Does spatial clustering help explaining differences in the inequality of income distribution? Evidence from Argentina

This paper analyzes the relationship between the spatial clustering of income distribution and inequality in the provinces of Argentina. The goal of the paper is to use spatial techniques to analyze to what extent the spatial clustering of income distribution affects the inequality of income distribution in a regional context of Argentina. In general, the literature of inequality implicitly considers each region or province as an independent entity and the potential for observational interaction across space has often gone ignored. However, spatial autocorrelation occurs when the spatial distribution of the variable of interest exhibits a systematic pattern. I compute three measures of global spatial autocorrelation: Moran´s I, Geary´s c, and Getis and Ord´s G, as the degree of provincial clustering between 1991 and 2002. The paper´s main conclusion is that there is evidence that relatively high (low) unequal provinces tend to be located nearby other high (low) unequal provinces more often than would be expected due to random chance. Therefore each province should not be viewed as an independent observation, as it has been implicitly assumed in previous studies of regional income inequality.