Imposto Predial e Territorial Urbano: tratando espacialmente desafios econômicos, sociais e políticos

Libório, Matheus Pereira; Laudares, Sandro; Abreu, João Francisco de; Ekel, Petr; Bernardes, Patrícia

doi:10.1590/2175-3369.012.e20190208

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…However, when limiting themselves to aggregating the variables in a CI, these methods disregard the effects that the variables and dimensions of the phenomenon have on each other. For example, they disregard that the income variable influences the infant mortality rate [46] or that the dimension of the housing conditions of families is influenced by the socioeconomic dimension [3].…”

Section: Composite Indicatorsmentioning

confidence: 99%

“…The variables were selected based on their theoretical and methodological relevance. From the theoretical perspective, Arretche's work [65] shows that inequality in Brazil over the past 50 years is related to characteristics of the population (e.g., social origin, family status, race, sex, and education), the provision of public goods and services to households (e.g., garbage collection, energy networks, treated water and sewage [3,66]), and the households' conditions (e.g., density of the household and existence of a bathroom). Among the countless variables associated with the study of social inequalities, income remains the most important [67,68].…”

Section: Latent and Observed Variables Of S-iiimentioning

confidence: 99%

“…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. Thus, 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.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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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.

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Section: Composite Indicatorsmentioning

confidence: 99%