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We use new PSID data on consumption and health, along with information on annual sick time, to estimate a structural labor supply model that incorporates a health capital stock with the traditional human capital learning-by-doing model. The estimates show strong evidence of learning by doing as well as strong persistence in health. However, the estimates reveal that time and money seem to have little effect on health consistent with 'flat of the curve' medicine. We find strong evidence that consumption and leisure are direct substitutes in preferences, and consumption and leisure are each utility complements with good health.

The burden of the COVID-19 pandemic has not been shouldered equally by American families. Black and Hispanic communities have been hit the hardest, with the pandemic often exacerbating existing disparities. Using nationally representative data, we assess the economic and public health effects of the pandemic among different socioeconomic groups and whether typical sources of protection from economic insecurity are uniformly protective across the U.S. population. Within these sociodemographic groups, we also explore differences by education and industry. We find higher levels of employment loss among Blacks and Hispanics, those without college degrees, and frontline workers. We also find evidence that individuals and families are facing mental health episodes and are turning to costly alternative financial strategies to cope throughout the pandemic.

Abstract
We test an improved imputation technique, sequential regression multivariate imputation (SRMI), for the Current Population Survey Annual Social and Economic Supplement to address match bias. Furthermore, we augment the model with administrative tax data to test for nonignorable nonresponse. Using data from 2009, 2011, and 2013, we find that the current hot deck imputation used by the Census Bureau produces different distribution statistics, downward for poverty and inequality and upward for median income, relative to the SRMI model-based estimates. Our results suggest that these differences are a result of match bias, not nonignorable nonresponse. Nearly all poverty, median income, and inequality estimates are not significantly different when comparing imputation models with and without administrative data. However, there are clear efficiency gains from using administrative data.