Composite Indicator of Poverty

Author(s):  
Louis-Marie Asselin
Keyword(s):  
Author(s):  
Laurens Cherchye ◽  
C.A. Knox Lovell ◽  
Wim Moesen ◽  
Tom Van Puyenbroeck

2018 ◽  
Vol 35 (4) ◽  
pp. 32-37
Author(s):  
M. U. Kazakov

The research of a condition of peripheral territories is of special interest within formation of a spatial paradigm of development of regional social and economic system. Differentiation of level of social and economic development of peripheral territories is natural process and is subject to complex studying for formation of adequate instruments of management within spatial social and economic policy. In article special attention is paid to formation of system and diagnostic approach for identification of level of social and economic development of territories on the basis of the composite indicator and also to justification and calculation of indicators of unevenness of development of peripheral territories.


2021 ◽  
Vol 11 (7) ◽  
pp. 3208
Author(s):  
Andrea De Montis ◽  
Vittorio Serra ◽  
Giovanna Calia ◽  
Daniele Trogu ◽  
Antonio Ledda

Composite indicators (CIs), i.e., combinations of many indicators in a unique synthetizing measure, are useful for disentangling multisector phenomena. Prominent questions concern indicators’ weighting, which implies time-consuming activities and should be properly justified. Landscape fragmentation (LF), the subdivision of habitats in smaller and more isolated patches, has been studied through the composite index of landscape fragmentation (CILF). It was originally proposed by us as an unweighted combination of three LF indicators for the study of the phenomenon in Sardinia, Italy. In this paper, we aim at presenting a weighted release of the CILF and at developing the Hamletian question of whether weighting is worthwhile or not. We focus on the sensitivity of the composite to different algorithms combining three weighting patterns (equalization, extraction by principal component analysis, and expert judgment) and three indicators aggregation rules (weighted average mean, weighted geometric mean, and weighted generalized geometric mean). The exercise provides the reader with meaningful results. Higher sensitivity values signal that the effort of weighting leads to more informative composites. Otherwise, high robustness does not mean that weighting was not worthwhile. Weighting per se can be beneficial for more acceptable and viable decisional processes.


2021 ◽  
Vol 50 ◽  
pp. 101308
Author(s):  
Emily Stebbings ◽  
Tara Hooper ◽  
Melanie C. Austen ◽  
Eleni Papathanasopoulou ◽  
Xiaoyu Yan

2014 ◽  
Vol 123 (2) ◽  
pp. 349-370
Author(s):  
Rosa Bernardini Papalia ◽  
Pinuccia Calia ◽  
Carlo Filippucci

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