Alcohol Consumption and Violent Offending: Findings From a Longitudinal Study on Korean Youth from Mid to Late Adolescence

Abstract

Although past research highlights a close relationship between alcohol and crime, its role as a static (i.e., stable) and dynamic (i.e., changing) risk factor of violent delinquency has been less studied. Cross-lagged dynamic panel models were employed to address this issue based on a longitudinal Korean adolescent sample. Despite the significant independent effects of baseline individual differences in alcohol use, its impact on violence was no longer statistically significant when accounting for within-individual changes in alcohol consumption. However, we also found interaction effects between baseline and within-individual changes in alcohol consumption; youth who consume more alcohol at earlier ages and engage in more alcohol use over time are more likely to engage in violence. Findings stress the importance of studying static and dynamic factors and their interaction to provide a greater understanding of violent delinquency. Limitations and implications for policy and practices are discussed.

Keywords

alcohol use stable risk factor dynamic risk factor violence

Although violent offending in and of itself is rare among young people, its occurrence remains a major issue throughout many countries. A key area of focus among researchers who study violent offending is identifying factors that can increase or decrease the likelihood of violent behavior, especially among adolescents. Mid to late adolescence is a developmental period in which criminal behavior is at its highest (Moffitt, 1993), and identifying risk factors related to violence in this developmental period can be helpful for developing prevention and intervention efforts geared toward reducing long-term criminal justice system involvement (Loeber & Farrington, 1998). One such factor consistently linked to violent offending among young people is alcohol consumption (Deutsch, 2021; Felson et al., 2011, 2008; Fergusson et al., 1996; Haggård-Grann et al., 2006; Huang et al., 2001; Kulis et al., 2019; MacLean & Moore, 2014; Resko et al., 2010; White et al., 2012).

Nationally representative data collected throughout multiple countries highlight that alcohol use among young people is a public health issue worldwide. For example, cross-cultural data from 15-year-olds in 24 Western countries indicate that the weighted average of monthly alcohol use was 45.4% in 1998, 43.6% in 2002, and 37.2% in 2006 (Simons-Morton et al., 2009). Similarly, in a systematic review of 41 studies from eight Asian countries, Jiang and colleagues (2018) found that the prevalence of alcohol consumption among young people (ages 15 to 29) over the previous 30 days ranged between 4.2% and 49.3%. Likewise, data from the 2019 National Survey on Drug Use and Health indicate that just over 400,000 young people aged 12 to 17 years reported alcohol use disorder in the United States (National Institute on Alcohol Abuse and Alcoholism, 2022).

In the case of South Korea, the annual prevalence of alcohol use among the general adolescent population was 36.6%, whereas 67.8% of justice-involved youth reported they drank alcohol in the past year (Korean Government Youth Commission, 2006). Although the overall alcohol consumption of youth has decreased in recent years, the onset age of first alcohol use is younger than that in previous years in South Korea (Korean Government Youth Commission, 2007). The prevalence of occasions of heavy drinking (i.e., five or more drinks for men, three or more drinks for women in the past month) was reported by over 50% of Korean adolescents (Heo, 2018). In addition, alcohol dependency among adolescents has steadily increased (Heo, 2018). The monthly alcohol use among young people between 15 and 18 years of age was 17 times greater than that among 12- to 14-year-olds (Korean Government Youth Commission, 2007).

While past research denotes correlations between violence and alcohol consumption among young people (Deutsch, 2021; Fagan, 1993; Felson et al., 2011, 2008; Fergusson et al., 1996; Haggård-Grann et al., 2006; Huang et al., 2001; Kulis et al., 2019; MacLean & Moore, 2014; Resko et al., 2010; White et al., 2012), there are certain limitations in these studies. One such limitation lies in identifying the causal connections between alcohol use and violence (Fagan, 1993; Felson et al., 2011). Using longitudinal data from a sample of Korean youth, we address these topics from mid to late adolescence while also controlling for dynamically changing correlates related to the variables of interest. Specifically, we studied the linkage between alcohol consumption and violent delinquency in the sample by considering alcohol use as static (measured as alcohol use at baseline) and dynamic (measured as changes in alcohol use over time) factors. We further examined the interaction effects between these two measures based on the notion of risk state, encompassing both static and dynamic factors, and previous violence risk assessment studies, suggesting the potential interplay between risk factors (Douglas & Skeem, 2005; Ward, 2016).

In our statistical models, we controlled for past violent delinquency throughout the study, as it was a strong predictor of subsequent violence (Gottfredson & Hirschi, 1987; Matsueda, 1986). We also measure earlier alcohol use and increases or decreases in alcohol consumption. The models allow us to address the temporal order of the relationship between alcohol consumption and violence over time and treat alcohol consumption as a static (i.e., stable) and dynamic (i.e., changing) risk factor for violent delinquency (Eisenberg et al., 2019). Assessing whether alcohol consumption acts as a static or dynamic risk factor for violent delinquency speaks to the amenability and treatment of alcohol use among young people (Bonta & Andrews, 2007).

Alcohol Consumption and Violent Offending

In summarizing the previous literature, Felson and colleagues (2011, p. 702) state previous “studies suggest that people are more likely to use violence during periods in which they engage in heavy drinking.” Evidence from a variety of studies highlights that many young people who engage in violence also report high levels of alcohol consumption (Douglas & Skeem, 2005; White et al., 2012, 1999) and that alcohol is often cited as a factor in the commission of violent crimes among young people (Felson et al., 2008). A recent meta-analysis examining alcohol use and violent behavior indicates a statistically significant and medium effect of alcohol use on violent behaviors and on violent victimization (Duke et al., 2018). Data from the Bureau of Justice Statistics (1998) and the National Incident-Based Reporting System (Bureau of Justice Statistics, 2010) also highlight that just under 9% of young people below the age of 21 years arrested for violent offenses were suspected of being under the influence of alcohol at the time of the offense. The relationship between alcohol consumption and violence perpetration among young people has been explained by alcohol “[playing] a more important role in unplanned offenses” (Felson et al., 2008, p. 800). Alcohol can lower inhibitions and increase impulsive behavior among young people. Furthermore, young people may consume alcohol with peers in the absence of adults in unstructured socialization settings (Felson et al., 2011; Hoeben et al., 2021; Osgood et al., 1996).

Violent offending itself is rare, and there is evidence that such behaviors are often unplanned and related to alcohol use (Felson & Massoglia, 2012). Alcohol consumption may reduce self-control, making young people who drink more susceptible to engaging in violence. From the perspective of Gottfredson and Hirschi’s (1990) general theory of crime, persons who exhibit low self-control are more likely to engage in “analogous behaviors” related to crime and violence, such as heavy alcohol use. Sellers (1999, p. 396) proposed that individuals “with low self-control, in addition to their propensity to use violence, are also more likely to pursue other immediate pleasures (e.g., drinking and drug use).”

In addition, the relationship between alcohol consumption and violence among young people and adolescents can be explained by the absence of structured environments or lack of parental monitoring (Beck et al., 2004; Osgood & Anderson, 2004; Osgood et al., 1996). Beck and colleagues (2004) used data from a sample of adolescents who participated in an intervention designed to reduce adolescent alcohol consumption. Among the 406 participants, aged 12-17 years, those who reported more parental monitoring at baseline were just under half as likely to report alcohol use 12 months later. When young people spend time with peers who engage in delinquency without adult monitoring, they are placed in situations that potentially expose them to opportunities to engage in crime and violence and consume alcohol (Hoeben et al., 2016; Osgood & Anderson, 2004).

Alcohol as a Static and Dynamic Characteristic of Violent Offending

Risk factors for violence can be stratified into two subtypes: static and dynamic. Static factors are risk factors for violence that cannot be changed and can include past antisocial behaviors associated with future violence (Eisenberg et al., 2019). In contrast, dynamic risk factors fluctuate over time as people age and can change through interventions (Eisenberg et al., 2019). Identifying static and dynamic factors for violence is essential for developing treatments and interventions to address risk factors of individuals involved in the justice system (Bonta & Andrews, 2007).

Much of the prior research on alcohol as a risk factor has treated it as a static risk factor, potentially as a result of data limitations. Researchers frequently measure past experiences with alcohol consumption (e.g., history of alcohol use or problems) to predict future crime and violence (Eisenberg et al., 2019). Criminological theories that propose static factors to explain crime may take the view that “stable individual differences” explain differences in outcomes (Brame et al., 1999, p. 601). Indeed, research is plentiful in showing that there is a positive correlation between past alcohol consumption and future violent offending in a variety of samples of young people and adults from different contexts (e.g., Deutsch, 2021; Duke et al., 2018; Felson et al., 2008; Fergusson et al., 1996; Haggård-Grann et al., 2006; Huang et al., 2001; Kulis et al., 2019; White et al., 1999).

There has been a recent increase in research considering alcohol use as a dynamic risk factor for violence. Douglas and Skeem (2005, p. 364) state that “substance use is almost by definition dynamic. That is, intoxication and use of substances ebb and flow relatively rapidly, even among heavy users.” Eisenberg and colleagues (2019) conducted a recent meta-analysis to assess the relationship between static and dynamic risk factors of violent and nonviolent recidivism. Among the 27 studies included in the project, they measured whether substance abuse was treated as a static (e.g., substance abuse history) and dynamic (e.g., current use) risk factor and their effect on outcomes. Results indicated that dynamic measures of substance abuse (including alcohol consumption) exhibited a medium and statistically significant effect on violent recidivism. Furthermore, they found that dynamic measures of substance abuse had stronger effects on outcomes than static measures.

However, research also highlights that controlling for alcohol consumption as a static risk factor is especially important when addressing dynamic change in such measures. Brame and colleagues (1999, p. 601) note “widespread agreement among criminologists that stable individual differences must be taken into account when estimating the effects of variables whose values are subject to change over time.” By specifically isolating the effects of static and dynamic measures of alcohol use on violence, researchers are better able to determine which risk factors are subject to potential change and add to the debate on the utility of including dynamic risk factors in risk assessment (Clarke et al., 2017; Vincent et al., 2011).

It is also possible that there is an interaction between static and dynamic risk factors that can increase the propensity for violence. Despite the necessity of examining potential interaction among risk factors in risk assessment literature (Douglas & Skeem, 2005; Ward, 2016), this question has not received enough empirical attention. Studying interactions between static and dynamic risk factors is important for gaining a greater understanding of violent behavior (Douglas & Skeem, 2005). Static risk factors can measure individuals’ “risk state” (Douglas & Skeem, 2005). The risk state incorporates a person’s likelihood of violence based on the combination of static and dynamic risk factors (Douglas & Skeem, 2005). Individuals with static risk factors, such as a history of alcohol use or abuse, and dynamic risk factors, such as increasing use of alcohol at any given time, may be more likely to engage in violence. Thus, it is also important to address the interactions between static and dynamic risk factors to assess the probability or likelihood of violence across the life course.

In addition, this study aimed to perform a more rigorous analysis by incorporating various time-variant and invariant correlates. In particular, to eliminate the potential influence of past behavior on future behavior (Brame et al., 1999), we utilized cross-lagged dynamic panel models to account for previous violent behavior over time. Such research has direct implications for the prevention of violence. It highlights the risks, needs, and potential responses to be addressed in young people who exhibit overlap in alcohol consumption and violent behavior (Andrews & Bonta, 2010).

Current Study

The current study utilizes data from a general population of young people from South Korea, followed from mid to late adolescence. Based on the prior literature, we propose two hypotheses surrounding the relationship between alcohol consumption and crime. Our first hypothesis (H1) is that static (baseline) and dynamic (longitudinal) measures of alcohol use throughout the study will be statistically significant and positively correlated with violent offending. We hypothesize that baseline alcohol use and changes in alcohol consumption each have independent effects on the outcome of interest. Static measures of alcohol, such as baseline measures of alcohol consumption or a history of alcohol use, have been positively correlated with violent offending (Douglas & Skeem, 2005). The same has also been found for dynamic measures of alcohol consumption or the fluctuation of alcohol use throughout the life course (Douglas & Skeem, 2005). Furthermore, criminologists have highlighted the importance of considering both baseline and static measures of risk factors to better understand how risk factors are related to violence.

The second hypothesis (H2) is that the interaction between static and dynamic measures of alcohol use will be a statistically significant predictor of more violent offending. Past research suggests that individuals with a higher risk state, that is, individuals with more static and dynamic risk factors, will be more likely to engage in violence (Douglas & Skeem, 2005). Exploring how static and dynamic risk factors of alcohol use interact to affect the likelihood of engaging in violent behavior enables us to examine whether jointly considering both types of factors enhances the prediction of violent behavior. Therefore, looking at the interaction between static and dynamic alcohol use and violence allows for a better understanding of the relationship between alcohol use and violence among young people.

We also incorporated a variety of time-variant control variables that are theoretically linked to alcohol consumption and violent offending when assessing these hypotheses. Although this study did not set out to test a specific criminological theory, we incorporate controls that are related to alcohol consumption and violence. Specifically, we measure and control for dynamic changes in self-control, victimization experiences, peer attachment, parental attachment, and parental monitoring.

Low self-control is positively correlated with both violence and antisocial behaviors, like alcohol consumption, in adolescence and adulthood (Gottfredson & Hirschi, 1990), and there is evidence that levels of self-control may fluctuate over time (Forrest et al., 2019). Victimization experiences are linked to higher alcohol consumption and violence. For instance, being victimized may encourage an individual to “cope” with their experiences through alcohol consumption (e.g., Meisel et al., 2018; Schuck & Widom, 2001). Victimization is also related to a higher likelihood of future violent offending among young people (Wojciechowski, 2019). Peers and friends play an important role in the lives of young people and can influence behavior (Haynie, 2001).

Furthermore, there is evidence of substantial change in peer networks over time that can explain fluctuations in offending and alcohol consumption (Weerman et al., 2018). Finally, parents can play a role in monitoring their children’s behavior, while attachment to their child can provide protective effects against problematic behaviors (Parker & Benson, 2004) by monitoring and providing structured environments (Beck et al., 2004; Wu et al., 2003). Controlling for these factors allows for a more robust statistical model that can rule out potentially spurious theoretical variables that may explain developmental processes over time.

Method

Data

The study participants comprised a nationally representative sample of Korean adolescents (Korean Youth Panel Survey; KYPS¹), which was led and collected by the only national youth research institute (National Youth Policy Institute) in South Korea. A stratified multistage cluster sampling strategy was used to ensure the representativeness of the sample. The first stage involved dividing the Korean adolescent population into three categories by metropolitan areas: (1) Seoul Metropolitan City (the capital), (2) an agglomeration of six other metropolitan cities, and (3) other cities and counties across South Korea. In the second stage, 104 schools were randomly chosen from each of these three clusters. Third, from each school, one class was randomly selected, and the students within that class participated in the survey.

Participants were followed up annually from 2003 (14 years old) to 2008 (19 years old). This study used five waves (baseline and four follow-up waves) when respondents were in junior high (second year at baseline) and high school (senior at Wave 4). Trained staff who visited randomly selected schools assured the confidentiality of the survey and introduced the survey to the students before completing the surveys. Only respondents who had parental consent were asked to complete questionnaires via paper-and-pencil methods in the classroom (K. S. Lee & Baek, 2007). Their parents were interviewed via telephone and provided information about their education level, family background, and socioeconomic status. The average household monthly income was about 2,800 U.S. dollars (Wave 1). High school graduation was the average parental education completed.

The retention rate of the KYPS demonstrates a high level of participant engagement, with approximately 80% of the original sample remaining throughout the study. Specifically, attrition rates were 7.6% at Wave 1, 2% at Wave 2, 0.1% at Wave 3, and 5% at Wave 4. Furthermore, there were no significant differences observed between the attrition cases and the analyzed sample in terms of key variables and control factors, including violent offending, alcohol use, low self-control, victimization, attachment with parents and peers, parental monitoring, gender, and parental education level (Y. Lee & Kim, 2017b). The analyzed sample contained 2,721 youths (1,351 girls and 1,370 boys), and there was no racial/ethnic diversity in the KYPS.

Measures

Key Variables

Alcohol use

One item was used, asking about the frequency of alcohol drinking during the last 30 days (Do & Shin, 2017; Lee, 2012; J. Lee & Kim, 2017a).² Responses were 0 (never), 1 (less than once per month), 2 (less than once every 2 weeks), 3 (less than once per week), 4 (2 or 3 times per week), and 5 (daily use). As we assessed alcohol use in two different variables, baseline and change, baseline alcohol use was adopted from the baseline wave of the data, and change was from Waves 1 to 4.

Violent offending

Respondents were asked whether they engaged in five different types of violence (e.g., assault, gang fighting, robbery, bullying, sexual assault) in the last year (Kim et al., 2023). We employed a variety scale to measure violent offending. Therefore, the original responses ranged from 0 (engaged in none of the violent behaviors) to 5 (engaged in all five types of violent behaviors). The variety scale was chosen because of its higher reliability and greater predictive validity relative to frequency measures and its higher correlation with official criminal measures compared to other types of self-reports (see Piquero et al., 2012; Sweeten, 2012). Due to the skewness of values, a natural logarithmic transformation was employed.

Time-Variant Control Variables

Lagged violent offending was assessed via the same method as described previously. When analyzing the models, t-1 violent offending is included in the analysis to control for the influence of prior violent offending on subsequent violent offending. In other words, baseline violent offending is used as a control when estimating violent offending at Wave 1, and Wave 1 is used as a control when estimating Wave 2, and so on. Self-control is the sum of six questions using a 5-point Likert-type scale (e.g., “Even if I have an exam tomorrow, I jump into exciting things; “I am apt to enjoy risky activities”) (Kim & Lee, 2019). The higher value indicates a lower level of self-control, and the internal consistency of the measure was 0.629. Victimization is the sum of five dichotomous questions asking whether the participants experienced each type of victimization (e.g., “being threatened”; “being beaten up”) (Y. Lee et al., 2022). The range of the variable is from 0 (experienced no victimization) to 5 (experienced all victimization). Peer attachment is measured with four questions (e.g., “I want to maintain the friendships I have with my close friends”; “I have candid conversations with my friends”). The sum of two questions based on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree) was used for parental attachment (e.g., “I am comfortable sharing my thoughts and feelings with my parents”; “I often talk with my parents about what happens to me outside the home”) (Kim & Lee, 2019). Higher values indicate greater parental attachment (α = .723). Parental monitoring is composed of a sum of four questions (“When I go out, parents usually know where I am”; “When I go out, parents usually know whom I am with”), and all four questions were measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) through 5 (strongly agree) (Y. Lee et al., 2022). Higher values represent more parental monitoring (α = .844).

Time-Invariant Control Variables

Respondents were asked to identify their gender as female (coded 0) or male (coded 1). Parental education level is coded respectively for father and mother (0 = elementary school, 1 = middle school . . . 6 = master’s degree, and 7 = doctoral degree).

Analytic Strategy

Considering that the primary purpose of the current study is to investigate within-individual effects of alcohol use on violence over time, a cross-lagged dynamic panel model was used. A cross-lagged dynamic panel model has several advantages for longitudinal analysis since it enables the ability to explore longitudinal links between variables, including time-variant and time-invariant covariates, as well as the use of a lagged measure of the dependent variable as a control variable (Allison et al., 2017; Williams et al., 2018). Given the well-established argument that “delinquent behavior is determined largely by previous delinquent behavior” (Gottfredson & Hirschi, 1987, p. 598; Matsueda, 1986), more rigorous testing is feasible by including a lagged outcome variable as a control.

Several consecutive steps were taken for analysis using STATA 15. First, we reported the descriptive statistics of all variables used in the analytic models. Second, a total of four cross-lagged dynamic panel models were estimated. To be more specific, the impacts of alcohol use at baseline and change in alcohol use were assessed, respectively, in Models 1 and 2. Next, Model 3 included both baseline and change in alcohol use, and then, the interactive term between the two was included in Model 4. Two traditional model fit indices were used to assess the closeness of model fit. Root mean square error of approximation (RMSEA) values are less than .05, and Bentler’s comparative fit index values greater than .9 are considered a good fit. Following the suggestions from Williams and colleagues (2018), we did not interpret the χ² statistics to assess the goodness of fit. Regarding missing data, full-information maximum likelihood estimation was used for the analyses.

Results

Table 1 summarizes the descriptive statistics of variables in the analyses. The mean for violent offending is 0.045 with a standard deviation (SD) of 0.196 (range 0–1.792). Its SD (between) and SD (within) are 0.128 and 0.148, respectively, which shows changes in violent offending over time. Descriptive information of alcohol use at baseline is 0.315 (mean) and 0.520 (SD). The mean for change in alcohol use is 0.517 (SD = 0.789, range = 0-5) with an SD (between) 0.564 and SD (within) 0.551, reflecting varying levels across waves.

Table 1:

Sample Descriptives (N = 2,721)

Variable	Mean	SD	Minimum	Maximum	SD (between)	SD (within)
Dependent variables
Violent offending	0.045	0.196	0	1.792	0.128	0.148
Independent variables
Alcohol (baseline)	0.315	0.520	0	4
Alcohol (change)	0.517	0.789	0	5	0.564	0.551
Control measures
Time variant
Lagged violent offending	0.086	0.267	0	1.792	0.168	0.208
Low self-control	15.956	3.968	6	30	3.109	2.467
Victimization	0.073	0.360	0	5	0.219	0.285
Peer attachment	16.935	2.538	4	20	1.836	1.756
Parental attachment	6.677	1.849	2	10	1.467	1.127
Parental monitoring	13.639	3.314	4	20	2.584	2.075
Time Invariant
Gender	0.503	0.500	0	1
Education: father	3.677	1.282	0	7
Education: mother	3.180	1.065	0	7

Note. SD = standard deviation.

Table 2 reports the results of the cross-lagged dynamic panel model assessing violent offending. In Model 1, examining the main effect of alcohol assumption at baseline, the baseline between-individual difference of alcohol use significantly relates to increased violence with small effect sizes (β = .032, SE = 0.008, p < .001). Prior violence (β = .149, SE = 0.010, p < .001), self-control (β = .100, SE = 0.018, p < .001), and victimization (β = .125, SE = 0.014, p < .001) were statistically significant time-variant factors. In other words, youth who engaged in violence, reported poor self-control, and experienced victimization were more likely to commit violent delinquency across waves. Model 2 shows that within-individual changes in alcohol consumption (β = .156, SE = 0.018, p < .001) are positively and significantly associated with violence with small effect sizes. Similar to Model 1, lagged violent delinquency (β = .155, SE = 0.010, p < .001), low self-control (β = .099, SE = 0.017, p < .001), and being victimized (β = .122, SE = 0.014, p < .001) were significantly associated with a greater likelihood of subsequent violence. The results of Models 1 and 2 support H1. Model 3, examining the independent effect of baseline differences and within-person changes in alcohol use, shows that only changes over time (β = .154, SE = 0.018, p < .001) are significantly related to increases in violence. Lagged violent delinquency (β = .155, SE = 0.010, p < .001), low self-control (β = .099, SE = 0.017, p < .001), and victimization (β = .122, SE = 0.014, p < .001) measures remained statistically significant. An interaction term was added in Model 4, which explores how baseline alcohol use and changes in alcohol consumption interact with each other to explain violent delinquency. The interaction term reaches statistical significance, supporting the H2; the interaction between static and dynamic measures of alcohol use is significantly associated with more violent behavior.

Table 2:

Cross-Lagged Dynamic Panel Data Models Assessing Violent Delinquency (N = 2,721)

Variable	Model 1			Model 2			Model 3			Model 4
Variable	β	SE	p	β	SE	p	β	SE	p	Β	SE	p
Independent variables
Alcohol
Baseline	0.032	0.008	<.001				−0.007	0.009	.446	−0.032	0.015	.031
Change				0.156	0.018	<.001	0.154	0.018	<.001	0.131	0.023	<.001
Interaction
Alcohol: Baseline × Change										0.052	0.026	.048
Control measures
Time variant
Lagged violent offending	0.149	0.010	<.001	0.155	0.010	<.001	0.155	0.010	<.001	0.156	0.010	<.001
Low self-control	0.100	0.018	<.001	0.099	0.017	<.001	0.099	0.018	<.001	0.099	0.017	<.001
Victimization	0.125	0.014	<.001	0.122	0.014	<.001	0.122	0.014	<.001	0.122	0.014	<.001
Peer attachment	0.020	0.015	.178	0.009	0.015	.539	0.009	0.015	.562	0.008	0.015	.580
Parental attachment	0.005	0.021	.822	0.002	0.020	.926	0.002	0.020	.924	0.003	0.020	.891
Parental monitoring	−0.027	0.019	.153	−0.030	0.018	.098	−0.029	0.018	.108	−0.028	0.018	.130
Time invariant
Gender	0.024	0.009	.005	−0.001	0.008	.889	−0.002	0.009	.827	−0.001	0.009	.895
Education: Father	−0.005	0.010	.620	0.002	0.010	.869	0.001	0.010	.906	0.000	0.010	.973
Education: Mother	0.011	0.010	.258	0.018	0.010	.056	0.018	0.010	.056	0.019	0.010	.046
Fit indices
χ² (Model vs. Saturated)	110.490			133.912			139.764			149.035
RMSEA	0.024			0.027			0.026			0.025
CFI	0.962			0.958			0.957			0.957

Note. Bold values are significant at p < .05. SE = standard error; RMSEA = root mean square error of approximation; CFI = comparative fit index.

Discussion

Although the link between alcohol and violent delinquency is not a new or uncommon finding in criminology, the present study extends the scope of studying this topic by adopting a developmental approach to examine alcohol consumption as a static and dynamic risk factor for violent delinquency. We utilized adolescent panel data from South Korea and found overall support for our two hypotheses, casting light on mechanisms of the alcohol consumption and violence phenomena.

The findings from the study are generally consistent with much past research; consuming alcohol is related to a higher likelihood of engaging in violence (Deutsch, 2021; Felson et al., 2011; Fergusson et al., 1996; Haggård-Grann et al., 2006; Huang et al., 2001; Kulis et al., 2019; MacLean & Moore, 2014; Resko et al., 2010; White et al., 2012). Some arguments suggest that alcohol consumption may be related to violence because alcohol may lower inhibitions, increase impulsivity, or increase risk-taking behaviors (Sellers, 1999). Also, alcohol affects decision-making through working memory disruption, cognitive-perceptual distortions, and attention deficits (Fagan, 1990; White, 2015), leading to alcohol myopia (Giancola et al., 2010) and increasing aggression (Exum, 2006; Felson et al., 2008).

The present study contributes to the literature in several ways. First, our initial results show statistically significant independent influences of baseline alcohol use on violent delinquency, reflecting the connections between more frequent alcohol consumption during an earlier developmental stage and an increase in violent delinquency. However, when its effect was simultaneously assessed with within-individual changes in alcohol use, the dominant explanations were derived from changes in alcohol use. This indicates that youth who report increased alcohol consumption over time are more likely to engage in violent behavior during adolescence. This finding highlights the fluid nature of alcohol use as a risk factor for violence. It reveals a time-varying link between alcohol use and antisocial behavior, indicating that changes in alcohol use are related to changes in subsequent violent offending (Eisenberg et al., 2019). In other words, dynamic risk factors of alcohol use are more predictive of violence than static ones. This is not only the case for our key variable, alcohol use. Unlike the time-invariant control factors across models, some time-variant control variables, such as self-control and victimization, were statistically significant predictors of violence over time. Given the main objective of these statistical models is to make an effort to accurately calculate the likelihood of a particular event happening within a specific time frame in the future (see Heilbrun, 1997), our study shows the predictive improvement of dynamic factors over static measures used in some previous studies (McGrath & Thompson, 2012; Vincent et al., 2011).

Second, the present study examined the interrelationships among risk factors. Although the potential interactions among predictors were recognized in the literature (Douglas & Skeem, 2005; Ward, 2016), it remains largely underexplored. We found statistically significant interaction effects between baseline measures of alcohol consumption and dynamic measures of alcohol consumption, indicating that individuals who consume higher amounts of alcohol at earlier ages and continue to increase their alcohol consumption over time are more likely to engage in violent behavior. In other words, the interaction effect suggests that the combination of early alcohol consumption and escalating alcohol use patterns amplifies the risk of involvement in violent activities among youth. By revealing statistically significant interaction effects between static and dynamic risk factors, the current study expands our knowledge of the complex nature of violence. This finding contributes to our understanding of the long-term phenomenon of violence across the life course, as recent research has documented the enduring associations between alcohol consumption and offending.

Longitudinal research has shed light on the significant impact of alcohol use not only on criminal behavior throughout an individual’s life but also on the process of desistance from offending (Hammerton et al., 2017; Jennings et al., 2015; White, 2015). These findings reveal that alcohol use exerts both between-individual and within-individual effects, influencing the initial engagement in criminal activities, the subsequent patterns of criminal behavior, and the likelihood of ceasing such behaviors over time. This comprehensive understanding of the complicated relationship between alcohol and offending underscores the importance of considering individual variations and dynamic factors in studying the interplay between alcohol use and criminal involvement.

In addition, our study adopted Beech and Ward’s (2004) hypothesis that suggests static and stable dynamic factors are not independent but measure the same construct. Rather than using multiple risk factors, we integrated two facets of alcohol use (i.e., baseline and changes over time). Our findings align with Lofthouse and colleagues’ (2014) study, which concluded dynamic risk factors are likely an indicator of the general risk assessed using static tools. This supports the idea that instead of solely focusing on risk factors themselves, it is crucial to pay attention to dynamic features of these factors over time. That is, “it is not the dynamic risk factors per se that should be focused on in research, but rather the changes in these factors” (Eisenberg et al., 2019, p.744), which provides valuable insights into understanding the overall risk of alcohol use on violence.

Furthermore, the current study fulfills methodological gaps in the literature. Limitations in prior literature are largely due to research designs using cross-sectional data and single-point estimation (Douglas & Skeem, 2005; Vincent et al., 2012). In cross-sectional designs, all explanatory variables are considered unchanging factors since they cannot differentiate between the impact of variables that vary over time and those that remain constant. Therefore, the previous literature yielded knowledge on interindividual outcomes but could not address intraindividual analysis. Our study used prospective longitudinal data to determine whether changes in risk factors account for changes in the probability of violence over time. Also, this study employed cross-lagged dynamic panel models, enabling us to conduct a more rigorous analysis by including a powerful predictor of future delinquency, lagged dependent variables over time (Gottfredson & Hirschi, 1987; Matsueda, 1986).

This study lends support to the concept of “risk state” rather than risk status, emphasizing the importance of accounting for the variability of risk factors. Combined static and dynamic risk factors help explain an individual’s likelihood of violence (Douglas & Skeem, 2005). Our findings show that the increased use of alcohol is an important factor positively related to violent delinquency while such effects are more substantial among adolescents who previously used alcohol frequently at an earlier age. Therefore, studies should “go beyond evaluating baseline risk status, which focuses on interindividual variability in risk, to assessing risk state, which focuses on intraindividual variability in violence potential” (Skeem & Mulvey, 2002, p. 118).”

Despite the contributions of our study, we acknowledge several limitations. First, we were not able to examine whether individuals who engaged in violence were under the influence of alcohol at the time that they engaged in violence. More comprehensive tests on the situational influence of alcohol are desirable with the inclusion of well-established correlates such as peer influence and levels of self-control. Second, these data only covered mid to late adolescence and did not allow us to examine how alcohol use impacts violence across the lifespan. The discipline would benefit from exploring the long-term influences of alcohol use and how it relates to life-course events or transitions.

Furthermore, while our alcohol frequency measure is commonly used by researchers, using various alcohol measures such as the quantity of alcohol consumption, consumption of different types of liquor, or alcohol use disorder diagnosis would provide a more complete picture of the role alcohol use plays in violence. Relatedly, the current study relied on self-reported alcohol use and delinquent behavior, which can be prone to issues of underreporting and overreporting (Krohn et al., 2010). It would be beneficial to supplement self-report data with additional sources of information, such as behavioral or archival indices. Finally, the present study focused on a particular population: a sample of Korean youth. Given the possible variations in youth behavior across nations and cultures, and the fact that much of the existing research on youth behavior is based on studies conducted in Western contexts, it is important to exercise caution in interpreting and generalizing the findings of this study.

Despite these caveats, the results of the current study have relevance to policy and practice. First, alcohol and delinquency control policies should not be separated but be used concordantly to prevent such behaviors in youth (Xue et al., 2009). Practitioners should also consider their converging effects in program design and implementation in terms of the risk-need-responsivity principles (Bonta & Andrews, 2017). In particular, the current study’s findings have meaningful implications for intervention and prevention efforts by considering the roles of static and dynamic risk factors in behavioral outcomes and by revealing the importance of dynamic change of alcohol use in violent delinquency among youth, which could potentially increase program effectiveness.

Second, the current study may help develop more informed decisions about the optimal timing of implementing practices: Prevention should be made in early adolescent stages to block or delay the onset of alcohol use that is predictive of subsequent violence (Scholes-Balog et al., 2013). Of course, preventing underage drinking in the first place would be desirable. But even though adolescents may begin consuming alcohol at an early age, it is still important to intervene and guard against subsequent alcohol use as early as possible. At the same time, more interventions are needed in mid to late adolescence in which alcohol consumption increases to reduce violent offending and collateral consequences of underage drinking.

Moreover, continuous monitoring and concerted efforts are needed to prevent alcohol use and delinquency. Supervision should be viewed as an ongoing process. That is, instead of evaluating risk on a one-time basis, it is better to continually assess it because risk levels may change over time and throughout interventions. Parents and caregivers pay more attention to youth’s attitudes toward alcohol and supervise their behavior. Although parental monitoring and attachment were used as control variables in this study, they did not exert statistically significant effects on violent outcomes. This does not necessarily mean that the role of parents is insignificant. Rather, it may reflect that more efficient and frequent supervision is needed to monitor young offsprings eager to spend more time outside of their parents’ radar. As research suggests, parental monitoring interventions can help reduce youth alcohol consumption and its accompanying risk by decreasing exposure to alcohol and risky and unstructured situations (Beck et al., 2004; Wu et al., 2003).

In addition, schools and communities should increase awareness of the harmful consequences of early alcohol use and expand substance-use-prevention programs. Programs that focus on developing life skills to refuse the temptation of alcohol use and to deal with everyday life challenges help encourage prosocial coping strategies rather than alcohol and violence. Intervention-building refusal skills would be helpful for youth to avoid pressure to consume alcohol in a social context.

Supplemental Material

sj-pdf-1-cjb-10.1177_00938548231202800 – Supplemental material for Alcohol Consumption and Violent Offending: Findings From a Longitudinal Study on Korean Youth from Mid to Late Adolescence

Supplemental material, sj-pdf-1-cjb-10.1177_00938548231202800 for Alcohol Consumption and Violent Offending: Findings From a Longitudinal Study on Korean Youth from Mid to Late Adolescence by Yeungjeom Lee, Stephanie M. Cardwell and Jihoon Kim in Criminal Justice and Behavior

Footnotes

ORCID iD

Jihoon Kim

Supplemental Material

Supplemental Appendices A-C are available in the online version of this article at .

Notes

Yeungjeom Lee is an assistant professor in the School of Economic, Political and Policy Sciences at the University of Texas at Dallas. Her primary interests include life course/developmental criminology, victimization, psychopathy, and juvenile delinquency.

Stephanie M. Cardwell is an assistant professor in the Department of Criminology & Criminal Justice at The University of Texas at San Antonio and an honorary research fellow in the School of Social Science at The University of Queensland. Her research focuses on relationship between risk factors and antisocial behavior.

Jihoon Kim is an assistant professor in the Department of Criminology and Criminal Justice at the University of Alabama. His research interests lie in the area of juvenile delinquency, life course/developmental theory, quantitative methods, and criminal justice policies.

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