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Federal Reserve Bank of Chicago

Reading, Writing, and Raisinets1:
Are School Finances Contributing to
Children’s Obesity?
Patricia M. Anderson and Kristin F. Butcher

WP 2004-16

Comments Welcome
October 2004

Reading, Writing, and Raisinets1:
Are School Finances Contributing to Children’s Obesity?

Patricia M. Anderson

Kristin F. Butcher

Department of Economics
Dartmouth College
Hanover, NH 03755-3514
and
National Bureau of Economic
Research

Federal Reserve Bank of
Chicago
Research Department
230 S. LaSalle Street
Chicago, IL 60604
kbutcher@frbchi.org

patty.anderson@dartmouth.edu

We thank Sara Christopher, Karenne Eng, AJ Felkey, Katharine Anderson, and Fred Yarger for
excellent research assistance. Nancy Brener provided advice in using the SHPPS data. We thank
Lisa Barrow, David Card, Kevin Hallock, Helen Levy, Thomas Lemieux, Doug Staiger, and
participants in UC-Berkeley’s labor lunch series, the 2003 NBER Summer Institute, and the
Chicago Fed’s brown bag series for helpful discussions. The views expressed here are the
authors’and do not necessarily represent those of the Federal Reserve Bank of Chicago or the
Federal Reserve System. All errors are our own.

1

This is in no way meant to impugn Raisinets, the Nestle Company or any of its other products.

Abstract
The proportion of adolescents in the United States who are obese has nearly tripled over the last
two decades. At the same time, schools, often citing financial pressures, have given students
greater access to “junk” foods and soda pop, using proceeds from these sales to fund school
programs. We examine whether schools under financial pressure are more likely to adopt
potentially unhealthful food policies. Next, we examine whether students’ Body Mass Index
(BMI) is higher in counties where a greater proportion of schools are predicted to allow these
food policies. Because the financial pressure variables that predict school food policies are
unlikely to affect BMI directly, this two step estimation strategy addresses the potential
endogeneity of school food policies. We find that a 10 percentage point increase in the
proportion of schools in a county that allow students access to junk food leads to about a one
percent increase in students’ BMI, on average. However, this average effect is entirely driven by
adolescents who have an overweight parent, for whom the effect of such food policies is much
larger (2.2%). This suggests that those adolescents who have a genetic or family susceptibility to
obesity are most affected by the school food environment. A rough calculation suggests that the
increase in availability of junk foods in schools can account for about one-fifth of the increase in
average BMI among adolescents over the last decade.

I. Introduction
Over the past three decades, weight problems among children have grown dramatically.
After holding fairly steady at around 5% during the 1970s, the percent of 12 to 19 year-olds that
were obese doubled by the early nineties and exceeded 15 percent by 2000 (Ogden, et al., 2002).2
At a basic physiological level, the cause of this increase in overweight status among children is
clear: children must be taking in more energy than they expend. What is unclear is what has
upset the balance between energy intake and expenditure.
Observers have begun to question the role played by schools, pointing in particular to
declines in physical education and increases in the availability of soft drinks and snack foods.
New accountability measures, which typically require that students achieve a certain minimum
level on standardized tests or the school suffers consequences, may give schools an added
incentive to invest resources in core academic curricula. At the same time, schools may try to
raise new money in order to meet the achievement goals while minimizing the need to cut noncore programs. However, the property tax reform movement during the 1970s and 1980s may
have limited schools’ ability to raise money through traditional means. One way schools can get
extra money to maintain optional programs or strengthen core academics is through soft drink
and vending contracts, or through other snack food sales. The media is rife with examples of
schools cutting deals with soda and snack vending companies in order to increase their
discretionary funds. For example, one high school in Beltsville, MD made $72,438.53 in the
1999-2000 school year through a contract with a soft drink company and another $26,227.49
through a contract with a snack vending company. The almost $100,000 obtained was used for a
variety of activities, including instructional uses such as purchasing computers, as well as
2

“Obese” here is defined as a BMI above an age-sex specific cutoff defined by the Centers for Disease control. This
cutoff corresponds closely to a BMI above the 95th percentile of age-sex specific BMI distributions from the late

1

extracurricular uses such as the yearbook, clubs and field trips (Nakamura, 2001). District level
contracts can be even more lucrative – one Colorado Springs district, for example, negotiated a
10-year beverage contract for $11.1 million dollars (DD Marketing, 2003).3
The purpose of our study is two-fold. First, we examine whether availability and
advertising of snack foods and beverages in schools are related to school financial pressures, as
captured by tax and expenditure limits, school accountability measures, state school financing
rules, and relative growth in the school aged (local) population. Second, we examine whether
availability and advertising of snack foods and beverages in schools can be linked to adolescent
obesity. As school districts nationwide debate the benefits and costs of entering into contracts
with soda companies or banning the sales or advertising of snacks and sodas on campus it is
important to have solid information on which to base these decisions. For example, high-calorie
snack foods and beverages may be so ubiquitous that adolescents will consume them whether or
not they are available through the school. If that is the case, policy-makers might prefer schools
to sell the foods students crave. In that way, at least students are not leaving school to buy snack
food (with all the attendant dangers that may entail) and schools can use the extra funds to
students’ advantage.
The effect of school food policies on children’s weight is an important, but difficult to
answer, question. Similar to questions about the impact of school quality on children’s academic
or labor market outcomes, one worries that school characteristics may be correlated with
individual or family characteristics in systematic ways. In addition, examining the effect of
school food policies on children’s weight problems is difficult because there is no one data set

1960s and early 1970s, prior to the advent of the current weight problems in children.
3
While on a per pupil basis these contracts amount to only about 0.5 percent of revenues (authors’ calculations
based on average revenues in NCES and reports on contracts from DD Marketing), they provide a source of
unrestricted funds that may be spent on elective programs.

2

that contains information on children’s height and weight, individual and family characteristics
that may affect weight problems, and school food policies. We use a two-sample procedure to
overcome these difficulties. The two- sample procedure overcomes the data availability
challenge because only the information needed to estimate the first stage – in our case these are
state and county characteristics -- must be in both samples. As an added benefit, the procedure
addresses the problem that school food policies may be endogenously determined. We combine
information from the School Health Policies and Programs Study (SHPPS), the National Center
for Education Statistics (NCES), the 1990 and 2000 U.S. Censuses, and the National
Longitudinal Survey of Youth 1997 (geocode version) to create the two samples that allow us to
examine the effect of school health policies on adolescent Body Mass Index (BMI). 4 We find
that there is a positive and often significant effect of predicted availability and advertising of
snack foods and beverages on BMI. However, this relationship is driven by those with an
overweight parent. We interpret our results to indicate that while for most students, school food
policies have no affect on their weight, for those with a family susceptibility for weight gain,
these policies that increase access to snack foods and beverages in school may be a contributing
factor.
The paper is organized as follows. Section II provides background on the issues of
school food policies and obesity in the United States. Section III describes our empirical
approach and discusses our main estimates of the relationship between school food policies and
finances and of the effect of school food policies on adolescent obesity. Section IV then
explores the interaction of school food policy and family susceptibility to weight problems, and
Section V concludes.

4

Body mass index is weight in kilograms divided by height in meters squared.

3

II. Background on School Food Policies and Obesity in the United States
Public health officials are alarmed at the increase in obesity in the United States. The
increase in childhood obesity is particularly worrisome as obesity in childhood has both
immediate and long-term health risks, including Type 2 diabetes, hypertension and
cardiovascular disease (Ebbeling et. al. 2002), as well as contributing to low quality of life scores
(Schwimmer et al. 2003). Using data from the National Health and Nutrition Examination
Surveys (NHANES), Anderson, Butcher and Levine (2003a) show that for both children and
adults, the BMI distributions for 1971-1974 and 1976-1980 are very similar, particularly in the
right tail of the distribution. However, beginning with the 1988-1994 NHANES, this right tail
gets thicker.5 The obesity “epidemic” does not appear to be a matter of a shift to the right of the
entire distribution of BMI. Rather, the distributions suggest that whatever changes have taken
place to upset the balance between energy in-take and energy expenditure have not affected
everyone in the same way. There appears to be some fraction of the population that is
particularly susceptible to obesity, and the conditions have become optimal for their disease to
flourish.
It is in this setting that school food policies are currently being hotly debated.
Policymakers are acting on the intuitive notion that having snacks and sodas readily accessible in
schools contributes to children’s obesity.6 Despite movements on the legal and policy fronts,
there are very few studies that address whether there is a direct relationship between school food

5

The timing of this change may help focus the investigation of the underlying cause in the increase in childhood
obesity. Researchers might fruitfully examine things that changed for children during this period. For example,
employment among mothers with young children increased in prevalence and intensity during this period. Anderson,
Butcher, and Levine (2003b) examines the role of maternal employment in the probability that a child is obese and
finds that increases in mothers’ hours of work can potentially explain between 12 to 35 percent of the increase in
childhood obesity among the children of highly educated mothers.
6
For example, New York City public schools recently banned candy, soda and other sugary snacks from school
vending machines (Perez-Pena, 2003), similarly the Los Angeles school district has banned the sale of soft drinks
during school hours (Fried and Nestle, 2002).

4

policies and obesity.7 What is clear, however, is the pervasiveness of snack foods and beverages
in U.S. schools. Data from the School Health Policies and Programs Study (SHPPS) from 1994
and 2000 form a nationally representative sample of schools, and include both public and private
schools. As shown in Anderson, Butcher and Levine (2003a), these data reveal two clear
patterns. First, availability of junk food increases with grade level. While only 27 percent of
elementary schools had vending machines available to students in 2000, that percentage rises to
67 percent for middle schools and to 96 percent for high schools. Second, in the few questions
asked consistently across the two years, availability has increased. For example, while 19
percent of high schools served a brand name fast food in 1994, 26 percent did by 2000.
Before examining the effect of school food policies on children’s weight, we first present
measures of BMI and obesity from the NHANES for comparison with the NLSY97 data used in
our study. BMI for individuals in the NLSY97 is constructed from self-reported height and
weight, while the NHANES includes an examination module where height and weight are
measured. In table 1 we report mean, median, and 95th percentile BMI for the NLSY97 analysis
sample and for the sub-sample of similarly aged (14 to 20 year old) respondents in the NHANES
(1999-2000). Since we control for parental BMI in the analysis below, we also report BMI for
parents in the NLSY97 and for similarly aged (32-67 year old) adults in the NHANES.
Columns 1 and 2 in Table 1 show measures of BMI for adolescents. Mean and median
BMI are both slightly higher in the NHANES, where height and weight are measured by an
examiner, than in the NLSY97, where height and weight are self-reported. However, BMI at the
95th percentile is close to 35 in the NHANES and only 32 in the NLSY97. This translates into

7

Studies on related topics include Ludwig, et al. (2001), Cullen et al. (2000), and Kubik et al. (2003) who examine
school food and overall nutrition. Additionally, Pateman, et al. (1995) and Weschler et al. (2001) examine the
availability of junk food in schools. Finally, Carter (2002) and Fried and Nestle (2002) speculate about the
correlation between school policies and adolescent obesity, but do not formally investigate the relationship.

5

about 4 percent more adolescents categorized as obese in the NHANES than in the NLSY97
sample. The data for adults shows a similar pattern. Both comparisons suggest that very heavy
people are under-reporting their weight in the self-reported data, such that the self-reported data
are prone to some measurement error.8

III. Effect of School Food Policies on Adolescent Obesity
A. Methodology
Much of our empirical strategy is dictated by the realities of the data at our disposal. In
particular, there are no available data sets that include school policies regarding junk food,
school financial pressure indicators and individual heights, weights and demographics. Thus, we
adopt a two-sample approach (Angrist and Krueger, 1992, 1995). If we had data with all of the
elements described above, we could directly investigate the relationship between school policies
and student obesity. However, even in this case one would be concerned about bias due to the
possible endogeneity of the key food policy variables. Thus, even with access to a richer data
set, we would still want to use an instrumental variables approach. While data limitations restrict
our ability to estimate the simple OLS relationship between obesity and school food policies in
the full sample, our two-sample methodology is essentially an IV estimator and should address
the endogeneity issue.9
We estimate the first stage using school food policy information from the SHPPS.
Specifically, we analyze three policies: junk food availability in schools, whether schools have
“pouring rights” contracts, and whether soda and snack food advertisements are allowed at
8

Empirical methods for correcting self-reported height and weight are developed in Cawley (1999), but make no
substantive difference in this analysis.
9
For a sub-sample of the data, we can directly match the county-level policy data with the individual data. In this
case, the true IV point-estimates are very close to those obtained using the two sample method with these data.

6

schools or school events.10 We aggregate these to the county level using the SHPPS school
weights. Our first stage, then, estimates the fraction of schools in a county with these policies as
a function of county, state, and regional characteristics. County characteristics include the
growth rate of the school age population relative to total population in the county, calculated
from the 1990 and 2000 U.S. Censuses.11 Next, we control for the fraction of school finances that
come from the state, calculated from the NCES data. The NCES provides this information at the
district level and we use district enrollments to aggregate to the county level. The state
characteristics include an indicator variable for whether the state has a tax or expenditure
limitation and an indicator for whether the state has passed a school accountability measure. 12
We also include a vector of three region dummies (the excluded category is the West).
Specifically, we estimate the following:
(1)

policyc = γ0 + γ1relative school-age population growth ratec + γ2fraction of revenues from
statec + γ3tax limitations + γ4accountabilitys + γ5Rs + ωc

where the c subscript represents county and the s subscript represents state.
The relative growth in the school-age population is meant to capture budgetary pressure
on the school system, thus we expect γ1 to be positive. If the share of children has grown relative
to the share of adults, then there may be more financial pressure on schools, and they may be

However, the two-sample method sacrifices efficiency, resulting in larger standard errors. See appendix table 2.
10
“Junk Food Available” means that students can buy chocolate, candy, cakes, ice cream, or salty snacks (that are
not fat free) from a machine or school store. “Pouring Rights” contract means the school has agreed to sell one
brand of soft drinks, often in exchange for a percentage of sales or other incentive packages. “Soda or Snack Food
Advertisements” means that advertisements are allowed at least at one type of school related activity or in one or
more places at the school – for example, on a school bus, at a school sporting event, on school grounds, or school
textbooks etc.
11
Specifically, this is defined for the county as the logarithm of the 5 to 17 year old population in 2000 minus the
logarithm of the 5 to 17 year old population in 1990, all divided by the logarithm of total population in 2000 minus
the logarithm of total population in 1990.
12
We thank David Figlio for the tax and expenditure limit indicators and Margaret Raymond for the accountability
indicators. See data appendix table 2 for a list of which states have tax and expenditure limits and which states have
accountability measures.

7

more likely to adopt these food policies that generate discretionary funds. Similarly, we expect
γ4 to be positive, since schools in states with accountability laws may be under pressure to meet
certain performance criteria. These criteria generally take the form of standardized tests scores
(not measures of students’ physical health), and thus schools may divert resources toward core
academics and away from other programs. In order to preserve optional programs, schools may
come up with creative ways to raise additional funds, including sales of snack foods and
beverages.
The tax and expenditure limit indicator and the fraction of school revenues that come
from the state are meant to capture how difficult it may be for schools to raise additional funds
for valued programs. It may be more difficult for schools to raise funds through traditional
means in a state with strict tax and expenditure limits, thus we expect γ3 to be positive.
Similarly, each state has a funding formula that specifies how much of a school district’s funds
will come from the state.13 Within state, this fraction tends to be negatively related with fiscal
capacity. However, there is also a good deal of variation at the state level.14 Across states, more
school funding coming from the state may reflect more difficulty for local school districts to
unilaterally decide to raise funds. This difficulty may simply be political – that is the district
relies more on the state because it is difficult to pass higher local property tax rates.
Alternatively, the difficulty may stem from characteristics of the local tax base. For example,
localities with a large commercial base may be in a better position to raise funds because the
businesses pay local taxes, but do not increase the number of children requiring education.

13

The most common approach is the foundation program, but other methods include a flat grant, percentage
equalizing, guaranteed tax base or guaranteed equal yield programs (Sielke and Holmes).
14
In our SHPPS data, the average ranges from 32 percent in South Carolina to 75 percent in New Mexico, while in
the NLSY97 data, New Hampshire has the minimum at 11 percent, and Hawaii the maximum at 88 percent.

8

Finally, we control for region of the country (R) in both the first and second stage to control for
additional factors that may influence school food policy decisions and children’s obesity.
Having obtained estimates of γ0, γ1, γ2, γ3, γ4 and γ5 using the SHPSS/NCES county-level
data, we then use these estimates to predict food policies in individual-level data from the
NLSY97. Because the independent variables in the first stage vary only at the county and state
level, and since we know the county of residence for the NLSY97, we can append the
appropriate fiscal, legal, and population change variables to the individual data and create
predicted food policies based on the first stage estimates. We can then estimate the effect of
predicted food policy on individual weight status, controlling for additional covariates.
To do so, we use a model of the following form:
(2)

ln(bmi)i = α + β1 predicted policyc + β2Xi + β3Fi + β4Ri + εi

where ln(bmi) is the log of the individual’s Body Mass Index; X is a set of individual-level
covariates, including age, race, sex and cigarette use; F is a set of family background covariates,
including family income, mother’s and father’s education and the log of the responding parent’s
BMI; and R is a set of region dummies. All of the data used in both stages are described in more
detail in the Data Appendix. Finally, we adjust the standard errors for arbitrary forms of
heteroskedasticity and within county correlation. In addition, we adjust the standard errors for
the fact that the policy variables are estimated (see Murphy and Topel, 1985).
Our maintained assumption is that the variables used in the first stage to estimate the
fraction of schools in a county with these three food policies – aside from region – do not
directly affect individual’s BMI. It seems quite plausible that individuals’ energy intake and
expenditure is not governed by state tax policy. Nonetheless, one might be concerned that there
are characteristics of individuals or places that affect BMI that are correlated with our predicted

9

school food policy variables. For example, one might be concerned that socioeconomic status,
which research shows is correlated with obesity, is also correlated with our predicted food
policy. We do several things to allay our worries on this score. First, we include a rich set of
controls for socioeconomic status in the second stage. If the variation in policy generated from
the first stage were coming from underlying variation in socioeconomic status, we would expect
the estimated impact of the policies in the second stage to be very sensitive to whether or not we
control for these characteristics. In addition, because we have more than one excluded
instrument, we can construct overidentification tests for the null hypothesis that our instruments
are exogenous. In all cases, we fail to reject the null hypothesis. Additionally, for a subset of the
data where the counties in the SHPPS and NLSY97 data overlap, we can estimate ordinary least
squares (OLS) and instrumental variables (IV) models. If our instruments were positively
correlated with low socioeconomic status (and we were not able to control for that adequately in
the second stage) we would expect the OLS estimates to be much larger than the IV estimates.
We find the opposite.
Finally, one might worry that there are unobserved characteristics at the state level that
drive both food policies and adolescent BMI. In this case, one would want to control for state
fixed effects in the second stage. However, we cannot do this because much of our variation in
our policy variables is at the state level. In order to see whether we are merely picking up state
differences, we estimated a set of “placebo” results where we estimated the first stage with a full
set of state dummies, but excluded these from the second stage. If this had given us similar
results to those reported below, we would have been concerned that our instruments were
working through unobserved state differences, as opposed to through the budgetary pressures

10

posited above. However, we find no effect of the food policy variables when predicted from
state fixed effects.
B. The Relationship between School Food Policies and Finances
The results from estimating the first stages described by equation (1) above are shown in
Table 2. The first column gives the means of the independent variables while the first row gives
the means for the dependent variables across the 180 counties in the SHPPS. First, note that all
of the estimated effects for the instruments are of the predicted sign. Since higher values for
each independent variable represents a higher level of local budgetary pressure, our hypothesis
predicts a positive coefficient. For both junk food and pouring rights, the fraction of total
revenues that come from the state is significantly positive. For both junk food and school
advertising, the tax and expenditure limit indicator is significant. Thus, at least one of the
variables is significant in each case. The F-statistics reported at the bottom of the table indicate
that the excluded instruments combined are significant for both junk food and pouring rights, but
not significant at conventional levels for school advertising. Note that the standard errors are
corrected for arbitrary forms of heteroskedasticity and within state correlation.
C. The Effect of Predicted School Policies on Obesity
The results from estimating equation (2) described above are reported in Table 3.
Columns (1) – (2) use junk food availability as the predicted policy, columns (3) – (4) use
pouring rights, and columns (5) – (6) use soda or snack food ads. For each policy, the first
model includes no covariates other than region dummies, while the second estimates equation (2)
in full. The first thing to note is that the additional covariates have very little impact on the key
policy coefficient. This result is not really unexpected, given that the policy variable is predicted
based on exogenous fiscal policy measures that are unlikely to be highly correlated with the

11

individual’s demographic background. We present the p-value for overidentification test with
the null hypothesis that the instruments are exogenous. We fail to reject the null hypothesis in all
cases (although we come somewhat close in the case of advertisements).
Focusing on the results that include the full set of controls, the column (2) estimates
imply that a 10 percentage point increase in the proportion of schools in a county that make junk
food available to their students, is correlated with a nearly 1 percent (0.90%) increase in
students’ BMI. The results are smaller for the fraction of schools with pouring rights contracts,
and marginally significant. The results for the fraction of schools that allow advertising are
smaller still, and are insignificant. This result suggests that it is the actual availability of
potentially unhealthful foods that affects adolescents’ weight, as opposed to the type of contract
the schools sign with food purveyors or the advertising they allow.
Turning to the estimates for the other variables, in all cases the point estimates imply that
females and those from richer families have lower BMIs, while blacks and Hispanics have higher
BMIs.15 Perhaps the most interesting control variable is the parental BMI, which implies an
elasticity of 0.22 between child and parent.16
For a nonrandom subset of the data, we can match the county-level policy from the
SHPPS data to the individual-level data from the NLSY97.17 There are 1007 individuals, living
in 78 different counties for which we can perform this match. As a check on our two-sample
procedure, we estimate several models with these matched data. First, we estimate equation (2)
using OLS, where “predicted policy” is no longer predicted, but is the matched county policy.
15

Note that in the NHANES females are not less likely to be obese, so our finding may be a reflection of sexspecific errors in the NLSY97 self-reported height and weight
16
Note that 83 percent of the time this parent is the biological mother, while 11% of the time it is the biological
father. Another 3% are a related female guardian and the rest are mainly step-mothers. If we limit the sample to
only those for whom the responding parent is the biological mother, we get nearly identical results.
17
Data appendix table 1 shows summary statistics for all the variables in the overall and matched samples of the
NLSY97. The means are very similar for the 2 samples, especially for BMI. The main differences seems to be that

12

We then estimate the same model by IV, using the state and county legal, finance, and
population change variables from equation (1) as the excluded instruments. Finally, we estimate
the two-sample procedure on the smaller matched sample and compare these results with the
actual IV results. The first stage results are reported in appendix table 1. The second stage results
are reported in appendix table 2.
The point estimates from the two-sample and IV models are very similar to each other,
and to those in Table 3. However, the smaller sample size results in relatively large standard
errors, such that none are significant at conventional levels. For junk food, recall that in table 3
the estimated effect was 0.090, with a standard error of 0.045. The IV model in the matched
sample produces an estimated effect of 0.140, but with a standard error of 0.084. Standard errors
are even larger with the two-sample method. The matched sample two-step procedure yields a
lightly larger estimate of 0.160, with a standard error of 0.117. Based on the matched sample,
we conclude that our two-sample method is producing estimates similar to what would be
obtained from a more standard IV model, but that we pay a price with slightly larger standard
errors.
A perhaps more interesting comparison is that between the OLS and IV estimates for the
matched sample. The OLS point estimates are uniformly smaller (between 0.019 and 0.034)
than the IV or two-sample estimates, and not significantly different from zero. The implication,
then, is that the OLS estimates are biased downward. One possibility is that the types of areas
with more junk food, pouring rights contracts and snack food ads are the types of areas for which
other unobservables would tend to produce leaner adolescents.18 For example, suppose that
parental demand for better academic achievement, more extracurricular activities (or both) is
there are more respondents in the West and fewer in the South in the matched sample.
18
Hausman tests for whether the policies are exogenous reject exogeneity in the cases of junk food availability and

13

behind schools raising additional funds through food policies. These same demanding parents
may provide more healthful foods and exercise opportunities in their homes than less involved
parents, resulting in a negative bias in the OLS estimates. Alternatively, simple attenuation bias
due to measurement error in the policy variable may explain the larger IV estimates.

IV. The Interaction of School Food Policy and Genetics (or Family Susceptibility)
The overall results can be summarized as implying that a 10 percentage point increase in
the proportion of schools with junk food is correlated with about a 1 percent higher BMI for the
average student. There are smaller and often insignificant effects for the other food policies
examined here. One should not read the estimates in table 3, however, as implying that every
student exposed to such food policies will increase their BMI by 1 percent. A fairly large
literature exists documenting a strong genetic component to weight (e.g. Grilo and Pogue-Geile,
1991). At the same time, the increases in obesity seen over the past 20 years clearly cannot be
attributed to changes in the gene pool. Thus, it seems reasonable to expect that some portion of
the population has a genetic susceptibility for weight gain, and under certain conditions will, in
fact, be more likely to gain weight. The idea of a genetic predisposition for weight gain is at the
heart of the theory of the “thrifty gene” by pioneering geneticist James Neel (Neel, 1962). In
studying the Pima Indians, he theorized that in difficult times, when many Pima died of
starvation, the survivors had a genetic advantage in storing energy as fat. This “thrifty gene” was
then passed on to future generations. In modern times, when the Pima live in an environment of
relative caloric abundance, this genetic predisposition toward more efficient fat storage results in
high rates of obesity.

snack food and beverage advertising.

14

It is with this idea of the interaction between nature and nurture in mind that we estimate
equation (2) separately by parental weight status. Table 4 shows results separately for those
whose responding parent is normal weight (BMI<25), overweight (25 <=BMI <30), and obese
(BMI>=30). For completeness we show the results for all the policies, although only those for
junk food availability are significantly different from zero. While it is difficult to get precise
estimates for these smaller subgroups (especially the obese parent group), the effect of junk food
on adolescents whose parents are overweight is statistically significantly different from those
whose parents are normal weight.19 For the students with a normal weight parent, there is
essentially no effect of junk food (or any of the other policies) on their BMI. However, for those
whose parent is overweight, a 10 percentage point increase in the proportion of schools with junk
food availability increases their BMI by over 2 percent (2.21%). The estimates for the other
school food policies are less precise. However, in each case, the point estimates for those with
overweight parents are much larger. Thus, there appears to be good evidence for the idea that for
those genetically susceptible to weight gain, the school environment may play an important role.
At the same time, for the 44 percent of students with normal weight parents, the school food
policy makes no difference at all.

V. Summary and Avenues for Future Research
Researchers and public health officials are currently at a loss to explain the rapid rise in
weight problems among children and adolescents. While it is clear that weight gain is
attributable to taking in more energy than one expends, it is unclear what has upset the balance
between energy intake and expenditure in recent decades. Thus, it is important to consider the
environmental factors that may affect either the intake or expenditure of energy. In looking at
19

We reject that the estimates are the same with a p-value of 0.047.

15

adult obesity, Cutler, Glaeser and Shapiro (2003) point to technological innovations over recent
decades that have lowered the time cost of food consumption. The result of these lower time
costs is increased caloric intake. In the adolescent context, the increasing number of schools
with vending machines provided for student use might be considered to be an example of this
type of technological innovation.
Perhaps not surprisingly, then, policy makers have begun to point to school food policies
as potentially important contributors to student weight problems. Although no solid evidence
currently exists on this link, several large school districts have banned or severely restricted the
availability of sodas and snack foods in schools. This paper takes a first step at assessing the
effect of school food policies on adolescent weight.
We find that schools that are under financial pressure (as captured by our available data)
are more likely to make junk food available to their students, have pouring rights contracts, and
allow food and beverage advertising to students. We use measures that capture financial pressure
to predict the fraction of schools in a county with these particular food policies, and then
estimate the effect of the fraction of schools in a county with these food policies on adolescent
BMI. This two-step method is meant to give us variation in school food policies that is correlated
with schools’ fiscal capacity, but not directly correlated with unobservable factors linked to the
prevalence of unhealthy weights among the students. We find fairly robust evidence that an
increase in the proportion of schools making junk food available to students is linked to an
increase in students’ BMI. Our results for the other school policies, pouring rights contracts and
food and beverage advertisements are smaller and less precise. The results suggest that a 10
point increase in the percentage of schools in a county that allow their students access to junk
food leads to a 1 percent increase in students’ BMI. Since average weight for adolescents in this

16

sample is about 148 pounds, this translates into about 1.5 extra pounds per 10 percentage point
increase in availability.
This result, however, masks large differences between those who we would think have a
family or genetic susceptibility to obesity and those who do not. For those students with normal
weight parents, there is no effect of school food policies on their BMI. However, for students
with overweight parents, the effect of a 10 percentage point increase in schools making junk food
available to students is a greater than 2 percent increase in students’ BMI. Since the average
weight among adolescents with an overweight parent is 166 pounds, this translates into 3.3 extra
pounds per 10 percentage point increase in junk food availability. This is consistent with the
facts of the obesity epidemic: people in the right hand tail of the BMI distribution have been
putting on weight more rapidly than those in the rest of the distribution. As mentioned earlier,
there appears to be a portion of the population that is susceptible to obesity, and the current
environment is one that promotes their disease. For students with this susceptibility, these school
food policies appear to be part of the environment that encourages their propensity to gain
weight.
Currently, policy makers are acting to reduce access to junk food and soda pop in a
number of school districts around the country, with the express purpose of curbing adolescent
obesity. We can do a rough “back of the envelope calculation” to bound how much of the recent
increase in adolescent BMI may be attributed to the increased availability of such foods in
schools over the last decade.20 Data from the 1994 and 2000 waves of the SHPPS show that the
percentage of high schools giving their students access to vending machines increased from 88
percent to 96 percent, or by 8 percentage points. Our estimates reported here imply that an 8

20

This calculation uses the authors’ calculations from the NHANES and SHPPS data reported in Anderson, Butcher,
and Levine (2003a) tables 1 and 6.

17

percentage point increase in the number of schools allowing students access to junk food would
translate into about a 0.8 percent increase in BMI on average.21 Data from the NHANES show
that BMI among 12-19 year olds increased by about 3.5 percent between the 1988-1994 and the
1999-2000 interviews. Roughly then, about a fifth (22%) of the average increase in adolescent
BMI could be attributed to the increase in availability of junk food in schools. While we would
like to do a similar calculation for those whose parents are overweight, we do not have
information on parental BMI in the NHANES.22
Thus, policy makers may be disappointed if they are expecting reductions in soda pop
availability in schools, for example, to be a “magic bullet” in the fight against adolescent obesity.
Future research might fruitfully examine the impact of these changes on the ground in New York
and Los Angeles and other school districts where they have banned certain “junk foods.”
Evaluations of the impact will need to consider which products are allowed to substitute for soda
pop and “junk” foods (for example, fruit juices, which are allowed under most of these revised
school policies, often have just as many calories as soda pop). In addition, evaluators should take
into account the benefits of existing school food policies. If it is the case that existing food
policies help generate funds for valuable programs, then the benefit (potentially to all students)
needs to be weighed against the health costs borne by the fraction of students with a
susceptibility to obesity.

21

Note that our estimates are for access to junk food and not access to a vending machine. The 1994 and 2000
waves of the SHPPs are not identical and we do not know what fraction of schools gave access to junk food in 1994.
One should keep in mind that access to junk food and access to a vending machine are not identical – students may
obtain junk food from places other than vending machines and vending machines may sell things that are not “junk.”
In particular, only about 75 percent of schools give students access to foods we have defined as “junk food” in 2000,
while 96 percent give students access to a vending machine.
22
One should not assume that just because we estimate the impact of junk food availability on BMI to be larger for
this group that increases in vending machines are responsible for a larger proportion of the increase in BMI among
this group – the percentage increase in BMI (the denominator in this calculation) is likely larger for this group as
well.

18

19

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22

Table 1: Comparison of BMI and Obesity Across Data Sets
NHANES
1999-2000

NLSY97
1999 Panel

NHANES
1999-2000

Age

14-20

14-20

Women 32-67

Parent’s of
NLSY97
1999 Panel
32-67

BMI Mean

24.07
(5.47)

22.99
(4.63)

28.52
(7.25)

26.53
(5.87)

BMI Median

22.77

21.95

27.55

25.10

BMI 95th
Percentile
Fraction
Obese

34.8

31.93

41.93

37.76

0.152
(0.359)

0.109
(0.312)

0.373
(0.484)

0.228
(0.419)

No. of Obs.
1742
3482
1293
3482
Notes: Summary statistics and standard deviations (in parentheses) from authors’ calculations
from the 1999-2000 National Health and Nutrition Examination Survey (NHANES) and the
1999 panel of the National Longitudinal Survey of Youth 1997 cohort. In the NLSY97 data on
parent’s BMI is for the responding parent: 83% are the biological mothers, 10% are the
biological fathers, 3% are related female guardians, 2% are stepmothers. To come close to
matching this, the NHANES data are for women in the relevant age range. The data are
weighted using sample weights.

23

Table 2: First Stage Predictions of Food Policies

Mean of
Independent
Variable
(Std. Dev.)
Mean of Dep. Var.
(Std. Dev.)
Share of Total Rev. from
State Sources
State has Tax and
Expenditure Limits
1990 to 2000 Relative
Growth Rate in County
Pop. Aged 5-17
State has School
Accountability Rules
Northeast
Midwest
South

0.495
(0.142)
0.472
(0. 501)
0.939
(10.18)
0.856
(0.353)
0.156
(0.363)
0.261
(0.440)
0.372
(0.485)

Constant
No. of Observations
R-squared
F-statistic for excluded
instruments
p-value for excluded
instruments

180

Fraction of
Schools in
County in which
can Purchase
Junk Food
0.748
(0.352)
0.350
(0.157)
0.113
(0.061
0.0003
(0.0016)

Fraction of
Schools in
County with
“Pouring Rights”
Contracts
0.671
(0.405)
0.502
(0.239)
0.077
(0.059)
-0.0004
(0.0025)

Fraction of
Schools in
County that
allow Food Ads.
at School
0.441
(0.406)
0.371
(0.241)
0.119
(0.061)
0.0020
(0.0020)

0.070
(0.100)
0.102
(0.089)
-0.156
(0.068)
0.047
(0.071)
0.469
(0.121)
180
0.1334
3.88

0.113
(0.073)
-0.061
(0.139)
0.212
(0.071)
0.140
(0.075)
0.193
(0.151)
179
0.1058
4.91

-0.042
(0.102)
-0.102
(0.122)
0.130
(0.095)
0.233
(0.077)
0.131
(0.167)
180
0.1344
2.04

0.0094

0.0026

0.1069

Notes: Dependent variables come from public middle and high schools sampled in the 2000 wave of the School
Health Policy and Programs Study (SHPPS). SHPPS school weights are used to aggregate the data to the county
level. Revenues come from the NCES Common Core Data for each district. District enrollment is used to aggregate
the data to the county level. Growth in county population of children aged 5-17 relative to total county population
growth from 1990 to 2000 comes from the U.S. Census. See data appendix for more details. See data appendix table
2 for information on which states have property tax revenue limits and which have accountability laws. “Junk Food
Available” means that the students can buy chocolate, candy, cakes, ice cream or salty snacks (that are not fat free)
from a machine or school store. “Pouring Rights” contracts means the school has agreed to sell one brand of soft
drinks. “Soda or Snack Food Advertisements” means that advertisements are allowed at least at one type of school
related activity or in one or more places at the school – for example, on a school bus, at a school sporting event, on
school grounds, or school textbooks etc. Standard errors (in parentheses) are adjusted for arbitrary correlation
within the state.

24

Table 3: Estimated Effect of Predicted School Food Policies on Ln(BMI)
Public School Students in the NLSY 1997
Dep Var: Dep Var:
Ln(BMI) Ln(BMI)
Policy:
Policy:
Junk Food Junk Food
Available Available
Predicted
Policy
African
American
Hispanic

(1)
0.106
(0.052)

Female
Age
Family Inc.
(in 100Ks)
Ln(Parent’s
BMI)
No. of Obs
R-squared
p-value for
Overid. test
(χ2~3 d.f.)

3482
0.0037

(2)
0.090
(0.045)
0.034
(0.012)
0.016
(0.011)
-0.027
(0.007)
0.019
(0.003)
-0.015
(0.009)
0.221
(0.018)
3482
0.1078
0.554

Dep Var:
Ln(BMI)
Policy:
Pouring
Rights

Dep Var:
Ln(BMI)
Policy:
Pouring
Rights

(3)
0.099
(0.052)

(4)
0.075
(0.042)
0.035
(0.012)
0.017
(0.011)
-0.027
(0.007)
0.019
(0.003)
-0.014
(0.009)
0.221
(0.018)
3482
0.1074
0.425

3482
0.0040

Dep Var: Dep Var:
Ln(BMI) Ln(BMI)
Policy:
Policy:
Beverage Beverage
or Snack
or Snack
Food Ads. Food Ads.
(6)
(5)
0.041
0.038
(0.041)
(0.033)
0.034
(0.012)
0.016
(0.011)
-0.027
(0.007)
0.019
(0.003)
-0.014
(0.009)
0.221
(0.018)
3482
3482
0.0022
0.1065
0.116

0.758
0.759
0.684
0.684
0.438
0.438
(0.119)
(0.119)
(0.137)
(0.137)
(0.168)
(0.168)
Mean (SD)
3.117
3.117
3.117
3.117
3.117
3.117
of Dep. Var.
(0.183)
(0.183)
(0.183)
(0.183)
(0.183)
(0.183)
Notes: Data are from the 1999 panel of the NLSY97. The sample includes individuals who
attended public schools during the 1999-2000 school year. In both sets of models we include
dummies for region (Northeast, Midwest, and South), and a constant. In the second set of
models we control for whether the individual reports smoking since the last interview, years of
education of the biological mother and father, and dummy variables indicating if this information
is missing. Log of Parent’s BMI is for the responding parent (see notes to table 1). Table 2
shows the model used to create the predicted policy. The results here are from two sample
estimation. The standard errors (in parentheses) are corrected both for arbitrary correlation
within county, and for the fact that the policy variable is estimated. The regressions are
weighted with NLSY sample weights. The overidentification test is for the null hypothesis that
the excluded instruments are exogenous. In all cases, we fail to reject exogeneity.
Mean (SD) of
Pred. Policy

25

Table 4: Estimated Effect of Predicted School Food Policies on Ln(BMI)
Public School Students in the NLSY 1997,
by Responding Parent’s Weight Status
Policy Variable
Junk Food Avail.
R-Squared
Pouring Rights
R-Squared
Any Ads
R-Squared

Under to Normal
Weight
0.014
(0.052)
0.0645

Overweight
(Not Obese)
0.221
(0.090)
0.0769

Obese
0.079
(0.110)
0.0360

0.024
(0.045)
0.0646

0.157
(0.081)
0.0737

0.076
(0.098)
0.0361

-0.009
(0.053)
0.0644

0.092
(0.065)
0.0718

0.0004
(0.049)
0.0351

No. of Obs.
1533
1043
906
Mean (SD) of
3.075
3.128
3.194
Dependent Var.
(0.154)
(0.180)
(0.212)
Notes: Data are from the 1999 panel of the NLSY97. The sample includes individuals who
attended public schools during the 1999-2000 school year. All models include the same set of
controls variables as columns 2, 4, and 6 in table 3. Table 2 shows the model used to create the
predicted policy. The results here are from two sample estimation. The standard errors (in
parentheses) are corrected both for arbitrary correlation within county, and for the fact that the
policy variable is estimated. The regressions are weighted with NLSY sample weights. Under
to normal weight is defined as a BMI<25, overweight (not obese) is defined as a 25<=BMI<30
and obese is defined as a BMI>=30. The responding parent is the biological mother 83% of the
time, and the biological father 10% of the time, while 3% are related female guardians and 2%
are stepmothers.

26

Appendix Table 1: First Stage Results for the “Matched” Sample.
Percent of Schools in the County with given Food Policy

Mean of Dependent Variable
(Std. Dev.)
Share of Total Revenues
From State Sources
State has Tax and
Expenditure Limits
1990 to 2000 Relative
Growth Rate in County Pop.
Age 5-17
State has School
Accountability Rules
Northeast
Midwest
South
African American
Hispanic
Smoked since last interview
Female
Age
Family Income
(in 100Ks)
Mother’s Education
Father’s Education
Ln(Parent’s BMI)

(1)
Junk Food Available

(2)
Pouring Rights

0.816
(0.277)
-0.188
(0.214)
0.093
(0.077)
-0.0007
(0.0015)

0.649
(0.385)
0.570
(0.415)
0.023
(0.118)
-0.002
(0.003)

(3)
Any Beverage or Snack
Food Ads
0.427
(0.376)
0.311
(0.377)
-0.045
(0.094)
0.002
(0.001)

0.334
(0.099)
0.006
(0.077)
-0.125
(0.084)
-0.066
(0.053)
-0.077
(0.051)
-0.045
(0.059)
-0.0001
(0.0153)
0.032
(0.016)
-0.001
(0.008)
0.017
(0.024)
-0.003
(0.004)
-0.002
(0.003)
0.001
(0.034)
1007
0.2921
10.87

0.097
(0.208)
-0.402
(0.126)
-0.037
(0.147)
0.127
(0.072)
0.132
(0.084)
-0.011
(0.070)
0.010
(0.017)
0.031
(0.026)
-0.007
(0.012)
0.021
(0.028)
-0.007
(0.007)
-0.007
(0.006)
-0.001
(0.060)
1007
0.3029
1.74

0.381
(0.138)
-0.460
(0.120)
0.063
(0.160)
0.159
(0.074)
0.015
(0.049)
-0.153
(0.049)
0.017
(0.021)
0.022
(0.023)
0.014
(0.011)
-0.051
(0.031)
-0.007
(0.004)
-0.002
(0.005)
0.023
(0.047)
1007
0.4013
6.14

No. of Observations
R-squared
F-statistic for excluded
instruments
p-value for excluded
0.0000
0.1709
0.0012
instruments
Notes: Data are for those individuals in the 1999 panel of the NLSY97 whose counties overlap with the SHPPS
2000 sample. The 1007 individuals live in 78 counties. The regressions also include dummy variables for whether
mother’s education and father’s education is missing and a constant. See notes to tables 2 and 3 for additional
information. Standard errors (in parentheses) are adjusted for arbitrary correlation within the state.

27

Appendix Table 2: Estimated Effect of Food Policy on Ln(BMI)
OLS, IV and Two-Sample Estimation with the “Matched” Sample
Policy Variable
& Est. Method
Junk Food
OLS
IV
Two-Sample
Pouring Rights
OLS
IV
Two-Sample

Coefficient
(Std Err)
0.034
(0.028)
0.140
(0.084)
0.160
(0.117)
0.019
(0.017)
0.084
(0.092)
0.125
(0.103)

R-Square
0.16

Mean (SD) of
Policy Var.

Mean (SD) of
Predicted Var.

0.816
(0.277)

0.14
0.16
0.16

0.758
(0.128)
0.649
(0.385)

0.15
0.16

Snack Food Ads
OLS

0.650
(0.124)

0.034
0.16
0.427
(0.026)
(0.376)
IV
0.146
0.13
(0.070)
Two-Sample
0.139
0.16
0.410
(0.135)
(0.146)
Notes: Models also include age, family income, ln(parent’s BMI), dummies for race, sex, region
(Northeast, Midwest, and South), whether the individual reports smoking since the last interview,
years of education for the biological mother and father, and dummy variables indicating if this
information is missing, and a constant. Standard errors (in parentheses) are adjusted for arbitrary
correlation within county. For the two-sample models, they are also adjusted for the fact that the
policy variable is estimated based on the model shown in appendix table 1. All models have
1007 observations and a dependent mean (SD) of 3.126 (0.190).

28

DATA APPENDIX
Because no one data set contains all of the variables necessary for our analysis, we must
build our data from several different sources. These include the School Health Policies and
Programs Study (SHPPS), the Common Core of Data for school districts from the National
Center for Education Statistics (NCES), county population data from the 1990 and 2000 Census,
and individual-level data on public school students from the National Longitudinal Survey of
Youth 1997 (NLSY97). We describe our use of each of these in turn.
The SHPPS is a national study conducted in 1994 and 2000 for the Center for Disease
Controls (CDC).23 While the study covers a broad range of school health policies and
procedures at the state, district, school and classroom level, we focus on the 2000 school
environment survey. This questionnaire asks about the school’s policies regarding such things as
the availability of snack foods through vending machines, school stores and snack bars; the
details of an exclusive contract with a soft drink manufacturer (if any); and the types of
advertising for sodas and snack foods allowed. Unfortunately, the majority of these questions
were not asked in 1994. While unlike the 1994 study, the 2000 study also includes elementary
schools, we do not include them in our main analysis, since we will be focusing on youths age 14
and older who are enrolled in public schools.
We choose three food policies from the SHPPS for the bulk of our analysis. First is an
indicator of student access to junk foods, defined as the availability through vending machines or
school stores of chocolate candy; other candy; cookies, crackers, cakes, pastries or other baked
goods that are not low in fat; or salty snacks that are not low in fat. Second is an indicator for
having an exclusive “pouring rights” contract with a soft drink manufacturer. Third is an

23

For more information on SHPPS, see http://www.cdc.gov/nccdphp/dash/shpps/.

29

indicator that advertisements promoting student consumption of candy, meals from fast food
restaurants or soft drinks are permitted in any number of ways, such as in the school building, on
textbook covers or food service menus, on buses, or at athletic fields.
Since we are focusing on public school financing issues, we limit ourselves to the public
schools in the SHPPS. We use data on the 451 public middle and high schools for which all the
variables we need have non-missing information. For these schools we can identify their school
district and merge on district-level information about school finances from the NCES Common
Core of Data.24 The SHPPS data give the QED (Quality Education Data) district codes. We
purchased the cross-walk between the QED and NCES district codes from QED. We use the
NCES district codes to merge data on district finance information from the NCES. While
detailed financial data is available, we want a simple summary measure of local fiscal capacity.
Thus, we choose to use the fraction of total district revenues that come from the state, since even
state funding formulas that are not explicitly equalization schemes, such as the most common
foundation grant formula, result in there being a negative correlation between local fiscal
capacity and the state share of funding.25
The lowest geographic level of detail available in our individual-level data sets is the
county. We therefore aggregate the SHPPS and NCES data up to the county level.26 Using the
school weights in the SHPPS, then, we calculate the probability that a school in the county has
each of these policies. There are 180 counties in 41 states covered in our SHPPS sample. The
NCES fiscal data is averaged across all districts in the county using district enrollment levels as

24

More information about the NCES Common Core of Data can be found at http://nces.ed.gov/ccd/. The latest
fiscal data available at the time the project began was for the 1998-1999 school year. Thus, the fiscal data lags the
policy data by one year.
25
See, for example, the summary discussion of Public School Finance Programs in the United States and Canada
by Sielke and Holmes at http://www.ed.sc.edu/aefa/reports/ch1.pdf.
26
The NCES identifies the county in which the district headquarters is located.

30

weights. Finally, we merge on two state-level indicators and the county-level relative growth in
the school-age population. The first indicator is for whether the state has tax and expenditure
limitations in place.27 These types of local limits were commonly passed beginning in the late
1970s and into the 1980s, with some states tightening their laws in the early 1990s. The second
indicator is for whether a state has passed a school accountability measure. These types of laws
are mainly of a much more recent vintage, with many not implemented until the mid-1990s.
Data appendix table 1 provides a complete list of states with each of these types of laws and the
dates they were implemented. Finally, we compute the relative growth rate for the school-age
population based on county-level population figures for those age 5-17 and the total population
from the 1990 and 2000 Census. The growth rate is then calculated as:
Ln(age 5-17 population in 2000)-Ln(age 5-17 population in 1990) divided by
Ln(total population in 2000)-Ln(total population in 2000).
Table 2 includes the sample means of each of these predictor variables.
The second major component of the project uses individual level data on adolescents that
includes height and weight, along with individual and family background demographics. The
National Longitudinal Survey of Youth, 1997 panel (NLSY97) is a survey of 8984 youths who
were age 12-16 as of December 31, 1996. The first round of the survey took place between
February of 1997 and May of 1998. Additional waves have been carried out annually during the
school year. We use data from wave 3, which was collected in the 1999-2000 school year, to be
in accord with the SHPPS data.
Wave 3 of the data contains information on 7958 individuals. We limit that to those who
are enrolled in public school, which reduces the sample to 4653 individuals. By the time we
eliminate those with missing information for variables used in our analysis, we are left with a
27

These laws are from Downes and Figlio (1999), updated to reflect the 2000 status.

31

working sample of 3482 individuals. In order to use our two step estimation, we append the data
on state tax limitations and state accountability laws to the NLSY97 data using state identifiers
provided in the confidential geocode data. Similarly, we append county level data on the share
of total school revenue from state sources, and the 1990 to 2000 relative growth rate in the
county population aged 5-17 using the county identifiers in the geocode data. The individuals in
our NLSY97 sample live in 426 counties.
We measure the adolescents’ weight status using the log of their body mass index (BMI).
BMI is defined as weight in kilograms divided by height in meters squared (kg/m2) and is a
commonly used measure to define obesity and overweight in adults. For example, according to
guidelines in National Institutes of Health (1998), adults are considered underweight if their BMI
is less than 18.5, overweight if their BMI is 25 or more, and obese if their BMI is 30 or more.
Use of the BMI to assess children and adolescents has been slightly more controversial, although
its use is fairly widespread.28 The Centers for Disease Control (CDC) has recently endorsed the
use of BMI to assess overweight status in children and adolescents, and has produced sexspecific BMI distributions for children aged 2 to 20 for just this purpose. While results are
similar using probability of overweight, we choose to use the continuous BMI measure.
Before calculating BMI, however, we make a few corrections for obvious typographical
problems with the recorded height and weight data. As a first step in cleaning the weight data,
we replace any weight value in a given year with the average from the years surrounding it if the
28

Ideally, one would prefer to measure overweight using a measure that reflects adiposity. Since it is impractical to
do so in large scale surveys, researchers have employed the BMI, which only requires the measurement of height
and weight. It is somewhat controversial when used to assess overweight among children because children
experience growth spurts at individual-dependent ages and this can weaken the relationship between height and
weight-based measures to adiposity. See Freeman, et al. (1995) and Whitaker, et al. (1997) for a discussion of the
use of BMI in children. Recently, Dietz and Bellizzi (1999) reporting on a conference convened by the International
Obesity Task Force, noted that the BMI “offered a reasonable measure with which to assess fatness in children and
adolescents.” Additionally, they conclude that a BMI above the 85th percentile for a child’s age and sex group is
likely to accord with the adult definition of overweight, and above the 95th percentile with the adult definition of

32

given value is less than half or more than double the average of those surrounding it. So for
example, for a wave 3 weight observation, we replace it with the average of the wave 2 and
wave 4 weight values if it is less than half or more than double the average of the wave 2 and 4
values.
Because several individuals are missing weight data for one or more years, this presents a
problem in comparing a given weight value to the values surrounding it. So, we employ an
additional screening method. We flag weight values less than 60 or greater than 300 and then
examine them by hand to distinguish between those individuals who have consistently high or
consistently low weights and those individuals who have a single weight value which is very
different from all other available weight data for that individual. For those individuals who have
an outlier weight value, we replace it with the average of the weight values from the closest
waves.
After creating the BMI values, we then flag any value which is less than 10 or greater
than 40. We chose 10 and 40 as our cutoff values because the 3rd percentile for a 12 year-old is
around 15 and the 97th percentile for a 20 year-old is 35. For those individuals with a flagged
BMI, we then examine the height values by hand in order to identify those height values which
clearly stand out from the rest. In all cases, only the feet value of height is altered since it is
virtually impossible to determine if the inches values are inaccurate. The outlier height values
are replaced with the nearest height value that makes sense. For example an individual who has
the height values 5’10”, 8’11”, 5’11”, 5’11”, 6’0” will have the 8’ replaced with 5’. An
individual who has the height values 4’11”, 7’2”, 5’2”, 5’3”, 5’3” will have the 7’ replaced with
5’. Finally, the BMI values are recomputed using the altered weights and altered heights.

obese.

33

Ultimately, only 5 sample BMI values are different from those computed from the recorded
values.
We also use parental BMI in the analysis. This information was collected in the initial
wave from the responding parent (88% are biological mothers, 10% are biological fathers, 3%
are related female guardians, and 2% are stepmothers). There are outliers in these data as well,
but with only 1 year of information, we cannot follow the procedure described above for
identifying incorrect information. Bad data leading to the misidentification of students as having
“obese” parents may explain why our results for that subgroup are relatively imprecise. Without
other information, our options for dealing with misreported data are limited. We re-estimated the
results in table 3 dropping those with parents with BMIs above 40, above 50, and above 60.
These three sets of results are very similar to those in table 3.
One other key explanatory variable needs some recoding. We are interested in using
parental income as a control for socioeconomic background. However, in the NLSY97, the
household income variables, which reflect the income of the household in which the youth
resides in, have missing data for a very large number of the respondents in all years except 1997.
In contrast, the 1997 data is relatively complete because in 1997, the initial year of the survey, a
parent survey was administered in addition to the youth survey and so reliable income
information was collected directly from the parents of the participating children. In all years
after 1997 (1998-2001), a household income update questionnaire was administered to a youth’s
parents if the youth still lived at home. For a given individual still living with his parents, this
questionnaire collected the following information:
-

whether or not the parent had an income

-

the amount of the parent’s income if it existed

34

-

whether or not the parent’s spouse/partner had an income

-

the amount of the spouse/partner’s income if it existed

-

whether or not the parent and spouse/partner collectively had any other form of income

-

the amount of the other income if it existed

In order to get a background income variable as many respondents as possible, we decided to
calculate one which is representative of the entire time period 1997-2001. We first compute an
income variable using the questions from the household income update described above. If the
parent does not have an income, we recode the parent’s income amount from missing to zero.
We do this because the income amount would be coded as missing since the income amount
question would not have been asked if the person responding said that the income did not exist.
We perform the same recoding for the parent’s spouse’s income amount and for the
parent/spouse other income amount. Also, if the parent answering the questionnaire does not
have a spouse/partner, we recode the parent’s spouse’s income amount from missing to zero.
Next, we add up the parent income value, the parent’s spouse income value, and the
parent/spouse other income value for each of the four years.
The constructed income variable still has a large number of missing values, so for the 19982001 incomes we replace any value that is missing in our constructed income variable, but not
missing in the household income variable, with the household income variable. For 1997, we
use only the household income variable since the other income questions do not exist for 1997.
We then use the CPI-U to convert the income variable for each survey year from nominal to
2000 dollars. Last, we define our income control variable to be the mean of the income variables
as long as there are no more than 3 years with missing income values.

35

Data Appendix Table 1: Summary Statistics
Full Sample
Matched Sample
0.686
0.587
(0.464)
(0.493)
Black
0.151
0.162
(0.358)
(0.369)
Hispanic
0.117
0.204
(0.322)
(0.403)
Smoked since last interview
0.348
0.302
(0.476)
(0.460)
Female
0.471
0.458
(0.499)
(0.499)
Age
16.2
16.26
(1.10)
(1.08)
Family Income
0.556
0.605
(in $100Ks)
(0.429)
(0.482)
Biological Mother’s Education
12.3
12.1
(3.84)
(4.16)
Biological Father’s Education
11.2
10.8
(5.25)
(5.70)
Bio. Mom’s Education Missing
0.051
0.064
(0.220)
(0.245)
Bio. Dad’s Education Missing
0.137
0.172
(0.344)
(0.378)
Northeast
0.163
0.162
(0.370)
(0.369)
Midwest
0.278
0.224
(0.448)
(0.417)
South
0.331
0.253
(0.471)
(0.435)
West
0.228
0.361
(0.419)
(0.481)
BMI
22.99
23.23
(4.63)
(4.95)
Ln(BMI)
3.12
3.13
(0.018)
(0.190)
Obese
0.109
0.116
(0.312)
(0.321)
Overweight
0.242
0.247
(0.428)
(0.432)
Parent’s BMI
26.53
26.38
(5.87 )
(5.50)
Ln(Parent’s BMI)
3.26
3.25
(0.204)
(0.198)
Parents Obese
0.228
0.230
(0.419)
(0.421)
Parents Overweight
0.518
0.533
(0.500)
(0.499)
Sample Size
3482
1007
Notes: Mean and (standard deviation) shown. The data are from the 1999 panel of the NLSY97. The “full” sample is for all
individuals who attend public school and for whom we have information for the full set of variables listed here. The “matched”
sample is for only those individuals who live in counties that overlap with those sampled in the School Health Policies and
Programs survey. See text for additional information about the samples.
White

36

Data Appendix Table 2: Tax and Expenditure Limits and School Accountability By State
Tax and Expenditure
Year of Enactment,
School
Year System
State
Limits
Tightening
Accountability
Implemented
Alabama
No
Yes
1997
Alaska
No
No
Arizona
Yes
1975, 1990
Yes
2000
Arkansas
Yes
1982
Yes
1999
California
Yes
1978
Yes
1999
Colorado
Yes
1974, 1990
Yes
1999
Connecticut
No
Yes
1984
Delaware
No
Yes
1998
District of Columbia
No
Yes
1997
Florida
No
Yes
1999
Georgia
No
Yes
2000
Hawaii
No
No
Idaho
No
No
Illinois
No
No
Indiana
Yes
1974
Yes
1995
Iowa
Yes
1972, 1992
No
Kansas
Yes
1974
Yes
1995
Kentucky
Yes
1980
Yes
1995
Louisiana
Yes
1979
Yes
1999
Maine
No
Yes
1999
Maryland
No
Yes
1999
Massachusetts
Yes
1981
Yes
1998
Michigan
Yes
1979
Yes
1998
Minnesota
Yes
1972
Yes
1996
Mississippi
Yes
1984
Yes
1994
Missouri
Yes
1981
Yes
1997
Montana
Yes
Yes
1998
Nebraska
No
No
Nevada
No
Yes
1996
New Hampshire
No
Yes
1993
New Jersey
Yes
1977
Yes
1995
New Mexico
Yes
1980
No
New York
No
Yes
1998
North Carolina
No
Yes
1993
North Dakota
No
No
Ohio
Yes
1977
Yes
1998
Oklahoma
No
Yes
1996
Oregon
Yes
1991
Yes
2000
Pennsylvania
No
Yes
1997
Rhode Island
No
Yes
1997
South Carolina
Yes
1981
Yes
1999
South Dakota
No
No
Tennessee
No
Yes
1996
Texas
Yes
1983
Yes
1994
Utah
No
No
Vermont
No
Yes
1999
Virginia
No
Yes
1998
Washington
Yes
1980
Yes
1998
West Virginia
No
Yes
1997
Wisconsin
No
Yes
1993
Wyoming
Yes
1999
No
Source: Tax and Expenditure Limit information is from David Figlio for 2000, from data used in “Do tax and expenditure limits
provide a free lunch? Evidence on the links between limits and public sector service quality” (Downes and Figlio, 1999). School
Accountability Information is from Mackie Raymond for 2000, from data used in “Improving Educational Quality: How Best to
Evaluate Our Schools” (Hanushek and Raymond, 2002).

37

Working Paper Series
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Real Effects of Bank Competition
Nicola Cetorelli

WP-04-03

Finance as a Barrier To Entry: Bank Competition and Industry Structure in
Local U.S. Markets?
Nicola Cetorelli and Philip E. Strahan

WP-04-04

6

Working Paper Series (continued)
The Dynamics of Work and Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-04-05

Fiscal Policy in the Aftermath of 9/11
Jonas Fisher and Martin Eichenbaum

WP-04-06

Merger Momentum and Investor Sentiment: The Stock Market Reaction
To Merger Announcements
Richard J. Rosen

WP-04-07

Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon

WP-04-08

Platform Competition in Two-Sided Markets: The Case of Payment Networks
Sujit Chakravorti and Roberto Roson

WP-04-09

Nominal Debt as a Burden on Monetary Policy
Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles

WP-04-10

On the Timing of Innovation in Stochastic Schumpeterian Growth Models
Gadi Barlevy

WP-04-11

Policy Externalities: How US Antidumping Affects Japanese Exports to the EU
Chad P. Bown and Meredith A. Crowley

WP-04-12

Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder

WP-04-13

Determinants of Business Cycle Comovement: A Robust Analysis
Marianne Baxter and Michael A. Kouparitsas

WP-04-14

The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau

WP-04-15

Reading, Writing, and Raisinets1: Are School Finances Contributing to Children’s Obesity?

WP-04-16

Patricia M. Anderson and Kristin F. Butcher

7