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Appendix B
Efforts to Model Workload and
Resource Requirements
T
his appendix reviews major efforts to model workload and resource
requirements for federal immigration enforcement and similar
criminal justice processes.
CHANGES IN THE WORKLOADS OF IMMIGRATION COURTS
In fiscal 2007, staff of the U.S. Department of Justice’s (DOJ’s) Office
of Planning, Analysis, and Technology (OPAT) worked with a statistician
to complete an analysis of immigration court workload. OPAT used data
from the Executive Office for Immigration Review (EOIR) and the U.S.
Department of Homeland Security (DHS).
The analysis (U.S. Department of Justice, 2008) indicated that EOIR
has limited tools available for predicting its future workload. The bulk of
the workload comes from “notice to appear” issued by DHS, and com-
plete information on the number, issuing agency, and place of issuance is
not available to EOIR in time for the predictions. Even if these data could
be obtained, the relatively short time lag for 80 percent of the cases of less
than 3 months between the issuance of a notice and intake by EOIR is not
enough to provide for meaningful advance planning or budgeting.
A more useful indicator of EOIR’s potential workload would be
the trend in apprehensions of non-Mexicans by the Border Patrol. Most
non-Mexicans cannot be returned directly to their native countries, and
they are likely to appear before immigration courts. Attempted unlawful
entries by non-Mexicans respond to a variety of causal factors, but for
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140 BUDGETING FOR IMMIGRATION ENFORCEMENT
most countries, abrupt increases or decreases in the level of those appre -
hended and issued notices are unusual and can often be traced to specific
events. During the study period, EOIR’s case intake tracked the number
of apprehensions of non-Mexicans along the southern border with a time
lag of several months. Again, this time lag does not permit long-range
planning. EOIR’s Mexican and non-Mexican caseloads are significantly
different. Mexicans who appear before EOIR generally do so because
they have records of previous immigration violations or criminal charges
are being brought against them. They are likely to be detained, and their
cases reach EOIR faster than those of others. They are somewhat less
likely to file for relief from removal than non-Mexicans, and even less
likely to file for asylum, which leads to swifter resolution of their cases.
Finally, the Mexican caseload that reaches EOIR has been growing since
fiscal 2004, while the trend for the other major nationalities was down
for fiscal 2006 and 2007. If EOIR is able in the future to obtain data on
apprehensions from the Border Patrol by month, nationality, and location
in a time-sensitive manner, it would be beneficial for short-term workload
planning.
EFFECTS OF HIRING INVESTIGATORS ON THE WORKLOAD
OF NONINVESTIGATIVE SYSTEM COMPONENTS
In 2005, the House Appropriations Committee expressed concern
that the budget request submitted by DOJ, whose highest priority was
the prevention of terrorism, did not fully support the budgetary needs
of the criminal justice components. DOJ was directed to submit a report
“describing how the hiring of an investigator impacts the workload of
the U.S. Attorneys, the U.S. Marshals Service, the Office of the Federal
Detention Trustee, and the Federal Prison System.” DOJ contracted with
BearingPoint, Inc., which built a prototype workflow model (based on
readily available data) to test the feasibility of the concept that mathemati-
cal relationships can be established and determine what areas should be
pursued to build a functional model (U.S. Department of Justice, 2005).
The prototype model illustrates the effects of hiring agents in the front
end of the criminal justice system on the workloads of downstream agen-
cies, such as the U.S. Marshals Service (USMS), the Office of the U.S.
Attorneys (USAO), and the Bureau of Prisons (BOP). It uses data on the
resources that were historically required to process the number of crimi-
nals received—explicitly assuming that the historical trends in these ratios
will continue into the future with little fluctuation.
The model consists of inputs (agents added to the Federal Bureau of
Investigation [FBI]; the Bureau of Alcohol, Tobacco, Firearms, and Explo-
sives [ATF]; and the Drug Enforcement Agency [DEA]), which will create
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APPENDIX B
the following outputs: number of U.S. attorneys; number of U.S. marshals;
number of correctional officers; number of criminals arrested; number
of arrestees detained; number of defendants prosecuted; and number of
defendants sentenced to prison.
The first test of the model determined how many data points had
predictive value for each component of the model. In this analysis,
BearingPoint used trends observed from 1999 to 2001 (with current initia-
tives and trends being more heavily weighted) and predicted a value for
2002 on the basis of these trends. To evaluate the quality of the reliability
of the predicted values, BearingPoint calculated the standard deviation of
data for 1999-2002. (The higher the standard deviation, the more difficult
it is to produce accurate predictive values in the future.) The prototype
model produced 19 of 32 data points within the standard deviation, or
approximately 60 percent.
Limitations of the model include the following:
• The assumption that historical case procedures used by DOJ com-
ponents and historical trends in types of criminal activity will
continue into the future with little fluctuation may not be realis-
tic. Account should be taken of changes in underlying trends in
criminal and law enforcement priorities and changing levels of
productivities over time.
• The model is based on comparisons of total personnel to total out-
puts, rather than focusing on marginal, or year-to-year, increases
in criminal processing due to the addition of investigative agents.
• Each district can focus on specific crimes and thus have statistics
that are different from the national average. A district/regional
approach would be needed, at least for some of the larger dis -
tricts whose statistics differ substantially from the national level.
However, understanding the historical workloads associated with
these district statistics would require going directly to the agen-
cies and gathering this information on a district level.
PROJECTING FEDERAL DETENTION POPULATIONS
Projecting future detention trends and estimating budgetary resource
requirements for the criminal detention program has historically been a
difficult task, at both macro and micro levels.
At the macro level, impediments to accurately projecting the deten-
tion population include the dynamic nature of the federal criminal justice
process; on-going changes in federal criminal law and policy; changes in
federal law enforcement priorities; and events external to the criminal
justice process, such as unforeseen events that might cause mass ille -
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142 BUDGETING FOR IMMIGRATION ENFORCEMENT
gal migration to the United States. At the micro-level, these macro-level
impediments translate to volatility in (1) the number of federal arrests and
bookings reported to the USMS, (2) prosecutorial priorities and declina-
tion criteria, (3) offender or offense characteristics necessitating pretrial
detention, and (4) case processing time that results from overburdened
criminal justice resources. Accordingly, projecting the impact of systemic
or short-term events or initiatives that will affect arrests and bookings is
the greatest challenge in projecting the detention population.
The Office of the Federal Detention Trustee (OFDT) documented the
challenge of doing such projections almost 10 years ago, and their basic
approach for projecting the detention population is still used (see Scalia,
2004). The primary source of data for the OFDT detention population
projection model is the USMS Prisoner Tracking System (PTS). OFDT
receives extracts of PTS that include individual records of each prisoner
processed by the USMS.
Time-series models lie at the heart of the population projection. These
atheoretical models are based on the assumption that historic trends—and
the factors that influenced those trends—are useful predictors of future
events and that the observed relationships will continue into the near
future. The time-series analysis produces weights that are used in a micro-
simulation model that generates future booking replicates.
Recognizing that simple time-series models may not produce reliable
results in an environment in which the underlying trend of a series can
be substantially affected by exogenous factors, OFDT incorporated law
enforcement and U.S. attorney staffing data into its process for estimat -
ing future detention. The staffing model has been described (by those
familiar with it) as useful for incremental changes, but not for levels; it
has also been characterized as informative but not definitive. The staffing
model uses aggregate staffing data for the U.S. Customs and Border Patrol
(CBP) and the U.S. Immigration and Customs Enforcement (ICE). In the
context of “modeling the past,” the staffing model also contains indicator
variables for things such as changes in administration.
At the tail end, OFDT tries to makes adjustments for policy initiatives
and changes (i.e., they are not built into the model itself and do not neces -
sarily have “data support”). With regard to the validity of predictions, the
model does best when the policy environment is relatively stable. Time
in detention, which is another model component, tends to be more stable
and predictable than how many people come into the system. However,
in the period immediately following the implementation of Operation
Streamline, the length of detention fell in a way that was not foreseen
by the existing model (although those predictions have since stabilized).
With regard to regional projections in the staffing model, OFDT can
link staffing data to specific duty stations. The OFDT model does account
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APPENDIX B
for district- and regional-level variations in law enforcement and prosecu-
torial productivity. Statistically, the accuracy of projections depends on the
size of the base population, the variability of the data series trends, and
the length of the forecast interval. One method for evaluating the validity
of the projection methodology and the resulting projections is to monitor
the individual components of future detention populations and identify
which component is the primary source of the observed error.
The reliability of the OFDT model is evaluated on a monthly basis by
using simple time-series methods to re-calibrate the original projections
with real-time population statistics.
ESTIMATING WORKLOADS FOR THE
FEDERAL CRIMINAL JUSTICE SYSTEM
The U.S. General Accounting Office (GAO) developed a model
designed to provide Congress and federal agencies with estimates of the
potential effect that budgetary changes for part of the federal criminal
justice system may have on the system as a whole (U.S. General Account-
ing Office, 1991). The work was undertaken after GAO evaluated the
existing criminal justice models and determined that they did not meet
the needs mandated by Congress: they were either designed to address
only a single part of the system or required data not routinely available
at the federal level.
The model developed by GAO is based on ordinary least squares
regression analysis with a zero intercept and no lag times. It assumes that
historic trends are useful predictors of future events and that the historic
relationships observed will continue into the near future. The accuracy of
the model’s estimates of future workload may be limited by a significant
change from the past budget and workload trends on which the model
relies.
Limitations of the model include the following:
• General crime categories were used to make the estimates reliable
(since specific crime types account for such a small portion of
the total). The use of broad crime categories is a drawback if the
user wants to estimate the impact of changes in resources for a
particular crime type that has been combined with others to form
a generic classification.
• The model provides only national estimates, which obscures dif-
ferences among individual judicial districts.
• The model can only provide reliable estimates of the impact
of resource changes within reasonable limits. For example, if
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144 BUDGETING FOR IMMIGRATION ENFORCEMENT
resources were increased by 50 percent in a single year, the esti-
mates produced by the model would be unreliable.
• In order to provide useful results over time, the model will
require annual updating of the mathematical formula on which
it is based. This is necessary to reflect changes in the criminal
justice system that may affect the relationships between resources
and outputs.
PROJECTING SPACE NEEDS IN JUVENILE
DETENTION AND CORRECTIONAL FACILITIES
The Office of Juvenile Justice and Delinquency Prevention (OJJDP)
projects juvenile commitment populations by using a mathematical flow
model (Butts and Adams, 2001). The model requires explicit assumptions
about the case processing factors that might influence the size of confine-
ment populations. The complexity of juvenile justice decision making
virtually guarantees that detention and corrections populations will not
closely follow arrest trends in the Violent Crime Index.
Analysts can produce more useful projections when they include
juvenile court processing data in projection models, and projection mod-
els are more useful if they can account for changing patterns in court
processing. Projection models are also likely to perform better when they
include more than a single source of information and when they analyze
more than a single point in the juvenile justice process.
The value of different projection scenarios is limited by the lack
of more detailed data. For example, the models used in this analysis
divided the population into only four categories of offenders, and projec -
tions would be more useful if offenses could be divided into additional
categories.
SIMULATING THE IMPACT OF SENTENCING
GUIDELINES ON PRISON POPULATIONS
In response to a congressional mandate that the U.S. Sentencing Com-
mission evaluate the impact of its sentencing guidelines on the future
prison population, the Bureau of Prisons adopted a simulation model
(Gaes et al., 1993), FEDSIM, in 1987 as its primary source for projecting
future inmate populations. The model overestimated (with a fairly high
margin of error) the percentages of cases receiving straight probation.
The explanations for this inaccuracy have to do with changes made to the
guidelines after the initial modeling efforts. The model also greatly over-
estimated the number of split sentences; this may have had something
to do with the modelers’ lack of prior experience with federal guideline
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APPENDIX B
sentencing that would have informed them about judges’ behavior. How-
ever, the 3-, 4-, and 5-year projections for the federal prison population
(the primary goal of the modeling effort) were quite accurate.
The overestimations reflect the fact that a simulation task is compli-
cated when people affected by modifications in the system behave differ-
ently than they did prior to the changes. (This problem can be approached
as an exercise as sensitivity analysis, which refers to the degree to which
the outputs of a model are affected by changes in assumptions about the
model’s inputs and its parameters.) The greatest error occurred in project-
ing future conviction rates trends for some of the offense categories. The
reason the model was relatively accurate, despite the errors in conviction
trends, was that the structural change in sentencing was so dramatic
that it dwarfed the impact of changes associated with conviction trends.
However, as time served stabilizes, it will become more important to
accurately predict future conviction trends. It will also be important to
separate out projections for certain groups of prisoners who have distinct
causes for changes in admission rates and length of stay than the typical
federal inmate.
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