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Measurement errors and multidimensional poverty

Abstract:

Under quite likely conditions, data measurement errors can cause an (upward) bias in unidimensional poverty estimates and thus mislead both conceptual analyses and policy implications. In the case of multidimensional poverty, we find that by proposing a dual cut-off strategy, the Alkire-Foster method will typically attenuate this bias. With data from a 2010 Living Standard Measurement Survey (LSMS) from Peru, we find empirical evidence in support of this virtue of the dual cut-off strategy.

Publication status:
Published
Peer review status:
Reviewed (other)

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Institution:
Universidad de Piura
Role:
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Institution:
Universidad de Piura
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Funder
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Robertson Foundation More from this funder
International Food Policy Research Institute More from this funder
Chilean Ministry of Planning More from this funder
Vanderbilt University More from this funder
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Publisher:
Oxford Poverty & Human Development Initiative (OPHI) Publisher's website
Series:
OPHI working paper
Publication date:
2012-01-01
ISSN:
2040-8188
Paper number:
50
ISBN:
9781907194368
Language:
English
Keywords:
UUID:
uuid:76f29c49-3c03-4d2d-92a3-37dd4cf5f539
Local pid:
ora:9462
Deposit date:
2014-12-02

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