Underage faculty students who acquire and use false identification (fake ID) are in danger for destructive outcomes. However, it’s at present 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-related risk factors (e.g., deviant peers, low self-management).
With a view to investigate whether the “fake ID impact” would hold after accounting for phenotypic risk, we utilized propensity rating matching (PSM) in a cross-sectional sample of n=1,454 students, and a longitudinal replication sample of n=three,720 undergraduates. People with a fake ID had been matched with people without a fake ID, when it comes to quite a lot of trait-based mostly and social risk factors. These matched groups had been then in contrast on 5 problematic outcomes (i.e., frequent binge ingesting, alcohol-related issues, arrests, marijuana use, and onerous drug use).
Findings showed that “fake ID effects” had been considerably—although not absolutely—diminished following PSM. The “fake ID impact” remained strongest for alcohol-related arrests. This may occasionally relate to problems with enforcement and students’ willingness to engage in deviant conduct with a fake ID, or it could be a operate of combined processes.
Total, the findings counsel that interventions should not only be aimed at decreasing fake ID-related alcohol access particularly, however also needs to be aimed extra typically towards at-risk youths’ access to alcohol. Future research might look at whether fake IDs have their strongest efficiency as moderators of the effects of risky traits—similar to impulsiveness—on ingesting outcomes.
Keywords: False identification, Fake IDs, underage alcohol use, heavy episodic ingesting, binge ingesting
Fake IDs, a novel mode of alcohol access, are increasingly wanted as people close to the minimum legal ingesting age (Martinez et al., 2007; Wagenaar et al., 1996). These forms of false identification may be borrowed (or duplicated) from an older peer or sibling (Myers et al., 2001), or they could be a specially crafted document obtained domestically or from an internet vendor (Murray, 2005). No matter their supply, there seems 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 (defined as 5+ drinks per event; Martinez, et al., 2007).
This bidirectional relation not only illustrates the general public well being dangers of this mode of alcohol access, however begs the question of whether it’s extra 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 risk on the part of the fake ID “owner” (e.g., deviant peer associations, low self-management). Although common alcohol access theories might support the previous speculation nearly entirely (particularly, fake ID possession increases alcohol access and subsequent hurt; see Gruenewald, 2011), common criminological theories of phenotypic risk support the latter (particularly, that broad classes of risk—or propensities to engage in risky conduct—are the true reason behind hurt; see Pratt & Cullen, 2000). Actually, such propensities is perhaps what predicts fake ID obtainment within the first place, and although the power of the fake ID impact seems to extend over time, it’s greatly diminished after controlling for intercourse, Greek status, and pre-faculty charges of ingesting (Martinez et al., 2007). In sum, it’s unclear how sturdy the fake ID impact is perhaps after accounting for people’ ranges of phenotypic or propensity risk—although this question has bearing on prevention and policy initiatives, which may concentrate on both strengthening enforcement of fake ID legal guidelines themselves, rising resources for trait-based mostly at-risk youth packages, 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 investigate the power of the fake ID impact, we matched students with and with out fake IDs on quite a lot of risk-based mostly covariates using propensity rating matching (PSM) techniques. We first in contrast matched groups’ ingesting- and drug-use-related outcomes in a cross-sectional sample of n=1,454 faculty students at a big Southeastern university. We also in contrast matched groups in an additional longitudinal replication sample of n=three,720 undergraduates at a big Midwestern university. We hypothesized that the effects of fake ID ownership on outcomes can be greatly diminished by—and subsequently largely attributable to—the pre-present trait-based mostly factors on which fake ID homeowners and non-homeowners could possibly be matched. These comparisons can inform the extent to which the relationship between destructive outcomes and false identification ownership are attributed to selection factors, which again, might have sensible software for intervention and policy.
Process and Contributors
Two samples had been individually investigated following Institutional Overview Board (IRB) approval: (1) A cross-sectional sample of n=1,454 underage faculty students from a big Southeastern University (IRB Protocol H12032) and (2) a prospective replication sample of n=three,720 undergraduates beneath the minimum legal ingesting age from a big Midwestern college (IRB Protocol 01-01-001). Of word, each samples supply distinctive insights into the relationship between false identification use and destructive outcomes. More particularly, the cross-sectional research contains objects that distinguish between using fake IDs in numerous conditions (at bars, at grocery shops, etc.) and the longitudinal research presents perception into the potential effects of fake ID ownership over time and establishes temporal order.
With regard to the cross-sectional sample, in the course of the educational year 2011–2012, participants had been recruited from forty randomly chosen large (>99 students) and average enrollment (30–99 students) classes. Contributors accomplished a one-page informed consent document within the chosen courses earlier than being given a six-page paper survey about faculty life and behaviors to finish with pencil or pen. Contributors were not compensated. All enrolled students had been invited to take part and the response rate was high at 80.four% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After these 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 particularly 51.6% feminine, 68.9% White/non-Hispanic, with a median age of 18.ninety five (SD=.795). Although this sample is cross-sectional, establishing temporal ordering of the covariates and fake ID ownership is essentially inconsequential for almost all of covariates as many are immutable (age, race, gender) or outdoors of the individual’s management (residence location, parental revenue, sexual orientation, etc.).
The longitudinal sample also utilized a self-report survey methodology. All incoming students in 2002 had been recruited to finish an instrument in the course of the summer season prior to school entrance using paper and pencil after which had been asked to finish on-line surveys each semester for the next four years (a complete of eight semesters). Students offered informed consent and had been compensated $25 in each wave. After excluding the n=35 who had been of age, 88% of the eligible entering class accomplished the survey (n=three,720). The sample was 53.7% feminine, 90.three% White/non-Hispanic, and averaged 17.9 (SD=.36) years of age (reflecting demographics which are representative of the college as an entire [University Registrar, 2013]). Students had been traditionally aged; by the beginning of their junior year, just one-third of the sample had reached the minimum legal ingesting age, climbing expectedly to 99.7% by the final semester of college, Sample retention was good, ranging from sixty nine% to 87% of baseline respondents collaborating at each subsequent wave. Retention biases had been low, although people had been extra more likely to remain within the sample in the event that they had been females (OR=2.33) and had been much less more likely to remain within the sample in the event that they had been frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the final time-point, the sample size was n=2,250, although 90% of scholars participated in or extra evaluation waves and eighty two% participated in three or extra waves. The longitudinal PSM offered inside the textual content utilized the primary years of college only (i.e., the primary four semesters, when the overwhelming majority of participants had been underage) and, consistent with most PSM research, only created matches between people in a way which is instantly similar to the analysis performed with the cross-sectional sample.1
For the purposes of replication, it was essential that the measures used in each the cross-sectional and longitudinal studies stayed as related as possible. For ease of presentation, measures are organized when it comes to their conceptual importance to the overall research with cross-sectional and longitudinal measures explained together in each section. Timing of the longitudinal measures was thought-about essential and is described as is appropriate. Particularly, although the eight-wave longitudinal sample included multiple measurements of many covariates across time, the primary longitudinal PSM only utilized measurements as they’d be anticipated to happen if observing a “fake ID impact” over a logical progression of time (i.e., Trait/propensity measures had been measured at Wave 1 and used to predict fake ID ownership at Wave 2 which in flip assessed as a predictor for final result measures at each Waves three and Wave four). The second semester of college (Wave 2) was chosen because the singular target time-point at which fake ID ownership (or the “fake ID impact”) was measured, as a result of it’s thought to be a peak time of risk for destructive ingesting-related outcomes and false ID ownership (see Martinez et al., 2008).
Important Outcomes Five outcomes related to substance use had been explored. First, a measure of frequent binge ingesting was created in each samples. A six-choice ordinal item asked respondents how many days within the final month did they eat 5 or extra alcoholic drinks. A intercourse-specific binge ingesting measure was not available. Those choosing both of the 2 highest frequency choices (10–19 days and 20+ days) had been categorised as frequent binge drinkers while all others had been not. This dichotomous item represents binge ingesting more than ten days within the final month. Second, we utilized an instrument created by Maney, Higham-Gardill, and Mahoney (2002) to characterize alcohol-related issues within the cross-sectional sample. This ten-item scale assesses the degree to which the individual feels that alcohol use has created relationship, family, well being, behavioral, and professional/college issues within the final year and shows satisfactory reliability (α=.822). In the longitudinal sample, this scale was approximated from ten objects taken from the Young Adult Alcohol Issues Screening Check (YAAPST; Hurlbut & Sher, 1992) with satisfactory reliability (α=.848 in second-year fall and α=.846 in second-year spring). A dichotomous alcohol-related arrest/citation measure was created within each samples using objects that asked respondents if they had ever been arrested or cited for driving beneath the influence, underage ingesting, public dysfunction (resulting from alcohol), being drunk in public, or an open container violation within the final year. The ultimate outcomes had been each dichotomous and measured equally in each sample; marijuana use and onerous drug use characterize whether the respondent self-reported any use of marijuana and cocaine, heroin, and/or methamphetamine, respectively, within the final year.
False Identification Current fake ID “ownership” was assessed dichotomously in each samples (0=No, 1=Yes). The cross-sectional research also included further objects that asked respondents whether they had used the fake ID in a bar or club and whether they had used it in a retailer to buy alcohol.
Trait and risk factor (matching) covariates Fifteen variables had been used in propensity rating matching within the cross-sectional sample and fourteen had been used within the longitudinal sample. Variables had been chosen resulting from their inclusion in each datasets and previous research suggesting that they could be related to the propensity to own a fake ID and expertise one of the 5 outcomes. These matching variables are: (1) age, (2) age of alcohol use onset, (three) employment status, (four) publicity to substance use, (5) family revenue, (6) gender, (7) GPA, (8) Greek membership, (9) well being, (10) low self-management, (eleven) peer substance use, (12) race, (13) rural residence location (only measured within the cross-sectional research), (14) sexual orientation (1=LGBT), and (15) subjective distress.
Eight of the fifteen variables had been measured identically and a ninth was measured almost identically. Amongst these identically measured had been age, age of first alcohol use, employment status (0= not employed; 1=employed), gender (0=feminine; 1=male), self-reported grade point 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 well being was measured with an item that asked respondents to rate their own well being—the cross-sectional research offered responses ranging from 1 (poor) to four (wonderful) whereas the longitudinal research choices ranged from 1 (poor) to 5 (wonderful).
The cross sectional research utilized four-item measures tailored from Lee, Akers, and Borg (2004) to characterize publicity to substance use (α=.786) and peer substance use (α=.801). As the longitudinal information didn’t include similarmeasures, each of those constructs was represented by a single item relatively than a four-item scale. The first (publicity) was measured dichotomously while the second (peer substance use) was measured on a six-choice ordinal scale. Low self-management was operationalized using the 24-item Grasmick et al. (1993) scale (α=.889) within the cross-sectional research and the NEO Five Issue Inventory conscientiousness scale (reverse-coded) within the longitudinal sample (α=.844; Costa & McCrae, 1992). Subjective misery was measured using Cohen and Williamson’s (1988) ten-item perceived pupil stress scale (α=.814) within the cross-sectional research and the World Severity Index from the Transient Symptom Inventory-18 within the longitudinal sample (Derogatis, 2000). Larger values on these scales characterize extra publicity to substance use, a bigger portion of peers that use substances, decrease self-management, and extra subjective misery, respectively.
Each studies included a single-item family socioeconomic status measure. In the cross-sectional research a measure of family revenue was used. Contributors selected between choices ranging from beneath $10,000 per year (coded 1) to over $one hundred seventy five,000 per year (coded 9). An item assessing whether or not students had been the primary of their family to attend faculty (0=No, 1=Yes) was utilized within the longitudinal study.
Rural residence location was used within the cross-sectional research, however no related measure was out there within the longitudinal data. This variable was essential to include despite creating differing matching criteria due to the characteristics of the research area. The cross-sectional sample was drawn from an space that may be very rural except one major 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 comparison, this was not a particular consideration for the longitudinal sample, which originated from a college of 35,000 that attracts students from large neighboring cities and its own reasonably large 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 also documented participants’ using of the fake ID in bars/clubs and stores. We estimated the bivariate associations of fake IDs with the 5 specified substance use outcomes in each samples—a rudimentary “fake ID effect.”
Subsequent, to raised determine the power of the “fake ID impact” after accounting for trait measures, propensity rating matching (PSM) was used for each samples. PSM presents a clearer image of the relationship between 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). Also of word, PSM is preferable to multivariate regression models in cases similar to this the place the variable of curiosity is probably not independently connected to the dependent variable, however is probably going correlated with these which are and likewise happens extra proximally. The propensity matching techniques developed by Rosenbaum and Rubin (1983, 1985) can be used to create a sample with groups which are related in all relevant variables apart from the “remedy” (i.e., fake ID possession). While their techniques do lead to a reduction in size of analytic sample (usually leading PSM to be referred to as resampling), they are efficient at creating a scenario whereby the impact of “remedy” may be estimated as the common distinction between these uncovered to the remedy and “counterfactuals,” defined because the anticipated outcomes had been it not for publicity to the remedy (Guo & Fraser 2009). On this case, the PSM methodology creates analytic groups whereby differences apart from false identification use are minimalized.
As instructed by Rosenbaum and Rubin (1983, 1985), we utilized logistic regression to estimate a propensity rating for each participant in each analytic sample. Fake ID possession was regressed on 15 covariates within the cross-sectional sample (age, age of firsts alcohol use, employment status, publicity to substance use, family revenue, gender, grade point common, membership in a campus Greek organization, self-assessed well being, low self-management, peer substance use, race, size of residence group, sexual orientation, and subjective misery) and 14 related variables (size of residence group excluded, as explained above) in the principle longitudinal PSM analysis (i.e. fake ID possession measured on the second semester and outcomes evaluated within the third and fourth semesters). Utilizing these models, each participant’s propensity rating was then calculated as their conditional likelihood of having a fake ID. Following an evaluation of regions of frequent support, we created comparison groups within each 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 anticipated lower in sample size (n=817 and n=518, respectively) however a ample number of circumstances had been retained for statistical comparisons.
Rates of fake ID ownership
Rates of fake ID ownership had been quite high, notably within the cross-sectional sample. That’s, of the 1,454 underage alcohol shoppers within the cross-sectional sample, 583 or 40.1% own or have owned a fake ID, 560 (38.5%) have used a fake ID at a bar, and 460 (27.8%) have used the ID to buy alcohol at a store. Prevalence charges of false ID use within the Midwestern sample changed over time. Fake ID ownership amongst students beneath 21 peaked in the course of the third year of college (pre-faculty=12.5%, first-year fall=17.1%, first-year spring=21.four%, second-year fall=28.1%, second-year spring=32.2%, third-year fall=34.9%, third-year spring=39.0%, fourth-year fall=38.1%, fourth-year spring= fewer than ten students had been beneath the minimum 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). Common scores for each final result (frequent binge ingesting [10 or extra days within the final month], self-reported alcohol related issues, alcohol-related arrests, marijuana use, and onerous drug use) are offered for people who have and have not owned a fake ID, used a fake ID at a bar/club (cross-sectional sample only), and used a fake ID at a retailer (cross-sectional sample only). Unbiased samples t-exams had been carried out to find out whether, on common, differences exist between fake ID users and non-users. Every of the exams reached significance. No matter whether the focus was ownership of a fake ID or using it at a specific kind of outlet, the results had been consistent. At the bivariate degree, extra people with false identification engage in frequent binge ingesting, have been arrested/cited for an alcohol violation, engage in marijuana use, and use onerous drugs. People with fake identification also, on common, report extra alcohol-related problems. These results would indicate that fake IDs are a vehicle of risk. However, it’s possible that fake ID ownership (and related dangers) are extra a operate of underlying risky traits.
Propensity rating matching (PSM)
Because of the consistency within the findings so far no matter false identification measure (ownership, bar use, and/or retailer use), the extra analyses with each sample utilizes just one false identification measure, possession of a false ID. We carried out PSM analyses in each samples, to examine whether people with and with out fake IDs proceed to differ on these outcomes after being matched on substantively essential traits. Desk 2 shows that people with and with out fake IDs indeed differed from each other on these trait propensity variables, suggesting that it’s these variables which may in the end be driving the fake ID effect. Desk 2 also shows that the PSM approach worked properly in each samples, persistently decreasing bias associated with the statistically significant differences between these with and with out fake IDs by more than 50% on all however one variable. Although three significant differences still remained within the cross-sectional sample (age of alcohol use onset, Greek affiliation, and having been raised in a rural space), the magnitude of the differences in age of onset and Greek affiliation had been minor in comparison with pre-matching. On this, matching was equally, if not more, successful within the longitudinal sample. It should be famous that matching did yield a reduction in sample size. Total, however, in each samples matching seems to have created remedy and comparison groups which are extra equal and extra acceptable for comparison than the unrivaled data.
The propensity scores that had been calculated for each case are graphically displayed in Figure 1. As may be seen within the figure, a area of frequent support exists, however only a few with low propensity scores had a fake ID and only a few with high 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 related outcomes. While significantly extra of these with fake IDs within the cross-sectional sample had been frequent binge drinkers prior to matching (t=9.81, df=815), the groups had been now not significantly different after matching (t=1.81, df=815) and the common remedy impact (ATE; e.g., differences in group means), as displayed in Desk three, was decreased by 59.2%. Equally, prior to matching, fake ID homeowners had significantly greater scores on the alcohol related issues scale than non-homeowners (t=9.83, df=815), but the groups had been now not significantly different after matching (t=1.31, df=815) and the ATE was decreased by 63.four%. However, when it comes to alcohol-related arrests, the 2 groups had been still significantly different and the ATE effectively remained unchanged. As was the case for the primary 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, 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 decreased by 38.four%.
Longitudinal sample As was the case within the cross-sectional sample, propensity rating matching led to a considerable lower within the ATE for four of the 5 outcomes (Desk three, columns three–6). However, in contrast to the cross-sectional sample, ATEs remained significant for alcohol related issues (t=4.00 wave three; t=4.17, wave four, df=516) and marijuana use (t=4.13, wave three; t=2.58, wave four, df=516) after propensity rating matching. The ATE also remained significant for frequent binge ingesting (t=3.26, df=516, wave three) and onerous drug use (t=2.06, df=516, wave four) at one wave however not the other. Again, these effects sizes had been considerably decreased, however within the aforementioned circumstances, not eliminated. As was the case within the cross-sectional sample, propensity rating matching had little influence on the ATE on alcohol-related arrests.three
This research’s initial results are consistent with previous research—a considerable number of underage students have fake IDs and are at greater risk for binge ingesting, alcohol-related issues, alcohol related arrests, and other substance use (see Arria et al., 2014; Martinez & Sher, 2010; Nguyen et al., 2011). Yet our work also showed that for some outcomes, it seems that what initially might have appeared to be a “fake ID impact” is essentially the result of factors 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, but the magnitude of those relationships had been considerably diminished, most by over 40%. Alcohol-related arrests had been an exception as the relationship was unaffected by PSM (i.e., after matching, these with a fake ID had been still at equally high ranges of risk for alcohol-related arrests [DUIs, open container, etc.]). The explanation this final result is distinct from the others is not readily clear; perhaps legislation enforcement officers usually tend to difficulty citations or arrests for other substance-related offenses when an individual can be found with a fake ID. If so, the “impact” would not seem smaller in propensity rating models because the distinction can be pushed by officers’ reactions to the fake ID relatively than people’ underlying propensity.
The pattern that emerges from Desk three seems to point that non-matched samples might have overestimated the impact of false identification use on destructive outcomes, however that fake ID ownership has an impact that extends beyond shared causal factors. This specific remaining “fake ID impact” might indeed support the concept the fake ID itself serves as a kind of threshold into other forms of deviant conduct, the place those that are willing to amass fake IDs change into increasingly willing to violate other legal guidelines (see Ruedy et al., 2013; Winograd et al., 2014). However in mild of the opposite findings, it’s extra likely that fake IDs extra typically average the effects of risky traits on behavior. For instance, fake IDs might have the highest efficiency of impact by way of offering impulsive people with further means and alternative for problematic behaviors that they’d not in any other case have engaged in. Indeed, underlying trait dangers are sometimes incorporated into alternative-principle-related examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).
As such, these findings have sensible implications. Although elevated server training, fake ID production/provider legal guidelines, and liability legal guidelines are an essential technique of addressing the dangers of fake IDs as a type of alcohol access (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), pretend-ID related outcomes may also partly be a operate of trait dangers that may additionally be addressed with intervention. One approach to begin addressing this combination of factors may be by way of motivational, normative suggestions-based mostly, or abilities interventions which are particularly aimed at lowering the probability that at-risk students acquire a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Furthermore, a fake ID obtainment-aimed intervention might presumably be broadly incorporated into interventions which are especially tailor-made towards addressing each people’ risky traits and their resulting behaviors (see Conrod et al., 2006).
Although an excellent power of this research rests in the similar findings found with distinctive faculty populations, these findings is probably not generalizable to non-faculty attending populations. Moreover, fake ID policies, enforcement, and concern of sanctions might fluctuate considerably in numerous localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For instance, some ingesting establishments may be lenient of their carding policies, deliberately accept false identification, and/or not be topic to rigorous regulatory enforcement (Murray, 2005). Also, 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 advantageous, suspension of driver’s license, and the potential of probation or jail time. Future research should consider the affect of fake ID relative to differential policies and enforcement of the minimum legal ingesting age, together with group efforts (Grube, 1997). Further, while fifteen distinct traits had been included within the matching course of, there stays the likelihood that further factors not measured in our information would have an effect on each the willingness to access a fake ID and the end result measures. If so, the “fake ID impact” may be even smaller than our matching models suggested.
In concluding that the “fake ID impact” is principally a operate of phenotypic risk, fake ID ownership might serve as an indicator of heightened risk for extra extreme ingesting related problems. Although most penalties for fake ID ownership are punitive (fines, probation/jail, and/or lack of driver’s license), policy-makers, college officials, and practitioners should target fake ID homeowners for intervention methods aimed at decreasing high-risk ingesting behaviors (and other problematic behaviors linked to phenotypic risk). Although elevated penalties and enforcement of the minimum legal ingesting age has the potential to reduce fake ID ownership, we caution policy-makers to judge and contemplate the destructive penalties of shifting faculty ingesting away from regulated establishments the place security and emergency providers are extra readily available (see Baldwin et al., 2012; 2014). Although our findings found that fake ID possession (no matter particular person risk characteristics) elevated the chance for alcohol related arrests, drug use, and alcohol related issues (Midwest sample only), we didn’t assess victimization and other harms associated with extreme alcohol consumption that could enhance in spaces not topic to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future research is needed to judge the affect that fake ID enforcement might have on problematic ingesting each in regulated and unregulated spaces.