Tools for Deriving Income Inequality Estimates from Grouped Income Data

Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census. These methods use different interpolation techniques to infer the distribution of incomes within income bins. One method is an implementation of Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB). The other method is based on a new technique, Lorenz interpolation, which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data. These methods can be used to estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index. Jargowsky and Wheeler (2018) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("lorenz")

0.1.0 by Andrew Carr, 20 days ago


Browse source code at https://github.com/cran/lorenz


Authors: Andrew Carr [aut, cre, cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports magrittr, dineq

Suggests testthat


See at CRAN