Spatial Analysis of Regional and Income Inequality in the United States

Abstract

Understanding spatial or geographical dependence of income and regional inequality is crucial in analyzing inequality. This paper employs a multi-scale, multi-mechanism framework to map and analyze historical patterns of regional and income inequality in the United States (US) by using state and regional panel data spanning over a century. To explore the patterns systematically and see the role of spatial partitioning, we organize the data around several established geographical partitions before conducting various Geographical Information System (GIS) analyses and statistical techniques. We also investigate the spatial dependence of income inequality and regional inequality. We find that spatial autocorrelation exists for both the regional and income inequality in the US; however, the magnitude of spatial dependence for regional inequality is declining while it is volatile for income inequality over time. We also notice that while income inequality is at its peak in the most recent decades, regional inequality is at its lowest point. As for the choice of partitioning, we observe that within inequality dominates for Census Divisions and the Bureau of Economic Analysis (BEA) Regions. Conversely, we see that between inequality overall contributes the most to the inequality among Census Regions.