AI Summary & Key Takeaways
AI InsightThe Gini coefficient increases with the dispersion of skills, the dispersion of productivities, and the labor share.\r\n
📌 3 Key Takeaways
Özet
\r\n 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.\r\n