Deciphering the Link Between Fast-Food Restaurants and Childhood Obesity

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Fast-Food Restaurants and Youth Obesity: Examining the Link

Youth obesity is a significant problem in the United States that is increasingly drawing attention from citizens and policymakers. Some estimates show that children age 10 to 17 have an obesity rate of around 15.3% (NSCH 2018). Children who struggle with obesity are more likely to develop a myriad of severe physical and mental health problems, including cardiovascular disease, diabetes, musculoskeletal disorders, depression, anxiety, and some types of cancer (CDC, 2016).

These health problems represent negative externalities on the economy as a whole since these issues increase healthcare costs and reduce workforce productivity (Bhattacharya et al., 2005). While rational adults are assumed to be capable of making health-related decisions for themselves, children often still lack the capacity to assess long-term risk. Thus, policy action to protect children is justified in reducing youth obesity.

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Available evidence suggests that there is a link between diet and levels of childhood obesity (Fryar et al., 2018). Often, fast-food chain restaurants are blamed for youth obesity. Despite public-relations campaigns, these restaurants, such as McDonald’s, Burger King, Taco Bell, and Pizza Hut, have an extremely negative reputation among health-conscious consumers (Downs, 2013). The food served at these restaurants is typically high in sodium, fat, and sugar. Still, fast food is a popular option in the US, with about 36.6% of American adults eating it every day (Fryar et al., 2018). Perhaps due to their convenience and low prices, fast-food restaurants continue to thrive. This short paper will estimate the effects of the number of fast-food restaurants per capita on youth obesity.

Literature Review

There is mixed evidence about the relationship between fast-food restaurants and obesity. Adults who consume fast food regularly tend to have much higher daily caloric intake (Bowman et al., 2003). Some studies show that exposure to advertising for fast-food restaurants can lead to increases in childhood obesity (Chou et al., 2008). Plus, there is research that suggests a school’s proximity to certain restaurants can drive trends in childhood obesity (Davis et al., 2009). With students located close to fast-food restaurants are, on average heavier than students with less direct access to these establishments.

Nevertheless, other research about policy measures designed to curtail fast-food consumption has yielded more negative results. One study found efforts to pass zoning laws limiting fast food in California had failed to have its intended effect of reducing weight (Strum et al., 2009). Another policy lever that some jurisdictions employ is raising taxes on fast food. Studies show that these taxes are rarely effective in improving obesity outcomes unless the tax is raised to extremely high rates (Franck et al., 2013). These studies suggest that altering the number of fast-food restaurants in a particular area may not improve youth obesity. Since there is some ambiguity regarding this question, more research is necessary.

Empirical Model

The multiple linear regression model will estimate the effects of fast-food restaurant prevalence on youth obesity. That model is the following:

Obesity= α+β_1 Fastfood+β_2 Income+β_3 Uninsured+β_4 Region+β_5 Poverty+u

The obesity rate measure used in this model is the proportion of children ages 10-17 who are obese, according to the NSCH. In order to measure fast-food prevalence, this paper takes the number of fast-food restaurants per 10,000 people in each state and Washington, DC. Moreover, income is measured as the median income in each state, and poverty is the proportion of the population below the federal poverty line. Plus, the uninsured variable refers to the proportion of children under 19 in each state who lack health insurance. The region variable refers to the geographic part of the country where each state is located.


The dependent variable in this model is youth obesity. It will be measured using state-level data from the 2018 survey by The National Survey of Children’s Health (NSCH). This survey asks parents or caregivers to report their child’s height and weight in order to produce a measurement of body mass index (BMI). The children’s ages vary from 10-17 in the dataset. If a child has a BMI at or above the 95th percentile of their age and sex distribution, they are classified as obese. This data can be found online at

The independent variable for this study is the number of fast-food restaurants per 10,000 people in each state plus Washington, DC. This will measure the prevalence of fast-food restaurants in different areas of the US. The data is from the year 2018, and it comes from the database Datafiniti which measures business activity in each state and records the number of each type of restaurant. They also list the 20 most common fast-food restaurants in the US, based on the number of locations each operates. This list includes Subway, McDonald’s, Burger King, Taco Bell, Pizza Hut, Wendy’s, Domino’s, KFC, Dairy Queen, Arby’s, Sonic, Hardee’s, Jimmy John’s, Jack in the Box, Chick-Fil-A, Chipotle, Panda Express, Carl’s Jr, Five Guys, and Whataburger. However, these restaurants only account for around 78.3% of total fast-food listings in the nation. The other restaurants included in the dataset are smaller regional chains. This information can be found at

Control variables such as state poverty rate, median income, and proportion of youths lacking health insurance were found for 2018 by the Kaiser Family Foundation. The URL to their website is

Table 1. Descriptive Statistics

  1. Variable Mean (St. dev) Minimum value Maximum value % youth obesity, aged 10-17 14.68 (3.25) 8.7 25.4
  2. Number of fast-food restaurants per 10,000 people 3.99 (.99) 1.9 6.3
  3. Median income 60,237.31 (10,257.51) 43,469 82,372
  4. Poverty rate 12.84 (2.96) 7 20 % uninsured youth 4.45 (2.13) 1 11
  5. West (excluded) 0.25 (0.44) 0 1
  6. South 0.33 (0.48) 0 1
  7. Midwest 0.24 (0.43) 0 1
  8. Northeast 0.18 (0.39) 0 1

Table 1. shows the various measures of obesity, income, poverty, and insurance. It includes the mean, maximum, and minimum number of fast-food restaurants per capita. Vermont has the fewest number of fast-food restaurants (1.9), followed closely by New Jersey (2.0) and New York (2.1). Alabama leads the country in terms of fast-food restaurants, with 6.3 of them for every 10,000 people. Surprisingly, Mississippi, which has the highest rate of youth obesity at 25.4%, has relatively few fast-food restaurants at only 2.1 per 10,000 people. While healthier Utah, with the lowest youth obesity rate in the country, has 3.6 fast-food restaurants per 10,000 people, placing it close to the mean of 3.99.

Empirical Results

The results found in Table 2. come to a somewhat startling and unexpected conclusion about the link between fast-food restaurants and youth obesity. A higher prevalence of fast-food restaurants seems to have an effect on decreasing youth obesity. The coefficient that describes the relationship between the number of fast-food restaurants and obesity is actually negative. An increase of 1 fast-food restaurant per 10,000 people in a state is predicted to result in an estimated reduction of youth obesity by .734% (p-valueTable 2). Regression Analysis

Poverty rate, region, and median income all have significant effects as well. States with high poverty tend to have high youth obesity (p-valueConclusions and Policy Implications.

The results of this research yielded the opposite result of what might be intuitive. The prevalence of fast-food restaurants in a given state seems to have an effect on decreasing the rate of youth obesity rather than increasing it. Therefore, it would seem policies that aim to tax or regulate the number of fast-food restaurants in a given area might actually produce the unintended result of increasing youth obesity.

Focusing government policy on the reduction of poverty may be the most useful option in fighting childhood obesity. Perhaps these restaurants’ food is not as bad for people’s health as commonly thought. Alternatively, maybe people in areas with a higher concentration of fast-food restaurants are making other healthy choices in their diet or exercise regime that offset the harmful effects of fast food.

However, there are serious limitations to this study that might challenge this policy conclusion. For one, this study measures the number of fast-food restaurants in each state per capita. It does not measure how often people eat at these establishments. Therefore, this raises the possibility that perhaps two states could have the same number of restaurants per capita, but one state’s population simply eats at these locations more. Plus, there might be a difference in the size of restaurants in different states. In some states, fast-food restaurants may be larger and designed to serve more people, leading to more people in that state eating fast food, even if they have the same number of restaurants.

Moreover, there might be a difference in the kind of fast-food restaurants available in different states. This dataset treats all fast-food restaurants the same, and as a result, may be missing some essential differences amongst the health effects of various fast-food options. Accordingly, this might lead to a slightly altered policy conclusion that categorically supporting or opposing fast food is a bad idea. Perhaps states should support healthier fast-food options.


1.NSCH 2018 (National Survey of Children’s Health, 2018)

2.CDC, 2016 (Centers for Disease Control and Prevention, 2016)

3.Bhattacharya et al., 2005 (Bhattacharya, J., Bundorf, K. M., & Pace, N. M. (2005). Does health insurance make you fat? NBER Working Paper No. 11529)

4.Fryar et al., 2018 (Fryar, C. D., Hughes, J. P., Herrick, K. A., & Ahluwalia, N. (2018). Fast food consumption among adults in the United States, 2013-2016. NCHS Data Brief, No. 322)

5.Downs, 2013 (Downs, J. S., Loewenstein, G., & Wisdom, J. (2010). Strategies for promoting healthier food choices. American Economic Review, 100(2), 45-50)

6.Bowman et al., 2003 (Bowman, S. A., Gortmaker, S. L., Ebbeling, C. B., Pereira, M. A., & Ludwig, D. S. (2004). Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics, 113(1), 112-118)

7.Chou et al., 2008 (Chou, S. Y., Rashad, I., & Grossman, M. (2008). Fast‐food restaurant advertising on television and its influence on childhood obesity. Journal of Law and Economics, 51(4), 599-618)

8.Davis et al., 2009 (Davis, B., Carpenter, C., & Procter, K. (2011). Proximity of fast-food restaurants to schools and adolescent obesity. American Journal of Public Health, 101(9), 1961-1968)

9.Strum et al., 2009 (Strum, R., Powell, L. M., Chaloupka, F. J., & Chriqui, J. F. (2009). Can zoning policy address public health concerns? The effect of fast food restrictions on obesity rates. Health Affairs, 28(6), w1068-w1077)

10.Franck et al., 2013 (Franck, C., Grandi, S. M., & Eisenberg, M. J. (2013). Taxing junk food to counter obesity. American Journal of Public Health, 103(11), 1949-1953)

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Deciphering the Link Between Fast-Food Restaurants and Childhood Obesity. (2023, Aug 18). Retrieved from

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