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Journals / Ekonomi-tek / 2012 / Cilt: 1 - Sayı: 1

On the Economic Content of the Gini Coefficient

Semih TÜMEN
Pages
97–110
Publication Date
2012-02-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The Gini coefficient increases with the dispersion of skills, the dispersion of productivities, and the labor share.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper argues that the canonical assignment model, which is widely used in the study of wage determination, provides natural links to the standardized tools of inequality analysis, such as the Lorenz curve and the Gini coefficient.
📊
Key Findings & Data:I show that an intuitive formula for the Gini coefficient of earnings can be derived using a standard assignment model.
💡
Conclusion & Impact:The Gini coefficient increases with the dispersion of skills, the dispersion of productivities, and the labor share.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Gini coefficient · earnings inequality · earnings equation

Abstract

This paper argues that the canonical assignment model, which is widely used in the study of wage determination, provides natural links to the standardized tools of inequality analysis, such as the Lorenz curve and the Gini coefficient. I show that an intuitive formula for the Gini coefficient of earnings can be derived using a standard assignment model. Such a model is useful in understanding the potential sources of earnings inequality, since it formulates the Gini coefficient as a function of the dispersion of worker skills, the distribution of firm productivities, and the strength of complementarities in production between capital and labor. The Gini coefficient increases with the dispersion of skills, the dispersion of productivities, and the labor share.

Özet

This paper argues that the canonical assignment model, which is widely used in the study of wage determination, provides natural links to the standardized tools of inequality analysis, such as the Lorenz curve and the Gini coefficient. I show that an intuitive formula for the Gini coefficient of earnings can be derived using a standard assignment model. Such a model is useful in understanding the potential sources of earnings inequality, since it formulates the Gini coefficient as a function of the dispersion of worker skills, the distribution of firm productivities, and the strength of complementarities in production between capital and labor. The Gini coefficient increases with the dispersion of skills, the dispersion of productivities, and the labor share.

🔖

Cite This Article

TÜMEN, S. (2012). On the Economic Content of the Gini Coefficient. Ekonomi-tek, Cilt: 1 - Sayı: 1, 97–110. https://ajindex.com/articles/753271

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