Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gini index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study.
Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gird index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study.
GiniInc: a Stata package for measuring inequality from incomplete income and survival data
Gigliarano, Chiara;
2018-01-01
Abstract
Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gird index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study.
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