U.S. Department of Justice

Office of Justice Programs

Office of Juvenile Justice and Delinquency Prevention

A Message From OJJDP

One of the most difficult challenges

facing State and local juvenile justice

systems is anticipating space needs

in detention and correctional facilities.

Underestimating future demands can

lead to overcrowded and less safe

facilities. Overestimating future

demands can lead to mismanaged

tax dollars and even misuse of the

extra space, such as detaining

juveniles who would not otherwise be

confined. In either case, the cost of

miscalculating the need for additional

space in secure juvenile facilities can

be considerable.

This Bulletin provides policymakers

with information that will help them to

determine the appropriate space

needed to accommodate the number

of juvenile offenders expected to be

placed in residential facilities. An

overview of juvenile justice system

policies and decisionmaking that

affect the process of assessing future

space needs is provided, and an

analysis of the different projection

models is included.

Given the dynamic nature of juvenile

justice policies, anticipating space

needs in detention and correctional

facilities will always be challenging.

Adoption of the ongoing systematic

forecasting approach set forth in this

Bulletin, however, should enable

policymakers to enhance the quality

and usefulness of their projections.

March 2001

Anticipating Space Needs

in Juvenile Detention and

Correctional Facilities

Jeffrey Butts and William Adams

At some point, every facility administrator in the juvenile detention and corrections system will be called upon to answer the same question: How many beds

do we need? In other words, how much

space will be needed to accommodate the

number of juvenile offenders expected to

be placed in residential facilities in the

future? The question may refer to a single

local jurisdiction or to an entire State. It

also may apply to the next budget period

or to the next 10 years.

Policymakers ask questions about space

needs for various reasons. Demographic

trends may indicate that a jurisdiction

will soon have a larger population of juveniles. Juvenile crimes may be occurring more frequently or less frequently,

and the crimes themselves may be becoming more severe or less severe. A

jurisdiction may be facing a financial crisis (or windfall). Deteriorating buildings

may necessitate new construction, or a

change in political leadership may bring

new policies to the juvenile justice system. No matter what compels State and

local officials to ask about future bedspace, their interest in the answer is

usually urgent and intense.

Juvenile justice professionals who must

respond to questions about space needs

may be tempted to answer with simple

statistical predictions based on recent

trends in juvenile arrests and court commitments or even recent changes in detention and corrections populations. Simple

answers are appreciated because they allow policymakers to proceed with budgeting and construction plans. Repeated experience with estimating future space needs,

however, has taught policymakers and

practitioners alike that there are no simple

answers or, more accurately, that there are

no simple and reliable answers. Statistical

prediction models are only as good as the

data elements that go into them and the

assumptions on which they are built. Every juvenile justice administrator eventually learns that the actual demand for detention and corrections space has a way of

proving statistical models wrong. Within a

few years, policymakers will likely return

to ask the same question: How many beds

do we need?

This Bulletin provides policymakers with

information to help them answer this

question. It presents an overview of the

roles of juvenile justice system policies

and decisionmaking in determining space

needs. It analyzes several methods for

projecting juvenile confinement populations, noting the limits of simple projection models and presenting a detailed example of a comprehensive projection

model. The Bulletin goes on to examine

the practical implications of projecting

detention and corrections populations

and to outline the differences between

forecasting and predicting future space

needs. (The background of the space

needs assessment study discussed in this

Bulletin is summarized on page 2.)
2

Space Needs

and System

Decisionmaking

Anticipating future space needs in juvenile

detention and correctional facilities can be

one of the most difficult challenges faced

by administrators and practitioners. The

costs of errors can be very high, considering the financial investment needed to

construct and operate new facilities. Underestimating future demands for space

can lead to overcrowding, inaccessible

facilities, and political conflict. Overestimating future demands can lead to charges

of financial mismanagement. In the worst

case, system officials may be tempted to

fill underused facilities with youth who

would not have been confined if excess

capacity had not been created.

The demand for confinement space is not

simply a function of juvenile population

trends and juvenile arrest rates. Policy

decisions will also, in part, determine demand. For a small number of juvenile offenders in any jurisdiction, justice system

intervention will always require secure

confinement. Few doubt the need for such

confinement in cases involving serious,

violent, and chronic offenders; juveniles

who have previously failed to appear for

scheduled court dates; or youth who

pose a serious danger to the community.

For another relatively small group of

offenders, justice system intervention

should almost never involve secure confinement. Youth who have not committed

prior offenses, very young offenders, and

youth charged with nonserious offenses

nearly always should be handled in the

community. The same is usually true for

highly vulnerable youth and those with

active, involved families and community

support systems that can competently

supervise the youth’s behavior.

For a large middle portion of the juvenile

offender population, however, the decision as to whether to use confinement

is not obvious. It is a complex, uncertain,

and sometimes highly contentious process involving a wide assortment of

policymakers, practitioners, and even

members of the community. Confinement

decisions depend on the actions and

beliefs of police officers, prosecutors,

judges, probation officers, elected officials who make policies that allocate resources across the spectrum of juvenile

justice programs, and members of the

community who support or oppose

Background of the OJJDP Space Needs Assessment Study

On November 26, 1997, as part of Public Law 105–119, Congress requested that the

U.S. Department of Justice conduct a “national assessment of the supply and demand for juvenile detention space,” including an assessment of detention and corrections space needs in 10 States. In particular, Congress expressed this concern:

The conferees are concerned that little data exists on the capacity of juvenile

detention and corrections facilities to handle both existing and future needs and

direct the Office of Justice Programs to conduct a national assessment of the

supply of and demand for juvenile detention space with particular emphasis on

capacity requirements in New Hampshire, Mississippi, Alaska, Wisconsin, California, Montana, West Virginia, Kentucky, Louisiana, and South Carolina, and to

provide a report to the Committees on Appropriations of the House and the

Senate by July 15, 1998.

OJJDP responded to this request by taking two actions. The first action was to submit

a report to Congress in July 1998 (see An Assessment of Space Needs in Juvenile

Detention and Correctional Facilities, Report to Congress, Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention, July 1998). That report provided some of the background for this

Bulletin. It was prepared by OJJDP with assistance from The Urban Institute, the

National Center for Juvenile Justice, the National Council on Crime and Delinquency,

and The American University in Washington, DC.

The second action taken by OJJDP was to fund a more extensive investigation as

part of the Juvenile Accountability Incentive Block Grants (JAIBG) program. The investigation, known as the Assessment of Space Needs in Juvenile Detention and

Corrections project, is being completed by researchers at The Urban Institute. The

Urban Institute is focusing on the methods used by States to anticipate future demand for juvenile detention and corrections space. Products of the work will include

new tools to forecast detention and corrections populations at State and local levels.

Project advisors and consultants are listed below.

Project Advisory Committee

Dr. Arnold Irvin Barnett, Massachusetts Institute of Technology

Dr. Donna M. Bishop, Northeastern University

Mr. Edward J. Loughran, Council of Juvenile Correctional Administrators

Dr. James P. Lynch, The American University

Dr. Samuel L. Myers, Jr., University of Minnesota

Ms. Patricia Puritz, American Bar Association

Project Consultants

Mr. Paul DeMuro, Independent Consultant, Montclair, NJ

Dr. William J. Sabol, Case Western Reserve University

Dr. Howard N. Snyder, National Center for Juvenile Justice

Mr. David J. Steinhart, Independent Consultant, Mill Valley, CA

For more information about this Bulletin or the Assessment of Space Needs in

Juvenile Detention and Corrections project, contact the OJJDP Program Specialist

responsible for the effort, Joseph Moone, at 202–307–5929 (phone) or

moone@ojp.usdoj.gov (e-mail).
3

those policies by electing some officials

and not others.

Moreover, the confinement space provided by detention and correctional facilities is just one type of resource available

for accomplishing the varied tasks of the

juvenile justice system—preventing juvenile crime, rehabilitating individual

offenders, controlling the behavior of offenders, and holding offenders accountable for their behavior through the use

of sanctions. Each of these responsibilities may sometimes involve the use of

secure confinement, but none always requires it. Even controlling offender behavior and holding youth accountable can be

achieved in certain cases without the use

of incarceration. Each jurisdiction’s particular combination of incarceration and

nonincarceration is a function of its experiences, resources, values, and policy

choices. (See “More Than One Type of

Space” on this page.)

Appropriate Confinement

Decisions

Every State or local jurisdiction with a

juvenile justice system must build and

manage a system that responds effectively to the actual (and, to some extent,

perceived) level of juvenile crime in the

community. To build an effective system,

policymakers must regularly receive information about the volume and characteristics of the juvenile offender population in their jurisdictions, the quality and

availability of their juvenile justice resources, and the mix of those resources,

both residential and nonresidential.

Confinement decisions can be best understood by analyzing three dimensions:

◆ Caseload. How many offenders are

coming into the juvenile justice system? What are the characteristics of

those offenders from either a public

safety or rehabilitation perspective?

◆ Process. What decisions does the juvenile justice system make concerning

the handling of individual offenders?

Who is involved in decisionmaking,

and what information is used to reach

decisions in individual cases?

◆ Preferences. What program options

are available for implementing decisions made within the juvenile justice

system? Who is involved in selecting

and supporting available program options, what information do they use,

and what values and beliefs underlie

their choices?

The answers to these questions will vary

from jurisdiction to jurisdiction and will

be determined by the choices and policies of a number of agencies. Even the

first dimension, caseload, is, in part, a

function of the choices and policies of

law enforcement agencies. One jurisdiction, for example, may arrest every

youth caught with even the smallest

amount of marijuana, while another may

elect to use unofficial diversion for every

first-time offender possessing less than

an ounce. The second and third dimensions, process and preferences, are exclusively shaped by policy choices, including the statutory choices of elected

officials.

Every young offender presents a challenge for juvenile justice officials. Which

program options are best? What are the

most cost-effective available options, not

only for ensuring the safety of the public

but also for preserving the chances of

youth to have positive and productive

lives? Every decision has ramifications.

Some are direct and immediately apparent. Others are indirect and difficult to

notice in the short term.

Impact of Preferences

and Policies

Decisions made by legislators, judges,

police and probation officials, social

workers, and juvenile facility administrators help to determine which juvenile

offenders are placed in detention or correctional facilities, when they are placed,

and how long they stay. Some factors involved in these decisions are similar to

the factors involved in adult jail and

prison commitments. These include the

severity of each offender’s most recent

offense and the extent and severity of his

or her record. The juvenile justice system, however, often has more discretion

in responding to these factors. For example, juvenile courts may sometimes

place offenders in secure custody for

their own protection and hold offenders

in custody because they failed to appear

for court hearings when released on previous charges.

Some aspects of juvenile justice decisionmaking may be unique to the juvenile justice system. Considerations that would be

prohibited in the criminal justice system

may influence a decision to place a youth

in a secure facility. A juvenile court judge

may decide to detain a youth or commit

him or her to a correctional facility in

More Than One Type

of Space

Space, in a juvenile justice context,

is often measured in terms of beds. The

number of juveniles that can be held in a

detention or correctional facility is equal

to its sleeping capacity. Thus, policy discussions about juvenile justice program

resources often focus on the availability

of “bedspace.”

Bedspace Sometimes a

Misnomer

Bedspace, however, can be a misnomer if the term is used too generally.

The number of beds available in a

jurisdiction is not equal to its juvenile

justice program resources. Some

programs can effectively supervise,

control, and hold young offenders accountable without requiring them to be

in residence for 24 hours each day.

Nonresidential programs may include

home detention, intensive supervision,

electronic monitoring, day reporting,

and vocational training. Young offenders may spend much of their day under the control of these programs but

then return to their own homes to

sleep at night.

Effective Policy Requires

a Broader View

To assess the validity of demands for

additional bedspace, policymakers

need information about all resources

available in a juvenile justice system,

not only the amount of residential

bedspace.

Ultimately, the need for additional

bedspace in a jurisdiction is related to

◆ The number of juveniles requiring

treatment, supervision, and control.

◆ The availability and quality of existing bedspace.

◆ The availability, quality, and use of

nonresidential program resources.

part because the youth is thought to have

a drug abuse problem, although no drug

charges may be involved in the case. A

juvenile with a precarious family situation

and chaotic home environment may be

placed in a secure setting to ensure the

delivery of social services.
4

Placement decisions may also be influenced by the availability and perceived

adequacy of program alternatives. Placement rates may be higher when juvenile

courts have fewer nonresidential options

to draw on in lieu of placement (e.g., in

rural areas and impoverished communities). For these reasons, the use of secure confinement in the juvenile justice

system is rarely a straightforward consequence of trends in juvenile populations

and crime rates. Some researchers might

even argue that a statistical model would

perform better using the availability of

bedspace to predict juvenile placement

decisions than it would using placement

decisions to predict bedspace.

Projections of

Juvenile Confinement

Populations

Sound projections require high-quality

data. Without data, policymakers have

only the opinions and beliefs of practitioners and administrators with which

to project future needs for bedspace.

The superintendent of a detention center

may offer his or her personal observations about crowding in detention. The

administrator of a corrections facility

may observe that young offenders are

being placed on waiting lists because of

insufficient space. A county sheriff may

complain that officers are required to

transport youth to a neighboring jurisdiction to find an opening in a secure

facility. Although personal observations

may be helpful in making projections,

relying on anecdotal information alone

may result in costly errors. Each individual involved in the juvenile justice

process can explain the process only

from his or her unique perspective.

Few are aware of every aspect of the

process and of the complex interactions

between decisions made at various

points in the process.

Once policymakers decide to look beyond

personal opinions, they need data about

the use of detention and corrections

space. Unfortunately, the easiest information to assemble is rarely ideal. In some

jurisdictions, the only readily available

data may be about past uses of detention

and corrections space. An agency might

only know that admissions to juvenile

corrections grew 50 percent during the

past 10 years. Some policymakers might

interpret this as a legitimate reason to

fund an additional 50-percent increase in

corrections space over the next 10 years,

but this could be a poor decision. Obviously, a jurisdiction that increased its

bedspace significantly in 1999 should not

rely on the increase in admissions from

1998 to 2000 to argue for yet more bedspace in 2001. Similarly, it would be unfair to use the lack of an increase to argue

that an agency does not require additional space. Perhaps a jurisdiction has

not funded any new corrections space

during the past 20 years. Flat funding

would explain the jurisdiction’s flat admission numbers, but this would not

necessarily mean that additional space

is unwarranted.

Policymakers are better served when agencies can generate additional information.

Figure 1: Using population alone, an analyst working in 1970 would

have recommended no expansion in detention and corrections space through the 1990’s—yet the number of delinquency cases nationwide doubled during that period

– 40

0

40

80

120

1970 1974 1978 1982 1986 1990 1994 1998

Year

U.S. population ages 10–17

Delinquency cases handled

by U.S. juvenile courts

Change Relative to 1970 (%)

◆ Between 1970 and 2000, the U.S. juvenile population declined from 32 million to 27

million, then rebounded to nearly 32 million again.

◆ Between 1970 and 1997, the number of delinquency cases handled by the Nation’s

juvenile courts more than doubled, from approximately 800,000 to nearly 1.8 million

annually.

Source: Data from U.S. Bureau of the Census’ National Residential Population Estimates

series and the National Center for Juvenile Justice’s (NCJJ’s) National Juvenile Court Data

Archive (NJCDA). For population estimates prior to 1980, see 1970 Census of the Population,

Vol. 1. Characteristics of the Population, Part 1: United States Summary, Section 1, U.S.

Department of Commerce, Bureau of the Census, June 1973. Estimates for 1971–79 were

interpolated using 1970 and 1980 single-year age estimates and 1975 estimates for grouped

ages. NJCDA national estimates prior to 1975 included status offenses. The average delinquency proportion of the delinquency/status totals for 1975–79 was used to adjust NJCDA

data before 1975.

For example, researchers could analyze

trends in the use of waiting lists and

early releases from confinement. An increase in these practices may indicate a

growing demand for space. Even this information, however, does not eliminate

the risk of misinterpretation. The fact

that a juvenile detention center is constantly full with no waiting lists or early

releases could have more than one explanation. It could mean that available

space is precisely equal to demand, or

it could mean that local decisionmakers

have learned to refer just enough youth

to detention so that a facility remains

full without being oversubscribed.

What would policymakers conclude if

the same correctional facility suddenly

began to report crowding, early releases,
5

and waiting lists for admission? Such a

development might indicate an increase

in juvenile crime and the need for more

space, or it might mean that local authorities had decided to begin referring

all potential detention cases for placement and not concern themselves with

availability. Projecting future space

needs requires more extensive analysis.

The question is what type of analysis?

Limits of Simple Models

Juvenile justice agencies often begin

their efforts to project detention and

corrections populations with relatively

simple models. Simple models may provide projections quickly and at relatively

little cost, but they can also produce

misleading information. One of the most

common simple models assesses the

need for secure confinement resources

according to expected changes in the

juvenile population (e.g., youth ages 10

through 17). If a jurisdiction has 100

detention beds and its juvenile population is expected to increase 20 percent

over the next 10 years, policymakers

might recommend expanding detention

capacity to 120 beds over the same period. This approach may be an improvement in a jurisdiction that has previously

used only anecdotal methods to anticipate future space needs, but it has great

potential for error. Consider the fact that

the national population of juveniles was

relatively unchanged between 1970 and

1998, a period when juvenile court caseloads more than doubled. An analyst

working with population data alone in

the 1970’s or 1980’s could have produced

very misleading projections (figure 1).

Most juvenile justice administrators

know that projection efforts must include at least some data about the juvenile justice process because the number

of offenders referred for placement can

differ considerably from trends in the

juvenile population. One approach

commonly used by State and local agencies is to monitor trends in juvenile arrests and then estimate future demand

for detention and corrections space

based on expected changes in the number of arrests. For example, some jurisdictions base their projections on

trends in juvenile arrests for the most

serious offenses, such as the Federal

Bureau of Investigation (FBI) Violent

Crime Index offenses (i.e., murder and

nonnegligent manslaughter, forcible

rape, aggravated assault, and robbery).

The logic behind this approach is that

youth charged with violent and other

serious offenses generate most of the

space needs in any jurisdiction.

The complexity of juvenile justice decisionmaking, however, virtually guarantees

that detention and corrections populations will not follow Violent Crime Index

arrest trends so closely. National changes

in juvenile arrests during the 1990’s underscore this point. The 1990’s were a

virtual case study in how difficult it can

be to predict juvenile justice trends. No

statistical model could have anticipated

the changes in serious juvenile crime that

occurred between 1985 and the end of the

1990’s (figure 2).

Consider what would have happened if

an analyst working in 1985 had projected

changes in the nationwide demand for

bedspace using 5-year trends in FBI Violent Crime Index arrests. The projection

of bedspace needs in 1990 would have

been produced by multiplying 1985 levels of placement resources by the percentage change in Violent Crime Index

arrests between 1980 and 1985—a decrease of 9 percent. Arrests for violent

offenses, however, were about to jump

sharply. A projection from 1985 would

have underestimated the volume of arrests in 1990 by 33 percent. An analyst

working in 1990 would have been more

fortunate using the percentage change in

arrests from 1985 to 1990 (up 37 percent)

to project space needs in 1995. Yet, a few

years later, in 1993, the same technique

would have produced estimates for 1998

that were far larger than actual need.

No statistician using this method in 1993

would have predicted that juvenile arrests for violent offenses would drop

25 percent between 1994 and 1998.

Extending the period of calculation by

using 10-year trends rather than 5-year

trends would ameliorate the problem

Figure 2: Predictions based on arrests since 1980 would have been

very different depending on when they were generated

1980 1982 1986 1988 1990 1992 1994

Year

Number of Arrests

0

60,000

120,000

1984 1996 1998

20,000

40,000

80,000

100,000

140,000

160,000

Juvenile arrests for FBI Violent 

Crime Index offenses

Five-Year Trend Predictions as Calculated in 1985, 1990, and 1993

Predicted

Date of Change in Arrests Actual Arrests

Prediction Prior 5 Years (%) in 5 Years in 5 Years Error

1985 –9 76,100 114,200 33% under

1990 37 156,400 147,700 6% over

1993 49 206,100 112,200 84% over

Source: Data from the FBI’s Crime in the United States annual series. National estimates

calculated by The Urban Institute using methods developed by NCJJ (see Snyder, 1999).
6

somewhat but not resolve it entirely

because the number of arrests is not directly linked to the number of placements. Analysts will produce more useful

projections when they include juvenile

court processing data in projection models. The juvenile court process is the

principal gatekeeper for placements in

juvenile bedspace. The juvenile court

usually approves detention decisions, or

at least it must approve the continuation

of detention beyond some statutorily defined limit (e.g., 72 hours). The juvenile

court is also the main access point for

placement in (or commitment to) longterm facilities. To be admitted to a juvenile correctional facility, young offenders

must be referred to court, officially

charged with delinquency, adjudicated

a delinquent, and then committed by the

court. Thus, changes in detention and

corrections populations are likely to be

more closely related to changing court

actions than to changes in juvenile

arrests.

This is clear when trends in juvenile arrests are compared over time with trends

in juvenile court delinquency cases (figure 3). Between 1980 and 1997, for example, increases in delinquency cases

outpaced increases in juvenile arrests.

According to the Office of Juvenile Justice

and Delinquency Prevention’s (OJJDP’s)

Juvenile Court Statistics program at the

National Center for Juvenile Justice, U.S.

juvenile courts handled slightly more

than 1 million delinquency cases in 1980,

just half the number of arrests involving

youth younger than age 18 that year. By

1997, the total number of delinquency

cases handled by juvenile courts represented 62 percent of the number of

arrests.1

Law enforcement’s increasing use of

court referrals for arrested youth is also

apparent when the analysis examines

only court cases and arrests that involved FBI Crime Index offenses (i.e.,

all offenses on the Violent and Property

Crime Indexes). In the early 1980’s, the

number of court cases involving Crime

Index offenses equaled about 70 percent

of the number of juvenile arrests involving Crime Index offenses. By the late

1990’s, the number of juvenile court

cases involving these offenses represented nearly 90 percent of the number

of arrests.

Projection efforts are more useful if they

can account for changing patterns in

court processing. A changing rate of

formal prosecution in juvenile courts, for

example, could have a dramatic effect on

the number of youthful offenders placed

in secure facilities. National data about

juvenile court processing reveal, in fact,

that the proportion of delinquency cases

handled formally (with prosecutor petitions rather than informal agreements

for diversion or dismissals) increased

from 49 percent to 57 percent between

1983 and 1997 (figure 4).

This shift toward more formal handling

could have been expected to increase

the number of juveniles eligible for outof-home placement. An analyst projecting future space needs with this information might still have made significant

errors, however, unless the analysis

was amended to include an additional

factor—namely, changes in the use of

formal adjudication. Between 1983 and

1997, as the use of formal petitioning

increased, the use of adjudication saw

a corresponding decrease from 68 percent to 58 percent. When both changes

are considered together, it is clear that

the total rate of adjudication (adjudication as a percentage of referrals) remained unchanged between 1983 and

1997 (33 percent in both years). This example demonstrates that projection

models are likely to perform better

when they include more than a single

source of information and when they

Figure 3: Predictions based on arrests since 1980 would fail to account

for changes in how juvenile arrests were processed by

prosecutors and the courts

Delinquency Cases and Juvenile Arrests: 1980, 1990, and 1997

1980 1990 1997

All offenses

Juvenile arrests 2,166,600 2,214,500 2,838,300

Delinquency cases 1,089,500 1,318,000 1,755,100

Ratio of arrests to cases 2 to 1 1.7 to 1 1.6 to 1

Index offenses

Juvenile arrests 839,900 822,800 824,900

Delinquency cases 544,900 631,300 705,100

Ratio of arrests to cases 1.5 to 1 1.3 to 1 1.2 to 1

Source: Data from NCJJ’s National Juvenile Court Data Archive and the FBI’s Crime in the

United States annual series. National estimates calculated by The Urban Institute using

methods developed by NCJJ.

40

50

60

90

1980 1982 1986 1988 1990 1992 1994

Year

Delinquency Cases as Percentage of 

Juvenile Arrests (National Estimates)

1984 1996 1998

70

80

FBI Crime Index offenses

All offenses
7

analyze more than a single point in the

juvenile justice process.

Example: Projecting the

Juvenile Commitment

Population in 2002

The following section presents an example of a projection model using data

about the national population of juvenile

offenders committed to residential facilities.2

 The analysis provides several different projections, each based on a different

set of assumptions. The results from each

set of assumptions reveal the sensitivity

of population projections to changes in

policy and practice, including changes in

the rate of referral, the rate of adjudication, the number of out-of-home placements, and the average length of those

placements. The range of projections

based on these varying assumptions

helps to set upper and lower bounds on

the future size of the national commitment population. The analysis uses data

from 1993 to 1997 to project populations

through 2002. The results suggest that a

major determinant of change in the commitment population originates outside

Figure 4: Despite changing patterns in the handling of delinquency

cases between 1983 and 1997, the overall use of adjudication

and out-of-home placement remained relatively consistent

1983 1985 1989 1991 1993 1995 1997

Year

Percentage of Cases

1987

20

30

40

50

60

70 Adjudications as percentage 

of formally petitioned cases

Formal petitions as percentage

of referred delinquency cases

Out-of-home placements as

percentage of adjudicated cases

◆ In 1983, 49 percent of delinquency cases were formally petitioned and 68 percent of these

were adjudicated, resulting in a total adjudication rate of 33 percent.

◆ In 1997, a 57-percent petition rate and 58-percent adjudication rate again resulted in a

total adjudication rate of 33 percent.

◆ The use of out-of-home placement was relatively consistent between 1983 and 1997,

varying between 28 and 32 percent of adjudicated cases throughout the period.

Source: Data from NCJJ’s National Juvenile Court Data Archive.

Delinquency Case Processing, 1993–97

The number of juveniles in commitment increased from 37,700 in 1993 to

52,500 in 1997. The increase was due

to a number of factors—the growth in

the number of referrals to juvenile court,

changes in the rate of adjudication,

changes in the rate of residential placement, and changes in lengths of stay.

Referral

◆ The total number of delinquency cases

referred to juvenile courts that involved

youth ages 10 to 17 increased 19 percent between 1993 and 1997, from

approximately 1.4 to nearly 1.7 million.

◆ Cases involving property offenses accounted for half of all court referrals in

both years.

◆ The rate of growth was largest among

drug cases, which more than doubled,

and for public order offenses, which

grew more than 30 percent.

Adjudication

◆ Between 1993 and 1997, the number of

cases resulting in adjudication increased

26 percent.

◆ The number of adjudicated cases increased in every major offense category.

◆ The rate of adjudication (the number

of adjudications divided by referrals) increased 2 percent. The rate was stable

for all major offense categories.

Placement

◆ From 1993 to 1997, the percentage of

adjudicated cases involving youth ages

10 to 17 that resulted in residential placement was relatively stable at 31 to 32

percent.

◆ The use of placement was constant for

property and public order offenses. For

drug offenses, the use of placement

decreased from 32 to 27 percent.

Note: These data differ from other published analyses of National Juvenile Court Data Archive data because cases involving youth under age 10 or

older than age 17 are excluded, as are technical violation cases. Percent changes were calculated using unrounded numbers.

Source: Urban Institute analysis of data from NCJJ’s National Juvenile Court Data Archive. National estimates of delinquency cases involving youth

ages 10 to 17.

◆ For person crimes, the use of residential placement dropped from 35 to 32

percent of adjudications.

Length of Stay

◆ The average length of stay for committed juveniles increased 14 percent

between 1993 and 1997, from 96 to

109 days.

◆ Most of the growth in length of stay

was driven by person crime offenders

(whose average length of stay increased from 162 to 180 days) and

by property crime offenders (89 to

104 days).

◆ Length of stay increased from 22 to

49 days for public order offenders and

decreased from 148 to 113 days for

drug offenders.
8

the juvenile court—namely, the number of

referrals by law enforcement. The relative

rates of adjudication and placement and

changes in average lengths of stay also

affect the size of commitment populations.

(Trends in these components of delinquency case processing between 1993

and 1997 are summarized on page 7.)

According to data collected for OJJDP by

the U.S. Bureau of the Census, the daily

size of the committed juvenile population

in custody for delinquency offenses increased 38 percent between 1993 and 1997,

from 52,000 to 71,700 (see table 1). For this

example, however, several adjustments to

these data are necessary.3

 First, the raw

Table 1: Juvenile Offenders in Residential Placement, 1993–97

One-Day Count of Juvenile Offenders in Custody

(delinquency offenses only)

Population 1993 1995 1997

Total population of juveniles

committed to residential

placement 52,000 59,500 71,700

Private-facility-adjusted

population* 55,200 61,600 71,700

Age-adjusted population† 37,700 43,500 52,500

Person offenders 14,800 18,300 19,800

Property offenders 16,600 17,800 21,300

Drug offenders 4,300 4,600 5,500

Public order offenders 1,900 2,800 5,900

* Adjustments were made to 1993 and 1995 committed populations to compensate for undercounts

of juveniles in placement in private facilities in those years. This was done by applying the ratio of

delinquent youth in private facilities to delinquent youth in public facilities in 1997 to the reported

population of youth in public facilities in 1993 and 1995, respectively, to obtain an estimate of the

number of delinquent youth in private facilities for those years. These estimates were added to the

reported number of delinquent youth in public facilities for 1993 and 1995, respectively, to obtain

private-facility-adjusted commitment populations for each year.

† The Children in Custody (CIC) census for 1993 and 1995 does not disaggregate committed and

detained delinquent populations by age. To obtain this information for youth ages 10–17, offensespecific adjustments were made based on the proportion of 10- to 17-year-olds in the overall

detained and committed populations in 1997, which is provided by OJJDP’s Census of Juveniles in

Residential Placement 1997. The assumption is that the proportion of 10- to 17-year-olds in the

detained and committed populations in 1993 and 1995 was the same as that actually observed in

1997. This assumption is supported by the age distribution of the overall custody population during

1993–97, which remained quite stable. (CIC data provide the age distribution for the overall juvenile

custody population but do not distinguish between offenders and nonoffenders or between delinquent and status offenders. The universe for this study is delinquent offenders only.) The 10- to

17-year-old portion of the overall custody population was remarkably stable during 1993–97: 87.4

percent in 1993, 87.8 percent in 1995, and 87.5 percent in 1997. These age-adjusted custody

populations also exclude youth in facilities for technical violations.

Note: Detail may not add to totals due to rounding. These counts include committed youth only;

detained youth are excluded.

Source: NCJJ analysis of OJJDP’s Children in Custody census 1993 and 1995 data files and

OJJDP’s Census of Juveniles in Residential Placement 1997 data file.

data most likely underestimate the number of juveniles in private facilities during

the 1993–95 period. Adjusting for this

undercount produces slightly higher figures.4

 The data are also adjusted to account for the fact that although many

youth in the commitment population at

any given time are older than 17, very few

are older than 17 at the time of their commitment. Adjusting the data for age allows

the analysis to compare more directly the

data on commitment populations with

data on commitment admissions.5

 The

analysis also limits the commitment

population to juveniles who were placed in

residential facilities for new offenses. Juveniles committed for technical violations of

probation are excluded. After making

these adjustments, the analysis suggests

that the juvenile commitment population

increased 39 percent between 1993 and

1997, from 37,700 to 52,500.

To generate estimates of the future commitment population, a statistical flow

model is used that analyzes the processing of delinquency cases to the point

of placement and models the lengths

of stay in placement. The model begins

with a starting population and calculates

transition rates (or probabilities that

cases will move from one stage of the

juvenile justice process to the next). The

flow model includes the following stages:

(1) referral to juvenile court, (2) adjudication, (3) commitment to residential

placement, and (4) length of stay for

youth in residential placement. Transition probabilities include the adjudication rate (the percentage of referred

cases that are adjudicated), the use of

residential placement (the percentage

of adjudicated cases that are committed

to residential facilities), and the average

length of stay in facilities (measured as

a stock-to-flow ratio; see discussion of

length of stay, pages 12–13).6

 These transition probabilities are shown in table 2.

Changes in the commitment population

can be shaped by a variety of case processing components, including the number of juvenile court referrals, the percentage of those referrals that result in

adjudication, the number of those cases

that end in residential placement, and

the length of those placements. As these

components increase or decrease, they

exert an influence on the size of the commitment population. It is possible to isolate the changes in each component and

determine the share of the total change

in the commitment population for which

each is responsible (see Methodology on

page 17). Certain components may contribute to growth, while others may have

the opposite effect. For example, if the

number of court referrals increases, this

will contribute to an expansion of the

commitment population. At the same

time, other elements of the system could

curtail growth. A decrease in the use of

placement could offset part or all of the

growth generated by increasing referrals.

Adding up the “shares” from all components of juvenile justice case processing

yields the overall net change in the commitment population.
9

Table 3 and figure 5 show how each component of the system contributed to the

amount of overall change in the commitment population between 1993 and 1997.

Several factors contributed to the expansion of this population from 37,700 to

52,500 juveniles. Increases in the number

of court referrals, the rate of adjudication, and the average length of stay all

contributed to the expansion, while the

decrease in the use of residential placement had a curtailing effect.

Of the four major offense categories (person, property, drugs, public order), person

and property offenses accounted for most

(each about one-third) of the total change

in the commitment population. Increases

in the number of commitments for public

order and drug offenses accounted for approximately 27 percent and 9 percent, respectively, of the change in the commitment population.

Increases in length of stay accounted for

80 percent of the growth in the commitment population of offenders charged

with public order offenses. For those

charged with drug offenses, increases

in the number of youth referred—which

more than doubled between 1993 and

1997—overrode the generally downward

trend of all other transition probabilities

Table 2: Referrals to Juvenile Court and Transition Probabilities for Youth in Residential Placement, 1993 and 1997

Rate of

Number of Referrals Rate of Residential Length of Stay

to Juvenile Court Adjudication (%) Placement (%)* (stock/flow ratio)†

Change 1993 1997 Change

Offense 1993 1997 (%) 1993 1997 1993 1997 (days) (days) (%)

Total 1,427,600 1,693,600 19 31 33 32 31 96 109 14

Person 309,200 378,200 22 31 33 35 32 162 180 11

Property 784,000 812,600 4 30 32 29 29 89 104 17

Drug 86,200 177,300 106 37 37 33 27 148 113 –24

Public order 248,200 325,500 31 34 37 37 37 22 49 123

* Percentage of adjudicated cases committed to residential facilities.

† Stock/flow ratio of the number of juveniles in residential facilities divided by the number of cases resulting in residential placement during the year.

The ratio is converted to the unit of days.

Source: OJJDP’s Children in Custody census 1993 data file, OJJDP’s Census of Juveniles in Residential Placement 1997 data file, and NCJJ’s National

Juvenile Court Data Archive 1993 and 1997 data files.

 Table 3: Change in Number of Juveniles Committed to Residential

 Placement Between 1993 and 1997, by Category of Offense and

 Components of Change

Number of Juveniles Committed

Offense 1993 1997 Net Change

Total 37,700 52,500 14,900

Person 14,800 19,800 4,900

Property 16,600 21,300 4,700

Drug 4,300 5,500 1,300

Public order 1,900 5,900 4,000

Change in the Juvenile Commitment Population

Between 1993 and 1997 Due To:

Use of Length

Offense Referral Adjudication Placement of Stay Net Change

Total 9,000 1,800 –2,600 6,600 14,900

Person 3,300 900 –1,200 2,000 4,900

Property 600 900 100 3,200 4,700

Drug 4,500 –100 –1,700 –1,500 1,300

Public order 600 200 0 3,200 4,000

Note: Detail may not add to totals due to rounding. Calculations were based on unrounded numbers.

Source: OJJDP’s Children in Custody census 1993 data file, OJJDP’s Census of Juveniles in

Residential Placement 1997 data file, and NCJJ’s National Juvenile Court Data Archive 1993 and

1997 data files.
10

(the adjudication rate, the use of placement, and average length of stay) associated with these offenders. Although

there were minor offense-specific variations from the overall sources of change,

all of the major offense categories contributed to the increase in the number of

juveniles committed to residential facilities (table 3).

The commitment population through

2002 is projected in the analysis by using

a mathematical flow model based on the

approach first developed by Stollmack

(1973) to project prison populations (see

“A Brief History of Corrections Population

Projection Methods” on page 14). Future

populations are projected by relating

flows to stocks by length of stay—the inverse of which represents the turnover

rate of the population. The model requires explicit assumptions about the

case processing factors that might influence the size of confinement populations.

For example, the model must include assumptions about changes in referrals and

length of stay. Will the number of court

referrals continue to rise through the year

2002, or will it stabilize at the 1997 level?

Figure 5: How much did each source of change contribute to the overall change in the population of juveniles

in commitment from 1993 to 1997?

Overall Age-Adjusted Commitment Population

0

10,000

20,000

30,000

40,000

50,000

60,000

1993 1997

(52,500)

Court referrals

An increase of 19 percent in juvenile court referrals accounted for 9,000 of

the net increase of 14,900 juveniles in the commitment population.

Cases adjudicated

An increased rate of adjudication (from 31 to 33 percent) accounted for

1,800 of the net increase of 14,900 in the commitment population.

Use of residential placement

A decrease in the percentage of adjudicated cases committed to residential 

placement (from 32 to 31 percent) curtailed growth in the commitment 

population by 2,600 juveniles.

Length of stay

An increase in average length of stay (from 96 to 109 days) accounted for 

6,600 of the net increase of 14,900 juveniles in the commitment population.

(37,700)

Combined effect of components of change in commitment population from 

1993 to 1997

Year 

Note: Components of change may not add to total due to rounding.

Source: Urban Institute analysis of OJJDP’s Census of Juveniles in Residential Placement 1997 data file, OJJDP’s Children in Custody census

1993 data file, and NCJJ’s National Juvenile Court Data Archive 1993 and 1997 data files.

Will average length of stay increase or

decrease? Assumptions about how these

components will or will not change after

1997 have a significant effect on projections of the juvenile population in facilities. The following analysis considers several possible scenarios to project a range

of 2002 commitment populations.

Five projections of the commitment population were developed, each based on a

different set of assumptions (figure 6).

These projections (referred to as A, B, C,

D, and E) yield commitment populations

ranging from almost 53,000 to more than

102,000 by the year 2002 (figure 7). For

example, if 1997 conditions were to persist for 5 years after 1997 (projection A),

the number of juveniles in commitment

facilities in 2002 would be expected to

remain at the 1997 level (about 53,000 juveniles). In other words, if juvenile courts

were to continue to commit juveniles to

residential placement at the 1997 rate, to

adjudicate cases at the 1997 rate, and to

hold juveniles in facilities for an average

of 109 days, just as in 1997, the commitment population would remain at the 1997

level.

Conditions in the juvenile justice system rarely remain unchanged for several years at a time. There are specific

reasons to doubt that the conditions

of 1997 would continue for very long

beyond 1997. First, the commitment

population was growing at an increasing rate between 1993 and 1997. Second, the number of cases referred to

juvenile courts also increased, and this

was responsible for a large part of the

total increase in the commitment population. In addition, the average length

of stay changed between 1993 and 1997,

growing from 96 to 109 days. Improbable changes in case processing would

have had to occur for admissions and

length of stay to have remained constant after 1997. For admissions to stabilize, for example, the increase in the

number of referrals to juvenile court

between 1993 and 1997 would have had

to reverse itself after 1997 or the use of

residential placement would have had

to decrease sharply. These changes

are unlikely, given trends observed

between 1993 and 1997.
11

On the other hand, if changes in caseprocessing practices were incorporated

into the projections, the expected population could follow the paths of projection lines B, C, D, or E. These projections

show how the juvenile population in

residential placement would change

based on varying assumptions about

admissions and the average length of

stay for committed youth. Under projection B (stable length of stay, admission

trends continue), the population would

increase to almost 69,000 in the year

2002. Under projection C (stable admissions, trends continue in length of stay),

the population would grow to about

75,000 by 2002. Projection D shows how

the population would change given the

assumption that admissions and length

of stay each continue the trend observed

from 1993 to 1997. It projects that the

commitment population would grow at

a steep rate, increasing to just more than

98,000 by 2002.

These projections point out the importance of the key policy variables (the rate

of referral to court, the rate of adjudication, the use of placement, and the length

of stay of youth in residential placement)

in anticipating future demand for bedspace. Each of these variables represents

important considerations for policy and

practice. The number of youth referred to

court reflects the volume of delinquent

acts in the community, but it also reflects

the policies and priorities of the juvenile

justice system, the availability of alternatives to secure confinement, and the range

of diversion options. The amount of time

juveniles spend in residential facilities is a

function of offense seriousness, but it also

reflects policy decisions about the use of

secure confinement and the availability of

postrelease supervision. (For a discussion

of why length of stay is important and

how it is measured, see pages 12–13.)

These relatively simple projection models

can also be used to consider different

policy and program choices and to simulate their effects. For example, suppose

juvenile justice officials know that the average length of stay for youth committed

for drug offenses will increase significantly

because of plans to administer more drug

treatment during confinement. Assume

that the new drug treatment programs will

increase the average length of stay for

drug offenders by 5 percent each year

between 1998 and 2002. For all other

offenders (nondrug), length of stay will

Figure 6: Five assumptions are used to define alternative projections

of the juvenile commitment population, 1998–2002

Assuming average length

of stay remains at 1997 level.

Projecting average length of stay

based on 1993–97 changes.

Fixing length of stay for drug

cases to increase 5 percent

annually; projecting length

of stay for all nondrug cases

based on 1993–97 changes.

Assuming admissions

remain at 1997 level

Projecting admissions based

on 1993–97 changes

Projection A

Projection C

Projection B

Projection D

Projection E

Figure 7: Projections of the juvenile commitment population vary

greatly according to assumptions about future conditions

1993 1997 1999 2001

Year

1995

20,000

40,000

60,000

80,000

100,000

120,000

Committed Juvenile Population

1991 2003

(E) 102,100

(D) 98,300

(C) 75,100

(B) 68,800

(A) 52,500

Actual age-adjusted* committed 

population, 1991–1997

Projected commitment

population, 2002

Assumptions:

A. Admissions and length of stay (LOS) remain at 1997 levels.

B. LOS remains at 1997 levels; admissions projected based on 1993–97 trends.

C. Admissions remain at 1997 levels; LOS projected based on 1993–97 trends.

D. Admissions and LOS projected based on 1993–97 trends.

E. Admissions based on 1993–97 trends; LOS for drug offenders increases by 5 percent each

year; LOS for all other offenders is projected based on 1993–97 trends.

* For the definition of the “age-adjusted” juvenile commitment population, see table 1, second

footnote.

Source: Urban Institute analysis of OJJDP’s Census of Juveniles in Residential Placement

1997 data files, OJJDP’s Children In Custody census 1991, 1993, and 1995 data files, and

NCJJ’s National Juvenile Court Data Archive 1993 and 1997 data files.
12

Length of Stay: Why It Is Important and How It Is Measured

Changes in the size of juvenile corrections populations can be

understood in relation to the number of people who move into and

out of facilities (or “flow”) and the length of time that they stay in

facilities (length of stay). Length of stay is a critical ingredient in

projections of juvenile custody populations. A corrections or detention population can change dramatically if a facility’s length of

stay begins to change, even if admissions are stable. Measuring

length of stay, however, can be challenging. There are three commonly used methods of estimating length of stay.

Estimation Methods

Exit Cohort

The most popular measure of length of stay is the average

amount of time spent in corrections by a group of youth released

during a given period of time. Known as an “exit cohort” estimate, this technique for estimating length of stay is easy to calculate and easy to interpret. However, it can underestimate the

length of time individuals actually spend in correctional facilities.

By definition, exit cohorts contain a disproportionate number of

individuals who had short stays.

Calculating an exit-cohort estimate of length of stay is easy

once the necessary data are assembled. The following example

shows the data for an exit cohort of five individuals released between April 1 and June 1. By combining their admission dates

and release dates and calculating each person’s length of stay,

it is possible to determine that this cohort’s average length of

stay was 87 days.

Calculating Average Length of Stay With Data

for an Exit Cohort

Cohort Admission Release Length of Stay

Members Date Date (in days)

Person A January 1 April 1 90

Person B January 1 April 10 100

Person C February 1 April 23 82

Person D February 1 May 15 104

Person E April 1 June 1 61

Average 87

Days Since Admission

Another common measure of length of stay is the average number of days that the current population of a detention or correctional facility has been in the facility as of a certain day. This

measure is easy to calculate, but it can also involve considerable bias. As with the exit-cohort estimation technique, it

involves just one source of data (the current “stock”).

In addition, average “days since admission” can significantly

overestimate length of stay because the current population

of any facility necessarily contains a disproportionately large

number of individuals who have had long stays.* If “days since

admission” is the only estimate possible with existing data, however, it can still be useful. The following is an example of a “days

since admission” estimate for a population containing five individuals. Using only today’s date and the admission dates for all

members of the population, it is possible to determine that the

average length of stay for this population is 39 days.

Calculating Average Length of Stay Using “Days

Since Admission”

Members

of the Admission Days Since

Population Date Today’s Date Admission

Person A January 1 April 1 90

Person B February 1 April 1 59

Person C March 1 April 1 31

Person D March 15 April 1 16

Person E March 31 April 1 1

Average 39

Stock/Flow Ratio

A third method of estimating length of stay is to calculate a ratio of

“stocks” and “flows,” where stock and flow are defined as follows:

Stock = the number of youth in a population on a given day (or

some measure of average daily population).

Flow = the number of youth released from the population over a

given period of time, usually monthly or annually. (If data on actual releases are not available, admissions data can be used to

estimate “flows,” but this assumes admissions and releases are

in equilibrium over the time period of interest.)

A stock/flow ratio can also be a biased estimator for length of

stay if the size of the population or the release rate is changing

rapidly. The extent of the bias, however, may be less than that

of other estimates since stock/flow ratios involve information

from two sources (stock and flow). Calculating length-of-stay

* Using “days since admission” to estimate a facility’s total length of stay

would be similar to estimating the life expectancy of Americans by

calculating the average age of all people alive now.

follow the average annual trends seen during the 1993–97 period. Under these assumptions, the commitment population

would nearly double from 53,000 in 1997 to

about 102,000 in 2002 (projection E). Thus,

the addition of drug treatment programs

and their effect on length of stay for drug

offenders could increase the commitment

population by almost 4,000 (the difference

between projection D and projection E).

These examples suggest how projection

models could be used to anticipate future

commitment populations, given varying

assumptions about future conditions. The

value of these examples is limited by the

lack of more detailed data. For instance,

the models presented here divide the

commitment population into only four
13

Length of Stay—Continued

estimates with stock/flow ratios can be fairly simple once the

appropriate information is available. The following two examples present length-of-stay estimates as stock/flow ratios.

Example 1: Assume that a juvenile correctional facility had an

average daily population of 300 during the preceding year, and

assume that 425 juveniles were released during the year. Using

this information, an analyst could estimate the facility’s length of

stay by dividing the stock (300) by the flow (425), which would

suggest that juveniles stayed in the center for an average of

(300/425) years—or 259 days.

Calculating Average Length of Stay as a Stock/Flow Ratio:

Example 1

Stock—average daily population in placement 300

Flow—juveniles released during previous year 425

Stock/flow ratio in years (300/425) 0.71

Length of stay in days (0.71 X 365) 259

Example 2: Assume that a juvenile detention center has a population of 100 today, and assume that the director of the center

considers today’s population typical. If 85 juveniles were released from the center during the previous month, a forecaster

could estimate the center’s length of stay by dividing the stock

(100) by the flow (85), which would suggest that juveniles stayed

in the center for an average of (100/85) months—or 36 days.

Calculating Average Length of Stay as a Stock/Flow Ratio:

Example 2

Stock—average daily population in placement 100

Flow—juveniles released during previous month 85

Stock/flow ratio in months (100/85) 1.18

Length of stay in days (1.18 X 30.4†) 36

Estimation Bias

As any measure of length of stay is likely to involve bias, corrections planners may want to use several estimators to understand

how the length of time served is changing. By understanding the

conditions that characterize the corrections system—such as

increasing admissions and slowing rates of release—the user

of length-of-stay information can assess the likely direction of

the bias in the measures of length of stay. Once the potential

direction of the bias in each measure is assessed, the measures

can be compared and conclusions can be drawn about whether

persons are spending more, less, or about the same amount of

time in custody.

Length of Stay in This Bulletin

This Bulletin presents an analysis of the change in the juvenile

commitment population between 1993 and 1997, and it projects

the commitment population for the year 2002. Both these analyses require measuring average length of stay. After considering

and computing several measures of length of stay, including “exit

cohort” and “days since admission” measures, the authors decided to use stock/flow measures to provide the estimates of

length of stay used in these analyses. The bias inherent in a

stock/flow ratio is usually less than it would be for other lengthof-stay measures (i.e., exit cohorts and days since admission),

and using the stock/flow ratio provided a consistent and uniform

method of measuring length of stay that was conducive to measuring the change in length of stay over the period.

A stock/flow measure for length of stay was calculated for 1993

and 1997 using data on the number of out-of-home placements

taken from NCJJ’s National Juvenile Court Data Archive (NJCDA)

and data on the number of youth in corrections taken from

OJJDP’s Children in Custody (CIC) census and its Census of

Juveniles in Residential Placement (CJRP). The use of admissions rather than releases is required because national-level

data on releases are not available. This choice assumes that

releases are estimated by admissions. Under this assumption, if

admissions are greater than releases (likely during the study

period), then a stock/flow ratio may underestimate length of stay.

Conversely, if admissions are less than releases (unlikely during

the study period), then a stock/flow ratio would overestimate

length of stay. The table below displays the stock/flow ratios

used in the analyses presented in this Bulletin.

1993 CIC 1997 CJRP

Stock/NJCDA Stock/NJCDA

Offense Flow Ratio (days) Flow Ratio (days)

Total 96 109

Person 162 180

Property 89 104

Drugs 148 113

Public order 22 49

† Number of days in the average month, 365/12.

categories of offenders—person, property, drug, and public order. Obviously,

projections would be even more useful if

offenses could be divided into additional

categories (e.g., felony or misdemeanor,

weapon or weaponless, drug possession

or drug sales). Moreover, when agencies

wish to apply projection models in actual

decisionmaking situations, they would

prefer even more data. In addition to dividing the juvenile population by offense,

projection models can sometimes be calculated separately for juveniles who are

drug dependent, those who are known

flight risks, those who have school problems, those with educational deficits, etc.

Ideally, projection models should be calculated for any categories or factors that

may be involved in actual agency decisions about the use of juvenile bedspace

in detention or correctional facilities.
14

A Brief History of Corrections Population Projection Methods

Beginning in the early 1970’s, corrections researchers began to develop increasingly sophisticated methods for

projecting adult prison populations. Their

methods drew largely from the fields of

demography and operations research.

Since the 1970’s, population projection

models and the data available for those

models have improved considerably. The

fundamentals of population projections,

however, are still based on the work of

a few original innovators.

In 1973, Stephen Stollmack published

one of the first “mathematical flow” models for projecting prison populations.

The model used an input-output analysis of the corrections system. It incorporated data about how offenders

“flowed” through the stages of the justice process—for example, from arrest

to indictment, conviction, and incarceration. Prison populations were projected

by relating flows to “stocks” (or the starting point of a prison population) and by

incorporating information on the average length of time individuals stay in

prison. The model even allowed for limited evaluations of policy changes (for

example, the impact of policies that

change length of stay can be built into

the model and their impacts can be assessed by seeing how the prison population is affected).

Stollmack’s model took population projections beyond traditional statistical

models (e.g., time series and regression). Statistical models projected future

populations by linear extrapolation of

trends in prior populations. Statistical

models continue to be used today because they allow forecasters to make

projections without having to assemble

a great deal of data about case processing. With statistical models, however,

forecasters cannot disaggregate projections for subpopulations, nor can they

analyze the impact of policy changes

that affect only certain types of offenders.

In addition, statistical models are effective

only when data are available for extended

periods, and they can be difficult to interpret for nontechnical audiences.

In 1980, Alfred Blumstein and his colleagues continued the development of

mathematical flow models by making two

enhancements to the Stollmack model

(Blumstein, Cohen, and Miller, 1980). First,

they disaggregated population projections

by racial and crime categories. Second,

instead of assuming a constant rate of admissions into the population, their model

projected admissions as age-specific proportions of the general population. They

developed these proportions with census

projections and historical data on prison

admissions. Their innovation acknowledged

that rates of crime, arrest, and incarceration varied among groups in the general

population. Population projections were

calculated as a weighted sum of the separate projections for each subpopulation.

Arnold Barnett (1987) introduced another

refinement to mathematical flow models

based on the concept of “criminal careers.”

Barnett’s model began with age-specific

probabilities that nonincarcerated offenders

are actively involved in crime. His model

estimated the incarceration rate for offenders based on several factors—age, criminal

activity, and the expected rate of desistance. The probability of criminal activity

could be revised within the model to account for policy changes, and the impact

of these changes could be factored directly

into projections of prison populations.

While Blumstein and his colleagues and

Barnett were improving Stollmack’s mathematical flow model, other researchers were

developing an entirely different approach

to population projections. This second approach would become known as “microsimulation.” By the end of the 1990’s, 24

States and the Federal Bureau of Prisons

were using some form of microsimulation

to project prison populations (Sabol, 1999).

Microsimulation models project prison

populations by simulating what happens

to individual offenders as they are processed by the justice system and enter

and leave prison. Early microsimulation

models began by estimating the length of

time individual offenders were likely to

remain in prison. For each prison admission, a path (or “trace vector”) is mapped.

Future prison populations are projected

by adding together the number of individuals remaining in prison at any given

point in the future. The California Department of Corrections developed one of

the first functional microsimulation models in the early 1970’s (Chaiken and

Carlson, 1988).

In the early 1980’s, the National Council

on Crime and Delinquency drew from the

experiences of California when it developed its “Prophet” model (National Council on Crime and Delinquency, n.d.). The

Prophet model was constructed on the

concept of “ID groups”—subpopulations

of offenders categorized according to how

they were likely to be handled in the justice system. Each group could be modeled through various decision points in

the criminal justice system, and lengths

of stay were estimated using sentencing

variables or data on time served by previous cohorts of released offenders. Incarcerated populations were projected by

estimating the number of offenders in

each ID group who were expected to be

in prison at certain points in the future.

Unfortunately, many State and local agencies are still unable to produce the detailed data necessary to make full use of

microsimulation models. In practice, most

jurisdictions continue to use grouped data

rather than individual-level data in their

population projections. Whenever grouped

data are used, microsimulation models

function essentially as disaggregated flow

models.

Note: Much of this history is drawn from Sabol (1999).

Population Projections

in Practice

The previous discussion demonstrates

how assumptions about future conditions

are critical to the results of projection

models. The most effective projection

models allow decisionmakers to consider a

wide range of policy choices and to incorporate those choices into a series of different models so that their effect on future

populations can be seen. (A brief history

of corrections population projection

methods is presented below and a summary of commonly used projection models follows on page 15.) If used in this

way, population projections can be flexible tools for understanding the ramifications of various policy choices and the

use of confinement resources. Projection

models, however, should not be offered
15

to policymakers as a simplistic mechanism for predicting future corrections

populations.

Because projection models are unable to

account for all of the details involved in

the juvenile justice process, they will

never be foolproof. Moreover, until State

and local agencies are able to support

Models Commonly Used To Project Corrections Populations

Projecting corrections populations is often incorrectly understood as an effort to “get the right number.” This assumes that a

projection is inferior if it produces a number that turns out to be

different from actual need or if a projection becomes irrelevant

after a change in policy. It is more appropriate to view projections as conditional statements of a future corrections population that will hold true only if current assumptions about the factors that generated past populations persist into the future.

A comprehensive forecasting effort should include not only

population projections but also policy debates and analyses to

understand why actual populations depart from projections and

to demonstrate the role of policy in shaping demands for space.

Type of Model Method or Approach Comments

Microsimulation

Disaggregated

flow

Statistical

Mathematical

◆ Projects the movement of individual entities

through the justice system using detailed information about real individuals who have gone

through the system or are still in process.

◆ Permits users to aggregate information at the

end of a simulation into whatever categories

are needed.

◆ Uses rates of flow between the stages of the

justice system (e.g., odds of adjudication after

arrest, odds of incarceration after adjudication).

◆ Rates can be entered and then altered for

various subpopulations for repeated projections over time.

Uses methods such as time series or multiple

regression to project populations based on

changes in other, related variables.

May involve various methods, ranging from

simple growth-rate projections to more sophisticated stochastic models.

◆ Offers the greatest flexibility/power in projecting

populations under various policy assumptions.

◆ Requires extensive data about individual

offenders.

◆ Most State and local jurisdictions are not able

to meet the data requirements.

◆ For national-level projections, data requirements

for microsimulation will likely never be met.

◆ Generates projections based on the movement

of groups through the justice system.

◆ Next to microsimulation, offers the most flexibility for anticipating future conditions.

◆ Requires grouped data only.

◆ Requires less data but does not provide much

flexibility for modeling future policy changes.

◆ Generates projections based on past values of

the variable to be projected and their relationship to other factors.

◆ May require the values of independent or

causal variables to be projected as well.

◆ Requires minimal data but is very inflexible.

◆ Projections are generated by adding a constant to existing populations or by multiplying

populations by calculated growth rates.

◆ Assumes future conditions will be the same as

past conditions.

◆ May include parameters that relate inflow to outflow or that model length of stay in corrections.

significant expansions in their data collection and analysis capabilities, it is unlikely that any projection model will ever

represent the true diversity of the juvenile population. For this reason, juvenile

justice agencies should resist the temptation to rely on any single prediction of

future demand for space. Instead, they

should invest in an extended process of

“forecasting.”

Forecasting Rather Than

Predicting

Forecasting is different from predicting,

although both strategies involve statistical

projections of corrections populations.
16

Forecasting relies on reflection instead of

speculation. In a prediction context, researchers focus on the future. They use

data about the past to speculate about the

future, and they encourage policymakers

to act on their statistical vision of the future. In a forecasting context, researchers

focus on the recent past. They use data to

understand how the recent past turned

out to be different from previous expectations. By identifying and examining these

differences, policymakers and other professionals increase their understanding of

the factors that are likely to influence future trends, but they do not place undue

faith in anyone’s ability to predict those

trends accurately.

A forecasting approach also encourages

decisionmakers to review their assumptions about their own policies and practices on a regular basis. Some agencies

may engage in a forecasting process on

an annual or even semiannual schedule.

They conduct repeated projections of

their corrections populations and compare actual developments with their

previous expectations of demand for

bedspace. Administrators and policymakers use the occasion of each forecasting exercise to review their assumptions

about their system and how it uses

bedspace. In such an environment,

population projections can be used to

encourage sound policy and practice

decisions. (See “Forecasting Juvenile Corrections Populations in Oregon” on this

page for a description of one agency’s approach to integrating forecasts into its

policy process.)

No single projection exercise should

drive policy and budgetary decisions.

Every projection should be used in conjunction with policy debates about the

type of programs a jurisdiction wishes to

support. Decisionmakers can use a forecasting process to reflect on current policies and practices and to ask critical

questions about their use of bedspace: If

current trends continue, which type of

offenders will be committed to secure

confinement and which will be placed in

community-based programs? What type

of offenders will stay the longest in secure facilities? Which facilities will see

the largest increases in daily populations

or length of stay? Which areas of the

State will experience the greatest

changes in expected demand? Projections of future custody populations

can be powerful learning tools that

serve the twin goals of making communities more secure and providing

appropriate treatment programs for

youth.

Forecasting and the

Policy Process

The juvenile justice process has many

unique features that need to be accounted for in projection methodologies.

These features include a wide use of diversion, great discretion at all levels, and

the juvenile court’s ability to base dispositions on not only the public safety but

also on the best interests of the juvenile.

Because juvenile court dispositions are

sometimes for indeterminate periods of

time, lengths of stay are often linked not

only to the severity of the offense but

Forecasting Juvenile

Corrections Populations

in Oregon

The Oregon Youth Authority obtains

twice-yearly forecasts of the number

of young offenders likely to be in its

“close custody” programs 10 years into

the future. (Close custody refers to

youth housed in the State’s MacLaren

and Hillcrest facilities and also those in

“accountability camps,” “work study

camps,” and Oregon’s Juvenile Intake

Center.) Forecasts are generated by

Oregon’s Office of Economic Analysis

using models developed by the office

and overseen by an interdisciplinary

advisory committee. Members of the

committee include researchers from

a local university, court and probation

officials, and the Director of the

Oregon Youth Authority.

Each forecast incorporates the most

recent data on intake trends, arrest

trends, and future population growth

for Oregon youth ages 12 through 17.

Separate models are used to forecast

important subpopulations within the

juvenile offender population, including

youth affected by Oregon’s “Ballot

Measure 11,” which automatically

transfers certain categories of offenders to the criminal court.

The forecasts are provided to policymakers and other officials in the State

to foster discussions about recent

trends and their effect on future corrections populations. The Office of Economic Analysis advises officials that

each “forecast is not what the population will be, but what the population

would be if current practices and policies were applied to future conditions”

(Oregon Youth Authority Close Custody

Population Forecast: Biennial Review of

Methodology, page 2).

Source: Oregon Youth Authority Close

Custody Population Forecast (April 2000),

a biennial series, and Oregon Youth Authority

Close Custody Population Forecast: Biennial

Review of Methodology (June 1998). Salem,

OR: Oregon Office of Economic Analysis.

Also available on the Internet at

www.oea.das.state.or.us/oya/oya.htm.

Differences Between Predicting and Forecasting

Predicting Forecasting

Focus Future Recent past

Goal Accurately predict Examine recent developthe future ments and their relevance

for the future

Methods Statistical projections Statistical projections,

policy discussions,

program reviews

Personnel Involved Analysts Policymakers, administrators, practitioners,

analysts

Frequency As needed Regularly

Definition of Success Accuracy Utility/learning
17

also to a youth’s progress in treatment

programs and the availability of space.

As a result, juvenile detention and corrections systems have much less stable

information on which to build forecasts

than criminal justice agencies.

Researchers must encourage policymakers and administrators to understand that no projection methodology

will ever be able to model the complexity

of the decisionmaking processes that

lead juvenile offenders to be placed in

secure facilities or that determine how

long juveniles will stay in those facilities.

It will always be necessary for decisionmakers to review the results of a projection model and consider its value for

policy and practice. However simple it

may appear at first, estimating a jurisdiction’s future need for detention and

corrections space requires an extensive

examination of the justice system and of

the processes used to select juvenile

offenders for placement.

Methodology

Decomposition Methods

A statistical flow model is used in this analysis to decompose

changes in the national juvenile commitment population between 1993 and 1997. The model segments the overall change

in the commitment population into offense-specific groups (person, property, drug, and public order). Within each group, the

model decomposes the overall change in the commitment population into the portions of total change that can be attributed to

the following factors:

◆ Changes in the number of juvenile court referrals.

◆ Changes in the number of referred cases that result in

adjudication.

◆ Changes in the number of adjudicated cases that result

in residential placement.

◆ Expected length of stay in residential placement (using

a stock/flow estimate of length of stay).

The offense-specific changes in these components of growth

are then aggregated to obtain the total change in the juvenile

commitment population over the period of analysis.

The population change model used in this Bulletin follows the

approach of Abrahamse’s (1997) method for assessing change

in prison populations. The number of juveniles committed to

residential placements at the end of a year is defined as follows:

POPULATION = REFERRALS x ADJUDICATION

x PLACEMENT x LENGTH OF STAY

Where each element is defined as follows:

POPULATION = the juvenile population committed to

residential placement facilities.

REFERRALS = the total number of delinquency cases

referred to the juvenile court system.

ADJUDICATION = the proportion of referred cases that

results in adjudication.

PLACEMENT = the proportion of adjudicated cases

that results in commitment to residential placement facilities.

LENGTH OF STAY = the expected length of stay, estimated

by a “stock/flow” ratio (see discussion

on pages 12–13).

The amount of change in the juvenile commitment population

between 1993 and 1997 is a function of the offense-specific

changes in each individual component of change as measured

in the above model. Thus, the difference in the population is a

“weighted sum” of differences in each component, where the

weights equal the offense-specific contribution to change in the

population. The decomposition of change is applied separately

to each offense group, and each of the offense-specific changes

in the juvenile commitment population can be summed to obtain

the total change in the population between 1993 and 1997.

Projection Calculation

Using data for the 1993–97 period, a mathematical flow model is

used to project the juvenile commitment population for the years

1998 through 2002. The model follows the approach developed

by Stollmack (1973) to project prison populations. The analysis

uses the following equation to project the juvenile committed

population for each year, from 1998 to 2002:

P(t) = A(t) x LOS(t) + [P(t–1) – (A(t) x LOS(t))] x exp[–1/LOS(t)]

Where each element is defined as follows:

P(t–1) = the population in the previous year (t–1).

A(t) = admissions or commitments to residential placement during the year.

LOS(t) = the estimated length of stay in commitment.

t = the time unit for flows (in this example, years).

This model requires three data inputs for each time period: the

starting population, which is the population from the previous

time period [P(t–1)]; admissions during time t; and length of

stay. The projection scenarios described in this Bulletin use

the 1997 juvenile commitment population as the initial starting

population and assume that admissions either remained at

1997 levels throughout the 1998–2002 period or that they increased each year based on applying the average annual

changes observed from 1993 to 1997. Similarly, average length

of stay is either assumed to remain at 1997 levels or projected

for each year based on the average annual change observed

from 1993 to 1997.

As with the decomposition model, the projection models presented in this Bulletin were apportioned into offense-specific

components (person, property, drug, and public order) and then

summed to obtain the total populations projected for each year

from 1998 to 2002. Since data on the number of committed

youth released from residential placement were not available for

all years in this analysis, the model presented in this Bulletin

must assume that admissions and releases were in equilibrium.
18

An effective forecasting process should

take into account the important role

played by each jurisdiction’s policy preferences and professional practices. Forecasting should include at least three general areas of activity:

◆ First, decisionmakers should have

regular access to extensive data about

trends in juvenile crime and juvenile

justice processing within their jurisdictions, and they should use that information to project the size of future detention and corrections populations.

◆ Second, they should develop a thorough understanding of their jurisdiction’s policies and practices regarding

the use of secure confinement for juvenile offenders, including how the

diversity and depth of juvenile justice

resources are related to the need for

secure space.

◆ Third, they should host a rotating series of strategy meetings with a variety

of audiences from the juvenile justice

system and the larger community.

These meetings should focus on the

relationships among the availability of

juvenile justice program resources,

recent trends in the use of those resources, and projections of future

confinement populations.

The validity of any projection model

rests on the reasonableness of its assumptions and the persistence of these

assumptions into the future. When projections fail to anticipate future conditions, forecasters should seek to explain

why actual populations differ from projected populations. Decisionmakers then

have the opportunity to learn about the

effects of practice and policy actions

that were not included in the projection.

The success of a forecasting process is

not determined by its predictive accuracy. A projection that turns out to be

wrong (or one that produces population

estimates that deviate from actual future

populations) is not necessarily an invalid

projection. An invalid projection is one

in which the differences between a projected population and the actual population cannot be explained. A projection

that turns out to be inaccurate as a prediction may still be a useful projection if

analysts are able to explain which critical assumptions were violated and what

impact these violations had on corrections populations.

Conclusion

Efforts to anticipate future space needs

in juvenile detention and juvenile corrections facilities should involve more than

an occasional analysis of juvenile arrest

trends. Ideally, juvenile justice decisionmakers should anticipate future demands

for space by engaging in a population

forecasting process on an annual or semiannual basis. Forecasting involves statistical predictions (or projections) of future

corrections populations, but the results

of such projections serve as the beginning of an agency’s decisionmaking process rather than the end. Forecasting encourages policymakers and practitioners

to use statistical projections to reflect on

recent trends and discuss their expectations of the future in light of those trends.

The accuracy of their expectations can

then be reviewed during the next forecasting session. Over time, a forecasting

process helps decisionmakers to anticipate the consequences of policies and

practices regarding secure bedspace without undue reliance on statistical analysis.

No projection method is infallible, but

juvenile justice officials must choose

some method for planning for future

space needs. Without careful projections

of the likely demand for detention and

corrections space, policymakers and administrators make important decisions

about the need for additional facilities

based primarily on the immediate pressures of crowding. However, crowding

is an indicator of past demand. Budgeting and policymaking must prepare an

agency for the future. Making important

decisions without attempting to project

future conditions can leave the juvenile

justice system unprepared and lead to

inefficient uses of costly resources.

Projecting future demand for bedspace

will always be challenging because the

policy environment in juvenile justice is

highly dynamic. As Allen R. Beck once

observed: “Using the past to ‘see’ the

future is like driving a car by looking into

the rear view mirror. As long as the road

is straight or curving in wide arcs, the

driver can stay on the road by looking

backward. However, if a sharp turn occurs or a bridge is out, the driver will

crash” (Beck, 1998). The policy environment in juvenile justice has taken many

sharp turns in recent decades. Agencies

can improve the usefulness of population

projections by investing in a forecasting

process that generates projections on a

regular basis and exposes each set of

projections to the scrutiny of a broad

range of audiences and stakeholders.

Endnotes

1. These numbers represent different

units of count, and this analysis should

not be interpreted as suggesting that

exactly 62 percent of all arrested youth

were referred to juvenile courts in 1997.

Changes in the relationship between juvenile arrests and juvenile court cases, however, do indicate law enforcement’s shifting emphasis on court referral.

2. This example is intended as a demonstration of projection methodology and

not an analysis of national custody populations that could be used to formulate

State or Federal policy. For this reason,

all data, including population counts, are

rounded.

3. The juvenile custody population numbers in table 1 are drawn from the Census

of Juveniles in Residential Placement

(CJRP) in 1997 and from the Census of

Public and Private Juvenile Detention,

Correctional, and Shelter Facilities, also

known as the Children in Custody (CIC)

census, in the years prior to 1997. CJRP

differs fundamentally from CIC, which collected aggregate data on juveniles held in

each facility. CJRP collects individual data

on each juvenile held in each residential

facility in the census. Since there was a

change in data collection instruments, it

is difficult to determine how much of the

increase in the number of delinquents in

custody is real and how much is due to

the change in methods. According to

OJJDP (see Snyder and Sickmund, 1999),

the “roster” format of the CJRP data,

along with electronic reporting, may have

facilitated a more complete accounting of

juveniles in facilities. In the years when

CIC was used, there were many private

facilities that did not report juveniles

in custody. It is therefore likely that the

reported number of juveniles in private

facilities is understated. The population

counts presented here do not match

the data reported in other analyses of

OJJDP’s CJRP data due to the various

adjustments in this analysis.

4. Adjustments were based on the assumption that the 1997 population represents an accurate count of juveniles in

custody in both private and public facilities. The ratio of the private to public

populations in 1997 was applied to the
19

1993 and 1995 reported counts of juveniles in public facilities to adjust the number of youth in private facilities in those

years.

5. The number of “admissions” into residential facilities is required to compute

the relative rate of placement for any

given year. A count of admissions is also

essential input for projecting future juvenile commitment populations. Data on

true admissions, however, are not available from any national data collection

program (e.g., the National Juvenile Court

Data Archive, the Census of Juveniles in

Residential Placement, or the Children in

Custody census). The National Juvenile

Court Data Archive, however, can provide

data on the number of adjudicated juvenile court cases resulting in commitment

to residential placement during each year

of the analysis. These data are used as a

proxy for the number of “admissions” into

residential placement.

6. Transition probabilities were calculated

for 1993 and 1997 on an offense-specific

basis. The overall change in the commitment population between 1993 and 1997

was then decomposed into the changes in

these transitions from stage to stage during the period.

References

Abrahamse, A. 1997. The impact of demography and criminal justice processing on prison population size. From the

National Workshop on Prison Population

Projection and Forecasting: Managing

Capacity. Washington, DC: National Institute of Justice, Bureau of Justice Statistics, and Corrections Program Office.

Barnett, A. 1987. Prison populations:

A projection model. Operations Research

35(1):18–34.

Beck, A.R. 1998 (August). Forecasting: Fiction and utility in jail construction planning. Correctional Building News. Available

online at www.justiceconcepts.com.

Blumstein, A., Cohen, J., and Miller, H.

1980. Demographically disaggregated

projections of prison populations. Journal of Criminal Justice 8(1):1–25.

Acknowledgments

This Bulletin was written by Jeffrey Butts, Ph.D., Director of the Assessment of

Space Needs in Juvenile Detention and Corrections project at The Urban Institute,

and William Adams, Research Associate with the project. The project is housed

within The Urban Institute’s Justice Policy Center, directed by Dr. Adele Harrell.

Development of the Bulletin benefited from significant contributions by Ojmarrh

Mitchell, Research Associate with The Urban Institute; Dr. William Sabol, formerly

of The Urban Institute and now Associate Director of the Center on Urban Poverty

and Social Change at Case Western Reserve University; Joseph Moone, Program

Specialist in OJJDP’s Research and Program Development Division; and

Dr. Helen Marieskind, a Writer/Editor in OJJDP’s Information Dissemination Unit.

The authors are also grateful for comments and criticisms provided by Dr. Howard

Snyder and Dr. Melissa Sickmund of the National Center for Juvenile Justice.

Both OJJDP and The Urban Institute gratefully acknowledge the efforts of the

State and local officials who assisted in the project. Their participation helped to

make this Bulletin possible. In particular, senior officials from the State-level

juvenile corrections agencies in Alaska, California, Kentucky, Louisiana, Montana,

New Hampshire, South Carolina, West Virginia, and Wisconsin provided critical

comments and insight.

This Bulletin was prepared under grant

number 98–JB–VX–K004 from the Office of

Juvenile Justice and Delinquency Prevention,

U.S. Department of Justice.

Points of view or opinions expressed in this

document are those of the authors and do not

necessarily represent the official position or

policies of OJJDP or the U.S. Department of

Justice.

The Office of Juvenile Justice and Delinquency Prevention is a component of the Office of Justice Programs, which also includes

the Bureau of Justice Assistance, the Bureau

of Justice Statistics, the National Institute of

Justice, and the Office for Victims of Crime.

Chaiken, J., and Carlson, K.E. 1988. Review

and Evaluation of the California

Department of Corrections’ Institution

and Parole Population Projections. Sacramento, CA: California Department of

Corrections, Offender Information Services Branch.

National Council on Crime and Delinquency. n.d. Introduction to the NCCD

Prophet Simulation Model: An Interactive

Microcomputer Simulation System. San

Francisco, CA: National Council on Crime

and Delinquency.

Sabol, W.J. 1999 (May). Prison population projection and forecasting: managing capacity. Unpublished report. Prepared for the Bureau of Justice Statistics,

Corrections Program Office, and the National Institute of Justice, Washington,

DC. NCJ 172844.

Snyder, H. 1999. Juvenile Arrests 1998.

Washington, DC: U.S. Department of Justice,

Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention.

Snyder, H., and Sickmund, M. 1999. Juvenile Offenders and Victims: 1999 National

Report. Washington, DC: U.S. Department

of Justice, Office of Justice Programs,

Office of Juvenile Justice and Delinquency Prevention.

Stollmack, S. 1973. Predicting inmate

populations from arrest, court disposition, and recidivism rates. Journal of

Research in Crime and Delinquency

10(1):141–162.
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