We investigate how changes in the supply of fast-food restaurants affect weight outcomes of 3 million children and 3 million pregnant women. Among 9th graders, a fast-food restaurant within .1 mile of a school results in a 5.2% increase in obesity rates. Among pregnant women, fast-food restaurant within .5 mile of residence results in a 1.6% increase in the probability of gaining over 20 kilos. The implied effects on caloric intake are one order of magnitude larger for children than for mothers, consistent with smaller travel cost for adults. Non-fast-food restaurants and future fast-food restaurants are uncorrelated with weight outcomes.

In the public debate over obesity it is often assumed the widespread availability of fast-food restaurants is an important determinant of obesity rates. Policy makers in several cities have responded by restricting the availability or content of fast food, or by requiring posting of the caloric content of the meals (Mair et al. 2005.)1 But the evidence linking fast food and obesity is not strong. Much of it is based on correlational studies in small data sets.

In this paper we seek to identify the effect of increases in the local supply of fast-food restaurants on obesity rates. Using a new dataset on the exact geographical location of restaurants, we ask how proximity to fast-food restaurants affects the obesity rates of over 3 million school children and the weight gain of 3 million pregnant women. For school children, we observe obesity rates for 9th graders in California over several years, and we are therefore able to estimate models with and without school fixed effects. For mothers, we employ the information on weight gain during pregnancy reported in the Vital Statistics data for Michigan, New Jersey, and Texas covering fifteen years. We focus on women who have at least two children so that we can follow a given woman across two pregnancies.

The design employed in this study allows for a more precise identification of the effect of fast-food restaurants on obesity than the previous literature. First, we observe information on weight for millions of individuals compared to at most tens of thousand in the standard data sets used previously. This large sample size substantially increases the power of our estimates. Second, we exploit very detailed geographical location information, including distances of only one tenth of a mile. By comparing groups of individuals who are at only slightly different distances to a restaurant, we can arguably diminish the impact of unobservable differences in characteristics between the groups. Since a fast-food restaurant’s location might reflect characteristics of the area, we test whether there are any observable patterns in restaurant location within the very small areas we focus on. Third, we have a more precise idea of the timing of exposure than many previous studies: The 9th graders are exposed to fast-food restaurants near their new school from September until the time of a spring fitness test, while weight gain during pregnancy pertains to the 9 months of pregnancy.

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