Underage college college students who obtain and use false identification (fake ID) are at risk for destructive outcomes. However, it is at the moment unclear how uniquely the fake ID itself serves as a car to subsequent hurt (i.e., the “fake ID effect”) over and above common and trait-related threat elements (e.g., deviant peers, low self-management).


With a purpose to examine whether or not the “fake ID effect” would hold after accounting for phenotypic threat, we utilized propensity rating matching (PSM) in a cross-sectional sample of n=1,454 college students, and a longitudinal replication sample of n=three,720 undergraduates. People with a fake ID were matched with individuals with out a fake ID, when it comes to quite a few trait-based mostly and social threat factors. These matched teams were then compared on 5 problematic outcomes (i.e., frequent binge drinking, alcohol-related problems, arrests, marijuana use, and hard drug use).



Findings showed that “fake ID effects” were substantially—though not totally—diminished following PSM. The “fake ID effect” remained strongest for alcohol-related arrests. This will relate to issues of enforcement and college students’ willingness to engage in deviant habits with a fake ID, or it might be a perform of mixed processes.


Overall, the findings recommend that interventions shouldn’t solely be geared toward lowering fake ID-related alcohol access specifically, but should also be aimed more typically towards at-threat youths’ access to alcohol. Future analysis would possibly study whether or not fake IDs have their strongest efficiency as moderators of the effects of risky traits—equivalent to impulsiveness—on drinking outcomes.

Key phrases: False identification, Fake IDs, underage alcohol use, heavy episodic drinking, binge drinking


Fake IDs, a novel mode of alcohol access, are increasingly sought after as individuals near the minimal authorized drinking age (Martinez et al., 2007; Wagenaar et al., 1996). These forms of false identification may be borrowed (or duplicated) from an older peer or sibling (Myers et al., 2001), or they may be a specially crafted document obtained regionally or from an online vendor (Murray, 2005). Regardless of their source, there seems to be a bidirectional relation between heavy drinking and fake IDs, such that (1) heavy drinking predicts subsequent obtainment of a fake ID, and (2) “possession” (i.e., possession) of a fake ID predicts subsequent frequency of heavy drinking (defined as 5+ drinks per event; Martinez, et al., 2007).

This bidirectional relation not solely illustrates the general public well being risks of this mode of alcohol access, but begs the question of whether or not it is more the case that a fake ID itself serves as a car to subsequent hurt (i.e., the “fake ID effect”) or whether or not such harms and outcomes are predominantly driven by a common stage of phenotypic threat on the a part of the fake ID “proprietor” (e.g., deviant peer associations, low self-management). Although common alcohol access theories would possibly assist the previous hypothesis nearly fully (particularly, fake ID possession increases alcohol access and subsequent hurt; see Gruenewald, 2011), common criminological theories of phenotypic threat assist the latter (particularly, that broad categories of threat—or propensities to engage in risky habits—are the true cause of hurt; see Pratt & Cullen, 2000). Definitely, such propensities may be what predicts fake ID obtainment within the first place, and though the strength of the fake ID effect seems to increase over time, it is enormously diminished after controlling for sex, Greek standing, and pre-college rates of drinking (Martinez et al., 2007). In sum, it is unclear how strong the fake ID effect may be after accounting for people’ ranges of phenotypic or propensity threat—though this question has bearing on prevention and policy initiatives, which can concentrate on either strengthening enforcement of fake ID legal guidelines themselves, increasing resources for trait-based mostly at-threat youth packages, or a neighborhood-driven combination of both (see Fell, Thomas, Scherer, Fisher & Romano, 2015; Fell, Scherer, Thomas & Voas, 2016; Fell, Scherer & Voas, 2015; Grube, 1997) .

Thus, with the intention to examine the strength of the fake ID effect, we matched college students with and with out fake IDs on quite a few threat-based mostly covariates utilizing propensity rating matching (PSM) techniques. We first compared matched teams’ drinking- and drug-use-related outcomes in a cross-sectional sample of n=1,454 college college students at a big Southeastern university. We additionally compared matched teams in an extra longitudinal replication sample of n=three,720 undergraduates at a big Midwestern university. We hypothesized that the effects of fake ID possession on outcomes could be enormously diminished by—and therefore largely attributable to—the pre-current trait-based mostly elements on which fake ID house owners and non-house owners may very well be matched. These comparisons can inform the extent to which the relationship between destructive outcomes and false identification possession are attributed to selection elements, which again, might have sensible utility for intervention and policy.

Process and Individuals

Two samples were individually investigated following Institutional Overview Board (IRB) approval: (1) A cross-sectional sample of n=1,454 underage college college students from a big Southeastern College (IRB Protocol H12032) and (2) a prospective replication sample of n=three,720 undergraduates underneath the minimal authorized drinking age from a big Midwestern college (IRB Protocol 01-01-001). Of be aware, both samples supply distinctive insights into the relationship between false identification use and destructive outcomes. Extra specifically, the cross-sectional study consists of objects that distinguish between the usage of fake IDs in several situations (at bars, at grocery shops, etc.) and the longitudinal study affords perception into the potential effects of fake ID possession over time and establishes temporal order.

With regard to the cross-sectional sample, throughout the tutorial yr 2011–2012, individuals were recruited from forty randomly selected large (>99 college students) and moderate enrollment (30–99 college students) classes. Individuals accomplished a one-web page knowledgeable consent document within the selected classes earlier than being given a six-web page paper survey about college life and behaviors to finish with pencil or pen. Individuals weren’t compensated. All enrolled college students were invited to participate and the response fee was excessive at 80.4% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After those above the authorized drinking threshold were eliminated, the analytic sample was n=1,454 underage individuals. The sample was largely representative of the college with regard to demographics and was specifically 51.6% feminine, 68.9% White/non-Hispanic, with a median age of 18.95 (SD=.795). Although this sample is cross-sectional, establishing temporal ordering of the covariates and fake ID possession is essentially inconsequential for the majority of covariates as many are immutable (age, race, gender) or outside of the person’s management (residence location, parental earnings, sexual orientation, etc.).

The longitudinal sample additionally utilized a self-report survey methodology. All incoming college students in 2002 were recruited to finish an instrument throughout the summer prior to college entrance utilizing paper and pencil and then were requested to finish on-line surveys each semester for the subsequent four years (a total of eight semesters). College students supplied knowledgeable consent and were compensated $25 in each wave. After excluding the n=35 who were of age, 88% of the eligible getting into class accomplished the survey (n=three,720). The sample was 53.7% feminine, 90.three% White/non-Hispanic, and averaged 17.9 (SD=.36) years of age (reflecting demographics which might be representative of the college as an entire [University Registrar, 2013]). College students were traditionally aged; by the start of their junior yr, only one-third of the sample had reached the minimal authorized drinking age, climbing expectedly to 99.7% by the ultimate semester of college, Sample retention was good, starting from sixty nine% to 87% of baseline respondents collaborating at each subsequent wave. Retention biases were low, though individuals were more likely to stay within the sample in the event that they were females (OR=2.33) and were less likely to stay within the sample in the event that they were frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the ultimate time-point, the sample dimension was n=2,250, though 90% of scholars participated in two or more evaluation waves and 82% participated in three or more waves. The longitudinal PSM presented inside the textual content utilized the first two years of college solely (i.e., the first four semesters, when the overwhelming majority of individuals were underage) and, in keeping with most PSM analysis, solely created matches between individuals in a manner which is instantly akin to the analysis performed with the cross-sectional sample.1


For the needs of replication, it was vital that the measures used in both the cross-sectional and longitudinal research stayed as related as possible. For ease of presentation, measures are organized when it comes to their conceptual importance to the general study with cross-sectional and longitudinal measures defined collectively in each section. Timing of the longitudinal measures was thought of vital and is described as is appropriate. Particularly, though the eight-wave longitudinal sample included a number of measurements of many covariates throughout time, the first longitudinal PSM solely utilized measurements as they would be anticipated to occur if observing a “fake ID effect” over a logical progression of time (i.e., Trait/propensity measures were measured at Wave 1 and used to foretell fake ID possession at Wave 2 which in flip assessed as a predictor for outcome measures at both Waves three and Wave 4). The second semester of college (Wave 2) was chosen as the singular target time-point at which fake ID possession (or the “fake ID effect”) was measured, as a result of it is regarded as a peak time of threat for destructive drinking-related outcomes and false ID possession (see Martinez et al., 2008).

Fundamental Outcomes Five outcomes related to substance use were explored. First, a measure of frequent binge drinking was created in both samples. A six-possibility ordinal merchandise requested respondents what number of days within the last month did they eat 5 or more alcoholic drinks. A sex-particular binge drinking measure was not available. These selecting either of the 2 highest frequency options (10–19 days and 20+ days) were classified as frequent binge drinkers whereas all others were not. This dichotomous merchandise represents binge drinking more than ten days within the last month. Second, we utilized an instrument created by Maney, Higham-Gardill, and Mahoney (2002) to represent alcohol-related problems within the cross-sectional sample. This ten-merchandise scale assesses the diploma to which the person feels that alcohol use has created relationship, family, well being, behavioral, and professional/faculty problems within the last yr and shows adequate reliability (α=.822). In the longitudinal sample, this scale was approximated from ten objects taken from the Younger Adult Alcohol Issues Screening Test (YAAPST; Hurlbut & Sher, 1992) with adequate reliability (α=.848 in second-yr fall and α=.846 in second-yr spring). A dichotomous alcohol-related arrest/quotation measure was created inside both samples utilizing objects that requested respondents if they had ever been arrested or cited for driving underneath the influence, underage drinking, public dysfunction (because of alcohol), being drunk in public, or an open container violation within the last year. The final two outcomes were both dichotomous and measured similarly in each sample; marijuana use and hard drug use represent whether or not the respondent self-reported any use of marijuana and cocaine, heroin, and/or methamphetamine, respectively, within the last year.

False Identification Present fake ID “possession” was assessed dichotomously in both samples (zero=No, 1=Sure). The cross-sectional study additionally included extra objects that requested respondents whether they had used the fake ID in a bar or club and whether they had used it in a store to buy alcohol.

Trait and threat issue (matching) covariates Fifteen variables were used in propensity rating matching within the cross-sectional sample and fourteen were used within the longitudinal sample. Variables were selected because of their inclusion in both datasets and previous analysis suggesting that they may be related to the propensity to own a fake ID and experience one of many 5 outcomes. These matching variables are: (1) age, (2) age of alcohol use onset, (three) employment standing, (4) exposure to substance use, (5) family earnings, (6) gender, (7) GPA, (eight) Greek membership, (9) well being, (10) low self-management, (11) peer substance use, (12) race, (thirteen) rural residence location (solely measured within the cross-sectional study), (14) sexual orientation (1=LGBT), and (15) subjective distress.

Eight of the fifteen variables were measured identically and a ninth was measured practically identically. Amongst those identically measured were age, age of first alcohol use, employment standing (zero= not employed; 1=employed), gender (zero=feminine; 1=male), self-reported grade point average (GPA), membership in a campus Greek organization (zero=non-member; 1=member), race (zero=white, 1=non-white), and sexual orientation (zero=heterosexual; 1=lesbian, homosexual, bisexual, or other). Self-reported well being was measured with an merchandise that requested respondents to fee their very own well being—the cross-sectional study offered responses starting from 1 (poor) to 4 (wonderful) whereas the longitudinal study options ranged from 1 (poor) to 5 (wonderful).

The cross sectional study utilized four-merchandise measures tailored from Lee, Akers, and Borg (2004) to represent exposure to substance use (α=.786) and peer substance use (α=.801). As the longitudinal knowledge did not embrace similarmeasures, each of those constructs was represented by a single merchandise quite than a four-merchandise scale. The primary (exposure) was measured dichotomously whereas the second (peer substance use) was measured on a six-possibility ordinal scale. Low self-management was operationalized utilizing the 24-merchandise Grasmick et al. (1993) scale (α=.889) within the cross-sectional study and the NEO Five Factor Inventory conscientiousness scale (reverse-coded) within the longitudinal sample (α=.844; Costa & McCrae, 1992). Subjective misery was measured utilizing Cohen and Williamson’s (1988) ten-merchandise perceived pupil stress scale (α=.814) within the cross-sectional study and the International Severity Index from the Brief Symptom Inventory-18 within the longitudinal sample (Derogatis, 2000). Greater values on these scales represent more exposure to substance use, a larger portion of peers that use substances, decrease self-management, and more subjective misery, respectively.

Both research included a single-merchandise family socioeconomic standing measure. In the cross-sectional study a measure of family earnings was used. Individuals chose between options starting from underneath $10,000 per yr (coded 1) to over $175,000 per yr (coded 9). An merchandise assessing whether or not or not college students were the first in their family to attend college (zero=No, 1=Sure) was utilized within the longitudinal study.

Rural residence location was used within the cross-sectional study, but no related measure was available within the longitudinal data. This variable was vital to include despite creating differing matching standards as a result of characteristics of the study area. The cross-sectional sample was drawn from an space that is very rural except one main city; thus, a dichotomous merchandise representing whether or not the scholar grew up in an urban / suburban space (coded zero) or a rural one (coded 1) was included. By comparability, this was not a special consideration for the longitudinal sample, which originated from a college of 35,000 that pulls college students from two large neighboring cities and its own moderately large population.


First, we estimated the proportions of fake ID possession in both the cross-sectional and longitudinal samples. In addition to possession of a fake ID, the cross-sectional sample additionally documented individuals’ utilizing of the fake ID in bars/clubs and stores. We estimated the bivariate associations of fake IDs with the 5 specified substance use outcomes in both samples—a rudimentary “fake ID effect.”

Next, to higher decide the strength of the “fake ID effect” after accounting for trait measures, propensity rating matching (PSM) was used for both samples. PSM affords a clearer picture of the relationship between two variables than bivariate analyses which can yield spurious outcomes (Guo & Fraser 2009) and has been used to assess issues related to substance use (Miller et al., 2011). Additionally of be aware, PSM is preferable to multivariate regression models in situations equivalent to this the place the variable of curiosity might not be independently related to the dependent variable, but is likely correlated with those which might be and also happens more proximally. The propensity matching techniques developed by Rosenbaum and Rubin (1983, 1985) can be utilized to create a sample with two teams which might be related in all relevant variables apart from the “therapy” (i.e., fake ID possession). While their techniques do lead to a reduction in dimension of analytic sample (typically main PSM to be referred to as resampling), they’re efficient at creating a state of affairs whereby the effect of “therapy” will be estimated as the common difference between those exposed to the therapy and “counterfactuals,” defined as the anticipated outcomes were it not for exposure to the therapy (Guo & Fraser 2009). On this case, the PSM methodology creates analytic teams whereby variations other than false identification use are minimalized.

As prompt by Rosenbaum and Rubin (1983, 1985), we utilized logistic regression to estimate a propensity rating for each participant in each analytic sample. Fake ID possession was regressed on 15 covariates within the cross-sectional sample (age, age of firsts alcohol use, employment standing, exposure to substance use, family earnings, gender, grade point average, membership in a campus Greek organization, self-assessed well being, low self-management, peer substance use, race, dimension of residence neighborhood, sexual orientation, and subjective misery) and 14 related variables (dimension of residence neighborhood excluded, as defined above) in the principle longitudinal PSM analysis (i.e. fake ID possession measured at the second semester and outcomes evaluated within the third and fourth semesters). Utilizing these models, each participant’s propensity rating was then calculated as their conditional chance of getting a fake ID. Following an evaluation of regions of frequent assist, we created comparability teams inside each sample utilizing a two-to-one nearest neighbor matching algorithm with a caliper calculated as .25σ of the propensity scores (see Guo & Fraser 2009). This caliper was .0725 within the cross-sectional sample and .0426 within the longitudinal sample. This matching method led to the anticipated lower in sample dimension (n=817 and n=518, respectively) but a adequate number of instances were retained for statistical comparisons.

Charges of fake ID possession

Charges of fake ID possession were fairly excessive, significantly within the cross-sectional sample. That is, of the 1,454 underage alcohol customers within the cross-sectional sample, 583 or 40.1% own or have owned a fake ID, 560 (38.5%) have used a fake ID at a bar, and 460 (27.eight%) have used the ID to buy alcohol at a store. Prevalence rates of false ID use within the Midwestern sample changed over time. Fake ID possession among college students underneath 21 peaked throughout the third yr of college (pre-college=12.5%, first-yr fall=17.1%, first-yr spring=21.4%, second-yr fall=28.1%, second-yr spring=32.2%, third-yr fall=34.9%, third-yr, fourth-yr fall=38.1%, fourth-yr spring= fewer than ten college students were below the minimal authorized drinking age).2

The “fake ID effect” previous to matching

Desk 1 presents imply scores for 5 substance use outcomes for fake ID house owners and non-house owners in both samples (outcomes at both Wave three and 4 are reported for the longitudinal sample). Common scores for each outcome (frequent binge drinking [10 or more days within the last month], self-reported alcohol related problems, alcohol-related arrests, marijuana use, and hard drug use) are presented for those who have and have not owned a fake ID, used a fake ID at a bar/club (cross-sectional sample solely), and used a fake ID at a store (cross-sectional sample solely). Unbiased samples t-checks were carried out to determine whether or not, on average, variations exist between fake ID customers and non-users. Each of the checks reached significance. Regardless of whether or not the main focus was possession of a fake ID or utilizing it at a specific sort of outlet, the results were consistent. On the bivariate stage, more individuals with false identification engage in frequent binge drinking, have been arrested/cited for an alcohol violation, engage in marijuana use, and use hard drugs. People with fake identification additionally, on average, report more alcohol-related problems. These outcomes would point out that fake IDs are a car of risk. However, it is potential that fake ID possession (and related risks) are more a perform of underlying risky traits.

Propensity rating matching (PSM)

Due to the consistency within the findings to this point no matter false identification measure (possession, bar use, and/or store use), the additional analyses with each sample makes use of only one false identification measure, possession of a false ID. We carried out PSM analyses in both samples, to look at whether or not individuals with and with out fake IDs proceed to differ on these outcomes after being matched on substantively vital traits. Desk 2 shows that individuals with and with out fake IDs indeed differed from one another on these trait propensity variables, suggesting that it is these variables which can in the end be driving the fake ID effect. Desk 2 additionally shows that the PSM method worked effectively in both samples, persistently lowering bias associated with the statistically significant variations between those with and with out fake IDs by more than 50% on all but one variable. Though three significant variations nonetheless remained within the cross-sectional sample (age of alcohol use onset, Greek affiliation, and having been raised in a rural space), the magnitude of the variations in age of onset and Greek affiliation were minor in comparison with pre-matching. On this, matching was equally, if no more, profitable within the longitudinal sample. It must be famous that matching did yield a reduction in sample size. Overall, nonetheless, in both samples matching seems to have created therapy and comparability teams which might be more equivalent and more acceptable for comparability than the unmatched data.

The propensity scores that were calculated for each case are graphically displayed in Figure 1. As will be seen within the determine, a region of frequent assist exists, but only a few with low propensity scores had a fake ID and only a few with excessive propensity scores did not.

Comparing fake ID house owners and non-house owners after PSM

Cross-sectional sample After matching, false identification house owners and non-house owners were compared on each of the 5 substance use related outcomes. While considerably more of those with fake IDs within the cross-sectional sample were frequent binge drinkers previous to matching (t=9.81, df=815), the teams were now not considerably different after matching (t=1.81, df=815) and the common therapy effect (ATE; e.g., variations in group means), as displayed in Desk three, was diminished by 59.2%. Equally, previous to matching, fake ID house owners had considerably larger scores on the alcohol related problems scale than non-house owners (t=9.83, df=815), however the teams were now not considerably different after matching (t=1.31, df=815) and the ATE was diminished by 63.4%. However, when it comes to alcohol-related arrests, the 2 teams were nonetheless considerably different and the ATE successfully remained unchanged. As was the case for the first two outcomes, fake ID possession was associated with marijuana use previous to matching (t=9.36), but not after (t=1.fifty two; ATE diminished by 60.4%). Finally, hard drug use was associated with fake ID possession both earlier than (t=7.26, df=815) and after matching (t=2.29, df=815), however the ATE was diminished by 38.4%.

Longitudinal sample As was the case within the cross-sectional sample, propensity rating matching led to a substantial lower within the ATE for four of the 5 outcomes (Desk three, columns three–6). However, unlike the cross-sectional sample, ATEs remained significant for alcohol related problems (t=4.00 wave three; t=4.17, wave 4, df=516) and marijuana use (t=4.thirteen, wave three; t=2.58, wave 4, df=516) after propensity rating matching. The ATE additionally remained significant for frequent binge drinking (t=3.26, df=516, wave three) and hard drug use (t=2.06, df=516, wave 4) at one wave but not the other. Once more, these effects sizes were substantially diminished, but within the aforementioned instances, not eliminated. As was the case within the cross-sectional sample, propensity rating matching had little influence on the ATE on alcohol-related arrests.three


This study’s initial outcomes are in keeping with earlier analysis—a substantial number of underage college students have fake IDs and are at larger threat for binge drinking, alcohol-related problems, alcohol related arrests, and other substance use (see Arria et al., 2014; Martinez & Sher, 2010; Nguyen et al., 2011). But our work additionally showed that for some outcomes, it appears that evidently what initially might have seemed to be a “fake ID effect” is essentially the result of elements that influenced both the acquisition of the false ID and the outcome. The numerous relationship between fake ID use and other substance use outcomes typically remained after PSM, however the magnitude of those relationships were substantially diminished, most by over 40%. Alcohol-related arrests were an exception as the relationship was unaffected by PSM (i.e., after matching, those with a fake ID were nonetheless at similarly excessive ranges of threat for alcohol-related arrests [DUIs, open container, etc.]). The rationale this outcome is distinct from the others isn’t readily clear; perhaps legislation enforcement officers usually tend to challenge citations or arrests for other substance-related offenses when an individual can be found with a fake ID. If this is the case, the “effect” would not seem smaller in propensity rating models as the difference could be driven by officers’ reactions to the fake ID quite than individuals’ underlying propensity.

The pattern that emerges from Desk three seems to point that non-matched samples might have overestimated the effect of false identification use on destructive outcomes, but that fake ID possession has an effect that extends past shared causal factors. This particular remaining “fake ID effect” might indeed assist the idea that the fake ID itself serves as a sort of threshold into other forms of deviant habits, the place those who are prepared to accumulate fake IDs change into increasingly prepared to violate other legal guidelines (see Ruedy et al., 2013; Winograd et al., 2014). However in gentle of the opposite findings, it is more likely that fake IDs more typically moderate the effects of risky traits on behavior. For instance, fake IDs might have the very best efficiency of effect via offering impulsive individuals with extra means and opportunity for problematic behaviors that they would not otherwise have engaged in. Certainly, underlying trait risks are often incorporated into opportunity-concept-related examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).

As such, these findings have sensible implications. Though increased server coaching, fake ID manufacturing/provider legal guidelines, and liability legal guidelines are an vital technique of addressing the risks of fake IDs as a form of alcohol access (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), pretend-ID related outcomes might also partly be a perform of trait risks that can moreover be addressed with intervention. One solution to begin addressing this mix of factors may be via motivational, normative suggestions-based mostly, or expertise interventions which might be specifically geared toward reducing the chance that at-threat college students obtain a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Moreover, a fake ID obtainment-aimed intervention would possibly probably be broadly incorporated into interventions which might be particularly tailor-made toward addressing both individuals’ risky traits and their resulting behaviors (see Conrod et al., 2006).

Although a fantastic strength of this study rests in the same findings found with two distinctive college populations, these findings might not be generalizable to non-college attending populations. Moreover, fake ID policies, enforcement, and worry of sanctions might vary substantially in several localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For instance, some drinking institutions may be lenient in their carding policies, deliberately accept false identification, and/or not be subject to rigorous regulatory enforcement (Murray, 2005). Additionally, penalties for possessing and/or utilizing a fake ID to buy alcohol varies substantially from state to state including the kind of offense, amount of tremendous, suspension of driver’s license, and the possibility of probation or jail time. Future analysis ought to evaluate the impact of fake ID relative to differential policies and enforcement of the minimal authorized drinking age, including neighborhood efforts (Grube, 1997). Further, whereas fifteen distinct traits were included within the matching course of, there stays the possibility that extra elements not measured in our knowledge would have an effect on both the willingness to access a fake ID and the outcome measures. If this is the case, the “fake ID effect” may be even smaller than our matching models suggested.

In concluding that the “fake ID effect” is mainly a perform of phenotypic threat, fake ID possession might serve as an indicator of heightened threat for more extreme drinking related problems. Although most penalties for fake ID possession are punitive (fines, probation/jail, and/or loss of driver’s license), policy-makers, college officials, and practitioners ought to target fake ID house owners for intervention strategies geared toward lowering excessive-threat drinking behaviors (and other problematic behaviors linked to phenotypic threat). Although increased penalties and enforcement of the minimal authorized drinking age has the potential to scale back fake ID possession, we caution policy-makers to evaluate and think about the destructive penalties of shifting college drinking away from regulated institutions the place security and emergency companies are more readily available (see Baldwin et al., 2012; 2014). Although our findings found that fake ID possession (no matter particular person threat characteristics) increased the danger for alcohol related arrests, drug use, and alcohol related problems (Midwest sample solely), we did not assess victimization and other harms associated with extreme alcohol consumption that could increase in spaces not subject to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future analysis is required to evaluate the impact that fake ID enforcement might have on problematic drinking both in regulated and unregulated spaces.