binequality: Methods for Analyzing Binned Income Data

Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.

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AuthorSamuel V. Scarpino, Paul von Hippel, and Igor Holas
Date of publication2016-12-17 01:36:01
MaintainerSamuel V. Scarpino <>
LicenseGPL (>= 3.0)

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binequality Man page
binequality-package Man page
county_bins Man page
fitFunc Man page
getMids Man page
getQuantilesParams Man page
giniCoef Man page
LRT Man page
makeFitComb Man page
makeInt Man page
makeIntWeight Man page
makeWeightsAIC Man page
mAvg Man page
midStats Man page
MLD Man page
modelAvg Man page
paramFilt Man page
run_GB_family Man page
school_district_bins Man page
SDL Man page
state_bins Man page
theilInd Man page

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