Estimates of differential mortality by socioeconomic status play an important role in several domains: in public policy for assessing distributional effects of public programs; in financial markets for the design of life insurance and annuities; and in individual decision making when figuring out how much to save for retirement. Traditionally, reliable estimates of differential mortality require rich panel data with large sample size. This paper proposes a new, less data-intensive approach relying on just a single cross-section of data. Rather than using observations on actual mortality in panel over time, the authors propose relating individuals' subjective probabilities of survival to variables of socioeconomic status in cross-section. They formulate the method in a model of survey response and provide an empirical validation based on data from the Health and Retirement Study comparing the alternative estimates to the traditional estimates of differential mortality for the same sample of baseline respondents. They present two applications. First, they document an increase in differential mortality in the U.S. over time, and second, they produce comparable estimates of differential mortality for 10 European countries and the U.S. based on subjective probabilities of survival.