An Axiomatic Approach to Multidimensional Poverty Measurement via Fuzzy Sets

Chakravarty, Satya R.

doi:10.1007/978-0-387-34251-1_4

Cited by 22 publications

(9 citation statements)

References 37 publications

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“…It is important to note that Chakravarty (2006) introduces such an axiom in a general form. Limiting our attention to the case in which the increase of income does not imply that the unit involved becomes rich, we consider two levels of monotonicity.…”

Section: Axiom Mon (Monotonicity) If the Income Of A Non-rich Increamentioning

confidence: 99%

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A fuzzy logic approach to poverty analysis based on the Gini and Bonferroni inequality indices

Giordani

Giorgi

2010

Stat Methods Appl

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In the poverty analysis framework, a great deal of attention has been paid to the poverty measurement in terms of monetary variables, such as income or consumption. In this context, a relevant open problem is connected with the distinction between poor and non-poor. In fact, the concept of poverty is rather vague and cannot be defined in a clear way. In this respect, following a fuzzy logic approach, some new poverty measures are proposed. In particular, the fuzzy extension of two existing poverty measures based on the Gini and Bonferroni inequality indices is provided. Some synthetic and real applications are given in order to show how the proposed poverty measures work.

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Section: Axiom Mon (Monotonicity) If the Income Of A Non-rich Increamentioning

confidence: 99%

“…In the fuzzy logic context, Chakravarty (2006) suggests a set of axioms for multi-dimensional poverty measures. Here, we consider such axioms limited to the univariate case.…”

Section: Poverty Axioms Fulfilled Bys Ands Bmentioning

confidence: 99%

A fuzzy logic approach to poverty analysis based on the Gini and Bonferroni inequality indices

Giordani

Giorgi

2010

Stat Methods Appl

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“…We could also consider alternative membership functions instead of (9). For instance, the class of membership functions proposed by Chakravarty (), in which

π_{n t} = {[\frac{z_{2} - x_{n t}}{z_{2} - z_{1}}]}^{θ}

z_{1 t} \leq x_{n t} \leq z_{2 t}

. An illustration of this membership function can be found in Figure in Appendix (in the online Supporting Information), though we note that these are not twice‐differentiable.…”

Section: Poverty Identification With Fuzzy Setsmentioning

confidence: 99%

Fuzzy Chronic Poverty: A Proposed Response to Measurement Error for Intertemporal Poverty Measurement

Porter¹,

Yalonetzky

2017

Review of Income and Wealth

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A number of chronic poverty measures are now being used by policymakers to quantify the prevalence and intensity of chronic poverty, vis-a-vis transient experiences. For this exercise, data on multiple time periods is used to assess the welfare trajectories of individuals, or households, over time, in order to distinguish the intertemporally poor from the non-poor, or the transient poor. The extent of intertemporal poverty can also be compared and profiled in the same way as static poverty. Our research examines the implications of measurement error for discontinuous chronic poverty measures, and proposes an alternative approach to measure chronic poverty that seeks to minimize the consequences of measurement error. The approach is based on a novel criterion for the identification of poverty that draws from fuzzy set theory.

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“…Following Cerioli and Zani [6] and Chakravarty [9], two poverty lines should be fixed 1 . Thus, incomes can be sorted into three types: extreme poverty, moderate poverty and non-poverty, whenever incomes are below the smaller poverty line, between both poverty lines or above the highest poverty line, respectively.…”

Section: Introductionmentioning

confidence: 99%

A new class of fuzzy poverty measures

García-Lapresta¹,

Vega²,

Pereira³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper, we introduce and analyze a class of fuzzy poverty measures based on exponential means. Since poverty is a vague notion, individuals should not be classified in poor or non-poor. In our proposal, we have associated a degree of poverty to each income through a fuzzy membership function. We have extended normalized gaps from the classical approach, where poverty is a dichotomous notion, to the fuzzy setting. The proposed family of fuzzy poverty measures decomposes into the three I's indicators: the incidence of poverty is captured through the headcount ratio, while intensity and inequality of poverty are measured by the core and the remainder, respectively, of a parameterized exponential mean over the normalized gaps of (somewhat) poor individuals. Taking into account the features of the dual decomposition of exponential means, we provide some properties of the proposed fuzzy poverty measures.

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An Axiomatic Approach to Multidimensional Poverty Measurement via Fuzzy Sets

Cited by 22 publications

References 37 publications

A fuzzy logic approach to poverty analysis based on the Gini and Bonferroni inequality indices

A fuzzy logic approach to poverty analysis based on the Gini and Bonferroni inequality indices

Fuzzy Chronic Poverty: A Proposed Response to Measurement Error for Intertemporal Poverty Measurement

A new class of fuzzy poverty measures

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