fraternity membership and binge drinkingeconomics.uta.edu/workshop/frat_r2.pdfjoin fraternities...
TRANSCRIPT
Fraternity Membership and Binge Drinking
Jeff DeSimone*
University of South Florida and NBER
26 December 2006
Abstract
This paper examines the relationship that social fraternity and sorority membership has
with binge drinking incidence and frequency among 18–24 year old full-time four-year college
students who participated in the 1995 National College Health Risk Behavior Survey. To net out
unobserved heterogeneity, several measures of situational and total alcohol use are entered into
the regressions as explanatory variables. Fraternity membership coefficients are substantially
reduced in size, but remain large and highly significant, suggesting a causal effect on binge
drinking. Otherwise, the estimates identify idiosyncratic selection into fraternities and binge
drinking across students with similar overall drinking profiles. Particularly notable is that
behavior by underage students appears to drive the relationship.
* Department of Economics, University of South Florida, 4202 E. Fowler Ave., BSN 3403, Tampa, FL 33620-5500; Phone: (813) 974-6514; E-mail: [email protected]. I thank Joseph Newhouse and two anonymous referees for helpful suggestions.
1. Introduction
Social fraternities and sororities play a prominent role in the lives of U.S. college
students. Eighteen percent of 18–24 year old full-time, four year college students in the 1995
National College Health Risk Behavior Survey (NCHRBS) were fraternity members, as were 12
percent of 17–25 year old four year college students in the 2001 Harvard College Alcohol Study
(CAS).1 Collegiate fraternities were founded largely to advocate community service, leadership
and learning. While many still fill these roles, fraternities now exist primarily as a focal point for
developing friendships among students interested in similar social activities.2
The main activity with which fraternities are associated is alcohol use. Fraternities often
connote a culture of drunkenness, as famously portrayed in the movie Animal House. Anecdotal
evidence of problem drinking at fraternity events abounds. Data confirm that fraternity members
drink more intensively than do non-members. In the NCHRBS, past month binge drinking,
defined as consuming at least five alcoholic beverages within a few hours, was reported by 69
percent of fraternity members as compared with 42 percent of non-members.3 Analyses of data
from the CAS (Chaloupka and Wechsler, 1996) and the Core Alcohol and Drug Survey (Alva,
1998; Cashin et al., 1998) have similarly documented that fraternity members drink more
frequently and heavily than do their non-member peers.
It is tempting to assume that fraternity membership is the reason that fraternity members
drink more excessively than do non-members. But does fraternity membership truly cause binge
drinking? Specifically, would the incidence or frequency of binge drinking among students who 1 Throughout the paper, the term “fraternity” applies in a gender-nonspecific way to both fraternities and sororities. 2 Analogous organizations are rare outside the U.S. and Canada, but do exist in countries such as the Netherlands, Belgium, Germany and the Philippines (see http://en.wikipedia.org/wiki/Fraternities_and_sororities). 3 A weakness of binge drinking as a proxy for drunkenness is that those who are heavier or otherwise have a greater tolerance to alcohol require more drinks to become intoxicated. The National Institute of Alcoholism and Alcohol Abuse and Centers for Disease Control have adopted a four-drink threshold to define binge drinking for women, who tend to be lighter and binge drink less frequently than men (see www.hsph.harvard.edu/cas/), but the NCHRBS used the five drink threshold for all respondents.
1
join fraternities decline in the absence of fraternities? That is the question this study addresses.
College student binge drinking is a concern because it is associated with many behaviors
that are harmful, particularly to others. These include drunken driving (Hingson et al., 2003b),
violence (Wechsler et al., 1995), vandalism and related disturbances (Wechsler et al., 2002),
sexual activity that is forced (Mohler-Kuo et al., 2004) or risky (Hingson et al., 2003a), and
reduced academic performance (Kremer and Levy, 2003). Efforts that most effectively limit
external binge drinking effects depend in part on whether fraternities increase binge drinking.
Furthermore, any direct effect of fraternity membership on binge drinking underestimates
the corresponding total effect, which also includes the impact on drinking by non-members. For
instance, Chaloupka and Wechsler (1996) find that having a fraternity on campus increases the
likelihood of binge drinking regardless of membership status, while Glindemann and Geller
(2003) estimate that intoxication levels at fraternity parties exceed those at other parties.
Moreover, fraternity-induced binge drinking might lead to binge drinking among roommates or
other peers (e.g. Kremer and Levy, 2003; Lundborg, 2006).
Students who join fraternities presumably perceive that membership will facilitate desired
binge drinking by matching them with students who share these preferences. For example,
Sacerdote (2001) found that high school drinkers in the Dartmouth senior classes of 1997 and
1998 were more likely to join a fraternity than were other classmates. Baer et al. (1995), Schall
et al. (1992) and Wechsler et al. (1996) obtain similar evidence and report that among students
who drank in high school, those who joined fraternities were more likely to have binge drank.
If self-selection of binge drinkers into fraternities is responsible for the correlation
between drinking and fraternity membership, members would binge drink even if fraternities did
not exist, and binge drinking therefore cannot be attributed to membership. In contrast, Borsari
2
and Carey (1999) outline three ways in which fraternity membership might increase binge
drinking. One is by applying social pressure to drink in order to gain acceptance among fellow
members. Another is by elevating perceptions of peer drinking norms, which college students
already tend to overestimate. The third is by providing an environment that makes alcohol
readily available and is insulated from students less tolerant of binge drinking. Consistent with
this, Lo and Globetti (1995) find that students who do not previously binge drink are three times
more likely to start doing so if they join a fraternity. Also, Sher et al. (2001) estimate that
fraternity members drank more heavily than non-members during college, even controlling for
previous alcohol use, but that this discrepancy disappeared within three years after college.
This study infers the effect of fraternity membership on binge drinking using a proxy
variable approach. It specifically includes various potentially endogenous factors as covariates
to control for unmeasured binge drinking determinants that might be correlated with fraternity
membership. Importantly, the main proxies are measures of current period alcohol use defined
more broadly than binge drinking. The fraternity effect is ultimately identified using differences
between members and non-members who drink in identical frequencies and situations.
As there is no way to know whether proxy variables completely eliminate spurious
regression correlations, it is overly ambitious to claim that the study estimates the causal effect of
fraternity membership on binge drinking. Yet, the alcohol use measures do explicitly control for
the exact type of unobserved heterogeneity that is most likely to contaminate the relationship of
interest. Moreover, this approach attributes a sizable portion of the observed variation in binge
drinking to related behaviors that in fact might be directly influenced by fraternity membership.
The analysis hence yields an estimated fraternity effect that is prospectively conservative.
If nothing more, it isolates a non-causal component of the relationship that fraternity membership
3
has with binge drinking, and outlines the selection mechanisms that must prevail to invalidate the
interpretation of the remaining correlation as causal. Namely, for the causality argument to fail,
fraternity membership must not influence non-binge drinking, and students must opt to join
fraternities because they binge drink more often than non-members who otherwise consume
alcohol with similar frequency, in analogous situations and over the same time period.
2. Data and Empirical Strategy
In estimating the binge drinking effect of fraternity membership, the main econometric
issue is the presence of unobserved factors that simultaneously determine membership and
binging. If binge drinking preferences are a reason that students join fraternities, members
would binge drink more than non-members even in the absence of fraternities. This omitted
variable problem must be addressed so that the fraternity membership regression coefficient
reflects only binge drinking that would not have occurred if fraternities did not exist. In
attributing endogeneity solely to omitted variables, I dismiss the possibility of reverse causation.
Student rarely leave fraternities, and holding constant unobserved confounding factors, it seems
unlikely that an exogenous increase in binge drinking would induce a student to join a fraternity.
The identification strategy includes observable factors that approximate omitted binge
drinking preferences likely correlated with fraternity membership. A distinctive aspect is the use
of several alcohol consumption measures to reflect these preferences, specifically measures of
drinking frequency, duration and the incidence of drinking in several circumstances that are
particularly risky. Any persistent self-selection would have to involve binge drinking differences
between members and non-members who have similar patterns of non-binge alcohol use.
Moreover, if non-binge drinking is also influenced by fraternity membership, this approach could
4
yield conservative estimated fraternity effects.
These alcohol covariates supplement a baseline model that controls for many standard
personal characteristics, and in some models are accompanied by proxies for additional omitted
factors. The vector of controls thus includes a fraternity membership indicator (F), variables
arguably exogenous with respect to unobserved binge drinking determinants (X), alcohol use
covariates representing unobservables that influence fraternity membership and binge drinking
(A), and additional heterogeneity controls (U). A regression equation incorporating this is
B = α0 + α1F + Xα2 + Aα3 + Uα4 + ε, (1)
where B represents binge drinking, ε ~ N(0, σ2) includes unobserved determinants of binge
drinking, and the α are the regression parameters.
Variants of equation (1) are estimated using data from the National College Health Risk
Behavior Survey (NCHRBS), developed by the Centers for Disease Control (CDC) and
administered during the first half of 1995.4 As described in CDC (1997), the NCHRBS was
designed to monitor college student health-risk behaviors. Two-stage cluster sampling produced
a nationally representative group of undergraduates aged 18 and over. From 16 strata with
varying percentages of black and Hispanic students, the first stage selected 148 institutions, half
two-year and half four-year, with probability proportional to undergraduate enrollment. The
second stage randomly sampled undergraduates in the 136 institutions that chose to participate,
targeting 72 and 56 students from each two- and four-year school, respectively. The
questionnaire was mailed to 7,442 students for self-administration and completed by 4,814.
The analysis sample is restricted to 18–24 year old, full-time undergraduates from four-
year schools. In 1995, 57 percent of the 12 million U.S. college students were ages 18–24, and
4 Specific interview dates are not reported.
5
few students who are older, part-time or at two-year schools are fraternity members.5 Sensitivity
analyses later verify that these exclusion criteria do not drive the estimation results.
The two dependent variables in the regressions are constructed from information on the
number of days in the past 30 on which the respondent binge drank. One is an indicator of any
binge drinking, for which probit models are estimated. Tables report average marginal effects.
The other dependent variable is binge drinking days. Because the survey reports only the
categorical choice between 0, 1, 2, 3–5, 6–9, 10–19, and 20 or more, binge days is analyzed
using an interval regression model. This handles the lower bound of zero and upper bound of 20
identically to a Tobit model. It further recognizes that while values of 1 and 2 represent exact
numbers of days, all that is known about the remaining three categories are the lowest and
highest number of days that corresponding values could represent.
Rewriting equation (1) as B = Zα + ε, the likelihood function for the interval model is
∑∑
∑∑
∑ ∑∑
∈∈
∈∈
= ==
⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −
Φ−+⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −
Φ−⎟⎠⎞
⎜⎝⎛ −
Φ+
⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −
Φ−⎟⎠⎞
⎜⎝⎛ −
Φ+⎥⎦
⎤⎢⎣
⎡⎟⎠⎞
⎜⎝⎛ −
Φ−⎟⎠⎞
⎜⎝⎛ −
Φ+
⎥⎥⎦
⎤
⎢⎢⎣
⎡+⎟
⎠⎞
⎜⎝⎛ −
−⎥⎥⎦
⎤
⎢⎢⎣
⎡+⎟
⎠⎞
⎜⎝⎛ −
−⎟⎠⎞
⎜⎝⎛−Φ=
]30,20[]19,10[
]9,6[]5,3[
0 2
22
1
22
201log1019log
69log35log
2log2212log1
21log
BB
BB
B BBL
σσσ
σσσσ
πσσ
πσσσ
ZαZαZα
ZαZαZαZα
ZαZαZα
, (2)
where Ф() is the cumulative standard normal distribution.6 The tables report marginal effects on
the observed B, calculated by multiplying the coefficients by ⎟⎠⎞
⎜⎝⎛−Φ−
σZα1 , i.e. the probability
that B > 0. This adjusts the coefficient downward in magnitude because the actual coefficients
represent marginal effects on the underlying B*, which can be negative, but the lower bound of
5 Also, one quarter of U.S. residents age 18–24 were college students. 6 Because the assumption of a normally distributed regression error is critical to both the probit and interval models, OLS results for both dependent variables are later presented as a robustness check.
6
zero is a true corner solution.7 Reported marginal effects again are averages across respondents.
The key explanatory variable, F, indicates whether the student is a member of a social
fraternity. This does not take into account fraternity house residence, which is later shown to be
irrelevant. Only 22 percent (57 of 259) of sample fraternity members live in a fraternity house.
The vector X of exogenous drinking determinants includes indicators for females, ages
18–24 (age 21 omitted), the freshman, sophomore and junior classes (seniors omitted), non-
Hispanic blacks, Hispanics, Asians and other non-white, non-Hispanics (non-Hispanic whites
omitted), married and separated, divorced or widowed (never married omitted), each parent not
finishing high school, graduating from high school, attending college and having unknown
education (graduating from college omitted), and each institution sampled except one. The
school fixed effects, though not strictly exogenous, are important to include as controls for
student selection into schools based on drinking prevalence and fraternity availability.
The vector A consists of the five alcohol measures alluded to above, which are intended
to reflect preferences that might influence fraternity membership and binge drinking. The
number of days alcohol was consumed, and number of times it was used before driving and in
combination with illegal drugs, each represent the past 30 days and are collapsed from
categorical responses by assigning midpoints and top-codes. Choices were zero, 1–2, 3–5, 6–9,
10–19, 20–29, and 30 for drinking days, zero, 1, 2–3, 4–5, and 6 or more for drunk driving, and
0, 1–2, 3–9, 10–19, 20–39, and 40 or more for use with drugs. Top-codes of 6.4 for drunk
driving (three percent of the sample) and 40 for use with drugs (0.1 percent) are imputed by
assuming an underlying normal distribution and rounding to the nearest tenth.
The other two alcohol use proxies are an indicator of whether alcohol or drugs were used
7 The constraint B* ≤ 30 need not be imposed because it is not binding: only seven respondents report 20 or more binge days, and the predicted B* for these (and all other) respondents is always less than 20.
7
before the most recent sexual encounter, and the number of years since alcohol was first
consumed. The latter is formed by subtracting from current age the reported age at which the
respondent first drank more than a few sips of alcohol, choices for which were never, 12 or
younger, 13–14, 15–16, 17–18, 19–20, and 21–24. The age of first drink was set equal to current
age for those who had never drank (which converts to zero years of drinking), 19 year olds
reporting 19–20, and 21 year olds reporting 21–24; 12 for those reporting the youngest category
(12 percent of the sample); interval midpoints for all reporting 13–14, 15–16 and 17–18, 20 year
olds reporting 19–20 and 24 year olds reporting 21–24; 21.5 for 22 year olds reporting 21–24;
and 22 for 23 year olds reporting 21–24.
Consumption frequency and duration are meant to broadly reflect tastes for alcohol. The
drinking before sex and concurrently with illegal drugs variables are motivated by Cashin et al.
(1998), who find that fraternity members are more likely to view drinking as a vehicle for sexual
opportunity and experience negative consequences from alcohol and other drugs. Meanwhile,
fraternity members might be more disposed to driving drunk if fraternities are non-residential
and host events at which alcohol is available. With these covariates included, the effect of
fraternity membership is identified by comparing members and non-members who have
consumed alcohol for the same length of time and roughly the same number of days in the past
month, drove and used an illegal drug while drinking about the same number of times, and had
the same drinking status the last time they had sex. Remaining self-selection into fraternities
picked up by the fraternity coefficient must occur on the basis of idiosyncratic tastes for binging.
Measurement error associated with constructing numerical variables from the categorical
information works against the identification strategy. Even if this error is random, the estimated
correlation between these variables and binge drinking will be smaller than if alcohol use was
8
measured precisely. This is exacerbated if the measurement error is correlated with fraternity
membership, which would thereby show lower correlation with the alcohol use controls.
Offsetting this is the possibility that the alcohol variables represent drinking behavior that in fact
responds to fraternity membership, which would deflate the estimated binge drinking effect.
Finally, the vector U includes several other covariates that potentially absorb some of the
unobserved heterogeneity between fraternity membership and binge drinking. To separate the
effects of physical environment and membership, a set of indicators reflect whether respondents
live in a residence hall, fraternity or sorority house, other institutional housing, off-campus
residence, or parent or guardian’s home, with “other” as the omitted category. Because paid
employment might have both substitution and income effects on membership and drinking, a
variable representing weekly hours of paid work is constructed from choices of zero, 1–9, 10–19,
20–29, 30–39, 40, and more than 40, using interval midpoints and a top code of 45. The number
of sports teams on which the respondent played (intra- or extramural), with a top-code of 3.3
assigned for the “3 or more” category, and height each might proxy for popularity (Persico et al.,
2004). Bodyweight helps determine the amount of alcohol necessary to cause inebriation.
The U vector has three additional elements. An indicator for always wearing a seat belt
when riding in a car (as opposed to most of the time, sometimes, rarely or never) is included as a
measure of risk aversion (Hersch and Pickton, 1995). The number of cigarettes smoked in the
past 30 days, formed by multiplying days smoked and the number smoked per day, proxies for
time preference (Evans and Montgomery, 1994; Fersterer and Winter-Ebmer, 2003). The days
smoked variable parallels that for alcohol, while midpoints and a top-code of 21 are assigned for
cigarettes per day categories of zero, less than one, one, 2–5, 6–10, 11–20, and more than 20.
Finally, the number of past 30 days uses of marijuana accounts for a potential interrelationship
9
between alcohol and marijuana, which Williams et al. (2004) report to be complementary for
college students. This is recoded, using a top-code of 41, from a variable with the same
categories as the alcohol and drug combination variable.
The analysis sample size is 1,401, from 66 different schools representing between four to
48 students. As table 1 documents, this includes only those for whom all variables are observed.
To verify robustness, models adding respondents for whom the only missing information
pertains to a U variable are also estimated. All regressions use NCHRBS sampling weights.
Because these do not incorporate my idiosyncratic exclusion criteria, I later show estimates from
an unweighted specification. Standard errors are robust to arbitrary forms of heteroskedasticity.8
Weighted sample means are provided in table 2. Column 1 shows unconditional means,
while columns 2 and 3, respectively, show means for fraternity members, who comprise 18
percent of the sample, and non-members. Nearly half of respondents binge drank at least once in
the past 30 days. The binge days mean of 2.5 thus implies that binge drinkers did so on five of
the past 30 days on average. As expected, binge drinking is more prevalent and frequent among
fraternity members. Fraternity binge drinkers did so an average of 6.7 days, compared to 4.8
days for non-member binge drinkers. Members are more likely than non-members to be male, in
the middle of the age distribution, juniors and seniors, white, and unmarried, and to have mothers
who attended college and fathers who graduated from college. Overall and situational alcohol
use is also more common among members. Fraternity housing appears to crowd out off campus
housing rather than dormitories. Members also work fewer hours in paid jobs, are taller and
heavier, play on more sports teams, and use cigarettes and marijuana more often.
8 The precise notation for equation (2), therefore, would have each term multiplied by a weight variable, and add a subscript to σ2 signifying that it can vary across observations.
10
3. Results
a. Baseline estimates
Table 3 shows results for baseline models that do not control for heterogeneity proxies.
Columns 1 and 4 indicate large and highly significant unconditional relationships between
fraternity membership and binge drinking, with implied semi-elasticities of 0.57 for any drinking
and 0.84 for drinking days at the dependent variable means. Adding student characteristics in
columns 2 and 5 reduces the fraternity coefficient sizes, but by only 18–19 percent. Rather than
absorbing some of the self-selection of binge drinkers into fraternities, entering school fixed
effects in columns 3 and 6 slightly increases the fraternity coefficients. Members are more likely
to binge drink by 23 percentage points (i.e. 48 percent), and binge drink on 1.7 additional days
(i.e. 68 percent). These seem too large to reflect causal effects, warranting the subsequent
insertion of the previously described controls for unobserved confounders.
Columns 2 and 3 also show the coefficients of the exogenous variables besides the school
indicators. Binge drinking is more common among males and whites, as holds the conventional
wisdom (e.g. http://kidshealth.org/college/drug_alcohol/getting_help/binge_drinking.html), as
well as the unmarried, but is not significantly related to age, grade, or parental education.9
b. Adding alcohol use covariates
Table 4 presents results for models that include alcohol use covariates to account for self-
selection of students into fraternities based on drinking preferences. The dependent variable is
the binge drinking indicator in columns 1 and 2 and binge drinking days in columns 3 and 4.
The starting point is the specification in columns 3 and 6 of table 3, i.e. with school fixed effects.
Columns 1 and 3 add days of drinking in the past 30 days. These models imply that 9 Age and grade are highly correlated, but all indicators for one remain insignificant even when the other is omitted.
11
fraternity membership causes binge drinking solely by increasing its likelihood on days when
alcohol would have been consumed regardless. This assumption seems particularly strong;
indeed, the total drinking days variable has the largest impact by far among the alcohol use
measures. Its insertion reduces the fraternity coefficient by 60 percent for any binging and 72
percent for binge days. The associated pseudo R-squared statistic, i.e. one minus the ratio of the
model and constant-only log likelihoods, rises by 0.31 for binge drinking propensity and 0.25 for
frequency. Six more days of alcohol use, roughly the standard deviation, predicts binge drinking
increases of 31 percentage points in probability and nearly two days. Importantly, however, the
fraternity effect remains highly significant, in part because the smaller regression standard errors
(i.e. larger pseudo R-squareds) translate to sizable coefficient standard error reductions.10
Estimates for models that include all five alcohol covariates appear in columns 2 and 4.
The total drinking days coefficient remains large, and the only variable to enter insignificantly is
using alcohol with drugs in column 2. But the additional alcohol measures increase the pseudo
R-squareds only slightly, while the fraternity coefficient actually grows in column 2 and shrinks
minimally in column 4. Fraternity membership now increases the likelihood of binge drinking
by 10 percentage points and binge days by about one-half. The associated semi-elasticities of
around 0.2 are economically meaningful, but much more plausible as potential causal effects.
Do these coefficients indeed represent causal impacts of fraternity membership? It is
easily imaginable that fraternity membership influences total drinking days as well as the other
alcohol covariates besides years since first drink. If so, including them might yield conservative,
10 Using non-binge drinking days, i.e. total drinking days less binge drinking days, in place of total drinking days results in much less conservative fraternity coefficients of .200 (6.36) for any binge drinking and 1.57 (6.54) for binge drinking days, where parentheses contain t statistics. By definition, the most frequent binge drinkers rarely drink without binging, which induces a negative correlation between binge and non-binge days. Indeed, when the remaining heterogeneity controls in tables 4 and 5 are inserted, the effect of non-binge days becomes marginally insignificant for any binge drinking and negative (albeit extremely small) for binge days.
12
rather than overstated, causal effect estimates. Even if not, for the fraternity coefficient to reflect
only spurious correlation, remaining selection would have to occur on binge drinking but not be
related to drinking experience, overall drinking frequency and three specific drinking behaviors
that are highly correlated with binge drinking. This scenario seems somewhat convoluted.
Yet, the pseudo R-squareds reveal that the regressions leave unexplained a substantial
portion of the variation in binge drinking. Combined with the reality that it is impossible to be
certain that all unobserved heterogeneity has been eliminated, the case for causality is not
unassailable. Still, the possible influence of further omitted factors can be investigated.
c. Adding other unobserved heterogeneity proxies
To do so, table 5 adds other self-selection controls. Starting with the models in columns
2 and 4 of table 4, which contain all the alcohol use covariates, columns 1 and 3 of table 5
append the three new heterogeneity proxies that are significantly related to binge drinking. Most
notably, students with greater sports involvement are heavier drinkers, with playing on an
additional team raising binge likelihood by 10 percent and days by eight percent. The coefficient
of not wearing a seat belt is slightly smaller in the likelihood equation and slightly larger in the
days equation. This suggests that students who are more popular and risk-tolerant have greater
involvement in binge drinking and fraternities, the latter given the accompanying decline in
fraternity coefficient magnitudes.
In several respects, though, the table 5 results continue to support a causal interpretation
of the fraternity effect. First, the relationship between cigarette smoking and binge drinking is
small and, in the case of any binge drinking, insignificant. Smoking an additional 100 cigarettes,
which is 250 percent of the sample mean, is associated with binge drinking increases of only 1
13
percentage point in likelihood and 0.09 days. The implication is that time preference has little
bearing on binge drinking behavior conditional on the included controls, and therefore does not
induce much spurious correlation between binge drinking and fraternity membership. Second,
the remaining heterogeneity proxies in columns 2 and 4 are highly insignificant both individually
and, as implied by the minimal pseudo R-squared increases, as a group. Third, the change in
fraternity coefficients from columns 1 to 2 and columns 3 to 4 is negligible for any binge
drinking and positive for binge days. Fourth, the net fraternity effect reduction from columns 2
and 4 in table 4 to the same columns in table 5 is only 8–9 percent. Fifth, much like total
drinking days, the main confounder in table 5, playing on sports teams, could in fact be a
mechanism by which fraternity membership influences binge drinking. In particular, fraternities
often field intramural sports teams and might treat competitions as binge drinking occasions.11
Pseudo R-squared statistics that are considerably less than one imply remaining scope for
other sources of spurious correlation between fraternity membership and binge drinking. To
further investigate, I obtain estimates for samples stratified on the covariates listed in tables 4
and 5. In many cases, I form two samples consisting of respondents with low and high values of
the covariate. If selection is important, coefficients in both sub-samples should be relatively
small, because some of the relationship between fraternities and binge drinking is explained by
changes from below average to above average rates of both membership and binging when
moving from one group to the other. Results, available from the author, suggest that self-
selection does not explain the fraternity coefficients. Effects remain significant and sizable for
frequent and infrequent drinkers, more and less experienced drinkers, and students who did not
drink and drive or combine alcohol with drugs or sex, and are quite large for students who drove,
11 No information is available regarding whether the teams represented by the sports variable are intercollegiate or intramural.
14
used drugs or had sex while drinking. Fraternity effects are also considerable for students who
smoke cigarettes or marijuana, are tall or heavy, live on campus, or work, yet retain practical and
statistical significance for the residually defined groups. Finally, although effects are smaller for
athletes and non-athletes alike, fraternity coefficients are still significant for both groups.
d. Method and sample permutations
Table 6 explores whether fraternity effects are sensitive to various method and sample
permutations. Columns 1 and 3 include only student characteristics and school indicators and
are thus comparable to columns 3 and 6 of table 3. Columns 2 and 4 also control for alcohol
covariates, sports participation, seat belt use and cigarette smoking, analogous to columns 1 and
3 of table 5. Each row represents a different method (A. and B.) or sample (C. through J.).
Estimated fraternity membership effects are robust to a wide array of changes, remaining
economically large and statistically significant. The widest deviations occur when the estimation
method is modified in the top two rows. Coefficients are generally smallest in the unweighted
models (row A.), but largest in the OLS models (row B.). The probit and interval models thus
yield conservative estimates relative to OLS, except in column 2 where the opposite is true. In
the rest of the table, the estimates vary little in response to altering the sample by adding students
who are missing values of unimportant variables, older, part-time, or at two-year schools (rows
C.– F.), or omitting students who are behind grade level, currently or previously married, or
attending schools with no or few sample fraternity members (rows G.–J.)
e. Stratifying on exogenous factors
Table 7 contains results of five separate exercises in which two subsamples are formed
15
based on values of a table 3 explanatory variable, with columns 1–4 mimicking table 6. Panels
A. and B. are instructive about how insufficiently accounting for selection can yield misleading
inferences. Columns 1 and 3 suggest that fraternity effects are much stronger for males and
whites, which would be a reason for higher binge drinking rates among these groups. Entering
the heterogeneity proxies in columns 2 and 4, however, considerably reduces these differences.
In A., the remaining discrepancy between male and female coefficients is smaller than their
standard errors. This indicates that targeting alcohol policies at fraternities rather than sororities
as well might be unwise. The gap between whites and nonwhites in B. is wider, and fraternities
no longer increase nonwhite binge days. Yet, the effect on any binging remains significant for
nonwhites, and the binge days effect for whites is lowered by nearly 80 percent.
Panel C. implies that fraternity membership might account for the conventional wisdom
that binge drinking is especially prevalent among underclassmen, given that table 3 showed no
direct association with class standing once membership is held constant. This is especially true
in columns 2 and 4, as selection effects are larger for juniors and seniors than for freshmen and
sophomores. Although NCHRBS data are insufficient to test this claim, one contributing factor
might be that fraternity rushing often occurs by sophomore year and involves binge drinking.
Panel D. divides the sample based on whether respondents are at least age 21 and hence
old enough to drink legally. This reveals a noteworthy result: fraternity coefficients are above
the baseline values for the underage, but are effectively zero for those of legal age. Providing
access to alcohol for those without legal means to obtain it thus appears to be an important
avenue through which fraternities increase binge drinking. This might help to explain the
absence of age effects in table 3. An implication is that directing underage drinking crackdowns
towards fraternities, and particularly their members, could be an efficient way to reduce
16
underage binge drinking as well as fraternity-related campus alcohol problems.
The estimates in C. and D. are also consistent with the other two reasons that Borsari and
Carey (1999) give to explain the means by which fraternities increase binge drinking. Social
pressure to binge drink could be more intense for new pledges trying to fit in than for veteran
members. Similarly, observed drinking at fraternity events might inflate peer drinking norms
less as students acquire experience in various campus social settings and thereby have
opportunities to gain a more representative perspective about drinking among their classmates.
In contrast, panel E. indicates that parental education is inconsequential for fraternity
effects on binge drinking. Parental college and fraternity experience therefore appears irrelevant.
4. Conclusion
Using data on 18–24 year old full-time four-year college students who participated in the
1995 NCHRBS, this study examined the relationship between binge drinking and membership in
social fraternities and sororities. The primary contribution was to enter various measures of
situational and overall alcohol use as explanatory variables in regressions of binge drinking on
fraternity membership. This addresses the specific type of unobserved heterogeneity expected to
inflate the estimated fraternity effect: students who like to drink heavily are the ones who join
fraternities. Including these alcohol use covariates substantially reduced fraternity membership
coefficients. But the continued significance of these coefficients, both statistically and
economically, supports the hypothesis that fraternity membership increases binge drinking.
The main caveat is that the alcohol use and other unobserved heterogeneity proxies might
not fully control for endogenous selection into fraternities. Thus it is impossible to be certain
that the fraternity coefficient represents a causal effect. At a minimum, however, a very
17
idiosyncratic selection mechanism must prevail for these results to not imply some causal effect.
In particular, fraternity members must binge drink more than non-members, yet consume alcohol
in similar frequencies and situations and over similar lengths of time. Moreover, an instrumental
variables method, which is infeasible with these data, would suffer from analogous uncertainties
regarding the correlation between the instruments and unobservable binge drinking determinants.
Also, the analysis does not attempt to correct for measurement error. While this could bias
estimates up if binging is over-reported by fraternity members or under-reported by non-
members, random measurement error would impart downward bias.
Combined with evidence on interventions intended to reduce binge drinking by college
students, the positive impact of fraternity membership on binge drinking suggests specific
strategies that could be targeted towards fraternity members. DeJong et al. (2006) find that
randomly assigned social norms marketing campaigns reduced perceived and actual drinking,
while Trockel et al. (2003) report that perceived drinking was related to actual drinking among
members of two large national fraternities surveyed on a number of campuses. This implies that
efforts to lower peer drinking norms within fraternity chapters might be useful. Also, Barnett et
al. (2006) indicate that mandating attendance in an alcohol education session following alcohol-
related medical treatment or disciplinary infractions can motivate students to change their
drinking behavior, particularly if they feel responsible for the corresponding incident. The
earlier-outlined theories for causal fraternity effects on binging suggest attempting analogous
fraternity-wide education efforts after adverse events involving binge drinking by members or at
fraternity-sponsored parties. Consistent with my findings for the underage, Larimer et al. (2001)
shows that first-year fraternity members randomly assigned an educational intervention of one-
hour individual and housewide discussions reported significant reductions in alcohol use one
18
year later, regardless of whether sessions were conducted by peers or professional staff.
Sacerdote (2001) found that peer effects among room- and dorm-mates are a major
determinant of whether Dartmouth students joined a fraternity. Along with my results, this
implies that fraternity membership is one way through which peers influence binge drinking.
Assigning freshmen who appear likely to join fraternities to the same rooms and dorms might
limit fraternity membership and thus binge drinking.
Still, increased drinking is only one of many potential effects of fraternities, some of
which might be positive. Hunt and Rentz (1994) report that fraternity membership provides a
sense of security and trust from belonging to a group with which to identify, which might lead to
advantageous outcomes. De Los Reyes and Rich (2003) propose that fraternity members are
more involved in campus life and more likely as alumni to maintain connections to their alma
mater. Indeed, Harrison et al. (1995) found that schools with greater fraternity and sorority
participation had higher alumni giving rates. Further, Marmaros and Sacerdote (2002) showed
that among the Dartmouth senior class of 2001, fraternity members and students networking with
them were more likely to obtain a high paying job. More generally, fraternity membership can
create lifelong friendships that ultimately improve various outcomes (Sacerdote, 2001). A
considerable leap would hence be required to conclude that fraternities should be banned or
otherwise restricted in ways that limit their ability to recruit members.
19
References
Alva, Sylvia Alatorre, “Self-reported Alcohol Use of College Fraternity and Sorority
Members,” Journal of College Student Development, January/February 1998, 39(1), 3–10.
Baer, John S., Daniel R. Kivlahan and G. Alan Marlatt, “High-risk Drinking Across the
Transition from High School to College,” Alcoholism: Clinical and Experimental Research,
February 1995, 19(1), 54–61.
Barnett, Nancy P., Abby L. Goldstein, James G. Murphy, Suzanne M. Colby and Peter
M. Monti, “‘I'll Never Drink like that Again’: Characteristics of Alcohol-Related Incidents and
Predictors of Motivation to Change in College Students,” Journal of Studies on Alcohol,
September 2006, 67(5), 754–763.
Borsari, Brian E., and Kate B. Carey, “Understanding Fraternity Drinking: Five
Recurring Themes in the Literature, 1980-1998,” Journal of American College Health, July
1999, 48(1), 30–37.
Cashin, Jeffrey R., Cheryl A. Presley and Philip W. Meilman, “Alcohol Use in the Greek
System: Follow the Leader?” Journal of Studies on Alcohol, January 1998, 59(1), 63–70.
Centers for Disease Control and Prevention, “Youth Risk Behavior Surveillance:
National College Health Risk Behavior Survey – United States, 1995,” Morbidity and Mortality
Weekly Report, 17 November 1997, 46(SS-6), 1–54.
Chaloupka, Frank J., and Henry Wechsler, “Binge Drinking in College: the Impact of
Price, Availability, and Alcohol Control Policies,” Contemporary Economic Policy, October
1996, 14(4), 112–124.
DeJong, William, Shari Kessel Schneider, Laura Gomberg Towvim, Melissa J.
Murphy, Emily E. Doerr, Neal R. Simonsen, Karen E. Mason and Richard A. Scribner, “A
20
Multisite Randomized Trial of Social Norms Marketing Campaigns to Reduce College Student
Drinking,” Journal of Studies on Alcohol, November 2006, 67(6), 868–879.
De Los Reyes, Guillermo, and Paul Rich, “Housing Students: Fraternities and Residential
Colleges,” Annals of the American Academy of Political and Social Science, January 2003, 585,
118–123.
Evans, William N., and Edward Montgomery, “Education and Health: Where There's
Smoke There's an Instrument,” NBER Working Paper 4949, December 1994.
Fersterer, Josef and Rudolf Winter-Ebmer, “Smoking, Discount Rates, and Returns to
Education,” Economics of Education Review, December 2003, 22(6), 561–566.
Glindemann, Kent E., and E. Scott Geller, “A Systematic Assessment of Intoxication at
University Parties: Effects of the Environmental Context,” Environment and Behavior, 35(5),
September 2003, 655–664.
Harrison, William B., Shannon K. Mitchell and Steven P. Peterson, “Alumni Donations
and Colleges’ Development Expenditures: Does Spending Matter?” American Journal of
Economics and Sociology, October 1995, 54(4), 397–412.
Hersch, Joni, and Todd S. Pickton, “Risk-Taking Activities and Heterogeneity of Job-
Risk Tradeoffs,” Journal of Risk and Uncertainty, December 1995, 11(3), 205–217.
Hingson, Ralph, Timothy Heeren, Michael Winter and Henry Wechsler, “Early Age of
First Drunkenness as a Factor in College Students’ Unplanned and Unprotected Sex Due to
Drinking,” Pediatrics, January 2003a, 111(1), 34–41.
Hingson, Ralph, Timothy Heeren, Ronda Zakocs, Michael Winter and Henry Wechsler,
“Age of First Intoxication, Heavy Drinking, Driving after Drinking and Risk of Unintentional
Injury Among U.S. College Students,” Journal of Studies on Alcohol, January 2003b, 64(1), 23–
21
31.
Hunt, Stephen, and Audrey L. Rentz, “Greek Letter Social Group Members’ Involvement
and Psychosocial Development,” Journal of College Student Development, July/August 1994,
35(4), 289–296.
Kremer, Michael, and Dan M. Levy, “Peer Effects and Alcohol Use Among College
Students,” NBER Working Paper 9876, July 2003.
Larimer, Mary E., Aaron P. Turner, Britt K. Anderson, Jonathan S. Fader, Jason R.
Kilmer, Rebekka S. Palmer and Jessica M. Cronce, “Evaluating a Brief Alcohol Intervention
with Fraternities,” Journal of Studies on Alcohol, May 2001, 62(3), 370–380.
Lo, Celia C., and Gerald A. Globetti, “The Facilitating and Enhancing Roles Greek
Associations Play in College Drinking,” International Journal of the Addictions, 1995, 30(10),
1311–1322.
Lundborg, Petter, “Having the Wrong Friends? Peer Effects in Adolescent Substance
Use,” Journal of Health Economics, March 2006, 25(2), 214–233.
Marmaros, David, and Bruce Sacerdote, “Peer and Social Networks in Job Search,”
European Economic Review, April 2002, 46(4-5), 870–879.
Mohler-Kuo, Meichun, George W. Dowdall, Mary P. Koss and Henry Wechsler,
“Correlates of Rape while Intoxicated in a National Sample of College Women,” Journal of
Studies on Alcohol, January 2004, 65(1), 37–45.
Persico, Nicola, Andrew Postlewaite and Dan Silverman, “The Effect of Adolescent
Experience on Labor Market Outcomes: The Case of Height,” Journal of Political Economy,
October 2004, 112(5), 1019–1053.
Sacerdote, Bruce, “Peer Effects with Random Assignment: Results for Dartmouth
22
Roommates,” Quarterly Journal of Economics, May 2001, 116(2), 681–704.
Schall, Matthew, Attila Kemeny and Irving Maltzman, “Factors Associated with Alcohol
Use in University Students,” Journal of Studies on Alcohol, March 1992, 53(2), 122–136.
Sher, Kenneth J., Bruce D. Bartholow and Shivani Nanda, “Short- and Long-term Effects
of Fraternity and Sorority Membership on Heavy Drinking: A Social Norms Perspective,”
Psychology of Addictive Behaviors, March 2001, 15(1), 42–51.
Trockel, Mickey, Sunyna S. Williams and Janet Reis. “Considerations for More
Effective Social Norms Based Alcohol Education on Campus: An Analysis of Different
Theoretical Conceptualizations in Predicting Drinking among Fraternity Men,” Journal of
Studies on Alcohol, January 2003, 64(1), 50–59.
Wechsler, Henry, George D. Kuh and Andrea Davenport, “Fraternities, Sororities, and
Binge Drinking: Results from a National Study of American Colleges,” NASPA Journal,
Summer 1996, 33(4), 260–279.
Wechsler, Henry, Jae Eun Lee, John Hall, Alexander C. Wagenaar and Hang Lee,
“Secondhand Effects of Student Alcohol Use Reported by Neighbors of Colleges: The Role of
Alcohol Outlets,” Social Science & Medicine, August 2002, 55(3), 425–435.
Wechsler, Henry, Barbara Moeykens, Andrea Davenport, Sonia Castillo and Jim Hansen,
“The Adverse Impact of Heavy Episodic Drinkers on Other College Students,” Journal of
Studies on Alcohol, November 1995, 56(6), 628–634.
Williams, Jenny, Rosalie Liccardo Pacula, Frank J. Chaloupka and Henry Wechsler,
“Alcohol and Marijuana Use Among College Students: Economic Complements or Substitutes?”
Health Economics, September 2004, 13(9), 825–843.
23
Table 1: Sample restrictions
Sample inclusion criterion Remaining students Targeted by NCHRBS 7,442 Completed survey 4,814 Ages 18–24 2,903 At four-year schools 1,713 Attend full-time 1,595 Enrolled four or fewer years 1,572 Report binge drinking and fraternity membership 1,528 Report race, marital status, parental education and school attended 1,514 Report all five alcohol explanatory variables 1,476 Report sports participation and use of seatbelts and cigarettes (corresponds to sample for row C of table 6)
1,447
Report housing type, labor supply, height, weight and marijuana use 1,401
The number in each row represents the number of students who meet the sample inclusion criteria in that and all preceding rows.
24
Table 2: Sample means (using NCHRBS weights)
Sample Full sample
Fraternity members
Non-members
(1) (2) (3) Sample size 1,401 259 1,142 Fraternity or sorority member .179 1 0 Binge drank in past 30 days .477 .700 .428 Days binge drank in past 30 days 2.54 4.72 2.06 Female .517 .492 .522 18 years old .110 .101 .112 19 years old .202 .189 .205 20 years old .203 .191 .206 21 years old .218 .268 .207 22 years old .139 .159 .134 23 years old .081 .080 .081 24 years old .047 .013 .055 Freshman .198 .141 .211 Sophomore .240 .235 .241 Junior .243 .279 .235 Senior .319 .345 .314 White .746 .810 .732 Black non-Hispanic .093 .083 .095 Hispanic .049 .026 .054 Asian .078 .047 .085 Other non-white .035 .034 .035 Never married .947 .968 .942 Married .045 .017 .052 Separated, divorced or widowed .008 .016 .006 Mother did not finish high school .053 .033 .057 Mother graduated from high school .252 .228 .257 Mother attended college .283 .328 .274 Mother graduated from college .400 .407 .399 Mother education is unknown .012 .004 .014 Father did not finish high school .063 .040 .068 Father graduated from high school .194 .194 .194 Father attended college .235 .218 .238 Father graduated from college .478 .518 .469 Father education is unknown .031 .029 .032 Days drank in past 30 days 5.39 8.34 4.74 Years since first alcoholic drink 4.51 4.71 4.46 Times used alcohol with drugs in past 30 days .759 1.33 .635 Times drank and drive in past 30 days .792 1.34 .673 Used alcohol last time had sex .195 .322 .167
25
Table 2 (continued): Sample means (using NCHRBS weights)
Sample Full sample
Fraternity members
Non-members
(1) (2) (3) Sample size 1,401 259 1,142 Lives in dormitory .356 .337 .360 Lives in fraternity or sorority house .045 .252 0 Lives in college housing other than fraternity or dorm .022 .009 .024 Lives in off-campus house or apartment .355 .277 .372 Lives in home of parent or guardian .218 .121 .239 Lives in other non-dorm residence .005 .004 .005 Hours per week works for pay 12.1 10.7 12.3 Height in inches 68.1 68.7 68.0 Weight in pounds 154.7 157.3 154.2 Sports teams played for this school year .574 1.06 .468 Always wears a seat belt when riding in a car .508 .471 .516 Cigarettes smoked in past 30 days 39.6 47.9 37.8 Times used marijuana in past 30 days 1.77 2.32 1.65
26
Table 3: Effects on binge drinking with only exogenous covariates
Any binge drinking in past 30 days
Days of binge drinking in past 30 days
(1) (2) (3) (4) (5) (6) Fraternity or sorority member
.271 (7.40) .568
.222 (6.90) .466
.228 (7.16) .478
2.12 (7.56) .835
1.66 (7.17) .653
1.72 (7.19) .679
Female –.127 (4.85)
–.114 (4.50)
–1.08 (5.82)
–.979 (5.40)
18 years old .084 (1.21)
.020 (0.30)
–.339 (0.60)
–.533 (1.00)
19 years old .080 (1.46)
.045 (0.85)
–.142 (0.32)
–.247 (0.61)
20 years old .077 (1.77)
.056 (1.33)
–.069 (0.20)
–.128 (0.38)
22 years old –.002 (0.04)
.029 (0.65)
–.153 (0.42)
.055 (0.16)
23 years old .063 (1.05)
.090 (1.50)
.362 (0.81)
.569 (1.30)
24 years old –.011 (0.15)
.052 (0.73)
–.682 (1.39)
–.258 (0.53)
Freshman –.039 (0.60)
.042 (0.66)
.279 (0.52)
.502 (1.07)
Sophomore –.008 (0.14)
.058 (1.17)
.085 (0.22)
.294 (0.81)
Junior –.030 (0.73)
.005 (0.13)
–.027 (0.09)
.166 (0.56)
Black non-Hispanic –.363 (7.98)
–.299 (5.51)
–2.73 (6.96)
–2.40 (5.27)
Hispanic –.125 (2.80)
–.124 (2.45)
–1.00 (2.97)
–.785 (2.00)
Asian –.402 (6.93)
–.374 (6.23)
–2.93 (6.13)
–2.76 (5.60)
Other non-white –.010 (0.14)
.044 (0.64)
–.515 (1.29)
–.161 (0.40)
Married –.338 (4.38)
–.270 (3.60)
–2.63 (4.28)
–2.17 (3.75)
Separated, divorced, widowed .210 (1.33)
.280 (1.44)
1.34 (1.27)
1.55 (1.35)
27
Table 3 (continued): Effects on binge drinking with only exogenous covariates Any binge drinking
in past 30 days Days of binge drinking
in past 30 days (1) (2) (3) (4) (5) (6) Mother did not finish HS –.093
(1.39) –.090 (1.33)
–.489 (0.89)
–.416 (0.76)
Mother graduated from HS –.025 (0.69)
–.008 (0.21)
–.292 (1.07)
–.240 (0.90)
Mother attended college –.005 (0.17)
.013 (0.39)
.006 (0.03)
.137 (0.62)
Mother education is unknown –.041 (0.27)
–.005 (0.04)
–.263 (0.22)
–.107 (0.09)
Father did not finish HS –.096 (1.49)
–.049 (0.76)
–.507 (1.05)
–.289 (0.59)
Father graduated from HS –.087 (2.22)
–.067 (1.73)
–.418 (1.44)
–.357 (1.25)
Father attended college –.002 (0.07)
.016 (0.48)
.115 (0.46)
.164 (0.68)
Father education is unknown –.020 (0.25)
.006 (0.08)
.121 (0.16)
.348 (0.45)
Includes school indicators? No No Yes No No Yes Pseudo R-squared .032 .152 .213 .018 .069 .093
The sample size is 1,401. Sample weights are used. Probit (interval) regressions are used to estimate the models in columns 1–3 (4–6). The average marginal effect across respondents is shown, along with the absolute value of the heteroskedasticity-adjusted t-statistic in parentheses. The semi-elasticity of the dependent variable with respect to fraternity membership, evaluated at the dependent variable mean, appears in italics. Omitted indicators are the modal categories, i.e. 21 years old, senior, white non-Hispanic, never married, mother graduated from college, and father graduated from college. All regressions also include a constant term.
28
Table 4: Effects on binge drinking with alcohol use covariates
Any binge drinking in past 30 days
Days of binge drinking in past 30 days
(1) (2) (3) (4) Fraternity or sorority member
.092 (3.57) .193
.099 (3.95) .208
.482 (4.02) .190
.457 (3.96) .180
Days drank in past 30 days
.052 (9.26)
.038 (7.30)
.309 (26.2)
.257 (19.2)
Years since first alcoholic drink
.017 (4.41)
.102 (5.14)
Times drank with drugs in past 30 days
.020 (1.56)
.045 (2.41)
Times drank and drive in past 30 days
.041 (2.89)
.119 (2.94)
Used alcohol last time had sex
.097 (2.99)
.419 (3.46)
Pseudo R-squared .5 0 2 .552 .343 .362
The sample size is 1,401. Sample weights are used. Probit (interval) regressions are used to estimate the models in columns 1–2 (3–4). The average marginal effect across respondents is shown, along with the absolute value of the heteroskedasticity-adjusted t-statistic in parentheses. The semi-elasticity of binge drinking with respect to fraternity membership, evaluated at the dependent variable mean, appears in italics. Regressions also control for all table 3 variables, including school indicators.
29
Table 5: Effects on binge drinking with additional covariates
Any binge drinking in past 30 days
Days of binge drinking in past 30 days
(1) (2) (3) (4) Fraternity or sorority member
.092 (3.70) .193
.091 (3.30) .191
.390 (3.40) .154
.416 (3.49) .164
Sports teams this school year
.046 (4.05)
.044 (3.79)
.194 (3.94)
.189 (3.79)
Always wears a seat belt
–.034 (1.74)
–.035 (1.79)
–.259 (2.56)
–.250 (2.43)
Cigarettes in past 30 days
.0001 (1.42)
.0001 (1.56)
.0009 (2.06)
.0009 (2.08)
Lives in fraternity house
–.015 (0.26)
–.128 (0.56)
Lives in other non-dorm college housing .026 (0.51)
.099 (0.40)
Lives in off-campus housing
–.032 (1.13)
–.062 (0.45)
Lives with parent or guardian
.016 (0.55)
.019 (0.12)
Lives in other housing .073 (0.83)
–.306 (0.72)
Hours per week works for pay
–.001 (1.05)
–.002 (0.46)
Height in inches
.003 (0.78)
.023 (1.18)
Weight in pounds
–.0002 (0.48)
.001 (0.74)
Times used marijuana in past 30 days .0000 (0.00)
.010 (0.84)
Pseudo R-squared .5 4 6 .567 .369 .371
The sample size is 1,401. Sample weights are used. Probit (interval) regressions are used to estimate the models in columns 1–2 (3–4). The average marginal effect across respondents is shown, along with the absolute value of the heteroskedasticity-adjusted t-statistic in parentheses. The semi-elasticity of binge drinking with respect to fraternity membership, evaluated at the dependent variable mean, appears in italics. Regressions also control for all variables listed in tables 3 and 4, including school indicators.
30
Table 6: Effects of fraternity membership using other samples and specifications
Sample size
Any binge drinking in past 30 days
Days binge drinking in past 30 days
Includes table 4 & main table 5 covariates? No Yes No Yes Modification to earlier model (1) (2) (3) (4) A. Does not use sample weights
1,401 .198 (6.47)
.085 (3.50)
1.36 (6.92)
.335 (3.15)
B. Uses OLS
1,401 .240 (7.33)
.076 (2.51)
2.49 (5.87)
.475 (2.10)
C. Includes students with missing extra table 5 variables
1,447 .228 (7.28)
.092 (3.74)
1.74 (7.46)
.439 (3.87)
D. Includes 25–34 year olds
1,557 .230 (7.35)
.099 (4.11)
1.75 (7.47)
.451 (4.08)
E. Includes part-time students
1,492 .226 (7.16)
.092 (3.87)
1.70 (7.29)
.377 (3.32)
F. Includes students at 2-year schools
2,066 .209 (6.93)
.091 (3.84)
1.50 (7.23)
.390 (3.82)
G. Excludes students not on pace to graduate in 4 years
1,160 .237 (7.18)
.079 (3.03)
1.66 (6.55)
.337 (2.78)
H. Excludes ever-married students
1,330 .236 (7.10)
.094 (3.73)
1.79 (7.20)
.426 (3.57)
I. Excludes schools with no sample fraternity members
1,319 .230 (7.18)
.093 (3.74)
1.70 (7.21)
.392 (3.47)
J. Excludes schools with < 10% sample fraternity members
976 .227 (6.34)
.090 (3.27)
1.69 (6.40)
.339 (2.64)
Sample weights are used in all models except A. Probit (interval) regressions are used to estimate the models in column 1–2 (3–4). The average marginal effect across respondents is shown, along with the absolute value of the heteroskedasticity-adjusted t-statistic in parentheses. All regressions also control for the covariates listed in table 3, including school indicators, and columns 2 and 4 also control for the covariates listed in table 4 as well as the first three covariates listed in table 5, i.e. sports teams, seat belts and cigarettes. Models D., E. and F. each include an indicator corresponding to the condition listed in the row heading.
31
Table 7: Effects of fraternity membership in exogenously stratified samples
Sample size
Any binge drinking in past 30 days
Days binge drinking in past 30 days
Includes table 4 & main table 5 covariates? No Yes No Yes Modification to earlier model (1) (2) (3) (4) A. Males
564 .356
(6.18) .087
(2.07) 3.19
(6.82) .431
(2.07) Females
837 .137 (3.30)
.074 (2.39)
.691 (2.92)
.305 (2.57)
B. Whites 928 .287 (6.90)
.117 (3.57)
2.41 (7.24)
.524 (3.23)
Nonwhites
473 .116 (1.92)
.070 (1.79)
.299 (1.19)
.053 (0.39)
C. Freshmen & sophomores 622 .316 (5.85)
.105 (2.52)
1.57 (4.92)
.426 (2.66)
Juniors & seniors
779 .178 (4.29)
.043 (1.34)
1.77 (5.29)
.224 (1.38)
D. 18–20 year olds 747 .325 (6.92)
.126 (3.27)
1.49 (6.01)
.453 (3.21)
21–24 year olds
654 .138 (3.00)
.004 (0.11)
1.84 (4.42)
–.015 (0.08)
E. Both parents attended or graduated from college
797 .221 (5.21)
.109 (3.19)
1.71 (5.47)
.435 (2.57)
At least one parent possibly did not attend college
604 .244 (4.62)
.082 (2.09)
1.56 (4.33)
.421 (3.06)
Sample weights are used. Probit (interval) regressions are used to estimate the models in column 1–2 (3–4). The average marginal effect across respondents is shown, along with the absolute value of the heteroskedasticity-adjusted t-statistic in parentheses. All regressions also control for the covariates listed in table 3, including school indicators, and columns 2 and 4 also control for the covariates listed in table 4 as well as the first three covariates listed in table 5, i.e. sports teams, seat belts and cigarettes.
32