Generally, econometric studies on income inequality consider regions as independent entities,
ignoring the likely possibility of spatial interaction particularly within a country. This interaction
may cause spatial dependency or clustering, which is referred to as spatial autocorrelation. This
chapter analyzes the relationship between the spatial clustering of income and education in the
districts of Pakistan by employing spatial exploratory data analysis (ESDA) techniques. Global
and local measures of spatial autocorrelation were computed using the Moran’s I index to obtain
estimates of the existing spatial autocorrelation in income and education levels across districts.
The results reveal a surprising absence of knowledge spillovers in terms of education attainment
rates across districts close to large cities with high education attainment rates. On the other hand,
district-wise incomes reveal a clear spatial autocorrelation pattern whereby high income districts
tend to be neighbors of other high income districts. By detecting outliers and clusters, ESDA
allows policy makers to focus on the geography of inequalities, hence highlighting the need to
pursue spatial analysis at lower geographical units such as the district level instead of the
common practice of provincial analysis in Pakistan