unpacking the relationship between adolescent employment and ...

\\server05\productn\C\CRY\45-1\CRY106.txt

unknown

Seq: 1

9-FEB-07

16:30

UNPACKING THE RELATIONSHIP BETWEEN ADOLESCENT EMPLOYMENT AND ANTISOCIAL BEHAVIOR: A MATCHED SAMPLES COMPARISON* ROBERT APEL SHAWN BUSHWAY School of Criminal Justice University at Albany

ROBERT BRAME Department of Criminology and Criminal Justice University of South Carolina

AMELIA M. HAVILAND RAND Corporation

DANIEL S. NAGIN Heinz School of Public Policy and Management Carnegie Mellon University

RAY PATERNOSTER Department of Criminology and Criminal Justice University of Maryland Maryland Population Research Center KEYWORDS: adolescent employment, delinquent offending, groupspecific treatment effects, developmental trajectories A large body of research has consistently found that intensive employment during the school year is associated with heightened antisocial behavior. These findings have been influential in prompting policy recommendations to establish stricter limits on the number of hours that students can work during the school year. We reexamine the *

We thank Wayne Osgood, Laurence Steinberg, and the anonymous referees for helpful comments on earlier drafts of this manuscript. This research has been supported by the National Science Foundation (NSF) (SES-99113700) and the National Institute of Mental Health (RO1 MH65611-01A2).

CRIMINOLOGY

VOLUME 45

NUMBER 1

2007

67


68

unknown

Seq: 2

9-FEB-07

16:30

APEL, ET AL. linkage between first-time work at age 16 during the school year and problem behaviors. Our analysis uses group-based trajectory modeling to stratify youths based on their developmental history of crime and substance abuse. This stratification serves to control for preexisting differences between workers and nonworkers and permits us to examine whether the effect of work on problem behaviors depends on the developmental history of those behaviors. Contrary to most prior research we find no overall effect of working on either criminal behavior or substance abuse. However, we do find some indication that work may have a salutary effect on these behaviors for some individuals who had followed trajectories of heightened criminal activity or substance abuse prior to their working for the first time.

Working during the school year has become a normative feature of adolescent development in the United States. The U.S. Department of Labor (2001) has estimated that, among 15-year-old students enrolled in school, approximately 44 percent of them had a formal paycheck-generating job at some point during the 1998–1999 school year. This increases to 78 percent for those 17 years old. Not only do most high-school students work during the school year, they frequently work long hours. Among seniors in high school who work for an employer, over half work more than 20 hours per week (National Research Council, 1998), which is a level regarded as “intensive” in the literature (Greenberger and Steinberg, 1986; Steinberg and Cauffman, 1995). It was once presumed that the experience of working while still attending school full time was beneficial. In the 1970s and 1980s, several prominent national commissions concluded that employment during the school year for high-school students increased structure on leisure time, heightened independence and maturity, provided greater responsibility in the use of money, enhanced exposure to adult authority figures and the adult world outside the classroom, improved self-esteem, and increased proficiency in the balancing of multiple responsibilities (Carnegie Council on Policy Studies in Higher Education, 1979; National Commission on the Reform of Secondary Education, 1973; National Commission on Youth, 1980; National Panel on High School and Adolescent Education, 1976; Panel on Youth, 1974). In the mid-1980s, however, researchers began to question the enthusiastic claims of adolescent work proponents. Greenberger and Steinberg (1986) were the first to suggest that high-school employment, particularly intensive employment, comes at a high price for students—lower grades and interest in school, increased absenteeism, difficulties with parents, and a greater risk of delinquency, alcohol use, and other antisocial behaviors. Subsequent research has corroborated these initial findings. Overall,


unknown

Seq: 3

9-FEB-07

EMPLOYMENT AND ANTISOCIAL BEHAVIOR

16:30

69

there has been consistent evidence linking adolescent employment, particularly intensive employment during the school year, to delinquency and other problem behaviors (Bachman and Schulenberg, 1993; Cullen, Williams, and Wright, 1997; McMorris and Uggen, 2000; Mihalic and Elliott, 1997; Mortimer, 2003; Mortimer and Finch, 1986; Mortimer et al., 1996; Ploeger, 1997; Staff and Uggen, 2003; Steinberg and Dornbusch, 1991; Steinberg, Fegley, and Dornbusch, 1993; Steinberg et al., 1980; Wright and Cullen, 2004; Wright, Cullen, and Williams, 1997). Partially in response to this evidence, the National Research Council (1998: 227) recommended that the federal government establish stricter limits on the number of hours that high-school students can work during the school year. It is also plausible, however, that the risks associated with intensive school-year work are a methodological artifact. Jessor and colleagues (Donovan and Jessor, 1985; Donovan, Jessor, and Costa, 1988; Jessor and Jessor, 1977) have argued that diverse expressions of problem behaviors among adolescents such as drinking and drug use, sexual misbehavior, and delinquency and premature entry into the labor force constitute a “syndrome,” that is, a set of behaviors with a common cause (Jessor, Donovan, and Costa, 1991: 117). They further argue (Donovan and Jessor, 1985: 891) that the cause of these different but related behaviors is an underlying individual trait of unconventionality. A propensity to be unconventional is established relatively early in life and is characterized by a concern with personal autonomy and a general disdain for society. Along similar lines, Gottfredson and Hirschi (1990) argue that delinquency and other analogous behaviors appearing in adolescence (such as promiscuous sex, smoking, and dropping out of school) are related because they are the ongoing reverberations of low self-control. Low self-control is established in childhood and reflects an inability to defer immediate gratification. Although intensive employment during the school year is not as obviously selfdestructive as violence or drug use, those who are unwilling to wait until summer or until their education has been completed to enter the labor market can also be seen as acting impulsively, with their sights on the short-term benefits of working (e.g., making immediate money or spending time away from parents), rather than considering the long-term implications of intensive employment for their academic success. Empirical research indeed indicates that preexisting differences between those who work long hours while still in school and their counterparts who do not work are consistent with the theories of Jessor and colleagues and Gottfredson and Hirschi. Prior to their employment, intensive workers have lower grades, reduced educational aspirations, spend less time on homework, and are less involved with school activities than nonworking or moderately working youth. They have more distant and hostile relationships with parents, they are more involved with delinquent and


70

unknown

Seq: 4

9-FEB-07

16:30

APEL, ET AL.

substance-using peers, and they are more involved in school deviance, delinquency, cigarette smoking, and alcohol and drug use (Bachman and Schulenberg, 1993; Johnson, 2004; Mihalic and Elliott, 1997; Mortimer, 2003; Staff and Uggen, 2003; Staff et al., 2005; Steinberg, Fegley, and Dornbusch, 1993). These preexisting differences greatly increase the difficulty of making valid statistical adjustments for differences between workers and nonworkers. Stated differently, the question is whether youngsters who work can be made statistically comparable with those who do not. Researchers have been aware of this comparability problem and have made conscientious efforts to take preemployment differences into account, usually by including observed covariates in their multivariate regression models. Because there are substantial preemployment differences between adolescents who do and do not work intensively, prior research has found that the positive relationship between work intensity and outcomes is markedly reduced (Bachman and Schulenberg, 1993; Mihalic and Elliott, 1997; Staff and Uggen, 2003) or eliminated (Paternoster et al., 2003) after controlling for these differences in a regression framework. Yet, it is worth noting that the original work by Steinberg, most notably Greenberger and Steinberg (1986) and Steinberg, Fegley, and Dornbusch (1993), did not use a regression framework but relied on a stratification approach. For example, Steinberg, Fegley, and Dornbusch (1993) stratified individuals based on whether they had not worked by a certain age, usually age 15. They then studied the difference between those who worked intensively versus those who did not in the next wave of the survey. By focusing on individuals who start work relatively late, Steinberg, Fegley, and Dornbusch were concentrating on a more homogenous group of people. In addition, their approach had the virtue of attempting to estimate a very well-defined treatment—intense work at approximately age 16–17 for those individuals who had not yet worked prior to that age. In this article, we also use a stratification framework; however, our stratification not only conditions on prior work experience but also on the developmental history of delinquent involvement. The method is described and demonstrated in Haviland and Nagin (2005). It uses groupbased trajectory modeling to identify clusters of individuals with similar developmental histories called trajectory groups. The trajectory groups can be thought of as latent strata in the data defined by their developmental history. Specifically, we use the first five periods of data in the Bureau of Labor Statistics’ 1997 National Longitudinal Survey of Youth (NLSY97) to estimate the effect of first-time intensive employment during the school year at age 16 on a general measure of delinquent offending and substance use. We apply group-based trajectory modeling (Muthen, ´ 2001; Nagin, 1999,


unknown

Seq: 5

9-FEB-07


16:30

71

2005; Nagin and Land, 1993) to these data to identify groups or strata of adolescents with comparable histories of delinquency and substance use. We then apply procedures described in Haviland and Nagin (2005) and Haviland, Nagin, and Rosenbaum (2006) for constructing comparable groups of workers and nonworkers. We then compare the problem behavior rates of intensive and nonintensive workers adjusting for group membership. Offending trajectories describe the pattern of criminal offending over time and reflect variation in developmental processes. Distinct offending trajectories, therefore, capture the unique impact of diverse known and unknown explanatory variables for each trajectory group. Lahey and Waldman (2005: 18) observe “It is now clear that youth who commit juvenile offenses do not all follow the same developmental trajectory—variations in developmental trajectories are central to understanding the causes of juvenile offending.” Based on this observation, we expect to find much greater comparability on a diverse range of background variables among youths sharing an offending trajectory than we would find among youths following different offending trajectories. The main contribution of this article is our use of developmental histories to stratify individuals for the purpose of comparing working and nonworking youths who up to the time of their first experience of work were similar on many background variables, including their prior histories of problem behavior. Our analytical strategy allows us to easily and transparently establish whether we have achieved comparability between workers and nonworkers on a long list of variables that have the potential for contaminating our estimate of the effect of work during the school year on problem behavior. This demonstration of comparability is important, because our approach assumes that conditional on group membership there are no systematic differences between workers and nonworkers that may be confounding the effect of intense work on problem behavior. One of our main objectives is to estimate trajectory group-specific effects of intensive adolescent employment. One’s developmental history of problem behavior may affect both the likelihood of work and the treatment effect of work on subsequent problem behavior. For individuals who have little or no history of problem behavior, entry into the workforce may mean one thing, whereas it could mean something quite different for individuals who have extensive histories of such behavior. Stated differently, the “average” effect of intensive work during the school year across all population members—called the population average treatment effect (PATE)—may conceal large differences in the response to intensive work across groups of individuals following different developmental trajectories. Thus, a set of group-specific average treatment effects (GATEs) across clusters of individuals with similar developmental trajectories may provide


72

unknown

Seq: 6

9-FEB-07

16:30

APEL, ET AL.

valuable information on treatment effect differences across trajectory groups.

DATA AND ANALYTIC OVERVIEW The NLSY97 comprises 8,894 youths who were 12 to 16 years of age at the end of 1996. The selection of our analysis sample proceeded in several stages. First, from the full sample, we selected 6,748 youths constituting the cross-sectional sample to avoid the additional complexity of using sampling weights. Second, from these we kept the 2,720 youths who were 12 or 13 years old by year-end 1996. Third, because our analysis is limited to the treatment effect of first-time employment at age 16, we then selected the 1,469 youths who had not yet made the transition into the formal labor market before age 16. Fourth, to ensure that there was close temporal correspondence between employment during the sixteenth year and delinquency and substance use during the sixteenth year, we further restricted our sample to 1,185 youths. Concerning this final restriction, consider a youth who is interviewed in the second month of his sixteenth year, and assume that 12 months have elapsed since the previous interview so that he was in the second month of his fifteenth year at the last interview. The reference period for employment is the first 2 months of his sixteenth year. The reference period for delinquency and substance use, on the other hand, is the 12 months since the last interview. Consequently, there are only 2 months of overlap between employment and antisocial behavior. Moreover, 10 months of the reference period for antisocial behavior are in the fifteenth year, with only 2 months in the sixteenth year. Because of the lack of correspondence, this hypothetical youth is eliminated from our estimation sample. To be included in the analysis sample, there had to be significant overlap between the reference period for crime, substance use, and delinquency and the reference period for employment at age 16.1 At the final step, we retained the 1,131 youths who had valid data on their age-16 employment and antisocial behavior. 1.

The lack of complete overlap derives from the fact that our employment measure is tied to age (the sixteenth year), whereas our delinquency measure is tied to the calendar year (time elapsed since the previous interview). Consequently, 100percent overlap is achieved for only a small proportion of the sample. To be included in our estimation sample, we require that a youth have at least 50-percent overlap between employment and delinquency during the sixteenth year. For example, if a youth is 16 years and 6 months at the interview, we require that no more than 1 year has elapsed since the last interview. Any more than 1 year, and this youth’s delinquency is mostly referenced during the fifteenth year. If a youth is 17 years and 0 months at the interview, we require that no fewer than 6 months and no more than 18 months have elapsed since the last interview.


unknown

Seq: 7

9-FEB-07


16:30

73

Our aim is to identify behavioral differences between individuals who report an average of more than 20 hours per week of paycheck-generating employment during the school year (with the requirement that an individual works at least 1 week during the school year) and those who do not. We limit our analysis to individuals who were not involved in paycheckgenerating work prior to age 16, the modal age for the first transition to work during the school year. One cost of restricting our attention to this group of first-time workers at age 16 is that our analysis may not be informative about the effects of work on individuals who work prior to age 16. Notwithstanding, it is important to note that this sample restriction does not bias the results; it only limits their generality.2 Balanced against this cost are several major benefits. Limiting our analysis to this group of firsttime workers allows us to avoid the challenging problem of having to model what Robins, Greenland, and Hu (1999) term feedback from output to input, which in this case involves how problem behavior may affect work even as work affects problem behavior.3 It also frees us from the difficult task of identifying comparable matches for the individuals who work prior to age 16. Had we ignored these problems and included the pre-16 workers in the analyses, their presence would have greatly increased the chance of bias in the putatively more general estimate of the effect of work. The outcomes of interest include binary indicators of self-reported involvement in substance use, criminal activities, and overall delinquency (involvement in either substance use or criminal activities) at age 16. For each year prior to age 16, involvement is measured with a “variety” score, which counts the number of different categories of substance use and criminal activities engaged in at least once in that year. For substance use, the variety score counts use/involvement in the following: 1) smoked cigarettes, 2) had at least one drink of alcohol, and 3) smoked marijuana. For 2.

3.

One reviewer expressed concern that restricting our analysis to first-time workers introduced bias. It is important to distinguish between sample restrictions based on preexisting covariates such as prior experience with treatment or demographic characteristics and sample restrictions based on the response variable itself. Sample selection based on the latter (e.g., requiring the response variable to exceed a specified threshold) can indeed introduce bias. However, selection on the former only limits the generalizability of the findings. For example, randomized medical trials routinely require no prior exposure to the treatment. This restriction does not bias the estimated treatment effect. However, it does imply that the estimated treatment effect may not apply to those who have been previously treated. Although we avoid the very challenging problem of modeling feedback, we do not completely eliminate the possibility of feedback from problem behavior to intensive work because for some individuals some proportion of the age-16 delinquency measurement may precede the entry into work.


74

unknown

Seq: 8

9-FEB-07

16:30

APEL, ET AL.

criminal activity, the variety score counts involvement in the following: 1) vandalism, 2) petty theft ($50), 4) motor vehicle theft, 5) other property crime, 6) aggravated assault, and 7) selling drugs. We use the variety score because of its capacity to capture dimensions of both prevalence/frequency and severity. Osgood, McMorris, and Potenza (2002) explored the power of item response theory (IRT) to combine information about frequency and severity of offending. They found that the variety score, which is much simpler than an IRT scale, is highly correlated with the IRT measure of delinquency. Sweeten (2006) has replicated this result using the NLSY97 data set.

RESULTS We begin our presentation of the results by conducting simple comparisons of age-16 criminal activity and substance use for age-16 workers and nonworkers. These simple comparisons are unadjusted for pre-age-16 substance use and criminal behavior. UNADJUSTED COMPARISONS Table 1 reports the percentage of individuals involved in crime and substance use at age 16 for the age-16 workers and the age-16 nonworkers separately. Consistent with virtually all previous research, individuals who work intensively during the school year at age 16 are significantly more likely to be involved in substance use, criminal acts, and overall delinquency.

Table 1. Age-16 Employment, Substance Use, Criminal Acts, and Overall Delinquency Group Age-16 Workers Age-16 Nonworkers Total

Number of Cases

Percent Using Substances

Percent Involved in Criminal Acts

Percent Involved in Overall Delinquency

248 883 1,131

60.5 53.8* 55.3

25.4 19.0* 20.4

65.7 57.2* 59.1

*The difference in proportion between the workers and the nonworkers is statistically significant at p < .05.

ESTABLISHING USEFUL COMPARISONS The most important limitation of the contrasts reported in table 1 is the possibility that the age-16 workers and nonworkers are not comparable with each other on other factors that might affect substance abuse and delinquency. Specifically, we expect those who work intensively during the school year at age 16 to be at higher risk of these behaviors than


unknown

Seq: 9

9-FEB-07


16:30

75

nonworkers based on pre-age-16 (and, thus, pre-age-16 employment) characteristics. A major advantage of the NLSY data is the availability of a rich set of first-interview and pre-age-16 measurements on characteristics that could potentially explain the relationship between age-16 employment and problem behavior. The appendix lists 111 such covariates. On 33 of the pre-age-16 characteristics, the workers and nonworkers differed significantly from each other on a two-tailed Z test conducted at the p < .05 significance level.4 In addition, we calculated a standardized difference statistic suggested by Rosenbaum and Rubin (1985: 36): D =

¯ w−X ¯n X

√ s +s2 2 w

2 n

When the absolute value of the standardized difference D is greater than 0.2, Rosenbaum and Rubin recommend concluding that the characteristic in question differs between the workers and the nonworkers. Using this benchmark, 13 of the 111 characteristics differ between the workers and the nonworkers. Clearly, intensive adolescent workers are fundamentally different from nonworkers in important ways before they even began working.5 To address the comparability problem and to better understand how the development of problem behavior can set up different responses to intensive school-year work at age 16, we use the group-based trajectory estimation to summarize developmental histories of substance use and criminal activities during the pre-age-16 adolescent years. The developmental trajectories of crime and substance use are based on the variety score for these respective behaviors from ages 11 to 15. The substance use score can range from 0 to 3 at each age, whereas the criminal behavior score can range from 0 to 7 at each age. The former are modeled with a censored normal probability distribution, whereas the latter are modeled with a zero-inflated Poisson distribution. The likelihood function for each model was maximized using the SAS-based procedure (SAS Institute, Inc., Cary, NC) described by Jones, Nagin, and Roeder (2001). To evaluate the fit of 4. 5.

This problem is generally referred to as “covariate imbalance” in the treatment effects literature. For example, among children with valid data on father’s education, workers’ fathers were less likely than fathers of nonworkers to have completed between 4 and 8 years of college education; workers reported spending fewer days on homework than nonworkers; workers had greater levels of exposure to antisocial peers at school than nonworkers; and prior involvement in crime and substance use was greater among workers than nonworkers. Other troubling examples of imbalance include number of fights in school, the percentage of respondents suspended from school, and hours spent watching television.


76

unknown

Seq: 10

9-FEB-07

16:30

APEL, ET AL.

our trajectory models, we calculated several statistics discussed in Nagin (2005). For example, the mean posterior probability of assignment was .84 for the four crime trajectories (range of .83 to .85) and .87 for the four substance use trajectories (range of .85 to .92). In addition, the mean odds of correct classification was 21.2 for the crime trajectories (range of 8.5 to 45.6) and 24.5 for the substance use trajectories (range of 14.7 to 35.3). Using the Bayesian Information Criterion and assessments of correspondence between observed and expected probabilities of group membership, we concluded that a four-group model was most suitable for both the substance use and the crime involvement histories. Figure 1 displays the developmental trajectories for the substance use model, whereas figure 2 presents the results for the criminal activity model.6 In both analyses, a plurality of individuals report virtually no involvement in either substance use (42.6 percent) or criminal activity (39.4 percent) throughout their pre-age-16 years. For both types of problem behaviors, we refer to this group as the Conformists. Both analyses also reveal a second group of individuals who are indistinguishable from the first group through about age 13. Beginning at age 13, however, the second group’s involvement in problem behavior begins to increase. We refer to this group of substance users and criminal offenders as Low Level Risers. The second group represents about 30 percent of the sample in the substance use analysis, whereas it comprises about 26 percent of the sample in the crime analysis. For both substance use and crime, the third and fourth groups are characterized by an early initiation into problem behaviors. The two groups are indistinguishable from each other until age 12, when they begin to diverge. For both behaviors, one group’s involvement continues to rise over time, through about age 14 for substance use and age 13 for crime. At these ages, there is a decline in each respective behavior, so we call these individuals the Decliners. This group of Decliners makes up about 14 percent of the substance use group and 23 percent of the criminal offending group. The other group’s involvement in substance use and crime continues to increase for the entire pre-age-16 time period. We call these individuals the High Level Risers, and they make up about 13 percent of the substance use group and 11 percent of the criminal offenders.

6.

The four groups in each analysis are not composed of the same individuals. For example, an individual can be assigned to group 1 in the substance use analysis but be a member of group 2 in the crime analysis. We note, however, that there is a good deal of overlap.


unknown

Seq: 11

9-FEB-07


Figure 1. Trajectories of Substance Use

Figure 2. Trajectories of Criminal Behavior

16:30

77


78

unknown

Seq: 12

9-FEB-07

16:30

APEL, ET AL.

TESTING FOR BALANCE The next step of our analysis is to assign each individual to a substance use group and a crime involvement group. As noted by Roeder, Lynch, and Nagin (1999) and Haviland and Nagin (2005), we cannot know with certainty the group to which each individual belongs. However, based on each individual’s history of substance use and crime involvement, we can calculate an estimated posterior probability of trajectory group membership. We then assign each individual to the substance use and crime groups that generate the highest posterior trajectory probabilities. The objective of classifying individuals in this fashion is to create groups of workers and nonworkers that are comparable with each other by conditioning on membership in substance use and crime trajectory groups. Table 2 presents the frequency distribution and rate of work within each pair of trajectory groups.

Table 2. Trajectory Group Membership and Age-16 Work Rates Substance Use Group CON CON CON CON LLR LLR LLR LLR DEC DEC DEC DEC HLR HLR HLR HLR

Crime Group

Number of Cases

Percent Working

CON LLR DEC HLR CON LLR DEC HLR CON LLR DEC HLR CON LLR DEC HLR

344 62 72 4 155 97 68 24 30 32 68 24 24 45 23 59

14.2 12.9 20.8 50.0 27.7 20.6 23.5 37.5 13.3 31.3 17.6 33.3 33.3 24.4 30.4 44.1

NOTE: CON = Conformists, LLR = Low Level Risers, DEC = Decliners, and HLR = High Level Risers. From table 2, it is clear that youths in different pairs of substance use and crime groups are not equally likely to work intensively during the


unknown

Seq: 13

9-FEB-07


16:30

79

school year at age 16. Consider the 344 individuals in the crime and substance use Conformist group: About 14 percent of these individuals work intensively during the school year at age 16. But, among the 59 individuals who seem to have the most serious problem behavior histories (substance use and crime High Level Risers), about 44 percent work intensively during the school year at age 16. Overall, the data in table 2 demonstrate that youngsters with a lower risk of problem behavior are also less likely to work intensively at age 16. We next sorted the sample into their pre-age-16 crime and substance abuse trajectories to create groups of age-16 workers and nonworkers who were comparable in terms of their pre-age-16 rates of substance abuse, crime, and other characteristics that might otherwise contaminate our estimate of the effect of work on problem behavior. We turn now to assessing our success in achieving such comparability. Previously, 33 of the 111 preage-16 covariates differed significantly between the workers and the nonworkers. Among these were the crime and substance abuse variety scores at each age from 12 to 15. By matching workers and nonworkers on their combined pre-16 substance abuse and crime trajectory groups, all pre-16 problem behavior rates were brought into balance (i.e., there was no significant difference between workers and nonworkers). Thus, our matching procedure seems to do a good job of ensuring that our worker/ nonworker comparisons are balanced, at least with respect to prior offending behavior.7 Based on the standardized difference diagnostic suggested by Rosenbaum and Rubin (1985), evidence of balance is even stronger, only 1 of the 13 covariates originally classified as “unbalanced” remains so.8 However, there is still cause for concern. After adjusting for trajectory group membership, ten of the pre-age-16 covariates still differ between the workers and the nonworkers (using a two-tailed Z test with a p < .05 significance level).9 Even in a randomized experiment, we would expect 7. 8.

9.

Additionally, the posterior probabilities of trajectory group membership are similar for the workers and nonworkers. This covariate is an indicator coded 1 if the respondent’s father attended graduate school and 0 otherwise. It is measured on 794 of the 1,131 respondents. The unadjusted difference between the workers and the nonworkers on this variable was -.1043, which implies that workers’ fathers were less likely than nonworkers’ fathers to have received graduate education. Both the adjusted and the unadjusted Z ratios are statistically significant for this covariate. In nine of the ten instances, these “unbalanced” covariates were also unbalanced in the initial comparisons described above. However, one of the ten post-adjustment differences was not significant prior to adjustment. This difference comes from an indicator variable observed on 1,107 individuals, which is coded 1 if an individual is Catholic and 0 otherwise. The unadjusted difference of -.0554 implies that workers were less likely to be Catholic than nonworkers, and the Z


80

unknown

Seq: 14

9-FEB-07

16:30

APEL, ET AL.

workers and nonworkers to differ from each other by chance on some covariates. But, at least by the Z test criterion, 9.0 percent (10 out of 111) of the comparisons are statistically significant—nearly twice what we would expect to see by chance. Table 3 lists the covariates on which the workers and nonworkers were not comparable. Although all of them are of interest, we are particularly concerned about the antisocial peer exposure difference between age-16 workers and nonworkers.10 Because of the theoretical and empirical importance of antisocial peer exposure in previous research, we decided to include it among our matching criteria. We also hoped that matching on this variable would help us to achieve comparability on some of the other variables listed in table 3. Our measure of antisocial peer exposure is based on the sum of the number of statements where the respondent agrees that about half or more of his/her school peers engage in various problem behaviors, including 1) smoking cigarettes; 2) getting drunk at least once per month; 3) belonging to a gang that does illegal activities;

Table 3. Pre- and Post-Adjustment Covariate Imbalance Covariate Mother’s graduate education Father’s graduate education Family income Early first birth Respondent is Catholic Respondent is Baptist No religious affiliation Weekdays on homework Time spent reading Antisocial peer exposure

Number of Cases

Pre-Adjustment Z Ratio

Post-Adjustment Z Ratio

1,029 794 861 1,053 1,107 1,107 1,107 1,114 1,097 1,066

–2.20 –4.95 –2.38 2.53 –1.73 2.43 –2.30 –2.98 –2.07 3.63

–2.65 –5.04 –2.13 2.15 –1.98 2.06 –2.56 –2.10 –2.26 2.36

NOTE: A negative Z ratio implies that workers were less likely than nonworkers to exhibit this characteristic.

10.

ratio for this comparison is -1.729, which is statistically significant at the p < .10 level. After adjustment, however, the average difference is -.066 and the Z ratio is -1.984. For several of these variables, the unbalance is in the direction of putting the workers at greater risk of delinquent behavior than the nonworkers. For example, workers are more likely to be raised in lower income families, and they spend less time on homework and reading. For other factors that remain unbalanced, it is not clear who is placed at greater risk. Workers are less likely than nonworkers to have parents with graduate educations, less likely to be Catholic, and less likely to have no religious affiliation. Compared with nonworkers, workers are more likely to have a mother who had a child before she was 20 years of age and are more likely to be Baptist.


unknown

Seq: 15

9-FEB-07


16:30

81

4) using marijuana, inhalants, or other drugs; and 5) cutting classes or skipping school. Our effort to match on antisocial peer exposure over and above the matching on trajectory group membership is not without cost. At the outset, we lose the 65 cases with missing data on this variable. In addition, in our previous analysis, each of the strata listed in table 2 was populated with both age-16 workers and nonworkers. After matching on antisocial peer exposure, this is no longer the case. For some of the age-16 workers, there are no comparable nonworkers (this happened in 8 instances). And, for some of the age-16 nonworkers, there were no comparable workers (this happened in 56 instances). Some nonworkers simply do not have any delinquent peers, and vice versa. This leaves us with a final analysis sample of 1,002 cases (a loss of about 8.1 percent of the worker sample and about 12.3 percent of the nonworker sample). We replicated the analysis presented in table 1 on the reduced sample and found that the unadjusted estimates of the effects of work are very similar; workers have significantly higher rates of substance use and crime at age 16. In addition, the additional matching improved the comparability of age-16 workers and nonworkers. With this added layer of matching, only five covariates are unbalanced: 1) father receives graduate education, 2) mother gave birth to first child at age 19 or younger, 3) respondent is Catholic, 4) respondent has no religious affiliation, and 5) respondent saw someone get shot.11 This is well within the range of significant difference rates we would expect by chance. There were also no covariates out of balance according to the Rosenbaum and Rubin standard. We, thus, conclude that matching on pre-age-16 substance abuse, crime, and antisocial peer exposure creates a plausible basis for inferring the causal impact of work at 16 on problem behavior at 16. Of course, the five covariates noted and other unmeasured covariates may bias our estimate of this impact, and the threat of such bias is an inevitable consequence of working with nonexperimental data. We note that, as the groups are balanced prior to age 16 on the variables used as the outcomes in later periods, any possible confounder would need to have a different effect on the outcomes at age 16 than it had at younger ages. Although this does not eliminate the possibility of confounding, it does narrow the scope of concern. ESTIMATES OF THE EFFECT OF WORK We now turn to the task of estimating the effect of first-time work by age 16 on age-16 problem behavior. The first quantity of interest is the 11.

Only two of these remaining unbalanced factors can be said to put workers at greater risk of delinquency than nonworkers: having a mother who gave birth to a child before the age of 20 and witnessing a shooting.


82

unknown

Seq: 16

9-FEB-07

16:30

APEL, ET AL.

PATE estimate for our selected sample of nonworkers up to age 16, adjusting for membership in the two sets of trajectory groups. The PATE estimate is obtained as the product of the outcome difference between workers and nonworkers in each strata with the number of cases in that strata, where strata is defined both by substance abuse and crime trajectory group and by the number of delinquent peers. This product is then summed across all pairs, and we divide the result by the total sample size of N = 1,002. The PATE formula for crime at age 16 is s PATEc = ∑ Ns × DCs s=1 N

where S refers to the number of strata, Ns refers to the number of individuals in stratum s, and Dc, is equal to the prevalence of criminal involvement for age-16 workers minus the prevalence for age-16 nonworkers in stratum s. The PATE estimates for substance use and overall delinquency are calculated the same way. An important issue with the PATE (for our purposes) is that individuals in some cells are more likely to work. The PATE assigns more weight to strata with larger sample sizes even though the fraction of individuals working in those strata may be quite small. Under this scenario, a stratum with a large number of cases can carry a good deal of weight in determining the magnitude of the treatment effect even though very few workers are actually observed in that stratum. An alternative to the PATE is to estimate the so-called “average effect of treatment on the treated” (ATE-T). The ATE-T is an estimate of a different parameter—the average effect of treatment on the subset of the population who is likely to be treated. It is obtained by weighting the stratum level estimates by the distribution of workers across the strata instead of the distribution of the full sample across the strata.12 To calculate ATE-T for crime at age 16, we make a straightforward modification to the PATE formula presented above: s ATE − Tc = ∑ NWs × DCs s=1 NW 12.

If the treatment effect is homogeneous across groups with different characteristics (i.e., the treatment effect within each stratum is the same), then the PATE equals the ATE-T. If treatment effects differ across strata, then how we weight the strata affects our average treatment effect. In this case, PATE and ATE-T are estimating different parameters. The PATE is an estimate of the average effect of intensive work at age 16 across the population of teenagers who have not yet worked prior to age 16. The ATE-T is an estimate of the average effect of intensive work on the type of teenager who works intensively and for the first time at age 16.


unknown

Seq: 17

9-FEB-07


16:30

83

where NW is the total number of individuals who work at age 16 and NWs is the number of individuals who work in stratum s. We also calculate the variances of the adjusted PATE and ATE-T to form the basis for statistical significance tests of the null hypothesis of a zero work effect. Let pcWs and pcNWs be the proportion of age-16 workers and nonworkers who report involvement in crime at age 16 in stratum s. Then, the sampling variance for the adjusted PATE for crime is given by s

V(PATE)c = ∑

s=1

( NN ) × ([N 2

s

Ws

× pcWs × (1−pcWs)]+[NNWs × pcNWs × (1−pcNWs)])

whereas the sampling variance for the ATE-T is s

V(ATE−T)c = ∑

s=1

( NN ) × ([N Ws

2

Ws

× pcWs × (1−pcWs)]+[NNWs × pcNWs × (1−pcNWs)])

W

and we obtain the standard errors by taking the square root of the variance.13 Table 4 presents the estimated effects of intensive school-year work at age 16 on substance use, crime, and overall delinquency at age 16. The PATE and ATE-T in this table are all very close to zero. Although not statistically significant, all of the average effects of treatment on the treated are negative. This suggests, contrary to previous findings, that intensive work during the school year among those just entering employment does not come with greater risk of either delinquent behavior or substance use. Our findings contrast with the results of other studies, particularly those of Steinberg and Dornbusch (1991) and Steinberg, Fegley, and Dornbusch (1993). They found a detrimental effect of intensive

Table 4. Adjusted Treatment Effect Estimates on Reduced Sample (N = 1,002) Outcome

PATE

Z Ratio

ATE-T

Z Ratio

Substance use Criminal acts Overall delinquency

–.025 .000 –.004

–.68 .03 –.11

–.040 –.021 –.024

–1.13 –.62 –.70

NOTES: No Z ratio is statistically significant at the one-tailed p < .05 significance level. A positive effect implies that workers are more likely than nonworkers to be involved in the problem behavior.

13.

We encountered two types of difficulties in the variance calculations within each stratum: 1) one observation in one group (either worker or nonworker) but more than one observation in the other group and 2) one observation in both the worker and the nonworker group. In case 1, we impute the variance from the group with more than one case to the other group. In case 2 where there is only one worker and one nonworker in a stratum, the comparison makes a contribution to the PATE or the ATE-T but not to the variance.


84

unknown

Seq: 18

9-FEB-07

16:30

APEL, ET AL.

school-time work for a sample similar to ours: high-school students entering the labor force for the first time. We also conducted several additional analyses to further ensure that our treatment effect estimates were not biased by potential confounders. We estimated a series of linear regression equations for each of the three outcome variables at age 16. Each equation included a system of dummy variables representing the matched group to which individuals had been assigned, a dummy variable indicating whether an individual works at age 16, and a potentially confounding covariate from the list of unbalanced covariates with statistically significant worker–nonworker differences after matching. Because of the missing data problems with the various covariates, we estimated a separate equation for each confounder. None of these regressions produced a statistically significant work effect. Although we seem to have been successful in averting bias from the long list of potential confounders listed in the appendix, this does not rule out bias from unmeasured confounders. One class of such confounders consists of variables that can be collapsed into a time-invariant fixed effect. Within our analytical framework, we can account for fixed effects by measuring the outcome for workers and nonworkers by the change in their crime/substance use between ages 15 and 16. The estimate of the treatment effect is the difference in the age-15 to age-16 change experienced by these two groups. Like the treatment effects reported in table 4, these “difference-in-difference” treatment effect estimates were small and statistically insignificant. Although these results reveal the importance of ensuring that workers and nonworkers are comparable with each other, they do not address the question of whether a PATE is a good summary of the effect of work in the population. In other words, we would like to know whether the effect of work varies in important ways for different groups within the population. The results of these analyses are presented in table 5. For youths in the Declining substance abuse trajectory and the High Rising crime trajectories, work is associated with a statistically significant reduction in substance use and crime, respectively. For these two groups, the effect of first-time intensive work seems to be beneficial. This finding that for some youth working intensively may actually be beneficial is contrary to virtually all of the existing literature. It may be that working a lot of hours while still attending school gives these high-risk youths some badly needed structure in their lives. Where intensive work might be harmful (although these results are not statistically significant) are for those youths in the three lower crime trajectories. Together, these findings suggest that the effect of intensive work during the school year may not be uniform, but it is dependent on the prior developmental history of the


unknown

Seq: 19

9-FEB-07


16:30

85

Table 5. Group-Specific Average Treatment Effects Group S1 S2 S3 S4

(CON) (LLR) (DEC) (HLR)

Number of Cases 428 308 141 125

Substance Use PATE

Z Ratio

ATE-T

Z Ratio

.058 –.011 –.315 –.017

.90 –.18 –3.36 –.27

.038 –.052 –.218 –.028

.59 –.83 –1.92 –.44

Crime

C1 C2 C3 C4

(CON) (LLR) (DEC) (HLR)

510 183 206 103

PATE

Z Ratio

ATE-T

Z Ratio

.007 .088 .052 –.285

.20 .93 .73 –2.96

.007 .088 .039 –.264

.22 .94 .48 –2.53

NOTES: S1–S4 refer to substance use trajectory groups 1–4. C1–C4 refer to crime trajectory groups 1–4. A positive effect implies that workers are more likely than nonworkers to be involved in the problem behavior.

worker. That is, the effect of work on subsequent behavior depends on the youth’s developmental history. As a final check, we considered the possibility that our results might change if our definition of “treatment” changed. In the foregoing analysis, a youth is in the treatment group if she is employed at least 1 week during the 9 months of the school year and averages more than 20 hours per week during that time. To check the robustness of our treatment effect estimates to the definition of work, we increased the duration threshold for membership in the treatment group. Specifically, in the sensitivity analyses reported below, we redefine the treatment as working an average of 20 hours per week or more for 4 or more weeks during the school year (1 month) and 8 or more weeks during the school year. In the first column of table 6, to establish a comparison, we reproduce the empirical results using our 1-week criterion. We report the unadjusted treatment effect estimates, as well as the unadjusted and adjusted balance diagnostics, based on our 119 potentially confounding variables. Note that in this table we do not condition on antisocial peer affiliation, although our results are virtually identical when we do. In the remaining columns, we present two sets of results using the 4-week and 8-week criteria. The first set of estimates, denoted as (A) for the comparison group, consists of the complement of the treatment group. Thus, if a youth is not in the treatment group, he or she is in the comparison group. In the second set of estimates, denoted (B) in the table below, the comparison group excludes


86

unknown

Seq: 20

9-FEB-07

16:30

APEL, ET AL.

those who are members of the treatment group by the 1-week criterion. Thus, if a youth works intensively using a 1-week criterion but nonintensively using a 4- or 8-week criterion, he or she is excluded from the estimation sample. This ensures that the composition of the comparison group is unchanged as we increase the duration threshold for treatment group assignment.

Table 6. Sensitivity Analysis 1 Week N # Nonworkers (%) # Workers (%) Unadjusted TE: Substance Use Unadjusted TE: Crime # Variables Reject Z Test (%) # Variables Fail D Test (%) Adjusted TE: Substance Use Adjusted TE: Crime # Variables Reject Z Test (%) # Variables Fail D Test (%)

Work Duration Criterion 4 Weeks 4 Weeks 8 Weeks (A) (B) (A)

8 Weeks (B)

1,131 1,131 1,110 1,131 1,066 883 (78%) 904 (80%) 883 (80%) 948 (84%) 883 (83%) 248 (22%) 227 (20%) 227 (20%) 183 (16%) 183 (17%) .067** .058 .061** .084* .085* .064* .075* .074* .082* .083* 37 (31%) 35 (29%) 35 (29%) 28 (24%) 28 (24%) 16 (13%) 20 (17%) 21 (18%) 18 (15%) 21 (18%) –.020 –.039 –.037 –.008 –.009 .025 .041 .038 .044 .043 10 (8%) 8 (7%) 8 (7%) 9 (8%) 8 (7%) 1 (1%) 3 (3%) 3 (3%) 4 (3%) 4 (3%)

* Z > 1.960 (p < .05, two tails); **Z > 1.645 (p < .10, two tails). NOTES: In column (A), the comparison group is the complement of the treatment group. In column (B), the comparison group is populated by the 883 youths in the comparison group by the 1-week criterion. The balance diagnostics include 119 variables. If a variable rejects the Z test, this refers to the fact that the mean comparison between workers and nonworkers for some confounding variable of interest is statistically significant (p < .05, two-tailed test). If a variable fails the D test, this refers to the fact that the ratio of the mean difference to the pooled standard deviation exceeds .20. TE = Treatment Effect.

Consider first the change in the number of youths in the treatment group as we increase the duration threshold. Of the 248 youths in the treatment group by a 1-week criterion, 227 or 91.5 percent remain in the treatment group using a 4-week threshold, and 183 or 73.8 percent remain in the treatment group using an 8-week criterion. The fact that the composition of the treatment group changes only modestly as we increase the threshold implies that most of our treated youths are indeed consistent, intensive school-year workers and are not, for example, holiday workers who work intensively only during school breaks. We are satisfied that our treatment definition is not capturing holiday workers, for example. There is a notable change in the magnitude of the unadjusted treatment effects for crime as we increase the duration threshold. Consider the estimates in column (B). The unadjusted crime treatment effects are .064, .074, and .083 for the 1-week, 4-week, and 8-week definitions, respectively. There is also some indication of the same pattern for substance use.


unknown

Seq: 21

9-FEB-07


16:30

87

The systematic increase in the unadjusted treatment effect estimates for crime suggests that the overall risk level of the working adolescents increases as we require a more consistent, intensive work history for inclusion. Despite this elevated risk, when we condition on developmental history, our adjusted treatment effect estimates remain nonsignificant. Furthermore, increasing the number of weeks worked for inclusion in the “intense work” group to 12, 16, and 20 weeks does not change our finding of no significant effect of intensive employment. We also carried out a similar exercise in which we systematically increased the work intensity threshold to 25 hours, 30 hours, and 35 hours, and our findings again remain unchanged. Thus, we are confident that the findings we report in this study are robust to modification of the definition of intensive work.

DISCUSSION AND CONCLUSIONS Our study of a nationally representative sample of youth was designed to analyze the effect of work within a developmental context. We sought to estimate the effect of intensive school-year employment on problem behaviors by explicitly attending to the developmental histories of those behaviors prior to the initiation of employment. Our analysis shows that careful consideration of how the development of problem behavior sets individuals up for different likelihoods of employment and future offending leads to useful insights about the effects of work on crime and substance use at age 16. Several findings were particularly noteworthy. First, working with a long list of such variables, we found strong evidence of within-trajectory group comparability or “balance” between firsttime workers and nonworkers on those variables that might confound our estimate of the effect of work on problem behavior. In itself, this is an important finding in that it confirms the theoretical claim that those who follow a similar developmental path of offending and substance use are demonstrably comparable with each other. Second, our analysis replicated the findings of previous research that there is a substantial positive relationship between youth who work intensively during the school year and involvement in delinquency and substance use prior to adjustment for differences between workers and nonworkers. However, after adjustment for systematic differences between workers and nonworkers based on within-trajectory group analysis, we found that, on average, the intensive workers were no more involved in either delinquency or substance use. Neither the PATE estimate nor the ATE-T estimate was statistically significant or substantively different from zero. The apparently harmful effects of intensive work seem


88

unknown

Seq: 22

9-FEB-07

16:30

APEL, ET AL.

to be largely due to preexisting differences in the developmental trajectories of problem behavior of first-time intensive workers at age 16 compared with their counterparts who refrain from intensive work. Third, we found that the treatment effect of work is not uniform throughout the population of interest and therefore that the PATE estimate conceals important variation across trajectory groups on the effect of work. For youths in the Declining substance abuse trajectory prior to age 16, work at age 16 is related to a further significant decline in substance use at age 16. Similarly, for individuals in the High Rising crime trajectory prior to age 16, work at 16 is related to a statistically significant lowering of their crime rate compared with their trajectory group counterparts who do not work. Working intensively may be helpful, then, for some youth who at an early age are at risk of antisocial behavior. These differences across trajectory groups in the effect of intensive school-year employment would not have been identified had we limited our attention to estimating a PATE. The findings thus highlight the usefulness of the trajectory group-based approach in developing group-based estimates of treatment effects. The findings also suggest that active U.S. Department of Labor policy, which attempts to attach at-risk high-schoolaged youth to work, may not be as wrong headed as extant research seems to suggest. Our research, of course, is not without its own limitations. We restricted our attention to the effects of first-time work at age 16. As we noted, this limits the generalizability of our findings to youths who begin intensive work during the school year prior to age 16. This group constitutes nearly half of youths who eventually work during the school year. Thus, they are important not only because of the size of the group but also because of the youthfulness of their entry into the working world. Even more importantly, no amount of statistical analysis can rule out the possibility of hidden biases from unmeasured confounders. Although our “difference-indifference” analysis would seem to rule out biases from time-invariant and additive fixed effects, there are many other possible sources of hidden bias, including the possibility that fixed characteristics of individuals or their circumstances do not have time-invariant and additive effects on the probability of work and on delinquency. Indeed it would not be surprising if these effects were age dependent. Finally, our findings pertain to the effect of intensive school-time work on only one class of behaviors—delinquency and substance use. Intensive adolescent employment may have effects on other types of behavior or outcomes. Recent research in this area that has seriously considered the selection issue is mixed. For example, Schoenhals, Tienda, and Schneider (1998) and Warren, LePore, and Mare (2000) found no relationship


unknown

Seq: 23

9-FEB-07


16:30

89

between intensive work and various measures of school success and performance, whereas Tyler (2003) found that youths who worked intensively during the school year had lower twelfth-grade math scores than those who did not. Clearly, more research on the possible consequences of teenage employment is required.

REFERENCES Bachman, Jerald G., and John Schulenberg. 1993. How part-time work intensity relates to drug use, problem behavior, time use, and satisfaction among high school seniors: Are these consequences or merely correlates? Developmental Psychology 29:220–35. Carnegie Council on Policy Studies in Higher Education. 1979. Giving Youth a Better Chance: Options for Education, Work, and Service. San Francisco, CA: Jossey-Bass. Cullen, Francis T., Nicolas Williams, and John Paul Wright. 1997. Work conditions and juvenile delinquency: Is youth employment criminogenic? Criminal Justice Policy Review 8:119–43. Donovan, John E., and Richard Jessor. 1985. Structure of problem behavior in adolescence and young adulthood. Journal of Consulting and Clinical Psychology 53:890–904. Donovan, John E., Richard Jessor, and Frances M. Costa. 1988. Syndrome of problem behavior in adolescence: A replication. Journal of Consulting and Clinical Psychology 56:762–65. Gottfredson, Michael R., and Travis Hirschi. 1990. A General Theory of Crime. Stanford, CA: Stanford University Press. Greenberger, Ellen, and Laurence D. Steinberg. 1986. When Teenagers Work: The Psychological and Social Costs of Adolescent Employment. New York: Basic Books. Haviland, Amelia M., and Daniel S. Nagin. 2005. Causal inferences with group-based trajectory models. Psychometrika 70:1–22. Haviland, Amelia, Daniel S. Nagin, and Paul R. Rosenbaum. 2006. Gang membership and teen violence: An observational study. Unpublished working paper. Jessor, Richard, and Shirley L. Jessor. 1977. Problem Behavior and Psychosocial Development: A Longitudinal Study of Youth. New York: Academic Press.


90

unknown

Seq: 24

9-FEB-07

16:30

APEL, ET AL.

Jessor, Richard, John E. Donovan, and Frances M. Costa. 1991. Beyond Adolescence: Problem Behavior and Young Adult Development. New York: Cambridge University Press. Johnson, Monica Kirkpatrick. 2004. Further evidence on adolescent employment and substance use: Differences by race and ethnicity. Journal of Health and Social Behavior 45:187–97. Jones, Bobby L., Daniel S. Nagin, and Kathryn Roeder. 2001. A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods and Research 29:374–93. Lahey, Benjamin B., and Irwin D. Waldman. 2005. A developmental model of the propensity to offend during childhood and adolescence. In Advances in Criminological Theory: Integrated Developmental and Life Course Theories of Offending, vol. 14, ed. David P. Farrington. New Brunswick, NJ: Transaction Publishers. McMorris, Barbara J., and Christopher Uggen. 2000. Alcohol and employment in the transition to adulthood. Journal of Health and Social Behavior 41:276–94. Mihalic, Sharon W., and Delbert S. Elliott. 1997. Short- and long-term consequences of adolescent work. Youth and Society 28:464–98. Mortimer, Jeylan T. 2003. Working and Growing Up in America. Cambridge, MA: Harvard University Press. Mortimer, Jeylan T., and Michael D. Finch. 1986. The effects of part-time work on adolescent self-concept and achievement. In Becoming a Worker, eds. Kathryn M. Borman and Jane Reisman. Norwood, NJ: Ablex Publishing. Mortimer, Jeylan T., Michael D. Finch, Seongryeol Ryu, Michael J. Shanahan, and Kathleen T. Call. 1996. The effects of work intensity on adolescent mental health, achievement, and behavioral adjustment: New evidence from a prospective study. Child Development 67:1243–61. Muthen, ´ Bent O. 2001. Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent curve modeling. In New Methods for the Analysis of Change, eds. Aline G. Sayers and Linda M. Collins. Washington, DC: American Psychological Association. Nagin, Daniel S. 1999. Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods 4:139–57.


unknown

Seq: 25

9-FEB-07


16:30

91

Nagin, Daniel S. 2005. Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. Nagin, Daniel S., and Kenneth C. Land. 1993. Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric mixed Poisson model. Criminology 31:327–59. National Commission on the Reform of Secondary Education. 1973. Reform of Secondary Education. New York: McGraw-Hill. National Commission on Youth. 1980. The Transition of Youth to Adulthood: A Bridge Too Long. Boulder, CO: Westview Press. National Panel on High School and Adolescent Education. 1976. The Education of Adolescents. Washington, DC: Office of Education, U.S. Department of Health, Education, and Welfare. National Research Council. 1998. Protecting Youth at Work: Health, Safety, and Development of Working Children and Adolescents in the United States. Washington, DC: National Academy Press. Osgood, D. Wayne, Barbara J. McMorris, and Maria T. Potenza. 2002. Analyzing multiple-item measures of crime and deviance I: Item response theory scaling. Journal of Quantitative Criminology 18:267–96. Panel on Youth. 1974. Youth: Transition to Adulthood. Report of the Panel on Youth of the President’s Science Advisory Commission. Chicago, IL: University of Chicago Press. Paternoster, Raymond, Shawn Bushway, Robert Brame, and Robert Apel. 2003. The effect of teenage employment on delinquency and problem behaviors. Social Forces 82:297–335. Ploeger, Matthew. 1997. Youth employment and delinquency: Reconsidering a problematic relationship. Criminology 35:659–75. Robins, James, Sander Greenland, and Fu-Chang Hu. 1999. Estimation of the causal effect of time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association 94:687–770. Roeder, Kathryn, Kevin Lynch, and Daniel S. Nagin. 1999. Modeling uncertainty in latent class membership: A case study in criminology. Journal of the American Statistical Association 94:766–76. Rosenbaum, Paul R., and Donald B. Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician 39:33–8.


92

unknown

Seq: 26

9-FEB-07

16:30

APEL, ET AL.

Schoenhals, Mark, Marta Tienda, and Barbara Schneider. 1998. The educational and personal consequences of adolescent employment. Social Forces 77:723–62. Staff, Jeremy, D. Wayne Osgood, John E. Schulenberg, Jerald G. Bachman, and Emily Messersmith. 2005. Paid work and deviant behavior during the early life course. Unpublished working paper. Staff, Jeremy, and Christopher Uggen. 2003. The fruits of good work: Early work experiences and adolescent deviance. Journal of Research in Crime and Delinquency 40:263–90. Steinberg, Laurence, and Elizabeth Cauffman. 1995. The impact of employment on adolescent development. Annals of Child Development 11:131–66. Steinberg, Laurence, and Sanford Dornbusch. 1991. Negative correlates of part-time work in adolescence: Replication and elaboration. Developmental Psychology 17:304–13. Steinberg, Laurence, Suzanne Fegley, and Sanford Dornbusch. 1993. Negative impact of part-time work on adolescent adjustment: Evidence from a longitudinal study. Developmental Psychology 29:171–80. Steinberg, Laurence D., Ellen Greenberger, Laurie Garduque, Mary Ruggiero, and Alan Vaux. 1980. Effects of working on adolescent development. Developmental Psychology 18:385–95. Sweeten, Gary A. 2006. Causal inference with group-based trajectories and propensity score matching: Is high school dropout a turning point? Unpublished PhD Dissertation. College Park, MD: University of Maryland. Tyler, John H. 2003. Using state child labor laws to identify the effect of school-year work on academic achievement. Journal of Labor Economics 21:381–408. U.S. Department of Labor. 2001. Report on the Youth Labor Force. Washington, DC: U.S. Department of Labor. http://www.bls.gov/opub. Warren, John Robert, Paul C. LePore, and Robert D. Mare. 2000. Employment during high school: Consequences for students’ grades in academic courses. American Educational Research Journal 37:943–69. Wright, John Paul, Francis T. Cullen, and Nicolas Williams. 1997. Working while in high school and delinquent involvement: Implications for social policy. Crime and Delinquency 43:203–21.


unknown

Seq: 27

9-FEB-07


16:30

93

Wright, John Paul, and Francis T. Cullen. 2004. Employment, peers, and life-course transitions. Justice Quarterly 21:183–205.

Robert Apel received his PhD in 2004 from the University of Maryland and is currently assistant professor in the School of Criminal Justice at the University at Albany. His current research interests include youth employment and antisocial behavior, violent victimization and injury, cohabitation and nontraditional family structure, and applied econometrics. Robert Brame is an associate professor in the Department of Criminology and Criminal Justice at the University of South Carolina. His current research emphasizes continuity and cessation of offending, collateral consequences of a criminal record, estimation of treatment effects in observational data, and law enforcement responses to domestic violence. Shawn D. Bushway is an associate professor of criminal justice in the School of Criminal Justice at the University at Albany. He received his PhD in public policy analysis and political economy in 1996 from the Heinz School of Public Policy and Management at Carnegie Mellon University. His current research focuses on the economics of crime, the process of desistance, the impact of a criminal history on subsequent outcomes, and the distribution of discretion in the criminal justice sentencing process. Amelia Haviland (PhD, statistics and public policy, Carnegie Mellon University) is an associate statistician at RAND. Her research focuses on causal analysis with observational data and analysis of longitudinal and complex survey data. She has published on delinquency outcomes related to gang membership and employment, economic outcomes related to racial and gender discrimination, and health outcomes related to gender and heart disease. She currently works on applications in criminology, health, and health economics. Daniel S. Nagin is Teresa and H. John Heinz III Professor of Public Policy and Statistics at Carnegie Mellon University. His research interests include the developmental course of violent and other criminal behavior, the preventive effects of criminal and noncriminal sanctions, and statistical methods for the analysis of longitudinal data. He is the author of GroupBased Models of Development (Harvard University Press, 2005). Raymond Paternoster is professor in the Department of Criminology and Criminal Justice at the University of Maryland. His research interests include criminological theory, offending over the life course, employment and adolescence, quantitative methods, and issues related to capital punishment.


94

unknown

Seq: 28

9-FEB-07

16:30

APEL, ET AL.

Appendix. Summary Statistics for Mean Differences in Confounding Variables by Intensive Work Status at Age 16

Variable Demographic and Residential Male Race/Ethnicity Non-Hispanic white Non-Hispanic black Hispanic Other race Housing Situation House, condo, or farm Apartment Other housing situation Residential Location Central city Suburbs Rural Census Region Northeast Midwest South West Family Composition and Background Deceased Parent(s) Family Structure Both biological parents Step parent Biological mother only Biological father only Other arrangement Mother Figure Employed Father Figure Employed Father Figure Education Less than high school High-school completion Some college Some graduate Mother Figure Education Less than high school High-school completion Some college Some graduate Household Size Residential Mobility Household Income ($1,000) Low SES Index Number of Family Assets Early First Birth Foreign-Born Parent(s)

Valid N

Mean (SD)

Unadjusted (N = 1,131) Z Ratio abs(D)

Trajectory- and TrajectoryAntisocialAdjusted Adjusted (N = 1,131) (N = 1,002) Z Ratio abs(D) Z Ratio abs(D)

1,131

47.2%

1.42

.10

1.83

.13

1.44

.11

1,131 1,131 1,131 1,131

63.9% 18.2% 16.7% 1.1%

−.82 −.22 1.03 .68

.06 .02 .08 .05

−1.10 −.15 1.19 .88

.08 .01 .09 .08

−.35 −.48 .58 .86

.03 .04 .05 .10

1,120 1,120 1,120

80.8% 11.4% 7.8%

−.96 −.53 1.84

.07 .04 .14

−.47 −.95 1.59

.03 .07 .12

−.75 −.80 1.89

.06 .06 .16

1,131 1,131 1,131

26.9% 54.2% 18.9%

.67 −.98 .48

.05 .07 .03

.98 −1.01 .15

.08 .08 .01

1.27 −1.70 .68

.10 .13 .05

1,131 1,131 1,131 1,131

18.3% 20.2% 37.3% 24.1%

−1.04 1.34 1.39 −2.10*

.07 .10 .10 .15

−1.33 1.18 1.56 −1.93

.10 .09 .12 .14

−.92 1.28 .89 −1.59

.07 .10 .07 .12

1,131

5.4%

−.45

.03

−.31

.02

−.54

.04

1,128 1,128 1,128 1,128 1,128 1,026 806

55.9% 13.7% 23.2% 3.1% 3.6% 71.2% 90.0%

−1.37 .39 1.06 −.30 .71 −.10 −.45

.10 .03 .08 .02 .05 .01 .04

−.91 −.05 1.05 −.77 .69 −.23 −.59

.07 .00 .08 .05 .05 .02 .06

−1.17 .28 .82 −.56 1.04 −.42 −.66

.10 .02 .07 .04 .10 .03 .07

794 794 794 794

18.1% 32.7% 36.6% 12.5%

1.27 .85 .57 −4.95*

.11 .07 .05 .36**

.70 1.07 .76 −5.05*

.06 .10 .07 .37**

1.19 1.04 .23 −3.55*

.12 .10 .02 .37**

1,029 1,029 1,029 1,029 1,131 1,014 861

18.5% 35.1% 38.0% 8.5% 4.5 (1.5) .6 (.3) 46.6 (30.9)

2.08* 1.10 −1.83 −2.20* 1.53 1.84 −2.38*

.16 .08 .14 .15 .12 .16 .19

1.79 1.14 −1.48 −2.65* 1.86 1.69 −2.14*

.15 .09 .12 .18 .17 .14 .19

1.47 1.19 −1.90 −1.22 1.48 .97 −1.61

.12 .10 .15 .10 .13 .08 .17

1,021 987 1,053 1,023

1.4 (1.3) 2.7 (1.6) 26.7% 14.8%

2.02* −1.33 2.53* .47

.16 .10 .19 .04

1.82 −1.42 2.15* 1.04

.15 .11 .17 .09

1.72 −.34 2.13* .91

.14 .03 .18 .08


unknown

Seq: 29

9-FEB-07


16:30

95

Appendix. (continued)

Variable Relational and Instrumental Control Attachment to Mother Figure Attachment to Father Figure Monitoring by Mother Figure Monitoring by Father Figure Mother Figure Relationship Discord Father Figure Relationship Discord Who Turn to With Problems Turn to parent(s) Turn to other relative Turn to friends Turn to other person Turn to no one (Ref.) Decision-Making Autonomy Permissive Disciplinary Style Inductive Disciplinary Style Authoritarian Disciplinary Style School Performance and Engagement P.I.A.T. Math Percentile Number of Fights in School Number of Times Late to School Number of Absences from School Positive School Attitudes Repeated a Grade Skipped a Grade Suspended from School Youth Background and Time Use Physical or Emotional Condition Disability or Chronic Condition Religious Preference Catholic Baptist Protestant No religious affiliation Other religion Victim of Home Burglary Victim of Repeat Bullying Saw Someone Get Shot



Valid N

Mean (SD)

1,093

6.4 (1.8)

−.79

.06

.56

.04

.29

.02

874

6.0 (2.1)

−2.15*

.18

−1.47

.12

−1.46

.15

1,090

2.5 (1.3)

−1.81

.13

−.84

.06

−.25

.02

868

1.8 (1.5)

−2.31*

.19

−1.24

.10

−.76

.07

721

.4 (.8)

1.47

.14

1.22

.12

.61

.04

719

.4 (.9)

1.55

.15

1.64

.17

.80

.06

1,128 1,128 1,128 1,128 1,128 1,104

58.6% 11.6% 21.8% 4.2% 3.8% 1.8 (.9)

−1.35 −1.39 1.81 .23 1.19 .65

.10 .10 .13 .02 .09 .05

.08 −1.66 .67 .21 .70 .23

.01 .12 .05 .02 .05 .02

−.59 −1.58 1.45 .11 .45 .54

.04 .12 .11 .01 .04 .04

1,100

1.0 (.9)

1.10

.08

−.22

.02

−.09

.01

1,100

1.8 (1.0)

−.88

.06

.54

.04

.54

.04

1,100

.2 (.6)

−.16

.01

−.58

.04

−.72

.06

1,074 1,129

52.2 (24.5) .3 (.9)

−2.36* 1.99*

.17 .15

−1.80 .94

.14 .07

−1.75 .97

.14 .07

1,126

1.0 (2.9)

1.53

.12

.51

.03

−.83

.05

1,110

3.7 (4.8)

1.23

.09

.44

.03

−.17

.01

1,127 994 853 1,131

4.3 (1.1) 10.6% .7% 19.4%

−1.16 .71 .61 2.53*

.08 .06 .06 .19

−.06 −.06 .40 1.16

.00 .00 .03 .08

−.22 .15 1.08 .76

.01 .01 .09 .06

1,023

11.1%

.10

.06

.55

.04

.58

.05

1,023

14.7%

.75

.01

−.56

.04

−.16

.01

1,107 1,107 1,107 1,107 1,107 1,116 1,119 1,118

29.5% 20.9% 34.3% 10.6% 4.7% 13.6% 17.4% 8.3%

−1.73 2.43* .75 −2.30* −.13 2.03* 1.12 −.12

.12 .18 .05 .16 .01 .15 .08 .01

−1.98* 2.06* 1.09 −2.56* .44 1.44 .19 −1.69

.15 .16 .09 .18 .04 .11 .01 .10

−2.54* 1.96 1.37 −2.26* 1.03 .88 .46 −2.51*

.19 .15 .11 .17 .09 .07 .03 .14


96

unknown

Seq: 30

9-FEB-07

16:30

APEL, ET AL.


Variable Number of Days Hear Gunshots # Weekdays Do Homework # Weekdays Do Extra Activities # Weekdays Read for Pleasure # Weekdays Watch T.V. # Hours Do Homework # Hours Do Extra Activities # Hours Read for Pleasure # Hours Watch T.V. # Days Do Household Chores # Days Have Dinner with Family # Days Do Something Fun # Days Do Something Religious Analogous Youth Behaviors Probability Arrested for Car Theft Antisocial Peer Association Prosocial Peer Association Earned an Allowance Monthly Allowance Worked in an Informal Job Weekly Informal TakeHome Pay Weekly Informal Work Intensity Friends in a Gang Reached Puberty Go On Unsupervised Dates Arrested for a Delinquent Offense Prior Crime and Substance Use Substance Use Variety, 11 Substance Use Variety, 12 Substance Use Variety, 13 Substance Use Variety, 14 Substance Use Variety, 15 Crime Variety, 11 Crime Variety, 12 Crime Variety, 13 Crime Variety, 14 Crime Variety, 15

Valid N

Mean (SD)


1,126

.5 (1.3)

1.67

1,114

3.6 (1.6)

−2.98*

1,120

.7 (1.5)

.25

1,117

2.0 (1.9)

1,119 1,110 1,115

1.22

.08

.97

.08

−2.10*

.15

−1.02

.08

.02

.57

.05

.70

.06

−1.25

.09

−1.13

.09

−.68

.05

4.3 (1.4) 2.0 (1.9) .5 (1.1)

1.23 −1.56 −.45

.09 .11 .03

1.02 −1.63 −.20

.08 .05 .02

.96 .38 −.40

.08 .04 .04

1,097

1.5 (2.1)

−2.07*

.14

−2.26*

.15

−1.77

.12

1,107 1,122

8.6 (6.6) 5.6 (1.9)

2.02* −.43

.15 .03

1.56 .19

.12 .01

1.56 .74

.13 .05

1,122

5.3 (2.2)

−.26

.02

.82

.06

1.21

.09

1,122

2.8 (2.0)

.21

.01

.94

.07

.75

.06

1,125

1.6 (2.0)

.63

.05

1.53

.12

1.61

.14

1,100

61.5 (40.8)

−.18

.01

−.07

.01

−.11

.01

1,066

.9 (1.3)

3.63*

.28**

2.36*

.18

—

—

1,085 1,130 1,104 1,131

2.8 (.9) 60.4% 12.7 (19.4) 44.1%

.96 1.09 1.84 1.67

.07 .08 .14 .12

1.16 .63 1.23 .82

.09 .05 .09 .06

1.03 .34 .93 1.29

.08 .03 .07 .10

1,110

8.7 (17.5)

1.31

.09

.41

.03

.71

.04

1,125

2.6 (5.9)

2.03*

.15

1.07

.08

1.12

.09

1,126 1,115 1,127

15.2% 41.8% 32.4%

1.93 −.24 2.19*

.14 .02 .16

.45 −1.12 .48

.03 .08 .03

−.44 −1.33 1.29

.03 .10 .10

1,131

4.0%

.05

.00

−.84

.06

−.53

.03

270 819 1,131 1,128 1,125 270 819 1,130 1,127 1,124

.2 (.5) .3 (.6) .5 (.9) .8 (1.0) .9 (1.1) .5 (.8) .5 (.9) .6 (1.1) .6 (1.1) .5 (1.0)

.20** .19 .27** .38** .28** .10 .20 .25** .28** .17

.59 .31 .43 1.62 −.27 .37 .85 .64 .76 −.64

.09 .02 .02 .07 .01 .04 .05 .03 .03 .03

−.85 .25 −.07 .61 −.77 −.07 .93 .44 .49 −1.09

.05 .01 .00 .02 .04 .01 .05 .02 .02 .06

1.23 2.15* 3.54* 5.08* 3.74* .66 2.25* 3.27* 3.58* 2.23*

.12


.22**


unknown

Seq: 31

9-FEB-07


16:30

97


Variable Overall Delinquency Variety, 11 Overall Delinquency Variety, 12 Overall Delinquency Variety, 13 Overall Delinquency Variety, 14 Overall Delinquency Variety, 15 Posterior Trajectory Probabilities Substance Use Posterior Probability Group 1 probability Group 2 probability Group 3 probability Group 4 probability Crime Posterior Probability Group 1 probability Group 2 probability Group 3 probability Group 4 probability Summary Statistics Excluding Posterior Probabilities Total number of variables Number of variables unbalanced Percent of variables unbalanced Including Posterior Probabilities Total number of variables Number of variables unbalanced Percent of variables unbalanced

Valid N

Mean (SD)



270

.6 (1.1)

1.03

.16

.52

.07

−.34

.03

819

.8 (1.3)

2.63*

.23**

.81

.04

.91

.04

1,130

1.1 (1.7)

3.91*

.30**

.70

.03

.30

.01

1,127

1.3 (1.8)

4.95*

.38**

1.44

.06

.67

.03

1,124

1.3 (1.8)

3.51*

.26**

.56

.03

−1.14

.06

1,131 1,131 1,131 1,131

39.2 30.6 16.6 13.6

(.5) (.4) (.3) (.3)

— — — —

— — — —

1.06 −.28 −.19 .08

.01 .00 .00 .00

1.26 −1.10 1.22 −.53

.01 .02 .03 .01

1,131 1,131 1,131 1,131

45.1 21.9 22.5 10.5

(.4) (.3) (.3) (.3)

— — — —

— — — —

1.41 −1.82 .79 −.06

.02 .05 .02 .00

1.58 −2.03* 1.38 −.79

.02 .05 .03 .01

111

111

111

111

110

110

33 29.7%

13 11.7%

10 9.0%

1 .9%

5 4.5%

1 .9%

—

—

119

119

118

118

— —

— —

10 8.4%

1 .8%

6 5.1%

1 .8%

*Z ratio is statistically significant when judged against a criterion of ±1.96 (p < .05, two-tailed). **D-statistic exceeds a criterion of .20 in absolute value. NOTES: All variables except for prior crime and substance use are from the initial interview (1997). Means for binary variables are displayed as percentages, and standard deviations for binary variables are not provided. A positive Z ratio implies that employed youths have more of the characteristic than nonemployed youths, whereas a negative Z ratio implies that employed youths have less of the characteristic than nonemployed youths.