2017
DOI: 10.1177/0974929217721764
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Nonlinearity between Infrastructure Inequality and Growth

Abstract: Infrastructure, through its direct and indirect effects, has a bearing on growth, equity and overall development of a country. Widening inequality has significant implications for growth and macroeconomic stability leading to suboptimal use of human resources and concentration of decision-making in the hands of a few. This article discusses the relationship between growth and infrastructure inequality in India since the 1990s. Tracing three dimensions of infrastructure inequality over time, this study tests th… Show more

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Cited by 10 publications
(12 citation statements)
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“…The relative weights assigned to infrastructure variables have been determined by using principal component analysis (PCA). Several studies employed PCA to formulate the composite indices (see, for example, Gayithri, 1997; Ghosh and De, 1998; Mohanty and Bhanumurthy, 2019; Nauriyal and Sahoo, 2010; Ouattara and Zhang, 2019; Sahoo and Dash, 2009; Simon and Natarajan, 2017). PCA transforms an original set of observations of correlated variables into a few linearly uncorrelated factors by applying an orthogonal transformation (Malhotra and Dash, 2018; Simon and Natarajan, 2017).…”
Section: Methodsmentioning
confidence: 99%
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“…The relative weights assigned to infrastructure variables have been determined by using principal component analysis (PCA). Several studies employed PCA to formulate the composite indices (see, for example, Gayithri, 1997; Ghosh and De, 1998; Mohanty and Bhanumurthy, 2019; Nauriyal and Sahoo, 2010; Ouattara and Zhang, 2019; Sahoo and Dash, 2009; Simon and Natarajan, 2017). PCA transforms an original set of observations of correlated variables into a few linearly uncorrelated factors by applying an orthogonal transformation (Malhotra and Dash, 2018; Simon and Natarajan, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…Several studies employed PCA to formulate the composite indices (see, for example, Gayithri, 1997; Ghosh and De, 1998; Mohanty and Bhanumurthy, 2019; Nauriyal and Sahoo, 2010; Ouattara and Zhang, 2019; Sahoo and Dash, 2009; Simon and Natarajan, 2017). PCA transforms an original set of observations of correlated variables into a few linearly uncorrelated factors by applying an orthogonal transformation (Malhotra and Dash, 2018; Simon and Natarajan, 2017). The core philosophy behind PCA is to account for the highest variance possible in the set of original variables in terms of as few components as possible (Ram, 1982).…”
Section: Methodsmentioning
confidence: 99%
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“…One of the areas that is prominently associated with inequality is the provision of urban infrastructure, and this is particularly the case in the Global South. In general, the discussion of these two issues is related to the issue Several studies (Lopez Garcia, 2017;Makmuri, 2017;Simon & Natarajan, 2017) have cited the linkages between unequal distribution of infrastructure and services and spatial inequality.…”
Section: Resultsmentioning
confidence: 99%