UNIVERSITY OF ILLINOIS LIBRARY AT URBANA-CHAMPAIGN ENGINEERING MAY 3 1080 The person charging trns u material is re- sponsible for its return to the library from which it was withdrawn on or before the Latest Date stamped below. Theft, mutilation, and underlining of books are reasons for disciplinary action and may result in dismissal from the University. To renew call Telephone Center, 333-8400 UNIVERSITY OF ILLINOIS LIBRARY AT URBANA-CHAMPAIGN MAR 2 198;? F £e 2 4 m L161— O-1096 ERENCE ROOi UNIVERSITY OF ILLINOIS URBANA. ILLINOIS U" URBANA-CHAMPAIGN URBANA. ILLINOIS 61801 CAC Document No. 184 ANTECEDENTS AND CONSEQUENCES OF INMATE POPULATION CHAPACTERISTICS IN ILLINOIS COUNTY JAILS by John R. Kimberly David B. Rottman August 31, 1975 IHE LIBRARY OK TH£ CAC Document No. l8U ANTECEDENTS AND CONSEQUENCES OF INMATE POPULATION CHAFACTERISTICS IN ILLINOIS COUNTY JAILS by John R. Kimberly, Ph.D. Principal Investigator and David B. Rottman, Ph.D. Research Director Center for Advanced Computation University of Illinois at Urbana-Champaign Urbana, Illinois 6l801 August 31, 1975 This research was supported in part by a grant from the Illinois Law Enforcement Commission. Digitized by the Internet Archive in 2012 with funding from University of Illinois Urbana-Champaign http://archive.org/details/antecedentsconse184kimb 11 ACKNOWLEDGEMENTS Many individuals gave generously of their time and their expertise in response to our inquiries seeking the most complete and accurate data available on the activities of criminal justice agencies in Illinois. We are particularly grateful to Jeff Ives of the Illinois Department of Law Enforcement's Crime Studies Section and to Tony Valaika of the Illinois Law Enforcement Commission for the help they provided. Some of the data used in this report were collected under grants to the authors by the University of Chicago Law School's Center for Studies in Criminal Justice and by the University of Illinois at Chicago Circle's Center for Research in Criminal Justice. We are grateful for that support. Finally, we are indebted to Joyce Fasnacht for the care and the expertise with which she typed anc prepared this report. Ill TABLE OF CONTENTS Page CONCLUSIONS AND RECOMMENDATIONS v CHAPTER I. INTRODUCTION 1 The Sample and the Data Set 5 Variables and Methodology 11 Overview of the Report Ik Relation to Previous Research 15 CHAPTER II. A PERSPECTIVE ON JAILS AND LOCAL CRIMINAL JUSTICE SYSTEMS 19 The Composition of Inmate Populations 19 Information Processes 22 The Structure of Jails 23 Interorganizational Relationships 27 Conclusion 30 CHAPTER III. COUNTY JAIL POPULATIONS AND THEIR ANTECEDENTS 32 County Jail Inmates in Comparative Perspective 32 Predicting Inmate Population Characteristics from Community Characteristics and Court Practices i+3 CHAPTER IV. THE CONSEQUENCES OF JAIL INMATE POPULATIONS 59 Predicting Jail Structure 60 Predicting Jail Effectiveness 68 Conclusion 80 CHAPTER V. SUMMARY OF THE FINDINGS 82 Predicting the Composition of Inmate Populations 82 Predicting Jail Structure Qk Predicting Jail Effectiveness 86 APPENDIX A. CHARACTERISTICS OF THE COUNTIES 87 APPENDIX B. COURT PRACTICES 88 APPENDIX C. JAIL CHARACTERISTICS 89 APPENDIX D. JAIL INMATE POPULATIONS 90 APPENDIX E. DEFINITIONS OF THE VARIABLES 91 APPENDIX F. ZERO -ORDER CORRELATION MATRIX 100 LIST OF REFERENCES 101 IV LIST OF TABLES Table Page 1. List of Variables 12 2. Comparison of Average Age in Arrest and Jail Populations ... 35 3. Comparison of the Percentage of Females in Arrest and Jail Populations 39 h. Comparison of Average Seriousness Scores in Arrest and Jail Populations Ul 5. Zero-Order Correlations: County Characteristics and Court Practices/Characteristics of Arrest Populations . . k-6 6. Zero-Order Correlations: Community Characteristics and Court Practices/Characteristics of Jail Inmate Population. ^8 7- Multiple Regression Equations: Predicting Court Practices and Jail Inmate Characteristics 50 8. Multiple Regression Equations: Predicting Inmate Characteristics from Community Characteristics 56 9. Multiple Regression Equations: Predicting Arrested-Jailed Differences 57 10. Multiple Regression Equations: Additive Effects on Structure Controlling for DEFT. SIZE 6l 11. Multiple Regression Euqations: Additive Effects on Structure Controlling for JAIL SIZE 62 12. Multiple Regression Equations: Additive Effects on Structure with foPLEA GUILTY Measuring Stability .... 65 13* Zero-Order Correlations of Inmate Characteristic/Structure Interaction Terms with their Components 70 Ik. Multiple Regression Equations: Additive Effects on EFFECTIVENESS 72 15. Zero-Order Correlations: Raw Mate rial/ Structure Interaction Terms and EFFECTIVENESS 7^ 16. Multiple Regression Equations: Testing the Importance of Interaction Terms for Predicting EFFECTIVENESS .... 77 17. Testing the Importance of Influences on EFFECTIVENESS 79 18. The Relationship between the Illinois Criminal Code and SERIOUSNESS Scores 9I+ 19« Individual Level Relationships among Inmate Characteristics . . 97 CONCLUSIONS AND RECOMMENDATIONS This report was written under a contract from the Illinois Law Enforcement Commission and had, as its major objectives, the description of county jail inmate populations and the analysis of the determinants of variability in these populations across counties and the consequences of this variability for jail structure and rated effectiveness. The report itself contains a variety of kinds of data, some of it presented in tabular and graph form, some of it in more highly technical statistical form. Because many of the analyses make use of multivariate analytic techniques and because the report itself is rather lengthy, the major conclusions drawn and recommendations formulated are presented in highly condensed form in the following pages in the Preface. It is no doubt the case that other researchers using similar data and finding similar results might have drawn quite different conclusions and seen quite different policy implications. For that reason, the following pages should be read both for informational purposes and with a critical eye. The conclusions and/or recommendations derived from the report fall into six major categories: 1. Ecological Effects When diversionary programs such as alcohol detoxification centers are introduced into a county, it can be anticipated that changes in the composition of the county jail's inmate population will follow. Specifically, it can be expected that the representation of minority group members among the jail's inmates and the duration of the average county jail confinement will increase as a consequence of such alterna- tives to jail confinement. The results of the data analyses carried out in this report indicate that it is the presence or absence of diversionary programs per se that underlies these increases. They are apparently not attributable to the fact that these programs are often comcomitant with other aspects of counties such as their socio-economic characteristics. VI The increases, in other words, are independent of county characteristics. While such a conclusion is an argument neither for nor against programs that divert individuals accused of 'Victimless crimes" from the criminal justice system, it does highlight one outcome of their implementation that ought to he made very clear so that it can be taken into account in various planning efforts. A similar effect can be attributed to the use made within a county of city jails. The presence of city jails and the manner in which they are utilized influence considerably the process by which some subset of all individuals arrested within a county ultimately becomes inmates in the county jail as well as the composition of that subset. Where city jails are both available and widely used, the county jail tends to become a specialized institution, confining mainly those individuals accused of relatively more serious criminal offenses. A city jail acts as a decision- point at which those accused of minor crimes may be released from custody. Where such opportunities for diverting some of those arrested prior to county jail confinement do not exist, then the county jail will act as a general repository for individuals arrested, and a lower level of seriousness in the charges facing its inmates will be found. 2. Court Practices The existence of a substantial backlog in felony cases in a county's courts tends to decrease the length of the average county jail confinement: the greater the backlog, the shorter the average confinement. Thus, support was not found for the argument of those who have contended that prosecutors systematically utilize jail incarceration as a method for contending with backlogs in the processing of cases. More precisely, counties with such backlogs did not evince lengthy average pretrial jail confinements. A definitive test for the existence of coercive uses of jail confinements, however, will require longitudinal research that employs a more representative sample of counties. In general, the present study suggests that the composition of jail inmate populations and the procedures and practices of the jails themselves are largely independent Vll of the practices and actions of local courts, at least insofar as these practices have "been measured here. 3. Inmate Populations There is considerable variability in county jail inmate popula- tions on two fronts. First, the composition of inmate populations varies according to age, race, sex and seriousness of charge across counties. There is, in other words, considerable heterogeneity in terms of who is confined in county jails in Illinois on a county-by-county basis. Second, when the composition of county, arrest and jail populations are compared, it is evident that they vary a great deal. In some counties, for example, the proportion of the general population that is black, the proportion of those arrested that is black and the proportion of those confined in the county jail that is black are more or less equal. In other counties, however, these proportions are not at all similar. A variety of processes, therefore, are at work which filter those arrested and result in confine- ment of a particular group in any given county's jail, and it may be inferred that these processes vary from county to county. This variability in inmate populations leads in two directions in its implications for policy. On the one hand, one may feel that such heterogeneity may be indicative of too much variability in the workings of local criminal justice systems. In this case, it might be desirable to explore policies designed to insure greater homogeneity, although this is an area which is clearly difficult and fraught with problems of juris- diction and control. On the other hand, one might be inclined to accept the variability across counties in inmate populations as a given and ask what may be done insofar as jails themselves are concerned. The appropriate response in this case appears to be to underline the need for contingency approaches in the development of standards for operating and programming county jails. In the face of the substantial differences in the types of inmates that such jails are called upon to confine, a monolithic approach that assumes one optimal basis for organizing a jail is clearly inadvisable. It is true that some aspects of jails, particularly those Vlll related to their physical plants and hygenic conditions, should he subject to minimal standards which can he universally specified. Other aspects of jails, however, such as inmate- staff ratios, staff skills and qualifica- tions, programming and procedures ought to he tailored to fit the particular types of inmates present in each jail. In promoting reform in Illinois jails, flexibility will be required, as what is optimal in one jail may well prove dysfunctional in another. h. Jail Size Within the range of jails considered in this report, in which Cook County's jails and jails in rural counties that are but infrequently used are excluded, size -- as measured by the number of inmates confined over a period of time -- appears to be unrelated to other aspects of jail structure and operation. Therefore, where economies of scale are feasible, they can be realized without incurring major costs in other aspects of jail operations. 5- Ratings of Jail Effectiveness The ratings of Illinois jails currently undertaken by the Bureau of Detention Standards and Services appear to reflect primarily the degree to which a jail has adopted bureaucratic procedures. The ratings assigned to a jail by the Bureau also appear to be strongly influenced by factors over which sheriffs and jailers themselves have very little direct control. It should be stressed that the analyses undertaken for the purposes of this report combine the ratings into a form and for a purpose other than that for which they were designed. Still, it is disquieting that the ratings had antecedents that were so far removed from the jails themselves and which were largely beyond the capacity of the jails themselves to modify in pursuit of higher ratings.. At the very least the results suggest the need to re-evaluate the role and intended consequences of both the methodology and the content of the rating system. IX 6. Data Needed for Policy Decisions and Planning If county jails are to be more than mere storage bins, it will be necessary to develop techniques that permit projections of program and facility requirements on the basis of both the types of inmates and the number of inmates. The present report provides a possible basis for doing so. When projections of the size and the composition of jail inmate populations are undertaken, the data that underlie those projections ought to be derived from cohort studies rather than from census enumerations. Data from a single census day tend to distort the description of the inmate population through the disproportionate weight given to individuals incar- cerated for a lengthy period. For studies of recidivism or for projections of future jail populations, a time frame of at least one month should be adopted and data should be collected on all individuals booked into the facility during that period. The data used and the analyses undertaken in this report demonstrate both the feasibility and the desirability of such an approach. When priorities are set for collecting information on the Illinois criminal justice system, the importance of acquiring data on the operating budgets and expenditures of local law enforcement and judicial agencies should be strongly emphasized. Such data must be comparable both across counties and over time. At present, definitions underlying avail- able expenditure and budgetary statistics are so inconsistent as to make their use an invitation to false conclusions and misleading results. There is an urgent need for information that can be used to assess the results produced through the expenditure of public dollars and to evaluate the cost of alternatives to those expenditures. But at present such information does not exist. It is not even possible, for example, to derive a figure for the average cost per county jail inmate per day which would be even vaguely comparable, valid or reliable across counties, and hence impossible to derive a meaningful figure for the state as a whole. Cost estimates for particular programs are even more difficult to derive. Such data will prove elusive until a highly specific set of instructions and definitions for compiling a detailed budget for each law enforcement and judicial function is established and a system for periodic collection of the information is implemented. Precisely how this should he done and under whose auspices is a matter for discussion; but it is clearly necessary. Analytic techniques exist for discriminating among alternative programs on the basis of the results achieved per dollar expended. In the absence of adequate expenditure data, however, the use of these techniques is precluded. CHAPTER I INTRODUCTION During 1973* the city and county jails of Illinois recorded 569,2^7 separate confinements (Illinois Bureau of Detention, 197*0 • How- many individuals are represented by that statistic, no one knows. A single individual may have been confined either on suspicion or on a con- viction for any number of different criminal acts during those 12 months. Further, in relation to any one of those acts, that person may have been transferred across jurisdictions from jail to jail, or, as state statutes mandate once kQ hours have elapsed, from city jail to county jail. It is also not uncommon for an individual to be incarcerated both while awaiting trial and, after first obtaining release through a bail bond, while serving a sentence. For these reasons, all that can be stated with certainty is that the number of jail inmates in Illinois is substantial. That number is also increasing, perhaps rapidly, though here, too, firm evidence is absent. In 19^7, "the Illinois Jail Survey (Mattick and Sweet, 1969:^+7) estimate'd that there were 169,192 separate confinements in Illinois' city and county jails. Projecting the trend in the rate of expansion of jail populations as evinced by the 1966 to 1967 increase, Mattick and Sweet (1969:60) anticipated that 1970 would witness a total of 2lU, 561 Illinois jail confinements. Even when the vagaries of inconsistent definitions, incomplete data, and parochial record keeping practices are acknowledged, ample justification exists for concern over the increase in jail use that has been seen recently. Between i960 and 1970, the popula- tion of Illinois increased by 10.2 percent (U. S. Bureau of the Census, 1973)* Apparently, then, the number of jail confinements is expanding at a rate that is greater than the rate of increase of the general population in the state as a whole. Jails not only handle a massive flow of human beings, they also perform an important role in the complex of arrangements through which Illinois attempts to respond to the problems of crime and delinquency. Little is known, however, about the workings of jails or about the clientele they house. This report is an attempt to begin to fill this void through a detailed analysis of the role of the jail in the Illinois criminal justice system. Data describing the activities of component agencies within that system are used to obtain answers to two vital questions. First, who is confined? To answer this question, the socio- demographic characteristics and the legal statuses of the inmates in 36 Illinois county jails are described. It can be anticipated that a substantial degree of variability exists among those jails concerning the distribution within their inmate populations on such traits as age, race, sex, seriousness of crimes with which inmates are accused or have been convicted of, and length of confinement. Second, what factors explain this heterogeneity, and what are the implications of such differences across jails for effectively planning, programming, and administering a jail? The second question is both the more important and the less readily answered of the two. Projections of jail inmate populations that estimate only the numbers and the rate of flow at some future date are inadequate for planning and policy-making purposes; it is also necessary that the composition of each jail's popula- tion be predictable. In a sense, the inmate population of a jail represents the use to which that jail is being put by the community it serves. What is appropriate in the way of facilities, programs, and administrative procedures is likely to differ in a jail with predominantly young inmates who are incarcerated due to relatively serious criminal acts from that appropriate in a jail with a predominantly middle-aged population charged with, or convicted of, crimes that are both minor and essentially victimless. The need to predict the composition of jail inmate populations becomes more acute when efforts are made to expand the role of jails beyond that of what Daniel Glaser (1970) terms a "storage bin for humans. " The provision of any programs or services other than those of simple housekeeping will require that the characteristics of the individuals to be included be known. Where the first question, that of establishing who Illinois' jail inmates are, is answerable on the basis of enumerations of jail inmates alone, to predict variations among jails in the composition of their inmate populations requires an expansion of the context considered. It is necessary to consider the workings generally of the criminal justice systems of the various localities and the characteristics of the localities themselves. This report examines the effect of a county's characteristics on its criminal justice system. In particular, it attempts to make mani- fest the interdependencies among components of the criminal justice system and to explain why those interdependencies exist. The underlying premise is that the composition of a county jail's inmate population is largely a consequence of the manner in which the police, prosecutors, judges, public defenders, probation officers, and parole officers within that county perform their tasks. Therefore, it can be anticipated that change intro- duced into the practices of those officials will ultimately be reflected in a changed county jail inmate population. The extent and type of plea bargaining that prevails in a local court system, for example, may well have implications for who is incarcerated in the county jail and for what lengths of time. A change in plea bargaining practices and tactics, whether it emerges through meticulous planning or through default, may well engender a dramatic shift in the range of charges pending against county jail inmates or in the age and racial composition of that population. Differences between counties in the practices of their criminal justice agencies are doubtlessly traceable, at least in part, to differences in the social and economic characteristics of the counties' residents. Such characteristics tend to establish the magnitude and the nature of the law enforcement problems that are present, and shape the responses to those problems that are feasible and that are viewed as desirable. The report takes as its given the composition of the inmate populations of Illinois county jails and inquires into the antecedents and the consequences of those populations. After the populations of the jails are described and contrasted to determine the extent and the nature of the variability they exhibit, a series of analyses is presented that attempt to answer the question of what factors act to produce the population-mix obtaining in a particular county jail. To do so, three levels of variables are employed. Variation in inmate populations among counties is assumed to be a function of the characteristics of each county's general population and alternative arrangements it has established for dealing with those accused of crimes, the characteristics of those arrested within that county, and the practices of each county's court system. The county jail inmate population is viewed, therefore, as the outcome of a filtering process starting with arrest decisions by the police and culminating in confinement in the jail. Once the factors that determine who becomes a jail inmate in the various counties are established, it is important that the implications of those variations be pursued. Analyses are presented that inquire into the degree that jails currently adapt their procedures and practices to accommodate the specific inmate population that they confine. The extent to which the linkage of certain inmate characteristics to certain jail characteristics enhances the effectiveness of jails will also be investigated. While the point of departure for this report is the inmates confined in Illinois' county jails, the analyses described above speak generally to the structure and functioning of criminal justice in Illinois. For example, while the inmates in city jails are not directly examined, the presence and the use made of such jails within a county will be one of the factors that determines who is incarcerated in the county jail. Indeed, it is the intent of this report to make manifest the interrela- tionships among organizations involved in law enforcement and the administration of justice. For that reason, data on a number of agencies engaged in those pursuits have been collected and combined in the analysis. This is in contrast to the majority of studies where each component agency in the criminal justice system is examined as if it functioned in a vacuum. In doing so, we wish both to provide information that can con- tribute to enhancing the level of the current debate on the role and the design of jails and to interpret that information so as to derive a set of conclusions and recommendations. While others may well disagree with the interpretations we reach, it is hoped that the evidence that has been assembled will not be ignored or rejected for that reason. Public discussion conducted in the absence of a realistic picture of the manner in which the criminal justice system functions is unlikely to be fruitful. Having established the broad outlines and the purpose of this report, the remaining sections of this chapter describe in detail the sample of jails selected for study, the methodology employed for that analysis, the specific variables used, and the sources of the data that measure those variables. In addition, the relationship of the present study to other research on criminal justice in Illinois, both past and ongoing, is discussed, as is the format to be followed in the chapters ahead. The Sample and the Data Set Of Illinois' 101 county jails, only those that in 1971 had an average daily population of ten or more inmates were included in the sample. This criterion was imposed to eliminate jails in which infre- quency of use causes them to be of limited interest as organizations. To avoid the opposite extreme, Cook County Jail was also excluded from the sample, leaving a total of Uo jails in the sample. The data set collected on the members of the sample brings together four series of records kept by state government agencies in Illinois on the activities of local criminal justice systems. This merger of official records creates both problems and opportunities. Each set of records has its own inadequacies imbedded in the collection process that generated it. As is inevitable with available data, one must live with those inadequacies. They can be controlled for, but never eliminated, It should also be noted that those records, by and large, represent the outcomes of agency activities and thus do not in any way portray the informal workings of the system. Despite these inadequacies, the data are used here "because of the potential they offer for understanding the structure and functioning of local criminal justice systems. Much has been written in a qualitative vein about the relationships among the agencies constituting such systems, but rarely has data been available for comparing a substantial number of them. The present data set permits such comparisons. Because of a lack of data on both inmate characteristics and jail characteristics, DuPage, Macon, McLean and Winnebago counties were removed from the sample, reducing its size from UO to 36. Court Activities The Administrative Office of the Illinois Courts receives monthly reports from each county's circuit court clerk and on that basis compiles the Annual Report to the Supreme Court of Illinois . Data gathered through this process provide a measure of the flow of all civil and criminal cases through the court and of the disposition reached for all defendants charged with felonies. Data on the flow of cases is presented through the number of cases initiated and terminated in a given year. Data on the disposition of felony cases is available for both the means of disposition used for each defendant (discharged at preliminary hearing, dismissed, reduced to misdemeanor charge, acquittal by court or by jury, plea of guilty, convicted by court or jury) and the penalty, if any, imposed. While uniformity in reporting practices is unlikely to be total, this reporting system has been in operation for the 11 years since the unifica- tion of Illinois courts into a single system. A high degree of uniformity can therefore be expected. Characteristics of Arrest Populations Law enforcement agencies within Illinois are requested to submit monthly arrest statistics to the Illinois Department of Law Enforcement's Bureau of Identification. The reporting practices that are used are identical with those of the FBI Uniform Crime Reporting Program, and thus a high degree of comparability across agencies is ensured in the data. From the Illinois Bureau of Identification, statistics were obtained that describe all individuals arrested within each county during January of 197^ • Data available on each such individual include age, sex, race, and criminal offense. A county's data will not, however, reflect arrests that were made by agencies that failed to submit a report to the Bureau of Identification. Also, the offenses that are reported include only those listed in the Illinois Uniform Crime Reporting Offense Codes for 197^* This eliminates most minor motor vehicle offenses from the arrest totals, though offenses such as reckless driving and all offenses involving improper licensing or registration are reported. Inmate Characteristics On a monthly basis, each county jail in Illinois submits to the Illinois Bureau of Detention a list of all persons booked during the previous month. Each inmate is carried on all subsequent reports until he or she is released. As a result, the following data are available on each inmate: date booked, age, sex, race, criminal charges pending, date of release and legal basis for release. From the reports submitted covering January 197^> & list of 4052 inmates was generated representing all inmates recorded as having been booked into the jails in the sample. These individuals were followed on all subsequent reports until their date of release. Eventually, a release date was obtained for all but 68 inmates. As their release date, these inmates were assigned the last day they were known to be confined. While these data are subject to bookkeeping errors, the material covered is factual and a high degree of accuracy should be present. Individuals sentenced to serve consecutive weekends in the county jail are included only at the time of their initial weekend during January. Jail Characteristics Annually, the Illinois Bureau of Detention conducts inspections of all city and county jails within the state. The resulting reports provide a wide range of descriptive data on each jail's staff, procedures, programs, and physical plant; it also provides a rating of the jail's conformance to state standards. The inspections follow a standardized format, though many descriptive items clearly require an evaluation "by the inspector rather than a determination of fact. Thus, along with a question on ages of the jailers, the inspectors are asked to judge the adequacy of the size of the jail staff. All descriptive items added to the data set are those requiring a purely factual determination. Confi- dence in the accuracy of such items is enhanced as each jail is inspected "by the Bureau's regional supervisor responsible for it who is intimately familiar with its operations. Assessing the quality of the inspectors ' ratings is more problematic. The formal ratings are done on the basis of scales ranging from excellent to unacceptable, and cover the following concerns: personnel, records, inmate property, commissary, receiving and discharge procedures, building and equipment, custody and security, supervision and discipline, medical and health services, food, housekeeping and sanitation, safety, and correctional services and programs. Eight inspectors partici- pated in the inspections carried out in the second half of 1973 on the 3° jails, with the resulting reports providing the data on jail characteristics and effectiveness to be used here. Missing Data For 3^- of the jails, a complete data set was obtained from the four record series. One jail, Sangamon County Jail, lacked effectiveness ratings, but was retained in the sample for all analyses except those using that variable. Also, data on those arrested in Clinton County was not available. As a result, for the major analyses, all coefficients will be based on the identical sample. The degree to which this facilitates interpretation of the results is considered sufficient to offset concern over the diminished sample size. The map on the next page shows the location of the 36 counties whose jails were included in the analysis. Together, those county jails accounted for ^,h.6 percent of all confinements to Illinois county jails during 1973* When the contribution of Cook County Jail to the state total is removed, the percentage of confinements to county jails occurring within the sample increases to 63.9 percent. The remaining non-Cook County jail confinements can largely be traced to the four jails originally included in the sample but ultimately discarded due to a lack of adequate data. The kO jails in the original sample account for 83.^ percent of all 1973 county jail confinements that were recorded outside of Cook County. Therefore, while the original premise on which the exclusion of 60 of the state's county jails was predicated apparently proved valid, the loss of the four cases does introduce a bias into the results reported here: the county jails of DuPage, Macon, McLean, and Winnebago Counties bear a substantial share of the load of confinements to county jails in Illinois, but are unrepresented in the analyses. To put the above statistics into perspective, it is worth noting that the 36 counties on which the data analysis will be performed comprise 32.6 percent of the total population of Illinois, as measured by the 1970 Census. Thus, the contribution of those counties to the county jail confinements total closely mirrors their contribution to the total popula- tion of the State. The four counties meeting the criterion for sample membership but excluded because of insufficient data together account for 8.7 percent of the state population and 10.6 percent of all county jail confinements. Cook County by itself comprises k-9-h percent of all Illinois residents, and i+5 • 9 percent of all confinements to county jails. For those 2 These percentages were derived from statistics given in the 1973 Annual Report of the Illinois Bureau of Detention Standards and Services. Where confinement data were not available for all months during 1973> projections were made so that each jail's total reflected the full 12-month period. 10 LOCATIONS OF THE THIRTY-SIX COUNTIES 11 three groupings of counties, the incidence of county jail confinements per 100,000 populations are, respectively, 1, 9&% 2,231, and 1,710. For the entire state, there were 1,841 such confinements for every 100,000 residents. Variables and Methodology To trace the origins and the consequences of a county jail inmate population, a total of 31 variables was operational! zed. Table 1 lists those variables by the mnemonics through which they will be identified throughout the report and offers a brief definition for each one. Of the first seven variables listed, five describe the residents of the counties in the sample (#C0. URBAN, #C0. BLACK, $C0. 17-24, INCOME, and SCHOOLING), and two variables indicate the extent to which alternatives to the county jail are available or are used (DETOX and CO. JAIL USE). The first of these variables (DETOX) indicates whether or not a county has an alcohol detoxification center, while the second (CO. JAIL USE) measures the extent to which city jails assume part of the burden of jail confinements within a county. The remaining variables describe the clientele or the charac- teristics of a component of the criminal justice systems of the various counties. For felony defendants, the manner in which cases are disposed of by the courts of those counties is described, as is the extent of the backlog in handling such cases. For each county, the characteristics of those arrested by its law enforcement agencies are indicated by a series of variables, as are the characteristics of the inmates in its county jail. The county jails themselves are then described in terms of their operating procedures, their ratings by the Illinois Bureau of Detention, and their size. Two alternative measures of size are offered. The first measures size on the basis of the sheriff's department of which the jail is a subunit, while the second measures size on the basis of the magnitude of the inmate load. The only data included in this report that are not readily available through widely disseminated sources are those that describe the individuals who in each county were either arrested or were incarcerated in the county jail. Therefore, while the other data are appended to the 12 Table 1 List of Variables 1. 2. 3- 4. 5- 6. ioCO. URBAN ioCO. BLACK DETOX $C0. 17-24 INCOME SCHOOLING 7. CO. JAIL USE 8. 9- 10. 11. BACKLOG foREDUCED foRLEA GUILTY foDISMISSED 12. /oIMPRISONED The percentage of a county's population that lives in urban areas The percentage of a county's population that is black Whether a county has an operational alcohol detoxification center (l = yes; = no) The percentage of a county's population aged 17-24 The median family income of a county's residents The median number of years of schooling completed by a county's residents The percentage of all 1973 jail confinements in a county that were to the county jail The backlog of felony cases in a county's court The percentage of felony cases in a county's court that are reduced to misdemeanant cases The percentage of felony defendants in a county's court who plead guilty The percentage of felony defendants that had their cases dismissed on a motion by the county's states attorney The percentage of convicted felons in a county who are sentenced to the Illinois Department of Corrections 13. 14. 15- X ARREST SERIOUSNESS The average seriousness of the criminal charges filed against those arrested in a county foBLACK ARRESTS /oFEMALE ARRESTS The percentage of blacks among those arrested in a county The percentage of females among those arrested in a county 13 Table 1 (continued) 16. X ARREST AGE 17. X INMATE SERIOUSNESS 18. $BLACK INMATES 19. foFEMALE INMATES 20. X INMATE AGE 21. X PRE-TRIAL STAY 22. X STAY 23. FORMALIZATION A 2k. FORMALIZATION B 25. CHIEF JAILER 26. TRUSTY CHOICE 27. X JAILER AGE 28. foTRUSTIES 29. EFFECTIVENESS 30. DEFT. SIZE 31. JAIL SIZE The average age of all those arrested in a county The average seriousness of the criminal charges filed against a county jail's inmates The percentage of blacks in the county jail population The percentage of females in the county jail population The average age of a county jail's inmates The average length of confinement in a county jail for pre-trial inmates The average length of confinement in a county jail for all inmates The degree to which a county jail's procedures for communicating with the outside are predetermined The degree to which a county jail's procedures for internal activities are predetermined Whether or not a county jail has a chief jailer (l = yes; = no) Who selects a jail's trusties (3 = Sheriff; 2 = Sheriff and jailers; 1 = jailers) The average of a jail's male jailers The percentage of a county jail's inmates who are given trusty status The ratings of a county jail made by the Illinois Bureau of Detention The number of employees of a county's sheriff's department The number of inmates booked into a county jail Ill report for the convenience of the reader, the text contains descriptions and comparisons of the 36 counties only for data on their arrest and jail populations. Since the bulk of the data being used is easily available, we have chosen to focus our efforts on combining them in analyses directed toward raising and answering some important questions about the inter- dependencies that exist between a community and its criminal justice system and among the various components of that system. Thus, once the discussion of the characteristics of the arrest and jail populations has been completed, the emphasis becomes more highly quantitative, and may not be equally comprehensible to all readers. To mitigate this to some extent, summary sections are included after every major series of results, and the final chapter states in as straightforward a manner as possible what was concluded and what evidence underlies those conclusions. In this manner, we hope to satisfy both the need to rigorously analyze the data and to communicate to the reader the essence of what was found. Overview of the Report The analysis that was undertaken emerged from a perspective on jails and on local criminal justice systems that emerged over several years of research. Chapter II summarizes that perspective and presents a set of specific expectations -- hypotheses -- about the relationships that will be found to exist among the variables listed in Table 1. Chapter III begins the data analysis with an examination of the inmate populations of the jails in our sample. Wherever possible, the inmates in each county's county jail are compared and contrasted with the characteristics of the general population and of the individuals arrested in that county. The extent of variability among the jail populations will be assessed, as will the different patterns that are present in the contrast between county, arrest, and jail populations. While this can be done through graphs and tables whose bases are self-evident, to establish the factors that combine to give a county jail its particular inmate population more sophisticated analyses are required, and multiple regression is used. 15 Chapter IV takes the characteristics of the inmate populations that Chapter III tried to predict and examines their implications for running a county jail. The degree to which the existing practices of those jails appear to have been tailored to fit the particular mix of inmates present in a jail is examined. Also, the ratings of effectiveness given the jails "by the Illinois Bureau of Detention are used to determine if such ratings depend on the degree to which a jail does adapt its procedures and staff to match its inmate population. The use of multiple regression as the principal means of obtain- ing answers to our research questions seriously narrows the audience to which we speak. Therefore, Chapter V leaves the more formalized predictive equations behind and discusses what they indicate about the degree of support found for our expectations and the reasons for that success or failure. The conclusions and the recommendations that were drawn from the results have already been presented as a preface to this report. To facilitate the use by others of the data analyzed here, the values for each county in the sample on all 31 variables are reported either within the text or within the appendices that follow the text. Appendix A presents data that describe the counties themselves, Appendix B data that describes the courts of those counties, with Appendix C data describing the counties' jails. Appendix D provides data on jail popula- tions that the text omits. Appendix E provides the precise definitions and the sources for all 31 variables, and in addition discusses such limitations as might influence the interpretations that can be made on the basis of these variables. Relation to Previous Research The origin of the research reported here goes back several years. An interest in county jails was translated into a study that employed observation of the practices of eight county jails in Illinois and inter- views with those who worked in the jails -- sheriffs, chief jailers, jailers, and matrons. That study, the results of which can be found in Kimberly and Rottman (1973) and Rottman and Kimberly (1975 )> combined our 16 interest in jails with our background in the comparative study of organi- zations. In short, we attempted to find out how jails carry out their work by comparing the eight jails with one another and by comparing jails with other organizations such as hospitals and prisons. It was the extent of the variability among jails that emerged as the central finding of that study. This variability appeared to be purposeful rather than capricious in its origins, and further research was then undertaken to establish the basis for the heterogeneity among Illinois jails. On the basis of what we already knew, the answer appeared to be that the differences represented the response made by each jail to the needs of the community it served. In particular, the practices and procedures in a jail seemed to reflect the type of inmates being incarcerated in the jail. To better understand the relationship between a community and a jail, a shift in emphasis and approach was necessary. Instead of observing and interviewing, data describing the activities of criminal justice agencies was used to see if lawful relationships could be found between community characteristics and jail inmate populations and between such populations and jail practices. Some of the data needed were already both collected and published in usable form. Other data, though collected from the appropriate agency in each county, was not reported in a manner useful for our purposes. For jails, information on the practices and actions of the courts and of the police appeared to be of paramount ■ importance. Those who are interested in Illinois jails are fortunate to have available the results produced by the Illinois Jail Survey of 1967-68. That survey enumerated the facilities and inmates of 160 of the State's city and county jails, establishing the parameters of the "jail problem" in Illinois. When the survey is combined with the more recent efforts of the Illinois Bureau of Detention Standards and Services, a more complete portrait of jails in Illinois is available than probably exists for any other state. 17 Therefore, in Illinois it is possible to go beyond describing the dimensions of the problem and to inquire systematically into the causes of the conditions and of the populations that were found in our jails. Other efforts are underway with much the same intent, notably the feasi- bility and planning studies undertaken for multi-county regions by the Illinois Bureau of Detention. An important component of those studies has been attempts at projecting the demand for jail use that will be generated within a particular area. The present report is, in part, an attempt to refine and to extend the methods and considerations included in such projections. In one important way, the present study uses data that are preferable to those employed in most previous efforts. When studying any population of inmates, data based on a single census day are likely to prove misleading. As Glaser (196k: lU) pointed out for research on penitentiary recidivism rates, a "blatant error" results from a failure to realize that: ...offenders with prior imprisonment generally get longer sen- tences, and are much less readily paroled, than first imprison- ment cases. Therefore, these "two- and three-time losers" accumulate in prison, so that they become higher as a percentage of men in the prison at any given time, than as a percentage of men received or released by the prison in a given period. This error can be found in the 1972 survey of jail inmates carried out by the U. S. Law Enforcement Assistance Administration. That study found the average length of pre-trial confinements up to the day of the census was about three months; the median confinement was one month. For the 3698 pre-trial inmates found among the inmates booked into the 36 county jails, the average length of confinement was 7*35 days. Similar comparisons between the two studies could also be made for the inmates* age and race. In part, this difference stems from the inclusion of large urban jails in the L.E.A.A. survey. For planning purposes, however, studies of inmate cohorts constitute the appropriate data set. Data from a single point in 18 time will result in facilities and programs that are not appropriate for the population that is to be housed. The present study was not, however, able to remedy a fundamental gap in what is known about the Illinois criminal justice system, a gap that creates a fundamental defect in nearly all existing research on that system. Reliable, complete information on the budgets of law enforcement and judicial agencies is simply not available. Until such data are forth- coming, cost-benefit analyses cannot be undertaken. While some reports of criminal justice expenditures are published on an annual basis, they are predicated upon a bewildering assortment of reporting practices that makes highly dubious their use in any study that includes more than one juris- diction. Therefore, at present, we are unable to differentiate among alternative programs and policies in terms of what will be gained from the dollars that will be expended. 19 CHAPTER II A PERSPECTIVE ON JAILS AND LOCAL CRIMINAL JUSTICE SYSTEMS The Composition of Inmate Populations A county jail is generally the only major facility for detaining those who have been arrested within a given county; its population can include individuals accused of every conceivable criminal offense. The variation in charges is, however, far greater than the variation in the social characteristics of those confined in the jail. Blacks and other minority groups are often represented in proportions well beyond that in the general population, and even among these groups, membership in the population of the local jail is highly exclusive. People rarely go from being an "ordinary citizen" one day to being a jail inmate the next. A deviant career pattern is generally involved. As the jailers are very much aware, the charge currently facing a prisoner is relatively meaning- less as an indicator of the person's criminality or character. While many people may commit and even be caught for an offense such as driving while intoxicated, those found in jail for this reason tend to come from a special group. The inmate population in jail for any single day, as compared with those placed in the jail over several months, will overrepresent those confined for more serious charges, as they are given higher bails and longer sentences, and often must await transfer to the penitentiary system. A large proportion of those confined on a given day will also owe their presence to having violated the conditions of their parole or proba- tion. The daily population will also be more non-representative of the general population of the county in terms of its members ' social charac- teristics, being more non-white, younger, and poorer. A "hard core" thus tends to accumulate in a jail, composed mostly of those waiting trial for a serious charge, and then waiting for some administrative action after they have been found guilty, or, more frequently, plead guilty. 20 The overwhelming majority of individuals booked into jails remain incarcerated only until a bail amount has been set and the requisite security posted. State statutes and local practices, along with personal finances, then, determine the duration of most jail confinements. Jailing is not an automatic consequence of the decision to take a suspect into custody -- that is, to arrest. With arrest, a variety of alternatives are presented to the police, only one of which is incarceration in the county jail. A study comparable to that of LaFave (1965) on the arrest decision could be undertaken on the decision to incarcerate, and, as with arrest decisions, wide variability among localities would doubtlessly emerge. The typical situation, however, involves a brief period of confinement, with a hard core of individuals accumulating over time for whom bail is either unavailable or unattainable. This hard core, however small its membership, is in a position to dominate the inmate social system in a jail. To a large extent, jail inmates know one another not only from previous confinement in the county jail, but in other isolating institu- tions as well. Contact among them often occurs on the outside also, and they appear to share a set of norms, values, beliefs, and other components of "culture" independent of whether they were in jail or not. The generality of this observation is untested, but Claude Brown's account of his experiences growing up in Harlem in the 1950 's indicates it may be equally prevalent in a metropolitan setting. One of Brown's friends (1965: ^25) asserts: The time I did in Woodburn, the times I did on the Rock, that was college man.... Every time I went there, I learned a little more. When I go to jail now, Sonny, I live, man. I'm right at home. That's the good part about it.... Now when I go back to the joint, anywhere I go, I know some people. If I go to any jails in New York, or if I do a slam in Jersey even, I still run into a lot of cats I know. It's almost like a family. Most jail inmates, and nearly all of those in the hard core, are involved in career patterns that involve a series of confinements in isolating institutions. Tied to many of the deviant careers identified by 21 Becker (1963:25-39) and others there may be a concomitant career of confinements in reformatories, jails, prisons, and mental hospitals. These confinements may play a vital role in many deviant subcultures, and these subcultures may themselves have a major impact on the organization of the jail and probably on many other institutions for housing "deviants. " As the most ubiquitous of all those organizations for confining those committing deviant behavior, the jail encompasses the greatest possible combination of career patterns. The various deviant subcultures in the catchment area of the jail combine into a general subculture that develops around the jail. In the course of the confinements in isolating institutions that are part of many deviant careers, one learns to live in the total institutional environment. Erving Goffman (1961:65-66) provides supportive evidence when he writes: Some lower-class mental hospital patients who have lived all their previous lives in orphanages, reformatories, and jails tend to see the hospital as just another total institution, to whom they can apply the adaptive techniques learned and perfected in similar institutions. The vacuum created by a lack of scheduled activities is filled by informal activities related to the various deviant subcultures repre- sented in the jail, which serve as communication mechanisms for them and for the more general subculture. The inmate's day is spent in almost constant conversation. In the course of these talkathons, they learn a great deal about each other, and this becomes part of the pool of knowledge held by the general subculture. Observers of inmate culture in prisons have noted that it is possible for large numbers of inmates to isolate themselves from their fellows and from the pervading culture (Mathiesen, 1965; Hood and Sparks, 1970:224-225). This is not an option for those in jail; conformity to the pervading expectations of behavior is necessary for survival. By virtue of repeated and long experience in a variety of total institutions and resultant knowledge of the informal workings of the legal system, certain inmates are able to assume leadership roles in the cell blocks. Power in jails might be usefully understood in terms of the 22 knowledge and experience of the various participants. Leaders emerge through their ability to offer assistance and reduce the uncertainties that inmates face in their dealings with law enforcement agencies, in adapting to the jail environment, and in activities they participate in on the outside. Information Processes Accommodations and agreements in a jail are possible only insofar as a pool of information on the inmates has been cultivated and maintained over time. Through this information, the jailers can assess the probable response an inmate will make to a given situation. Jails vary considerably in the importance of such information. In some jails, decisions must continually be made on how to treat individual inmates in a wide variety of situations where they could, if they chose, easily be a security threat to the entire jail. The primary basis for these decisions appears to be prior familiarity with the inmate. This familiarity derives from non-formal, as opposed to official, sources of information and contact. Where a new arrival is unknown to the jailers, in some jails inmates already in the jail will be asked to supply the necessary information. In such instances, jailers often prefer to leave the choice of a specific cell for the new inmate up to the other inmates in the wing selected. The decisions made by the jailers often appear to be relatively independent of the past criminal history or current charge facing an inmate. In certain instances, jailers appear far more willing to trust inmates awaiting transfer to a state penitentiary to begin serving a long sentence for a violent crime and who had lengthy criminal records than those brought in for minor offenses such as traffic violations. The conclusions reached by the jailers in evaluating a prisoner are based on a set of data and evaluative criteria different from those an outsider to the jail would be likely to employ. Instead, they are likely to conform quite closely to the conclusions that might be reached about an individual by his fellow inmates. It is those inmates about whom the jailers can 23 obtain little information that will generally "be treated as posing the greatest security risks. What basically needs to be learned by the jailer is the degree to which the unknown inmates are attuned to the culture that prevails in guiding interaction among inmates and between inmates and jailers, as well as any individual idiosyncrasies such an inmate might possess that might upset the balance present. The inmate population present in a jail at a given point in time represents the basic problem to which a jail must respond. Several charac- teristics of inmates can be suggested as particularly salient on the basis of interviews and observations. One important way in which inmates vary is in the consequences that would be engendered should they escape or cause an incident. Inmates also differ in the length of their current confine- ment in the jail and in the amount of time they are expected to remain incarcerated there. Perhaps most important, however, is the variation that exists in the degree to which individual inmates are known to the jailers through prior contact. This is crucial in that the kinds of information about an inmate that are likely to be of use to the jailers must perforce come from non-formal sources. It is not, however, sufficient that a jailer be able to evaluate individual inmates. The inmate population as a whole must be considered in terms of the likely consequences of the various individuals present interacting. For the jailers, the more diverse the inmate population, the more difficult it will be to predict these consequences and also the more important it is to do so. Diverse inmate populations are likely to be quite volatile in that they provide endless possibilities for inter-group friction. The Structure of Jails In the aegis under which they operate and in the ends they pursue, county jails present the student of organizations with some features which, though not unique, do require consideration. County jails lack a clearly demarcated operational charter that legitimates their authority and their right to resources. While state statutes in Illinois and in many other states mandate that the county sheriff serve as warden of the 2k county jail and operate it with members of his staff, the responsibility for allocating the funds to operate and maintain the jail is given to the county board. Like the sheriff, county board members are elected officials, and the result is that while the sheriff has little control over the financial resources he receives, the county board in turn has very limited power to oversee the manner in which those resources are expended. Another dimension of the external regulation of jails is found in the requirement that the chief judge of each circuit oversee the internal workings of the jail and that at least once during each session the grand jury inspect the county jail and file a report with the court (Illinois Revised Statutes, 1971* Chapter 58* Section 26). Other states have their own specific requirements, but Illinois' pattern is typical: statutory requirements regarding jails are dispersed throughout the statute books, fail to clearly allocate powers or responsibilities, and rarely provide an adequate mechanism for state-level enforcement of their provisions. The "mission" of a jail -- what it is intended to do -- is similarly clouded. On the basis of field research in 11 counties, it can be suggested that the operational goal of jails, that is, the end pursued on a day to day basis, is that of maintaining a low profile. Jails are successful in the view of sheriffs, county boards, judges, grand juries, and the general public insofar as they are unobtrusive. When observed over time, it can be seen that a jail evinces cycles of visibility in which periodic scandals bring the jail into local prominence. Efforts then follow to reduce the jail's visibility (Mattick, 1974:821-823). In Illinois, the creation during 1970 of a division within the State Department of Corrections responsible for overseeing the operations of city and county jails added a new element to the general environment within which jails function. This agency, the Illinois Bureau of Detention, was formed to establish a set of standards applicable to all jails in the State, and to provide the technical assistance and such periodic inspec- tions as are needed to ensure compliance with those standards. By statute, the standards specify only a minimum level of acceptable performance. The Bureau is empowered to go to court to have condemned as unfit for human 25 habitation jails failing to meet that level, but has no intermediate sanctions to impose. Thus, the Bureau has largely been limited to using persuasion. Nevertheless, the result appears to have been a gradual convergence in the operating practices and the procedures of jails in Illinois. To carry out their "mission" and to operate within the dispersed supervisory mechanism described above, jails can employ a number of alter- native strategies. These strategies must all respond to the dual problem of control that is present: how can the sheriff maintain control over the actions of his deputies in their jail work and how can the jail staff maintain control over the inmates? One basic strategy is that of specifying the procedures to be followed for jailer-inmate contact in advance. By setting down what the appropriate response is to a wide range of events, the sheriff retains controls over what transpires within the jail. At the same time, the jailers are relieved of any real need to treat inmates as individuals. Jails that evince such a reliance on routine procedures can be expected to resolve such discretionary decisions as are encountered by referring them to the sheriff. Thus, the sheriff will deal personally with all problems involved in administering discipline or in responding to inmate complaints. In addition, security will be sought largely through formal inspection checks of the cell blocks that are carried out by the jailers at pre- determined intervals. Careful records will be maintained on these checks and on the aspects of jailer- inmate interaction. Through these records, the sheriff can routinely monitor the performance of the jail staff. Two variations on this general theme can be suggested, variations that hinge on whether or not a jail uses trusties. The physical layout of most jails and the presence of but one jailer at any given time dictate that much of the work involved in maintaining the jail be carried out by carefully selected prisoners. In the course of their duties, which typically include getting food to the other inmates, acting as messengers, and distributing mail and supplies, the trusties are given access to all parts of the jail and interact with everyone there. Where a jail relying 26 on routine procedures uses trusties, they will tend to be selected by the sheriff, given as limited freedom of movement as is feasible, and their numbers will be limited to that minimum needed to get their job done. Nevertheless, the use of trusties is a deviation from the general idea of routine procedures, which is to lock away all inmates and avoid contact with them to the maximum extent possible. A second organizing strategy available to jails is built around the use of non-formal, non-routine procedures. Here, the sheriff abdicates direct scrutiny over decisions made in running the jail. The jailers gain discretion, with only the most extreme situations being brought to the sheriff for resolution. Given that the jailers are in a position to make evaluations on inmates to a degree far more precise than could the sheriff, more latitude can be granted to the trusties. This is particularly true where an evaluation is reached on the basis of observed behavior. People are not constants, and by observing behavior jailers can adjust their evaluations as proves necessary and have confidence in them. In such a situation, it is likely that the sheriff will designate one of the jailers as "chief jailer" and delegate to him operational responsibility for running the jail, though he retains his statutory liability. Maintenance of formal records on jail activities is likely to be limited in jails using this organizing strategy. A further attribute of jails that suggests these types is the use to which the central area is put. In jails that have been charac- terized as using non-routine procedures, the central area is frequently used to accommodate inmates with whom the jailers are well acquainted and feel they can trust. This can involve permission to use the jail's phone, to receive a visitor at other than the specified times, or simply a bull session with the jailers. Such uses of the central area are generally rare in jails with formalized procedures. These ideal-types emerge from observations and interviews with jail staff members. No research literature exists that can in any meaning- ful way attest to their veracity or lack thereof. Each ideal type, however, appears to be appropriate to a particular inmate population. Where the 27 inmates tend to "be middle-aged or elderly, white, and charged with minor, non-violent crimes, the first strategy is appropriate. Young, non-white inmates, charged with serious crimes of violence argue for adoption of the second strategy. An inmate population can he thought of as environmentally imposed uncertainty that must "be transformed into information. Jails develop a structure geared toward removing a particular level of uncer- tainty. Where information can be obtained, it is possible to assign probabilities. The situation becomes one of risk rather than one of uncertainty. Interorganizational Relationships An urbanized community with a sizable minority group population is likely to have a more serious crime problem than a homogeneous rural community. While this difference may well be reflected in the composition of each community's jail inmate population, more potent influences are likely to stem from the practices of their respective local criminal justice systems. In effect, that system, consisting of the community's police departments, courts, parole and probation officers, prosecutors, and public defenders, determines who out of the total set of individuals suspected of a criminal act ends up in jail. Of course, the practices prevailing in the criminal justice system are not unrelated to the degree to which the community is urbanized or otherwise complex. Local criminal justice systems are marked by the extent to which the processing of those brought before them is accomplished through informal arrangements and accommodations. The heart of these agreements is the negotiated plea of guilty, a procedure which Katz (1972:193-215) has described in this way: During the period of time following the arrangement, the prose- cutor and defense attorney consume a considerable amount of time merely jockeying for position. .. .To dispose of cases and to keep the system moving, the prosecutor is often ready to make some concession to the defendant. At the same time, the defense attorney and the defendant are interested in the lightest penalty available and are willing to plead guilty if the punish- ment offered is less severe than might be decreed by a trial court. The etiquette of plea bargaining differs from city to city (193-I9U). 28 In 1967> the President's Commission on Law Enforcement and the Adminis- tration of Justice estimated that pleas of guilty accounted for 90 percent of all convictions, and Katz (1972:195) cites statistics from Philadelphia that show that in 1970 negotiated pleas of guilty resolved 79 percent of all cases in that jurisdiction. If a defendant is out of jail on bond, delaying disposition of his case works in his favor. Witnesses disappear and memories fade, while the prosecutor gets more anxious to dispose of the case. For those defendants incarcerated in jail, time generally works against them: The defendant who is incarcerated is more likely to agree to an initial offer from the prosecutor, even though it involves addi- tional imprisonment, than is the defendant who is free on "bail and can take the chance that a more acceptable offer will be forthcoming (Katz, 1972:202-205). Thus the jail, and more specifically jail conditions, are a major source of pressure that the prosecutors can employ against an inmate awaiting trial toward obtaining both a speedy disposition of a case, as well as a disposition reflecting positively on his performance -- a conviction. Landes (1971) placed these plea bargaining maneuverings within the framework of an economic model in which both defendants and prosecutors seek to maximize some objective function within the limits of their resources. The role played by the jail in the plea bargaining process remains crucial. As described by O'Neill (l^k), Landes' central hypothesis is that, increases in the proportion of defendants released on bail will, ceteris paribus, increase the proportion of cases that are brought to trial. This result follows from Landes' model because being held without bail greatly increases the pecuniary cost of waiting to go to and complete a trial. . .these effects operate to make incarcerated defendants more susceptible to plea bargaining at relatively high (though less than the maximum) sentences. Once the role jails perform in the plea bargaining process is made manifest, it becomes possible to understand the curious tendency for those awaiting trial to be confined in conditions which are inferior to those used to house convicted felons. 29 Not only is the prevalence of plea "bargaining likely to influ- ence who ends up in jail and the length of their confinement, the type of plea bargaining is also likely to have an impact. Sudnow (1965) described plea bargaining as it is practiced in a major metropolitan center. There, public defenders and prosecutors used categories of "normal crimes" to dispose of cases without recourse to the evidence or to discretionary judgment. Once a defendant was classified, a specific "bargain" became appropriate and the dispo'sition of the case was automatic. Typically, disposition entailed the defendant pleading guilty to a lesser charge than that originally filed against him. On the basis of a nation-wide study of plea bargaining, Newman (1956) identified three other patterns of plea bargaining, each based on negotiating something other than reduced charges: a lesser sentence, a concurrent sentence, or a dismissal of some of the pending charges. Neubauer (197M> i- n a study of the criminal justice system of Macon County, Illinois, one of the counties included in the sample of the present study, found that negotiations there centered around sentences. Negotiating for a reduced charge occurred only where the prosecutor was in a weak bargain- ing position. In all other cases, the defendant would plead to the original charge. Neubauer (I97^ ; 22l) also found that plea bargaining in Macon County was essentially a "mini-trial conducted in the prosecutor's office." Negotiating began with the evidence and only later turned to establishing a sentence acceptable to both sides. The generalizability of Neubauer's observations to communities of similar size is unclear. One possible implication is that where prose- cutors face substantial backlogs of cases, they will rely more heavily on reduced pleas. The possible implications of this for the role of the jail in the negotiating process will be pursued in the next chapter. The jail itself plays a broader role than that of storage-bin in the bargaining process. Frequently, it is other inmates who suggest to a defendant the possibility of pleading guilty for consideration (Blumberg, 1969:235)- The jailers serve as communication links between an inmate and the various officials involved in deciding his fate. Here, the informal 30 relations between the jailers and prosecutors, probation officers, and even judges are brought into play. The courts essentially act as filters, taking the total popula- tion of those arrested in a county and determining who ends up in the county jail. This filtering process can be seen when the ages, serious- ness of offenses, and racial composition of those arrested are compared with those of the jail inmates. The differences reflect the local practices of the court regarding plea bargaining, the setting of bail, the use of pre-trial dismissal of cases, and bond forfeiture. It is assumed that these practices all tend to complement each other in any given local criminal justice system. Court practices, therefore, ought to be strong predictors of the composition of jail inmate populations. No direct relationship is antic- ipated between environmental complexity, as measured by urbanization or presence of minority groups, and the composition of the inmate populations, Such a claim is supported by the scant research literature on jails which suggests that such basic environmental characteristics as the size of the community served by a jail are poor predictors of the number of inmates that will be confined in the jail at any given time (Nebraska, 1970: 97-105). If jail inmates are representative of the total population of individuals arrested within the county, then the courts would be having little impact on the filtering process which determines who is confined in the county jail at some point prior to the disposition of their case. Conclusion Certain court practices and characteristics tend to lead to specific types of inmates accumulating in the county jail. A substantial court backlog implies a disadvantage for the prosecutors in their nego- tiations with defense attorneys. To restore a balance within which they can achieve a satisfactory rate of convictions, prosecutors will endeavor to maximize the number of defendants on felony charges who will remain in jail while awaiting trial. Landes (1971) suggests this occurs through manipulation of bail setting practices aimed at making the monetary 31 payment required for release as unobtainable as possible. The assumption Landes attributes to the prosecutors is that being confined in jail is a powerful incentive to the inmate-defendant to expeditiously resolve his or her case. In short, jail inmates as opposed to non- incarcerated defendants, will be amenable to pleading guilty, perhaps even independent of any offer of "consideration. " Where a jail is used primarily as a processing center (that is, only briefly until bail is established and bond payment achieved), a wide range of charges and demographic characteristics will be present in the inmate population. Large court backlogs and plea bargaining tend, in contrast, to argue for the accumulation over time in jails of the "hard core" described earlier in this chapter. It is suggested that differences between jails in the types of inmates they incarcerate lead, in turn, to differences in the manner in which jails are organized. Where inmates are young, black, and accused of serious crimes, the tendency will be for the sheriff to grant discretion to the jailers as only they have the knowledge about the inmates in the jail that is required if reliable decisions are to be made. In such situations, jails will tend to acquire autonomy within the sheriff's department, with the sheriff retaining only general supervision over what takes place within the jail. Where inmates tend to be middle-aged, white, and facing minor criminal charges, the sheriff can directly involve himself in running the jail. 32 CHAPTER III COUNTY JAIL POPULATIONS AND THEIR ANTECEDENTS This chapter has a two-fold purpose. First, the inmates of 35 Illinois county jails are described. The inmates present in each jail are compared with those found in the other jails, and the characteristics of each jail's inmates are compared with those of the general population and the arrest population of the county in which that jail is located. While this information provides a portrait of county jail inmates, it does not provide a basis for explaining the differences that exist across counties in who is incarcerated in the county jail. The second purpose of this chapter is to move toward such an explanation. In so doing, multiple regression analysis is used. For each characteristic considered, multiple regression equations can suggest which antecedent factor is most important and how it is important. Readers unfamiliar with this technique are invited to skim the tables and text involved in the analysis and await the first section of Chapter V where the findings are summarized. County Jail Inmates in Comparative Perspective In this section, the age, race, and sex of county jail inmates will be considered, as will be the criminal charges facing those individuals , For the first two characteristics, comparisons with both county and arrest populations are possible. Such comparisons are important as indicators of similarities and differences across counties in the outcomes of the filter- ing process involved in their local criminal justice systems. As will be demonstrated, there are considerable differences between counties, differ- ences which raise interesting questions about the current and future functioning of their criminal justice systems. For the latter two characteristics, only comparisons to arrest populations can be undertaken. In each instance, what is being compared is the composition of a jail's inmate population in January of 197^j as shown either through an average score or a percentage. 33 Figure 1 compares the percentage of county, arrest, and jail populations formed by those aged 17 to 2k. For the general population that percentage is generally around 12 percent, rising above that only for counties such as Champaign, Coles, DeKalb, and Jackson that have large universities located within them. In all 35 counties, those aged 17 to 2k make up a much greater percentage of those arrested and jailed than their representation in the general population. In 17 of the 35 counties, individuals arrested tended to be younger than those incarcerated in the county jail. For 16 counties, the exact opposite relationship was found, while for two counties, the percentages of individuals 17 to 2k was identical for those arrested and those jailed. Thus, when compared to a county's general population, those arrested and those incarcerated in the county jail are disproportionately young. No clear pattern was present, however, that indicates that jail inmates are younger than the total group of all individuals arrested within a county. The jail with the largest proportion of inmates between 17 and 2k was Whiteside County where ~j6.k percent of all inmates fell within that group. The lowest percentage was found in Jefferson County Jail where 28.1 percent of all inmates were between 17 and 2k years of age. For all 35 jails, the average percent in that age group is 52.9 percent. The standard deviation for that mean, indicating the degree of dispersion around the average score, is 10.5* The average percentage of those aged 17 to 2k among all individuals arrested in the counties was 5^«3 percent. Overall, then, jail inmates are not very different in their age composition from the larger group comprised of all individuals arrested, although there are particular counties, notably Adams, Livingston, and Whiteside, where striking differences do exist. The reasons for these differences are not at all clear, but it does appear tnat the filtering process unfolds in various ways across counties in Illinois. The information found in Table 2 confirms that conclusion. There, the average or mean age is used as the summary measure of the age composi- tion of a jail's inmate population. In contrast to percentages, means tend to be heavily influenced by extreme scores. Marion County had the 70% 60% 50% 40% 30% 20% 10% 80% 70% 60% 50% 40% 30% 20% 10% \ V v \ \ S \ \ \ % \ \ % X \ % \ % 80% 70% 60% 50% 40% 30% 20% 10% V J \ % \ Ptrcontog* of County Population Ag.d 17-24 Porcontag* of Arroit Population Ag»d 17-24 Parcantag* of Jail Population Agod 17-24 Figure 1. Comparison of those Aged Y(-2k as a Percentage of County, Arrest, and Jail Populations 35 i U CD CD <+H CJ <*H d •H 0) « VD 99. h_v ^ 1 r— H VO I s - I s - H CO LP\CO LfA OO LfA CVJ r-H^j- m Q OO VO CO CO OO O I H O I O i o o + I o + o + o + OJ + o o + + LfA o H i ON o OO CO a; H P •H aj cd B ^3 C H -H- CO o on LfA cvj OA OO OJ vo vo oo o CO -H- LfA CO I s - LfA oa VO I s - CO O o UA OA CO CJ OA OJ OJ C\J H OO cvj I s - LfA CVJ co OJ -3- VO CVJ CO CVJ CO CVJ CVJ CVJ CD H € EH CD a O •H P CCS H <£ H •H cd hi p CD CD d •H CD p CO -=t oo On VO H 0,1 H LfA vo VO o -H- no rH CO CD rH CO -H- t- ON LfA cvj a\ H CO oo H 00 H O OA 00 0- co VO I s - H I s - CO LfA ON I s - I s - On r— CVJ <^ cvj CVJ CM CVJ CVJ OO oj cvj CVJ CVJ cvi CVJ OJ OJ d O ■H P cd •H > X3 CD d d d T3 cd rH O d CD O H •H d CD H o TJ cq T3 >S >> CD cr! O d H •H •H a Jh P rH cd H rH a; B a; 0 Pn a; S P H H d n o Uh H O CJ • rH d N 5h •H H H cd cd o CJ bD ,9 o P cd cd P cd CD ,d •H •H CD -p s o l-H ffi w W ra CQ EH > ^ D5 3: 8 co CD M cd fH CD O 5h oo H -H- -H- H -H- on OO 00 oo CVJ UA o H CO -3- CO -=J- vo -H; vo -H; CD CD CO O oo m H H oo LfA o o\ -H- -H- o\ o LfA LfA -H- oo CVJ OA o co tH CJ > p d B o o a d cd d d o CD d o p •H •H CD ■H d CQ CI) CD CQ d 3 cd P rd +J H !>» O rH Jsj H t>0 o d CD cd Pk CQ CQ H +- 1 ^ T3 >5 CQ CD cd H d d CQ o 8 a; td •H CD cd 9 J d d M X > cd ?i d a bO Ti m Ti d ^ 4J O fi cd M u CD cd fl) cd CD d cd cd (1) ■H o cd cd < m o o O l^H |X| C5 w 1-3 1-3 W w W A 1-3 J J ^ S 2 36 jail with the highest average inmate age; LaSalle County the jail with the lowest average age. The range between the highest and lowest averages was exactly nine years. The most interesting observation that can be made on the basis of Table 2 has to do with the number of counties in which the jail inmate population was substantially older than the arrest population. The most dramatic instance of this is found in Henry County where, on average, jail inmates were almost 13 years older than the population of all those arrested within the county. In four other instances, DeKalb, Fayette, Grundy, and Livingston Counties, jail inmates averaged four or more years older than those arrested. Though in 16 of the counties the average inmate age exceeded the average age for those arrested, nowhere was that differential more than three years. The general tendency, there- fore, appears to be that certain factors -- unknown to us but subject to informed speculation -- combine to take the total of those arrested in a county and filter them into the county jail population in such a manner as to exclude more young individuals than middle-aged and older individuals , Figure 2 compares the percentage of blacks found in county, arrest, and jail populations. In all counties in which blacks comprise over one percent of the general population, except for Livingston County, blacks are represented in the arrest and jail populations considerably in excess of their representation in the total population. Whether this results from discrimination or from disproportionate involvement in criminal activities, or both, is beyond the ability of the present study to determine. On average, the county, arrest, and jail populations are, respectively, 3«7; U.^b and 12.1 percent black. In 11 of the 35 counties, blacks formed a greater percentage of the arrest than of the jail populations. In 17 counties the reverse is true, while for three counties the percentages are nearly identical. In three other counties, no blacks were present in any of the populations being considered. As previously noted, for one county -- Livingston County -- blacks represented 2-3 percent of the county's population and were entirely absent from both the arrest and the jail populations. 37 c o c o <1> <-. 0> Q. 4> °0 °O0L °0 w c »_ u o 5 <■> o a> o "- X o (BOlO ° 5 t co a. < «> c -* a> o o ■2 £ m a. D EZZZZ 3 i///, /////,//, T r////// .T.TTI (ZZ2 55! 3 t: ^^^^^^^^^w e5 o o o o o m ^- io cm — ^ ^ ^ ^ %«■ ^ e \<* >1* .**° ^ 0^ \* ^ ** ?* o> H •H S3 CQ P >> co cd O c c u cd H H a; B 0) pi a •H O a cd 43 O CU 0) H ^ •H bO w o w H o o • H a a; o M CD o P cd cd p 2 O ft w CO W CQ CO. 0J H O rH C CU H O T3 H ■H •H ^ rH CQ £ •H a) a; B p N u •H cd EH CD > i OJ o rH rH On I s - ON I s — ON -H- S3 O •H P cd •H > cu rH cd d cd p co cu bO cd p a cu CJ 5h cu ^1 p

5 O Jh JsJ H bu O S3 CQ od ^ CQ CQ H p J*i TD >» CQ 03 cd ■d $3 S3 CO O e cu d •H 03 cd cu S3 S3 rH X CH C13 J^ XI Q) •H cd •H •H cd Jh cd rH H W >5 cd J3 S3 o bU ra Fh TD s 43 43 o O O M fe fe o w h) h) W X W PI hJ PI J PI S S ko and are therefore more likely than men to be released prior to jail con- finement and particularly before confinement in the county jail. This may well be abetted by chivalry on the part of law enforcement officials. Simon (1975) has argued that as women increase the rate and scope of their participation in the labor force, a concomitant increase will appear in their participation in crime. Both increases may act to reduce the degree to which women receive preferential treatment in the criminal justice system, as well as to expand the representation of women among those accused of serious crimes. Comparison of the results found in the Illinois Jail Survey (1969) with those of the present study tends to support the existence of such a trend. While the two studies are not comparable in the range of jails they include, the evidence is strengthened in that Mattick and Sweet (1969:61) found that the month of January accounted for only about 7.5 percent of all confinements of adult women to county jails, a percentage below that which would indicate that January bore an equal or greater share of such annual confinements. If the contribution of January to the annual total of county jail confinements of women has remained constant over time, then women in 197*+ comprise more than 7*33 percent of all confinements to county jails. The seriousness of the charges pending or proven against the inmates in each of the 35 jails is presented in Table h. Quantification of the seriousness of criminal offenses has always posed complex and vexing problems for researchers. The Sellin and Wolfgang method (I96U) is perhaps the most widely used and best known of the techniques currently available, and the technique used here is based on their finding (196^: 327-328) that scores assigned on the basis of their method correlated highly with the maximum imprisonment specified by statute for a given offense. To derive our measure of seriousness, the nine categories of criminal offenses specified in the Illinois Uniform Code of Corrections (1972) were assigned weights to reflect variability in the maximum period of confinement permitted upon conviction. Each inmate booked into each county jail during January of 197*+ was then assigned a score on the basis kl I u CU CD •H CD Q H C\l -=1- C\J VO oo C\l ON VO oo o oo vo oo ON oo rH ON oo L/N ON LT\ VO H oo t— t- LTN H o- I s - I s - H rH VO -=f I s - VO co -3- O CO O VO o oo rH 1 on OO ON 1 CVJ 1 H i CM i C\J 1 1 no 1 oo OJ -3- CQ c O •H -P o3 H H •H 0} l"3 Crj -P CD 01 co cu rH O o w cq cu H p •H cri cri £3 h) C! H ■P c o o ON LTN -=f LT\ I s - oo CO oo oo VO LTN O OJ VO ON OJ OJ CM LTN o LTN LT\ on ON OO VO H oo LTN VO I s - LT\ o rH CO O CO -=t OJ o VO LfA VO OO oo VO J- ON I s - LTN H UN co t— co LTN CO CO oo t- OJ co OO o OO rH O VO -=f I s - oo LTN r— vo oo I s - ON H m CM LTN vo o vo o- o ON oo I s - -4- -4- ON CO co o rH LTN H I s - -=t VO LTN VO LTN t— r— CM CVJ LTN -=f VO CM 0- -=1- oo CVJ -=t -3- H oo rH T3 C crj >^ CQ rH crj H a •H CD CO rH J*! w rH O o f ) tm

^ S £ £3 CQ rQ crj EH CQ CQ CU CQ O •H rH CU CQ CU tjJD crj U CU r+H O C o CQ •H £h crj §< O O I H CU CU r+H O •H 0) Q CQ CU H -p •H cri cd £3 h> a H -=r -3" i CO CO OO OJ LTN CO O OJ I s - I s - ON OJ OJ I s - oo H VO -=r ON I s - O H OJ H ON VO oo co oo oo VO I s - J- O J- I s - OJ OO OJ vo vo I s - rH I I On OO oo ON -3" I OJ I ON VO I OO LTN CO -J" OJ I s — CO I s - rH I . I OO CM I OO H I s - CO l l OJ OJ I o OJ I CO OO ON OJ LTN LTN I I I OO co OJ OO I s - OO OJ OO OJ LTN ON LTN OJ OJ o OJ C0 LTN ON o LTN I s - CO CO LT\ CO LTN H rH -3" OJ OJ H CO oo o vo o I s - OJ vo oo ON -3- ON VO o ON ON I s - o vo I s - O -4- oo -3- OO H VO -Ct" ON H H I s - I s - oo VO OJ H H OJ H OJ ON I s - LTN VO vo I s - ON H I s - OJ -* -=f I s - -=h CM LTN CO -=f- -* H CVI CVJ co CM ON CM CO ON -3" -* -* CM VO CO I s - vo oo ON I s - ON co vo CO LTN 00 o LTN I s - o vo CO UACMCO H I s - O ON^t" t-LTNOOVO CMCO ON CO H ONVOCO OOH OJ I s — OO OJ OO OO OO OJrHrH-d-LrNOOOOVOVOOOOOJ-rH a pi o o C C C O tU) crj c O CU -p •H •H 0) •H G CQ d) CQ C pi crj +J P -p H >> O Sh -^ H W) O fl CQ cri f-r 1 CQ CQ H -p M 'O >5 CQ 0) crj H c C CQ O £3 (1) £3 •H <\) crj a; a fl ^H A! Cu CD X X (U crj •H cri •H •H crj fn crj ^H rH W >5 3 3 iH o !+H S a O ^ CQ aj f> W) TJ rH TD 3 ^ 43 O a crj 5h fn a; crj CD crj crj CJ crj crj CD •H o crt cri <: P4 O o o hH pq O ffi h) ha W W W h-J. hJ l-H 1-^ J 2 S U2 of the most serious charge facing him or her, these scores were summed, and an average was computed for each jail. The range in average serious- ness among the jails is considerable. The highest score is found in St. Clair County -- I58.I. The lowest score is slightly more than one-fifth of the highest score -- Fayette County Jail's 3^.22. While the jails with scores of 90 and above are generally large, urban facilities, Bureau County Jail is an exception. In all 35 counties, the average seriousness for those jails exceeds that found in the arrest population by a large margin. The average of the counties scored is 73*6 for the jail populations, and 37«^ for the arrest population. Clearly, a filtering process is at work in the localities, shunting those charged with serious crimes into the county jail and diverting others either before or during confinement in the city jails. Doubtlessly, this follows in large measure from the differences in the dollar amount required to obtain release on bail. Still, the magnitude of the differences in the levels of seriousness found among those arrested and those jailed is often striking. In Marion County, which had the second lowest score for arrest populations, the score for jail inmates is almost precisely six times as great. For Fayette County, however, both the seriousness in the arrest and jail populations were the lowest among the jails in the sample. Curiously, it was Bureau County that proved to have the highest average seriousness among its arrest population, rather than, as one might have expected, one of the more highly urbanized counties. In total, 17 counties had average arrest seriousness scores that were less than one-half their jail inmate seriousness scores. There was considerable variability among the counties both in the magnitude of the average inmate seriousness and in the degree of difference between the seriousness of the crimes of those arrested and those charged. Generally speaking, however, the scores for jail inmates were high: the lowest jail score was 3*7 times the size of the lowest score for those arrested. Similarly, the highest score for those incarcerated in county jails was more than twice as great as the highest score found among those arrested. ^3 The comparative analysis of the characteristics of county popula- tions, arrest populations and county jail inmate populations raises some interesting and important questions. The most striking finding is the extent of heterogeneity across counties that exists, both within and between populations. Whether one is interested in, for example, the range in the average seriousness of crimes for which inmates have been booked or convicted across county jails or in the differences in such scores in arrest versus jail populations or in the range in such differences across counties, one finds extensive variability. And this variability exists for all inmate characteristics considered — age, sex and race as well as seriousness of charge. What do these differences mean? If they at least partially reflect differences across counties in the workings of criminal justice at the local level, should they be encouraged? Or should the emphasis be on the development of policies to ensure greater uniformity? What kinds of extra- local initiatives can be developed to encourage less heterogeneity? Or is the status quo as reflected in the data presented in fact desirable from the perspective of usual standards of criminal justice? These are questions without easy answers, but it is hoped that the data presented here will provide a basis for framing them in meaningful terms. In the next section of this chapter, an attempt is made to answer some of these questions on one level by asking a somewhat different question, why it is that the differences found in inmate populations in fact exist. Predicting Inmate Population Characteristics From Community Characteristics and Court Practices In this section, the results of the statistical analyses designed to explain the differences that were found among the inmate populations of county jails are presented. Chapter II suggested that these differences might well be a function of differences in the nature of the community in which a jail is located and the court practices that prevail in that community. The degree to which that expectation is supported empirically uu is examined through a series of multiple-regression analyses. To interpret the regression results, however, it is necessary first to consider the zero-order relationships that exist. The first consideration is the zero-order correlations in Appendix F. The most vital question to ask of the relationships found in that correlation matrix is the likelihood of multicollinearity among variables that are to be used simultaneously as predictors. Multi- collinearity is present when predictors are sufficiently highly inter- correlated to prevent individual variables from exerting an independent effect: One of the uses of multiple regression as an interpretive tool is to evaluate the relative importance of the independent variables. The situation is somewhat paradoxical, however. The more strongly correlated the independent variables are (excluding, of course, extreme multicollinearity which prevents the coefficients from being calculated at all), the greater the need for controlling the confounding effects. However, the greater the intercorrela- tion of the independent variables, the less the reliability of the relative importance indicated by the partial regression coefficients (Nie, Hull, Jenkins, Steinbrenner, and Bent, 1975: 3^0)o While it is impossible to offer an infallible criterion, it is wise to suspect extreme multicollinearity whenever correlations among predictors reach .80, and to be cautious in situations where such correlations exceed .60. The most troubling potential for collinearity between variables is found in the .'jk correlation between #C0. BLACK and foIMVIATES BLACK and the .84 correlation between the two measures of organization size. The only solution is to prohibit either pair from being in the same equation. As a result, it will be impossible to test directly for their independent effect on other variables. The two alternative measures of length of confinement correlate at .93* Their relationships to other variables are closely parallel, with X STAY generally having the stronger connection. For most of the analyses, therefore, X STAY will be used. h5 The correlations among other sets of predictors do not indicate problems of collinearity. The two scales developed to measure the extent of formalization correlate at a moderate . 39> and each scale demonstrates its own pattern of relationships to other variables. The same can be said for the two measures of plea bargaining practices. Collinearity is prob- able, however, in the -.68 relationship between $C0. URBAN and CO. JAIL USE. Table 5 presents the zero-order correlations of the populations and court practices of the 35 counties with the characteristics of those arrested within those counties. While a correlation is a measure of association, and thus devoid of causal implications, in interpreting the coefficients it will be assumed that community characteristics affect arrest population characteristics, with the characteristics of those arrested, in turn, affecting court practices. In scanning the table, the asterisks indicate which coefficients are of sufficient size to dismiss chance as an explanation for the associ- ation that was found. Generally, #C0. URBAN, INCOME, and SCHOOLING have the same general relationship with arrest population characteristics, though $C0. URBAN usually has the strongest degree of association. There is, however, one exception. INCOME and SCHOOLING have significant negative relationships with the average age of those arrested while $>C0. URBAN does not: the counties with high average family incomes and high average number of school years completed are also those with low average ages among all individuals arrested. $>C0. l r J-2h, indicating the age structure of a county's population, however, is unrelated to X ARREST AGE, suggesting that the size of the pool of potential young criminals bears no real relationship to the representation of that age group among all individuals arrested. By and large, court practices are unrelated to the character- istics of those arrested. In the single exception, a low average age among those arrested in a county is associated with a large number of felony cases being reduced to misdemeanant charges. Two other correlations are intriguing. Counties with a substantial share of their populations kG Table 5 Zero-Order Correlations: County Characteristics and Court Practices/Characteristics of Arrest Populations Arrest Population Characteristics Co\inty Population and Court Characteristics $C0. URBAN foCO. BLACK foCO. 17-21+ INCOME SCHOOLING DETOX BACKLOG foREDUCED $PLEA GUILTY ^DISMISSED ^IMPRISONED X ARREST SERIOUS- NESS foBLACK ARRESTS .488** ^FEMALE ARRESTS .370* X ARREST AGE .338* .022 • 330* . 762** .225 .102 -.127 .176 .35^* -.066 .359* .263 .279 -.353* .311 .23I1 • 30^ - . 38O* .173 . 1+86** .169 .182 .285 .107 .05I+ -.01+5 -.01+2 -.279 -.098 -.399* -.123 .178 -.192 .129 -.007 -.19I+ .059 .0^8 .21+5 .016 -.031+ .218 N = 35 *P < .05 **p < .01 hi between ages 17 and 2h are also those that tend to have a high percentage of females among those arrested. Though for all those arrested no rela- tionship was found to exist between county age structure and average age of those arrested, perhaps such a relationship does exist among females. The other relationship that merits mentioning is the .1+9 correlation between DETOX and $,BLACK ARRESTS. That relationship may, however, be spurious due to the relationships between #C0. URBAN, DETOX, and /oBLACK ARRESTS. In Table 6, the characteristics of jail populations replace those of arrest populations. Here, too, court practices are with one exception unrelated to those characteristics. That exception is the correlation between BACKLOG and X INMATE AGE, with counties with substan- tial court backlogs also having jail inmates that are predominantly young. Also, like the pattern in Table 5* $>C0. URBAN evinces much the same pattern of relationships as INCOME and SCHOOLING, with the exception that the latter two variables are more strongly related to the average age of the jail population: the wealthier and better educated counties are also those with young average ages in their county jail inmate populations. There is, however, no relationship between the age structure of a county's population and the age structure of that county's county jail inmate population. Apparently, then, participation in crime by various age groups, or at least the propensity to be arrested and jailed, is different in urbanized counties with well-to-do, well educated residents than in other counties. CO. JAIL USE proved to have important relationships to the level of seriousness and the length of the average confinement in the county jails' inmate populations. Specifically, where alternatives in the form of city jails are available and used, the average seriousness score for the county jail increases as does the length of both the average pre-trial confinement and the average confinement for pre-trial and sentenced inmates. An urban county shows the same relationships to inmate populations, and is also likely to be a county in which city jails are available. It remains questionable, therefore, whether it is urbanization or the U8 fl T3 O fi -H OJ -P 0j cq H CJ •H Ph p o [ Oh •H f-< aj CD -P -P ct5 o S aj C 1h m ct3 43 H O -H aj >l 1-3 P •H Ch C O ^o 3 S 03 a; S cj H O -H & O -P CO CO H •H • • Jh co O Ah O -P Jh !h CD 3 Csl O o co o •H -P CO ■H fn CD -P o CO Jh aj & o o •H -P a$ rH H •H aj hi H K Eh Ph p CXI ! o co p^; H r£| H g IX CO * * * * I s - H VO O -=f on VO VD VO LTN CO VO C\l CO CVJ O OJ LA -=J" OJ -cl- H t- OO OO OO H H o OO 0O OJ in H O r-\ * * * O co t- I s — o OO H CVJ ON 00 ON H ON I s - t- -3- o OO -=(■ o -4- ON o H CVJ OO o o H OO OO OO H O O OJ * * * * * * * a) OO -=f O H H On VO LTN VO OO CO a) OJ LT\ -J- OO O I s - CM H i_r\ H Ol OO o H LTN VO O OJ -=}■ O H O H VO H H H VO ON * o CO OO OO I s - CO Ol o ^J- OO I s - H H oo LT\ I s - o o OO o o OO H H o * * % * * * I s - o t- I s - OO ON H VO -H/ ON ON -=f o LT\ ^t I s - H OJ OJ LT\ OO o I s - oo OJ ON I s - ON CO LTN CO OJ O H 1 % * * * * CVJ 00 H o cvj o LTN O _H/ VO t- LTN VO OO LTN -3- H ON O LTN vo -3- oo CO LTN LTN o -3- OO OJ LTN H o H O O a CO •H CJ P •H aj ■P -P H U en a 2 •H ^H ^ o CD ■P TH CJ >» a 0) p a Jh a Crf ? A n C_) o H W -3- CO pi o cvj o a 1 I s - H ft 3 w H LxJ i-h" ^ O X hi • • o o o o o u K EH • o o S O W o y< ^. H CO O o a p B ^ ^ CO co CO M ft ft O co ft >£. LTN H o O LTN • • OO V V II Ph Ph S3 * * * h9 proportion of the total confinement load that the county jail assumes that is associated with more serious, less transient jail population. This, and the other relationships in Table 6 that appear interesting, can be more profitably investigated in the multiple regression analysis that follows. On the basis of the zero-order correlations presented thus far, a subset of the variables is included in the multiple regression analysis that follows. The choice of variables is based on the need to avoid multicollinearity and to limit the predictive equations to an interpretable number of variables. Also, an effort will be made to select variables that are promising in terms of the criteria Blalock (1972:^56) put forward for multiple regression: ...if we wish to explain as much variation in the dependent vari- able as possible, we should look for independent variables which are relatively unrelated to each other but which have at least moderately high correlations with the dependent variable. Where variables describing those arrested within the counties are present in the analysis, the sample size of 35 will be used. When such variables are not involved, Clinton County will be included, bringing the sample size to 36- From the correlations it appears that community characteristics bear strong relationships to the characteristics of the inmate populations. The theoretical perspective argues that these correlations exist because of the impact of court practices; were court practice variables controlled for, the relationships between community and inmate population would disappear. Table 7 presents the multiple regression results necessary for testing that assertion. In this and in all future tables, each column represents a multiple regression equation predicting the dependent vari- able listed above it. The entries in the columns are standardized regression coefficients (beta weights), each in the row of the appropriate independent variable. At the bottom of each column, the multiple correla- tion coefficient "R" for the equation can be found. When squared, that 50 r— ■S CO CJ •H P CO ■H Fh CD -P O o3 oi ,G o (L) -P oi •H oS h) g o3 w CD o ■H -P CJ o3 P P O o G ■H P CJ •H TD CJ CO G o •H P 05 & g o •H CO CO CD P b0 & K CD rP ft •H P H I rP o * * rP o o on vo ON OJ en LTN ON H H 00 O m * OJ l H H 1 P" C\J VO rP t— H -P o ON en O 0.1 OJ rP CM C~) rP O ON * * en h- H CO ON -P UJ VO [P- LTN rP cm no en O P O H CO CD H ■3 •H P 03 > -P G 0) G 0) ft CD P CO * * H V0 -P ir\ VO H LfA en o PO ON CM -P o CO -P cm H r— * * H O CO CO CO H O CO o -p O H VD CM o o o VO * C— LTN CD ON CO -P" en H H on rP o -p * o p- o en en p- vo cn n o O rH o kj bd o l 3 o EH si Uj 9 S ^ CM m -p P o B VO -eR t— CO en CO 03 o H W 03 IX ON EH CD IX o H * ON ON LTN * oS en CO P LTN O • ip VO en II * VO a o 1*- • »\ • . V co p Xi M ■H * 0) rP 5 P" VO oi • P ,0 Fh O * P ON rP P CO co • ai p p T3 * 01 CM rH O •H -P 03 • P cu G o * -P rP o CD LTN > • CU H LT\ o • ON ■ p O 03 H • P G o3 o •p

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URBAN and ^REDUCED. If it is accepted that blacks as a population group are less powerful in a community than are whites, it becomes reasonable that prosecutors with many black defendants are able to secure guilty pleas to the original charges. Urbanization, despite its positive correlation with $>C0. BLACK, is itself unrelated to the incidence of guilty pleas. Highly urbanized counties are, however, also likely to be those that extensively use misdemeanant convictions as a means for disposing of felony cases. One possible explanation for this points to the existence of factors in an urbanized community that tend to place the prosecutor at a disadvantage and thus pressure him to make concessions as a means for case disposition. The remaining five columns in Table 7 use both community charac- teristics and court practices to predict inmate characteristics. Contrary to what was anticipated, at least one of the community variables demon- strates a direct impact on each of the inmate characteristics, despite the court variables* presence. Of the three court variables, only BACKLOG has an independent effect, or at least has one in the predicted direction. BACKLOG is a strong predictor of the average age of jail inmates: the greater the backlog, the younger the inmate population. In addition, BACKLOG has a sizable impact on the two lengths of stay variables that is not in the direction anticipated. 53 Thus, though all five equations predicting inmate characteristics explained a statistically significant proportion of the variance, the hulk of the predictive ability stems from community characteristics. One clear conclusion that emerges is that the three community characteristics do have independent effects on the composition of jail inmate populations. In support of the use of DETOX as a general community characteristic, that variable demonstrates a significant tendency to raise the presence of blacks in jails and to increase the average length of confinement. It is also of interest that while $URBAN has no independent effect on foINMATES BLACK, for X INMATE AGE it is /oURBAN that successfully predicts, this time with $C0. BLACK being the community characteristic without an independent impact. For the present, it is sufficient to note that of the court variables only BACKLOG asserted itself: a substantial backlog is associated with a low average inmate age. This effect was independent of the strong relationship in the same direction from the degree of urbaniza- tion. While court backlogs also successfully predicted the length of the average confinement, the direction of the relationship was the opposite of that expected. Therefore, the argument that prosecutors manipulate con- finement length to adjust their backlog of cases is not supported on the basis of the data examined here. One explanation that can be offered is that backlogs are adjusted through the outright dismissal of cases, with the average length of jail confinement being unrelated to such actions. A second possible explanation reverses the implicit causal order of Landes ' approach and argues that it is the failure to manipulate length of pre- trial confinement that creates backlogs in the first place. To test the hypotheses implicit in these two interpretations would require longitud- inal data. All that can be concluded here is that for the 36 counties there is no relationship between court backlog, plea behavior on the part of prosecutors or defendants, and jail confinement length. In sum, the three community characteristics and the three court practices when used together as independent variables are strong predictors of the characteristics of the inmate populations in the 35 counties. The 5U amount of variance explained ranges from a high of 67 percent for the proportion of "blacks in the inmate population to a low of 34 percent for the average length of pre-trial confinements. Prosecutors in moderate-sized Illinois counties do not appear to utilize jails in the manner suggested by Landes (1971). Where court back- logs are present, they act to decrease the average length of pre-trial confinement, not lengthen them as Landes suggests. The other major impact of backlogs in the courts is to lower the average age of the inmates. Perhaps this reflects a large proportion of cases that involve crimes of violence. Generally speaking, however, it does not appear that a county's court practices will prove to be a useful basis for projecting future jail requirements in terms of the size or the composition of the inmate popula- tions to be accommodated. The data were more clear in what they say about the likely impact on jails of diversionary programs. The presence of a detoxification center for alcoholics in a county tends to add to the presence of minority group members within the jail and to increase the length of the average jail confinement. While such a finding is neither an argument for nor against such programs, it does warn of an outcropping of them that ought to be considered and provided for when diversionary programs are initiated. In the light of Landes's (1971) findings, it is also interesting that plea bargaining does not appear, at least upon the basis of the indirect measures of plea bargaining used here, to dominate the local courts to the degree that researchers in other regions have found. In 1973> the average proportion of felony defendants pleading guilty was 28.8 percent for the 36 counties. Far more felony cases were disposed of through a motion by the prosecutor to dismiss the charges -- 1+5.1 percent. Dismissals, then, would appear to be the most important discretionary judgment being made in those court systems. The available research literature has little to say about such practices, however, perhaps reflecting a preoccupation with plea bargaining. 55 Table 8 presents another series of regression equations. Here, the interest is in determining the impact of the use of city jails as an alternative to confinement in the county jail. From Appendix F, it can be seen that CO. JAIL USE and foCO. URBAN have a zero-order correlation of -.70: the more urbanized a county, the smaller the proportion of the total burden of jail confinements that the county jail assumes. That correlation is sufficiently high to indicate multicollinearlity. There- fore, Table 8 substitutes CO. JAIL USE for $C0. URBAN rather than including it in the equations in Table 7* By and large, the effect of CO. JAIL USE mirrors closely that of $>C0. URBAN. Where the county jail assumes a large share of all jail con- finements within a county, the average seriousness of the jail inmates will be low and the average length of confinement short. In such counties, a filtering process diverting individuals charged with minor offenses before they reach the county jail does not appear to be operating. Where city jails are widely used, however, the inmates in the county jail will be a more select group. The other variable added to the analysis in the present table is INCOME. As might be anticipated, its effect follows closely that found previously for foCO. URBAN. Also, /oFEMALE INMATES is included here as a dependent variable. Basically, community variables appear to be largely irrelevant in determining the proportion of a jail's inmates that are female -- only 13 • 7 percent of the variance is explained by the four variables considered. One interesting finding, though it is not statis- tically significant, does emerge. The presence of a detoxification center tends to increase the presence of women in the county jail inmate population. Table 9 considers the difference scores between the characteris- tics of jail and arrest populations. The dependent variable is the difference between those arrested and those jailed within each county, with a positive score indicating that the average for the arrest popula- tion was greater than the average for the jail population. 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C0. URBAN as a predictor; the remaining equations use CO. JAIL USE. The equations indicate that the more urbanized a county is, the less alike will be the two groups in their seriousness, with urbanized counties being those in which the jail population is facing more serious criminal charges than the arrest population. The effect is more pronounced when CO. JAIL USE replaced $C0. URBAN. The greater the proportion of jail inmates who are confined in the county jail rather than in city jails, the more similar will be the arrest and jail populations in their average seriousness. In interpreting this beta weight, the only one to achieve statistical significance in the table, it must be recalled that urbanized counties are those with alternatives available in the form of city jails. Other general tendencies, though not significant, are interesting. In more urbanized counties, county jail inmates tend to be younger than those arrested. Also, in counties with high average family incomes, women are more likely to be found in the arrest than in the jail population. When city jails are both available and utilized in a county, the result is that the county jail becomes a specialized institution. As proved to be the case when an alcohol detoxification center operates within a county, the availability of city jails as an alternative to incarcera- tion in the county jail tends to increase the average seriousness of the criminal charges pending against county jail inmates and to increase the length of the average incarceration. Further, the effects of DETOX are independent of those produced by $>C0. URBAN, and on the basis of Table 9 it can be argued that it is CO. JAIL USE, not foCO. URBAN, that underlies the filtering process by which some subset of all those arrested within a county are booked into the county jail. 59 CHAPTER IV THE CONSEQUENCES OF JAIL INMATE POPULATIONS In Chapter II, it was suggested that jails may vary the manner in which they carry out their tasks so as to fit the type of inmates incar- cerated in the jail. Using multiple regression analysis, this chapter inquires into the extent to which those working with and in Illinois county jails in fact make such accommodations to the composition of inmate popu- lations. The six aspects of jails listed in Table 1 are the dependent variables in this analysis. The question, then, is the ability of X STAY, X SERIOUSNESS, foBLACK INMATES, and X INMATE AGE to produce statistically significant beta weights linking them to the six jail characteristics. Together, those six characteristics will be termed the structure of the jails, denoting the fact that they are intended to measure regularities in their operating procedures. Of the four inmate characteristics entered into the analysis, the first will be considered to indicate the degree of stability present within the jail population, while the three remaining variables represent the degree to which it can be assumed that jailers will be able to predict the behavior of the inmates confined. Jailers and sheriffs have traditionally been middle-aged whites. For such individuals, black, young inmates facing serious criminal charges represent a high degree of uncertainty, with uncertainty suggesting that the reactions of such inmates are difficult for the jailers to predict or understand, and that jailer-inmate communication will be strained. This will be exacerbated when the average duration of jail confinements is short. In Chapter II, it was argued that when uncertainty in inmate populations is high, the jail might be expected (l) to adopt flexible procedures in which formalization in how things get done is low, (2) to become autonomous from the sheriff's department through the appointment of a chief jailer and (3) to have the jail staff, rather than the sheriff, selecting the trusties. Also younger jailers can be expected to be added to the jail staff, and more extensive use made of trusties. 6o Finally, it is anticipated that those factors that were found to determine the composition of jail inmate populations -- C0. BLACK, and DETOX will not prove to he important influences on the jails themselves. The analysis will permit the testing of this assertion, and will also allow conclusions to be drawn on the degree to which the size of a jail has a major impact on its structure. Predicting Jail Structure Tables 10 and 11 together present the necessary regression equations to test the assertions outlined above. The two tables differ only in that Table 10 controls for DEPT. SIZE, while Table 11 controls for JAIL SIZE. Scanning the tables suggests that the inmate characteristics variables are only moderately important predictors of structure. In part, however, such a conclusion is an artifact of the use of a large number of predictors with a small sample. By definition, such a situation is one where statistical significance will prove elusive. Another factor contributing to the lack of strong relationships between either community characteristics or inmate characteristics to structure is the presence of controls for size. When size variables are removed from the equations, other variables become significant. Those results are not presented, however, as they are spurious. The most straightforward relationship in the tables is that between size and TRUSTY CHOICE. Organization size, however measured, predicts who selects the jail's trusties. The larger the sheriff's department or the larger the jail, the more likely trusty selection will take place within the jail itself. While their impact is essentially the same, DEPT. SIZE seems preferable to JAIL SIZE as a control. JAIL SIZE appears to produce instability in other beta weights when it is in an equation. More interesting is the relationship between FORMALIZATION B and the various inmate characteristics. In Table 10, that variable is sub- stantially influenced by "/oBLACK INMATES: the greater the proportion of the inmates who are black, the more formalized is the jail. No similar 61 Table 10 Multiple Regression Equations: Additive Effects on Structure Controlling for DEFT. SIZE Dependent Variables Predictors FORMALIZATION A FORMALIZATION B CHIEF JAILER DURBAN -.42 .08 -.01 foCO. BLACK .15 .28 •19 DETOX .28 .21 .15 /oBLACK INMATES .21 -.05 .^5 • 35 .21 • 13 X INMATE AGE .01 .10 .13 -.12 -.16 -.15 -.28 - •32 -.29 X SERIOUSNESS -.09 -.18 -.13 -.16 -.25 -.19 -.12 - .22 -.14 X STAY .20 .11 .03 .20 .24 •17 .06 .08 .03 DEFT. SIZE .28 .18 • 23 -.17 .06 -.08 .28 •37 .33 R • 39 • 31 •35 .45 • 32 .47 • 50 •49 .51 Predictors TRUSTY CHOICE X JAILER AGE foURBAN -.03 -.00 $C0. BLACK .16 •31 DETOX -.06 -.04 foBLACK INMATES • 19 .21 .16 .17 X INMATE AGE .14 .12 .15 -.50 -.59*_. 5 o X SERIOUSNESS -.06 ■ -.14 -.05 -.53 -.69 -.52 X STAY -.13 ■ -.12 -.12 .05 .05 .06 DEPT. SIZE -.72*- :6f -.74* .07 .07 .05 foTRUSTIES 10 ,08 .09 05 -.07 23 .16 .20 05 -.12 -.07 39 .42 •39 11 -.09 -.05 R .78* .78* .78* 39 .44 .39 45 .45 .45 Significant at .05 level (two-tailed t-test for beta weights); n = 36 for all coefficients except those involving X JAILER AGE where n = 31 • 62 Table 11 Multiple Regression Equations: Additive Effects on Structure Controlling for JAIL SIZE Dependent Variables Predictors FORMALIZATION A FORMALIZATION B CHIEF JAILER foURBAN -•3h .00 -.02 /oCO. BLACK .17 .23 •13 DETOX .22 .21+ .10 ^INMATES BLACK •27 .12 •39 .26 .15 .10 X INMATE AGE .04 .12 .16 -.16 -.21 -.17 -33 - .36 -.33 X SERIOUSNESS .02 -.Ok .Ok -.26 -.1+0 -.28 -.17 - .26 -.18 X STAY .18 .10 .02 .23 .29 .18 •13 .15 .10 JAIL SIZE •03 -.01 -.10 .06 .27 .10 •33 .1+0 .3k R .& ,2k .32 A3 .38 .Vf •50 .50 .50 Predictors TRUSTY CHOICE X JAILER AGE foTRUSTIES /oURBAN •03 .12 .11+ ioCO. BLACK .25 •35 .10 DETOX .01+ -.06 • .08 ^INMATES BLACK •31 .29 .22 .25 .01 -.02 X INMATE AGE .26 .18 21+ -.1+2 -.57*-. 1+8 .27 .18 .22 X SERIOUSNESS .06 - -.11 05 -36 -.58 -.1+0 .02 -.07 -.01 X STAY -.29 - -.23 - 29 -.01+ .02 .03 •33 .38 .36 JAIL SIZE -.82*- .,6k*-, 79 -.19 -.10 -.12 -.23 -.16 -.11+ R .76 -* • 75 * 77 .1+0 .kk ,ko .kG .1+5 .1+5 Significant at .05 level (two-tailed t-t£st for beta weights); n = 36 for all coefficients except those involving X JAILER AGE where n = 31« 63 relationship is found for FORMALIZATION A. The confidence in the effect from $>BLACK INMATES is enhanced in that the equation omitting that vari- able to allow for the inclusion of $C0. BLACK has a substantially smaller multiple correlation than the equations in which $BLACK INMATES is included, Here again, however, the beta weights in Table 11 show considerably less stability than do those in Table 10. While the equations predicting X JAILER AGE appear to be interesting, when examined closely, the results are far from interpret- able. While the strong negative relationship between X SERIOUSNESS and X JAILER AGE is as might be expected, the relationship between inmate age and jailer age is the opposite of what was expected. Given the -.58 zero- order correlation between X SERIOUSNESS and X INMATE AGE, the betas are difficult to accept. However, as the zero-order correlation between the two age variables themselves is negative (-.213), it is unlikely that the "true" relationship between those variables could be positive. To test further the relationship between X SERIOUSNESS, X INMATE AGE, and X JAILER AGE, the two inmate characteristics variables were removed one at a time from the equation: without X SERIOUSNESS present, X INMATE AGE's beta weight dropped to -.213; without X INMATE AGE present, the beta weight for X SERIOUSNESS fell to -.10^. While multicollinearity is not the cause, clearly these relationships are not trustworthy. The most reasonable interpretation is that these are not the proper variables for predicting average jailer age. Two other findings in Tables 10 and 11 merit attention. ^TRUSTIES is influenced by the average length of jail confinement. No other predictor has a major impact on that structural variable. It is also worth noting that, generally speaking, community characteristics fail to exercise a major direct impact on any aspect of jail structure. The sole exception appears in the equation that predicts FORMALIZATION A using DURBAN as a control. There, the degree to which a county is urbanized relates negatively to the degree its jail is in that sense formalized. 6h Thus far, it has been assumed that stability in the raw material is a property of the inmates themselves. On that basis, stability was measured as the average length of stay of each jail's inmates. Stability is potentially important in another sense as well -- the constancy of the flow of the inmates. While no direct measure of stability of flow is available, it might be expected that court systems relying on pleas of guilty as the basis for obtaining conviction tend to rend a more homogeneous (stable) group of inmates to the jail on a regular basis. Table 12 presents the multiple regression equations needed to assess the impact of this form of stability on jail structure. Where a jail is located in a county in which pleas of guilty resolve a large proportion of all felony cases, it is argued that the composition of jail inmate populations will be relatively constant over time. Idiosyncrasies of judges and prosecutors would in such counties be less likely to intrude into the disposition of cases, and the demands of the courts on the county jail would tend to be more uniform from week to week. When a jail has a large black population, such stability makes it likely that a sheriff will appoint a chief jailer. In the absence of such a black presence, however, stability in this sense makes such an appointment unlikely. When the beta weights in Table 12 are compared with those found in Tables 10 and 11, the assertion that the effects produced by the two alternative measures of stability are often dissimilar can be confirmed. foELEA GUILTY does not have the impact on /oTRUSTIES that X STAY demonstrates, but does show a strong negative impact on CHIEF JAILER that X STAY does not share. That negative relationship, though it is significant in only one of the equations, indicates that the greater the stability in the sense tapped by /oPLEA GUILTY, the less need there is for jail autonomy. It should be pointed out that while the relationship between foFLEA GUILTY and DEFT. SIZE when entered into the same equation shows some of the signs suggestive of collinearity, the zero-order correlation between the two variables is a mere .20. Barring further evidence, then, their often opposite effect on structural variables can be accepted as genuine. Stability of flow makes 65 Table 12 Multiple Regression Equations: Additive Effects on Structure with #PLEA GUILTY Measuring Stability Dependent Variables Predictors FORMALIZATION A FORMALIZATION B CHIEF JAILER Durban -.35 ,1k .05 $C0. BLACK .25 •29 .32 DETOX .22 .27 .04 ^INMATES BLACK .19 -.01 .kk • 32 .20 .19 X INMATE AGE .20 .22 .22 -.03 -.03 -.11 -.12+ - .21 -.16 X SERIOUSNESS .13 -.07 -.02 -.00 -.04 -.09 .01 - .14 .00 iFLEA GUILTY -.23 -.31 -.18 -.03 -.09 .06 -.30 - .37*-. 28 DEPT. SIZE .28 .20 .22 -.19 .06 -.07 • 30 AO* .33 1+2 .38 .38 1+2 .27 .k6 .57 .59* .57 Predictors TRUSTY CHOICE X JAILER AGE $T RUSTI ES Durban -.06 .02 .21 $C0. BLACK .22 .36 .01+ DETOX -.13 -.06 .19 foINMATES BLACK .20 .25 .16 .20 -.08 -.11+ X INMATE AGE .10 .09 .11+ -.1+1+ -.53*-. 1+3 .43 .36 .34 X SERIOUSNESS -.13 - -.23 -.09 ,.k6 -.61+*-. 1+3 .23 .21+ .19 $PLEA GUILTY -.07 • -.12 -.11 -.09 -.16 -.11 -.02 -.02 .05 DEPT. SIZE -.6?- -.61+*-. 75* .06 .08 .05 -.16 -.11 -.01+ R .78* .78* .78* .1+0 .1+5 .1+0 31+ .30 . 3^ Significant at .05 level (two-tailed t-test for beta weights); n = 36 for all coefficients except those involving X JAILER AGE where n = 31. 66 less likely the appointment of a chief jailer. Large jails, however, are more likely than small jails to have a chief jailer. Despite their zero-order correlation of . 6l, DEPT. SIZE and $>URBAN also have very different effects on a number of structural variables. For FORMALIZATION A, low levels of formalization in external relations are present in highly urbanized counties. It is also true, however, that jails that are a part of a large sheriff's department tend to be more formalized. These relationships are all non-significant: though the beta weights involved are often substantial, so are their standard errors. Nonetheless, it is interesting that an environmental condition such as urbanization is independent in its impact on structure from an organizational characteristic such as size. It was anticipated that four aspects of inmate populations would influence the internal arrangements of county jails. With the exception of X SERIOUSNESS, relationships were found that linked inmate characteristics to structure, relationships that were independent of the effects produced by size or by the type of county in which the jail was located. Nowhere, however, was there convincing evidence that jails modify their internal arrangements to fit the seriousness of the charges pending or proven against the individuals they incarcerate. This was true despite the quite considerable variation in the average seriousness scores of the 36 jails. FORMALIZATION A and FORMALIZATION B proved to have very different relationships to size and to the extent of urbanization. FORMALIZATION A has a low value in counties that, within the range present within the sample, are heavily urbanized. An urbanized county is likely to be one where a considerable amount of communication takes place between the jail and outside individuals, groups, and organizations. To deal with this extensive communication, formalized procedures are apparently abandoned and the jail employees granted discretion to supervise such contacts. Perhaps the sheer diversity of external linkages in such a jail inhibits the use of formal procedures. On the other hand, formalization of internal procedures -- FORMALIZATION B -- is high in counties that are highly urbanized. 67 For both variables used to measure autonomy, size had an impor- tant effect. In large jails, trusties were likely to be chosen internally, indicating a high degree of autonomy. Together, size and the inmate characteristics explain 59- 3 percent of the variance in TRUSTY CHOICE. For CHIEF JAILER, large jails were more likely than small ones to have a chief jailer. Though that relationship is non- significant, it again suggests that large size argues for autonomy. The two formalization variables represent the degree of flexi- bility that jail employees have in dealing with inmates. The theoretical perspective of Chapter II argues that formalization ought to be minimal where the uncertainty in the raw material is considerable. Each formali- zation variable proved to have its own pattern of relationships to the predictors. For FORMALIZATION A, jails that were part of large sheriffs' departments were likely to have highly formalized procedures for communicating with the outside. No similar effect is evident for jail size itself. This supports the argument that large parent organizations have procedures that they impose on their "child" organizations. The only other important effect on FORMALIZATION A stems from foURBAN. Given that size is not related to FORMALIZATION B, it appears that for jails such a transfer is more likely for procedures specific to external communication than for procedures covering purely internal concerns , Sheriffs* departments are perhaps more likely to have applicable existing procedures for the former than for the latter. Though the relationship was not statistically significant, the major factor determining the degree of internal formalization appears to be the proportion of the inmate popu- lation that is black: the greater the proportion of blacks, the more formalized the jail. It is largely for this reason that the equations predicting FORMALIZATION B explained a greater proportion of the variance than did those predicting FORMALIZATION A. At best, 22.1 percent of the variance in FORMALIZATION B was explained, and 15.2 percent of the variance in FORMALIZATION A. The results, then, do not demonstrate a major impact from size on the internal structures of jails. Or, more precisely, within the range 68 of size "being considered here, large jails and small jails appear to have very similar ways of carrying out their work. Such a finding may have important implications for issues such as regionalization of jails which are based on a desire to benefit from economies of scale. Predicting Jail Effectiveness As can be seen from the previous analyses, those working in jails only partially modify their procedures to fit the particular inmate population in a given jail. It may be, however, that such adaptations are nonetheless important for determining how well a jail does its job. In other words, it may be that where the procedures do "fit" the inmate population effectiveness is enhanced. Effectiveness is likely to be high, therefore, when certain structural arrangements and certain inmate charac- teristics are simultaneously present. A jail, in this view, is not likely to be effective merely because the jail staff rather than the sheriff select the trusties, it is only likely to be effective where such a selection process is used on an inmate population whose membership is predominantly young, black, and facing serious criminal charges. In other jail populations, that selection process will impede effectiveness, not enhance it. Broadly stated, this view holds that where procedures are appropriate for the clientele being processed, a jail will be effective. The measure of effectiveness used in subsequent analyses was derived from ratings of each jail by the Illinois Bureau of Detention. The equations presented in Tables 10 through 12 are exclusively additive. That is, whatever the values of a jail on foBLACK INMATES and TRUSTY CHOICE, an increase of one standard deviation unit in TRUSTY CHOICE would produce the identical change in EFFECTIVENESS. Additive models do not allow for the possibility that the effect of one variable on a given dependent variable can change depending on the value assumed by a third variable. Where it is hypothesized that the effect of, say, TRUSTY CHOICE, on EFFECTIVENESS differs when there are many blacks in the inmate population from the effect produced when few blacks are present, a model is necessary that allows for statistical interaction. Such a model can be tested by 69 multiplying each case's value for /oBLACK INMATES by that case's value for TRUSTY CHOICE and entering the resulting amount into the equation as a new variable. An interaction effect can be confirmed when it can be concluded that the intercept and the slope of the relationship between TRUSTY CHOICE and EFFECTIVENESS change according to the proportion of the inmate popula- tion that is black. To establish the presence of such an interaction effect^ it is not sufficient simply to find an interaction term with a statistically significant beta weight. The real issue is the ability of the interaction term to make a unique contribution to the variance that is explained in EFFECTIVENESS. A unique contribution in the present example is one that explains a portion of the variance in EFFECTIVENESS that the additive effects from TRUSTY CHOICE and foBLACK INMATES leave unexplained. The traditional approach to establishing whether or not such a unique contribution is made involves a comparison of the explained variance (R ) achieved by an equation that enters simultaneously the two additive terms and the interaction term that describes their joint effect with an equation that contains only the two additive terms. The null hypothesis that the interaction term does not produce a statistically significant unique contribution to the explained variance in EFFECTIVENESS can then be tested. If that null hypothesis can be rejected, then an interaction effect has been identified. Such a straightforward approach is available only where the interaction term is not collinear with its components. The manner in which interaction terms are constructed in multiple regression, however, generally imposes mult ic oil inearity on equations into which both the interaction term and its two additive components have been entered. For this reason, Althauser (1971) concludes that the traditional basis for examining interaction effects is not valid. Table 13> which presents the zero-order correlations between the interaction terms hypothesized as important for predicting effectiveness and the variables that are the components of those terms, confirms that multicollinearity will distort equations containing the interaction terms and their components. 70 *° >r >r & a o •H 0) •P S O fc H V ■p a c o H ■H ■P IV o i id ■p (U o - -p on e c H ■p CO V H o fl iH 3 •P Eh a *r xT" * ON fc m II C E S3 w O O « o H EH H Ej a o H a M 3 X o i £j £ Eh (0 o 1 o 1 to s Eh pti fc u Ei -6*. **■ IX IX >r r-i 1 71 Given the presence of multicollinearity, it will be necessary to adopt a circuitous route to establish the presence of interaction. First, the presence of additive effects will be tested for as an indirect test for interaction. Therefore, it is being argued that when the additive effects from the structural variables and from the inmate characteristics are examined, they will be found to be non- significant. X JAILER AGE will be omitted from this analysis both because of the erratic nature of its relationship to the inmate characteristic variables and because of the availability of data on that variable for only 31 of the 36 jails. Of the three inmate characteristics, five structural variables, and two measures of organization size included in Table Ik, only FORMALIZATION A has an independent effect on jail effectiveness: the more formalized a jail's relationships to its environment, the more likely it is to be assessed effective. Aside from FORMALIZATION A, there are no significant or even substantial additive effects on effectiveness. Also, all of the equations are non- significant in the amount of variance they explain. In the case of the two equations that include FORMALIZATION A, however, the absence of significance is attributable to a large number of variables whose effects are negligible. The zero-order correlation tetween FORMALIZATION A and effectiveness is . ULU. Despite the absence of significance, it is also of interest that the two size variables evince beta weights of opposite signs when combined with most sets of predictors. Apparently, then, inmate characteristics and jail characteristics rarely have direct implications for the ratings received by the 35 jails included in this part of the analysis. This does not, however, demonstrate that the relationships of those variables with effectiveness is interactive. In total, 15 interaction terms have been suggested as potentially important for predicting jail effectiveness. Each interaction term is the product of multiplying the value for a jail on a jail characteristic variable by its value on an inmate characteristic variable. Once those interaction 72 Table 14 Multiple Regression Equations: Additive Effects on EFFECTIVENESS Predictors FORMALIZATION A .40*. 39* FORMALIZATION B .23 .24 CHIEF JAILER .25 .22 TRUSTY CHOICE -.09 .03 foTRUSTIES .16 .17 foBLACK INMATES .19 -07 .12 .07 .18 .13 .25 .16 .23 .15 X INMATE AGE .04 .02 .12 .12 .17 . l6 .11 .10 .05 .03 X SERIOUSNESS .08 -.04 .06 .02 .09 .02 .06 .01 .03 -.05 DEFT. SIZE -.15 -.06 -.17 -.16 -.08 JAIL SIZE .11 .04 -.01 .07 .10 r .45 .44 .31 .31 .32 .29 .24 .22 .27 .27 Statistically significant at .05 level (one-tailed t-test for beta weights); n = 35 for all coefficients. 73 terras are created, a preliminary test of their importance for jail effectiveness can be made on the basis of their zero-order correlations with EFFECTIVENESS. Table 15 presents those correlations. Overall, those coefficients suggest it is unlikely that the criterion of statistical significance will be achieved. Only the interaction between FORMALIZATION A and X INMATE AGE is significant in its zero-order relationship to effectiveness. There, however, the fact that Table 13 shows the zero-order correlation between FORMALIZATION A and the interaction term to be .97 raakes the second criterion needed to assert an interaction effect -- a unique contribution to the explained variance -- unlikely to be met. With this, an impasse has been reached. Additive effects from inmate or jail characteristics do not have, with one exception, direct implications for the ratings it received. Multicollinearity, however, prohibits use of the traditional test for establishing whether or not interaction is present in an empirical relationship. A resolution of this impasse can be obtained, however, by returning to the definition of the effect whose presence or absence we wish to ascertain. Statistical inter- action is active wherever both the slope and the intercept of one variable's relationship to another changes according to the value assumed by a third variable. The addition of a control variable, in contrast, affects only the intercept -- never the slope -- of the relationship. Blalock (1972:38^) suggests such a resolution in passing, but fails to elaborate. Althauser (1971) > in his critique of existing tests for interaction effects, does not propose any alternatives to them. The examination of slopes must be justified by an analogy to the test for statistical interaction that Blalock (1972: 483-^89) advocates for analysis of covariance. There, the slopes of the least-squares equation regressing Y on X. is compared across the categories of a third variable, X p . The present adaptation of that approach substitutes a range of scores on X p for those categories. Here, the interest is in determining whether the slope of the relationship between Y and X, differs markedly at high values 7h Table 15 Zero-Order Correlations: Raw Material/ Structure Interaction Terms and EFFECTIVENESS Interaction Terms EFFECTIVE- NESS FORMALIZATION A x $BLACK INMATES) FORMALIZATION A x X INMATE AGE) FORMALIZATION A x X SERIOUSNESS) FORMALIZATION B x ^BLACK INMATES) FORMALIZATION B x X INMATE AGE) FORMALIZATION B x X SERIOUSNESS) CHIEF JAILER x JBLACK INMATES) CHIEF JAILER x X INMATE AGE) CHIEF JAILER x X SERIOUSNESS) TRUSTY CHOICE x JBLACK INMATES) TRUSTY CHOICE x X INMATE AGE) TRUSTY CHOICE x X SERIOUSNESS) foTRUSTIES x ^BLACK INMATES) foTRUSTIES x X INMATE AGE) foTRUSTLES x X SERIOUSNESS) 251 ,401* 308 ,222 279 ,281 204 239 235 187 033 0^5 ,248 156 270 Statistically significant at .05 level; n = 35< 75 of X p from the slope at low values of X p . No textbook sanctioned rules can be offered for defining what is a marked difference. It will be assumed, however, that where the slope at the high value is three times that at the low value, interaction will have been confirmed. The difference in slopes will be most dramatic where opposite signs are found. Through this process, the slopes of the relationships between FORMALIZATION B, CHIEF JAILER, TRUSTY CHOICE, and foTRUSTIES and effective- ness were found to change substantially as inmate populations moved from low levels of X SERIOUSNESS to high levels on that variable. None of the remaining interaction terms proved to be accurate descriptions of the relationship of their components to jail effectiveness. The relationship of FORMALIZATION A to effectiveness can, therefore, be assumed to be additive. To the extent that C0. BLACK is statistically significant. The equation itself is also statistically significant at the .05 level. When the three court practice variables are combined into a similar equation, the results explain only 2.6 percent of the variance in effectiveness: EFFECTIVENESS = -.011BACKL0G - .UNREDUCED + .080> oJ o •H -P co •H 13 •p CO 80 Conclusion The effectiveness ratings given the jails can be informative not only about the jails themselves, but about the agency that made the evalu- ations as well. The Bureau of Detention rewards the use of predetermined procedures -- formalization -- with higher ratings. While improvement in the quantity and quality of the records maintained by jails is doubtlessly worth encouraging, caution should be exercised so that reliance on procedures does not preclude an ability to respond to particular charac- teristics of particular inmates. Perhaps the Bureau should be more precise in its recommendations on what aspects of jail procedures ought to be rigorously applied to all inmates and in which areas discretion should be permitted. Apart from the degree of formalization, no clear antecedents to a high rating of effectiveness were found, or at least none that were within the ability of a jail to effect in pursuit of higher effectiveness. This is unfortunate as the inspections by the Bureau of Detention provide an incentive to change, but apparently no real guidance on how or where to do so. Whether or not a jail had a chief jailer, who selected the trusties, the extensiveness of trusty use, and the degree to which internal procedures were formalized, all were without a major influence on the ratings. What impact these aspects of jails did have, however, depended on the level of seriousness among a jail's inmates. The Bureau of Detention should be encouraged to incorporate such contingencies into its standards and its ratings. Instead of a monolithic approach that assumes that there is one best way of organizing a jail, allowance should be made for the particular demands and requirements of a specific inmate population. Clearly, while there are basic standards for facilities and for the treatment of prisoners that must be adhered to by all jails in Illinois, some flexibility in operating procedures should be both permitted and encouraged. At present, county jails in Illinois apparently do alter some of their operating procedures to fit the type of inmates they incarcerate. 81 Whether this emerged through planned intent or through gradual trial and error cannot be stated with certainty. The latter possibility is, however, the more likely. Such adaptations appear primarily to be a response to the age and racial composition of a jail's inmate population, not to the level of seriousness found in charges pending and proven against the inmates. Those working in. jails do not appear to consider criminal charges to be a useful basis for differentiating among people in a manner useful to jail work. This finding is not inconsistent with the observations obtained through field research in Illinois county jails. Jailers routinely grant trust and favor to inmates facing the most serious of criminal charges while at the same time confining in the most maximum security possible individuals charged with traffic violations (Kiraberly and Rottman, 197^> Rottman and Kimberly, 1975)* Like all institutions confining those accused or convicted of crimes, jails confront the dilemma that doing time and doing crime are not intimately related. An individual can be a model inmate and a genuine menace to society. If jails are ever to become involved in programs with goals more lofty than mere confinement, then some basis for interjecting concern over the nature of a person's involvement in criminal pursuits into his or her disposition within the institution will have to be developed. 82 CHAPTER V SUMMARY OF THE FINDINGS This chapter summarizes the results that were obtained through the multiple regression analyses presented in the second half of Chapter III and in Chapter IV. It is intended to aid those readers who became lost in the mire of tables and technical jargon by stating directly and concisely what was found. The obvious limitation of such a summary is that it does not directly present the evidence that led to each conclusion. A full appreciation of the empirical nuances can only come from a thorough reading of the step-by-step progress of the analysis. What the summary does do is to tell how the characteristics of counties and their courts act to establish the composition of inmate populations of county jails, how jails adapt their operating strategies to match the specific inmate population with which they must cope, and what factors underlie the ratings of effectiveness that county jails receive from the Illinois Bureau of Detention. Predicting the Composition of Inmate Populations Inmates in 36 Illinois county jails were described in terms of their race, sex, age, criminal charges, and length of confinement in the jail. This description was general, and was based on the average for all inmates in each jail on each characteristic. In general, it is the charac- teristics of the counties themselves, not the practices of their courts, that determine the composition of the jails* inmate populations. In those counties in which a high percentage of residents live in urban areas, county jail inmates tend to be young and to be booked for relatively serious criminal charges. The inmates of such jails also, on average, spend a longer period of time within the jail than do inmates in more rural counties: the more urbanized a county, the younger, the more seriously involved in crime, and the more stable the inmate population in its county jail. 83 The extent of a county's black population has a very straight- forward effect on the representation of blacks in the inmate population of the county jail: the more blacks there are in a county, the more blacks there will be in the county jail, although it was found in the first part of Chapter III that blacks are found in greater proportion in the county jail than this representation in the county population. Also, the greater the percentage of blacks among a county's residents, the more serious are the charges facing county jail inmates. The average length of confinement, however, did not vary on the basis of the size of the counties ' black populations. The presence in a county of an operational alcohol detoxification center acts to increase the percentage of blacks in the county jail inmate population and to increase the duration of the average county jail confine- ment. It can be suggested that detoxification centers have this impact on who is incarcerated in the county jail and for what length by shunting off a group of potential inmates who are largely white and whose jail confinement is likely to be brief. Of the court practices considered, only the presence or absence of a backlog in felony cases has important implications for the composi- tion of inmate populations. A substantial backlog of cases leads to a predominantly young county jail inmate population. There is also a tendency for such a backlog to decrease the length of confinements for that population. Such a finding is in conflict with a viewpoint that argues that prosecutors use pre-trial jail confinements as a leverage with which to induce defendants to plead guilty. In those counties where city jails assume a major share of the burden of jail confinements, the county jail in effect becomes a special- ized institution: the smaller the share of all confinements within a county attributable to the county jail, the greater will be the difference between the average seriousness score for all those arrested from that of all those booked into the county jail, with the score for the jail inmates having the higher magnitude. This is taken to be evidence of a filtering 84 process, similar to that identified for the effects of a detoxification center, in which alternative routes are taken by those accused of minor charges, routes that lead in directions other than incarceration in the county jail. For the most part, community and court characteristics were unable to explain the variation among the counties in the percentage that females comprise of county jail inmate populations. An operational detox- ification center tends to increase the representation of females in the jail, though the basis for such an effect is uncertain. In terms of understanding the difference between the percentage of women among those arrested from the percentage among county jail inmates, the average income of the counties' families proved to be the most important factor: the higher the average family income, the greater will be the disparity between the percentage of women among those arrested from those jailed, with the representation of women proving to be greater among those arrested. Predicting Jail Structure Having successfully predicted the characteristics of inmate populations in the 36 counties, the question becomes that of whether or not jails currently make allowance in their procedures for the types of inmates they incarcerate. The answer provided by the data is a qualified yes. The qualifications interjected into the affirmative response note that this was not true for all aspects of jail operations considered, and that other factors independent of inmate characteristics are also doubtlessly important. For the degree to which a jail's procedures governing communica- tion with outside groups are pre-determined, it is the nature of the county in which the jail is located that is the most important factor. The more urbanized the county, the less its county jail will rely on pre-determined procedures. Perhaps the sheer volume of communication of this sort in an urban county requires the abandonment of carefully specified procedures to oversee it. 85 Where the jail characteristic of interest is the degree of pre- determination of internal procedures, it is the percentage of blacks in the inmate population that is the most important factor. The greater the percentage of black inmates, the greater the reliance on pre-established procedures, and, therefore, the smaller the amount of discretion granted the jailers. While there was a tendency for larger jails to be more likely than small jails to have a chief jailer, it was also true that chief jailers were found in jails with inmate populations in which the average age was low. Also, when a county's court extensively uses pleas of guilty to resolve felony cases, then it is less likely that the county jail will have a chief jailer. Somehow, then, it appears that the incidence of such pleas has implications for what happens within the jail. Based on the discussion in the previous section, however, that effect does not occur through differences in inmate characteristics attributable to the use of pleas of guilt in the local courts. Inmate characteristics did not significantly affect the manner in which trusties are chosen in a jail. Instead, the important influence there proved to be the size of the jail: the larger the jail, the more likely it is that trusties will be selected by the jail staff rather than by the sheriff. Also, while the effect was not as strong, larger jails were also those that tended to have a chief jailer. Therefore, it can be concluded that the larger a jail is, the more autonomy it will have from the remainder of the sheriff's department and the less involved will be the sheriff in making routine decisions within the jail. In the 36 jails studied, two inmate characteristics were important for understanding the percentage of inmates granted trusty status. The more powerful of these influences comes from the average length of confinement: the longer the average jail confinement, the greater will be the percentage of the inmates used as trusties. There is also a tendency for greater use to be made of trusties in jails in which the average inmate age is high. If long average confinements and middle- age inmates represent stability, then these relationships are readily 86 interpretable. The size of a jail, though it had major implications for who selected the trusties, does not affect the percentage of the inmate population that is used as trusties. The analysis also considered the average age of the jailers in the 36 county jails. Here, however, the results do not permit any con- clusions. Neither county, court, nor inmate characteristics can "be said to have any real implications for that variable. Predicting Jail Effectiveness Having examined the connection between the characteristics of inmate populations and the internal structures of jails, it is worthwhile to inquire into the degree to which both inmate and jail characteristics have implications for how effective a jail is. Effectiveness was gauged on the basis of the ratings given each jail by the Illinois Bureau of Detention. For the analysis, those ratings were summed across categories into a single total. In reviewing the results of the analysis, it should be remembered that the Bureau's ratings were not originally intended for such use. We, as researchers, have attached the label "effectiveness" to the Bureau's ratings. A jail's ratings proved to follow largely from the degree to which it is located in a county with a substantial black population and the degree to which its procedures governing communication with the outside are formalized. In addition, there was a tendency for inmate characteristics to interact with jail characteristics in their impact on effectiveness. Specifically, the effect of four of the jail characteristics varied depending on the level of seriousness in the jail's inmate popula- tion. That is, when the average seriousness was high, jail characteristics such as who selects the trusties had a different impact on effectiveness than when the average seriousness was low. Apparently, what enhances effectiveness in jails varies depending on the nature of the inmate population. These relationships, however, were far less important in explaining the ratings given the jails than the implications of the percentage of blacks in the county population and the degree to which external communication is through pre-determined procedures. 87 APPENDIX A CHARACTERISTICS OF THE COUNTIES Counties *C0. URBAN 63-9* £co. BLACK 2.1*. DETOX No 5fco. 17-2U 12.7* INCOME $8883 SCHOOLING 12.0 CO. JAIL USE Adams 51*. 0* Bureau 32.5 0.0 No 10.1* 888U 12.0 98.8 Champaign 77.0 6.5 Yea 27.9 1011*5 12.7 5»*.3 Christian 1*7.6 0.3 No 10.2 8556 11.1 95. <♦ Clinton 27-0 0.7 No 11.9 8557 9-0 100.0 Coles 75-0 0.6 Yes 23.0 8899 12.1 71*. 1 DeKalb 68.1 1.2 No 29-7- 10735 12.1* 79.9 Fayette 2l».9 1.2 No 11.5 7306 9-3 100.0 Franklin 48.9 0.0 No 9-7 6833 9-5 100.0 Grundy 1*2.0 0.0 Yes ll.l* 10982 12.1 100.0 Henry 51.7 1.2 No 10.5 9553 11-7 8lt. 9 Jack6on 59-6 7.2 No 1*3.6 7818 12.1* 59.1 Jefferson 51.1 3.8 Yes 10.3 7292 10.3 85.1* Kane 87.5 3-6 Yes 11.9 119l»7 12.2 21*. 6 Kankakee 53-6 11.3 Yes 12.3 101*1*1 11.6 66.7 Knox 70.2 3-1 No 13.O 91*99 12.1 59.1* Lake 81.1* ' 5-1 No 16.8 12998 12.5 1*7.7 LaSalle 61*. 6 0.6 Yes 10.8 9953 11.9 72.3 Lee 1*7.8 2.2 No 12.5 9636 ' H.3 100.0 Livingston 1*0.0 2.1* No 12.7 9611 12.0 100.0 Logan 52.3 1-7 No 13.9 9330 11.8 89.7 Madison 71.7 5-2 No 12.1 1021*9 11.6 38.6 Marion 50.2 3-7 Yes 10.6 75U2 10.7 51.0 McHenry 51.6 0.0 No 10.6 11965 12.2 80.8 Ogle 1*2.2 0.0 Yes 11.1 10166 12.1 73.6 Peoria 83.9 7.6 Yes 13.6 10663 12.1 99.7 Rock Island 85.8 <*-3 No 12.5 10573 12.1 1*9.5 St. Clair 83.2 22.2 Yes 12.3 951*0 11.2 1*1*. 7 Saline 52.1 2.7 No 9.8 6857 9.1* 100.0 Sangamon 78.0 l*.9 Yes 11.1* 10301 12.2 57.7 Stephenson 56.8 k.9 Yes 11.3 9087 12.1 100.0 Tazewell 75.2 0.0 No 11.7 10787 12.1 55.5 Vermilion 62.0 6.3 Yes 11.5 91*1*6 11-5 57.2 Whiteside 51*. 7 0.5 No 11.6 10011* 11.5 79.1* Will 72.0 6.8 Yes 12.1 11790 12.0 83.7 Williamson 57.2 1.6 3.6 No 11.7 7687 11-3 100.0 mean 59.5 W 13-9 9570 11.6 r5.lt standard deviation 16.6 5.2 .500 6.8 11*88.5 • 95 22.1 Yes=l; No=0 88 APPENDIX B COURT PRACTICES Counties BACKLOG 1.16 * REDUCED ll*.l** DISMISSED 37-7* ^IM- PRISONED 31*. 1* 5&PLEA GUILTY Adams 25.9* Bureau 1.22 33-9 3^-0 75-0 20.8 Champaign 1.19 18.1 1*1-5 1*8.8 11.. 6 Christian 0.98 11*. 9 35.7 69.0 38.6 Clinton 1.55 0.0 26.3 33-3 31.6 Coles 1.05 18.2 31*. 2 11.8 29.8 DeKalb 1.06 2.9 62.1 12.5 21.0 Fayette 1.22 13.2 1*3.9 11*. 3 24.lt Franklin 0.95 11.1 67.5 1*7-1 22.1 Grundy 1.27 29.2 33-9 62.5 27.1 Henry 0.86 1*2.0 29.2 09.5 30.8 Jackson 0.95 2.2 58.7 25.O 31-5 Jefferson 0.1*6 U.7 52.3 20.8 11*. 1* Kane 1.05 0.1 1*1*. 2 0.0 18.1* Kankakee 1.03 0.0 29.8 39-6 1*9.1 Knox 0.9^ 21*. 1 1*7.3 52.9 17-9 Lake 0.91 0.0 16.1* 32.8 1*6.6 LaSalle' 1.25 8.9 5^.5 28.1 27.7 Lee 1.1J» 8.6 66.0 1*8.7 18.0 Livingston 0.63 22.8 1*7.1* 28.6 11*. 3 Logan 1.26 l*.l 1*6.8 55-6 35-1 Madison 1.08 fc.5 1*1*. 1* 33-3 23.U Marion 0.90 3-6 59-5 21.6 31.9 Mc Henry 1.02 U.5 1*9.1* 12.8 1*1.5 Ogle 1.22 13-9 55-3 25.8 17.1* Peoria 1.1+0 7.0 3I*. 2 1*0.1* 28.2 Rock Island 0.97 3-6 1*5.2 30.7 33-9 St. Clair 1.11 9-0 23.7 28.6 59-8 Saline 1.12 0.0 1*1.9 31*. 1 51-1* Sangamon 1.12 2.8 53.0 26.6 9-7 Stephenson 1.10 8.9 59.8 35-3 17.2 Tazewell 1.23 1.0 32.7 33-3 1*9.1 Vermilion 1.15 3.3 28.5 35-0 1*1.7 Whiteside O.98 2.0 59.7 28.1* 21.6 Will 0.95 10.6 72.1* 51*. 2 12.6 Williamson 0.93 0-7 56.0 19.1 38.3 mean 1.07 9-7 1*5.1 33-6 28.8 standard deviation • 19 10.1 13.1* 16.7 12.2 89 APFENPIX C JAIL CHARACTERISTICS Counties FORMALI- ZATION A 3-0 FORMALI- ZATION B u.o CHIEF JAILER No TRUSTY CHOICE 3 I JAILER AGE ^TRUSTIES 1.6 IFFICTTVX NESS Adams 29.6 U2.0 Bureau 1.0 2.0 Yes 3 35. h 2.2 35.0 Champaign u.o 6.0 Yea 3 36.8 0.9 U5.0 Christian fc.o 2.0 No 3 37-0 3-6 UO.O Clinton 6.0 2.0 No 3 • 3-7 U6.0 Coles i».o 6.0 Yes 3 22.7 6.8 Ul.O DeKalb 6.0 6.0 Yea 3 U2.8 2.U Ul.O Fayette 5-6 U.O No 3 39-0 0.0 51.0 Franklin 6.0 U.O Yee 3 2U.5 0.0 38.0 Grundy 8.0 U.o No 3 UU.U 0.0 3U.0 Henry U.O 0.0 No 3 2U.0 11.5 U2.0 Jackson 8.0 U.O No 3 3U.3 1.3 53.0 Jefferson 8.0 6.0 No 3 U6.8 6.3 39.0 Kane 6.0 6.0 Yea 1 UO.O l.U 37.0 Kankakee 8.0 6.0 Yes 3 35-5 1.9 U5.U Knox 1.0 0.0 Yea 3 fiO.ii 0.0 UO.O Lake 6.0 6.0 Yes 1 31.8 1.6 U9.5 LaSalle 6.0 2.0 No 1 36.0 1.8 35.0 Lee 8.0 6.0 Yes 2 29.0 6.3 52.5 Livingston 6.0 2.0 Yes 2 « 1.2 U7.0 Logan 7-0 U.O Yes 3 U2.0 1.6 37-0 Madison 6.0 0.0 Yes 1 U2.3 1.6 38.0 Marion 5-6 6.0 Yea 3 25.8 U.5 50.0 Mc Henry 8.0 2.0 Yes 2 • 2.6 52.0 Ogle 8.0 6.0 Yes 2 Ul.8 6.0 61.0 Peoria 6.0 6.0 Yes 1 * 2.7 uu.o Rock Island 5-0 U.O Yes 1 3U.0 6.6 Ul.O St. Clair 6.0 6.0 Yea 1 3U.5 5.2 58.0 Saline 3-0 3-0 No 3 32.3 1-5 35.0 Sangamon 6.0 2.0 Yes 2 30.2 3-3 • Stephenson 8.0 U.o Yes 3 U1.3 • l.U 60.0 Tazewell 2.0 U.O No 1 U3.5 1-7 U3.0 Vermilion u.o 2.0 Yes 2 UO.U 5.3 U6.0 Whiteside 8.0 6.0 Yes 3 56.3 1.8 U7.5 Will 6.0 6.0 Yes 1 « 2.5 37.0 Williamson 5-0 5.6 6.0 No 1 2.28 U6.0 0.0 U5.0 mean U.03 • 67 1 37. u 2.9 UU.2 standard deviation 2.0 2.01 .US .88 8.6 2.5 7.2 •Data not available 1 Yes=l; No=0 90 x CO O CM H UN UN CO VO UN rH -J" ^O f— o r- o o oo on co s no OJ On r— un r— I*- vo vo vo h co on on ON H H ON -J- CO UN CO ON OJ UN CO UN UN OJ O H r- r— CO o\ r— Cvj ON CO H OJ CO CO CVJ o CO J" oo vo i> J IA UN oo r— un o CO O ON o ON r— J" oo co OJ M N < M »-3 CO VO .-t CO r— CO r- o -4" On oo L/\ OO VO ON m vo oo co vo co r- H H UN o VO O oo rH OJ H H H LVJ H OJ OJ CO 13 c a C c o crt U O C OJ o ■H H •H n to H o T3 to T3 P f>5 CO crt o c H •H •H a h crt V| crt H H (1) g (1) s> H CO crt Crt -rl ri •H n c crt x: > ■H OJ •H T3 > OJ r) 4rf •H bl) P) CD a +J H H c^ C 0) Cd T3 Ml rH O u • rH c N >H ■H rH rH o nn * o ■p crt CO -p crt a) X! •H •H 0) -p 2 o « 03 CO CO CO H > S£ IS s e CO CO IX ON O UN ON ON r— CO OJ ON' 0\ CO 4 rH Jp OJ 0-4; VO on r— OJ vo UN ON CO ON LTN O O eh <; K CO K IX CO LTN VO oo ON ON OO CO VO VO CO CO OJ oo E— o -3- OJ -3- r- VO oo CO On I — UN UN _3" VO O J- vo ON -3- ON OJ OJ t— CO J- oo r— OJ OJ UN OO OO CO 4 4 H ON VO un t— oo H OJ n tt ■r-\ LTN rvi CD -=J- ON Ol UN VO CO VO VO < H VO -=t OJ OJ UN UN CO VO ON o-i CVJ t— i-D CO cvj Ol ^t UN VO UN O oo UN H -J- -4" m H O H t— o OO H H VO CO vo ao H J- S, 9 C o cu •iH •H c CI) •H c CO OJ -) crt P o P -p H >>» o u ^ m rrt ft CO -p co ■d p J>d s CO co crt F> CD P ■H c 0) a> § rj M AJ VH 0J Ai X crt U crt r4 •H H « >> P c o bu T3 M crt crt (1) •H o crt crt J iJ ►J J J 2 2 91 APPENDIX E DEFINITIONS OF THE VARIABLES 1. ^URBAN: The percentage of a county's population that resides in an urban area, as presented in the 1972 County and City Data Book. 2. $>C0. BLACK: The percentage of a county's total population that is black as determined by the 1970 Census of the Population. 3. DETOX: A dichotomized variable, where a county that has an operational voluntary detoxification center for alcoholics receives a score of one, and counties without such a facility are coded zero. Data to code each county is available in the Bureau of Detention inspection reports. These three variables at their high values all indicate a complex and diverse community. The first two variables represent the level of pressure imposed on the local criminal justice system by the nature of the community itself. The presence of a detoxification center represents the presence of an alternative institutional framework apart from that system for dealing with those pressures. That presence or absence may also be a reflection of a community's value system. k. $C0. 17-24: The percentage of a county's population aged 17-24 as reported in the 1970 Census of the Population. 5. INCOME: The median family income in a county during 19^9 as reported in the County and City Data Book, 1972 . 6. SCHOOLING: The median number of school years completed by a county's adult residents as reported by the 1970 Census of the Population. 7« CO. JAIL USE: The percentage of all jail confinements, both in city and in county jails, within a county that were to the county jail. Based on statistics for 1973 as reported in the 1973 Annual Report of the Illinois Bureau of Detention Standards and Services. Where data were not available on a jail for the full 12 month period, projections were made to replace the missing information. 92 8. BACKLOG: The degree to which a backlog is present in a county's courts as of January 1, 197^ • This variable is measured "by the formula Cases Added During 1973 - Cases Terminated During 1973 + i. Cases Added During 1973 Low values on this variable indicate a court system that in 1973 terminated more cases than it began, while a high value indicates a substantial backlog. The denominator controls for the raw size of the caseload. 9* foREDUCED: The percentage of felony defendants whose case was disposed of through a conviction on a misdemeanant offense. 10. $PLEA GUILTY: The percentage of felony defendants whose case was disposed of through a plea of guilty. While a case may include several defendants, all variables based on defendant data represent action taken on or by an individual. The last two court variables are indirect measures of, respectively, the incidence and the type of plea bargaining. As Neubauer (197^) suggests, a prevalence of felony defendants being convicted of misdemeanant offenses may represent a weak prosecutor's office. For this reason, $PLEA GUILTY will be included to measure the incidence of plea bargaining; ^REDUCED to measure the type of plea bargaining. 11. ^DISMISSED: The percentage of all felony defendants that had their cases dismissed through a motion by the State's Attorney. 12. ^IMPRISONED: The percentage of convicted felons committed into the custody of the Illinois Department of Corrections. All five court variables are measured using court statistics for 1973 as shown in the 1973 Annual Report to the Supreme Court of Illinois. 13- X ARREST SERIOUSNESS: The mean seriousness of the criminal charges filed against all individuals arrested within a county during January of 197^ • For a description of the system by which seriousness scores are assigned to individuals, see the discussion following the definition of Variable 17. 93 14. $BLACK ARRESTS: The percentage of blacks among all those arrested within a county during January of 1974. 15. ^FEMALE ARRESTS: The percentage of females among all those arrested within a county during January of 1974. 16. X ARREST AGE: The average age of all individuals arrested within a county during January of 197^* 17. X SERIOUSNESS: The mean of all scores received on a seriousness scale by inmates booked in a jail during January, 197^. The scale is applied to the criminal charges responsible for confinement. The greater the seriousness, the less understood is the raw material. Adherence to the statutory definitions of criminal offenses has posed a formidable obstacle to research interested in quantifying the impact of criminal acts -- its seriousness. Criminal codes are formed of a myriad of nominal categories each of which encompasses a wide range of misconduct that cannot readily be utilized to gauge the seriousness of the criminal behavior. Sellin and Wolfgang (1964) proposed an alternative approach that anchors the measure of delinquency to an event. By summing the "objective features" of each event, a total seriousness score can be determined that reflects the harm done by the individual or group. The quantitative score given each objective feature is based on the ratings given by respondents to an attitude questionnaire. Such a procedure perforce demands an extensive amount of infor- mation on each arrest. For most research situations, data of that degree of refinement is simply not available. Further, Sellin and Wolfgang report (1964:327-328) that when they collapsed their events to correspond to the offenses in the Pennsylvania state statutes, the correlation between the resulting seriousness score and the maximum imprisonment specified by the statute as the penalty was between .875 and .938. Therefore, seriousness scores were assigned to each individual as to the maximum confinement permitted by the Illinois Uniform Code of Corrections (1972). Taking effect in January of 1973> this code divides all criminal offenses into nine categories. Table 18 presents these categories, the maximum penalty attached to them, and the seriousness scores. 9k Table 18 The Relationship between the Illinois Criminal Code and SERIOUSNESS Scores Category Murder Felony I Felony II Felony III Felony IV Misdemeanant A Misdemeanant B Misdemeanant C Petty Offense Maximum Penalty Death (100 years) Life (kO years) 20 years 10 years 3 years 1 year 6 months 1 month SERIOUSNESS Score 1200 1+80 2i+0 120 36 12 6 1 95 These scores will be assigned to all inmates booked into the county jail during January of 197*+ • The scores, like the statutes, are weighted to reflect the presence of inmates charged with crimes of violence. In preparing the Uniform Crime Reports, the Federal Bureau of Investigation collects information only on the most "serious" offense committed by a person. While the assumption made by Sellin and Wolfgang (1964) that seriousness is additive has advantages, the reality of arrest reporting systems argues that seriousness be assigned to individuals on the basis of the most serious offense. In this way, inmate seriousness in a given geographical area can be compared with the seriousness of arrests. 18. /oBLACK INMATES: The percentage blacks represent among all adult inmates booked into a jail during January, 197*+ • The greater the proportion of blacks, the less well understood will be the inmate population, in part, because blacks are likely to prove more difficult than whites to classify. 19. /oFEMALE INMATES: The percentage of females among all adults booked into a county jail during January of 197*+* 20. X INMATE AGE: The near age of all inmates booked during January, 197*+' Younger inmates are assumed to be less well understood than older inmates. In Illinois, jails are prohibited from incarcerating individuals under the age of 17, and while this prohibition is often ignored, the averages will be computed excluding juveniles. 21. X PRETRIAL STAY: The average length of confinement for all inmates booked into the jail during January, 197*+ who were awaiting trial. 22. X STAY: The average length of confinement for all inmates booked into the jail during January, 197*+* Given the incidence of extreme scores, the distribution for each jail on these two variables is markedly skewed in the direction of lengthy confinements. Therefore, logarithmic transformations were performed on each inmate's length of confinement, and averages were 96 computed from those scores. While this contributes toward compliance with the assumptions underlying multiple regression analysis, it was performed only on the basis of an adequate theoretical rationale. It is argued that the difference "between a confinement of three days and one of four days is more important theoretically than the difference between a confinement of kO days and one of kl days. The use of logarithms accomplishes this by reducing the weight given to extreme scores. While X STAY will be the main stability measure used in the analysis, X PRETRIAL STAY will also be considered in some analyses. Neither criminal acts nor arrests are phenomena randomly distributed among the population. Relationships can be expected, therefore, among the various inmate characteristics. Table 19 presents the zero-order correlations found between the characteristics of the 4052 inmates booked into the 36 jails during January of 197^+ • Missing or inapplicable data attenuate the sample size used in computing each coefficient. These relationships are in the direction suggested by the available literature on criminal conduct and on arrests. For example, in the birth cohort of Philadelphia boys that Wolfgang, Figlio, and Sellin (1972:122) studied, it was found that between the ages of 10 and 18: as nonwhites grow older, they are involved in more violent crime but less property crime and other index offenses. As whites grow older, they are slightly more violent, but show no truly distinguishing patterns of crime. The strong relationships between seriousness of charges and length of confinement are themselves evidence that some rationality is loose both in the world and in the sample. 23. FORMALIZATION A: A scale measuring external formalization created by summing the values of a jail on these items: fingerprints, temporary absences, phone calls, and mail. The mean score for the sample is 5*^2 out of a possible range of zero to eight; the standard deviation is 1.99* 2k. FORMALIZATION B: A scale measuring internal formalization, created by summing the values of a jail on these items: visitors, 97 Table 19 Individual Level Relationships among Inmate Characteristics S.D. 1 foBLACK INMATES 2 X INMATE AGE 3 X SERIOUSNESS 4 X PRETRIAL STAY 5 X STAY .008 (3965) .145 -.124 (3686) (36145) .150 -.114 .446 (3698) (3617) (3371) .141 -.096 .396 (4051) (3966) (3687) ** .1745 .380 27.80 11.10 84.14 114.94 .8858 1.168 .9951 1.233 All coefficients are zero order correlations. All coefficients except r, p are significant at the .001 level. *# No coefficient can be computed , 98 attorney visits, and cell shakedowns. The mean score for the sample is 4.03 out of a possible range of zero to six; the standard deviation is 2.01. From items in the Illinois Bureau of Detention Inspection reports on a jail's operating procedures, seven items were selected as both relevant and capable of differentiating among the jails in the sample. On the basis of the information in each jail's report, these procedures were rated on the degree to which their application to inmates was specified in advance. A value of two was assigned to an item where the jail applied the procedure to all inmates, a value of one was assigned where the jail applied the procedure only to certain categories of inmates, and a value of zero was assigned where the procedure was not specifically applied to any category of inmates. The seven items so measured are: fingerprinting new inmates, recording temporary absences from the jail, recording inmate phone calls, recording incoming and outgoing mail, maintenance of a list of authorized and actual visitors, recording attorney visits, and cell shakedowns. These procedures both directly constrain a jailer's treatment of an inmate, and have an indirect effect. For example, where a record is kept of all phone calls made by each inmate, jailers will be unable to allow extra phone calls as favors to inmates if the formal procedures are followed . The seven items were factor analyzed. The number of variables relative to the number of cases, however, allows little confidence to be placed in the stability of the factors that emerged. Instead, two scales were constructed on a theoretical basis, aided by familiarity with the way jails are operated. One scale can be labeled external formalization as its items all relate to the jail's communication to the outside. Each procedure governs an action that relates those within the jail to some group or individual on the outside. Fingerprints, for example, are not used internally but are instead transmitted to the Federal Bureau of Investigation and to the sheriff's departmental records. The second scale can be labeled internal formalization as it refers to procedures governing 99 activities that are entirely contained within the jail building. As such, the internal procedures may be relatively independent of the procedures included in external formalization, however, have close analogues in departmental procedures and may simply be reflections of them. Also, the sheriff is more likely to be concerned over procedures that govern commun- ication with the outside than he is with what is limited to the jail facility itself; the potential for scandal in the former is greater. On this basis, two distinct measures of formalization are offered. While factor analysis also produces these two scales, their validity must be assessed on the degree to which distinguishing them makes sense theoretically. 25. CHIEF JAILER: A dichotomized variable, assigned a value of one where a chief jailer has been appointed and a value of zero where a chief jailer has not been appointed. A value of one represents autonomy. 26. TRUSTY CHOICE: Where the sheriff selects all trusties, a value of three is assigned; where both the sheriff and jail staff make the selection, a value of two is assigned; where the jail staff alone selects trusties, a value of one is assigned. Where a jail does not use trusties, a value of three is given this variable, as the sheriff has eliminated any jail staff discretion in this area. Here, a low value represents autonomy. 27. X JAILER AGE: the average age of a jail's male jailers. 28. ^TRUSTIES: the number of trusties a jail uses as a percentage of inmates booked into the jail during January, 197^ • 29. EFFECTIVENESS: The sum of the ratings given a jail by the Bureau of Detention. Each of the 13 items previously mentioned is scored for this purpose on a range of five to one, with five repre- senting a rating of "excellent. " For the 35 jails on which it could be measured, this variable had a mean of 44. 2 out of a possible range of 13 to 65, and a standard deviation of 7*2. 30. DEFT. SIZE: The number of employees in the sheriff's department of which a jail belongs. 31. JAIL SIZE: The number of inmates booked into a jail during January, 197** • 100 ^VJ fc Q Ol CVJ O ^ in M h- C\ > h- IA IA H i-t m «8 fc fc OS o la j O rH O tO 1a *- tH O no j- .* 1j*n Ha ij- ON rH O NO en 00 O h- en la o 8 LA ON CO NO -» CVJ l»l N H NO lb -3 CVJ rH la q la rH la evi n5 evi en rH 0\ V) W la to O fi w rn h j in h j r- o o o H *b ON r— h- in OJ co Oj IA O O rH rH 8 LA O NO O 8 H H -3- O O Q O H r-t -J rH H O O H 8 r— r— en t— rH O O -3- CM vf) O 8 O OJ r— rH NO -3- H OJ ON m 3 CVJ 8 "to H oj O -I ON rH V 1 * CI 1 O rH O CVJ 1 cvj * j- 1 > J- CM in *VJ I VI LT\ rH ^-1 O () _-t en -3- ON -3" Fn en ITN r- (VI (VI NO .11 H IVI CVJ n> UN l/N J- en L/N r^ H OJ CVJ O J- l/N ON CN en o en co la cvj la no OJ On en co LH H (V OJ O rH ON J- CVJ O en cu on o H in, u*n cvj in in OJ vo co co m CM ro w C) m cr} in c^ O CVJ -Lj H . O ON OJ CO rH in VJO O -3- «H -* m 04 ON ON in on r— O O r— o O O O O OJ H O 1 * t'n On 1 CO * CVJ * LA ON On 01 f 1 e 1 rH NLi en CVJ I J -3 ^f 1 * cvj en 1 NO * en 1 to j- -3- ( > r— On LA OJ en NO OJ t— .-f ON LA O co H en CVJ NO O H Q 'O O r- en r- O CD OJ CJ CJ § 5 o o ^H -H <»H EH P O E C/J L7J O M M fl B a $ k g a s CO Eh CO C/J § r, d B Li fc ■«■'- xh. «?- -^ |x Ix IX <; m o o M HH El EH p o &. I>, a g eI R c/j o S rlil Eh IX -WS- ^ &, K hi & ft g ?, •3 -3 CJ OJ OJ OJ (VJ 101 LIST OF REFERENCES Althauser, Robert P. 1971 'Multicollinearity and non-additive regression models. " Pp- 1 +53- 1 +72 in H. Blalock (ed.), Causal Models in the Social Sciences. Chicago: Aldine. Becker, Howard S. I963 Outsiders: Studies in the Sociology of Deviance. New York: Free Press. Blalock, Hubert M. 1972 Social Statistics: Second Edition. New York: McGraw-Hill. Bl umber g, Abraham S. 1969 "The criminal court as organization and communication system. " Pp. 267-291 in R. 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