Unpacking the MPI : A Decomposition Approach of Changes in Multidimensional Poverty Headcounts
Multidimensional measures of poverty have become standard as complementary indicators of poverty in many countries. Multidimensional poverty calculations typically comprise three indices: the multidimensional headcount, the average deprivation shar...
| Main Authors: | , , , |
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| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2015
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2015/12/25669944/unpacking-mpi-decomposition-approach-changes-multidimensional-poverty-headcounts http://hdl.handle.net/10986/23478 |
| Summary: | Multidimensional measures of poverty
have become standard as complementary indicators of poverty
in many countries. Multidimensional poverty calculations
typically comprise three indices: the multidimensional
headcount, the average deprivation share among the poor, and
the adjusted headcount ratio. While several decomposition
methodologies are available for the last index, less
attention has been paid to decomposing the multidimensional
headcount, despite the attention it receives from policy
makers. This paper proposes an application of existing
methodologies that decompose welfare aggregates--based on
counterfactual simulations--to break up the changes of the
multidimensional poverty headcount into the variation
attributed to each of its dimensions. This paper examines
the potential issues of using counterfactual simulations in
this framework, proposes approaches to assess these issues
in real applications, and suggests a methodology based on
rank preservation within strata, which performs positively
in simulations. The methodology is applied in the context of
the recent reduction of multidimensional poverty in
Colombia, finding that the dimensions associated with
education and health are the main drivers behind the poverty decline. |
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