ABSTRACT
The United States government’s clearance adjudication process examines past behavior to determine soldier eligibility for a security clearance. For young recruits with a short-documented history, however, little information is available. While informal social controls generally associate with criminal desistance, desistance speaks little about those who have yet to offend. This work extends informal social control theory to better understand military clearance eligibility adjudication outcomes as measured in terms of a self-equity construct. This analysis looks at a twelve-year cohort of US Army recruits who received clearance eligibility adjudication within the first five years of service, as recorded in military archival data in the Person-event Data Environment (PDE) database. Laub, Rowan, & Sampson’s (2018) age-graded theory of informal social control is tested to estimate models, capturing the self-equity effects of moral waiver, qualification-test percentiles, service time, rank, education, and childbirth–marriage interaction. The results demonstrate that self-equity substantively relates to security clearance eligibility adjudication outcomes. These findings carry policy implications for the creation of an objective trustworthiness measure in the absence of reliable documented history.
KEYWORDS: Self-equity, trustworthiness, informal social control theory, security clearance eligibility
“The most dangerous creation of any society is the man who has nothing to lose.”
– James Baldwin
What is the public significance of this article?—This study presents “self-equity” as a trustworthiness measure to better inform automated threat assessments for the US Army insider threat program. The work empirically tests a self-equity construct in terms of security clearance eligibility adjudication results for US Army recruits and externally validates the results with Guard/Reserve component data. The findings strongly suggest self-equity can be profitably employed as a theoretical trustworthiness construct.
Introduction
The United States government grants personnel security clearances through an adjudicative process that culminates in a characterization of individual security risk. Thirteen guidelines (32 U.S.C 147) are applied in reviewing a subject’s trustworthiness in terms of clearance-worthiness. Notwithstanding this protocol, adjudicators routinely grant security clearance eligibility to teenagers in some cases who may require access to classified information in the course of their duties, presupposing their clearance-worthiness in the absence of a known history of delinquent behavior and guideline violations (Adjudicative Desk Reference [ADR], 2014). Criminological research indicates that past delinquency is a good predictor of future misbehavior (Moffitt, 1993; Piliavin, Gartner, Thornton, & Matsueda, 1986); however, the short life histories of many young recruits render this basis of prediction unreliable as a consequence of a fundamental ignorance of any history (Kelly, 2017, p. 32; Denby & Gammack, 1999; Holtzman, 1988, p. 27).
Well-established research indicates the passage of certain life-course events tends to reduce recidivism in those with criminal backgrounds (Laub, Rowan, & Sampson, 2018). According to Laub’s age-graded theory of informal social control, desistance tends to follow specific turning points in life, such as marriage, military service, and employment. Deterrence theories rely heavily on the loss-aversion principle, in which delinquency decreases as the perceived cost of such behavior exceeds the perceived benefit. This work tests Laub’s informal social control theory as applied to US Army recruits to demonstrate the protective effects of informal social controls.
This work explores how self-equity may be applied to identify dissuasive, anti-delinquency forces that explain favorable security clearance adjudications. The age-graded theory of informal social control is tested through the following research question:
How well does the age-graded theory of informal social control explain clearance adjudication in Army recruits?
Research motivation
Insider threats are a pervasive problem of escalating scope and impact (Proudfoot, Boyle, & Schuetzler, 2016). By design, security controls allow access to authorized personnel, and are thus minimally effective in blocking malicious insiders with legitimate access (Guo, Yuan, Archer, & Connelly, 2011). The cost of repairing information security breaches is mounting, and federal spending on information security is massive (Fischer, 2016). While sophisticated defensive strategies are in place, breaches are ever harder to remedy (Wilshusen, 2014) and nearly half are inside jobs (Ponemon Institute, 2016). Executive Order 13,587, issued in 2011, was crafted to address this existential threat to national security.
Executive Order 13,587 directs structural reforms for the responsible sharing and safeguarding of classified information and issues responsibility to “implement an insider threat detection and prevention program consistent with guidance and standards developed by the National Insider Threat Task Force.” In November 2012, the National Insider Threat Policy and Minimum Standards for Executive Branch Insider Threat Programs directed military departments to “build and maintain an insider threat analytic and response capability to manually and/or electronically gather, integrate, review, assess, and respond to information derived from counter intelligence, security, information assurance, law enforcement, the monitoring of user activity, and other sources as necessary and appropriate.” The Department of Defense executed the order through DoD Directive 5205.16 (DoD Insider Threat Program), which directs DoD component heads to “establish or maintain a multidisciplinary threat management capability to conduct and integrate the monitoring, analysis, reporting, and response to insider threats” to “facilitate timely, informed decision making.” Army leadership, interpreting the directive as requiring an analysis hub, issued Directive 2013–18 in response, which requires the Deputy Chief of Staff (DCS) G-2 to “evaluate and use all required personnel security information [and] support an integrated, centralized analysis, reporting, and response capability to detect and mitigate threats.” Insider threat monitoring and analysis tend to be laborious – difficult to automate and prone to false positives. A more promising strategy would be to inform a theoretical trustworthiness measure that may assist in the design of automated insider threat detection algorithms by better understanding factors associated with those unlikely to exhibit concerning behavior.
This work defines an “insider threat” as a clearance-eligible individual who is not trustworthy for clearance eligibility. The adjudicative process evaluates a person against 13 guidelines to determine his or her eligibility for access to classified information (32 U.S.C. 147). Following the guidelines, the absence of concerning behaviors or disqualifying conditions indicates that the subject is trustworthy for security clearance eligibility. However, the mere absence of negative indicators does not imply trustworthiness. Even were it demonstrated that past criminal history will always lead to future criminal activity, the assumption cannot be turned around: to assert that those with no past criminal behavior will continue to be free of criminal history in the future is to deny the antecedent, a classic logical fallacy. The ADR states that “the absence of information in criminal record files should not be viewed as positive evidence of reliability or trustworthiness” (2014, p. 53). In other words, trustworthiness for clearance eligibility cannot be assumed simply by an absence of criminal history, but involves an assay of trustworthiness not yet explicitly defined.
According to the ADR “three positive qualities, among others, are associated with trustworthiness “(1) a strong sense of social responsibility; (2) self-control, or the ability to exercise responsible and rational control over one’s impulses; and (3) the ability to maintain personal or job commitments over time” (ADR, 2014, p. 5). These qualities speak to a predictability measure, but the ADR admits that “the complexity of human behavior severely limits any ability to codify such thresholds for making adjudicative decisions” (ADR, 2014, p. 4). However, the ADR’s elements of social responsibility, self-control, and time relate closely to extant trustworthiness theories.
Trustworthiness, self-equity, and informal social control
Well-established scholarly literature provides some useful insights toward a theory of trustworthiness, describing multiple conceptions of trust (Hosmer, 1995). Greenwood and Van Buren (2010) and Blois (1999) define three elements in the granting or withholding of trust: morality, emotion, and predictability. Peccei and Guest (2006) conceive the moral element as a subjective belief in the benevolent intent of a trusted party; Hosmer (1995) views it as a belief that the trusted party will act consistent with the greater good and identifies the emotional component as an irrational willingness to increase vulnerability. Irrational, unwarranted trust tends to accrue to the physically attractive (Mulford, Orbell, Shatto, & Stockard, 1998) and charismatic (Conger, Kanungo, & Menon, 2000). Wicks, Berman, and Jones (1999, p. 103) identify trust as the “rational prediction” relative to risk. Wicks and Blois’s predictability concepts fit firmly within an objective epistemic view consistent with the ADR’s concept of trustworthiness.
Trustworthiness and trust differ according to perspective: trustworthiness derives from perceived characteristics and trust is a belief about those characteristics. Kharouf, Lund, and Sekhon (2014) identify “trustworthiness” with beliefs about past behaviors and “trust” as beliefs about future behaviors; thus, trustworthiness is a measure of how likely something will behave as expected, or, in a word, predictability. Mayer, Davis, and Schoorman (1995) point out that the perception of trustworthiness does not directly relate to trust. Assuming one is trustworthy, and trust is a rational willingness to expose vulnerability, there must be reasons to render trust. In sum, trustworthiness is measurable through observable characteristics relative to the trustor.
Gambetta (1988) declares trustworthiness is not provable; and it must be acknowledged that indisputable proof is rare in the social sciences, where theories and evidence are commonly presented as generalizations (Salmon, 1989). Dasgupta (1988, p. 250) proposes that with knowledge of a subject’s disposition, available options, and the consequences of alternative actions, it is possible to predict which behavior will be rationally chosen. However, identifying a subject’s disposition requires personal knowledge that is not objectively measurable. Luhmann (1995) proposes the stratagem of assessing past behaviors to determine trustworthiness through a confirmatory process, beginning with small risks, as a reason for trust.
Security perspectives tend to qualify distrusted agents in terms of past delinquent behavior: trusted agents tend to be those who demonstrate an absence of recorded delinquent behavior (ADR, 2014). But insights from the field of economics may offer an alternative perspective with which to formulate risk and trustworthiness. In economics, risk tends to be a measure of uncertainty – and behavior is unpredictable (Mun, 2015). Similarly, trust is a measure of certainty – and behavior is predictable. If security risks are persons deemed likely to misbehave because of past misbehavior, and trusted agents are those deemed unlikely to misbehave because of an absence of past misbehavior, what then is the basis for trustworthiness?
Self-equity
Self-equity is simply a measure of the utility loss one risks by engaging in delinquent behavior. Thus, one who has less to lose is free to engage in delinquent behavior than one with more, though it does not necessarily imply one would. Four decades ago, Gary Becker concluded that “the economic approach is a comprehensive one that is applicable to all human behavior” (1976, p. 6). Insights into the economic dynamics of brand equity may shed light on the conundrums of personal trustworthiness explored in this research. The marketing term “brand equity” refers to customer beliefs about a brand (Keller, 1993); Aaker (1991, p. 15) defines it as “a set of brand assets and liabilities linked to a brand, its name and symbol, that add to or subtract from the value provided by a product or service to a firm and/or to that firm’s customers.” Brand equity has real market value, as regularly measured in merger and business-acquisition negotiations. This indicates brand equity is a quantifiable measure of consumer belief that a product will meet expectations, i.e., exhibit trustworthiness. McKnight and Chervany (2001) echo this economic phenomenon in their definition of reputation as an element that supports trusting beliefs, even without firsthand knowledge.
Brand equity is measurable in the form of goodwill, that is, “the expectation that a partner intends to fulfill their role in the relationship” (Lui & Ngo, 2004, p. 474). Likewise, Sako (1998, p. 268) describes goodwill in terms of predictability, as the reduction of uncertainty that the trusted party will refrain from taking unfair advantage. Blois and Ryan (2013) present trustworthiness in an economic context through the theoretical vantage of Alan Fiske’s relational theory. Fiske’s relational market-pricing theoretical model indicates the value of actions or services provided can be expressed in contractual terms (Fiske, 1992). Blois and Ryan (2013) suggest that goodwill develops through a series of positive past interactions that imply future positive interactions. Goodwill is a known intangible asset priced through generally accepted accounting practices, according to Gu and Lev (2011). Blois and Ryan (2013, p. 189) point out that “the [market pricing] model organizes interactions with reference to rational calculations of value in terms of rations, rates, interests, or proportional costs and returns.” Following Fiske’s model, an individual’s goodwill may theoretically demonstrate quantifiable value at the individual level of analysis.
Goodwill implies contractual trust (Sako, 1998), i.e., a belief that both parties will deliver on written or implied contractual obligations, often supported by written guarantees (Chenhall & Langfield-Smith, 2003). This contractual form of trust is a necessary component of Fiske’s market-pricing model (Blois & Ryan, 2013). US government-cleared persons receive classified access by agreeing to protect all classified information they handle under penalty of, inter alia, clearance revocation, termination, and criminal prosecution. A known trust breach that results in unfavorable clearance eligibility adjudication negatively impacts career paths that require a clearance, effectively voiding the operations of personal goodwill, save for the rare exception of regaining eligibility.
The concept of personal goodwill is elaborated in Apel and Sweeten's (2010) work on Laub and Sampson (1993) age-graded theory of informal social control. Apel and Sweeten present personal goodwill in economic terms, citing the ways in which “social capital” moderates the effects of delinquent history in predicting delinquent behavior. They interpret social capital as an investment much like Sampson, Laub, and Wimer in that delinquent behavior will “impose significant costs for translating criminal propensities into action” (2006, p. 1) and suggests a paucity of social capital factors may contribute to late onset criminal behavior. Conversely, Laub et al. (2018) point out that the resources invested in family life and career create conditions such that risk of loss becomes non-negotiable for the reward of delinquent behavior. The overlap between behavioral economics and informal social control theory tends to intersect with a more general principle of loss aversion. The problem with Sweeten’s interpretation is a loose definition of social capital and a focus on the value of the social network rather than the specific personal value assigned within the network (Dolfsma & Dannreuther, 2003; Coleman, 1990, p. 302; Nahapiet & Ghoshal, 1998, p. 243). The present work adds rigor to this important line of inquiry by introducing the concept of self-equity as an informal social control construct that provides reason for trust based on past security clearance eligibility adjudications.
Informal social control theory
Life course criminology research provides a theoretical framework for determining the conditions that tend to mitigate delinquency in known criminals. This work extends the theoretical framework to include those who have no known disqualifying criminal history by offering the supposition that desistence factors also have a protective effect against entering into delinquency. Laub et al. (2018) highlight the discourse with the turning point and human agency perspectives of desistance. Informal social control theories suggest that recidivism is contingent on the circumstances wrought by intervening life events, such as marriage (Bersani & Doherty, 2013; Sampson, Laub, & Wimer, 2006), parenthood (Pyrooz, Mcgloin, & Decker, 2017; Warr, 1998), employment (Skardhamar & Savolainen, 2014), academic achievement (Giordano, Cernkovich, & Rudolph, 2002; Harlow, 2003; Lochner, 2004; Tan, 2014), religion (Giordano et al., 2002), and occupational seniority (D’arcy & Herath, 2011; Giordano, Schroeder, & Cernkovich, 2007). The research tends to agree that positive life-course factors, controlling for age, tend to negatively associate with delinquent behavior. Following Laub et al. (2018) this work presents informal social control theory as a framework to understand the psychological mechanisms that underlie compliant behavior in recruits.
Identity- and cognitive-transformation theories stress the primacy of human agency in desistance in known offenders (Giordano, Cernkovich, & Holland, 2003; Paternoster & Bushway, 2009). According to Giordano et al. (2002), desistance necessarily follows an “openness to change,” where the value of desistance is important to the delinquent actor. Paternoster, Bachman, Bushway, Kerrison, and O’Connell (2015, p. 213) assert further that desistance is a conscious decision that is conducive to Laub and Sampson’s social-control factors. To Paternoster and Bushway (2009), some delinquents eventually conceive a “feared” self-image that grows incompatible with a desirable self-image. These researchers propose that the conscious decision to migrate to a positive possible future self is self-regulating, such that those with the means and will for self-improvement will naturally diverge from the feared self-image. Transformation theory offers that social control factors like marriage, parenthood, employment, academic achievement, religion, and occupational seniority are the result of a-priori desistance as an artifact of human agency. Irrespective of primacy, social control, and identity theories agree that certain life course factors negatively associate with delinquency.
Human agency tends to moderate the perceived value of delinquency. Classical social-control theory from Travis Hirschi () offers an anecdotal example of the association between IQ and crime: “being relatively unrewarded by conformity [they] are simply freer to commit criminal acts and will be more likely to do so should the opportunity arise” (p. 112). To Hirschi, those who are less gifted typically accrue less from conformity than those who are more gifted. This implies that those who are more rewarded for conformity are not as free to commit criminal acts – that is to say, those who enjoy the rewards of conformity (e.g., good marriage, children, employment, academic achievement, and occupational seniority) will be less likely to risk these rewards for the benefit of nonconformity. Cognitive-transformation and social-control theories explained by human agency tend to agree on a more general principle of loss aversion, a concept mined in Kahneman and Tversky (1979) prospect theory.
While these authors focus on desistance, they concur that certain events tend to act informally as a form of social control, perhaps at a subconscious level (Laub et al., 2018). Giordano et al. (2003) find that certain life-course events tend to be “hooks for change,” influencing delinquents in a predictable way. Assuming a delinquent is open to change, he will generally desist and opt out of delinquency if exposed to hooks such as marriage or good employment. Laub et al. (2018) mention an analysis of Glueck and Glueck (1950), indicating that those who “invested resources and time in a marriage or a job” produced a situation in which “risking this investment became non-negotiable” (p. 299). Laub et al.’s observation directly speaks to loss aversion; according to informal social control theory, those who have invested resources in themselves should be less likely to risk loss than those with lesser investments. Surprisingly, none of the authors assess the protective nature of informal social controls in non-offenders, a gap this article seeks to fill. Following Laub et al. (2018) this work proposes invested resources as an objectively measurable form of self-equity, empirically testable against adjudicative guideline violations that result in unfavorable security clearance adjudication.
Empirical background
The adjudicative guidelines serve as the US government standard for determining eligibility for a security clearance (32 U.S.C. 147). The ADR provides insight into the interpretation and application of the guidelines, drawing on past delinquency as a strong predictor of future delinquency.
This research derived nine hypotheses from academic literature to understand the relationship between self-equity factors and trustworthiness in terms of security clearance eligibility adjudication. Hypothesis testing statistically controls for known effects of socio-demographics by including the control terms in multivariate logistic regression models. Hypothetically, self-equity factors should negatively relate to unfavorable security clearance eligibility adjudication outcomes.
Security clearance eligibility adjudication
According MILPER 14–306 (2014), soldiers who have security clearances denied or revoked [due to an unfavorable adjudication] will be either reclassified or separated from service, both cases will negatively impact the soldier’s prospects in the Army. The security-clearance adjudication process involves self-reporting, investigation, and verification. Recruits submit “Standard Form 86 – Questionnaire for National Security Positions” (SF-86) to apply for a government security clearance (Shedler & Lang, 2015). Recruits self-report historical information, which clearance investigators verify. Department of Defense (DoD) Consolidated Adjudications Facility (CAF) adjudicators process results from the investigator (who verifies the SF-86 information) to determine clearance eligibility for the military services. Unfavorable adjudications generally fall within the categories of alcohol abuse, criminal conduct, drug use, financial problems, sexual misbehavior, personal-information falsification, and mental problems (Fischer & Morgan, 2002).
The purpose of adjudication is to determine if a person is an acceptable security risk by “weighing a number of variables known as the whole person concept” (Adjudicative Guidelines for Determining Eligibility for Access to Classified Information, 32 C.F.R. § 147.7, 2016). The process first identifies disqualifying adverse information that raises a security concern; the adjudicator then considers mitigating factors to determine whether the concerning information is serious enough to warrant an unfavorable adjudication. Recruits cannot hold a security clearance unless they receive a favorable adjudication outcome. Adjudication is measured in terms of favorable (0) or unfavorable (1). This research identifies the relationship between self-equity factors and security-clearance adjudication outcome for 93,577 Regular Army recruits within the first five years of service.
Self-equity factors
Moral Waiver is measurable by an administrative waiver that allows a soldier to join the armed forces despite disqualifying criminal convictions. The sample assumes no observations with known criminal histories that did not receive a waiver. The present study provides a test for the relationship between criminal history and unfavorable security clearance eligibility adjudication for those who have been suitability vetted and allowed enlistment despite their criminal history.
The general consensus in criminology research agrees with Moffitt’s (1993) claim that the best predictor of criminal behavior is the presence of past criminal behavior. The ADR informs clearance decisions citing research linking past delinquency to future delinquency. According to the ADR (2014, p. 243), those with a single arrest “[are] 65% more likely than other[s]” to receive an unfavorable discharge before the end of their four-year enlistment. The empirical basis for predicting risky persons strictly by their criminal history may be unwarranted, owing to the ADR’s twenty-five-year-old statistic for discharges and US military recruits (Flyer, 1995).
Recent research finds US military enlistees with previous felony arrests are no more likely to receive an unfavorable discharge than non-felons (Lundquist, Pager, & Strader, 2018). Furthermore, authors of similar studies from the 1990s claimed that much of the military criminal history data was self-reported and waivers granted generally did not match the actual crime (Flyer, 1995; Lake, 1996). Interestingly, Lundquist et al.’s (2018) research found recruits with felony history are more likely to receive fast promotion and attain the rank of sergeant (within their initial enlistment contract) than non-offenders. Advances in computer networks could explain the contradictory results between Flyer (1995) and Lundquist et al. (2018) as those with criminal history are better detected with modern information technology. However, it does little to explain the beneficial effect of the waiver. A better explanation is the effect of a waiver is the value of a second chance which is measurable in the strength of relationship with favorable adjudication outcomes. Alternatively, a waiver may require some compensating attributes to justify eligibility for service. If present, compensating attributes should increase an adjudicator’s likelihood of granting a favorable clearance adjudication to those who received a moral waiver. This work will partially replicate the research from the 1990s to determine the association as it relates to adjudications as applied to a 21st century dataset under modern Army enlistment policy.
H1: Moral waiver will negatively associate with unfavorable adjudication.
Armed Forces Qualification Test percentile (AFQT percentile) measures mental aptitude as a percentile score on a standardized test. Substantial research identifies a negative relationship between mental aptitude and criminal behavior (Lochner, 2004). Several researchers agree that higher AFQT scores associate negatively with unfavorable discharges for initial-contract recruits (Flyer, 1995; Frabutt, 1996). Rosellini et al. (2017) found that AFQT below the 49th percentile predicted violent crime in Army soldiers. The Stanford marshmallow experiments reveal that those with higher self-control tend to score higher on standardized tests (Mischel, Shoda, & Rodriguez, 1989). Granted, the ADR (p. 133) enumerates self-control as characteristic of trustworthiness, the beneficial relationship remains without a convincing theory. However, Johnston and Powers (2017, p. 2) note “the services often tie enlistment incentives to high AFQT scores. High AFQT scores also help you pick the job you want.” The AFQT percentile thus may have value measured as perceived preferential treatment. Explained in terms of self-equity, AFQT percentile should negatively relate to behaviors of adjudicative concern.
H2: AFQT percentile will negatively associate with unfavorable adjudication.
Rank is measurable as the paygrade of Army soldiers. D’arcy and Herath (2011) research demonstrates that being in a management position has a crime-deterrent effect. The results are interpretable in at least two ways: management is a deterrent, or the deterrence effect applies to any position of relative seniority – perhaps those predisposed to compliance tend to secure management positions. This work controls for the effects of management position by evaluating enlisted recruits only. Time in service and rank at enlistment should associate with rank at the time of adjudication because the Army promotes on a merit system with constraints of time in grade and time in service (AR 600-8-19, 2016). This work controls for time- and rank-at-enlistment confounders by including both factors in a model. Ceteris paribus, those who have higher rank likely advanced at a faster rate than their peers, controlling for time on the job and rank at enlistment. The loss-aversion principle holds that loss of rank looms as a greater disincentive than not receiving promotion. Thus, those with more rank to lose should be less likely to exhibit behaviors that result in unfavorable clearance adjudication.
H3: Rank will negatively associate with unfavorable adjudication.
Enlistment Age is a form of self-equity measured in terms of the value of youth remaining to fit entry-level positions. The measure of time elapsed between the exit from school and reliable entry to the labor market is a known indicator for low earnings relative to peers (Fernandes-Alcantara & Gabe, 2009). Coined “disconnected youth,” social researchers claim the disconnected follow a trajectory of negative outcomes, such as poverty and criminality (Loprest, Spaulding, & Nightingale, 2019). Belfield, Levin, and Rosen (2012) calculated the monetary cost of disconnection to society thereby demonstrating a measurable value. Likewise, value may extend to an individual’s perceived cost of disconnectedness. Take for instance the coupling clock; one may be reckless with relationships in his early twenties, but in his thirties, time invested in a relationship becomes more valuable as fewer years remain on the coupling clock. The cost to “start over” is thus greater as the clock times out. This work hypothesizes a similar relationship should exist for those who are aging out of their prime as less time remains to “start over” for a 30-year-old recruit than with a 20-year-old recruit. In this sense, one with a greater enlistment age has more to lose from delinquency than one with a lower enlistment age.
H4: Enlistment Age will negatively associate with unfavorable adjudication.
Service years are a form of paid-in capital measured by time investment in a career. Mitchell et al. (2001) identify this form of self-equity as “job embeddedness.” Job embeddedness assigns three costs to leaving a job: social network, fit perception, and sacrifice (switching costs). Job embeddedness relates to other concepts from classic organization theory, including March and Simon (1958) “perceived ease of movement” and Mobley’s (1977) “loss of seniority.” Uggen (2000) finds employment reduces the likelihood of recidivism, but gives no mention of how employment duration associates with criminal activity. Related research by Rosellini et al. (2017) finds those within their first five years of Army service commit 68% of minor violent crimes in the Army. Following Rosellini et al. (2017); this work tests how embeddedness, as measured in service years, relates to unfavorable adjudications in terms of soldiers with less than five years of service.
H5: Service years will negatively associate with unfavorable adjudication.
Education is the knowledge and skills an individual acquires through school and training (Tan, 2014, p. 413). Harlo’s (2003) report reveals prisoners without a high-school education are disproportionate with the national population. Tan (2014) presents education as an investment, citing the perception of future benefits as a return on investment, and also several limitations beyond rational choice, such as cognitive ability. There is surprisingly little empirical research evaluating the relationship between higher education levels and delinquency while controlling for the effects of aptitude in terms of AFQT percentile. This work evaluates how academic achievement affects unfavorable adjudication while controlling for the effects of AFQT.
H6: Education will negatively associate with unfavorable adjudication.
The concept of Marriage involves a spousal relationship. Early life-course criminology theorized that “good marriages” may provide alternatives to crime (Moffitt, 1993). More recent studies offer that courtship leading to marriage tends to associate with criminal desistance (Laub et al., 2018; Sampson et al., 2006). Warr (1998) reports a negative relationship between marriage and delinquent behaviors.
Laub and Sampson (1993) note that marriage interdependency imposes “significant costs” for acting on criminal propensities. According to Waite and Gallagher (2000, p. 24), “Marriage makes people better off in part because it constrains them from certain kinds of behavior, that, while perhaps immediately attractive (e.g., staying up all night drinking beer, or cheating on your partner) do not pay off in the long run.” Substantial research indicates that marriage is a mitigating factor for delinquency. Sampson et al. (2006) find marriage reduces the offense rate by 43%, citing decreased exposure to delinquent peer groups. Lyngstad and Skardhamar (2013) extend Sampson et al.’s (2006) work, stating that courtship leading up to marriage also has a negative association with crime and indicating that even the anticipation of marriage is strong enough to reduce criminal propensity. Uecker’s (2012) research concludes the stabilizing effect of matrimony generalizes to alcoholism, citing that the engaged or married had nearly half the odds of alcoholism. Bersani and Doherty (2013) measured marriage as a life course factor in terms of “ever-married,” consistent with Giordano et al.’s (2003) “hook” hypothesis. Sampson et al. (2006) find an association between crime and marriage probabilities by age, with a negative association between matrimony and delinquency, notwithstanding marriage compelled by unwed pregnancy. This work tests the marriage social-control hypothesis in terms of unfavorable adjudication.
H7: Marriage will negatively associate with unfavorable adjudication.
Children quantity is the count of minor dependents that a recruit claims for military family benefits. Sampson et al. (2006) note that the traditional view of “getting serious” may explain why offspring may constrain a caregiver from deviant behavior. Kreager, Matsueda, and Erosheva (2010) find a negative association between teen motherhood and drug, alcohol, delinquency, fighting, and stealing, indicating that having children reduces delinquent behavior in the high-risk (< 26 year) age group. Pyrooz et al. (2017) provided evidence that for males, having children did not reduce gang membership, and for females, having additional children did lower the likelihood of gang membership. Other research found that parenthood in itself is not a factor in the reduction of property offending (Zoutewelle-Terovan & Skardhamar, 2016). The inconsistent results are tested with a negative hypothesized relationship between children and unfavorable adjudication outcomes.
H8: Children quantity will negatively associate with unfavorable adjudication.
There is relatively little research evaluating the interaction between quantity of children and marriage. Bersani and Doherty (2013) present related research that gives evidence for the relationship between household size, marriage, family structure, and probability of arrest. According to Bersani and Doherty, an intact family structure tends to negatively relate with probability of arrest. Their work measures household size in terms of number of persons 18 years or younger living in the household, but the study involves disadvantaged, incarcerated, and separated parents. The researchers operationally define family structure as intact or not, but neglect testing for any interactive effect between household size and family structure. The present work specifically tests how number of dependent children relates to a recruit’s adjudication outcome, controlling for the effects of marriage.
Several researchers present evidence that marriage is a hook for change. But marriage in itself may not always mitigate delinquency, as life-course criminology strongly suggests (Laub et al., 2018; Lyngstad & Skardhamar, 2013; Sampson et al., 2006; Gallager, 2000). Children and assets tend to impose significant costs to the dissolution of a legally binding union, but there is little penalty if neither exists. Pyrooz et al. (2017) empirically demonstrate the desistance effect of parenthood in those who reside with children. These findings strongly indicate an interaction between marriage and parenthood. This work extends the findings of Bersani and Doherty (2013) in terms of self-equity by making the claim that a breadwinner for a nuclear family has more to lose from termination than one who is not, including those married without dependent children. Thus, marriage and children together should have a stronger negative association with unfavorable adjudication than when either occur independently.
H9: The association of children quantity with adjudication outcome will be moderated by marriage.
This research considers socio-demographic factors to control for latent cultural, racial, and biological effects. Although these factors are not specifically of interest in this work, any variability due to sex, race, religious preference, and military service is statistically accounted for.
Socio-demographic controls
Department of the Army Pamphlet 600–26 prohibits the Army from discrimination related to socio-demographic factors such as religion, race, and sex. The analysis statistically controls for the effects of religion, race, and sex to assess the effects of self-equity independent of socio-demographic factors.
Religion is a self-reported characterization. Well-established research reveals a negative association between religion and crime (Baier & Wright, 2001). Bainbridge (1989) reports a negative association between churchgoing and rape, assault, burglary, and larceny. Jang (2018), citing Johnson and Jang (2011), notes that 244 studies published over 65 years confirm a negative relationship between religion and crime and asserts that the relationship is causal, empirically demonstrating that religion reduces crime and drug use, and not vice versa. McCullough and Willoughby (2009) differentiate “spirituality” from religion by empirically demonstrating a positive relationship between religion and self-control and a negative relationship between spirituality and self-control. He notes that religious practices such as generosity and fasting require self-regulatory strength. “Catholic guilt” has become a cliché in religious popular culture, but there is little supporting empirical research for this soul-searching phenomenon, and that which is available tends to conflict (Sheldon, 2006; Vaisey & Smith, 2008). These findings suggest associations between various religious preferences and unfavorable outcomes.
Cornwell et al.’s (2005, p. 532) research provides evidence that interreligious morality differs in terms of relativism, claiming that those with more relativistic religion “reject the idea of moral universality and have weaker ethical beliefs.” Cornwell et al.’s findings demonstrate empirically that those with more-relativistic morality tend to cluster with each other and be more idealistic, while those with a less-relativistic morality tend be less idealistic. Different perspectives may influence self-equity in terms of salvation. For instance, those who subscribe to “once saved, always saved” believe salvation is eternally secure regardless of any act of depravity, while others believe salvation can be lost through depravity and yet others may perceive no path to eternal reward. Irrespective of guilt or shame, depravity thus carries a cost for some and none for others. Thus, differing views of religion may affect the subjective value of a major form of self-equity – the promise of eternal reward.
Race is categorized by the main ethnic codes as assigned at enlistment. Campbell, Vogel, and Williams (2015, p. 199) find “race was and remained an important predictor of higher incarceration” and Caetano, Schafer, Field, and Nelson (2002) demonstrate that domestic-violence incidence varies by race. Race tends to have measurable effects in related studies (Bersani & Doherty, 2013, 2013; Boman & Mowen, 2018; Giordano et al., 2003, 2002; Kirk, 2012; Kryvonos, 2013). Following extant research, this work seeks to limit any latent confounding relationship between race, marriage, and children by controlling for the effects of race.
Sex represents chromosomal sexual identity as documented at birth. Jung, Herrenkohl, Klika, Lee, and Brown (2015) provide evidence that sex strongly relates to crime, as does a range of related research (Agnew & White, 1992; Giordano et al., 2003, 2002). Following extant research, this work seeks to limit any confounding relationship among children and marriage by controlling for the effects of sex.
Age is measured in years since birth and has a known negative relationship with delinquency, as people are known to age out of crime (Agnew & White, 1992; Bersani & Doherty, 2013; Boman & Mowen, 2018; Kirk, 2012; Sampson et al., 2006). However, age in of itself offers little explanatory power. This work controls for the effects of age to determine the independent effects of life-course events.
Military service represents “the quintessential life-course event, as it encapsulates many of the core principles of the life-course paradigm” effectively knifing off delinquent influences (Laub et al., 2018, p. 303). MacLean and Elder (2007) cite the educational opportunities of the GI Bill as a positive effect of service, but also delays in professional progression, drug- and alcohol-abuse prevalence for older recruits, and increased divorce rates among combat Veterans. This work tests informal social control theory controlling for the effects of military service, as all subjects are Army recruits. This work also tests the social-control effects of Regular Army service relative to Guard/Reserve service.
Method
This work moves away from the glut of criminology survey research to examine verifiable measures within archival data. Granted “self” tends to indicate subjectivity, there are problems with objectively measuring self-perception within a post-positive epistemic perspective. A recent article by Bowman and Mowen explains the difficulties with survey research, stating “the independent variables also contain subjectivity in how they could be interpreted” (Boman & Mowen, 2018, p. 217). This research intentionally excludes subjective measures from surveys to provide replicable results from common Army datasets in the Person-event Data Environment (PDE); it is exploratory, evaluating the benefit of applying the theory of informal social control to real-world Army problems by leveraging the Army’s vast archives.
Laub et al.’s (2018) age-graded theory of informal social control is tested by drawing from US Army recruits who have received clearance eligibility adjudication within the first five years of service, between 2006 and 2018. The PDE database provided streamlined data acquisition, human-research-protection governance, and analytical tools (Vie et al., 2015).
Operational variables
Following well-established theories of informal social control, self-equity factors should relate to trustworthiness as assessed by security clearance eligibility adjudications.
Dependent variable Adjudication (1 = unfavorable/0 = favorable) is measurable by the presence of an adjudication-result code in the “favorable” element in the clearance adjudications database.1 All security clearance eligibility is assessed in accordance with the “Adjudicative Guidelines for Access to Classified Information” (32 C.F.R. § 147). The adjudication result element (favorable) contains two values: true and false, where “true” represents a favorable adjudication and “false” an unfavorable adjudication. Favorable adjudications take a 0 value and unfavorable adjudications, a 1. The design retains only the first adjudication result, unless an unfavorable adjudication exists, in which case it retains the first unfavorable adjudication only.
Independent variables include moral waiver (1 = waiver/0 = no waiver), AFQT percentile (1 per percentile), rank (0 = Private 1, 1 = Private 2, 2 = Private First Class, 3 = Specialist, 4 = Sergeant, 5 = Staff Sergeant, 6 = Sergeant First Class), entry rank (0 = Private 1, 1 = Private 2, 2 = Private First Class, 3 = Specialist, 4 = Sergeant), age (1 per year), enlistment age (1 per year), service years (1 per year), education (0 = less than high school, 1 = high school, 2 = some college, 3 = undergraduate degree, 4 = graduate degree or higher), marriage (1 = marriage/0 = no marriage), and children quantity (1 per child).
Control variables are age (1 per year), race (White, Black, Southeast Asian, Pacific Islander, Native American, and Interracial), religion (Protestant, Buddhist, Catholic, Atheist, Hindu, Islam, Hebrew, no preference), and sex (M/F). Age at the time of adjudication is a control such that the effects of enlistment age are measurable controlling for the effects of age at the time of adjudication.
Descriptive statistics
The primary analytical focus evaluates how self-equity factors relate to security clearance eligibility adjudication in Regular Army recruits within the first five years of service. Guard/Reserve components and Regular Army recruits within the first 9 years of service augment the analysis for internal and external validity. Table 1 presents the descriptive statistics.
Table 1.
United States Army recruits: Regular Army, Guard/Reserve security clearance eligibility adjudications
| Enlisted Army Recruits within first: | Regular Army 5-year | Guard/Reserve 9-year | Regular Army 9-year | ||
|---|---|---|---|---|---|
| Adjudications | Adjudications | Adjudications | |||
| Variable | Mean [S.D.] Range | Mean [S.D.] Range | Mean [S.D.] Range | ||
| Unfavorable Outcome | 3,061 (3.3%) | 4,894 (2.3%) | 4,384 (3.1%) | ||
| Socio-demographic controls | |||||
| Age | 23.45 [4.523] 32 | 22.64 [5.316] 32 | 24.61 [4.860] 32 | ||
| Race (Reference = White) | 66,601 (71.2%) | 160,339 (75%) | 988,872 (69.5%) | ||
| Southeast Asian | 5,163 (5.5%) | 7,738 (3.6%) | 7,835 (5.5%) | ||
| Black | 19,725 (21.1%) | 38,223 (17.9%) | 30,445 (21.4%) | ||
| Pacific Islander | 342 (.4%) | 894 (.4%) | 602 (.4%) | ||
| Interracial | 1,052 (1.1%) | 5,022 (2.4%) | 3,568 (2.5%) | ||
| Native American | 964 (.7%) | 1,429 (.7%) | 1,022 (.7%) | ||
| Sex (Reference = Male) | 79,917 (85.4%) | 168,335 (78.8%) | 121,504 (85.4%) | ||
| Religion (Reference = Protestant) | 68,345 (73.0%) | 186,951 (77.2%) | 101,885 (71.6%) | ||
| Buddhist | 503 (.5%) | 678 (.3%) | 729 (.5%) | ||
| Catholic | 15,947 (17.0%) | 29,509 (13.8%) | 25,454 (17.9%) | ||
| Atheist | 840 (.9%) | 1,316 (.6%) | 1,145 (.8%) | ||
| Hindu | 312 (.3%) | 220 (.1%) | 377 (.3%) | ||
| Islam | 589 (.6%) | 580 (.3%) | 816 (.6%) | ||
| Hebrew | 301 (.3%) | 434 (.2%) | 459 (.3%) | ||
| No preference | 6,740 (7.2%) | 14,972 (7.0%) | 11,479 (8.1%) | ||
| Self-Equity at Recruitment | |||||
| Moral Waiver | 2,787 (3%) | – | – | ||
| AFQT Percentile | 61.59 [20.021] 89 | 61.08 [19.988] 98 | 61.29 [19.966] 98 | ||
| Entry Rank | 1.04 [1.025] 4 | – | – | ||
| Enlistment Age | 21.77 [4.220] 24 | – | – | ||
| Self-Equity at Outcome | |||||
| Service Years | 1.01 [1.320] 4 | 1.49 [2.505] 8 | 2.35 [2.641] 8 | ||
| Rank | 1.91 [1.262] 6 | 1.55 [1.536] 7 | 2.47 [1.502] 6 | ||
| Education | 1.19 [.752] 4 | 1.06 [.819] 4 | 1.19 [.729] 4 | ||
| Marriage | 36,514 (39.0%) | 40,193 (18.8%) | 71,396 (50.2%) | ||
| Children quantity | .32 [.770] 9 | .17 [.604] 9 | .50 [.943] 9 | ||
| N = | 93,577 | 213,645 | 142,344 | ||
*ABBREVIATIONS: S.D. = standard deviation
Regular Army recruits clearance eligibility adjudications within the first five years of service, between the years of 2006 and 2018 totals 93,577. The mean age at adjudication is 23 years old with 85.4% males. Whites comprise 71.2% of the sample, followed by 21.1% black, 5.5% Southeast Asian, and less than 2% for each of Pacific Islander, Native American, and those of interracial descent. The mean enlistment age is 21 years. Religious preference is 73% Protestant, 17% Catholic, 7.2% of no preference and less than a percent for each of Atheist, Buddhist, Hindu, Islam, and Hebrew beliefs. The mean recruit AFQT percentile is 61 and the mean entry rank is Private at time of enlistment. At time of adjudication, recruits hold the average rank of Private with 1 year of service and a high school diploma. Nearly two-fifths (39%) are married at the time of adjudication and the mean quantity of children is less than one. Relatively few (3.3%) of the recruits received an unfavorable security clearance eligibility adjudication within the first five years of service.
Generalizability validation tests examined Guard/Reserve components and Regular Army recruits who received clearance adjudication, within the first nine years of service, between the years of 2006 and 2018. Those Guard/Reserve component soldiers who received adjudication in the first 9 years of service total 213,645 and 142,344 for Regular Army soldiers. The distributions do not substantially differ with the exception of Guard/Reserve components service time averaging 6 years. The difference is an artifact of the duration of service requirement for Guard/Reserve components that extend up to eight years (AR 135–91, p. 4).
This work considers only those Army recruits with available enlistment contract records in the primary analysis. Those without enlistment contract records include cadets who failed to commission, commissioned officers who failed to complete educational requirements, inter-service transfers, and other nontraditional enlistments, which are beyond the scope of this research.
Recruits with a criminal history are relatively few (3%). This is evidence that predicting delinquency based on criminal history may not be the best indicator for the Army. Whether from barriers to entry or insufficient time to offend, relatively few criminals enter the Army. This lack of actionable information limits the predictive power of models that focus on criminal history. Soldiers who obtained a waiver for criminal history are assumed to have a disqualifying criminal record, but those who have such a criminal record and were not eligible for a waiver cannot enlist, and therefore are not represented in the samples.
Table 2 presents the bivariate correlates between independent variables for the adjudication outcomes. Measures known at enlistment, such as obtaining a moral waiver, AFQT percentile, and entry rank, do not correlate highly with age, except enlistment age. Rank at adjudication, entry rank, enlistment age, service years, and academic progress tend to increase over time, thus correlating more with age. The low criminal history correlations are artifacts of the small sample size but the direction of the association provides theoretical support. Without controlling for moderating factors, the correlates indicate that while education negatively relates to moral waivers, it also positively relates to marriage and children. Furthermore, marriage and children positively associate with moral waiver, indicating marriage and children may not fit theoretically as self-equity factors. The analytical strategy specifies the method to study the phenomenon in greater detail.
Table 2.
Bivariate correlations for security clearance eligibility adjudications – Regular Army within 5 years’ service
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Adjudication Age | – | – | – | – | – | – | – | – | – | – |
| 2 | Moral Waiver | .05 | – | – | – | – | – | – | – | – | – |
| 3 | AFQT Percentile | .12 | .01 | – | – | – | – | – | – | – | – |
| 4 | Entry Rank | .38 | −.02 | .29 | – | – | – | – | – | – | – |
| 5 | Enlistment Age | .93 | .03 | .13 | .38 | – | – | – | – | – | – |
| 6 | Service Years | .35 | .05 | −.00 | .06 | .04 | – | – | – | – | – |
| 7 | Adjudication Rank | .47 | .04 | .15 | .50 | .24 | .73 | – | – | – | – |
| 8 | Education | .36 | −.04 | .24 | .62 | .38 | .02 | .34 | – | – | – |
| 9 | Marriage | .41 | .04 | −.03 | .10 | .30 | .38 | .35 | .05 | – | – |
| 10 | Child Quantity | .43 | .03 | −.04 | .06 | .35 | .29 | .25 | .02 | .46 | – |
| 11 | Unfavorable Adjudication | .14 | −.03 | −.02 | −.00 | .12 | .08 | .07 | −.01 | .09 | .13 |
aN = 93,577
* All correlations with magnitudes greater than .01 are statistically significant at p < .001
Analytical strategy
This work tests the association between self-equity and unfavorable adjudication outcomes. The analysis tests nine hypotheses drawn from the academic literature in a nonexperimental, cross-sectional design. IBM’s Statistics Program for Social Sciences (SPSS) version 22 was used to create correlation tables and regression models. The analysis pertains to archival data and does not claim to demonstrate causation; the results, however, qualify theoretical claims based on association strength.
The analysis proceeded with logistic regression to assess a binary outcome with observations that are independent of each other. Logistic regression has the assumption of a large sample size (n = 93,577) and low multicollinearity. Although there were relatively few who received unfavorable adjudications, the population size was sufficiently large, totaling 3,061 observations. Of the quantitative variables, age produced a significant non-linear relationship, thus the quadratic effect of age and is included in the models. The analysis produced four models to account for individual self-equity effects while controlling for the effects of socio-demographics. Self-equity at recruitment variables included moral waiver, AFQT percentile, entry rank, and enlistment age. Self-equity at outcome variables included service years, rank, education, marriage, and child quantity. The model controls for temporal effects between self-equity factors by including age at adjudication, enlistment age, and service years in the same regression models. This method produces the independent effects of each self-equity factor while controlling for the effects of counterpart self-equity factors.
Logistic regression does not produce a traditional R-squared value to assess the variability in adjudication outcomes explained by the independent variables. Nagelkerke’s pseudo-R-squared value is provided in Tables 3, 4 to demonstrate how additional self-equity predictors add to the models. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are provided to facilitate model comparison. According to Table 3, the reduction in AIC/BIC values in each successive model verifies that additional self-equity values meaningfully add to model fit. Furthermore, Model 4’s AIC indicates the best fit of all models regardless of the additional self-equity variables and interaction term.
Table 3.
Regular Army adjudications for enlisted soldiers within their first 5-year term of service
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | |||
| Socio-demographic controls | (S.E.) exp(B) | (S.E.) exp(B) | (S.E.) exp(B) | (S.E.) exp(B) | |||
| Age | (.036)2.518*** | (.036)2.531*** | (.037)2.933*** | (.043)2.692*** | |||
| Age Squared | (.001).986*** | (.001).986*** | (.001).986*** | (.001).986*** | |||
| Race (Ref = White) | |||||||
| Southeast Asian | (.123).364*** | (.123).360*** | (.124).379 | (.124).436*** | |||
| Black | (.043)1.414*** | (.043)1.401*** | (.045)1.243*** | (.045)1.286*** | |||
| Pacific Islander | (.455).505 | (.455).498 | (.456).422 | (.457).430 | |||
| Interracial | (.206).604* | (.206).601* | (.207).453*** | (.208).450*** | |||
| Native American | (.222)1.215 | (.222)1.222 | (.223)1.116 | (.224)1.090 | |||
| Sex (Ref = Male) | (.056).759*** | (.056).754*** | (.057).772*** | (.057).787*** | |||
| Religion (Ref = Protestant) | |||||||
| Buddhist | (.290).961 | (.290).959 | (.291)1.024 | (.292)1.090 | |||
| Catholic | (.054).840** | (.054).840** | (.055).812*** | (.055).835** | |||
| Atheist | (.208)1.125 | (.208)1.117 | (.206)1.331 | (.209)1.392 | |||
| Hindu | (1.007).108* | (1.007).107* | (1.008).134* | (1.008).161 | |||
| Islamic | (.295).276*** | (.295).273*** | (.296).288*** | (.297).320*** | |||
| Hebrew | (.300).976 | (.300).977 | (.302)1.145 | (.303)1.201 | |||
| No Preference | (.072).990 | (.072).989 | (.073).966 | (.073).979 | |||
| Self-equity trustworthiness factors | |||||||
| Self-equity at recruitment | |||||||
| Moral Waiver | – | (.114).676** | (.115).596*** | (.115).568*** | |||
| AFQT Percentile | – | – | (.001).990*** | (.001).991*** | |||
| Entry Rank | – | – | (.020).775*** | (.026).887*** | |||
| Enlistment Age | – | – | (.011).913*** | (.022).961*** | |||
| Self-equity at adjudication | |||||||
| Service Years | – | – | – | (.030)1.054 | |||
| Adjudication Rank | – | – | – | (.030).981 | |||
| Education | – | – | – | (.032).770*** | |||
| Marriage | – | – | – | (.049)1.103* | |||
| Child Quantity | – | – | – | (.068)1.544*** | |||
| Child Quantity x Marriage | – | – | – | (.070).765*** | |||
| Nagelkerke R Square | .107 | .108 | .128 | .137 | |||
| X2 Step (df) | – | 13.241(1)*** | 494.503(3)*** | 212.409(6)*** | |||
| X2 Model (df) | 2546.045(15)*** | 2559.286(16)*** | 3053.789(19)*** | 3266.198(25)*** | |||
| Akaike Information Criteria | 24,382.22 | 24,366.98 | 23,866.476 | 23,642.066 | |||
| Bayesian Information Criteria | 24,240.47 | 24,227.23 | 23,686.926 | 23,405.816 | |||
aN = 93,577
* p < .05; ** p < .01; *** p < .001
Table 4.
External validation between Regular Army and Guard/Reserve component soldiers
| Adjudications | |||
|---|---|---|---|
| Variable | Regular Army | Guard/Reserve | |
| Socio-demographic controls | (S.E.) exp(B) | (S.E.) exp(B) | |
| Age | (.031)2.361*** | (.023)2.317*** | |
| Age Squared | (.001).988*** | (.000).988*** | |
| Race (Ref = White) | |||
| Southeast Asian | (.101).471*** | (.105).587*** | |
| Black | (.037)1.349*** | (.036)1.687*** | |
| Pacific Islander | (.296).639 | (.322).426** | |
| Interracial | (.132).495*** | (.103).582*** | |
| Native American | (.179)1.113 | (.160)1.530** | |
| Sex (Ref = Male) | (.048).771*** | (.039).861*** | |
| Religion (Ref = Protestant) | |||
| Buddhist | (.277).900 | (.263)1.421 | |
| Catholic | (.045).864** | (.046).864** | |
| Atheist | (.178)1.431* | (.235).906 | |
| Hindu | (1.006).153 | (.514)1.296 | |
| Islamic | (.242).379*** | (.253).165 | |
| Hebrew | (.261)1.166 | (.299)1.288 | |
| No preference | (.056)1.041 | (.053)1.155** | |
| Self-equity trustworthiness factors | |||
| AFQT Percentile | (.001).990*** | (.001).993*** | |
| Service Years | (.010).891*** | (.008)1.020* | |
| Rank | (.020).897*** | (.017)1.018 | |
| Education | (.024).721*** | (.022).666*** | |
| Marriage | (.043)1.186** | (.041).895** | |
| Child quantity | (.056)1.624*** | (.031)1.536*** | |
| Child quantity x Marriage | (.057).748*** | (.034).822*** | |
| N = | 142,344 | 213,645 | |
| Nagelkerke R Square | .106 | .153 | |
| X2 Model (df) | 3670.017(22)*** | 6490.239(22)*** | |
| Akaike Information Criteria | 35,432.964 | 40,103.269 | |
| Bayesian Information Criteria | 35,215.912 | 39,877.284 | |
a B = coefficient; S.E. = standard error; exp(B) coefficient exponent; N = population size; df = degrees of freedom* p < .05; ** p < .01; *** p < .001
Table 3 provides regression analysis for the primary research population. Model 1 presents the effects of controls alone. It assesses the effects of socio-demographics, including age, race, sex, and religion. The model includes an age-squared term to account for a nonlinear relationship with the dependent variable, following Sampson et al. (2006), Kreager et al. (2010), and Bersani and Doherty (2013). Model 2 includes moral waiver for criminal history, controlling for the effects of age, race, sex, and religion. Theoretically, a criminal history should be a strong predictor for future delinquency, but this work does not seek to confirm that well-established hypothesis, evaluating instead the effects of self-equity associated with the waiver. The primary theoretical claim is that self-equity should negatively relate to unfavorable adjudication outcomes. Model 3 evaluates the effects of self-equity at recruitment, including AFQT percentile, entry rank, and enlistment age, while controlling for the effects of socio-demographic factors. Model 4 includes self-equity measures at time of adjudication, including service years, rank, educational level, marriage, and child quantity while controlling for the effects of socio-demographics and counterpart self-equity factors. The model additionally evaluates the hypothetical interaction between marriage and child quantity.
Results
The results of this research produce evidence that self-equity has a measurable negative relationship with unfavorable adjudication outcomes as presented in Table 3. Model 1 reveals that those who are older are more likely to receive unfavorable clearance eligibility adjudication. Older recruits would have more history to inform a risky behavior profile, something younger recruits would have less of owing to youth. Life-course criminology research tends to agree that people age out of crime, but the SF-86 asks “have you EVER been charged with an offense [emphasis in original]” and specifically cites a list of charges, including felonies. Thus, the population with criminal record that may result in an unfavorable security clearance eligibility should accumulate over time.
Interestingly, the positive linear effect and the negative quadratic effect of age indicate unfavorable adjudications increase until age 38 and then level off and decrease. The point at which the effect reverses can be estimated using the linear quadratic slope coefficients is calculated as ln[exp(B) Age]/[2{ln[exp(B) Age Squared]} = [.924/[2(.012)] = 38.5 years (Darlington & Hayes, 2017, p. 356). This finding tends to agree that people generally age out of delinquency, yet those who are young generally have little history of delinquency by which to base an adjudicative decision.
The effect of moral waiver in Model 2 demonstrates a negative relationship between criminal history moral waiver and unfavorable adjudication outcome. The relationship is an artifact of the enlistment waiver process because a criminal waiver in itself indicates the recruit demonstrated sufficient redemption to justify his entry to enlistment. This finding offers evidence that predicting clearance-worthiness based on criminal history offers little when criminals select out prior to enlistment. According to H1, those who obtain a moral waiver to enlist in the Army are less likely to receive unfavorable clearance adjudication than those who have no moral waiver. This finding provides evidence that a moral waiver has a protective effect beyond the detrimental effect of the criminal background that compelled the waiver for enlistment. This waiver accounts for a 32.4% decrease in the odds of unfavorable adjudication controlling for the effects of demographic factors alone and 43.2% decrease in Model 4 when including controls for counterpart self-equity factors.
The magnitude of the moral waiver effect speaks to the value of a second chance for those with criminal backgrounds that may find opportunity in the Army that would otherwise be unobtainable. Strain theorists argue criminal history itself tends to reduce the chances of employment, thereby limiting positive employment effects (Apel & Sweeten, 2010). A 2012 Society for Human Resource Management (SHRM) survey found that over 70% of employers require background checks, reinforcing the strain hypothesis (Holzer, Raphael, & Stoll, 2004). Criminal history negatively affects eligibility for professional licensures, effectively constituting a proxy employment barrier in the absence of a pre-employment background check (Love, Roberts, & Klingele, 2013). The Urban Institute, citing a 2017 report from the National Inventory of Collateral Consequences of Conviction, reveals that “47% of local employment regulations restrict people convicted of any felony from being hired,” indicating policy can negatively influence hiring decisions for those with a criminal record (Duane, La Vigne, Lynch, & Reimal, 2017). The waiver is thus a major form of self-equity that moderates behaviors of adjudicative concern.
The results in Model 3 support H2, indicating that the prospect of preference afforded by a higher AFQT percentile score negatively relates to unfavorable adjudication outcomes. Each increment increase in AFQT percentile is associated with a 1% decrease in unfavorable adjudication. This finding has at least two interpretations: those with greater self-control typically have higher test scores (Mischel et al., 1989) and those with higher test scores have preferential treatment (Johnston & Powers, 2017, p. 2). Both interpretations speak to a form of control but differ in causal direction; that is, preexisting self-control prevents delinquent behavior which is made manifest in high scores vs. high scoring recruits risk loss of preferential treatment with delinquent behavior. The cross-sectional analytical design does not establish causation but offers a self-equity explanation as a theoretical basis for the effect of the percentile score itself.
According to Model 4, each additional grade of entry rank is associated with an 11.3% decrease in the odds of unfavorable adjudication, confirming H3. That is to say, those recruits with a leg up in rank have status to lose from delinquent behavior that would warrant adjudicative concern. Interestingly, the effect of adjudication rank demonstrates no significance when controlling for entry rank. This non-finding is likely because little variability exists in the sample after controlling for the effects of entry rank. Those who start with a leg up in the initial ranks will tend to maintain the disparity throughout the first five years of service owing to a regular early-career promotion schedule. The entry rank control was not available for the 9-year sample in Table 4 but there remained an approximate 11% decrease in odds of unfavorable adjudication for each paygrade increment. The specter of losing status among peers is thus a major form of self-equity that negatively relates to unfavorable adjudication outcomes.
It is interesting to note the effects of age at enlistment as it tends to demonstrate the value of remaining time on the career clock. Those who are older at enlistment are less likely to receive an unfavorable adjudication outcome controlling for the effects of demographics, moral waiver, and age at time of adjudication. For each year of age prior to enlistment, the odds of unfavorable clearance adjudication will decrease by 8.7%, controlling for the effects of age at the time of adjudication decision. This finding supports H4 in that those who enlist at a later age are less likely to risk losing the utility of time invested in service due to behaviors of adjudicative concern than those who enlist younger. A reasonable explanation is those who are younger value time less than those who are older because, for the young, there is plenty of time to start again. An 18-year-old would little value the sunk cost of two years in the Army because plenty of time remains to try other options; however, a 30-year-old recruit would more value the same two years as his acceptability to entry-level employment has nearly timed out. Thus, the value of time increases as the recruit ages, as measured in terms of utility loss from delinquent behavior, and fits firmly within the definition of self-equity.
Model 4 presents the effects of self-equity at the time of adjudication. Years in service produced no significant relationship for recruits within the first five years of service, failing to confirm H5; however, the effect is made manifest in the 9-year validation cohort shown in Table 4. This is explained by the high correlation between service years and rank in the 5-year sample (Table 2), owing to a relatively common promotion schedule among recruits. Promotions later in service become more competitive and, as hypothesized, demonstrate significance in the 9-year sample.
Model 4 also validates known beneficial effects of education, controlling for the effects of demographics and other self-equity factors. Each level of education associates with a 25% decrease in the odds of unfavorable adjudication outcome. The Army provides numerous educational benefits such as the proverbial GI-Bill, tuition assistance, student loan repayment, among others which may be revoked if the recruit is in an unfavorable status (AR 621–5, 2019, p. 24; AR 600-8-2, 2016, p. 2). Hypothetically, those who invested more time and student loans in education have more to lose from the Army defunding loan repayments and future education progression. Furthermore, higher education credits translate to additional promotion points and a degree meets a major requirement for promotion to commissioned officer. Thus, the value of career progression afforded by education is a major form of self-equity that agrees with H6.
The effects of marriage and children seemingly contradict prevailing research in the theory of informal social controls. Model 4 provides that neither marriage nor having children is beneficial unless they occur together. However, little available research assessed the interaction of marriage and children in a statistical model. Extant research concurs that having children and being married provide a beneficial effect, but these factors generally occur together and models should account for the effects of the interaction. This finding conflicts with Lyngstad and Skardhamar (2013), Uecker (2012), Laub and Sampson (1993), Warr (1998), and Waite and Gallagher (2000, p. 24) who concur that marriage is a protective factor. However, the Guard/Reserve population in Table 4 agrees with the consensus findings involving civilians, indicating Regular Army life may moderate how marriage affects adjudication outcomes.
Model 4 demonstrates that marriage and children interact. The finding provides evidence indicating self-equity arises in terms of the nuclear family, rather than having children or being married in isolation. For instance, marriage associates with a 20% increased odds of unfavorable clearance adjudication and each child associates with 19% increased odds of unfavorable clearance adjudication when assessed independently. However, calculating the simple slopes reveals that when marriage is present, each child is associated with a 15.7% decrease in the odds of unfavorable clearance adjudication, controlling for the effects of socio-demographics. These findings refute both H7 (marriage negatively associates with unfavorable adjudication) and H8 (children negatively associates with unfavorable adjudication). However, the findings strongly support H9 (marriage moderates the association between children and adjudication outcome). That is to say, those who have children and never married, or ever married without children, are both more likely to exhibit behaviors of adjudicative concern than those ever married with children.
Validation
The secondary objective of this research was to determine how self-equity trustworthiness generalizes to populations beyond Regular Army recruits within their first five years. The Guard/Reserve components generally require the recruit serve the Army two weeks a year and one weekend a month with service obligations extending up to eight years (AR 135–91, p. 4). This analysis accommodates the full enlistment duration and, for more comparative samples, includes the statistics for Regular Army recruits within the same service duration.
Table 4 reveals that increasing the service term by four years does not fundamentally change the self-equity effects on adjudication outcomes for Regular Army Recruits with the exception of service years. The effect of rank at adjudication was not significant with Guard/Reserve component soldiers. These findings indicate reliable self-equity effects when controlling for age and service duration in support of H2 (AFQT), H6 (education), and H9 (marriage/children interaction). Furthermore, the validation results show that H3 (rank), H5 (service years), and H7 (marriage) vary with component. This finding indicates that rank and service years render little self-equity effects in Guard/Reserve recruits, likely because a typical Guard/Reserve recruit risks little in terms of rank because Army is a part-time job, in contrast to a Regular Army recruit who risks his livelihood. The same Guard/Reserve recruit would, however, risk the utility loss of educational benefits and more satisfying opportunities resulting from a high AFQT score similar to the Regular Army counterparts. These results show that service component moderates some self-equity effects, yet are well explained in terms of self-equity.
Marriage demonstrates an opposite and beneficial effect for Guard/Reserve Solders, contributing to an 11% decrease in the odds of unfavorable adjudication outcome. However, marriage associates with an 18% increase in the odds of unfavorable adjudication outcome for Regular Army Soldiers in the comparative sample. One reason for this finding is single Regular Army recruits reside in Army barracks, but married recruits outside of initial training may move out of the barracks and receive a housing allowance. For instance, a married Private stationed at the Army’s Defense Language Institute is entitled to a 2421 USD monthly housing allowance in addition to his regular monthly pay of 1680 USD.2 Thus, recruits may be financially motivated to hasty marriages resulting in conditions conducive for behaviors of adjudicative concern. Barracks residency does not apply to Guard/Reserve recruits on part-time duty; and consequentially, marriage is a beneficial factor for those part-time recruits, which agrees with prevailing scientific consensus. This finding conditionally confirms H7 (marriage).
Discussion and conclusion
This study applies informal social control theory for measuring trustworthiness in US Army personnel in terms of security clearance eligibility adjudications. Current risk measures are prone to false-positives which impede the operational utility of predictive models based on delinquent history measures alone. The problem with false-positives stem from the absence of criminal history, owing to youth or enlistment exclusion criteria. This work presents a theoretical method for reducing false positives in Army risk rating tools by introducing the concept of trustworthiness as a risk-mitigating construct. This research measures trustworthiness, as assessed by adjudication outcome. Theoretical concepts from Laub et al.’s (2018) age-graded theory of informal social control informed the selection of nine “self-equity” factors that relate positively to trustworthiness (with the exception of the marriage and children factors, which interact to reveal a positive relationship). This work proposes new ways to predict delinquency by turning the question around: who is sufficiently trustworthy that they are unlikely to engage in delinquent behavior? The findings strongly suggest that the informal social controls in terms of self-equity may be profitably used to inform an Army trustworthiness prediction capability.
The purpose of this investigation is to test how social control theory explains security clearance eligibility adjudications, as a proxy for trustworthiness. The question is relevant for an objective trustworthiness measure of mitigating factors that will augment models that rely on criminal history. Laub et al.’s (2018) theoretical framework guided this research, the primary analysis of which included 93,577 adjudications from regular Army recruits within the first five years of enlistment. A validation analysis processed 142,344 Regular Army adjudications and 213,645 Guard/Reserve component soldier adjudications within the first nine years of enlistment.
This work presents the self-equity construct to better understand trustworthiness in terms of adjudication decisions. Computational user activity monitoring software overwhelms analysts with alerts based on the pre-defined risk level of a specific network behavior, e.g., accessing sensitive information. However, a trustworthiness construct may help provide context with mitigating factors when rank ordering the alerts so threat analysts would look at those risky alerts demonstrating fewest mitigating factors first. This research tests the validity of self-equity as a trustworthiness measure in the light of informal social control theory.
Review of the informal social control literature provided the basis for nine testable hypotheses drawn from previous empirical tests. The validation analysis demonstrated that the major theoretical findings apply to the Guard/Reserve components, indicating partial generalizability to the civilian population. This work reveals that certain factors, including educational achievement, AFQT, rank seniority, and child producing marriages are protective factors, confirming the age-graded theory of informal social control (Laub et al., 2018) as measured in terms of self-equity.
Researchers should consider replicating these findings to determine whether they can be generalized to the broader military and civilian populations. The marriage–child interaction, for example, may not hold for those without military experience. Though the detrimental marriage effect demonstrated in Regular Army recruits conflicts with broad scientific consensus; there is a dearth of research that assesses the interaction term between marriage and children.
The applicability of this study is subject to several limitations. First is the exclusive use of Army recruits – replications should consider other military services and civilian populations for external validity. Second, owing to research restrictions, this work did not assess arrest data; a future study should control for the effects of prior arrests in addition to convictions. Third, divorce status was unverifiable. As an alternative, this work followed Bersani and Doherty (2013) by assessing those who had ever married against those who had not. Fourth, this study performed analysis on relatively few self-equity factors. Replications should include other self-equity factors – the effects of accrued leave, retirement contributions, and military awards, for example – to determine the extent to which additional factors may contribute to predictive models. Fifth, this work does not articulate a definitive causal relationship between self-equity and adjudication outcomes. It is important to note that youthful recruits do not generally have sufficient time to accumulate self-equity so the results of this study cannot be applied as a trustworthiness sieve implicating as risky those without self-equity. The strength of these associations and the scope of the sample size combine to confirm existing informal social control theory in terms of clearance adjudication.
In conclusion, self-equity tends to associate with favorable security clearance eligibility adjudications when controlling for the effects of socio-demographics. Discrepancies in the empirical analysis of family data are reconciled by demonstrating the interaction of marriage and children. This study provides an option for an objective trustworthiness measure in the absence of reliable known history and extends the age-graded theory of informal social control as a theoretical basis for the measure (Laub et al., 2018). The use here of clearance adjudications as a proxy for trustworthiness is exploratory and the results demonstrate that trustworthiness may be measurable in terms of self-equity.
Notes
Variable measures in (parenthetical).
2019 Defense Finance and Accounting Service (https://www.dfas.mil/dam/jcr:a8fe14fa-9234-406b-8b2a-27ab459a6666/MilPayTable2019_3.pdf)/Defense Travel Management Office (https://www.defensetravel.dod.mil)
Data availability statement
The data that support the findings of this study are available from the Army Analytics Group Research Facilitation Laboratory. Restrictions apply to the availability of these data, which were used under license for this study. Data are available in the Person-event Data Environment (PDE) at pde.army.mil with the permission of the Department of Defense.
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the Army Analytics Group Research Facilitation Laboratory. Restrictions apply to the availability of these data, which were used under license for this study. Data are available in the Person-event Data Environment (PDE) at pde.army.mil with the permission of the Department of Defense.