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.) 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
to policymakers as a simplistic mechanism
for predicting future corrections
populations.
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 inputoutput 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 agespecific 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 agespecific
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 individuallevel 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).

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
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.”
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 popula tion 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   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.

 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 nationallevel projections, data requirements for microsimulation will likely never be met.

Disaggregated
flow 
 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.

 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.

Statistical 
Uses methods such as time
series or multiple regression to
project populations based on
changes in other, related variables. 
 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.

Mathematical 
May involve various methods, ranging from
simple growthrate projections to more sophisticated
stochastic models. 
 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.


Forecasting Rather Than Predicting
Forecasting is different from predicting,
although both strategies involve statistical
projections of corrections populations.
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” for a description of one agency’s approach
to integrating forecasts into its
policy process.)
Forecasting Juvenile
Corrections Populations
in Oregon
The Oregon Youth Authority obtains
twiceyearly 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.

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
communitybased 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.
Differences Between Predicting and Forecasting

 Predicting  Forecasting 

Focus 
Future 
Recent past

Goal 
Accurately predict the future 
Examine recent developments 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

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
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 offensespecific 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 offensespecific 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). 
The amount of change in the juvenile commitment population
between 1993 and 1997 is a function of 
the offensespecific 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 offensespecific contribution to change in the
population. The decomposition of change is applied separately
to each offense group, and each of the offensespecific 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)xLOS(t)+[P(t–1)–(A(t)xLOS(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 offensespecific
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. 
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.

Anticipating Space Needs
in Juvenile Detention and Correctional Facilities 
Juvenile
Justice Bulletin • March 2001 
