Spatial Analysis of Regional Income Inequality

Questions surrounding regional economic convergence have commanded a great deal of recent attention in economics literature. As in other recent cases in the social sciences, the application of spatially explicit methods of data analysis to the convergence question has yielded important insights on regional economic growth. By contrast, the literature on regional income inequality, although somewhat older than the convergence literature, has been slower to adopt new spatially explicit methods of data analysis. This chapter helps to speed that adoption by investigating the role of spatial dependence and spatial scale in the analysis of regional income inequality in the US over the 1929-2000 period. The findings reveal a strong positive relationship between measures of inequality in state incomes and the degree of spatial autocorrelation. Additionally, a geographically based decomposition of inequality highlights a strong positive relationship between the interregional inequality share (as opposed to intraregional inequality) and spatial clustering. Finally, a new approach to inference in regional inequality analysis is suggested and demonstrated as providing a formal explanatory framework to complement the broad, but descriptive approaches in the existing literature.