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Income Inequality and Health: Expanding our Understanding of State Level Effects by using a Geospatial Big Data Approach


Timothy Haithcoat






Leadership Auditorium, 2501 Student Center

The income inequality hypothesis proposes that ecological income inequality is harmful for population health but findings from extant work are inconsistent across health outcomes and levels of geography. We contribute to this debate by applying a big data geospatial approach to create three innovative measures of uniformity in income inequality across space within US states. Controlling for relevant individual and contextual characteristics, we evaluate multilevel models of individuals within states using data from the Behavioral Risk Factor Surveillance System and American Community Survey to examine the ways that income inequality, operationalized as the Gini coefficient, and the three uniformity measures are associated with several health outcomes. Specifically, the uniformity measures capture the extent to which 1) inequality is uniformly distributed spatially in states regardless of whether the level is high or low, 2) the extent to which states are more uniformly high in inequality across space, and 3) the extent to which they are more uniformly low in inequality. We conclude that state income inequality does not predict worse health across these outcomes (and indeed was associated with lower odds of depression and obesity) but that residents of states that have more uniformly high inequality across space are more likely to report below average health, cardiovascular disease, difficulty concentrating, and that they have not sought care because it was too expensive. We conclude with a discussion of the ways that a big data geospatial approach can further contribute to research on this and other public health topics wherein scholars primarily rely on traditional survey data.