The Estimation and Interpolation of Inequality Measures

Abstract

Alternative methods of computing estimates of inequality measures from grouped data are critically examined in terms of their theoretical and empirical properties. The use of a simple “split-histogram” technique of interpolation is explained and supported. Theoretical and empirical support is also provided for the “⅓/⅔ rule”—a simple computational procedure for a point estimate of an inequality measure derived from its standard grouping bounds.