The Role of Monthly Spending Money in College Student Drinking Behaviors and Their Consequences

Transcription

1 JOURNAL OF AMERICAN COLLEGE HEALTH, VOL. 57, NO. 6 The Role of Monthly Spending Money in College Student Drinking Behaviors and Their Consequences Barbara Alvarez Martin, MPH; Thomas P. McCoy, MS; Heather Champion, PhD; Maria T. Parries; Robert H. DuRant, PhD; Ananda Mitra, PhD; Scott D. Rhodes, PhD Abstract. Objective: Alcohol use among college students is pervasive and affected by economic factors such as personal income and alcohol price. The authors examined the relationship among students spending money, drinking rate, and alcohol-related consequences. Participants: In 2005, the authors conducted a Web-based survey among a random sample of 3,634 undergraduate students from 2 large universities. Methods: The authors used multiple logistic regression to model drinking behaviors and multiple linear regression to model alcohol-related consequences. Results: The lowest reported levels of average monthly spending money were associated with reduced levels of drinking and getting drunk. Spending money was independently associated with experiencing alcohol-related consequences caused by a student s own drinking, even after the authors controlled for personal drinking behaviors. The effects for consequences caused by others drinking were significant for students who had gotten drunk. Conclusions: These findings have implications for alcohol price and marketing, particularly around colleges, and suggest actions for parents to consider. Keywords: alcohol, alcohol marketing, college students, money management Much is known about the characteristics of students who drink alcohol while attending college and the risks of excessive consumption. 1 However, little is known about whether the amount of spending money college students have is associated with their alcohol use and related consequences. Recent national find- Ms Alvarez Martin, Ms Parries, and Dr Rhodes are with the Department of Social Sciences and Health Policy at Wake Forest University School of Medicine in Winston-Salem, NC. Mr McCoy is with the Department of Biostatistical Sciences at Wake Forest University School of Medicine. Dr Champion is an Enterprise Associate at the Center for Creative Leadership in Greensboro, NC. Dr DuRant is in Winston-Salem, NC. Dr Mitra is with the Department of Communication at Wake Forest University. Copyright 2009 Heldref Publications ings indicate that more than two-thirds of undergraduate students report drinking alcohol in the past 30 days 2,3 and that 38.0% consumed 5 or more drinks the last time they partied or socialized. 3 O Brien et al 4 found that across 10 campuses in 1 southeastern US state, 54.4% of past-month undergraduate drinkers reported getting drunk at least once in a typical week. Students who drink heavily tend to be male, Caucasian, members of fraternities or sororities, and varsity athletes. 2,5 11 Furthermore, several studies have demonstrated that students increase their alcohol use during their freshman year of college; 12 however, rates of alcohol use or consequences experienced vary little across the cross-section of undergraduate class years. 13 Drinking patterns also vary by where students live. Students who live in Greek houses tend to drink the most, followed by those who reside in residence halls on campus. 14,15 Students tend to congregate in these types of settings, which, in turn, serve a socializing function. Students with more roommates tend to drink more than do students living alone. 16 In contrast, students who live independently off campus or who live at home and commute to the university drink less. 17,18 Researchers must consider these demographic characteristics when examining spending money s possible effects on college drinking patterns. High-risk drinking among college students is associated with many adverse consequences and poses problems for drinkers and nondrinkers alike. Alcohol use is the greatest single contributor to college student injury and death. 19 Not surprisingly, researchers have found that those students who drink the heaviest and get drunk suffer the most alcoholrelated consequences. High-risk drinking has been associated with greater rates of injury, traffic-related fatalities, physical and sexual assault, dating violence, unsafe and unplanned sexual activity, sexual harassment, health problems, impaired sleep and study time, and interpersonal problems. 10,

2 Martin et al Consequences that students may experience because of someone else s drinking include having sleep or study interrupted, taking care of or seeking medical attention for a peer who drank too much, finding trash or vomit around one s residence, being verbally harassed or threatened with physical violence, and having one s property damaged. 4,10,23 26 Compared with female drinkers, male drinkers tend to experience more consequences that negatively affect others around them. 13,27 Much of the research to date regarding economic influences (eg, income, employment, discretionary spending) on alcohol use and alcohol-related consequences has been conducted at a general population level. Cook 28 found that people in higher income brackets (> $50,000/y) are more likely to drink than are those in the lowest income group (< $20,000/y). He estimated that a 10% increase in average income leads to a 3.4% increase in average alcohol sales. Research on adolescent samples yields a relationship between having a part-time job and heavier alcohol use; however, these findings may not be applicable to a college population because work intensity tended to be negatively correlated with adolescents plans to pursue a college education. 29 Typical sources of income for college students include parttime jobs, parental support, or previous savings. Although we found no literature regarding the relationship between college student alcohol use and either college student employment or income, we can make inferences. If college students regard their discretionary spending as a proxy for their income, Cook s findings 28 would suggest that the likelihood of drinking among college students would increase as their availability of discretionary funds increases. A student s sex may also influence the relationship between money and alcohol use. For example, Hayhoe et al 30 found that women used credit cards to purchase appearance items, whereas men used credit purchases for entertainment and leisure items. Thus, the relationship between discretionary spending and alcohol use may be stronger for men than for women. In the present study, we explored differences among college students in distributions of spending money by demographic variables and drinking behaviors, the association between spending money and getting drunk, and the association between alcohol-related consequences and spending money. We hypothesized that students who report having more monthly spending money are more likely to drink and experience alcohol-related consequences. METHODS This study was part of an initiative funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) to reduce college high-risk drinking. Wake Forest University School of Medicine participated in a cooperative agreement with NIAAA and another university to evaluate its campus s intervention. A second university agreed to serve as a comparison site. Procedure In the fall of 2005, a random sample of 3,634 undergraduate college students attending 2 large, urban universities in the Midwest and Mid-Atlantic United States completed a Web-based survey as part of a study examining differences in alcohol use and party behaviors among college students. We fielded the survey for 5 days, beginning on the Sunday evening of 2 specific weekends, which we selected on the basis of the level of expected risk for heavy alcohol use, parties, and student disturbances. We selected a high-risk weekend (HRW) and a low-risk weekend (LRW) at each university on the basis of predefined criteria that we provided and on which the schools agreed. For HRWs, criteria included home football games, traditional campus-wide parties or events, sporting events of importance (eg, conference game, ranked opponent, traditional rival, nationally televised), and sporting events that occurred later in the season. LRW criteria included away games, nonconference sporting events, games of less importance (eg, against lesser rivals), and sporting events occurring earlier in their season. Thus, the weeks were comparable across schools. The Wake Forest University Health Sciences Institutional Review Board and the institutional review boards of participating schools approved the following protocol. For each wave of the survey, the target sample for completion comprised 900 undergraduate students (225 each of freshmen, sophomores, juniors, and seniors) from each university. For both the HRW and LRW waves of data collection, each campus provided us the names of a sample of 4,000 full-time undergraduate students (1,000 from each class), randomly selected from enrollment lists. We requested this number (n = 4,000 per school, for a total of 16,000 for 2 weekends each at 2 campuses) on the basis of expected response rates of approximately 30% to 35% obtained by researchers from other Web-based surveys measuring alcohol use among college students, 4,22,31,32 as well as on our need to have enough additional sample members to reach our target if we did not meet the expected response rate. Our primary goal was to obtain 900 completed surveys; however, we also attempted to optimize response rates by staggering the release of the sample. The initial sample released (n = 1,125) would result in an 80% response rate if 900 completions were reached on the first day. On the basis of the actual day 1 response rate, we released an additional sample on day 2 to reach 900 completions by no later than 5:00 PM on Day 5, the Friday after we fielded the survey. (The total sample that we ultimately released to meet the goal [900 students per campus, per wave] was 12,300, which breaks down as follows: campus 1 = 2,722 HRWs, 3,070 LRWs; campus 2 = 3,022 HRWs, 3,486 LRWs.) We ed each student, inviting him or her to participate in a Web-based survey. The informed students that we would deposit $10 into their campus account for completing the survey. We sent daily reminder s to noncompleters. We shut down the survey either after 900 completions was reached or by 5:00 PM on day 5, the Friday after the targeted weekend. The survey fielding periods lasted from 2 to 5 days, achieving an overall response rate of 29.5% (range = 588 JOURNAL OF AMERICAN COLLEGE HEALTH

3 Monthly Spending Money, Drinking Behavior, and Consequences %), which is similar to what is emerging as the expected response rate for college students. 32 Sample A total of 3,634 students completed the survey across both campuses and the HRWs and LRWs (campus 1: 902 HRWs, 860 LRWs; campus 2: 861 HRWs, 1,011 LRWs). Survey We designed the Student Alcohol Use Survey (SAUS) to measure student alcohol use, party attendance, party behaviors and observations, alcohol-related consequences, and perceptions of alcohol policies and enforcement. We developed this instrument by drawing extensively from college survey instruments we have used in previous research and investigated for validity and reliability. 4,22 We field-tested the SAUS for completion time and respondent comprehension. For the present study, we focused on alcohol use and related consequences. We included several demographic characteristics in the present analysis, including sex, race or ethnicity (non- Hispanic white, African American, Asian/Pacific Islander, Hispanic, and other), class year (freshman to senior or 5th-year undergraduate), paid work status (yes, no), Greek affiliation (yes, no), Greek pledge status (yes, no), and student status (full-time, part-time). We measured living arrangements by 2 questions. We dichotomized residence at school as on or off campus. A second question asking with whom the respondent lived during the school year offered 7 options: alone; 1, 2, and 3 or more roommates (for which each was a separate indicator variable); parents/other adult relatives; steady boyfriend/girlfriend/ partner/spouse; and children. We combined the latter 3 into 1 option in the analysis (parents/partner/children). Because of the frequency distribution, we broke marital and relationship status into 3 categories: single without partner, single with partner, and married. Table 1 shows demographic characteristics. Alcohol Use Several items measured alcohol use, including whether respondents had ever consumed alcohol, had consumed it in the past 30 days, and had gotten drunk in the past 7 days. Ever drinkers were those respondents who gave an age when asked when they had had their first drink of alcohol other than a few sips. We measured past-month use as yes or no. Respondents reported whether they had gotten drunk on each of the 7 days preceding the survey. For this study, we categorized anyone who responded affirmatively for at least 1 of those 7 days as got drunk. Alcohol-Related Consequences Caused by Own Drinking Respondents reported whether they had experienced specific consequences while drinking (yes, no) and whether the most recent occurrence had happened in the past 7 days, past 30 days, past year, or more than 1 year ago. The 17 consequence items included got drunk, passed out, had a hangover, memory loss, got sick/vomited, missed a class, performed poorly on a test or project, did something later regretted, was hurt or injured, required medical treatment, urinated in a public setting, flashed and/or mooned, damaged property/committed vandalism, got into a verbal argument, strained a relationship, drove a car while under the influence of alcohol, and received a ticket for a DUI/DWI. We conceptually grouped these consequences as 1 factor and evaluated it for adequate reliability using the Kuder- Richardson Formula 20 (KR-20) for internal consistency in an item analysis. For each item, participants who indicated they had experienced the consequence at least once in the past 30 days received a score of 1; we summed scores to create a scale of consequences caused by own drinking (range = 0 17; KR-20 =.86). Alcohol-Related Consequences Caused by Others Drinking Respondents indicated whether they had experienced consequences caused by another person s alcohol use by choosing from a list of 14 variables and indicated whether the most recent occurrence happened in the past 7 days, past 30 days, past year, or more than 1 year ago. The 14 items included took care of someone who drank too much; took someone for emergency medical care; had sleep or study interrupted; found cans, bottles, or other litter in or around your residence; found vomit in or around your residence; thought of transferring to another school; was harassed because of sexual orientation, race/ethnicity, religion, sex; had a verbal argument; was threatened with physical violence; got into a physical fight; was sexually victimized; and had personal property or residence damaged. We used methods similar to those described earlier to develop a single scale of consequences caused by others drinking (range = 0 14; KR-20 =.75). Spending Money To measure this construct, a single question asked How much spending money do you have in an average month? Response categories were < $100, $100 $199, $200 $299, $300 $399, $400 $499, or > $500. Statistical Methods The goals of the statistical analyses were to examine (1) differences in distributions of spending money by demographic variables and past-month drinking, (2) the association among spending money and past-month drinking and getting drunk, and (3) the association between alcoholrelated consequences and spending money. We hypothesized that having more average spending money would lead to a greater likelihood of getting drunk and of experiencing alcohol-related consequences. We considered the primary study variable, spending money, using 5 indicator variables for the 6 categories with < $100 as the reference group. This functional form allowed us to estimate the effects of individual ranges of spending money on outcomes. We conducted an analysis to determine whether spending money was associated with consequences independent VOL 57, MAY/JUNE

4 Martin et al Table 1. Demographics and Past 30-Day Drinking, by Amount of Spending Money, 2005 Student Alcohol Use Survey (N = 3,634) < $100 $100 $199 $200 $299 $300 $399 $400 $499 $500 Overall Characteristic n % n % n % n % n % n % n % Sex ** Male , Female , Race * White (non-hispanic) , African American Asian/Pacific Islander Hispanic Multiracial/Other Class year Freshman Sophomore Junior Senior th year/other Greek member ** Yes No , , Greek pledge Yes No , , Job/Work for wages ** Yes No Part-time school status ** Yes No , , Single without partner Yes , No , Married ** Yes No , , Campus residence ** On , Off , Living... ** Alone With roommates , With relatives School S , S , Weekend Low risk , High risk , Drank in past 30 days ** Yes , No * p <.05 from trend test. ** p <.01 from trend test. p <.01 from test of nonzero Spearman correlation. S1 = school 1; S2 = school 2. of other hypothesized study variables or whether different reported levels of spending money modified the risk of consequences. Thus, we first tested for interaction effects of money, past-month drinking, and getting drunk, before evaluating main effects. We used past-month drinking to determine descriptive statistics (see Table 1). We used the variable of got drunk in prior week for the logistic regression to model drinking behaviors because it was a more sensitive variable for indicating heavy alcohol use than was ever drinker or past-month drinker. 4 We included 30-day 590 JOURNAL OF AMERICAN COLLEGE HEALTH

5 Monthly Spending Money, Drinking Behavior, and Consequences use and got drunk in prior week in the linear regressions to model alcohol-related consequences. We used multiple logistic regression to model getting drunk and multiple linear regression to model alcohol-related consequences. We assessed collinearity with correlation matrixes and variance inflation diagnostics from the regression models. The largest variance inflation factor in the models for getting drunk, consequences from own drinking, and consequences from others drinking were 3.8, 3.9 and 5.0, respectively. Because they were lower than 7, we deemed there was no multicollinearity. 33 We examined study covariables in bivariate analyses and included them in multivariable analyses after a priori identification from conceptual considerations. For the present analysis, we used HRW and LRW samples. We adjusted for survey weekend and campus indicator variables in multivariable analyses. We first tested the interaction of survey weekend and campus in models with the included indicators and retained them if significant (p <.05). We performed all analyses using Stata version 9.2 (StataCorp, Inc, College Station, TX). RESULTS Sample Description by Spending Money, Demographics, and Past-Month Drinking Among the 3,634 students in the sample, 43% were men, 73% were white (non-hispanic), 7% were African American, 11% were Asian/Pacific Islander, 4% were Hispanic, and 98% were full-time students (see Table 1). The mean age was 20.2 years (SD = 2.1 years), with 62% of the students aged younger than 21 years. Overall, 78% of the students were past-month drinkers, and 41% had gotten drunk in the week prior to the survey. The distribution of average monthly spending money was as follows: 25.7% reported having < $100, 32.9% had $ , 18.3% had $ , 9.6% had $ , 4.1% had $ , and 9.6% had > $500. Of the 61 married students, 33% reported having > $500 in average monthly spending money; 9% of unmarried students reported this amount. Table 1 shows the distributions by spending money categories and overall. Several demographic characteristics were significantly associated with higher levels of spending money, including being male, being a Greek organization member, being married, living off campus, going to school part-time, living alone or with parents or children, and having a job. Being white (non-hispanic; p =.016) or a freshman (p <.01) was associated with having less spending money. Students with the most spending money (> $500) were African American (15%) and Asian/Pacific Islander (13%), compared with 9% of white students. Past-month drinkers were more likely to have more spending money. We found no differences in spending money by school or weekend. Spending Money and Getting Drunk We observed a significant positive linear trend in spending money for those students who had gotten drunk in the past 30 days (Spearman r =.13; for Cuzick trend test, p <.01) and for those who had gotten drunk in the previous 7 days (Spearman r =.12; Cuzick trend test p <.01). Because of the riskier nature of getting drunk and its relationship with experiencing alcohol-related consequences, 4 we used the past-week drunkenness variable in the logistic regression model (see Table 2). Spending money amount was significantly positively associated with likelihood of getting drunk after we adjusted for the other factors in the model (p <.001). The size of the effect (adjusted odds ratio [AOR]) quantifying the risk of drunkenness generally increased as the categories of reported spending money increased (from AOR = 2.00 for $ vs < $100 to AOR = 2.61 for > $500 vs < $100). Spending Money and Alcohol-Related Consequences Looking at the 2 indexes of consequences, we first tested for interaction effects with past-month drinking and getting drunk. We found no evidence of these for own drinking consequences (p >.10). Table 3 shows results from the subsequent regression analyses of consequences caused by own drinking. Spending money was independently associated with own drinking consequences in the multivariable model (df = 5, p =.004). Compared with < $100, all levels of average spending money > $200 were associated with an increase in the average number of past-month consequences as a result of the student s drinking (p <.05). Students who had $ were not statistically different from those who had < $100 (p =.074). The effect was highest for those who had $ , followed by > $500 in a typical month. Other characteristics that were significantly related to increased consequences from own drinking (p <.05) were having an off-campus residence, being male, being a Greek organization member or pledge, being single, drinking in the past month, and getting drunk in the past week. We found a difference in consequence reporting between the 2 schools on the HRWs and LRWs of the fielded surveys (School Weekend interaction, p =.009). Table 4 presents results from the regression analysis of consequences caused by others drinking. This analysis indicated evidence of interaction effects of spending money and getting drunk (df = 5, p =.005). Specifically, 2 levels of spending money displayed interaction effects: $ (p <.001) and > $500 (p =.029). Regression coefficients of these interaction effects were significantly higher than 0, indicating that students who had gotten drunk and had levels of spending more than $399 were at an additional increased risk of experiencing consequences from others drinking compared with respondents reporting only 1 of these characteristics or those in the referent groups. The interaction effects monotonically increased from $ to $ , but then the regression coefficient was smaller in magnitude for > $500. Other characteristics significantly related to increased consequences from others drinking (p <.05) were living with 3 or more roommates, being a Greek member or pledge, being a freshman, and getting drunk (main effect). We also found a difference in reporting between survey weekends (p =.014) and campuses (p =.037). Living with VOL 57, MAY/JUNE

6 Martin et al TABLE 2. Logistic Regression Modeling for Getting Drunk in Previous 7 Days (N = 3,022) 95% Confidence Characteristic AOR interval for AOR p Monthly spending money (df = 5, p <.001) <$100 (RC) $ <.001 $ <.001 $ <.001 $ $500 or more <.001 On vs off campus <.001 number of roommates (df = 4, p <.001) 0 (RC) <.001 Live with parents or children <.001 Male vs female Race/Ethnicity (df = 4, p <.001) White (RC) African American <.001 Asian/Pacific Islander <.001 Hispanic Other Greek membership (df = 2, p <.001) Fraternity/Sorority member <.001 Fraternity/Sorority pledge Academic classification (df = 4, p =.464) Freshman (RC) Sophomore Junior Senior th year/other Varsity athlete Marital status (df = 2, p <.001) Single without partner (RC) Single with steady partner <.001 Married Job/Work for wages School: S1 vs S Weekend: high risk vs low risk Note. AOR = adjusted odds ratio; RC = reference category. S1 = school 1; S2 = school 2. a spouse or children and being academically classified as a sophomore or higher had a protective effect on experiencing such consequences. COMMENT We found that students with higher levels of spending money tended to be male, nonwhite, Greek members, married, attending school part-time, employed, living off campus, and living either alone or with parents or children. In addition, their level of spending money was associated with past-month drinking. Having the lowest amount of monthly spending money (< $100) was associated with reduced levels of drinking and getting drunk. Furthermore, having more spending money was associated with experiencing more alcohol-related consequences caused by one s own drinking and others drinking; the latter was significant only for those students who had gotten drunk. We observed a positive association between level of spending money and experiencing alcohol-related consequences even after controlling for drinking behavior, 592 JOURNAL OF AMERICAN COLLEGE HEALTH

7 Monthly Spending Money, Drinking Behavior, and Consequences TABLE 3. Regression Analysis of Consequences From Own Drinking (N = 2,997) 95% Confidence interval Characteristic b Upper limit Lower limit p Monthly spending money (df = 5, p =.004) <$100 (RC) $ $ $ <.001 $ $500 or more On vs off campus # of roommates (df = 4, p =.394) Live alone (RC) Live with parents or children Male vs female Race/Ethnicity (df = 4, p =.055) White (RC) African American Asian/Pacific Islander Hispanic Other Greek membership (df = 2, p <.001) Fraternity/Sorority member <.001 Fraternity/Sorority pledge Academic classification (df = 4, p =.226) Freshman (RC) Sophomore Junior Senior th year/other Varsity athlete Marital status (df = 2, p <.001) Single without partner (RC) Single with steady partner Married Job/Work for wages Ever had alcohol Drank in past 30 days <.001 Got drunk in prior week <.001 School: S1 vs S Weekend: high risk vs low risk School Weekend Note. RC = reference category. S1 = school 1; S2 = school 2. suggesting an independent relationship. This was an unexpected finding because alcohol consumption is generally a prerequisite to experiencing alcohol-related consequences. A plausible explanation is that students with more spending money have the financial resources to place themselves in social or contextual situations that expose them to more risk for experiencing consequences. For example, perhaps students with more discretionary funds are also more likely to have access to a car, enabling them to attend off-campus parties, or the cash to pay taxi fares and cover charges, making it more possible to go bar hopping. We observed a nonmonotonic relationship between spending money and alcohol-related consequences in that the effect (regression coefficient) was higher at the $ level (b VOL 57, MAY/JUNE

8 Martin et al TABLE 4. Regression Analysis of Consequences From Others Drinking (N = 2,999) 95% Confidence interval Characteristic b Upper limit Lower limit p Monthly spending money (main effects; df = 5, p =.967) <$100 (RC) $ $ $ $ $500 or more Got drunk in prior week (main effects) <.001 Monthly Spending Money Got Drunk (df = 5, p =.005) $ Got Drunk $ Got Drunk $ Got Drunk $ Got Drunk <.001 $500 or More Got Drunk On vs off campus number of roommates Live alone (RC) Live with parents or children <.001 Male vs female Race/Ethnicity (df = 4, p =.050) White (RC) African American Asian/Pacific Islander Hispanic Other Greek membership (df = 2, p <.001) Fraternity/Sorority member <.001 Fraternity/Sorority pledge Academic classification (df = 4, p <.001) Freshman (RC) Sophomore <.001 Junior <.001 Senior <.001 5th year/other <.001 Varsity athlete Marital status (df = 2, p =.348) Single without partner (RC) Single with steady partner Married Job/Work for wages Ever had alcohol Drank in past 30 days School: S1 vs. S Weekend: high risk vs low risk Note. RC = reference category. S1 = schoo1; S2 = schoo1 2. =.61) but then estimated to be lower at the $ (b =.46) and > $500 (b =.49) levels. This decrease in the effect of money on consequences at the highest levels of spending money can be explained in part by marital status. Married 594 JOURNAL OF AMERICAN COLLEGE HEALTH

9 Monthly Spending Money, Drinking Behavior, and Consequences students had significantly more monthly spending money than did nonmarried students (p <.05), yet they had a lower prevalence of engaging in risky drinking behaviors and experiencing consequences from their own and others drinking. Overall, our findings suggest that public health interventions aimed at affecting alcohol-related consequences may consider addressing students disposable income or their alcohol-related expenses. Regarding income, administrators could focus interventions on increasing expectancies about students setting and maintaining reasonable budgets while in college. In this study, students reported having approximately $172 (SD = $107) in monthly spending money, a figure similar to that found in other studies. 34 Typical college expenditures for noneducation-related items include clothing, cosmetics, gifts, music, travel, and entertainment (including alcohol). 34 For parents who contribute to their children s college education, the expectation of financial planning and management can serve as a catalyst for addressing the costs of drinking, financial and otherwise. Whether students receive financial support from their parents or cover college-related costs on their own, students should plan for expected expenses, how they will cover them, and how they will save for the future. Campus-based social marketing campaigns can contribute to building these expectancies on a broader level. In terms of alcohol-related expenses, increasing the price of alcohol can place it further out of the reach of the typical college student s spending allowance. According to an analysis of data from the Harvard College Alcohol Survey conducted by Powell et al, 35 the price of alcohol significantly reduced demand among college students by 0.6; that is, a dollar increase in the average price reduced the average number of drinks consumed per drinking occasion by 0.6 of a drink. In addition, they found that alcohol purchased for a flat fee, which typically happens at off-campus parties and fraternity keggers, significantly increased the average number of drinks consumed by 1.6 drinks. Several researchers have found that increasing the price of alcohol can produce greater percentage declines in drinking rates among youths who are frequent drinkers compared with those who seldom drink. In their 2005 literature review of college student morbidity and mortality, Hingson et al 19 concluded that younger heavier drinkers are more price sensitive than are older heavy drinkers and postulated that this could be because younger drinkers have less discretionary income. In addition, the research generally has concluded that alcohol price increases lead to reductions in the negative consequences of alcohol use and abuse, 39 many of which are of particular concern on college campuses, including rape, 40 sexually transmitted diseases, 41 and motor vehicle fatalities The most efficient way for the public to increase the price of alcohol would be to raise excise taxes. 28 Although the authority to raise taxes rests with federal and state legislatures (and in some states, local municipalities), college administrators, and faculty, trustees and parents have significant credibility when it comes to talking about the health and safety of students. They are in a unique position to raise awareness about the link between price and consequences and to advocate for such policy change. In addition, administrators can play a direct role by regulating the price of alcohol sold in campus pubs and provided by student organizations or at campus-sponsored events. The price of alcohol can also be affected by prohibiting bars and restaurants from offering 2-for-1 drink specials, penny drafts, and similar promotions at bars and restaurants, particularly in college towns. Such restrictions could be enacted by state or local policymakers or self-imposed by individual retailers. A considerable amount of the alcohol that students drink is obtained at bars that entice student patrons with steep discounts or at parties where alcohol is available free or at low costs. This reality may somewhat diminish the youth price sensitivity effect for the college population 39,45 and suggests a future area of research. Limitations We used a cross-sectional sample, and responses were self-reported. The study was limited to undergraduate students attending 2 large public universities, which could affect generalizability. In addition, we achieved the sample using a quota approach, which affected response rates. However, we deemed this approach necessary because of the limited time we had to obtain data about alcohol use and party behaviors during the specific 7-day period immediately preceding the fielding period. Future researchers should examine where alcohol fits into students spending priorities. In addition, a considerable amount of the alcohol that students drink is obtained at parties where alcohol is available free or at low costs. We could not determine how much students spent to buy alcohol or to facilitate drinking. More research is needed to ascertain how party patronage, distance to bars, and density of alcohol retailers affects the relationship among spending money, alcohol use, and consequences. NOTE For comments and further information, address correspondence to Barbara Alvarez Martin, Dept of Social Sciences and Health Policy, Wake Forest University School of Medicine, 2000 W. First Street, Suite 271, Winston-Salem, NC 27104, USA ( bamartin@wfubmc.edu). REFERENCES 1. The National Center on Addiction and Substance Abuse at Columbia University. Wasting the Best and Brightest: Substance Abuse at America s Colleges and Universities. New York, NY: Columbia University; Johnson LD, O Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future: National Survey Results on Drug Use, : Volume II: College Students and Adults Ages Bethesda, MD: National Institute on Drug Abuse; Report No American College Health Association. 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