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Alkire S., FosterJ., Seth S. et al.. Multidimensional Poverty Measurement and Analysis. Oxford University Press,2015. — 368 p.. 2015

Multidimensional poverty measurement and analysis is evolving rapidly. A particular counting approach to multidimensional poverty measurement, developed by Sabina Alkire and James Foster, has created considerable interest. Notably the publication of the Multidimensional Poverty Index (MPI) estimates in the Human Development Reports of the United Nations Development Programme since 2010, and national poverty measures released in Mexico, Colombia, Bhutan, and the Philippines. The academic response has been similarly swift, with related articles published in both theoretical and applied journals.

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ACKNOWLEDGEMENTS
CONTENTS
Introduction
‘I live under the roof of falling tiles.' This self-description of poverty, tucked away in Victor Hugo's Les Miserables, is by a character called Bossuet, who was, it seems, both merry and unlucky.
Normative Motivation
Empirical Motivations
Policy Motivation
The Framework
This chapter introduces the notation and basic concepts that will be used throughout the book.
Review of Unidimensional Measurement and FGT Measures
Notation and Preliminaries for Multidimensional Poverty Measurement
Scales of Measurement: Ordinal and Cardinal Data
Comparability across People and Dimensions
Properties for Multidimensional Poverty Measures
Overview of Methods for Multidimensional Poverty Assessment
Since the early twentieth century, poverty measurement has predominantly used an income approach.[77]
Dashboard of Indicators and Composite Indices
Venn Diagrams
The Dominance Approach
Statistical Approaches
Fuzzy Set Approaches
Axiomatic Measures
Counting Approaches: Definitions, Origins, and Implementations
An assessment of measurement methodologies based on their properties and normative characteristics is illuminating and draws our attention to many interesting distinctions among measures, as we saw in Chapter 3.
Definition andOrigins
Measures of Deprivation in Europe and their Influence
Measures of Unsatisfied Basic Needs in Latin America and Beyond
Counting Approaches in Targeting
Final Comments on Counting Approaches
The Alkire-Foster Counting Methodology
This chapter provides a systematic overview of the multidimensional measurement methodology of Alkire and Foster (2007, 2011a), with an emphasis on the first measure of that class: the Adjusted Headcount Ratio or M0.
The AF Class of Poverty Measures: Overview and Practicality
Identification of the Poor: The Dual-Cutoff Approach
Aggregation: The Adjusted Headcount Ratio
Distinctive Characteristics of the Adjusted Headcount Ratio
The Set of Partial and Consistent Sub-Indices of the Adjusted Headcount Ratio
A Case Study: The Global Multidimensional Poverty Index (MPI)
AF Class Measures Used with Cardinal Variables
Some Implementations of the AF Methodology
Appendix
Normative Choices in Measurement Design
The modern field of inequality measurement grew out of the intelligent application of quantitative methods to imperfect data in the hope of illuminating important social issues.
The Adjusted Headcount Ratio: A Measure of Capability Poverty?
Normative Choices
Elements of Measurement Design
Concluding Reflections
Data and Analysis
Chapter 6 transitioned from considerations about selecting a measurement methodology (Chapters 3-5) to issues met in implementing real-world measures that undergird and re­inforce policies to fight poverty.
Data for Multidimensional Poverty Measurement
Issues in Indicator Design
Relationships among Indicators
Robustness Analysis and Statistical Inference
Chapter 5 presented the methodology for the Adjusted Headcount Ratio poverty index (M0) and its different partial and consistent sub-indices;
Robustness Analysis
Statistical Inference
Robustness Analysis with Statistical Inference
Distribution and Dynamics
This chapter provides techniques required to measure and analyse inequality among the poor (section 9.1), to describe changes over time using repeated cross-sectional data (section 9.2), to understand changes across dynamic subgroups (section 9.3), and to measure chronic multidimensional poverty (section 9.4).
Inequality among the Poor
Descriptive Analysis of Changes over Time
Changes over Time by Dynamic Subgroups
Chronic Multidimensional Poverty
Some Regression Models for AF Measures
From a policy perspective, in addition to measuring poverty we must perform some vital analyses regarding the transmission mechanisms between policies and poverty measures.
Micro and Macro Regressions
Generalized Linear Models
Micro Regression Models with AF Measures
Macro Regression Models for M0 and H
REFERENCES
INDEX
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  2. Baldwin Richard, Weder di Mauro Beatrice. Economics in the Time of COVID-19. London (UK): Centre for Economic Policy Research,2020. — 123 p. - 2020 ãîä
  3. Banerjee A., Rajan R.G. et al.. What the Economy Needs Now. Penguin Press,2019. — 400 p. - 2019 ãîä
  4. Banerjee Abhijit V., Duflo Esther. Good Economics for Hard Times. PublicAffairs,2019. — 403 p. - 2019 ãîä
  5. Bauer J., Latzer M. (Eds.). Handbook on the Economics of the Internet. Edward Elgar,2016. — 603 p. - 2016 ãîä
  6. Atkinson Anthony, Bourguignon François. Handbook of Income Distribution. Volume 2A. North Holland,2014. — 2366 p. - 2014 ãîä
  7. Alan Griffiths, Stuart Wall (eds.). Applied Economics. 12th ed. — Financial Times/ Prentice Hall,2011. — 729 p. - 2011 ãîä
  8. Backhaus Jürgen G. (ed.). The Elgar Companion to Law And Economics. Second Edition. Edward Elgar,2005. – 777 p.2 - 2005 ãîä
  9. Barry A., Slater D.. The Technological Economy. London: Routledge,2005. — 256 p. - 2005 ãîä
  10. Atkinson Anthony, Bourguignon François. Handbook of Income Distribution. Volume 2B. North Holland, 2014. — 2366 p. - ãîä
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