The Measurement of Social Exclusion

Chakravarty, Satya R.; D’Ambrosio, Conchita

doi:10.1111/j.1475-4991.2006.00195.x

Cited by 213 publications

(99 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 We note that this flexibility makes it particularly useful for measurement efforts at the country level where these decisions can fit the purpose of the 1 Foster, Greer and Thorbecke (1984). 2 The literature is growing rapidly; some key papers in the axiomatic tradition include Anand and Sen (1997), Brandolini and D'Alessio (1998), Chakravarty et al (1998), Tsui (2002), Atkinson (2003), Bourguignon and Chakravarty (2003), Deutsch and Silber (2005), Duclos, Sahn and Younger (2006), Chakravarty and D'Ambrosio (2006), Kakwani and Silber (2008a, b) and Thorbecke (2008). For recent critiques of multidimensional measurement, see Ravallion (2010Ravallion ( , 2011.…”

Section: Introductionmentioning

confidence: 99%

Understandings and misunderstandings of multidimensional poverty measurement

2011

View full text Add to dashboard Cite

Multidimensional measures provide an alternative lens through which poverty may be viewed and understood. In recent work we have attempted to offer a practical approach to identifying the poor and measuring aggregate poverty (Alkire and Foster 2011). As this is quite a departure from traditional unidimensional and multidimensional poverty measurement -particularly with respect to the identification step -further elaboration may be warranted. In this paper we elucidate the strengths, limitations, and misunderstandings of multidimensional poverty measurement in order to clarify the debate and catalyse further research. We begin with general definitions of unidimensional and multidimensional methodologies for measuring poverty. We provide an intuitive description of our measurement approach, including a 'dual cutoff' identification step that views poverty as the state of being multiply deprived, and an aggregation step based on the traditional FGT measures. We briefly discuss five characteristics of our methodology that are easily overlooked or mistaken and conclude with some brief remarks on the way forward.Keywords: poverty measurement, multidimensional poverty, deprivation, FGT measures, decomposability, joint distribution, axioms. This publication is copyright, however it may be reproduced without fee for teaching or non-profit purposes, but not for resale. Formal permission is required for all such uses, and will normally be granted immediately. For copying in any other circumstances, or for re-use in other publications, or for translation or adaptation, prior written permission must be obtained from OPHI and may be subject to a fee. JEL classification: I3, I32, D63, O1

show abstract

Section: Introductionmentioning

confidence: 99%

Understandings and misunderstandings of multidimensional poverty measurement

2011

View full text Add to dashboard Cite

show abstract

“…Yoksunluk kavramı, genellikle statik bir kavram olarak ele alınırken, sosyal dışlanma önemli dinamik görünümlere sahiptir (Bossert vd., 2007;Sapancalı, 2005Sapancalı, , 2009. Eğer bireylerin yoksunluğu devam eder veya zaman içinde daha kötüye giderse, birey sosyal olarak dışlanmış olmaktadır (Chakravarty & D'Ambrosio, 2006). Townsend (1993) temel gereksinimleri karşılayamama olan maddi yoksunluk düzeyi (yeme-içme, giyinme, barınma vb.)…”

Section: Introductionunclassified

“…Sosyal dışlanma, hem ekonomik-yapısal hem de sosyo-kültürel biçimde olmak üzere ortaya çıkan çok boyutlu bir olaydır (Bhalla & Lapeyre, 1997;Chakravarty & D'Ambrosio, 2006;Jehoel-Gijsbers & Vrooman, 2007). Commins (1993) sosyal dışlanmayı, vatandaşlık entegrasyonundan dışlanma, işgücü piyasasından dışlanma, sosyal yardım durumundan dışlanma ve aile-toplumdan dış-lanma şeklinde dört boyutta ele alırken; Silver (1994) sosyal dışlanma kavramını ekonomik, sosyal, politik ve kültürel boyutlar ile incelemiştir.…”

Section: Introductionunclassified

Yaşam Tatmini Ve Sosyal Dışlanma

BAYRAM¹,

SAM²,

Aytaç³

et al. 2010

ISGUC

View full text Add to dashboard Cite

“…Some of the multidimensional poverty functions in the literature are of the R form (e.g. Chakravarty and D'Ambrosio (2006), Bossert et al (2009)).…”

Section: A General Family Of Counting Measuresmentioning

confidence: 99%

“…Based on multivariate stochastic dominance techniques, the conditions are relevant for a broad range of counting measures including the AF family and most of the multidimensional poverty measures in the literature (e.g. Tsui (2002), Bourguignon and Chakravarty (2003), Chakravarty and D'Ambrosio (2006), Bossert et al (2009)). The latter also rely on a counting approach, albeit some implicitly.…”

Section: Introductionmentioning

confidence: 99%

Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables

Yalonetzky

2014

Soc Choice Welf

View full text Add to dashboard Cite

A natural concern with multivariate poverty measures, as well as with other composite indices, is the robustness of their ordinal comparisons to changes in the indices' parameter values. Applying multivariate stochastic dominance techniques, this paper derives the distributional conditions under which a multidimensional poverty comparison based on the popular counting measures, and ordinal variables, is fully robust to any values of the indices.parameters. As the paper shows, the conditions are relevant to most of the multidimensional poverty indices in the literature, including the Alkire-Foster family, upon which the UNDP.s "Multidimensional Poverty Index" (MPI) is based. The conditions are illustrated with an example from the EU-SILC dataset. Conditions for the Most Robust Multidimensional Poverty Comparisons Using Counting Measures and ordinal variablesGaston Yalonetzky University of Leeds, OPHI July 3, 2012Abstract A natural concern with multivariate poverty measures, as well as with other composite indices, is the robustness of their ordinal comparisons to changes in the indices' parameter values. Applying multivariate stochastic dominance techniques, this paper derives the distributional conditions under which a multidimensional poverty comparison based on the popular counting measures, and ordinal variables, is fully robust to any values of the indices'parameters. As the paper shows, the conditions are relevant to most of the multidimensional poverty indices in the literature, including the AlkireFoster family, upon which the UNDP's "Multidimensional Poverty Index" (MPI) is based. The conditions are illustrated with an example from the EU-SILC dataset.JEL Classi…cation: I32.

show abstract

The Measurement of Social Exclusion

Cited by 213 publications

References 12 publications

Understandings and misunderstandings of multidimensional poverty measurement

Understandings and misunderstandings of multidimensional poverty measurement

Yaşam Tatmini Ve Sosyal Dışlanma

Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables

Contact Info

Product

Resources

About