Tag Archives: multidimensional poverty

Why Multidimensional Poverty Measures?

Gaston Yalonetzky

  • Conceptual and philosophical arguments for multidimensional poverty measures.
  • Overview of public debates, Stiglitz Sen Fitoussi Commission.
  • The challenges for, and debates of, multidimensional poverty measures.

 

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Guide to the video

00:00 Introduction

Part 1: Why a Renewed Interest in Multidimensional Poverty Measures?

06:28 Overview

11:06 1: Relevant data is increasing

13:12 2: Multiple poverty measures are exploding

14:50 3: The 2010 Human Development Report – three new indices

16:50 4: Technical resources do not reflect human development

17:33 5: The political demand is increasing

24:18 Introduction to the Stiglitz Sen Fitoussi Commission

32:33 Focus of the Stiglitz Sen Fitoussi Commission

Part 2: Why Multidimensional Poverty Measures?

34:28 Income measures are incomplete

38:45 Justifications of multidimensional poverty measures

Part 3: The Debates of Multidimensional Poverty Measures

40:57 The two major challenges for multidimensional poverty measures; why replace incomes? why a composite index?

45:09 First challenge posed by (among other Ravallion): monetary poverty is multidimensional

47:29 The answer to the challenge: the problems with monetary poverty

55:23 Second challenge: why not use the dash board approach to multidimensional poverty measures?

61:23 The answer to the challenge: the problems with the dash board approach to multidimensional poverty

70:17 The empirical challenge to multidimensional poverty

74:47 Challenges, improvements and best practices of multidimensional poverty measures

83:00 An overview “the reasons for using a multidimensional poverty measure”

OPHI Research in Progress 20a

This paper applies the Alkire Foster method for multidimensional measurement as a targeting mechanism for conditional cash transfers (CCTs), and finds that it significantly improves selection of households with children who are most deprived in dimensions relevant to CCTs.

This paper was published in an OPHI special issue of Social Indicators Research (Volume 112, Issue 2, June 2013). You can read the SIR article here.

Restricted and Unrestricted Hierarchy of Weights

The increasing interest in multidimensional poverty and well-being analysis added complexity to the way these phenomena are conceptualized and measured. A further source of arbitrariness typically derives from the choice of the ‘weights’ to be attached to poverty dimensions. In the literature there has not been thus far a specific attempt to conceptualise the nature of the desired hierarchy among the selected poverty dimensions: That is, the possible meanings of the statement “dimension h is more important than dimension k” have not critically been searched for. The aim of this paper is to move a first step into that direction. We envisage two simple and highly alternative ways in which such a statement can be understood, restricted and unrestricted hierarchy. The analytical conditions allowing to incorporate them into a poverty index are derived and their implications in terms of the understanding of poverty are discussed. An empirical application shows how the choice of the hierarchical scheme for poverty dimensions can lead to opposite conclusions on the trend of poverty.

Citation: Esposito, L. and Chiappero-Martinetti, E. (2008). “Restricted and Unrestricted Hierarchy of Weights.” OPHI Working Paper 22.

Characterizing Weights in the Measurement of Multidimensional Poverty: An Application of Data-Driven Approaches to Cameroonian Data

The study seeks to compare multidimensional poverty indices in Cameroon generated by different multivariate techniques. After carefully exploring the theoretical and empirical review of the statistical methods of setting weights in the measurement of multidimensional poverty, the study employs three different statistical or data-driven methods – principal components analysis, multiple correspondence analysis, and fuzzy set approach to set weights in the aggregation procedure. Use is made of the 2001 Cameroonian household survey data to estimate the models. The poverty distributions obtained from the three approaches are submitted to stochastic dominance tests to investigate the sensitivity of the resultant poverty index rankings to changes in the weighting characterization. It comes out of the empirical analysis that the principal components analysis index distribution unambiguous shows less poverty than the multiple correspondence analysis and fuzzy set composite indices, while comparison of the two latter index distributions shows no clear dominance.

Citation: Njong, A.M. and Ningaye, P. (2008). “Characterizing Weights in the Measurement of Multidimensional Poverty: An Application of Data-Driven Approaches to Cameroonian Data.” OPHI Working Paper 21.

Measuring Multidimensional Poverty in India: A New Proposal

This paper focuses on the methodology by which India’s 2002 Below the Poverty Line (BPL) census data identify the poor and construct a BPL headcount. Using the BPL 2002 methodology it identifies which rural families would have been considered BPL if NFHS (National Family Health Survey) data had been used rather than BPL census data. It compares these to poor families that would be identified using the same variables with the Alkire and Foster multidimensional poverty methodology. It finds that up to 12 per cent of the poor sample population and 33 per cent of the extreme poor could be misclassified as non-poor by the pseudo-BPL method. The paper also develops a sample Index of Deprivation that responds to criticisms regarding BPL data. We compare these results with income poverty and with pseudo-BPL status for sample respondents and disaggregate the index by state and break it down by dimension.

Citation: Alkire, S. and Seth, S. (2008). “Measuring Multidimensional Poverty in India: A New Proposal.” OPHI Working Paper 15, University of Oxford.

Multidimensional Measurement of Poverty in Sub Saharan Africa

Since the seminal work of Sen, poverty has been recognized as a multidimensional phenomenon. The recent availability of relevant databases renewed interest in this approach. This paper estimates multidimensional poverty in fourteen Sub-Saharan African countries using the Alkire and Foster multidimensional poverty measures, whose identification method is based on a counting approach. Four dimensions are considered: assets, health, schooling and empowerment. The results show important differences in poverty among the countries of the sample. The multidimensional poverty estimates are compared with some standard measures such as the Human Development Index (HDI) and the income poverty headcount ratio. It is found that including additional dimensions into the analysis leads to country rankings different from those obtained with the two standard measures. Geographical decompositions and dimensions-break down indicate that rural areas are significantly poorer than urban ones and that schooling is in general the highest contributor to poverty. Finally, robustness and sensitivity analyses are done with respect to the number of dimensions in which deprivation is required so as to be considered poor (the across-dimensions cutoff) as well as to the poverty lines within each dimension. Several cases of dominance between countries are found in the first robustness test.

Citation: Batana, Y. (2008). “Multidimensional Measurement of Poverty in Sub-Saharan Africa.” OPHI Working Paper 13, University of Oxford.

Multidimensional Poverty Measures from an Information Theory Perspective

This paper proposes to use an information theory approach to the design of multidimensional poverty indices. Traditional monetary approaches to poverty rely on the strong assumption that all relevant attributes of well-being are perfectly substitutable. Based on the idea of the essentiality of some attributes, scholars have recently suggested multidimensional poverty indices where the existence of a trade-off between attributes is relevant only for individuals who are below a poverty threshold in all of them (Bourguignon & Chakravarty 2003, Tsui 2002). The present paper proposes a method which encompasses both approaches and, moreover, it opens the door to an intermediate position which allows, to a certain extent, for substitution of attributes even in the case in which one or more (but not all) dimensions are above the set threshold. An application using individual well-being data from Indonesian households in 2000 is presented in order to compare the results under the different approaches.

Citation: Lugo, M. A., Maasoumi, E. (2008, revised 2009). Multidimensional poverty measures from an information theory perspective. OPHI Working Paper 10, University of Oxford.

Counting and Multidimensional Poverty Measurement (Short Version)

This paper proposes a new methodology for multidimensional poverty measurement consisting of an identification method ρk that extends traditional approaches, and a class of poverty measures Ma that satisfies several desirable properties including decomposability. Our identification step employs two forms of cutoff: one within each dimension and a second across dimensions that identifies the poor by counting their deprivations. We aggregate using Foster-Greer-Thorbecke measures adjusted for multidimensionality. Our adjusted headcount ratio is well suited for use with ordinal data. Examples from Indonesia and the US illustrate our methodology.

Citation: Alkire, S. & Foster, J. (revised in 2009) Counting and multidimensional poverty measurement (short version). OPHI Working Paper 7.5, University of Oxford.

Counting and Multidimensional Poverty Measurement

This paper proposes a new methodology for multidimensional poverty measurement consisting of an identification method ρκ that extends traditional approaches, and a class of poverty measures Mα that satisfies several desirable properties including decomposability. Our identification step employs two forms of cutoff: one within each dimension and a second across dimensions that identifies the poor by counting their deprivations. We aggregate using Foster-Greer-Thorbecke measures adjusted for multidimensionality. Our adjusted headcount ratio is well suited for use with ordinal data. Examples from Indonesia and the US illustrate our methodology.

Citation: Alkire, S. & Foster, J. (2007, revised in 2008). Counting and multidimensional poverty measurement. OPHI Working Paper 7, University of Oxford.