Research on Sentencing The Search for Reform, Volume II (1983) / Chapter Skim
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8 THE IMPACT OF CHANGES IN SENTENCING POLICY ON PRISON POPULATIONS
8 THE IMPACT OF CHANGES IN SENTENCING POLICY ON PRISON POPULATIONS
Pages 460-490
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From page 460... ...
Since changes in sentencing 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)
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From page 461... ...
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 sentencing policy.
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From page 462... ...
The Pennsylvania Commission on Sentencing did not adopt current populations as a constraint on its eventual schedule but did try to keep informed of the estimated effect of the evolving sentencing schedule on Pennsylvania's prison population. Any impact estimate is associated with a future time after the sentencing policy is adopted and implemented.
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From page 463... ...
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.
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From page 464... ...
When there are such important influences in progress-demographic shifts, for example -- then it does become important to have an accurate baseline projection, especially when saturation of prison capacity becomes relevant. 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.
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From page 465... ...
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.
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From page 466... ...
So simple a model, of course, invokes only one variable, 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)
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From page 467... ...
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)
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From page 468... ...
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 variables. 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.
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From page 469... ...
Finally, in the context of the other exogenous political, demographic, and socioeconomic factors that influence 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.
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From page 470... ...
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.
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From page 471... ...
In aaa~zon, there Goes not appear to be important differences between the growth rate for blacks and that for the population generally. If values of incarceration rates were available at several points in time, and if those values displayed a trend instead of a constant rate, then one might try to extrapolate the incarceration rates to generate estimates gi*
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From page 472... ...
Unfortunately, while the data systems in most jurisdictions will support calculation of incarceration rates because of good records on prison populations, the data based on court records are sufficiently inadequate that estimates of demographic- and offense-specific conviction rates will be extremely difficult to generate. Disaggregated Flow Models The data problems become much more manageable in those jurisdictions in which some form of offender-based transaction statistics (OBTS)
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From page 473... ...
In particular, there is no behavioral model built into the program that, as the prison population builds up beyond the prison's capacity, reduces the flow rate from a sentencing stage to prison (by reducing the probability of prison given conviction) , even though such accommodation is likely.
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From page 474... ...
This demographic disaggregation becomes particularly necessary when demographic changes are important factors influencing prison populations, as they certainly are in many regions of the United States and in other countries experiencing the postwar baby boom of 1947 to 1962. Thus, a flow model like JUSSIM, augmented by retaining demographic disaggregation of Processing parameters, could be used to generate a projection ot prison populations that was sensitive to demographic shifts.
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From page 475... ...
Their analyses provided a basis for projecting numbers of arrests, commitments to prison, and prison populations to the year 2000. Those projections reflected the strong effects of the postwar baby boom on the criminal justice system as the trailing edge of that group (the 1962 birth cohort)
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From page 476... ...
for independent testing of alternative policy changes. Microsimulation Models The flow models discussed in the previous section simply treat average flow rates at each stage of the criminal justice system and examine the distribution of those units of flow across the processing network.
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From page 477... ...
Even though a number of models can project prison population reasonably, only the subset that specifically contains the sentencing policy variables Q and S can also serve the policy-impact-estimation function. The approaches that are most appropriate are likely to be the disaggregated flow models and the microsimulations.
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From page 478... ...
Calculating the projected change in prison population resulting from the response to the changed policy. Identification of Population Subsets Any sentencing policy ordinarily specifies at least the following attributes of those to whom it applies: (1)
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From page 479... ...
For those groups that remain unaffected by the law or policy, then Qi can remain at its prior value. A similar approach pertains to sentence under a mandatory minimum sentencing policy.
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From page 480... ...
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.
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From page 481... ...
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 specified by the bill. A similar partition was conducted for the offenses involving firearms.
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From page 482... ...
Analysis was carried out only on the first two of these scenarios, and their effects on state prison populations 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.
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From page 483... ...
995) It was also interesting to identify the offenses that contributed to the major growth in prison populations under the mandatory minimum scenario.
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From page 484... ...
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.
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From page 485... ...
It is also important that the impact be examined in the context of projections of prison populations 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 Northeast 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 constructed.
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From page 486... ...
NOTES 1. In this paper, the term sentencing policy is used generically to refer to guidance or mandates to sentencing 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 judicial council or by a legislatively created sentencing commission.
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From page 487... ...
11. This is the approach used by the Minnesota Sentencing Guidelines Commission in estimating the effect of any guideline sentencing schedule on prison populations, enabling the commission to adhere to the policy it adopted of avoiding any policy that would lead to an increase in prison populations (see Knapp and Anderson, 1981)
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From page 488... ...
Miller 1980 Demographically disaggregated projections of prison populations. Journal of Criminal Justice 8(1)
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From page 489... ...
Beverly Hills, Calif.: Sage Publications. Miller, Harold 1981 Projecting the impact of new sentencing laws on prison populations.
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