Juvenile Incarceration versus Alternative Sanctions:
Effects on Youth Outcomes
Alison Cuellar
George Mason University & NBER
Department of Health Administration and Policy
College of Health and Human Services
4400 University Drive, MS 1J3
Fairfax, VA 22030
[email protected]
Dhaval Dave
Bentley University & NBER
Department of Economics
175 Forest St., AAC 195
Waltham, MA 20452
[email protected]
Very Preliminary
Please do not cite or circulate
This project was supported by a grant from the MacArthur Foundation Models for Change
Initiative.
1
Abstract
One significant policy issue is how best policymakers should respond to juvenile crime. Once
delinquent youth are indentified by the justice system, key sanctioning alternatives include
minimal or no supervision, community treatment services, or incarceration. To date the
empirical work has generally focused on adults; very few studies have assessed how sentencing
alternatives impact juvenile recidivism or affect other youth outcomes. The literature has also
been challenged in disentangling causality and in bypassing confounding bias in the estimated
effects due to non-random selection across different sanctions. To this end, this study assesses
causal effects on recidivism as well as education and schooling among juvenile offenders who
were incarcerated versus those receiving other alternate sentences. The analyses are based on
a unique multi-year longitudinal data set of juvenile court administrative data, screening and
assessment data, and education data for all juvenile offenders processed in Washington state
between 2005 and early-2010, linked at the person-level. The screening data include rich
information on the juvenile’s family, social, and mental health/substance abuse history. The
justice data include arrest, offense type, sanction, and prior criminal history, including all
relevant dates. The education data include completion of high school and GED, advancement,
absences, drop-out status, cumulative GPA and selected test scores.
We exploit discontinuities in the program eligibility screening scores to address non-random
selection into alternate sentences. Eligibility cut-offs for services based on the assessment data
allow us to compare youth just above and below cut-offs and identify the effect of alternative
treatment-based sanctions. To identify the effect of incarceration, we also exploit
discontinuities in the determinate sanctioning grid imposed by state law. We find some
evidence that incarceration reduces the likelihood of recidivism in the short-term but raises this
likelihood over a longer duration; thus, on the net, incarceration does not have any significant
net deterrent effect on subsequent criminal behavior among juveniles observed over the
sample period. We also assess differential responses across offenders with a dysfunctional
social history comprising high risk factors for criminal behavior and across offenders with a
history of mental health problems. These estimates suggest that, especially among juvenile
offenders with a history of mental health problems, incarceration in a state facility may raise
the probability of recidivism.
2
I.
Introduction
One of the most consistently-documented “stylized facts” in criminology is the presence
of an age-crime gradient. The likelihood of both official as well as self-reported criminal activity
tends to peak during late adolescence and early adulthood for each cohort (Figure 1). While
there is no national recidivism rate for juveniles due to variations across state systems, statelevel re-offending data suggest that the re-arrest rate within a 12-month follow-up period may
be as high as 45 – 55% (National Center for Juvenile Justice, 2006). Certain factors or processes,
including elements of juvenile justice, must exist that cause young offenders to desist
altogether or slow down their rate of offending (Mulvey et al. 2004).
Approaches that intervene with adolescents in the juvenile justice system to reduce
criminal outcomes generally have not been studied over the long term leaving the possibility
that intensive intervention specifically for individuals at greatest risk for later violent and
delinquent behavior may be highly efficient. Nor has their success been measured for
particular subsets of youth in the juvenile justice system who represent the most
disadvantaged. These are youth who are most likely to be involved in multi-generational
criminal justice-involvement, which could continue to affect future generations of children.
Thus, a significant policy issue is how best policymakers should respond to juvenile crime and
break the cycle of delinquency.
Much of the economics literature on crime is predicated on Gary Becker’s economic
framework (Becker, 1968; Stigler 1970; Ehrlich 1973) wherein a person weighs the expected
benefits and costs of the criminal act in deciding whether to commit the offense. Becker’s
model indicates that criminal acts are a function of the probability of getting caught, the
3
probability of being convicted, and the subsequent punishment. Other factors that often enter
the cost-benefit calculus of offending include income available from legal and illegal sources,
the local unemployment rate, and prior experience in offending. In contrast to Becker’s
rational offender, Bennett, DiIulio and Walters (1996) had described the violent juvenile
offender as a “super-predator” who is impulsive, irrational, and does not fear arrest or
imprisonment. These models represent the two ends of a spectrum of how potential offenders
respond to economic factors and to sanctions in the juvenile justice system. Once delinquent
youth are identified by the justice system, key sanctioning alternatives include minimal or no
supervision, community treatment services, or detention and incarceration. These sanctioning
alternatives embed potentially significant trade-offs. Hence, responses of juveniles, even if not
perfectly informed or rational, have important implications for whether resources are devoted
to sanctions, or in the case of youth with mental health problems, to treatment intervention
programs.
The sociology literature focuses on community factors and elements of social
disorganization to explain juvenile crime. Social disorganization theory (Shaw and McKay, 1969;
Sampson and Groves, 1989) highlights the importance of community structure, shared values,
and effective social controls in motivating youths to desist from offending. In this context,
social disorganization can be caused by low socioeconomic status, family dysfunction, ethnic
heterogeneity, and other environmental disruptions (Hjalmarsson, 2002).
Deterrence is a traditional argument for favoring financing policies that are “tough on
crime.” However, Paternoster and Piquero (1995) find evidence of a defiance effect wherein
punishment may actually increase subsequent to offending. This may be particularly relevant
4
for minority juvenile offenders if they believe that the sanctions and punishment have been
unfairly imposed. While the general deterrent effect of incarceration may reduce recidivism,
congregating delinquent youth together in confinement may exacerbate antisocial behavior, as
offenders learn from each other (Holman and Ziedenberg, 2006); in this framework,
incarceration and detention may lead to an increase in criminal-specific human capital. Along
the same lines, the labeling school of deviance suggests negative consequences converge when
juvenile offenders are processed officially (Manski and Nagin, 1998). In the context of these
pathways, confinement may turn into a cycle of offending, making it more likely that the
juvenile thinks of himself as a deviant. Confinement away from the normal routines or
opportunities and with other offenders may reinforce the juvenile’s negative perception of
himself.
Alternatives to sanctions can impact desistance by modifying the juvenile’s social
environment (Mulvey et al., 2004). Treatment facilities may provide juvenile offenders
alternative peer networks, critical skills, and other opportunities for community involvement, at
the same time removing offenders from the original setting that promoted the antisocial
behavior. Many of the theories on how incarceration and alternative sanctions will affect youth
lead to ambiguous predictions. Furthermore, juvenile response to incarceration versus other
forms of sanctions may be heterogeneous, with either deterrent or iatrogenic effects taking on
different orders of relative importance for different groups of offenders. Ultimately, it becomes
an empirical issue to identify how strategies financed by the juvenile justice system affect
outcomes related to recidivism, schooling, and peers.
5
Previous evidence on mental health treatment for criminally involved youth, for
example, by the Washington State Institute for Public Policy (WSIPP) addresses the importance
of providing evidence-based services (EBS) to justice-involved youth (summarized in Barnoski,
2004). This report concludes that high-fidelity EBS to youth can be cost effective in comparison
to usual probation services. However, these results do not address the effects of incarceration
on one extreme or no-sanction (diversion) on the other. Several studies are able only to
address recidivism, but not other important social outcomes, such as education or peer effects.
Furthermore, many of these studies provide only correlational evidence and do not address
potential bias due to systematic selection into treatment, sanctions, and recidivism. The
handful of studies in the economics literature, which do attempt to tackle this selection bias,
are mostly focused on adults (Levitt 1996, 1998; Kessler and Levitt 1999; Drago, Galbiati, and
Vertova 2009; Owens 2009; Green and Winik 2010; Abrams 2011; Di Tella and Schargrodsky
2013; Nagin and Snodgrass 2013).1
The current study expands on this prior work by shifting the lens, and adding to the very
sparse evidence, on juveniles, with a special focus on identifying the causal effects of
incarceration versus treatment-based sanctions on their likelihood of recidivism. Two recent
studies have exploited natural experiments to explore the effects of juvenile sentencing. Aizer
and Doyle (2013) study offenders in Chicago and exploit variation in incarceration propensity
across randomly-assigned judges to estimate causal effects.2 They find that incarceration
reduces high school completion and raises adult recidivism, though they do not directly study
effects on juvenile crime. Hjalmarrson (2009) analyzes juvenile offenders who were
1
See Levitt and Miles (2006, 2007) for an excellent survey of this literature.
See Di Tella and Schargrodsky (2013) and Nagin and Snodgrass (2013) for an analyses of adults using
a similar identification strategy based on random judge assignment.
2
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adjudicated of at least one offense between 1998 and 2000, and exploits presumptive
sentencing to study the effects of incarceration on re-conviction rates. She finds that youth
sentenced to state incarceration have a 37% lower hazard rate of recidivating.
Similar to Hjalmarsson (2009), we exploit discontinuities in Washington State’s eligibility
requirements for diverting juvenile offenders into evidence-based programs and discontinuities
in the state’s sentencing guidelines with respect to incarceration versus other milder local
sanctions, with an instrumental-variables (IV) framework, to isolate plausibly causal effects.
The administrative dataset comprises almost 113,000 offenders processed in WA’s juvenile
justice system between 2005 and 2009, and contain rich information on the offender’s criminal
and social history as well as information on key family domains, which have generally remained
unobserved in prior studies. We expand on the limited prior evidence in several ways. First, we
move beyond estimating just an average population effect, and explore heterogeneous
responses across both time (that is, how is criminal activity affected both in the short term
upon release as well as over longer periods), across various domains of social history, including
mental health, and other margins. Second, in ongoing work, in addition to informing the effects
at the incarceration versus non-incarceration margin, we also exploit discontinuities in eligibility
for diversion into various treatment programs for juvenile offenders who are not incarcerated.
These analyses inform the relative effects of various treatment interventions versus probation
and community supervision on youth recidivism.
While estimates of recidivism are important because they allow us to compare the
estimated costs of victimization of these policy alternatives, they give only a narrow picture of
youth outcomes. An important extension, broadening the focus on a range of important social
7
outcomes, is to examine youth human capital investments. To this end, our study also assesses
causal effects on education and schooling among juvenile offenders who were incarcerated
versus those who received other sentences. From this vantage point, given the
intergenerational transmission of criminal activity and risky behaviors, secondary prevention
efforts that stem future recidivism and increase educational attainment among at-risk
adolescents can be viewed as primary prevention for the next generation.3 Successful
education completion is an important dimension of human capital accumulation and is strongly
associated with later success in the labor force. There is also a direct causal link from higher
educational attainment to healthy behaviors, better health, and a reduced propensity to take
up crime in adulthood – all of which can disrupt the intergenerational cycle of delinquency.
At this preliminary stage, we find some evidence that incarceration reduces the
likelihood of recidivism in the short-term but raises this likelihood over a longer duration; thus,
on the net, incarceration does not have any significant net deterrent effect on subsequent
criminal behavior among juveniles observed over the sample period. We also assess
3
Although early intervention may be recommended on several grounds, others argue (see for instance,
Cook et al. 2014; Heller, Pollack, Ander, and Ludwig, 2013) for programs which intervene in a targeted
fashion on the basis of observed risk levels or which are directed at children who have begun to display
negative outcomes. Such secondary prevention occurs in older children who may have begun to engage
in risky behaviors or are at high proximal risk of engaging in such behaviors due to their social or family
environment, and involves efforts to minimize or correct the course of a problem once it has begun to
manifest. Behaviors that emerge in adolescents, such as juvenile crime, may lend themselves to such
targeted approaches. Three arguments potentially favor investments in secondary prevention. First,
many children will engage in delinquent behavior as they grow older, despite early intervention. Policy
makers will continue to require strategies to address these youth. Even proponents of early intervention
recognize the marginal value of secondary prevention and do not contend that such early intervention
efforts should crowd-out or “starve later educational and skill enhancement efforts” (Cunha, Masterov,
Lochner and Heckman 2005). Rather, primary and secondary preventive interventions should be viewed
as complementary strategies. Second, research has not established that all later investment in older,
disadvantaged children produces lower returns than early intervention. The one area that has been
established as relatively less efficient than early intervention is investment in teen job skills, such as Job
Corps programs. Yet, the knowledge base for delinquency interventions is relatively sparse. Third, such
interventions, even if they are intensive and more costly on a per participant basis than early intervention
programs, could be narrowly targeted to individuals at greater risk for later violent behavior, thus
increasing their cost-effectiveness (Cook et al 2014).
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differential responses across offenders with a dysfunctional social history comprising high risk
factors for criminal behavior and across offenders with a history of mental health problems.
These estimates suggest that, especially among juvenile offenders with a history of mental
health problems, incarceration in a state facility may raise the probability of recidivism. Several
robustness checks are implemented in order to assess the credibility of the identification
strategy and the validity of the exclusion restrictions.
II.
Background
Mental Health Interventions and Treatment Allocation
Washington State offers three mental health treatment programs4 that are evidencebased to eligible youth. The state-implemented behavioral interventions for justice-involved
youth include: 1) aggression replacement training (ART); 2) functional family therapy (FFT); and
3) multi-systemic therapy (MST). These are evidence-based interventions that take a holistic
approach to preventing further delinquency and juvenile crime. ART is a cognitive behavioral
intervention that trains adolescents to cope with their aggression and violent behaviors, with a
focus on promoting social skills, anger control, and moral reasoning. FFT and MST are more
intensive treatment programs that combine family-based approaches with individualized
supports, seeking to reduce youth antisocial behaviors by empowering them and their families
with the requisite skills and resources to address their difficulties and to cope with various
environmental and social problems (Henggeler et al. 1998). Both MST and FFT are listed as
model programs by the Blueprints for Violence Prevention initiative. However, neither is
associated with overwhelming evidence, in part because of a leaner research base than is found
4
We do not address coordination of Services a limited intervention provided to low-risk youth over 2
consecutive Saturdays.
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for early intervention, and furthermore neither MST nor FFT research has reported long-term
outcomes. Furthermore, very little credible evidence exists which relate interventions for
juvenile justice youth on their human capital outcomes.
In Washington all youth who are referred to juvenile justice are given a mental health
prescreen which determines whether they are potentially eligible for a full assessment and
treatment. The pre-screen assessment is a subset of the full assessment, and denotes whether
an offender is of low, moderate, or high risk of re-offending. Specifically, the pre-screen
assessment includes scores on two broad domains: 1) criminal history; and 2) social history.
Criminal history captures the following information regarding the juvenile offender: 1) age at
first offense; 2) misdemeanor referrals (prior contact with juvenile justice); 3) felony referrals;
4) weapon referrals; 5) against-person misdemeanor referrals; 6) against-person felony
referrals; 7) confinement orders to detention; 8) confinement orders to state institution; 9)
escapes; and 10) failure to appear warrants. Social history captures information on the
following components: 1) male; 2) school; 3) friends; 4) court-ordered or DSHS placements; 5)
runaways; 6) family; 7) current risk factors such as substance abuse and family members in
prison/jail; 8) alcohol and drugs; 9) victim of abuse; 10) victim of neglect; and 11) mental
health. The total score on all criminal history domains ranges from 0 to 31, and that for all
social history domains ranges from 0 to 18. Youth are classified as low, moderate, or high risk
based on the interaction between their criminal history and social history scores, as shown in
Figure 2.
Only youth who score high enough on the pre-screen, specifically on criminal, social risk
and the separate family dysfunction domain are eligible for either of two intensive mental
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health treatments, depending on their scores for subsets of the component domains. Youth
who score moderate on criminal and social risk and who score high on aggression are
potentially eligible for aggression replacement training. Not all youth who are assessed as
eligible ultimately receive treatment. In our sample, 22% of youth are not referred to treatment
because treatment is not available or geographically accessible. In addition, some youth have
probation periods that are not long enough to complete treatment. Further, not all youth
complete treatment. Among the major reasons for dropping out of treatment are nonparticipation in the sessions, whereabouts unknown, youth or family is involved in other
services, non-successful completion, and termination due to behavior problems.
Commitment in a Juvenile Rehabilitation Administration Facility
About 850 youth are committed annually to facilities of the Juvenile Rehabilitation
Administration (JRA) by county juvenile courts in Washington. The average age of juvenile
offenders in custody is 16.6 years, with about 25 percent of the offenders ages 15 and younger.
About 92 percent of those in residential custody are males, 17.1 percent are black, 43.5 percent
are white, and 18.2 percent are Hispanic. Assault and robbery constitute the most common
offenses, with each representing 22.1 percent of those who are committed.5
A presumptive, determinate sentencing grid in Washington took effect for all offenses
committed on or after July 1, 1998 (see Figure 3). If subject to juvenile court jurisdiction,
sentencing for a convicted juvenile offender is a function of the interaction between only two
factors: 1) the severity of the current offense; and 2) number of prior convictions.6 The offense
5
These figures are obtained from the Washington State Department of Social and Health Services. See
http://www.dshs.wa.gov/jra/facts.shtml#jra.
6
An offender’s sentence may fall outside of the standard sentencing range denoted in the grid above, for
three reasons: 1) chemical dependency disposition alternative; 2) special sex offender disposition
11
severity is based on 10 categories ranging from the most severe, level A+ (for instance, murder
1 and murder 2), to the least severe, level E (for instance, driving without a license and
attempted assault 4). These offense levels comprise the vertical axis of the sentencing grid,
with offense classes A through C representing felonies and classes D and E representing
misdemeanors. The horizontal axis reflects the juvenile offender’s prior adjudications (meaning
convictions) score. Each prior violation or misdemeanor adjudication contributes 1/4 point to
the score, and each prior felony adjudication contributes a full point to the score; fractional
scores are rounded down to an integer score.
III.
Analytical Framework
The objective of this study is to assess the causal impact of evidence-based
interventions and incarceration on the likelihood that the juvenile will subsequently engage in
criminal behavior. Consider the following “production function” that relates post-disposition
criminal activity to prior incarceration.
(1)
Rit+k = α + β Custodyit + PriorFelonyit Ω + PriorMisdemeanorit ψ + OffenseSeverityit Π +
Xit Φ + Yeart+k λ + Countyξ + μit + εit+k
Specifically, recidivism (R), for the ith juvenile over some period k beyond their prior disposition
at time t, is a function of any prior incarceration (Custody). Specifically, we study recidivism
over any period of time post-release as well as recidivism occurring over a window of 30 days,
alternative; and 3) manifest injustice. If the offender is subject to local sanctions or 15-36 weeks of
confinement, and has not committed a class A- or B+ offense, and the court finds that the offender is
chemically dependent and amenable to treatment, then it may suspend the standard disposition and
impose one outside of the range. Under the special sex offender disposition alternative, if the juvenile is
found to have committed a sex offense (other than a serious violent offense) with no history of a prior sex
offense, the court may place the offender on community supervision with certain conditions, upon
determining that the offender is amenable to treatment. Finally, under manifest injustice, the judge may
sentence outside the standard range if he or she determines that the standard range penalty is excessive
or when the standard range penalty poses a clear and serious danger to society. See WA Caseload
Forecast Council (2013).
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120 days, and more than 120 days (k=30 days; 120 days; >120 days) after their prior conviction
and sentence. The parameter of interest is β, which captures the net structural effect of
incarceration on recidivism, operating through any potential deterrent and/or iatrogenic effect
of serving time in prison.
The juvenile’s propensity to recidivate also depends on the extent of their prior criminal
experience, measured as indicators for the total number of prior felony convictions
(PriorFelony) and the number of prior misdemeanor convictions (PriorMisdemeanor), and their
offense severity, measured as indicators for the 10 offense classes. The vector X represents a
set of observed time-variant and invariant individual-specific characteristics, including
indicators for age, gender, race, and ethnicity, that account for observed differentials in
criminal behavior across socio-demographic factors. All models include year fixed effects,
which control for overall trends in juvenile crime in WA, and county fixed effects, which control
for observed and unobserved stable local characteristics such as infrastructure, policing, area
demographics and economic factors, composition of peers, and quality/conditions of the JRA
facility and detention facilities. To the extent that juvenile court referrals are unique to each
county in Washington, the county fixed effects further capture any unobserved differences in
procedural referral protocols.
A challenge in any such analysis relates to disentangling the causal effect of
incarceration from other unobserved factors (μ) that may also affect recidivism -- for instance,
family background and parental characteristics, mental health, substance abuse, and other risk
factors. Sentencing by judges is not random, and may reflect factors that are correlated with
the offender’s current and past history, expected outcomes, and other unobservables. The bias
13
due to this selection may be positive or negative. For instance, unobservable factors such as
propensity for risk-taking, dysfunctional family background or adverse neighborhood peer
effects may raise criminal engagement as well as the likelihood of committing more severe acts
and being sentenced to harsher penalties. Manski and Nagin (1998) term this the “skimming
model”, in which judges may be classifying offenders based on “risk” and assigning those
deemed higher risk into detention and incarceration. In this case, the estimated impact of
incarceration on recidivism in equation (1) may be biased upwards. On the other hand, if
individuals are “sorted” into different sentences based on the court’s private assessment
regarding expected benefits, then the estimated effect may be biased downwards. In this
scenario, judges make sentencing decisions that minimize expected recidivism, which Manski
and Nagin (1998) term the “outcome optimization model”.
While we are able to control for a large set of observable risk factors, such as prior
criminal history, offense severity, socio-demographics, parental and family history, mental
health and substance abuse history, and other social domains, the possibility of non-random
selection bias remains. We address this identification threat by exploiting discontinuities in the
determinate sentencing grid in Washington.
As shown in Figure 3, sentencing between incarceration in a state detention facility
operated by the Juvenile Rehabilitation Administration (JRA) and other local sanctions depends
on the intersection between the juvenile offender’s prior criminal history (captured in the prior
adjudications score) and the severity class of their current offense. Local sanctions include
combinations of 0-30 days confinement in a local detention facility, 0-12 months of community
supervision, 0-150 hours of community restitution, and $0-$500 fine. The cells also indicate the
14
standard sentencing (or disposition, in the jargon of the juvenile justice system) range. Thus,
even among juveniles who are sentenced to custody in a JRA facility, the length of the custody
is also dependent on the intersection between the prior adjudications score and their current
offense class.
Sentencing varies discontinuously across the shaded and unshaded cells, and across
cells in the unshaded region. For instance, a juvenile offender with no prior criminal history,
whose current offense is a felony of class B+, would be sentenced to 15-36 weeks of custody in
a JRA detention facility whereas an otherwise similar offender whose current offense is a felony
of class B would be punished with local sanctions. The discontinuity also arises across the prior
adjudication score. For two juvenile offenders with a current offense class of B, the offender
whose prior adjudication score is 2 would be sentenced to 15-36 weeks in a state detention
facility whereas the offender with a prior adjudication score of 1.75 (which gets rounded down
to 1) would only be sentenced to local sanctions. Thus, small changes in offense class and/or
prior adjudications lead to a large discontinuous increase in penalties -- from local sanctions to
at least 15 weeks confinement in a JRA facility.
We exploit these discontinuities to identify the causal effect of incarceration on
recidivism. Specifically, we estimate equation (1) based on an instrumental-variables (IV)
methodology, utilizing the relevant interactions in the determinate sentencing grid for plausible
exclusion restrictions.7 Our first-stage prediction equation comprises the following, with the
“~” differentiating coefficients from this first-stage model relative to the structural secondstage model:
7
Our identification strategy can be thought of in term of a fuzzy regression discontinuity design (RDD),
which is equivalent in this case to the IV-based approach we implement (Angrist and Pischke 2009).
15
(2)
Custodyit
α + (PriorAdjudicationsScoreit*CurrentOffenseClassit)
+ PriorFelonyit Ω +
PriorMisdemeanorit ψ + OffenseSeverityit Π + Xit Φ + Yeart+k λ + County ξ + μit + νit+k
It should be noted that only the relevant cells, across which sentencing varies, are used as
excluded instrumental variables. These are based on the interactions between the prior
adjudications score and the current offense class. In alternative models that assess the effects
of any incarceration (as opposed to a continuous measure of incarceration length), we also
define a single IV indicator delineating the shaded cells (wherein the individual would be
sentenced to local sanctions) versus the non-shaded cells (wherein the individual would be
sentenced to incarceration). We do this in order to maximize statistical power and minimize
small-sample bias associated with weak instruments, which increases with the number of
instruments.
Since the structural model (equation 1) non-parametrically controls for the offender’s
full criminal history and their current offense severity, identification is achieved only off these
requisite interaction terms. Causal effects are thus essentially identified only through
comparisons of otherwise similar juvenile offenders on both sides of the multiple
discontinuities in the grid, across both the horizontal and vertical axes. That is, the effects of
incarceration are estimated based on juvenile offenders with identical prior criminal history but
slightly different offense classes that result in substantially different sentencing (one gets
incarcerated but the other does not) as well as juvenile offenders committing the same class
offense but slightly different criminal history, again resulting in substantially different
sentences, conditional on all other observables.
16
Figures 4-10 graph the mean probability of being incarcerated, within rows (holding
offense class constant, and comparing individuals just above and below the prior adjudication
score threshold) and within columns (holding the prior adjudication score constant, and
comparing individuals with slightly higher or lower severity of current offense.) These figures
underscore two points. First, as would be predicted by the sentencing grid within each offense
type or prior score, there is a discontinuous jump in the incarceration probability surrounding
the relevant threshold. For instance, the grid predicts that among offenders who have
committed a class B felony, there should be a large increase in the likelihood of being
incarcerated for those who have a prior adjudication score of 2 versus those with a slightly
lower score (for instance, 1.75 which would be rounded down to 1). Figure 4 confirms this
discrete jump in the probability of around 36 percentage points, and discontinuous increases
between 20-40 percentage points are also found around the other thresholds.8 The first-stage
results confirm this and suggest that the grid discontinuity leads to about a 35 percentage point
increase in the probability of being arrested.
The second notable point is that the increase in probability is not one; that is, for many
individuals, sanctions do not quite follow the determinate sentencing grid. An offender’s
sentence may fall outside of the standard sentencing range denoted in the Figure 3, for various
reasons. There are two special juvenile enhancements for crimes involving firearms, which
carry additional commitment time in addition to any other sanctions. Additionally, if the
offender is subject to local sanctions or 15-36 weeks of confinement, and has not committed a
8
Except for differences in race/ethnicity (offenders who fall just above the discontinuity and would be
subject to incarceration are more likely to be black and slightly less likely to be Hispanic), there are no
other significant differences in observable characteristics among offenders on either side of the
discontinuity.
17
class A- or B+ offense, and the court finds that the offender is chemically dependent and
amenable to treatment, then it may suspend the standard disposition and impose one outside
of the range. Under the special sex offender disposition alternative, if the juvenile is found to
have committed a sex offense (other than a serious violent offense) with no history of a prior
sex offense, the court may place the offender on community supervision with certain
conditions, upon determining that the offender is amenable to treatment. The court may also
generally suspend disposition if it deems that the offender and the community may benefit on
the condition that the offender partake in required educational or treatment programs. Finally,
under manifest injustice, the judge may sentence outside the standard range if he or she
determines that the standard range penalty is excessive or when the standard range penalty
poses a clear and serious danger to society. Hence, identification is being derived from those
offenders whose incarceration is being “moved” by the grid discontinuities, the so-called
“compliers” in the context of an IV estimation strategy. We perform several diagnostic tests
(detailed below) to assess the validity of our identification strategy.
IV.
Data
We obtained several administrative datasets from Washington State Administrative
Office of the Courts (AOC), which contain information on all juvenile offenders with a referral
filed between January 1st, 2005 and December 31st 2009. Referrals (or intakes) represent all
youth that come through the juvenile justice by way of a police officer or probation officer -analogous to an arrest for adults. There were 112,766 juvenile offenders over this period, who
were referred to court intake 227,260 times, with 108,840 of these referrals progressing to a
formal charge and legal case. For each offender, referral, and legal case, we can observe all
18
prior convictions, prior offenses and violations, current charges and offense categories,
adjudication (guilty or not guilty), disposition (action taken by the Juvenile Court postadjudication), disposition condition (commitment to a JRA facility versus other detailed
sanctions and conditions), disposition duration (length of commitment if incarcerated and the
duration of the other treatment/rehabilitation conditions), and the relevant dates with respect
to each of these factors.
The Juvenile Rehabilitation Administration (JRA) provides rehabilitation services to
juvenile offenders convicted for crimes. The JRA operates five secure residential facilities, three
of which are maximum security institutions, and also operates six state-run community facilities
with 72 minimum-security beds. We obtain information on whether the offender was
sentenced to commitment to a JRA facility from their case disposition records. In our analyses
sample, among offenders who are charged and convicted, just over five percent are sentenced
to incarceration. This comprises 4,211 unique juvenile offender sentenced to a JRA
commitment facility between 2005 and 2009. We construct two measures relating to
incarceration: 1) an indicator for whether the offender was sentenced to incarceration; and 2)
total time in days that the juvenile was sentenced to the commitment facility, with those
individuals who are not incarcerated coded as 0 days. In certain cases, if the offender is subject
to a standard range sentencing that involves JRA confinement, and the court determines that
the offender and the community may benefit from a suspended disposition, it may suspend
execution of the confinement sentence on the condition that the offender comply with local
sanctions and any treatment, educational, or counseling requirement. If the offender fails to
comply with these conditions, the court can revoke the suspended disposition and order the
19
execution of the confinement. We do not include these suspended confinement dispositions in
our measure of incarceration. Such suspended commitment sentences were handed out in 572
cases; estimates are also not sensitive to excluding these cases altogether from the analyses.
Outcomes
In addition to constructing a measure for any recidivism, meaning any new referral
(arrest) post-disposition over the sample period, we construct four additional measures to
capture short-term and long-term recidivism. These comprise indicators for any new referral
within 30 days, 90 days, 120 days, or greater than 120 days. It is possible that youth who
receive mental health interventions are more likely to "get caught" due to the additional
oversight during treatment. This would bias our estimates downwards if the oversight is
greater than the oversight among the comparison group. However, to make observing
recidivism among the treated and non-treated groups comparable, we begin the starting point
of our calculated recidivism window with the prescreen date and add the average length of
treatment. For functional family therapy we add three months, for multi-systemic therapy we
add four months, and for aggression replacement training we add ten weeks, and for
coordination of services we add 14 days (Alexander et al. 2000; Henggeler et al. 1998; Goldstein
and Glick 1994).9 We do this for both the treatment and comparison groups.
For our recidivism measures for JRA, we define a second recidivism window using the
latest date on which there was an action on a youth’s case. For example, if a youth was given 6
months of community supervision we begin the recidivism window 6 months after the
community supervision decision was made. If a youth was confined to a JRA facility for 6
9
http://www.wsipp.wa.gov/rptfiles/07-06-1201.pdf
20
months, then the recidivism window commences after the youth is expected to be released. By
definition, the offender is heavily “incapacitated” from engaging in a criminal act while in
confinement, and other sentenced youth would also be significantly constrained from engaging
in criminal activity due to heightened supervision while fulfilling their local sanctions.10 Thus,
we bypass the incapacitation effect of incarceration and isolate the pure deterrent and/or
iatrogenic effect.
Independent Variables and Controls
We include indicators for gender, age, race, and ethnicity in all specifications, along with
fixed effects for the year of the referral violation and the county. In models that assess the
impact of JRA confinement, we also non-parametrically control for indicators of the offender’s
prior felony convictions, prior misdemeanor convictions, and the current offense class. In
supplementary models, we assess the robustness of our IV estimates to controlling for a full
vector of characteristics from the social and criminal history domains in the offender’s prescreen assessment.
V.
Results
We present estimates of the effects of incarceration on measures of recidivism, based
on equation 1, in Tables 1-8. Table 1 presents single-equation OLS estimates of any
incarceration on various measures of recidivism. Specification 1 suggests that spending time in
a JRA detention facility is associated with a statistically significant 6 percentage point decrease
in the probability of any subsequent criminal violation (approximately 18 percent relative to the
10
It is validating that the effects on recidivism become more negative when we set the counter for the
non-incarcerated offenders to commence post-disposition (that is post-sentencing) rather than postdisposition plus sentence length. One would expect a large deterrent/incapacitation effect while the
individual is serving their local sanctions and is under community or probationary supervision.
21
average probability of 33.3 percent over our observation window). Specification 2 controls for
an extended set of risk factors, measures which are typically unobserved in other studies.
These include: 1) indicator for parental employment problems; 2) indicator for parental mental
health problem; 3) indicators for parental authority and control (whether youth usually
obeys/follows rules, sometimes obeys or obeys some rules, or consistently disobeys and/or is
hostile); 4) indicator for any family history of jail/imprisonment; 5) indicators for age of first
offense (over age 16, age 16, age 15, ages 13-14, or under age 13); 6) indicator for parental
drug problem history; 7) indicators for current parental problems with drugs and alcohol; 8)
indicator for current parental physical health problem; and 9) indicators for social history score
(ranging from 0 to 18), which captures these and additional domains of the youth’s social
environment and history including school performance, friends/peers, own substance abuse,
mental health problems, physical and sexual abuse, neglect, aggression, runaways, and other
family domains. These extended characteristics are obtained from the youth’s pre-screen
assessment, which is administered to all juvenile offenders. However, since the pre-screen
assessment is missing for a large part of our sample, the sample size is substantially reduced in
specification 2.11 The OLS estimate is robust in sign to controlling for these risk and/or
protective factors, though the magnitude increases to a 9.7 percentage point decrease in
recidivism. This is consistent with positive selection on unobservables – the “skimming model”
of Manski and Nagin (1998) – wherein there is positive bias if these unobserved risk factors
remain unaccounted.
11
There are no significant differences, beyond a Type I error, in offense class, prior history, and
observable socio-demographic factors (age, gender, race/ethnicity) across offenders for whom the prescreen assessment is available and those for whom it is missing.
22
The effects of race, ethnicity and gender are generally consistent with the literature.
Among offenders for whom this information is not missing,12 blacks and American Indians have
a significantly higher probability of recidivating relative to whites, and whites have a
significantly higher probability relative to Asians. The likelihood of recidivating is also
significantly higher among males (relative to females) and among Hispanics (relative to nonHispanics). The effects of age suggest a progressively lower probability of recidivating with age,
though these should be interpreted with caution since they confound both a life-cycle effect as
well as a sample truncation effect. Given that our sample is truncated in 2010 or at the age of
majority, whichever occurs first, we observe younger offenders over a larger period of time
than older offenders by definition.
In order to partly account for the differential in the observation window due to this
truncation, we also consider recidivism over specific periods of time: 30 days, 90 days, and 120
days. For these specifications (reported in models 3-8), we restrict the terminal period so that
we are able to observe all offenders for at least the period under consideration postdisposition. That is, when studying recidivism over 30 days, we restrict the analyses to
individuals whom we are able to observe for at least 30 days after their release or fulfillment of
their sentence. These models suggest that incarceration has a strong deterrent effect, on the
net, on subsequent criminal behavior on the order of a 4-15 percentage point decrease in
recidivism over respective periods of 30, 90, and 120 days. As before, the effect magnitude
become somewhat more negative in the extended specifications (even-numbered models) that
12
Gender is missing for approximately 0.06 percent of the sample, race is missing for 8.91 percent of the
sample, and Hispanic ethnicity is missing for 48.3 percent of the sample. In order to maximize sample
size, we include these observations and control for a missing indicator with respect to each of these
characteristics.
23
control for the offender’s social history and related risk factors. These estimates suggest that
incarceration appears to reduce subsequent criminal activity, at least over the short term.
The final two specifications (models 9 and 10) assess effects on recidivism in the longer
term -- any subsequent criminal violation that resulted in contact with the juvenile justice
system over a period of more than 120 days subsequent to serving the prior sentence. These
estimates suggest much weaker deterrent effects, on the order of 2-5 percentage points.
In order to bypass any potential bias due to non-random selection into incarceration
and subsequent criminal activity, Table 2 presents IV-based estimates of Equation 1.
Specification 1 suggests that incarceration has no discernible impact on the probability of
engaging in subsequent criminal activity. This specification employs a single indicator, which
separates the shaded cells from the non-shaded cells in the determinate sentencing grid (Figure
3) as the excluded IV. We employ this as the preferred IV since the key measure relates to any
incarceration rather than the length of the incarceration. With respect to incarceration versus
local sanctions, this IV captures the essential discontinuity reflected in the grid while
maximizing the strength of the instrument. This exclusion restriction is highly significant in the
first-stage prediction, as shown by the F-statistic of 106.7.13 In specification 2, we utilized
separate indicators for all JRA cells relative to the cells for local sanctions as excluded
instruments. While the F-statistic on the IVs becomes smaller (11.1) due to reduced predictive
power across some of the multiple cells, it remains statistically significant at the one-percent
level. And, the IV estimate of incarceration is robust, again suggesting no significant effects on
13
Due to this equation being just-identified, we cannot conduct the Sargan test of overidentifying
restrictions. However, as a crude test, we confirm that the single interaction IV is statistically insignificant
with a magnitude close to 0 when included in an OLS estimate of Equation 1, suggesting that it has no
direct effect on recidivism beyond its impact on sentencing
24
recidivism. Owing to these multiple IVs, we can implement the Sargan overidentification test,
which further validates the exclusion restriction.14
That the IV estimate is more positive than the OLS estimate (after controlling for the
offender’s extended social history and risk factors) is consistent with the remaining negative
bias in the OLS estimate being driven by the “outcomes optimization” model. Given that we are
able to observe and control for generally all of the objective information on the offender as the
court, the bias due to unobserved confounding risk factors is likely bypassed or mitigated. As
noted earlier, there are some alternatives under which the court may deviate from the
sentencing grid, most of which are related to the court’s assessment of expected benefits to the
offender of a particular sentence. If judges sort offenders into sentences that maximize
benefits and minimize recidivism, then OLS estimates of incarceration may be biased
downwards. The IV estimates appear to correcting for this potential bias.
Model 3 suggests that incarceration leads to a significant decline in the likelihood of
recidivating over a window of 30 days, by about 7.7 percentage points (which appears as a large
effect given the mean 30-day recidivating probability of 7%, though given that only about 5% of
the sample is incarcerated, the elasticity is estimated at 0.065). Models 4 and 5 also suggest a
decrease in criminal activity over 90 days and 120 days, with the latter effect suggesting a 15.8
percentage points (62 percent relative to the mean) decrease in recidivism. This is in stark
contrast to the null effects noted for any recidivism. Thus, the decrease in recidivism over the
14
The overidentification test is predicated on the assumption that there is no heterogeneity in the
structural IV effect across the multiple margins captured by the multiple instruments. Hence, inference
should be made with caution. That is, a rejection of the overidentifying restrictions can signal a rejection
of homogeneous treatment effect or a rejection of the exclusion restrictions. We present some
suggestive evidence later that we do not find substantial differences in the effect of incarceration across
alternate margins.
25
short-term must be counteracted by an increase over a longer period. The final specification
(Model 6) confirms this; incarceration leads to an 8.3 percentage points increase (34 percent) in
the likelihood of recidivating longer than 120 days post-sentencing. This estimate is imprecise
though of a substantial magnitude. When assessing longer-term recidivism, we restrict the
terminal period so that we are able to observe all offenders for at least 180 days postdisposition.
Table 2 also tests for the endogeneity of incarceration, based on the Hausman test,
distributed as a Chi-squared density function. For all measures of recidivism, except for the
long-term measure shown in model 6, we are unable to reject the null hypothesis that
incarceration is exogenous in these models. Nevertheless, since a handful of the models
suggest that sentencing may not be exogenous for certain measures of recidivism, we report
and discuss the IV estimates rather than the single-equation estimates.15
If our instrumental variables are valid and thus uncorrelated with the unobservables in
the structural equation, then adding further controls for risk factors (which remain typically
unmeasured in other studies and datasets) should yield similar IV estimates. We assess this
robustness in Table 3, where we include the extended measures of risk factors and social
history to the specifications. The smaller sample size reduces the power of the instruments
somewhat and inflates standard errors (though the F-statistic remains substantially large and
ranges from 26.3 to 34.8). Nevertheless, it is validating that the structural IV effect of
incarceration on recidivism remains robust in terms of direction and magnitude. Over relatively
short observation windows, incarceration leads to a decrease in recidivism, though this effect is
15
The relatively large confidence intervals for some of the IV estimates, which contain the OLS estimate,
furthermore makes it difficult to reject the null of exogeneity.
26
reversed over the longer term (more than 120 days). Overall, therefore, incarceration has no
deterrent effect on the net on recidivism measured over all periods, and may even raise
subsequent criminal activity.
Table 4 assesses the effects of a continuous measure of incarceration (measured in days
of commitment to a JRA facility among committed youth and 0 for others) on the five recidivism
indicators. Among incarcerated youth, the average length of commitment is 92 days. The
pattern of results mirrors those reported in Tables 2 and 3 for any incarceration. Specification 1
suggests that incarceration has no discernible effect on recidivism measured over the sample
period. The next three specifications (model 2-4) suggest net deterrent effects in the shortterm. Specifically, model 4 indicates that an additional 4 weeks of incarceration reduces the
probability of recidivism within 120 days by 8.3 percentage points. Model 5 suggests that the
impact on recidivism is reversed and becomes positive in the longer term, though this effect is
imprecisely estimated. The joint F-statistic on the set of excluded instruments is substantially
smaller, though highly significant, ranging from 22 to 27. Thus, the sentencing grid is a stronger
predictor of incarceration versus local sanctions, relative to predicting incarceration length
(given that courts can exercise some judgment over the sentencing range). In all cases, the
Sargan overidentification test validates the exclusion restriction of the IV set, and for two of the
five models, the endogeneity test confirms that incarceration length is endogenous. All of the
models reported in Table 4 capture effects at both the extensive margin (from local sanctions to
27
some incarceration) as well as the intensive margin (from shorter to longer incarceration
duration).16
One concern in all of these models relates to selection sample attrition over time, which
affects the window over which we can observe a juvenile offender post-release or postsentence completion. We cease to observe an offender once they turn 18, regardless of year,
and we cease to observe an offender after 2009, regardless of their age. Since the probability
of recidivism increases with the observation window, it is possible that the differential
estimates for short-term versus long-term recidivism may be confounded by the nature of the
sample truncation. Specifically, since confounding due to truncation is more likely to affect
measures of recidivism over the longer term, we expect that the effects of any recidivism (over
the entire sample period) and long-term recidivism (greater than 120 days) may be especially
sensitive to this compositional selection.
We address this possibility in Table 5. Specifically, in order to bypass truncation related
to the terminal year of our analysis sample, we restrict the analyses to all juvenile offenders
who were sentenced in 2006 or earlier when they were at least 14 years of age. Given that the
terminal year is 2009 and we observe youth up to age 18, this restriction ensures that we
observe the reported criminal activity of all youth until they reach the age of majority. Inflated
standard errors due to reduced sample sizes and smaller (but still significant) F-statistics on the
IVs render most of the IV estimates imprecise. However, the general pattern of results remains
robust, and the heterogeneity in the effects between short-term and long-term recidivism
becomes more pronounced. Model 1 suggests that incarceration raises the probability of any
16
Since only about 850 offenders enter JRA confinement each year, we lack statistical power to isolate
the effects of confinement duration at the intensive margin, conditional on any confinement.
28
recidivism (while the offender is a juvenile) by 12.6 percentage points. As hypothesized, this
effect was especially sensitive to the correction for sample truncation. Estimates for the effects
of incarceration on short-term recidivism (30-days, 90-days, and 120-days in models 2-4)
remain robust, though are imprecise. Specification 5 indicates that commitment in a state
detention center raises longer-term recidivism (greater than 120 days) by a marginally
significant 17.4 percentage points. Models 6-10 suggest similar-patterned effects for
incarceration duration – net deterrent effect on criminal activity in the short-term, but a
positive and significant effect on recidivism post-120 days.
Juveniles released from JRA residential facilities may be supervised in the community for
up to six months.17 Thus, the net deterrent effect over the short-term may be reflective of this
heightened supervision and probation. The estimates suggest that this deterrent effect fully
depreciates over time as the community supervision fades. Thus, over the longer term,
incarceration appears to have an iatrogenic effect and raise recidivism.
Models reported in Table 6 assesses heterogeneous responses across gender. In
general, the pattern of effects noted above is primarily driven by males; among male juvenile
offenders, incarceration leads to a net 5 percentage point increase (11% relative to the mean)
in recidivism over the sample period. This net effect masks a strong decrease in the short-term
but a significant increase in the long-term. Among female offenders, incarceration has a
substantial negative effect on recidivism over all observations windows, though these effects
are imprecisely estimated due to a reduced sample size (about 24% of juvenile offenders are
17
Most sex offenders are supervised for 24 to 36 months.
29
female, and among convicted female offenders 3.3% are sentenced to incarceration, compared
to 6.9% of convicted male offenders) and a relatively smaller F-statistic on the IVs.
Table 7 assesses differential effects based on the offender’s social history, which
comprises their current/past substance abuse, aggression, peers, history of runaways, school
attendance, history of mental health, physical and sexual abuse, and neglect, parental control,
and family history relating to jail/imprisonment, mental health, physical health, and substance
abuse. The pre-screen assessment constructs a pre-determined social history risk score,
ranging from 0 to 18, based on these component domains, with a score of 10 or above
considered high risk for recidivism. This risk score is pre-determined in the sense that the
assessment predates the juvenile case processing and disposition. We stratify the sample
across low/moderate and high social history risk. The negative (positive) effect of incarceration
on recidivism in the short-term (long-term) is evident for both groups. However, among
offenders with a history of a more dysfunctional social environment, incarceration appears to
have a larger positive effect on recidivism in the longer term.
This heterogeneity across social risk is especially evident with respect to one of the
component domains - a history of mental health problems such as schizophrenia, bi-polar,
mood, thought, personality, and adjustment disorders, confirmed by a professional in the social
service or healthcare field. Among juvenile offenders with no history of mental health
problems, incarceration has a strong and significant negative effect on recidivism, especially in
the short-term. However, among offenders with a history of mental health problems,
incarceration leads to a large positive effect on recidivism, especially in the short-term. These
estimates suggest that incarceration is not an effective deterrent among individuals with any
30
history of mental health problems, and may have an iatrogenic effect for them, leading to
greater subsequent criminal engagement.
Robustness Checks18
One concern is that a rational forward-looking offender, being aware of the sentencing
grid, may engage in criminal activity while minimizing their expected sentence. For instance,
an individual with a prior adjudication score of 0 may be careful not to engage in criminal
offenses of class B+ or greater since the sentencing jumps discontinuously between offense
classes B and B+. Alternately, there may be revisions of the charge prior to the adjudication
and disposition; for instance, charges may be revised upwards or downwards
disproportionately more around the discontinuity. Since we are able to observe both the initial
and amended charges, we re-estimate all models based on the initial charges. As expected, the
strength of the IVs decline somewhat since sentencing is based on final rather than initial
charges. Nevertheless, the IVs pass all weak identification tests and the estimates of the
structural effects of incarceration on recidivism are not materially affected. Additionally, we do
not find any statistically significant differences in the conditional probability of committing an
offense of a slightly lower severity among those offenders who would benefit from doing so
(below the incarceration grid) relative to those offenders who would not.
We also re-estimate all specifications, utilizing only the discontinuities within each
column, in order to assess heterogeneity across alternate margins as well as robustness to
alternate identification. Specifically, we note that this effect is mostly driven by lowexperienced offenders (prior score of 0 or 1) due to a higher density of offenders in those cells.
18
Results are not reported.
31
These estimates also indicate very similar patterns and magnitudes to those reported in Table
3, suggesting that the responses are generally similar for the low-experienced offenders as they
are for others.
The IV estimate (β in Equation 1) of incarceration on recidivism is a local average
treatment estimate (LATE) based on the compliers (Angrist and Pischke 2009), that is those
juvenile offenders who receive the harsher sentence of incarceration because they are slightly
above the threshold with respect to the offense severity or with respect to the prior
adjudication score. Always-takers, offenders who are not impacted by the IV and would receive
an incarceration sentence regardless of the criminal history, and never-takers, similarly
offenders who would not receive an incarceration sentence regardless of their criminal history
and thus are also not “moved” by the IV, do not contribute to the estimation of the structural
causal effect. Hence, under the assumptions of our identification strategy, our estimates
should be robust to excluding potential always-takers and never-takers from the analyses.
Based on the sentencing grid, offenders whose current offense class is B+ or above would be
sentenced to incarceration and those whose current offense is a misdemeanor (class D+ and
lower) would receive local sanctions, regardless of their prior convictions. We assess the
robustness of our estimates by restricting the analyses to only those offenders immediately
surrounding the row discontinuity -- offense classes B+ through D+. As expected, our IV
estimates are not materially affected by this restriction.
VI.
Discussion and Next Steps
In this study we utilize a unique natural experiment, afforded by Washington’s
presumptive sentencing grid and its discontinuous thresholds determining eligibility for
32
evidence-based interventions, to isolate the causal effects of incarceration and these various
programs on recidivism. We find consistent evidence that incarceration has no discernible
deterrent effect on subsequent re-offending. This effect, however, masks a strong net
deterrent effect in the short-term, when the probability of recidivism drops significantly
possibly due to heightened community supervision, but a net iatrogenic effect in the long-term,
when the probability of recidivism increases due to incarceration. We also find that these
mean effects mask further heterogeneity across males and females; among female offenders,
incarceration generally has a deterrent effect in all periods, though among male offenders, the
pattern noted above with respect to short-term and long-term recidivism is especially
pronounced.
Juvenile offenders with a history of mental health problems are at especially high-risk of
re-offending as a result of confinement. The adverse impact of incarceration among those with
a history of mental health problems may reflect a disruption in the continuity of mental health
care through two opposite channels. First, mental health care in state detention centers may
be sub-par to pre-incarceration care, which may explain the increase in recidivism post-release.
Or, mental health care in state detention centers may be more focused and continuous, and
hence upon release the offender suffers a deterioration in care, which may also explain the
higher recidivism.19 Both scenarios however underscore the importance of maintaining
continuity of adequate mental health care services for this at-need population. Most juvenile
residential facilities do offer some counseling and mental health services, though a recent
survey also found that many are not adequately prepared to address the needs of the offenders
19
About 25-60% of offenders enter JRA custody with unmet mental health care needs. A majority of
juvenile offenders in residential facilities also had at least one mental illness (McPherson and Sedlak
2010; Hammond 2007).
33
in custody and lack early identification and screening mechanisms (McPherson and Sedlak
2010).
The mechanisms that influence the risk of recidivism and future offenses also may affect
outcomes related to schooling. Here as well, the confluence of factors may have both positive
and negative effects on human capital accumulation. Mandatory schooling laws require youth,
including confined juveniles, to attend school until a specified age that generally varies across
states. To the extent that the offender is also receiving a “criminal education” from his or her
peers at the same time, any education through mandated schooling in confinement is
undermined (Hjalmarsson, 2008). Incarceration and detention may disrupt the education
process and the accumulation of human capital, making it more difficult for the youth to catch
up, and potentially leading the youth to repeat a grade or drop out of school altogether. In
follow-up work, we will therefore assess the impact of incarceration as well as the impact of
various evidence-based treatment interventions versus community supervision and probation
on human capital and school-based outcomes among juvenile offenders.
34
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National Academy of State Health Policy. December 2008. A Multi-Agency Approach to Using
Medicaid to Meet the Health Needs of Juvenile Justice-Involved Youth (Portland, ME: NASHP)
National Center for Juvenile Justice 2006. Juvenile Offenders and Victims: 2006 National Report.
US Department of Justice, Office of Juvenile Justice and Delinquency Prevention.
Owens E., “More Time, Less Crime? Estimating the Incapacitative Effect of Sentence
Enhancements,” Journal of Law and Economics, 2009, 52(3) 551-579.
Paternoster, R. & Piquero, A. (1995), “Reconceptualising deterrence: An empirical test of
personal and vicarious experiences”, Journal of Research in Crime & Delinquency, vol. 32, pp.
251-286.
Sampson, Robert J., and W. Byron Groves. 1989. Community structure and crime: Testing
social-disorganization theory. American Journal of Sociology 94, no. 4: 774-802.
Shaw, Clifford R. and McKay, Henry D. Juvenile Delinquency and Urban Areas. Chicago: The
University of Chicago Press, 1969.
37
Snowden, LR, Masland, MC, Wallace, NT and Cuellar, AE. “Strict Medicaid EPSDT Enforcement:
Effects on Outpatient and Emergency Mental Health Care,” American Journal of Public Health,
February 2007
Stigler, George. (1970). The optimum enforcement of laws. Journal of Political Economy.
78:526-536.
Teplin LA, Abram KA, McClelland GM, Washburn JJ, Pikus AK. Detecting mental disorder in
youth detainees: who receives services. Am J Public Health. 2005; 95(10):1773-1780.
Washington Caseload Forecast Council. 2012 Washington State Juvenile Disposition Guidelines
Manual, State of Washington 2013.
38
Figure 1
Age – Crime Relationship
Arrest Rate (per 100,000 population)
12000
10000
8000
6000
4000
2000
65 and over
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
24
23
22
21
20
19
18
17
16
15
13-14
10-12
Under 10
0
Source: Arrest data from FBI Uniform Crime Reports and population data from U.S. Census
39
Figure 2
Risk level definitions based on Criminal History and Social History Risk Scores
Criminal History
Social History Risk Score
Risk Score
0–5
6–9
10 - 18
0–2
Low
Low
Moderate
3–7
Low
Moderate
High
8 - 31
Moderate
High
High
Notes: Reproduced from Washington State Juvenile Court Assessment Manual Version 2.1,
Washington State Juvenile Court Administrators Association.
40
Current Offense Class
Figure 3
Juvenile Offender Sentencing Grid
A+
180 weeks to Age 21 for all category A+ offenses
A
103 - 129 weeks for all category A offenses
A15 - 36
52 - 65
80 - 100
103 - 129
103 - 129
(30 - 40 for
15-17 year
olds.)
B+
15 - 36
15 - 36
52 - 65
80 - 100
103 - 129
B
LS
LS
15 - 36
15 - 36
52 - 65
C+
LS
LS
LS
15 - 36
15 - 36
C
LS
LS
LS
LS
15 - 36
D+
LS
LS
LS
LS
LS
D
LS
LS
LS
LS
LS
E
LS
LS
LS
LS
LS
0
1
2
3
4 or more
Prior Adjudications Score
Notes: Reproduced from Juvenile Disposition Manual 2006, Sentencing Guidelines Commission,
State of Washington. Sentences are noted in weeks. LS refers to local sanctions.
41
Figure 4
Figure 5
Offense Class B
Offense Class C+
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0 - 1.75
1.75
2
0-2.75
2.75
3
Figure 6
Offense Class C
0.4
0.3
0.2
0.1
0
0-3.75
3.75
4
Figure 7
Figure 8
Priors = 0 or 1
Priors = 2
0.6
0.6
0.4
0.4
0.2
0.2
0
0
B or lower
B
B+
C+ or lower
Figure 9
C+
B
Figure 10
Priors = 3
Priors = 4+
0.6
0.6
0.4
0.4
0.2
0.2
0
0
C or lower
C
C+
D+ or lower
D+
C
42
Outcome
Model
Any JRA
Confinement
Any Recidivism
1
2
-0.05989***
(0.00546)
-0.09676***
(0.00769)
Table 1
Effects of Any JRA Confinement on Recidivism
OLS Estimates
Recidivism 30 Days
Recidivism 90 Days
3
4
5
6
-0.04249***
(0.00352)
-0.05433***
(0.00547)
-0.10696***
(0.00554)
-0.13229***
(0.00817)
Recidivism 120 Days
7
8
-0.12592***
(0.00616)
-0.15352***
(0.00898)
Recidivism > 120 Days
9
10
-0.02337***
(0.00505)
-0.04803***
(0.00713)
Black
0.07267***
0.05863***
0.02381***
0.00671
0.05808***
0.03650**
0.06808***
0.03675**
0.06494***
0.07620***
(0.00781)
(0.01355)
(0.00515)
(0.00991)
(0.00808)
(0.01467)
(0.00897)
(0.01613)
(0.00723)
(0.01257)
American Indian
0.05268***
0.01179
0.02305***
-0.00027
0.04461***
-0.01569
0.05880***
-0.01397
0.04613***
0.02976**
(0.00917)
(0.01566)
(0.00600)
(0.01143)
(0.00940)
(0.01693)
(0.01043)
(0.01862)
(0.00849)
(0.01452)
Race missing
-0.02793***
0.00446
-0.01725***
-0.03374***
-0.03473***
-0.05373***
-0.03265***
-0.04351**
-0.01785**
0.02526*
(0.00885)
(0.01534)
(0.00581)
(0.01121)
(0.00912)
(0.01664)
(0.01012)
(0.01832)
(0.00819)
(0.01422)
White
0.01856**
0.02072
0.00891*
-0.00401
0.01823**
-0.00132
0.02399***
0.00356
0.01795***
0.03739***
(0.00733)
(0.01275)
(0.00483)
(0.00931)
(0.00757)
(0.01378)
(0.00839)
(0.01515)
(0.00678)
(0.01182)
Male
0.06155***
0.05182***
0.02385***
0.02852***
0.05750***
0.05843***
0.06679***
0.06528***
0.05297***
0.04289***
(0.00285)
(0.00480)
(0.00185)
(0.00345)
(0.00290)
(0.00513)
(0.00321)
(0.00562)
(0.00264)
(0.00445)
Gender missing
0.03399
0.00564
-0.04702
-0.10464
0.07854
0.15427
0.15782**
0.25031*
0.05384
0.10002
(0.05859)
(0.08742)
(0.04186)
(0.07576)
(0.06406)
(0.10994)
(0.07462)
(0.13706)
(0.05419)
(0.08106)
Non-Hispanic
-0.02971***
-0.01416*
-0.01517***
-0.01914***
-0.03025***
-0.03657***
-0.03563***
-0.03781***
-0.01899***
-0.01237*
(0.00450)
(0.00762)
(0.00295)
(0.00557)
(0.00463)
(0.00831)
(0.00514)
(0.00914)
(0.00416)
(0.00707)
Hispanic missing
-0.09495***
-0.06663***
-0.03265***
-0.03275***
-0.07001***
-0.05640***
-0.08535***
-0.06527***
-0.07385***
-0.05248***
(0.00439)
(0.00754)
(0.00288)
(0.00553)
(0.00452)
(0.00824)
(0.00503)
(0.00909)
(0.00406)
(0.00699)
Notes: Coefficients from OLS models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals over time. All
models further include indicators for: age at referral, number of prior felony convictions; number of prior misdemeanor convictions; severity of current offense; year of violation;
and juvenile court. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05, *0.05<p-value≤0.10.
43
Table 2
Effects of Any JRA Confinement on Recidivism
IV Estimates
Outcome
Model
Any JRA
Confinement
Weak Identification Test: F-statistic for IVs
Sargan Overidentification Test: Chi-squared
Endogeneity Test: Chi-squared
Instrumental Variables (IV)
Indicators for number of prior felonies
Indicators for number of prior misdemeanors
Indicators for maximum offense severity
Age indicators
Gender indicators
Race/Ethnicity indicators
Court/County Indicators
Year indicators
Observations
Any Recidivism
1
2
Recidivism
30 Days
3
Recidivism
90 Days
4
Recidivism
120 Days
5
Recidivism
> 120 Days
6
0.007306
(0.072473)
-0.004003
(0.068733)
-0.077373*
(0.044771)
-0.203576***
(0.067740)
-0.157963**
(0.070007)
0.082892
(0.070299)
106.71***
1.5351
11.09***
11.58716
1.302574
86.23***
.6114751
87.73***
2.054205
96.17***
.2112365
106.71***
3.274918*
Indicator for
sentencing grid
separating JRA
vs. local
sanctions
Indicators for all
differentiated
sentencing grid
cells for JRA vs.
local sanctions
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
87783
87783
107959
102947
95506
87783
Indicator for sentencing grid separating JRA vs. local sanctions
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals over time. All
models include all covariates listed in Table 1. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05, *0.05<p-value≤0.10.
44
Table 3
Effects of Any JRA Confinement on Recidivism
IV Estimates: Controlling for Extended Social / Family History
Outcome
Model
Any JRA
Confinement
Weak Identification Test: F-statistic for IVs
Sargan Overidentification Test: Chisquared
Endogeneity Test: Chi-squared
Indicators for social & parental History
Indicators for number of prior felonies
Indicators for number of prior
misdemeanors
Indicators for maximum offense severity
Age indicators
Gender indicators
Race/Ethnicity indicators
Court/County Indicators
Year indicators
Observations
Any Recidivism
1
Recidivism
30 Days
2
Recidivism
90 Days
3
Recidivism
120 Days
4
Recidivism
> 120 Days
5
0.031377
(0.118001)
-0.046875
(0.085076)
-0.117373
(0.119200)
-0.142466
(0.126649)
0.047179
(0.114498)
34.81***
-
26.25***
-
27.52***
-
27.27***
-
34.81***
-
1.570685
0.0077065
0.0157298
0.0076566
1.124066
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
34219
42687
40615
37476
34219
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within
individuals over time. In addition to indicators for parental history, juvenile’s social history, and current home/parental conditions, all models include all
covariates listed in Table 1. IV for Any JRA is an indicator for cells in the sentencing grid that separate JRA sentence from other local sanctions.
Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05, *0.05<p-value≤0.10.
45
Table 4
Effects of JRA Confinement Duration on Recidivism
IV Estimates
Outcome
1
Recidivism
30 Days
2
Recidivism
90 Days
3
Recidivism
120 Days
4
Recidivism
> 120 Days
5
JRA Confinement Duration
-0.001032
(0.001001)
-0.000924
(0.000602)
-0.003092***
(0.000867)
-0.002988***
(0.000941)
0.000652
(0.000970)
Weak Identification Test: F-statistic for IVs
Sargan Overidentification Test: Chisquared
Endogeneity Test: Chi-squared
27.29458***
10.50469
21.72032***
7.417538
26.59514***
6.538225
26.62337***
5.989163
27.29458***
11.03067
0.590647
2.370597
12.89654***
10.02577***
0.9091186
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
87783
107959
102947
95506
87783
Model
Indicators for number of prior felonies
Indicators for number of prior
misdemeanors
Indicators for maximum offense severity
Age indicators
Gender indicators
Race/Ethnicity indicators
Court/County Indicators
Year indicators
Observations
Any Recidivism
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within
individuals over time. All models include all covariates listed in Table 1. IV variables for JRA are a set of separate indicators for all differentiated
sentencing grid cells for JRA vs. local sanctions. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05, *0.05<p-value≤0.10.
46
Table 5
Effects of Any JRA Confinement and Duration on Recidivism
IV Estimates: Disposition during 2006 or earlier among Juveniles ages 14+
Sample
Outcome
Any Confinement
Recidivism
Recidivism
90 Days
120 Days
3
4
Recidivism
> 120 Days
5
Any
Recidivism
6
Recidivism
30 Days
7
Duration
Recidivism
90 Days
8
Recidivism
120 Days
9
Recidivism
> 120 Days
10
-0.167621
(0.120879)
-
0.174022*
(0.117546)
-
-
-
-
-
-
0.001461
(0.001435)
-0.000207
(0.000635)
-0.000937
(0.000989)
-0.001694
(0.001234)
0.002035*
(0.001300)
32.67***
32.66***
37.02***
10.95***
10.67***
10.67***
12.47***
11.03***
-
-
-
-
4.613464
10.90973
9.475926
6.099323
5.795637
2.189621
.0301525
.008662
.1912882
2.899161*
2.057723
.0017034
.7296334
1.509361
4.009536**
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
24605
25002
24926
24806
24605
24605
25002
24926
24806
24605
Any
Recidivism
1
Recidivism
30 Days
2
Any JRA
Confinement
JRA Confinement
Duration
0.125958
(0.122956)
-
-0.023328
(0.071406)
-
-0.106565
(0.112047)
-
Weak
Identification Test:
F-statistic
Sargan
Overidentification
Test: Chi-squared
Endogeneity Test:
Chi-squared
35.34***
33.14***
-
Model
Indicators for
number of prior
felonies
Indicators for
number of prior
misdemeanors
Indicators for
maximum offense
severity
Age indicators
Gender indicators
Race/Ethnicity
indicators
Court/County
Indicators
Year indicators
Observations
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals over time. In
addition to indicators for parental history, juvenile’s social history, and current home/parental conditions, all models include all covariates listed in Table 1. IV for Any JRA is an
indicator for cells in the sentencing grid that separate JRA sentence from other local sanctions. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05,
*0.05<p-value≤0.10.
47
Table 6
Effects of Any JRA Confinement on Recidivism
IV Estimates: Differential Effects by Gender
Sample
Outcome
Model
Any
Recidivism
1
Recidivism
30 Days
2
Males
Recidivism
90 Days
3
Recidivism
120 Days
4
Recidivism
> 120 Days
5
Any
Recidivism
6
Recidivism
30 Days
7
Females
Recidivism
90 Days
8
Recidivism
120 Days
9
Recidivism
> 120 Days
10
Any JRA
Confinement
0.050211
(0.076558)
-0.068773
(0.048984)
-0.213900***
(0.073207)
-0.156833**
(0.075351)
0.130676*
(0.074596)
-0.343377
(0.251204)
-0.155862
(0.130860)
-0.074988
(0.209494)
-0.081540
(0.222459)
-0.325881
(0.240755)
96.62***
76.93***
78.61***
86.14***
96.62***
9.47***
8.88***
8.79***
9.31***
9.47***
-
-
-
-
-
-
-
-
-
-
3.120623*
0.2665299
2.108197
0.1820144
5.838634**
1.34951
.8741977
0.0047367
0.0285169
1.839391
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Weak
Identification Test:
F-statistic
Sargan
Overidentification
Test: Chi-squared
Endogeneity Test:
Chi-squared
Indicators for
number of prior
felonies
Indicators for
number of prior
misdemeanors
Indicators for
maximum offense
severity
Age indicators
Gender indicators
Race/Ethnicity
indicators
Court/County
Indicators
Year indicators
66667
82102
78249
72541
66667
21085
25820
24661
22934
21085
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals over time. In
addition to indicators for parental history, juvenile’s social history, and current home/parental conditions, all models include all covariates listed in Table 1. IV for Any JRA is an
indicator for cells in the sentencing grid that separate JRA sentence from other local sanctions. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05,
*0.05<p-value≤0.10.
Observations
48
Table 7
Effects of Any JRA Confinement on Recidivism
IV Estimates: Differential Effects by Social History
Sample
Outcome
Model
Any
Recidivism
1
Low or Moderate Social History Risk
Recidivism
Recidivism
Recidivism
30 Days
90 Days
120 Days
2
3
4
Recidivism
> 120 Days
5
Any
Recidivism
6
High Social History Risk
Recidivism
Recidivism
Recidivism
30 Days
90 Days
120 Days
7
8
9
Recidivism
> 120 Days
10
Any JRA
Confinement
0.005856
(0.149833)
-0.076949
(0.097847)
-0.120250
(0.143870)
-0.160859
(0.150857)
-0.050530
(0.145386)
0.038683
(0.196550)
-0.024361
(0.165868)
-0.214719
(0.212838)
-0.192745
(0.226640)
0.107441
(0.196231)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
24069
30011
28528
26364
24069
10150
12676
12087
11112
10150
Indicators for
number of prior
felonies
Indicators for
number of prior
misdemeanors
Indicators for
maximum offense
severity
Age indicators
Gender indicators
Race/Ethnicity
indicators
Court/County
Indicators
Year indicators
Observations
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals over time. In
addition to indicators for parental history, juvenile’s social history, and current home/parental conditions, all models include all covariates listed in Table 1. IV for Any JRA is an
indicator for cells in the sentencing grid that separate JRA sentence from other local sanctions. Asterisks denote significance as follows: *** p-value≤0.01, **0.01<p-value≤0.05,
*0.05<p-value≤0.10.
49
Table 8
Effects of Any JRA Confinement on Recidivism
IV Estimates: Differential Effects by Mental Health History
Sample
Outcome
Model
Any
Recidivism
1
No History of Mental Health Problems
Recidivism
Recidivism
Recidivism
30 Days
90 Days
120 Days
2
3
4
Any JRA
Confinement
-0.026756
(0.134771)
-0.123254
(0.093537)
-0.293213**
(0.132821)
Yes
Yes
Yes
Indicators for
number of prior
felonies
Indicators for
number of prior
misdemeanors
Indicators for
maximum offense
severity
Age indicators
Gender indicators
Race/Ethnicity
indicators
Court/County
Indicators
Year indicators
Recidivism
> 120 Days
5
Any
Recidivism
6
History of Mental Health Problems
Recidivism
Recidivism
Recidivism
30 Days
90 Days
120 Days
7
8
9
-0.343728**
(0.142896)
-0.001822
(0.131328)
0.034701
(0.287052)
0.134167
(0.220199)
0.203490
(0.303649)
0.261353
(0.317189)
0.019395
(0.283080)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Recidivism
> 120 Days
10
25630
31906
30368
28031
25630
8589
10781
10247
9445
Notes: Coefficients from IV models are presented, with standard errors in parentheses. Standard errors are adjusted for arbitrary correlation within individuals
over time. In addition to indicators for parental history, juvenile’s social history, and current home/parental conditions, all models include all covariates listed in
Table 1. IV for Any JRA is an indicator for cells in the sentencing grid that separate JRA sentence from other local sanctions. Asterisks denote significance as
follows: *** p-value≤0.01, **0.01<p-value≤0.05, *0.05<p-value≤0.10.
8589
Observations
50