Measuring human development: a stochastic dominance approach

Pinar, Mehmet; Stengos, Thanasis; Topaloglou, Nikolas

doi:10.1007/s10887-012-9083-8

Cited by 44 publications

(39 citation statements)

References 33 publications

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“…Anderson et al (2011) adopted a nonparametric approach, by imposing monotonicity and quasiconcavity on the welfare function and deriving upper and lower bounds on welfare levels for the various alternatives/agents. Finally, Pinar et al (2013) focused on the HDI index and used concepts from stochastic dominance to determine the set of weights that results in maximum human development over time.…”

Section: Previous Workmentioning

confidence: 99%

Multidimensional welfare rankings under weight imprecision: a social choice perspective

Athanassoglou¹

2015

Soc Choice Welf

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Ranking alternatives based on multidimensional welfare indices depends, sometimes critically, on how the different dimensions of welfare are weighted. In this paper, a theoretical framework is presented that yields a set of consensus rankings in the presence of such weight imprecision. The main idea is to consider a vector of weights as an imaginary voter submitting preferences over alternatives. With this voting construct in mind, the well-known Kemeny rule from social choice theory is introduced as a means of aggregating the preferences of many plausible choices of weights, suitably weighted by the importance attached to them. The axiomatic characterization of Kemeny's rule due to Young and Levenglick (1978) and Young (1988) extends to the present context. An analytic solution is derived for an interesting special case of the model corresponding to generalized weighted means and the -contamination framework of Bayesian statistics. The model is applied to the ARWU index of Shanghai University. Graph-theoretic insights are shown to facilitate computation significantly. JEL Classification

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Section: Previous Workmentioning

confidence: 99%

Multidimensional welfare rankings under weight imprecision: a social choice perspective

Athanassoglou¹

2015

Soc Choice Welf

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“…In its most recent versions, the HDI is the geometric mean of the three dimension scores, where each dimension is assigned equal weight. In light of its importance in international policy circles, testing the robustness of the HDI rankings with respect to changes in weights has been pursued in a number of alternative ways (e.g., Anderson et al [3], Foster et al [12], Pinar et al [23]). I proceed to apply Procedure 1 to the most recent version of the HDI published in 2013.…”

Section: United Nations Human Development Indexmentioning

confidence: 99%

Robust Multidimensional Welfare Comparisons: One Vector of Weights, One Vote

Athanassoglou

2013

SSRN Journal

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Many aspects of social welfare are intrinsically multidimensional. Composite indices attempting to reduce this complexity to a unique measure abound in many areas of economics and public policy. Comparisons based on such measures depend, sometimes critically, on how the different dimensions of performance are weighted. Thus, a policy maker maker may wish to take into account imprecision over composite index weights in a systematic manner.In this paper, such weight imprecision is parameterized via the -contamination framework of Bayesian statistics. Subsequently, combining results from polyhedral geometry, social choice, and theoretical computer science, an analytical procedure is presented that yields a provably robust ranking of the relevant alternatives in the presence of weight imprecision.The main idea is to consider a vector of weights as a voter and a continuum of weights as an electorate. The procedure is illustrated on recent versions of the Rule of Law and Human Development indices.

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“…The most general approach for this type of analysis involves searching for stochastic dominance rankings over multivariate distributions. Considerable effort has been expended in developing these techniques (see works by Tsui, ; Duclos et al ., ; Anderson, ; Muller and Trannoy, ; Gravel and Moyes, ; Pinar et al ., ; Yalonetzky, ; Sonne‐Schmidt et al ., ), which has built upon classic earlier papers by Kolm () and Atkinson and Bourguignon (). Dominance results are powerful when they occur and are motivated by the attractive welfare properties they imply.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Inequality Across Three Developed Countries

Rohde

Guest

2017

Review of Income and Wealth

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This paper produces comparable estimates of multidimensional inequality for the U.S., Germany, and Australia. Two alternative approaches with differing interpretations are employed. The first method projects all facets of welfare onto a single variable which is then analyzed using standard univariate techniques. The second approach establishes equivalent‐income distributions that would lead to an equalization of welfare, such that the difference between this counterfactual and the true income distribution can be measured. This difference is then interpreted as the degree of income redistribution required to offset welfare inequality. Using data on permanent incomes, health scores, years of education, and leisure times, we observe much higher levels of inequality in the U.S. than in Germany or Australia. Our results are highly statistically significant and hold over a large variety of weighting specifications.

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Measuring human development: a stochastic dominance approach

Cited by 44 publications

References 33 publications

Multidimensional welfare rankings under weight imprecision: a social choice perspective

Multidimensional welfare rankings under weight imprecision: a social choice perspective

Robust Multidimensional Welfare Comparisons: One Vector of Weights, One Vote

Multidimensional Inequality Across Three Developed Countries

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