GAHAI AGROPOLITAN PROJECT IN ERADICATING POVERTY: MULTIDIMENSIONAL POVERTY INDEX

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.

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

Advances in Social Work ◽

10.18060/21635 ◽

2018 ◽

Vol 18 (3) ◽

pp. 853-873 ◽

Cited By ~ 1

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.

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Weighting the Dimensions of the Multidimensional Poverty Index: Findings from Sri Lanka

Social Indicators Research ◽

10.1007/s11205-021-02656-0 ◽

2021 ◽

Author(s):

N. P. Ravindra Deyshappriya ◽

Simon Feeny

Keyword(s):

Sri Lanka ◽

Multidimensional Poverty ◽

Poverty Index ◽

Multidimensional Poverty Index

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Relationship analysis between the spread of COVID-19 and the multidimensional poverty index in the city of Manizales, Colombia

The Egyptian Journal of Remote Sensing and Space Science ◽

10.1016/j.ejrs.2021.04.002 ◽

2021 ◽

Author(s):

Vladimir Henao-Cespedes ◽

Yeison Alberto Garcés Gómez ◽

Silvia Ruggeri ◽

Tatiana María Henao-Cespedes

Keyword(s):

Multidimensional Poverty ◽

Poverty Index ◽

Multidimensional Poverty Index ◽

Relationship Analysis ◽

The City

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Measuring Vulnerability to Multidimensional Poverty

Social Indicators Research ◽

10.1007/s11205-019-02192-y ◽

2019 ◽

Vol 148 (1) ◽

pp. 67-103 ◽

Cited By ~ 3

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.

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Combining disparate data sources for improved poverty prediction and mapping

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1700319114 ◽

2017 ◽

Vol 114 (46) ◽

pp. E9783-E9792 ◽

Cited By ~ 25

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.

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