Economic Preferences and Obesity among a Low-Income African American Community.

Obesity has reached epidemic proportions in the US, with a significantly higher fraction of African Americans who are obese than whites. Yet there is little understanding of why some individuals become obese while others do not. We conduct a lab-in-field experiment in a low-income African American community to investigate whether risk and time preferences play a role in the tendency to become obese. We examine the relationship between incentivized measures of risk and time preferences and weight status (BMI), and find that individuals who are more tolerant of risk are more likely to have a higher BMI. This result is driven by the most risk tolerant individuals. Patience is not independently statistically related to BMI in this sample, but those who are more risk averse and patient are less likely to be obese.

Systematic neighborhood observations at high spatial resolution: methodology and assessment of potential benefits.

There is a growing body of public health research documenting how characteristics of neighborhoods are associated with differences in the health status of residents. However, little is known about how the spatial resolution of neighborhood observational data or community audits affects the identification of neighborhood differences in health. We developed a systematic neighborhood observation instrument for collecting data at very high spatial resolution (we observe each parcel independently) and used it to collect data in a low-income minority neighborhood in Dallas, TX. In addition, we collected data on the health status of individuals residing in this neighborhood. We then assessed the inter-rater reliability of the instrument and compared the costs and benefits of using data at this high spatial resolution. Our instrument provides a reliable and cost-effect method for collecting neighborhood observational data at high spatial resolution, which then allows researchers to explore the impact of varying geographic aggregations. Furthermore, these data facilitate a demonstration of the predictive accuracy of self-reported health status. We find that ordered logit models of health status using observational data at different spatial resolution produce different results. This implies a need to analyze the variation in correlative relationships at different geographic resolutions when there is no solid theoretical rational for choosing a particular resolution. We argue that neighborhood data at high spatial resolution greatly facilitates the evaluation of alternative geographic specifications in studies of neighborhood and health.