Underage college college students who acquire and use false identification (fake ID) are in danger for negative outcomes. However, it’s at present unclear how uniquely the fake ID itself serves as a car to subsequent hurt (i.e., the “fake ID effect”) over and above normal and trait-related risk elements (e.g., deviant friends, low self-management).


In order to examine whether the “fake ID effect” would hold after accounting for phenotypic risk, we utilized propensity score matching (PSM) in a cross-sectional pattern of n=1,454 college students, and a longitudinal replication pattern of n=three,720 undergraduates. Individuals with a fake ID have been matched with people with out a fake ID, by way of quite a few trait-primarily based and social risk factors. These matched groups have been then in contrast on five problematic outcomes (i.e., frequent binge consuming, alcohol-related problems, arrests, marijuana use, and onerous drug use).



Findings confirmed that “fake ID results” have been considerably—though not fully—diminished following PSM. The “fake ID effect” remained strongest for alcohol-related arrests. This will likely relate to problems with enforcement and college students’ willingness to engage in deviant habits with a fake ID, or it may be a perform of combined processes.


Overall, the findings counsel that interventions shouldn’t solely be aimed toward lowering fake ID-related alcohol entry specifically, but should also be aimed more generally in direction of at-risk youths’ entry to alcohol. Future analysis might examine whether fake IDs have their strongest efficiency as moderators of the results of risky traits—equivalent to impulsiveness—on consuming outcomes.

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


Fake IDs, a unique mode of alcohol entry, are more and more wanted as people close to the minimum legal consuming age (Martinez et al., 2007; Wagenaar et al., 1996). These types 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 internet vendor (Murray, 2005). No matter their supply, there seems to be a bidirectional relation between heavy consuming and fake IDs, such that (1) heavy consuming predicts subsequent obtainment of a fake ID, and (2) “possession” (i.e., possession) of a fake ID predicts subsequent frequency of heavy consuming (outlined as 5+ drinks per event; Martinez, et al., 2007).

This bidirectional relation not solely illustrates the general public health dangers of this mode of alcohol entry, but begs the question of whether it’s more the case that a fake ID itself serves as a car to subsequent hurt (i.e., the “fake ID effect”) or whether such harms and outcomes are predominantly pushed by a normal level of phenotypic risk on the part of the fake ID “proprietor” (e.g., deviant peer associations, low self-management). Although normal alcohol entry theories might assist the former hypothesis almost completely (namely, fake ID possession increases alcohol entry and subsequent hurt; see Gruenewald, 2011), normal criminological theories of phenotypic risk assist the latter (namely, that broad classes of risk—or propensities to engage in risky habits—are the true reason behind hurt; see Pratt & Cullen, 2000). Definitely, such propensities could be what predicts fake ID obtainment in the first place, and though the power of the fake ID effect seems to increase over time, it’s enormously diminished after controlling for sex, Greek status, and pre-college charges of consuming (Martinez et al., 2007). In sum, it’s unclear how robust the fake ID effect could be after accounting for people’ ranges of phenotypic or propensity risk—though this question has bearing on prevention and coverage initiatives, which may concentrate on either strengthening enforcement of fake ID laws themselves, growing assets for trait-primarily based at-risk youth programs, or a group-pushed combination of each (see Fell, Thomas, Scherer, Fisher & Romano, 2015; Fell, Scherer, Thomas & Voas, 2016; Fell, Scherer & Voas, 2015; Grube, 1997) .

Thus, as a way to examine the power of the fake ID effect, we matched college students with and without fake IDs on quite a few risk-primarily based covariates utilizing propensity score matching (PSM) techniques. We first in contrast matched groups’ consuming- and drug-use-related outcomes in a cross-sectional pattern of n=1,454 college college students at a big Southeastern university. We additionally in contrast matched groups in an extra longitudinal replication pattern of n=three,720 undergraduates at a big Midwestern university. We hypothesized that the results of fake ID possession on outcomes could be enormously diminished by—and subsequently largely attributable to—the pre-existing trait-primarily based elements on which fake ID owners and non-owners could be matched. These comparisons can inform the extent to which the connection between negative outcomes and false identification possession are attributed to choice elements, which once more, may have sensible application for intervention and policy.

Procedure and Participants

Two samples have been individually investigated following Institutional Assessment Board (IRB) approval: (1) A cross-sectional pattern of n=1,454 underage college college students from a big Southeastern University (IRB Protocol H12032) and (2) a prospective replication pattern of n=three,720 undergraduates beneath the minimum legal consuming age from a big Midwestern college (IRB Protocol 01-01-001). Of note, each samples provide unique insights into the connection between false identification use and negative outcomes. More specifically, the cross-sectional examine consists of objects that distinguish between using fake IDs in numerous conditions (at bars, at grocery stores, etc.) and the longitudinal examine presents insight into the potential results of fake ID possession over time and establishes temporal order.

With regard to the cross-sectional pattern, throughout the tutorial 12 months 2011–2012, individuals have been recruited from forty randomly selected giant (>ninety nine college students) and reasonable enrollment (30–ninety nine college students) classes. Participants accomplished 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 finish with pencil or pen. Participants were not compensated. All enrolled college students have been invited to participate and the response rate was high at 80.four% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After those above the legal consuming threshold have been eliminated, the analytic pattern was n=1,454 underage individuals. The pattern was largely consultant of the college with regard to demographics and was specifically 51.6% female, 68.9% White/non-Hispanic, with an average age of 18.ninety five (SD=.795). Although this pattern is cross-sectional, establishing temporal ordering of the covariates and fake ID possession is basically inconsequential for almost all of covariates as many are immutable (age, race, gender) or outside of the person’s management (house location, parental income, sexual orientation, etc.).

The longitudinal pattern additionally utilized a self-report survey methodology. All incoming college students in 2002 have been recruited to finish an instrument throughout the summer season prior to university entrance utilizing paper and pencil after which have been requested to finish online surveys every semester for the following 4 years (a complete of eight semesters). Students offered knowledgeable consent and have been compensated $25 in every wave. After excluding the n=35 who have been of age, 88% of the eligible getting into class accomplished the survey (n=three,720). The pattern was 53.7% female, 90.three% White/non-Hispanic, and averaged 17.9 (SD=.36) years of age (reflecting demographics which are consultant of the college as a complete [University Registrar, 2013]). Students have been historically aged; by the beginning of their junior 12 months, just one-third of the pattern had reached the minimum legal consuming age, climbing expectedly to 99.7% by the final semester of faculty, Sample retention was good, starting from 69% to 87% of baseline respondents collaborating at every subsequent wave. Retention biases have been low, though people have been more more likely to remain in the pattern if they have been females (OR=2.33) and have been much less more likely to remain in the pattern if they have been frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the final time-level, the pattern size 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 within the text utilized the first two years of faculty solely (i.e., the first 4 semesters, when the overwhelming majority of individuals have been underage) and, in keeping with most PSM analysis, solely created matches between people in a way which is instantly comparable to the analysis carried out with the cross-sectional sample.1


For the purposes of replication, it was vital that the measures utilized in each the cross-sectional and longitudinal studies stayed as related as possible. For ease of presentation, measures are organized by way of their conceptual importance to the general examine with cross-sectional and longitudinal measures defined collectively in every section. Timing of the longitudinal measures was thought-about vital and is described as is appropriate. Particularly, though the eight-wave longitudinal pattern included multiple measurements of many covariates across time, the first longitudinal PSM solely utilized measurements as they would be anticipated to happen if observing a “fake ID effect” over a logical development of time (i.e., Trait/propensity measures have been 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 each Waves three and Wave four). The second semester of faculty (Wave 2) was chosen as the singular goal time-level at which fake ID possession (or the “fake ID effect”) was measured, because it’s considered a peak time of risk for negative consuming-related outcomes and false ID possession (see Martinez et al., 2008).

Main Outcomes Five outcomes related to substance use have been explored. First, a measure of frequent binge consuming was created in each samples. A six-possibility ordinal item requested respondents how many days in the last month did they consume five or more alcoholic drinks. A sex-specific binge consuming measure was not available. These deciding on either of the two highest frequency choices (10–19 days and 20+ days) have been categorized as frequent binge drinkers whereas all others have been not. This dichotomous item represents binge consuming more than ten days in the last month. Second, we utilized an instrument created by Maney, Higham-Gardill, and Mahoney (2002) to characterize alcohol-related problems in the cross-sectional sample. This ten-item scale assesses the degree to which the person feels that alcohol use has created relationship, household, health, behavioral, and professional/school problems in the last 12 months and reveals adequate reliability (α=.822). In the longitudinal pattern, this scale was approximated from ten objects taken from the Younger Adult Alcohol Issues Screening Check (YAAPST; Hurlbut & Sher, 1992) with adequate reliability (α=.848 in second-12 months fall and α=.846 in second-12 months spring). A dichotomous alcohol-related arrest/citation measure was created inside each samples utilizing objects that requested respondents if they had ever been arrested or cited for driving beneath the influence, underage consuming, public dysfunction (due to alcohol), being drunk in public, or an open container violation in the last year. The ultimate two outcomes have been each dichotomous and measured similarly in every pattern; marijuana use and onerous drug use characterize whether the respondent self-reported any use of marijuana and cocaine, heroin, and/or methamphetamine, respectively, in the last year.

False Identification Present fake ID “possession” was assessed dichotomously in each samples (0=No, 1=Sure). The cross-sectional examine additionally included extra objects that requested respondents whether or not they had used the fake ID in a bar or membership and whether or not they had used it in a retailer to buy alcohol.

Trait and risk issue (matching) covariates Fifteen variables have been utilized in propensity score matching in the cross-sectional pattern and fourteen have been used in the longitudinal sample. Variables have been selected due to their inclusion in each datasets and former analysis suggesting that they may be related to the propensity to own a fake ID and expertise one of many five outcomes. These matching variables are: (1) age, (2) age of alcohol use onset, (three) employment status, (four) exposure to substance use, (5) household income, (6) gender, (7) GPA, (8) Greek membership, (9) health, (10) low self-management, (11) peer substance use, (12) race, (thirteen) rural house location (solely measured in the cross-sectional examine), (14) sexual orientation (1=LGBT), and (15) subjective distress.

Eight of the fifteen variables have been measured identically and a ninth was measured almost identically. Amongst those identically measured have been age, age of first alcohol use, employment status (0= not employed; 1=employed), gender (0=female; 1=male), self-reported grade level common (GPA), membership in a campus Greek organization (0=non-member; 1=member), race (0=white, 1=non-white), and sexual orientation (0=heterosexual; 1=lesbian, homosexual, bisexual, or other). Self-reported health was measured with an item that requested respondents to rate their very own health—the cross-sectional examine provided responses starting from 1 (poor) to four (glorious) whereas the longitudinal examine choices ranged from 1 (poor) to five (glorious).

The cross sectional examine utilized 4-item measures adapted from Lee, Akers, and Borg (2004) to characterize exposure to substance use (α=.786) and peer substance use (α=.801). Because the longitudinal data didn’t embody similarmeasures, every of these constructs was represented by a single item slightly than a 4-item 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-item Grasmick et al. (1993) scale (α=.889) in the cross-sectional examine and the NEO Five Factor Stock conscientiousness scale (reverse-coded) in the longitudinal pattern (α=.844; Costa & McCrae, 1992). Subjective distress was measured utilizing Cohen and Williamson’s (1988) ten-item perceived student stress scale (α=.814) in the cross-sectional examine and the World Severity Index from the Temporary Symptom Stock-18 in the longitudinal pattern (Derogatis, 2000). Larger values on these scales characterize more exposure to substance use, a larger portion of friends that use substances, lower self-management, and more subjective distress, respectively.

Each studies included a single-item household socioeconomic status measure. In the cross-sectional examine a measure of household income was used. Participants chose between choices starting from beneath $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 have been the first of their household to attend college (0=No, 1=Sure) was utilized in the longitudinal study.

Rural house location was used in the cross-sectional examine, but no related measure was accessible in the longitudinal data. This variable was vital to include despite creating differing matching criteria due to the traits of the examine area. The cross-sectional pattern was drawn from an space that may be very rural excluding one main city; thus, a dichotomous item representing whether the scholar grew up in an urban / suburban space (coded 0) or a rural one (coded 1) was included. By comparability, this was not a particular consideration for the longitudinal pattern, which originated from a college of 35,000 that draws college students from two giant neighboring cities and its own reasonably giant population.


First, we estimated the proportions of fake ID possession in each the cross-sectional and longitudinal samples. Along with possession of a fake ID, the cross-sectional pattern additionally documented individuals’ utilizing of the fake ID in bars/golf equipment and stores. We estimated the bivariate associations of fake IDs with the five specified substance use outcomes in each samples—a rudimentary “fake ID effect.”

Subsequent, to higher determine the power of the “fake ID effect” after accounting for trait measures, propensity score matching (PSM) was used for each samples. PSM presents a clearer picture of the connection between two variables than bivariate analyses which may yield spurious results (Guo & Fraser 2009) and has been used to evaluate issues related to substance use (Miller et al., 2011). Additionally of note, PSM is preferable to multivariate regression fashions in situations equivalent to this the place the variable of curiosity will not be independently connected to the dependent variable, but is probably going correlated with those which are and also occurs more proximally. The propensity matching techniques developed by Rosenbaum and Rubin (1983, 1985) can be utilized to create a pattern with two groups which are related in all relevant variables aside from the “treatment” (i.e., fake ID possession). While their techniques do lead to a reduction in size of analytic pattern (typically main PSM to be known as resampling), they are effective at creating a state of affairs whereby the effect of “treatment” can be estimated as the average distinction between those exposed to the treatment and “counterfactuals,” outlined as the anticipated outcomes have been it not for exposure to the treatment (Guo & Fraser 2009). On this case, the PSM methodology creates analytic groups whereby variations other than false identification use are minimalized.

As instructed by Rosenbaum and Rubin (1983, 1985), we utilized logistic regression to estimate a propensity score for every participant in every analytic sample. Fake ID possession was regressed on 15 covariates in the cross-sectional pattern (age, age of firsts alcohol use, employment status, exposure to substance use, household income, gender, grade level common, membership in a campus Greek organization, self-assessed health, low self-management, peer substance use, race, size of house group, sexual orientation, and subjective distress) and 14 related variables (size of house group excluded, as defined 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). Using these fashions, every participant’s propensity score was then calculated as their conditional likelihood of having a fake ID. Following an evaluation of regions of common assist, we created comparability groups inside every pattern 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 in the cross-sectional pattern and .0426 in the longitudinal sample. This matching technique led to the anticipated decrease in pattern size (n=817 and n=518, respectively) but a enough number of cases have been retained for statistical comparisons.

Rates of fake ID possession

Rates of fake ID possession have been fairly high, significantly in the cross-sectional sample. That’s, of the 1,454 underage alcohol consumers in the cross-sectional pattern, 583 or 40.1% own or have owned a fake ID, 560 (38.5%) have used a fake ID at a bar, and 460 (27.8%) have used the ID to buy alcohol at a store. Prevalence charges of false ID use in the Midwestern pattern modified over time. Fake ID possession among college students beneath 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 spring=39.0%, fourth-12 months fall=38.1%, fourth-12 months spring= fewer than ten college students have been below the minimum legal consuming age).2

The “fake ID effect” previous to matching

Table 1 presents mean scores for five substance use outcomes for fake ID owners and non-owners in each samples (outcomes at each Wave three and four are reported for the longitudinal pattern). Average scores for every outcome (frequent binge consuming [10 or more days in the last month], self-reported alcohol related problems, alcohol-related arrests, marijuana use, and onerous drug use) are presented for people who have and have not owned a fake ID, used a fake ID at a bar/membership (cross-sectional pattern solely), and used a fake ID at a retailer (cross-sectional pattern solely). Impartial samples t-exams have been carried out to find out whether, on common, variations exist between fake ID users and non-users. Every of the exams reached significance. No matter whether the main target was possession of a fake ID or utilizing it at a specific kind of outlet, the outcomes have been consistent. On the bivariate level, more people with false identification interact in frequent binge consuming, have been arrested/cited for an alcohol violation, interact in marijuana use, and use onerous drugs. Individuals with fake identification additionally, on common, report more alcohol-related problems. These results would indicate that fake IDs are a car of risk. However, it’s potential that fake ID possession (and associated dangers) are more a perform of underlying risky traits.

Propensity score matching (PSM)

Due to the consistency in the findings so far regardless of false identification measure (possession, bar use, and/or retailer use), the additional analyses with every pattern makes use of just one false identification measure, possession of a false ID. We carried out PSM analyses in each samples, to look at whether people with and without fake IDs proceed to vary on these outcomes after being matched on substantively vital traits. Table 2 reveals that people with and without fake IDs indeed differed from one another on these trait propensity variables, suggesting that it’s these variables which may in the end be driving the fake ID effect. Table 2 additionally reveals that the PSM technique labored properly in each samples, consistently lowering bias associated with the statistically important variations between those with and without fake IDs by more than 50% on all but one variable. Although three important variations nonetheless remained in the cross-sectional pattern (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 have been minor in comparison with pre-matching. On this, matching was equally, if no more, successful in the longitudinal sample. It needs to be noted that matching did yield a reduction in pattern size. Overall, nonetheless, in each samples matching seems to have created treatment and comparability groups which are more equal and more applicable for comparability than the unmatched data.

The propensity scores that have been calculated for every case are graphically displayed in Determine 1. As can be seen in the determine, a area of common assist exists, but very few with low propensity scores had a fake ID and very few with high propensity scores did not.

Evaluating fake ID owners and non-owners after PSM

Cross-sectional pattern After matching, false identification owners and non-owners have been in contrast on every of the five substance use related outcomes. While significantly more of those with fake IDs in the cross-sectional pattern have been frequent binge drinkers previous to matching (t=9.eighty one, df=815), the groups have been not significantly different after matching (t=1.eighty one, df=815) and the average treatment effect (ATE; e.g., variations in group means), as displayed in Table three, was diminished by 59.2%. Similarly, previous to matching, fake ID owners had significantly larger scores on the alcohol related problems scale than non-owners (t=9.eighty three, df=815), but the groups have been not significantly different after matching (t=1.31, df=815) and the ATE was diminished by 63.four%. However, by way of alcohol-related arrests, the two groups have been nonetheless significantly 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.four%). Finally, onerous drug use was associated with fake ID possession each earlier than (t=7.26, df=815) and after matching (t=2.29, df=815), but the ATE was diminished by 38.four%.

Longitudinal pattern As was the case in the cross-sectional pattern, propensity score matching led to a substantial decrease in the ATE for 4 of the five outcomes (Table three, columns three–6). However, unlike the cross-sectional pattern, ATEs remained important for alcohol related problems (t=4.00 wave three; t=4.17, wave four, df=516) and marijuana use (t=4.thirteen, wave three; t=2.58, wave four, df=516) after propensity score matching. The ATE additionally remained important for frequent binge consuming (t=3.26, df=516, wave three) and onerous drug use (t=2.06, df=516, wave four) at one wave but not the other. Once more, these results sizes have been considerably diminished, but in the aforementioned cases, not eliminated. As was the case in the cross-sectional pattern, propensity score matching had little influence on the ATE on alcohol-related arrests.three


This examine’s preliminary results are in keeping with previous analysis—a substantial number of underage college students have fake IDs and are at larger risk for binge consuming, 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 confirmed that for some outcomes, plainly what initially might have appeared to be a “fake ID effect” is basically the results of elements that influenced each the acquisition of the false ID and the outcome. The significant relationship between fake ID use and other substance use outcomes typically remained after PSM, but the magnitude of these relationships have been considerably diminished, most by over forty%. Alcohol-related arrests have been an exception as the connection was unaffected by PSM (i.e., after matching, those with a fake ID have been nonetheless at similarly high ranges of risk for alcohol-related arrests [DUIs, open container, etc.]). The reason this outcome is distinct from the others shouldn’t be readily clear; maybe regulation enforcement officers are more likely to subject citations or arrests for other substance-related offenses when a person can be found with a fake ID. If this is the case, the “effect” wouldn’t appear smaller in propensity score fashions as the distinction could be pushed by officers’ reactions to the fake ID slightly than people’ underlying propensity.

The sample that emerges from Table three appears to point that non-matched samples may have overestimated the effect of false identification use on negative outcomes, but that fake ID possession has an effect that extends past shared causal factors. This specific remaining “fake ID effect” might indeed assist the concept the fake ID itself serves as a type of threshold into other types of deviant habits, the place those that are prepared to amass fake IDs turn out to be more and more prepared to violate other laws (see Ruedy et al., 2013; Winograd et al., 2014). But in mild of the opposite findings, it’s more probably that fake IDs more generally reasonable the results of risky traits on behavior. For instance, fake IDs may have the best efficiency of effect by way of offering impulsive people with extra means and opportunity for problematic behaviors that they would not otherwise have engaged in. Certainly, underlying trait dangers are often integrated into opportunity-principle-related examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).

As such, these findings have sensible implications. Although increased server training, fake ID production/provider laws, and liability laws are an vital technique of addressing the dangers of fake IDs as a form of alcohol entry (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), fake-ID related outcomes might also partly be a perform of trait dangers that can additionally be addressed with intervention. One option to start addressing this mixture of things may be by way of motivational, normative feedback-primarily based, or abilities interventions which are specifically aimed toward decreasing the probability that at-risk college students acquire a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Furthermore, a fake ID obtainment-aimed intervention might possibly be broadly integrated into interventions which are especially tailor-made toward addressing each people’ risky traits and their resulting behaviors (see Conrod et al., 2006).

Although an ideal power of this examine rests in the similar findings found with two unique college populations, these findings will not be generalizable to non-college attending populations. Additionally, fake ID policies, enforcement, and concern of sanctions may differ considerably in numerous localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For instance, some consuming institutions may 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 utilizing a fake ID to buy alcohol varies considerably from state to state together with the type of offense, quantity of high-quality, suspension of driver’s license, and the potential of probation or jail time. Future analysis should evaluate the impact of fake ID relative to differential policies and enforcement of the minimum legal consuming age, together with group efforts (Grube, 1997). Additional, whereas fifteen distinct traits have been included in the matching process, there remains the likelihood that extra elements not measured in our data would have an effect on each the willingness to entry a fake ID and the outcome measures. If this is the case, the “fake ID effect” may be even smaller than our matching fashions suggested.

In concluding that the “fake ID effect” is principally a perform of phenotypic risk, fake ID possession may serve as an indicator of heightened risk for more extreme consuming related problems. Although most penalties for fake ID possession are punitive (fines, probation/jail, and/or loss of driver’s license), coverage-makers, college officers, and practitioners should goal fake ID owners for intervention methods aimed toward lowering high-risk consuming behaviors (and other problematic behaviors linked to phenotypic risk). Although increased penalties and enforcement of the minimum legal consuming age has the potential to cut back fake ID possession, we caution coverage-makers to guage and contemplate the negative consequences of transferring college consuming away from regulated institutions the place safety and emergency providers are more readily available (see Baldwin et al., 2012; 2014). Although our findings found that fake ID possession (regardless of particular person risk traits) increased the risk for alcohol related arrests, drug use, and alcohol related problems (Midwest pattern solely), we didn’t assess victimization and other harms associated with extreme alcohol consumption that could improve in areas not topic to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future analysis is required to guage the impact that fake ID enforcement may have on problematic consuming each in regulated and unregulated spaces.