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Final_Paper_52616
1. A BRIEF ANALYSIS OF THE RELATIONSHIP BETWEEN FRATERNITY
MEMBERSHIP AND BINGE DRINKING
Dmitry Polonskiy
ECO 722
MAY 2016
2. Introduction
For a significant percentage of college students, joining a fraternity or sorority is seen as
an essential part of college life. In order to properly analyze college life, the 2001 Harvard
College Alcohol Study, or CAS for short, selected students from 120 colleges, across 40 states,
in order to achieve a sample that represented the nation. Internal to this study, the percentage of
students who answered that they belonged to a fraternity or sorority was 14.38%.
It should come as no surprise that fraternities, from this point forward the term
fraternities will encompass sororities as well, are most commonly associated with heavy alcohol
consumption (Wechsler, Dowdall, Davenport, & Castillo, 1995). Mass media often portray
fraternities as having raucous parties where alcohol is readily available to all students who
attend. There is a slew of illicit behaviors that tend to arise from heavy alcohol consumption,
some of which are drinking while driving and being involved in physical altercations. Roughly
33% of the sample responded that they had in fact driven after drinking, and 11% claimed that
they had been involved in a physical altercation due to alcohol consumption. While the
percentage of fraternity members engaging in these acts is less than non-fraternity members, this
can be attributed to this particular dataset being less rich than previous studies. When using all
four rounds of the CAS, from 1993-2001, the results show that a larger percentage of fraternity
members engage in these activities (DeSimone, 2007).
Looking at these issues from both a legal and health perspective, the illicit activities that
arise from heavy alcohol consumption, warrant intercession in fraternity functions that expose
students to alcohol. The case for these interventions is strengthened when it becomes clear that
63% of students claim that they are able to obtain alcohol at these fraternity functions without an
I.D.
3. There is however, a major caveat to the results obtained in this paper. Fraternity
membership is done willingly, where most of the students joining have a certain view about
alcohol (Wechsler, Kuh, & Davenport 2009). There is also a case to be made that heavy alcohol
consumption in college is not only facilitated by fraternity membership but by other external
factors (Weitzman, Nelson, & Wechsler 2003). That is why this experiment does not seek to
prove that fraternity membership necessarily results in binge drinking. This experiment instead
attempts to show if there is in fact any extra effect of being in a fraternity on the likelihood of
binge drinking.
In order to help relinquish some of the bias from the results I obtain, I am following the
framework that DeSimone (2007), presents in his NBER paper. The CAS is fortunate enough to
include a large collection of variables from which several proxies are readily available. Self-
selection into fraternities is controlled for by including a variety of normative variables, such as
parent’s approval of drinking and frequency of alcohol consumption in high school. External
factors which contribute to binge drinking are controlled for by including motives such as
drinking to forget your troubles and drinking as a reward for working hard (Jasinski & Ford
2007).
Ultimately, the results we obtain from our proxies do in fact reveal that the correlation we
predicted between fraternity members and drinking, is due to this self-selection bias. However,
even after all these proxies have been adjusted for, the results reveal that fraternity membership
has a statistically significant impact when it comes to observing heavy drinking patterns and the
potential illicit behaviors that arise as a result of these drinking patterns.
4. Literature Review
Much of the recent literature examining correlations between fraternity membership and
heavy alcohol consumption tends to favor the argument that fraternity membership does in fact
lead to increased binge drinking (Harford et al., 2002; Caron et al., 2004). However, many of
these studies fail to adjust properly for the potential bias that their results may exhibit, due to
both self-selection and social norms. For instance, Crawford & Novak (2007) who explicitly
controlled for social norms found that males were more likely to consume alcohol at a level,
which they perceived as normal from their peers. However, this effect is only intensified as
students typically perceive that their peers drink more heavily than them, causing them to
attempt to consume similar quantities (Prentice & Miller 1993). This ties to the notion that being
in fraternities does lead to binge drinking, as the peer pressure, and perceived alcohol
consumption faced by members is greater than that of non-members (Neighbors & Knee 2002).
However, in their analysis, Crawford & Novak (2007) do not account for the self-selection bias
into fraternities. There have been quite a few studies which explore this topic of self-selection
into fraternities, and the findings always yield the same results, heavy high school drinkers tend
to join fraternities as they promote the same behavior they are accustomed to (Borsari et al.
1999; Larimer et al. 2000). In a study which sought to control for this self-selection bias, it was
discovered that amongst students entering college as non-drinkers, 46.5% of the sample would
begin consuming college in alcohol (Lo & Globetti 1993). To examine the effects of self-
selection into fraternities by heavy drinkers, they examined the effect fraternities had on their
non-drinking sample of students. Their results found that if a student entered a fraternity or
sorority, and did not possess any friends who discouraged them from drinking, they were three
5. times as likely to start consuming alcohol as their counterparts who did not meet these two
criteria (Lo & Globetti 1993).
Most recently, an analysis on the 2001 CAS survey found that those who belong to a
fraternity or sorority have 114% greater odds to binge drink than those who are not affiliated
(Harris 2014). Unfortunately, this study exhibits a major flaw; it fails to account for social
norms and drinking motives. The only bias that Harris (2014) accounts for is the time-varying
proxy by including variables that correspond to drinking behaviors in high school. This study
aims to remedy this situation and build upon previous findings by attempting to minimize the
bias in the model.
The two papers, which this analysis most closely resembles, are that of Jasinski & Ford
(2007) and DeSimone (2007). Whilst, Jasinski & Ford (2007) examine the effects of sexual
preference on alcohol consumption, they use a rich set of vectors in order to control for social
norms, as well as prior drinking preferences. The key addition of DeSimone (2007) comes in the
form of introducing a proxy for time-varying preferences, which he encapsulates by using a
variable that specifies the amount of alcoholic drinks the student had in the month leading up to
the CAS questionnaire. His results were consistent with previous assumptions, that the effects of
fraternity membership on binge drinking were in fact upward biased. When this time-varying
variable was added to his regression, the effect of fraternity membership on binge drinking
decreased. However, as mentioned previously, the coefficient on fraternity participation is still
highly significant.
6. Data
The regressions this paper examines utilizes data from the 2001 CAS, which was
distributed to various schools throughout the nation, to only be completed by full-time students
currently en route to a four-year degree. The CAS was administered a total of four times,
spanning from 1993 to 2001, respectively. The selection process began with the selection of 195
schools that would receive the questionnaire in 1993, however only 140 of these schools agreed
to participate in 1993 (Wechsler et al. 2004). From this group of 140 schools, only 128 agreed to
the study in 1997 and 1999, with the number dropping to 119 in 2001 (DeSimone 2007). Of
these 119 schools in the 2001 CAS, 113 participated from the inception of the survey in 1993,
with six new schools joining to fill out the group (DeSimone 2007).
Each of the four rounds of the CAS followed a similar distribution. The questionnaires
were mailed out in February to students who met the criteria for the study. Students were
selected from the student registry, by selecting every ith student (DeSimone 2007). By June the
questionnaires had been returned with a completion rate of 69% (DeSimone 2007). The 2001
CAS finished with 10,904 completed questionnaires. From this sample size 115 respondents
were dropped since they did not answer the question pertaining to fraternity membership. This
left the sample size of respondents at 10,789, which was further trimmed down by an additional
6,108 respondents, due to missing responses of independent variables. The analysis therefore,
focuses on the remaining 4,681 students who answered all the relevant questions pertaining to
this analysis. I recognize that the sample size is smaller than previous studies conducted on the
CAS (Wechsler et al. 2009; Jasinski & Ford 2007; DeSimone 2007), therefore the issue of
validity of coefficients may arise. This problem is further exacerbated by the possibility of
7. endogeneity in this model, since there is a possibility of excluded variables affecting the impact
of fraternity membership on binge drinking.
The dependent variable of this study is binge, which is a dichotomous variable signifying
one, if the respondent has in fact binge drank in college. From this sample, 61.2% of respondents
claim they have binge drank in college. As mentioned previously, the independent variable we
are most interested in is fraternity membership. From this sample of students, 14.38% answered
that they belong to either a fraternity or sorority. When we encompass fraternity membership
with the variable binge drinking, the statistics show the stark difference of binge drinking levels
between those members and non-members. In our sample, 41% of non-members responded that
they do not binge drink, whereas only 25% of fraternity members responded in such a manner.
When it comes to the act of binge drinking, 59% of non-members responded that they engage in
such behavior, where as 75% of fraternity members responded that they binge drink. These
numbers are shown in Table 1. If we go a step further, by encompassing fraternity membership
and high school binge drinking with college binge drinking we are able to see the potential
influence a fraternity has on binge drinking. The percentage of fraternity members who binge
drink in college, but did not in high school, is 40.5%. Alternatively, the percentage of fraternity
members who both binge drink in high school and college is only 34.2%. These figures would
suggest, that joining a fraternity as a non-drinker increases your chances of becoming a binge
drinker in college. The numbers and relevant percentages calculated from these numbers are
shown in Table 2. When it comes to fraternities and binge drinking, illicit behaviors such as
unprotected sex, raise cause for concern, especially when previous studies have found that being
in a fraternity raises the probability of such behavior by 9.1% (DeSimone 2007). By going one
step further and encompassing fraternity membership and high school binge drinking with
8. college binge drinking and unprotected sex we observe a difference between members and non-
members. The percentage of fraternity members who have had unprotected sex two or more
times, and are both college and high school binge drinkers is half a percentage point greater than
those who are not. The previous analysis conducted by DeSimone (2007) found that this number
was five percentage points higher in fact, this can be attributed to his sample being significantly
larger than this one. A more recent study also found that fraternity housing residents were twice
as likely to engage in unprotected sex as those who did not live in fraternity housing (Wechsler
et al. 2009).
Empirical Strategy
In order to properly estimate the effect of fraternity membership on binge drinking I
derive a model which uses a logistic regression. My model takes the form of:
(1) Pr(di = 1| xi) = β0 + mβ1 + xiβ2 + γiβ3 + αiβ4 + µi.
In my model the letter m signifies fraternity membership, the i catalogs the respective student in
the sample, the β’s are the coefficients we are generating in order to find the best estimate of, the
letters x, γ, and α represent a column of vectors that control for various discrepancies and biases,
and lastly the µ simply represents our unseen error term. I have used the format that DeSimone
(2007) uses and thus the subscripts on the respective β’s will correspond to the columns in Table
5. By running three separate rounds of regressions, where an additional column of controls is
introduced, we are able to observe how the coefficient on m changes as it becomes less biased.
The first column of vectors, represented by the letter x, accounts for the controls in our
model, or the variables we assume to be exogenous to this regression. These variables in order
are, gender, race, age, whether the respondent lives off campus, if they are an athlete, if their
9. GPA is higher than a B+, if their mother’s highest education level is college, and how many
drinks they consumed in the past 30 days prior to the questionnaire. The variable gender is a
dichotomous variable where 1 represents that the respondent is a female. Race is accounted for
with the variables white and black, both of which are dichotomous, and where the number 1
signifies the variable is true. The variable that represents if they live off campus, if they are an
athlete, if their GPA is higher than B+, and if their mother finished college, are all dichotomous,
and follow the same format where the number 1 signifies that they are true. The last control
variable is how many drinks the respondent had in the 30 days leading up to filling out the
questionnaire. This variable is continuous variable starting from 1, which represents they had one
to two drinks, and ending at 6, which represents they had 40 or more drinks in the past 30 days.
The final category in this variable, which represents the upper echelon of heavy drinkers, only
describes .34% of the sample. The mean of this variable however, is 2.29, with a standard
deviation of 1.17. These statistics can be seen in Table 4 in greater depth.
The next column of vectors, represented by the letter γ, accounts for some of the motives,
that respondents expressed as reasons to drink. These variables in order are, drinking to forget
their troubles, drinking to feel comfortable around the opposite sex, drinking to help get work
done, drinking as a reward for hard work, drinking to relieve tension, drinking because it’s an
inexpensive activity, drinking because they are bored, drinking because everyone else around
them is drinking, drinking to celebrate, and drinking because they enjoy the taste of alcohol. All
of these variables were originally on a scale from 1, which represented the student found this
motive not important, to 4, represented that the student found this motive very important. I
recoded these variables by dropping all rows with missing answers, and combining the categories
of somewhat important, important, and very important into one category, represented by the
10. number 1 as being true. If any of these variables are equal to one our analysis treats the
respondent as viewing them as important. The variable associated with drinking to help get work
done had the lowest mean, at .03, with a standard deviation of .177. On the other hand, the
variable with the highest mean at .88, with a standard deviation of .31, was drinking to celebrate.
The final column of vectors, represented by the letter α, accounts for the social norms
and normative reasons that a respondent might choose to join a fraternity. These variables in
order are, if their parents drink, if their parents approve them drinking alcohol, on how many
occasions they drank in high school per month, their stance on drinking policies in their
respective school, how important it is to attend parties, if they feel having six drinks is an
acceptable amount, and if they binge drank in high school. The variables associated with, their
parents drinking, their parents approval of them drinking, if they find attending parties important,
if they feel having six drinks is an acceptable amount, and if they binge drank in high school are
all dichotomous and follow the same format as the other dichotomous variables in this analysis.
The variable associated with the amount of drinks they had in high school per month is a recode
of an ordinal variable which started from 1, which represented never drinking in high school, and
ending at 7, represented drinking on 40 or more occasions per month in high school. The new
variable starts at 1, representing having never drank in high school, and ending at 4, which
represents 20 or more occasions a month. The final variable in this column of vectors is their
views on the drinking policy at their respective school, which starts from 1, stating they agree
with the current policy, and ends at 4, meaning they don’t know the policy well enough to make
a decision. The two choices in the middle represent both, a more aggressive, and less aggressive
alcohol policy. The variable with the highest mean, at 1.92, with a standard deviation of .78, is
11. the one that represents on how many occasions a month they would drink. The mean of 1.92
corresponds to a student drinking between one and five times a month, on average.
The second part of this analysis estimates the effect of binge drinking and fraternity
membership on the probability of engaging in one of three illicit behaviors. For this portion I
have derived a multinomial model which takes the form of:
(2) zij = β0 + xiβj + γiβj + wiβj + µij.
In this model the letter z corresponds to one of three illicit behaviors we are analyzing, having
unprotected sex, getting injured as a result of intoxication, and getting in trouble with the police
as a result of intoxication. All three dependent variables range from 0, representing that the
responder has never been involved in such behavior, to 2, which represents that the responder
has been involved in such behavior two or more times. The subscript i, represents the respondent
in the sample, the subscript j represents one of the three possible choices from our dependent
variable, and as always µ represents the error term, which varies now both by i and j. As before,
the letter x, represents a column vector of controls, that are assumed to be exogenous, and the
letter γ, represents a column vector of motives, that respondents expressed as reasons to drink. In
addition to these two column vectors, I have also added a new column consisting of sexual
preference signified by the letter, w. This vector will be important when examining the
dependent variable associated with unprotected sex.
It is important to note, as mentioned before, that a weakness of this study is the presence
of endogeneity. A majority of the independent variables are associated with drinking behavior in
order to strip away bias from the coefficient on fraternity membership. However, it is highly
possible that this model suffers from endogeneity, as I performed an instrumental variable probit
regression in which I tested the notion that fraternity membership suffered from endogeneity.
12. The Wald test of exogeneity gave me a test statistic which was .078, so I could not reject the null
hypothesis that my model suffers from endogeneity. While at the 5% significance level this
hypothesis holds true, if I increased my estimates to the threshold of the 10% significance level,
then my model is revealed to suffer from endogeneity. Therefore, all results should not be taken
as a given.
Results
Tables 4 & 5 display the main results from both the logistic and multinomial regression
analysis’. As mentioned previously each of the columns in Table 4 corresponds to subscript for
each respective β that is associated with our proxy vectors. As the columns progress from left to
right, the next successive column of vectors is added in order to observe how the old variables
change when new variables are introduced. Therefore, Column 4 of Table 4 can be viewed as the
complete model as it contains all the explanatory variables used in this analysis. All marginal
effects from this model will be computed using the output from Column 4.
One of the first results we observe is that fraternity membership is significant across all
three columns. We also observe that the coefficient on fraternity membership decreases from
Column 2 to Column 4 by 9.73 percentage points. Ultimately, being a fraternity member
increases the likelihood that a college student engages in binge drinking. Another major group
that students are associated with are collegiate athletes. In my sample, collegiate athletes make
up 13.72% of the population, of which 17.13% also belong to a fraternity. The results reveal that
being a collegiate athlete increases the likelihood of binge drinking in college, with the
coefficient being significant across all three columns in Table 4. When it comes to race, white
students are more likely to binge drink than non-white students. This result is exemplified by the
coefficient on black, which is negative, showing that black students are less likely to binge drink
13. than non-black students. The two terms that help to control for time-varying preferences, binge
drinking in high school and the amount of drinks consumed in the past 30 days, have a strong
positive impact on the likelihood of binge drinking. Analysis results also reveal that students
with a GPA of B+ or higher are less likely to binge drink, which matches the results achieved by
Jasinski & Ford (2007) and Harris (2014).
To obtain a more accurate estimate of the impact that being in a fraternity has on the
likelihood of binge drinking, I have taken the derivative of the function with respect to fraternity
membership, over gender, assuming the respondent binge drank in high school. The derivative
also takes into account all the levels of alcohol consumption the respondent has had the past 30
days, starting from none, which is specified by left-most number in Column 1 of Table 6. The
right-most number in Column 1 of Table 6 corresponds to whether the derivative is treating the
respondent as a female, signified by 1, or a male, signified by 0. From this table we can see that
being a male fraternity member who binge drank in high school and had one to two drinks the
past 30 days, increases the likelihood of binge drinking by 9.25 percentage points, while being a
female with the same qualities increases the likelihood by 9.22 percentage points. The difference
is small enough that it does not warrant a distinction, and this pattern of effects being similar for
both men and women remains true throughout Table 6. The table also reveals that the likelihood
of binge drinking given these characteristics decreases as the amount of drinks consumed over
the past 30 days increases. For example, being a male who has consumed 40 or more drinks in
the past 30 days only increases the likelihood of binge drinking by .68 percentage points. Table 7
evaluates the same marginal effects, but of a fraternity member who did not binge drink in high
school. This method is used to see the potential persuasion power a fraternity has on the
likelihood of a fraternity member drinking. A male who has consumed one or two drinks in the
14. past 30 days is 9.17 percentage points more likely to binge drink. This effect seems to be smaller
than the effect observed from Table 6. The main difference however is the intensity at which the
marginal effects decrease. The effects in Table 7 decrease at a slower pace than Table 6 and this
is evidenced when looking at a person who drank in the past 30 days at an intensity of 2 (if you’ll
recall, drinking in the past 30 days is a variable on a scale from 1 to 6, where amount of drinks
increases as the variable increases), as this effect is larger than in Table 6. Every effect thereafter
follows the same pattern of being greater than the effect in Table 6. For example, being a male
who has consumed 40 or more drinks in the past 30 days but did not binge drink in high school
increases your likelihood of binge drinking in college by 1.31 percentage points. This effect is
almost 100% larger than the effect we observe in Table 6.
The second part of the analysis is directed at the three multinomial regressions, from
which the results can be seen in Table 5. From this table we are able to see that being a fraternity
member and engaging in binge drinking behavior leads to a higher chance of having unprotected
sex two or more times, getting injured while drunk, and having at least one run-in with the
police. An interesting result of this analysis, is that of living off-campus. A student who lives on
campus is more likely to have unprotected sex at least once, compared to a student who lives on-
campus. However, a student who lives off-campus is less likely to get injured while drunk or
have altercations with the police. Specific to the model analyzing effects on unprotected sex, we
observe that identifying as bisexual increases the likelihood of having unprotected sex two or
more times. The results also reveal, that being a female decreases the likelihood of getting in
trouble with the police, while race does not appear to have much significance when it comes to
being involved in these illicit behaviors.
15. When it comes to marginal effects, being a bisexual increases the likelihood of having
unprotected sex by 3.59 percentage points. These results can be seen in Table 8. To evaluate the
effect of being a fraternity member on unprotected sex, we take the derivative of the function
with respect to living off-campus, since most fraternity houses are not located on campus. The
results in Table 8 show that living off-campus and being a fraternity member who binge drinks,
increases the probability of having unprotected sex at least once by 2.99 percentage points. To
examine the results of being injured two times or more while drunk, once again I take the
derivative with respect to living off campus and indicate both fraternity membership and binge
drinking as being true. Surprisingly the effects reveal that fraternity members who binge drink
and live off campus are 2.27 percentage points less likely to get injured while drunk. If we regard
as living off campus as a constant and simply see the effect of binge drinking and being a
fraternity member on the likelihood of getting hurt two or more times while drunk, we find that
this likelihood is increased by 4.10 percentage points. This result may appear trivial since getting
hurt while being drunk requires a person to be drunk, which is a side effect of binge drinking,
however, when we derive this same marginal effect for a non-member we find that their
likelihood of getting hurt two or more times while drunk is 2.77, which is significantly lower
than that of a fraternity member. The final multinomial model of police altercations
unfortunately does not reveal much predictive power of fraternity membership, so this model
will be left, to be revisited and revised at a future time.
Conclusion
The results this analysis has shown, indicate that fraternity membership, is in fact
correlated with binge drinking. As mentioned previously, the model potentially suffers from
endogeneity, thus I can not claim that the relationship between the two variables is causal. The
16. variable associated with fraternity membership remained significant throughout the model, even
as social norms, motives, and time-varying variables were added to account for potential bias.
Final results also showed that fraternity membership had an effect on committing several illicit
behaviors. Possible model revisions using instrumental variables, clustering on a school and/or
state level, and adding more proxies may help to estimate more accurate effects of fraternity
membership on binge drinking, however, this is best left for another time.
17. Table 1
Table 2
Binge in High School and Fraternity Member
Binge in ---No Binge in HS -------------- Yes Binge in HS---
College No Yes Total No Yes Total
No 1,347 147 1,494 299 23 322
Yes 1,296 273 1,569 1,066 230 1,296
Relevant Percentages from Table 2
Fraternity Member Binge in College Total Percentages
Binge in College
No Yes No Yes
No 1,646 2,362 4,008 41% 59%
Yes 170 503 673 25.3% 74.7%
Total 1,816 2,865 4,681
Percent who binge drink and are Percent who binge drink and are
fraternity members and not high school fraternity members and high school
binge drinkers binge drinkers
40.5% 34.2%
Percent who don’t binge drink and are Percent who don’t binge drink and are
fraternity members and not high school fraternity members and high school
binge drinkers binge drinkers
21.8% 3.5%
18. Table 3
Unsafe Binge in High School and Fraternity Member
Sex and
Binge in --- No Binge in HS ------- Yes Binge in HS ---
College No Yes Total No Yes Total
No Sex
No 1,301 143 1,444 285 20 305
Yes 1,163 241 1,404 904 193 1,097
1 Time
No 33 2 35 7 2 9
Yes 69 18 87 82 20 102
2 or More
No 13 2 15 7 1 8
Yes 64 14 78 80 17 97
Relevant Percentages from Table 3
Percentage who binge drink and are Percentage who binge drink and are not
fraternity members and are not high fraternity members and are not high
school binge drinkers who have had school binge drinkers who have had
unprotected sex two or more times unprotected sex two or more times
2.1% 1.6%
Percentage who binge drink and are Percentage who binge drink and are not
fraternity members and are high fraternity members and are high school
school binge drinkers who have had binge drinkers who have had
unprotected sex two or more times unprotected sex two or more times
2.5% 2.0%
19. Table 4
(2) (3) (4)
VARIABLES Binge
Controls
Binge
Motives
Binge Social
Norms
Frat Member 0.493*** 0.505*** 0.445***
(0.109) (0.111) (0.116)
Female 0.0754 0.123 0.197**
(0.0784) (0.0819) (0.0856)
White 0.475*** 0.457*** 0.383***
(0.0983) (0.101) (0.104)
Black -0.425** -0.523*** -0.420**
(0.181) (0.188) (0.190)
Age -0.174*** -0.132*** -0.0602***
(0.0205) (0.0213) (0.0229)
Live Off Campus -0.0233 -0.0297 -0.0887
(0.0812) (0.0839) (0.0892)
Athlete 0.369*** 0.383*** 0.297***
(0.108) (0.111) (0.114)
GPA > B+ -0.220*** -0.212*** -0.173**
(0.0727) (0.0749) (0.0775)
Mother College -0.196*** -0.202*** -0.174**
(0.0732) (0.0752) (0.0783)
HS Binge 0.714***
(0.0997)
Drink 30 Days 1.121*** 0.942*** 0.855***
(0.0404) (0.0425) (0.0440)
Forget Troubles 0.282*** 0.246***
(0.0892) (0.0919)
Opposite Sex 0.334*** 0.289***
(0.102) (0.105)
Help Work -0.0829 0.0442
(0.231) (0.234)
Hard Work 0.316*** 0.248***
(0.0795) (0.0822)
Relieve Tension 0.0722 0.0575
(0.0945) (0.0978)
Cheap Activity 0.465*** 0.410***
(0.122) (0.125)
Drink When Bored 0.531*** 0.385***
(0.0999) (0.103)
Everybody Drinking 0.168* 0.167*
(0.0958) (0.0988)
Drink to Celebrate 0.509*** 0.316**
(0.121) (0.126)
20. Like the Taste 0.0282 0.0132
(0.0848) (0.0880)
Parent’s Drink -0.141*
(0.0854)
Parent’s Approve 0.146
(0.0899)
HS 1-5 Occasions 0.314***
(0.0885)
HS 6-19 Occasions 0.358**
(0.144)
HS 20+ Occasions 0.0764
(0.237)
More Policy -0.302**
(0.120)
Less Policy 0.0260
(0.0984)
Don’t Know Policy -0.0336
(0.110)
Party Important 0.791***
(0.0957)
Six Drinks OK 0.412***
(0.0933)
Constant 1.452*** -0.0878 -2.358***
(0.430) (0.461) (0.529)
Observations 4,681 4,681 4,681
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
21. Table 5
Had
Unprotected
Sex Once
Had
Unprotected
Sex Two or
More Times
Injured
While
Drunk Once
Injured
While
Drunk Two
or More
Times
Police
Run-In
Once
Police
Run-In Two
or More
Times
VARIABLES
Frat Member#Binge Drink
No#Yes 0.562*** 0.868*** 0.683*** 1.312*** 0.497* 1.336
(0.207) (0.268) (0.184) (0.388) (0.262) (1.066)
Yes#No 0.142 0.641 -0.0718 -0.0798 0.622 2.686*
(0.537) (0.633) (0.486) (1.075) (0.557) (1.444)
Yes#Yes 0.776*** 0.769** 0.487** 1.581*** 0.655** 0.955
(0.264) (0.329) (0.236) (0.420) (0.311) (1.167)
Female 0.0108 0.00822 0.123 0.266 -0.500*** -1.066***
(0.154) (0.167) (0.129) (0.183) (0.163) (0.402)
White -0.475** -0.170 0.111 0.321 0.177 -0.381
(0.193) (0.234) (0.186) (0.303) (0.256) (0.522)
Black 0.597* 0.359 -0.419 -0.0848 -0.950 -13.90
(0.305) (0.415) (0.430) (0.665) (0.756) (999.4)
Age -0.0922** 0.0642 -0.188*** -0.188*** -0.138** -0.269*
(0.0441) (0.0474) (0.0408) (0.0601) (0.0548) (0.145)
Live Off Campus 0.502*** 0.0186 -0.167 -0.510*** -0.582*** -1.105**
(0.165) (0.183) (0.133) (0.191) (0.182) (0.466)
Athlete 0.00620 -0.401* 0.325** 0.0635 0.155 0.692*
(0.194) (0.242) (0.149) (0.217) (0.195) (0.396)
GPA -0.235* -0.577*** 0.0680 -0.138 -0.314** -0.429
(0.141) (0.157) (0.118) (0.165) (0.156) (0.374)
Mother College 0.0266 -0.0567 -0.0320 -0.0140 0.124 -0.0869
(0.142) (0.156) (0.117) (0.163) (0.154) (0.365)
HS Binge 0.247 0.0606 0.220 0.211 0.582*** -0.213
(0.168) (0.179) (0.138) (0.200) (0.188) (0.416)
Drinks Past 30 Days 0.200*** 0.469*** 0.271*** 0.517*** 0.274*** 0.306
(0.0696) (0.0749) (0.0588) (0.0838) (0.0777) (0.188)
Forget Troubles 0.0809 0.331** 0.326*** 0.178 0.0988 0.00133
(0.152) (0.164) (0.125) (0.172) (0.171) (0.386)
Opposite Sex 0.376** 0.243 0.251* 0.219 0.0529 0.369
(0.158) (0.172) (0.131) (0.179) (0.175) (0.395)
Help Work 0.535* 0.00760 -0.215 -0.0697 0.0746 0.608
(0.284) (0.334) (0.299) (0.370) (0.362) (0.591)
Reward for Hard Work 0.279* 0.139 0.0560 0.393* 0.0243 0.499
(0.168) (0.189) (0.137) (0.213) (0.187) (0.498)
Relieve Tension 0.175 0.187 -0.118 0.281 -0.366 -0.0225
(0.224) (0.273) (0.182) (0.302) (0.224) (0.658)
Cheap Activity 0.362** 0.338* 0.372*** 0.171 0.187 -0.0498
24. Table 8
Delta
Method
Marginal
Effect
Standard
Error
z P>z [95% Confidence
Interval]
Bisexual .0358729 .0180783 1.98 0.047 .0004401 .0713058
Live Off
Campus
.0289396 .0103845 2.79 0.005 .0085864 .0492927
Getting
Injured and
Living Off
Campus
-.0227073 .0089979 -2.52 0.012 -.0403428 -.0050719
Binge
Drinking
Member
Getting Hurt
.0409555 .0145498 2.81 0.005 .0124384 .0694727
Binge
Drinking Non-
Member
Getting Hurt
.0277459 .0060342 4.60 0.000 .015919 .0395727
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