Gogas, PeriklisGupta, RanganMiller, Stephen M.Papadimitriou, TheophilosSarantitis, Georgios Antonios2017-08-142017-10Gogas, P., Gupta, R., Miller, S.M., Papadimitriou, T. & Sarantitis, G.A. 2017, 'Income inequality : a complex network analysis of US states', Physica A : Statistical Mechanics and its Applications, vol. 483, pp. 423-437.1873-2119 (online)0378-4371 (print)10.1016/j.physa.2017.04.102http://hdl.handle.net/2263/61637This 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.en© 2017 Elsevier B.V. All rights reserved. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Physica A: Statistical Mechanics and its Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Physica A: Statistical Mechanics and its Applications, vol. 483, pp.423-437, 2017. doi : 10.1016/j.physa.2017.04.102.Income inequalityGraph theoryUnited States (US)SharesTradePostprint Article