Investigators of social differentials in health outcomes commonly augment incomplete microdata by appending socioeconomic characteristics of residential areas (such as median income in a zip code) to proxy for individual characteristics. But little empirical attention has been paid to how well this aggregate infromation serves as a proxy for the individual characteristics of interest. The authors build on recent work addressing the biases inherent in proxies and consider two health-related examples within a statistical framework that illuminates the nature and sources of biases. Data from the Panel Study of Income Dynamics and the National Maternal and Infant Health Survey are linked to census data. The authors assess the validity of using the aggregate census information as a proxy for individual information when estimating main effects and when controlling for potential confounding between socioeconomic and sociodemographic factors in measures of general health status and infant mortality. They find a general, but not universal, tendency for aggregate proxies to exaggerate the effects of micro-level variables and to do more poorly than micro-level variables at controlling for confounding. The magnitude and direction of these biases vary across samples, however. The authors' statistical framework and empirical findings suggest the difficulties in and limits to interpreting proxies derived from aggregate census data as if they were micro-level variables. The statistical framework that we outline for our study of health outcomes should be generally applicable to other situations where researchers have merged aggregate data with microdata samples.