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Does Early Adolescent Arrest Alter the Developmental Course of Offending into Young Adulthood?

Abstract

Adolescent involvement in risky behavior is ubiquitous and normative. Equally pervasive is the rapid decline in risky behavior during the transition to adulthood. Yet, for many, risky behavior results in arrest. Whereas prior research finds that arrest is associated with an increased risk of experiencing a host of detrimental outcomes, less understood is the impact of an arrest on the developmental course of offending compared to what it would have looked like if no arrest had occurred—the counterfactual. This study examines the developmental implications of an arrest early in the life course. The sample (N = 1293) was 37% female, 42% non-white, with a mean age of 13.00 years (SD = 0.82, range = 12–14) at baseline and followed annually for 15 years. Analyses combine propensity score matching and multilevel modeling techniques to estimate the impact of early arrest (i.e., 14 or younger) on the development of offending from adolescence into adulthood. The results indicate that early arrest alters the developmental course of offending in two primary ways. First, early arrest heightens involvement, frequency, and severity of offending throughout adolescence and into early young adulthood even after controlling for subsequent arrests. The detrimental influence of early arrest on the developmental course of offending is found regardless of gender or race/ethnicity. Second, even among youth with an early arrest, offending wanes over time with self-reported offending among all youth nearly absent by the mid- to late-twenties. The findings advance understanding of the developmental implications of early arrest beyond typical and expected offending.

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Fig. 1
Fig. 2

Notes

  1. The 2004 wave (round 8 interview) is dropped from the analysis. The random subsample responding to self-reported offending questions in this round was reselected in 2005; the sample selected in 2005 continues to answer self-reported offending questions in subsequent waves (confirmed via personal correspondence with NLS User Services). Thus, we use the 2005 subsample to define our analytic sample across all waves.

  2. This alternative approach involved assigning a given number of control observations (neighbor = 10) to the treatment case with the closest propensity score.

  3. Results of MLM prior to PSM (not shown) are consistent with those of Table 3 but the latter are tempered by the PSM. For example, the effect of early arrest on the prevalence of offending declines by more than half (from b = 1.110; se = 0.136 to b = 0.454; se = 0.143) in the matched-groups models.

References

  • Allison, P. D. (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods & Research, 28(3), 301–309

    Google Scholar 

  • Apel, R. (2016). The effects of jail and prison confinement on cohabitation and marriage. The Annals of the American Academy of Political and Social Science, 665(1), 103–126

    Google Scholar 

  • Apel, R. J., & Sweeten, G. (2010a). Propensity score matching in criminology and criminal justice. In A. R. Piquero & D. Weisburd (Eds), Handbook of quantitative criminology (pp. 543–562). Springer

  • Apel, R., & Sweeten, G. (2010b). The impact of incarceration on employment during the transition to adulthood. Social Problems, 57(3), 448–479

    Google Scholar 

  • Association of Maternal and Child Health Programs. (n.d.). Project area: Adolescent development. Retrieved December, 2020

  • Augustyn, M. B., Ward, J. T., Krohn, M. D., & Dong, B. (2019). Criminal justice contact across generations: Assessing the intergenerational labeling hypothesis. Journal of Developmental and Life-course Criminology, 5(2), 137–175

    PubMed  PubMed Central  Google Scholar 

  • Beaver, K. M., DeLisi, M., Mears, D. P., & Stewart, E. (2009). Low self‐control and contact with the criminal justice system in a nationally representative sample of males. Justice Quarterly, 26(4), 695–715

    Google Scholar 

  • Bernburg, J. G. (2019). Labeling theory. In M. Krohn, N. Hendrix, G. Penly Hall & A. Lizotte (Eds), Handbook on crime and deviance (pp. 179–196). Springer

    Google Scholar 

  • Bernburg, J. G., & Krohn, M. D. (2003). Labeling, life chances, and adult crime: The direct and indirect effects of official intervention in adolescence on crime in early adulthood. Criminology, 41(4), 1287–1318

    Google Scholar 

  • Bernburg, J. G., Krohn, M. D., & Rivera, C. J. (2006). Official labeling, criminal embeddedness, and subsequent delinquency: A longitudinal test of labeling theory. Journal of Research in Crime and Delinquency, 43(1), 67–88

    Google Scholar 

  • Bersani, B. E., & Doherty, E. E. (2018). Desistance from offending in the twenty-first century. Annual Review of Criminology, 1, 311–334

    Google Scholar 

  • Brame, R., Bushway, S. D., Paternoster, R., & Turner, M. G. (2014). Demographic patterns of cumulative arrest prevalence by ages 18 and 23. Crime & Delinquency, 60(3), 471–486

    Google Scholar 

  • Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Sage Publications, Inc

  • Bureau of Labor Statistics, U.S. Department of Labor. (2019). National Longitudinal Survey of Youth 1997 cohort, 1997-2011 (rounds 1-15). Produced and distributed by the Center for Human Resource Research (CHRR). The Ohio State University

    Google Scholar 

  • Bushway, S. D., & Tahamont, S. (2016). Modeling long-term criminal careers: What happened to the variability? Journal of Research in Crime and Delinquency, 53(3), 372–391

    Google Scholar 

  • Chenane, J. L., Wright, E. M., & Wang, Y. (2020). The effects of police contact and neighborhood context on delinquency and violence. Victims & Offenders, 16(4), 1–24

  • Chesney-Lind, M., & Eliason, M. (2006). From invisible to incorrigible: The demonization of marginalized women and girls. Crime, Media, Culture, 2(1), 29–47

    Google Scholar 

  • Chiricos, T., Barrick, K., Bales, W., & Bontrager, S. (2007). The labeling of convicted felons and its consequences for recidivism. Criminology, 45(3), 547–581

    Google Scholar 

  • Cottle, C. C., Lee, R. J., & Heilbrun, K. (2001). The prediction of criminal recidivism in juveniles: A meta-analysis. Criminal Justice and Behavior, 28(3), 367–394

    Google Scholar 

  • Crutchfield, R. D., Skinner, M. L., Haggerty, K. P., McGlynn, A., & Catalano, R. F. (2012). Racial disparity in police contacts. Race and Justice, 2(3), 179–202

    Google Scholar 

  • Del Toro, J., Lloyd, T., Buchanan, K. S., Robins, S. J., Bencharit, L. Z., Smiedt, M. G., Reddy, K. S., Pouget, E. R., Kerrison, E. M., & Goff, P. A. (2019). The criminogenic and psychological effects of police stops on adolescent black and Latino boys. Proceedings of the National Academy of Sciences, 116(17), 8261–8268

    Google Scholar 

  • Doherty, E. E., & Bersani, B. E. (2016). Understanding the mechanisms of desistance at the intersection of race, gender, and neighborhood context. Journal of Research in Crime and Delinquency, 53(5), 681–710

    Google Scholar 

  • Doherty, E. E., & Bersani, B. E. (2018). Mapping the age of official desistance for adult offenders: Implications for research and policy. Journal of Developmental and Life-Course Criminology, 4(4), 516–551

    Google Scholar 

  • Doherty, E. E., Cwick, J. M., Green, K. M., & Ensminger, M. E. (2016). Examining the consequences of the “prevalent life events” of arrest and incarceration among an urban African-American cohort. Justice Quarterly, 33(6), 970–999

    PubMed  Google Scholar 

  • Duell, N., Steinberg, L., Icenogle, G., Chein, J., Chaudhary, N., Di Giunta, L., Dodge, K. A., Fanti, K. A., Lansford, J. E., Oburu, P., Pastorelli, C., Skinner, A. T., Sorbring, E., Tapanya, S., Tirado, L. M. U., Alampay, L. P., Al-Hassan, S. M., Takash, H. M. S., Bacchini, D., & Chang, L. (2018). Age patterns in risk taking across the world. Journal of Youth and Adolescence, 47(5), 1052–1072

    PubMed  Google Scholar 

  • Erosheva, E. A., Matsueda, R. L., & Telesca, D. (2014). Breaking bad: Two decades of life-course data analysis in criminology, developmental psychology, and beyond. Annual Review of Statistics and Its Application, 1, 301–332

    Google Scholar 

  • Ge, X., Donnellan, M. B., & Wenk, E. (2003). Differences in personality and patterns of recidivism between early starters and other serious male offenders. Journal of the American Academy of Psychiatry and the Law Online, 31(1), 68–77

    Google Scholar 

  • Goffman, A. (2009). On the run: Wanted men in a Philadelphia ghetto. American Sociological Review, 74(3), 339–357

    Google Scholar 

  • Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford University Press

  • Gottfredson, M., & Hirschi, T. (2020). Modern control theory and the limits of criminal justice. Oxford University Press

  • Guo, S., & Fraser, M. W. (2014). Propensity score analysis: Statistical methods and applications (Vol. 11). SAGE Publications, Inc

  • Hirschfield, P. J. (2008). The declining significance of delinquent labels in disadvantaged urban communities. Sociological Forum, 23(3 Sep), 575–601

    Google Scholar 

  • Huizinga, D., & Henry, K. L. (2008). The effect of arrest and justice system sanctions on subsequent behavior: Findings from longitudinal and other studies. In A. M. Liberman (Eds.), The long view of crime: A synthesis of longitudinal research (pp. 220–254). Springer

    Google Scholar 

  • Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self‐competence and values: Gender and domain differences across grades one through twelve. Child Development, 73(2), 509–527

    PubMed  Google Scholar 

  • Jacobsen, W. C., Ragan, D. T., Yang, M., Nadel, E. L., & Feinberg, M. E. (2021). Arrested friendships? Justice involvement and interpersonal exclusion in rural schools. Journal of Research in Crime and Delinquency

  • Jones, N. (2014). “The regular routine”: Proactive policing and adolescent development among young, poor black men. New Directions for Child and Adolescent Development, 2014(143), 33–54

    PubMed  Google Scholar 

  • Kirk, D. S. (2009). Unraveling the contextual effects on student suspension and juvenile arrest: The independent and interdependent influences of school, neighborhood, and family social controls. Criminology, 47(2), 479–520

    Google Scholar 

  • Kirk, D. S., & Sampson, R. J. (2013). Juvenile arrest and collateral educational damage in the transition to adulthood. Sociology of Education, 86(1), 36–62

    Google Scholar 

  • Laub, J. H, Rowan, Z. R., & Sampson, R. J. (2019). The age-graded theory of informal social control. In D. P. Farrington, L. Kazemian, & A. R. Piquero (Eds.) The Oxford handbook of developmental and life-course criminology (pp. 295–322). Oxford University Press

  • Laub, J. H., & Sampson, R. J. (2003). Shared beginnings, divergent lives. Harvard University Press

    Google Scholar 

  • Laub, J. H., & Sampson, R. J. (2020). Life-course and developmental criminology: Looking back, moving forward—ASC division of developmental and life-course criminology inaugural David P. Farrington Lecture, 2017. Journal of Developmental and Life-Course Criminology, 6, 1–14

    Google Scholar 

  • Le Blanc, M., & Loeber, R. (1998). Developmental criminology updated. Crime and Justice, 23, 115–198

    Google Scholar 

  • Liberman, A. M., Kirk, D. S., & Kim, K. (2014). Labeling effects of first juvenile arrests: Secondary deviance and secondary sanctioning. Criminology, 52(3), 345–370

    Google Scholar 

  • Liu, J., Francis, B., & Soothill, K. (2011). A longitudinal study of escalation in crime seriousness. Journal of Quantitative Criminology, 27(2), 175–196

    Google Scholar 

  • Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using stata. 2nd ed. Stata Press

    Google Scholar 

  • Lopes, G., Krohn, M. D., Lizotte, A. J., Schmidt, N. M., Vasquez, B. E., & Bernburg, J. G. (2012). Labeling and cumulative disadvantage: The impact of formal police intervention on life chances and crime during emerging adulthood. Crime & Delinquency, 58(3), 456–488

    Google Scholar 

  • McAra, L., & McVie, S. (2007). Youth justice? The impact of system contact on patterns of desistance from offending. European Journal of Criminology, 4(3), 315–345

    Google Scholar 

  • McGlynn-Wright, A., Crutchfield, R. D., Skinner, M. L., & Haggerty, K. P. (2020). The usual, racialized, suspects: The consequence of police contacts with black and white youth on adult arrest. Social Problems

  • Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100(4), 674

    PubMed  Google Scholar 

  • Moffitt, T. E. (1994). Natural histories of delinquency. In E. G. Weitekamp, & H. Kerner (Eds.), Cross-national longitudinal research on human development and criminal behavior (pp. 3–61). Springer

    Google Scholar 

  • Monahan, K. C., & Piquero, A. R. (2009). Investigating the longitudinal relation between offending frequency and offending variety. Criminal Justice and Behavior, 36(7), 653–673

    PubMed  PubMed Central  Google Scholar 

  • Morris, R. G., & Piquero, A. R. (2013). For whom do sanctions deter and label? Justice Quarterly, 30(5), 837–868

    Google Scholar 

  • Mowen, T. J., Brent, J. J., & Bares, K. J. (2018). How arrest impacts delinquency over time between and within individuals. Youth Violence and Juvenile Justice, 16(4), 358–377

    Google Scholar 

  • Natsuaki, M. N., Ge, X., & Wenk, E. (2008). Continuity and changes in the developmental trajectories of criminal career: Examining the roles of timing of first arrest and high school graduation. Journal of Youth and Adolescence, 37(4), 431–444

    Google Scholar 

  • Nguyen, H., & Loughran, T. A. (2018). On the measurement and identification of turning points in criminology. Annual Review of Criminology, 1, 335–358

    Google Scholar 

  • Osgood, D. W. (2010). Statistical models of life events and criminal behavior. In A. R. Piqeruo, & D. Weisburg (Eds.), Handbook of quantitative criminology (pp. 375–396). Springer

    Google Scholar 

  • Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36(4), 859–866

    Google Scholar 

  • Patterson, G. R., & Yoerger, K. (2002). A developmental model for early-and late-onset delinquency. In J. B. Reid, G. R. Patterson, & J. Snyder (Eds.), Antisocial behavior in children and adolescents: A developmental analysis and model for intervention (pp. 147–172). American Psychological Association

  • Pettit, B., & Western, B. (2004). Mass imprisonment and the life course: Race and class inequality in US incarceration. American Sociological Review, 69(2), 151–169

    Google Scholar 

  • Piquero, A. R. (2008). Taking stock of developmental trajectories of criminal activity over the life course. In A. M. Liberman (Ed.), The long view of crime: A synthesis of longitudinal research (pp. 23–78). Springer

    Google Scholar 

  • Puzzanchera, C., & Hockenberry, S. (2021). Trends and characteristics of delinquency cases handled in Juvenile Court, 2019. Office of Juvenile Justice and Delinquency Prevention. https://www.ojjdp.gov/ojstatbb/snapshots/DataSnapshot_JCS2019.pdf

  • Raphael, S., & Rozo, S. V. (2019). Racial disparities in the acquisition of juvenile arrest records. Journal of Labor Economics, 37(S1), S125–S159

    Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). SAGE Publications, Inc

  • Rengifo, A. F., & Pater, M. (2017). Close call: Race and gender in encounters with the police by black and Latino/a youth in New York City. Sociological Inquiry, 87(2), 337–361

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38

    Google Scholar 

  • Rutter, M., Giller, H., & Hagell, A. (1998). Antisocial behavior by young people. Cambridge University Press

    Google Scholar 

  • Sampson, R. J., & Laub, J. H. (1997). A life-course theory of cumulative disadvantage and the stability of delinquency. Developmental Theories of Crime and Delinquency, 7, 133–161

    Google Scholar 

  • Sampson, R. J., & Winter, A. S. (2018). Poisoned development: Assessing childhood lead exposure as a cause of crime in a birth cohort followed through adolescence. Criminology, 56(2), 269–301

    Google Scholar 

  • Schmidt, N. M., Lopes, G., Krohn, M. D., & Lizotte, A. J. (2015). Getting caught and getting hitched: an assessment of the relationship between police intervention, life chances, and romantic unions. Justice Quarterly, 32(6), 976–1005

    Google Scholar 

  • Steffensmeier, D., Zhong, H., & Lu, Y. (2017). Age and its relation to crime in Taiwan and the United States: Invariant, or does cultural context matter? Criminology, 55(2), 377–404

    Google Scholar 

  • Steinberg, L. (2004). Risk taking in adolescence: What changes, and why? In R. E. Dahl, & L. P. Spear (Eds.) Annals of the New York Academy of Sciences: Vol. 1021. Adolescent brain development: Vulnerabilities and opportunities (pp. 51–58). New York Academy of Sciences

  • Stevens, T., Morash, M., & Chesney-Lind, M. (2011). Are girls getting tougher, or are we tougher on girls? Probability of arrest and juvenile court oversight in 1980 and 2000. Justice Quarterly, 28(5), 719–744

    Google Scholar 

  • Stolzenberg, L., & D’Alessio, S. J. (2004). Sex differences in the likelihood of arrest. Journal of Criminal Justice, 32(5), 443–454

    Google Scholar 

  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1), 1

    Google Scholar 

  • Sweeten, G. (2012). Scaling criminal offending. Journal of Quantitative Criminology, 28(3), 533–557

    Google Scholar 

  • Tapia, M. (2011). Gang membership and race as risk factors for juvenile arrest. Journal of Research in Crime and Delinquency, 48(3), 364–395

    Google Scholar 

  • von Hippel, P. T. (2007). Regression with missing Y’s: An improved strategy for analyzing multiply imputed data. Sociological Methodology, 37(1), 83–117

    Google Scholar 

  • Ward, J. T., Krohn, M. D., & Gibson, C. L. (2014). The effects of police contact on trajectories of violence: A group-based, propensity score matching analysis. Journal of Interpersonal Violence, 29(3), 440–475

    PubMed  Google Scholar 

  • Widom, C. S., Fisher, J. H., Nagin, D. S., & Piquero, A. R. (2018). A prospective examination of criminal career trajectories in abused and neglected males and females followed up into middle adulthood. Journal of Quantitative Criminology, 34, 831–52

    Google Scholar 

  • Wiesner, M., & Capaldi, D. M. (2003). Relations of childhood and adolescent factors to offending trajectories of young men. Journal of Research in Crime and Delinquency, 40(3), 231–262

    Google Scholar 

  • Wiley, S. A. (2015). Arrested development: does the grade level at which juveniles experience arrest matter? Journal of Developmental and Life-Course Criminology, 1(4), 411–433

    Google Scholar 

  • Wiley, S. A., & Esbensen, F. A. (2016). The effect of police contact: Does official intervention result in deviance amplification? Crime & Delinquency, 62(3), 283–307

    Google Scholar 

  • Yun, I., Cheong, J., & Walsh, A. (2014). The relationship between academic achievement and likelihood of police arrest among delinquents. International Journal of Offender Therapy and Comparative Criminology, 58(5), 607–631

    PubMed  Google Scholar 

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Acknowledgements

We wish to thank John Laub and D. Wayne Osgood for their insights and comments on earlier versions of the manuscript.

Authors’ Contributions

B.E.B. conceived of the study, participated in its design and coordination, and drafted the manuscript; W.C.J. performed statistical analyses, participated in the interpretation of the data and writing of the manuscript; E.E.D. participated in the conception of the study, modeling approach, and writing of the manuscript. All authors read and approved the final manuscript.

Data Sharing and Declaration

The datasets generated and/or analyzed during the current study are available in the National Longitudinal Surveys repository, https://www.nlsinfo.org/investigator.

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Correspondence to Bianca E. Bersani.

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Bersani, B.E., Jacobsen, W.C. & Doherty, E.E. Does Early Adolescent Arrest Alter the Developmental Course of Offending into Young Adulthood?. J Youth Adolescence 51, 724–745 (2022). https://doi.org/10.1007/s10964-022-01576-7

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Keywords

  • Early arrest
  • Developmental course of offending
  • Race and gender
  • Propensity score modeling
  • Counterfactual