Project Summary Racial and ethnic disparities in both economic and health outcomes are pervasive in the United States, so it is plausible that racial and ethnic minorities may experience worse health and economic burden resulting from occupational injuries and illnesses. Research on the financial losses caused by workplace injury has relied on administrative data with little or no information on race and ethnicity, however, and so racial and ethnic disparities in the economic consequences of workplace injuries have not yet been studied. This study would overcome this data limitation by imputing race and ethnicity information in a large database of earnings linked to workers' compensation claims from California. Our key innovation will be to apply a validated imputation algorithm known as modified Bayesian Improved First Name Surname Geocoding (mBIFSG) to workers' compensation claims with linked earnings data, allowing us to estimate racial and ethnic differences in economic outcomes after injury. Our observational research design will use workers with minor, medical-only injuries as a comparison group for workers with lost-time injuries, a strategy that has been widely used in past research. This approach will enable us to document, for the first time, whether there are racial and ethnic disparities in post-injury employment, earnings, workers' compensation benefits, or wage replacement rates. We will also use Oaxaca-Blinder decompositions and related methods to explore mechanisms that drive differences in post-injury economic outcomes, focusing on those that are amenable to policy interventions. The specific aims of this project are: 1. Using administrative data on California workers' compensation claims linked to unemployment insurance earnings data, impute race and ethnicity using the validated RAND mBIFSG algorithm. 2. Estimate the causal effect of workplace injury on earnings and employment for up to five years after injury, and test whether there are race and ethnicity disparities in the economic consequences of injury. 3. Measure the extent to which racial and ethnic disparities in the economic consequences of injury reflect differences in target characteristics such as job physical demands and, workplace job security, observed non-target characteristics like age, or unobserved factors (including employment discrimination). 4. Estimate the degree to which workers' compensation benefits replace lost earnings and determine the extent to which benefits and replacement rates vary by race and ethnicity. This work will advance our understanding of disparities in the burden of occupational injury and illness and the mechanisms that drive them. Study findings will offer guidance for policymakers and researchers about where to focus future efforts at reducing disparities. By developing data necessary to study disparities in the nation's largest workers' compensation system, this project will also build a foundation for future study.