9320386 Buchinsky Much recent research in labor economics is devoted to understanding the wage structure of American workers. This research investigates changes in the returns to schooling and experience for the male and female labor force, using a newly developed econometric technique for quantile regression. The technique describes the entire conditional distribution of wages and also allow an examination of within-group inequality. The use of the quantile regression technique raises serious theoretical concerns related to sample selection bias. In order to handle this, the first part of the proposed project is devoted to the theoretical derivation of the asymptotic properties of the quantile regression estimator. The second part proposes to use nonparametric methods to investigate changes in the female labor force wage structure. The application uses Census of Population Survey data from 1964 to 1992. The findings from this empirical enquiry would shed light on important issues that relate to changes in the entire conditional distribution of the female labor force. The third part of the project uses the results from the second part to investigate educational choices of the female labor force over time. The application uses data from the Michigan Panel Study of Income Dynamics and the National Longitudinal Survey to track individuals over time. The findings from the two empirical analyses would help explain mobility of wages and earnings. If wages are immobile, then changes in wage inequality would be indicative of real welfare implications, otherwise differences among individuals would average out over time. ***
