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Examining the interstate variations and interlinkage between water poverty and multidimensional poverty in India: evidence from household-level data

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dc.contributor.author Mohapatra, Geetilaxmi
dc.contributor.author Arora, Rahul
dc.date.accessioned 2023-01-30T07:21:16Z
dc.date.available 2023-01-30T07:21:16Z
dc.date.issued 2022
dc.identifier.uri https://www.ingentaconnect.com/content/mcb/006/2022/00000049/00000010/art00008
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8840
dc.description.abstract The purpose of the present study is to examine the inter-relationship between the multi-dimensional poverty and water poverty using household level data for Indian states. A modified water poverty index (MWPI) for both rural and urban households was created using the five components approach and various quantifiable proxies. Principal component analysis (PCA) has been used for the construction of MWPI. Multidimensionality of poverty (MPI) is measured using the Alkire and Foster methodology. Further, the study has utilized correlation and Tobit regression analysis to show the relationship between MWPI and MPI. The empirical findings suggest that there is a positive and significant relationship between multidimensional poverty and water poverty, with the extent of relationship being greater in rural areas. The results show that in rural areas all the components of water poverty has significant impact on multidimensional poverty, whereas in urban areas except use component all others have significant impact on multidimensional poverty. Further, components of multidimensional poverty were also found to be significantly impacting water poverty. en_US
dc.language.iso en en_US
dc.publisher Emerald en_US
dc.subject Economics and Finance en_US
dc.subject Multidimensional poverty en_US
dc.subject Water poverty en_US
dc.subject Tobit regression en_US
dc.title Examining the interstate variations and interlinkage between water poverty and multidimensional poverty in India: evidence from household-level data en_US
dc.type Article en_US


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