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Adolescent marijuana use in the United States : an age-period-cohort analysis, 1991 to 2017 Gu, Jiaxin 2019

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ADOLESCENT MARIJUANA USE IN THE UNITED STATES:  AN AGE-PERIOD-COHORT ANALYSIS, 1991 TO 2017  by  Jiaxin Gu  B.A., The University of British Columbia, 2017  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF ARTS in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Sociology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2019  © Jiaxin Gu, 2019    ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled:  Adolescent marijuana use in the United States: an age-period-cohort analysis, 1991 to 2017  submitted by Jiaxin Gu in partial fulfillment of the requirements for the degree of Master of Arts in Sociology  Examining Committee: Qiang Fu Supervisor  Gerry Veenstra Supervisory Committee Member                  iii Abstract Marijuana, the most widely used illicit drug in North America, has been a key focus of health-related social science research. Research shows that marijuana use among youth has increased in recent years with the gradual decriminalization and legalization of marijuana in the U.S. There is also evidence of disparities in frequency of adolescent marijuana use by gender, racial group, and socioeconomic status. Using the national representative Monitoring the Future annual survey from 1991 to 2017, this study investigates how marijuana use among middle-school and high-school students in the U.S. varies by gender, racial group, age group, time period, and birth cohort. Hierarchical Age-Period-Cohort Logistic method and multiple structural breaks in time series tests were used to illuminate temporal trends and identify vulnerable populations. The results reveal a steady increase in marijuana use in recent decades. Adolescents from four populations – male, non-Black, metropolitan residence, and low-educated parents – are at all-time high risks of using marijuana. Significant structural breaks identified in eight sub-groups coincide with economic recessions that severely hit the American economy, and adolescents from different socio-economic groups reacted differently during these periods. This study aims to raise awareness of the current high risks of adolescent marijuana use and to help parents, schools, and communities design and implement substance use prevention and intervention programs among adolescents.     iv Lay Summary This study examines temporal trends in marijuana use among adolescents in the United States from 1991 to 2017 and identifies risk factors that increase the odds of marijuana use among youth. Adolescents who are male, non-Black, live in metropolitan-statistical areas, or raised by low-educated  parent(s) are at all-time high risks of marijuana use. Further, economic recessions affect adolescent marijuana use, but the effects vary for different socio-economic groups. This study aims to raise awareness of the current high risks of adolescent marijuana use and to help parents, schools, and communities design and implement substance use prevention and intervention programs among adolescents.        v Preface This thesis is the original work by the author, Jiaxin Gu. This research did not require ethics approval, did not involve collaboration, and has not been previously published in whole or in part.         vi Table of Contents  Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables .............................................................................................................................. viii List of Figures ............................................................................................................................... ix Acknowledgements ...................................................................................................................... xi 1. Introduction ................................................................................................................................1 2. Literature Review ......................................................................................................................3 2.1 Socio-economic gradients ............................................................................................... 3 2.2 Temporal trends of American adolescent marijuana use: APC effects .......................... 4 3. Data, Variables and Methods ...................................................................................................6 3.1 Data ................................................................................................................................. 6 3.2 Variables ......................................................................................................................... 8 3.2.1 Measure of marijuana use ....................................................................................... 8 3.2.2 Measure of socio-demographic characteristics ....................................................... 8 3.3 Methods........................................................................................................................... 9 3.3.1 Hierarchical Age-Period-Cohort Models ................................................................ 9 4. Results .......................................................................................................................................13 4.1 Temporal effects based on HAPC analysis ................................................................... 13 4.2 Breakpoints based on time series tests .......................................................................... 23    vii 5. Discussion and Conclusion ......................................................................................................29 References .....................................................................................................................................32                         viii List of Tables Table 1 Descriptive statistics, Monitoring the Future 1991-2017 .................................................. 7 Table 2 Results from logistic hierarchical age-period-cohort models of adolescent marijuana use in the U.S., Monitoring the Future 1991-2017 (N= 245, 604) ...................................................... 15       ix List of Figures Figure 1 Predicted probabilities of adolescent marijuana use across age groups based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: age effects. ............... 13 Figure 2 Predicted probabilities of adolescent marijuana use across birth cohorts based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: cohort effects. .......... 14 Figure 3 Predicted probabilities of marijuana use for adolescents with different gender identities based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects........................................................................................................................................................ 21 Figure 4 Predicted probabilities of marijuana use for adolescents with different racial identities based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects........................................................................................................................................................ 22 Figure 5 Predicted probabilities of marijuana use for adolescents living in different residential area based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects........................................................................................................................................................ 22 Figure 6 Predicted probabilities of marijuana use for adolescents with different parental education levels based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects. ........................................................................................................................................... 23 Figure 7 Probability of marijuana use for female adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017. ......................... 24 Figure 8 Probability of marijuana use for male adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2002, and 2008, Monitoring the Future 1991-2017. ......................... 25 Figure 9 Probability of marijuana use for black adolescents in the U.S. across 27 years, and breakpoints in 1994, 1999, 2008, and 2012, Monitoring the Future 1991-2017. ......................... 25    x Figure 10 Probability of marijuana use for non-black adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017. ......................... 26 Figure 11 Probability of marijuana use for adolescents living in metropolitan statistical area (MSA) in the U.S. across 27 years, and breakpoint in 1994, 1998, 2003, 2008, Monitoring the Future 1991-2017............................................................................................................................................... 26 Figure 12 Probability of marijuana use for adolescents living in non-metropolitan statistical area (non-MSA) in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017 ................................................................................................. 27 Figure 13 Probability of marijuana use for adolescents raised by college-educated parents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017............................................................................................................................................... 27 Figure 14 Probability of marijuana use for adolescents raised by below-college-educated parents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017. .................................................................................................................................... 28         xi Acknowledgments I would like to express my sincere gratitude to Professor Qiang Fu and Professor Gerry Veenstra for their guidance and kind support throughout my Master’s program. I am also thankful to Dr. Heqing Liu and Ellie Piao Han for their help. I would also like to extend my gratitude to Ms. May Chen and the entire staff at the Department of Sociology for their generous assistance all this time.   Special thanks to my parents and my partner Yan Zhi for the encouragement and invaluable guidance.   Any omission in this brief acknowledgment does not translate to lack of gratitude.    1 1. Introduction Marijuana, the most widely used illicit drug in North America, is always a central focus in health-related social science research. Literature on the topic shows that the use of marijuana among youth has significantly increased in recent years, especially with the gradual decriminalization and legalization of marijuana in several states in the U.S. (Khatapoush et al., 2004). There is also evidence supporting the disparity in frequency of marijuana usage among genders, racial groups, and individuals of different social-economic status (Salas-Wright et al., 2015). With regards to the youth, researchers find that their household living arrangements, family social-economic status, as well as academic performance are primary predictors of marijuana use (Finn, 2006). Adverse life events such as traumas and experiences of abuse are strong indicators for adolescent substance dependence, including marijuana use (Yamaguchi and Kandel, 1985). Previous study discovered that there exists an age-related increase in marijuana use among youth (Fergus and Zimmerman, 2005), as significant historical events such as the legalization of recreational drugs at the state level were proposed to impact the trend of marijuana use among adolescents (Choo et al., 2014). Marijuana use during adolescence is found to be aggregated by birth cohorts (Keyes et al., 2011).  Marijuana use at an early life stage has shown to be a significant predictor for adverse consequences, including higher risks of dropping out of high school and underwhelming academic performance (Bray et al., 2000; Lynskey and Hall, 2000), long-term cognitive impairments (Volkow et al., 2014), higher likelihood of using other illicit drugs (Fergusson et al., 2006), and clinical outcomes such as anxiety and depressive symptoms (Patton et al., 2002). Marijuana use during adolescence can be especially traumatizing because the brain is actively developing during this period (Mechoulam and Parker, 2013). Individuals who started to use marijuana during    2 adolescence are two to four times more likely to develop cannabis dependence and addiction, compared to adult users (Chen et al., 2009).  To reduce the potential adverse outcomes and thus be able to improve the efficiency and effectiveness of the current intervention strategies, it is crucial to understand the particularity of the adolescent marijuana use trend and to identify the impact factors. Using the national representative Monitoring the Future annual survey from 1991 to 2017, this study explores the marijuana use among middle-school and high-school students in the United States by asking, 1) How does the trend of marijuana use among adolescents vary across age groups, time periods, and birth cohorts?  2) Which demographic in the United States has been at higher risk concerning adolescent marijuana use for 27 years? Common research designs of other studies on adolescent marijuana use either analyze impact factors and the temporal trends net to each other (Reisner et al., 2015; Hamilton et al., 2019), or only focused on the temporal difference across one particular socio-economic gradient (Miech and Koester, 2012; Johnson and Gerstein, 2000). This study intends to present the temporal trend of marijuana use among adolescent from 1990s to the most recent, and to compare the differences across multiple socio-economic risk indicators during this era. The study takes one step forward by probing the significant structural changes occurring during this period across socioeconomic groups, using multiple structural breaks tests in time series.  It also tries to relate the individual substance use among adolescents to key macroeconomic trends. The next section provides the background to the research by introducing the general trend of marijuana use among American adolescents.        3 2. Literature Review 2.1 Socio-economic gradients Research using cross-sectional data and national surveys consistently support the argument that there is a higher level of illicit drug use, including marijuana, for male than female students overtime (Finn, 2006; Svensson, 2003). With regards to the long-term trajectory, a study using longitudinal survey (Chen and Jacobson, 2012; Duncan et al., 2006) find evidence that females during early adolescence exhibit higher levels of marijuana use than males, while males report higher levels of usage during late adolescence to early adulthood, with greater increase overtime. One explanation raised by a study implemented in the United States ascribes this gender disparity to the different opportunities to try drugs for boys and girls (Van Etten and Anthony, 2001); others regard it as the result of changing social norms of marijuana use for women only (Keyes et al., 2011). Racial/ethnic differences of marijuana use among adolescents are widely discussed but with inconsistent findings. Non-white Hispanics report the highest rank of marijuana use during adolescence compared to other racial or ethnic groups in some study (Finn, 2006; Johnston et al., 2012). However, some argue that White young adults ranked the highest for marijuana use compared to African-Americans and Hispanics (SAMHSA, 2004), and it is suggested that social norms play an important role in this racial disparity (Keyes et al., 2012). Adolescents from LGB groups report higher risks of using marijuana compared to their heterosexual peers, moderated by gender and their definition of sexual orientation (Marshal et al., 2008). Other socio-economic factors that are related include academic performance and school experiences, parental support, parental cannabis use history, living arrangements in the household, household income, and prior history of using tobaccos, alcohol, or other illicit drugs. Low-achieving academic performance and    4 school misbehaviors are risk factors that increase substance use, while high academic achievement and positive school experiences have protective effects during adolescences (Hawkins et al., 1992). Parental support and relatively higher household income protect adolescents from using marijuana by moderating the effects of emotional distress, delinquent behaviours, drug availability, and community/neighborhood problems stemming from adolescent marijuana use (Fergus and Zimmerman, 2005; Duncan et al., 2000). Individuals who grew up in single-parent households or households where parent(s) had history of substance use are likely to be negatively affected, but family connectedness (Lloyd-Richardson et al., 2002), religiosity (Brook et al., 2001), and personal decision-making skills (Botvin et al., 1998) shed away the adverse influences.    2.2 Temporal trends of American adolescent marijuana use: APC effects  To distinguish the age, period, and cohort effects on adolescent marijuana use is to separately estimate for each – the impact aggregated by being a particular age, historical events during certain time periods, and the life experiences shared by the same birth cohort. Risks of using marijuana are believed to increase with the maturation process for youth (Finn, 2006). Findings from study elsewhere, depending on the periods they focused on, suggest various trends in adolescent marijuana use. Evidence found in the study of Bachman et al. show that marijuana use trend among students in high school increased during the 1990s after a decrease throughout the 1980s (Bachman et al., 1998). They speculate that the decline of anti-drug ads covered by the media, low percentages of parents and educators talking about the hazards of drugs, and few opportunities for younger generations to learn the traumatizing outcomes of drugs as their everyday peers were using them, possibly contributed to the increase of marijuana use in the 1990s. Several studies found that there appears to be an increase in youth marijuana consumption beginning in the late    5 2000s across all ages and birth cohorts (Miech and Koester, 2012; Keyes et al., 2011). Studies that examine cohort effects on youth marijuana use largely focus on the social values, norms, disapproval toward marijuana use and the generational differences they lead to. Johnson and Gerstein found evidence that the shift in socialization on drug-related values brought on the increasing use of marijuana for individuals born between 1970-1979 (Johnson and Gerstein, 2000). Keyes and colleagues analyze the adolescent marijuana use in the United States from 1976 to 2007 and propose that the odds of using marijuana are lower for individuals born in birth cohorts which had more individuals disapprove of marijuana use (Keyes et al., 2011). Considering the macro-level impact factors, higher risks of marijuana dependence are found among residents of the states with medical marijuana legalized (Cerdá et al., 2012). Study conducted by Arkes found a counter-cyclical model positing that greater teenage marijuana use and more dealers would show up during a weaker economy at the state level, because access to substance would be easier (Arkes, 2007).           6 3. Data, Variables and Methods 3.1 Data  This research is based on twenty-seven waves (from 1991 to 2017) of data from the Monitoring the Future: A Continuing Study of the Lifestyle and Values of Youth (MTF), an ongoing national annual survey targeting school-aged adolescents in the United States since 1975. This survey invites approximately 50,000 students from the 8th, 10th, and 12th grade nationwide in the United States to participate and respond to questionnaires each year. A three-stage random sampling strategy including the selection of geographic areas (stage 1), the selection of schools within each area (stage 2), and the selection of classes within each school chosen (stage 3), is used. As a nationally representative survey with a multistage sampling design, around 420 public and private high schools are selected and invited to participate. The school that declined to participate would be replaced by another school with geographic and demographic similarities. Information in the MTF study is collected by asking participants to complete self-administered questionnaires at school. Questions in the MTF survey cover multiple dimensions of students’ life experiences, including mental health condition, physical health, civic engagement, social activities, and drug-substance abuse.  Surveys on 8th, 10th, and 12th graders from the years 1991-2017 are used because the 2017 survey is the most up-to-date wave with public access, and students from 8th and 10th grade did not join the MTF survey until 1991. The analytic sample is limited to a sub-sample who responded to the frequency of marijuana yearly usage and reported valid socio-economic information such as racial identities, gender, residential area, and so on. The final sample, in total, consists of 245,604 respondents, and the socioeconomic distribution is shown in Table 1. The sample used in this study    7 comes from the public-use cross-sectional datasets, which means variables that can be used to identify the individual respondent such as the state they live in or ZIP code were omitted.   Table 1 Descriptive statistics, Monitoring the Future 1991-2017  Grade 8 Grade 10 Grade 12  (N=97,654) (N=103,411) (N=44,539) Male 48.72% 48.85% 48.04% Black 14.57% 11.99% 12.92% School Region    Northeast 19.81% 23.10% 21.38% Northcentral 25.94% 28.86% 27.38% South 37.47% 32.85% 34.52% West 16.78% 15.19% 16.71% MSA 77.20% 78.20% 77.78% Parent College-educated 57.94% 57.34% 53.51% High GPA 79.22% 74.62% 82.81% Intact Family 80.34% 81.00% 72.68%    8 3.2 Variables  3.2.1 Measure of marijuana use  To measure adolescent marijuana use, students were asked on how many occasions they had used marijuana in the past 12 months, with response categories of “0 occasion”, “1-2 times”, “3-5 times”, “6-9 times”, “10-19 times”, “20-39 times”, and “40 and above times”. The responses are dichotomously coded – students who reported “0 occasion” were defined as “never used” and those who reported to have used it at least once in the past 12 months as simply “used”. This dichotomy strategy is commonly used by other scholars who use MTF survey data to study the marijuana usage of adolescents (Keyes et al., 2011).   3.2.2 Measure of socio-demographic characteristics  The MTF survey covers basic socio-demographic information of the respondents at the individual level including racial/ethnic identity, gender, academic performance (GPA), parental education background, household living arrangements, geographical regions of schools, and whether the respondents lives in a MSA (Metropolitan Statistical Area). Male refers to whether the individual is male or female, Black denotes whether the individual self-identified as non-Hispanic African American, MSA denotes whether the individual lives in a “Metropolitan Statistical Area”, and College-educated Parents measures whether at least one side of the student’s parents has some college education. Since the information of birth year was omitted from the original dataset, 30 birth cohorts were generated based on 27 survey years and 3 grade levels (birth year = survey year – age).      9  3.3 Methods  3.3.1 Hierarchical Age-Period-Cohort Models  Separating age, period, and cohort effects from the three perfectly correlated temporal indicators (age = survey year – birth year) has been regarded as a major methodological challenge in demography and social sciences in general since the 1950s (Mason et al., 1973). This study uses the recently developed hierarchical age-period-cohort (HAPC) regression models to examine the patterns of marijuana use among American adolescents from 1991 to 2017. HAPC model is an innovative method widely adopted by demographers, sociologists, and epidemiologists to consider age effects as the person-level fixed effect while estimating the period and cohort effects as random effects at the population-level using best linear unbiased predictors (BLUPs) (Fu, 2016; Reither, Hauser and Yang, 2009). The Monitoring the Future study, as a repeated cross-sectional survey, provides an ideal opportunity for conducting age-period-cohort (APC) analysis. All HAPC models were estimated using SAS PROC GLIMMIX (Littell et al., 2006).  The level-1 within-group fixed effects of the HAPC model can be defined as:  log( !"#$%&"'(	*+,"#%+'+-.	!"#$%&"'(	*+,"#%+'+ ) = 	0123 +	0-5678 + 0956798 + 0:2;<=>?8 + 0@A7B8 + 0C2DA58 +0EFG8 + 0HIJ8 + 0K2LM8 + 0N2OP + 0-12A +	0--2Q  for  i = 1,2,…n individuals in jth period and kth birth cohort;        j = 1,2,…27 survey years (periods);         k = 1,2,…30 birth cohorts (1 birthyear/birth cohort);    10  Where R823ST8UV	WXY82SXUX  refers to the predicted probability of the ith adolescent using marijuana from the kth birth cohort in the ith period year. As for the covariates in level-1 model, Black denotes whether respondent self-identified as black or non-black; Sex denotes the gender identity (male or female); MSA denotes the current residential area (whether it is a Metropolitan-statistical-area or not); PE denotes at least one side of the parents had college (or above) education. For other control variables in the model, HG denotes the good academic performance (B- above or below); IF denotes the intact family structure; and NC, S, W denotes the school regions (northcentral, south, and west, respectively). The 0U s represent the coefficients in level-1,  0123 is the level-1 intercept that denoting the group means for respondents in the reference group who were interviewed in the jth year at the age zero, and born in the kth cohort.   The level-2 between-group random effects of the HAPC model can be defined as:  For intercept: 																																								0123 = 	 Z1 +	R12 +	>13   (1)  For Sex effect:																																						0@2 = 	 Z@ +	R@2     (2)          For Race effect:																																				0:2 = 	 Z: +	R:2    (3)  For MSA/Residential area effect:					0C2 = 	 ZC +	RC2    (4)  For Parental Education effect:											0K2 = 	 ZK +	RK2    (5)   For equation (1), Z1 refers to the mean averaged over 27 periods and 30 cohorts when age, all covariate variables, and control variables at level 1 are zero; R12 is the residual random effects of period j averaged over all 30 birth cohorts; and >13 is the residual random effects of cohort    11 averaged over 27 time periods. This study also aims to consider the differences in terms of adolescent marijuana use across social-demographic groups by using level-2 random effect models to testify whether the effects of the Sex, Race, Residential area, and Parental Education vary across different time periods by including the interaction terms of the four indicators and each survey eave. From equation (2) to (5), ZU  is the main effect of the corresponding socio-demographic gradient and RU2  are the random period effect on corresponding level-1 sociodemographic indicator 	0U2, which, in another word, denotes the interaction term of jth period and the level-1 indicator.   3.3.2 Structural change in Time Series tests  In their papers published in 1998 and 2003, Bai and Perron came up with an endogenous method which makes it possible to detect multiple breakpoints in one linear model1 (Bai and Perron, 1998; 2003). Endogenous method here refers to the fact that no pre-judgement was made on the numbers and locations of the possible structural breaks; instead, it let the data speak for itself.  In other words, the structural changes detected were due to endogenous factors such as historical events, change of social structural or of social economic policy. To see whether there is any significant structural change of youth marijuana use across 27 years and to locate the breakpoints where changes happened, time-series tests with multiple structural breaks based on the design of Bai and Perron (1998; 2003) were used. After verifying that the time series for all eight linear models (correspond to eight sub-groups from the four socio-economic indicators identified in HAPC model) are stationary around one or several breaks, the multiple break tests suggested by Bai and                                                1 See Bai and Perron 1998; 2003, for more details on multiple structural breaks in time series.     12 Perron (1998; 2003) were performed. In particular, the R package strucchange (Zeileis et al., 2002) was used to locate all the possible structural breaks using the predicted probability obtained from the previous HAPC analysis. To compare the variances within and between gender (male vs. female), race (black vs. non-black), residential area (MSA vs. non-MSA), and parental education (above college or below college), one multiple structural change test was conducted for each sub-group, which resulted in eight tests overall.                      13 4. Results  4.1 Temporal effects based on HAPC analysis  The observation of the age and birth cohort effects of adolescent marijuana use in the U.S. are shown in Figure 1 and 2. The general pattern of age effects on adolescent marijuana use probability echoes the findings in the previous study (Fergus and Zimmerman, 2005; Finn, 2006) and suggests a clear age-related increase in marijuana use. Risks of adolescent marijuana use ascend with youth aging mutually, but it slows down after entering grade 10. The cohort effects on marijuana use among adolescent dipped to a low point around the mid-1970s cohorts, followed by stepwise increase peaks in the mid-1980s birth cohorts, with a final downward trend emergent in the late 1990s cohorts.    Figure 1 Predicted probabilities of adolescent marijuana use across age groups based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: age effects.    50.00%55.00%60.00%65.00%70.00%75.00%80.00%85.00%90.00%GRADE 8 GRADE 10 GRADE 12Age Effect   14  Figure 2 Predicted probabilities of adolescent marijuana use across birth cohorts based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: cohort effects.  Table 2 reports the coefficient based on the hierarchical Age-Period-Cohort analysis. Model 1 presents age, period, and cohort effects, along with the control variables. To compare the results with and without period and cohort effects while investigate the potential relationship between important socio-economic predictors and marijuana use among adolescents, model 2 includes age, control variables, and four additional socio-economic predictors: gender, race, residential areas, and parental education levels. Being a male or living in a metropolitan area significantly and positively increases the odds for adolescents to use marijuana. Self-identified as black or coming from a family with college-educated parent(s) significantly lower the odds for the adolescents to use marijuana. In Model 3, period and cohort effects were added on the base of Model 2 to do further comparison. Model 4-7 present the odds of adolescent marijuana use with interactions between socio-economic indicators and period effects added. All four socio-economic indicators and the interactions with periods were included in model 8 with age, period, and cohort effects listed. 60.00%62.00%64.00%66.00%68.00%70.00%72.00%74.00%birthyear 1972birthyear 1974birthyear 1976birthyear 1978birthyear 1980birthyear 1982birthyear 1984birthyear 1986birthyear 1988birthyear 1990birthyear 1992birthyear 1994birthyear 1996birthyear 1998birthyear 2000birthyear 2002Cohort Effect   15 Table 2 Results from logistic hierarchical age-period-cohort models of adolescent marijuana use in the U.S., Monitoring the Future 1991-2017 (N= 245, 604)  Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Fixed effects         Intercept -0.951*** -1.006*** -1.103*** -1.059*** -0.840*** -1.112*** -0.939*** -1.099*** Age = 17 0.999*** 0.984*** 0.994*** 1.001*** 0.992*** 1.001*** 1.002*** 0.998*** Age = 19 1.458*** 1.450*** 1.454*** 1.459*** 1.449*** 1.463*** 1.461*** 1.459*** Northcentral -0.190*** -0.173*** -0.164*** -0.192*** -0.195*** -0.164*** -0.190*** -0.166*** South -0.155*** -0.072*** -0.076*** -0.155*** -0.109*** -0.122*** -0.156*** -0.072*** West 0.011 -0.007 0.006 0.012 0.002 0.020 0.011 0.012 High GPA -0.947*** -0.906*** -0.942*** -0.926*** -0.973*** -0.947*** -0.943*** -0.945*** Intact Family -0.401*** -0.476*** -0.476*** -0.409*** -0.471*** -0.400*** -0.399*** 0.206*** Male  0.190*** 0.193*** 0.194***    0.191*** Black  -0.428*** -0.429***  -0.406***   -0.425*** MSA  0.219*** 0.217***   0.175***  0.206*** Parent College Educated  -0.042*** -0.056***    -0.036 -0.061** Random period effects         1991 wave -0.569***  -0.560*** -0.580*** -0.519*** -0.623*** -0.544*** -0.556***   Male    0.021    0.016   Black     -0.505***   -0.514***   MSA      0.091  0.111   Parent College Educated       -0.017 -0.040 1992 wave -0.555***  -0.536*** -0.522*** -0.472*** -0.478*** -0.571*** -0.385***   Male    -0.071    -0.073   Black     -0.663***   -0.658***   MSA      -0.081  -0.061   Parent College Educated       0.047 0.031 1993 wave -0.347***  -0.331*** -0.353*** -0.315*** -0.332** -0.407*** -0.349***   Male    0.014    0.013   Black     -0.284**   -0.275   MSA      0.002  -0.001   Parent College Educated       0.107* 0.082 1994 wave 0.044  0.056 0.002 0.078 -0.173* -0.033 -0.217*   Male    0.074    0.069   Black     -0.331***   -0.311***   MSA      0.322***  0.304***   Parent College Educated       0.129** 0.087 1995 wave 0.290***  0.305*** 0.283*** 0.294*** 0.241** 0.271*** 0.242**   Male    0.017    0.015   Black     -0.026   -0.011   MSA      0.086  0.068   Parent College Educated       0.043 0.027 1996 wave 0.404***  0.412*** 0.428*** 0.411*** 0.357*** 0.380*** 0.380***   Male    -0.050    -0.058    16  Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8   Black     -0.144   -0.152   MSA      0.092  0.095*   Parent College Educated       0.049 0.031 1997 wave 0.382***  0.395*** 0.406*** 0.399*** 0.404*** 0.333*** 0.394***   Male    -0.046    -0.051   Black     -0.189*   -0.201*   MSA      -0.011  0.006   Parent College Educated       0.081 0.073 1998 wave 0.251***  0.271*** 0.279*** 0.262*** 0.255*** 0.260*** 0.290***   Male    -0.058    -0.054   Black     -0.046   -0.060   MSA      0.017  0.033   Parent College Educated       -0.002 0.000 1999 wave 0.174**  0.186** 0.178 0.182** 0.255*** 0.214** 0.290***   Male    -0.004    0.003   Black     -0.056   -0.063   MSA      -0.102  -0.084   Parent College Educated       -0.046 -0.043 2000 wave 0.094  0.105 0.089 0.089 0.087 0.136 0.121   Male    0.015    0.011   Black     0.009   0.013   MSA      0.026  0.030   Parent College Educated       -0.055 -0.059 2001 wave 0.145*  0.148* 0.121 0.149* 0.140 0.149* 0.123   Male    0.051    0.045   Black     -0.109   -0.112   MSA      0.014  0.028   Parent College Educated       0.001 -0.005 2002 wave 0.051  0.057 0.046 0.059 0.043 0.100 0.073   Male    0.007    0.008   Black     -0.069   -0.088   MSA      0.014  0.044   Parent College Educated       -0.066 -0.061 2003 wave -0.008  -0.002 0.005 0.002 0.067 0.013 0.087   Male    -0.024    -0.026   Black     -0.052   -0.048   MSA      -0.099  -0.076   Parent College Educated       -0.024 -0.015 2004 wave -0.122  -0.124 -0.121* -0.117 -0.017 -0.104 -0.005   Male    0.006    0.005   Black     -0.111   -0.106   MSA      -0.143**  -0.129*   Parent College Educated       -0.024 -0.022    17  Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 2005 wave -0.095  -0.105 -0.096 -0.116 -0.178* -0.134 -0.222**   Male    -0.000    -0.005   Black     0.112   0.104   MSA      0.107  0.099   Parent College Educated       0.058 0.046 2006 wave -0.178**  -0.187** -0.162* -0.191*** -0.110* -0.122 -0.068   Male    -0.029    -0.024   Black     0.041   0.048   MSA      -0.098  -0.098   Parent College Educated       -0.078 -0.064 2007 wave -0.217***  -0.230*** -0.227** -0.214*** -0.132* -0.197** -0.132   Male    -0.019    0.018   Black     -0.081   -0.064   MSA      -0.113  -0.114*   Parent College Educated       -0.026 -0.012 2008 wave -0.169**  -0.167** -0.174** -0.162* -0.088 -0.213** -0.124   Male    0.015    0.015   Black     -0.044   -0.029   MSA      -0.118*  -0.131*   Parent College Educated       0.060 0.061 2009 wave -0.035  -0.040 -0.067 -0.054 -0.079 -0.075 -0.158   Male    0.072    0.072   Black     0.196*   0.221**   MSA      0.053  0.035   Parent College Educated       0.056 0.049 2010 wave -0.005  -0.018 -0.025 -0.035 -0.003 0.056 -0.005   Male    0.036    0.039   Black     0.261**   0.265**   MSA      -0.019  -0.028   Parent College Educated       -0.087 -0.066 2011 wave 0.023  -0.004 0.002 -0.003 0.029 0.064 0.015   Male    0.042    0.044   Black     0.278**   0.275**   MSA      -0.012  -0.021   Parent College Educated       -0.059 -0.044 2012 wave 0.007  0.073 0.000 -0.005 -0.006 0.013 -0.027   Male    0.007    0.008   Black     0.133   0.128   MSA      -0.001  -0.006   Parent College Educated       -0.008 0.006 2013 wave 0.082  0.037 0.060 0.043 0.090 0.137 0.074   Male    0.045    0.047   Black     0.434***   0.427***    18  Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8   MSA      -0.016  -0.027   Parent College Educated       -0.081 -0.061 2014 wave 0.034  0.053 0.042 0.012 0.013 0.005 -0.030   Male    -0.014    -0.013   Black     0.275**   0.269**   MSA      0.008  -0.002   Parent College Educated       0.040 0.048 2015 wave 0.059  0.053 0.059 0.030 -0.009 0.145* 0.035   Male    0.003    0.006   Black     0.371***   0.363***   MSA      0.081  0.071   Parent College Educated       -0.122 -0.101 2016 wave 0.072  0.071 0.078 0.045 0.127 0.091 0.119   Male    -0.017    -0.013   Black     0.349***   0.356***   MSA      -0.087  -0.101   Parent College Educated       -0.024 -0.012 2017 wave 0.193*  0.198* 0.253** 0.168* 0.212* 0.163 0.212**   Male    -0.119**    -0.114**   Black     0.383***   0.391***   MSA      -0.026  -0.039   Parent College Educated       0.043 0.052 Random cohort effects         1972 -0.058  -0.059 -0.062 -0.055 -0.060 -0.065 -0.068 1973 -0.154  -0.159 -0.151 -0.151 -0.166 -0.165 -0.169 1974 -0.059  -0.050 -0.060 -0.044 -0.062 -0.067 -0.056 1975 -0.234***  -0.236*** -0.227** -0.234*** -0.243*** -0.245*** -0.247*** 1976 -0.188***  -0.198*** -0.190** -0.196** -0.194** -0.196*** -0.212*** 1977 -0.211***  -0.213*** -0.209*** -0.213*** -0.221*** -0.218*** -0.228*** 1978 -0.180**  -0.191** -0.182** -0.179** -0.189*** -0.186** -0.197*** 1979 -0.047  -0.049 -0.045 -0.040 -0.061 -0.051 -0.058 1980 0.077  0.079 0.077 0.084 0.075 0.074 0.076 1981 0.018  0.024 0.022 0.029 0.009 0.017 0.022 1982 0.067  0.074 0.067 0.076 0.069 0.066 0.077 1983 0.104  0.108 0.106 0.108 0.103 0.109* 0.114* 1984 0.222***  0.230*** 0.224*** 0.227*** 0.225*** 0.223*** 0.233*** 1985 0.171**  0.178** 0.169*** 0.183*** 0.172*** 0.177** 0.187** 1986 0.103  0.115* 0.102 0.119* 0.107 0.107 0.124* 1987 0.186***  0.202*** 0.188*** 0.203*** 0.193*** 0.193*** 0.218*** 1988 0.118*  0.130* 0.118* 0.126* 0.120* 0.122* 0.133* 1989 0.078  0.078 0.076 0.081 0.087 0.086 0.096 1990 0.090  0.094 0.090 0.091 0.095 0.096 0.101 1991 0.104  0.105 0.103 0.105 0.115* 0.111 0.122*    19  Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 1992 0.108  0.115* 0.104 0.108 0.111 0.114* 0.114 1993 0.074  0.075 0.072 0.074 0.087 0.081 0.092 1994 0.135*  0.137* 0.133* 0.130* 0.138* 0.143* 0.140* 1995 0.142*  0.147* 0.144* 0.137* 0.156** 0.149* 0.160** 1996 0.103  0.105 0.105 0.094 0.106 0.113 0.107 1997 0.060  0.057 0.059 0.053 0.070 0.067 0.068 1998 0.016  0.014 0.012 0.004 0.016 0.028 0.011 1999 -0.055  -0.054 -0.055 -0.063 -0.042 -0.048 -0.043 2000 -0.154*  -0.153* -0.155* -0.170* -0.153* -0.150* -0.166* 2001 -0.268**  -0.276** -0.268** -0.286*** -0.257** -0.264** -0.269** 2002 -0.365***  -0.391*** -0.366*** -0.392*** -0.366*** -0.361*** -0.387*** *p<0.05; **p<0.01; ***p<0.001 (two-tailed tests)   20 Figures 3 to 6 show the calculated probabilities of marijuana use based on the logistic-regression results from model 8 (using the equation ! = #$$%	'()*#+,#$$%	'()*# ) in each socio-economic group. Overall, the risk of marijuana use among American adolescents starts to increase since 1991 and peaks in 1996. With an acute decline until late 2000s, it eventually rises slowly with several small fluctuations in most recent periods. Figure 1.3 shows the gender disparities in adolescent marijuana use, where male adolescents are at higher risks to use marijuana across all 27 years than female and the gender disparities enlarged after 1996. It aligned with findings in other study (Guxens et al., 2007) that school-aged boys are at higher risks of using cannabis and marijuana. As shown in Figure 1.4, adolescents who are non-Black (including those self-identified as White, Hispanics, or other groups) are at all time higher risks of using marijuana compared to African-American adolescents excepts for 2013 and 2017. However, the racial difference on marijuana use narrows rapidly after later 2000s because the risks for African-American adolescents start to boost since 2008. The valley point appeared in 2012 for African-Americans suggests that the downward shift of overall adolescent marijuana use from 2011 to 2013 is because of the sudden drop of the marijuana use among African-American adolescents only. The gradually diminishing racial difference indicates that the difference of using marijuana risks for black versus non-black adolescents in the U.S. was getting closer in recent years. Figure 1.5 shows that adolescents who live in metropolitan statistical areas are constantly facing higher risks of using marijuana compared to their counterparts across 27 years. The disparities between adolescents whose parent(s) received college education or otherwise, as shown in Figure 1.6, were relatively close. Although there appears a time period from 1992 to 1997 where there are very minor differences indicated, those who have college-educated parent(s) are experiencing significantly lower risks of using marijuana compared to their peers before 1992 and after 1997. Those findings indicate that, mostly, having    21 highly-educated parent(s) effectively protect the adolescents from using marijuana. Being female, being Black, residing in non-metropolitan statistical areas, or having parent(s) who were college-educated, respectively, has protective effect which cut down the probability for adolescents to use marijuana. Inversely, being male, being non-Black living in metropolitan areas, or having relatively less-educated parent(s) is strong risky factor that leads to higher possibilities of using marijuana for adolescents.     Figure 3 Predicted probabilities of marijuana use for adolescents with different gender identities based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects.   10.00%15.00%20.00%25.00%30.00%199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Gendermale female general population   22  Figure 4 Predicted probabilities of marijuana use for adolescents with different racial identities based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects.     Figure 5 Predicted probabilities of marijuana use for adolescents living in different residential area based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects.   4.00%6.00%8.00%10.00%12.00%14.00%16.00%18.00%20.00%22.00%24.00%26.00%28.00%30.00%199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Raceblack non-black general population9.00%11.00%13.00%15.00%17.00%19.00%21.00%23.00%25.00%27.00%29.00%199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Residential Areamsa non-msa general population   23  Figure 6 Predicted probabilities of marijuana use for adolescents with different parental education levels based on a hierarchical age-period-cohort model, Monitoring the Future 1991-2017: period effects.    4.2 Breakpoints based on time series tests  To locate the exact time point where a statistically significant structural change happened, time series tests with multiple break points are conducted for each sub-group of the four socio-economic indicators identified from HAPC model. Figure 7 to 14 present the breakpoints identified using the time series tests with its corresponding confidence interval at 95% level2.                                                 2 Note that the confidence intervals of the breakpoints identified by structural change models does not have to be symmetrical. As Zeileis & Kleiber 2005: 687 discussed in a paper that replicated Bai & Perron’s design, “The confidence intervals are derived from the distribution of the argmax functional of a process composed of two independent Brownian motions with different linear drifts and scales.” See Zeileis and Kleiber 2005: 687, Bai and Perron 1998; 2003, for more detailed discussions on the asymmetric confidence interval. 10.00%12.00%14.00%16.00%18.00%20.00%22.00%24.00%26.00%28.00%199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Parent Educational Backgroundcollege educated parent(s) highschool and below general population   24 All eight sub-groups share notable structural breaks in year 1994, 1998/1999, and 2008; all sub-groups except for adolescents who were Black have a breakpoint in year 2003; the self-identified Black population has an unique breakpoint in year 2012. There are upward shifts with continuously positive slopes appeared after the significant structural break in 1994 across all sub-groups. On the contrary, downward shifts with negative slopes showed up after the significant structural break in 1998 across all sub-groups. Among all sub-groups which have a significant structural break in 2002/2003, there is a notable decrease of marijuana use after the break points for all adolescents but males and those who had less-educated parent(s). Following the break point in 2008, all groups but the non-MSA residents show dramatical rises in marijuana use compared to the trends before the 2008 breaks, not to mention the direction of the slope for below-college-educated parents group changed. For black adolescents, there is a unique breakpoint in 2012, and the pattern of black adolescents marijuana use reached its bottom at this break followed by a sharply upward shift.   Figure 7 Probability of marijuana use for female adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017. Timefemale0.120.140.160.180.200.220.24199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017   25   Figure 8 Probability of marijuana use for male adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2002, and 2008, Monitoring the Future 1991-2017.      Figure 9 Probability of marijuana use for black adolescents in the U.S. across 27 years, and breakpoints in 1994, 1999, 2008, and 2012, Monitoring the Future 1991-2017. Timemale0.140.160.180.200.220.240.26199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Timeblack0.050.100.150.20199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017   26   Figure 10 Probability of marijuana use for non-black adolescents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017.   Figure 11 Probability of marijuana use for adolescents living in metropolitan statistical area (MSA) in the U.S. across 27 years, and breakpoint in 1994, 1998, 2003, 2008, Monitoring the Future 1991-2017. Timenonblack0.160.180.200.220.240.26199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Timemsa0.140.160.180.200.220.240.260.28199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017   27  Figure 12 Probability of marijuana use for adolescents living in non-metropolitan statistical area (non-MSA) in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017   Figure 13 Probability of marijuana use for adolescents raised by college-educated parents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017. Timenonmsa0.120.140.160.180.200.22199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017Timepaeduc0.140.160.180.200.220.240.26199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017   28  Figure 14 Probability of marijuana use for adolescents raised by below-college-educated parents in the U.S. across 27 years, and breakpoints in 1994, 1998, 2003, and 2008, Monitoring the Future 1991-2017.  These eras where the commonly-shared breakpoints were identified coincide with several waves of economic recessions that affected the American society, including the early 1990s recession as the result of the 1990 oil price shock, the 1997 Asian financial crisis which hit the American markets severely, the early 2000s recession that widely affected developed countries, and the 2007-2008 global financial crisis. The 2012 break found in black adolescents group coincides with the United States debt ceiling crisis in 2011, the “Black Monday”. The findings above reveal the repercussions of the economic recession and financial crisis on the adolescent marijuana use, but the effect varies across gender, race, residential areas, and SES (parental educational attainment) differently.    Timepaloweduc0.140.160.180.200.220.240.26199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017   29 5. Discussion and Conclusion Using 1991 to 2017 waves of data from the Monitoring the Future survey, this study examines the temporal trend and the socio-economic disparities in adolescent marijuana use in the U.S. from 1991 to 2017. To investigate the age, period, and cohort effects and compare the patterns within and between socio-economic groups across time, the hierarchical age-period-cohort (HAPC) analysis was conducted using SAS PROC GLIMMIX. The R package strucchange was employed to locate all the possible structural breaks of the adolescent marijuana use probability from 1991 to 2017. Several limitations of this study should be noted. First, the public-use dataset omitted certain identifiable personal information including the state the respondent resides in. Consequently, the possibility to compare the temporal trend of marijuana use between states that already legalized recreational marijuana use and those did not was not supported. Second, Monitoring the Future survey does not include youths who dropped out of school. It can be foreseen that the probability of marijuana use among adolescents would change if the responses from high school dropouts are included. Third, racial group “Hispanic or Latino” was not an independent option in the survey until 2005. To standardize the categorization for racial identity, race was dichotomized into “Black” and “Non-Black (including White, Hispanic and all others), and more detailed information on racial identities is impossible to be retrieved due to the inconsistency of the data collection. This dichotomy of racial identity variable precludes the opportunity to conduct comparisons across multiple racial groups.  Results show the temporal trend of marijuana use among American adolescents and identify four vulnerable socio-economic groups that are at all-time risks of using marijuana during the ages while they are in school. Several structural breaks are detected and they coincide with several waves of economic recessions that affected American economy. A notable increase in    30 marijuana use among American adolescents is found from 1992 to 1996 following by a steady decrease. The risk of using marijuana for adolescents later bounced back over the most recent decade. This trend should be regarded as an early alert that the risks of marijuana use among underaged Americans are gradually increasing and may continue to grow stronger. Social disparities are found and evidences show that American adolescents who are male, self-identified as non-Black, living in metropolitan statistical areas, or having less-educated parent(s) are at all-time higher risks of using marijuana compared to their counterparts and the average American youth. The findings also suggest that certain shifts and fluctuations emerged in the overall temporal trend of American adolescent marijuana use are brought by the drastic change happened within certain socio-economic groups in certain periods. Those findings from the HPAC analysis emphasize the fact that American adolescents from the identified groups are the most vulnerable population regarding risks of marijuana use during their school ages, and, therefore, require more specific targeted intervention and prevention efforts from family, school, and community. Regarding the age effects, it is consistent with the findings elsewhere that the trends are age-related maturational. It starts with a large than 50% probability for students in grade 8, rises up to around 75% probability for students in grade 10, and finally ends with an over 80% probability for students in grade 12 to use marijuana. Considering the potential adverse health consequences of using marijuana, such as addiction and mental illness in the later life stages (Bachman et al., 1991), and its gate-way effect that leads to the use of other illicit drugs (Lynskey et al., 2003), the high risks across all age groups is no-doubt a highly noteworthy phenomenon for parents, educators, and researchers. The trend of cohort effects reveals a peak in the mid to late 1980s cohorts and late 1990s cohorts, which could be explained by the “change of public acceptance and social norms on marijuana” hypothesis proposed earlier (Keyes et al., 2011).     31 Significant structural breaks in 1994, 1998, 2003, and 2008 detected using endogenous multiple structural breaks in time series tests coincide with several economic recessions that hit the American economy severely. The pattern of marijuana use varies differently around the structural breaks for male vs. female, black vs. non-black, MSA-residents vs. non-MSA residents, and those with high-educated vs. less-educated parent(s). This suggests that the economic recessions affect the risks of adolescent marijuana use in the America and this impact is distinct for adolescents coming from each socio-economic sub-groups. Some of the possible mechanisms to explain the relationship of economy and adolescent marijuana use include (1) the increasing/decreasing amount of time youth spent with parental supervision (parents may work longer to compensate for the lower income, or, in the contrary, parents may work less due to the loss of jobs); (2) the decrease of family income changed the accessibility of substance drugs for adolescents; and (3) the change of parental stress level due to unemployment may affect parental support or trigger substance use of parent(s). 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