US Income Inequality Measures Over Time Using Generalized Gamma Distribution

Three different years of US income data are analyzed using inequality measures derived from the generalized gamma distribution. The dataset includes Gini coefficient, Theil, Atkinson, and Pietra indexes, with parameters estimated via a minimum distance estimator for grouped data. It accompanies a simulation study and a web-tool named GGIneq for practical application.

Use Cases

• Modeling income distribution based on the generalized gamma distribution framework described.
• Comparing inequality trends over time based on the analysis of US income data for three years.
• Evaluating parameter estimation methods for grouped data based on the described simulation study.
• Applying standard inequality measures (Gini, Theil, Atkinson, Pietra) to economic data based on the described methodology.

Strengths

• Includes a simulation study to evaluate the performance of the parameter estimation method.
• Provides access to a companion web-tool (GGIneq) for generating inequality measures and graphical results.
• Applies a specific statistical method (minimum distance estimator based on Kullback-Leibler divergence) for grouped data.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), indicating limited scope or aggregated summary data.

Provenance

Source

Author Emrah Altun, published on figshare.

Collection Method

Parameters estimated using a minimum distance estimator based on Kullback-Leibler divergence for grouped data.

Time Range

Covers three different years (specific years not stated).

Freshness

Last updated 2026-05-19 17:37:53; freshness should be verified.

Geography

United States income data.