Multidimensional Poverty and Inequality

Aaberge, Rolf; Brandolini, Andrea

doi:10.1016/b978-0-444-59428-0.00004-7

Cited by 250 publications

(103 citation statements)

References 267 publications

(267 reference statements)

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“…Third, when the function in (1) is linear (or in (2) is equal to 1), the measure in (1) and (2) is equivalent to the simple counting measure (head-count method). The simple counting measure is a special case of more elaborate counting measures that have attracted much attention and have been extensively studied in the literature (see Aaberge and Brandolini, 2015 for an extensive survey on related studies). However, when the function in (1) is not linear, the family of measures defined in (1) behaves very differently from counting measures, and these measures can avoid many pitfalls suffered by various counting measures (Pattanaik and Xu, 2018).…”

confidence: 99%

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Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework

Dhongde

Pattanaik

2019

Review of Income and Wealth

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We study changes in social well-being and deprivation in the U.S. during the Great Recession and the subsequent recovery. We outline an analytical framework for measuring well-being and deprivation in a multidimensional fashion when data on achievement in each dimension is assumed to be ordinal and binary in nature. We use data from the American Community Survey between 2008 and 2015 and find that there was a decline in social well-being and a rise in social deprivation in the U.S. during the recession followed by a reversal of trends during the recovery. Despite low deprivation levels among the White population, this population experienced the largest increase in deprivation during the recession and the least decline in deprivation in the recovery period. These results underscore the fact that the impact of recession and the subsequent recovery varied significantly across population groups. JEL Codes: D36, I31, J10focus on the overall deprivation of the deprived individuals only, we introduce a benchmark level t (t > 0) such that an individual is considered deprived if and only if her overall achievement falls short of t. Our measure of social deprivation is then computed as the sum of overall deprivations of all individuals who are classified as deprived.Our measures are related to some existing measures introduced in the literature on measuring multi-dimensional well-being and deprivation (see, for example, Aaberge and Brandolini (2015), for an extensive survey on related studies). For example, if the transformation function that is used to transform an individual's overall achievement to well-being is linear, then our measure is equivalent to the counting measure (Atkinson, 2003) widely used in the literature. However, if the transformation function is not linear, then the family of our measures behaves very differently from counting measures, and can avoid many pitfalls suffered by various counting measures. By appropriately choosing a transformation function to transform an individual's overall achievement to her well-being (see our discussions in Section 3), we ensure that our measure satisfies certain attractive properties.We estimate the proposed indices using data from the American Community Survey (ACS), which is the largest household level surveys in the U.S. Our sample comprises more than 2 million individuals each year from ACS rounds: 2008 to 2015. We use the recommendations of the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz et al., 2009) as a guide in choosing the different dimensions or dimensions in terms of which we assess an individual's well-being. We choose 9 variables from the ACS which broadly capture the well-being dimensions mentioned in the Commission's report. We estimate trends in overall well-being and deprivation over time and test their sensitivity to multiple thresholds and weights. We also estimate these indices for population groups based on age, gender, nativity, race and ethnicity and find some interesting differences among the different...

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confidence: 99%

“…Education and ability to speak English fluently are categorical (ordinal and discrete) variables whereas being employed or having health insurance are binary variables. Typically, indices based on the counting approach use dichotomous or binary variables (Aaberge and Brandolini, 2015); we convert data on all 9 indicators to a binary 0-1 form.…”

Section: Datamentioning

confidence: 99%

Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework

Dhongde

Pattanaik

2019

Review of Income and Wealth

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“…However, there is not a consensus on how to perform poverty comparisons in this multivariate setting; see Ferreira and Lugo (2013). The approach most widely used has been based on comparing multidimensional poverty measures that aggregate somehow the information across dimensions and individuals; see the reviews of Alkire et al (2015) and Aaberge and Brandolini (2015) and the references therein. However, as in the unidimensional case, this approach could suffer from lack of robustness, because the choice of different measures of multidimensional poverty may lead to different orderings.…”

Section: Bivariate Stochastic Dominance and Bidimensional Povertymentioning

confidence: 99%

“…The EU has also adopted a multidimensional approach to measuring poverty on the basis of an aggregate indicator of 'At Risk of Poverty or social Exclusion' (AROPE) that takes into account relative income poverty, material deprivation and work intensity. The literature on methodological aspects and applications of these and other multivariate poverty indices is enormous; to mention but a few, see Aaberge and Brandolini (2015) and Alkire et al (2015).…”

Section: Introductionmentioning

confidence: 99%

A Review of Stochastic Dominance Methods for Poverty Analysis

García‐Gómez

Pérez

Prieto‐Alaiz

2019

Journal of Economic Surveys

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Stochastic dominance techniques have been mainly employed in poverty analyses to overcome what it is called the multiplicity of poverty indices problem. Moreover, in the multidimensional context, stochastic dominance techniques capture the possible relationships between the dimensions of poverty as they rely upon their joint distribution, unlike most multidimensional poverty indices, which are only based on marginal distributions. In this paper, we first review the general definition of unidimensional stochastic dominance and its relationship with poverty orderings. Then we focus on the conditions of multivariate stochastic dominance and their relationship with multidimensional poverty orderings, highlighting the additional difficulties that the multivariate setting involves. In both cases, we focus our discussion on first‐ and second‐order dominance, though some guidelines on higher order dominance are also mentioned. We also present an overview of some relevant empirical applications of these methods that can be found in the literature in both univariate and multivariate contexts.

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“…Different weighting schemes (or classes of such schemes) have been derived by imposing different sets of norms on the index (or on the underlying social values). For a recent review of MIIs, see Aaberge and Brandolini (2015).…”

Section: Introductionmentioning

confidence: 99%

Normative properties of multidimensional inequality indices with data‐driven dimensional weights: The case of a Gini index

Banerjee

2018

Int J of Economic Theory

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A central problem in constructing multidimensional inequality indices is that of devising weights on the dimensions. There are two different approaches to the problem: the normative and the data‐driven. Indices derived from data‐driven weights have the limitation that they may violate normatively desired properties. This paper asks whether it is possible to obtain normatively acceptable inequality indices from a data‐driven approach. A multidimensional Gini index is derived from an endogenous weighting scheme and is shown to possess a number of desired properties. The existing literature does not seem to contain a Gini index that satisfies all of these properties.

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Multidimensional Poverty and Inequality

Cited by 250 publications

References 267 publications

Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework

Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework

A Review of Stochastic Dominance Methods for Poverty Analysis

Normative properties of multidimensional inequality indices with data‐driven dimensional weights: The case of a Gini index

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