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.

Obesity and access to chain grocers.

Recent empirical work in the obesity literature has highlighted the role of the built environment and its potential influence in the increasing prevalence of obesity in adults and children. One feature of the built environment that has gained increasing attention is the role of access to chain grocers and their impact on body mass index (BMI). The assessment of the impacts of spatial access to chain grocers on BMI is complicated by two empirical regularities in the data. There is evidence that health outcomes such as BMI are clustered in space and that there is spatial dependence across individuals. In this article, we use an econometric model that takes into account the spatial dependence, and we allow the effect of access to differ for a person depending on whether he or she lives in a low-income community or peer group. We categorize this community using the characteristics of the people who immediately surround the individual rather than using census tracts. Using georeferenced survey data on adults in Marion County, Indiana, we find that the effect of improvements in chain grocer access on BMI varies depending on community characteristics.

Zoning for health: the obesity epidemic and opportunities for local policy intervention.

Several states and local communities have started to experiment with policy initiatives that affect the built-up environment in an attempt to decrease the prevalence of obesity. The focus of these policy measures has generally been to eliminate geographical disparities in access to food. Recent policy proposals include the use of zoning laws to create a healthier food environment by providing incentives for chain grocers to open stores in disadvantaged, underserved areas and providing incentives for existing food retailers to offer healthier products. The economic feasibility of implementing these types of interventions depends on the policymaker's ability to identify communities most at need. We use computer simulations, based on introducing new chain grocers in targeted areas, to map the effects on BMI of this modification in the food environment. In this study, we show that targeting economically disadvantaged communities with high prevalence of obesity-related diseases can provide an effective means of identifying areas where policy implementation will be most beneficial for improvements in health outcomes such as BMI.

The value of statistical life in road safety: a meta-analysis.

Costs of accidents make up an important part of the total external cost of traffic. A substantial proportion of accident costs is related to fatal accidents. In the evaluation of fatal accident costs the availability of an estimate of the economic value of a statistical life is pivotal. We present an overview of the empirical literature on the value of statistical life in road safety (VOSL), and use meta-analysis to determine variables that explain the variation in VOSL estimates reported in the literature. We show that the magnitude of VOSL estimates depends on the value assessment approach (particularly, stated versus revealed preference), and for contingent valuation studies also on the type of payment vehicle and elicitation format. We explain that VOSL estimates cannot simply be averaged over studies. The magnitude of VOSL is intrinsically linked to the initial level of the risk of being caught up in a fatal traffic accident and to the risk decline implied by the research set-up.