Distribution-Sensitive Multidimensional Poverty Measures
This paper presents axiomatic arguments to make the case for distribution-sensitive multidimensional poverty measures. The commonly-used counting measures violate the strong transfer axiom which requires regressive transfers to be unambiguously pov...
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2018
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| Online Access: | http://documents.worldbank.org/curated/en/156261519136911696/Distribution-sensitive-multidimensional-poverty-measures http://hdl.handle.net/10986/29406 |
| Summary: | This paper presents axiomatic arguments
to make the case for distribution-sensitive multidimensional
poverty measures. The commonly-used counting measures
violate the strong transfer axiom which requires regressive
transfers to be unambiguously poverty-increasing and they
are also invariant to changes in the distribution of a given
set of deprivations amongst the poor. The paper appeals to
strong transfer as well as an additional cross-dimensional
convexity property to offer axiomatic justification for
distribution-sensitive multidimensional poverty measures.
Given the nonlinear structure of these measures, it is al
also shown how the problem of an exact dimensional
decomposition can be solved using Shapley decomposition
methods to assess dimensional contributions to poverty. An
empirical illustration for India highlights distinctive
features of the distribution-sensitive measures. |
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