High School Athletics Participation and Future Health Behavior
Vasilios D. Kosteas
Cleveland State University
2121 Euclid Avenue, RT 1719
Cleveland, OH 44115-2214
b.kosteas@csuohio.edu
Tel: 216-687-4526, fax: 216-687-9206
Abstract
This paper investigates whether participation in high school athletics has a positive impact on
health behaviors for US adults in their early 30s to mid-50s, using data from the National
Longitudinal Surveys of Youth 1979 cohort. We ask whether being a high school athlete makes
someone more likely to exercise regularly and less likely to smoke or drink excessively at those
ages. The results show that participation in athletics during high school leads to a higher
probability of being physically active later in life, a lower probability of smoking on a daily
basis, but a higher probability of exceeding consumption guidelines for alcoholic beverages.
Furthermore, these effects are fairly consistent for the same cohort over a ten-to-fourteen year
period.
Key Words: exercise, smoking, alcohol, high school athletics
JEL Codes: I12
1
Introduction
A lack of adequate physical activity for a large segment of the adult US population, in
spite of numerous attempts to raise overall activity levels remains a source of concern for policy
makers. So do the prevalence of binge drinking and the fact that nearly one-in-five non-elderly
US Americans smoke on a regular basis. 1 There is a sizable literature examining the
determinants of health behaviors such as smoking, and drinking. While smoking and
consumption of alcohol consumption have traditionally received greater attention, the literature
examining the determinants of regular exercise has been growing. These studies have focused on
the importance of demographic characteristics, education, income and community level
amenities, such as the availability of parks and recreation centers. However, relatively little
attention has focused on the potential role of participation in high school athletics on promoting
healthy behaviors throughout life. The present paper combines the literature examining the
determinants of healthy behavior with the literature examining the impacts of participation in
high school athletics on future outcomes. In the latter, existing studies have looked into the effect
of participation in high school extracurricular activities on human capital formation and labor
market outcomes, with a particular emphasis on participation in high school athletics. These
studies show a wide array of positive outcomes associated with participation in high school
extracurricular activities, including greater educational attainment, higher wages and benefits and
a greater likelihood of being a supervisor and holding higher level job responsibilities such as
hiring and firing and deciding pay increases.
Surprisingly, there are few studies linking these two areas of research. What literature
exists tends to be outside of economics and focuses simply on establishing correlations between
high school activities and future health behaviors. In this paper, we examine the effect of
participation in high school athletics on health related behaviors for US adults in their early 30s
to mid-50s. We hypothesize that participation in high school athletics helps to foster a preference
for healthy behavior. We postulate this relationship works (at least partially) through establishing
those healthy behaviors during the high school years. In an effort to tease out the extent to which
the established correlations between these variables represent causal relationships, we employ
1
See the CDC website for information on binge drinking: http://www.cdc.gov/alcohol/fact-sheets/bingedrinking.htm and smoking statistics:
http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/.
2
propensity score matching estimation and a relatively new instrumental variables approach
developed by Lewbel (2012).
All analyses are conducted using data from the National Longitudinal Surveys of Youth
1979 cohort. Specifically, we ask whether being a high school athlete makes someone more
likely to exercise regularly and less likely to smoke or drink excessively at those ages. The
results show that participation in athletics during high school leads to a higher probability of
being physically active later in life, a lower probability of smoking on a daily basis, but a higher
probability of exceeding consumption guidelines for alcoholic beverages. These effects are fairly
consistent for the same cohort over a ten-to-fourteen year period.
Background
The theoretical motivation for the empirical models is straightforward. Participation in
HS athletics helps to encourage future physical activity by fostering a love of sports and/or
through habit formation that can continue for decades into the future. Previous studies have
established the importance of habit formation and/or addiction in exercise (Acland and Levy
2012, Charness and Gneezy 2009, Royer at al 2012), smoking (e.g. Jones 2014), and drinking
(e.g. Williams 2005). Participation in HS athletics may have an impact on whether the individual
drinks alcohol or smokes tobacco during high school. High school athletes may be less likely to
smoke since it can interfere with athletic performance. On the other hand, they may be more
likely to drink alcohol since this is often a group activity in high school and participation in
athletics may increase the opportunities to drink socially. Evidence indicates that HS athletes are
indeed less likely to smoke compared with non-athletes (Naylor et al 2001, Terry‐McElrath and
O'Malley 2011) but more likely to drink alcohol (Terry‐McElrath and O'Malley 2011, Wetherill
and Fromme 2007). By impacting the decision to smoke or drink during high school,
participating in HS athletics may affect future smoking and drinking behavior through habit
formation (which may also be interpreted as addiction in the case of cigarette smoking and also
for some individuals with respect to the consumption of alcohol). Participating in HS athletics
may also affect future smoking and drinking by nurturing a desire to lead a healthy lifestyle. We
could test the first mechanism if we had good information on smoking and drinking during the
HS years in our dataset. Unfortunately, we do not. Thus, we will not directly test either of these
mechanisms. Instead, we simply examine whether there is any effect of participation in HS
3
athletics on future health behaviors and defer to earlier studies on the question of whether
participation in HS athletics affects drinking and smoking during the high school years.
The existing empirical literature in economics examining the long term effects of
participation in high school athletics has focused on educational and labor market outcomes.
Barron, Ewing and Waddell (2000) analyze the relationship between participation in high school
athletics on high school rank, educational attainment, future employment and weekly wages.
Eide and Ronan (2001) estimate the relationship between athletics participation in high school
and educational attainment and future wages. Their results are mixed, finding a negative
correlation with educational attainment for white men, a positive correlation for black men and
white women, and no correlation for Hispanics or black women. They do not find a significant
correlation between athletics and wages for any of these groups. Ewing (2007) finds that in
addition to earning higher wages, former high school athletes also receive more benefits.
Anderson (2001) finds a positive link between participation in sports and educational outcomes
for white athletes. Stevenson (2010) finds that women who go to high school in states with a
higher female participation rates in athletics have greater educational attainment. Kosteas (2011)
shows that former high school athletes are more likely to be supervisors at work and hold high
level responsibilities, such as setting pay and making hiring and firing decisions. Pfeifer and
Cornelisen (2010) find a positive link between childhood sports and educational attainment for
Germany. There are also several empirical papers in the sociology literature which investigate
the link between high school athletics and various educational outcomes (see Troutman and
Dufur 2007 for a summary papers in this literature).
There are several papers in the applied psychology and public health literatures
examining the impact of playing high school sports on future health behaviors. However, these
studies tend to use relatively small samples which are often not representative of the broader
population. Furthermore, these papers tend to employ relatively limited models and do not go
beyond establishing a correlation between participation in HS athletics and future outcomes. For
recent studies in this literature see Dohle and Wansink (2013), Geisner et al (2012), Hartmann
and Massoglia (2007), and Wichstrom and Wichstrom (2008).
The literature examining the determinants of exercise, smoking and drinking is
voluminous. Cabane and Lechner (2014) provide a nice review of the literatures on both the
determinants and long term effects of physical activity, including papers in economics,
4
epidemiology, sports science, and other fields. In this section we highlight some of the key
studies in the economics literature examining the labor market effects of physical activity.
Existing studies can be grouped according to whether they focus on the effect of individual
characteristics (education, income, age) or aspects of the local environment (access to public
parks, weather conditions) on physical activity. With respect to individual characteristics, studies
have found relationships between levels of physical activity and age (Breuer and Wicker 2009,
Downward et al 2011, Eberth and Smith 2010, Garcia et al 2011, Humphreys and Ruseski 2015,
Stamatakis and Chaudhury 2008), marital status and the presence of young children in the
household (the latter for women only) (Eberth and Smithe 2010 and Garcia et al 2011), and
education and income (Downward and Rasciute 2010, Fridberg 2010, Hovemann and Wicker
2009, Humphreys and Ruseski 2015, Lechner 2009, Mletzer and Jena 2010). Regarding
environmental factors, existing research has uncovered a positive link between physical activity
for children and both a state physical education requirement and state spending on parks and
recreation (Cawley et al 2007). Studies focusing on adults have established connections between
levels of physical activity and education (Mullahy and Robert 2010) and the number of gyms,
parks and other recreational areas per capita (McInnes and Shinogle 2011, Humphreys and
Ruseski 2007). Multiple studies have uncovered a connection between poor weather conditions
and physical activity (Eisenberg and Okeke 2009, Humphreys and Ruseski 2011, Witham et al
2014). A few studies in the experimental economics literature have established the importance of
peer effects (Babock and Hardman 2010, Carrel et al 2011, Leslie and Norton 2012). Finally,
there are also a couple of studies which have investigated the tradeoff between exercise intensity
and duration, finding that high wage Americans substitute intensity for duration (Meltzer and
Jenna 2012) while high income Australians are more likely to exercise more frequently and with
greater intensity (Maruyama and Shin 2012).
The literature on the correlates and determinants of smoking and alcohol consumption is
vast. Here, we will highlight some of the more recent papers in the health economics literature.
As with the exercise literature, we can group these studies into those which look at
individual/household characteristics and those which look at economic/environmental factors. In
spite of the strong correlation between education and both drinking and smoking, Park and Kang
(2008) do not find a causal link running from the former to the latter for Korean men. Dohmen et
al (2011) show that greater preference for risk is a strong predictor of smoking. Several papers
5
have found important peer effects on drinking for US teens (Powell et al 2005), Irish college
students (Delaney et al 2007) and Swedish teens (Lundborg (2006). Delaney et al (2013) find a
strong association between drinking for Irish college students and the alcohol consumption of
their fathers and older siblings. Gohlmann et al 2010) also find a significant impact of parental
smoking on children’s smoking in Germany. Argys et al (2006) show that birth order has a
significant correlation with smoking, drinking and other behaviors for US teenagers.
Participation in Head Start leads to reduced smoking probability in young adulthood (Anderson
et al 2010). Finally, perceptions of risk are important determinants of smoking (Lundborg and
Lindgren 2004) and drinking (Lundborg and Lindgren 2002). Economic factors such as
significant stock market crashes (Cotti et al 2015) also lead to greater smoking and drinking,
while income is negatively related to smoking and positively related to drinking alcohol (CostaFont et al 2014). Cigarette prices are a robust determinant of youth smoking, while the effect of
cigarette taxes is mixed (Nonnemaker and Farrelly 2011) and adults who are current smokers are
more likely to quit in response to rising prices (Goel and Naretta 2011).
The current paper adds to these literatures by examining the effect of participation in high
school athletics on three health behaviors: physical activity, smoking, and consumption of
alcohol. To our knowledge, this is the first paper to empirically investigate these relationships.
Furthermore, we attempt to push beyond merely establishing correlations between participation
in HS athletics and health behaviors to estimating causal relationships by using propensity score
matching and a relatively new instrumental variables estimator developed by Lewbel (2012).
Data
The data come from the 1998-2002 and 2012 waves of the NLSY79 cohort, unless indicated
otherwise. The NLSY79, which conducted surveys every year starting in 1979 through 1994,
then in even numbered years, began with an initial sample of 12,686 individuals. The initial
sample contained oversamples of poor white individuals and members of the armed forces. The
military and poor white oversamples were dropped in 1985 and 1991, respectively. Data for each
of our three health behaviors are not available for all years. We use data on exercise/physical
activity from the 2000, 2002 and 2012 waves, data on smoking from the 1998 and 2012 waves
and data on consumption of alcohol from the 2002 and 2012 waves. We chose the earliest and
6
most recent years for which data on these activities is available in order to examine whether the
impact of participation in HS athletics on these activities changes as individuals get older.
The NLSY is a good source of labor market data for individuals, containing information
for a variety of background variables in addition to current information. One drawback with this
dataset is the narrow age range of the respondents; they were between the ages of fourteen and
twenty-one in 1979. However, by taking advantage of the longitudinal nature of the data and
estimating the models using data from different years, we are able to examine these relationships
for individuals ranging in age from 33-57 years of age for the smoking models, 35-57 years of
age for the exercise/physical activity models, and 37-57 years of age for the consumption of
alcohol models.
We examine three different health behaviors: physical activity, smoking, and drinking
alcohol. The NLSY79 used two different approaches to gathering information on physical
activity (PA). In 1998 and 2000, respondents were asked to answer the following question: “How
often do you participate in vigorous physical exercise or sports - such as aerobics, running,
swimming, or bicycling?” Responses were placed in the following categories: never, less than
once a month, one to three times each month, once or twice a week, three times or more each
week. Using this information, we construct two different indicator variables, one to reflect
whether the individual exercises at least once per week and another reflecting regular exercise,
which takes a value of one if the individual exercises at least three times per week and zero
otherwise. The surveys also ask about participation in light-to-moderate physical activity.
However for the sake of the present analysis, the exercise models using the 2000 wave data focus
on vigorous exercise. 2 A strong point of these measures is that they focus on leisure time
physical activity (LTPA), over which individuals exert significant control, unlike work-related
physical activity (WRPA) and to a lesser extend physical activity related to household work.
However, they also suffer from a significant drawback in that they do not capture variations in
the total amount of time spent exercising or the intensity of the activities, but simply exercise
frequency. We chose to work with the 2000 data on exercise frequency in order to provide a
closer in time comparison of results with the models estimated using the data from the 2002
wave of the surveys.
2
Estimates show a positive effect of participation in high school sports on the probability an individual engages in
light-to-moderate physical activity at least three times per week. These results are available upon request.
7
Beginning in 2002, the NLSY79 employed a different set of questions to measure PA.
Respondents were asked how frequently they participate in vigorous as well as light to moderate
physical activities and the average duration of each episode. For vigorous activity, respondents
are first asked “How often do you do vigorous activities for at least 10 minutes that cause heavy
sweating or large increases in breathing or heart rate?” Respondents then provide a number of
times they engage in vigorous activity and the time unit for their response (per day, per week, per
month, per year). Similarly, regarding participation in light to moderate physical activity,
respondents are asked the following question “How often do you do light or moderate activities
for at least 10 minutes that cause only light sweating or slight to moderate increase in breathing
or heart rate?” As with the question on vigorous physical activity, respondents report a frequency
(number of times) and a time unit as well as information on the duration of a typical episode of
light-to-moderate physical activity.
Using this information, we calculate the number of minutes spent engaging in both
vigorous and light-to-moderate PA on a weekly basis. Then we construct indicator variables
capturing whether the individual meets the guidelines for physical activity established by the
Office of Disease Prevention and Health Promotion. These are also the same guidelines
espoused by the World Health Organization
(http://www.who.int/dietphysicalactivity/factsheet_adults/en/). The guidelines call for at least
150 minutes a week of moderate-intensity, or 75 minutes a week of vigorous-intensity aerobic
physical activity, “or an equivalent combination of moderate- and vigorous intensity aerobic
activity.” To achieve even greater health benefits, the Office of Disease Prevention and Health
Promotion recommends doubling these numbers. We create a variable measuring the number of
“equivalent minutes” of activity per week:
(1)
equivalent minutes = 2*minutes vigorous activity + minutes light-to-moderate activity,
and construct an indicator variable for the basic guideline which takes a value of one if the
number of equivalent minutes is greater than or equal to 150 and zero otherwise, and an indicator
for the higher guideline which takes a value of one if the number of equivalent minutes per week
is greater than or equal to 300 and zero otherwise.
Unlike the exercise variables contained in the 1998 and 2000 waves of the NLSY79, the
questions and responses in the more recent waves of the survey capture total physical activity,
including WRPA and PA associated with household work (such as gardening and cleaning).
8
Working a with a more complete measure of PA is important since many individuals may be able
to meet the PA guidelines through their normal daily activities, including walking to work
(which individuals may not be likely to report as exercise) or physical demands on the job.
Additionally, these measures give a better sense of total activity compared to the categorical
responses provided in the 1998 and 2000 waves. On the other hand, aside from switching
occupations (which is less likely with workers in their forties and fifties) individuals may not
have much control over their WRPA. Thus, we are less likely to see a relationship between
individual characteristics and these measures of PA.
The indicator variables are preferred to measures of total time spent engaged in PA
because the latter are subject to significant measurement error. In the 2002 wave, the median
number of minutes spent engaged in vigorous physical activity per week is 70, while the 75th
percentile value is 240 minutes (or four hours per week). These are very reasonable numbers, but
the 95th percentile value is 2,160 minutes. While 36 hours per week may not seem like an
excessive amount of time for a triathlete or other professional athletes (even here the number is
questionable), it his highly unlikely 5 percent of the population spends at least this much time per
week engaged in vigorous physical activity. Focusing on the indicator variables should minimize
the measurement error issues without requiring ad hoc decisions regarding capping the minutes
of PA or excluding observations with large values.
Using the frequency of exercise measure in 2000 and the indicator variables based on
total PA in 2002 allows us to compare results using very different measures of PA. Obtaining
similar estimates using these different measures will give us greater confidence in the robustness
of the estimated relationship between PA and HS athletics.
We use two measures of smoking: whether the individual currently smokes daily, and
whether the individual has smoked at least 100 cigarettes in her lifetime. The NLSY first asked
respondents whether they have smoked 100 cigarettes in their lifetime. Those who answer
positively are asked a follow up question “Do you now smoke daily, occasionally or not at all?”
We could use the responses to these two questions to create a categorical variable taking three
values, one for does not smoke, one for occasionally smokes and one for smokes daily. However,
the smokes occasionally category is rather vague. Instead we construct an indicator variable for
whether the individual smokes daily since this is the best measure of whether the person is a
regular smoker.
9
We develop two variables measuring alcohol consumption: the first is an indicator
variable for whether the individual consumed any alcohol in the past thirty days, while the
second is an indicator variable taking a value of one if the individual averages one (two) or fewer
drinks per day if female (male) on the days the individual drinks and zero otherwise. Both
variables are constructed from the same series of question. First, respondents are asked whether
they have consumed any alcoholic beverages in the last 30 days. Those who respond in the
affirmative are asked a series of follow up questions, starting with the number of days over the
past 30 on which they consumed any alcohol and then an additional question asking how many
drinks they have on average on the days they drink. The second variable attempts to capture
whether an individual tends to keep their alcohol consumption in the zero to moderate range. The
CDC defines moderate consumption as up to one drink per day for women and two drinks per
day for men (http://www.cdc.gov/alcohol/faqs.htm). The CDC’s guidelines are meant to apply to
each day, while our consumption variable measures average consumption on a typical, creating a
potential discrepancy between the guidelines and the guideline variable. It is the closest
approximation to the CDC’s guidelines given the available information.
Methodology
Our primary models examine the relationship between participation in HS athletics and health
behaviors for US adults, regardless of work status. As a robustness check, additional results are
presented for models estimated for working individuals and controlling for wages and hours
worked per week. Since each of our outcome variables is binary, exploratory results are obtained
via logit estimation for all models. Each model includes control variables which fall into three
categories: time invariant demographics (indicators for being female, black, and Hispanic),
contemporaneous control variables (age, highest grade completed, log family income, and
whether the individual is married), and background variables (Armed Forces Qualifying Test
(AFQT) score percentile from 1980, mother and father’s highest grade completed, a measure of
the frequency of religious services attendance in 1979, the Rosenborg self-esteem score in 1979,
the high school athletics participation rate in 1974 for the respondent’s reported state of
residence in 1979, and indicator variables for whether the individual participated in youth
organizations, hobby clubs, student government, yearbook or newspaper, performing arts clubs,
or national honors society).
10
Several papers have also shown a link between various measures of time preference and
exercise (Kosteas 2015), smoking (Adams and Nettle 2009, Anderson and Mellor 2008, Brown
et al 2014, Khwaja et al 2007), alcohol consumption (Anderson and Mellor 2008, Richards and
Hamilton 2012), risky sexual behavior (Chesson et al 2006) and body composition (Adams and
Nettle 2009, Anderson and Mellor 2008, Smith et al 2005, Borghans and Golsteyn 2006, Chabris
et al 2008, Zhang and Rashad 2008, Richards and Hamilton 2012). In addition to serving as
determinants of these health behaviors, time preference may also be correlated with participation
in HS sports. Thus, the omission of a measure of or proxy for time preference may bias our
estimates. Unfortunately, the NLSY79 does not contain a measure of time preference for any of
the years of data used in the primary analysis. However, there are some questions aimed at
eliciting information on time preference in the 2006 wave. As a robustness check, we estimate
the models for PA and drinking using the 2006 data and include two different measures of time
preference. 3
The Logit estimates likely suffer from two significant issues: measurement error in the
high school athletics participation variable and bias introduced by unobserved characteristics
affecting both participation in high school athletics and future health behaviors. In particular, the
key unobservable for the exercise/PA models is the individuals’ baseline taste for exercise. It is
quite likely that individuals who are more inclined towards exercising regularly are also more
likely to participate in high school athletics. Likewise, these individuals may also be more health
conscious and thus less likely to undertake unhealthy behaviors such as smoking and heavy
consumption of alcohol. Thus, for the exercise/PA and smoking models, the estimates based on
logistic regression are likely to overstate the causal effect of participation in HS athletics on
these future behaviors. In contrast, unobserved taste for exercise/healthy lifestyle is likely to lead
to underestimation of the causal effect of HS athletics participation on future consumption of
alcohol if the former causes an increase in the latter. In all models, assuming the measurement
error is random, it will lead to attenuation bias. Without more information on the relative
importance of these two issues, it is not possible to sign the direction of the bias for the exercise
and smoking models. However, both omitted variables and measurement error lead to
underestimation of the effect of HS athletics on drinking. In an attempt to address these issues,
3
The 2006 wave did not contain information on cigarette smoking.
11
we employ two alternative estimation routines: propensity score matching (PSM) and Lewbel’s
(2012) instrumental variables estimator (LIV).
In order to estimate the average treatment effect, propensity score matching takes place in
three stages. First, a logit model is estimated for the probability of belonging to the treatment
group and the propensity score estimated. Next, observations from the treatment group are
matched to those not in the treatment group based on their propensity scores and the sample is
tested to see if the samples are balanced. Rubin (2001) proposes the difference in the mean of the
propensity scores for the treated and matched samples should be less than half a standard
deviation and the ratio of the variances of the two samples’ propensity scores should be between
0.5 and 2.0. If the balancing requirement is satisfied, observations undergoing treatment are
matched with observations that did not undergo the treatment and the effect of treatment is
obtained by comparing the mean difference of the dependent variable between each treated
observations and its matching observations. We employ nearest neighbor matching where each
treated observation is matched to three non-treated observations. 4
While PSM estimation provides an alternative to regression analysis, there are potential
drawbacks to this approach. In order to produce unbiased estimates of the treatment effect, PSM
requires large sample sizes (not a problem in the present study), substantial overlap between the
treatment and comparison groups and a rich set of covariates to estimate the propensity score.
PSM rests on the assumption that assignment to treatment and control groups is random after
conditioning on observable characteristics. Omitting variables which affect both assignment to
the treatment (participation in high school athletics) and the outcome variable (engagement in
regular exercise, regular smoking, or drinking alcohol) from the first stage can lead to biased
estimates (Heckman et al 1997). Thus, estimates obtained via PSM may eliminate some, but not
all of the bias present when estimating treatment effects using more traditional estimators
(logistic regression in the present case). In particular, the unobservable characteristics of greatest
concern in the present study are baseline taste for exercise/healthy behaviors (predating
participation in HS athletics) and discount rates. The dataset does provide variables which
measure/proxy for the discount rate, but these data are only available for 2006. Furthermore, the
dataset does not have any measures of the baseline preference for health behaviors (predating
4
Changing the number of matching observations in a range from 1-5 does not substantially alter the estimated
average treatment effects.
12
participation in HS athletics). This leads to the question of how discipline affects earnings. Lack
of an adequate proxy means that PSM may not eliminate all of the bias found when using least
squares estimation. The inclusion of several variables representing household/family
characteristics during adolescence and participation on other HS club activities should
considerably strengthen the first stage estimation and improve the performance of the PSM
estimation. For each model, statistics show the samples (treated vs. not-treated) are sufficiently
balanced for PSM estimation to perform properly. 5
We employ a two-stage instrumental variables estimation technique developed by Lewbel
(2012). This technique uses heterogeneity in the residuals from the first stage estimates to
construct instruments for the second stage equation. Specifically, the instruments are constructed
by multiplying the residuals from each first stage regression (one for each endogenous regressor)
by the exogenous variables’ deviations from their means. Thus, the first stage regression
generates one instrument for each explanatory variable in the first stage equation plus each
traditional instrumental variable (we do not specify any traditional IVs, so the number of
instruments generated from each first stage regression is equal to the number of exogenous
explanatory variables in the second stage). Identification requires the residuals from the first
stage regressions to be heteroskedastic. This technique may generate less reliable estimates
compared to standard IV approaches, but serves as a reasonable alternative when other valid
instruments are not available, and can augment traditional IVs, leading to lesser inflation of the
standard errors in the second stage regression.
Before turning to the results, we briefly discuss some of the key summary statistics for
the data. Table 1 presents the mean for each of the dependent variables and for the participation
in HS athletics variable for each sample. In 2000, 39.1 percent of the sample reported exercising
at least once per week while 20.4 percent exercised three or more times per week. In 2002, we
see that 57.3 percent of the respondents met the basic PA guidelines espoused by the federal
government and the WHO, while 45.8 percent met or exceeded the higher guidelines. The
difference between the two estimates can be attributed to WRPA and PA associated with
commuting to work. Data from the 2011-2012 round of the National Health and Nutrition
Examination Surveys (NHANES) show that only 25 percent of US adults engage in at least 40
5
In general, the difference between the means of the propensity scores is around 10% while the ratio of the
variances is very close to one.
13
minutes of vigorous LTPA per week, while median total PA per week is 4.67 hours. The
numbers for the 2012 wave are similar; with somewhat lower fractions of the sample meet either
guideline. The smoking data show a reduction in the rate of daily smoking from 24.9 percent in
1998 to 19.4 percent in 2012. The data on consumption of alcohol show fairly steady rates of
consumption, with just over 56 percent of individuals consuming any alcohol in both 1998 and
2012 and roughly 40 percent meeting the guidelines for the consumption of alcohol. The
reported rate of participation in HS athletics is fairly consistent over the different samples,
hovering around 40 percent.
Results
Exercise/Physical Activity
Table 2 presents the estimates for the exercise/PA models. All models contain the full set
of covariates listed in the methodology section. However, for the sake of expediency, only the
estimates for the HS athletics variable are presented. The logit estimates for frequency of
vigorous exercise (Panel A) show that individuals who participated in HS athletics are 7.9
percentage points more likely to exercise at least one time per week and 5.7 percentage points
more likely to exercise three or more times per week. These represent a 20 percent and 27.9
percent increase over the participation rate for the sample. The PSM estimates are highly similar,
showing a 7 (5.7) percentage point increase in the probability of exercising one (three) day(s) or
more per week. The Lewbel IV estimates show even larger effects of participation in HS
athletics on future exercise frequency. However, while the J-statistic supports validity of the
instruments, the Kleinbergen-Paap (KP) statistic indicates the instruments are weak, possibly
introducing bias into the estimates.
Panel B presents the estimates for the impact on meeting the guidelines for PA in
2002.The results from logit estimation show participation in HS athletics increases the
probability of meeting the basic (higher) PA guidelines by 5 (6) percentage points. The PSM
estimates show a smaller, but still statistically significant effect. The Lewbel estimates vary
significantly. The basic guideline model shows not significant relationship between PA and HS
athletics, while the higher guideline model shows an effect of HS athletics similar to the frequent
exercise model estimates presented in panel A. The consistency in the estimates between the
frequent exercise models in panel A and the higher guideline models in panel B gives us greater
14
confidence that these relationships are robust to different (in this case drastically different)
measures of PA.
Finally, the logit and PSM based estimates for both the basic PA guideline and higher PA
guideline models using the 2012 data are very similar to those using the 2002 data. In fact, the
estimates show an even larger effect of participation in HS athletics on PA as individuals get
older. Overall, the results indicate that participation in HS athletics has a positive, significant
(both statistically and economically), and long lasting effect on future levels of PA.
Cigarette Smoking
The cigarette smoking models (table 3) also show a significant effect of participation in
HS athletics. Being an athlete lowers the probability that an individual smokes daily in 1998 by
2.9 percentage points according to the logit model and by 3.2 percentage points according to the
PSM routine, and the probability the individual has smoked 100-plus cigarettes in her lifetime by
4.4 and 5.5 percentage points, respectively. The Lewbel IV estimates are similar to those
obtained via logistic regression and PSM, but are no longer statistically significant due to the
marked increase in the standard error which is a result of the instruments’ weakness. HS athletics
continues to have a negative effect on smoking in 2012, but with a reduced magnitude. The PSM
estimates for being a daily smoker are no longer statistically significant, while the estimates for
ever having smoked 100-plus cigarettes is only significant at the ten-percent level. Part of the
decline may be due to the decline in overall rates of current smoking. Overall, there does appear
to be a significant, negative impact of HS athletics on smoking, at least for individuals in their
thirties.
Consumption of Alcohol
Participation in HS athletics results in a higher probability of consuming alcohol and
lower probability of staying within the CDC guidelines for alcohol consumption on a typical day
during which the individual consumes alcohol. As with the exercise/PA models, the logit and
PSM estimates are very similar for most of the models. Participation in HS athletics leads to a
2.8 percentage point increase in the probability of consuming any alcohol during the previous
thirty days and a 4.8 percentage point decrease in the probability of meeting the guideline for
alcohol consumption on a typical day. These effects are even stronger in 2012. HS athletics
15
results in a 5.1 percentage point increase in the probability of consuming any alcohol and a 5.4
percentage point reduction in the probability of meeting the guideline. In general, the Lewbel IV
estimates are not significant as tests again show them to be valid, but weak. The one exception is
the guideline model in 2012, which shows a large, negative effect of participation in HS athletics
on the probability of maintaining the guidelines on a given day. However, given the weakness of
the instruments, this estimate should be viewed with some hesitation. Overall, the results show a
significant effect of participation in HS athletics on consumption of alcohol later in life. As with
the PA models, these effects are long-lasting and may actually grow stronger over time.
Robustness check- controlling for a measure of time preference
As discussed in the methodology section, it has been shown that time preference is
correlated with a variety of health behaviors and may also be correlated with participation in HS
athletics. However, the NLSY79 only contains questions aimed at eliciting measures of time
preference in the 2006 wave. Thus, we recreate each model using data from 2006 for the physical
activity and drinking models (the NLSY79 does not contain information on smoking for the full
sample in 2006). Following Smith et al (2005) who used an indicator variable representing
whether the individual saved or dis-saved to proxy for time preference, we use information on a
hypothetical savings question as an alternative to the time preference variables. The savings
variable is constructed from a series of question aimed at eliciting information on the
individual’s preferences over risk. First, individuals were asked “Suppose you have been given
an item that is either worth nothing or worth $1, 000. Tomorrow you will learn what it is worth.
There is a 50-50 chance it will be worth $1,000 and a 50-50 chance it will be worth nothing. You
can wait to find out how much the item is worth, or you can sell it before its value is determined.
What is the lowest price that would lead you to sell the item now rather than waiting to see what
it is worth?” Then, they were asked the follow up question “If you received [$ (value in RISK3)/your selling price], what percentage (0 -100) of this would you save for the future rather than
spend in the next 12 months?” We use the response to the second question, recoded to take
values between zero and one, as our proxy for time preference.
The NLSY79 data provide an alternative to the hypothetical savings rate variable
described above. Respondents were asked: “Suppose you have won a prize of $1000, which you
can claim immediately. However you have the alternative of waiting one month to claim the
16
prize. If you do wait, you will receive more than $1000. What is the smallest amount of money in
addition to the $1000 you would have to receive one month from now to convince you to wait
rather than claim the prize now?” While this information might seem to provide a more direct
measure of time preference, discount rates constructed from this information do not show a
significant correlation with any of the PA or drinking variables. Kosteas (2015) also found the
savings variable to be a stronger predictor of PA compared to discount rates calculated from the
responses to this question.
Table 5 presents the estimates for the PA and drinking models using the 2006 data. Each
model is first estimated without the time preference proxy (columns 1-3) and then again
including the time preference measure (columns 4-6). When excluding the time preference proxy
from list of control variables, the estimated effect of participation in HS athletics on PA in 2006
is qualitatively similar to the estimates obtained using the 2002 and 2012 samples, however the
magnitudes of the coefficients from the logit models are somewhat smaller. The PSM estimates
are very similar to those obtained using the 2002 data. It is important to note here that the PSM
estimates are actually slightly larger in magnitude compared with the logit estimates.
Interestingly, the estimated effect of participation in HS athletics on current PA levels is large
and statistically significant. However, as before, some caution is warranted as the KP tests
indicate the instruments are weak. Including the hypothetical savings variable has very little
impact on the coefficient estimates. The proxy behaves exactly as we would expect. Individuals
who are more future oriented (i.e. would save a larger fraction of the hypothetical award) are
more likely to meet both the basic and the higher PA guidelines. Overall, the estimates for the
PA models are robust to the inclusion of the time preference proxy.
Switching to the models for consumption of alcohol, we see similar patterns in the
models where we do not control for time preference. As with the PA models, the logit estimates
are smaller in magnitude for 2006 compared with the 2002 and 2012 estimates. However, the
PSM estimates are similar in magnitude to those for 2002. Consistent with the results for the
2012 data, the Lewbel IV estimates show a strong, negative effect of participation in HS athletics
on the probability of adhering to the guidelines for alcohol consumption on a typical day.
Inclusion of the savings variable does not have a significant impact on either the logit or IV
estimates, however the PSM estimates decline significantly and are no longer statistically
17
significant. Thus, the estimates for the alcohol consumption models are not as robust to the
inclusion of the savings variable as are the PA models.
Robustness check- controlling for log wage and hours worked per week
As an additional robustness check, we estimate the models including the log hourly wage and
average hours worked per week as additional control variables. The inclusion of these variables
restricts the sample to working individuals. Given the weakness of the generated instruments in
the Lewbel IV approach, we focus here on the logit and PSM estimates. Generally, the results
(Table 6) are highly consistent with those presented in tables 2-4. It does not appear the effect of
participation in HS athletics on health behaviors depends on labor market status. Furthermore,
the coefficient estimate on the hours worked per week variable is not statistically significant in
any of the models.
The consistency of our results when conducting these specification checks increases our
confidence in the primary findings presented in tables 2-4. In general, there is strong evidence
suggesting participation in HS athletics leads to higher levels of PA and lower rates of smoking
in middle age, with a positive, but less robust effect on consumption of alcohol.
Conclusions
We estimate the effect of participation in high school athletics on several health
behaviors during adulthood, through middle age. Specifically, we examine whether playing
sports in high school affects the probability of meeting established guidelines for physical
activity, being a daily smoker or ever having been a regular smoker, and whether and how much
alcohol an individual consumes. We look at these relationships over a twelve year period,
allowing us to determine whether these effects are persistent over time. We find that
participation in HS athletics raises the level of PA, leads to lower rates of smoking, but higher
rates of drinking alcohol. These findings are consisting with existing studies showing that high
school and college athletes drink more but are less likely to smoke cigarettes when compared
with their classmates who do not play sports, suggesting that these results may at least be the
result of habit formation. Furthermore, the estimated effects are long-lasting and may, grow
stronger over time for certain behaviors.
18
This study adds to our understanding of the many potential benefits of participation in
athletics during high school. In addition to greater educational attainment and improved labor
market outcomes, it appears former athletes also invest more in their health capital. The results
provide additional justification for the continuing financial support for high school athletics by
school districts. By using multiple estimation techniques, including propensity score matching
and a relatively new instrumental variables routine, the present paper attempts to push beyond
simply establishing correlation and towards the estimation of causal effects. The results indicate
these relationships are indeed causal. However, more work is needed, using other estimators in
order to more confidently assert the causal nature of these correlations. Additionally, future work
is needed to assess the extent of participation of high school athletics that is needed to generate
these benefits. Unfortunately, the limited information on participation available in the NLSY79
does not allow us to do so in the present study.
19
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Table 1: Summary Statistics
2000 Exercise Models
Exercise 1+ times per week
Exercise 3+ times per week
Participated in HS athletics
Mean
0.391
0.204
0.403
Std Dev
0.488
0.403
0.491
N
5,538
2002 Physical Activity Models
Met Basic PA Guideline
Meet Higher PA Guideline
Participated in HS athletics
Mean
0.573
0.458
0.399
Std Dev
0.495
0.498
0.49
N
4,900
2012 Physical Activity Models
Met Basic PA Guideline
Meet Higher PA Guideline
Participated in HS athletics
Mean
0.565
0.422
0.402
Std Dev
0.496
0.494
0.49
N
5,198
1998 Smoking Models
Currently Smokes Daily
Has Smoked 100+ Cigarettes
Participated in HS athletics
Mean
0.249
0.479
0.405
Std Dev
0.432
0.5
0.491
N
5,702
2012 Smoking Models
Currently Smokes Daily
Has Smoked 100+ Cigarettes
Participated in HS athletics
Mean
0.184
0.579
0.398
Std Dev
0.387
0.494
0.49
N
5,617
1998 Alcohol Models
Consumed any alcohol past 30 days
Meets Guidelines for Alcohol
Participated in HS athletics
Mean
0.562
0.686
0.407
Std Dev
0.496
0.464
0.491
N
5,671
2012 Alcohol Models
Consumed any alcohol past 30 days
Meets Guidelines for Alcohol
Participated in HS athletics
Mean
0.564
0.708
0.399
Std Dev
0.496
0.454
0.49
N
5,588
26
Table 2: Determinants of Exercise/Physical Activity
Panel A: 2000 (n=5,538)
Athletics
Logit
Exercise 1+ Times
Per Week
PSM
Lewbel IV
0.079**
(0.013)
0.07**
(0.016)
0.129+
(0.068)
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Panel B: 2002 (N=4,900)
Athletics
Athletics
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Lewbel IV
0.057**
(0.011)
0.057**
(0.013)
0.115+
(0.061)
11.02 (0.89)
6.7
0.0426
Logit
Met Basic PA Guideline
PSM
Lewbel IV
0.05**
(0.015)
0.039*
(0.017)
0.006
(0.069)
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Panel C: 2012 (N=5,198)
Logit
Exercise 3+ Times
Per Week
PSM
13.46 (0.76)
6.7
0.0426
Logit
Meet Higher PA Guideline
PSM
Lewbel IV
0.06**
(0.015)
0.048*
(0.017)
0.125+
(0.072)
17.89 (0.46)
6.03
0.0462
Logit
Met Basic PA Guideline
PSM
Lewbel IV
0.048**
(0.015)
0.049**
(0.018)
0.04
(0.066)
18.7 (0.41)
7.26
0.0503
27
6.03
0.0462
Logit
Meet Higher PA Guideline
PSM
Lewbel IV
0.058**
(0.015)
0.058**
(0.018)
0.081
(0.068)
13.8 (0.74)
7.26
0.0503
Table presents marginal effects for logit models and average treatment effects for PSM models.
All models contain the full set of control variables.
Robust standard errors are in parentheses.
+,*, ** denote significance at the 10%, 5%, 1% level, respectively.
KP critical values: 20% = 6.31, 10% = 11.46, 5% = 21.36.
28
Table 3: Determinants of Smoking
Panel A: 1998 (N=5,707)
Athletics
Logit
Currently Smokes Daily
PSM
Lewbel IV
-0.029*
(0.012)
-0.032*
-0.014
0.036
(0.056)
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Panel B: 2012 (N=5,617)
Athletics
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Logit
Has Smoked 100+ Cigarettes
PSM
Lewbel IV
-0.044**
(0.014)
-0.05**
(0.016)
0.042
(0.07)
13.9 (0.74)
6.06
0.038
Logit
Currently Smokes Daily
PSM
Lewbel IV
-0.028*
(0.011)
-0.021
(0.014)
-0.022
(0.041)
21.3 (0.26)
6.06
0.038
Logit
Has Smoked 100+ Cigarettes
PSM
Lewbel IV
-0.036**
(0.014)
-0.031+
(0.017)
0.058
(0.063)
27.1 (0.08)
8.31
0.0526
Table presents marginal effects for logit models and average treatment effects for PSM models.
All models contain the full set of control variables.
Robust standard errors are in parentheses.
+,*, ** denote significance at the 10%, 5%, 1% level, respectively.
KP critical values: 20% = 6.31, 10% = 11.46, 5% = 21.36.
29
16.0 (0.59)
8.31
0.0526
Table 4: Determinants of Alcohol Consumption
Panel A: 2002 (N=5,671)
Athletics
Logit
Consumed any Alcohol
Past 30 Days
PSM
Lewbel IV
0.041**
(0.014)
0.028+
(0.015)
-0.017
(0.067)
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Panel B: 2012 (N=5,588)
Athletics
Logit
Meets Guidelines
for Alcohol
PSM
Lewbel IV
-0.051**
(0.013)
-0.048**
(0.015)
-0.032
(0.064)
21.3 (0.26)
6.39
0.0411
Logit
Consumed any Alcohol
Past 30 Days
PSM
Lewbel IV
0.046**
(0.014)
0.051**
(0.016)
0.062
(0.06)
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
16.3 (0.57)
8.24
0.0525
Table presents marginal effects for logit models and average treatment effects for PSM models.
All models contain the full set of control variables.
Robust standard errors are in parentheses.
+,*, ** denote significance at the 10%, 5%, 1% level, respectively.
KP critical values: 20% = 6.31, 10% = 11.46, 5% = 21.36.
30
12.2 (0.84)
6.39
0.0411
Logit
Meets Guidelines
for Alcohol
PSM
Lewbel IV
-0.056**
(0.013)
-0.054**
(0.014)
-0.155**
(0.056)
15.46 (0.63)
8.24
0.0525
Table 5: Robustness Check- controlling for time preference
Excluding Time Preference
Including Time Preference
Panel A: Basic PA guideline
Logit
PSM
Lewbel IV
Logit
PSM
Lewbel IV
Athletics
0.034*
0.036*
0.141*
0.034*
0.035*
0.149*
(0.015)
(0.018)
(0.067)
(0.015)
(0.017)
(0.071)
Time Preference
Observations
5,519
5,519
5,519
0.067**
0.065**
(0.017)
(0.017)
5,278
5,278
5,278
First-stage statistics
Hansen's J-statistic
21.3 (0.26)
Kleibergen-Paap statistic
Shea Partial R-squared
Panel B: Higher PA guideline
Athletics
6.48
5.63
0.0467
0.0441
Logit
PSM
Lewbel IV
Logit
PSM
Lewbel IV
0.043**
0.048**
0.203**
0.039**
0.04*
0.208**
(0.014)
(0.018)
(0.068)
(0.015)
(0.017)
(0.071)
Time Preference
Observations
24.1 (0.19)
5,519
5,519
5,519
0.062**
0.061**
(0.017)
(0.017)
5,278
5,278
5,278
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
24.4 (0.14)
25.0 (0.16)
6.48
5.63
0.0467
0.0441
Panel C: Any drinks past 30 days
Logit
PSM
Lewbel IV
Logit
PSM
Lewbel IV
Athletics
0.034*
0.034*
0.083
0.032*
0.015
0.09
(0.013)
(0.016)
(0.062)
(0.014)
(0.016)
(0.065)
Time Preference
Observations
6,178
6,178
6,178
First-stage statistics
31
-0.068**
-0.068**
(0.015)
(0.015)
5,889
5,889
5,889
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
Panel D: Alcohol guideline
Athletics
25.9 (0.08)
27.9 (0.09)
7.46
6.31
0.0465
0.0428
Logit
PSM
Lewbel IV
Logit
PSM
Lewbel IV
-0.045**
-0.048**
-0.158**
-0.044**
-0.02
00.168**
(0.012)
(0.016)
(0.056)
(0.013)
(0.015)
(0.059)
Time Preference
Observations
6,178
6,178
6,178
0.067**
0.067**
(0.015)
(0.015)
5,889
5,889
5,889
First-stage statistics
Hansen's J-statistic
Kleibergen-Paap statistic
Shea Partial R-squared
27.0 (0.08)
26.5 (0.12)
7.46
6.31
0.0465
0.0428
Table presents marginal effects for logit models and average treatment effects for PSM models.
All models contain the full set of control variables.
Robust standard errors are in parentheses.
+,*, ** denote significance at the 10%, 5%, 1% level, respectively.
32
Table 6: Robustness Check- including only employed individuals
Panel A: Exercise
Met Basic PA
Guidelines
(2002)
0.048**
(0.016)
Met Higher PA
Guidelines
(2002)
0.056**
(0.016)
Met Basic PA
Guidelines
(2012)
0.047**
(0.017)
Met Higher PA
Guidelines
(2012)
0.062**
(0.017)
PSM (ATE)
0.033+
(0.018)
0.055**
(0.018)
0.044*
(0.019)
0.057**
(0.02)
Observations
4,245
4,245
3,974
3,974
Currently Smokes
Daily (1998)
-0.038**
(0.012)
Has Smoked
100+
Cigarettes (1998)
-0.054**
(0.015)
Currently Smokes
Daily (2012)
-0.023*
(0.012)
Has Smoked
100+
Cigarettes (2012)
-0.037*
(0.016)
PSM (ATE)
-0.048**
(0.013)
-0.055**
(0.017)
-0.0057
(0.012)
-0.053**
(0.017)
Observations
4,966
4,963
4,306
4,306
Consumed Alcohol
Past 30 days
(2002)
0.046**
(0.015)
Meets Alcohol
Guidelines
(2002)
-0.06**
(0.014)
Consumed Alcohol
Past 30 days
(2002)
0.048**
(0.016)
Meets Alcohol
Guidelines
(2012)
-0.071**
(0.015)
PSM (ATE)
0.043**
(0.016)
-0.06**
(0.016)
0.058**
(0.018)
-0.065**
(0.017)
Observations
4,911
4,911
4,300
4,300
Logit (ME)
Panel B:
Smoking
Logit (ME)
Panel C:
Drinking
Logit (ME)
Table presents marginal effects for logit models and average treatment effects for PSM models.
All models contain the full set of control variables, including the log hourly wage and hours worked per
week.
Robust standard errors are in parentheses.
+,*, ** denote significance at the 10%, 5%, 1% level, respectively.
33