Abstract
Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted—or not—as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and consequently distort the assessment of intra-urban inequality. In this paper, we explore the direct and indirect effects that the different underlying dimensions of intra-urban inequality have on indicators that represent this phenomenon. Structural equation modeling was used to build a composite indicator that captures the direct and indirect effects of the underlying dimensions of intra-urban inequality. From this modeling that combines confirmatory factor analysis with a system of simultaneous equations, the intra-urban inequality of the urban conurbation of Maringá–Sarandi–Paiçandu, Brazil was measured. The model comprises first- and second-order structures. The first-order structure is composed of non-observed variables that represent three underlying dimensions of intra-urban inequality. The second-order structure is the intra-urban inequality composite indicator that synthesizes the non-observed variables of the first-order structure. The model aims at demonstrating how to perform a theorized measurement of urban inequality so that it makes it possible to identify which dimensions most influence the others, as well as which dimensions are more relevant to this purpose.
Highlights
Researchers have focused on the development and use of composite indicators to represent complex phenomena [1]
This article presents a case study to explore the direct and indirect effects that the different dimensions of intra-urban inequality have on the S-III that represents such a phenomenon
Its emergence occurred in the late 1940s through the actions of Companhia Melhoramentos Norte do Paraná in the vicinity of the Rede Ferroviária Federal station, as it happened with countless other Brazilian cities [59]
Summary
Researchers have focused on the development and use of composite indicators to represent complex phenomena [1] These indicators’ construction is almost always done by methods that aggregate a reasonable number of manifested variables, which can be weighted or not, in a new synthesis variable [2]. The problem is that this aggregation and weighting do not allow one to capture the effects that the multiple underlying dimensions of the phenomenon have on each other It is disregarded, for example, that the socioeconomic condition of families influences their housing conditions [3]. This limitation means that intra-urban inequality composite indicators constructed from methods based on aggregation and weighting (e.g., see [4,5]) do not capture the effects among the underlying dimensions of the inequality. Such methods do not allow for consideration of the influence that indicators have on each other, such as socioeconomic [6], neighborhood [7], and household [8] inequalities
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
