We construct a tractable, flexible-functional-form estimator of cumulative distribution functions for non-negative random variables which admits large numbers of covariates. The estimator adopts and extends techniques from the spell-duration literature for estimating hazard functions to distribution functions for wages, earnings, and income. We apply these methods to investigate sources of wage inequality for full-time male workers between Canada and the United States, finding that the Canadian wage density has a thinner left tail because low-educated workers have higher pay and a thinner right tail because of a lower proportion of highly-educated workers. Unions appear to play a large role in these outcomes.

This paper uses microeconomic data from the U.K. Family Expenditure Surveys (FES) and the General Household Surveys (GHS) to describe and explain changes in the distribution of male wages. Since the late 1970s wage inequality has risen very fast in the U.K., and this rise is characterized both by increasing education and age differentials. We show that a large part of the changes in the U.K. can be summarized quite simply as increases in eduction differentials and a decline of growth of entry level wages which persist subsequently. This fact we interpret as cohort effects. We also show that, like in the U.S., an important aspect of rising wage inequality is increased within-group wage dispersion. Finally we use the GHS to evaluate the role of alternative education measures.