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

dc.contributor.authorMohapatra, Geetilaxmi
dc.contributor.authorArora, Rahul
dc.date.accessioned2023-01-30T07:21:16Z
dc.date.available2023-01-30T07:21:16Z
dc.date.issued2022
dc.description.abstractThe 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.identifier.urihttps://www.ingentaconnect.com/content/mcb/006/2022/00000049/00000010/art00008
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8840
dc.language.isoenen_US
dc.publisherEmeralden_US
dc.subjectEconomics and Financeen_US
dc.subjectMultidimensional povertyen_US
dc.subjectWater povertyen_US
dc.subjectTobit regressionen_US
dc.titleExamining the interstate variations and interlinkage between water poverty and multidimensional poverty in India: evidence from household-level dataen_US
dc.typeArticleen_US

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