scholarly journals Assessing Refugee Poverty Using Capabilities Versus Commodities: The Case of Afghans in Iran

2018 ◽  
Vol 18 (3) ◽  
pp. 853-873 ◽  
Author(s):  
Mitra Naseh ◽  
Miriam Potocky ◽  
Shanna L. Burke ◽  
Paul H. Stuart
Keyword(s):  
Multidimensional Poverty ◽  
Poverty Line ◽  
Poverty Measurement ◽  
Poverty Index ◽  
Policy Makers ◽  
Multidimensional Poverty Index ◽  
Afghan Refugees ◽  
Poor Households ◽  
Standards Of Living ◽  
Using Data

This study is among the first to calculate poverty among one of the world’s largest refugee populations, Afghans in Iran. More importantly, it is one of the first to use capability and monetary approaches to provide a comprehensive perspective on Afghan refugees’ poverty. We estimated poverty using data collected from a sample of 2,034 refugee households in 2011 in Iran. We utilized basic needs poverty lines and the World Bank’s absolute international poverty line for our monetary poverty analyses and the global Multidimensional Poverty Index (MPI) for our capability analyses of poverty. Findings show that nearly half of the Afghan households were income-poor, approximately two percent of the households had less than USD 1.25 per person per day, and about 28% of the surveyed households were multidimensionally deprived. Results suggest that 60% of the income-poor households were not deprived from minimal education, health, and standards of living based on the MPI criteria, and about 32% of the multidimensionally deprived households were not income-poor. These findings call for more attention to poverty measurement methods, specifically for social workers and policy makers in the field, to gain a more realistic understanding about refugees’ wellbeing.

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 GAHAI AGROPOLITAN PROJECT IN ERADICATING POVERTY: MULTIDIMENSIONAL POVERTY INDEX

2018 ◽  
Vol 16 (7) ◽  
Author(s):  
Mohd Khairi Ismail ◽  
Chamhuri Siwar ◽  
Rospidah Ghazali
Keyword(s):  
Multidimensional Poverty ◽  
Living Standards ◽  
Poverty Index ◽  
Policy Makers ◽  
Extreme Poverty ◽  
Multidimensional Poverty Index ◽  
Household Members ◽  
Project Participants ◽  
Design And Construction

The planning and development of Agropolitan Project in Malaysia began in 2007 and was aimed at eradicating extreme poverty in Malaysia. This study aims to discuss the design and construction of Agropolitan Project in eradicating extreme poverty among its participants. This study uses the Multidimensional Poverty Index (MPI) found in the 11th Malaysian Plan, which includes the dimension of education, health, living standards, and earning. In addition, this study utilizes a survey involving 45 participants of an agropolitan project from Gahai, Lipis,Pahang. The result shows that only 5 of the respondents fall into the multidimensional poverty category, which involves 11.9 percent of the household members. The result of this study shows that the planning and development of Gahai Agropolitan Project, Lipis has succeeded in eradicating extreme poverty among the project participants. Deprivation faced by the respondents based on the MPI analysis can help policy makers in the design and construction of future agropolitan projects.

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 Accounting for drinking water quality in measuring multidimensional poverty in Ethiopia

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243921
Author(s):  
Alemayehu Azeze Ambel ◽  
Harriet Kasidi Mugera ◽  
Robert E. S. Bain
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Rural Areas ◽  
Urban Areas ◽  
Multidimensional Poverty ◽  
Water Source ◽  
Poverty Measurement ◽  
Poverty Index ◽  
Poverty Level ◽  
Multidimensional Poverty Index

The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5–13 percentage points, which would mean that 5–13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures.

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.

scholarly journals Analysis of Deprivations Suffered by Children Under Five in Benin

2020 ◽  
Vol 2 (2) ◽  
pp. 125
Author(s):  
Fawaz A. Adéchinan Aminou ◽  
Pam Zahonogo
Keyword(s):  
Multiple Correspondence Analysis ◽  
Multidimensional Poverty ◽  
Poverty Line ◽  
Jel Classification ◽  
Demographic And Health Surveys ◽  
Children Under Five ◽  
Multidimensional Poverty Index ◽  
Under Five ◽  
Child Deprivation ◽  
Poor Children

Children are seriously affected by poverty and suffer particular deprivations. In addition, their situation is most of the time ignored in the strategies devoted to tackling poverty. This study examined the multidimensional poverty of children under five in Benin by identifying its determinants. It used the data from the demographic and health surveys in Benin from 2011-2012 and adopted the approach by Alkire & Foster (2011) to generate multidimensional poverty profiles of children. The weightings of the dimensions were generated by multiple correspondence analysis. The GLM and Logit models were used to identify the driving factors of child deprivation. Findings indicate that 54 percent of children were multidimensionally poor when the poverty line k = 1 against 32 percent of poor children when k = 3.  Nutrition and sanitation dimensions had the highest relative contributions of 41.12 and 28.77 percent respectively to the global multidimensional poverty index. JEL Classification:  D63, I32, O10

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