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Measurement Errors and Multidimensional Poverty

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  • Cesar Calvo, Fernando Fernandez

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.

Suggested Citation

  • Cesar Calvo, Fernando Fernandez, 2012. "Measurement Errors and Multidimensional Poverty," OPHI Working Papers 50, Queen Elizabeth House, University of Oxford.
    • Handle: RePEc:qeh:ophiwp:ophiwp050
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    File URL: https://ophi.org.uk/measurement-errors-and-multidimensional-poverty/
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    Cited by:

    1. Alkire, Sabina & Roche, José Manuel & Vaz, Ana, 2017. "Changes Over Time in Multidimensional Poverty: Methodology and Results for 34 Countries," World Development, Elsevier, vol. 94(C), pages 232-249.
    2. Alkire, Sabina & Santos, Maria Emma, 2014. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," World Development, Elsevier, vol. 59(C), pages 251-274.
    3. Gaurav Datt, 2017. "Distribution-sensitive multidimensional poverty measures with an application to India," Monash Economics Working Papers 06-17, Monash University, Department of Economics.

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