Obesity and Fast Food Essay Example for Free

Obesity and Fast Food Essay January 2009 Abstract. We investigate the health consequences of changes in the supply of fast food using the exact geographical location of fast food restaurants. Specifically, we ask how the supply of fast food affects the obesity rates of 3 million school children and the weight gain of over 1 million pregnant women. We find that among 9th grade children, a fast food restaurant within a tenth of a mile of a school is associated with at least a 5. 2 percent increase in obesity rates. There is no discernable effect at . 25 miles and at . 5 miles. Among pregnant women, models with mother fixed effects indicate that a fast food restaurant within a half mile of her residence results in a 2. 5 percent increase in the probability of gaining over 20 kilos. The effect is larger, but less precisely estimated at . 1 miles. In contrast, the presence of non-fast food restaurants is uncorrelated with obesity and weight gain. Moreover, proximity to future fast food restaurants is uncorrelated with current obesity and weight gain, conditional on current proximity to fast food. The implied effects of fast-food on caloric intake are at least one order of magnitude smaller for mothers, which suggests that they are less constrained by travel costs than school children. Our results imply that policies restricting access to fast food near schools could have significant effects on obesity among school children, but similar policies restricting the availability of fast food in residential areas are unlikely to have large effects on adults. The authors 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 New York and Chicago, The New School, the Tinbergen Institute, the Rady School at UCSD, and Williams College for helpful comments. We thank Cecilia Machado, Emilia Simeonova, Johannes Schmeider, and Joshua Goodman for excellent research assistance. We 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 Pennsylvania 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 data. The authors are solely responsible for the use that has been made of the data and for the contents of this article. 1 1. Introduction The prevalence of obesity and obesity related diseases has increased rapidly in the U. S. since the mid 1970s. At the same time, the number of fast food restaurants more than doubled over the same time period, while the number of other restaurants grew at a much slower pace according to the Census of Retail Trade (Chou, Grossman, and Saffer, 2004). In the public debate over obesity it is often assumed that the widespread availability of fast food restaurants is an important determinant of the dramatic increases in 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 (Mcbride, 2008; Mair et al. 2005). 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 causal effect of increases in the supply of fast food restaurants on obesity rates. Specifically, using a detailed dataset on the exact geographical location restaurant establishments, we ask how proximity to fast food affects the obesity rates of 3 million school children and the weight gain of over 1 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 cross-sectional as well fixed effects models that control for characteristics of schools and neighborhoods. 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. 1 We focus on women who have at least two children so that we can follow a given woman across two pregnancies and estimate models that include mother fixed effects. The design employed in this study allows for a more precise identification of the effect of fast-food on obesity compared to the previous literature (summarized in Section 2). First, we observe information on weight for millions of individuals compared to at most tens of thousand in the standard data sets with weight information such as the NHANES and the BRFSS. This substantially increases the power 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. One advantage of focusing on a longitudinal measure of weight gain instead of a measure of weight in levels is that only the recent exposure to fast-food should matter. 1 2 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 two groups. Third, we have a more precise idea of the timing of exposure than many previous studies: The 9th graders are exposed to fast food 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. While it is clear that fast food is generally unhealthy, it is not obvious a priori that changes in the availability of fast food should be expected to have an impact on health. On the one hand, it is possible that proximity to a fast food restaurant simply leads local consumers to substitute away from unhealthy food prepared at home or consumed in existing restaurants, without significant changes in the overall amount of unhealthy food consumed. On the other hand, proximity to a fast food restaurant could lower the monetary and non-monetary costs of accessing unhealthy food. In addition, proximity to fast food may increase consumption of unhealthy food even in the absence of any decrease in cost if individuals have self-control problems. Ultimately, the effect of changes in the supply of fast food on obesity is an empirical question. We find 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 percentage points in the fraction of students in a class who are obese relative to the presence at. 25 miles. This effect amounts to a 5. 2 percent increase in the incidence of obesity. Since grade 9 is the first year of high school and the fitness tests take place in the Spring, the period of fast-food exposure is approximately 30 weeks, implying an increased caloric intake of 30 to 100 calories per school-day. The effect is larger in models that include school fixed effects. Consistent with highly non–linear transportation costs, we find no discernable effect at . 25 miles and at . 5 miles. The effect is largest for Hispanic students and female students. Among pregnant women, we find that a fast food restaurant within a half mile of a residence results in 0. 19 percentage points higher 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 races and educational levels. It 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 findings suggest that increases in the supply of fast food restaurants have a significant effect on obesity, at least in some groups. However, it is in principle possible that our estimates reflect unmeasured shifts in the demand for fast food. Fast food chains are likely to open new restaurants where they expect demand to be strong, and higher demand for unhealthy food is almost certainly correlated with higher risk of obesity. The 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 entirely rule out this possibility. However, three pieces of evidence lend some credibility to our interpretation. First, we find that observable characteristics of the schools are not associated with changes in the availability of a fast food in the immediate vicinity of a school. Furthermore, we show that within the geographical area under consideration, fast food restaurants are uniformly distributed over space. Specifically, fast food restaurants are equally likely to be located within . 1, . 25, and . 5 miles of a school. We also find that after conditioning on mother fixed effects, the observable characteristics of mothers that predict high weight gain are negatively (not positively) related to the presence of a fast-food chain, suggesting that any bias in our estimates may be downward, not upward. While 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 consistent with our identifying assumption. Second, while 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 discernible effect on obesity rates or weight gains. This suggests that our estimates are not just capturing increases in the local demand for restaurant establishments. Third, we find that while 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 lowering food prices or by tempting consumers with self-control problems. 2 Differences in travel costs between students and mothers could explain the different effects of proximity. Ninth graders have higher travel costs in the sense that they are constrained to stay near the 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. Our 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 similar attempt to reduce access to fast food in residential neighborhoods would be unlikely to have much effect on adult consumers. The remainder 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. Background While the main motivation for focusing on school children and pregnant women is the availability of geographically detailed data on weight measures for a very large sample, they are important groups to study in their own right. Among school aged 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 increasingly suffering from diseases associated with obesity while still in childhood (Krebs and Jacobson, 2003). At 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 temptation of the fast-food looms large. For an overview of the role of self-control in economic applications, see DellaVigna (2009). A model of cues in consumption (Laibson, 2001) has similar implications: a fast-food that is in immediate proximity from the school is more likely to trigger a cue that leads to over-consumption. 5 pounds during pregnancy doubled between 1989 and 2000 (Lin, forthcoming). Excessive weight gain during pregnancy is often associated with higher rates of hypertension, C-section, and large-for-gestational age infants, as well as with a higher incidence of later maternal obesity (Gunderson and Abrams, 2000; Rooney and Schauberger, 2002; Thorsdottir et al. , 2002; Wanjiku and Raynor, 2004). 3 Moreover, Figure 1 shows that the incidence of low APGAR scores (APGAR scores less than 8), an indicator of poor fetal health, increases sharply with weight gain above about 20 kilograms. Critics 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 portions, and high calorie density 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 average recommended dietary intakes (Prentice and Jebb, 2003). Some consumers may be particularly vulnerable. In two randomized experimental trials involving 26 obese and 28 lean adolescents, Ebbeling et al. (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 also characterized by aggressive marketing to children. One 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 McDonald’s (Robinson et al.2007). Chou, Grossman, and Rashad (forthcoming) use 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 specifications. 4 3 According to the Centers for Disease Control, obesity and excessive weight gain are independently associated with poor pregnancy outcomes. Recommended weight gain is lower for obese women than in others. (http://www. cdc.gov/pednss/how_to/read_a_data_table/prevalence_tables/birth_outcome. htm) 4 They also estimate instrumental variables (IV) models using the price of advertising as an instrument. However, while they find a significant â€Å"first stage†, they do not report the IV estimates because tests 6 Still, a recent review of the considerable epidemiological literature about the relationship 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 consumption and weight gain or obesity†. Most epidemiological studies have longitudinal designs in which large groups of participants are tracked over a period of time and changes in their body mass 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 potential 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). Economic studies place varying amounts of emphasis on increased caloric consumption as a primary determinant of obesity (a trend that is consistent with the increased availability of fast food). Using 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 due to reduced physical activity in market and home production. Bleich et al. (2007) examine data from several developed countries and conclude that increased caloric intake is the main contributor to obesity. Cutler et al. (2003) examine food diaries as well as time use data from the last few decades and conclude that rising obesity is linked 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? † 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 further suggest that technological change has lowered the time cost of food preparation which in turn has lead to more frequent consumption of food. Finally, they speculate that people 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. 7 Courtemanche and Carden examine the impact on obesity of Wal-Mart and warehouse 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 from the Behavioral Risk Factor Surveillance System (BRFSS. ) They find that non-grocery selling 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 expand real 7 A series of recent papers explicitly focus on fast food restaurants as potential contributors to obesity. Chou 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 association 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 Butcher (2005) investigate the effect of school food policies on the BMI of adolescent students using data from the NLSY97. They assume that variation in financial pressure on schools across counties provides exogenous variation in availability of junk 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 elasticity 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. Anderson, Butcher, and Levine (2003) find that maternal employment is related to childhood obesity, and speculate that employed mothers might 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 closest to ours is a recent study by Anderson and Matsa (2009) that focuses on the link between eating out and obesity using the presence of Interstate highways in rural areas as an instrument for restaurant density. Interstate highways increase restaurant density for communities adjacent to highways, reducing the travel costs of eating out for people 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 –people who eat out also consume more calories when they eat at homeand the fact that large portions at restaurants are offset by lower caloric intake at other times of the day. Our paper differs from Anderson and Matsa (2009) in four important dimensions, and these four differences are likely to explain the difference in our findings. incomes, enabling households to substitute away from cheap unhealthy foods to more expensive but healthier alternatives. 8 (i) First, our data allow us to distinguish between fast food restaurants 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. This 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 differently, based on their sample size, our statistically significant estimates would have been considered statistically insignificant. (iii) Third, our data give us the exact location of each restaurant, school and mother. The spatial richness 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. It 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 and Matsa focus on predominantly white rural communities, while we focus on primarily urban 9th graders and urban mothers. We 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 used 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 show 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. 8 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 tap new data in an attempt to test the causal connection between fast food and obesity. 3. Data Sources and Summary Statistics Data for this project comes from three sources. (a) School Data. Data on children comes from the California 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. In 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, aerobic 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. Since grade 9 is the first year of high school and the fitness tests take place in the Spring, 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/teacher ratios, fraction eligible for free lunch etc. ) from the National Center for Education Statistic’s Common Core of Data, as well as to the Start test scores for the 9th grade. The location of the school was also geocoded using ArcView. Finally, we merged in information. 8 9 See also Brennan and carpenter (2009). In very few cases, a high school is in the same location as a middle school, in which case the estimates reflect a longer-term impact of fast-food. 10 about the nearest Census 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, New Jersey, and Texas. These data are from birth certificates, and cover all births in these states from 1989 to 2003 (from 1990 in Michigan). For these three states, we were able to gain access to confidential data including mothers names, birth dates, and addresses, which enabled us both 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. 5 million births. (c) Restaurant Data. Restaurant data with geo-coding information come from the National Establishment Time 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 yellow pages and business directories. 10 We obtained a panel of virtually all firms in Standard Industrial Classification 58 from 1990 to 2006, with names and addresses. Using 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 definition of fast-food restaurants includes only the top-10 fast-food chains, namely, Mc Donalds, Subway, Burger King, Taco Bell, Pizza Hut, Little 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. The definition of â€Å"other restaurants† changes with the definition of fast food. Appendix Table 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 match the schools and mother’s residence to the closest restaurants using ArcView software. For 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 indicators for whether 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. Here, 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. They 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 students than the average California school. Table 1b shows a similar summary of the mother data. Again, mothers who live 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. 2 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 + ? Zst + 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 indicator 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. The 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), share of IEP students. Zst 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.