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Income inequality: A complex network analysis of US states

Periklis Gogas (), Rangan Gupta (), Stephen Miller (), Theophilos Papadimitriou () and Georgios Sarantitis ()

Physica A: Statistical Mechanics and its Applications, 2017, vol. 483, issue C, 423-437

Abstract: This study performs a long-run, inter-temporal analysis of income inequality in the US spanning the period 1916–2012. We employ both descriptive analysis and the Threshold-Minimum Dominating Set methodology from Graph Theory, to examine the evolution of inequality through time. In doing so, we use two alternative measures of inequality: the Top 1% share of income and the Gini coefficient. This provides new insight on the literature of income inequality across the US states. Several empirical findings emerge. First, a heterogeneous evolution of inequality exists across the four focal sub-periods. Second, the results differ between the inequality measures examined. Finally, we identify groups of similarly behaving states in terms of inequality. The US authorities can use these findings to identify inequality trends and innovations and/or examples to investigate the causes of inequality within the US and implement appropriate policies.

Keywords: Income inequality; Graph theory; US states (search for similar items in EconPapers)
JEL-codes: D63 (search for similar items in EconPapers)
Date: 2017
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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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