Underage faculty college students who obtain and use false identification (fake ID) are in danger for destructive outcomes. However, it’s 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 general and trait-associated risk factors (e.g., deviant peers, low self-control).
In an effort to investigate whether or not the “fake ID effect” would hold after accounting for phenotypic risk, we utilized propensity rating matching (PSM) in a cross-sectional sample of n=1,454 college students, and a longitudinal replication sample of n=3,720 undergraduates. People with a fake ID were matched with people without a fake ID, when it comes to quite a lot of trait-based and social risk factors. These matched teams were then compared on five problematic outcomes (i.e., frequent binge consuming, alcohol-associated problems, arrests, marijuana use, and exhausting drug use).
Findings confirmed that “fake ID results” were substantially—though not absolutely—diminished following PSM. The “fake ID effect” remained strongest for alcohol-associated arrests. This may increasingly relate to problems with enforcement and college students’ willingness to interact in deviant habits with a fake ID, or it may be a operate of mixed processes.
Overall, the findings suggest that interventions shouldn’t only be geared toward lowering fake ID-associated alcohol access specifically, however must also be aimed more typically towards at-risk youths’ access to alcohol. Future analysis might examine whether or not fake IDs have their strongest efficiency as moderators of the consequences of risky traits—akin to impulsiveness—on consuming outcomes.
Keywords: False identification, Fake IDs, underage alcohol use, heavy episodic consuming, binge consuming
Fake IDs, a singular mode of alcohol access, are increasingly sought after as people close to the minimum authorized 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 doc obtained locally or from an online vendor (Murray, 2005). Regardless of their source, 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 occasion; Martinez, et al., 2007).
This bidirectional relation not only illustrates the general public health risks of this mode of alcohol access, however begs the query of whether or not 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 or not such harms and outcomes are predominantly driven by a general degree of phenotypic risk on the a part of the fake ID “proprietor” (e.g., deviant peer associations, low self-control). Though general alcohol access theories might assist the former hypothesis nearly completely (specifically, fake ID possession increases alcohol access and subsequent hurt; see Gruenewald, 2011), general criminological theories of phenotypic risk assist the latter (specifically, that broad classes of risk—or propensities to interact in risky habits—are the true reason behind hurt; see Pratt & Cullen, 2000). Actually, such propensities could be what predicts fake ID obtainment within the first place, and although the energy of the fake ID effect seems to extend over time, it’s greatly diminished after controlling for intercourse, Greek standing, and pre-faculty rates of consuming (Martinez et al., 2007). In sum, it’s unclear how sturdy the fake ID effect could be after accounting for individuals’ ranges of phenotypic or propensity risk—though this query has bearing on prevention and coverage initiatives, which can concentrate on both strengthening enforcement of fake ID legal guidelines themselves, rising assets for trait-based at-risk youth applications, or a neighborhood-driven combination of each (see Fell, Thomas, Scherer, Fisher & Romano, 2015; Fell, Scherer, Thomas & Voas, 2016; Fell, Scherer & Voas, 2015; Grube, 1997) .
Thus, in order to investigate the energy of the fake ID effect, we matched college students with and with out fake IDs on quite a lot of risk-based covariates using propensity rating matching (PSM) techniques. We first compared matched teams’ consuming- and drug-use-associated outcomes in a cross-sectional sample of n=1,454 faculty college students at a big Southeastern university. We also compared matched teams in an extra longitudinal replication sample of n=3,720 undergraduates at a big Midwestern university. We hypothesized that the consequences of fake ID possession on outcomes could be greatly diminished by—and therefore largely attributable to—the pre-existing trait-based factors on which fake ID owners and non-owners could be matched. These comparisons can inform the extent to which the connection between destructive outcomes and false identification possession are attributed to choice factors, which once more, may have practical utility for intervention and policy.
Procedure and Individuals
Two samples were individually investigated following Institutional Overview Board (IRB) approval: (1) A cross-sectional sample of n=1,454 underage faculty college students from a big Southeastern College (IRB Protocol H12032) and (2) a potential replication sample of n=3,720 undergraduates under the minimum authorized consuming age from a big Midwestern university (IRB Protocol 01-01-001). Of observe, each samples offer distinctive insights into the connection between false identification use and destructive outcomes. Extra specifically, the cross-sectional examine consists of items that distinguish between using fake IDs in several situations (at bars, at grocery shops, etc.) and the longitudinal examine gives insight into the potential results of fake ID possession over time and establishes temporal order.
With regard to the cross-sectional sample, through the tutorial 12 months 2011–2012, individuals were recruited from forty randomly selected large (>ninety nine college students) and moderate enrollment (30–ninety nine college students) classes. Individuals completed a one-page informed consent doc within the selected classes earlier than being given a six-page paper survey about faculty life and behaviors to finish with pencil or pen. Individuals were not compensated. All enrolled college students were invited to take part and the response fee was excessive at 80.four% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After these above the authorized consuming threshold were eliminated, the analytic sample was n=1,454 underage individuals. The sample was largely consultant of the university with regard to demographics and was specifically 51.6% feminine, 68.9% White/non-Hispanic, with a median age of 18.95 (SD=.795). Though this sample 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 individual’s control (residence location, parental income, sexual orientation, etc.).
The longitudinal sample also utilized a self-report survey methodology. All incoming college students in 2002 were recruited to finish an instrument through the summer prior to college entrance using paper and pencil and then were requested to finish on-line surveys every semester for the subsequent four years (a total of eight semesters). Students supplied informed consent and were compensated $25 in every wave. After excluding the n=35 who were of age, 88% of the eligible coming into class completed the survey (n=3,720). The sample was 53.7% feminine, 90.3% White/non-Hispanic, and averaged 17.9 (SD=.36) years of age (reflecting demographics that are consultant of the university as a whole [University Registrar, 2013]). Students were historically aged; by the beginning of their junior 12 months, only one-third of the sample had reached the minimum authorized consuming age, climbing expectedly to 99.7% by the final semester of college, Pattern retention was good, ranging from 69% to 87% of baseline respondents taking part at every subsequent wave. Retention biases were low, though people were more more likely to remain within the sample in the event that they were females (OR=2.33) and were much less more likely to remain within the sample in the event that they were frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the final time-point, the sample size was n=2,250, though ninety% of students participated in or more evaluation waves and eighty two% participated in three or more waves. The longitudinal PSM presented inside the textual content utilized the first years of college only (i.e., the first four semesters, when the overwhelming majority of individuals were underage) and, in keeping with most PSM analysis, only created matches between people in a fashion which is immediately akin to the evaluation 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 similar as possible. For ease of presentation, measures are organized when it comes to 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 of important and is described as is appropriate. Particularly, though the eight-wave longitudinal sample included multiple measurements of many covariates throughout time, the primary longitudinal PSM only utilized measurements as they’d be expected 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 predict fake ID possession at Wave 2 which in flip assessed as a predictor for consequence measures at each Waves 3 and Wave four). 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’s regarded as a peak time of risk for destructive consuming-associated outcomes and false ID possession (see Martinez et al., 2008).
Predominant Outcomes Five outcomes associated to substance use were explored. First, a measure of frequent binge consuming was created in each samples. A six-option ordinal merchandise requested respondents what number of days within the last month did they devour five or more alcoholic drinks. A intercourse-specific binge consuming measure was not available. Those deciding on both of the 2 highest frequency options (10–19 days and 20+ days) were classified as frequent binge drinkers while all others were not. This dichotomous merchandise represents binge consuming more than ten days within the last month. Second, we utilized an instrument created by Maney, Higham-Gardill, and Mahoney (2002) to represent alcohol-associated problems within the cross-sectional sample. This ten-merchandise scale assesses the diploma to which the individual feels that alcohol use has created relationship, family, health, behavioral, and professional/faculty problems within the last 12 months and exhibits sufficient reliability (α=.822). In the longitudinal sample, this scale was approximated from ten items taken from the Young Grownup Alcohol Problems Screening Take a look at (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 requested respondents if they’d ever been arrested or cited for driving under the affect, underage consuming, public disorder (attributable to alcohol), being drunk in public, or an open container violation within the last year. The ultimate outcomes were each dichotomous and measured similarly in every sample; marijuana use and exhausting 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 Current fake ID “possession” was assessed dichotomously in each samples (zero=No, 1=Sure). The cross-sectional examine also included further items that requested 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 store to buy alcohol.
Trait and risk 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 attributable to their inclusion in each datasets and previous analysis suggesting that they may be associated to the propensity to personal a fake ID and experience one of many five outcomes. These matching variables are: (1) age, (2) age of alcohol use onset, (3) employment standing, (four) publicity to substance use, (5) family income, (6) gender, (7) GPA, (eight) Greek membership, (9) health, (10) low self-control, (eleven) peer substance use, (12) race, (thirteen) rural residence location (only measured within the cross-sectional examine), (14) sexual orientation (1=LGBT), and (15) subjective distress.
Eight of the fifteen variables were measured identically and a ninth was measured nearly identically. Amongst these 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 common (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, homosexual, bisexual, or other). Self-reported health was measured with an merchandise that requested respondents to fee their own health—the cross-sectional examine supplied responses ranging from 1 (poor) to four (excellent) whereas the longitudinal examine options ranged from 1 (poor) to five (excellent).
The cross sectional examine utilized four-merchandise measures tailored from Lee, Akers, and Borg (2004) to represent publicity to substance use (α=.786) and peer substance use (α=.801). Because the longitudinal data did not embody similarmeasures, every of these constructs was represented by a single merchandise fairly than a four-merchandise scale. The first (publicity) was measured dichotomously while the second (peer substance use) was measured on a six-option ordinal scale. Low self-control was operationalized using the 24-merchandise Grasmick et al. (1993) scale (α=.889) within the cross-sectional examine and the NEO Five Factor Stock conscientiousness scale (reverse-coded) within the longitudinal sample (α=.844; Costa & McCrae, 1992). Subjective misery was measured using Cohen and Williamson’s (1988) ten-merchandise perceived scholar stress scale (α=.814) within the cross-sectional examine and the Global Severity Index from the Brief Symptom Stock-18 within the longitudinal sample (Derogatis, 2000). Increased values on these scales represent more publicity to substance use, a bigger portion of peers that use substances, lower self-control, and more subjective misery, respectively.
Each studies included a single-merchandise family socioeconomic standing measure. In the cross-sectional examine a measure of family income was used. Individuals chose between options ranging from under $10,000 per 12 months (coded 1) to over $one hundred seventy five,000 per 12 months (coded 9). An merchandise assessing whether or not or not college students were the first in their family to attend faculty (zero=No, 1=Sure) was utilized within the longitudinal study.
Rural residence location was used within the cross-sectional examine, however no similar measure was accessible within the longitudinal data. This variable was important to include despite creating differing matching standards as a result of characteristics of the examine area. The cross-sectional sample was drawn from an area that is very rural except for one main city; thus, a dichotomous merchandise representing whether or not the student grew up in an urban / suburban area (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 university of 35,000 that attracts college students from large neighboring cities and its personal moderately large population.
First, we estimated the proportions of fake ID possession in each the cross-sectional and longitudinal samples. In addition to possession of a fake ID, the cross-sectional sample also documented individuals’ using of the fake ID in bars/clubs 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.”
Next, to better determine the energy of the “fake ID effect” after accounting for trait measures, propensity rating matching (PSM) was used for each samples. PSM gives a clearer picture of the connection between variables than bivariate analyses which can yield spurious results (Guo & Fraser 2009) and has been used to assess issues associated to substance use (Miller et al., 2011). Also of observe, PSM is preferable to multivariate regression models in situations akin to this where the variable of interest will not be independently linked to the dependent variable, however is likely correlated with these that are and in addition occurs more proximally. The propensity matching strategies developed by Rosenbaum and Rubin (1983, 1985) can be utilized to create a sample with teams that are similar in all relevant variables except for the “therapy” (i.e., fake ID possession). Whereas their strategies do lead to a discount in size of analytic sample (often main PSM to be known as resampling), they’re effective at creating a scenario whereby the effect of “therapy” may be estimated as the typical distinction between these exposed to the therapy and “counterfactuals,” outlined as the anticipated outcomes were it not for publicity to the therapy (Guo & Fraser 2009). In this case, the PSM methodology creates analytic teams whereby differences apart from false identification use are minimalized.
As urged by Rosenbaum and Rubin (1983, 1985), we utilized logistic regression to estimate a propensity rating for every participant in every analytic sample. Fake ID possession was regressed on 15 covariates within the cross-sectional sample (age, age of firsts alcohol use, employment standing, publicity to substance use, family income, gender, grade point common, membership in a campus Greek group, self-assessed health, low self-control, peer substance use, race, size of residence neighborhood, sexual orientation, and subjective misery) and 14 similar variables (size of residence neighborhood excluded, as defined above) in the main longitudinal PSM evaluation (i.e. fake ID possession measured on the second semester and outcomes evaluated within the third and fourth semesters). Using these models, every participant’s propensity rating was then calculated as their conditional probability of having a fake ID. Following an evaluation of areas of frequent assist, we created comparison teams within every sample using a -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 approach led to the expected decrease in sample size (n=817 and n=518, respectively) however a sufficient variety of instances were retained for statistical comparisons.
Rates of fake ID possession
Rates 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% personal 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 under 21 peaked through the third 12 months of college (pre-faculty=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.zero%, fourth-12 months fall=38.1%, fourth-12 months spring= fewer than ten college students were below the minimum authorized consuming age).2
The “fake ID effect” prior to matching
Table 1 presents imply scores for five substance use outcomes for fake ID owners and non-owners in each samples (outcomes at each Wave 3 and four are reported for the longitudinal sample). Average scores for every consequence (frequent binge consuming [10 or more days within the last month], self-reported alcohol associated problems, alcohol-associated arrests, marijuana use, and exhausting drug use) are presented for people who have and haven’t owned a fake ID, used a fake ID at a bar/club (cross-sectional sample only), and used a fake ID at a store (cross-sectional sample only). Independent samples t-exams were performed to determine whether or not, on common, differences exist between fake ID customers and non-users. Every of the exams reached significance. Regardless of whether or not the main focus was possession of a fake ID or using it at a specific sort of outlet, the outcomes were consistent. On the bivariate degree, more people with false identification engage in frequent binge consuming, have been arrested/cited for an alcohol violation, engage in marijuana use, and use exhausting drugs. People with fake identification also, on common, report more alcohol-associated problems. These results would point out that fake IDs are a car of risk. However, it’s potential that fake ID possession (and related risks) are more a operate of underlying risky traits.
Propensity rating matching (PSM)
Because of the consistency within the findings to this point regardless of false identification measure (possession, bar use, and/or store use), the extra analyses with every sample makes use of only one false identification measure, possession of a false ID. We carried out PSM analyses in each samples, to examine whether or not people with and with out fake IDs proceed to differ on these outcomes after being matched on substantively important traits. Table 2 exhibits that people with and with out fake IDs indeed differed from one another on these trait propensity variables, suggesting that it’s these variables which can finally be driving the fake ID effect. Table 2 also exhibits that the PSM approach labored properly in each samples, consistently lowering bias associated with the statistically important differences between these with and with out fake IDs by more than 50% on all however one variable. Although three important differences nonetheless remained within the cross-sectional sample (age of alcohol use onset, Greek affiliation, and having been raised in a rural area), the magnitude of the differences in age of onset and Greek affiliation were minor compared to pre-matching. In this, matching was equally, if no more, profitable within the longitudinal sample. It must be famous that matching did yield a discount in sample size. Overall, nonetheless, in each samples matching seems to have created therapy and comparison teams that are more equivalent and more applicable for comparison than the unrivaled data.
The propensity scores that were calculated for every case are graphically displayed in Figure 1. As may be seen within the determine, a region of frequent assist 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 owners and non-owners after PSM
Cross-sectional sample After matching, false identification owners and non-owners were compared on every of the five substance use associated outcomes. Whereas considerably more of these with fake IDs within the cross-sectional sample were frequent binge drinkers prior to matching (t=9.81, df=815), the teams were no longer considerably totally different after matching (t=1.81, df=815) and the typical therapy effect (ATE; e.g., differences in group means), as displayed in Table 3, was decreased by 59.2%. Similarly, prior to matching, fake ID owners had considerably increased scores on the alcohol associated problems scale than non-owners (t=9.eighty three, df=815), but the teams were no longer considerably totally different after matching (t=1.31, df=815) and the ATE was decreased by 63.four%. However, when it comes to alcohol-associated arrests, the 2 teams were nonetheless considerably totally different and the ATE effectively remained unchanged. As was the case for the first outcomes, fake ID possession was associated with marijuana use prior to matching (t=9.36), however not after (t=1.fifty two; ATE decreased by 60.four%). Lastly, exhausting 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 decreased by 38.four%.
Longitudinal sample As was the case within the cross-sectional sample, propensity rating matching led to a substantial decrease within the ATE for four of the five outcomes (Table 3, columns 3–6). However, unlike the cross-sectional sample, ATEs remained important for alcohol associated problems (t=4.00 wave 3; t=4.17, wave four, df=516) and marijuana use (t=4.thirteen, wave 3; t=2.fifty eight, wave four, df=516) after propensity rating matching. The ATE also remained important for frequent binge consuming (t=3.26, df=516, wave 3) and exhausting drug use (t=2.06, df=516, wave four) at one wave however not the other. Once more, these results sizes were substantially decreased, however within the aforementioned instances, not eliminated. As was the case within the cross-sectional sample, propensity rating matching had little affect on the ATE on alcohol-associated arrests.3
This examine’s preliminary results are in keeping with previous analysis—a substantial variety of underage college students have fake IDs and are at increased risk for binge consuming, 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 also confirmed that for some outcomes, plainly what initially might need appeared to be a “fake ID effect” is basically the result of factors that influenced each the acquisition of the false ID and the outcome. The significant relationship between fake ID use and other substance use outcomes often remained after PSM, but the magnitude of these relationships were substantially diminished, most by over forty%. Alcohol-associated arrests were an exception as the connection was unaffected by PSM (i.e., after matching, these with a fake ID were nonetheless at similarly excessive ranges of risk for alcohol-associated arrests [DUIs, open container, etc.]). The explanation this consequence is distinct from the others shouldn’t be readily clear; perhaps legislation enforcement officers are more likely to subject citations or arrests for other substance-associated offenses when a person is also discovered with a fake ID. If that is so, the “effect” would not seem smaller in propensity rating models as the distinction could be driven by officers’ reactions to the fake ID fairly than people’ underlying propensity.
The pattern that emerges from Table 3 appears to indicate that non-matched samples may have overestimated the effect of false identification use on destructive outcomes, however that fake ID possession has an effect that extends past shared causal factors. This specific remaining “fake ID effect” may indeed assist the concept the fake ID itself serves as a sort of threshold into other types of deviant habits, where those who are willing to accumulate fake IDs turn into increasingly willing to violate other legal guidelines (see Ruedy et al., 2013; Winograd et al., 2014). However in gentle of the other findings, it’s more probably that fake IDs more typically moderate the consequences of risky traits on behavior. For instance, fake IDs may have the very best efficiency of effect via providing impulsive people with further means and opportunity for problematic behaviors that they’d not otherwise have engaged in. Certainly, underlying trait risks are often included into opportunity-concept-associated examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).
As such, these findings have practical implications. Although increased server training, fake ID production/supplier legal guidelines, and legal responsibility legal guidelines are an important technique of addressing the risks of fake IDs as a form of alcohol access (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), pretend-ID associated outcomes may also partly be a operate of trait risks that can additionally be addressed with intervention. One approach to begin addressing this mix of things may be via motivational, normative suggestions-based, or skills interventions that are specifically geared toward lowering the likelihood that at-risk college students obtain a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Moreover, a fake ID obtainment-aimed intervention might presumably be broadly included into interventions that are especially tailored towards addressing each people’ risky traits and their ensuing behaviors (see Conrod et al., 2006).
Though an incredible energy of this examine rests in the similar findings discovered with distinctive faculty populations, these findings will not be generalizable to non-faculty attending populations. Moreover, fake ID policies, enforcement, and fear of sanctions may range substantially in several localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For instance, some consuming establishments may be lenient in their carding policies, deliberately settle for false identification, and/or not be subject to rigorous regulatory enforcement (Murray, 2005). Also, penalties for possessing and/or using a fake ID to buy alcohol varies substantially from state to state including the kind of offense, quantity of positive, suspension of driver’s license, and the potential of probation or jail time. Future analysis ought to consider the influence of fake ID relative to differential policies and enforcement of the minimum authorized consuming age, including neighborhood efforts (Grube, 1997). Additional, while fifteen distinct traits were included within the matching course of, there stays the possibility that further factors not measured in our data would affect each the willingness to access a fake ID and the outcome measures. If that is so, the “fake ID effect” may be even smaller than our matching models suggested.
In concluding that the “fake ID effect” is mainly a operate of phenotypic risk, fake ID possession may function an indicator of heightened risk for more extreme consuming associated problems. Though most penalties for fake ID possession are punitive (fines, probation/jail, and/or loss of driver’s license), coverage-makers, university officers, and practitioners ought to target fake ID owners for intervention strategies geared toward lowering excessive-risk consuming behaviors (and other problematic behaviors linked to phenotypic risk). Though increased penalties and enforcement of the minimum authorized consuming age has the potential to cut back fake ID possession, we caution coverage-makers to evaluate and consider the destructive consequences of transferring faculty consuming away from regulated establishments where safety and emergency companies are more readily available (see Baldwin et al., 2012; 2014). Though our findings discovered that fake ID possession (regardless of individual risk characteristics) increased the danger for alcohol associated arrests, drug use, and alcohol associated problems (Midwest sample only), we did not assess victimization and other harms associated with excessive alcohol consumption that could increase in spaces not subject to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future analysis is required to evaluate the influence that fake ID enforcement may have on problematic consuming each in regulated and unregulated spaces.