Social contexts, age and juvenile delinquency: a community perspective.

Journal of Child & Adolescent Mental Health

ISSN: 1728-0583 (Print) 1728-0591 (Online) Journal homepage: http://www.tandfonline.com/loi/rcmh20

Social contexts, age and juvenile delinquency: a community perspective Catherine L Ward & James E Laughlin To cite this article: Catherine L Ward & James E Laughlin (2003) Social contexts, age and juvenile delinquency: a community perspective, Journal of Child & Adolescent Mental Health, 15:1, 13-26, DOI: 10.2989/17280580309486536 To link to this article: http://dx.doi.org/10.2989/17280580309486536

Published online: 12 Nov 2009.

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Journal of Child and Adolescent Mental Health 2003, 15(1): 13–26 Printed in South Africa — All rights reserved

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JOURNAL OF CHILD AND ADOLESCENT MENTAL HEALTH ISSN 1728–0583

Research Paper

Social contexts, age and juvenile delinquency: a community perspective Catherine L Ward1,2* and James E Laughlin1 1

Department of Psychology, University of South Carolina, USA Department of Psychiatry and Mental Health, University of Cape Town, Groote Schuur Hospital, Observatory 7925, South Africa * Corresponding author, e-mail: [email protected]

Downloaded by [University of Exeter] at 01:12 03 November 2015

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Objective — Social disorganisation of communities, family bonds, school bonds, the peer group and age, have been shown to be related (either positively or negatively) to delinquency. This study addressed gaps in the literature by (1) using a large and randomly drawn sample of adolescents, within a large number of randomly selected communities; (2) investigating the influence of community social disorganisation directly on delinquency, while including in the same model the moderating effect of community social disorganisation on the micro-contexts of family, school, and peer group, as well as the direct effects of these micro-contexts; and (3) including age as a variable likely to have both direct effects on delinquency and moderating effects on the micro- and macrolevel social contexts. Method — The public-use data set of Wave I of the (US) National Longitudinal Study of Adolescent Health was used. The model was tested using hierarchical linear modelling and included the social disorganisation of communities; adolescents’ bonds to school and family, family controls and involvement with a deviant peer group; and age and its interaction with these social contexts. Results — Community social disorganisation was found to be positively related to delinquency, but effects of micro-level contexts were not found to be moderated by social disorganisation. Family bonds and controls, and school bonds, were negatively related to delinquency. No effect of peer group was found. Age was found to have a direct effect; effects of the interaction of age with family controls and age with school bonds were also significant. Conclusions — Results strengthen those from previous studies (using more limited samples) which show that integrated views of macro- and micro-level social contexts and developmental trends are necessary to understand delinquency.

Introduction Delinquency is an important area of adolescent behaviour that is strongly related to various social contexts. Families, schools and peer groups appear to play a key role in the development or prevention of delinquency (Hirschi 1969, Gottfredson and Hirschi 1990, Catalano and Hawkins 1996). Communities, which provide a macro-context for the microcontexts of families, schools and peer groups, also have an effect (Elliott et al. 1997, Sampson and Laub 1997). Aside from social contexts, other variables are strongly related to delinquency. One of the chief amongst these is age (Hirschi and Gottfredson 1989), suggesting that developmental trends also play a role in delinquency. It is increasingly recognised that a comprehensive, integrated understanding of development and individuals’ microand macro-social contexts is necessary to understanding delinquent behaviour (Bursik and Grasmick 1993, Le Blanc 1997). Delinquency and micro-level contexts Social control theory (Hirschi 1969) provides a useful framework for viewing the micro-level processes that have been found to be related to delinquency. The central tenet of this theory is that the bond between the individual and society restrains him or her from becoming deviant. This bond, as conceptualised by Hirschi (1969), consists of the following

four parts: (1) Attachment: the psychological and emotional connection that the individual feels toward others; (2) Commitment: the rational counterpart of attachment — the individual weighs the costs and benefits of delinquency; (3) Involvement: involvement in conventional activities leaves less time and opportunity for involvement in deviant activities; and (4) Belief: believing in, accepting, conventional values weakens the likelihood of delinquency. Beyond merely such a bond, however, the child is socialised by direct controls: he or she is supervised and disciplined when necessary (Gottfredson and Hirschi 1990). The bond makes it possible for social controls to become internalised. Control theory thus emphasises the strength of bonds to social institutions. For adolescents, the most important social institutions are family, school and peers. Within the family, aspects of social control that are important for preventing delinquency encompass affective attachment between parents and children, commitment to conventional activities, involvement with family members in prosocial activities, and encouraging children to internalise conventional values (Hirschi 1969, Gottfredson and Hirschi 1990). Direct family controls — supervision and monitoring of children’s activities — may be understood as an expression of these four aspects of social control (Gottfredson and Hirschi 1990, Masten and Coatsworth 1998). Beyond the


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parents’ attachment to the child, the child’s attachment to the parents is also important in ensuring that parental controls are effectively internalised (Fischer 1983, Wilson H 1987, Gottfredson and Hirschi 1990). Association with delinquent peers is shown in some studies to correlate positively with delinquency (Gottfredson and Hirschi 1990, Shoemaker 1996), whereas association with prosocial peers is a protective factor: through forming bonds with prosocial peers, modelling of prosocial behaviour is facilitated and so is control over adolescents’ behaviour (Loeber 1990, Catalano and Hawkins 1996). The more time adolescents spend with delinquent peers, the more likely it is that they will become violent offenders (Sampson and Lauritsen 1994), and association with delinquent peers seems to precede an individual’s own delinquency (Menard and Elliot 1990). The school is an important institution in the lives of adolescents, and poor school performance is strongly associated with delinquency (Henggeler 1989). Liking school, having higher grades, and being involved in school activities, are evidence of a bond to the school; whereas low grades and dropping out are evidence of a weak or absent bond (Gottfredson and Hirschi 1990). In turn, academic incompetence and dropping out of school are predictors of delinquency (Loeber and Le Blanc 1990, Stouthamer-Loeber et al. 1993). Delinquency and macro-level contexts Several studies show that there are various physical and demographic characteristics of communities, such as poverty, that tend to vary with delinquency and gang activity (Johnstone 1981, Galster and Mincy 1993, Kasarda 1993, Gracia, Garcia and Musitu 1995). Shaw and McKay (1942, 1969), in their classic study of neighbourhoods and delinquency, found that delinquency rates were negatively correlated with distance from the city centre, and hence with low economic status, ethnic heterogeneity, and residential mobility. They propose the concept of social disorganisation as the construct that accounts for these correlations. Social disorganisation refers to the “inability of a community structure to realise the common values of its residents and maintain effective social controls” (Sampson 1992, p. 66). Structural factors and delinquency are hypothesised to be causally connected by the impact of structure on the ability of the neighbourhood to develop common values or to solve common problems together (Shaw and McKay 1942, 1969, Kornhauser 1978, Sampson 1992). Three aspects of social organisation appear in particular to be related to juvenile delinquency (Sampson 1992, Sampson and Lauritsen 1994): (1) the supervision of teenage peer groups: the greater the ability of a community to control groups of teenagers (for instance, by intervening in groups congregated at street corners), the lower the delinquency rate; (2) local friendship networks and the density of acquaintanceship are key to (a) restraining the behaviour of those within the network, and (b) recognising strangers and so guarding against victimisation; and (3) the more community residents participate in local formal and voluntary organisations, the more the community will be able to control its youth by maintaining consistent standards for behaviour across different contexts.

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Links between macro- and micro-contexts Community social organisation also appears to affect delinquency rates indirectly, however, through strengthening either community institutions themselves, or attachment to them. The effect of the community on the family cannot be ignored, although studies reveal conflicting results. Some suggest that while neighbourhood has an indirect effect on delinquency, this effect is mediated by the ability of parents to supervise their children and good family relationships (Wilson 1982, Sampson and Laub 1994, Stern and Smith 1995, McCord 2000). Other studies, however, suggest that the relationship between family functioning and delinquency depends on the neighbourhood context, with family relations and parenting practices moderating the influence of stress on likelihood of delinquency in poor urban neighbourhoods, but in inner-city neighbourhoods, family factors are unlikely to play such an ameliorating role (Gorman-Smith, Tolan and Henry 1999, 2000). At the community level, social disorganisation implies a breakdown in the common value system, so that within the socially disorganised community, there will be groups that have developed their own norms and values (Sutherland 1939, Sutherland and Cressey 1978, Le Blanc 1997). The more disorganised the community, the more likely youth are to come into contact with deviant peer groups. In more organised communities, it is more likely, of course, that prosocial groups will be encountered. Evidence for this comes from the work of Elliott et al. (1997), who found that neighbourhood informal control is related to the likelihood of youths having conventional friends. The community also influences the role played by the school in the lives of adolescents. For instance, rates of high school suspension (which correlates with delinquency) have been shown to be related to elements of neighbourhood context (Hellman and Beaton 1986). Suspension from school is a sign of a lack of a bond between the adolescent and the school (Hirschi 1969), so that Hellman and Beaton’s (1986) work implies that the neighbourhood context is related to bond to the school. Further, in a study of the relationships between community-level factors and individual offending, Simcha-Fagan and Schwartz (1986) found that both community’s aggregate levels of organisational participation by residents and residential stability (aspects of community social organisation) affected delinquency, by affecting the child’s attachment to the school. Delinquency and age With only subtle variations in the curve, depending on factors such as sex and type of crime, all age-crime curves show an increase in offending which starts in the early teen years, to a peak in the middle to late teen years, followed by a decline in offending (Farrington 1986, Hirschi and Gottfredson 1989, Gottfredson and Hirschi 1990). In their longitudinal study of youth, Jessor and Jessor (1977) conceptualise delinquency as one of a class of problem behaviours that have age-graded social controls, with these controls changing with age so that what might be proscribed for one age group is accepted or even prescribed in an older one. Such behaviours might include engaging in sexual intercourse and drinking.


Journal of Child and Adolescent Mental Health 2003, 15: 13–26

Age-related changes appear to affect both macro- and micro-level contexts. For instance, age has been shown to affect association with delinquent peers (Simcha-Fagan and Schwartz 1986) and the delinquent peer group seems to fulfil, for the developing adolescent, the same functions as a prosocial peer group (Henggeler 1989). Similar results are found for the influence of the school and the community, as adolescents begin to explore beyond the family and so become increasingly involved in these contexts (Stern and Smith 1995, Catalano and Hawkins 1996, Santrock 1996). There is some evidence that neighbourhood effects also vary by age, having less effect in the lives of younger adolescents but more for older youth, who have had more time to be exposed to the environment and who are likely to be more involved in neighbourhood social networks (Barry 1994, Elliott et al. 1997). Furthermore, as adolescents grow older, they move beyond the family and so social control over their behaviour comes also from the school, peers and community (Farrington 1986, Catalano and Hawkins 1996, Sampson and Laub 1997). Towards an integrated approach From the theoretical perspective, social control and social disorganisation theories provide compatible explanatory models for the phenomenon of juvenile delinquency, models that, respectively, address delinquency from the perspectives of the individual and of the larger social context. Both may be understood as control theories, since both view social controls — either at the macro-level (social disorganisation theory) or at the micro-level (social control theory) — as crucial in preventing crime and delinquency (Sampson 1992, Shoemaker 1996). Previous work therefore makes it clear that micro- and macro-level contexts, together with age, play an integrated role in influencing the likelihood of delinquency. Yet previous studies are limited in crucial respects. Firstly, previous studies have tended to use carefully drawn probability samples of high-risk youth or high-risk communities (Simcha-Fagan and Schwartz 1986, Stern and Smith 1995, Elliott et al. 1997). While these methods have power to answer certain questions, few other studies include such a very large sample of randomly sampled communities and the adolescents who live in them. Secondly, few studies include all three of the family, peer group and school with the community social disorganisation, and so a comprehensive picture of the adolescents’ world is not available. Stern and Smith (1995), for instance, look at the family context (including neighbourhood) and parenting, while Elliott et al. (1997) include neighbourhood and youth outcomes (such as the presence of conventional friends, or problem behaviour) without including other contexts in their model. Thirdly, studies that integrate age or developmental issues with contextual factors are lacking. Where Tolan (1988) did investigate the relationship between developmental tasks and the age patterns of delinquency, his sample was restricted to males only, and did not include the community context. While both the community and the family exert controls on delinquency, these should change in their relative importance as adolescents grow older and are

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more able to be independent in their exploration of community contexts. This study addresses the gaps in previous studies by (1) using a large and randomly drawn sample of adolescents living in a large number of randomly selected communities; (2) investigating the influence of community social disorganisation directly on delinquency, while including in the same model the moderating effect of community social disorganisation on the micro-contexts of family, school, and peer group, as well as the direct effects of these micro-contexts; and (3) including age as a variable likely to have both direct effects on delinquency and moderating effects on the microand macro-level social contexts. Method Participants Data for this study are drawn from the core sample of the public-use data set of Wave I of the National Longitudinal Study of Adolescent Health. Students and their parents were accessed through schools. A database of schools in the USA, developed by Quality Education Data, was used as the sampling frame (Bearman, Jones and Udry 1997). Schools were defined as such if they included an eleventh grade and had an enrolment of more than thirty students. Of all the eligible schools in the USA, a stratified, random sample yielded a high school and its feeder school (schools that included the seventh grade and whose graduates usually attend the selected high school) in each of eighty different communities. In some cases, the selected high school was its own feeder school. Stratification ensured that schools were representative of schools across the USA in terms of region of the country, urbanisation, ethnicity, and school type and size. Schools that refused to participate were replaced by another school within that stratum. The sample included 132 schools in total. Students in grades seven through twelve in these schools completed a questionnaire at school between September 1994 and April 1995 (Bearman, Jones and Udry 1997). This self-report In-School Questionnaire was completed during a class period by more than 90 000 adolescents. Parents were informed in advance of the study and could indicate if they did not wish their children to participate. All students at each school (including those who did not complete a questionnaire) were stratified by school, age and sex; and a sample of adolescents was randomly selected for an interview in their homes (Bearman, Jones and Udry 1997). Also included in the public-use data set is an oversample of Black adolescents with a parent with a college degree (n = 520). The total n for this study (the public-use data set) is 6 504 students in grades seven through twelve. Of these, 5 984 cases are in the core sample only, 432 are in the oversample (Bearman, Jones and Udry 1997), and 88 cases belong to both the core and the oversample. Since social stratification implies that minorities are most likely to be living in ‘underclass’ environments — environments most likely to be characterised by social disorganisation (Wilson WJ 1987) — the oversample was included in this study in order to assist in teasing out a more accurate picture of the relationship among race, community variables and delinquency.

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A parent of each adolescent interviewed at home, usually the resident mother, was also asked to complete a questionnaire (Bearman, Jones and Udry 1997). Questionnaires completed by the adolescents thus included the In-School Questionnaire (self-report) and the In-Home Questionnaire (completed partly by a trained interviewer, partly by selfreport, under conditions that assured confidentiality and anonymity). Additional data was available in the Parental Questionnaire (Bearman, Jones and Udry 1997).


Measures Variables of interest in this study include delinquency, community social disorganisation, family bonds, family controls, school bonds, peer involvement and age. These variables conceptually fall into two ‘levels’: family bonds, family controls, peer involvement and school bonds (Level One) are nested within communities (Level Two). Dependent variable: delinquency The Delinquency Scale is used intact from the In-Home Questionnaire and consists of 15 items, similar to those in the Short-Nye Self-Report Delinquency Scale and the Seattle Self-Report Instrument, two well-researched delinquency scales (Hindelang, Hirschi and Weis 1981). The use of self-report data in delinquency studies is criticised on several counts: self-report may ignore more serious forms of delinquency which may best be captured by official records; and self-report data do not show the same relationships to sex, race and social class as the official statistics do (Hindelang, Hirschi and Weis 1979, Langner et al. 1979, Tolan and Lorion 1988). However, official statistics underestimate the extent of general delinquency and tend to be biased with regard to race, socio-economic status and gender (Langner et al. 1979, Lorion, Tolan and Wahler 1987, Figueira-McDonough 1992). Furthermore, since most delinquency begins with more minor acts and then escalates (Loeber 1990), the focus of self-report data on less serious acts provides a better picture of how delinquent behaviour patterns develop, and of the factors associated with those behaviour patterns and their development (Tolan 1988). The scale used here measures the frequency of acts, which has been shown to have a strong correlation with other methods of measuring delinquency, such as ‘seriousness’ (which reflect their legal meaning), and ‘variety’ (which reflects the adolescent’s differential involvement in behaviours at different levels of seriousness) scores (Tolan and Lorion 1988). Community social disorganisation and Level One independent variables: family bonds and controls, peer involvement and school bonds Working with an existing data set meant that scales that measured these variables needed to be developed from the existing item pool. Items with face validity for the various constructs were drawn from the In-Home, In-School and Parent Questionnaires. If necessary, items were reversescored before being included in any analysis. Exploratory factor analyses were then used to determine the final item pools to be used in each scale. The items finally forming each variable are given in Table 1. When the factor analyses

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had been completed, values for each scale were calculated for each adolescent by summing the values of each of the items. The names assigned to these variables, the meaning of high and low scores, and Cronbach’s alpha for each scale, are given in Table 2, as are details of the covariates. Community social disorganisation Social disorganisation, as a construct, accounts for an observed relationship between physical and demographic characteristics of communities that tend to vary with delinquency (Shaw and McKay 1942). Population turnover and ethnic heterogeneity, two of Shaw and McKay’s (1942, 1969) proposed correlates, are assumed to affect the ability to achieve social organisation, in that (Bursik 1988, p. 521): 1. Institutions pertaining to internal control are difficult to establish when many residents are ‘uninterested in communities they hope to leave at the first opportunity’ (Kornhauser 1978, p. 78) 2. The development of primary relationships that result in networks of friendship and association (informal structures of social control) is less likely when networks are constantly changing (Berry and Kasarda 1977) 3. Ethnic heterogeneity impedes communication, making it more difficult to solve common problems and reach common goals (Kornhauser 1978). It seems then that the community, as a system, affects delinquency rates via elements of social control (Bursik and Grasmick 1993): 1. Informal networks of kinship and friendship, which exercise control by threatening to withdraw affection, support and esteem 2. Relationships among residents that do not have the same affective aspects, but are based in part on the ability of communities to supervise their residents. This might include informal surveillance (casual but active observation of the neighbourhood by residents as they go about their daily activities), informal rules about avoiding certain areas that are viewed as unsafe, and direct intervention, such as questioning strangers and residents about suspicious activities or admonishing others for unacceptable behaviour. This thus involves direct controls on behaviour. To the latter, Kornhauser (1978) also adds that strong community institutions and high involvement of neighbourhood residents in these institutions enable the community to develop and to enforce common values. Items reflecting these social elements of the community and its networks were thus chosen from the questionnaires. The scale was first developed using individual respondents’ perceptions of the social organisation of their communities. But since community social disorganisation is a systemic rather than an individual construct, the mean for each school of individuals’ perceptions of community social disorganisation was used to reflect social disorganisation in each community. This procedure also effectively operationalises ‘community’ as the area around the school. Although this is somewhat problematic — there is no way of knowing whether respondents’ subjective sense of their communities overlap with each other or with the whole of the area around the school — such proxies are frequently regarded as the best possible practical alternative to more cumbersome


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Table 1: Measurement scales Delinquency scale In the past 12 months, how often did you... 1. paint graffiti or signs on someone else’s property or in a public place? 2. deliberately damage property that didn’t belong to you? 3. lie to your parents or guardians about where you had been or whom you were with? 4. take something from a store without paying for it? 5. get into a serious physical fight? 6. hurt someone badly enough to need bandages or care from a doctor or nurse? 7. run away from home? 8. drive a car without its owner’s permission? 9. steal something worth more than $50? 10. go into a house or building to steal something? 11. use or threaten to use a weapon to get something from someone? 12. sell marijuana or other drugs? 13. steal something worth less than $50? 14. take part in a fight where a group of your friends was against another group? 15. act loud, rowdy, or unruly in a public place? Community social disorganisation scale: adolescent perspective 1. You know most of the people in your neighbourhood. 2. In the past month, you have stopped on the street to talk with someone who lives in your neighbourhood. 3. People in this neighbourhood look out for each other. 4. Do you usually feel safe in your neighbourhood? 5. On the whole, how happy are you with living in your neighbourhood? 6. If, for any reason, you had to move from here to some other neighbourhood, how happy or unhappy would you be? Community social disorganisation scale: parent perspective 1. Please tell me whether each of the following statements is true with regard to your present neighbourhood: A. You live here because there is less crime in this neighbourhood than there is in other neighbourhoods. B. You live here because there is less drug use and other illegal activity by adolescents in this neighbourhood. C. You live here because this neighbourhood is close to your friends or your relatives. D. You live here because the schools here are better than they are in other neighbourhoods. E. You live in this neighbourhood because there are children here who are the same ages as children in your household. F. You (or your spouse or partner) were born in this neighbourhood. 2. If you saw a neighbour’s child getting into trouble, would you tell a neighbour about it? 3. If a neighbour saw your child getting into trouble, would your neighbour tell you about it? 4. How much would you like to move away from this neighbourhood? 5. In this neighbourhood, how big a problem is litter or trash on the streets and sidewalks? 6. In this neighbourhood, how big a problem are drug dealers and drug users? Family bonds scale 1. How close do you feel to your (mother figure)? 2. How much do you think she cares about you? 3. How close do you feel to your (father figure)? 4. How much do you think he cares about you? 5. Most of the time, your mother is warm and loving toward you. 6. Overall, you are quite satisfied with your relationship with your mother. 7. Most of the time, your father is warm and loving toward you. 8. Overall, you are satisfied with your relationship with your father.

9. How much do you feel your parents care about you? 10. How much do you feel that people in your family understand you? 11. How much do you feel that your family pays attention to you? 12. Which of the things listed on this card have you done with your (mother figure) in the past 4 weeks? A. talked about your school work or grades B. worked on a project for school C. talked about other things you’re doing in school 13. Which of the things listed on this card have you done with your (father figure) in the past 4 weeks? A. talked about your school work or grades B. worked on a project for school C. talked about other things you’re doing in school 14. Your mother encourages you to be independent. Direct controls scale 1. When you do something wrong, your mother talks about it with you and helps you understand why it is wrong. Do your parents let you make your own decisions about... 2. the time you must be home on weekend nights? 3. the people you hang around with? 4. what you wear? 5. how much television you watch? 6. which television programs you watch? 7. what time you go to bed on week nights? 8. what you eat? School bonds scale (Questions are rephrased slightly if the child was interviewed during the summer, to refer to the past school year.) 1. Since school started this year, how often have you had trouble: A. Getting along with your teachers? B. Paying attention in school? C. Getting your homework done? D. Getting along with other students? 2. You feel close to people at your school. 3. You feel like you are a part of your school. 4. Students at your school are prejudiced. 5. You are happy to be at your school. 6. The teachers at your school treat students fairly. 7. You feel safe in your school. Involvement with deviant peers Items used to assess peer deviance During the past twelve months, how often did you... 1. smoke cigarettes? 2. drink beer, wine, or liquor? 3. get drunk? 4. race on a bike, on a skateboard or roller skates, or in a boat or car? 5. do something dangerous because you were dared to? 6. lie to your parents or guardians? 7. skip school without an excuse? Items used to measure peer involvement Male friends: Darken the oval under his name if: 1. You went to his house in the last seven days. 2. You met him after school to hang out or go somewhere in the last seven days. 3. You spent time with him last weekend. 4. You talked with him about a problem in the last seven days. 5. You talked with him on the telephone in the last seven days. Female friends: Darken the oval under her name if: 1. You went to her house in the last seven days. 2. You met her after school to hang out or go somewhere in the last seven days. 3. You spent time with her last weekend. 4. You talked with her about a problem in the last seven days. 5. You talked with her on the telephone in the last seven days.

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Table 2: Variables and covariates

Variables and the meaning of their values Scale Cronbach’s alpha Delinquency 0.85 Community social disorganisation 0.69 Family bonds Family controls School bonds Peer involvement


Covariates Variable Race

SES

Poverty

Gender

Age Family Structure

Number of Siblings Tenure

0.86 0.64 0.77 0.79

Meaning of values A high score indicates high delinquency. A high score indicates high social disorganisation in the community, a low score indicates better social organisation. A high score indicates strong bonds with the family. A high score indicates strong family controls. A low score indicates strong bonds with the school. Since this variable is a product, higher scores indicate higher involvement with more deviant peers — i.e. higher scores reflect both more deviance in the peer group, and more involvement with that peer group.

Description The adolescent’s race, as coded by the interviewer’s observation (after the respondent had identified his or her own understanding of his or her race). Coded from interviewer observation, as follows: “1” = White “2” = Black or African American “3” = American Indian or Native American “4” = Asian or Pacific Islander “5” = Other A status attainment measure of family socio-economic status. “55.27” for managerial and specialty occupations “35.45” for technical, sales and administrative support occupations “31.51” for precision production, craft, and repair occupations “24.14” for service occupations “23.58” for operators, fabricators, assemblers, and laborers “23.34” for farming, forestry and fishing occupations “0” if both parents unemployed An underclass measure of family socio-economic status: a binary variable, indicating whether either of the parental fiures in the household was unemployed and looking for work (i.e. the unemployment was likely to be a family stressor) or receiving public assistance. Coded “1” if either of the parental figures in the household was either (a) unemployed and looking for work; or (b) receiving public assistance. If neither of these was true, then POVERTY was coded “0”. Biological sex of the adolescent. “1” = male “2” = female The adolescent’s age, in years, calculated by subtracting the adolescent’s birth year and month from the year and month of the interview. A binary variable indicating whether or not the adolescent lived in an intact family (i.e. both biological parents resident in the household). Coded “1” if both a biological mother and a biological father were present in the household; other wise, it was coded “0”. The number of siblings identified by the respondent. The number of years the adolescent has lived in his or her current home, calculated by subtracting the year of the interview from the year in which he or she moved to the current home. If he or she was born there, tenure was set equal to the respondent’s age.

methods, such as asking each respondent to indicate community boundaries on a map (Figueira-McDonough 1991, Coulton et al. 1995). Cronbach’s alpha for this measure, as calculated across individual respondents, is 0.69, which is within acceptable limits (Sampson 1991, O’Brien 1990). In addition, the coefficients of variability (standard deviation divided by the mean — see Blalock 1960) for each community reveal that dispersion around the mean within each community is relatively small — below the upper limit of 0.5 suggested by Blalock (1960). This suggests that there is generally acceptable agreement within communities on ratings. In addition, examination of the values of community social disorganisation across communities reveals that they range from –1.63 to

4.01, covering more than five standard deviations. The measure is thus capable of differentiating between neighbourhoods on the dimension of community social disorganisation, another condition suggested by O’Brien (1990) as increasing confidence in the reliability of the measure. Family bonds and controls Following Hirschi (1969), family bonds are conceptualised in terms of affective attachment, involvement and beliefs. Commitment could not be measured directly from these data, but involvement can be viewed as the observable aspect of commitment to conventional values (Hirschi 1969, Krohn and Massey 1980). Factor analysis retained only factors relating to attachment and involvement. In addition to



family bonds, family controls, such as supervision and discipline, are also examined. Peer involvement While delinquency of the peer group could not be directly assessed in the public-use data set of the National Longitudinal Study of Adolescent Health, each respondent had, in filling out the In-School Questionnaire, nominated up to ten alters — five male friends and five female friends. Each respondent had also answered a seven-item scale that gave an indication of deviance, including alcohol and drug use, risk-taking, and lying to authority figures, all of which are strongly correlated with delinquency (Jessor and Jessor 1977). The mean of these deviance scores for the peer group is available in the public-use data set. The similarity of this scale to the delinquency scale is further strengthened since both are frequency measures and assess the frequency of acts over the same time period (twelve months). Peer group involvement is measured by a simple tenitem scale, asking participants to indicate whether they had engaged in a particular activity with peers (such as talking on the phone, or spending time together) in the past seven days. These are the same peers whose deviance was assessed. Some studies simply use peer involvement as a measure (e.g. Menard, Elliott and Wofford 1993), or use both peer deviance and peer involvement as two separate scales (e.g. Paetsch and Bertrand 1997). It is, however, the involvement with deviant peers that is really of interest in delinquency studies. A relatively isolated child may have deviant peers but is less likely to be as affected by them as a child who is heavily involved with deviant peers. Similarly, a child who is much involved with prosocial peers is unlikely to model deviant behaviours. This study, therefore, uses the product of peer deviance and involvement to reflect the construct of involvement with deviant peers. In this way, a relatively isolated respondent (low involvement), even with a more deviant peer group, will have a low score. Similarly, high involvement with a prosocial peer group (low deviance) will also yield a low score. School bonds Attachment to the school, beliefs about the value of education, and commitment to the school and to education — including grades (evidence of commitment to school, Hirschi 1969) — can all be assessed from the In-Home Questionnaire. Involvement in school could be assessed from the In-School Questionnaire. After factor analysis, however, only items relating to attachment and to beliefs were retained. Covariates Table 2 lists the covariates included in this study. All covariates are variables identified as potentially affecting delinquency at the micro-level, and so are included at Level One. These variables include socio-economic status, race, family structure and size, length of residence in the neighbourhood and gender. While some studies find that socio-economic status or economic stress is associated with delinquency (Andrew 1981, Thornberry and Farnworth 1982, Lempers and ClarkLempers 1990), others find no relationship between social

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class and delinquency (Tittle, Villemez and Smith 1978). Such discrepancies appear to be related to differences between the types of measure of delinquency (self-report or other) and of the type of measure of socio-economic status: status attainment or underclass measures (Farnworth et al. 1994). Self-report measures of general delinquency typically show no relationship to class; whereas in official reports that include more serious acts, there appears to be a relationship between underclass measures of social class and delinquency. Status attainment measures, based on income and education, are more related to social mobility, whereas underclass measures assess the presence or absence of chronic poverty (Farnworth et al. 1994). It would appear that a measure of persistent underclass status is most consistent with social disorganisation theory, since chronic poverty and unemployment, at the neighbourhood level, are known to be associated with higher crime rates (Bursik 1988, Bursik and Grasmick 1993, Kasarda 1993). However, these issues have not been conclusively resolved and so both underclass and status attainment measures of socio-economic status are included here. Farnworth et al. (1994) use persistent unemployment and persistent welfare receipt, over two years, as measures of underclass status. In these data, longitudinal measures of unemployment, welfare receipt and public assistance are not available, although they are assessed cross-sectionally for both the respondent parent and his or her current spouse or partner. These variables are combined to form an ‘underclass’ measure (poverty), which is scored “0” if either (a) one spouse/partner is or has been unemployed in the past year; or (b) one spouse/partner receives public assistance. In this way, the effects of unemployment and poverty, both known to affect delinquency (Lempers and Clark-Lempers 1990, Sampson and Laub 1994), can be included in the best possible approximation to a theoretically appropriate measure of socio-economic status. The status attainment measure of Socio-Economic Status (SES) is calculated as follows (Farnworth et al. 1994): family Socio-Economic Status (SES) for a two-parent household is determined by the occupational status of the father figure’s occupation, and by that of the mother figure in a single parent household. Values are assigned according to total socio-economic indices (i.e. combined socio-economic indices for both men and women in these occupations) of the major occupation categories of the 1990 census, as developed by Hauser and Warren (1997). Both family structure and family size appear to exert an effect on family process, and hence on delinquency (Putnins 1984, Rickel and Langner 1985). Family structure is measured as a dichotomy indicating whether the child lives with both biological parents, or in some other arrangement. Family size is indicated by the number of siblings with whom the respondent lives. There is much controversy about the relationship between race and delinquency. It is routinely included as a variable in studies, even though it shows at best a weak correlation with self-reported delinquency (Hindelang et al. 1981, Peeples and Loeber 1994) and positive findings may reflect effects of living in racially stratified neighbourhoods, rather than of race itself (Bronfenbrenner, Moen and

20

Ward and Laughlin


Garbarino 1984). As this issue has not been resolved, however, race is included in this study as a covariate. Since this study is an investigation of the effect of the neighbourhood on adolescents and their social contexts, it is important that length of residence be included as a control variable. Those who have lived in the neighbourhood longest are most likely to be affected by it (Elliott et al. 1997). The prevalence and incidence of delinquency in females are much lower than in males (Hagan, Gillis and Simpson 1985, Henggeler 1989), making it apparent that gender is an important correlate of delinquency. Gender is therefore included as a covariate in this study. Method of statistical analysis: hierarchical linear modelling Hypotheses were tested using Hierarchical Linear Modelling (HLM), a statistical technique that allows one to model effects of having one contextual level (families, schools and peer groups) nested within another (communities). In other words, HLM allows exploration, in one study, of the macro and micro aspects of the various social contexts that influence delinquency. HLM overcomes several problems inherent in other techniques for the analysis of multi-level data (Bryk and Raudenbush 1992): it incorporates a unique random error component for each higher-level unit (in this case, communities), so that similarities among responses of people within a community, resulting from community factors, are accounted for; and it is able to give insight into heterogeneity of regression across higher-level units (in this case,

to provide explanations for why communities might differ in their effects on delinquency). Of these scales, the distributions of the Delinquency, Family Controls, and Peer Involvement Scales were very positively skewed. Square root transformations improved the normality of the data to within acceptable limits. Since the measures were individually designed for this study, this should not significantly affect their interpretability (Tabachnick and Fidell 1996). All scale scores were also standardised before being entered into the analysis. Multicolinearity amongst the covariates is low and so all variables were included in the analyses. Table 3 gives the correlations among the variables. Pairwise deletion of missing data was used in the formation of the sufficient statistics matrix, since listwise deletion would have resulted in the loss of so much data that analyses would have been severely compromised. Prior to beginning hierarchical linear modelling, variables are entered into a sufficient statistics matrix, the data matrix on which hierarchical linear modelling is performed. During this process, data for eight communities were dropped from the matrix as cases within these communities had too much missing data for the HLM analysis to be valid. Although these communities were eliminated, the range of community social disorganisation (2.76 to –1.63) remained the same before and after the formation of the sufficient statistics matrix. Since the extremes of community social disorganisation were retained, ability to detect an effect should not have been compromised by the loss of data. Descriptive statistics for each variable are given in Table 4.

Table 3: Correlations amongst variables entered into HLM analysis Delinquency Delinquency Family bonds Family controls School bonds Peer involvement Age Age x Family bonds Age x Family controls Age x School bonds Age x Peer involvement Gender Tenure Race SES Poverty No. of siblings Family structure

Gender Tenure Race SES Poverty No. of Sibilngs Family Structure

1.00 –0.29 –0.04 0.37 0.23 0.02 –0.28 –0.04 0.36 0.22 –0.14 –0.03 0.02 –0.01 0.04 –0.01 0.01

Family controls 1.00 0.03 –0.04 –0.10 –0.16 0.10 0.02 –0.40 –0.10 –0.05 0.01 –0.05 0.04 –0.05 –0.05 –0.07

Age x Peer Involvement 0.09 0.09 –0.13 0.07 –0.06 –0.05 0.01

Family bonds

School Peer bonds involvement

1.00 –0.05 –0.21 –0.38 0.02 0.10 –0.05 –0.22 0.01 –0.06 0.05 –0.04 0.06 1.00 –0.05

1.00 0.16 0.11 –0.40 –0.04 0.99 0.16 –0.08 –0.07 –0.03 –0.12 0.08 –0.02 0.02

Gender 1.00 –0.02 0.01 –0.02 0.00 0.01 0.01

1.00 0.25 –0.10 –0.20 0.16 0.99 0.10 0.09 –0.13 0.07 –0.06 –0.05 0.01

Age

1.00 –0.15 –0.38 0.10 0.23 –0.04 0.13 0.01 –0.02 –0.02 –0.07 0.03

Age x Family Age x Family bonds controls

1.00 0.02 –0.04 –0.09 –0.05 0.01 –0.05 0.04 –0.05 –0.04 –0.08

Tenure

Race

SES

1.00 –0.04 0.06 –0.10 –0.04 –0.02

1.00 –0.08 0.10 0.10 –0.02

1.00 –0.02 –0.04 –0.02

1.00 –0.04 –0.21 0.02 –0.06 0.05 –0.04 0.06 0.10 –0.05

Age x School bonds

1.00 0.15 –0.07 –0.07 –0.04 –0.12 0.08 –0.02 0.02

Poverty No. of siblings

1.00 0.13 0.03

1.00 0.00


21


Table 4: Descriptive statistics for variables in sufficient statistics matrix

Delinquency Family bonds Family controls Peer involvement School bonds Poverty Family structure Number of siblings Race Gender Tenure (years) SES Age (years) Age by family bonds Age by school bonds Age by family controls Age by peer involvement Community social disorganisation

6 4 6 3 6 5 6 6 6 6 5 6 6 4 6 6 3

N 401 264 337 649 302 641 477 477 498 503 075 351 501 264 302 337 649 72

Mean 0.00 0.00 0.00 0.00 0.00 – – 1.37 – – 7.59 – 16.04 –0.28 0.18 –0.65 0.42 0.02

Results Initially, an unconditional model was tested. This model included simply the outcome variable, intercepts and error terms, without any predictor variables. It is, in effect, a oneway random effects ANOVA model, and computed the grand mean of delinquency, and the within- and between-group variabilities (Bryk and Raudenbush 1992). Results from this are given in Table 5. Since the delinquency variable is centred on the mean delinquency of the communities, these results indicate that there is relatively more variability in delinquency within each community than between communities. The intra-class correlation coefficient of 0.03 indicates that approximately 3% of the variance in delinquency is between communities: variance between communities = 0.03/(0.03 + 0.95) = 0.03 (Bryk and Raudenbush 1992). The next model tested includes the full Level One model (i.e. all the micro-context predictors) without the Level Two predictor (community social disorganisation). This enables examination of the effects of the micro-context predictors without consideration of the social disorganisation of the community. Results for the micro-level-only model are given in Table 6. Introducing the Level One predictor variables explained 14.7% of the variance within communities. Of the predictor variables, school bonds, age, family controls, and the interactions of age with school bonds and with family controls are all significantly related to delinquency. School bonds had the strongest effect, followed by family controls, gender, and then age and the interaction of age and direct controls, and age and school bonds. Poor school bonds are positively related to delinquency and strong family controls have a negative relationship with delinquency; age is negatively related to delinquency; and girls are less likely than boys to carry out delinquent acts. The interaction of age and family controls, and of age and school bonds, are also both significantly related to delinquency, but have less influence than the other significant variables.

Std. Dev. 1.00 1.00 1.00 1.00 1.00 – – 1.19 – – 5.38 – 1.75 16.22 16.08 16.35 16.10 0.97

Maximum 4.18 1.69 2.10 4.20 4.74 – – 12.00 – – 19.00 – 21.33 31.88 74.79 42.18 76.58 2.76

Minimum –1.32 –4.95 –1.64 –1.92 –2.10 – – 0.00 – – 0.00 – 11.42 –88.21 –39.67 –34.96 –38.33 –1.63

Finally, the full model was tested and the results are summarised in Table 7. In the hierarchical linear modelling analysis, for those variables presumed to be affected by community social disorganisation, both an intercept and a slope are reported. The intercept may be understood as the direct effect of the variable, and the slope as indicating the moderating effect of community social disorganisation on that variable. This full model explains a third of the variance in delinquency across communities (i.e. a third of the 3% that existed between communities), and community social disorganisation is significantly and positively associated with delinquency. As hypothesised, the positive coefficient for community social disorganisation imply that the greater the social disorganisation of the community, the greater the delinquency. This is a small effect, however, compared with the effects of the micro-level variables. Contrary to what was hypothesised, no moderating effect of community social disorganisation on other variables predicting delinquency is found. Family bonds, family controls and school bonds are each found to have a direct effect on delinquency. From the relative size of the coefficients, school bonds appear to have the greatest effect, followed by family bonds, family controls, and then by community social disorganisation. But — contrary to hypothesis — no direct effect of association with a deviant peer group is found. Age is found to be directly related to delinquency, as are the interaction of age with bonds to the school and the interaction of age with family controls. Age is negatively related to delinquency. The negative coefficient of the interaction of age and school bonds implies that as children get older, the bonds to their school have an increasingly weaker relationship with delinquency. A similar result is found for the interaction of age and family controls. Neither the interaction of age with attachment to the family, nor that of age with peer group involvement, is significant. In terms of relative strength, these age-related effects are weaker than the other effects.

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Ward and Laughlin

Table 5: Results of test of fully unconditional model Final estimation of fixed effects: Fixed Effect For Level One Intercept: Average community delinquency Final estimation of variance components: Random Effect Variability between communities Variability within communities

Coefficient

Standard Error

t-ratio

0.01

0.02

0.30

Standard Deviation 0.17 0.97

Variance Component 0.03 0.95

df 71

Chi-square 260.06 a

a

P < 0.001 Intra-class correlation coefficient: 0.03


Table 6: Results of the micro-context-only model. Proportion of variance in delinquency within communities explained by this model: 0.147 (14.7%) Final estimation of fixed effects: Fixed Effect Level Two intercepts School bonds Age Gender Family controls Interaction of age and family controls Interaction of age and school bonds Mean delinquency across communities Family bonds Involvement with deviant peers Interaction of age and family bonds Interaction of age and involvement with deviant peers Family structure Number of siblings Length of tenure in the neighbourhood Race Poverty (underclass measure of socio-economic status) SES (status attainment measure of socio-economic status) Final estimation of variance components: Random Effect Variability between communities Variability within communities a

Coefficient 0.55 –0.04 –0.24 –0.31 0.02 –0.02 0.01 –0.25 0.10 0.01 0.00 0.07 0.00 0.00 0.02 0.04 0.00


Standard Error

t-ratio

0.12 0.01 0.02 0.12 0.01 0.01 0.02 0.13 0.12 0.01 0.01 0.24 0.01 0.00 0.01 0.04 0.00


4.58 –4.83 –9.91 –2.62 2.47 –2.07 0.34 –1.95 0.85 1.48 –0.03 0.28 –0.20 0.05 1.83 1.16 0.79

df 71

a a a b c c

Chi-square 279.98 a

P < 0.001, b P < 0.01, c P < 0.05

Discussion The results of this study are significant in that, using a broader and more representative sample than other studies, it is yet shown that the social organisation of the community does play a role in juvenile delinquency. Similarly, in the integration of micro-context variables and age with community variables, some light is shed on the relative importance of these contexts and of developmental trends. The small amount of variance in delinquency across communities that is explained by community social disorganisation is in accord with the work of others (for instance, Stern and Smith 1995, Elliott et al. 1997). This study’s key contribution is that it uses a far larger and broader sample of communities, and still finds that community social disorganisation has a significant positive association with delinquen-

cy. Other samples have been restricted to a few communities in urban areas and/or to carefully selected samples of high- and low-risk youth (Simcha-Fagan and Schwartz 1986, Stern and Smith 1995, Elliott et al. 1997). This sample includes 72 diverse neighbourhoods (urban, rural and suburban), and a random sample of adolescents. Using this large and diverse sample of communities and youth strengthens the finding that the greater the community social disorganisation, the greater the likelihood of delinquency. The low variance in delinquency across communities, and the relative higher variance within them, may in part have resulted from the necessary pragmatic definition of ‘community’ as equivalent to school zones. Future studies will be strengthened by making every effort to ensure that community boundaries match as closely as possible with residents’ more subjective impressions of boundaries. Given


23

Table 7: Results of test of full model. Proportion of variance in delinquency across communities explained: 0.333 (33.3%)


Final estimation of fixed effects: Fixed Effect

Coefficient

Standard Error

0.57 0.03

0.12 0.12

4.72 0.24

a

–0.24

0.02

–9.92

a

–0.04

0.01

–4.86

a

0.01 0.08

0.02 0.02

0.39 3.50

–0.32 –0.09

0.12 0.13

–2.76 –0.73

b

0.02 0.00

0.01 0.01

2.62 0.41

b

–0.02 0.00

0.01 0.01

–2.22 –0.31

c

–0.25 0.03

0.13 0.13

–2.00 0.21

c

0.12 –0.13

0.12 0.12

1.02 –1.08

0.01 0.00

0.01 0.01

1.52 –0.09

0.00 0.01

0.01 0.01

–0.20 0.79

0.07

0.24

0.30

0.00

0.01

–0.17

0.00 0.00

0.00 0.00

0.04 0.95

0.02

0.01

1.35

0.03

0.04

0.92

0.00

0.00

0.71

School bonds: Level Two Intercept MCSD Gender: Level Two Intercept Age: Level Two Intercept Mean delinquency in each community: Level Two Intercept MCSD Family controls: Level Two Intercept MCSD Interaction of age and family controls: Level Two Intercept MCSD Interaction of age and school bonds: Level Two Intercept MCSD Family bonds: Level Two Intercept MCSD Involvement with deviant peers: Level Two Intercept MCSD Interaction of age and family bonds: Level Two Intercept MCSD Interaction of age and involvement with deviant peers: Level Two Intercept MCSD Family structure: Level Two Intercept Number of siblings: Level Two Intercept Length of tenure in the neighbourhood: Level Two Intercept MCSD Race: Level Two Intercept Poverty (underclass measure of socio-economic status): Level Two Intercept SES (status attainment measure of socio-economic status): Level Two Intercept Final estimation of variance components: Random Effect Variability between communities Variability within communities a



df 70

t-Value

b

Chi-square 241.26 a

P < 0.001, b P < 0.01, c P < 0.05

that this study perforce uses a relatively weak measure of community, it is all the more striking that an effect of community is found. Yet it is equally clear that micro-contexts have an impact on juvenile delinquency. As hypothesised, bonds to the family and to the school, and parents’ direct controls of the child’s behaviour, are found to have an influence on delinquent behaviour. These effects are in the expected direction:

as bonds to the family and to the school increase, and as control increases, delinquency decreases. One of the unique contributions of this study, however, is that combining the macro- and micro-level variables in one model enables one to examine their relative impact on delinquency. This model suggests that school bonds have relatively more impact than family bonds, which in turn have more effect than direct controls and that all of these have more


24

impact than community social disorganisation. Again, the relative weighting of macro- and micro-level contexts is in accord with other studies, but the findings are strengthened here by the use of a very large, random sample of both adolescents and neighbourhoods. The effect of age and of the interactions of age with family controls and with school bonds suggests that developmental trends do play a role in delinquency. These results suggest that age in and of itself plays a role in delinquency, and that it also influences the roles of school bonds and family controls. Developmental effects, however, appear to have less impact than the micro- and macro-contexts. This study, then, indicates that social organisation does indeed have an influence on delinquency, but that microcontexts are more important than macro-contexts, which are again more important than developmental trends. While these findings are not strikingly new, this study adds considerable confidence to such findings since it is the first to use such a large and randomly selected sample of communities and adolescents and model of macro- and micro-contexts together with developmental trends. Clear implications for interventions to prevent or interrupt juvenile delinquency can also be drawn from these findings. The direct effects of family and school contexts underscore the importance of interventions aimed at improving family relationships, families’ monitoring of their children, and particularly the ability of schools to engage and support children as they develop. While the effect of the community on delinquency is comparatively small, it, too, is important in facilitating the development of either delinquent or prosocial behaviour. Interventions that assist in bringing neighbours together, in involving parents in schools, in helping communities develop social organisation, may be of assistance in combating delinquency. Two key areas for future research are suggested by this study: longitudinal studies, and models that can take greater account of the complexities of interrelationships among social contexts. Effects found here, although significant, are small, suggesting that there is much more complexity in our social lives than could be captured in this model. Furthermore, some of the results found in this study are not consistent with some other evidence, such as the failure to show a relationship between involvement with a deviant peer group and delinquency. It is possible that the family and school contexts do indeed have more important roles than the peer group, but it is also possible that there are more complex relationships between contexts than could be modelled in this study. For instance, parental monitoring may affect choice of friends, and good bonds with the family may translate into more time spent with family and less with friends, thus reducing the power of the peer group (Catalano and Hawkins 1996, Sampson and Laub 1997). Alternatively, this may be a result of the measurement used for peer group delinquency: some studies suggest that associations tend to depend on whether it is the focal adolescent, the parent or a peer group member who reports the delinquency (Haynie 2000, Zhang and Messner 2000). In addition, this measure includes an inherent assessment of network density, and this too has been shown to affect the association between delinquency

Ward and Laughlin

of the focal adolescent and the peer group (Haynie 2001). Future studies should investigate both issues inherent in measurement format and the more complex influences that social contexts have on each other. Secondly, this is a cross-sectional study and understanding of juvenile delinquency would be strengthened by a focus on the ways in which relationships between contexts (and the people in them) develop over time. For instance, it is entirely possible that delinquency and social disorganisation are mutually influential, so that if delinquency increases, so does social disorganisation, in turn leading to further delinquency (Sampson and Lauritsen 1994, Le Blanc 1997). This study thus adds to a growing body of literature that demonstrates that community social organisation is a factor associated with social ills such as juvenile delinquency, together with the influence of families, schools and other groups and institutions. Future research and interventions need to take account of our growing understanding of the complexities of the relationships between the individual, the community, and the social contexts in which we spend our lives. Acknowledgements — This research is based on data from the (US) Add Health project, a program project designed by J Richard Udry (PI) and Peter Bearman, and funded by grant PO1–HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Cancer Institute; the National Institute of Alcohol Abuse and Alcoholism; the National Institute on Deafness and Other Communication Disorders; the National Institute of Drug Abuse; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Institute of Nursing Research; the Office of AIDS Research, NIH; the Office of Research on Women’s Health, NIH; the Office of Population Affairs, HHS; the National Center for Health Statistics, Centers for Disease Control and Prevention, HHS; the Office of Minority Health, Centers for Disease Control and Prevention, HHS; the Office of Minority Health, Office of the Assistant Secretary for Health, HHS; the Office of the Assistant Secretary for Planning and Evaluation, HHS; and the National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Jo Jones, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516–3997 (e-mail: [email protected]). This paper is based on the first author’s doctoral dissertation. In preparing that original work, the assistance of Herman Salzberg, Jim Laughlin, Kathy Wilson and Emilie Smith, and the University of South Carolina’s Laura Griffin Fund, is gratefully acknowledged.

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