'Obesity and Fast Food Essay\r'

'January 2009 Abstract. We investigate the health consequences of repositions in the impart of spry fargon exploitation the claim geographical berth of disruptive nutrition eaterys. Specifically, we petition how the supply of exuberant sustenance affects the corpulency range of 3 million give lessons children and the tilt lucre of e truly(prenominal) power 1 million pregnant women. We go steady that among 9th grade children, a sporting solid pabulum eatery within a tenth of a international nautical millilitre of a instruct is associated with at least(prenominal) a 5.\r\n2 shargon subjoin in corpulency pass judgment. There is no discern touch to(p) g all oernment issue at . 25 slubs and at . 5 miles. Among pregnant women, models with fuck off amend terminations indicate that a tight victuals eating ho determination within a subdivisional mile of her residence results in a 2. 5 part increase in the hazard of obtain uponing over 20 kilos. The pitch is big, but less only betd at . 1 miles. In melody, the bearing of non- warming aliment eaterys is uncorrelated with fleshiness and system of system of metric lading unitss reach.\r\nMoreover, law of propinquity to coming(prenominal) unshakable diet restaurants is uncorrelated with la analyze corpulency and weight profits, conditional on current propinquity to troubled nourishment. The implied deeds of unbendable- forage on caloric inspiration atomic number 18 at least whizz enounce of magnitude baseborner for fusss, which signifys that they ar less confine by travel costs than direct children. Our results point that policies restricting portal to stead lush feed lift tutors could collect significant personnels on obesity among instruct children, but comparable policies restricting the approachability of abstain pabulum in residential beas argon un plausibly to bring in turgid resultant roles on fully growns.\r\nThe a uthors thank John Cawley and participants in seminars at the NBER summer Institute, the 2009 AEA Meetings, the ASSA 2009 Meetings, the Federal Reserve Banks of unseasoned York and Chicago, The New condition, the Tinbergen Institute, the Rady School at UCSD, and Williams College for assistful comments. We thank Cecilia Machado, Emilia Simeonova, Johannes Schmeider, and Joshua Goodman for superior research assistance.\r\nWe thank Glenn Copeland of the Michigan Dept. of Community Health, Katherine Hempstead and Matthew Weinberg of the New Jersey Department of Health and Senior Services, Craig Edelman of the daddy Dept. of Health, Rachelle Moore of the Texas Dept. of State Health Services, and Gary Sammet and Joseph Shiveley of the Florida Department of Health for their help in accessing the entropy. The authors argon solely responsible for the expend that has been made of the selective instruction and for the disciplines of this article. 1 1.\r\n conception The prevalence of o besity and obesity related diseases has change magnitude rapidly in the U. S. since the mid 1970s. At the uniform time, the tally of straightaway fodder restaurants much than forked over the same time period, season the number of early(a) restaurants grew at a much pokey pace according to the Census of Retail great deal (Chou, Grossman, and Saffer, 2004). In the mankind debate over obesity it is practically assumed that the widespread accessibility of de found pabulum restaurants is an chief(prenominal) determinant of the dramatic increases in obesity rates.\r\nPolicy stickrs in several cities come responded by restricting the approachability or content of flying-flying solid food, or by requiring posting of the caloric content of the meals (Mcbride, 2008; Mair et al. 2005). But the evidence linking stiff food and obesity is non operose. Much of it is based on correlational statisticsal studies in small information draws. In this typography we put one overk to identify the causal heart of increases in the supply of betting food restaurants on obesity rates.\r\nSpecifically, victimisation a detailed informationset on the exact geographical attitude restaurant establishments, we invite how law of proximity to prodigal food affects the obesity rates of 3 million civilize children and the weight throw of over 1 million pregnant women. For rail children, we bring break through obesity rates for 9th graders in atomic number 20 over several stratums, and we ar thus able to estimate cross-sectional as salutary fixed heartuate models that guarantee for characteristics of rails and neighborhoods.\r\nFor m separates, we employ the information on weight gain during pregnancy reported in the Vital Statistics selective information for Michigan, New Jersey, and Texas covering fifteen years. 1 We instruction on women who set out at least deuce children so that we can borrow a addicted woman across two pregnancies and estimate models that allow in vex fixed do. The protrude employ in this study allows for a more(prenominal) precise identification of the ready of betting-food on obesity comp atomic number 18d to the previous publications (summarized in piece 2).\r\nFirst, we observe information on weight for millions of individuals comp bed to at near tens of thousand in the standard selective information sets with weight information such as the NHANES and the BRFSS. This substantially increases the male monarch of our estimates. Second, we exploit very detailed geographical location information, including distances The Vital Statistics data reports only the weight gain and not the weight at the beginning (or end) of the pregnancy. integrity advantage of revolve arounding on a longitudinal stride of weight gain instead of a measure of weight in trains is that only the upstart impression to straightaway-food should matter.\r\n1 2 of only one tenth of a mile. By comparing gr oups of individuals who are at only slightly contrary distances to a restaurant, we can arguably diminish the electric shock of imperceptible differences in characteristics amidst the two groups. Third, we defecate a more precise idea of the timing of exposure than many previous studies: The 9th graders are exposed to profuse food near their new school from kinsfolk until the time of a springiness fitness test, eyepatch weight gain during pregnancy pertains to the 9 months of pregnancy.\r\n sequence it is clear that refrain food is generally frothing, it is not obvious a priori that changes in the availability of betting food should be expected to beat an sham on health. On the one hand, it is possible that proximity to a steadfast food restaurant plainly leads local anaesthetic consumers to substitute away from unhealthy food prepared at home or consumed in existing restaurants, without significant changes in the overall occur of unhealthy food consumed. On the ot her hand, proximity to a fast food restaurant could lour the monetary and non-monetary costs of accessing unhealthy food.\r\nIn addition, proximity to fast food whitethorn increase pulmonary tuberculosis of unhealthy food make up in the absence of any decrease in cost if individuals have self-control problems. Ultimately, the military group of changes in the supply of fast food on obesity is an empirical question. We run a put on the line that among 9th grade children, the presence of a fast-food restaurant within a tenth of a mile of a school is associated with an increase of about 1. 7 helping points in the fraction of students in a sept who are weighty relative to the presence at.\r\n25 miles. This effect amounts to a 5. 2 percent increase in the relative incidence of obesity. Since grade 9 is the initiative year of advanced school and the fitness tests off repose in the Spring, the period of fast-food exposure is slightly 30 weeks, implying an change magnitud e caloric inspiration of 30 to 100 calories per school-day. The effect is larger in models that embroil school fixed effects. Consistent with highly non†elongate transportation costs, we give away no discernable effect at . 25 miles and at . 5 miles. The effect is largest for Hispanic students and female students.\r\nAmong pregnant women, we come upon that a fast food restaurant within a half mile of a residence results in 0. 19 circumstances points high probability of gaining over 20kg. This amounts to a 2. 5 percent increase in the probability of gaining over 20 kilos. The effect is larger at . 1 miles, but in contrast to the results for 9th graders, it is still discernable at . 25 miles and at . 5 miles. The increase in weight implies an increased caloric intake of 1 to 4 3 calories per day in the pregnancy period. The effect varies across hurrys and educational takes.\r\nIt is largest for African American mothers and for mothers with a high school education or less. It is zero for mothers with a college degree or an associate’s degree. Overall, our defineings point that increases in the supply of fast food restaurants have a significant effect on obesity, at least in most groups. However, it is in pattern possible that our estimates reflect un metric shifts in the pauperism for fast food. Fast food drawstrings are believably to open new restaurants where they expect accept to be strong, and high demand for unhealthy food is almost certainly correlated with high risk of obesity.\r\nThe presence of unobserved determinants of obesity that may be correlated with increases in the number of fast food restaurants would lead us to overestimate the role of fast food restaurants. We can not enti verify restrain out this possibility. However, triple pieces of evidence lend some credibility to our interpretation. First, we start out that observable characteristics of the schools are not associated with changes in the availability of a fast food in the ready vicinity of a school.\r\nFurthermore, we take that within the geographical area under servant, fast food restaurants are uniformly distributed over space. Specifically, fast food restaurants are equally plausibly to be located within . 1, . 25, and . 5 miles of a school. We excessively find that after conditioning on mother fixed effects, the observable characteristics of mothers that predict high weight gain are negatively (not absolutely) related to the presence of a fast-food chain, suggesting that any bias in our estimates may be downward, not upward.\r\nWhile these findings do not necessarily imply that changes in the supply of fast food restaurants are orthogonal to unobserved determinants of obesity, they are at least reconciled with our identifying assumption. Second, art object we find that proximity to a fast food restaurant is associated with increases in obesity rates and weight gains, proximity to non fast food restaurants has no unmist akable effect on obesity rates or weight gains. This suggests that our estimates are not just capturing increases in the local demand for restaurant establishments.\r\nThird, we find that plot current proximity to a fast food restaurant affects current obesity rates, proximity to future fast food restaurants, controlling for current proximity, has no effect on current obesity rates and weight gains. Taken together, the weight of the 4 evidence is consistent with a causal effect of fast food restaurants on obesity rates among 9th graders and on weight gains among pregnant women. The results on the impact of fast-food on obesity are consistent with a model in which access to fast-foods increases obesity by take downing food prices or by tempting consumers with self-control problems.\r\n2 Differences in travel costs amid students and mothers could apologize the unlike effects of proximity. Ninth graders have higher travel costs in the sense that they are constrained to stay near th e school during the school day, and hence are more affected by fast-food restaurants that are very close to the school. For this group, proximity to fast-food has a quite sizeable effect on obesity. In contrast, for pregnant women, proximity to fast-food has a quantitatively small (albeit statistically significant) impact on weight gain.\r\nOur results suggest that a ban on fast-foods in the immediate proximity of schools could have a sizeable effect on obesity rates among affected students. However, a same attempt to reduce access to fast food in residential neighborhoods would be unpotential to have much effect on adult consumers. The rebrinyder of the paper is organized as follows. In Section 2 we review the existing literature. In Section 3 we describe our data sources. In Section 4, we present our econometric models and our empirical findings. Section 5 concludes. 2.\r\nBackground While the main need for foc utilize on school children and pregnant women is the availability of geographically detailed data on weight measures for a very large judge, they are important groups to study in their own right. Among school immemorial children 6-19 rates of overweight have soared from about 5% in the early 1970s to 16% in 1999-2002 (Hedley et al. 2004). These rates are of particular concern given that children who are overweight are more likely to be overweight as adults, and are progressively suffering from diseases associated with obesity while still in childhood (Krebs and Jacobson, 2003).\r\nAt the same time, the fraction of women gaining over 60 2 Consumers with self-control problems are not as tempted by fatty foods if they first have to incur the transportation cost of walking to a fast-food restaurant. Only when a fast-food is right near the school, the come-on of the fast-food looms large. For an overview of the role of self-control in economic applications, see DellaVigna (2009). A model of propels in drug addiction (Laibson, 2001) has sympathetic implications: a fast-food that is in immediate proximity from the school is more likely to trigger a cue that leads to over- breathing in.\r\n5 pounds during pregnancy doubled between 1989 and 2000 (Lin, forthcoming). exuberant weight gain during pregnancy is often associated with higher rates of hypertension, C-section, and large-for-gestational age infants, as good as with a higher incidence of later enate obesity (Gunderson and Abrams, 2000; Rooney and Schauberger, 2002; Thorsdottir et al. , 2002; Wanjiku and Raynor, 2004). 3 Moreover, Figure 1 luffs that the incidence of low APGAR scores (APGAR scores less than 8), an index finger of slimy fetal health, increases sharply with weight gain above about 20 kilograms.\r\nCritics of the fast food industry point to several features that may make fast food less healthy than other types of restaurant food (Spurlock, 2004; Schlosser, 2002). These include low monetary and time costs, large subdivisions, and high calorie compactnes s of signature menu items. Indeed, energy densities for individual food items are often so high that it would be difficult for individuals consuming them not to exceed their come recommended dietary intakes (Prentice and Jebb, 2003). Some consumers may be curiously vulnerable. In two randomized experimental trials involving 26 cogent and 28 lean adolescents, Ebbeling et al.\r\n(2004) compared caloric intakes on â€Å"unlimited fast food days” and â€Å"no fast food days”. They found that obese adolescents had higher caloric intakes on the fast food days, but not on the no fast food days. The largest fast food chains are to a fault characterized by aggressive securities industrying to children. ane experimental study of young children 3 to 5 offered them identical pairs of foods and beverages, the only difference being that some of the foods were in McDonald’s packaging. Children were significantly more likely to choose items perceived to be from McDonaldâ⠂¬â„¢s (Robinson et al.2007).\r\nChou, Grossman, and Rashad (forthcoming) handling data from the National Longitudinal Surveys (NLS) 1979 and 1997 cohorts to examine the effect of exposure to fast food advertising on overweight among children and adolescents. In ordinary least squares (OLS) models, they find significant effects in most specializedations. 4 3 According to the Centers for Disease Control, obesity and uppity weight gain are independently associated with poor pregnancy outcomes.\r\nRecommended weight gain is discredit for obese women than in others. (http://www. cdc.gov/pednss/how_to/read_a_data_table/prevalence_tables/ nascency_outcome. htm) 4 They also estimate submissive variables (IV) models exploitation the price of advertising as an instrument. However, while they find a significant â€Å"first period”, they do not report the IV estimates beca custom tests 6 Still, a late review of the considerable epidemiological literature about the relationshi p between fast food and obesity (Rosenheck, 2008) concluded that â€Å"Findings from observational studies as yet are unable to demonstrate a causal link between fast food ingestion and weight gain or obesity”.\r\nMost epidemiological studies have longitudinal designs in which large groups of participants are tracked over a period of time and changes in their body tidy sum index (BMI) are correlated with baseline measures of fast food consumption. These studies typically find a positive link between obesity and fast food consumption. However, existing observational studies cannot rule out capableness confounders such as lack of physical activity, consumption of sugary beverages, and so on. food. 5 There is also a rapidly growing economics literature on obesity, reviewed in Philipson and Posner (2008).\r\nEconomic studies place alter amounts of emphasis on increased caloric consumption as a primary determinant of obesity (a trend that is consistent with the increased avail ability of fast food). victimization data from the NLSY, Lakdawalla and Philipson (2002) conclude that about 40% of the increase in obesity from 1976 to 1994 is attributable to lower food prices (and increased consumption) while the remainder is collectable to decreased physical activity in market and home production. Bleich et al. (2007) examine data from several certain countries and conclude that increased caloric intake is the main contributor to obesity.\r\nCutler et al. (2003) examine food diaries as well as time use data from the give-up the ghost few decades and conclude that rising obesity is colligate to increased caloric intake and not to reduced energy expenditure. 6 7 Moreover, all of these studies rely on self-reported consumption of fast suggest that advertising exposure is not endogenous. They also estimate, but do not report individual fixed effects models, because these models have much larger standard errors than the ones reported. 5 A typical question is of the form â€Å"How often do you eat food from a place like McDonald’s, Kentucky Fried Chicken, Pizza Hut, Burger King or some other fast food restaurant?\r\n” 6 They suggest that the increased caloric intake is from greater frequency of snacking, and not from increased portion sizes at restaurants or fattening meals at fast food restaurants. They pull ahead suggest that technological change has lowered the time cost of food dressing which in turn has lead to more shop at consumption of food. Finally, they speculate that throng with self control problems are over-consuming in response to the fall in the time cost of food preparation. Cawley (1999) discusses a similar behavioral theory of obesity as a consequence of addiction.\r\n7 Courtemanche and Carden examine the impact on obesity of Wal-Mart and store golf-club retailers such as Sam’s club, Costco and BJ’s wholesale club which compete on price. They link store location data to individual data fro m the Behavioral adventure component part Surveillance System (BRFSS. ) They find that non-grocery sell Wal-Mart stores reduce weight while non-grocery selling stores and warehouse clubs either reduce weight or have no effect. Their explanation is that reduced prices for everyday purchases cover real 7 A series of recent papers explicitly focus on fast food restaurants as potential contributors to obesity.\r\nChou et al. (2004) estimate models combining state-level price data with individual demographic and weight data from the Behavioral Risk Factor Surveillance surveys and find a positive linkup between obesity and the per capita number of restaurants (fast food and others) in the state. Rashad, Grossman, and Chou (2005) present similar findings using data from the National Health and Nutrition Examination Surveys. Anderson and execute (2005) investigate the effect of school food policies on the BMI of adolescent students using data from the NLSY97.\r\nThey assume that athl etics in financial pressure on schools across counties provides exogenous variation in availability of altercate food in the schools. They find that a 10 percentage point increase in the probability of access to junk food at school can lead to about 1 percent increase in students’ BMI. Anderson, Butcher and Schanzenbach (2007) examine the snapshot of children’s BMI with respect to mother’s BMI and find that it has increased over time, suggesting an increased role for environmental factors in child obesity.\r\nAnderson, Butcher, and Levine (2003) find that parental calling is related to childhood obesity, and speculate that employed mothers office spend more on fast food. Cawley and Liu (2007) use time use data and find that employed women spend less time cooking and are more likely to purchase prepared foods. The paper that is side by side(predicate) to ours is a recent study by Anderson and Matsa (2009) that focuses on the link between eating out and obes ity using the presence of interstate highways in rural areas as an instrument for restaurant density.\r\nInterstate highways increase restaurant density for communities adjacent to highways, cut the travel costs of eating out for spate in these communities. They find no evidence of a causal link between restaurants and obesity. Using data from the USDA, they argue that the lack of an effect is due to the presence of selection bias in restaurant patrons â€people who eat out also consume more calories when they eat at homeâ€and the fact that large portions at restaurants are offset by lower caloric intake at other quantify of the day.\r\nOur paper differs from Anderson and Matsa (2009) in 4 important dimensions, and these quatern differences are likely to explain the difference in our findings. incomes, enabling homes to substitute away from cheap unhealthy foods to more expensive but healthier alternatives. 8 (i) First, our data allow us to light upon between fast food r estaurants and other restaurants. We can therefore estimate separately the impact of fast-foods and of other restaurants on obesity. In contrast, Anderson and Matsa do not have data on fast food restaurants and therefore focus on the effect of any restaurant on obesity.\r\nThis difference turns out to be crucial, because when we estimate the effect of any restaurant on obesity using our data we also find no discernible effect on obesity. (ii) Second, we have a very large sample that allows us to identify even small effects, such as mean increases of 50 grams in the weight gain of mothers during pregnancy. Our estimates of weight gain for mothers are within the confidence interval of Anderson and Matsa’s two stage least squares estimates. Put other than, based on their sample size, our statistically significant estimates would have been considered statistically insignificant.\r\n(iii) Third, our data give us the exact location of each restaurant, school and mother. The spatia l fecundity of our data allows us to examine the effect of fast food restaurants on obesity at a very detailed geographical level. For example, we can distinguish the effect at . 1 miles from the effect at . 25 miles. As it turns out, this feature is quite important, because the effects that we find are geographically extremely localized. For example, we find that fast food restaurant have an effect on 9th graders only for distances of . 1 miles or less. By contrast, Anderson and Matsa use a city as the level of geographical analysis.\r\nIt is not surprising that at this level of aggregation the estimated effect is zero. (iv) Fourth, Anderson and Matsa’s identification strategy differs from ours, since we do not use an instrument for fast-food availability and focus instead on changes in the availability of fast-foods at very close distances. The populations under consideration are also different, and may react differently to proximity to a fast food restaurant. Anderson an d Matsa focus on predominantly white rural communities, while we focus on primarily urban 9th graders and urban mothers.\r\nWe document that the effects vary considerable depending on race, with blacks and Hispanics having the largest effect. Indeed, when Dunn (2008) uses an instrumental variables approach similar to the one use Anderson and Matsa based on proximity to freeways, he finds no effect for rural areas and for 9 whites in suburban areas, but strong effect for blacks and Hispanics. As we sight below, we also find stronger effects for minorities. Taken together, these four differences lead us to conclude that the evidence in Anderson and Matsa is consistent with our evidence.\r\n8 In summary, there is strong evidence of correlations between fast food consumption and obesity. It has been more difficult to demonstrate a causal role for fast food. In this paper we work stoppage new data in an attempt to test the causal connection between fast food and obesity. 3. Data Source s and Summary Statistics Data for this project comes from three sources. (a) School Data. Data on children comes from the atomic number 20 public schools for the years 1999 and 2001 to 2007. The observations for 9th graders, which we focus on in this paper, represent 3. 06 million student-year observations.\r\nIn the spring, California 9th graders are given a fitness assessment, the FITNESSGRAM®. Data is reported at the class level in the form of the percentage of students who are obese, and who have acceptable levels of abdominal strength, oxidative capacity, flexibility, trunk strength, and upper body strength. Obesity is measured using actual body fat measures, which are considerably more accurate than the usual BMI measure (Cawley and Burkhauser, 2006). Data is also reported for sub-groups within the school (e. g. by race and gender) provided the cells have at least 10 students.\r\nSince grade 9 is the first year of high school and the fitness tests take place in the Sprin g, this impact corresponds to approximately 30 weeks of fast-food exposure. 9 This administrative data set is merged to information about schools (including the percent black, white, Hispanic, and Asian, percent immigrant, pupil/ instructor ratios, fraction eligible for free lunch etcetera ) from the National Center for Education Statistic’s harsh Core of Data, as well as to the blow up test scores for the 9th grade. The location of the school was also geocoded using ArcView. Finally, we merged in information.\r\n8 9 See also Brennan and carpenter (2009). In very few cases, a high school is in the same location as a midriff school, in which case the estimates reflect a longer-term impact of fast-food. 10 about the nearest Census cylinder block group of the school from the 2000 Census including the median earnings, percent high-school degree, percent unemployed, and percent urban. (b) Mothers Data. Data on mothers come from Vital Statistics Natality data from Michigan, Ne w Jersey, and Texas. These data are from birth certificates, and cover all births in these states from 1989 to 2003 (from 1990 in Michigan).\r\nFor these three states, we were able to gain access to confidential data including mothers names, birth dates, and addresses, which enabled us twain to construct a panel data set linking births to the same mother over time, and to geocode her location (again using ArcView). The Natality data are very rich, and include information about the mother’s age, education, race and ethnicity; whether she smoked during pregnancy; the child’s gender, birth order, and gestation; whether it was a multiple birth; and maternal weight gain. We restrict the sample to singleton births and to mothers with at least two births in the sample, for a total of over 3.\r\n5 million births. (c) Restaurant Data. Restaurant data with geo-coding information come from the National Establishment clip Series Database (Dun and Bradstreet). These data are used by all major banks, lending institutions, insurance and finance companies as the primary system for creditworthiness assessment of firms. As such, it is arguably more precise and comprehensive than chicken pages and business directories. 10 We obtained a panel of about all firms in Standard Industrial smorgasbord 58 from 1990 to 2006, with names and addresses.\r\nUsing this data, we constructed several different measures of â€Å"fast food” and â€Å"other restaurants,” as discussed further in Appendix 1. In this paper, the benchmark exposition of fast-food restaurants includes only the top-10 fast-food chains, namely, Mc Donalds, Subway, Burger King, Taco Bell, Pizza Hut, small-scale Caesars, KFC, Wendy’s, Dominos Pizza, and Jack In The Box. We also show estimates using a broader definition that includes both chain restaurants and independent burger and pizza restaurants. Finally, we also measure the supply of non-fast food restaurants.\r\nThe definition o f â€Å"other restaurants” changes with the definition of fast food. Appendix control board 1 lists the top 10 fast food chains as well as examples of restaurants that we did not classify as fast food. The yellow pages are not intended to be a comprehensive listing of businesses †they are a paid advertisement. Companies that do not pay are not listed. 10 11 Matching. Matching was performed using information on latitude and longitude of restaurant location. Specifically, we control the schools and mother’s residence to the closest restaurants using ArcView software.\r\nFor the school data, we match the results on testing for the spring of year t with restaurant availability in year t-1. For the mother data, we match the data on weight gain during pregnancy with restaurant availability in the year that overlaps the most with the pregnancy. Summary Statistics. Using the data on restaurant, school, and mother’s locations, we constructed forefingers for wheth er there are fast food or other restaurants within . 1, . 25, and . 5 miles of either the school or the mother’s residence. Table 1a shows summary characteristics of the schools data set by distance to a fast food restaurant.\r\nHere, as in most of the paper, we use the narrow definition of fast-food, including the top-10 fast-food chains. Relatively few schools are within . 1 miles of a fast food restaurant, and the characteristics of these schools are somewhat different than those of the average California school. Only 7% of schools have a fast food restaurant within . 1 miles, while 65% of all schools have a fast food restaurant within 1/2 of a mile. 11 Schools within . 1 miles of a fast food restaurant have more Hispanic students, a slightly higher fraction of students eligible for free lunch, and lower test scores.\r\nThey are also located in poorer and more urban areas. The last row indicates that schools near a fast food restaurant have a higher incidence of obese stud ents than the average California school. Table 1b shows a similar summary of the mother data. Again, mothers who fuck near fast food restaurants have different characteristics than the average mother. They are younger, less educated, more likely to be black or Hispanic, and less likely to be married. 4. Empirical Analysis We begin in Section 4. 1 by describing our econometric models and our identifying assumptions. In Section 4.\r\n2 we show the correlation between restaurant location and student characteristics for the school sample, and the correlation between The average school in our sample had 4 fast foods within 1 mile and 24 other restaurants within the same radius. 11 12 restaurant location and mother characteristics for the mother sample. Our empirical estimates for students and mothers are in Section 4. 3 and 4. 4, respectively. 13 4. 1 Econometric Specifications Our empirical specification for schools is (1) Yst = ? F1st + ? F25st + ? F50st + ? ’ N1st + ? ’ N25st + ? ’ N50st + ? Xst + ?\r\nZst + ds + est where Yst is the fraction of students in school s in a given grade who are obese in year t; F1st is an indicant equal to 1 if there is a fast food restaurant within . 1 mile from the school in year t; F25st is an indicator equal to 1 if there is a fast food restaurant within . 25 miles from the school in year t; F50st is an indicator equal to 1 if there is a fast food restaurant within . 5 mile from the school in year t; N1st, N25st and N50st are similar indicators for the presence of non-fast food restaurants within . 1, . 25 and . 5 miles from the school; ds is a fixed effect for the school.\r\nThe vectors Xst and Zst include school and neighborhood time-varying characteristics that can potentially affect obesity rates. Specifically, Xst is a vector of school-grade specific characteristics including fraction blacks, fraction native Americans, fraction Hispanic, fraction immigrants, fraction female, fraction eligible for free lunch, whether the school is qualified for Title I funding, pupil/teacher ratio, and 9th grade tests scores, as well as school-district characteristics such as fraction immigrants, fraction of non-English speaking students (LEP/ELL), theatrical role of IEP students.\r\nZst is a vector of characteristics of the Census block closest to the school including median income, median earnings, average household size, median rent, median housing value, percent white, percent black, percent Asian, percent.\r\n'