Access to Fast Food and Food Prices: Relationship with
Fruit and Vegetable Consumption and Overweight among Adolescents
April 2006
Lisa M. Powell, Ph.D.
Department of Economics and Institute for Health Research and Policy
University of Illinois at Chicago
M. Christopher Auld, Ph.D.
Department of Economics, University of Calgary and
Institute for Health Research and Policy, University of Illinois at Chicago
Frank J. Chaloupka, Ph.D.
Department of Economics and Institute for Health Research and Policy
University of Illinois at Chicago
Patrick M. O’Malley, Ph.D
Survey Research Center, Institute for Social Research
The University of Michigan
Lloyd D. Johnston, Ph.D.
Survey Research Center, Institute for Social Research
The University of Michigan
We gratefully acknowledge research support from the Robert Wood Johnson Foundation through
ImpacTeen and Youth, Education, and Society studies. Monitoring the Future survey data were
collected under a grant from the National Institute on Drug Abuse. We thank Yanjun Bao and
Donka Mirtcheva at the University of Illinois at Chicago and Deborah Kloska at the University
of Michigan for their excellent research assistance.
1
ABSTRACT
We examine the extent to which food prices and restaurant outlet density are associated with
adolescent fruit and vegetable consumption, body mass index (BMI), and the probability of
overweight. We use repeated cross-sections of individual-level data on adolescents from the
Monitoring the Future Surveys from 1997-2003 combined with fast food and fruit and vegetable
prices obtained from the American Chamber of Commerce Researchers Association and fast
food and full-service restaurant outlet density measures obtained from Dun & Bradstreet. The
results suggest that the price of a fast food meal is an important determinant of adolescents’ body
weight and eating habits: a 10% increase in the price of a fast food meal leads to a 3.0% increase
in the probability of frequent fruit and vegetable consumption, a 0.4% decrease in BMI, and a
5.9% decrease in probability of overweight. The price of fruits and vegetables and restaurant
outlet density are less important determinants, although these variables typically have the
expected sign and are often statistically associated with our outcome measures. Despite these
findings, changes in all observed economic and socio-demographic characteristics together only
explain roughly one-quarter of the change in mean BMI and one-fifth of the change in
overweight over the 1997-2003 sampling period.
2
INTRODUCTION
The prevalence of overweight among American adolescents aged 12-19 has tripled over the last
three decades, reaching 16.1% by 1999-2002 (Hedley et al. 2004). Parallel to this rising obesity
epidemic, data based on nationwide surveys of food consumption patterns and household
expenditures show a marked upward trend in total energy intake derived from away-from-home
sources (Stewart et al., 2004). In 2002, nearly half of Americans’ food expenditures went
toward an away-from-home food facility compared to one-quarter in 1960. The greatest growth
over the twenty year period from 1982 to 2002 by outlet type in the away-from-home food
market as a percent of sales was in the fast food industry. In this study, we provide econometric
evidence on whether adolescent fruit and vegetable consumption and body weight respond to
changes in the prices of fruit and vegetables and fast food and the availability of full-service and
fast food restaurants.
Previous research suggests that one explanation for the increase in overweight is higher caloric
intake associated with fast food meals. Based on food consumption surveys categorizing caloric
intake from food source locations by home, restaurants, fast food establishments, schools or day
care and other non-home locations, Guthrie et al. (2002) reported significant increases over the
last few decades in the portion of food prepared away-from-home with particularly large
increases in fast food consumption. The percentage of total caloric intake from fast food sources
increased between 1977-78 and 1994-96 by a factor of three for adults aged 18 years and over
and by a factor of five for children aged 2-17 years. Nielson et al. (2002) reported a significant
increase in the consumption of particular foods such as salty snacks, french fries, cheeseburgers,
pizza and soft drinks and showed that the portion of total energy coming from restaurant and fast
food places tripled among adolescents (aged 12-18) and doubled among young adults (aged 1929) over the period from 1977 to 1996. These trends in dietary patterns mirror obesity trends for
adolescents and adults. Research that has examined associations between fast food consumption
and nutrient intake find that, controlling for individual and socioeconomic characteristics, for
both children and adults fast food consumption is associated with higher total energy intake and
density and higher intake of fat, saturated fat, carbohydrates, sugar, carbonated soft drinks and
lower intake of micronutrients and fruit and vegetables (Lin et al.1999; Binkley et al. 2000;
French et al. 2000, 2001a; Lin and Morrison 2002; Paeratakul et al. 2002; Bowman et al. 2004;
Bowman and Vinyard 2004) .
In addition to the literature tracking dietary consumption over time, a large body of research has
examined the determinants of food consumption patterns. Individual, household, and regional
characteristics are associated with dietary intake patterns (Lutz and Blaylock 1993; Siega-Riz et
al. 2000; Neumark-Sztainer et al. 2003; Xie et al. 2003) and the demand for food away from
home (Stewart et al. 2004). Lack of data on food prices is often a limitation of this research.
Stewart et al. noted that “prices are an important determinant of demand” (p.829) but due to a
lack of data the authors were unable to include them in the study. In the absence of price data,
several studies have examined the impact of household expenditures on food consumption
patterns, but these expenditure data are likely to suffer from endogeneity problems as prices and
other unobserved determinants of food expenditures are likely to be correlated with consumption
patterns. Park and Capps (1997) found the demand for prepared meals to be price sensitive
based on an imputed food price from expenditure data. Other research examines the
determinants of total expenditure on food consumption patterns, but such studies do not allow
the researcher to disentangle price and quantity (McCracken and Brandt 1987).
3
Studies that do include food prices show that prices and access are important determinants of
food consumption. Recent research shows that own-price reductions in vending machines
significantly increase children’s food purchases of low-fat snacks (French et al. 2001b; French et
al. 1997a). Fruit and vegetable consumption in cafeterias is responsive to price (Jeffery et al.
1994; French et al.1997b; Hannan et al. 2002). Specific food demand models show that prices
are important determinants of consumption of fats and oils (Yen et al. 2002).
Economic research emphasizes changes in incentives, particularly prices, as causes of changes in
body weight. Lakdawalla and Philipson (2002) argued on the basis of both theory and evidence
from microdata that the obesity epidemic in the U.S. is a result of two changes in incentives: the
relative price of consuming a calorie has fallen over time while the opportunity cost of burning a
calorie has risen over time. Other econometric studies focusing on incentives as determinants of
body weight show that increases in the per capita number of restaurants, lower real food prices,
and higher cigarette prices may have significantly contributed to the upward trend in obesity
(Chou et al. 2004) 1 . Only one study to date has examined the effects of restaurant outlet density
and food prices on the body mass index (BMI) of children: Sturm and Datar (2005) analyzed
weight changes in children in kindergarten through the third grade and found that lower fruit and
vegetable prices, but not generally prices of other food items or outlet density, predicted smaller
increases in body weight. Lakdawalla et al. (2005) reported that lower food prices are associated
with better nutritional status even in a highly developed country such as the U.S. Overall,
econometric analysis of individual-level data on adults and young children indicates decreases in
food prices are an important cause of the obesity epidemic.
This study examines the extent to which food prices and restaurant outlet density are associated
with adolescent body weight and fruit and vegetable consumption. We use repeated crosssections of individual-level data on adolescents from 1997 through 2003. We observe
adolescents’ weight outcomes and fruit and vegetable consumption along with detailed sociodemographic data from the Monitoring the Future (MTF) surveys. Geographic identifiers at the
zip code level are exploited to merge these data with fast food and full-service restaurant outlet
density measures obtained from Dun & Bradstreet (D&B) and the price of fast food and fruits
and vegetables obtained from the American Chamber of Commerce Researchers Association
(ACCRA). Following economic explanations for changes in body weight and assuming food
types substitute for one another, we expect consumption of fruits and vegetables to decrease with
their own price and increase with the full price of fast food. Similarly, we expect consumption
of fast food to be higher when its full price (including travel costs) is lower or when fruits and
vegetables are more expensive. We do not observe fast food consumption but estimate reduced
form models of BMI and overweight. We expect that weight will be higher in times and places
where the full price of fast food is lower or where the price of fruits and vegetables is higher.
This is the first study to estimate the importance of such contextual factors on eating habits and
weight outcomes among adolescents.
The results suggest that the price of a fast food meal is an important determinant of adolescents’
body weight and eating habits. The price of fruits and vegetables and restaurant outlet density
are less important determinants, although these variables typically have the expected sign and are
often statistically associated with our outcome measures. Despite the evidence we present that
1
Also see comment by Gruber and Frakes (2005) and reposnse by Chou et al. (2006).
4
changes in incentives are economically and statistically significant determinants of BMI, we find
that in total changes in these incentives and changes in all other observed socio-demographic
characteristics can only explain about one-quarter of the increase in mean BMI observed
between 1997 and 2003. Further, only a small portion of the explainable variation is due to
changes in food prices and restaurant densities. Similarly, most of the change in overweight is
not explained by changes in observed characteristics.
METHODS
DATA
This study combines individual-level national data for 8th and 10th grade students from the MTF
surveys with data on restaurant outlets obtained from business lists developed by D&B and food
price data obtained from the ACCRA. The external outlet density and food price measures are
matched to the individual-level data at the school zip code level for each year 1997 through
2003.
Monitoring the Future Survey Data
The MTF study, which has annually surveyed nationally representative samples of high school
seniors in the coterminous United States since 1975, is conducted by the University of
Michigan’s Institute for Social Research (ISR). Since 1991 the MTF surveys have also included
30,000 eighth and tenth grade students annually. Located in approximately 280 schools, these 8th
and 10th grade students are selected annually for the MTF survey based on a three-stage sampling
procedure (Johnston et al. 2004). Stage 1 involves geographic area selection. Stage 2 involves
selection of one or more schools in each area based on establishing the probability for inclusion
proportionate to the size of the respective grade to be sampled. Stage 3 selects students within
each selected grade. Within each school, up to 350 students per grade are selected for the study.
For those schools with a smaller student body for the respective grade, all students are selected.
If a school has more than 350 students then a random sample of classrooms or other random
method is used to choose the final sample. This study draws on the 8th and 10th grade student
samples. The data are weighted to correct for any inequalities in selection probabilities at the
various stages of sampling. The summary statistics and regression analyses are weighted.
Questionnaires are administered by an ISR representative in classrooms during normal class
periods whenever possible. Students are informed of the importance of accurate responses and
assured that their confidentiality will be protected. Neither parents nor the school are informed
of individual student responses. In order to cover the range of topic areas in the study, 8th and
10th grade students are administered four different forms. This occurs in an ordered sequence, so
as to ensure virtually identical sub-samples for each form. Approximately one-third of the
questions on each form are common to all forms; these include the demographic variables used
in this study. Questions that relate to food consumption, physical activity and height and weight
are all form specific and are included on only a subset of forms.
MTF Student Samples: For the seven years of data from 1997 through 2003 for 8th and 10th
students our sample has a total of 72,854 observations on which we have information on height
and weight and non-missing information on our covariates. Our sample with information on
food consumption behavior totals 47,675 observations. As noted above, the reason for the
differing sample sizes is that MTF consists of multiple forms that include both core and form-
5
specific questions. The questions on food consumption behaviors and height and weight are
form-specific.
Fruit and Vegetable Consumption Measure: We create a measure of frequent fruit and
vegetable consumption based on the answers to the following two questions: ``How often do you
eat at least some green vegetables?’’ and ``How often do you eat at least some fruit?’’ Student
responses include the following possible categories for each question: never, seldom, sometimes,
most days, nearly every day, and every day. Based on these answers we created a dichotomous
indicator for frequent consumption of fruit and vegetables equal to unity if the student answered
most days, nearly every day or every day for both questions and equal to zero otherwise. Table 1
shows that the majority (57%) of students frequently consume both fruits and vegetables. Table
2 shows that frequent fruit and vegetable consumption trended downward until 2002 but bounced
up in 2003.
BMI and Overweight Measures: Anthropometric information is available in the MTF survey
based on self-reports. Such data are likely to contain measurement error. Wang et al. (2002)
found under-reporting of both overweight and obesity in self-reported data of older adolescents
(15-19 years). Concerns stemming from measurement error are mitigated by Strauss (1999) who
found that 94% of children were in the correct classification of obesity and Goodman et al.
(2000) who found that examining self-report data among teens correctly classified 96% as obese
or not obese. Using height and weight, we calculate BMI (=weight(kg)/height(m)2).
Individuals’ body weight status is classified based on BMI for children and teens using the 2000
CDC Growth Chart (Kuczmarski et al. 2001); obesity is classified as BMI>=age-sex-specific
95th percentile (based on data from 1963-1994). Note that for children the CDC recommends
using the term of “overweight.” We create a dichotomous indicator equal to one if the student is
“overweight” and zero otherwise. Table 1 shows that average BMI is 21.8 and prevalence of
overweight is 10.3% for the full sample of students. Table 2 shows that over the 1997-2003
period BMI trended upwards reaching 22 in 2003. Over the same period, the prevalence of
overweight peaked at 11.5% in 2001 and fell somewhat to 11% by 2003.
Socio-demographic Measures: We control for demographic measures available in the student
surveys including: gender; grade; age; race/ethnicity; highest schooling completed by father;
highest level of schooling completed by mother; a rural/urban area neighborhood designation;
total student real ($82-84) income (earned and unearned such as allowance; specified in $100s in
the regressions); weekly hours of work by the student and whether the mother works part-time or
full-time. In our sensitivity analyses for our overweight models, we also control for participation
in physical activity based on the following question: ``How often do you do actively participate
in sports, athletics or exercising?’’ Responses were based on a 5-point scale that included:
never, a few times a year, once or twice a month, at least once a week, and almost every day.
Based on these answers we created a dichotomous indicator for frequent participation in physical
activity equal to unity if the student answered at least weekly or almost every day and equal to
zero otherwise. The summary statistics in Table 1 show that just under half of the sample is
male and that approximately 69% of the students are white, 11% are black, 10% are Hispanic
and 10% are of other (or mixed) racial/ethnic backgrounds. The average age of the sample is
14.7 and just under half of the sample is in 8th grade, whereas the second half is in 10th grade.
The majority of students’ parents have at least some college education (58% of fathers and 61%
6
of mothers). Most (80%) students live with both of their parents and just under one quarter live
in a rural area. Students work on average just under 4 hours per week. Students’ average weekly
real income is about $23. Approximately 64% of students’ mothers work full-time whereas 18%
of students have mothers who work part-time. The sample is evenly distributed across years
with about 14% in each of the seven years from 1997-2003.
Restaurant Outlet Density Measures
Data on restaurant outlets were obtained from a business list developed by D&B. 2 This list is
obtained through use of D&B MarketPlace software. MarketPlace contains information on more
than 14 million businesses in the U.S. and D&B employs a staff of more than 1,300 individuals
to compile and update these records through interviews, public documents, and directories. The
records are then updated quarterly in order to optimize their accuracy and completeness.
Specifically, D&B utilizes the following sources to help update its database: yellow page
directories that are matched against its database to identify new businesses; news and media
sources are monitored daily to identify businesses that have merged, been acquired, closed, or
claimed bankruptcy; government registries to identify business registration information; and,
websites, including its own where businesses have the ability to review and update their own
information. In addition to these sources, D&B has telecenters that place approximately 100
million phone calls annually to update and verify business list information.
D&B has a number of quality assurance protocols in place to ensure accuracy of the data. For
instance, D&B utilizes a “match grade” method to consolidate multiple business listings into one
complete record. This method ensures that there are no duplicate entries of the same business
and that data are not matched to the wrong business. D&B also assigns each business a unique
numerical identifier to ensure validity of its data over time. This nine-digit number is never
recycled and allows D&B to easily track changes and updates for all businesses contained in its
database.
MarketPlace allows sorting by multiple criteria such as zip code, company, size, location,
metropolitan area, county, state, physical addresses, subsidiaries, and Standard Industry
Classification (SIC) codes. SIC codes allow for searching for, and selection of, specific types of
businesses at varying levels of detail/specificity. Facilities may appear on the Marketplace list by
both "primary" and "secondary" SIC codes. Therefore, there is initially some level of
duplication in the listings. To eliminate such duplications, we draw on the primary SIC code
listing in creating the list of outlets used for this analysis.
Information on restaurant outlets available in the D&B data set was pulled by zip code for the
years 1997 through 2003. The outlet density data are linked to the individual-level data by
student’s school’s zip code. While this may be a good proxy for the student’s home zip code at
lower grade levels (in this case grade 8), high schools may draw their student population from
beyond its own zip code. If a child lives in a different zip code to that of their school, the extent
to which neighboring zip codes are similar will help to mitigate this potential source of error for
2
Information on D&B’s methods was obtained from several sources that include: 1) www.zapdata.com;
2) "The DUNSright Quality Process: The Power behind Quality Information" (2005) Dun and Bradstreet; and,
3) Personal communication with Todd Mertz, Relationship Leader, US DUNSright Customer Solutions, D&B,
February 2, 2004.
7
differing access between time spent near and around school and time spent around their home.
Information on the total number of restaurants was pulled at the 4-digit SIC code level and the
number of fast food restaurant outlets was pulled at the 6-digit SIC code level. Non-fast food
restaurants, referred to as full service restaurants, are defined as the number of total restaurants
minus the number of fast food restaurants. Table 1 shows that on average in each zip code there
are 2.4 fast food and 12.8 full service restaurants per 10,000 people. Table 2 reveals that the per
capita number of full service restaurants remained fairly constant over the 1997-2003 period
while the per capita number of fast food restaurants trended upwards, increasing by 56%.
Food Price Measures
Food and fast food price data were obtained from the American Chamber of Commerce
Researchers Association (ACCRA) Cost of Living Index reports. These reports contain quarterly
information on prices across more than 300 US cities. The ACCRA collects 62 different prices for
a range of products. Price data collection is based on establishment samples that reflect a midmanagement standard of living. For consistency, national brands are stipulated where possible.
Otherwise, “lowest price” is specified and is the average of the lowest prices found in all stores
surveyed. These price data are matched to the MTF sample based on the closest city match
available in the ACCRA data using school zip code geocode data. Price data are drawn from
quarters one and two as these reflect the time frame of the MTF surveys. From the items
provided in the ACCRA data we create two prices indices: a fruit and vegetable price index and
fast food price index. All prices are deflated by the BLS Consumer Price Index (CPI) (19821984=1). The ACCRA also reports a cost of living index for each city which could be used to
further deflate prices. Following Chou et al. (2004) we do not use this index because, given the
ACCRA sample structure based on cost differentials among mid-management households,
homeownership costs are likely to be over-weighted compared to an index based on an average
consumer.
The fruit and vegetable price index is based on the food prices available for this food category:
potatoes, bananas, lettuce, sweet peas, tomatoes, peaches, and frozen corn. ACCRA reports
weights for each item based on expenditure shares derived from the Bureau of Labor Statistics
(BLS) Consumer Expenditure Survey. These weights are used to compute a weighted fruit and
vegetable price based on the seven food items noted above. Table 1 shows that the average real
price of the fruit and vegetable bundle in our sample is 72 cents. Table 2 reveals that the real fruit
and vegetable price increased by 17% over the sampling period.
The fast food price is based on the following three items included in the ACCRA data: 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 Friend Chicken and/or Church’s
Friend Chicken. The fast food index is computed as an average of these three food prices since
they have equal weights. Table 1 shows that the average real fast food price is $2.71. Table 2
shows that the real fast food price trended downwards over the 1997-2003 period, falling by
roughly 5%.
ANALYTIC FRAMEWORK
We follow Lakdawalla and Philipson (2002) in viewing body weight within a rational choice
framework in which individuals choose food intake and physical activity in order to achieve ends
8
such as health, social acceptance, and gastronomic pleasure. Observed weight is determined by
the costs and benefits of gaining or losing weight at the margin, and these costs and benefits
depend in part on the prices individuals face to obtain a given bundle of food products, to prepare
food, to use energy through exercise, and the prices of goods and services other than food.
Demand for various types of food and for physical activity obtains as a function of these
parameters, and weight in turn is determined by these demands. We estimate reduced form
statistical models for measures of body weight and demand equations for consumption of fruit
and vegetables.
Generally, weight depends on the composition of the amount and kind of food consumed, on
genetics, on physical activity, and possibly other environmental and behavioral factors. We
focus on the total price of several forms of food intake as determinants of observed weight.
Since detailed decompositions of food intake are not available in our data, we simplify by
considering the availability of full service and fast food restaurants, the price of fruits and
vegetables, and the price of fast food meals as key determinants of weight. Fast food meals are
energy dense and the rise in obesity over time is speculated to be partially explained by decreases
in price for these meals and increases in demand for food away from home caused in part by
increases in female labor force participation (Lakdawalla and Philipson 2002, Chou et al. 2004).
Anderson et al. (2003) provide evidence on the causal effect of greater maternal hours of work
and increased overweight status for children.
The total price of food intake includes both the prices of various types of food and the
opportunity cost of the time spent acquiring the food. The time cost depends in turn on how far
one must travel to obtain a given type of food. We proxy these time costs using measures of percapita fast food and full service restaurant densities. The extent to which fast food alternatives
are conveniently available within local communities compared to full-service restaurants that
may offer more healthful food alternatives is likely to shape eating patterns. Differences in the
supply of alternative choice sets for dining out, or having food delivered, or obtaining take-out
food across different communities may result in systematic differences in eating patterns and
weight status.
Our statistical models disentangle the effects of changes in incentives, as measured by money
prices and outlet densities, from other determinants of body weight and eating habits using either
ordinary least squares regression or maximum likelihood probit regression. In some models, we
include variables that may be simultaneously determined with the outcome (such as measures of
physical activity), and our price and outlet measures may be correlated with unobserved regional
characteristics which also affect weight such as peer or selection effects. We do not attempt to
correct for such potential endogeneity problems but rather report sensitivity checks and bestow
causal interpretations to our estimates cautiously.
Model of fruit and vegetable consumption: We report marginal effects from maximum
likelihood probit models when our outcome is binary, such as the indicator for frequent fruit and
vegetable consumption. Assume respondent i’s propensity to frequently consume fruit and
vegetables, FVCi*, is given by
FVCi * = β 0 + β 1ODis + β 2 PFFis + β 3 PFVis + β 4 Xi + εi
(1)
9
where ODis is a vector of full-service food and fast food restaurant outlet density available to
individual i in geographic area s, PFFis is the price of fast food, PFVis is the price of fruits and
vegetables, Xi is a vector of individual and household characteristics, β are parameters to be
estimated and εi is a normally distributed disturbance term. Individual i is observed to frequently
consume fruits and vegetables if FVCi*>0.
The marginal effect associated with the coefficient estimate β3 gives the own-price effect on fruit
and vegetable consumption, whereas the effect associated with the estimate of β2 measure the
cross-price effect between fruit and vegetable consumption and the price of fast food. We report
alternative specifications, varying the control variables in X.
Reduced form models of BMI and overweight status: We estimate reduced form models of
individual BMI and OVERWEIGHT specified by:
BMI / OVERWEIGHT i = β 0 + β 1ODis + β 2 PFFi + β 3 PFVis + β 4 Xi + εi
(2)
where the variables are defined as in equation (1) above. The BMI model is estimated by OLS
and the OVERWEIGHT model is estimated as a probit model similar to the frequent fruit and
vegetable consumption model.
Interpretation of year effect dummies: In our preferred specifications, we include a set of
dummy variables indicating survey waves 1998 through 2003. Inclusion of these dummies is
equivalent to nonparametrically detrending each variable in the analysis such that the estimates do
not reflect common trends. For example, in our data mean BMI trends up and mean fast food
prices trend down, but this common trend is removed by the time dummies and we identify the
effects of interest using regional variation within years. We also report some specifications
without time dummies in order to contrast the magnitude of the estimates on our key variables.
In models with year dummies, we vary the included additional covariates to assess the ability of
these covariates to explain trends in BMI and overweight. Consider the equation,
Yi = γ 0 + γ 1Di + γ 2 Zi + εi
(3)
where Yi is an outcome of interest, Di is a set of year dummies indicating the years from 1998 to
2003, and Z are covariates. If we estimate this model including only the time dummies using
ordinary least squares regression (that is, set γ 2 =0), the estimate of the constant γ0 is the mean of
Yi in 1997 and each estimated parameter on the year dummies recovers changes in the mean of Yi
relative to 1997. If we then include the covariates Z, the estimates of the year effects γ1 measure
changes in the mean of Yi over time that cannot be attributed to changes in Z.
RESULTS
The results from the regression models of the relationship between access to fast food and food
prices and adolescent frequent fruit and vegetable consumption, BMI and overweight status are
presented in Table 3. Table 3 includes results from models with the full set of control covariates
10
with and without the year dummies. Examining first the results for fruit and vegetable
consumption, we find that increased availability of full service restaurants has a statistically
significant relationship with the likelihood of adolescent frequent fruit and vegetable
consumption: ten more full service restaurants per capita in the respondent’s region is associated
with a 1.9 percentage point increase in the probability of frequent consumption. A dollar
increase in the price of fast food is statistically significantly associated with a reduction in
frequent consumption of fruit and vegetables; by 7.3 percentage points when year effects are not
included and by 6.7 percentage points when they are. A $1 increase in the price of fruit and
vegetables is estimated to decrease fruit and vegetable consumption by 6.3 percentage points
(z=2.05). The estimated effect does not change when time dummies are included but loses some
statistical significance (z=1.79).
Turning to the BMI and overweight models, the results show that the inclusion of the year
dummies substantially reduces the magnitude and statistical significance of the estimated access
and price effects. Model 3, with no year effects, suggests that fast food and fruit and vegetable
prices both statistically significantly impact BMI. When year effects are included, in Model 4,
the magnitude of the fruit and vegetable price effect drops by more than half and loses statistical
significance. The estimated effect of a one dollar change in the price of a fast food meal falls by
almost half to 0.31 m/kg2, but remains statistically significant. Model 4 also suggests that BMI
is higher when there are fewer full service restaurants, more fast food restaurants, or higher fruit
and vegetable prices, although none of these latter results are statistically significant. In the
overweight model, the only statistically significant contextual factor is the price of fast food.
Controlling for year effects, Model 6 suggests that a dollar increase in the price of a fast food
meal reduces the prevalence of overweight by 2.2 percentage points. The signs on the effects of
the other price and access measures are also as expected but, as with BMI, these effects are not
statistically significant.
Sensitivity analyses to check the robustness of the results of our outlet density and price
variables to alternative specifications are shown in Table 4. 3 Results are shown that examine the
sensitivity to specifications that exclude mother’s work status for both outcomes and that include
student frequent participation in physical activity in the specification of the BMI model.
Maternal work status is highly statistically significant but its inclusion has little effect on the
estimated price and restaurant access coefficients. Similarly, Model 5 shows that conditioning
on physical activity has little effect on the price and restaurant access coefficients. Since
physical activity may proxy unobserved region-varying determinants of weight outcomes,
robustness to the inclusion of this variable mitigates concern over omitted variable bias. For
example, social attitudes towards health may vary across regions and may be correlated with
food prices and restaurant density. Since these attitudes may also be correlated with physical
activity, if such unobserved factors were biasing our results we would not expect the results to be
robust to the inclusion of the activity measure.
3
We also undertake sensitivity analyses (not shown in this table) to assess the robustness of our results to our
sample restriction on missing data. Due to a high number (13%) of missing observations on parental education, we
reran our analyses including dummy indicators for missing on these variables and found that the results for all of our
key contextual variables are robust to their sample exclusion. One difference does occur for the control variables;
with missing information on parental education, the mother’s full-time work covariate is no longer statistically
significant in the BMI and overweight regressions.
11
Turning to the covariates other than prices and restaurant densities, the results from the frequent
fruit and vegetable consumption model (Table 3, Model 2) show that older students within grade,
those who are African American or Hispanic, and those students whose mother or father have
less than a high school education are significantly less likely to frequently consume fruit and
vegetables. The strongest associations are found for race: compared to their white counterparts,
African American and Hispanic students are 14.5 and 9.8 percentage points less likely to
frequently consume fruit and vegetables. Student’s income has a very small marginally
statistically significant impact on consumption. Students who live in an intact family and those
with college-educated parents are significantly more likely to frequently consume fruit and
vegetables. However, controlling for mothers’ education (and the other covariates), students
with a mother who works full-time are four percentage points less likely to frequently consume
fruit and vegetables compared to students whose mothers do not work. The year dummy
variables are not statistically significant individually, nor are they jointly statistically significant.
The results for the control covariates in the BMI (Model 4) and overweight (Model 6) models are
as expected. African American and Hispanic students are more likely to be overweight by 5.4
and 3.6 percentage points, respectively, than white students, and white students have lower mean
BMI. Male and 10th versus 8th grade students are have higher BMI and are more likely to be
overweight. Controlling for grade, BMI increases with age but the risk of overweight does not.
Students with parents who have higher education and those who live with both parents have
lower BMI and are less likely to be overweight. Living in a rural area is associated with higher
BMI and overweight prevalence. Controlling for maternal education, mothers’ full-time work is
significantly associated with 0.13 m/kg2 higher in BMI and marginally associated with just over
half a percentage point increase in the probability of overweight.
Table 5 shows the estimated year effects from several specifications. Model 1 includes no other
covariates and so recovers deviations in unadjusted means from 1997. As already shown in
Table 2, mean BMI generally trends up over our sampling interval. Model 2 adds fast food and
fruit and vegetable prices and the restaurant density measures as additional covariates. If
changes in these variables over time explain a substantial portion of changes in obesity over
time, then the estimated year effects should fall substantially towards zero, which we do not
observe. In Model 3, we include all covariates (these replicate Models 2, 4 and 6 in Table 3).
Figure 1 shows that the significant drop in fruit and vegetable consumption that occurred by
2002 is substantially explained by our observed covariates with the contextual price and outlet
density factors accounting for a large part of this trend. Figure 2 shows that changes in all
covariates, including prices and densities and mother’s employment, explain only roughly onequarter of the ½ point change in mean BMI. Similarly, Figure 3 displays the results for
overweight status over time. Overweight status trends up until 2001, then levels off or starts to
fall. Contextual factors and other covariates explain little of this variation: if prices, restaurant
densities, and all other characteristics were unchanged across the sampling period, the pattern of
changes in overweight would not have been markedly different.
12
CONCLUSIONS
We contribute to the literature examining the causes for the obesity epidemic in the United States
by examining the determinants of BMI, overweight and fruit and vegetable consumption among
adolescents. We analyze repeated cross-sections from the Monitoring the Future surveys,
merged with data on fruit and vegetable and fast food prices and fast food and full service
restaurant outlet density measures matched at the zip code level. The results indicate that
changes in prices and restaurant densities change outcomes in the manner predicted by the
standard economic model. Fruit and vegetable consumption is lower when fruit and vegetable
prices are higher, or when the price of fast food is lower, or when full service restaurants are less
readily available. BMI is lower when fast food is more expensive. The price of fast food is the
only contextual factor consistently associated with both fruit and vegetable consumption and
with the weight outcomes and it is the only factor that is statistically significantly associated with
BMI and overweight. Our preferred specifications suggest that a 10% increase in the price of a
fast food meal leads to a 3.0% increase in the probability of frequent fruit and vegetable
consumption, a 0.4% decrease in BMI, and a 5.9% decrease in prevalence of overweight. These
results suggest that individuals at risk of overweight are more responsive to changes in price than
the average individual.
We also find that changes in prices and access explain very little of the one-half point increase in
mean BMI between 1997 and 2003. Over this period mean BMI increases and simultaneously
fast food prices fall, fruit vegetable prices rise, and fast food outlet density rises. Together these
changes suggest that substitution towards fast food driven primarily by fast food price changes
may explain increases in weight outcomes. This explanation is correct but only accounts for
perhaps 5% of the observed change in BMI and 7% of the observed change in overweight.
Changes in all observed economic and socio-demographic characteristics together only explain
roughly one-quarter of the change in mean BMI and one-fifth of the change in overweight. The
bulk of the change in weight outcomes remains in the residual, not attributable to changes in
prices, restaurant density, mother’s employment, race, age, student’s income or employment,
parental education, or family structure.
These results are subject to several limitations. Some of the key variables are subject to
measurement error which will tend to reduce the magnitude of our estimated effects and
understate the importance of, in particular, our economic explanatory variables. Information on
family income is unavailable in our data; previous research has shown that income is highly
correlated with body weight. To the extent that our control variables, such as parental education,
do not capture variation in income our results may be subject to omitted variables bias. Also, we
identify the effects of prices and densities using variation across geographic regions within years
such that unobserved determinants of weight outcomes and eating habits across regions may bias
our results. Despite these limitations, our results for adolescents are consistent with findings for
other age ranges and suggest that food prices are statistically significant predictors of weight
outcomes and eating habits.
Taxing unhealthful foods, such as fast food meals, or subsidizing healthful foods are
controversial policy proposals to address the obesity epidemic. Our results suggest that such
policy instruments may be effective, to some extent, in reducing adolescent overweight.
13
References
Anderson, Patricia M., Kristin F. Butcher, and Phillip B. Levine (2003) Maternal Employment
and Overweight Children, Journal of Health Economics 22:477-504.
Binkley, JK; J Eales and M Jekanowski (2000) The Relation Between Dietary Change and
Rising US Obesity. International Journal of Obesity 24: 1032-1039.
Bowman, Shanthy A.; Steven L. Gortmaker; Cara B. Ebbeling; Mark A. Pereira and David S.
Ludwig. (2004) Effects of Fast-Food Consumption on Energy Intake and Diet Quality Among
Children in a National Household Survey. Pediatrics 113(1): 112-118.
Bowman, Shanthy A, Bryan T. Vinyard (2004) Fast Food Consumption of US Adults: Impact on
Energy and Nutrient Intakes and Overweight Status, Journal of the American College Nutrition
23(2): 163-168.
Chou, Shin-Yi, Michael Grossman and Henry Saffer (2004) An Economic Analysis of Adult
Obesity: Results From the Behavioral Risk Factor Surveillance System, Journal of Health
Economics 23: 565-587.
Chou, Shin-Yi, Michael Grossman and Henry Saffer (2006) Replt to Jonathan Gruber and
Michael Frakes, Journal of Health Economics 25: 389-393.
French, Simone A; L Harnack and RW Jeffery (2000) Fast Food Restaurant Use Among Women
in the Pound of Prevention Study: Dietary, Behavioral and Demographic Correlates.
International Journal of Obesity 24: 1353-1359.
French, Simone A; M Story; D Neumark-Sztainer; JA Fulkerson and P Hannan. (2001a) Fast
Food Restaurant Use Among Adolescents: Associations With Nutrient Intake, Food Choices and
Behavioral and Psychosocial Variables. International Journal of Obesity 25: 1823-1833.
French, Simone A.; Robert W Jeffery; Mary Story; Kyle K Breitlow; Judith S. Baxter; Peter
Hannan and M Patricia Snyder (2001b) Pricing and Promotion Effects on Low-Fat Vending
Snack Purchases: The CHIPS Study. American Journal of Public Health 91(1): 112-117.
French, Simone A.; Robert W. Jeffery; Mary Story; Peter Hannan and Patricia Snyder (1997a) A
Pricing Strategy to Promote Low-Fat Snack Choices through Vending Machines. American
Journal of Public Health 87(5): 849-851.
French, Simone A; A. Story; M. Jeffery, RW; Snyder, P.; Eisenberg, M.; Sidebottom, A. and
Murray D (1997b) Pricing Strategy to Promote Fruit and Vegetable Purchase in High School
Cafeterias. Journal of American Dietetic Association 97: 1008-1010.
Goodman, E; Hinden BR and Khandelwal S. (2000) Accuracy of Teen and Parental Reports of
Obesity and Body Mass Index. Pediatrics 106(1 Pt 1): 52-58.
14
Gruber, Jonathan and Michael Frakes (2005) Does Falling Smoking Lead to Rising Obesity?
NBER Working Paper No. 11483.
Guthrie JF, Lin B-H and Frazao E. (2002) Role of Food Prepared Away From Home in the
American Diet, 1977–78 versus 1994–96: Changes and Consequences. Journal of Nutrition
Education and Behaviors 34: 140-150.
Hannan, P.; French, SA; Story, M and Fulkerson, JA (2002) A Pricing Strategy to Promote
Purchase of Lower Fat Foods in a High School Cafeteria: Acceptability and Sensitivity Analysis.
American Journal of Health Promotion 17: 1-6.
Hedley, Allison A., Cynthia L. Ogden, Clifford L. Johnson, Margaret D. Carroll, Lester R.
Curtin, Katherine M. Flegal (2004) Prevalence of overweight and obesity among US children,
adolescents, and adults, 1999-2002. JAMA 291(23): 2847-50.
Jeffery, Robert W, Simone A French, Cheryl Raether and Judith E. Baxter (1994) An
Environmental Intervention to Increase Fruit and Salad Purchase in A Cafeteria. Preventive
Medicine 23: 788-792.
Johnston LD, O'Malley PM, Bachman JG, and Schulenberg JE (2004) Monitoring the Future
National Survey Results on Drug Use, 1975-2003, Vol. 1. Secondary School Students. NIH
Publications no. 04-5507. Bethesda, MD: National Institute on Drug Abuse.
Kuczmarski, MF, Kuczmarski RJ, Najjar M. (2001) Effects of Age on Validity of Self-Reported
Height, Weight, and Body Mass Index: Findings From the Third National Health and Nutrition
Examination Survey, 1988-1994. Journal of the American Dietetic Association 101(1): 28-34.
Lakdawalla, Darius, Thomas Philipson and Jay Bhattacharya (2005) Food Prices and Nutritional
Status. Presented at the International Health Economic Association Meetings, July 2005.
Lakdawalla, Darius and Thomas Philipson (2002) The Growth of Obesity and Technological
Change: A Theoretical and Empirical Examination. NBER Working Paper No. 8946.
Lin, Biing-Hwan and Rosanna Mentzer Morrison (2002) Higher Fruit Consumption Linked With
Lower Body Mass Index. Food Review 25(3): 28-32.
Lin, B.-H., Guthrie, J., and Frazao, E. (1999) Away-From-Home Food Increasingly Important to
Quality of American Diet. Agriculture Information Bulletin No. 749, Economic Research
Service, U. S. Department of Agriculture, 12 pp.
Lutz, Steven M and James R. Blaylock (1993) Household Characteristics Affect Food Choices.
Food Review 16(2): 12-18.
McCraken, Vicki A. and Jon A. Brandt (1987) Household Consumption of Food-Away-FromHome: Total Expenditure and by Type of Food Facility. American Journal of Agricultural
Economics (May): 274-284.
15
Neumark-Sztainer, Dianne; Melanie Wall; Cheryl Perry and Mary Story (2003) Correlates of
Fruit and Vegetable Intake Among Adolescents Findings from Project EAT. Preventive
Medicine 37: 198-208.
Nielsen, Samara Joy; Anna Maria Siega-Riz and Barry M. Popkin (2002) Trends in Food
Locations and Sources among Adolescents and Young Adults. Preventive Medicine 35: 107-113.
Ogden CL, Flegal KM, Carroll MD, Johnson CL. (2002) Prevalence and trends in overweight
among US children and adolescents, 1999-2000. JAMA 288: 1728-1732.
Paeratakul S., JC Lovejoy, DH Ryan and GA Bray (2002) The Relation of Gender, Race and
Socioeconomic Status to Obesity and Obesity Comorbidities in A Sample of US Adults.
International Journal of Obesity 26: 1205-1210.
Park, John L. and Oral Capps Jr. (1997) Demand for Prepared Meals by US Households.
American Journal of Agricultural Economics 79: 814-824.
Philipson, Tomas J and Richard A Postner (1999) The Long-Run Growth in Obesity As A
Function of Technological Change, NBER Working Paper No. 7423.
Siega-Riz, Anna Maria; Barry M. Popkin and Terri Carson. (2000) Differences in Food Patterns
at Breakfast by Sociodemographic Characteristics among a Nationally Representative Sample of
Adults in the United States. Preventive Medicine 30: 415-424.
Stewart, Hayden; Noel Blisard; Sanjib Bhuyan and Rodolfo M. Nayga Jr. (2004) The Demand
for Food Away From Home: Full-Service or Fast Food? Food and Rural Economics Division,
Economic Research Service, USDA. Agricultural Economic Report No. 829.
Strauss, RS (1999) Comparison of measured and self-reported weight and height in a crosssectional sample of young adolescents. International Journal of Obesity, 23, 904-908.
Sturm, Roland and Ashlesha Datar (2005) Body Mass Index in Elementary School Children,
Metropolitan Area Food Prices, and Food Outlet Density. Public Health, 119 (12): 1059-1068.
Wang Z, Patterson CM, Hills AP. (2002). A comparison of self-reported and measured height,
weight and BMI in Australian adolescents. Aust N Z J Public Health; 26(5):473-478.
Xie, Bin; Frnak D. Gilliland; Yu-Fen Li and Helaine RH Rockett. (2003) Effects of Ethnicity,
Family Income, and Education on Dietary Intake among Adolescents. Preventive Medicine 36:
30-40.
Yen, Steven T.; Kamhon Kan and Shew-Jiuan Su (2002) Household Demand For Fats and Oils:
Two-Step Estimation of a Censored Demand System. Applied Economics 14: 1799-1806.
16
Change in fruit&veg consumption from 1997
.01
0
-.01
-.02
No controls
Controlling for:
Prices and densities
All covariates
-.03
1997 1998 1999 2000 2001 2002 2003
year
Figure 1: Changes in frequent fruit and vegetable consumption over time
Figure 1 shows changes in prevalence of frequent fruit and vegetable consumption, relative to
1997. The solid line shows unadjusted changes in the sample means. The dashed line shows
changes that can be attributed to neither changes in fast food and fruit and vegetable prices nor
changes in restaurant outlet densities. The dotted line shows changes that cannot be attributed to
prices, densities, or any of the other covariates listed in Table 1.
17
Change in mean BMI from 1997
.5
.4
.3
.2
No controls
Controlling for:
Prices and densities
All covariates
.1
0
1997
1998
1999
2000
2001
2002
2003
year
Figure 2: Changes in mean BMI over time
Figure 2 shows changes in mean BMI, relative to 1997. The solid line shows unadjusted changes
in the sample means. The dashed line shows changes that can be attributed to neither changes in
fast food and fruit and vegetable prices nor changes in restaurant outlet densities. The dotted line
shows changes that cannot be attributed to prices, densities, or any of the other covariates listed
in Table 1.
18
Change in overweight from 1997
.03
.02
.01
No controls
Controlling for:
Prices and densities
All covariates
0
1997
1998
1999
2000
2001
2002
2003
year
Figure 3: Changes in overweight over time
Figure 3 shows changes in prevalence of overweight, relative to 1997. The solid line shows
unadjusted changes in the sample means. The dashed line shows changes that can be attributed
to neither changes in fast food and fruit and vegetable prices nor changes in restaurant outlet
densities. The dotted line shows changes that cannot be attributed to prices, densities, or any of
the other covariates listed in Table 1.
19
Table 1: Summary Statistics: Outcomes, Access, Price, and Control Variables
Body Mass Index (BMI)
Prevalence of Overweight
Frequent Consumption of Fruit and Vegetables
Per Capita Number of Full Service Restaurants
Per Capita Number of Fast Food Restaurants
Price of Fast Food ($82-84)
Price of Fruit and Vegetables ($82-84)
Weekly or More Frequent Physical Activity
Participation
Male
BMI/Overweight
Sample
21.8061
(4.2932)
0.1025
12.8358
(9.3673)
2.4492
(2.2019)
2.7130
(0.1743)
0.7206
(0.1047)
Fruit and Vegetable
Consumption Sample
0.7484
-
0.4754
14.6570
Age
(1.1637)
8th Grade
0.4852
th
10 Grade
0.5148
White
0.6957
Black
0.1065
Hispanic
0.0971
Other Race
0.1007
Father Less Than High School
0.1308
Father Complete High School
0.2933
Father College or More
0.5759
Mother Less Than High School
0.1110
Mother Complete High School
0.2798
Mother College or More
0.6092
Live With Both Parents
0.7997
Live In Rural Area
0.2382
22.8239
Students’ Weekly Income ($82-84)
(26.6573)
3.8568
Hours Worked by Student
(7.1437)
Mother Works Part-Time
0.1827
Mother Works Full-Time
0.6415
Year 1997
0.1471
Year 1998
0.1473
Year 1999
0.1398
Year 2000
0.1380
Year 2001
0.1392
Year 2002
0.1381
Year 2003
0.1505
N
72,854
Notes: Standard deviations are shown in parentheses for non-dummy variables.
0.5687
12.8491
(9.3779)
2.4512
(2.2089)
2.7137
(0.1744)
0.7204
(0.1046 )
0.4722
14.6606
(1.1649)
0.4828
0.5172
0.6972
0.1048
0.0970
0.1010
0.1328
0.2916
0.5756
0.1114
0.2760
0.6126
0.8004
0.2378
23.1516
(26.2636)
4.0074
(7.1902)
0.1839
0.6408
0.1474
0.1498
0.1397
0.1365
0.1392
0.1370
0.1504
47,675
20
Table 2: Trends in Outcomes, Access and Prices
Year
1997
1998
1999
0.0876
Per Capita
Number of Full
Service
Restaurants
12.6969
Per Capita
Number of
Fast Food
Restaurants
2.0169
2.7901
0.6680
(0.2827)
[N=10720]
(9.3399)
[N=10720]
(1.6154)
[N=10720]
(0.2167)
[N=10720]
(0.0696)
[N=10720]
21.6446
0.0913
12.3911
2.2918
2.7788
0.7021
(0.4929)
[N=7018]
(4.1767)
[N=10543]
(0.2881)
[N=10543]
(7.5903)
[N=10543]
(1.9633)
[N=10543]
(0.1685)
[N=10543]
(0.0680)
[N=10543]
0.5683
21.7325
0.0984
12.3945
2.1182
2.7699
0.7053
(0.4953)
[N=6709]
(4.2945)
[N=10237]
(0.2978)
[N=10237]
(9.1817)
[N=10237]
(1.9544)
[N=10237]
(0.1522)
[N=10237]
(0.1287)
[N=10237]
Frequent
Fruit and
Vegetable
Consumption
0.5795
21.5454
(0.4937)
[N=7057]
(4.0497)
[N=10720]
0.5841
BMI
Overweight
Status
Price of
Fast Food
Price of Fruit
and
Vegetables
2000
0.5696
21.8110
0.1063
11.7724
2.0792
2.6936
0.6939
(0.4952)
[N=6445]
(4.2290)
[N=9979]
(0.3082)
[N=9979]
(7.3920)
[N=9979]
(1.6964)
[N=9979]
(0.1404)
[N=9979]
(0.0649)
[N=9979]
2001
0.5576
21.9347
0.1151
13.6342
2.6355
2.6481
0.6770
(0.4967)
[N=6665]
(4.4579)
[N=10198]
(0.3192)
[N=10198]
(11.6241)
[N=10198]
(2.5242)
[N=10198]
(0.1514)
[N=10198]
(0.0796)
[N=10198]
2002
0.5471
21.9865
0.1099
14.2231
2.9521
2.6592
0.8141
(0.4978)
[N=6525]
(4.4200)
[N=10074]
(0.3128)
[N=10074]
(11.0536)
[N=10074]
(2.7759)
[N=10074]
(0.1478)
[N=10074]
(0.1086)
[N=10074]
2003
0.5719
21.9979
0.1102
12.7800
3.1363
2.6477
0.7835
(0.4948)
[N=7256]
(4.3998)
[N=11103]
(0.3131)
[N=11103]
(0.8493)
[N=11103]
(2.3519)
[N=11103]
(0.1524)
[N=11103]
(0.0998)
[N=11103]
0.5687
21.8061
0.1025
12.8358
2.4492
2.7130
0.7206
(0.4953)
[N=47675]
(4.2932)
[N=72854]
(0.3034)
[N=72854]
(9.3673)
[N=72854]
(2.2019)
[N=72854]
(0.1743)
[N=72854]
(0.1047)
[N=72854]
All
Years
Notes: Standard deviations are shown in parentheses and numbers of observations are shown in brackets.
21
Table 3: Effects of Access to Fast Food and Food Prices on Adolescent
Frequent Fruit and Vegetable Consumption, BMI and Overweight Status
Per Capita Number of
Full Service Restaurants
Per Capita Number of
Fast Food Restaurants
Price of Fast Food
Price of Fruit and
Vegetables
Male
Age
10th Grade
Black
Hispanic
Other Race
Father Less Than High
School
Father College or More
Mother Less Than High
School
Mother College or More
Live With Both Parents
Live In Rural Area
Students’ Weekly Income
Hours Worked by
Student
Mother Works Part-Time
Mother Works Full-Time
Year 1998
Year 1999
Year 2000
Year 2001
Year 2002
Frequent Fruit and Vegetable
Consumption
Model 1
Model 2
0.0019***
0.0019***
(0.0005)
(0.0005)
-0.0028
-0.0029
(0.0018)
(0.0019)
0.0730***
0.0669***
(0.0197)
(0.0201)
-0.0633**
-0.0632*
(0.0308)
(0.0353)
-0.0037
-0.0038
(0.0058)
(0.0058)
-0.0183***
-0.0183***
(0.0049)
(0.0049)
-0.0046
-0.0043
(0.0118)
(0.0118)
-0.1445***
-0.1450***
(0.0105)
(0.0104)
-0.0984***
-0.0978***
(0.0099)
(0.0099)
-0.0079
-0.0077
(0.0097)
(0.0097)
-0.0438***
-0.0436***
(0.0100)
(0.0100)
0.0777***
0.0778***
(0.0066)
(0.0066)
-0.0419***
-0.0421***
(0.0106)
(0.0106)
0.0796***
0.0796***
(0.0064)
(0.0064)
0.0670***
0.0669***
(0.0076)
(0.0076)
0.0118
0.0118
(0.0073)
(0.0072)
-0.0002
-0.0002
(0.0001)
(0.0001)
-0.0004
-0.0004
(0.0005)
(0.0005)
-0.0132
-0.0130
(0.0090)
(0.0090)
-0.0411***
-0.0410***
(0.0073)
(0.0073)
0.0135
(0.0110)
-0.0018
(0.0113)
0.0058
(0.0116)
-0.0115
(0.0115)
-0.0084
(0.0128)
Year 2003
-
0.0107
(0.0123)
Constant
-
-
47,675
47,675
N
BMI
Model 3
-0.0048
(0.0029)
0.0187
(0.0122)
-0.5757***
(0.1321)
0.6874***
(0.2027)
0.8053***
(0.0410)
0.2693***
(0.0342)
0.7503***
(0.0810)
1.1206***
(0.0646)
0.7244***
(0.0888)
-0.0482
(0.0667)
0.4569***
(0.0706)
-0.4586***
(0.0483)
0.1248
(0.0817)
-0.2013***
(0.0429)
-0.2609***
(0.0499)
0.2962***
(0.0530)
0.0006
(0.0010)
0.0084**
(0.0037)
-0.1063*
(0.0636)
0.1305***
(0.0509)
-
18.3290***
(0.6246)
72,854
Overweight
Model 4
-0.0039
(0.0029)
0.0084
(0.0124)
-0.3066**
(0.1397)
0.2688
(0.2392)
0.8041***
(0.0409)
0.2719***
(0.0341)
0.7324***
(0.0804)
1.1301***
(0.0639)
0.7015***
(0.0864)
-0.0615
(0.0669)
0.4551***
(0.0705)
-0.4558***
(0.0483)
0.1250
(0.0816)
-0.2095***
(0.0430)
-0.260***
(0.0499)
0.2917***
(0.0526)
0.0007
(0.0010)
0.0088**
(0.0037)
-0.1038
(0.0635)
0.1335***
(0.0508)
0.0373
(0.0731)
0.1226
(0.0767)
0.1569*
(0.0802)
0.2936***
(0.0795)
0.2864***
(0.0866)
0.3376***
(0.0836)
17.7112***
(0.6323)
72,854
Model 5
-0.0002
(0.0002)
0.0005
(0.0009)
-0.0398***
(0.0088)
0.0159
(0.0138)
0.0698***
(0.0029)
-0.0026
(0.0022)
0.0110**
(0.0051)
0.0529***
(0.0054)
0.0378***
(0.0061)
0.0083*
(0.0047)
0.0157***
(0.0044)
-0.0261***
(0.0032)
0.0071
(0.0052)
-0.0153***
(0.0032)
-0.0119***
(0.0034)
0.0208***
(0.0036)
0.00001
(0.00006)
-0.000001
(0.0002)
-0.0075
(0.0045)
0.0063*
(0.0034)
-
Model 6
-0.0002
(0.0002)
0.00003
(0.0009)
-0.0224**
(0.0097)
-0.0049
(0.0153)
0.0697***
(0.0029)
-0.0023
(0.0022)
0.0097*
(0.0051)
0.0535***
(0.0054)
0.0358***
(0.0059)
0.0073
(0.0047)
0.0154***
(0.0043)
-0.0260***
(0.0032)
0.0072
(0.0052)
-0.0158***
(0.0032)
-0.0118***
(0.0034)
0.0207***
(0.0035)
0.00001
(0.00006)
-0.00002
(0.0002)
-0.0074
(0.0045)
0.0064*
(0.0034)
0.0010
(0.0053)
0.0080
(0.0056)
0.0135**
(0.0060)
0.0227***
(0.0060)
0.0186***
(0.0068)
-
0.0194***
(0.0063)
-
-
72,854
72,854
Notes: Standard errors are clustered at the school zip code level and are shown in parentheses.
The symbols *, **, and *** represent statistical significance at the 10 percent, 5 percent, and 1 percent levels, respectively.
22
Table 4: Sensitivity Analyses of BMI and Frequent Fruit and
Vegetable Consumption to Alternative Model Specifications
Per Capita Number of
Full Service Restaurants
Per Capita Number of
Fast Food Restaurants
Price of Fast Food
Price of Fruit and
Vegetables
Mother Works Part-Time
Mother Works Full-Time
Frequent Fruit and
Vegetable Consumption
Model A
Model B
0.0019***
0.0019***
(0.0005)
(0.0005)
-0.0029
-0.0028
(0.0019)
(0.0019)
Model A
-0.0039
(0.0029)
0.0084
(0.0124)
Model B
-0.0039
(0.0030)
0.0082
(0.0124)
Model C
-0.0033
(0.0029)
0.0074
(0.0124)
0.0669***
(0.0201)
-0.3066**
(0.1397)
-0.3148**
(0.1402)
-0.3097**
(0.1396)
0.2688
(0.2392)
-0.1038
(0.0635)
0.2592
(0.2407)
0.3007
(0.2384)
-0.0926
(0.0635)
-0.0632*
(0.0353)
-0.0130
(0.0090)
0.0410***
(0.0073)
0.0684***
(0.0201)
-0.0611*
(0.0354)
-
BMI
0.1335***
(0.0508)
-
0.1484***
(0.0509)
Frequent Participation in
-0.5593***
Physical Activity
(0.0468)
N
47,675
47,675
72,854
72,854
72,854
Notes: 1. Standard errors are clustered at the zip code level and are shown in parentheses.
2. The symbols *, **, and *** represent statistical significance at the 10 percent, 5 percent,
and 1 percent levels, respectively.
3. Model A: Model 2 specification from Table 3.
Model B: Model A without mothers’ work status.
Model C: Model A with students’ sports participation.
All of the models include socioeconomic variables: gender, age, grade, race/ethnicity,
fathers’ education, mothers’ education, living arrangement, urbanization of residence,
students’ weekly income, hours worked by student and year dummies.
23
Table 5: Explaining Trends in Fruit and Vegetable Consumption, BMI and Overweight with Alternative Model Specifications
Frequent Fruit and Vegetable
Consumption
Year Effects
(Omitted Year 1997)
BMI
Overweight
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
Model 1
0.0046
(0.0123)
-0.0113
(0.0139)
-0.0100
(0.0139)
-0.0220*
(0.0133)
-0.0325**
(0.0143)
-0.0076
(0.0136)
0.0086
(0.0123)
-0.0064
(0.0137)
0.0012
(0.0138)
-0.0115
(0.0134)
-0.0154
(0.0159)
0.0128
(0.0146)
0.0135
(0.0110)
-0.0018
(0.0113)
0.0058
(0.0116)
-0.0115
(0.0115)
-0.0084
(0.0128)
0.0107
(0.0123)
0.0992
(0.1000)
0.1870*
(0.1119)
0.2656**
(0.1142)
0.3893***
(0.1149)
0.4411***
(0.1185)
0.4525***
(0.1123)
0.0951
(0.1011)
0.1818
(0.1130)
0.2376**
(0.1177)
0.3713***
(0.1191)
0.4406***
(0.1393)
0.4299***
(0.1277)
0.0373
(0.0731)
0.1226
(0.0767)
0.1569*
(0.0802)
0.2936***
(0.0795)
0.2864***
(0.0866)
0.3376***
(0.0836)
0.0042
(0.0059)
0.0119*
(0.0063)
0.0204***
(0.0070)
0.0298***
(0.0066)
0.0243***
(0.0069)
0.0246***
(0.0066)
Food Prices and
Restaurant Densities
-
Included
Included
-
Included
Included
All Other Covariates
-
-
Included
-
-
Included
Year 1998
Year 1999
Year 2000
Year 2001
Year 2002
Year 2003
Model 2
Model 3
0.0044
(0.0059)
0.0118*
(0.0063)
0.0177***
(0.0070)
0.0263***
(0.0068)
0.0241***
(0.0080)
0.0229***
(0.0073)
0.0010
(0.0053)
0.0080
(0.0056)
0.0135**
(0.0060)
0.0227***
(0.0060)
0.0186***
(0.0068)
0.0194***
(0.0063)
-
Included
Included
-
-
Included
Notes: 1. Standard errors are clustered at the zip code level and are shown in parentheses.
2. The symbols *, **, and *** represent statistically significant changes in BMI relative to year 1997 at the 10 percent, 5 percent, and 1 percent levels, respectively.
24