This dissertation contributes to the understanding of employer-employee bargaining over hours of work, and the use of administrative data to better understand labor market statistics. In chapter 1, Hours Off the Clock,'' I address a simple but confounding research question: To what extent do workers work more hours than they are paid for? The relationship between hours worked and hours paid, and the conditions under which employers can demand more hours off the clock," is not well understood. The answer to this question affects worker welfare, as well as wage and hour regulation. In addition, work off the clock has important implications for the measurement and cyclical movement of productivity and wages. In this chapter, I construct a unique administrative dataset of hours paid by employers linked to a survey of workers on their reported hours worked to measure work off the clock. Using cross-sectional variation in local labor markets, I find only a small cyclical component to work off the clock. The results point to labor hoarding rather than efficiency wage theory, indicating work off the clock cannot explain the counter-cyclical movement of productivity. I find workers employed by small firms, and in industries with a high rate of wage and hour violations are associated with larger differences in hours worked than hours paid. These findings suggest the importance of tracking hours of work for enforcement of labor regulations. In chapter 2, Hours Adjustments: Evidence from Linked Employer-Employee Data,'' coauthored with fellow graduate student Nellie Zhao, we provide the first look at administrative data on hours worked within firms. We document the extent to which part-time work varies across industries, and confirm that part-time work is concentrated in relatively low-wage service sectors. Further, we take advantage of the longitudinal nature of our dataset and analyze the prevalence of transitions between part-time and full-time work within the same job. We show that the share of new full-time or part-time jobs that are created due to within-job hours changes varies greatly across industries. In chapter 3, Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in OnTheMap,'' coauthored with Kevin L. McKinney, Lars Vilhuber, and John M. Abowd, we report results from the first comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI). We conducted the evaluation by generating multiple threads of the edit and imputation models used in the LEHD Infrastructure File System. These threads conform to the Rubin (1987) multiple imputation model, with each thread or implicate being the output of formal probability models that address coverage, edit, and imputation errors. Design-based sampling variability and finite population corrections are also included in the evaluation. We derive special formulas for the Rubin total variability and its components that are consistent with the disclosure avoidance system used for QWI. These formulas allow us to publish the complete set of detailed total quality measures for QWI and LODES. The analysis reveals that the five publication variables are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs have acceptable quality. Tabulations involving zero, one or two jobs have substantial total variability.