Underage school college students who receive and use false identification (fake ID) are in danger for unfavourable outcomes. Nonetheless, it’s presently unclear how uniquely the fake ID itself serves as a automobile to subsequent hurt (i.e., the “fake ID effect”) over and above general and trait-associated danger elements (e.g., deviant friends, low self-management).
With a view to investigate whether the “fake ID effect” would hold after accounting for phenotypic danger, 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. People with a fake ID have been matched with individuals with no fake ID, in terms of plenty of trait-primarily based and social danger factors. These matched teams have been then compared on five problematic outcomes (i.e., frequent binge ingesting, alcohol-associated problems, arrests, marijuana use, and onerous drug use).
Findings confirmed that “fake ID effects” have been considerably—although not absolutely—diminished following PSM. The “fake ID effect” remained strongest for alcohol-associated arrests. This will likely relate to problems with enforcement and college students’ willingness to have interaction in deviant habits with a fake ID, or it could be a perform of combined processes.
General, the findings counsel that interventions shouldn’t only be aimed at reducing fake ID-associated alcohol entry specifically, however also needs to be aimed extra typically in direction of at-danger youths’ entry to alcohol. Future research may examine whether fake IDs have their strongest efficiency as moderators of the results of dangerous traits—corresponding to impulsiveness—on ingesting outcomes.
Key phrases: False identification, Fake IDs, underage alcohol use, heavy episodic ingesting, binge ingesting
Fake IDs, a novel mode of alcohol entry, are increasingly wanted as individuals near the minimum legal ingesting 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 specifically crafted doc 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 (outlined as 5+ drinks per event; Martinez, et al., 2007).
This bidirectional relation not only illustrates the public well being risks of this mode of alcohol entry, however begs the query of whether it’s extra the case that a fake ID itself serves as a automobile to subsequent hurt (i.e., the “fake ID effect”) or whether such harms and outcomes are predominantly pushed by a general stage of phenotypic danger on the a part of the fake ID “proprietor” (e.g., deviant peer associations, low self-management). Though general alcohol entry theories may support the previous speculation virtually totally (particularly, fake ID possession will increase alcohol entry and subsequent hurt; see Gruenewald, 2011), general criminological theories of phenotypic danger support the latter (particularly, that broad categories of danger—or propensities to have interaction in dangerous habits—are the true reason behind hurt; see Pratt & Cullen, 2000). Actually, such propensities might be what predicts fake ID obtainment within the first place, and though the energy of the fake ID effect seems to increase over time, it’s greatly diminished after controlling for intercourse, Greek status, and pre-school rates of ingesting (Martinez et al., 2007). In sum, it’s unclear how sturdy the fake ID effect might be after accounting for people’ levels of phenotypic or propensity danger—although this query has bearing on prevention and policy initiatives, which may focus on either strengthening enforcement of fake ID laws themselves, increasing assets for trait-primarily based at-danger youth programs, or a community-pushed mixture of each (see Fell, Thomas, Scherer, Fisher & Romano, 2015; Fell, Scherer, Thomas & Voas, 2016; Fell, Scherer & Voas, 2015; Grube, 1997) .
Thus, with a purpose to investigate the energy of the fake ID effect, we matched college students with and without fake IDs on plenty of danger-primarily based covariates using propensity score matching (PSM) techniques. We first compared matched teams’ ingesting- and drug-use-associated outcomes in a cross-sectional pattern of n=1,454 school college students at a large Southeastern university. We also compared matched teams in a further longitudinal replication pattern of n=three,720 undergraduates at a large Midwestern university. We hypothesized that the results of fake ID ownership on outcomes can be greatly diminished by—and due to this fact largely attributable to—the pre-present trait-primarily based elements on which fake ID owners and non-owners could possibly be matched. These comparisons can inform the extent to which the relationship between unfavourable outcomes and false identification ownership are attributed to selection elements, which again, could have sensible application for intervention and policy.
Process and Participants
Two samples have been separately investigated following Institutional Evaluation Board (IRB) approval: (1) A cross-sectional pattern of n=1,454 underage school college students from a large Southeastern College (IRB Protocol H12032) and (2) a potential replication pattern of n=three,720 undergraduates underneath the minimum legal ingesting age from a large Midwestern university (IRB Protocol 01-01-001). Of notice, each samples supply unique insights into the relationship between false identification use and unfavourable outcomes. Extra specifically, the cross-sectional study includes items that distinguish between the usage of fake IDs in numerous situations (at bars, at grocery stores, etc.) and the longitudinal study gives perception into the potential effects of fake ID ownership over time and establishes temporal order.
With regard to the cross-sectional pattern, in the course of the tutorial 12 months 2011–2012, participants have been recruited from forty randomly chosen large (>99 college students) and average enrollment (30–99 college students) classes. Participants completed a one-web page informed consent doc within the chosen courses before being given a six-web page paper survey about school life and behaviors to complete with pencil or pen. Participants were not compensated. All enrolled college students have been invited to take part and the response charge was high at 80.4% (Stogner & Miller, 2013; 2014; Hart et al., 2014). After those above the legal ingesting threshold have been removed, the analytic pattern was n=1,454 underage individuals. The pattern was largely representative of the university with regard to demographics and was specifically 51.6% feminine, 68.9% White/non-Hispanic, with an average age of 18.ninety five (SD=.795). Though this pattern 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 outside of the person’s management (residence location, parental income, sexual orientation, etc.).
The longitudinal pattern also utilized a self-report survey methodology. All incoming college students in 2002 have been recruited to complete an instrument in the course of the summer prior to college entrance using paper and pencil after which have been requested to complete on-line surveys each semester for the subsequent four years (a complete of eight semesters). Students provided informed consent and have been compensated $25 in each wave. After excluding the n=35 who have been of age, 88% of the eligible getting into class completed the survey (n=three,720). The pattern was 53.7% feminine, 90.three% White/non-Hispanic, and averaged 17.9 (SD=.36) years of age (reflecting demographics that are representative of the university as an entire [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 ingesting age, climbing expectedly to 99.7% by the final semester of faculty, Pattern retention was good, ranging from sixty nine% to 87% of baseline respondents participating at each subsequent wave. Retention biases have been low, although individuals have been extra prone to stay within the pattern in the event that they have been females (OR=2.33) and have been much less prone to stay within the pattern in the event that they have been frequent binge drinkers (OR=.88; Sher & Rutledge, 2007). By the final time-level, the pattern measurement was n=2,250, although 90% of students participated in two or extra assessment waves and 82% participated in three or extra waves. The longitudinal PSM introduced inside the textual content utilized the first two years of faculty only (i.e., the first four semesters, when the overwhelming majority of participants have been underage) and, in step with most PSM research, only created matches between individuals in a way which is straight akin to the analysis carried out with the cross-sectional sample.1
For the purposes of replication, it was essential that the measures utilized in each the cross-sectional and longitudinal studies stayed as similar as possible. For ease of presentation, measures are organized in terms of their conceptual importance to the overall study with cross-sectional and longitudinal measures explained together in each section. Timing of the longitudinal measures was considered essential and is described as is appropriate. Particularly, although the eight-wave longitudinal pattern included multiple measurements of many covariates across time, the primary longitudinal PSM only utilized measurements as they’d be anticipated to occur 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 ownership at Wave 2 which in turn assessed as a predictor for consequence measures at each Waves three and Wave 4). The second semester of faculty (Wave 2) was chosen because the singular target time-level at which fake ID ownership (or the “fake ID effect”) was measured, as a result of it’s thought to be a peak time of danger for unfavourable ingesting-associated outcomes and false ID ownership (see Martinez et al., 2008).
Predominant Outcomes Five outcomes associated to substance use have been explored. First, a measure of frequent binge ingesting was created in each samples. A six-option ordinal merchandise requested respondents what number of days within the final month did they consume five or extra alcoholic drinks. A intercourse-specific binge ingesting measure was not available. These deciding on either of the 2 highest frequency choices (10–19 days and 20+ days) have been categorized as frequent binge drinkers while all others have been not. This dichotomous merchandise 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 symbolize alcohol-associated problems within the cross-sectional sample. This ten-merchandise scale assesses the diploma to which the person feels that alcohol use has created relationship, household, well being, behavioral, and professional/college problems within the final 12 months and reveals satisfactory reliability (α=.822). Within the longitudinal pattern, this scale was approximated from ten items taken from the Young Grownup Alcohol Issues Screening Check (YAAPST; Hurlbut & Sher, 1992) with satisfactory 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 had ever been arrested or cited for driving underneath the affect, underage ingesting, public dysfunction (due to alcohol), being drunk in public, or an open container violation within the final year. The ultimate two outcomes have been each dichotomous and measured similarly in each pattern; marijuana use and onerous drug use symbolize whether the respondent self-reported any use of marijuana and cocaine, heroin, and/or methamphetamine, respectively, within the final year.
False Identification Present fake ID “ownership” was assessed dichotomously in each samples (zero=No, 1=Sure). The cross-sectional study also included extra items 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 purchase alcohol.
Trait and danger factor (matching) covariates Fifteen variables have been utilized in propensity score matching within the cross-sectional pattern and fourteen have been used within the longitudinal sample. Variables have been chosen due to their inclusion in each datasets and previous research suggesting that they may be associated to the propensity to personal a fake ID and experience one of the five outcomes. These matching variables are: (1) age, (2) age of alcohol use onset, (three) employment status, (4) publicity to substance use, (5) household income, (6) gender, (7) GPA, (eight) Greek membership, (9) well being, (10) low self-management, (11) peer substance use, (12) race, (thirteen) rural residence location (only measured within the cross-sectional study), (14) sexual orientation (1=LGBT), and (15) subjective distress.
Eight of the fifteen variables have been measured identically and a ninth was measured nearly identically. Among those identically measured have been age, age of first alcohol use, employment status (zero= not employed; 1=employed), gender (zero=feminine; 1=male), self-reported grade level average (GPA), membership in a campus Greek group (zero=non-member; 1=member), race (zero=white, 1=non-white), and sexual orientation (zero=heterosexual; 1=lesbian, homosexual, bisexual, or other). Self-reported well being was measured with an merchandise that requested respondents to charge their own well being—the cross-sectional study provided responses ranging from 1 (poor) to 4 (wonderful) whereas the longitudinal study choices ranged from 1 (poor) to 5 (wonderful).
The cross sectional study utilized four-merchandise measures tailored from Lee, Akers, and Borg (2004) to symbolize publicity to substance use (α=.786) and peer substance use (α=.801). Because the longitudinal data did not include similarmeasures, each of these constructs was represented by a single merchandise relatively 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-management was operationalized using the 24-merchandise Grasmick et al. (1993) scale (α=.889) within the cross-sectional study and the NEO Five Issue Inventory conscientiousness scale (reverse-coded) within the longitudinal pattern (α=.844; Costa & McCrae, 1992). Subjective distress was measured using Cohen and Williamson’s (1988) ten-merchandise perceived scholar stress scale (α=.814) within the cross-sectional study and the Global Severity Index from the Transient Symptom Inventory-18 within the longitudinal pattern (Derogatis, 2000). Increased values on these scales symbolize extra publicity to substance use, a larger portion of friends that use substances, decrease self-management, and extra subjective distress, respectively.
Both studies included a single-merchandise household socioeconomic status measure. Within the cross-sectional study a measure of household income was used. Participants chose between choices ranging from underneath $10,000 per 12 months (coded 1) to over $175,000 per 12 months (coded 9). An merchandise assessing whether or not college students have been the first of their household to attend school (zero=No, 1=Sure) was utilized within the longitudinal study.
Rural residence location was used within the cross-sectional study, however no similar measure was obtainable within the longitudinal data. This variable was essential to incorporate despite creating differing matching criteria as a result of characteristics of the study area. The cross-sectional pattern was drawn from an area that is very rural aside from one major city; thus, a dichotomous merchandise representing whether the scholar 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 pattern, which originated from a university of 35,000 that attracts college students from two large neighboring cities and its personal reasonably large population.
First, we estimated the proportions of fake ID ownership in each the cross-sectional and longitudinal samples. Along with possession of a fake ID, the cross-sectional pattern also documented participants’ 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 decide the energy of the “fake ID effect” after accounting for trait measures, propensity score matching (PSM) was used for each samples. PSM gives a clearer picture of the relationship between two variables than bivariate analyses which may yield spurious outcomes (Guo & Fraser 2009) and has been used to evaluate points associated to substance use (Miller et al., 2011). Also of notice, PSM is preferable to multivariate regression models in cases corresponding to this where the variable of curiosity may not be independently connected to the dependent variable, however is probably going correlated with those that are and in addition occurs extra proximally. The propensity matching methods developed by Rosenbaum and Rubin (1983, 1985) can be utilized to create a pattern with two teams that are similar in all related variables except for the “remedy” (i.e., fake ID possession). Whereas their methods do lead to a reduction in measurement of analytic pattern (usually main PSM to be referred to as resampling), they are effective at creating a state of affairs whereby the effect of “remedy” can be estimated as the average distinction between those exposed to the remedy and “counterfactuals,” outlined because the anticipated outcomes have been it not for publicity to the remedy (Guo & Fraser 2009). On this case, the PSM methodology creates analytic teams whereby differences other than false identification use are minimalized.
As prompt by Rosenbaum and Rubin (1983, 1985), we utilized logistic regression to estimate a propensity score for each participant in each analytic sample. Fake ID possession was regressed on 15 covariates within the cross-sectional pattern (age, age of firsts alcohol use, employment status, publicity to substance use, household income, gender, grade level average, membership in a campus Greek group, self-assessed well being, low self-management, peer substance use, race, measurement of residence community, sexual orientation, and subjective distress) and 14 similar variables (measurement of residence community excluded, as explained above) in the main longitudinal PSM analysis (i.e. fake ID possession measured at the second semester and outcomes evaluated within the third and fourth semesters). Utilizing these models, each participant’s propensity score was then calculated as their conditional likelihood of having a fake ID. Following an assessment of areas of frequent support, we created comparison teams within each pattern using a two-to-one nearest neighbor matching algorithm with a caliper calculated as .25σ of the propensity scores (see Guo & Fraser 2009). This caliper was .0725 within the cross-sectional pattern and .0426 within the longitudinal sample. This matching technique led to the anticipated lower in pattern measurement (n=817 and n=518, respectively) however a enough variety of circumstances have been retained for statistical comparisons.
Charges of fake ID ownership
Charges of fake ID ownership have been quite high, significantly within the cross-sectional sample. That’s, of the 1,454 underage alcohol customers within the cross-sectional pattern, 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 purchase alcohol at a store. Prevalence rates of false ID use within the Midwestern pattern changed over time. Fake ID ownership amongst college students underneath 21 peaked in the course of the third 12 months of faculty (pre-school=12.5%, first-12 months fall=17.1%, first-12 months spring=21.4%, 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 have been below the minimum legal ingesting age).2
The “fake ID effect” prior 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 4 are reported for the longitudinal pattern). Average scores for each consequence (frequent binge ingesting [10 or extra days within the final month], self-reported alcohol associated problems, alcohol-associated arrests, marijuana use, and onerous drug use) are introduced for those who have and haven’t owned a fake ID, used a fake ID at a bar/membership (cross-sectional pattern only), and used a fake ID at a retailer (cross-sectional pattern only). Impartial samples t-exams have been conducted to find out whether, on average, differences exist between fake ID customers and non-users. Each 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 outcomes have been consistent. At the bivariate stage, extra individuals 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 average, report extra alcohol-associated problems. These outcomes would indicate that fake IDs are a automobile of risk. Nonetheless, it’s attainable that fake ID ownership (and associated risks) are extra a perform of underlying dangerous traits.
Propensity score matching (PSM)
As a result of consistency within the findings to this point no matter false identification measure (ownership, bar use, and/or retailer use), the additional analyses with each 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 individuals with and without fake IDs continue to vary on these outcomes after being matched on substantively essential traits. Table 2 reveals that individuals with and without 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. Table 2 also reveals that the PSM technique worked well in each samples, consistently reducing bias related to the statistically significant differences between those with and without fake IDs by more than 50% on all however one variable. Though three significant differences nonetheless remained within the cross-sectional pattern (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 have been minor compared to pre-matching. On this, matching was equally, if no more, profitable within the longitudinal sample. It needs to be noted that matching did yield a reduction in pattern size. General, nonetheless, in each samples matching seems to have created remedy and comparison teams that are extra equivalent and extra applicable for comparison than the unmatched data.
The propensity scores that have been calculated for each case are graphically displayed in Determine 1. As can 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.
Evaluating fake ID owners and non-owners after PSM
Cross-sectional pattern After matching, false identification owners and non-owners have been compared on each of the five substance use associated outcomes. Whereas significantly extra of those with fake IDs within the cross-sectional pattern have been frequent binge drinkers prior to matching (t=9.eighty one, df=815), the teams have been not significantly different after matching (t=1.eighty one, df=815) and the average remedy effect (ATE; e.g., differences in group means), as displayed in Table three, was reduced by 59.2%. Equally, prior to matching, fake ID owners had significantly greater scores on the alcohol associated problems scale than non-owners (t=9.eighty three, df=815), however the teams have been not significantly different after matching (t=1.31, df=815) and the ATE was reduced by 63.4%. Nonetheless, in terms of alcohol-associated arrests, the 2 teams have been nonetheless significantly different and the ATE successfully remained unchanged. As was the case for the first two outcomes, fake ID possession was related to marijuana use prior to matching (t=9.36), however not after (t=1.52; ATE reduced by 60.4%). Lastly, onerous drug use was related to fake ID possession each before (t=7.26, df=815) and after matching (t=2.29, df=815), however the ATE was reduced by 38.4%.
Longitudinal pattern As was the case within the cross-sectional pattern, propensity score matching led to a considerable lower within the ATE for four of the five outcomes (Table three, columns three–6). Nonetheless, not like the cross-sectional pattern, ATEs remained significant for alcohol associated problems (t=4.00 wave three; t=4.17, wave 4, df=516) and marijuana use (t=4.thirteen, wave three; t=2.58, wave 4, df=516) after propensity score 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 4) at one wave however not the other. Once more, these effects sizes have been considerably reduced, however within the aforementioned circumstances, not eliminated. As was the case within the cross-sectional pattern, propensity score matching had little affect on the ATE on alcohol-associated arrests.three
This study’s initial outcomes are in step with previous research—a considerable variety of underage college students have fake IDs and are at greater danger for binge ingesting, alcohol-associated problems, alcohol associated arrests, and other substance use (see Arria et al., 2014; Martinez & Sher, 2010; Nguyen et al., 2011). Yet our work also confirmed that for some outcomes, plainly what initially may need gave the impression to be a “fake ID effect” is essentially the results of elements that influenced each the acquisition of the false ID and the outcome. The numerous relationship between fake ID use and other substance use outcomes usually remained after PSM, however the magnitude of these relationships have been considerably diminished, most by over forty%. Alcohol-associated arrests have been an exception as the relationship was unaffected by PSM (i.e., after matching, those with a fake ID have been nonetheless at similarly high levels of danger for alcohol-associated arrests [DUIs, open container, etc.]). The rationale this consequence is distinct from the others is not readily clear; maybe regulation enforcement officers usually tend to problem citations or arrests for other substance-associated offenses when a person can also be found with a fake ID. If this is the case, the “effect” would not seem smaller in propensity score models because the distinction can be pushed by officers’ reactions to the fake ID relatively than individuals’ underlying propensity.
The sample that emerges from Table three appears to indicate that non-matched samples could have overestimated the effect of false identification use on unfavourable outcomes, however that fake ID ownership has an effect that extends past shared causal factors. This specific remaining “fake ID effect” could indeed support the concept that the fake ID itself serves as a sort of threshold into other types of deviant habits, where those that are keen to acquire fake IDs become increasingly keen to violate other laws (see Ruedy et al., 2013; Winograd et al., 2014). But in gentle of the other findings, it’s extra seemingly that fake IDs extra typically average the results of dangerous traits on behavior. For instance, fake IDs could have the best efficiency of effect by way of offering impulsive individuals with extra means and alternative for problematic behaviors that they’d not in any other case have engaged in. Indeed, underlying trait risks are sometimes included into alternative-concept-associated examinations of crime (Grasmick et al., 1993; Lagrange & Silverman, 1999).
As such, these findings have sensible implications. Though elevated server coaching, fake ID manufacturing/supplier laws, and legal responsibility laws are an essential means of addressing the risks of fake IDs as a type of alcohol entry (Fell, Scherer, Thomas & Voas, 2014; Yörük, 2014), pretend-ID associated outcomes might also partly be a perform of trait risks that can moreover be addressed with intervention. One way to start addressing this combination of things may be by way of motivational, normative feedback-primarily based, or abilities interventions that are specifically aimed at lowering the chance that at-danger college students receive a fake ID (see Fromme & Orrick, 2004; Larimer & Cronce, 2007). Moreover, a fake ID obtainment-aimed intervention may presumably be broadly included into interventions that are particularly tailored towards addressing each individuals’ dangerous traits and their resulting behaviors (see Conrod et al., 2006).
Though a great energy of this study rests in the similar findings found with two unique school populations, these findings may not be generalizable to non-school attending populations. Additionally, fake ID insurance policies, enforcement, and concern of sanctions could differ considerably in numerous localities (Erickson, Lenk, Toomey, Nelson & Jones-Webb, 2016). For instance, some ingesting establishments may be lenient of their carding insurance 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 purchase alcohol varies considerably from state to state including the type of offense, amount of nice, suspension of driver’s license, and the opportunity of probation or jail time. Future research ought to consider the impression of fake ID relative to differential insurance policies and enforcement of the minimum legal ingesting age, including community efforts (Grube, 1997). Further, while fifteen distinct traits have been included within the matching course of, there remains the possibility that extra elements not measured in our data would affect each the willingness to entry a fake ID and the result measures. If this is the case, the “fake ID effect” may be even smaller than our matching models suggested.
In concluding that the “fake ID effect” is especially a perform of phenotypic danger, fake ID ownership could serve as an indicator of heightened danger for extra extreme ingesting associated problems. Though most penalties for fake ID ownership are punitive (fines, probation/jail, and/or loss of driver’s license), policy-makers, university officials, and practitioners ought to target fake ID owners for intervention methods aimed at reducing high-danger ingesting behaviors (and other problematic behaviors linked to phenotypic danger). Though elevated penalties and enforcement of the minimum legal ingesting age has the potential to scale back fake ID ownership, we caution policy-makers to judge and consider the unfavourable consequences of moving school ingesting away from regulated establishments where security and emergency companies are extra readily available (see Baldwin et al., 2012; 2014). Though our findings found that fake ID possession (no matter individual danger characteristics) elevated the danger for alcohol associated arrests, drug use, and alcohol associated problems (Midwest pattern only), we did not assess victimization and other harms related to excessive alcohol consumption that might improve in spaces not subject to regulatory controls (Miller, Levy, Spicer & Taylor, 2006). Future research is needed to judge the impression that fake ID enforcement could have on problematic ingesting each in regulated and unregulated spaces.