Multidimensional Human Deprivation in India: Does Club Convergence Exist?

A new measure of multidimensional human deprivation index (MHDI) across 24 states (over 4 time points corresponding to NFHS 1, 2, 3 and 4) is suggested here using principal component analysis (PCA). We observe that our weighted MHDI is found to be consistent with the Human Poverty Index (HPI) and the Multidimensional Poverty Index (MPI) introduced by UNDP in 1997 and 2010, respectively. Though most of the states in India have been experiencing a decline of the incidence of MHDI, but club convergence clearly proves that five major states such as Rajasthan, Madhya Pradesh, Bihar, Uttar Pradesh and Odisha are found consistently to be stable in the higher group of MHDI. Lagged MHDI, human capital investments along with availability of infrastructure are the underlying factors of differential MHDI across states. Therefore, allocation of grants should consider these issues of chronic MHDI found in five major states in order to ensure regional balance, equity and social justice in our federal structure.

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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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Towards a Multidimensional Poverty Index for Germany

SSRN Electronic Journal ◽

10.2139/ssrn.2583764 ◽

2015 ◽

Cited By ~ 5

Author(s):

Nicolai Suppa

Keyword(s):

Multidimensional Poverty ◽

Poverty Index ◽

Multidimensional Poverty Index

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Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis

Social Indicators Research ◽

10.1007/s11205-017-1819-6 ◽

2017 ◽

Vol 140 (3) ◽

pp. 1131-1157 ◽

Cited By ~ 4

Author(s):

Marko Kallio ◽

Joseph H. A. Guillaume ◽

Matti Kummu ◽

Kirsi Virrantaus

Keyword(s):

Principal Component Analysis ◽

Spatial Variation ◽

Principal Component ◽

Component Analysis ◽

Poverty Index ◽

Weighted Principal Component ◽

Water Poverty Index

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GAHAI AGROPOLITAN PROJECT IN ERADICATING POVERTY: MULTIDIMENSIONAL POVERTY INDEX

PLANNING MALAYSIA JOURNAL ◽

10.21837/pmjournal.v16.i7.503 ◽

2018 ◽

Vol 16 (7) ◽

Cited By ~ 1

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.

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