Fast food consumption is a dietary factor associated with higher prevalence of childhood obesity in the United States. The association between food prices and consumption of fast food among 5th and 8th graders was examined using individual-level random effects models utilizing consumption data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K), price data from American Chamber of Commerce Researchers Association (ACCRA), and contextual outlet density data from Dun and Bradstreet (D&B). The results found that contextual factors including the price of fast food, median household income, and fast food restaurant outlet densities were significantly associated with fast food consumption patterns among this age group. Overall, a 10% increase in the price of fast food was associated with 5.7% lower frequency of weekly fast food consumption. These results suggest that public health policy pricing instruments such as taxes may be effective in reducing consumption of energy-dense foods and possibly reducing the prevalence of overweight and obesity among US children and young adolescents.
The incidence of obesity increased rapidly among children and adolescents in the United States over the last few decades. In 2007-08, obesity prevalence (with body mass index (BMI) greater than or equal to the 95th percentile of the CDC growth chart) was 19.6% for children 6 to 11 years of age and 18.1% for adolescents aged 12 to 19 years [
Fast food consumption has been associated with higher total caloric intake, higher total fat and sodium intake, poorer nutrient and vitamin intake, higher BMI, and increased likelihood of obesity [
A systematic review of time series and household survey studies on price elasticity of demand for food found that consumption of food away from home was more responsive to price changes than any other food category with a 10% increase in price associated with a 8.1% reduction in consumption [
Linking food price to fast food consumption is important given that a number of recent studies have found statistically significant negative associations between fast food prices and the prevalence of overweight among children and adolescents [
This study drew on two waves of individual-level data (5th graders in 2004 and 8th graders in 2007) from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K). Trained evaluators assessed children in their schools, collected information from their parents over the telephone, and contacted teachersand school administrators to complete the questionnaires. Information on the frequency of fast food consumption was self-reported by the children, anthropometric variables such as height and weight were measured by the evaluators, home environment and school characteristics were self-reported by parents, teachers, and school officials. The outcome of interest was child fast food consumption, which was comprised of how many times in the past 7 days the child ate a meal or snack from a fast food restaurant such as McDonald’s, Pizza Hut, Burger King, Kentucky Fried Chicken, Taco Bell, Wendy’s, and other similar establishments. The responses given by the children were converted from a categorical scale to a numerical scale using midpoints to facilitate analysis.
The contextual data consisted of food prices from the American Chambers of Commerce Researchers Association (ACCRA), fast food outlet density data from Dun and Bradstreet, and median household income from the Census 2000. Food price indices were computed from the ACCRA Cost of Living Index reports, which contain quarterly information on prices in the United States, and which were matched to each child for each year based on the closest zip code match available using the child’s home zip code identifier. An index of fast food price was computed using three food items in the ACCRA data, which includes a McDonald’s Quarter-Pounder with cheese, a thin-crust regular cheese pizza at Pizza Hut and/or Pizza Inn, and fried chicken (thigh and drumstick) at Kentucky Fried Chicken and/or Church’s Fried Chicken. In order to compute the price index of food at home, a basket of grocery items was utilized, which included meats (steak, ground beef, fried chicken, and tuna), fruits and vegetables (potatoes, bananas, lettuce, sweet peas, peaches, and frozen corn), dairy products (half gallon of whole milk, a dozen eggs, margarine, and parmesan cheese), and soft drinks. Both price indices were weighted based on expenditure shares provided by ACCRA derived from the Bureau of Labor Statistics (BLS) Consumer Expenditure Survey. All prices were deflated by BLS Consumer Price Index (CPI; 1982–1984 = 1). In addition to the price data, a number of economic contextual variables, including the Census median household income and fast food restaurant density were merged to the restricted geocoded version of ECLS data at the zip code level to account for differences in neighborhood characteristics. The median household income data for each zip code was obtained from the Census 2000. Information on fast food restaurant outlet density was obtained by year and zip code from the Dun and Bradstreet (D&B) business list data at the primary 8-digit SIC code level for categories of “fast food restaurants and stands” and “chain and independent pizzerias” but not coffee or ice cream shops.
The basic controls consisted of standard individual and household characteristics including age, gender, race (white, African American, Hispanic, other race, and more than one race), mother’s highest level of education completed (less than high school, high school, some college, bachelor’s degree or more), family income (categories consisting of income below $20,000, $20,001–$35,000, $35,001–$50,000, $50,001–$75,000, $75,001–$100,000, and $100,001 and above), degree of urbanicity (urban, suburban, and rural), and an indicator for the year. Two variables indicating the number of times per week a child ate breakfast and dinner with parents were included to account of unobservable characteristics such as the importance the parents place on children’s food consumption patterns. Number of hours of television watched per week was included to control for lifestyle behaviors. All data analysis for this project was conducted using STATA 11.0 [
Children’s food consumption may be influenced by a number of economic contextual factors, including food prices. Changes in relative prices of different food products are expected to influence the relative demand for these products. Consumption of fast food will have own-price effects from fast food prices, and cross-price substitution effects from prices of other foods (such as food at home).
A model of children’s fast food consumption of the following form was estimated:
To take advantage of the longitudinal nature of data, an individual-level random effects model was estimated to provide a weighted average of the between and within estimates where
Sensitivity analysis was conducted to assess the robustness of the fast food price estimates to exclusions of additional contextual variables. The data showed (not shown in tables) that the frequency of fast food consumption was significantly higher (
The basic summary statistics are shown in Table
Summary statistics.
Mean (SD) | |
---|---|
Outcome variable | |
Number of times fast food consumed in past 7 days | 2.468 (3.955) |
Price measures | |
Price of fast food ($1982–84) | 2.662 (0.168) |
Price of food at home ($1982–84) | 1.185 (0.136) |
Contextual factors | |
Median household income in $10,000 ($2000) | 4.756 (1.838) |
Fast food restaurants per 10,000 per capita | 3.598 (3.000) |
Individual, household and local area characteristics | |
Female | 0.502 (0.500) |
Male | 0.498 (0.500) |
White | 0.665 (0.472) |
African American | 0.092 (0.290) |
Hispanic | 0.152 (0.359) |
Other | 0.069 (0.253) |
More than one race | 0.022 (0.147) |
Mother completed less than high school | 0.127 (0.332) |
Mother completed at least high school | 0.237 (0.426) |
Mother completed at least some college | 0.335 (0.472) |
Mother completed bachelor’s degree or more | 0.301 (0.459) |
Parental income 0–20 K | 0.106 (0.307) |
Parental income 20 K–35 K | 0.152 (0.359) |
Parental income 35–50 K | 0.159 (0.366) |
Parental income 50–75 K | 0.199 (0.399) |
Parental income 75–100 K | 0.168 (0.374) |
Parental income 100 K+ | 0.216 (0.412) |
8th grade round (year = 2007) | 0.513 (0.500) |
Household residence urban | 0.673 (0.469) |
Household residence suburban | 0.134 (0.341) |
Household residence rural | 0.193 (0.394) |
Days per week eat breakfast with parents | 3.497 (2.399) |
Days per week eat dinner with parents | 5.287 (1.752) |
Hours of television child watches weekly | 21.94 (15.18) |
Notes:
Table
Longitudinal regression estimates for individual level random effects model of the determinants of fast food consumption.
Consumption of Fast Food | (SE) | |
---|---|---|
Price measures | ||
Price of fast food | −0.527** | (0.241) |
Price of food at home | 0.175 | (0.401) |
Contextual factors | ||
Median household income | −0.131*** | (0.021) |
Fast food restaurants | 0.025** | (0.013) |
Individual, household and local area characteristics | ||
Female | −0.267*** | (0.079) |
African American | 1.932*** | (0.022) |
Other | 0.231 | (0.141) |
Hispanic | 0.627*** | (0.139) |
More than one race | 0.331 | (0.242) |
Mother completed high school | −0.221 | (0.176) |
Mother completed some college | −0.350** | (0.170) |
Mother completed bachelors or more | −0.625*** | (0.175) |
Parental income 20–35 K | −0.635*** | (0.207) |
Parental income 35–50 K | −0.784*** | (0.194) |
Parental income 50–75 K | −0.825*** | (0.195) |
Parental income 75–100 K | −0.913*** | (0.192) |
Parental income 100 K+ | −0.776*** | (0.192) |
8th grade round ( | −0.787** | (0.362) |
Household residence is suburban | 0.165 | (0.123) |
Household residence is rural | −0.085 | (0.120) |
Days per week eat breakfast with parents | −0.053*** | (0.016) |
Days per week eat dinner with parents | −0.054** | (0.023) |
Hours of television child watches weekly | 0.014*** | (0.004) |
Note: regressions include a full set of age dummy variables and average distance between closest ACCRA city and ECLS-K zip code. Standard errors (SE) are reported in parentheses and are robust and clustered at the home zip code level. **significance at 5%; ***significance at 1%.
Longitudinal regression estimates for individual-level random effects model of the determinants of fast food consumption and price elasticity of consumption, by alternate model specifications.
Fast food price coefficient estimates | Fast food price elasticity | |
---|---|---|
Model 1: full specification as shown in Table | −0.527** (0.241) | −0.565** (0.258) |
Model 2: model 1 without median household income | −0.563** (0.240) | −0.603** (.257) |
Model 3: model 1 without fast food restaurant density | −0.548** (.243) | −0.586** (0.260) |
Model 4: model 1 without median household income and fast food restaurant density | −0.589** (0.242) | −0.630** (0.259) |
Model 5: model 4 without price of food at home | −0.644*** (0.198) | −0.689*** (0.211) |
Notes: the regression models include all variables shown in Table
The results for the remaining contextual factors were consistent with expectations. Higher median household income was associated with lower fast food consumption: a $10,000 rise in median household income lowered fast food consumption by 0.13 times per week. Greater availability of fast food outlets measured as outlets per capita was associated with higher frequency of fast food consumption. Higher prices of food at home were associated with higher frequency of fast food consumption although the estimate was not statistically significant.
Table
Longitudinal regression estimates for individual level random effects model of the determinants of fast food consumption, by sub groups.
Price of Fast Food | Price of Food at Home | Fast Food Restaurant Outlet Density | Median Household Income | |
---|---|---|---|---|
Full sample | −0.527** (0.241) | 0.175 (0.401) | 0.025** (0.013) | −0.131*** (0.021) |
By gender | ||||
Female | 0.070 (0.345) | −0.783 (0.555) | 0.022 (0.012) | −0.146*** (0.028) |
Male | −0.190*** (0.351) | 1.163** (.056) | 0.039 (0.026) | −0.108*** (0.030) |
By income | ||||
0–35 K | −0.627 (0.610) | 1.223 (0.991) | 0.074 (0.043) | −0.285*** (0.084) |
36–75 K | −0.407 (0.443) | −0.284 (0.647) | 0.035 (0.024) | −0.139*** (0.041) |
75 K+ | −0.534 (0.292) | 0.046 (0.433) | 0.016 (0.011) | −0.076*** (0.019) |
By weight status | ||||
Overweight | −0.787** (0.391) | 0.944 (0.703) | 0.080*** (0.029) | −0.170*** (0.038) |
Nonoverweight | −0.397 (0.332) | 0.083 (0.482) | 0.007 (0.011) | −0.118*** (0.025) |
By race | ||||
White | −0.844*** (0.239) | −0.065 (0.368) | 0.039*** (0.012) | −0.081*** (0.018) |
African American | 0.172 (1.389) | −2.360 (2.066) | 0.063 (0.070) | −0.228 (0.133) |
Hispanic | 0.073 (0.741) | 0.893 (1.143) | 0.022 (0.064) | −0.210*** (0.069) |
By TV viewing | ||||
9 hours or more per week | −0.595** (0.261) | 0.229 (0.435) | 0.028** (0.014) | −0.140*** (0.023) |
Less than 9 hours per week | 1.050 (0.617) | −0.794 (0.870) | −0.028 (0.028) | −0.080 (0.042) |
Notes: the regression models include all variables shown in Table
Policymakers continue to consider a number of potential public health policy interventions aimed at reducing the negative health implications from the increasing rates of obesity prevalence among children and adolescents in the United States. Public health policy may help to reduce obesity prevalence in children and adolescents by utilizing mechanisms that improve diets through the influence of food prices. Children and adolescents often have disposable income and are able to make independent meal selections when food is not purchased for them directly by their parents [
Earlier studies suggested that pricing mechanisms may have the strongest impact on those individuals who were in the upper tail of the BMI distribution [
The results from this study were subject to a number of limitations. First, the fast food consumption data were self-reported total number of days per week consumed, not actual amounts of consumption (e.g., caloric intake). Second, the ACCRA price data had a number of limitations, which included that the data were only collected in a limited number of cities and metropolitan statistical areas, the data were based on establishment samples that reflect a higher standard of living, and they did not always sample the same cities continuously and hence the data were not fully comparable over time. Third, the outlet density count measures were subject to count error and we were limited to using SIC codes which may have classification errors. Fourth, this study was only able to assess the contextual variables at the zip code level due to data limitations in the availability of more proximate geographic identifiers in the ESLS-K data.
Despite these limitations, the results from the individual-level random effects model in this paper suggested that higher fast food prices were associated with lower frequency of children’s fast food consumption. Therefore, public health policy interventions such as taxes may be effective in reducing consumption of fast food and possibly reducing the prevalence of overweight and obesity among children. Given that the price effects were stronger among children who were male, white, overweight, and frequent television viewers, additional studies based on longitudinal data are needed to develop the evidence base with regard to the potential effectiveness of pricing interventions among various groups of children to help improve food consumption patterns, overall diet, and health outcomes that may possibly translate into healthier outcomes later in adulthood.
Support for this paper was provided by Grant number R01HL096664 from the National Heart, Lung, and Blood Institute and the Robert Wood Johnson Foundation Bridging the Gap ImpacTeen project. The views expressed herein are solely those of the authors and do not reflect the official views or positions of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the Robert Wood Johnson Foundation.