This thesis is comprised of three chapters. The first chapter develops a statistical model that enriches the role that firms playin wage determination, allowing firms to influence both average wages as well as the returns to observable worker characteristics. I exploit the hierarchical nature of a unique employer-employee linked dataset maintained by the U.S. Census Bureau's Longitudinal Employer-Household Dynamics Program, estimating a multilevel statistical model of earnings that accounts for firm-specific deviations in average wages as well as the returns to components of human capital -- race, gender, education, and experience - while also controlling for person-level heterogeneity in earnings. Results suggest that there is significant variation in the returns to worker characteristics across firms. First, estimates of the parameters of the covariance matrix of firm-specific returns are statistically significant. Firms that tend to pay higher average wages also tend to pay higher than average returns to worker characteristics; firms that tend to reward highly the human capital of men also highly reward the human capital of women. Second, the firm-specific returns account for roughly 7% of the variation in wages - approximately 30% of the variation in wages explained by firm-specific intercepts alone.

The second chapter (joint with John M. Abowd and Lars Vilhuber) and third chapter discuss the confidentiality protections and imputation of place-of-work in the Quarterly Workforce Indicators produced by the Census Bureau's Longitudinal Employer-Household Dynamics Program as a part of its Local Employment Dynamics Estimates partnership with 37 state Labor Market Information offices. Both chapters provide a discussion of methodology as well as assessments of the impact of the confidentiality protections and imputation of place-of-work on the Quarterly Workforce Indicators public use measures.