Obesity and fast food in urban markets: a new approach using geo-referenced micro data.

This paper presents a new method of assessing the relationship between features of the built environment and obesity, particularly in urban areas. Our empirical application combines georeferenced data on the location of fast-food restaurants with data about personal health, behavioral, and neighborhood characteristics. We define a 'local food environment' for every individual utilizing buffers around a person's home address. Individual food landscapes are potentially endogenous because of spatial sorting of the population and food outlets, and the body mass index (BMI) values for individuals living close to each other are likely to be spatially correlated because of observed and unobserved individual and neighborhood effects. The potential biases associated with endogeneity and spatial correlation are handled using spatial econometric estimation techniques. Our application provides quantitative estimates of the effect of proximity to fast-food restaurants on obesity in an urban food market. We also present estimates of a policy simulation that focuses on reducing the density of fast-food restaurants in urban areas. In the simulations, we account for spatial heterogeneity in both the policy instruments and individual neighborhoods and find a small effect for the hypothesized relationships between individual BMI values and the density of fast-food restaurants.