Obesity in Urban Food Markets: Evidence from Geo-referenced Micro Data

This paper provides quantitative estimates of the effect of proximity to fast food restaurants and grocery stores on obesity in urban food markets. Our empirical model combined georeferenced micro data on access to fast food restaurants and grocery stores with data about salient personal characteristics, individual behaviors, and neighborhood characteristics. We defined a "local food environment" for every individual utilizing 0.5-mile buffers around a person's home address. Local food landscapes are potentially endogenous due to 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 were handled using spatial econometric estimation techniques. Our policy simulations for Indianapolis, Indiana, focused on the importance of reducing the density of fast food restaurants or increasing access to grocery stores. We accounted for spatial heterogeneity in both the policy instruments and individual neighborhoods, and consistently found small but statistically significant effects for the hypothesized relationships between individual BMI values and the densities of fast food restaurants and grocery stores.