Towards a measure of income inequality freed from the volatility caused by variations in the rate of unemployment

By mixing together inequalities based on cyclical variables, such as unemployment, and on structural variables, such as education, usual measurements of income inequality add objects of a di§erent economic nature. Since jobs are not acquired or lost as fast as education or skills, this aggreagation leads to a loss of relavant economic information. Here I propose a di§erent procedure for the calculation of inequality. The procedure uses economic theory to construct an inequality measure of a long-run character, the calculation of which can be performed, though, with just one set of cross-sectional observations. Technically, the procedure is based on the uniqueness of the invariant distribution of wage o§ers in a job-search model. Workers should be pre-grouped by the distribution of wage o§ers they see, and only between-group inequalities should be considered. This construction incorporates the fact that the average wages of all workers in the same group tend to be equalized by the continuous turnover in the job market.

Measurements of income inequality: a critique. Inflation and income inequality: a link through the job- search process

In this paper I claim that, in a long-run perspective, measurements of income inequality, under any of the usual inequality measures used in the literature, are upward biased. The reason is that such measurements are cross-sectional by nature and, therefore, do not take into consideration the turnover in the job market which, in the long run, equalizes within-group (e.g., same-education groups) inequalities. Using a job-search model, I show how to derive the within-group invariant-distribution Gini coefficient of income inequality, how to calculate the size of the bias and how to organize the data in arder to solve the problem. Two examples are provided to illustrate the argument.