Elsevier

Economics & Human Biology

Volume 12, January 2014, Pages 110-119
Economics & Human Biology

The effect of fast-food restaurants on childhood obesity: A school level analysis

https://doi.org/10.1016/j.ehb.2013.05.001Get rights and content

Highlights

  • We model the effects of proximity to fast-food restaurants on school obesity rates.

  • We use an IV model identifying the effects of fast-food restaurant on school weight.

  • Results show fast food restaurants within a mile from the school affect obesity.

Abstract

We analyze, using an instrumental variable approach, the effect of the number of fast-food restaurants on school level obesity rates in Arkansas. Using distance to the nearest major highway as an instrument, our results suggest that exposure to fast-food restaurants can impact weight outcomes. Specifically, we find that the number of fast-food restaurants within a mile from the school can significantly affect school level obesity rates.

Introduction

Childhood obesity is a major public health issue and is presently receiving a great deal of attention due to its broader economic consequences and long term effects on children's overall health, academic accomplishments, quality of life and productivity as they become adults (Currie, 2009). Fortunately, the most recent statistics indicate that growth in the proportion of children classified as overweight or obese has finally leveled off (Ogden et al., 2010). However, rates of overweight and obese children remain high. Nearly 35% of children and adolescents aged 6–19 are overweight and just under 19% are obese (Ogden et al., 2010). With regard to health, the consequences of overweight/obesity among children include increased risks for a variety of conditions such as hypertension, coronary heart disease, type 2 diabetes, respiratory problems and orthopedic abnormalities (Must and Strauss, 1999, Ebbeling et al., 2002).

One of the sectors of the food industry that is being blamed for the prevalence of childhood obesity is the food-away-from-home (FAFH) sector, particularly fast-food. From the late 1970s up to the mid 1990s, the proportion of meals eaten away from home increased significantly from 16% to 29% (Lin et al., 1999, Cawley, 2006). Chou et al. (2004) attribute the rapid expansion of the fast-food industry to major structural changes. These include technological developments in storage and preparation that have enabled food companies to mass produce ready-to-cook meals (Philipson and Posner, 2003, Lakdawalla and Philipson, 2009, Cutler et al., 2003) and developments in the US labor market that led to increased labor force participation of women (Anderson et al., 2003b), which consequently reduced the time allocated for food preparation and child care within the home (Anderson and Butcher, 2006). Indeed, these changes have occurred in tandem (Cawley and Liu, 2012). The availability of fast-foods and other market replacements for household-produced meals has facilitated the entry of women into the labor force. At the same time, improvements in labor market opportunities have increased the demand for and spurred the development of market-provided alternatives to household-produced meals.

Considering the public policy issues and implications surrounding the need to reduce child obesity, our main goal in this paper is to determine whether the availability of fast-food restaurants is a significant driver of obesity rates in children. Fast-food items typify the dietary characteristics that may increase the likelihood of obesity (Ebbeling et al., 2002). Fast-foods tend to have high glycemic indexes, are often high in fats, and are sold in large portion sizes. Moreover, individual food items are generally bundled and sold as energy dense “value” meals. Finally, fast-foods are heavily promoted on television and the volume of marketing messages reaching children has been statistically linked to the problem of overweight children and adolescents (Chou et al., 2008, Andreyeva et al., 2011).

A number of studies have attempted to estimate the causal impact of the number of fast-food restaurants on obesity levels. Currie et al. (2010) and Davis and Carpenter (2009) assessed the impact of fast-food proximity on obesity rates among California schoolchildren. These two studies employed indicator variables measuring the presence or absence of fast-food restaurants within a given distance of the school. However, the two studies differed in terms of level of aggregation and age of children in the samples. The main response variable used in the Currie et al. (2010) study was the proportion of obese 9th graders, a school-level aggregate. They found that the presence of fast-food restaurants within a tenth of a mile increased obesity rates by at least 0.81% points. The dataset analyzed by Davis and Carpenter (2009) included 7th through 12th graders and contained BMIs for individual students. Their findings indicate that students had 1.06 times the odds of being overweight and 1.07 times the odds of being obese when a fast-food restaurant was located within a half mile of their school. Depending on model specification, Davis and Carpenter's (2009) estimates suggest that the presence of a fast-food establishment close to the school caused a student's BMI to increase by 0.08–0.14 BMI points. Currie et al.’s (2010) fast-food measure included the top ten fast-food chains: McDonalds, Subway, Burger King, Taco Bell, Pizza Hut, Little Caesars, KFC, Wendy's, Domino's Pizza and Jack in the box. They also considered a broader measure that included other chain restaurants along with independent pizza and burger restaurants but excluded donut, coffee shops, and ice cream parlors in their analysis. Davis and Carpenter (2009) included the top limited service restaurants by Technomic (a food consulting firm), but they did not specify the names of these limited service restaurants. Davis and Carpenter (2009) also performed a separate analysis including restaurants not found in the Technomic list, which included smaller chain and non-chain and limited service restaurants.

Based on earlier findings by Currie et al. (2010) and Davis and Carpenter (2009), transportation costs are probably an important determinant of students’ ability to access fast-food and so we would expect fast-food restaurants located close to the school to have a greater impact on obesity outcomes than those that are more distant. Another possible mechanism is the effect of peers. A number of studies have shown that there is a significant associative link between the student's own weight and the BMI of his peers (Halliday and Kwak, 2009). Moreover, adolescent students who are obese are likely to engage in unhealthy weight control behaviors and eating patterns and have limited social network ties (Fiese et al., 2012). The presence of fast-food dining opportunities near schools may facilitate the development and reinforcement of peer-influences that encourage consumption of fast-foods.

Other recent studies have assessed the relationship between fast-food availability and adult – as opposed to child or adolescent – obesity levels (Dunn, 2010, Anderson and Matsa, 2011, Chen et al., 2009). In each of these studies the authors acknowledge that fast-food availability is endogenous. In other words, the spatial distribution of fast-food establishments and consumer residences is determined, in part, by preferences and behaviors that would otherwise affect weight outcomes. Consequently, each study makes use of instrumental variable (IV) models to account for this endogeneity problem. Chen et al. (2009) characterized food environments in Marion County, Indiana. They examined the relationship between fast-food restaurant and grocery store access on individual adult BMI scores. They used the amount of zoned non-residential land as an instrumental variable and incorporated spatial dependence and heteroscedasticity across observations given the spatial characteristics of the data. Their results show that BMI scores were directly related to proximity of fast-food restaurants and were inversely related to availability of supermarkets. However, these effects, while statistically significant, were small in magnitude.

Both Dunn (2010) and Anderson and Matsa (2011) analyzed data from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System. While these two studies differed in spatial scales and sample population, the instrumental variable used in both was based on the proximity of interstate highways to consumer residences. The argument here is that fast-food establishments tend to cluster near highway off-ramps and so the presence of highways substantially augments the availability of fast-food in a particular locale. Dunn's (2010) results show statistically significant responses in BMI to fast-food proximity among female and minority populations within counties of medium population density. BMI scores among other subpopulations were not statistically linked to fast-food availability. Anderson and Matsa (2011) find little causal evidence linking fast-food restaurants to obesity levels among adults. Unlike Dunn's study, their focus was centered primarily on Caucasian residents in rural areas. Their analysis suggests that calories from fast-food are offset by reduced consumption of food from other sources. Also, they argue that obese individuals eat more nutritionally deficient foods in general, regardless of whether it is prepared in the home or sourced from fast-food restaurants. In addition, Anderson and Matsa (2011) provided separate analysis on full service restaurants (Pizza Hut, Applebee's Chili's and Olive Garden) and limited service restaurants (McDonalds, Burger King, Subway, Wendy's Taco Bell and KFC). In contrast, Dunn's (2010) fast-food definition focused only on limited service restaurants.

Two general conclusions from the above studies are as follows. First, the impact of fast-food availability on weight outcomes appears to be context specific. In particular, clustering of fast-food restaurants near schools has detrimental effects on weight outcomes among schoolchildren whereas the evidence directly linking fast-food availability to weight outcomes in adults is substantially weaker, both statistically and in terms of overall economic importance.1 Second, it is important to account for potential endogeneity when attempting to estimate the relationship between fast-food and obesity outcomes. Failure to address this issue will generally bias empirical findings toward an understatement of the importance of fast-food to weight gain (Dunn, 2010).

In this paper, we look at the impact of the number of fast-food restaurants on rates of obese children in Arkansas public schools.2 Arkansas is an interesting case to study since it is one of the poorest states and is also one of the least healthy. More importantly for our study, Arkansas's childhood obesity rate has doubled in the last couple of decades and is one of the highest in the country (Arkansas Center for Health Improvement, 2009). Our study is similar to Currie et al. (2010) in that we use school-level data to examine the impact of fast-food availability on the proportion of students that are classified as obese. Also, we model proximity to fast-food restaurants by counting the number of restaurants within a mile from schools because fast-food restaurants are likely to be located within this distance (Currie et al., 2010, Davis and Carpenter, 2009). However, our study differs from Currie et al. (2010) in many respects. First, our sample covers a broader range of student ages since it includes measurements on children from kindergarten through 10th grade. Second, we measure the actual count of restaurants within a given distance. Currie et al. (2010) model the changes in the supply of fast-food using binary variables to indicate the presence of a fast-food establishment within a given distance of the school. Third, we use an IV regression approach to identify the effects of fast-food proximity on school-level weight outcomes.

We identify our model with the use of a highway proximity measure (i.e., distance between school and nearest major highway) because fast-food restaurants often choose locations to take advantage of business from highway travelers. In this case we included US highways in our nearest highway definition as these provide further additional sources of exogenous variation (Dunn et al., 2012). Furthermore, earlier studies have found the highway proximity measure to be an adequate instrument in identifying the effects of fast-food availability on obesity. We extensively discuss the validity of our instrument in a separate section of the paper.

Section snippets

Model specification

In modeling the effect of fast-food restaurants on school-level obesity rates, the assumption that the error term is uncorrelated with one or more columns of the regressor matrix is likely to be violated due to omitted variables, measurement error, and/or reverse causality (Baum, 2006, Murray, 2010). In this context we argue that decisions regarding child health outcomes are made by the parents. Specifically, children's food choices and preferences are largely dependent on parental decisions (

Data description

The proportion of obese students in Arkansas schools was obtained from the Arkansas Center for Health Improvement. The obesity rates are based on BMI measurements that were taken during the 2008–09 school year on children in even numbered grades: kindergarten, 2nd grade, 4th grade, 6th grade, 8th grade and 10th grade. One advantage of the obesity rates used here is that they are based on actual weight and height measurements and were assessed by trained personnel within the school. However,

Instrument validity

As mentioned above, the instrumental variable used to identify our model is the distance to the nearest US highway or interstate highway. Measures of proximity to highways have been used in earlier studies to identify the relationship between fast-food establishments and adult obesity outcomes (Dunn, 2010, Anderson and Matsa, 2011). The logic here is that fast-food establishments tend to cluster near highways in order to capture sales from traveling customers and so the presence of highways

Results

Table 4 presents the first stage estimation results. As expected, restaurant counts decrease when the distance to the nearest major highway increases. We find that coefficient of the nearest major highways instrument is statistically significant at the 1% level and the first stage F-statistic of excluded instruments is 14.10 which is higher than 10. This implies that the weak instrument critique may not apply to our sample.

Table 5 provides results by estimation procedure. From the results, the

Conclusion

The principal aim of our paper is to ascertain the impact of the number of fast-food restaurant on school-level obesity rates. While our study is similar to Currie et al. (2010) who looked at the proportion of obese 9th graders, we cover a broader range of student ages – from kindergarten through 10th grade. Moreover, in contrast to Currie et al. (2010), we measure the actual count of restaurants within a mile from the school. Our results suggest that fast-food restaurant counts within one mile

References (33)

  • K. Morland et al.

    Neighborhood characteristics associated with the location of food stores and food service places

    American Journal of Preventive Medicine

    (2002)
  • L. Powell et al.

    Food store availability and neighborhood characteristics in the United States

    Preventive Medicine

    (2007)
  • L.M. Powell et al.

    Food prices, access to food outlets and child weight

    Economics & Human Biology

    (2009)
  • P. Anderson et al.

    Childhood obesity: trends and potential causes

    The Future of Children

    (2006)
  • P. Anderson et al.

    Economic perspectives on childhood obesity

    Economic Perspectives

    (2003)
  • M. Anderson et al.

    Are restaurants really supersizing America?

    American Economic Journal: Applied Economics

    (2011)
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    This research was supported by the Agriculture and Food Research Initiative of the USDA National Institute of Food and Agriculture, Grant Number 2011-68001-30014. This work was also partly supported by the National Research Foundation of Korea (NRF-2011-330-B00074). The authors thank Richard Dunn, Susan Chen and Bruce Dixon for helpful comments. The authors also thank the Arkansas Center for Health Improvement (ACHI) for use of their data.

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