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While athletic success may improve the visibility of a university to prospective students and thereby benefit the school, it may also increase risky behavior in the current student body. Using the Harvard School of Public Health College Alcohol Study, we find that a school's participation in the NCAA Basketball Tournament is associated with a 47% increase in binge drinking by male students at that school. Additionally, we find evidence that drunk driving increases by 5% among all students during the tournament. (JEL I12, I23, Z28)
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While athletic success may improve the visibility of a university to prospective
students and thereby benet the school, it may also increase risky behavior in the current
student body. Using the Harvard School of Public Health College Alcohol Study, we nd
that a school’s participation in the NCAA Basketball Tournament is associated with a
47% increase in binge drinking by male students at that school. Additionally, we nd
evidence that drunk driving increases by 5% among all students during the tournament.
(JEL I12, I23, Z28)
Alcohol consumption is one of the primary
public health concerns on college campuses in the
United States (NIH-NIAAA 2016; theAmethyst- 2015). Of particular concern is the
amount of binge drinkingdened as ve or
more drinks at one timeamong students. Binge
drinking is associated with increased rates of
drunk driving, sexual assault, and other nega-
tive outcomes among young people (Miller et al.
2007). A survey conducted by Glassman et al.
(2010) revealed that 16% of respondents aged
1824 consumed more than double the binge
drinking threshold on college football game days
and that 36% of respondents reported binge
drinking during game day festivities.
Studies of alcohol consumption associ-
ated with athletic events have been conducted
previously, but these focus almost entirely on a
single occasion or a single school (Neal et al.
2005; Neal and Fromme 2007). In this paper, we
We thank Toben Nelson for providing the College Alco-
hol Study data for this project. We also thank participants
in the Southern Economic Association Conference, Beero-
nomics Conference, and the SES Seminars at Washington
State University for their feedback. All errors are the authors’
White: Assistant Professor, Economics, University of
Nebraska at Omaha, Omaha, NE 68182. Phone 402-554-
3303, Fax 402-554-2853, E-mail
Cowan: Associate Professor, Economics, Washington State
University, Pullman, WA 99164; NBER, Cambridge, MA
02138. Phone 509-335-2184, Fax 509-335-1173, E-mail
Wooten: Assistant Teaching Professor of Economics, Eco-
nomics, The Pennsylvania State University, University
Park, PA 16802. Phone 814-865-7352, Fax 814-863-4775,
explore the impact of the NCAA Men’s Basket-
ball Tournament on student alcohol consumption
during the 1993, 1997, 1999, and 2001 seasons at
more than 40 schools using the Harvard School
of Public Health College Alcohol Study (CAS).
We isolate the effect of tournament participation
on student alcohol consumption by examining
how patterns of consumption change around
the tournament for schools that do and do not
participate in a given year.
Intercollegiate athletics have played a part in
the college experience for more than a century;
currently, over 1,200 colleges and universities
are members of the National Collegiate Athletic
Association (NCAA 2010). The broadcast rights
deal signed by the NCAA for the Division I
Men’s Basketball tournament ($10.8 billion
over 14 years) provides proof of the tremendous
demand both on and off campus for college
basketball (O’Toole 2010). The increasing pop-
ularity of intercollegiate athleticsparticularly
football and men’s basketball— drives universi-
ties to invest more in their athletic programs each
year (Berkowitz 2014).
Athletic programs have the potential to
increase the exposure of an institution and
thereby increase the quality of applicants to
their school. Toma and Cross (1998) nd that
winning a national championship in either men’s
basketball or football increases the number of
CAS: College Alcohol Study
DD: Difference-in-Differences
GPA: Grade Point Average
NCAA: National Collegiate Athletic Association
Contemporary Economic Policy (ISSN 1465-7287) doi:10.1111/coep.12425
© 2019 The Authors. Contemporary Economic Policy published by Wiley Periodicals, Inc. on behalf of the Western Economic Association International.
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applicants to a school, and that these increases
were both absolute and relative to peer insti-
tutions. Pope and Pope (2009, 2014) nd that
improved performance in football or basketball
in a given year can increase the number of
Scholastic Assessment Test scores submitted to
a school by up to 10%. A larger pool of appli-
cants allows a school to be more selective when
making admissions decisions.
Lindo, Swensen, and Waddell (2012) exam-
ine variation over time in athletic performance of
football teams at the University of Oregon and
nd that academic performance suffers during
years in which the team performs exceptionally
well. This difference is most pronounced among
males. Responses to a student survey in Lindo,
Swensen, and Waddell (2012) suggest that alco-
hol consumption plays a role in the decline of
grades. This agrees with previous results that nd
alcohol consumption is associated with worse
grades among students and that peer effects are
particularly inuential in determining the extent
of consumption among college students (Eisen-
berg, Golberstein, and Whitlock 2014; Lindo,
Swensen, and Waddell 2013). In a follow-up arti-
cle, Hernández-Julián and Rotthoff (2014) con-
rm the ndings of Lindo, Swensen, and Waddell
(2012) by observing a similar effect at Clemson
University, although they nd that females suf-
fer a larger decrease in academic performance.
While better athletic performance may have a
positive effect for the university in terms of dona-
tions and improving the quality of future classes,
there appears to be an adverse effect on the cur-
rent student body.
More recently, Lindo, Siminski, and Swensen
(2018) investigate the incidence of sexual assault
on college football game days. They nd that
reported sexual assaults increase signicantly
surrounding these events and that alcohol-related
arrests rise as well. The authors suggest that
increased alcohol consumption and a college
party culture are the primary drivers of the spike
in the incidence of sexual assault surrounding
college football games.
In this paper, we examine self-reported alco-
hol use during a major college sporting event
at schools that do and do not participate in the
tournament in a given year using a difference-in-
differences (DD) framework. We are thus able
to study a direct link between college sports and
drinking. Furthermore, while previous papers
have largely focused on college football, we
examine the effects of a post-season basket-
ball tournament that involves many schools
simultaneously and takes place in the spring
rather than the fall.1
Identication in our paper is established by
comparing the change in drinking that occurs
among students at participating (treatment)
schools during the time of the tournament (mid-
March to early April in our sample years) to any
change that occurs at nonparticipating (control)
schools around that time. Thus, time-invariant
differences in alcohol use across schools by
participation status as well as seasonal changes
in drinking that are common to all college stu-
dents are “differenced out” of our estimated
treatment effect.
In the next section of this paper, we present
our empirical model to estimate the impact of
NCAA tournament participation and games on
alcohol consumption. We then provide a sum-
mary of the data used in our analysis. In the
results section, we show that tournament par-
ticipation increases the binge drinking rate of
male students by approximately 47% (relative to
the average binge rate among males at tourna-
ment schools). Furthermore, we nd that students
are more likely to self-report drunk driving dur-
ing the tournament. We conclude by consider-
ing the meaning of these results and their pol-
icy implications.
The NCAA Men’s Basketball Tournament
extends the basketball season for participating
teams by up to six games during the years studied
in this paper. The winner of the tournament is
deemed the national champion for that season.2
These games are watched or attended by tens of
millions of fans.3For college students, if alcohol
consumption is in fact complementary to viewing
or attending (important) games by the college’s
team (as is suggested by Lindo, Swensen, and
Waddell 2012), then drinking among students
will rise when the school’s team participates in
tournament games.
Using reports of alcohol consumption tak-
ing place between January and May in each of
1. This is a necessity of working with the CAS data, as
surveys are collected each spring.
2. Information on the selection of participating teams can
be found at and-
3. In our sample years, television viewership for the
national championship game alone was between 23 and 33
million people:
nal-four-ratings-history-most-watched-games-cbs- tbs-
our sample years, we can observe alcohol con-
sumption of students during the NCAA tourna-
ment. We exploit both time differences (whether
a respondent’s survey covers the time period for
the tournament) as well as school differences
(whether the respondent was attending a tourna-
ment school in that year) in a DD framework to
identify the effect of NCAA tournament partici-
pation by an individual’s institution on their alco-
hol consumption.
Our regression model is expressed as follows:
Drepresents the drinking behavior of interest,
and i,s,m, and yare indices for individual,
school, month, and year, respectively. We focus
primarily on the number of occasions that an
individual binged (measured as at least ve drinks
at one time) over a 2-week period, but consider
other measures of alcohol use as well.
Our treatment variable, treatedismy, is a binary
variable that is equal to one if the individual’s
retrospective survey covers the date(s) of one or
more games played by their school team in the
tournament that year and is zero otherwise. Thus,
it is zero for individuals attending schools that did
not participate in the tournament in a given year
as well as for individuals attending tournament
schools but whose survey did not cover any of
that school’s tournament games. μmrepresents
month xed effects, and γsy represents school by
year xed effects that account for school-specic
trends across years. Xismy is the vector of all other
individual and school explanatory variables, and
ϵismy is a disturbance term.
Our main parameter of interest, β1, is identi-
ed through any change in drinking that occurs in
tournament schools during the time of the tourna-
ment compared to other times within the year, all
relative to the same difference at nontournament
Time-invariant differences in alcohol con-
sumption by institution are not a threat to
identication: if students at tournament schools
tend to drink more than students at nontourna-
ment schools, but this difference does not change
during the tournament, we will fail to reject
the hypothesis that β1=0, and the difference
4. We include a full set of month effects in our model
rather than an indicator for “during tournament.” We also
performed specications with unique dummies for all month-
year pairs as a robustness check (the results are very similar
to those of our baseline model; see Section IV.C).
will be reected in the school-year xed effects.
Similarly, seasonal changes in drinking behavior
across all schools cannot account for a positive
β1, since those changes will be absorbed by the
month xed effects. We note that student drink-
ing could rise during spring break (when school
is out), but we do not observe that tournament
schools are more likely to hold their spring break
during the time of the tournament.5
Xismy includes factors that are commonly
associated with alcohol consumption by college
students, according to Glassman et al. (2010):
race, gender, age, membership in a fraternity or
sorority, and year in school. Xismy also includes
measures of grade point average (GPA), mari-
tal status, men’s basketball regular season win
percentage, and athletic conference (because
conference afliation varies for some teams
during our study period, it is not collinear with
school xed effects). As we show in the results
section, the inclusion of these variables makes
little difference in our estimates of interest.
We use the Harvard School of Public Health
CAS to examine the relationship between col-
lege basketball postseason play and the amount
of alcohol consumed by an institution’s student
body. CAS constitutes a nationally representa-
tive sample of full-time college students attend-
ing 4-year institutions in 1993, 1997, 1999, and
2001.6Forty-one of the institutions in the data
participated in NCAA Division I athletics dur-
ing the sample frame; students from these insti-
tutions make up the sample used for this paper.
The names of the schools included in our sample
can be found in Table A1 of Appendix S1, Sup-
porting Information. In each year, CAS students
were sent a mail survey, which included a series
of questions regarding alcohol and other drug use,
5. In 2015, 79% of spring breaks across 529 institutions
occurred entirely within the month of March and 97% over-
lapped March (STSTravel 2014). Though we do not know the
spring break dates of all the school-year pairs in our data,
we think it is unlikely that schools schedule spring break to
overlap the NCAA tournament from year to year. Thus, our
analysis in Section IV.C is unlikely to be biased by any dif-
ferences in spring break dates by tournament status, and we
provide alternative specications to support this opinion.
6. For details on the survey design, see Wechsler et al.
(2002). Cowan and White (2015) provide a comparison of
CAS with Monitoring the Future and the National Longitudi-
nal Survey of Youth (1997 cohort), and nd similar drinking
patterns and other characteristics of college students across
the data sources.
Mean Characteristics for Students and Schools by Tournament Status
Number of binge occasions in past 2 weeks 1.370 1.526 1.477 1.812
Drank last month 0.651 0.697 0.709 0.632
Number of drinks in past month 24.142 27.234 26.078 33.980
Survey overlaps any of college team’s tournament games 0 0.146 0 1
Number of tournament games overlapped by survey 0 0.235 0 1.606
Men’s basketball season win percentage 40.617 67.586 67.832 66.156
Age 21.043 20.676 20.685 20.627
Belongs to fraternity/sorority 0.153 0.207 0.210 0.190
Married 0.098 0.068 0.069 0.060
Female 0.577 0.576 0.575 0.584
1993 respondent 0.268 0.591 0.671 0.125
1997 respondent 0.315 0.115 0.054 0.471
1999 respondent 0.297 0.230 0.216 0.315
2001 respondent 0.121 0.064 0.060 0.089
Freshman 0.204 0.219 0.216 0.236
Sophomore 0.202 0.217 0.213 0.238
Junior 0.248 0.237 0.238 0.229
Senior 0.236 0.228 0.231 0.209
White 0.767 0.857 0.862 0.829
Black 0.064 0.036 0.035 0.043
Asian 0.075 0.048 0.048 0.051
Other race 0.093 0.059 0.055 0.077
GPA 3.159 3.151 3.147 3.177
N21,987 3,990 3,406 584
Notes: Bold values indicate that the difference in means is signicant at the 5% level. Tournament respondents are those
individuals whose survey overlaps at least one of their own institution’s team’s games in the NCAA tournament. Nontournament
Respondents are individuals at Tournament Colleges whose survey covers dates either before or after the tournament. Students at
Tournament Colleges attended institutions whose NCAA Division I Men’s Basketball Team participated in that season’s NCAA
tournament. Students at Nontournament Colleges also attended institutions with an NCAA Division I Men’s Basketball Team,
but their team did not participate in the NCAA tournament in that year.
experiences in college, and limited demographic
and family background information.
Of the 31,184 student observations attending
Division I institutions, 25,977 have nonmissing
values for all variables used in this paper.7This
is our regression sample. Student surveys were
distributed early in the year and returned over the
next several months: 4,396 surveys were returned
in February, 7,804 in March, 11,054 in April, and
2,527 in May, accounting for about 99% of our
sample over the 4 years.
According to Table 1, the majority of respon-
dents reporting during a year in which their
school’s team participated in the NCAA
Tournament are surveyed in 1993 (approxi-
mately 60% of such responses). The bulk of
7. In order to determine that missing values are not more
prevalent among tournament or nontournament schools, we
calculate the number of observations with missing values for
each group, and present those results in Table A2 of Appendix
S1. Nontournament schools have a higher frequency of obser-
vations with missing values, with March responses (prior to
the tournament) being the primary driver of this difference.
nontournament responses occurred in the 1997
and 1999 surveys, simply due to the fact that
fewer schools that participated in the CAS were
invited to participate in the NCAA tournament
during those survey years, especially in compari-
son to 1993. Because the primary response years
for each type of respondent differ, it may also
be that the response dates within years for each
type of respondent also vary. Table 2 provides
the average response date for tournament and
nontournament school-years. There is certainly
variation between the response dates, but the
average response date for tournament respon-
dents is within one standard deviation of the
response date for nontournament respondents in
each year (and the overall sample), mitigating
concerns that there are systematic differences
in response patterns between tournament and
nontournament schools.
A limitation of our analysis is that we only
know the date a survey was processed by CAS
administrators; we do not know the exact dates
on which individuals completed their survey. As
Average Response Date by Year and Tournament Status
1993 1997 1999 2001 Overall
Average response date — tournament school-years March 5 April 30 April 2 April 12 March 17
(17 days) (10 days) (18 days) (26 days) (25 days)
Average response date — nontournament school-years March 12 April 27 April 8 April 4 April 2
(21 days) (19 days) (23 days) (27 days) (26 days)
Note: Standard deviations are provided in parentheses.
a result, when questions refer to retrospective
alcohol consumptionsuch as binge drinking
over the previous 2 weeks— we do not know
the exact period of time to which a student
is referring.
In lieu of information on the dates over which
each student is measuring their drinking, we
assume that 4 weeks pass between the completion
of the survey by the individual and its process-
ing date. This accounts for any lag between the
date a student lled out the survey and the date
they mailed it, time spent in the mail, and any
lag between receipt of the survey and its process-
ing by CAS administrators. The question “How
many times did you binge drink in the past 2
weeks?” would therefore be interpreted as “How
many times did you binge drink between 28 and
42 days prior to the processing date of the sur-
vey?,” and questions regarding drinking in the
previous month would be interpreted with respect
to the time period 2858 days prior to the pro-
cessing date.
Since the choice of a 4-week lag to estimate
the dates of the retrospective drinking period is
somewhat arbitrary, we also estimated our mod-
els using 2-, 3-, 5-, and 6-week lags. Figure 1
shows how our point estimate of interest (the
effect of the tournament on binge drinking occa-
sions) and corresponding condence intervals
change with the choice of lag. As seen in the
gure, point estimates diminish as the lag is
moved away from 4 weeks in either direction,
with 2- and 6-week lags yielding a near-zero
coefcient. Figures 2 and 3 show that this pattern
holds among male respondents, but that the binge
drinking behavior of female students appears to
have no relationship to the tournament window.8
8. The following is a quote from Wechsler et al. (2002)
regarding the mailing and collection of CAS surveys in all
four survey years: “Following the same practice as that used
in the 3 prior surveys, we mailed questionnaires directly to
students beginning in February and sent 3 separate mailings
within a minimum span of 3 weeks. The initial mailing con-
sisted of a letter of invitation to participate in the study and
It is unfortunate that we cannot corroborate
our assumption about the lag between survey
completion and survey processing, especially
because our results are sensitive to this assump-
tion. In all years, there are many more responses
received prior to the NCAA tournament than
after it. It is comforting, however, to nd that
the results (for men) diminish symmetrically
with a shorter or longer lag, since this means
that shifting individuals who respond late to
the survey into the “treatment” group does not
produce a different result than shifting treat-
ment toward individuals responding somewhat
earlier. Thus, concerns about self-selection of
response times and its own correlation with
drinking behavior, even if that relationship
is correlated with tournament school status,
are diminished.
A. Drinking Measures
We focus on three drinking measures to eval-
uate the impact of the NCAA Men’s Basketball
Tournament on alcohol consumption patterns:
(1) the number of binge drinking incidents
reported in the past 2 weeks, (2) whether or
not an individual reported drinking at all in the
past month, and (3) the number of alcoholic
beverages an individual reported consuming
in the past month. Two of our dependent vari-
ables are quasi-continuous measures based
on binned responses to questions regarding
alcohol consumption, and the third depen-
dent variable is a binary value based on these
original measures.
Our measure of binge drinking is based on
respondents’ answer to the question “Think back
a questionnaire. We followed this mailing with a reminder
postcard and a separately mailed second questionnaire. Mail-
ings were different for each school, and we scheduled them
to avoid the period immediately preceding and following
spring break to capture behavior that occurred on campus
and to avoid responses that reected behavior during spring
Varying the Response Lag (w/95% CI)
Varying the Response Lag among Male Respondents (w/95% CI)
Varying the Response Lag among Female Respondents (w/95% CI)
over the last two weeks. How many times have
you had ve or more drinks in a row?” Potential
answers are 0, 1, 2, 3 to 5, 6 to 9, and 10+
times. For answers with single values, that value
is coded as the number of binge occasions in the
past 2 weeks. For bins with multiple values, the
midpoint is taken, and for individuals choosing
the 10+bin, a value of 10 binge occasions in the
past 2 weeks is assigned.
The number of drinks consumed in the past
month (30 days) is generated by combining the
values of two questions. First, “On how many
occasions have you had a drink of alcohol in the
past 30 days?” Responses to this question were
categorized as 0, 1 to 2, 3 to 5, 6 to 9, 10 to 19,
20 to 39, and 40+occasions. Where bins were
bounded, we coded the number as the midpoint
value. When using the top code, a response was
coded as 40 drinks. The next question, “In the
past 30 days on the occasions when you drank
alcohol, how many drinks did you usually have?”
had possible responses of 1, 2, 3, 4, 5, 6, 7,
8, 9+drinks per occasion. Only the top code
needed clarication, and any response using the
top code was assigned a value of 9 drinks per
occasion. The number of drinks consumed in the
past month was then calculated as the product of
the number of days an individual drank in the past
30 days and the number of drinks an individual
typically consumed when they did drink.
The binary variable indicating any alcohol
consumption in the past month (30 days) is gener-
ated from the question about the number of occa-
sions on which the respondent consumed alcohol.
Where this value is 0, the binary variable is also
0; where the response was a value greater than 0,
the binary variable was coded as 1.
B. Preliminary Data Evaluation
Summary statistics for variables included
in our analysis by tournament school status
(whether an individual’s team participated in
the tournament in that year, or not) are pro-
vided in Table 1. Students at tournament schools
are more likely to have consumed alcohol
and, on average, engaged in more drinking
and binge drinking than students at nontour-
nament schools. There are also signicant
differences in the demographics of these two
groups, with students from tournament schools
being whiter, younger, less frequently married,
and more likely to be members of fraternities
and sororities.
Table 1 also shows how respondents whose
survey covers games played by their institution’s
Effects of Own-Institution NCAA Tournament Participation on Student Drinking Behaviors
All Observations Males Females
Number of
Binges Drink?
Number of
Number of
Binges Drink?
Number of
Number of
Binges Drink?
Number of
Primary specication
0.353** 0.001 5.041** 0.724** 0.037 10.947** 0.062 0.031 0.645
(0.122) (0.020) (1.331) (0.206) (0.031) (4.046) (0.146) (0.027) (1.923)
Including school*month FE’s
0.334** 0.03 0.912 0.766** 0.069** 4.867 0.022 0.002 2.288
(0.121) (0.017) (1.061) (0.205) (0.026) (3.177) (0.144) (0.023) (1.576)
Including month*year FE’s
0.297** 0.04** 3.669** 0.596** 0.007 7.179** 0.062 0.064** 0.818
(0.106) (0.014) (0.919) (0.179) (0.022) (2.696) (0.127) (0.02) (1.397)
Primary specication, tournament
school subsample (n=9,774)
0.209** 0.001 2.919** 0.435** 0.023 6.985** 0.011 0.024 0.402
(0.077) (0.013) (0.872) (0.124) (0.019) (2.391) (0.093) (0.018) (1.256)
Primary specication, including
spring break (n=22,970)
0.321*0.009 5.682** 0.774** 0.046 12.713** 0.04 0.053 0.286
(0.138) (0.022) (1.417) (0.23) (0.034) (4.187) (0.165) (0.03) (2.148)
Notes: Standard errors are robust to heteroskedasticity and clustered at the institution level. Other controls included in the regressions but not
shown include all those described in Section IV.A.
Signicant at the 5% level.
∗∗Signicant at the 1% level.
team in the tournament compare to other respon-
dents at tournament schools (who returned their
survey at different times of the year).9We actu-
ally see a lower probability of drinking among
during-tournament respondents, but that is
accompanied by more binge drinking and drinks
over the past month, indicating that during-
tournament respondents consume more alcohol
conditional on drinking at all. Other than per-
centages of respondents by year (which indicates
that responses covering the tournament were
more common in some years than others), there
are few signicant differences across other vari-
ables between during-tournament respondents
and other respondents at tournament schools.
This assuages concerns that those who choose
to return their survey at a date that indicates
tournament overlap are selected on unobserved
factors that are correlated with drinking behavior.
A. Primary Specication Regression Results
The results from our primary regression anal-
ysis are presented in the rst row of Table 3. All
9. Table 1 indicates that the bulk of Tournament-
observing respondents at tournament schools reported during
the 1997 and 1999 surveys. According to the information pro-
vided by the surveyors in Wechsler et al. (2002), this is due
primarily to randomness in the response rates of the surveyed
individuals. This is unlikely to affect our results, since each
specication will include year xed effects to account for dif-
ferences across time.
regressions are performed using ordinary least
squares with robust standard errors clustered
by school. Each column represents a differ-
ent dependent variable (drinking measure).10
Control variables for this specication include
team win percentage, membership in a frater-
nity or sorority, marital status, gender, year in
school, race, GPA, age, and xed effects for
athletic conference, month, and school-year
interaction terms. Detailed regression results
for our primary specication are presented in
Table A3 of Appendix S1. “Treated” respon-
dents attended a school that participated in the
NCAA tournament during their survey year,
and had surveys that covered the date(s) of
at least one of their school team’s tournament
We nd that treatment raises the number
of binge occasions in the past 2 weeks by
roughly 0.35, or 23% at the mean for tourna-
ment schools.11 The number of drinks in the
past month rises by 5 (a 19% increase). Treat-
ment appears to have no signicant effect on
the likelihood that students drank in the past
10. While not reported in the tables, our results are robust
to using a binary measure of “binge drank in the past 2 weeks”
as a dependent variable. These results are available upon
11. While the results presented in Table 3 employ linear
models, the results are robust to using other estimation tech-
niques. The results of negative binomial and logistic regres-
sion models are available in Table A6 of Appendix S1.
As presented in Table A3 of Appendix S1,
other coefcients in our models are gener-
ally as expected: students who are members
of fraternities and sororities drink more alco-
hol; married and female students drink less
(DeSimone 2007). Freshmen are less likely to
have drunk at all in the previous month but are
more likely to binge. White students consume
substantially more alcohol than “other race”
students, who in turn consume more than black
and Asian students. A higher GPA is associated
with less drinking (according to any measure).
Lastly, the fact that the men’s basketball team
regular season win percentage has little effect
on drinking behaviors suggests that participation
in the tournament, rather than the quality of
the team itself, drives the observed increase in
alcohol consumption.
B. Effects by Gender
When results are estimated on samples of
men and women separately, stark differences
emerge. As seen in Table 3, the measured
increase in binge drinking and number of drinks
consumed is concentrated almost exclusively
among male students.
The observed increase in the number of binge
drinking occasions in the past 2 weeks for men
represents an approximately 47% increase at
the mean among students at tournament schools
(which is just under two binge occasions). Males
report consuming almost 11 additional drinks in
the past month when their college team partici-
pated in the tournament (a 40% increase at the
mean). Because past and emerging research sug-
gests that drinking surrounding sporting events is
associated with highly negative outcomes, these
results are important from a public health per-
spective (Lindo, Siminski, and Swensen 2018).
C. Robustness
In this subsection, we examine the robustness
of our main results in several different ways.12
We begin by examining how sensitive our treat-
ment effect estimate is to the set of controls in
the model.
The rst three rows of Table 3 show, respec-
tively, results from models including (1) the
12. In addition to the robustness tests described in this
section, we also include results from a falsication analysis
and analysis of various subgroups of schools in Table A4 of
Appendix S1. Each of these analyses supports the generality
of our ndings above.
primary specication, which includes separate
dummies for each school-year pair (school and
year xed effects are interacted), (2) interacted
xed effects for each school-month pair (interact-
ing school and month rather than school and year
xed effects), and (3) interacted xed effects for
each year-month pair (interacting year and month
rather than school and year xed effects). The
primary specication controls for the possibility
that some schools experience a different trend
in alcohol use over our sample years than oth-
ers, which could be correlated with tournament
status. The second accounts for the possibility
of different seasonal variation between schools.
The third is the most exible way to account
for differences in drinking over time (within
and across years). Each specication afrms the
results from our primary specication.
As discussed in Section II, a potential threat
to the interpretation of our results is spring break,
a time (usually a week) off from school given
to students at most U.S. institutions, typically
during the month of March. Spring break at
many colleges likely coincides with part of the
NCAA tournament, and alcohol use increases
during spring break, particularly among males
(Lee, Lewis, and Neighbors 2009).13
Because the vast majority of schools hold
spring break during the month of March, we
expect that any “spring break” effect on drinking
is likely to be absorbed by the month effects in our
model. This may confound our results if spring
break is more likely to be scheduled over NCAA
tournament dates at tournament schools than at
nontournament schools. Since some schools con-
sistently play in the tournament each year while
others rarely do, it is possible that these teams
schedule spring break during the tournament
while others do not, which would challenge our
ability to accurately estimate a treatment effect.
To counter this possibility, we estimate our
model with only students from schools whose
team plays in the tournament in at least 1 year
of our data using only those students whose sur-
vey overlaps the tournament dates in a given
year (thus, month effects are removed from the
13. Another potential confounding factor in our analysis
is the possibility that students do not consume more alcohol
overall when they observe the NCAA tournament. It is possi-
ble that they instead change the time period during which they
consume alcohol. In Table A7 of Appendix S1, we attempt to
detect intertemporal substitution of alcohol consumption, and
nd little evidence that students consume less alcohol before
or after the NCAA tournament in order to compensate for con-
sumption during the tournament window.
Spring Break Dates by School-Year
model). Identication in this model comes from
year-to-year variation in drinking at schools who
reach the tournament in some years but not oth-
ers. Because academic calendars are set well in
advance of any invitation to participate in the
NCAA tournament, we think it is highly unlikely
that a college’s spring break is more likely to
occur during the tournament in years in which
their team participates than in years in which they
do not.
The results of this exercise are contained in the
fourth row of Table 3. Treatment is again dened
based on overlap with any tournament games (as
in all other rows of the table). The point estimates
are smaller in magnitude than in the primary
specication, and some are less precisely esti-
mated (owing to the much reduced sample size).
The broad picture is remarkably similar, how-
ever: binge drinking and total drinking rise during
the tournament when a student’s own school team
participates, and this effect is again focused on
male students.
Finally, we successfully obtained detailed
spring break dates during our study years from
34 schools,14 in order to include a control for
spring breaks in our regression analysis. A his-
togram of spring break dates by the tournament
status of schools is presented in Figure 4, and
shows that the spring break dates of schools
participating in the NCAA tournament closely
14. Some schools indicated that they do not maintain
archives of academic calendars that reach back 25 years,
while others simply did not respond to our request for
information when past academic calendars were unavailable
resemble the distribution of spring break dates
for nonparticipating school-year observations.
In order to control for spring break in our
regression analysis, we created a binary vari-
able for whether or not the survey response
window overlapped the school’s reported spring
break in respondents’ survey year. The results of
the specication including this measure can be
found in the nal row of Table 3. These estimates
are nearly identical to our primary specication.
The evidence of each of our specications tar-
geting potential interference resulting from the
timing of spring break suggests that the effect
of spring break on our measurement of alco-
hol consumption during the NCAA tournament
is negligible.
D. Effects of Individual Rounds
In the rst row of Table 4, the denition of
treatment is changed from a binary variable indi-
cating whether an individual’s survey overlapped
with any games played by their college team in
the tournament to the number of the team’s games
(rounds) overlapped by the student’s survey. The
results are qualitatively and quantitatively con-
sistent with those from our baseline treatment
denition, since the average number of rounds a
team plays in the tournament is a little less than
two games.
The rest of the rows in Table 4 display
the effects of overlapping exactly one round,
two rounds, and three or more rounds sepa-
rately (within the same regression). Though the
increases are not perfectly monotonic in all cases,
and some individual coefcients are no longer
Effects of Own-Institution NCAA Tournament Rounds on Student Drinking Behaviors, by Gender
All Observations Males Females
Number of
Binges Drink?
Number of
Number of
Binges Drink?
Number of
Number of
Binges Drink?
Number of
Treated (number of rounds) 0.196*0.003 2.585** 0.414** 0.025 6.677** 0.001 0.031 0.918
(0.077) (0.013) (0.847) (0.123) (0.019) (2.356) (0.091) (0.018) (1.231)
One round observed 0.290*0.011 3.734*0.520*0.027 8.846 0.110 0.039 0.199
(0.147) (0.023) (1.463) (0.25) (0.035) (4.702) (0.178) (0.03) (2.044)
Two rounds observed 0.303 0.039 8.311*0.821*0.059 13.921 0.202 0.002 2.437
(0.231) (0.049) (3.537) (0.389) (0.071) (8.526) (0.267) (0.07) (6.815)
Three plus rounds observed 0.586 0.01 4.301 1.139*0.066 16.548 0.088 0.092 6.698
(0.331) (0.051) (3.403) (0.501) (0.072) (9.023) (0.431) (0.074) (4.742)
N25,438 25,438 25,438 10,845 10,845 10,845 14,593 14,593 14,593
R2.179 .124 .148 .165 .112 .129 .149 .132 .126
Notes: Standard errors are robust to heteroskedasticity and clustered at the institution level. Other controls included in the
regressions but not shown include all those described in Section IV.A.
Signicant at the 5% level.
∗∗Signicant at the 1% level.
statistically signicant at conventional levels, the
broad pattern is that “dosage” matters: when an
individual’s survey overlaps more tournament
games, they engage in more binge drinking and
total drinking. As before, these effects are highly
concentrated among male respondents.
E. Drunk Driving
Our last analysis deals with the question of
whether the increase in drinking as a result of the
tournament leads to behaviors that could cause
harm to others. We are limited in the behaviors
we can analyze given that many questions in CAS
ask respondents about behaviors or experiences
over the course of the past year (which is not
well suited to our analysis of changes around
tournament time). However, we are able to exam-
ine self-reported drunk driving and self-reports of
riding in a car with a drunk driver over the past
month. Table 5 shows the results of this analysis.
In the rst row, drunk driving and riding with a
drunk driver are pooled together— the dependent
variable is equal to one if the student reported
either behavior in the previous month. The sec-
ond row shows the results for drunk driving only,
and Row 3 shows the results for riding with a
drunk driver only.
Both males and females experience a roughly
4 percentage point increase in the probability of
experiencing any kind of drunk driving episode
(a 9.1% difference at the mean for males and a
Effects of Own-Institution NCAA Tournament
Participation on Drunk Driving Involvement
Self-reported drunk
driving incident
0.038 0.036 0.038
(0.022) (0.034) (0.029)
Self-reported driver
0.052*0.047 0.053*
(0.021) (0.034) (0.027)
0.024 0.049 0.005
(0.02) (0.033) (0.026)
Notes: Standard errors are robust to heteroskedasticity
and clustered at the institution level. Other controls included
in the regressions but not shown include all those described
in Section IV.A. Self-reported drunk driving incidents are
calculated based on responses to a question asking “In the past
30 days, how many times did you...,” where the items related
to drunk driving are “drive after drinking alcohol,” “drive
after having 5 or more drinks,” and “ride with a driver who
was drunk or high.” For each item, respondents selected from
options of “Not At All,” “Once,” and “Twice or More.” The
rst two behaviors were combined to generate our measure
of drunk driving, while the third behavior was used as our
measure of riding with a drunk driver. Since we utilized a
binary dependent variable in each case, the responses “Once”
and “Twice or More” (to at least one question) triggered a
value of 1, while “Not At All” triggered a value of 0. The
value of the rst row, measuring any drunk driving incidents
was assigned a value of 1 if the response to any of the
three questions is “Once” and “Twice or More,” and was 0
Signicant at the 5% level.
∗∗Signicant at the 1% level.
13.9% difference for females). However, statisti-
cal signicance in these models is weak; we only
observe signicant effects on the rate of drunk
driving for the group as a whole, and for the
female only group.
Overall, these results suggest that the increase
in (binge) drinking occurring during the NCAA
tournament among students at participating
schools may in fact lead to behavior that is harm-
ful to others, although there is too much noise
to make this assertion with condence. Future
work with more focused information on student
behavior during tournament time could measure
how the tournament affects other problematic
behaviors that are associated with alcohol use
and abuse.
Athletic success presents many exciting
opportunities for schools, but this paper adds
to a growing body of research suggesting that
participation and success in major sports is
associated with increased binge drinking (and
its associated negative consequences) among
the current student body. Our ndings suggest
a large increase in binge drinking episodes
reported by male students during the NCAA
men’s basketball tournament.
Determining how to combat the increase in
drinking arising during the NCAA tournament
among students at participating schools is beyond
the scope of this paper. However, we note a few
possibilities here. Given the enormous popular-
ity of the NCAA tournament on and off cam-
pus, we believe that a dramatic change to the
event itself is highly unlikely.15 As such, col-
lege administrators and other interested parties
might focus on interventions targeting the sup-
ply or demand of alcohol around major sporting
events. For example, changing perceptions about
what constitutes “normal” drinking (Haines and
Spear 1996Perkins 2002) and providing moti-
vational interventions (Borsari and Carey 2000)
have been shown to reduce drinking (at least in
the short run) in other contexts. Increase in law
enforcement (surrounding underage drinking or
drunk driving) during the tournament is another
15. It should be noted that a modest expansion in the
number of tournament teams, from 64 to 68, has taken place
in recent years. A large-scale expansion in the number of
tournament teams is sometimes proposed (AP 2010). Our
results suggest such a move would increase drinking around
the tournament, though we cannot know for sure, since our
data do not include such an expansion.
possibility. The difference in behavior between
men and women suggests that the lowest cost
intervention would be to target interventions at
male students, since the increase in risky alco-
hol consumption observed during the tournament
occurs among males.
Intervention to reduce risky alcohol consump-
tion becomes more urgent when considering the
increased incidence of trafc fatalities surround-
ing major sporting events observed by Wood,
McInnes, and Norton (2011). Interventions,
such as the beer sales ban at the University of
Colorado’s Folsom Field in 1996, have also
been demonstrated to reduce negative outcomes
(Bormann and Stone 2001). Additional methods
of intervention might include harm reduc-
tion through education (e.g., regarding sexual
assault), ride services, or other means, adminis-
tered just prior to or during the tournament itself.
Future work might examine how these different
policy options substitute for or complement each
other and determine cost-effective ways to limit
high levels of alcohol use and its consequences
during the NCAA Men’s Basketball Tournament.
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Additional supporting information may be found online
in the Supporting Information section at the end of the article.
Appendix A1: NCAA Division I Schools Surveyed in the
Harvard School of Public Health College Alcohol Study.
Appendix A2: Missing Values by Month and School
Tournament Status.
Appendix A3: Effects of Own-institution NCAA Tourna-
ment Participation on Student Drinking Behaviors.
Appendix A4: Falsication Test of NCAA Tournament
Participation on Student Drinking Behaviors.
Appendix A5: Effects of Own-institution NCAA Tour-
nament Participation on Student Drinking Behaviors: Alter-
native Specications.
Appendix A6: Effects of Own-institution NCAA Tour-
nament Participation on Student Drinking Behaviors Using
Count and Binary Models.
Appendix A7: Intertemporal Substitution of Alcohol
Consumption Surrounding NCAA Tournament.
... Alternatively, Lindo, Swensen, and Waddell (2012) and Hernandez-Julian and Rotthoff (2014) both discovered that athletic success in football lowers students' academic performance during a successful season. In addition, White, Cowan, and Wooten (2019) found current students increased alcohol consumption when their university team participates in the NCAA postseason men's basketball tournament. Lastly, Lindo, Siminski, and Swensen (2018) empirically surmised that there are increased incidents of reported rapes on the home team's campus during football games. ...
National accolades and positive media attention are frequently lavished upon successful collegiate sports programs. Correspondingly, studies have demonstrated that universities often benefit from the achievements of their athletic teams by increasing the schools’ application numbers, student quality, and alumni donations. This study demonstrates that the opposite effect occurs when a university’s sports team is accused of engaging in impropriety. Our findings suggest that the negative attention given to the National Collegiate Athletic Association postseason tournament ban of a men’s basketball program could serve as a signal to prospective students regarding the quality of the institution. This perception ultimately leads to a decrease in the infracting university’s enrollment the year before the ban that then rebounds the year after the ban. However, the ban reduces the percentage of high-achieving students who choose to attend the university after the ban has been implemented.
... Student grades decline as the football team is more successful (Lindo et al. 2012). Student alcohol consumption and binge drinking rises when the basketball team plays in the NCAA tournament (White et al. 2017) while library research declines (Clotfelter 2011). The hypothesis is that behavior can also be influenced prior to matriculation with successful college sports serving as a signal of the presence of a party pathway that leads some accepted students to attend the school. ...
Full-text available
Colleges and universities face pressure to maintain enrollments in a time of demographic shifts in the college-going population and reductions in state funding. One indicator of successfully maintaining enrollments is the percentage of accepted students who matriculate—the enrollment yield. Factors known to contribute to yield include school size, cost, research, and reputation. Of interest in the present study is the import of academic reputation as measured by U. S. News and World Report rankings and social reputation as measured by designation as a ‘party school’ relative to accomplishments of the school’s high-profile athletic teams. I use a 21 year panel to model yield for all institutions competing at the highest level of intercollegiate athletics. The results show that yield rates consistently respond to USNWR rankings, but being named a party school has a more sporadic influence. Athletic success has little effect on a school’s enrollment yield. The findings suggest that the signals sent by academic rankings are stronger and better received than the signals sent by social or sports accomplishments.
Cambridge Core - Political Economy - Big-Time Sports in American Universities - by Charles T. Clotfelter
This paper considers the degree to which events that intensify partying increase sexual assault. Estimates are based on panel data from campus and local law enforcement agencies and an identification strategy that exploits plausibly random variation in the timing of Division 1 football games. The estimates indicate that these events increase daily reports of rape with 17-24-year-old victims by 28 percent. The effects are driven largely by 17-24-year-old offenders and by offenders unknown to the victim, but we also find significant effects on incidents involving offenders of other ages and on incidents involving offenders known to the victim.
We study the effect of state-level merit aid programs (such as Georgia's HOPE scholarship) on alcohol consumption among college students. Such programs have the potential to affect drinking through a combination of channels - such as raising students' disposable income and increasing the incentive to maintain a high GPA - that could theoretically raise or lower alcohol use. We find that the presence of a merit-aid program in one's state generally leads to an overall increase in (heavy) drinking. This effect is concentrated among men, students with lower parental education, older students, and students with high college GPA's. Our findings are robust to several alternative empirical specifications including event-study analyses by year of program adoption. Furthermore, no difference in high-school drinking is observed for students attending college in states with merit-aid programs.
We revisit a recent study by Lindo et al. (2012), who found a negative relationship between the success of the University of Oregon football team and the academic performance of students as measured by grades. Using data from Clemson University, we also find that the football team's winning percentage is negatively related to academic performance. Although Lindo et al. (2012) found that the academic performance of male students was more sensitive to changes in the winning percentage than the academic performance of female students, we find evidence of the opposite phenomenon in the Clemson data. Moreover, the negative relationship between wins and academic performance at Clemson appears to persist into the spring semester.
Social scientists continue to devote considerable attention to spillover effects for risky behaviors because of the important policy implications and the persistent challenges in identifying unbiased causal effects. We use the natural experiment of assigned college roommates to estimate peer effects for several measures of health risks: binge drinking, smoking, illicit drug use, gambling, having multiple sex partners, suicidal ideation, and non-suicidal self-injury. We find significant peer effects for binge drinking but little evidence of effects for other outcomes, although there is tentative evidence that peer effects for smoking may be positive among men and negative among women. In contrast to prior research, the peer effects for binge drinking are significant for all subgroups defined by sex and prior drinking status. We also find that pre-existing risky behaviors predict the closeness of friendships, which underscores the significance of addressing selection biases in studies of peer effects.
Among large universities, success inhigh-profile intercollegiate athletics appears to be onefactor among several in the college choice process foraspiring undergraduates. Winning a national championship in one of the two most visible college sports— football and men's basketball — isroutinely accompanied by significant positive attentionfor an institution. That attention appears to translateinto increases in applications received for undergraduateadmission in subsequent admissions cycles. We comparedyear-to-year and multiyear changes in the number ofapplications submitted following winning a national championship with changes in parallel sets ofdata from peer institutions that did not experience achampionship, thereby isolating athletic success as afactor in the college choice process. We found that notable increases generally occurred inadmissions applications received-both in absolute termsbut more importantly relative to peer schools — inthe years following the championship season.
The 2001 Harvard School of Public Health College Alcohol Study surveyed students at 119 4-year colleges that par-ticipated in the 1993, 1997, and 1999 studies. Responses in the 4 survey years were compared to determine trends in heavy alcohol use, alcohol-related problems, and encounters with college and community prevention efforts. In 2001, approximately 2 in 5 (44.4%) college students reported binge drinking, a rate almost identical to rates in the previous 3 surveys. Very little change in overall binge drinking occurred at the individual college level. The percentages of abstainers and frequent binge drinkers increased, a polarization of drinking behavior first noted in 1997.A sharp rise in frequent binge drinking was noted among students attending all-women's colleges. Other significant changes included increas-es in immoderate drinking and harm among drinkers. More stu-dents lived in substance-free housing and encountered college educational efforts and sanctions resulting from their alcohol use. Key Words: alcohol, alcohol-related problems, binge drinking, college students, secondhand effects of alcohol, prevention
We consider the effect of legal access to alcohol on student achievement. Our preferred approach identifies the effect through changes in one's performance after gaining legal access to alcohol, controlling flexibly for the expected evolution of grades as one makes progress towards their degree. We also report RD-based estimates but argue that an RD design is not well suited to the research question in our setting. We find that students' grades fall below their expected levels upon being able to drink legally, but by less than previously documented. We also show that there are effects on women and that the effects are persistent. Using the 1997 National Longitudinal Survey of Youth, we show that students drink more often after legal access but do not consume more drinks on days on which they drink.
Using a unique, national dataset that indicates where students choose to send their SAT scores, we find that college sports success has a large impact on student application decisions. For example, a school that has a stellar year in basketball or football on average receives up to 10% more SAT scores. Certain demographic groups (males, blacks, out-of-state students, and students that played sports in high school) are more likely to be influenced by sports success than their counterparts. We explore the reasons why students might be influenced by these sporting events and present evidence that attention/accessibility helps explain these findings.
We consider the relationship between collegiate-football success and non-athlete student performance. We find that the team's success significantly reduces male grades relative to female grades. This phenomenon is only present in fall quarters, which coincides with the football season. Using survey data, we find that males are more likely than females to increase alcohol consumption, decrease studying, and increase partying in response to the success of the team. Yet, females also report that their behavior is affected by athletic success, suggesting that their performance is likely impaired but that this effect is masked by the practice of grade curving.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at
For sports fans, great games are the close ones—those between evenly matched opponents, where the game remains undecided until the very end. However, the dark side to sporting events is the incidence of traffic fatalities due to game-related drinking. Here, we ask whether the closeness of the game affects the number of fatalities that occur. Two opposing predictions can be made. Games that are not close (“blowouts”) may be less engaging, thus increasing drinking. Alternatively, close games may be more dangerous, increasing competition-associated testosterone that spills over into aggressive driving. An analysis of major sporting events (2001–8) shows that closer games are significantly correlated with more fatalities. Importantly, increased fatalities are observed only in locations with winning fans (game site and/or winners’ hometown), congruent with a testosterone-based account. Ultimately, this finding has material consequences for public welfare on game days and suggests that one silver lining for losing fans may be a safer drive home.