
doi: 10.2307/2938303
This paper clarifies and extends the classical Roy model of self selection and earnings inequality. The original Roy model, based on the assumption that log skills are normally distributed, is shown to imply that pursuit of comparative advantage in a free market reduces earnings inequality compared to the earnings distribution that would result if workers were randomly assigned to sectors. Aggregate log earnings are right skewed even if one sectoral distribution is left skewed. Most major implications of the log normal Roy model survive if differences in skills are log concave. However few implications of the model survive if skills are generated from more general distributions. We consider the identifiability of the Roy model from data on earnings distributions. The normal theory version is identifiable without regressors or exclusion restrictions. Sectoral distributions can be identified knowing only the aggregate earnings distribution. For general skill distributions, the model is not identified and has no empirical content. With sufficient price variation, the model can be identified from multimarket data. Cross-sectional variation in regressors can substitute for price variation in restoring empirical content to the Roy model.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 401 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 0.1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
