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Juvenile Arrest and Later Economic Attainment: Strength and Mechanisms of the Relationship

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Abstract

Objectives

We tested the impact of juvenile arrest on asset accumulation, debt accumulation, and net worth from ages 20–30. We also examined whether indicators of family formation, school and work attainment, and subsequent justice system contacts explained any effects.

Methods

We used longitudinal data on 7916 respondents from the National Longitudinal Survey of Youth 1997 Cohort. Our treatment variable was a dichotomous indicator of whether respondents were arrested as juveniles. Our focal outcomes were combined measures of the values of 10 types of assets, 6 types of debt, and net worth (assets minus debt) at ages 20, 25, and 30. We used propensity score methods to create matched groups of respondents who were and were not arrested as juveniles, and we compared these groups on the outcomes using multilevel growth curve analyses.

Results

Arrested juveniles went on to have lower assets, debts, and net worth during young adulthood compared to non-arrested juveniles. These differences were most pronounced at age 30. The differences were largely explained by educational attainment, weeks worked, and income.

Conclusions

The fact that juvenile arrest predicted early adult economic attainment net of 43 matching covariates provides strong evidence that these effects are not merely artifacts of selection. The additional finding that education, employment, and income explain much of the juvenile arrest effect highlights several potential areas of intervention for protecting young arrestees’ later net worth.

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Notes

  1. We compared our focal juvenile arrest group with the group of respondents who were arrested before wave 1. The groups differed on 12 of the 43 matching variables. Specifically, those arrested before wave 1 had more school problems, poorer health, less prosocial and more antisocial peers, and higher levels of deviant behavior. The groups had comparable demographic characteristics and socioeconomic and family backgrounds.

  2. We imputed all variables with item missingness (i.e., cases where respondents refused or skipped the question, or did not know the answer; or in some cases, where respondents were purposely skipped as part of the NLSY97 design). Each predictive equation included the other study variables, both those with no missingness (i.e., gender, age, region, concentrated disadvantage, percent Black, number of siblings, and an indicator for asset interview) and those that had missingness. The treatment variable (juvenile arrest) was not included in the imputation model. The average amount of missingness across all variables was low (2.7%). There were some variables with notable missingness, including parental education (5.0%), assets (9.9%) and net worth (11.7%), ASVAB score (19.9%), household income (26.6%), and income-to-poverty ratio (26.9%). As noted below, the substantive conclusions were the same under listwise deletion.

  3. Information on respondents’ state of residence is not in the public use version of the NLSY97; instead, the second author applied to the Bureau of Labor Statistics for access to the restricted geocode file and was granted access. The geocode file contains respondents’ the state and county of residence at each wave.

  4. The NLSY97 also collected asset and debt information at age 35. However, because only a small percentage (14.4%) of respondents had reached age 35 by wave 17, most respondents had missing information on the age 35 asset/debt interview. We therefore excluded the age 35 asset/debt observations.

  5. Although almost all of the items come from the Asset interview section of the survey, the items for age 20 government and family student loan come from the education section of the survey. We did this because the NLSY97 did not collect student loan information until wave 7 in the Asset interview section, and using student loan information from that section would have resulted in about half of cases having missing student loan data at age 20.

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Author information

Authors and Affiliations

  1. College of Criminology and Criminal Justice, Florida State University, 112 S. Copeland Street, Tallahassee, FL, 32306, USA

    Sonja E. Siennick

  2. Department of Criminal Justice, University of Louisville, Louisville, KY, USA

    Alex O. Widdowson

Authors
  1. Sonja E. Siennick
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Correspondence to Sonja E. Siennick.

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Appendices

Appendices

Appendix A

Description of Pretreatment Covariates Used in Matching Algorithm.

Variable

Definition

Demographic characteristics

 Male

Respondent’s gender is male (0 = no, 1 = yes)

 Age

Respondent’s age (in years) at wave 1

 Race

Set of dummy variables with indicators for White (0 = no, 1 = yes), Black (0 = no, 1 = yes), American Indian (0 = no, 1 = yes), Asian or Pacific Islander (0 = no, 1 = yes), and Other race/Something else (0 = no, 1 = yes)

White is the reference category

 Ethnicity

Respondent is Hispanic (0 = no, 1 = yes)

 Birth cohort

Set of dummy variables with indicators for 1980 cohort (0 = no, 1 = yes), 1981 cohort (0 = no, 1 = yes), and 1982 cohort (0 = no, 1 = yes), 1983 cohort (0 = no, 1 = yes), and 1984 cohort (0 = no, 1 = yes). 1980 cohort is the reference category

Community characteristics

 Census region

Set of dummy variables with indicators for South (0 = no, 1 = yes), Northeast (0 = no, 1 = yes), Midwest (0 = no, 1 = yes), and West (0 = no, 1 = yes). South is the reference category

 Residential location

Set of dummy variables with indicators for rural (0 = no, 1 = yes), central city (0 = no, 1 = yes), and suburban (0 = no, 1 = yes). Rural is the reference category

 Concentrated disadvantage

Mean of the county’s proportion of families living below the poverty line proportion of female-headed households, the median family income (reverse coded and logged), unemployment rate, proportion of the population without a high school diploma, and the proportion of households receiving public assistance, from the 1990 Census (α = .89)

 Percent Black

Percentage of the county’s population that was non-Hispanic Black, from the 1990 Census

Household characteristics

 Two parent household

Respondent lives with two parents (0 = no, 1 = yes)

 Number of siblings

Number of siblings in respondent’s home

 Parental education

Highest level of education attained by a parent

 Household income

Total household income in logged dollars

 Income-to-poverty ratio

The ratio of gross household income variable to the previous year’s federal poverty level (for households of that size)

 Mother’s age at R’s birth

Mother’s age at respondent’s birth

Family characteristics

 Mother supportive

Respondent’s mother figure is supportive (0 = no, 1 = yes)

 Mother strict

Respondent’s mother figure is strict (0 = no, 1 = yes)

Educational characteristics

 ASVAB score

Cognitive abilities were assessed by the Armed Service Vocational Aptitude Battery (ASVAB). Scores reflect percentiles

 School tardies

Number of days late to school without an excuse in the past semester

 School absences

Number of days absent from school in the past semester

 School suspension

Suspended from school in the past year (0 = no, 1 = yes)

 Fought at school

Been in a physical fight at school in the past year (0 = no, 1 = yes)

 School attachment

Mean index based on 7 items assessing whether: (1) teachers are good, (2) teachers are interested in students, (3) there are disruptions by other students, (4) students are graded fairly, (5) there is a lot of cheating on tests, (6) discipline is fair, and (7) feels safe at school (α = .68)

 Property stolen at school

Belongings stolen at current school (0 = no, 1 = yes)

 Threatened at school

Threatened at current school (0 = no, 1 = yes)

 Private school

Attended a private school at wave 1 (0 = no, 1 = yes)

Youth background

 Victimization index

Variety score indicating the number of types of victimization respondents experienced before age 12: (1) home burglarized, (2) bullied, and (3) witnessed violence

 Youth’s health

Respondent’s general state of health (1 = poor, 5 = excellent)

Peer influences

 Antisocial peer association

Mean index based on 5 items assessing the percentage of respondents’ peers who (1) smoke, (2) get drunk, (3) belong to a gang, (4) use illegal drugs, and (5) skip class (α = .84)

 Prosocial peer association

Mean index based on 4 items assessing the percentage of respondents’ peers who (1) go to church, (2) participate in sports, (3) plan to go to college, and (4) volunteer (α = .59)

Antisocial characteristics

 Perceived risk of arrest

Percent chance respondent believes he/she would be arrested if stole a car

 Gang member

Respondent reports belonging to a gang (0 = no, 1 = yes)

 Delinquency

Variety score indicating the number of different delinquent acts ever committed: (1) vandalism, (2) theft under $50, (3) theft over $50, (4) other property crime, (5) sold or helped sell drugs, and (6) assault (α = .70)

 Substance use

Variety score indicating the number of different substances ever used: (1) cigarettes, (2) alcohol, and (3) marijuana (α = .74)

Appendix B

Random Effects Regressions Predicting Mediators From Juvenile Arrest (N = 22,081 observations on 7908 respondents).

 

Marrieda

Childrena

Highest grade completedb

Weeks workedb

Personal incomeb

b

95% CI

b

95% CI

b

95% CI

b

95% CI

b

95% CI

Juvenile arrest

0.24

− 0.10, 0.59

0.45***

0.21, 0.70

− 0.56***

− 0.73, − 0.38

− 0.20**

− 0.35, − 0.06

− 0.38

− 0.76, − 0.00

Age 25

1.34***

1.18, 1.51

1.05***

0.93, 1.17

0.85***

0.79, 0.92

0.06

− 0.02, 0.15

1.19***

1.00, 1.38

Age 30

2.07***

1.89, 2.24

1.79***

1.66, 1.93

1.29***

1.20, 1.37

0.60***

0.52, 0.68

1.09***

0.87, 1.30

Juvenile arrest* Age 25

− 0.28

− 0.62, 0.05

− 0.11

− 0.35, 0.13

− 0.24**

− 0.39, − 0.10

0.02

− 0.16, 0.20

− 0.22

− 0.68, 0.24

Juvenile arrest* Age 30

− 0.68**

− 1.07, − 0.29

− 0.33*

− 0.61, − 0.05

− 0.40***

− 0.60, − 0.20

0.00

− 0.20, 0.20

− 0.34

− 0.86, 0.17

Intercept

− 2.45***

− 2.62, − 2.28

− 1.71***

− 1.83, − 1.58

11.85***

11.77,11.92

3.20***

3.13, 3.26

7.26***

7.10, 7.42

 

Adult arresta

Adult convictiona

Adult incarcerationa

b

95% CI

b

95% CI

b

95% CI

Juvenile arrest

0.93***

0.64, 1.22

1.15***

0.79, 1.52

1.19***

0.63, 1.75

Age 25

− 0.28*

− 0.54, − 0.01

− 0.07

− 0.42, 0.27

0.00

− 0.54, 0.53

Age 30

− 0.58***

− 0.87, − 0.29

− 0.10

− 0.43, 0.23

0.19

− 0.34, 0.72

Juvenile arrest* Age 25

− 0.28

− 0.71, 0.14

− 0.44

− 0.97, 0.08

− 0.26

− 0.98, 0.47

Juvenile arrest* Age 30

− 0.01

− 0.46, 0.45

− 0.23

− 0.74, 0.28

− 0.23

− 0.97, 0.52

Intercept

− 2.42***

− 2.60, − 2.24

− 3.01***

− 3.26, − 2.76

− 3.87***

− 4.30, − 3.43

  1. Results are adjusted for kernel density weight and NLSY97 population weights
  2. †p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
  3. aLogistic coefficients shown
  4. bLinear coefficients shown

Appendix C

Random Effects Linear Regressions Predicting Each Measure of Debt from Juvenile Arrest (N = 22,081 observations on 7908 respondents).

 

Motor vehicle debt

Consumer debt

Government student loan debt

Family student loan debt

Personal loan debt

Mortgage debt

b

95% CI

b

95% CI

b

95% CI

b

95% CI

b

95% CI

b

95% CI

Juvenile arrest

− 0.58**

− 0.93, − 0.23

0.25

− 0.10, 0.59

− 0.33**

− 0.56, − 0.11

− 0.02

− 0.11, 0.07

0.07

− 0.09, 0.24

− 0.14

− 0.30, 0.02

Age 25

0.92***

0.69, 1.15

2.03***

1.82, 2.24

0.87***

0.75, 0.99

0.02

− 0.02, 0.06

− 0.10

− 0.20, 0.00

1.24***

1.07, 1.42

Age 30

1.10***

0.87, 1.32

1.38***

1.16, 1.60

1.28***

1.11, 1.45

0.08*

0.02, 0.14

− 0.18***

− 0.26, − 0.09

2.67***

2.44, 2.90

Juvenile arrest* Age 25

− 0.10

− 0.58, 0.37

− 0.32

− 0.84, 0.19

− 0.51***

− 0.79, − 0.23

− 0.08

− 0.18, 0.02

− 0.07

− 0.28, 0.15

− 0.16

− 0.55, 0.22

Juvenile arrest* Age 30

− 0.52*

− 1.01, − 0.04

− 0.26

− 0.76, 0.25

− 0.33

− 0.72, 0.06

0.00

− 0.16, 0.16

− 0.03

− 0.25, 0.20

− 1.01***

− 1.50, − 0.52

Intercept

2.27***

2.10, 2.44

1.94***

1.80, 2.08

0.95***

0.84, 1.05

0.13***

0.10, 0.16

0.38***

0.31, 0.45

0.34***

0.25, 0.43

  1. Results are adjusted for kernel density weight and NLSY97 population weights
  2. *p < 0.05; **p < 0.01; ***p < 0.001

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Siennick, S.E., Widdowson, A.O. Juvenile Arrest and Later Economic Attainment: Strength and Mechanisms of the Relationship. J Quant Criminol 38, 23–50 (2022). https://doi.org/10.1007/s10940-020-09482-6

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