This paper develops a model of wage distribution and wage dynamics based on assignment and Pareto learning. The model matches a large number of key facts about wage distribution and wage dynamics. The tractability of Pareto learning allows us to derive joint implications on the wage distribution and wage dynamics as assignment becomes more important. Our model also provides a natural framework for decomposing the earning variance into a permanent component and a transitory one, and it helps explain why the growing importance in assignment can lead to both higher wage inequality and higher wage instability.
|Original language||English (US)|
|Number of pages||36|
|State||Published - Jan 11 2013|