Patterns of jail use

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PATTERNS OF JAIL USE

JOHN KLOFAS Department of Criminal Justice Illinois State University Normal, Illinois 61761

ABSTRACT Jails continue to be the most neglected component of the criminal justice system. One consequence of this neglect has been that important conceptual issues regarding jails remain unresolved. This study developed a method for describing the patterns of jail use in counties. Using data from Illinois, rates of jail use and the diversity of use patterns were described. The influence of crime rate and county population si:e on jail use were explored, and jail crowding was examined by focusing on different patterns of jail use. Implications for jail research and policy are discussed.

Discussions of local jails frequently begin by noting the limited research in the area.

Such descriptions of the current state of knowledge are often accompanied by evidence attesting to the importance of the local jail as a social institution. Over eight million people are processed through jails annually, and nearly 250,000 can be found in them at any given time (Bureau of Justice Statistics, 1985). Furthermore, jails are said to be utilized in dealing with a whole gamut of social problems-they act as everything from contemporary poorhouses to agencies dealing with problems of alcoholism and drug addiction to houses of confinement for serious violent offenders (Goldfarb, 1976). Despite the obvious social significance of the jail, few explanations of its neglect in the criminal justice research are available. When this issue is addressed directly, the lack of research is attributed to such things as the fact that jails are locally managed, that their 403

population is diverse and complicated in that inmates exhibit a variety of legal and demographic characteristics and that jails differ greatly by size (Flynn, 1973; Mattick, 1974; Handberg, 1982). These concerns generally relate to either technical difficulties regarding the generalizability of research findings or tactical difficulties in conducting research across numerous diverse institutions. Regardless of the explanation, the dearth of jail research has meant that not only have specific hypotheses not often been generated and tested but even basic conceptual issues have not been resolved. For example, there appears to be little consensus about the best way to define and describe local jails. Thus, research and policy in the area have not had the benefit of a sufficiently clear conceptual foundation. The problem is illustrated in what still must rank as one of the most comprehensive discussions of jails available. Hans Mattick (1974)

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JOHN KLOF.-lrS

noted the inadequacy of relying solely on nomenclature to identify jails. instead of getting confused over terms such as “House of Correction” and “House of Detention.” Mattick suggested a functional approach. in which a jail is defined as “an institution for (1) general detention, and (2) the serving of short sentences” (p.777). In his research, however, Mattick found this definition unsatisfactory and chose not to consider some institutions that fell within its parameters. Instead, he resolved the problem by relying on the operational definition originally used by the Law Enforcement Assistance Administration in its census of jails. This definition hinges on the authority to detain people for forty-cight or more hours. Conceptual problems continue to hamper jail research. One consequence of these problems is that methodological assumptions in jail studies may go unexamined. Irwin (1984: 18), for example, concluded in his study of the San Francisco City and County jails that “ . . the jail, unlike the prison, has little to do with serious crime. Its primary purpose is to receive and hold persons because they arc ‘offensive’.” This conclusion, however, must be considered in light of the fact that Irwin’s inmate sample was randomly selected from a list of those booked into the jail. Other researchers have concluded that there is in fact a relationship between rates of serious crime and jail populations. Sykes and Vito (1987) found significant correlations between changes in index crimes and jail populations in the nation’s fifty-nine largest jails. Their conclusion, however, was clearly related to their use of gross cross-sectional data obtained from the jail census. The differences between these two studies relate to different assumptions about the jail population. Irwin assumed that the best way to describe the jail is to focus on those booked into the jail, whereas Sykes and Vito opt for a focus on the people in jail on any given day. These studies, therefore, were based on different populations. The need for precision in identifying exactly what is being studied is also seen in other research. A recent investigation of the

inmate subculture in jail relied on a survey of inmates conducted within hours of their booking (Garofalo and Clark, 1985). The study concluded that evidence of adherence to subcultural norms was greatest among recidivists who had previously experienced jail confinement. By investigating subcultural attachments among recently booked inmates. the authors appeared to assume that their sample was similar to a sample of inmates in jail on any given day. Those samples, however, are likely to differ on a host of subculturally relevant variables, including seriousness of offense and criminal history. Differences regarding how best to describe the jail population also call attention to the need for care in interpreting the Bureau of Justice Statistics’ Surve)~ of Inmates of Local Jails (1985). In 1983 this survey was conducted with a stratified random sample of nearly 6,000 inmates in 407 jails. The resulting descriptions of inmates’ demographic characteristics, criminal histories, histories of substance abuse, etc., were said to represent those of persons housed in the nation’s jails, The descriptions, however, would not accurately reflect the socio-demographic and criminal characteristics of the much larger number of persons booked into jail and detained for periods ranging from a matter of hours to several days. The data, therefore, say little about the most common clients of jails. In fact, since the longer a person spent in jail, the more likely he or she was to be in the sample, the survey results were heavily affected by a residual class of serious offenders, which would not have occurred had the sample been based on all jail admissions. This is similar to the problem of measuring recidivism by studying inmates in prison Glaser (1969:4) pointed out that such approaches inevitably inflate recidivism rates because those with longer criminal histories are likely to receive longer sentences and thus be overrepresented in cross-sections of the prison population. The survey, therefore, cannot be used to describe who goes to jail, but rather, only who is in jail on any given day. In fact, the survey can only describe those inmates

Patterns

forced to remain in jail a relatively long time. that is, inmates similar to inmates in jail when the sampling frame was constructed and still in jail when the survey was conducted. The Bureau of Justice Statistics (1985b) reported that approximately five percent of those in the sampling frame were not available for the survey. This group probably included a large number of short term and, therefore, low seriousness offenders. One implication of this is that the BJS survey is of limited use in studying bail decisions since it disproportionately discounts low seriousness offenders, who may spend days rather than months awaiting release. The conceptual problem is not a trivial one. It has broad implications for both research and practice. The issue is relevant to the study of many jail concerns. For example, should researchers of mental health problems sample from bookings or lists of jail inmates? Either method will have limitations that should be appreciated. A research design involving samples of bookings, inmates who have been in jail for a short time, and a separate sample of longtermers may be needed for an accurate description of mental health problems among those served by jails. Likewise, should jails be planned and administered based on inmate profiles from the Survey of Waif Inmates, or should profiles developed from samples of people booked into jails be utilized? The resolution of that issue may mean the difference between jails that resemble prisons in appearance and operation and jails that are more comparable with processing centers. All of this suggests the need to clarify definitions of jails and jail populations. One step in this direction is to focus not simply on differences within the jail population but rather on differences in the way jails function. Differences in the use of jail resources across local jurisdictions will have implications for many other jail-related issues. The goal of this study was to describe patterns of jail use in the state of Illinois. Such a modest undertaking was deemed a useful step in strengthening the conceptual foundation needed for research and policy regarding local jails.

of Jail Use

405 DESCRIBING

JAIL USE

Comparing the size of jails on the basis of either their number of beds or the number of inmates they hold provides little information about jail use because of the need to control for different population bases in the counties. At the time this study was conducted, Illinois counties ranged in population from 4,404 to 5,253,655, and jail populations ranged from one inmate to 4,933’. To control for diverse population bases, it is necessary to convert measures of jail use to rates. Summary data for standardized measures of jail use are reported in Table 1. Capacity rate, average daily population rate, and total annual booking rate for the ninety-six county jails in Illinois2 are presented using a population base of 10,000 county residents. There is considerable variety in both the cell space available for use in counties and in the way that space was used. Nearly twenty percent of counties had fewer than six jail cells per 10,000 county residents, whereas approximately the same percentage had over fifteen cells available for each 10,000 residents. The counties also filled those cells at vastly different rates. Twenty-two percent of the counties had average daily population rates of three or less whereas the six percent of the counties with the highest confinement rate exceeded a rate of ten per 10,000 residents, and one had a jail population rate of nearly seventeen. Still greater diversity is seen in the annual rate of bookings rather than a measure of the number of inmates being held on any given day. In twenty of the ninety-six counties, 100 or fewer people were booked into the jail annually out of each 10,000 residents. At the other extreme, eleven counties had a booking rate of over 350, with one reaching over 450. One additional measure of jail use is the ratio of annual bookings to the average daily population of the jails. On an average, Illinois jails annually booked in nearly fortyfive times their daily inmate population. This measure, however, also showed the greatest variability across counties. Ten per-

JOHN KLOFAS

406

TABLE: I : DESCRIPTIVE STATISTICS FOR COUNTY JAILS

Average Daily Population Rate

Booking Rate

9.60 4.48

4.87 2.63

187.05 100.23

44.94 30.19

9.00 3.40 29.80 26.40

4.15 1.10 16.60 15.70

167.20 45.40 450.50 405.10

36.45 8.52 189.29 180.77

Capacit) Rate Mean Standard deviation Median Minimum Maximum Range

Ratio of Bookings to Daily Population

NOTE: N=96

cent of the jails turned over their population twenty or fewer times a year. At the other extreme, over 100 times their average daily population were booked annually into the ten percent of jails with the highest turnover. In fact, two counties annually booked over 170 times their average daily population. One of those counties had an average daily inmate population of three, whereas the other housed an average of 111 inmates. Standardized measures of jail use each showed wide diversity across Illinois jails. Counties differed greatly by jail capacity rate, average daily population rate, and the rate at which they booked new inmates into their jails. The extent to which these differences reflect fundamental differences in approaches to justice and social control or seemingly trivial differences in policies is not known. What is evident is that there are wide differences across jails in the same state and those differences probably have implications for other jail issues. Before further discussion of the diversity in local jail use, it is necessary to consider the possible contributions of other variables to those patterns. As noted earlier, there are differences of opinion regarding the relationship between crime and incarceration. Although there has been considerable explora-

tion of this with regard to prisons (Blumstein et al., 1980; Abt Associates, 1980; McGuire and Sheehan, 1983), the research with regard to jails has been more limited. Still, it is possible to hypothesize that levels of jail use will be affected by crime levels. The hypothesis that crime rates will be affected by levels of jail use has also been advanced (Sykes and Vito, 1987). It is also desirable to investigate the relationship between county population size and the measures of jail use. Considerable differences between urban and rural jails have been noted (Flynn, 1973; Handberg, 1982). Irwin’s (1985) conclusions that the San Francisco jails have little to do with crime may imply that in jurisdictions where jails serve a large and diverse population, jails may be used to control a large social “rabble”. On the other hand, the argument has been made that smaller rural counties have historically used interventions such as probation sparingly (Rothman, 1980:83). They may, therefore, also use jail resources at a high rate. Table 2 presents the zero order correlations between the jail use measures and crime rate and population size. There was virtually no relationship between the measure of jail size and crime rate” or between crime rate and the number of times a jail

407

Patterns of Jail USC

TABLE

2:

CORRELATIONS OF JAIL

USE MEASURES WITH CRIMERATE AND POPULATION

Capacity Rate

Crime rate Populatior+

.16 .25Q

Average Daily Population Rate

.360 .06

Booking Rate

.3? .06

NOTES: N=96 * significant beyond .Ol

b N=95, Cook County (Chicago) is excluded.

population turned over annually. Likewise, the relationships between crime rate and average daily population as well as the rate of bookings were modest at best. These bivariate relationships suggest that patterns of jail use have little to do with patterns of crime. One possible explanation for this is that crime rate, as measured by the Uniform Crime Reports, taps a domain of seriousness of criminal behavior that is not characteristic of the most numerous clients of jails. Table 2 also shows that county population size did little !o explain variation in jail use. The correlations of county population size were practically zero with all but the capacity measure. Even with that measure, the relationship was very small. Population size did little to account for the patterns of jail use. Ordinary least squares regression was also used to examine the contributions of variables. The analysis used the capacity rate, crime rate, and county population figures to account for differences in average daily population and booking rates. Forty-one percent of the variance in average daily population rates was accounted for by these variables, but only the contributions of capacity and crime rates were significant. Likewise, twenty-one percent of the variance in annual booking rate was accounted for by the .variables, and once again the county population variable was not significant.4

RELATIONSHIPS BETWEEN MEASURES

THE

The measures of jail capacity, population, and bookings all were constructed independently of one another and revealed considerable variability. It is also important to examine diversity across the measures. One may overstate the case for variability if the measures of jail use are highly correlated and are, therefore, alternative measur& of the same property. In fact, there is every reason to expect high correlations among the measures. It seems only logical, for example, that jails that book and hold inmates at a high rate will also be high on capacity rate and that jails with a low population rate will also be low on the capacity and booking measures. Table 3 presents the zero order correlations of the jail use measures. Although two of the three correlations reached statistical significance,5 it is difficult to argue that the relationships are substantively significant. Only twenty percent of the variance in average daily population rate was explained by the capacity measure, and only a similar proportion of the population rate variance was explained by the booking measure. Similarly, the relationship between the size measure and the booking measure was insignificant. The magnitude of these relationships continues to suggest that local jails may function very differently from one another. The nature of those differences is further TABLE3: CORRELA-IXONS OF JAIL

USE

MEASURES

Average Daily Population Rate

Capacity rate Average daily population rate NOTE: a significant beyond .Ol

.49

Booking Rate

.21 .46@

408

JOHN

TABLE 4 TYPES OF JAIL USE PATTERNS Average

Daily Populatiorl

Rate

Low

Medium

High

17

11

4

Booking Rate

Low

Holding jails

Low use jails 10 11

Medium

10 10

11 211 3”

10 lh 4

High

11

17 High use jails

Processing jails 10 lb

NOTES: N=96 0 Number of county b Number of county

40 3h

jails over 80 percent capacity jails over 100 percent capacity

explored in Table 4. The range of theoretically possible patterns of jail use can be elaborated by presenting the average daily population rate and annual booking rate as independent dimensions. Jails fitting particular patterns can then be empirically identified by dividing the distributions of the variables into thirds and locating the jails along the dimensions. This table reveals that Illinois counties could be identified that fit each of the possible patterns. In the center of the matrix, ten jails illustrated the medium levels on both dimensions. Forty-two of the ninety-six Illinois jails exhibited patterns described by extremes along the dimensions and filling the four corners of the grid. The upper right hand corner includes four jails that might be described as

KLOFAS

“holding” jails. These facilities booked inmates at a low rate but held them at a high rate. One possible explanation of this pattern is that it may represent counties that made heavy use of policies of citation and release but also relied heavily on confinement as a sanction or made infrequent use of pretrial release programs. Opposite the “holding” jails on the matrix arc four jails that may be called “processing” jails. These facilities may have functioned primarily to process individuals toward release after they were arrested. They may represent the pattern in counties that made heavy use of pretrial (but post booking) release mechanisms and in which there was not a heavy reliance on local incarceration as a sentence for crimes. It is worth noting that the jails described as “processing” and “holding” jails accounted for the smallest categories of Illinois jails. Jails that were either low or high on both dimensions reflected the most common use patterns among Illinois jails. Seventeen institutions ranked low on both average daily population rate and annual booking rate. These facilities are considered “low use” jails because of the rate at which they booked and held people. On the other hand, seventeen jails can be described as “high use” jails in that they ranked in the highest third along each dimension. These jails may have made comparatively little use of pretrial release policies and extensive use of confinement at sentencing. Comparisons of the high and low use jails do indicate that the jurisdictions in which they were located differed significantly in terms of crime rate but not in county population size.6

DESCRIBING

JAIL CROWDING

The elaboration of patterns of use may also be helpful in describing the problem of jail crowding. In Illinois, eighteen of the ninetysix jails had an average daily population that was over eighty percent of the capacity of the jail,’ and seven of those jails were filled to over 100 percent of capacity. The patterns of jail use for those facilities are also described in Table 4. The table reveals that crowding in

Patterns of Jail USC

jails was not limited to “high use” facilities. The high use pattern, however, was the most prominent among the crowded jails. Seven of the jails above eighty percent capacity and three of the jails above 100 percent of capacity reflected the “high use” pattern. The remaining eleven crowded jails were spread across six cells in the table. This supports the view that relief from overcrowding may be pursued effectively by addressing policies regarding jail use rather than simply through construction of additional cell space. Of course, the question of jail capacity continues to figure centrally in discussions of jail crowding. These discussions, however, may benefit from a focus on capacity rates rather than simply on the number of available beds. By juxtaposing the capacity rate and average daily population rate for the crowded jails it is possible to investigate the contribution of jail capacity to crowding problems. Table 5 reveals that all crowded jails ranked either low in capacity rate or high on average daily population rate. The remaining four cells of the grid contain no crowded jails. Six of the crowded jails ranked at medium or below on average daily population. Compared to the

TABLE 5 CAPACITY RATE BY AVERAGE DAILY POPULATION RATE

Capacity Rate

Low

Medium

High

18

10

4

Average daily population rate Low

2. Medium

8 40

8

16

High

6 4a

14

12

5.

Y

NOTES: N=% * Number of county jails over 80 percent capacity

409

problem in other Illinois jails, the crowding problem in these jails may have been chiefly the result of a capacity rate per 10,000 residents that was below the state average. Four crowded jails suffered from the dual problem of being below the average rate of capacity but above the average daily population rate. For eight of the crowded facilities, the problem appears to have been chiefly a problem of a high average daily population rate. For these jails, then, more inmates than expected, rather than fewer cells than expected (compared to the mean) was the cause of overcrowding.8

DISCUSSION Describing prisons and prisoners based solely on cross-sectional data would appear to distort our understanding of imprisoned offenders only minimally. Although crosssectional views will reveal higher seriousness of offenses, longer criminal histories, and higher recidivism rates than would appear if all offenders who go to prison were examined, these distortions appear to be minimal compared to the distortions that would appear if jails and the jail population were examined similarly. Prisoners, after all, stay in prison for relatively lengthy periods, and the less serious violators with the less serious offense records have been weeded out. Jails differ fundamentally from prisons in that they hold inmates who have not yet been convicted as well as those already under sentence. Many inmates stay in jail for only brief periods, and low seriousness violators are not excluded from jail. These differences mean that jail studies have not been insulated from the potential distortion of cross-sectional research. To avoid distortions, it is imperative that descriptions of the jail population be based both on studies of those booked into jail as well as those present on any given day. The difference between studying inmates as they are booked into jail and studying inmates in jail on any given day has implications beyond the resulting descriptions of jail clients. It suggests the need for a differ-

410

JOHN

em approach to characterizing jails and possibly even the local criminal justice networks in which they arc embedded. That need is reinforced by the independence of the average daily inmate population rate and booking rate measures. Minimal correlations of the jail use measures with county population size and crime rate suggests that thcsc diffcrences cut across differences already acknowledged in the jail literature. The description of the diversity in patterns of jail use by counties also suggests the need for further research to elaborate on these differences. On one hand, there is a need to investigate the causes of the identified differences. Specifically, variability in the patterns of crime, criminal processing, and community characteristics should be studied to determine their impact on jail use patterns. On the other hand, patterns of jail use should also be studied as independent variables. That is, there is a need to study the effect of different patterns of jail use on such things as violence and victimization in jail, subcultural attachments, mental health problems, and attitudes of staff. One could easily hypothesize about differences on these variables between “processing” and “holding” jails or between “low use” and “high use” facilities. There are also policy implications based on this approach to studying jails. These relate to the use of this analysis to provide a data base for decisionmaking regarding jail use. Statewide mean rates and patterns of jail use, for example, may provide useful comparisons for counties contemplating expansion of jail space or changes in detention or sentencing policies. The approach also calls attention to patterns of jail use rather than simply overcrowded jails. “High Use” jails represent significant (and possibly unnecessary) local expenditures, whether or not those facilities are filled beyond capacity. Focusing on use patterns, therefore, may be more productive than simply attending to overcrowded facilities. And too, the approach permits a refined analysis of crowding problems by describing the relative contributions of deficient capacity and higher than expected numbers of inmates when compared with other jails.

KLOFAS

Finally, the most significant use of the analysis may be in supporting a cybernetic approach to jail use planning. Such an approach would be similar to the sentencing guidelinesmodel developed by Wilkins, Gottfredson, and Kress (Kress, 1980). Under this model, judges base their decisions on feedback about typical sentences they have given for similar offenses. With regard to jail use, data on the impact of policies that raise or lower jail use rates or alter patterns of jail use can be fed back to relevant decisionmakers including judges, prosecutors, and police officials as well as county officials responsible for the jail budget. Informed decisions to maintain or change policies can then become part of the local political process.

ACKNOWLEDGEMENTS The author wishes to express his appreciation to Don Gibbons and Joseph F. Jones for their comments on an earlier draft of this article.

NOTES Data for 1985 were obtained from the Ilhnois Department of Corrections, which is responsible for overseeing jails in the state. Population bases were obtained from the 1980 U.S. Census. The raw jail data are described below (N=95):

Mean S.D. Median Minimum Maximum

Capacuy

Averagt~ Daily Populalion

Afl!llMl Bookings

50.86 66.18 27.69 4.00 356.00

32.68 53.11 12.08 1.00 299.00

1264.22 2128.71 538.90 28.00 10730.00

Cook County (Chicago) was excluded from this table because of its effect on the mean as an extreme outlier. Since most of the analysis used rates, Cook County was included except where specifically noted otherwise. Five of 102 Illinois counties do not operate jails. These counties hold prisoners in neighboring counties. All counties reported that their average number of prisoners held was less than one and their total number of bookings for the year was less than twenty. One other county was excluded from the analysis because the jail was temporarily closed during the year. Two jails in the study are under court order limiting their population. Crime figures used Uniform Crime Report categories and definitions as reported by the Illinois Department of Law Enforcement (1985)

Patterns

Average Daily Population Ram= .075+(.31*Capacity Rate)+(.OOO7*Crime)+(.OOOOOO5*Population~, R squared=.41, F=21.36. Booking Rate= 60.49+(6.26’Capacity Rate)+(.03*Crime)+(.OOOOO4*Population). R Squared=.21, F=8.31. In all correlations using raw population figures, Cook County (Chicago) was excluded because its population far exceeded all other counties. Since all Illinois County jails were used in this study, statistical significance is presented only for the purpose of highlighting the relationship and not as an indicator of generalizability. T=4.13,

significant

beyond

.Ol.

All jails with an average daily population of over 80 percent of their capacity were considered crowded in this analysis. This figure is based on interviews with jail administrators who indicated that they lose placement options when the jail population exceeds this level. This analysis can also be extended to consider the cost of overcrowding. One “high use” county, for example, was well below the mean on available cell space per 10,000 residents but well above the mean for the average daily population rate. This county housed inmates in a neighboring county at a cost of $35 per day. The deficiency in cell space rate (compared to the average) could cost the county as much as $268,275 a year while the policies that result in a higher than expected number of inmates could cost as much as $459,000 per year.

REFERENCES

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of Jail USC

Bureau of Justice Statistics. (1985a). Jail inmates 1983. Washington: U.S. Department of Justice. (1985b). Survey of inmates of local jails, 1983. Ann Arbor, Ml: Inter-University Consortium for Political and Social Research. Flynn, E. E. (1973). Jails and criminal justice. In Prisoners in America, ed. L. Ohlin. Englewood Cliffs. NJ: Prentice-Hall. Garafalo. J.. and Clark, R. (1985). The inmate culture in jails. Crim jusr B 12: 415-34.

sub-

Glaser, D. (1969). The effecriveness of (I prison and parole system. Indianapolis: Bobbs-Merrill. Goldfarb, R. (1975). Jails: The ultimate gherro of the criminal justice sysfem. Garden City, NY: Anchor Books. Handberg. R. (1982). Jails and correctional The neglected half of rural law enforcement. of Correctional Education 32: 20-23.

farms: Journal

Illinois Department of Law Enforcement. Crime in Illinois. Springfield, IL: Illinois ment of Law Enforcement.

(1985). Depart-

Irwin, J. (1985). The jail: managing the underclass in American sociery. Berkeley: University of California Press. Kress, J. (1980). Prescriprions for jusrice: The rheor) and practice of senrencing guidelines. Cambridge, MA: Ballinger. Mattick. H. (1974). The contemporary jails of the United States: An unknown and neglected area of justice. In Handbook of criminology. ed. D. Glaser. Chicago: Rand McNally. McGuire, W.J.,andSheehan, R.G. (1983). Relationships between crime rates and incarceration rates: Further analysis. J Res Crime20: 73-85.

Abt Associates. (1980). Americot~ prisorls and jails, Vol. I. Washington: U.S. Department of Justice.

Rothman, D. (1980). Conscience Boston: Little, Brown.

and convenience.

Blumstein. A.; Cohen, J.; and Miller, H. (1980). Demographically disaggregatcd projections of prison populations. J Crim Jusr8: l-26.

Sykes, G.. and Vito. G. (1987). Jail populations and crime rates: An exploratory analysis of incapacitation. J Police Sci Adm.