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Wiley InterScience

The Economic Journal

The Economic Journal

Volume 116 Issue 514, Pages 943 - 968

Published Online: 12 Oct 2006

Journal compilation © 2010 by the Royal Economic Society (Registered Charity No. 231508)



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Robust Multidimensional Poverty Comparisons*
Jean-Yves Duclos 1 , David E. Sahn 2 and Stephen D. Younger 3
  1 Université Laval and Institut d 'Anàlisi Económica (CSIC), UAB, Barcelona
  2 Cornell University
  3 Cornell University

  * Duclos' research was supported by SSHRC, FCAR and IDRC's MIMAP programme. Sahn's and Younger's participation was supported by the USAID and AERC. An early version of this work was written as a supporting paper for the World Development Report (2000), regarding which we are grateful to Christian Grootaert for his comments and support. We also thank Rob Strawderman and Steve Westin for technical advice, Nicolas Beaulieu and Wilson Perez for their excellent research assistance, and Peter Lambert, Paul Makdissi and Lars Osberg for very useful comments.

Copyright 2006 The Author(s). Journal compilation Royal Economic Society 2006

ABSTRACT

We demonstrate how to make poverty comparisons using multidimensional indicators of well-being, showing in particular how to check whether the comparisons are robust to aggregation procedures and to the choice of multidimensional poverty lines. In contrast to earlier work, our methodology applies equally well to what can be defined as 'union', 'intersection' or 'intermediate' approaches to dealing with multidimensional indicators of well-being. To make this procedure of some practical usefulness, the article also derives the sampling distribution of various multidimensional poverty estimators, including estimators of the 'critical' poverty frontiers outside which multidimensional poverty comparisons can no longer be deemed ethically robust. The results are illustrated using data from a number of developing countries.


Submitted: 1 July 2004 Accepted: 17 June 2005

DIGITAL OBJECT IDENTIFIER (DOI)
10.1111/j.1468-0297.2006.01118.x About DOI

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