The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Easily Overstated: Estimating the 

Relationship Between State Justice Policy 

Environments and Falling Rates of Youth 

Confinement 

Author(s): Douglas N. Evans, Gina Moreno, Kevin T. 

Wolff, Jeffrey A. Butts 

Document Number: 254460 

Date Received: January 2020 

Award Number: 2017-JF-FX-0064 

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Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Easily Overstated:

Estimating the Relationship Between State Justice Policy 

Environments and Falling Rates of Youth Confinement

January 2020 Douglas N. Evans 

Gina Moreno 

Kevin T. Wolff

Jeffrey A. Butts
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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Easily Overstated: January 2020

Douglas N. Evans Estimating the Relationship Between Gina Moreno 

State Justice Policy Environments and Kevin T. Wolff

Jeffrey A. Butts

Falling Rates of Youth Confinement
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Acknowledgments

The authors appreciate the contributions of the entire research team from the 

Research and Evaluation Center at John Jay College of Criminal Justice, especially 

those who contributed directly to the study: Wogod Alawlaqi; Nicole Alexander; 

Patricia Cobar; Richard Espinobarros; Sebastian Hoyos-Torres; Jason Szkola; Kathleen 

Tomberg; and Anthony Vega. 

The research team is very grateful to Benjamin Adams, social science analyst at 

the National Institute of Justice, for his generous support, feedback, and guidance 

throughout the project. 

This report and its presentation of research findings were improved by pre-publication 

criticisms from: Nate Balis, Kwan-Lamar Blount-Hill, Felipe Franco, Josie Hahn, Marc 

Schindler, Vinny Schiraldi, Dana Shoenberg, Victor St. John, Jordan Stockdale, 

and Tanya Washington. Undoubtedly, the authors did not resolve all the group’s 

objections, but the report findings are more nuanced as a result of these comments. 

Funding

This report is the final product of a project supported by Grant # 2017-JF-FX-0064

from the Office of Juvenile Justice and Delinquency Prevention, as managed by the 

National Institute of Justice, Office of Justice Programs, United States Department of 

Justice. The opinions, findings, and conclusions or recommendations expressed in 

this publication are those of the authors and do not necessarily reflect those of the 

Department of Justice. 

Recommended Citation

Evans, Douglas N., Gina Moreno, Kevin T. Wolff and Jeffrey A. Butts (2020). Easily 

Overstated: Estimating the Relationship Between State Justice Policy Environments 

and Falling Rates of Youth Confinement. Final report of research project 2017-JFFX-0064, U.S. Department of Justice. New York, NY: Research and Evaluation Center, 

John Jay College of Criminal Justice, City University of New York. 
This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

 

Table of Contents

1 ABSTRACT

2 INTRODUCTION

3 BACKGROUND

4 Popular Claims

7 The Changing Rate of Youth Confinement

8 Factors that Influence Incarceration

10 RESEARCH QUESTIONS AND OBJECTIVES

10 Study Objectives

10 Hypothesis

10 Research Questions

11 STUDY METHODS

11 Dependent Variable

11 Independent Variables

13 Covariates

14 Analytic Technique

15 FINDINGS

15 Bivariates Analyses

15 Multivariate Analyses

18 Variation in Youth Confinement

22 CONCLUSIONS

23 DISCUSSION

24 Study Limitations

25 IMPLICATIONS FOR POLICY, PRACTICE AND FUTURE RESEARCH

26 REFERENCES

29 APPENDIX
PAGE 

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AY C Y UNIVERSITY OF NEW YORK 

ALUA 

JOHN J OLLEGE OF CRIMINAL JUSTICE / CIT 1

RESEARCH AND EV TION CENTER

Abstract 

Researchers used state-level data on youth justice 

policies and practices to explore the association 

between state policy environments and recent 

changes in the use of residential placements for 

adjudicated youth (i.e., confinement). The study 

assigned a score to each of the 50 states based 

on the extent to which their youth justice policy 

environments could be considered “progressive” as 

opposed to punitive or regressive. Using datafrom the 

National Center for Juvenile Justice’s compendium 

of justice system characteristics, “Juvenile Justice, 

Geography, Policy, Practice & Statistics” (JJGPS), 

the research team created an index that accounts 

for 16 policies that are more or less progressive in 

terms of rehabilitative intent, compatibility with 

developmental science, focus on the use of “least 

restrictive” settings, and consistency with civil 

liberties and the need for balanced restraint on the 

powers of government to ensure public safety. The 

maximum score was 16, with higher scores reflecting 

more progressive policy environments. Researchers 

then used a series of latent growth curve analyses 

to estimate associations between this index and 

state confinement rates calculated with data from 

the Office of Juvenile Justice and Delinquency 

Prevention’s “Census of Juveniles in Residential 

Placement” (CJRP). Covariates included annual per 

capita income data for each state, unemployment 

rates, political ideology scores, and lagged variables 

for youth confinement rates and violent crime arrest 

rates. Results of the study indicated little evidence of 

a relationship between state policy environments and 

changes in youth confinement rates between 1997 

and 2015. Youth confinement declined significantly 

across the country (modeled by a function of time), 

but states with more progressive policy environments 

did not demonstrate significantly steeper declines. 

Of course, the 16 JJGPS indicators provide an 

incomplete measure of state policy environments 

and the study lacks any data about local policies and 

practices. Unfortunately, more complete data are 

not available for national analyses. Until more useful 

data are available, researchers will be unable to 

explain exactly how youth justice policies did or did 

not contribute to falling rates of youth confinement 

across the United States. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK PAGE 1 

RESEARCH AND EVALUATION CENTER 

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Introduction 

State officials, policy advocates, and journalists often 

describe falling rates of youth confinement across the 

United States as the result of reform. Indeed, youth 

confinement (out-of-home placements ordered by 

juvenile justice authorities) began to decline in the 

1990s after growing steadily during the 1970s and 

1980s. By 2015, the rate of youth confinement per 

capita was less than half the rate of 1997 (Sickmund et 

al. 2017). During the same period, the number of adults 

in U.S. prisons and jails grew 23 percent (BJS 2019). 

Periods of confinement in the youth justice 

system are much shorter, of course, than sentence 

lengths in the adult prison system. Youth confinement 

rates may react more quickly to a general decline in 

incarceration. But, what specific factors are involved?

Policymakers and advocates make ambitious 

claims about the effects of changes in policy and 

practice, suggesting that more progressive youth 

justice approaches are responsible for the declining rate 

of confinement. While the use of confinement often 

declines after the implementation of various policies, 

this is insufficient evidence of a causal relationship. Are 

youth justice policies actually responsible for creating 

declines in youth confinement, or do public officials 

and advocates overstate the association between 

policy reforms and reductions in confinement? 

Why have youth confinement rates dropped 

sharply in the past two decades? Is it simply the result 

of falling youth crime, or may officials rightfully take 

credit for reducing confinement with policy reform? If 

so, which practices and policies are most effective in 

lowering the youth confinement rate and which states 

had the most success in reducing confinement? Is 

the youth justice policy environment a likely cause of 

recent changes in confinement rates? 

In this study, researchers analyzed data about 

economic factors, crime rates, political ideology, and 

youth justice policy to test the association between 

state-level policy environments and recent changes in 

the use of confinement for adjudicated youth. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 3 

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Background

Surprisingly little research has examined fluctuations 

in rates of youth confinement and their relationship 

to policy structures. Research on adult incarceration 

is far more common. The discrepancy may be due 

to the limited range of data available on youth 

confinement—especially before the mid-1990s—as 

well as the fact that the confined youth population 

is very small relative to the number of incarcerated 

adults.1

 The national number of incarcerated adults 

is often 40 to 50 times larger than the population of 

youth in confinement, including those in long-term 

secure facilities, residential facilities, and group homes 

(Sawyer and Wagner 2019; Sawyer 2018). 

Some factors shaping the use of confinement 

may be similar in both the youth system and adult 

system, including crime rates, financial factors, political 

climate, and the policies and decisions of police, 

prosecutors, and courts. Reviewing research literature 

on adult incarceration, therefore, may lead to sufficient 

understanding of confinement trends. Some factors, 

however, may be quite different in youth justice. 

Research focusing specifically on youth confinement is 

essential for policymakers and practitioners who need 

to identify the best methods for reducing unnecessary 

and ineffective uses of youth placements. 

The declining use of confinement for youth 

began more than a decade before the decline in adult 

incarceration. Adult incarceration rates began to climb 

in the 1970s and continued for more than three decades, 

leaving the U.S. with the highest incarceration rate in 

the world (Carroll and Cornell 1985; Phelps and Pager 

2016). The adult prison population dropped slightly 

beginning in 2010, down seven percent according 

to recent data (Carson 2018; Guerino, Harrison and 

Sabol 2011). Youth confinement numbers, on the other 

hand, began falling in the mid-1990s. Between 1997 

and 2015, the national number of youth in residential 

placements decreased 54 percent—from 105,055 to 

48,043 (Hockenberry 2018). 

1. The Census of Public and Private Juvenile Detention, Correctional, and Shelter 

Facilities, also known as the Children in Custody census, was conducted from the 

early 1970s until the 1990s when the Census of Juveniles in Residential Placement 

(CJRP) became the nation’s primary data series about juvenile confinement. 

Many factors likely contribute to the declining 

use of confinement, including some of the reforms 

celebrated by advocates. Researchers, however, cannot 

test the direct effect of myriad reforms at state and 

local levels. First, the concept of justice reform is so 

broad it makes the term almost meaningless. Second, 

the detailed data required to study numerous state 

and local youth justice reforms do not exist. 

The popular notion of reform in the context 

of justice policy could mean any intentional effort to 

improve the effectiveness and fairness of the justice 

process as well as the impact of any subsequent 

interventions. Typical reforms include reducing punitive 

sentences and expanding the use of alternatives, 

or limiting the influence of race and gender bias in 

sentencing decisions (Harmon 2013). 

The term reform, however, could also describe 

changes in policy and practice of a very different 

type. Some policymakers could think of reform as 

increasing police surveillance, imposing stricter 

sentences, or making the terms of probation longer 

and more restrictive. In such a framework, reforms 

could contribute to increases in incarceration, such 

as mandatory minimum sentences and Three Strikes 

Laws, or they could be designed to reduce the ability of 

courts to impose non-incarcerative sanctions and limit 

access to diversion for broad categories of offenses. 

“Tough on crime” and “zero tolerance” policies during 

the 1980s and 1990s led to an expansion of secure 

facility space in the U.S. and increased the use of 

confinement (Scott and Saucedo 2013). Some officials 

may view these policies as “reform.” 

This study defined reform as the use of laws, 

policies, rules, and regulations to advance a more 

“progressive” approach to youth justice. In other 

words, the study focused on reforms designed to be 

rehabilitative and restorative rather than punitive, 

those that are compatible with the science of 

adolescent development, that promote the use of the 

“least restrictive” setting for adjudicated youth, and 

are generally respectful of civil liberties and maintain 

appropriate restraint on the power of government even 

when those policies purport to ensure public safety. 

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RESEARCH AND EVALUATION CENTER 
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Even narrowing the meaning of reform in this 

way, however, does not make a national analysis of youth 

justice policies simple or straightforward. To establish 

a statistical connection between progressive reforms 

and youth confinement rates, researchers would need

detailed, historical measures about policies and practices 

implemented in states, counties, and cities over several 

decades. Anything less than a comprehensive, national 

database of reform measures organized at the local 

level would render such research a type of case study. Of 

course, some useful research derives from case studies 

focused on just one state or city (e.g., Fabelo et al. 2015).

Case studies, however, fail to account for nationwide 

trends. Researchers examining one jurisdiction alone 

may be tempted to see causation in a finding that

actually reflects a broader, national phenomenon

(McDowall and Loftin 2009). To create more robust

measures, researchers need comprehensive and reliable 

data about policies and practices in all states and for 

many years. Unfortunately, this kind of information does 

not exist, but it is possible to obtain a few key variables 

from data series managed by the U.S. Department of 

Justice and to use these data to model state-level 

characteristics pertaining to crime, justice processing, 

demographics, economics, and political ideology across 

all 50 states. 

State-level data about law and policy will still 

produce a limited reflection of youth justice in practice.

Removing a juvenile from home and placing them in 

a facility follows a complex series of policy decisions. 

Because long-term confinement facilities are often an

endpoint in the justice process, the number of youth in 

these facilities depends on the decisions of many actors 

throughout the justice system. Police officers have the

discretion to arrest someone (or not) for an offense.

Prosecutors have the authority to file charges (or not).

Judges and executive branch agencies usually decide 

whether a particular case merits the use of confinement.

Manipulating any one of these factors may contribute to 

some change in the total rate of youth confinement, but

no single factor is likely to drastically reduce the volume 

of placements. Many other social and political factors 

influence such decisions, from recent crime trends and

public opinion to budget crises and even the lasting 

effects of one or two notorious cases that generate

public anxiety (Butterfield 1995).

Most importantly, the United States (as with 

many other countries) has been experiencing falling

crime for 25-years. Adult and youth arrests have 

decreased nearly every year since the mid-1990s. The

per capita rate of violent crimes fell 29 percent between

1999 and 2018 (FBI 2019). When crime falls, especially

serious and violent crime, policymakers are more willing 

and more able to implement reforms. If the rate of 

confinement continues to decline, officials are tempted

to claim their reforms are responsible—even if rigorous

research would show the two measures (reforms and 

confinement rates) are associated but not causally

related. With fewer arrests coming to court, the demand 

for secure confinement may fall regardless what policies

are being pursued to reduce confinement.

Popular Claims 

Researchers, politicians, and advocates often claim 

incarceration rates fall due to the effects of intentional

reform. Referencing the nationwide drop in adult 

incarceration rates, researchers Lofstrom and Raphael 

asserted that declines were “driven by sentencing 

reforms at the state level explicitly designed to reduce 

incarceration rates” (2016: 197). Governor John Bel

Edwards of Louisiana was quick to celebrate his state’s

“significant decreases in prison populations and prison

admissions following the first year of the state’s historic

criminal justice reforms” (Toohey 2018). Advocacy

organizations are pleased to describe the declining use 

of incarceration for adults as the result of reform. 

“...South Carolina enacted a modest 

criminal justice reform package... intended 

to safely reduce the prison population, 

save taxpayer money, and produce a 

better public safety outcome. And it did

just that” (Center for Criminal Justice 

Reform 2018). 

Similarly, youth advocates credit an array 

of reforms for the declining number of youth held 

in secure facilities and other forms of placement. 

Typical reforms include funding for community-based 

alternatives, diversionary policies, policies requiring

the least restrictive placement for adjudicated youth, 

bans on out-of-home placement for youth adjudicated 

for certain types of offenses (non-felony, non-violent

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misdemeanor, or low-level offenses, drug possession,

prostitution), bans on confinement of foster care youth,

and raising the age of juvenile jurisdiction overall. Some 

of these reforms may have contributed to reductions 

in the use of confinement for adjudicated youth, but

without considering the effect of other factors it may be

wrong to claim or even imply that reforms are entirely 

responsible. Making causal claims, however, is quite

popular. Two authors of this study, in fact, published a 

John Jay College report in 2011 that identified policy

choices as a key influence on youth confinement.

“The scale of incarceration is not simply

a reaction to crime. It is a policy choice.

Some lawmakers invest heavily in youth

confinement facilities. In their jurisdictions, 

incarceration is a key component of the

youth justice system. Other lawmakers 

invest more in community-based

programs” (Butts and Evans 2011).

Other organizations have been even more 

confident in their attributions of cause and effect.

Nationally known organizations such as the Council 

of State Governments, the National Juvenile Justice 

Network, the National Center for Youth Law, the 

National Council on Crime and Delinquency, the Crime

and Justice Institute, and Pew Charitable Trusts have 

published reports suggesting that policy and practice 

reforms were responsible for lowering the rate of secure 

confinement for adjudicated youth.

In 2019, the Crime and Justice Institute 

examined changes in the demand for secure youth 

confinement space in West Virginia and implied that 

a small amount of improvement was due to the recent 

passage of state legislation. 

“One primary goal of the S.B. 393 policy

changes was to reduce the number of 

West Virginia youth in secure facilities. 

Examining the number of youth admitted

to restrictive settings versus the number 

referred to community alternatives like 

the YRCs is a quantifiable way to measure 

progress toward this goal. From 2015 

through 2017, the overall number of 

BJS admissions decreased from 2,073 to 

1,877. At the same time, the data show 

a steady increase in the percentage of 

juveniles referred to community-based

interventions, from 37 percent in 2015 to 

39 percent in 2017. The trend continued 

into 2018, with 42 percent of admissions 

referred to non-residential programs”

(Crime and Justice Institute 2019: 4).

A 2012 report from the National Center for Youth 

Law and the National Council on Crime and Delinquency

reviewed youth justice reforms implemented by the State 

of Arkansas and confidently declared them successful

without investigating other explanations, such as the 

national crime decline and subsequent reductions in

demand for secure space. 

“Arkansas leaders, like their counterparts

in other states, have embarked on a 

planned course to transform the state’s 

juvenile justice system. ... In just a short

period of time, Arkansas has achieved

significant positive results from reform 

efforts. From 2008 to 2011, commitments 

to state custody have been reduced by

20%, including those for low-level, nondangerous youthful misbehaviors; the 

average length of stay in state residential 

treatment centers has been shortened 

by 19%; and the number of beds at the 

state’s largest juvenile secure facility,

the Arkansas Juvenile Assessment and 

Treatment Center, was reduced by 30%”

(Arthur and Hartney 2012: 1).

The National Juvenile Justice Network 

collaborated with the Texas Public Policy Foundation 

to publish a 2013 report on youth confinement trends.

While the report acknowledged that declines in youth 

arrests “helped explain” the falling rate of youth 

confinement, it also asserted that state policies “shape”

the changes leading to reduced confinement (National

Juvenile Justice Network 2013: 2). In 2017, the Council

of State Governments Justice Center described the 

nationwide drop in youth confinement as the result of 

efforts by “state and local leaders.”

“State and local leaders across the country 

have made concerted efforts to scale 

back juvenile incarceration, and their 

efforts have yielded significant results: 

the national juvenile incarceration rate 

has been cut in half over the last decade. 

As a result, a greater number of youth 

in the juvenile justice system are now 

being supervised in their communities,

which research shows leads to lower rearrest rates, and states are increasingly

allocating the majority of their juvenile

justice resources to community-based

supervision and services” (CSG 2017).

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Similarly, the enactment of youth justice reforms in 

Kentucky inspired the Pew Charitable Trusts to conclude 

the policies had a direct effect. 

“Juvenile justice reforms enacted 

by Kentucky in 2014 are creating

substantial benefits for youth, families,

and communities throughout the state. 

Between fiscal years 2014 and 2017, the 

number of youths held in Department of

Juvenile Justice facilities fell 34 percent,

reflecting a reduction in detentions and 

commitments for lower-level offenses” 

(Horowitz and Pheiffer 2018). 

Researchers at Pew saw other causal connections 

in Georgia: “After Georgia enacted a 2013 reform package, 

the state’s juvenile residential population fell 35 percent” 

(Horowitz and Carlock 2017). An assistant commissioner

of the Georgia Department of Juvenile Justice joined the 

argument, saying “juvenile reform in Georgia has made it 

possible not only to avoid construction of new facilities, 

but to reduce the population in existing facilities” (Vignati 

and Edwards 2018).

An online data brief from Pew used federal data 

to depict changes in youth confinement from 2006 to 

2015 and reported that all 50 states and Washington, 

DC showed decreases ranging from -1 percent to 

-83 percent. The Pew brief ended by implying that 

reforms were likely behind the change because the 

general trend of reduced youth confinement “comes 

as a growing number of states adopt policies that 

prioritize costly space in residential facilities for youths 

adjudicated for serious crimes” (Horowitz 2017). 

A comprehensive report from Texas used data 

from 2007 to 2012 to claim that policy changes deserved

credit for reduced confinement in that state: “the first of

a series of reforms was enacted, and over the next five

years, the number of youth incarcerated in state facilities 

did not grow as projected but instead plunged” (Fabelo 

et al. 2015: 30). The authors concluded that:

“State efforts to reduce the number of youth in 

state juvenile correctional facilities have delivered 

on the promise made when they were enacted. 

Thousands more youth are living at home now 

(or are being supervised closer to home) than 

before the reforms” (Fabelo et al. 2015: 81).

Advocacy groups are often eager to 

report a connection between reforms and rates of 

confinement. The Vice President of the Advocates

for Children of New Jersey (ACNJ) wrote that the

“[Annie E. Casey Foundation’s Juvenile Detention 

Alternatives Initiative] has resulted in far fewer youth 

being incarcerated in longer-term Juvenile Justice 

Commission facilities without risk to public safety” 

(Coogan 2017). ACNJ relied on a subtle turn of phrase

to imply a causal relationship between reforms and 

reduced confinement, stating that “New Jersey is a

national leader in a rapidly advancing juvenile justice 

reform movement. The state is locking up thousands 

fewer young offenders, while safely addressing their

needs in their communities” (ACNJ 2018).

The 2014 Annual Report of the Annie E. Casey 

Foundation was less subtle, lauding a 43 percent drop 

in juvenile detention in several states as the “result” of 

its efforts to reduce detention in those states (Annie

E. Casey Foundation 2014).

Journalists often endorse correlational claims 

by public officials and advocates. The Arkansas Times

reported that “[statewide youth justice] reforms have 

reduced both the number of youths detained locally 

in juvenile detention centers and those committed to 

(state) facilities” (Hardy 2017). A Kansas news outlet

noted that, “juvenile arrests and placement of youths 

in group homes or detention facilities declined at the 

same time Kansas moved to funnel budget savings into 

community-based therapy and intervention programs 

designed to keep families together” (Carpenter 2019).

Another article about Kansas opened with the 

following assertion: “Reforms to the Kansas juvenile 

justice system have slashed the number of young 

people in confinement by 63 percent over the past

two years” (The Crime Report 2019).

A recent article about Vice, the HBO

documentary about criminal justice reform, opened 

with this observation: “The U.S. has significantly

reduced the amount of incarcerated youths via state 

reforms from 2001 to 2015” (Vice Impact 2018).

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RESEARCH AND EVALUATION CENTER 
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Even celebrities join those seeing a direct, causal 

connection. Musician and justice advocate John Legend 

once observed: 

“When Illinois instituted comprehensive reforms 

over the past several years to build ageappropriate responses to crime, day-reporting 

centers, and community-based mental health

services for youth in cities including Chicago, 

the state incarcerated 44 percent fewer youth,

reserving incarceration only for those who were 

a public safety threat” (Spark Action 2017). 

Policy reforms may contribute to confinement 

reductions, but other factors—social conditions, 

economic trends, cultural shifts—likely play a part. 

This study tested the claim that state policies are 

responsible for falling youth confinement rates and 

examined what happens to that relationship when 

other explanatory factors are included. If the study 

indicates that the effects of the policy environment are 

reduced or nullified by covariates, it would cast doubt 

on the widely assumed causal relationship between 

reforms and reductions in youth confinement. 

The Changing Rate of

Youth Confinement

The rise and fall of youth confinement occurred in 

the midst of America’s wave of mass incarceration. 

The number of incarcerated adults surged in the U.S. 

during the past 50 years. The adult incarceration rate 

had been relatively stable until the early 1970s, when 

it started to increase exponentially. From 1972 to 2008 

the rate of individuals incarcerated in jails and state 

or federal prisons increased from 161 inmates per 

100,000 residents to nearly 756 per 100,000 residents 

(Travis, Western, and Redburn 2014; Maguire, n. d.). The 

incarceration boom affected people of all age groups, 

as adult crime and incarceration rates tend to affect 

youth confinement rates as well (Mears 2006). 

Most research on incarceration continues to 

focus on adult populations, but this study concentrates 

on youth confinement and factors that influence it. The 

widespread decline in youth confinement that began 

in the mid-1990s followed a period of growth during 

the 1980s and early 1990s. 

During periods of either increasing or 

decreasing confinement, nationwide trends obscure 

differences between states. In 1997, for example, rates 

of placement for juveniles age 10 and older varied 

from a low of 70 per 100,000 in Vermont to a high of 

583 per 100,000 in Louisiana (Sickmund 2000). Sizeable 

disparities in confinement rates present an opportunity 

for researchers. By analyzing state variations, it may 

be possible to identify key factors affecting the use of 

youth confinement. Some factors, such as demographic 

shifts, economic changes, and the introduction of 

new policies, may be measurable. Others, such as the 

political climate and cultural norms, are more difficult 

to capture in national studies. 

“Tough on crime” legislation, for example, 

certainly contributed to increasing confinement rates 

in the 1980s (Tonry 1999). Being “tough” became the 

standard for elected officials in the criminal (adult) 

justice arena and had spillover effects on youth justice 

policy as well (Wool and Stemen 2004; Mears 2006). 

Aggressive policies were also a response to sharp 

increases in youth violence during the 1980s and early 

1990s (Butts and Mears 2001; Van Vleet 1999). News 

media at the time sensationalized crimes involving 

youth and some prominent academics even argued 

that certain juveniles were “superpredators... capable of 

committing the most heinous acts of physical violence 

for the most trivial reasons” (Dilulio 1995). Such claims 

likely motivated policymakers to implement more 

tough-on-crime policies aimed especially at youth. 

After peaking in the mid-1990s, youth crime 

rates—especially violent crimes—began to fall. Youth 

confinement rates followed suit. The national number 

of confined youth decreased nearly every year since the 

late 1990s (Sickmund et al. 2015). The reduced demand 

for confinement was largely a response to falling youth 

crime. Juvenile courts placed fewer youth out-of-home 

in absolute terms, but the proportion of court cases 

resulting in out-of-home placement remained stable 

between 1996 and 2016 (Butts and Pfaff 2019). If the 

overall decline in youth confinement coincided with 

falling crime rates, is it still possible that some of the 

recent changes were also due to policy changes? 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 8

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Factors that Influence 

Incarceration 

Incarceration rates respond to a combination of micro 

factors pertaining to individuals involved in the criminal 

justice process (defendants, police, prosecutors) and 

macro factors (societal changes potentially affecting 

the entire population) (Pfaff 2013). At the micro-level, 

police officers have the discretion to arrest someone 

suspected of committing a crime, or they can decide 

not to make an arrest. Prosecutors can charge an 

arrestee with a range of criminal offenses or they can 

decide not to file charges. Judges usually determine a 

convicted defendant’s length of sentence, and parole 

boards determine if an inmate can leave prison before 

some pre-determined release date. Parole officers issue 

restrictions on their parolees and have the discretion 

to revoke parole and send them back to prison. 

Micro factors affect the likelihood and severity/

length of criminal sanctions and may explain some 

of the increase in incarceration that started in the 

1970s and continued into the 2010s. Macro factors, 

of course, affect these micro factors, including efforts 

by lawmakers to restrict the discretion of justice 

officials to make decisions about individual cases. 

Other macro factors include the broad array of 

social policies, economic conditions, unemployment 

rates, demographic characteristics, and the political 

ideologies prevalent in a given area. 

Disentangling how all these factors affect 

incarceration is difficult because changes in one factor 

may correlate with changes in others. The war on 

drugs was a macro-level policy that influenced police 

departments and officers to target people suspected 

of drug offenses, which helps to explain the increase 

in people incarcerated for drug offenses (Blumstein 

and Beck 1999). The likelihood of prison became 

more common as prosecutors recommended severe 

sentences more often and judges agreed with them 

more often (Travis, Western, and Redburn 2014). One 

study of prosecutions in 34 states (the only states 

for which felony filing data were available) indicated 

that while arrests fell about 10 percent between 1994 

and 2008, felony court filings increased more than 37 

percent (Pfaff 2013). 

Other research shows that macro-economic 

factors, such as income and financial inequality, 

unemployment, and poverty, may also influence 

incarceration rates. Crime rates tend to be higher in 

communities beset by financial inequality and the 

association is consistent across multiple countries and 

time periods (Fajnzylber, Lederman and Loayza 2002). 

Although inequality may not have a direct effect on 

incarceration, its effect may operate through other 

factors. Even government assistance programs may 

be related to incarceration. A study exploring welfare 

recipients and incarceration rates found that states 

spending less on social welfare had higher incarceration 

rates (Beckett and Western 2001). 

Unemployment and poverty are often positively 

related to incarceration rates (Sorensen and Stemen 

2002). Poor and unemployed people do not necessarily 

commit more crime, but there is a relationship between 

the number of people living with limited financial means 

and the justice system’s prioritization of incarceration. 

Researchers find that a rise in unemployment can 

affect increases in crime (Raphael and Winter-Ebmer 

2001). The relative poverty rate in a neighborhood 

may be indicative of the extent of incarceration among 

residents from that neighborhood. The criminal justice 

system may disproportionately affect individuals from 

high poverty communities along with other social 

problems such as violent crime, mental illness, and 

substance abuse (Travis, Western, and Redburn 2014). 

On the other hand, unemployment could actually 

contribute to reductions in youth confinement. 

Unemployed parents may spend more time at home, 

allowing them to supervise their children directly, 

potentially leading to fewer delinquent acts and fewer 

youth confinements.

The political preferences and voting behavior of 

an area may also affect incarceration rates. Conservative 

politicians could use claims of “crime problems” to 

offer tough-on-crime solutions that result in political 

gains, or conservative citizens may demand harsher 

punishments for law violators (Jacobs and Carmichael 

2001). Politically conservative states may adopt more 

punitive justice policies, perhaps in response to 

increases in populations of color in areas that were 

once predominantly white (McGarrel 1991; Tolbert 

and Grummel 2003). Conservative-leaning states 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 9

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also tend to adopt more severe sentences for some 

crimes, including rape, assault, and robbery (Bowers 

and Waltman 1993). Some researchers find a positive 

correlation between the number of conservative 

citizens and a state’s incarceration rate (Sorensen and 

Stemen 2002; Greenberg and West 2001). 

Policies in the criminal and juvenile justice 

systems are not easily comparable because criminal 

justice policies tend to be more punitive. However, 

criminal justice policies can demonstrate the effects of 

reforms on incarceration rates. Determinate criminal 

justice sentences, including mandatory minimums 

and three-strikes laws, are often cited as a driver of 

mass incarceration. Some argue that a shift toward 

determinate sentencing and longer sentences 

influenced the growth of incarceration (Mauer 2001). 

Others, however, suggest that determinate sentencing 

laws are not associated with increases in incarceration 

for most states and may actually decrease incarceration 

in some states (Marvell and Moody 1996). A study of 

sentencing practices in all 50 states, including voluntary 

sentencing, presumptive sentencing, three-strikes laws, 

parole abolition, and truth-in-sentencing laws, found 

that with the exception of parole abolition, sentencing 

practices were not strongly correlated with changes in 

prison populations (Zhang, Maxwell and Vaughn 2009). 

One could assume that a rising crime rate 

would trigger punitive reactions that increase the 

number of people sentenced to jails and prisons, but 

prior research suggests the relationship is not direct 

(Greenberg and West 2001). Although crime rates in 

any given year affect crime rates in consecutive years 

(Field 1992), the relationship between crime and 

incarceration is less than robust. For the past 25 years, 

the two have often varied in opposite directions. After 

crime rates began to decline in the 1990s, incarceration 

continued to increase for 20 years. Patterns could be 

different in the youth justice system, but research has 

not fully explored relationships between delinquency 

and confinement. 

Demographic trends play a role in incarceration. 

A large-scale analysis of incarceration trends found 

that demographic changes explained 20 percent of 

the growth in the prison population (Langan 1991). 

Race and gender in particular influence someone’s 

likelihood of receiving a prison sentence. Black males 

have a one in three chance of going to prison in their 

lifetime compared to only one in 17 White males 

(Bonczar 2003). In 1972 men were incarcerated at a 

rate 24 times greater than women (Travis, Western, 

and Redburn 2014). Incarceration rates for females 

increased considerably since then. By 2011 males 

were incarcerated at a rate only 14 times greater than 

females (Carson and Sabol 2012). 

Blacks and people from lower social classes 

have been incarcerated at disproportionate rates in the 

United States as long as such data existed (Western 

and Pettit 2010). There is also an inverse relationship 

between education and incarceration. Among high 

school dropouts, Black males are twice as likely as 

White males to spend some time in prison (Travis, 

Western, and Redburn 2014). Race, and particularly 

skin tone, may override all other demographic factors. 

A study of nearly 67,000 males incarcerated for the 

first time in Georgia between 1995 and 2002 found 

that after controlling for offense type, socioeconomic 

status, and other demographic factors, dark-skinned 

Black men received average sentences at least 18 

months longer than those imposed on White men. In 

comparison, light-skinned Black men received average 

sentences that were just 3.5 months longer than the 

sentences of White men (Hochschild and Weaver 

2007). Neighborhood factors also matter. Incarceration 

is more prevalent in communities that deal with family 

instability, poor health, and residential segregation 

(Travis, Western, and Redburn 2014). 

Prior research suggests a number of factors 

affect confinement rates. It can be difficult to determine 

each factor’s relative contribution to the actual rate 

of confinement and more research is needed to 

measure how micro and macro factors combine to 

affect confinement rates. The lack of research is even 

more apparent in the youth justice system, where very 

little is known about the key drivers of confinement 

rates. Policymakers, advocates, and journalists often 

celebrate the implementation of reforms designed to 

reduce confinement and then simply assume those 

reforms are responsible for changes in the number of 

youth confined out-of-home following the introduction 

of reform. There are many reasons to question such 

assumptions. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 

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ALUA 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 10

RESEARCH AND EV TION CENTER

Research Questions

and Objectives 

This study explores how often confinement is used by youth justice systems in the United States, and statistical 

relationships among state-level youth justice policy environments and changes in rates of confinement since the 

1990s. Given the lack of comprehensive data on the myriad of youth justice policies across the country, the study 

relies on a defined set of policy characteristics measured at the state level. The analysis assigns a score to each 

state based on the presence or absence of 16 progressive policies. A state scoring high on the scale is consistent 

with a progressive approach while a state scoring low is less progressive and may be more inclined to implement 

a punitive or retributive approach to youth justice. 

Study Objectives 

This study tests whether factors other than 

progressive policy environments could explain the 

nationwide decline in the rate of youth confinement 

during the past 20 years. The objectives are to 

understand state-level variations in juvenile justice 

policy environments, to improve knowledge about 

the relationship between such policies and changes 

in confinement rates, and to address a gap in 

research regarding factors that influence out-ofhome placement rates in the juvenile justice system. 

Research Questions 

RQ1: 

Were state policies generally 

related to youth confinement 

rates as of the mid-1990s? 

RQ2: 

Were specific youth justice 

policies associated with 

decreases in state-level youth 

confinement after the mid1990s? 

Hypothesis 

If there is an association between progressive youth 

justice policies and reductions in youth confinement, 

states that have demonstrated the largest reductions 

in youth confinement should be those demonstrating 

the most progressive approaches to youth justice by 

taking aggressive actions to rehabilitate adjudicated 

youth, minimizing punitive interventions, restricting 

unnecessary placements, and relying on policies and 

practices that 

principles. 

are consistent with developmental 

RQ3: 

Did states with more 

progressive youth justice policy 

environments experience 

steeper declines in youth 

confinement since the 1990s 

when compared with less 

progressive states? 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK PAGE 10 

RESEARCH AND EVALUATION CENTER 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
PAGE 11 

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Study Methods 

This analysis explored the effects of progressive 

youth justice policies on youth confinement rates 

from 1997 to 2015. The dependent variable was the 

youth residential commitment rate across all 50 states 

expressed as a rate per 100,000 (excluding Washington, 

DC due to excessive missing data for political ideology 

and crime). Committed youth refers to youth placed in 

facilities as a result of court dispositions. 

The Office of Juvenile Justice and Delinquency 

Prevention (OJJDP) publishes state counts of juveniles 

in residential placement in the Census of Juveniles 

in Residential Placement (CJRP), but the data are 

published intermittently: 1997, 1999, 2001, 2003, 2006, 

2007, 2010, 2011, 2013, and 2015. To account for gap 

years, all annual data were grouped into waves. Each 

wave includes a three-year average for all data points. 

Ten waves were generated for the following 

time periods: 1996-98, 1998-2000, 2000-02, 2002-04, 

2005-07, 2006-08, 2009-11, 2010-12, 2012-14, and 

2014-16. In addition to data on youth in residential 

placements, the study used data on demographic 

composition, arrests, and economic activity to control 

for differences between states. All data sources are 

described below. 

Dependent Variable 

The U.S. Census Bureau collects data for OJJDP on 

the number of juveniles in residential placement and 

OJJDP publishes the data online in the “Easy Access to 

the Census of Juveniles in Residential Placement: 1997-

2015” (EZACJRP) data analysis tool. The data collection 

process starts with identification of all juvenile facilities 

in the United States. Officials send notification letters 

and requests for information to approximately 2,200 

public and private residential facilities holding juveniles 

charged with or adjudicated for delinquency or status 

offenses. 

Response rates are typically very high, 

approaching and sometimes exceeding 90 percent. 

CJRP data provide a snapshot (one-day count) of the 

total populations of all juvenile facilities in a given 

state. 

The research team used CJRP data—specifically 

the number of delinquency commitments in each 

state—as the numerator in calculating a confinement 

rate for all states across the study time period. The 

denominator for the calculation of confinement rates 

was the juvenile population in each state and each time 

period as defined in OJJDP’s “Easy Access to Juvenile 

Populations” (EZAPOP) data analytic tool. Confinement 

rates were expressed as the number of juveniles in 

resident placements due to court dispositions per 

100,000 juveniles ages 10 to 17.2 

Independent Variables 

The National Center for Juvenile Justice maintains 

the Juvenile Justice, Geography, Policy, Practice, & 

Statistics (JJGPS) website. The site provides data 

on state-level juvenile justice policies and system 

characteristics. In this study, researchers compiled data 

about a range of indicators and dichotomized each 

according to whether it reflected a policy environment 

that was more punitive and retributive (scored as 0) 

or relatively progressive and developmental (scored 

as 1)3

. The technique resulted in an index summarizing 

the policy environment of each state (Figure 1). The 

scoring approach built upon previous research about 

state variations in youth justice policy characteristics 

(Willison, Mears, and Butts 2011).4 

2. The two sources of data used to calculate youth confinement rate did not have 

the same age group categories. The upper and lower age categories for EZACJRP 

data (upper age of 20 and lower age of “12 and under”) and EZAPOP data (ages 10 

through 17) do not align perfectly, and EZACJRP commitment data pulls do not allow 

for selection of individual states and specific age groups at the same time. Given the 

small number of youths under age 10 and young adults (above 18) expected to be 

confined in the juvenile justice system, the discrepancy should have little effect on 

the results, and EZACJRP does not publish state-level population data on specific 

ages. In this study, CJRP data were organized into 10 waves—i.e. 1 (1997), 2 (1999), 3 

(2001), 4 (2003), 5 (2006), 6 (2007), 7 (2010), 8 (2011), 9 (2013), and 10 (2015). 

3. See Table A2 in the Appendix for more information about the coding scheme used 

in this study. 

4. The study team surveyed individuals in the nation’s youth justice community 

(N=20), asking respondents to rank each of the indicators of juvenile justice policy 

environments in terms of its ability to have a major, considerable, minor, or no 

influence on juvenile placement rates. All policy indicators were scored as having 

some influence on juvenile placement rates, but the top-ranked items were diversion 

policies, community-based programming, and formal bans on out-of-home 

placement for non-felony offenses, which JJGPS does not track.

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 12 

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Figure 1:

State Scores on 16-Point Scale of Youth Justice Policy Environment 

Most Progressive 

Moderately Progressive 

Least Progressive 

The study’s index of progressive policy 

characteristics comprised 16 individual indicators 

(Table 1). For example, states that set no lower age limit 

for children subject to the delinquency jurisdiction 

received a score 0 on that particular indicator, as did 

those setting the upper age of delinquency jurisdiction 

at less than 17 and those allowing automatic, legislative 

transfer to criminal court for youth under age 18. States 

that terminate juvenile jurisdiction prior to age 21 were 

scored 0, while states that extend juvenile age to 21 

and over were scored 1. States whose delinquency laws 

do not have a stated purpose clause scored 0 while 

those with purpose clauses supporting due process, 

balanced and restorative justice, parens patriae, or 

research on adolescent developmental scored 1. 

States without juvenile competency standards or those 

that apply the adult standard to juveniles scored 0, 

and states that have a separate juvenile competency 

standard scored 1. States that do not consider youth 

immaturity in competency standards scored 0; states 

that do consider immaturity scored 1. 

Intake and diversion decisions are handled 

differently across states. Where prosecutors—either 

solely or in conjunction with juvenile court intake 

officers (JCIO)—make diversionary decisions or when 

such decisions are based on the offense in question, 

states were scored 0. States in which a JCIO solely 

decides were scored 1. States that have some or no 

limits were scored 0 and states that prohibit solitary 

confinement were scored 1. States that have no 

restriction on shackling juveniles in court scored 0 

and states that have restrictions scored 1. States that 

register juveniles convicted of sex crimes scored 0 

while states that do not scored 1. 

States received another score of 0 if the 

agency charged with managing the administration 

and operations of juvenile corrections was the adult 

corrections authority or an independent agency (of 

equal stature to a state department of corrections), 

while states were scored 1 if their youth corrections 

agency was part of or under a family/child welfare or 

human services agency. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 13 

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States with no mental health screening were 

scored 0 and states that use mental health screens 

from detention through probation and juvenile 

corrections were scored 1. States in which courts make 

the decisions to release juveniles from out-of-home 

placement scored 0 and states in which placement 

agencies make release decisions scored 1. States that 

do not track recidivism scored 0 and states that have 

a system for tracking recidivism (determined by the 

presence of at least two of the following: population 

specified for tracking, event indicating recidivism, 

follow-up period specified) scored 1. 

States with statutory or regulatory support for 

the use of evidence-based programs scored 1 while 

states without such supports or those providing no 

information about their supports for evidence-based 

programs scored 0. States that allow prosecutorial 

discretion for waivers or have legislation for automatic 

transfer of juveniles to adult court scored 0 and states 

in which only judges have the authority to decide if a 

minor should be tried in adult court scored 1. 

The final scale summed all scores for the 

16 indicators, with higher scores reflecting more 

progressive characteristics. Two conceptualizations 

of the scale measure were used for the growth curve 

modeling analysis: a continuous scale measure and 

an ordinal measure where states were grouped into 

Least Progressive, Moderately Progressive, and Most 

Progressive based on their total scores. The scale is 

intended as a general measure of a state’s youth justice 

policy environment. It does not reflect specific reforms 

or practices and it does not characterize policies that 

vary at the level of local (city or county) governments. 

Covariates 

The Bureau of Economic Analysis (BEA) provides 

information on annual per capita income for each state. 

Per capita income is the average income per person 

each year and is calculated by dividing a state’s total 

income by its population. The Bureau of Labor Statistics 

(BLS) provides data on estimates of unemployment 

rates for each month and for each state, from 1996 

to 2016. In BLS data, unemployment rates reflect the 

percentage of the labor force (i.e. persons age 16 and 

older) who are not employed. 

Table 1: 

Indicators of Progressive Youth Justice

Policy Environments 

Juvenile Justice Court Characteristics 

States that include a purpose clause in their juvenile 1 point delinquency laws

States where judicial or administrative actors (not 1 point prosecutor) make diversion decisions

States that restrict shackling of juveniles in court 1 point 

States that have a juvenile-specific competency 1 point standard 

States that consider immaturity in their competency 1 point standards 

States that do not register juveniles convicted of sex 1 point crimes 

Juvenile Justice Service Characteristics 

States that prohibit solitary confinement of juveniles 1 point 

States in which family/child welfare or human 1 point services agency oversees juvenile placement system 

States in which administrative agencies (not courts) 

determine when to release youth from out-of-home 1 point 

placements 

States that use mental health screens for juvenile 1 point dispositions 

States that track recidivism of juveniles processed by 1 point juvenile courts and placement agencies 

States with statutory or regulatory supports for use 1 point of evidence-based programs 

Juvenile Justice Jurisdictional Boundary Characteristics 

States that set lower age of juvenile delinquency 1 point jurisdiction 

States that set upper age of juvenile delinquency 1 point jurisdiction at 17 or older

States that have an extended age of juvenile 1 point delinquency jurisdiction over age 20

States where only judges have authority to try 1 point juveniles as adults 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 14 

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Ideological data reflect the political attitudes 

of residents and governments. Higher values indicate 

more liberal attitudes and lower values indicate more 

conservative attitudes. Each score was calculated 

using congressional election results, political parties of 

governors and state legislators, voting scores of state 

congressional delegations, and other assumptions 

about voters. Created by Berry and colleagues (1998), 

the measure has been found to be related to state 

incarceration rates (Jacobs and Carmichael 2001; Smith 

2004; Stemen and Rengifo 2011). 

Since 1930, the FBI has aggregated and 

published data from state, local, and tribal agencies and 

colleges/universities on arrests for crimes that come 

to their attention (FBI 2019). Arrests cover categories 

such as violent crimes (murder and non-negligent 

manslaughter, forcible rape5

, robbery, and aggravated 

assault), property crimes (burglary, larceny/theft, auto 

theft, and vandalism), financial (embezzlement, fraud, 

forgery), victimless (drugs, prostitution, gambling), 

domestic, and alcohol-related offenses among others. 

Data are organized as total arrests (all ages) or total 

juvenile (under age 18) arrests. Arrest counts indicate 

that a law enforcement agency made a custodial 

arrest of a person and recorded information about the 

arrest in an official police report. Police departments 

across the country send arrest data to the4 FBI for 

compilation in the annual report of crime statistics. This 

study used two principal measures: all youth arrests 

and youth arrests for violent crimes.6 The research 

team also generated lagged variables for the underage-18 violent crime arrest rate and the total underage-18 arrest rate. Lagged variables accounted for the 

influence of these variables in the preceding year on 

juvenile confinement rates in the following year. 

5. The FBI’s Uniform Crime Reporting program recently modified the definitions of 

forcible rape and sexual assaults, but this study uses data from the period prior to 

those changes taking effect. 

6. Total arrests include: murder and non-negligent man slaughter, forcible rape, 

robbery, aggravated assault, burglary, larceny-theft, motor vehicle theft, arson, other 

assaults, forgery and counterfeiting, fraud, embezzlement, stolen property (buying, 

receiving, possessing), vandalism, weapons (carrying, possessing), prostitution and 

commercialized vice, other sex offenses, drug abuse violations, gambling, offenses 

against family and children, driving under the influence, liquor laws, drunkenness, 

disorderly conduct, vagrancy, all other non-traffic offenses, suspicion, and curfew and 

loitering law violations. 

Researchers then took the square root of per 

capita income and of the lagged arrest rate and the 

natural log of the lagged under-18 violent crime arrest 

rate to account for their skewed distribution. 

Analytic Technique

Two analyses estimated the effect of state-level 

progressive characteristics on changes in confinement 

rates. First, the research team conducted bivariate 

analyses to examine whether specific progressive 

characteristics were associated with significantly 

larger declines in youth confinement across the 10 

waves (1996-2016). Next, growth curve modeling was 

used to measure the effect of youth justice policy 

environments on youth confinement rates across time 

while controlling for a number of theoretically relevant 

predictors of confinement drawn from past research. 

In bivariate analyses, researchers tested the 

effects of greater or lesser progressive youth justice 

policies using t-tests to determine if state-level policy 

characteristics were associated with declines in youth 

confinement rates between Wave 1 (1996-1998) and 

Wave 10 (2014-2016). Declines in youth confinement 

rates were measured both as percentage change 

and absolute change over time. Latent growth curve 

models then estimated the impact of state-level policy 

characteristics on the dependent measure (change in 

youth confinement rates over time) while controlling 

for youth arrests, unemployment, per capita income, 

and state political ideology. Originally adapted from 

hierarchical linear modeling techniques designed to 

analyze cross-sectional data, growth curve modelling 

is a useful way to assess variability in outcomes across 

different states over time (Raudenbush and Bryk 

2002). Given the small number of time periods in the 

analysis and the time-invariant independent variable 

(state policy environment), growth curve models are 

suitable for determining whether progressive policy 

characteristics are significantly related to statelevel declines in youth confinement (Phillips and 

Greenberg 2008). The approach was used by Kubrin 

and Hearting (2003) to study trends in homicide 

across neighborhoods in St. Louis, and by Rosenfeld 

and colleagues (2007) to assess the impact of order 

maintenance arrests on precinct-level robbery and 

homicide trends in New York City. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 15 

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FINDINGS 

Bivariate Analyses

Researchers calculated the percentage decline in 

each state’s youth confinement rate by dividing the 

difference in rates between the first and last waves 

over the initial rate in Wave 1, and multiplying the 

result by -1 to capture the decline. With the exception 

of Idaho and West Virginia, all states had lower outof-home placement rates in Wave 10 (2014-2016) 

than in Wave 1 (1996-1998). States varied in the size 

of their percentage declines. The mean decline was 49 

percent, while the greatest decline was 88 percent and 

the smallest was seven percent. 

States were divided into 2 groups across all 16 

dichotomized variables that comprised the progressive 

policy scale: those with more progressive youth justice 

environments and those with less progressive or 

punitive environments. An analysis using t-tests across 

each of the 16 policy variables revealed no significant 

differences between the two groups in their percentage 

declines in confinement rates from Waves 1 to 10. In 

other words, the rate of decline in youth confinement 

in states with more progressive youth justice policy 

environments did not differ significantly from states 

with less progressive environments (Table 2). 

Researchers calculated the absolute change in 

youth confinement rate for each state by subtracting 

the confinement rate in the initial wave (1996-1998) 

from the rate during the final wave (2014-2016). 

Examining absolute change could provide additional 

information on the magnitude of decline in youth in 

confinement as states may have seen meaningful 

declines in absolute terms but not as a percentage of 

previous rates. Researchers conducted t-tests on all 

16 policy environment indicators to test for significant 

differences in absolute change. Only juvenile 

competency standards showed a statistically significant 

difference in confinement. States with juvenile-specific 

competency standards experienced significantly larger 

declines in placement rates over time (t = 2.74, p = 

0.0085) compared with those states without juvenilespecific competency standards (Table 3). 

Multivariate Analyses 

To assess the impact of progressive policy characteristics 

on the dependent variable, the research team 

constructed two models. The ‘‘Level 1’’ model regressed 

the dependent variable (youth confinement) against a 

function of time (usually taken to be a polynomial). The 

‘‘Level 2’’ model regressed the regression coefficients 

from the level one estimation against a vector of 

predictors that characterize the cases (i.e. state and 

policy environments). The Level 1 equation may be 

written as: 

JCit= β0i+β1iT1i+β2iX1i+Ɛ1i

where JCit is the youth confinement rate at time t for 

state i, β0i is the youth confinement rate at Wave 1 

(1996-1998) for state i, β1i is the average linear change 

in the confinement rate between Wave 1 and Wave 10 

for state i, T1i is a linear time trend with Wave 1 equal to 

0, β2i is the average effect of the mean-centered, timevarying covariate X1i

 for state i, and Ɛ1i is the Level 1 

error term at time t for state i. Given this specification, 

the annual youth confinement rate is a function of both 

a linear time trend and state-specific circumstances 

that fluctuate from wave to wave. 

Level 2 equations treat the intercepts and 

slopes as dependent variables. For instance, suppose 

the analysis hypothesizes that a time-invariant 

characteristic (Wi

) has an impact on states’ youth 

confinement trajectories via their impact on the 

intercept (level of confinement) and slope (trend). The 

Level 2 equations may be written as:

β0i = γ00 + γ01Wi + u0i

β1i= γ10 + γ11Wi + u1i

β2i = γ20 

In the first Level 2 equation γ00 represents the 

average confinement rate in Wave 1 (1996-1998) 

across states, γ01 is the effect of the state-specific, timeinvariant covariate Wi on the initial confinement rate, 

and u0i is the residual, or random-effect, for state i. In 

the second equation, γ10 represents the average linear 

trend in confinement rates between Wave 1 and Wave 

10 (1996-2016) across states, γ11 is the effect of a stateJOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 16 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 2: 

Percentage Change in Youth Confinement Rate, Wave 1 to 10 (t-test results)

Indicator of State Youth Justice Policy Environment 

No (0) 

Yes (1) N 

Percentage Change in 

Confinement Rate t-value p-value 

0 33 -46.89% Sets a lower age of juvenile delinquency jurisdiction 0.776 0.442 1 18 -54.49%

0 09 -61.98% Sets the upper age of juvenile jurisdiction at 17 or older -1.240 0.221 1 42 -46.91%

0 42 -48.81% Has an extended age of juvenile jurisdiction over age 20 0.350 0.728 1 09 -53.13% 

0 02 -40.06% Includes purpose clause in juvenile delinquency laws 0.409 0.684 1 49 -49.96%

0 36 -50.35% Judicial / administrative actor makes diversion decisions -0.255 0.800 1 15 -47.71%

0 20 -48.83% Prohibits solitary confinement of juveniles 0.126 0.900 1 31 -50.05% 

0 29 -51.47% Restricts the shackling of juveniles in court -0.464 0.645 1 22 -47.07%

0 19 -39.47% Has a juvenile-specific competency standard 1.701 0.095 1 32 -55.57%

0 48 -49.13% Considers immaturity in competency standards 0.375 0.709 1 03 -56.63% 

0 40 -51.58% Does not register juveniles convicted of sex crimes -0.821 0.416 1 11 -42.25% 

0 28 -48.27% Family/child welfare agency oversees youth placement system 0.305 0.762 1 23 -51.15% 

0 21 -42.31% Administrative agencies determine release of youth from placement 1.313 0.195 1 30 -54.65% 

0 09 -44.04% Mental health screen used for juvenile dispositions 0.655 0.516 1 42 -51.27%

0 18 -42.75% Has system for tracking recidivism of youth released from placement 1.084 0.284 1 33 -53.29%

0 16 -43.93% Demonstrates support for evidence-based programs 0.815 0.419 1 35 -52.15% 

0 43 -47.36% Does not provide prosecutorial / legislative criminal court transfers 1.100 0.277 1 08 -61.43% 

*p < 0.05 **p < 0.01 ***p < 0.001. 

Source: Data on the 16 state-level indicators of youth justice policy environment are from the National Center for Juvenile Justice’s compendium of justice 

system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. Confinement rates are calculated with data from the federal Census of Juveniles in 

Residential Placement: 1997-2015. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 17

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 3: 

Absolute Change in Youth Confinement Rate, Wave 1 to 10 (t-test results)

Indicator of State Youth Justice Policy Environment 

No (0) 

Yes (1) N 

Absolute Change in 

Confinement Rate t-value p-value 

0 33 -116.00 Sets a lower age of juvenile delinquency jurisdiction -0.152 0.880 1 18 -112.87

0 09 -144.20 Sets the upper age of juvenile jurisdiction at 17 or older -1.400 0.168 1 42 -108.62

0 42 -114.69 Has an extended age of juvenile jurisdiction over age 20 0.047 0.963 1 09 -115.90

0 02 -095.28 Includes purpose clause in juvenile delinquency laws 0.402 0.689 1 49 -115.70

0 36 -121.36 Judicial / administrative actor makes diversion decisions -1.024 0.311 1 15 -099.40

0 20 -131.09 Prohibits solitary confinement of juveniles -1.341 0.186 1 31 -104.45 

0 29 -124.15 Restricts the shackling of juveniles in court -1.088 0.282 1 22 -102.71

0 19 -082.18 Has a juvenile-specific competency standard 2.741 0.009 ** 1 32 -134.32 

0 48 -114.66 Considers immaturity in competency standards 0.096 0.924 1 03 -118.67

0 40 -119.69 Does not register juveniles convicted of sex crimes -0.932 0.356 1 11 -097.50

0 28 -120.56 Family/child welfare agency oversees youth placement system -0.635 0.528 1 23 -108.01

0 21 -120.85 Administrative agencies determine release of youth from placement -0.505 0.616 1 30 -110.74

0 09 -120.89 Mental health screen used for juvenile dispositions 0.069 0.946 1 42 -113.62 

0 18 -101.12 Has system for tracking recidivism of youth released from placement 1.042 0.303 1 33 -122.42 

0 16 -107.27 Demonstrates support for evidence-based programs 0.524 0.603 1 35 -118.39

0 43 -112.26 Does not provide prosecutorial / legislative criminal court transfers 0.623 0.536 1 08 -129.10

*p < 0.05 **p < 0.01 ***p < 0.001. 

Source: Data on the 16 state-level indicators of youth justice policy environment are from the National Center for Juvenile Justice’s compendium of justice 

system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. Confinement rates are calculated with data from the federal Census of Juveniles in 

Residential Placement: 1997-2015.

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 18

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

specific, time-invariant covariate Wi

 on the linear trend 

in youth confinement, and u1i is the random effect on 

the trend for state i. In the final equation, the withinstate average effect of a time-varying covariate β2i

 is 

estimated as γ20. Nesting the four equations within 

a single equation, the full random coefficient model 

estimated in the current analysis is: 

JCit = γ00 + γ01Wi + γ10 + γ11Wi + γ20 X1i+ 

(Ɛ1i + u0i+ u1i T1i) 

This equation breaks down the annual youth 

confinement rate into its within-state and betweenstate components and allows the analysis to estimate 

the impact of progressive youth justice environments 

while controlling for other predictors of confinement. 

The various steps of the analysis are discussed in more 

detail below. 

Variation in Youth 

Confinement

The first step of the growth-curve estimation procedure 

was to establish whether rates of confinement (at 

Wave 1) vary significantly across states as well as assess 

both the linear and curvilinear trends present in the 

data. To do this, researchers first estimated a model in 

which only the intercept was allowed to vary, and the 

only measures included in the model were the linear 

and curvilinear time trends (see Model 1 in Table 4). 

In Model 1, the average intercept (237.95) represents 

the mean level of confinement across all 50 states 

at Wave 1 (1996-1998). The coefficient for the linear 

time trend (-11.864) represents the average decline 

(trend) in youth confinement across all states over the 

entire period. The model indicates that on average, 

confinement rates declined in a linear fashion over 

the time period analyzed. In the case of confinement 

across the 50-state sample, adding the time-squared 

term did not substantially improve the fit of the model 

and therefore it was removed from the more complex 

models estimated later. Importantly, the variance 

component estimated for the first model suggests 

significant variation exists in the initial level of youth 

confinement across states. That is, rates of placement 

varied substantially and significantly from state to state 

during the first wave of data (1996-1998). 

The second step was to assess whether 

variability exists between states in the slope (trend) 

of youth confinement—i.e. whether confinement was 

declining faster in some states compared with others. 

The research team estimated a model allowing the effect 

of time to vary across states (a random slope). Variance 

components in Model 2 indicate significant variation 

in confinement trends across states. Thus, states vary 

not only in initial levels of confinement (Model 1), but 

also in confinement trends since 1996. Researchers 

attempted to explain this variation using information 

on progressive youth justice policy characteristics and 

other predictors of youth confinement. 

Model 3 includes the measure of progressive 

policy characteristics in each state (time-invariant) as 

a predictor of the intercept (levels of confinement). 

The coefficient shown indicates that states with more 

progressive policy characteristics, on average, had 

significantly lower rates of youth confinement at Wave 

1 (1996-1998). Each unit increase in progressive policy 

characteristics was associated with a decrease of 11.75 

confined youth per 100,000 population. 

To assess whether state-level progressive policy 

environments were associated with trends in youth 

confinement, the research team included a cross-level 

interaction between the measure of time at Level 1 

and state policies at Level 2. Adding this term to the 

regression equation produced an estimate of the effect 

of progressive characteristics on the slope of time, or the 

trend in confinement (see Model 4 in Table 4). Estimates 

suggest that progressive policy environments had a 

significant impact on the intercept (placement levels at 

Wave 1), but no evidence was found that progressive 

policies in general were associated with trajectories of 

youth confinement observed across states. In other 

words, states with varying levels of progressive policy 

characteristics did not see significantly different trends 

in youth confinement between 1996 and 2016. 

Model 5 included each of the time-varying 

covariates defined above at Level 1. Each measure was 

group-mean-centered, allowing estimation of effects 

of within-state changes in each covariate independent 

from their average differences across states. Progressive 

policy characteristics were also included in this model 

at Level 2 as a predictor of both the intercept and the 

slope. The results in Model 5 are consistent with those 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 19

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

- - - -

Table 4: 

Youth Confinement Rate Regressed on Indicators of Youth Justice Policy 

Environment Using Continuous Measure of the Policy Scale (n=50)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

237.950 *** 245.732 *** 337.843 *** 342.292 *** 304.426 *** 

Intercept (12.889) (13.03) (48.494) (52.851) (72.856)

-11.864 ** -15.881 *** -15.877 *** -19.111 *** -17.262 *** 

Time 

(4.308) (1.147) (1.147) (5.012) (4.886)

-0.393 - - - -

Time2 

(-0.387) - - - -

Youth Violent Crime Arrest Rate t-1 -3.055 

- - - -

(Logged) (6.69)

Youth Arrest Rate t-1 0.630 * 

- - - - (Square root) (0.274)

Unemployment Rate -3.199 ** 

(1.104)

Per Capita Income 0.117

- - - - (Square root) (0.362)

State Ideology - - - -

0.190

(0.197)

TIME-STATIC INDICATOR 

Scale - - -11.750 * -12.322 * -14.087 * 

- - (5.851) (6.416) 5.844

CROSS-LEVEL INTERACTION 

Scale x Wave - - - 0.413 0.411 

- - - (0.596) (0.524)

RANDOM-EFFECTS VARIANCES 

State 67.869 *** 81.179 *** 77.898 *** 77.820 *** 75.427 *** 

(-10.647) (12.197) (11.511) (11.526) (8.378)

Wave - 6.283 *** 6.253 *** 6.202 *** 5.964 *** 

- (0.940) (0.930) (0.986) (0.878)

Residual 34.959 *** 29.824 *** 29.842 *** 29.846 *** 29.512 *** 

(2.624) (2.513) (2.516) (2.516) (1.169)

State Sample 50 50 50 50 50 

State-period Sample 444 444 444 444 444 

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Source: Data on the 16 state-level indicators of youth justice policy environment are from the National Center for Juvenile Justice’s compendium of justice system 

characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. Confinement rates are calculated with data from the federal Census of Juveniles in Residential 

Placement: 1997-2015.

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 20 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Least Pr

Moderat

ogressiv

ely Pr

e 

Most Pr

ogressiv

ogressiv

e 

e 

All S tat es 

presented earlier. While states with more progressive 

youth justice policy environments tended to have lower 

rates of confined youth, progressive characteristics 

were not related to trends (i.e. the slope) in youth 

confinement between 1996 and 2016. Of the timevarying measures included at Level 1, the youth arrest 

rate was positively related and rates of unemployment 

were negatively related to placement rates. 

Researchers repeated the analyses in Table 

4 using a different operationalization of the key 

independent variable (Table 5). Instead of the 16-point 

scale, an ordinal measure was generated to categorize 

states as (1) least progressive, (2) moderately, or 

(3) most progressive in terms of the number of 

progressive policies in place. States scoring 4, 5, or 6 on 

the full scale were coded as least progressive (n=16), 

while those scoring 7 or 8 were coded as moderately 

progressive (n=18) and those scoring 9 or higher were 

coded as most progressive (n=16). The research team 

re-estimated each growth curve model and the results 

were consistent with previous analyses. Compared 

with the least progressive states, states coded most 

progressive exhibited lower confinement rates on 

average across 10 waves, but the lack of significance in 

cross-level interaction (scale and wave) indicates that 

states with more progressive policy environments did 

not experience greater declines in confinement during 

the period of the study. 

Finally, in order to assess the sensitivity of these 

results, states were grouped as least, moderate, and 

most progressive using other cut-off points, including 

the 25th and 75th percentiles of the full scale and one 

standard deviation above and below the scale mean. 

The statistical significance of the cross-level interaction 

term (scale and wave) was not sensitive to these other 

groupings of states and time (wave) lost significance 

in the full models using alternative criterion (See the 

Appendix). 

Researchers created a data graphic to capture 

the nature of the long-term trends in youth confinement 

across states with varying degrees of progressive policy 

characteristics (Figure 2). States with a moderate extent 

of progressive policy characteristics exhibit slightly 

steeper declines than states categorized as low or high, 

but the overall trajectories are similar among all three 

groups. 

The results of the analysis begin to answer to the 

key question explored in this study—i.e. do states with 

more progressive youth justice policies show greater 

declines in youth confinement? Can the remarkable 

and long-term decline in youth confinement during 

the last 20 years be attributed to the efforts of 

state policymakers and the extent to which states 

demonstrate more consistently progressive youth 

justice policy environments? 

Figure 2: 

Youth Confinement in States with 

Varying Degrees of Progressive Youth 

Justice Policy Environments 

Estimated Confinement Rate Holding All Other Factors Constant

Year 

Note: Based on results from growth curve models. All controls held at 

their means. Results from growth curve modeling did not include 

estimates for juvenile confinement in Wave 1 (1996 - 1998) as data 

from this wave was used to create lagged measures. The research 

team extrapolated a value based on the average rate of decline 

between Waves 2 to 10. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 21 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

Table 5: 

Youth Confinement Rates Regressed on Indicators of Youth Justice Policy 

Environment Using Ordinal Measure of the Policy Scale (n=50)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

Intercept 237.950 *** 245.732 *** 274.764 *** 274.78 *** 235.908 *** 

(12.889) (13.030) (19.359) (20.794) (63.935)

Time -11.864 ** -15.881 *** -15.877 *** -15.889 *** -13.357 *** 

(4.308) (1.147) (1.147) (2.284) (3.111)

Time2 -0.393 - - - -

(-0.387) - - - -

Youth Violent Crime Arrest Rate t-1 

(Logged) - - - -

-3.221 

(7.854)

Youth Arrest Rate t-1 

(Square root) - - - -

0.670

(.327)

* 

Unemployment Rate - - - -

-3.235 

(1.071)

** 

Per Capita Income - - - -

0.040 

(Square root) (.415)

State Ideology - - - -

0.160 

(.239)

TIME-STATIC INDICATOR 

Moderately progressive - - -28.746 -24.462 -22.828

- - (27.534) (30.354) (28.709)

Most progressive - - -58.468 * -62.874 * -68.114 * 

- - (27.630) (30.299) (30.529)

CROSS-LEVEL INTERACTION 

Moderately progressive x wave - - -2.840 -3.178

- - (2.737) (2.556)

Most progressive x wave 3.167 3.093

(2.956) (2.997)

RANDOM-EFFECTS VARIANCES 

State 67.869 *** 81.179 *** 77.729 *** 77.143 *** 74.494 *** 

(10.647) (12.197) (11.455) (11.345) (10.751)

Wave - 6.283 *** 6.277 *** 5.800 *** 5.525 *** 

- (0.940) (0.932) (0.973) (0.978)

Residual 34.959 *** 29.824 *** 29.827 *** 29.847 *** 29.535 *** 

(2.624) (2.513) (2.515) (2.515) (2.565)

State Sample 50 50 50 50 50 

State-period Sample 444 444 444 444 444 

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Source: Data on the 16 state-level indicators of youth justice policy environment are from the National Center for Juvenile Justice’s compendium of justice system 

characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. Confinement rates are calculated with data from the federal Census of Juveniles in Residential 

Placement: 1997-2015.

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 22 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

CONCLUSIONS 

Were state policies generally related to 

youth confinement rates in the 1990s?

YES. The number of state-level progressive policy 

characteristics had a significant effect on initial rates 

of youth confinement. States with higher scores 

on the 16-point scale (i.e. more progressive policy 

characteristics) had lower rates of youth confinement 

at wave 1 (1996-1998) than did states with lower 

scores. This could suggest that states with more 

progressive characteristics were less inclined to rely 

on confinement in the mid-1990s. On the other hand, 

states experiencing the greatest reductions in youth 

confinement before the mid-1990s may have been 

more able to create the budgetary space and political 

climate to implement targeted reforms that further 

reduced youth confinement. States may have adopted 

such reforms in response to confinement declines 

rather than as a means of creating declines. Due to 

data limitations, this study cannot rule out such a 

possibility. 

Were specific youth justice policies 

associated with decreases in youth

confinement after the mid-1990s?

NO. Progressive youth justice policy environments 

were not significantly associated with declines in 

youth confinement rates. Although nearly all states 

showed declines in youth confinement during the 

study period, the analysis could not attribute the 

extent of decline to specific policy characteristics.

Only one of the 16 policy characteristics 

tested in the study (i.e. juvenile-specific competency 

standards) was associated with an absolute decline (as 

opposed to percentage decline) in youth confinement 

rates. States with any of the remaining 15 progressive 

policy characteristics did not show significantly larger 

declines in youth confinement when compared with 

states not having those policies. 

Of course, general policy environments are 

different than specific reforms and the study did 

not have the data to examine all possible youth 

justice policies and system reforms. A state’s general 

policy environment, however, should be more or 

less conducive to the implementation of various 

reforms intended to reduce youth confinement. Given 

widespread claims that reform policies are responsible 

for recent reductions in youth confinement, states 

with more progressive policies in general should 

experience greater reductions in confinement. This 

analysis failed to produce such a finding.

Did states with more progressive policy

environments experience steeper

declines in youth confinement rates?

NO. States with more progressive youth justice 

policy characteristics did not see significantly larger 

declines in youth confinement after the 1990s than 

did states with fewer progressive characteristics. The 

analysis confirmed the significant decline in youth 

confinement over the study period, but progressive 

policy characteristics were not associated with the 

magnitude of decline. The progressive quality of 

youth justice policy environments across all states did 

not significantly affect state-level reductions in youth 

confinement during the years examined by the study. 

Other factors did affect the decline in youth 

confinement. Youth arrest rates were positively 

associated with youth confinement trends, 

suggesting that decreases in arrests were related 

to decreases in confinement. Unemployment was 

negatively associated with confinement. Increased 

unemployment was related to lower rates of youth 

confinement. 

Importantly, progressive policy characteristics 

were negatively associated with youth confinement 

in general, meaning that more progressive states had 

lower rates of youth confinement across time. The 

interaction between time and level of progressive 

policy characteristics, however, was not significant, 

suggesting that the extent of progressive youth 

justice policy did not affect the downward trend 

in youth confinement. In other words, every state 

benefited from the nationwide crime decline by 

experiencing reductions in youth confinement, but 

the pace of falling confinement was not associated 

with the progressive quality of youth justice policy.

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 23 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

DISCUSSION 

Confinement rates declined over time in all states 

except two (Idaho and West Virginia), but this was 

expected given the national decline in youth crime. 

Youth arrest rates in prior years and unemployment 

rates also significantly affected declines in youth 

confinement. The relationship between decreases 

in youth arrest rates for violent crimes in prior years 

and confinement rates could suggest that juveniles 

were involved in fewer violent crimes over time. There 

could be aggregate changes in youth behavior that 

are associated with a reduced likelihood of engaging 

in delinquency. It could also indicate law enforcement 

awareness of the drop in violent crime among juveniles. 

Even if such awareness was not overt, it could influence 

police behavior. Police could be arresting fewer 

juveniles for violent crimes or they could be reducing 

charges in cases with ambiguous severity. 

The relationship between unemployment and 

confinement rates is noted. Prior research suggests 

that increases in unemployment are associated with 

increases in crime (Raphael and Winter-Ebmer 2001). 

Thus, higher unemployment could be linked to higher 

confinement rates. However, the findings indicate that 

increases in unemployment rates were associated with 

decreases in youth confinement. There are a number of 

possible explanations for this. Scholars have suggested 

financial resources are necessary to administer justice 

system punishment. When the economy is down 

and unemployment is high, costly punishment is 

unsustainable (Aviram 2015). An inverse relationship 

between unemployment and crime might also be 

explained by the guardianship effect— less time spent 

on work-related activities increases the availability of 

guardians of people and places which might in turn 

reduce opportunities for criminal activity to occur 

(Cantor and Land 1985). Finally, perhaps declines in 

youth confinement are so durable across states and 

over time that growth in unemployment rates has no 

appreciable effect. Notably, the violent youth crime 

drop continued nearly uninterrupted from the mid1990s and into the 2010s, even during the period of 

recession that started in 2008. 

The findings of this study suggest that 

states with more progressive youth justice policy 

environments did not demonstrate steeper reductions 

in youth confinement compared with other states. The 

confinement rate was already starting a downward 

trend in the mid-1990s, and there is no evidence that 

policies measured in the current study were significantly 

associated with variations in the decline across states. 

If progressive policy characteristics had an influence 

on youth confinement rates, one would expect states 

with more progressive policy environments to show 

steeper rates of decline. That is, states that scored 

higher on measures of progressive youth justice policy, 

should have seen steeper declines. Nearly all states 

experienced declines in youth confinement during the 

20-year study period, but the rate of decline was not 

associated with the degree of progressive youth justice 

policies generally present in each state. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 24 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Study Limitations

This study did not identify or test the policy and JJGPS is limited in terms of the breadth of 

practice reforms most likely to reduce youth 

confinement. Without comprehensive, time-stamped 

data on the implementation of state-level (or even 

better, local-level) justice reforms aimed at reducing 

youth confinement, analyzing the effect of reforms on 

confinement is not possible. More research on the effect 

of reforms and other drivers of increases and decreases 

in youth confinement is sorely needed. Moreover, the 

lack of continuous or at least annualized data on statelevel residential placement rates for youth in the justice 

system required the research team to calculate multiyear waves. Each wave reflected a three-year average 

that covered all 20 years of the period under study, 

but the use of waves limits the statistical power of the 

analyses. In addition, not all residential facilities report 

to the CJRP. Missing values are imputed for both unit 

and item non-response, and imputation rates vary by 

collection year and state. 

Other data limitations pertain to the timing 

of available indicators of the youth justice policy 

environment. The majority of indicators on the JJGPS 

website do not have associated dates so it is not 

possible to analyze how reforms affect youth placement 

rates by analyzing time periods before and after their 

enactment. Researchers can only code the presence 

of reforms using binary variables– whether a state 

has a policy or not. Analyses would be more robust 

with an array of policy measures coded according to 

their dates of enactment. Even knowing the year of 

enactment would allow for the use of complex analytic 

techniques, such as interrupted time-series or panel 

regression modeling. 

characteristics and policies it includes. Notably, this 

study surveyed a group of national youth justice 

experts and found that some of the policies most likely 

to drive the use of confinement are not represented 

in JJGPS. Even when the JJGPS database includes 

important variables, data are not always reported by 

every state. For example, some states do not report 

whether or not they use mental health screenings tools 

or if they have policies to support the use of evidencebased practices. Two states (AR, MO) did not respond 

to inquiries from JJGPS on the first domain and three 

states (MD, MO and NY) did not respond to the second 

domain. In these cases, states were coded as not having 

these policy measures (i.e. coded 0 on the scale), but 

their nonresponse could introduce error.* 

A final limitation concerns the crime data 

disseminated by the FBI’s Uniform Crime Reporting 

program (UCR). Federal crime data are the best source 

of information for youth justice research that is national 

in scope, but such research is necessarily restricted 

to information about arrests rather than all reported 

crimes. It is not possible to divide crime data according 

to the age of offenders until arrests are made, which 

means that youth justice research cannot account 

for crimes that do not result in arrest, a well-known 

limitation of the database for youth justice researchers. 

The UCR program is also voluntary, which results in 

some law enforcement agencies failing to report data 

on time, and this varies from year to year. For example, 

Washington, DC had several years of missing data 

during the study period and the District was removed 

from the study for most analyses. 

* The research team re-estimated growth curve models, excluding Arkansas, 

Missouri, Maryland and New York. The estimates did not change substantively; 

direction and statistical significance of each estimate obtained remained 

unaltered. See tables the Appendix for ancillary growth curve model estimates. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

ALUA 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 25

RESEARCH AND EV TION CENTER

IMPLICATIONS FOR POLICY, PRACTICE,

AND FUTURE RESEARCH 

This study explored the relationship between statelevel progressive policy environments and changes 

in youth confinement rates since the late 1990s. 

The findings suggest the need for more in-depth 

analyses. Many factors are likely to affect the use of 

confinement, including macro factors (e.g., social 

policies, economics, unemployment, racial and ethnic 

demographics, and the general political climate) and 

micro factors (e.g., justice system decisions and the 

actions of police, prosecutors, judges, and probation 

officers). The sheer number of potential influences on 

youth confinement make it vulnerable to changing 

politics and the ideology of crime control, which only 

increases the need for persuasive research on the costs 

and benefits of various policies. 

Youth crime declined nearly every year since the 

mid-1990s. Falling crime rates provide an opportunity 

for lawmakers and other officials to experiment with 

progressive policies to limit incarceration, lower costs, 

and protect public safety by diverting and rehabilitating 

youth rather than relying on confinement. If crime rates 

rebound, however, what happens to these policies 

and practices? Unless they are already convinced that 

progressive policies are consistent with public safety, 

policymakers may be inclined to scale back on reforms 

when crime rises and instead renew their focus on 

punitive responses. Future studies on the factors 

actually shaping youth confinement are essential for 

sound policy development. The key issue: how and to 

what extent do progressive policies affect confinement 

rates, independently of other factors? 

The current study is a small step in this direction, 

but more complete data are needed for a thorough 

analysis of policy effects on youth confinement. In 

addition to annual data on the number of youth 

arrested, adjudicated, and confined, researchers 

need detailed information regarding reforms and 

policy changes, including the goals of reforms, their 

purposes, dates of enactment, and other details about 

implementation. Until a new federal data series of state 

and local initiatives is launched, state agencies and 

youth justice advocates should make such information 

available in a central repository so that future claims 

about the impact of policy reforms may be rigorously 

evaluated. 

With more detailed data, researchers could 

assess the impact of policy changes using appropriate 

statistical methods that account for other factors 

known to affect youth confinement. For example, with 

a national database of monthly rates of youth arrests 

and confinements at the local level as well as the dates 

on which various policy reforms were implemented, 

researchers could use a series of autoregressive 

integrated moving average (ARIMA) models to assess 

the ability of reforms to change youth confinement 

trends while accounting for existing trends and/or 

other sources of spuriousness. This approach has been 

used to evaluate the impact of programs designed to 

reduce gun violence (Roman, Klein, and Wolff 2018), 

and could be effective in assessing how policy changes/

reforms impact youth confinement across the country. 

Policymakers, advocates, and even some 

researchers claim that youth confinement rates across 

the United States dropped in recent years due to 

changes in policy and practice. Such claims remain 

unproven, but voters and elected officials are inclined 

to accept them as factual because they are offered 

by reputable agencies and repeated in news media 

sources. Without reliable evidence, however, the 

notion that state-level youth confinement rates fall 

primarily in response to progressive policy reforms is 

merely appealing rhetoric. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK PAGE 25 

RESEARCH AND EVALUATION CENTER 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
PAGE 26 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

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RESEARCH AND EVALUATION CENTER 
PAGE 28

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JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 29

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Table A1: State Policy Characteristics 

Policy Environment Scale: Juvenile Court Policies 

Laws include purpose

clause 

AL 

■

AK 

■

AZ AR

■

CA 

■

CO 

■

CT 

■

DC 

■

DE 

■

FL 

■

GA 

■

HI 

■

ID 

■ 

IL 

■

IN 

■

IA 

■

KS 

■

KY 

■

LA 

■

ME 

■

MD 

■

MA 

■

MI 

■

MN 

■

MS 

■

MO 

■

Judicial/admin actor

decides diversion ■ ■ ■ ■ ■ ■ ■ ■ ■

Restricts court shackling ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Has youth-specific

competency standard ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Considers immaturity in

competency standards ■

Does not register for sex

crime convictions ■ ■ ■ ■ ■ ■

Policy Environment Scale: Juvenile Justice Service Policies 

Prohibits solitary

confinement

AL AK 

■

AZ 

■

AR

■

CA CO 

■

CT 

■

DC 

■

DE FL 

■

GA 

■

HI 

■

ID 

■

IL 

■

IN IA KS KY LA ME 

■

MD 

■

MA 

■

MI MN MS MO 

■

Family/child welfare 

agency oversees

placement system 

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Admin agencies

determine release from 

out-of-home placements 

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Mental health screen used 

for dispositions ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Tracks recidivism ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Support for EBP ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Policy Environment Scale: Jurisdictional Boundary Policies

Sets lower age of juvenile

delinquency jurisdiction

AL AK AZ 

■

AR

■

CA CO 

■

CT 

■

DC DE FL GA HI ID IL IN IA KS 

■

KY LA 

■

ME MD 

■

MA 

■

MI MN 

■

MS 

■

MO 

Sets upper age of juvenile

delinquency jurisdiction at

17 or older

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Has extended age of

juvenile delinquency

jurisdiction over age 20 

■ ■ ■ ■

Without prosecutorial /

legislative adult transfer

discretion 

■ ■ ■ ■ ■

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 30 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Table A1: State Policy Characteristics (continued)

Policy Environment Scale: Juvenile Court Policies 

MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY 

Laws include purpose

clause ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Judicial/admin actor

decides diversion ■ ■ ■ ■ ■ ■

Restricts court shackling ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Has youth-specific

competency standard ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Considers immaturity in

competency standards ■ ■

Does not register for sex

crime convictions ■ ■ ■ ■ ■

Policy Environment Scale: Juvenile Justice Service Policies 

MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY 

Prohibits solitary

confinement ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Family/child welfare 

agency oversees

placement system 

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Admin agencies

determine release from 

out-of-home placements 

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Mental health screen used 

for dispositions ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Tracks recidivism ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Support for EBP ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Policy Environment Scale: Jurisdictional Boundary Policies

MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY 

Sets lower age of juvenile

delinquency jurisdiction ■ ■ ■ ■ ■ ■ ■ ■

Sets upper age of juvenile

delinquency jurisdiction at

17 or older

■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■

Has extended age of

juvenile delinquency

jurisdiction over age 20 

■ ■ ■ ■ ■

Without prosecutorial /

legislative adult transfer

discretion 

■ ■ ■

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 31 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Table A2: 

Coding Scheme for 16 Indicators of State Youth Justice Policy Environments 

Jurisdictional Boundary 

Policies Provisions 

Scale 

Score 

Lower Age 

No age specified 0 

Age 6, 7, 8 or 10 1 

Upper Age 

Age 15 or 16 0 

Age 17 or older 1 

Extended Age 

Age 18, 19, or 20 0 

Age 21 or older 1 

Discretion Over Criminal Court 

Transfer 

Prosecutor, legislature, or both 0 

Juvenile/family court judge only 1 

Juvenile Court Policies Provisions 

Scale 

Score 

Purpose Clause 

No purpose clause 0 

Purpose clause based on: balanced & restorative justice; developmental; due process; parens patriae 1 

Discretion Over Intake 

Diversion 

Determined by offense, by court, or by prosecutor 0 

Within the discretion of juvenile court intake officer 1 

Courtroom Shackling of 

Juveniles 

No restrictions on shackling use 0 

Judiciary or legislative restrictions 1 

Competency Standard 

No standard or adult standard used 0 

Juvenile-specific standard 1 

Competency Standard 

Considers Youth Maturity 

No 0 

Yes 1 

Sex Offender Registry Includes 

Juveniles 

Yes 0 

No 1 

Juvenile Justice Service 

Policies Provisions 

Scale 

Score 

Solitary Confinement of 

Juveniles 

Permitted with or without limits 0 

Prohibited 1 

Agency Overseeing Youth 

Confinement

Adult corrections or independent youth corrections agency 0 

Child welfare or human services agency 1 

Authority Over Youth Releases 

from Placement 

Court with or without agency concurrence 0 

Agency or parole board without court 1 

Mental Health Screening 

None or state provides no information 0 

Used by probation, detention or corrections, or multiple 1 

Regular Tracking of Youth 

Recidivism 

Does not track recidivism 0 

Tracks recidivism for at least some youth populations 1 

Support for Evidence-Based 

Programs 

No formal support for EBP or State provides no information 0 

Support for EBP through statute, administrative regulations, or the inclusion of an EBP support entity 1 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 32 

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Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

Table A3: 

Grouped Youth Justice Policy Environment Scores 

Least Progressive Moderately Progressive Most Progressive 

Score States 

4 KY 

Score States 

7 IL MD MS OH OK 

Score States 

9 AK ID NE NJ OR VT 

5 MI WV WY 8 AR AZ DC DE FL GA KS 

MT NC NY SD TN TX WI 

10 CA MA NM PA 

6 AL IA IN LA MN MO NV 

ND RI SC VA WA 

11 CO HI NH UT 

12 CT ME 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 33 

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Table A4: 

Youth Confinement Rates Regressed on Indicators of Juvenile Justice Policy 

Environment Using Ordinal Measure of Scale with States Grouped Based on 25th 

and 75th Percentiles of Scale Score (n=50)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

Intercept 237.950 *** 245.732 *** 286.736 *** 281.763 *** 247.532 ** 

(12.889) (13.030) (64.428) (69.741) (81.343)

Time -11.864 ** -15.881 *** -15.882 *** -12.229 * -10.618

(4.308) (1.147) (1.147) (5.599) (6.131)

Time2 -0.393 - - - -

(.387) - - - -

Youth Violent Crime Arrest Rate t-1 

(Logged) - - - - -3.326 (7.706)

Youth Arrest Rate t-1 

(Square root) - - - -

0.662 

(.331)

* 

Unemployment Rate - - - - -3.166 

(1.072)

** 

Per Capita Income 

(Square root) - - - - 0.076(.432)

State Ideology 

TIME-STATIC INDICATOR (SCALE)

- - - - 0.148

(.244)

Moderately Progressive - - -30.746 -22.63 -31.831

(25th-75th Percentile) (65.311) (71.264) (63.955)

Most Progressive -70.456 -69.853 -84.254

 (Above 75th Percentile) - - (67.121) (73.14) (67.451)

CROSS-LEVEL INTERACTION (SCALE*WAVE)

Moderately Progressive x Wave - - - -5.846 -5.313 

- - - (5.749) (5.902)

Most Progressive x Wave -0.494 0.077

(5.905) (6.115)

RANDOM-EFFECTS VARIANCES 

State 67.869 *** 81.179 *** 78.248 *** 77.441 *** 74.594 *** 

(10.647) (12.197) (10.465) (10.398) (9.876)

Wave - 6.283 *** 6.293 *** 5.715 *** 5.508 *** 

- (.940) (.926) (.849) (.833)

Residual 34.959 *** 29.824 *** 29.817 *** 29.852 *** 29.532 *** 

(2.624) (2.513) (2.521) (2.531) (2.579)

State Sample 50 50 50 50 50 

State-period Sample 444 444 444 444 444 

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Data Source: Data on the 16 state-level indicators of youth justice policy environment from the National Center for Juvenile Justice’s compendium of justice system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 34 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

Table A5: 

Youth Confinement Rates Regressed on Indicators of Juvenile Justice Policy 

Environment Using Ordinal Measure of Scale with States Grouped based on Scale 

Score One Standard Deviation Above and Below the Mean Scale Score (n=50)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

Intercept 237.950 *** 245.732 *** 286.808 *** 281.762 *** 243.888 ** 

(12.889) (13.030) (64.354) (69.741) (82.046)

Time -11.864 ** -15.881 *** -15.882 *** -12.229 * -10.714

(4.308) (1.147) (1.147) (5.599) (6.094)

Time2 -0.393 - - - -

(0.387) - - - -

Youth Violent Crime Arrest Rate t-1 

(Logged) - - - - -1.858(7.905)

Youth Arrest Rate t-1 

(Square root) - - - -

0.575

(.323)

Unemployment Rate - - - - -3.228

(1.065)

** 

Per Capita Income 

(Square root) - - - - 0.082(0.424)

State Ideology 

TIME-STATIC INDICATOR (SCALE)

- - - - 0.195

(0.240)

Moderately Progressive 

(25th-75th Percentile)

Most Progressive 

 (Above 75th Percentile)

CROSS-LEVEL INTERACTION (SCALE*WAVE)

-

-

-

-

-36.295

(65.151)

-74.668

(67.969)

-29.991

(71.172)

-71.803

(74.233)

-40.020 

(65.209)

-85.432

(69.297)

Moderately Progressive x Wave 

Most Progressive x Wave 

RANDOM-EFFECTS VARIANCES 

-

-

-

-

-

-

-4.511 

(5.744)

-2.074

(6.064)

-3.951

(5.894)

-1.681

(6.166) 

State 67.869 *** 81.179 *** 78.720 *** 78.436 *** 76.410 *** 

(10.647) (12.197) (11.008) (10.990) (10.559)

Wave - 6.283 *** 6.238 *** 6.067 *** 5.843 *** 

- (0.940) (0.938) (0.827) (0.815)

Residual 34.959 *** 29.824 *** 29.850 *** 29.866 *** 29.532 *** 

(2.624) (2.513) (2.523) (2.527) (2.579)

State Sample 50 50 50 50 50 

State-period Sample 444 444 444 444 444 

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Data Source: Data on the 16 state-level indicators of youth justice policy environment from the National Center for Juvenile Justice’s compendium of justice system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 35 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

Table A6: 

Youth Confinement Rates Regressed on Indicators of Juvenile Justice Policy 

Environment Using Continuous Measure of Juvenile Justice Policy Environment 

Scale, Excluding Arkansas, Missouri, Maryland and New York (n=46)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

Intercept 244.614 *** 251.496 *** 355.771 *** 362.65 *** 309.568 *** 

(13.554) (13.757) (48.988) (53.705) (74.725)

Time -12.886 ** -16.430 *** -16.426 *** -21.065 *** -19.241 *** 

(4.574) (1.188) (1.187) (5.108) (4.990)

Time2 -0.347 - - - -

(0.412) - - - -

Youth Violent Crime Arrest Rate t-1 

(Logged) - - - - -1.157(6.932)

Youth Arrest Rate t-1 

(Square root) - - - -

0.669

(0.282)

Unemployment Rate - - - - -3.529

(1.161)

** 

Per Capita Income 

(Square root) - - - - 0.168(0.382)

State Ideology - - - - 0.145 

(0.203)

TIME-STATIC INDICATOR 

Scale - - -13.217 * -14.092 * -16.250 ** 

(5.878) (6.486) (5.825)

CROSS-LEVEL INTERACTION 

Scale x Wave - - - 0.588 0.595

- - - (0.605) (0.522)

RANDOM-EFFECTS VARIANCES 

State 69.448 *** 81.783 *** 77.405 *** 77.225 *** 73.888 *** 

(11.029) (12.666) (11.810) (11.836) (8.593)

Wave - 6.116 *** 6.091 *** 5.977 *** 5.762 *** 

- (1.043) (1.024) (1.120) (0.921)

Residual 35.444 *** 30.638 *** 30.653 *** 30.663 *** 30.219 *** 

(2.784) (2.621) (2.623) (2.622) (1.250)

State Sample 46 46 46 46 46 

State-period Sample 408 408 408 408 408

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Data Source: Data on the 16 state-level indicators of youth justice policy environment from the National Center for Juvenile Justice’s compendium of justice system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
PAGE 36 

This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

 

Table A7:

Youth Confinement Rates Regressed on Indicators of Juvenile Justice Policy 

Environment Using Ordinal Measure of Scale, Excluding Arkansas, Missouri,

Maryland and New York) (n=46)

Model 1 Model 2 Model 3 Model 4 Model 5 

TIME-VARYING INDICATORS 

Intercept 244.614 *** 251.496 *** 282.478 *** 282.986 *** 233.932 *** 

(13.554) (13.757) (19.104) (20.480) (64.704)

Time -12.866 ** -16.430 *** -16.428 *** -16.776 *** -13.838 *** 

(4.574) (1.188) (1.188) (2.256) (3.297)

Time2 -0.347 - - - -

(0.412) - - - -

Youth Violent Crime Arrest Rate t-1 

(Logged) - - - - -1.233 (7.958)

Youth Arrest Rate t-1 

(Square root) - - - -

0.718

(0.339)

* 

Unemployment Rate - - - - -3.581

(1.126)

** 

Per Capita Income 

(Square root) - - - - -0.042 (0.433)

State Ideology 

TIME-STATIC INDICATOR (SCALE)

- - - - 0.091

(0.252)

Moderately Progressive - - -25.694 -20.144 -18.908

(29.451) (32.385) (30.282)

Most Progressive -65.017 * -71.016 * -76.750 * 

- - (27.383) (30.083) (30.082)

CROSS-LEVEL INTERACTION (SCALE*WAVE)

Moderately Progressive x Wave - - - -3.348 -3.881

- - - (2.702) (2.444)

Most Progressive x Wave 4.044 3.974

(2.934) (3.013)

RANDOM-EFFECTS VARIANCES 

State 69.448 *** 81.783 *** 77.294 *** 76.307 *** 72.636 *** 

(11.029) (12.666) (11.469) (11.320) (10.618)

Wave - 6.116 *** 6.144 *** 5.385 *** 5.112 *** 

- (1.043) (1.024) (1.115) (1.138)

Residual 35.444 *** 30.638 *** 30.074 *** 30.660 *** 30.244 *** 

(2.784) (2.621) (2.586) (2.620) (2.705)

State Sample 46 46 46 46 46 

State-period Sample 408 408 408 408 408

*p < 0.05 **p < 0.01 ***p < 0.001. Note: Standard errors are in parentheses. 

Data Source: Data on the 16 state-level indicators of youth justice policy environment from the National Center for Juvenile Justice’s compendium of justice system characteristics, Juvenile Justice, Geography, Policy, Practice & Statistics. 

JOHN JAY COLLEGE OF CRIMINAL JUSTICE / CITY UNIVERSITY OF NEW YORK 

RESEARCH AND EVALUATION CENTER 
This resource was prepared by the author(s) using Federal funds provided by the U.S. Department of Justice. 

Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the U.S. Department of Justice.