Underage college college students who get hold of and use false identification (fake ID) are in danger for adverse outcomes. Nevertheless, it’s presently unclear how uniquely the fake ID itself serves as a vehicle to subsequent hurt (i.e., the “fake ID impact”) over and above common and trait-associated threat elements (e.g., deviant friends, low self-control).


In an effort to investigate whether the “fake ID impact” 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. Individuals with a fake ID had been matched with individuals with out a fake ID, in terms of a variety of trait-primarily based and social threat factors. These matched teams had been then in contrast on 5 problematic outcomes (i.e., frequent binge ingesting, alcohol-associated problems, arrests, marijuana use, and arduous drug use).



Findings confirmed that “fake ID effects” had been considerably—although not fully—diminished following PSM. The “fake ID impact” remained strongest for alcohol-associated arrests. This may relate to problems with enforcement and college students’ willingness to engage in deviant habits with a fake ID, or it may be a operate of combined processes.


General, the findings recommend that interventions mustn’t solely be aimed toward lowering fake ID-associated alcohol entry specifically, however must also be aimed more usually in the direction of at-threat youths’ entry to alcohol. Future analysis might look at whether fake IDs have their strongest potency as moderators of the results of dangerous traits—corresponding to impulsiveness—on ingesting outcomes.

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


Fake IDs, a novel mode of alcohol entry, are more and more wanted as individuals near the minimal legal ingesting age (Martinez et al., 2007; Wagenaar et al., 1996). These types of false identification could also be borrowed (or duplicated) from an older peer or sibling (Myers et al., 2001), or they might be a specially crafted document obtained regionally or from an internet vendor (Murray, 2005). No matter their supply, there appears to be a bidirectional relation between heavy ingesting and fake IDs, such that (1) heavy ingesting predicts subsequent obtainment of a fake ID, and (2) “ownership” (i.e., possession) of a fake ID predicts subsequent frequency of heavy ingesting (outlined as 5+ drinks per occasion; Martinez, et al., 2007).

This bidirectional relation not solely illustrates the general public well being dangers of this mode of alcohol entry, however begs the query of whether it’s more the case that a fake ID itself serves as a vehicle to subsequent hurt (i.e., the “fake ID impact”) or whether such harms and outcomes are predominantly pushed by a common degree of phenotypic threat on the part of the fake ID “owner” (e.g., deviant peer associations, low self-control). Although common alcohol entry theories might help the previous hypothesis nearly fully (specifically, fake ID possession increases alcohol entry and subsequent hurt; see Gruenewald, 2011), common criminological theories of phenotypic threat help the latter (specifically, that broad categories of threat—or propensities to engage in dangerous habits—are the true reason behind hurt; see Pratt & Cullen, 2000). Definitely, such propensities may be what predicts fake ID obtainment in the first place, and although the strength of the fake ID impact appears to increase over time, it’s tremendously diminished after controlling for sex, Greek status, and pre-college charges of ingesting (Martinez et al., 2007). In sum, it’s unclear how robust the fake ID impact may be after accounting for individuals’ ranges of phenotypic or propensity threat—although this query has bearing on prevention and policy initiatives, which may focus on both strengthening enforcement of fake ID laws themselves, increasing sources for trait-primarily based at-threat youth programs, or a community-pushed combination of each (see Fell, Thomas, Scherer, Fisher & Romano, 2015; Fell, Scherer, Thomas & Voas, 2016; Fell, Scherer & Voas, 2015; Grube, 1997) .

Thus, with a view to investigate the strength of the fake ID impact, we matched college students with and without fake IDs on a variety of threat-primarily based covariates using propensity rating matching (PSM) techniques. We first in contrast matched teams’ ingesting- and drug-use-associated outcomes in a cross-sectional sample of n=1,454 college college students at a large Southeastern university. We additionally in contrast matched teams in a further longitudinal replication sample of n=three,720 undergraduates at a large Midwestern university. We hypothesized that the results of fake ID ownership on outcomes could be tremendously diminished by—and therefore largely attributable to—the pre-existing trait-primarily based elements on which fake ID homeowners and non-homeowners could possibly be matched. These comparisons can inform the extent to which the connection between adverse outcomes and false identification ownership are attributed to selection elements, which again, may have practical utility for intervention and policy.

Procedure and Contributors

Two samples had been individually investigated following Institutional Evaluate Board (IRB) approval: (1) A cross-sectional sample of n=1,454 underage college college students from a large Southeastern College (IRB Protocol H12032) and (2) a prospective replication sample of n=three,720 undergraduates under the minimal legal ingesting age from a large Midwestern college (IRB Protocol 01-01-001). Of be aware, each samples provide unique insights into the connection between false identification use and adverse outcomes. More specifically, the cross-sectional study includes items that distinguish between the use of fake IDs in numerous situations (at bars, at grocery shops, etc.) and the longitudinal study gives insight into the potential effects of fake ID ownership over time and establishes temporal order.

With regard to the cross-sectional sample, throughout the academic 12 months 2011–2012, individuals had been recruited from forty randomly selected massive (>ninety nine college students) and average enrollment (30–ninety nine college students) classes. Contributors completed a one-page knowledgeable consent document in the selected classes earlier than being given a six-page paper survey about college life and behaviors to complete with pencil or pen. Contributors were not compensated. All enrolled college students had been invited to participate and the response price was excessive at 80.four% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After those above the legal ingesting threshold had been removed, 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 mean age of 18.ninety five (SD=.795). Although this sample is cross-sectional, establishing temporal ordering of the covariates and fake ID ownership is basically inconsequential for the majority of covariates as many are immutable (age, race, gender) or exterior of the individual’s control (home location, parental earnings, sexual orientation, etc.).

The longitudinal sample additionally utilized a self-report survey methodology. All incoming college students in 2002 had been recruited to complete an instrument throughout the summer time prior to university entrance using paper and pencil and then had been asked to complete online surveys each semester for the following 4 years (a total of eight semesters). College students supplied knowledgeable consent and had been compensated $25 in each wave. After excluding the n=35 who had been of age, 88% of the eligible coming into class completed 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 that are representative of the college as an entire [University Registrar, 2013]). College students had been traditionally aged; by the beginning of their junior 12 months, only one-third of the sample had reached the minimal legal ingesting age, climbing expectedly to 99.7% by the final semester of faculty, Pattern retention was good, ranging from 69% to 87% of baseline respondents collaborating at each subsequent wave. Retention biases had been low, although individuals had been more prone to remain in the sample in the event that they had been females (OR=2.33) and had been less prone to remain in the sample in the event that they had been frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the final time-level, the sample dimension was n=2,250, although ninety% of students participated in two or more assessment waves and eighty two% participated in three or more waves. The longitudinal PSM offered within the text utilized the first two years of faculty solely (i.e., the first 4 semesters, when the overwhelming majority of individuals had been underage) and, in line with most PSM analysis, solely created matches between individuals in a manner which is straight similar to the analysis carried out with the cross-sectional sample.1


For the purposes of replication, it was important that the measures used in each the cross-sectional and longitudinal studies stayed as related as possible. For ease of presentation, measures are organized in terms of their conceptual importance to the general study with cross-sectional and longitudinal measures explained together in each section. Timing of the longitudinal measures was thought-about important and is described as is appropriate. Particularly, although the eight-wave longitudinal sample included a number of measurements of many covariates across time, the first longitudinal PSM solely utilized measurements as they’d be expected to occur if observing a “fake ID impact” over a logical development of time (i.e., Trait/propensity measures had been measured at Wave 1 and used to foretell fake ID ownership at Wave 2 which in flip assessed as a predictor for end result measures at each Waves three and Wave four). The second semester of faculty (Wave 2) was chosen because the singular goal time-level at which fake ID ownership (or the “fake ID impact”) was measured, as a result of it’s considered a peak time of threat for adverse ingesting-associated outcomes and false ID ownership (see Martinez et al., 2008).

Important Outcomes 5 outcomes associated to substance use had been explored. First, a measure of frequent binge ingesting was created in each samples. A six-possibility ordinal item asked respondents how many days in the final month did they consume 5 or more alcoholic drinks. A sex-particular binge ingesting measure was not available. Those choosing both of the 2 highest frequency choices (10–19 days and 20+ days) had been labeled as frequent binge drinkers while all others had been not. This dichotomous item represents binge ingesting more than ten days in the final month. Second, we utilized an instrument created by Maney, Higham-Gardill, and Mahoney (2002) to represent alcohol-associated problems in the cross-sectional sample. This ten-item scale assesses the diploma to which the individual feels that alcohol use has created relationship, family, well being, behavioral, and professional/school problems in the final 12 months and shows sufficient reliability (α=.822). In the longitudinal sample, this scale was approximated from ten items taken from the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992) with sufficient reliability (α=.848 in second-12 months fall and α=.846 in second-12 months spring). A dichotomous alcohol-associated arrest/citation measure was created within each samples using items that asked respondents if they had ever been arrested or cited for driving under the influence, underage ingesting, public dysfunction (because of alcohol), being drunk in public, or an open container violation in the final year. The final two outcomes had been each dichotomous and measured equally in each sample; marijuana use and arduous drug use represent whether the respondent self-reported any use of marijuana and cocaine, heroin, and/or methamphetamine, respectively, in the final year.

False Identification Current fake ID “ownership” was assessed dichotomously in each samples (zero=No, 1=Sure). The cross-sectional study additionally included additional items that asked respondents whether or not they had used the fake ID in a bar or club and whether or not they had used it in a retailer to buy alcohol.

Trait and threat factor (matching) covariates Fifteen variables had been used in propensity rating matching in the cross-sectional sample and fourteen had been used in the longitudinal sample. Variables had been selected because of their inclusion in each datasets and former analysis suggesting that they might be associated 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 status, (four) exposure to substance use, (5) family earnings, (6) gender, (7) GPA, (eight) Greek membership, (9) well being, (10) low self-control, (eleven) peer substance use, (12) race, (13) rural home location (solely measured in the cross-sectional study), (14) sexual orientation (1=LGBT), and (15) subjective distress.

Eight of the fifteen variables had been measured identically and a ninth was measured nearly identically. Amongst those identically measured had been age, age of first alcohol use, employment status (zero= not employed; 1=employed), gender (zero=feminine; 1=male), self-reported grade level average (GPA), membership in a campus Greek group (zero=non-member; 1=member), race (zero=white, 1=non-white), and sexual orientation (zero=heterosexual; 1=lesbian, gay, bisexual, or other). Self-reported well being was measured with an item that asked respondents to price their own well being—the cross-sectional study supplied responses ranging from 1 (poor) to four (excellent) whereas the longitudinal study choices ranged from 1 (poor) to five (excellent).

The cross sectional study utilized 4-item measures adapted from Lee, Akers, and Borg (2004) to represent exposure to substance use (α=.786) and peer substance use (α=.801). Because the longitudinal information did not embody similarmeasures, each of these constructs was represented by a single item reasonably than a 4-item scale. The first (exposure) was measured dichotomously while the second (peer substance use) was measured on a six-possibility ordinal scale. Low self-control was operationalized using the 24-item Grasmick et al. (1993) scale (α=.889) in the cross-sectional study and the NEO 5 Factor Stock conscientiousness scale (reverse-coded) in the longitudinal sample (α=.844; Costa & McCrae, 1992). Subjective misery was measured using Cohen and Williamson’s (1988) ten-item perceived scholar stress scale (α=.814) in the cross-sectional study and the World Severity Index from the Brief Symptom Stock-18 in the longitudinal sample (Derogatis, 2000). Greater values on these scales represent more exposure to substance use, a bigger portion of friends that use substances, lower self-control, and more subjective misery, respectively.

Each studies included a single-item family socioeconomic status measure. In the cross-sectional study a measure of family earnings was used. Contributors chose between choices ranging from under $10,000 per 12 months (coded 1) to over $one hundred seventy five,000 per 12 months (coded 9). An item assessing whether or not college students had been the first of their family to attend college (zero=No, 1=Sure) was utilized in the longitudinal study.

Rural home location was used in the cross-sectional study, however no related measure was out there in the longitudinal data. This variable was important to include despite creating differing matching standards due to the characteristics of the study area. The cross-sectional sample was drawn from an space that is very rural with the exception of one main metropolis; thus, a dichotomous item representing whether the student grew up in an urban / suburban space (coded zero) or a rural one (coded 1) was included. By comparison, this was not a special consideration for the longitudinal sample, which originated from a college of 35,000 that pulls college students from two massive neighboring cities and its own reasonably massive population.


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

Subsequent, to better determine the strength of the “fake ID impact” after accounting for trait measures, propensity rating matching (PSM) was used for each samples. PSM gives a clearer image of the connection between two variables than bivariate analyses which may yield spurious outcomes (Guo & Fraser 2009) and has been used to assess points associated to substance use (Miller et al., 2011). Additionally of be aware, PSM is preferable to multivariate regression models in instances corresponding to this where the variable of interest may not be independently related to the dependent variable, however is likely correlated with those that are and in addition happens more proximally. The propensity matching techniques developed by Rosenbaum and Rubin (1983, 1985) can be utilized to create a sample with two teams that are related in all relevant variables except for the “therapy” (i.e., fake ID possession). Whereas their techniques do lead to a discount in dimension of analytic sample (usually main PSM to be known as resampling), they are efficient at making a scenario whereby the impact of “therapy” could be estimated as the average distinction between those exposed to the therapy and “counterfactuals,” outlined because the anticipated outcomes had been it not for exposure to the therapy (Guo & Fraser 2009). On this case, the PSM technique creates analytic teams whereby differences other than false identification use are minimalized.

As steered 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 in the cross-sectional sample (age, age of firsts alcohol use, employment status, exposure to substance use, family earnings, gender, grade level average, membership in a campus Greek group, self-assessed well being, low self-control, peer substance use, race, dimension of home community, sexual orientation, and subjective misery) and 14 related variables (dimension of home community excluded, as explained above) in the primary longitudinal PSM analysis (i.e. fake ID possession measured on the second semester and outcomes evaluated in the third and fourth semesters). Utilizing these models, each participant’s propensity rating was then calculated as their conditional probability of getting a fake ID. Following an assessment of regions of widespread help, we created comparison teams within each sample using 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 in the cross-sectional sample and .0426 in the longitudinal sample. This matching approach led to the expected lower in sample dimension (n=817 and n=518, respectively) however a ample number of instances had been retained for statistical comparisons.

Rates of fake ID ownership

Rates of fake ID ownership had been quite excessive, significantly in the cross-sectional sample. That’s, of the 1,454 underage alcohol customers in 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 charges of false ID use in the Midwestern sample modified over time. Fake ID ownership amongst college students under 21 peaked throughout the third 12 months of faculty (pre-college=12.5%, first-12 months fall=17.1%, first-12 months spring=21.four%, second-12 months fall=28.1%, second-12 months spring=32.2%, third-12 months fall=34.9%, third-12 months, fourth-12 months fall=38.1%, fourth-12 months spring= fewer than ten college students had been under the minimal legal ingesting age).2

The “fake ID impact” prior to matching

Desk 1 presents mean scores for 5 substance use outcomes for fake ID homeowners and non-homeowners in each samples (outcomes at each Wave three and four are reported for the longitudinal sample). Average scores for each end result (frequent binge ingesting [10 or more days in the final month], self-reported alcohol associated problems, alcohol-associated arrests, marijuana use, and arduous drug use) are offered 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 retailer (cross-sectional sample solely). Independent samples t-assessments had been conducted to find out whether, on average, differences exist between fake ID users and non-users. Every of the assessments reached significance. No matter whether the main target was ownership of a fake ID or using it at a particular type of outlet, the results had been consistent. At the bivariate degree, more individuals with false identification interact in frequent binge ingesting, have been arrested/cited for an alcohol violation, interact in marijuana use, and use arduous drugs. Individuals with fake identification additionally, on average, report more alcohol-associated problems. These outcomes would point out that fake IDs are a vehicle of risk. Nevertheless, it’s possible that fake ID ownership (and related dangers) are more a operate of underlying dangerous traits.

Propensity rating matching (PSM)

As a result of consistency in the findings so far regardless of false identification measure (ownership, bar use, and/or retailer use), the extra analyses with each sample utilizes only one false identification measure, possession of a false ID. We carried out PSM analyses in each samples, to examine whether individuals with and without fake IDs proceed to differ on these outcomes after being matched on substantively important traits. Desk 2 shows that individuals with and without fake IDs indeed differed from one another on these trait propensity variables, suggesting that it’s these variables which may ultimately be driving the fake ID effect. Desk 2 additionally shows that the PSM approach worked well in each samples, consistently lowering bias associated with the statistically important differences between those with and without fake IDs by more than 50% on all however one variable. Though three important differences still remained in the cross-sectional sample (age of alcohol use onset, Greek affiliation, and having been raised in a rural space), the magnitude of the differences in age of onset and Greek affiliation had been minor compared to pre-matching. On this, matching was equally, if not more, profitable in the longitudinal sample. It must be famous that matching did yield a discount in sample size. General, nonetheless, in each samples matching appears to have created therapy and comparison teams that are more equal and more appropriate for comparison than the unmatched data.

The propensity scores that had been calculated for each case are graphically displayed in Figure 1. As could be seen in the figure, a region of widespread help exists, however only a few with low propensity scores had a fake ID and only a few with excessive propensity scores did not.

Comparing fake ID homeowners and non-homeowners after PSM

Cross-sectional sample After matching, false identification homeowners and non-homeowners had been in contrast on each of the 5 substance use associated outcomes. Whereas significantly more of those with fake IDs in the cross-sectional sample had been frequent binge drinkers prior to matching (t=9.eighty one, df=815), the teams had been no longer significantly completely different after matching (t=1.eighty one, df=815) and the average therapy impact (ATE; e.g., differences in group means), as displayed in Desk three, was decreased by 59.2%. Similarly, prior to matching, fake ID homeowners had significantly increased scores on the alcohol associated problems scale than non-homeowners (t=9.eighty three, df=815), however the teams had been no longer significantly completely different after matching (t=1.31, df=815) and the ATE was decreased by 63.four%. Nevertheless, in terms of alcohol-associated arrests, the 2 teams had been still significantly completely different and the ATE effectively remained unchanged. As was the case for the first two outcomes, fake ID possession was associated with marijuana use prior to matching (t=9.36), however not after (t=1.52; ATE decreased by 60.four%). Lastly, arduous drug use was associated with fake ID possession each earlier than (t=7.26, df=815) and after matching (t=2.29, df=815), however the ATE was decreased by 38.four%.

Longitudinal sample As was the case in the cross-sectional sample, propensity rating matching led to a substantial lower in the ATE for 4 of the 5 outcomes (Desk three, columns three–6). Nevertheless, unlike the cross-sectional sample, ATEs remained important for alcohol associated problems (t=4.00 wave three; t=4.17, wave four, df=516) and marijuana use (t=4.13, wave three; t=2.fifty eight, wave four, df=516) after propensity rating matching. The ATE additionally remained important for frequent binge ingesting (t=3.26, df=516, wave three) and arduous drug use (t=2.06, df=516, wave four) at one wave however not the other. Once more, these effects sizes had been considerably decreased, however in the aforementioned instances, not eliminated. As was the case in the cross-sectional sample, propensity rating matching had little influence on the ATE on alcohol-associated arrests.three


This study’s initial outcomes are in line with previous analysis—a substantial number of underage college students have fake IDs and are at increased threat for binge ingesting, alcohol-associated problems, alcohol associated arrests, and other substance use (see Arria et al., 2014; Martinez & Sher, 2010; Nguyen et al., 2011). Yet our work additionally confirmed that for some outcomes, plainly what initially may need appeared to be a “fake ID impact” is basically the result of elements that influenced each the acquisition of the false ID and the outcome. The numerous relationship between fake ID use and other substance use outcomes usually remained after PSM, however the magnitude of these relationships had been considerably diminished, most by over 40%. Alcohol-associated arrests had been an exception as the connection was unaffected by PSM (i.e., after matching, those with a fake ID had been still at equally excessive ranges of threat for alcohol-associated arrests [DUIs, open container, etc.]). The rationale this end result is distinct from the others shouldn’t be readily clear; perhaps legislation enforcement officers are more likely to problem citations or arrests for other substance-associated offenses when an individual can also be found with a fake ID. If that is so, the “impact” would not seem smaller in propensity rating models because the distinction could be pushed by officers’ reactions to the fake ID reasonably than individuals’ underlying propensity.

The pattern that emerges from Desk three appears to indicate that non-matched samples may have overestimated the impact of false identification use on adverse outcomes, however that fake ID ownership has an impact that extends past shared causal factors. This particular remaining “fake ID impact” could indeed help the idea that the fake ID itself serves as a type of threshold into other types of deviant habits, where those that are willing to amass fake IDs become more and more willing to violate other laws (see Ruedy et al., 2013; Winograd et al., 2014). However in gentle of the opposite findings, it’s more seemingly that fake IDs more usually average the results of dangerous traits on behavior. For example, fake IDs may have the very best potency of impact by way of providing impulsive individuals with additional means and alternative for problematic behaviors that they’d not otherwise have engaged in. Certainly, underlying trait dangers are often included into alternative-theory-associated examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).

As such, these findings have practical implications. Though elevated server coaching, fake ID production/provider laws, and legal responsibility laws are an important means of addressing the dangers of fake IDs as a form of alcohol entry (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), pretend-ID associated outcomes may partly be a operate of trait dangers that may moreover be addressed with intervention. One technique to start addressing this mix of factors could also be by way of motivational, normative feedback-primarily based, or expertise interventions that are specifically aimed toward reducing the probability that at-threat college students get hold of a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Furthermore, a fake ID obtainment-aimed intervention might probably be broadly included into interventions that are particularly tailored towards addressing each individuals’ dangerous traits and their resulting behaviors (see Conrod et al., 2006).

Although an excellent strength of this study rests in the similar findings found with two unique college populations, these findings may not be generalizable to non-college attending populations. Moreover, fake ID policies, enforcement, and worry of sanctions may fluctuate considerably in numerous localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For example, some ingesting institutions could also be lenient of their carding policies, intentionally settle for false identification, and/or not be topic to rigorous regulatory enforcement (Murray, 2005). Additionally, penalties for possessing and/or using a fake ID to buy alcohol varies considerably from state to state together with the type of offense, quantity of positive, suspension of driver’s license, and the potential of probation or jail time. Future analysis ought to consider the affect of fake ID relative to differential policies and enforcement of the minimal legal ingesting age, together with community efforts (Grube, 1997). Additional, while fifteen distinct traits had been included in the matching process, there remains the possibility that additional elements not measured in our information would have an effect on each the willingness to entry a fake ID and the result measures. If that is so, the “fake ID impact” could also be even smaller than our matching models suggested.

In concluding that the “fake ID impact” is especially a operate of phenotypic threat, fake ID ownership may function an indicator of heightened threat for more extreme ingesting associated problems. Although most penalties for fake ID ownership are punitive (fines, probation/jail, and/or lack of driver’s license), policy-makers, college officers, and practitioners ought to goal fake ID homeowners for intervention strategies aimed toward lowering excessive-threat ingesting behaviors (and other problematic behaviors linked to phenotypic threat). Although elevated penalties and enforcement of the minimal legal ingesting age has the potential to scale back fake ID ownership, we warning policy-makers to judge and consider the adverse penalties of shifting college ingesting away from regulated institutions where security and emergency services are more readily available (see Baldwin et al., 2012; 2014). Although our findings found that fake ID possession (regardless of particular person threat characteristics) elevated the danger for alcohol associated arrests, drug use, and alcohol associated problems (Midwest sample solely), we did not assess victimization and other harms associated with extreme alcohol consumption that could increase in spaces not topic to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future analysis is needed to judge the affect that fake ID enforcement may have on problematic ingesting each in regulated and unregulated spaces.