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OCR for page 460
8
The Impact of Changes in
Sentencing Policy on
Prison Populations
Alfred Blumstein
THE NEED FOR ESTIMATES
Widespread activity oriented toward structuring sentenc-
ing policy has generated a need for the development of
improved methodology for estimating the impact of changes
in sentencing policies on prison populations.
The need for such estimates is particularly intense
today because prisons in the United States are now effec-
tively filled and are likely to get more crowded even in
the absence of a policy change. Since changes in sen-
tencing policy tend much more often to be directed at
increasing rather than decreasing prison populations,
failure to account for the impact of a policy change will
result in two kinds of undesirable consequences: (1)
Judges will adhere to the policy change, and prisons will
become severely overcrowded, with the attendant dehumanr
ization and associated risks of violence, misconduct,
riot, and recidivism; and (2) Judges will adhere to
existing capacity limits, and will do so by accommodating
in ways they choose, which may well violate the mandated
policies adopted.
If a proposed policy change does involve a need for
significant new prison capacity, then it is important
that the body adopting the policy, and certainly the
legislature, weigh the desirability of the policy change
against the cost of that increment of capacity. If the
policy change is worth that cost, then the legislature
should appropriate the funds for the extra capital cost
460
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and consider the anticipated operating cost of the extra
capacity. If not, then adoption of an empty policy is
likely to serve only to further discredit the criminal
justice system. Thus, finding reliable means for esti-
mating the prison impact--and the corresponding budget
impact--of a sentencing policy is a necessary part of
ensuring responsible consideration of such policies. The
resulting "prison impact statement" and its associated
"budget impact statement" can then be as helpful in this
case as it is with many other kinds of legislation.
In determining sentencing policies, only rarely is any
consideration given to the downstream implications of
such policies by the judiciary or by legislative judi-
ciary committees, perhaps because such considerations of
impact seldom enter their concerns. That limited per-
spective may have been satisfactory when resources were
available to accommodate any reasonable policy adopted,
when the increment of resources are costless, or when
they can be expanded rapidly and easily to accommodate
the demand imposed by the court. It is certainly not the
situation that prevails in the criminal justice system of
today, and the situation is likely to become even more
severe throughout the decade of the 1980s.
On one hand, such impact estimates are necessary
because those capacity limits, which are being severely
pressed, should enter into any consideration of sentenc-
ing policy. A policy that fails to take such considera-
tions into account will simply be violated, but on the
basis of ad hoc considerations of individual judges or
prosecutors in individual cases, rather than on the basis
of the considerations of those responsible for establish-
ing policy. This accommodation could take the form of
shifts in plea bargaining, greater use of mitigating
circumstances, and the development of various "front
door" diversion strategies and "back door" early-release
strategies to accommodate the resource or capacity con-
straint in prison space.
Even if the body establishing the sentencing policy
chooses to ignore such capacity considerations in reach-
ing their policy choices--and there are many who insist
not only that such considerations can be ignored but also
that they should be ignored--it is necessary to be able
to estimate the impact of their choices on prison
resource requirements. Such estimates enable
legislatures charged with reviewing or adopting such
policies to assess the reasonableness of any sentencing
policy. Then, when a policy is adopted and implemented,
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impact estimates are necessary to begin to plan for the
resources to accommodate the new policy.
In many cases a body charged with establishing sen
tencing policy is specifically mandated to establish that
policy without generating any increase in prison popula-
tions or capacity. The Minnesota legislature, for exam-
ple, in establishing the Minnesota Sentencing Guidelines
Commission, suggested that they "take into substantial
correctional resources . . ." and the
consideration .
.
commission took that suggestion as a constraint, so that
any sentencing schedule it adopted would have a zero net
aggregate prison impact. The Pennsylvania Commission on
Sentencing did not adopt current populations as a con-
straint on its eventual schedule but did try to keep
informed of the estimated effect of the evolving sen-
tencing schedule on Pennsylvania's prison population.
Any impact estimate is associated with a future time
after the sentencing policy is adopted and implemented.
The impact estimate must therefore use as a baseline a
projection of future prison populations under current
The policy change,
or alternative changes being considered, can then be
viewed as a perturbation to that projected baseline
level. The difference between the two projections is the
estimated impact associated with the policy change.
In developing the estimate of the impact projection,
the time dimension must be taken into account. That is,
_
policies prior to the policy change.
different policies involve different build-up rates of
prison populations, and those differences can be very
important. For example, a policy that involves a large
increase in numbers of prison commitments will display a
more rapid growth in prison populations compared with a
policy that involves a similar fractional increase in
time served. Even though both policies will require the
same capacity eventually, in the latter case, the build-
up will take place more slowly over time as release dates
are extended.
Any impact estimate must take account of compliance
with the planned policy. This requires some behavioral
assumptions about how judges, prosecutors, and defense
counsel respond to the imposition of the changed policy.
The simplest--and most simplistic--assumption is that
they will fully comply with the policy. Another simple
assumption is that they will ignore the policy and con-
tinue their prior practices. Even though this assumption
is not so simplistic, the associated impact estimate is
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463
zero, and the estimate of future prison populations is
merely the baseline projection.
Most often, of course, the response is somewhere
between the two extremes. There does tend to be some
compliance with a policy change, but it often is less
than total compliance. In considering a 5-year mandatory
minimum sentencing law for rape, for example, it is
entirely possible that introduction of the law will bring
about no Change in charging behavior by the prosecutor
and that everyone charged under the law will be sent to
prison with certainty for a sentence no less than the
specified 5-year mandatory minimum sentence; this would
represent total compliance. It is more likely, however,
that under the new law a larger fraction of the rape
arrests would appear as assault cases, or that judges
faced with a rape indictment would be more likely to
dismiss the charge or to find mitigating circumstances
that would enable them to assign probation if the only
available prison sentence is 5 years or more. These
kinds of accommodation behaviors must somehow be
reflected in any impact assessment that is made.
In discussing impact assessment, therefore, we begin
first with a discussion of approaches to the projection
of future prison populations, then consider means of
incorporating policy changes into those projections.
PROJECTION OF FUTURE PRI SON POPULATIONS
AS A BASELINE FOR THE IMPACT ESTIMATE
In considering approaches to estimating future prison
populations, it is useful to organize them roughly in
order of increasing complexity of the projection model
and the associated increase in the richness or subtlety
of the assumptions involved in generating a projection.
Naive Projection--Current Situation as a Baseline
The simplest, most simplistic projection is the naive one
that suggests that any subsequent year's prison popula-
tion will be the same as that of the current year. This
has the obvious benefit of requiring only one assumption
(however gross), instead of many more complex and
challenging, subtle and simple ones. (Clearly, the
number of assumptions is not necessarily a good indicator
of the parsimony of a model).
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Such an assumption can be invoked even in the absence
of any conviction that it represents a good approximation
of reality. This form of projection is implied, for
example, when current practice is used as a baseline on
which to estimate the impact of a policy change. This
clearly avoids the many concerns that arise in attempting
to project the baseline to reflect continuation of cur-
rent practice in the absence of a policy change. This is
probably a very reasonable approach when there are no
external changes affecting criminal justice operations.
When there are such important influences in progress--
demographic shifts, for example--then it does become
important to have an accurate baseline projection, espe-
cially when saturation of prison capacity becomes rele-
vant. If current practice results in a prison population
well below current prison capacity, and if the external
changes in the absence of policy shifts would generate
prison populations that exceed prison capacity, then it
is important to have that baseline estimate to plan
future resource requirements. The cost of a policy is
appreciably greater if it requires creating new capacity
than if it can be accommodated within existing capacity.
This approach of using current practice as the base-
line level was used by the Pennsylvania Commission on
Sentencing in estimating the impact of its sentencing
schedule. The commission collected a sample of con-
viction cases in Pennsylvania in 1977 and assigned each
case to the appropriate cell of the sentencing guideline
grid.2 Then, with Ni cases assigned to the ith cell in
the sentencing grid and Mi of them given prison sen-
tences, the sentences imposed in that sample of cases
provide estimates of the principal sentencing parameters
in each cell, Qi' the probability of imprisonment, and
Si, the sentence served. The probability of
imprisonment, Qi' is estimated as Qi = Mi/Ni, and the
mean sentence, Si, is estimated as
Mi
Si = ~ Sip
3=1
where Sij is the sentence assigned to the jth case (j =
1, 2, . . . , Mi) that falls in the ith cell. The prison
capacity associated with cell i in the baseline case is
then
NiQi Si '
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465
and the total prison capacity required would be
n n
C = ~ C i - ~ NiQiSi
i=1 i=1
where the summation is taken over the n cells in the
sentencing grid.
The impact of a recommended guideline structure can
then be estimated as a change in this baseline. If a
prison sentence of Si' years is recommended for cell i
(with Si' - 0 for cells in which no imprisonment is
recommended), then the prison capacity for cell i under
the recommended schedule is
Ci' = NiSi '
and the total capacity required is
n
C' = ~ NiSi'
i-1
If the sentence recommendation for cell i is a range,
Sio ~ Si1 (e.g., 3-4 years), then one can generate a
conservative estimate of capacity requirements by using
the upper Si1 value in each cell, a risky estimate by
using the lower Sio value, or a median estimate by using
the average,
(Sin + Sil)/2 .
For a heterogeneous jurisdiction with diverse sentenc-
ing practices across its counties (as is certainly the
case in Pennsylvania), a better estimate can be obtained
by assuming that the lower values are applied in the
metropolitan counties and the upper values in the rural
counties. This additional refinement--as is the case
with most refinements--requires additional information.
The extra information required is the
distribution of cases across counties, Nik, the number of
cases falling within cell i from the kth type of county.
Extrapolation of the Time Series of Prison Populations
One of the least helpful approaches to projecting future
prison populations is linear extrapolation of recent
trends. After a number of years of fairly steady
increases (or decreases) in prison populations, it can be
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particularly tempting to simply draw a line through the
points and extrapolate that line into the future.
This approach has a number of serious pitfalls. If
the points were following a downward trend, even the most
naive extrapolator would know enough not to draw that
line far enough so that it took on negative values for
prison population. Projecting an increasing trend does
not yield such obviously absurd results, but it could be
equally inaccurate. Simple linear extrapolation--even
though widely used--fails to recognize the fact that most
trends at some point saturate and reverse themselves, and
are certainly more likely to do that than to continue
indefinitely. While a linear extrapolation may be rea-
sonable for a short-term projection of one or two years,
going beyond that can be very risky.
The underlying model of the linear extrapolation is:
Yt= aO* + al*t,
to be fit from recent data.
(1)
where t represents time, Yt represents the prison
population at time t, and aO* and al* are two parameters
~ ~ ~ Here, aO is the prison
population in the year when t is set at 0, and a1 is the
average annual increase in prison population.
So simple a model, of course, invokes only one vari-
able, time, and no other information about the other
factors influencing imprisonment. Most important, from
the viewpoint of using this projection as a policy tool
for impact estimation, such a model contains no policy
variables, reflecting sentencing practice (the Qi and S
of the previous section) whose impact on prison popu~a
tion can be directly measured.
If the time series of imprisonment has been moving in
other than a linear way, one might become somewhat more
elaborate in the extrapolation by adding additional terms
involving higher powers of t for example, be adding a
term, a2*t2, to equation (1).
, . _
Such an elaboration of
t~ng a n~gner-uegree polynomial to the data can be
very risky. Even though adding terms can give a closer
fit to the data, that higher-degree polynomial is much
more vulnerable to radical deviation outside the fitted
data points. In contrast, one of the virtues of the
linear equation is the severe limitation on how rapidly
it can change.
~ much more sophisticated form of extrapolation in-
volves the use of ARIMA models, introduced by Box and
Jenkins (1976; see also McCleary and Hay, 1980), as a
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means of using data on a time series (Y1, Y2, · · · , Yt)
to forecast future values, (Yt+l, Yt+2, , Yt+k)
The basic approach in developing such a forecast involves
first identifying the form of the underlying process that
generated the original series, then estimating the param-
eters of that process, and finally, by assuming that the
same underlying process continues into the future, gener-
ating estimates of the expected future values of the
series. As with all such forecasts, the farther the look
into the future, the more sources of error there are that
can lead to an erroneous forecast, and the more likely it
becomes that the underlying assumption of a continuation
of the prior underlying process will be violated by a
distortion of the process.
Such univariate time series have the limitation that
they do not include the relevant policy variables.
Multivariate ARIMA processes, which are used to establish
the link between two or more time series--for example,
prison population Y and the sentencing policy variables,
Q (the probability of imprisonment given conviction) and
S (the average sentence imposed)--can then be used to
test the effect of a change in one of those policy
variables.
.
Multivariate Regression
One can go beyond models that use only the single vari-
able time as an exogenous determinant of future prison
populations by invoking a variety of other variables
known to be causally related to prison populations. This
equation takes the following form:
Yt = aO + alXlt + a2X2t =
.
· + anXnt i (2)
where Yt is the prison population in year t and the x
vector,
{xit I
i = 1, 2, . . . , n} ,
includes exogenous determinants of prison population in
year t. Factors that have been proposed for x include
unemployment rates (see Greenberg, 1977, for example)
consumer price index (see Fox, 1978,), or demographic
variables (e.g., population of men ages 20-30) and other
such variables. They could also include sentencing
parameters, Qt (the probability of imprisonment in year
t) and St (average sentence imposed in year t).
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Such a model could be estimated by collecting data on
the specified xt variables and on the associated Yt
over a period of years, using standard multivariate
techniques to estimate the values of the coefficients
(aO *, al*, a2 *, · , an*)
Using such a model for projecting future prison popu-
lations obviously requires projections of the values of
the x vector (xl, . . . , xn). For many variables
that would be important candidates for inclusion in x, it
may be far more difficult to generate a projected esti-
mate of that variable than of prison population itself.
If that is the case for the unemployment rate, for exam-
ple, then a model that depends strongly on a projection
of the unemployment rate contributes little to the
capability of projecting prison population (Y).
Some variables, such as demographic variables, are
more easily projected. For example, the number of men in
a particular high-imprisonment age group (for example,
ages 20-29), is relatively easy to project for at least
20 years into the future. Aside from migration, all
individuals who will be in that age group are already
born, so the only uncertainty is that associated with
death and migration. Death rates are fairly small for
ages 1-20 and are also reasonably predictable. Migration
can be a major distorting factor in a small region like a
city or in a rapidly growing state, and it must certainly
be taken into account in projecting the demographic
variables.
.
Some variables can reasonably serve as leading indi-
cators of Y (e.g., xt_k is one of the components of the
Xt vector in equation (2) for Yt). When that is the
case, then such a variable can be helpful for projecting
as many as k years ahead.
An important limitation of the multivariate regression
approach, especially for estimating the effect of changes
in the sentencing policy variables, is the anticipated
insensitivity of the regression equation to those vari-
ables. First, as with most complex phenomena, one can
expect only limited success in accounting for the factors
contributing to the variation in prison population
through a linear regression equation.
Second, the regression of Yt on the sentencing policy
variables, Q and S. must involve, in addition to Qt and
St, (Q,S)t_l, (Q,S)t_2' etc., since the prison population
in year t (Yt) includes people sentenced one or more
years earlier, and so was determined by sentencing poli-
cies more than several years prior to t. One might try
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to avoid this complexity by considering only commitments
during t, but that strategy would fail to recognize the
effects of sentence length, S. or changes in S.
Finally, in the context of the other exogenous politi-
cal, demographic, and socioeconomic factors that influ-
ence prison populations, each of which is difficult to
capture totally, it is likely to be very difficult to
discern the separate effects of sentencing policy through
a multiple regression model. Thus one cannot have strong
confidence that the coefficients associated with the
sentencing policy variables will be reliably estimated.
Projections Based on
Demographic-Specific Incarceration Rates
It is well known that different age, race, and sex groups
differ markedly in many aspects of their involvement with
the criminal justice system. This is particularly true
in prison populations: in 1979 females made up only 4
percent of the total state prison population; the incar-
ceration rate for males (i.e., prisoners per capita) was
disproportionately large by a factor of 25 to 1 compared
with females; black males made up 46 percent of the total
U.S. male prison population, a disproportionate represen-
tation of 6.7 to 1 compared with white males; and the
incarceration rate by age was also markedly different
across the different age groups.3
Table 8-1 shows the incarceration rate by race and age
for males in U.S. state prisons in 1979.4 The peak
incarceration rate for white males occurs at age 23 and
is 2.2 times that at ages 35-39 and 8.8 times that at age
40 or older. The incarceration rate for black males
reaches its peak at ages 25-29 and is 7.5 times the peak
for white males (at age 23). The age falloff for blacks
is comparably fast, the peak being 2.5 times the rate at
ages 35-39 and 9.1 times the rate at age 40 or older.
These striking age, race, and sex differences suggest
another approach to projecting prison populations. The
current prison population can be partitioned into demo-
graphic subgroups and the incarceration rate calculated
for each subgroup; if that rate is assumed constant (or
projected), that incarceration rate can then be applied
to any projection of the general population.
Thus, for example, one can generate a vector of incar-
ceration rates,
g i = Yi/Ni
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TABLE 8-1 Demographic-Specific Incarceration
Rates (prisoners per 100,000 population) in 1979
for U.S. Males by Race and Age
Total
Age U.S.WhiteBlack
18-19 4322421,657
20 6784272,234
21 7344362,826
22 8194763,208
23 8895133,485
24 8314653,543
25-29 7964163,856
30-37 5262802,716
35-39 3622331,515
40+ 9258424
Total 2541451,062
NOTE: Incarceration rates were calculated from
Yi/N i, where Yi is the number of prisoners in
demographic group i at time of the 1979 survey of
the Bureau of Justice Statistics and the Bureau of
the Census, and Ni is the number of persons in
the general population in demographic group i in
1979.
SOU=E: U.S. Bureau of the Census (1980).
where Yi is the number of prisoners in the ith demogra-
phic group, Hi is the number in the general population
within the ith demographic group, and gi is the incar-
ceration rate for the ith subgroup. Then, if one has a
demographic projection of the population for time t'
say, N i* ( t' ), then the estimate of the pr ison popu-
lation in demographic group i at time t' is given by
Yi*(t') = giNi*(tt)
and
Y* (t') =
~ Yi* (t')
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3. The prosecutor could revise charging behavior so
that individuals for whom the mandatory minimum
sentence seems excessively severe could be charged
under one of the many related offense types, there-
by decreasing the fraction charged with a pre-
scribed offense and correspondingly increasing some
of the other offenses.
In all of these cases, the response could be
characterized in terms of a corresponding change in Qi or
Si as well as in changes in the number of persons
associated with each subset, {Gi}.
Calculation of the Effects of the Sentencing Policy Change
Once the parameters in the estimation models have been
formulated to generate estimates of the numbers in each
subset, {Gi}, and their associated Qi and Si under each
of the alternative sentencing policies being considered,
and for each of the behavioral adaptation assumptions, it
then becomes possible to calculate the prison populations
associated with each sentencing policy. That calculation
could be accomplished using a disaggregated flow model
(equations (3) to (5)) or a microsimulation, each with
the appropriate sentencing policy variables, Qi and Si.
By comparing Pt', the prison population in year t under
the new policy to the corresponding Pt under the old,
the difference (Pt' - Pt) represents the incremental cost
(or savings) associated with the policy change.
Estimation of the Impact of a
Mandatory Minimum Sentencing Bill
In order to illustrate some of the methodological issues
discussed earlier and also to convey some of the substan-
tive insights that emerged, this section summarizes the
results of an impact analysisl2 in Pennsylvania, build-
ing on projections of prison populations in Pennsylvania
through the demographic-specific flow model discussed
above.
The particular policy change examined is a mandatory
minimum sentencing bill, S.B. 995, that was one of sev-
eral such bills being considered by the Pennsylvania
legislature during its 1976 session. The bill addressed
10 felony offenses ranging from murder to sale of nar
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cotics and burglary. Recidivists with one prior con-
viction were to receive a one-year mandatory minimum
sentence upon reconviction, and those with two or more
prior convictions were to receive a two-year minimum.13
If a firearm was used in the current offense, an addi-
tional year was to be added to the minimum sentence.
These provisions of the bill provided the necessary
guidance for generating the offense and offender subsets
that fall under its provisions. The relevant offenses
were clearly specified in the bill and also were avail-
able in the court OBTS records.
_
The prior record provi-
s~ons were clear in the bill but, as is often the case,
were not available in the individual records from the
court; court records, at best, might include a single
number (such as prior felony convictions) but are not
likely to provide more detailed information on convic-
tions for a specified group of felonies. Thus it became
necessary to draw a separate sample of convicted persons
and to examine their prior records in detail in order to
determine the fraction associated with each combination
of current offense type and prior record that were speci-
fied by the bill. A similar partition was conducted for
the offenses involving firearms.
This information provided the basis for partitioning
convicted offenders in any year into appropriate subsets,
{Gi}, corresponding to each of the combinations of
conditions specified in the bill. For each such group,
the fraction of cases involved and the sentencing pattern
prior to enactment of the bill, Qi and Si, could be
determined. For each such group, the provisions of the
bill indicated Qi' (either Qi' = Qi if the group was not
relevant to the bill or Qi' = 1 if imprisonment was
mandatory). The average sentence under the bill, Si',
depended on the distribution of sentences in prior
practice. For the groups not addressed by the bill,
Si' = Si. For those for whom prison was mandated, the
lower tail (below Sol, the group's relevant mandatory
minimum) of the sentence distribution was set at Sol,
those previously assigned to probation were set at Sol,
and the upper tail of the sentence distribution (above
Sol) remained unaffected.
These statements reflect literal interpretation of the
bill's provisions. The next step involved characterizing
the judges' behavioral responses to those provisions.
Since the bill afforded judges the opportunity to avoid
imposing a sentence if they found that mitigating factors
warranted such an action, three possible scenarios were
considered:
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482
1. Mandatory Prison Scenario: Literal interpretation
of the bill so that anyone who satisfied the condi-
tions of the bill was sent to prison for the speci-
fied mandatory minimum sentence.
2. Conforming to Minimum Only Scenario: Only those
who formerly were sent to prison but served less
than the specified minimum had their sentences
raised to the minimum; others,-and particularly
those who formerly were assigned to probation--
remained unaffected by the bill.
3. Undermining Scenario: Those who were formerly sent
to prison for a sentence less than the mandatory
minimum were put on probation in order to avoid
having to increase their sentences; others remained
unaffected by the bill.
Analysis was carried out only on the first two of
these scenarios, and their effects on state prison popu-
lations were examined. Since the impact will accumulate
over a number of years as new offenders are convicted
under the new bill, the impact estimate was calculated
over time as a perturbation to the population projections
assuming continuation of current practice. These effects
under the two scenarios, reflecting the different beha-
vioral responses to the bill, are shown in Figure 8-1.
The striking observation is that full implementation of
the legislation as written (the mandatory prison sce-
nario) would have involved an increase of about 50 per-
cent in prison populations at the peak. A much less
dramatic change is associated with the conforming to
~ninimum only scenario, in which the prior probation
decisions remain unaffected. Under this scenario, prison
population would increase only about 10 percent, certain-
ly well within any forecasting or impact estimation error
and certainly a tolerable impact on any prison system.
This also indicates that the major change called for by
the bill is the increase in the use of imprisonment for
people who otherwise are put on probation, and it does
not call for major increases in time served by those who
already do go to prison.
These two scenarios undoubtedly encompass the judicial
response that would be anticipated. The results also
suggest that if the bill were passed, most judges would
probably invoke the mitigating factors option, at least
for a sizable fraction of the cases they had formerly put
on probation.
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483
1 5,000
LL
At:
1 0,000
-
o
U)
Ct:
Cal
On
mandatory prison
scenario
/
/
5,000
-
-
conforming to minimum
only scenario
-
current sentencing
scenario
of 1 1 1 1 1 1
1 970 1 97 5 1 980 1 985
YEAR
1990 1995 2000
FIGURE 8-1 Pro jec ted Pr ison Populations Under
Alternative Sentencing Scenarios (S.B. 995)
It was also interesting to identify the offenses that
contributed to the major growth in prison populations
under the mandatory minimum scenario. The changes in the
number of commitments for five offense categories are
shown in Table 8-3. It is clear from the table that th e
bill would have very little effect on those convicted of
murder and rape--virtually all of them go to prison
already. The major influence would be on those convicted
of relatively minor offenses, burglary in particular.
The predominant portion of those new commitments would be
those convicted of burglary with relatively few prior
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TABLE 8-3 Changes in Commitments to State Prison Under
S.B. 995
Number of Number of Increase
Current Commit- in Percentage
Commit- ments Number of Increase
meets Under Commit- in Commit
(1975) S.B. 995 meets meets
Burglary 685 1,626 941 137
Robbery 686 1,148 462 67
Drugs 401 582 181 45
Murder 406 436 30 7
Rape 120 145 25 21
convictions, largely because they were not committed
under prior policy.
In view of the considerable disparity in sentencing
practice across a state as heterogeneous as Pennsylvania,
the predominant increase in sentences to state prison
comes from Philadelphia. For example, Philadelphia would
provide 58 percent of the new commitments for robbery.
This is partly a result of the disproportionate number o f
robbery convictions in Philadelphia, partly a result of
the greater leniency with which Philadelphia treats
robbery--many of its convicted robbers go to the county
jail for sentences less than the mandatory minimum, and
S.B. 995 would require that they be sent to the state
prison.
The results of this analysis were presented in testi-
mony to the Pennsylvania legislature in 1977 and in 1978,
when it was considering a number of mandatory minimum
bills, along with a proposal to create a sentencing
commission. The sentencing commission was intended, at
least in part, as a means of heading off the politically
attractive mandatory minimum bills. The magnitude of the
estimated impact estimate of S.B. 995 was surprising to
many of the legislators; that provided one basis for
arguing for the necessity of formulating sentencing
policy in a forum like a sentencing commission, which
they hoped would be more deliberative than is normally
the case on the floor of a state legislature. The sew
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fencing commission bill was passed with a majority of
only one vote.
SUMMARY AND FURTHER RESEARCH NEEDS
This paper has focused on the importance and the feast
bility of providing estimates of the impact on prison
populations of proposed changes in sentencing policy and
of the need to develop improved methods for generating
them. Such estimates are necessary to ensure that the
debate over sentencing policy is balanced and that the
political attractiveness of a tougher policy is respon-
sibly weighed against the costs of such a policy.
This issue will be particularly important in the
coming decade, when prisons, already largely filled to
capacity, can expect significant growth in sentenced
populations. It is also important that the impact be
examined in the context of projections of prison popula-
tions over an interval of at least 20 years in order to
estimate the degree to which the anticipated future
prison population growth warrants provision of additional
prison capacity. At least in those states of the North-
east and the Midwest in which prison populations can be
expected to reach a peak and to decline after about 1990,
there may be a serious question about the advisability of
creating that extra capacity, especially if one considers
the limited excess demand after it is finally con-
structed. The availability of impact estimates provides
legislatures and the public generally the opportunity to
make responsible and explicit trade-offs between the
desired level of punitiveness and its cost.
There are a number of research approaches that could
make important contributions to the ability to develop
such impact estimates:
1. A number of readily available models for calculat-
ing prison impact should be formulated and made
available to criminal justice planning agencies for
their use in assisting a legislature or sentencing
commission in estimating the impact of their policy
choices.
2. Some pilot trials ought to be undertaken in states
with OBTS systems to develop good means for pro-
jecting future prison populations and to estimate
impacts.
3. In jurisdictions that have adopted significant new
sentencing policies, the impact should be estimated
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using data that were available prior to the policy
changes, and these projections should be compared
with the changes that actually occurred.
4. Cros~jurisdictional studies should be conducted to
discern how judges respond to changes in sentencing
policy. It is particularly important to be able to
compare courts in jurisdictions in which the prison
system is fully saturated with those in which there
is slack capacity in the prison.
NOTES
1. In this paper, the term sentencing policy is used
generically to refer to guidance or mandates to sentenc-
ing judges, whether that guidance is established by a
legislated determinate sentencing schedule as embodied in
California's SB-42, by a mandatory minimum sentencing
law, or by sentencing guidelines established by a judi-
cial council or by a legislatively created sentencing
commission. Statutory sentence maximums are also a form
of sentencing policy, but they are largely ignored in
this paper, partly because their role in the courtroom is
insignificant, but primarily because the questions of
prison impact addressed here are much more related to
concerns over the effects of sentences that are con-
strained from below than from above.
2. The grid was created by generating an offense score
of 12 levels based on a ranking of offense seriousness
and an offender score of 7 levels based on the prior
conviction history of the defendant.
3. The estimates of number of prisoners by sex and race
are based on Tables 2 and 3 (for sex) and Table 7 (for
race of males) of U.S. Department of Justice (1981). The
general population estimates are based on U.S. Bureau of
the Census (1980:Table 2).
4. The rates in Table 8-1 were calculated on the basis
of the age and race of the male prisoners responding to a
survey in 1979 conducted by the U.S. Bureau of the Census
(1980) for the Bureau of Justice Statistics, and the
demographic composition of the U.S. population was deter-
mined from U.S. Bureau of the Census (1980).
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487
5. These are not the same incarceration rates presented
in Table 8-1. The values in Table 8-2 are based on the
demographic features of interviewed prisoners at the time
of their admission to prison. The more appropriate
numerators are the demographic features at the time of
the survey, which were used in Table 8-1. That numer-
ator, however, was available only for the 1979 survey,
and so, for comparability, the less appropriate measure
of age at admission is used in Table 8-2.
6. For a description of the JUSSIM model, see Blumstein
(1980). That article contains detailed references on the
program and its operation.
7. These substantive observations are, of course, pre-
cisely true only for Pennsylvania. The important role of
the demographic shifts associated with the baby boom,
however, is likely to apply broadly to the states of the
Northeast and the Midwest, with their numerically stable
and aging populations. Even within those states, the
large cities would have to be examined separately because
of their large rates of migration and the strong effect
those migration patterns could have on demographic struc-
tures. At the state level, however, the level at which
concern over prison population is most relevant, demogra-
phy is less sensitive to shifting migration patterns. In
contrast to the Northeast, the rapid population growth in
the West and the Southwest could dominate the age shifts
that cause the peaking observed in Pennsylvania.
8. In 1981, Pennsylvania's prison population was about
300 prisoners over capacity.
9. These projections were based on the processing
parameters remaining constant throughout the period, a
situation that did prevail in the early 1970s. In the
late 1970s, however, sentences were observed to increase,
thereby intensifying the anticipated saturation.
10. Literally, C enters equation (4), but Q influences C
through equation (3).
11. This is the approach used by the Minnesota Sentenc-
ing Guidelines Commission in estimating the effect of any
guideline sentencing schedule on prison populations,
enabling the commission to adhere to the policy it adopt-
ed of avoiding any policy that would lead to an increase
in prison populations (see Knapp and Anderson, 1981).
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488
12. The details of the impact analysis are reported in
Blumstein et al. (1979). The results are summarized in
Miller (1981).
13. One interesting indication of the shift in attitudes
since 1976 is the fact that the mandatory minimum bills
considered by the Pennsylvania general assembly in 1981
call for minimum sentences of five years rather than one
or two years.
REFERENCES
Blumstein, Alfred
1980 Planning models for analytical evaluation. Pp.
237-257 in Malcolm W. Klein and Katherine S.
Teilmann, eds., Handbook of Criminal Justice
Evaluation. Beverly Hills, Calif.: Sage
_
Publications.
Blumstein, Alfred, Jacqueline Cohen, and Harold D. Miller
1980 Demographically disaggregated projections of
prison populations. Journal of Criminal Justice
8(1):1-25.
Blumstein, Alfred, Harold Miller, Wendy Bell, Deborah
Kahn, and Stewart Szydlo
1979 The Impact of New Sentencing Laws on State
Prison Populations in Pennsylvania: Final
Report. Urban Systems Institute, Carnegie-
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Box, George E.P., and Gwilym M. Jenkins
1976 Time Series Analysis: Forecasting and Control.
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Fox, James A.
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Lexington, Mass.:
Lexington Books.
Greenberg, David F.
1977 The dynamics of oscillatory punishment
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Knapp, Kay A., and Ronald E. Anderson
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Program--User's Manual. Minnesota Sentencing
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Lettre, Michel A., et al.
1978 A Jurisdiction-Based Description of the Maryland
Criminal Juvenile Justice System. Maryland
Governor's Commission on Law Enforcement and
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McCleary, Richard, and Richard A. Hay, Jr.
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Miller, Harold
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_ . . ~ .
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Representative terms from entire chapter:
sentencing policy
