Measuring inequality through a non-compensatory approach

Libório, Matheus Pereira; Martinuci, Oséias da Silva; Ekel, Petr; Hadad, Renato Moreira; Lyrio, Renata de Mello; Bernardes, Patrícia

doi:10.1007/s10708-021-10519-x

Cited by 8 publications

(7 citation statements)

References 91 publications

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“…The heterogeneity concept is considered when the weight of the sub‐indicators varies according to the unit of analysis. Three classes of methods are capable of operationalizing the heterogeneity concept 1 : BoD and OWA that operationalize the heterogeneity concept of the analyzed units disregarding the influence of distance in defining the sub‐indicators' weights (Libório, Martinuci, Ekel, et al, 2022; Libório, Martinuci, Machado, et al, 2022); Spatial BoD (Fusco, Vidoli, and Sahoo, 2018) and the Geographically Weighted Principal Components Analysis (GWPCA—Cartone and Postiglione, 2021) that operationalize the heterogeneity considering the latitude and longitude of units in defining, providing statistical information on the spatial context; Multiscale analysis that are based on aggregating scale‐weighted sub‐indicators (Doeffinger and Hall, 2021) and neglect the mutual influence of the units of analysis in defining the sub‐indicators' weights subjectively. Literature review 2 shows that scholars consider only the second class of methods for operationalizing the concept of spatial heterogeneity. None of the main works on BoD (Cherchye, Moesen, and Van Puyenbroeck, 2004; Nardo et al, 2005; Cherchye et al, 2007; OECD, 2008) consider this method compatible with the spatial heterogeneity concept.…”

Section: Literature Reviewmentioning

confidence: 99%

“…None of the main works on BoD (Cherchye, Moesen, and Van Puyenbroeck, 2004; Nardo et al, 2005; Cherchye et al, 2007; OECD, 2008) consider this method compatible with the spatial heterogeneity concept. Only Libório, Martinuci, Ekel, et al (2022) and Libório, Martinuci, Machado, et al, 2022 argue that the BoD definition of weights operationalizes the concept of spatial heterogeneity. On the contrary, researchers agree that the Spatial BoD and the GWPCA operationalize the concept of spatial heterogeneity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this regard, the Ordered Weighted Averaging (OWA) operator (Yager and Kacprzyk, 2012) is a suitable multicriteria decision‐making method as it is structurally non‐compensatory and weights the sub‐indicators according to the characteristics of the analysis units (Yager, 1988). Besides, implementing linguistic quantifiers makes it possible to regulate the sub‐indicators' compensation levels (Ekel, Pedrycz, and Pereira Júnior, 2020) and the heterogeneity of the sub‐indicators' weights (Libório, Martinuci, Ekel, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Fusco,

Libório,

Rabiei‐Dastjerdi

et al. 2023

Geographical Analysis

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Spatially heterogeneous weights and a non‐compensatory aggregation scheme, are two important properties needed to construct a composite indicator capable of summarizing properly the multidimensional phenomenon of local spatial units. Such a composite indicator takes into account, on the one hand, the latent characteristics of the specific units related to their location in the territory, and on the other hand, the relative importance of sub‐indicators highlighting both positive and negative aspects of the studied phenomena. Under these premises, this article proposes a new method called Ordered Geographically Weighted Averaging (OGWA), which can consider different degrees of non‐compensability between sub‐indicators and, at the same time, the spatial heterogeneity for continuous, ordinal, and mixed data. The properties of the method are evaluated through a simulation study. Finally, the method is applied to construct a composite indicator to map the urban public infrastructure of São Sebastião do Paraíso, a city located in the southeastern region of Brazil.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Fusco,

Libório,

Rabiei‐Dastjerdi

et al. 2023

Geographical Analysis

Self Cite

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“…It means that, with the application of different linguistic quantifiers, numerous scenarios that consider different country circumstances can be obtained from the initial weights supplied by the experts. Furthermore, the OWA operator solves non-compensatory aggregation issues [61], resulting in a statistically consistent composite indicator [77]. Definition 7.…”

Section: The Ordered Weighted Averaging (Owa) Operatormentioning

confidence: 99%

Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo

et al. 2022

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In economic development, in addition to comparing the gross domestic product (GDP) between nations, it is critical to assess the quality of life to gain a holistic perspective of their different aspects. However, the quality of life index (QOLI) is a subjective term that can be difficult to quantify. Although this composite index is typically calculated using universal weights proposed by experts to aggregate indicators, such as safety indexes, healthcare indexes, pollution indexes, and housing indicators, it is complicated to balance multiple dimensions whose weights are adjusted to account for different countries’ circumstances. Therefore, this paper aims to construct various scenarios of the QOLI, using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic model. Numbeo, one of the largest quality of life information databases, was used in this paper to estimate the QOLI in 85 countries. Uncertainty and sensitivity analyses were employed to assess the robustness of the QOLI. The results of the proposed model are compared with those obtained using the Numbeo formulation. The results show that the proposed model increases the linguistic interpretability of the QOLI, and obtains different QOLIs, based on diverse country contexts.

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“…On the other side, the major limitation of the approach proposed is related to the lack of context, as countries are considered equally without considering any contextual factor, such as the population. However, non-compensatory aggregations are often used in the broad context of composite social indicators [11]. We believe that, despite some possible potentially misleading result, this approach provides a more transparent and relatively unbiased perspective.…”

Section: Introductionmentioning

confidence: 99%

Walking Together Indicator (WTI): Understanding and Measuring World Inequality

Pileggi

2023

Sustainability

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Socio-economic inequality may be addressed at a different scale. Its impact at a micro and macro level on very many social issues is well-known, as well as its effects on global development being extensively reported in literature, where inequality is often seen as a clear barrier on the path to a sustainable development. That becomes extremely critical in light of major global challenges, such as climate change. The quantification of inequality in the different contexts, its interpretation, as well as its impact on society at a different level are object of major interest and discussion within the scientific community. Inspired by the famous African proverb “if you want to go fast go alone; If you want to go far go together”, we propose an indicator—Walking Together Indicator (WTI), based on a simple model to foster transparency and broad communication. It relies on the statistical standard deviation to facilitate the measurement of inequality looking at single metrics (e.g., GDP) as well as considering broad categories (e.g., Economy) composed of multiple indicators. Despite evident limitations, simplifications (context-less comparison among countries) and approximations (significant lack of data), the study conducted on well-known macro indicators presents a fundamental coherence in the result. Indeed, it shows a reducing inequality in the main trends. On the other side, the performed computations also point out a relevant exception for the main economic indicator, which is characterised, overall, by an increasing inequality among the considered countries. Such a contrasting trend is partially explainable looking at the simplifications in the model, which neither considers dependencies among indicators nor assumes weighting. The indicator provides a very encouraging and optimistic figure. However, the recent pandemic has shown a world running at different speeds. It advises a more conservative interpretation of the indicator as the exception related to economy is relevant.

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Measuring inequality through a non-compensatory approach

Cited by 8 publications

References 91 publications

Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Multiple Scenarios of Quality of Life Index Using Fuzzy Linguistic Quantifiers: The Case of 85 Countries in Numbeo

Walking Together Indicator (WTI): Understanding and Measuring World Inequality

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