
Current Article ◽  
Different Dimensions ◽  
Labelling Theory

Income-based poverty and multidimensional poverty are two major paradigms currently in use to define and measure poverty. Both these paradigms, however, take individuals as units of analysis and classify them on the basis of certain poverty lines and cut-offs as poor and non-poor. Social stigma and labelling theory suggest that the label of poverty negatively impacts the self-esteem of people or contributes to the tendencies of paternalistic dependency among them. This article suggests that poverty should be measured using dimensions of life as units of analysis. In this direction, it offers a variant of the Alkire and Foster (Counting and multidimensional poverty measures, OPHI Working Paper No. 7, Oxford Poverty & Human Development Initiative, 2007) multidimensional poverty index in the form of a multidimensional deprivation spectrum. Along with using different dimensions as units of analysis, the current article presents a whole spectrum of indices built to measure inequality for a more nuanced picture.

scholarly journals Multidimensional Relative Poverty of Farmers in Tibetan Areas – Based on a Data Analysis on Micro Surveys

2021 ◽  
Vol 4 (6) ◽  
pp. 84-90
Author(s):  
Shilian Zhu
Keyword(s):  
Poverty Reduction ◽  
Multidimensional Poverty ◽  
Poverty Index ◽  
Absolute Poverty ◽  
Relative Poverty ◽  
One Dimensional ◽  
Multidimensional Poverty Index ◽  
Head Of Household ◽  
Multidimensional Deprivation ◽  
Per Capita

In 2020, the issue of absolute poverty has been solved, and China is building a well-off society in an all-round way. The issue of relative poverty is an important content of poverty reduction. Based on a survey data from Danba County in October 2020, this paper uses the AF method to calculate the incidence of multidimensional poverty and the multidimensional poverty index. The results showed that 44.65% of the farmers have multidimensional deprivation of any three indicators of relative poverty, and 2.79% of the farmers have serious multidimensional deprivation; the incidence of one-dimensional poverty in terms of “educational level index of head of household,” “per capita non-transfer income of households in 2019,” and “per capita household income in 2019” is the highest; at the same time, the contribution rate of the three indicators to the multidimensional poverty index is also higher than other indicators. Therefore, several suggestions have been put forward to alleviate the multidimensional relative poverty in the region from the aspects of industry development and education.

scholarly journals Monetary and Multidimensional Poverty of the Tea Garden Labour Community of Dibrugarh District of Assam, India

2019 ◽  
Vol 8 (4) ◽  
pp. 3759-3764
Keyword(s):  
Rural Poverty ◽  
Multidimensional Poverty ◽  
Poverty Line ◽  
Poverty Index ◽  
Primary Survey ◽  
Multidimensional Poverty Index ◽  
Poverty Measures ◽  
Tea Garden

This paper aims at measuring the level of both monetary poverty and multidimensional poverty of the tea garden labour community of the Dibrugarh district of Assam. The paper also aims at comparing the monetary poverty and multidimensional poverty of the tea garden labour community of the Dibrugarh district of Assam. The present study is mainly a primary survey based study. Monetary poverty is measured on the basis of the official state specific rural poverty line and using Foster-Greer-Thorbecke class of poverty indices. Multidimensional poverty is measured using Alkaire-Foster methodology. Then for comparing monetary and multidimensional poverty the study used the simple cross tables. The findings of the study show that monetary poverty headcount ratio of the sample tea garden labour community is 48.89 percent. The value of the multidimensional poverty index declines with higher multidimensional poverty cutoffs. The comparison of the monetary and multidimensional poverty shows that for all the three multidimensional poverty cutoffs the similarity between the two poverty measures is higher than the mismatch between them

scholarly journals Interdisciplinary Perspectives on Poverty Measurement, Epistemic Injustices and Social Activism

2020 ◽  
pp. 1-20
Author(s):  
Valentin Beck ◽  
Henning Hahn ◽  
Robert Lepenies
Keyword(s):  
Social Activism ◽  
Explanatory Power ◽  
Multidimensional Poverty ◽  
Early Career ◽  
Poverty Measurement ◽  
Global Poverty ◽  
Multidimensional Poverty Index ◽  
Normative Concepts ◽  
Poverty Measures ◽  
International Experts

AbstractAs we enter the 2020s, global poverty is still a grave and persistent problem. Alleviating and eradicating poverty within and across the world’s societies requires a thorough understanding of its nature and extent. Although economists still standardly measure absolute and relative poverty in monetary terms, a consensus is emerging that poverty is a socially relational problem involving deprivations in multiple dimensions, including health, standard of living, education and political participation. The anthology Dimensions of Poverty advances the interdisciplinary debate on multidimensional poverty, and features contributions from leading international experts and early career researchers (including from the Global South). This introductory chapter gives an overview of formative debates, central concepts and key findings. While monetary poverty measures are still dominant in public and academic debate, their explanatory power has been drawn into question. We discuss relevant criticisms before outlining the normative concepts that can inform both multidimensional poverty and monetary measures, including basic capabilities, basic needs and social primary goods. Next, we introduce several influential multidimensional poverty indices, including the Human Development Index and the Multidimensional Poverty Index. The anthology shows in detail how such measures can be improved, from a variety of disciplinary perspectives. It shows that there are different methods of poverty research that require further investigation, including participatory studies, (value) surveys, public consensus building, the constitutional approach, and financial diaries. Finally, we show that there is an ongoing problem of epistemic asymmetries in global poverty research, and discuss responsibility for addressing poverty, including the responsibilities of academics. The remainder of the chapter is dedicated to a more detailed preview of the volume’s 20 contributions, which are assembled along the following five themes: (I) poverty as a social relation; (II) epistemic injustices in poverty research; (III) the social context of poverty; (IV) measuring multidimensional poverty; and (V) country cases.

Feminisation of Multidimensional Poverty in Rural Odisha

2020 ◽  
Author(s):  
Surya Narayan Biswal ◽  
◽  
S. K. Mishra ◽  
M. K. Sarangi ◽  
◽  
...  
Keyword(s):  
Rural Areas ◽  
Social Groups ◽  
Multidimensional Poverty ◽  
Cumulative Distribution ◽  
Policy Framework ◽  
Living Standard ◽  
Individual Level ◽  
Multidimensional Poverty Index ◽  
Order Stochastic Dominance ◽  
The Individual

UNDP’s 2030 agenda of Sustainable Development Goals (SDGs) emphasized gender equality in augmenting human capital and alleviating poverty. For eradication of extreme poverty and building resilience for persons who are vulnerable to poverty, SDGs calls for a pro-poor and gender-sensitive policy framework. In this context, a gender-based study on multi-dimensional aspects of poverty is highly significant. Extant literature reveals that females are more deprived in different dimensions of poverty such as education, health, living standard, empowerment, environment, autonomy and social relationship. The present study is conducted with the basic objective of examining feminization of poverty in rural areas of Jagatsinghapur district of Odisha. Seven socio-economic dimensions comprising sixteen indicators have been taken into consideration to construct the Multidimensional Poverty Index (MPI) using the Alkire-Foster (AF) Method at the individual level. The novelty of the study lies in analyzing MPI at the individual level for rural Odisha. Higher female deprivation is observed across social groups and all occupation categories except services. Dummy variable regression analysis also supports the major findings of the study. Complementary Cumulative Distribution Function satisfies strict first-order stochastic dominance condition and substantiates the feminisation of poverty at each level of poverty cut-off across all social groups and occupational categories except for services. The findings of the study have significant implications for developing suitable policies for gender equalization and poverty alleviation.

scholarly journals Measuring Vulnerability to Multidimensional Poverty

2019 ◽  
Vol 148 (1) ◽  
pp. 67-103 ◽  
Author(s):  
Mauricio Gallardo
Keyword(s):  
Multidimensional Poverty ◽  
Risk Behaviour ◽  
Poverty Measurement ◽  
Multidimensional Space ◽  
Poverty Index ◽  
Cross Sectional ◽  
Multidimensional Poverty Index ◽  
Wide Range ◽  
Vulnerability Indicators ◽  
Vulnerability To Poverty

Abstract A method to measure vulnerability to multidimensional poverty is proposed under a mean–risk behaviour approach. We extend the unidimensional downside mean–semideviation measurement of vulnerability to poverty towards the multidimensional space by incorporating this approach into Alkire and Foster’s multidimensional counting framework. The new approach is called the vulnerability to multidimensional poverty index (VMPI), alluding to the fact that it can be used to assess vulnerability to poverty measured by the multidimensional poverty index (MPI). The proposed family of vulnerability indicators can be estimated using cross-sectional data and can include both binary and metric welfare indicators. It is flexible enough to be applied for measuring vulnerability in a wide range of MPI designs, including the Global MPI. An empirical application of the VMPI and its related indicators is illustrated using the official MPI of Chile as the reference poverty measurement. The estimates are performed using the National Socioeconomic Characterisation Survey (CASEN) for the year 2017.

scholarly journals Combining disparate data sources for improved poverty prediction and mapping

2017 ◽  
Vol 114 (46) ◽  
pp. E9783-E9792 ◽  
Author(s):  
Neeti Pokhriyal ◽  
Damien Christophe Jacques
Keyword(s):  
Bayesian Learning ◽  
Pearson Correlation ◽  
Gaussian Process Regression ◽  
Multidimensional Poverty ◽  
Environmental Data ◽  
Policy Makers ◽  
Multidimensional Poverty Index ◽  
Fine Grained ◽  
Disparate Data ◽  
Single Data

More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of “Big Data” to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84–0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication.

Socioeconomic Variation of Multimorbidity in Colombian Older Adults

2021 ◽  
Author(s):  
Silvia Marcela Ballesteros ◽  
José Moreno-Montoya ◽  
Wilhelmus Johannes Andreas Grooten ◽  
Pedro Barrera-López ◽  
José A. De la Hoz-Valle
Keyword(s):  
Mortality Rate ◽  
Rural Areas ◽  
Urban Areas ◽  
Structural Equation ◽  
Strong Association ◽  
High Body Mass Index ◽  
Multidimensional Poverty ◽  
The Elderly ◽  
Cross Sectional ◽  
Multidimensional Poverty Index

Abstract BackgroundMultimorbidity prevalence in the elderly is increasing worldwide. Variations regarding the socioeconomic characteristics of the individuals and their context have been described, mostly in high-income scenarios. This study aims to assess the magnitude and the socioeconomic factors associated with variations on multimorbidity in Colombia.MethodsA cross-sectional multilevel study with a nationally representative sample of 23 694 Colombian adults aged 60 years and older was conducted. Individual socioeconomic, demographic, childhood and health related characteristics, as well as group level variables (multidimensional poverty index and infectious diseases mortality rate) were analyzed. A two-level stepwise structural equation model was used to simultaneously adjust the individual and contextual effects. ResultsMultimorbidity prevalence was 62.3% (95% CI 61.7–62.9). In the multilevel adjusted models, age, female sex, having functional limitations, non-white ethnicity, high body mass index, higher income, physical inactivity, poverty during childhood and living in urban areas were associated with multimorbidity. The mediation analysis showed that living in rural areas was significantly associated with infectious disease mortality rate and other individual associations with multimorbidity were mediated by the multidimensional poverty variable. ConclusionsThis paper demonstrates a strong association between multimorbidity and poverty in a low-middle income country. Differences in the factors involved in the etiology of multimorbidity are expected among wealthy and poor countries regarding availability and prioritization of health services.

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