In common sociological research, income inequality is measured only at the aggregate level. The main purpose of this article is to demonstrate that there is more than meets the eye when inequality is indicated by a single measure. In this article, I introduce an alternative method that evaluates individuals’ contributions to inequality as well as the between-group and within-group components of these individual contributions. I first highlight three common inequality measures, the Gini index and two generalized entropy measures—Theil’s T and Theil’s L indices—by presenting their individual components as a method for evaluating inequality. Five artificial data examples illustrate the use of these individual components first. An empirical analysis of the 2007 and 2017 Current Population Survey data then focuses on the differences in inequality revealed by such individual inequality components between the 2007 and 2017. The individual-level inequality measures can reveal patterns of inequality concealed by single measures at the aggregate level. In particular, the Gini individual measures differentiate cases better than the generalized entropy measures and tend to have smaller standard errors in a regression analysis.
Generalized entropy measures of long-run inequality and stability among male headed households
