Robust Multidimensional Poverty Comparisons

Duclos, Jean–Yves; Sahn, David E.; Younger, Stephen D.

doi:10.1111/j.1468-0297.2006.01118.x

Cited by 322 publications

(159 citation statements)

References 24 publications

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“…r k (t h ) is the estimation of the asymptotic variance of b D r k (t h ). Using the same reasoning as Davidson and Duclos (2000) and Duclos et al (2006) based on the law of large numbers and the central limit theorem, these estimations of the asymptotic variance are de…ned by:…”

Section: Appendix 2 Testing Methodologymentioning

confidence: 99%

Robust normative comparisons of socially risky situations

Gravel

Tarroux

2014

Soc Choice Welf

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In this paper, we characterize and empirically implement robust normative criteria for comparing societies on the basis of their allocations of risks among their members. Risks are modelled as lotteries on the set of distributions of state-contingent pecuniary consequences. Individuals are assumed to have individualistic Von NeumanMorgenstern preferences for these risks. Appealing to Harsanyi's aggregation theorem, we provide empirically implementable criteria that coincide with the unanimity, over all such individual preferences, of anonymous and Pareto-inclusive Von Neuman Morgenstern social rankings of risks. The empirically implementable criteria can be interpreted as sequential expected poverty dominance. Illustrations of the usefulness of the criteria for comparing the exposure to unemployment risk of different segments of the French and US workforce and for appraising the evolution, over time, of risks of violent crimes in India are also provided.

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Section: Appendix 2 Testing Methodologymentioning

confidence: 99%

Robust normative comparisons of socially risky situations

Gravel

Tarroux

2014

Soc Choice Welf

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“…For s x = 1 and s y = 1 and for s x = 2 and s y = 1, the proof follows from Theorems 1 and 2 in Duclos, Sahn, and Younger (2006). For s x = 2 and s y = 2, start with…”

mentioning

confidence: 97%

“…This can be seen as a generalization of the procedures followed in Duclos, Sahn, and Younger (2006) to the case of surfaces, curves and indices whose thresholds and individual functions of contributions to total poverty are subject to sampling variability because they depend on unknown characteristics and moments of the distribution (e.g., the means µ χ and µ ξ of the variables).…”

Section: Estimation and Inferencementioning

confidence: 99%

“…First, rather than consider absolute values of multiple measures of well-being, we normalize them by a reference value, usually their mean. The robust poverty comparisons of Duclos, Sahn, and Younger (2006) thus become robust relative poverty comparisons. These can be of interest in their own right (see for instance Sen 1983), but they also permit sensible analysis of inequality if the poverty lines are allowed to span a suitably large range, extending beyond the least deprived people in the population.…”

Section: Introductionmentioning

confidence: 99%

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Partial multidimensional inequality orderings

Duclos

Sahn

Younger

2011

Journal of Public Economics

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Abstract:The paper investigates how comparisons of multivariate inequality can be made robust to varying the intensity of focus on the share of the population that are more relatively deprived. It follows the dominance approach to making inequality comparisons, as developed for instance by Atkinson (1970), Foster andShorrocks (1988) and Formby, Smith, and Zheng (1999) in the unidimensional context, and Atkinson and Bourguignon (1982) in the multidimensional context. By focusing on those below a multidimensional inequality "frontier", we are able to reconcile the literature on multivariate relative poverty and multivariate inequality. Some existing approaches to multivariate inequality actually reduce the distributional analysis to a univariate problem, either by using a utility function first to aggregate an individual's multiple dimensions of well-being, or by applying a univariate inequality analysis to each dimension independently. One of our innovations is that unlike previous approaches, the distribution of relative well-being in one dimension is allowed to affect how other dimensions influence overall inequality. We apply our approach to data from India and Mexico using monetary and non-monetary indicators of well-being.

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“…In the context of applied welfare economics, such methods were popularized by Atkinson and Bourguignon (1982) who showed how stochastic dominance techniques for comparisons of probability distributions can be used to make comparisons of populations across broad classes of underlying social welfare functions. Such techniques have been further refined and applied by, e.g., Atkinson and Bourguignon (1987), Bourguignon (1989), Atkinson (1992), Bourguignon and Chakravarty (2003), Duclos et al (2006Duclos et al ( , 2007, Gravel et al (2009), Gravel andMukhopadhyay (2010), Muller andTrannoy (2011), Gravel andMoyes (2012), and many others.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional First-Order Dominance Comparisons of Population Wellbeing

Siersbæk¹,

Østerdal²,

Arndt³

2016

Measuring Poverty and Wellbeing in Developing Countries

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Robust Multidimensional Poverty Comparisons

Abstract: We investigate how to make poverty comparisons using multidimensional indicators of well-being, showing

Cited by 322 publications

References 24 publications

Robust normative comparisons of socially risky situations

Robust normative comparisons of socially risky situations

Partial multidimensional inequality orderings

Multidimensional First-Order Dominance Comparisons of Population Wellbeing

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