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«The Social Control of Childhood Behavior via Criminalization or Medicalization: Why Race Matters DISSERTATION Presented in Partial Fulfillment of the ...»

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ln � � = 0 + 1 + 2 2 + 3 3 Pr(=1;) 1−Pr(=1;) In this model, Pr(y=1;j) is the probability of involvement with the criminal justice or mental health systems, respectively, for individuals in group j in a given year and β 0, β 1, β 2, and β 3 are possible population parameters that may determine the shape of the trajectory. Importantly, because each trajectory can theoretically have its own unique shape, group-based models allow for estimating a unique set of parameters for each group (Nagin 2005).

Because the decision on how many groups to estimate is made a priori, it is important to consider both theoretical and methodological criteria (Nagin 2005).

Following prior research and in keeping with the goals of the current project, I estimate a three-group model of criminalization, with one group following a linear trajectory, a second group following a quadratic trajectory, and a third group following a cubic trajectory, and a three-group model of medicalization, with one group following a linear trajectory, a second group following a quadratic trajectory, and a third group following a linear trajectory. This strategy aligns well with literature discussed earlier, which finds that, for a large proportion of the population, the risk of involvement with either the criminal justice system or mental health system will remain relatively low throughout adolescence and young adulthood (Piquero et al. 2013). On the other hand, for those that do experience criminalization or medicalization during adolescence or young adulthood, only a small proportion will actually maintain contact throughout adulthood (Moffitt 1993; Nagin and Land 1993; Piquero et al. 2013). Instead, their involvement with the criminal justice and/or mental health systems is short-lived, often waning by the early twenties (Moffitt 1993; Nagin and Land 1993).

To build an appropriate model and test for model fitness, I ran a series of tests during throughout the analysis. I used Bayesian Information Criterion (BIC) as the primary methodological test to determine both the optimal number of groups and the best fitting model. For both criminalization and medicalization, I started with a one-group model and added groups until reaching the model with the largest BIC. For both dependent variables, BIC peaked in a three-group model. I then moved on to testing the appropriate number of parameters for age to estimate the shape of each trajectory (Nagin 2005). Finally, following Nagin (2005), I calculated posterior probabilities to determine the likelihood than an individual would follow a given trajectory based on his own pattern of involvement with either the criminal justice or mental health system. For both models (see Appendix H), posterior probabilities (average probabilities group of membership for group members) were all above the recommended cutoff of.70 (Nagin 2005).

Time-invariant Variables To examine how events during childhood can contribute to different long-term trajectories of social control, this project focuses on several time-invariant variables captured before the respondents’ fifteenth birthdays. Because school punishment and therapy/medication are measured before young adulthood, it reduces the chances of a reverse causal relationship between childhood labeling and trajectories of social control during adulthood. The central independent variables in this paper capture the labeling of child problem behavior during childhood. This was accomplished by creating a series of dummy variables designed to capture a range of possible responses to childhood misbehavior involving school punishment and/or the use of therapy or medication for behavior problems before the age of fifteen. School punishment is measured using the Mother’s response to the question “Has your child ever been suspended or expelled from school?” and coded “one” if the mother responds “yes.” The use of therapy or medication is intended to capture medical or psychological services for behavior problems and is taken from the child’s response to one of two questions: (1) whether or not the child had seen a psychiatrist or psychologist for troubles in school or for tantrums, hyperactivity, or disruptive behavior and; (2) whether or not the child was taking drugs to control his/her behavior. I then construct a categorical measure, coded as follows: (1) neither punished nor therapy/medication; (2) Therapy/Medication only; (3) Punishment only; (4) both punishment and therapy/medication. For all analyses, the “neither punished nor therapy/medication” is considered the reference group. Childhood misbehavior and race play key roles in the analysis as well.

To capture childhood problem behavior, I use an externalizing behavior scale adopted from the Child Behavior Checklist (Guttmannova, Szanyi, and Cali 2007).

Externalizing behaviors are those behavior characterized by a lack of emotional control or an inability to suppress impulses, leading to rule breaking (Guttmannova, Szanyi, and Cali 2007). Importantly, as you can see from the list, the externalizing behavior scale includes a number of behaviors that could possibly lead to school discipline, including getting into trouble with teachers, being disobedient at school, and bullying or being cruel to others. To note, these behaviors are also listed by mental health professionals as “symptoms” of childhood behavior disorders. For example, cheating/lying and bullying are included in some conduct disorder symptom checklists, disobedience is often a sign of ODD, and confusion, restlessness, and inattention are considered to be classic ADHD symptoms. Finally, many of these variables capture aspects of low self-control, a theoretically time-invariant explanation of criminal activity that develops in childhood and remains relatively stable across the life-course (Blokland and Nieuwbeerta 2010; Hay and Carter 2006; Gottfredson and Hirshi 1990). A full list of variables in the externalizing behavior scale is available in Appendix G. Following Currie and colleagues (2014), I create a time-stable measure of behavior by averaging the scores on the CBCL between the ages of 6 and 14.





Race is captured with a dummy variable equal to 1 for African-American respondents and 0 for White males. I focus on males because of their overrepresentation in school punishment, early diagnosis of behavior problems, and involvement in the criminal justice and mental health system during young adulthood (Bertrand and Pan 2013; CDC 2012; Miller, Nigg, and Miller 2009). In addition, many of the mechanisms behind social control for young Hispanic males may differ than those of young White and African-American males. In particular, the legacy of slavery and the use of the criminal justice system to further entrench a racialized caste system are less salient in the Hispanic community (Alexander 2012; Wacquant 2001; Western 2006).

I also include a number of time-invariant control variables to capture social and structural conditions during childhood that may increase the risk of entrance into different social control trajectories. To control for early academic experience, I include timeinvariant measures of academic achievement using the child’s standardized score the last year he took the PIAT Reading Recognition and Mathematics test 4 and dummy variables equal to “1” if the respondent repeated a grade due to academic issues or enrolled in Head Start, respectively. To capture socioeconomic status, I use mother’s education (in years) and a dummy variable equal to “1” if total family income (total money family members earned from wages, tips, and salaries in the past year, adjusted for inflation and reported in 2010 dollars) was ever less than the poverty level between the ages of 6 and 14 5. I also include dummy variables indicating whether the respondent ever lived in a single-mother household as a child. Similar to externalizing behaviors, all continuous variables were created by averaging the scores between the ages of 6 and 14 6.

While children in the NLSY-C take the PIAT Reading Recognition and Math tests each year they are surveyed, I chose to include their score the final year they took the test because young adults in the sample do not take the tests, group-based models require that time-invariant variables be measured before group membership is measured, and children generally performed better on the exams as they got older Supplemental analysis included these variables measured as the mean score during childhood (similar to externalizing behaviors) and the maximum score (typically equal to the final score) with no change to the substantive findings.

I chose to include a measure of ever having lived in poverty, as opposed to household income, for important methodological and conceptual reasons. First, all variables predicting risk of entering a given trajectory must be time-stable and measured before the dependent variables of interest. Second, prior research suggests that living in poverty is a strong predictor of both school punishment and involvement with the criminal justice system during young adulthood (Kupchik 2010; Lyons and Drew 2006). As a result, I chose to include a time-stable measure of poverty over the averaging of family income during childhood to capture socioeconomic status during childhood. Results for models using average family income during childhood are available by request.

While the PIAT Reading Recognition and Math tests were issued with every survey, I I chose the mean over other options, for example the maximum score or the scores for the last time the child took the test on the survey for two reasons. First, First, all variables predicting risk of entering a given trajectory must be time-stable and measured before the dependent variables of interest. Second, similar to externalizing behaviors, taking the mean allows me to use the most information possible and takes into account severe Additional variables include time-invariant measures of whether the respondent spent the majority of their childhood in a suburban, rural, or urban residence during school, the region of the country in which the respondent spent the majority of their time during elementary and middle school (Northeast, Midwest, South, or West) and whether the mother was under 18 years old when the respondent was born. Finally, because the stigma of childhood mental disorders has changed over time (Pescosolido 2013), I include a measure of year born to control for changing perception of punishment and therapy/medication.

Time-variant variables I also control for a number of time-variant variables, measured each survey year during young adulthood, that serve as potential pathways between early childhood labeling events and involvement in either the criminal justice or mental health systems for individuals following different criminalized or medicalized trajectories, respectively.

Given the relationship between participation in conventional activities such as work or school and the likelihood of offending and conviction during young adulthood, I include a measure of economic idleness using a dummy variable equal to “1” if the respondent neither worked nor was enrolled in school for a period of at least two months in the past year (Deming 2009). Additionally, because school punishment and therapy or medication may contribute to criminalization or medicalization through school intrapersonal changes in performance over time. Results from models using other specifications, including the maximum and final scores, did not differ significantly from those presented in this analysis and are available by request.

performance, I include time-varying measures of recent grades and high school graduation. Because the NLSY-CYA does not include a measure of grade point average, recent grades are measured with a dummy variable equal to “1” if the respondent’s most recent grades were at least a C+ or better during school. High school graduation is measured with a dummy variable equal to “1” if the respondent received his high school diploma.

Childhood labeling is argued to significantly influence self-perception and sense of self-worth, particularly during important transitions from childhood to adolescence and young adulthood. I capture self-esteem using a ten-item scale based on the Rosenberg Esteem Scale (Rosenfield 1997). Respondents were asked whether they (1)strongly disagree to (4) strongly agree with the questions: I feel that I'm a person of worth, at least on an equal basis with others; I feel that I have a number of good qualities; all in all, I am inclined to feel that I am a failure (reverse-coded); I am able to do things as well as most people; I feel that I do not have much to be proud of (reverse-coded); I take a positive attitude toward myself; on the whole, I am satisfied with myself; I wish I could have more respect for myself (reverse-coded); I certainly feel useless at times (reverse-coded);

at times I think I am no good at all (reverse-coded).

To measure self-control, I use a six-item scale based on Gottfredson and Hirschi’s (1990) argument that attitudinal measures of self-control better capture the concept during adolescence. Similar to the other measures, respondents are asked whether they (1) strongly disagree to (4) strongly agree with the questions: I often get in a jam because I do things without thinking; I think that planning takes the fun out of things; I have to use a lot of self-control to keep out of trouble; I enjoy taking risks; I enjoy new and exciting experiences, even if they are a little frightening or unusual; life with no danger in it would be too dull for me. In order to be consistent across my attitudinal measures, I recode self-esteem and self-control so that higher scores on each measure indicate positive or “better” attitudes and standardize each variable before the analysis to ease interpretation of the coefficients.

Finally, I include a measure of illegal behavior using a scale (0-3) developed from questions relating to whether or not the respondent had engaged in any of the following behaviors: violent crime in the past year (hurt someone badly enough to need doctor, gotten into a physical fight at school or work, seriously threatened or hit someone);

property crime in the past year (damaged school property intentionally, taken something worth $50 or more, taken something from store without paying); drug use in the past month (marijuana, powder cocaine, crack cocaine, heroin, methamphetamine, or hallucinogens).



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