Multidimensional Poverty and Multiple Correspondence Analysis

Louis-Marie Asselin and Vu Tuan Anh

Chapter 5 in Quantitative Approaches to Multidimensional Poverty Measurement, 2008, pp 80-103 from Palgrave Macmillan

Abstract: Abstract This chapter aims to offer a succinct presentation of the use of a particular factorial technique, Multiple Correspondence Analysis (MCA), in the area of multidimensional poverty measurement. We will not discuss conceptual issues concerning the choice of poverty indicators, and will not review other approaches found in the literature.2 Neither will we present the statistical foundations of the basic factorial techniques, found in many textbooks. We will rather highlight in section 5.2 some characteristics of MCA which render it particularly attractive for measuring multidimensional poverty and making poverty comparisons across space, time and socioeconomic groups.

Keywords: Poverty Rate; Composite Indicator; Primary Indicator; Poverty Measurement; Multiple Correspondence Analysis (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-0-230-58235-4_5

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DOI: 10.1057/9780230582354_5

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