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The Rand Inmate Survey: A Reanalysis Christy A. Visher In 1982 the Rand Corporation released its findings from a 1978 survey of jail and prison inmates and presented provoca- tive information about the individual of- fencling patterns of criminals. Rand's "second inmate survey," as it is called, involved nearly 2,200 inmates in three states who completed detailed] question- naires about the variety and intensity of their criminal activity. Analysis of these self-report data re Christy A. Visher is research associate at the National Research Council, National Academy of Sciences. She prepared this paper while a National Research Council Fellow at the National Academy of Sciences in 1983. The data used in this paper were made available by the Inter-university Consortium for Political and Social Research in Ann Arbor, Michigan. The data were originally collected by the Rand Corporation of Santa Monica, California. Neither the original source or collectors of the data nor the consortium bear any responsibility for the analyses or interpre- tations presented here. The author would like to thank Alfred Blumstein, Jeffrey Roth, and Douglas Smith for many helpful comments and suggestions in preparing this paper and Allan Abrahamse, Jan Chaiken, Peter Green- wood, and Charles Wellford for providing addi- tional data, documentation, and assistance. ~6~ vented that the distribution of the annual number of crimes an offender commits, often referred to as lambda (A), is highly skewed. Most of the inmates in the Rand survey reported small values of A, about five crimes per year, for most crime types. Some individuals, however, committed crimes at very high frequencies more than 100 crimes per year. These results suggest that most criminals, including the majority of those who are incarcerated, actually commit few crimes. High-rate offenders make up only a small propor- tion of the inmate population, but they may account for most of the crime prob- lem. This finding makes it particularly desirable to identify them. In one ofthe Ranc! reports based on the survey data, Varieties of Criminal Behav- ior, Chaiken and Chaiken (1982a) cIassi- fiec] the surveyed inmates into 10 groups according to the combination of crimes in which they engaged. One important re- sult of their research was the identifica- tion of a single category of serious crimi- nals, whom they designated as "violent predators." These offenders engaged in assault, robbery, and (lrug dealing at very

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162 high rates, but they also committed prop- erty crimes at high rates. In fact, these "violent predators" committed more bur- glaries and other thefts than the criminals who specialized in those crimes. Chaiken and Chaiken concluded that these partic- ular offenders are especially troublesome and become entrenched in a deviant life- style in their juvenile years. The extreme skewness in offending fre- quencies ant] the identification of a small group of violent predators have intensi- fied interest in iclenti*ing high-rate, seri- ous offenders. If the most serious offencI- ers can be distinguished with information about their patterns of behavior and indi- vidual characteristics, the criminal justice system could become more efficient in identifying the most appropriate cancli- dates for long periods of incarceration. The Rand study made an important con- tribution to this effort by using self- reported information from the inmate sur- vey to identify serious offenders. Chaiken and Chaiken (1982a) showed that per- sonal factors and life-styTe characteristics, including persistent drug use, certain types of juvenile criminal involvement, and unstable employment, were strongly related to a violent, predatory pattern of offending. Other types of offending groups were similarly distinguishecl by particular observable behavior patterns and demographic attributes. The researchers at Rand also went a step further and attempted to translate findings about the characteristics of high- rate offenders into a policy instrument that could be used to guicle decisions in the criminal justice system. One sug- gested approach for addressing simulta- neously the problems of prison crowding and high aggregate crime rates is to em- phasize incarceration for the particularly serious high-rate offenders and to Reemphasize it for the others. Another Rand report (Greenwood, 1982) exam- inec3 the possibilities and consequences CRIMINAL CAREERS AND CAREER CRIMINALS of using this strategy selective incapac- itation as a specific policy in sentencing convicted offenders. Using a simple scale of seven variables that correlated with high annual offending frequencies, Green- wood estimated that a particular selective incapacitation policy could reduce rob- bery rates by 20 percent without increas- ing the prison population in California. As a crime control strategy, the idea of selective treatment of some offenders is not new. The concept of"predictive sen- tencing" has a long history (for a review, see Morris anct Miller' 1985), and it un- derlies the common use of risk-factor scales in (recisions regarding parole re- lease (see Gottfrecison and Gottfredson, this volume). The Rand research has en- hancect the potential value of predictive sentencing because of the skewness of the reported distribution of A. It has also generated considerable controversy. The criticisms that have been directec! at the Greenwood report and at the Ranc! study in general have both methodologi- cal and ethical elements. Some critics argue that the analysis is methoclologi- cally flawed and that Rand's sample of prisoners is not representative of the con- victec3 offenders judges have to sentence. Others are skeptical of the truthfulness of inmates' reports concerning the crimes they hac! committed. Observers are also concerned that most of the variables in the seven-point scale are baser! on self- report rather than official data and would be much less reliable if based on official records. Still others regard the variables involved as inappropriate as a basis for sentencing in any event. These and other criticisms are reviewed in a later section of this paper. Criticism of the Rand results has been stimulated by the extensive public atten- tion the seven-point scale has received. Some state legislators introduced bills in 1982 and 1983 to implement selective incapacitation as part of new sentencing

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TTIE RAND INMATE SURVEY: A REANALYSIS policies (see Blackmore and Welsh, 1983~. Some police and prosecutors may already be using the scale informally to guide their decisions. An experimental program in Illinois is testing the predic- tive accuracy of the Ranc3 scale, along with other types of guidelines, in identi- fying offenders who are likely to recicli- vate. These actions have raised serious concerns that the results of this single study, which has a number of readily identifiable technical flaws (see Cohen, 1983) ant! which has not been subjectec] to internal or external validation, couIcI be implemented widely in making decisions regarding individual liberty. Thus, an intensive review of the Rand study is necessary. This paper provides a first internal validation basect on an exten- sive reanalysis of the actual inmate re- sponses to validate the findings and test their robustness to variations in the ana- lytic procedures used. An external vaTicia- tion using different settings is also neces- sary to assess the generalizability of the Ranc] results to a new sample of inmates and to samples of convicted offenders who are not in prison, but that test is beyonc! the scope of this effort. Three interrelated objectives are cen- tral to this reanalysis. The first objective is to validate the reported estimates of A and to assess the sensitivity ofthose and other findings to the interpretation of ambigu- ous ant! incomplete survey responses, ar- bitrary choices in constructing variables, treatment of missing clata, and decisions regarding scale development. The sec- ond objective is to examine the predictive accuracy of the seven-point scale in the three states, for specific crime types, and in other subsamples. The third objective is to reevaluate the reported incapacita- tion effects in light of the reestimation of A and reconstruction of the prediction tables. Data for the reanalysis were obtained from a machine-readable, public-use tape 163 of the inmate responses, supplied by the Inter-university Consortium for Political and Social Research, which maintains a data archive for the research community. Data obtained directly from RancT pro- vided adclitional cletail on how the ana- lysts translated the survey responses into the variables used in their analyses. With the generous help of the Rand research- ers, every effort was macle to determine Rand's analytic procedures. Copies of cocking manuals and computer source codes were studied, and persons at Rancl who were familiar with the analysis were consultecI. This reanalysis is limited to two key findings in the Rand reports: the esti- mates of annual individual offending fre- quencies, A (Chaiken ant! Chaiken, 1982a), and the use of the survey data to clevelop a prerliction instrument to iclen- tify high-rate offenders (Greenwood, 19821. Robbery ant! burglary offenses are the exclusive focus because of the prom- inence they received in the Rand reports and because of their prevalence among the sampler] prisoners. The remainder of this paper is orga- nizecl into three major sections. First, the purposes and general methods of Rand's second inmate survey and specific fincI- ings reported by Chaiken and Chaiken and by Greenwood are summarized. Pub- lished critiques of the Rand studies are also reviewed in this section. In the sec- onc1 section the results of the reanalysis are presented and compared win Rand's publishecl finclings. In the final section major findings and conclusions are presented. THE SECOND RAND INMATE SURVEY The Rand Corporation's 1978 survey of inmates extended previous work at Rand on studies of incarcerated offenders. In an exploratory study Petersilia, Greenwood,

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164 and Lavin (1977) conducted extensive in- terviews with 49 convicted robbers in California prisons. Rancl's "first inmate survey" (Peterson and Braiker, 1981) was a self-administered questionnaire given to 642 prison inmates in California. The findings from both studies indicated that most inmates committee! few crimes per year and that a small group reported much higher frequencies of offending. The researchers considerecl their findings preliminary because the information on individual offending frequencies was im- precise, serious offenders were overrepre- sented in the sample relative to sen- tenced offenders, and only one state was involves] in the studies. Thus, a third, more intensive research project was cle- signed. Data and Methods The sample for the second inmate sur- vey, actually the third research project, covered three states, California, Michi- gan, and Texas. The sample was drawn to represent a typical cohort of incoming inmates for those states; a weighting scheme was used in which "each inmate was given a sampling weight proportional to the inverse of the length of his prison term" (Peterson et al., 1982:541. In addi- tion, to obtain a range of severity among the conviction offenses, inmates from both prisons and jails were sampled. Re TABLE Type CRIMINAL CAREERS AND CAREER CRIMINALS placement procedures were used to re- duce the usual problems of nonresponse bias. ESee Peterson et al. (1982) for other details of the sampling design, site selec- tion, and pretesting procedures.] The inmates selected for the study were asked to complete a cletailect ques- tionnaire that elicited information about their juvenile criminal behavior, aclult criminal behavior in the period (up to 2 years) prior to the arrest leading to their current incarceration, past and recent use of illegal drugs and alcohol, as well as information concerning employment his- tory, attitudes, and demographic clata. The survey was not anonymous so that official record data, which were collected on all prison inmates, couIc3 be matched to the inmates' self-reports. More than 2,500 inmates actually completed the questionnaire, but jail respondents in Texas were excluded from the analysis because, unlike jail inmates in over states, they were predominantly sen- tenced offenders awaiting transfer to prison. The final sample consisted of 2,190 inmates. The distribution of the 2,190 prison and jail inmates from the three states is shown in Table 1. Given the focus on robbery ant! bur- glary in the reanalysis, of particular inter- est are the inmates who reported commit- ting robbery or burglary during the 1- to 2-year period before they were arrested for their conviction offense, referred to as Distribution of Sample Across States, by Type of Institution and Crime Total Survey Robbers Burglars State PrisonJail Prisonfail PrisonJail California 357437 16894 182163 Michigan 422373 15466 174112 Texas 6010 1450 2520 Total 1,380810 467160 608275 NOTE: Data for robbers and burglars were computed as part of the reanalysis. Offenders in the two groups are defined by their reports of whether they committed any robberies or burglaries during the measurement period. Some individuals are included in both groups. SOURCE: Chaiken and Chaiken (1982a:6).

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THE BAND INMATE SURVEY: A REANALYSIS the "measurement period." Table 1 shows that Texas had somewhat fewer inmates who reported committing rob- bery (24 percent) than either California (33 percent) or Michigan (28 percent). Respondents who reported committing burglary were more prevalent and were distributed more evenly among the three states 43 percent, 36 percent, and 42 percent in California, Michigan, and Texas, respectively. Many potential sources of error exist in a survey of this type. The most readily apparent systematic error arises from members ofthe sample refusing to partic- ipate; those nonresponclents couIc3 well be different in their crime patterns from those who were willing to respond. To correct partially for this potential source of bias, a "replacement respondent" was selected for each sampled prison inmate prior to the survey's administration. The replacement was matched with the sam- pled inmate on several criteria, including age, record, and conviction offense. The actual response rate varied consid- erably across states and type of institu- tion. In jails in all three states, the re- sponse rate averaged 70 percent. In Michigan and California prisons the rate was 49 percent and in Texas prisons, 82 percent. Replacement respondents in all three states were asked to complete the survey, but the replacement data from Texas were not used because of the Tow number of refusals among the main sam- ple. After including the replacements, Peterson et al. (1982:viii) concluded that "no statistically significant differences were found between responding and nonresponding inmates in any Michigan or Texas prisons, in terms of age, race, record or conviction offense." In some prisons in California, Chicano inmates were less likely than others to participate. Inmates with reading problems were underrepresentecT in all three states. Over major sources of error in surveys eliciting self-reportec! information are un 765 reliable responses and nonvalid survey instruments. Researchers at Rand carried out extensive analyses of these problems (Marquis, 1981; Chaiken and Chaiken, 1982a; Peterson et al., 19821. Two design strategies were built into the survey for later use in the analysis of reliability: redundant questions were asked within the survey, and 250 respondents were retested 1 week later. Chaiken ant] Chaiken (1982a:Appendix B) relied on the first approach and developed mea- sures of the internal quality and external reliability of the survey responses. The internal checks inclucled looking for cor- rect skip patterns, consistent answers, minimal confusion, and few omitted questions. The external checks relied on comparisons between each inmate's offi- cial record and his responses to 14 self- reportecl items (e.g., conviction offense, arrest incidents, ant] prior prison terms). The two measures were strongly corre- lated in each state. Chaiken and Chaiken concluclec! that most individual characteristics and be- havior patterns, including age, race, con- viction offense, and reports of crimes committal, were unrelated to the quality and reliability of inmates' responses. About 83 percent of the inmates "passed" the internal quality test, whereas only 56 percent achieved a similar level of exter- nal reliability.) Scattered evidence sug- gested that respondents who gave consis- tent and reliable answers were less likely to report very high offending frequencies and less likely to deny committing crimes. Finally, key regression analyses were carried out with and without inconsistent 1"Failure" is defined as having more than 20 percent "bad" indicators on the external or internal reliability measures (see Chaiken and Chaiken, 1982a:9, 222-239). Other data reported suggest that the low level of external reliability is partly the result of incomplete of ficial records, especially juve- nile records (p. 229~. Inmates often reported juve- nile convictions or incarcerations that were not found in their records.

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166 or unreliable respondents (42 percent of sample), anal no "meaningful differences" were found between the two analyses (Chaiken and Chaiken, 1982a:9), although the actual results were not reported. Purposes of the Rand Study The Rand survey was designed to achieve a number of purposes (see Peter- son et al., 19821. One major purpose was to gather information on incliviclual pat- terns of criminal behavior types of crimes committed, degree of speciaTiza- tion in crime types, and changes in crim- inal patterns over time. Questions were asked about juvenile criminal activity and criminal behavior during the 6 years prior to incarceration to explore hypotheses about whether offenders progress through stages of increasing crime seri ousness. A large section of the survey was de- voted to obtaining offenders' estimates of the number of times they committee] each of 10 crime types2 during the measure- ment period. Estimates of annual offencI- ing frequencies, A, have been calculated by other researchers using a variety of techniques based primarily on inferences from arrest records (e.g., Greenberg, 1975; Blumstein and Cohen, 1979), but no broad consensus has yet been reached in these estimates (for a review, see Cohen, 19831. The Rand survey was the first to use a self-report technique to ob- tain annual estimates of )< for a group of known adult offenders. The use of a self-administerec] ques- tionnaire also permitted researchers to collect richer data on offenders' personal 2The 10 crimes that were included in the ques- tionnaire were burglary, business robbery, personal robbery, assault during robbery, other assaults, then, auto theft, forgery/credit card swindles/bad checks, fraud, and drug dealing. The specific word- ing of the questions is available in Chaiken and Chaiken (1982a:19-201. CRIMINAL CAREERS AND CAREER CRIMINALS characteristics than can typically be found in official records. Extensive infor- mation was gathered on (1) criminal ex- periences at young ages; (2) use of illegal drugs as a juvenile and as an adult; (3) adult offender histories, including arrests, convictions, and incarceration; and (4) life-style characteristics, such as marital status, employment record, and geo- graphic mobility. The survey also con- tained a number of questions about atti- tudes toward crime. The researchers at Rand believed that the self-report data on personal characteristics and annual of- fencling frequencies might help to distin- guish different types of offenders and, particularly, to identify the serious "ca- reer criminals." Such information could be helpful to criminal justice agencies in making decisions regarding sentencing, parole, work release, or (lrug treatment programs. The Rand Results The findings from the Rand study ap- pear in several reports (Petersilia ant! Honig, 1980; Rolph, Chaiken, ant! Houchens, 1981; Chaiken ant] Chaiken, 1982a; Greenwood, 19821. Three results are especially relevant to policy decisions in the criminal justice system: (1) esti- mates of A and its skewed distribution (Chaiken and Chaiken, 1982a), (2) the development of an offender typology and the use of a multivariate approach to clis- tinguish among types of offenders (Chaiken and Chaiken, 1982a), and (3) the identification of high-rate offenders using self-reportecl information (Green- woo(l, 1982~. Rancl's summary statistics for the annu- alizecl incliviclual offending frequencies are shown in Table 2. The statistics for each crime type are based on only those inmates who reported committing that crime. The distribution of A, as noted, is highly skewed. More than one-half the

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THE RAND INMATE SURVEY: A REANALYSIS TABLE 2 Estimates of A for Respon- clents Who Reported Committing the Crime Crime Type Median Value at the 90th Percentilea Burglary Robbery Assault Theft Forgery and credit cards Fraud All except drug dealing 5.45 232 5.00 87 2.40 13 8.59 425 4.50 5.05 14.77 206 258 605 aTen percent of the respondents who commit the crime commit it at or above the rate indicated. SOURCE: Chaiken and Chaiken (1982a:44). inmates who committed robbery or bur- glary in the measurement period dic3 so at rather Tow rates about five crimes per year. On the other hand, the worst 10 percent committee] robbery and burglary at the rate of two to four crimes per week, or 20 to 40 times as frequently as the median offender. In this highly skewed situation, the mean does not accurately represent the central tendency of such a distribution. Further, the mean is ex- tremely sensitive to the values of the few 767 offenders in the right tad] of the clistribu- tion anti, therefore, the median rate is preferable for estimates of a "typical" offencler's crime rate. The survey also providecI the data for Chaiken and Chaiken's development of an offender typology. They found that inmates could be categorized according to the combination of crimes they com- mit, such as robbery and assault or bur- glary and drug dealing. In Table 3 the medians and 90th percentile values of A are compared for offender types that in- clucle robbery or burglary as one of the defining crimes. These six types consti- tute 62 percent of the inmate sample (Chaiken and Chaiken, 1982a:271. As seen from Table 3, violent predators com- mitted robbery and burglary at very high frequencies; however, the me(lian A was 9. The most active 10 percent in this group reportecITy committed at least 516 burglaries per year, whereas the 90th per- centile of the "burglar-(leaTers" (who commit burglary and other property crimes and sell cirugs) committed 113 burglaries per year. The violent preda- tors, especially the worst 10 percent, thus appear responsible for the majority of robberies ant] burglaries committed by the inmates in the Rand survey. Realizing TABLE 3 Estimate of A for Robbery and Burglary for Six Offender Types Percent Robberya A Burglary A Offender Type of Sample Median Mean 90th Pet. Median Mean 90th Pet. Violent predatorsb 15 9 70 154 9 172 516 Robber-assaulters 8 5 50 141 5 69 315 Robber-dealers 9 4 32c 87 14 122 377 Low-level robbers 12 2 10 13 4 48 206 Burglar-dealers 10 4 42 113 Low-level burglars 8 2 36 105 Others 38 aIncludes both business robbery and personal robbery. bThose who commit robbery, assault, and drug dealing concurrently. COne outlier has been removed. Includes "mere assaulters," property and drug offenders, low-level property offenders, drug dealers, and about 13 percent who did not report committing any of the crimes studied. SOURCE: Chaiken and Chaiken (1982a:27, 219).

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168 the impact of these serious offenders on the crime problem, the Rand researchers used several techniques to identify them. Using a multivariate approach Chaiken and Chaiken founcI that some self-report- ed information could distinguish violent predators from other inmates. These of- fenders were often young people with a history of serious juvenile criminal activ- ity, including initiation of delinquent be- havior before age 16, involvement in both violent and property crimes, frequent use of illegal drugs, and multiple commit- ments to state juvenile institutions. They were generally unmarriecl, unemployed, and extremely heavy drug users, often at costs exceeding $50 per day for heroin. A regression mode! using these variables explainec! 35 percent of the variance in annual offending frequencies. However, many inmates predicted to be high-rate robbers with this moclel actually reported committing no robberies at all. Greenwood (1982) independently at- tempted to identify the high-rate offend- ers with a simple, seven-point scale. He selectee] seven variables (six self-report and one official record variable available only for prison inmates-see below) that correlated fairly well with high annual robbery and burglary offending frequen- cies anct whose use might be appropriate for sentencing purposes. The resulting additive scale (variables were scorer! as 1 or O clepending on the presence or ab- sence of the attribute) could be used to identify high-rate offenders. Inmates were classified as low-rate (scoring O or 1), medium-rate (scoring 2 or 3), or high- rate (scoring 4 or more) offenders. The mean annual offending frequencies were reported to stiffer sharply across these groups. For inmates in Califomia, the respective mean As for robbery were 2.0, 10.1, and 30.8. This pattern is consistent for robbery in the other states and for burglary, but the group differentials are widest in CaTifomia. CRIMINAL CAREERS AND CAREER CRIMINALS Variables Used in Scale to Distinguish Inmates by Individual Crime Rates Convicted previously for same charge (official criminal record; prison inmates only) Incarcerated more than 50% of preceding 2 years (self-report) Convicted before age 16 (self-report) Served time in state juvenile facility (self-report) Used drugs in preceding 2 years (self-report) Used drugs as a juvenile (self-report) Employed less than 50% of preceding 2 years (self-report) Using the model of incapacitation de- velopecl by Avi-Itzhak and Shinnar (1973), Greenwood estimated the poten- tial crime control effects of increased sen- tences for the identified high-rate offend- ers. For California, he reported that a policy of sentencing predicted high-rate robbers to S-year terms and all other rob- bers to 1-year jail terms could reduce the robbery rate by a maximum of 20 percent, without increasing the prison population. Such a strategy does not work as well for burglary. (A detailed analysis of the sev- en-point scale and its use in identifying high-rate offenders is presented in a later section in conjunction with the reanalysis of the Rand data.) Criticisms of the Rand Study Because of the provocative policy im- plications of the Rand results, the inmate study has received a considerable amount of attention, and not all of it has been positive. Some researchers have raised moral objections to the mechanical use of any such scale for determining sentences. Others argue that the findings are flawed and therefore policy proposals should not be based on Rand's results. Ethical Concerns Ethical concerns emerged largely in response to the analyses presented in the Greenwood report. The Rand study has also mobilized arguments about se

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THE RAND INMATE SURVEY: A REANALYSIS lective incapacitation as a sentencing phi- Tosophy and, especially, the use of ex- plicit predictions in sentencing. These debates have become quite vigorous. The issues are discussed only briefly here, however.3 One of the most frequent objections to the Greenwood report concerns the se- lection of variables for the seven-point scale. In particular, critics argue that some of the variables in the scale are past behaviors or social characteristics that cannot be changed. Employment status, drug use, and juvenile criminal history account for five of the seven variables. Retributivists and others have pointed out that using these criteria as a basis for sentencing is contrary to the widely accepted "just deserts" philosophy, whereby differences in sentences are based on the seriousness of the convic- tion offense. Greenwood (1982:Table 4.11) anticipated these criticisms and tested his scale without three of the most "objectionable" predictors Juvenile drug use, recent drug use, and recent employ- ment history). The limited scale, how- ever, was less effective in distinguishing high-rate offenders from medium-rate of- fenders compared with the full seven- variable scale. A more fundamental ethical objection has been raised to the concept of sentenc- ing offenders according to a prediction of their future behavior. Because of its ex- plicit relationship to sentencing policy, Greenwood's analysis was the recent tar- get ofthese critics. Some critics argue that this type of sentencing policy would vio- late principles of fairness and "just deserts" (von Hirsch, 1976, 1981) ant! others question whether future high-rate 3For other discussions of these topics, see Dershowitz (1973, 1974), Cohen (1983), von Hirsch (1976, 1981, 1984), Floud and Young (1981), Hinton (1982), Moore et al. (1984), and Morris and Miller (1985~. |69 offenders can be accurately iclentifiect (Blackmore anci Welsh, 1983; van Hirsch and Gottfrecison, 1984~. Any classification system is likely to misidentify offencI- ers-classifying some low-rate offenders as high-rate ("false positives") and some high-rate offenders as Tow-rate ("false negatives". The expected level of error is totally unacceptable to some (von Hirsch and Gottfredson, 1984) but considered reasonable within some definitions by others (Morris and Miller, 19851. Green- wood also raised some of these same issues in his report, but he differs from his critics in believing that these ethical (and some empirical) problems are only limi- tations on the usefulness of selective in- capacitation and not barriers to its poten- tial use. In summary, the Ranc3 reports have intensified the ethical debate about selec- tive incapacitation and predictive sen- tencing. Any resolution will involve hard choices about acceptable error rates and appropriate prediction instruments. Em- pirical information about the predictive capability of different scales may help to inform those choices for some. Empirical Concerns Empirical concerns regarding the Ranc! study cover a wide range of issues, in- cluding reliability of the inmates' re- sponses, construction of the seven-point scale, and the robustness of the incapaci- tation effects to variations in the model. The following discussion reviews pub- lishec! critiques and raises some addi- tional concerns. The examination of po- tential limitations to Rancl's results provides direction for the reanalysis that follows. First, some observers have questioned the reliability of the inmates' self-report- ecl responses, especially the data used to estimate the annual number of crimes, A, an inmate committed prior to his incarcer

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170 ation (Blackmore and Welsh, 1983; von Hirsch and Gottiredson, 1984~. The many sources of error in self-report methods have been widely discussed (e.g., GoIc3, 1966; Farrington, 1973; Reiss, 19731. The Rand study presented further problems because of its sample-convicted offend- ers. Some inmates could have concealed crimes they committed, and others might have exaggerated their criminal activities, ant! these practices could contribute to the observed skewness in the reporting of offending frequencies. The Ranc3 finding that a small group of inmates reported committing hundreds, or even thousands, of robberies and burglaries a year has led critics to speculate that some respondents inflated their illegal behavior to appear "tough" or important (von Hirsch ant] Gottfredson, 19841. The opposite type of response error, concealment, is also plau- sible, especially since between 24 ant] 36 percent of all convicted robbers in the sample denied committing any robberies in the measurement period (Greenwood, 1982:Table 4.1~. The accuracy of estimates of A also depends on an assumption of stable of- fending patterns over time (Cohen, 19831. But criminals may operate erratically, committing many crimes in a short period and then ceasing their illegal activities for a while. If the "crime spurting" phenom- enon describes even a minority of Rand's inmate sample, the estimates of annual offending frequencies might well be in- flated (Cohen, 19831. Second, the criticism directed against the Greenwood scale was even more vig- orous. The variables in the seven-point scale were mostly self-report measures, and the only scale variable that was con- structed from official record information was whether the inmate had a prior con- viction for the same offense. Some critics were concerned about the availability of necessary information if predictions re- garding future criminal behavior were to CRIMINAL CAREERS AND CAREER CRIMINALS be made (Blumstein, 1983; Cohen, 19831. Of course, Greenwoocl's scale, basecl on self-reportec3 information, was only sug- gestive of the kinds of factors that may be predictive of high-rate offending. If the scale was to be used operationally, the needed information would have to come from inclependent sources, such as official records or other inquiries, like those re- flected in presentencing investigations. But the use of official records invariably involves some clecay in reliability be- cause of missing records, recording er- rors, ant! other mistakes. Data from inde- penclent sources are also likely to be incomplete and less helpful because some information, such as drug use, is not gathered consistently. Third, the treatment of missing data in the scale is another source of concern. Each of the variables in this scale was coded 1 or O to indicate the presence or absence of the attribute, and missing in- formation on any scale item was also codecl 0. However, for at least one of the scale variables- prior conviction for the same offense-the missing-data problem was systematic: official records were only available for the prison sample, and so all jail inmates were assigned a zero for this variable. In the analysis the past- convictions variable thus becomes a measure that distinguishes jail and prison inmates (Cohen, 19831. Since high-rate offenders are probably already more likely to be sentencee] to prison than to jail, this variable is more a "predictor" of who was sent to prison than of any other inmate characteristics. Missing data was a prob- lem for another variable, juvenile (lrug use; 14 percent of the respondents failed to answer the questions on this topic (Greenwood, 1982:52~. Fourth, the predictive accuracy of the seven-point scale turns out to be no better than that for other prediction instruments developed over the past 10 years. The final sample used in the prediction anal

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THE RAND INMATE SURVEY: A REANALYSIS ysis was prison and jail inmates in the three states who were currently serving a sentence for a robbery or burglary convic- tion. Among inmates predicted to be high-rate offenders, only 45 percent (Cohen, 1983) actually were, according to estimates of their annual offending fre- quencies. Stated another way, 55 percent of the predicted high-rate group was in- correctly iclentifiecI. This level of"false positives" is close to the average false- positive rate (60 percent) reported in a review of other prediction studies (Monahan, 1981~. The scale floes much better among predicted Tow-rate offenc3- ers: the accuracy rate is 76 percent (Cohen, 19831. These differences are due in part to the different base rates of the two groups arbitrarily specified as the lowest 50 percent for the low-rate group and the highest 25 percent for the high- rate group. However, the data reported by Greenwood and reanalyzed by Cohen focus on overall accuracy rates for the entire analysis sample, and there has been no examination to date of whether the scaTe's predictive accuracy is consis- tent across states, crime types, and other important subgroups. Finally, another area of major concern about the results of the RancI study relates to the validity of the incapacitation effects reported by Greenwood (Blackwore and Welsh, 1983; Cohen, 1983; von Hirsch and Gottirecison, 19841. In her review of research on incapacitation, Cohen (1983) notes! that Greenwooc3's clevelopment of a prediction scale is based on retrospec- tive clata. The reporter! incapacitation ef- fects, therefore, clo not take into account the possibility that future rates of offend- ing might change (e.g., regress towarc! a mean) or that there might be a differential likelihood of terminating criminal activ- ity. Thus, the prospective accuracy of the seven-point scale in identifying high-rate offenders can only be judged with an appropriate longitudinal panel (resign. In other 171 fact, the use of retrospective data may lead to an overestimate of the crime-reduc- tion effects. Another serious validation problem is the lack of any test of the scale on an inclepen~lent sample. This is particularly important because a selective sentencing policy wouIc3 be applied to convicted of- fenclers, and predictive information in that population may be different from that in a sample of inmates (Cohen, 19831. Other research has shown that predictive accuracy for the initial sample for which a prediction scale is constructed tends to be greater than for a separate validation sam- ple (GottEredson and Got~redson, 1980; Farrington and TarTing, 1985~. Thus, re- ported reductions in aggregate robbery rates that are tied to any particular scale will diminish for new samples. However, Greenwood argues (1982:91) that, since his 0-1 prediction scale was not closely fitted to the inmate sample (as is the case with regression models), the expected shrinkage wouIc3 be less than with regres- sion weights. This characteristic of Bur- gess ((~1) scales, compared with closely weighted scales, is cliscussecI by Gottfrec3- son and GottErecison in Chapter 6. The state-specific results obtained in the crime control analysis for California and Texas also illustrate the sensitivity of the Rand findings to the population being studied. For California, it is reported that aggregate robbery rates could be reduced by 20 percent and burglary by 12 percent without any increase in prison population by using a selective sentencing strategy (Greenwood, 1982:79~. In Texas, how- ever, a similar sentencing policy would actually increase the robbery rate be- cause there are so few high-rate offenders (Greenwood, 1982:Figure 5.31. In summary, several critical reviews of the Ranc3 inmate study have raised impor- tant questions about the sensitivity of the results reported in Chaiken and Chaiken (1982a) and Greenwood (1982) to the in

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THE RAND INMATE SURVEY: A REANALYSIS the robbery rate reported by Greenwood is relatively unaffected by using Tower estimates of A. This reanalysis and Cohen's (1983) replication of Green- wood's data both indicate the possibility of about a 13 percent reduction in rob- bery. But the prison population would remain essentially unchanged using esti- mates of the input variables obtained from the reanalysis.24 Further reductions in the robbery rate beyond 13 percent can only be achieved by increasing the expected sentence length. The end points of the dashed and solid lines in Figure 3 and those in Greenwood's report are based on the hy- pothetical sentencing policy of 1-year jail terms for Tow- and medium-rate offenders and about 8-year expected terms for high- rate offenders. Thus, any extension to a lower crime level actually involves a change in the sentencing policy. To see if Greenwood's finding of a possible 20 per- cent reduction in robbery could be achieved, Greenwood's hypothetical pol- icy was revised and the average time served for high-rate offenders was in- creased by a factor of 3, to slightly over 12 years. This modification is represented by dotted lines in Figure 3. These data reveal that with extremely stiffexpected sentences for high-rate rob- bers (actual prison sentences would prob- ably be 16 to 24 years), the robbery rate 24The model that is used to estimate these inca- pacitative effects is based on a series of calculations involving a wide range of magnitudes; therefore rounding and truncation error (for example, using 2 decimal places instead of 4 or 5) may slightly alter the estimates of changes in crime rates and prison populations. Details of the model and the interme- diate calculations can be found in Cohen (1984a) and Volume I (Chapter 5) of the panel's report. The projections reported here for incapacitative effects are conditional on assumptions stated in those sources, and actual effects are likely to differ from these projections because the assumptions may be violated in ways that are discussed later in the paper. 20] couIct be reduced by only 18 percent. Moreover, the prison population might have to be increased (according to the reanalysis estimates) to accommodate the longer sentence lengths. But more impor- tant, a sentencing policy Mat gives 1-year jail teens to most convicted robbers arid sentences a small group of predicted high-rate offenders (which inclucles an error rate of at least 50 percent, according to the prediction tables presented earlier) to about 20 years would represent ex- ~eme disparity in sentencing. Incapacitative effects for Michigan were not estimated in Me Greenwood report because the necessary data on cur- rent sentencing policies were not avail- able. The data for Michigan robbers were obtained for the reanalysis, and the incapacitative effects that wouIc] be ex- pected under Greenwoocl's model were computed.25 The results were quite dif- ferent from hose for CaTifomia. With S- year sentence lengths for predicted high- rate robbers and 1-year jai] terms for all over robbers, the robbery rate in Michi- gan would increase by 33 percent, but Me prison population would decrease by 25The data on current sentencing policies in Michigan are taken from the official records of a large sample of Michigan arrestees (Blumstein and Cohen, 1984, personal communication) and state- level summary data supplied by the Michigan State Police. These sources gave nearly identical esti- mates of the parameters needed for the incapacita- tion analysis based on Michigan robbers and they were averaged to arrive at the following estimates: conviction rate .44; number of robbery arrests in 1977~,281; probability of incarceration given con- viction .86; prison commitment rate .86; jail com- mitment rate-.05. These parameters were substi- tuted into Table B.4 (Greenwood, 1982:112) to estimate current numbers of robbers in Michigan prisons and jails. Then, those estimates and data on the 150 convicted robbers in the Michigan subsam- ple were used to generate a table similar to Table B.6 (p. llS) for Michigan. Further details about estimating the potential incapacitative effects of a selective sentencing policy for robbers in Michigan are available from this author.

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202 nearly 50 percent. The hypothetical pol- icy is clearly not satisfactory in Michigan because incarcerated high-rate offenders, as cleaner] by a minimum score of four on the seven-point scale, are apparently a very small group in Michigan prisons and jails, compared with CaTifomia. More- over, all convicted robbers in Michigan are aIreacly serving Tong prison terms (an average of 5 years) and few robbers are sentenced to jail. Thus, in Michigan a policy that reserves Tong prison sentences for only the small group of predicted high-rate offenders actually would in- crease the crime rate and reduce the prison population. This would occur be- cause most robbers (those cleaned as low- anc! meclium-rate) would spend a smaller portion of their offending careers in prison or jail under this policy than under Michigan's current policy ant] would have more "free time" in which to com . . mat more crimes. The crime rate was also increased when Greenwood applied his incapacita- tion moclel ant! selective sentencing pol- icy to the Texas robbers and burglars (GreenwoocI:79~811. In Texas preclicted high-rate offenders, using the seven-point scale, were also a small group; conse- quently, Greenwooc3's selective sentenc- ing policy would reduce the prison pop- ulation but would not reduce the robbery or burglary rate. Finally, one important parameter of the original incapacitation moclel was omit- ted from the Greenwood version- of- fender's career length. Other analyses of the Rand data reveal that when career length is included in the model for CaTi- fomia, estimates of crime reduction Mat could be achieved by a selective sentenc- ing policy drop to about 5 to 10 percent (Cohen, 1984a; Spelman, 19841. In a re- cent report on the duration of criminal careers, Blumstein, Cohen, and Hsieh (1982:55) estimated that the maximum mean resi(lual career length for robbery CRIMINAL CAREERS AND CAREER CRIMINALS (the number of years left in a criminal career at any given age) is only 7 years. Therefore, many of the targeted high-rate offenders wouIcl likely have ended their careers before the ens! of their 8-year prison term anyway, in which case the projected reductions in crime would be overstated. Thus, these and other analy- ses suggest that, under the best assump- tions, significant reductions in crime can- not be easily achieved by identifying the high-rate offenders and targeting them for long prison terms. Selecting Scale Cut Points One of the fundamental parameters of Greenwood's calculations is the choice of cut points on the seven-point scale that (refines the distribution of incarcerated ofl~enclers across the three preclictecl oF fence-rate groups. The cut points are used to estimate the total offender population in California and the probability of prison (versus jail) for convicted offenders in each group. In Greenwood's incapacita- tion analysis, the Tow-, medium-, and high-rate groups are clefinecl by the scores ~1, 2<, and 4-7 on the seven-point scale derived from the survey data. The distri- bution of these scores within the inmate sample is used, along with information about California's current sentencing pol- icy for robbers, to estimate the total an- nual jail and prison population in CaTifor- nia. Using these methods, predicted high-rate offenders turned out to be 43 percent of the incarcerated robber popu- lation (Greenwood:771. Greenwoocl introcluced his scale, how- ever, as a device for identifying a rela- tively small group of high-rate offend- ers specifically, the most active 25 percent of the convicted robbers, accor(l- ing to their self-reports. (Chaiken and Chaiken, 1982a, chose the top 20 per- cent.) As Loeber and Dishion (1983) noted generally, this excess of selection

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THE RAND INMATE S URVEY: A REANALYSI S ratio (43 percent) over base rate (25 per- cent) guarantees a false-positive rate of at least 18 percent. To maximize predictive efficiency, the seiecuon ratio (me per- centage of respondents predicted to be high-rate offenders) should be equivalent to the 25 percent base rate (the percent- age of respondents defined as high-rate according to their reported crime rates). Therefore, in the reanalysis the model was reestimated to assess the sensitivity of the results to alternative scale cut points for Califomia robbers-. Adjusting the cut points to equalize approximately the selection ratio and base rate and substituting the lower aver- age estimates of A for the three groups dramatically altered the potential crime reductions associated with Greenwood's hypothetical selective sentencing policy. The cut points of the predictor scale were changed to ~2 (low). 3 - (me- dium), and ~7 (high).26 The values of A . . , . ~, 26There are actually two closely related issues: one is substantive and the other is technical. The cut point decision is also a policy issue-how much error in prediction is acceptable and how are pre- dicted high-rate offenders to be defined (e.g., having four or five of seven attributes), given the character- istics of a specific offender population. The techni- cal issue relates to the estimated distribution of offenders across the three offense-rate groups. In Greenwood's model (and this reanalysis), this esti- mate is dependent on the cut points because the distribution of the three groups defined by the cut points in the sample is used, in conjunction with other parameters, to estimate the distribution of the three groups in the general offender population. These estimates were necessarily based on the small number of convicted robbers in the California inmate sample (N = 178~. Changing the cut points of the prediction scale reduces the high-rate group to 22 percent of the estimated total incarcerated population, which is closer to the 25 percent figure that Greenwood initially thought would be appropriate. Once dif- ferent proportions of "street time" among the three offender groups (most for the low-rate group and least for the high-rate group) are taken into account, Greenwood's model estimated that about 13 percent of the total population of robbers in California are 203 for the newly defined groups indicate less differentiation between the medium- and high-rate groups-2.2 (Iow), 16.9 (me- dium), and 20.8 (high), compared with 0.9, 8.1, and 20.8 using the other scale cut points. Surprisingly, changing the cut points did not alter the average high rate A, which highlights the difficulty of dis- tinguishing between medium-rate and high-rate offenders with the prediction scale. With the alternative scale cut points and resulting changes in the model's pa- rameter values, the California robbery rate would actually increase about 6 per- cent under Greenwood's selective sen- tencing policy, although the imprisoned population would decrease about 20 per- cent. As with the Michigan and Texas estimates discussed earlier, the h,vpothet- ical increase in the crime rate and the reduction in prison population would oc- cur because the large majority of robbers would spend a smaller portion of their careers incarcerated, under the assump- tions of this revised model, and so would be free to commit crime. Thus, it appears clear from these anal- yses that the potential incapacitative ef- fects derived from a model that assumes a selective sentencing structure are sensi- tive to the choice of scale cut points and high-rate offenders (see Greenwood:771; the reestimation here of the model with the revised scale cut points makes the explicit assumption that fewer California robbers (i.e., only 6 percent) are high-rate offenders. Of course, since it is impossible to know how many "active" robbers actually exist in any state or how they are distributed across low-, medium-, and high-rate groups, these numbers must be estimated. But using Greenwood's method could distort the estimates! number of offenders in each group if, for example, the incarcerated population contained an unusually large group of predicted high-rate of- fenders, as was the case in California. An alternate method would be to estimate the total offender population using seven groups (one for each score value on the scale) rather than the three groups arbitrarily defined by the cut points.

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204 the nature of the offender population. The appropriate cut points on the predic- tion scale may depend on the definition of "high-rate" offender, which could dif- fer across states. The most active 10 per- cent of the robbers in the Texas sample each reported committing at least 15 crimes per year, but the top 10 percent of the California robbers reported 100 or more crimes a year (see Table 10~. Chaiken and Chaiken (1984:223) suggest that the low rates of robbery reported by inmates in Texas compared with inmates in Michigan and California could reflect unmeasured aspects of the environment on patterns of criminality. California o~- cials may be more willing to tolerate some forms of criminal behavior than their counterparts in Texas. The probable interstate differences in offender populations, criminal justice sys- tem practices, and projections of incapac- itative effects highlight the need for cus- tomizing the development of prediction rules, the selection of cut points, and the implementation of selective sentencing policies within each jurisdiction. Factors specific to the local situation should be consi(lered before any prediction instru- ment is adopted, even one having some degree of accuracy. Moreover, cut points for decision rules may also be influenced by local values as to the relative costs of the criminal behavior and the sanctions being imposed according to the rule (Blumstein, Farrington, and Moitra, 1985; Morris and Miller, 1985~. In any choice of cut points, the lower the cutoff defining the high-rate offenders, the greater the risk of incorrectly classifying some of- fenders in this group. SUMMARY AND CONCLUSIONS The single most important contribution of Ranc3's second inmate survey is the highlighting of the extreme skewness of CRIMINAL CAREERS AND CAREER CRIMINALS the distribution of A for a sample of known serious criminals. Although the technique used to elicit this information an(1 the Rand sample of incarcerated of- fenders may have introcluced errors into these estimates, the Rand study has sig- nificantly advanced our unclerstanding of individual patterns of criminal behavior. Although some minor differences exist in the precise numbers in the distribution, this reanalysis of the Rand data confirms that the distribution of A is highly skewed at least for the offenders sam- plecl from the prisons and jails of Califor- nia, Michigan, and Texas. Half the of- fenders report committing no more than five crimes a year, while a small but very important group may commit several hundred crimes a year. The estimates of A for robbery ant] burglary, however, are sensitive to choices in computation, such as the inter- pretation of ambiguous survey responses, the treatment of missing data, and the computation of the length of responclents' "street time." Moreover, the veracity of some respondents, particularly the large group of convicted robbers and burglars who clenied committing any robberies or burglaries and the few respondents whose reports implied annual rates of 1,000 or more robberies or burglaries, may be affecting the observed distribu- tion of A. Another problem is obtaining accurate annualized rates for those re- sponclents who are incarcerated for long portions of the observation period and who have intensive, but short, street time, or for those who commit crime sporadi- cally. Changes in the clesign of the Rand questionnaire or some analytic adjust- ments to the estimates of annual offend- ing rates may be necessary to provide more valid estimates of crime rates for such respondents. Finally, A varies con- siderably across the three state samples and further research is needed to deter

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THE RAND INMATE SURVEY: A REANALYSIS mine whether this variation is- due to differences in the states' offender popula- tions or is a consequence of different selectivity arising from the criminal jus- tice processes in these states. The Rand finding that has received the greatest public attention is also the one about which the most questions are raised in this reanalysis: the Greenwood formulation of a particular scale for iclen- tifying high-rate offenders. A fundamen- tal problem relates to how well this iclen- tification can be accomplished in an operational setting and how well the Rand report demonstrates the feasibility of cloing so. Although the scale certainly does bet- ter than chance in all the jurisdictions examined, one would expect improve- ment from any scale that invoked the predictors it did and that was fitted to the sample data. There is no indication that Greenwood's scale would perform any better, even in California, than any other scale that has been used operationally. The relative improvement over chance varied consiclerably across the three states; the best performance was ob- served for California (57 percent for rob- bery and 43 percent for burglary) and the worst for Michigan (21 percent for rob- bery and 19 percent for burglary). The prediction scale also seems to work some- what better in identifying Tow-rate of- fenders than the high-rate offenders at whom it was targeted, even adjusting for the higher prevalence of low-rate offenc3- ers in the population. These results em- phasize the importance of each jurisdic- tion's developing and vaTiclating its own scale rather than simply applying the sev- en-point prediction instrument devel- oped by Greenwood or any other instru ment. If one could identify the high-rate of 205 tainly make it possible to reduce crime by selectively incarcerating those high-rate offenders. This reanalysis of the Ranct data found that Greenwood overesti- mated the anticipated reduction in the California robbery rate. Using a seven- item scale and a sentencing policy that would double sentence lengths for high- rate offenders, the most favorable effect achieved in the reanalysis was a reduc- lion of about 13 percent. However, the scale used to identify high-rate offenders is more sensitive to the attributes of those offenders in California than to the at- tributes of high-rate offenders elsewhere. If the same sentencing policy and predic- tion scale were appliecl in Michigan and Texas, the crime rate would probably increase because of differences in current criminal justice practices and offender populations in the three states. More importantly, even in California, the assumptions necessary to make the calculation inflate the estimate of inca- pacitation effects. The estimate of a 13 percent reduction in crime with a selec- tive sentencing policy, which has been demonstraterl only with California clata, will clecline further if any ofthe following obtain: 1. Predictive power decreases as the model is applied to any new population ("shrinkage") and especially to a popula- tion of all convicted offenders rather than prisoners; 2. The comprehensive self-report data used in the Rand analyses are replaced by less complete official records of the pre dictor variables; 3. The reports of A gathered retrospec tively in the Ranc] survey fait to persist into the future, especially after the longer periods of incarceration impliecl by the selective incapacitation policy; fenders prospectively, the extreme skew- 4. The criminal justice system limits ness in the distribution of A should cer- the proposecl policy through judicial dis

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206 cretion or other adaptive responses in ways Mat reduce We disparity that arises from a sentence of 8 years for predicted high-rate offenders compared win 1 year for other convicted persons. Thus, future research is needed to identify characteristics of high-rate of- fenders and how those characteristics vary across offender populations. Re- search is also needed to develop and test locally appropriate, prediction-based se- lection rules to distinguish high-rate of- fenders from other offenders using oper- ationally available data. On the basis of this reanalysis, much more realistic esti- mates of the true operational effective- ness of a prediction instrument are needed before the current enthusiasm about the estimated reduction in crime through selective incapacitation is war- rantecI. APPENDS A: SUPPLEMENTAL TABLES TABLE A. 1 Comparison of the Cumulative Percentage Distribution of Rand Estimates of A with Esti- mates Produced from a Reanalysis of the Rand Data Robbery Burglary ~Rand Reanalysis Rand Reanalysis A < 1 13.1 16.8 9.4 < 2 24.9 33.0 20.8 < 3 35.3 41.8 32.7 < 4 43.8 52.9 40.1 < 5 49.4 56.1 48.2 < 10 65.8 71.9 62.1 < 20 77.5 81.2 70.3 < 30 82.7 85.4 75.0 < 40 85.1 87.4 77.3 <50 86.6 87.7 78.8 < 100 90.9 92.1 82.4 2 100 99.9 99.9 100.0 SOURCE: Chaiken and Chaiken robbery; 203, burglary). 12.1 26.5 35.8 46.0 52.2 65.7 73.1 77.0 79.1 80.4 83.9 99.9 (1982a:206, Variable CRIMINAL CAREERS AND CAREER CRIMINALS TABLE A.2 Regressions of Estimates of A on the Seven Variables in Greenwood's Scale: Inmates Convicted of Robbery or Burglary Unstandardized Coefficients Robbery Burglary I. Three States (N = 848) Past conviction Recent incarceration Early conviction Juvenile incarceration Recent drug use Juvenile drug use Recent unemployment Constant Adjusted R2 II. California (N = 311) Past conviction Recent incarceration Early conviction Juvenile incarceration Recent drug use Juvenile drug use Recent unemployment Constant Adjusted R2 III. Michigan (N = 245) Past conviction Recent incarceration Early conviction Juvenile incarceration Recent drug use Juvenile drug use Recent unemployment Constant Adjusted R2 IV. Texas (N = 292) Past conviction Recent incarceration Early conviction Juvenile incarceration Recent drug use Juvenile drug use Recent unemployment Constant Adjusted R2 -.01 .43a .11 .4oa .46a .48a .33a -.39 .12 -.01 l.l8a .12 .SOa .31 .62a .22 -.41 .20 .15 _ .71a -.01 .46 .s7a .39 .6la -.30 .08 -.14 .14 .19 -.11 .39a .29 .10 -.27 .05 .46a .28 .44a .18 gga .7la .47a -.37 .12 .58a .16 .32 .35 .soa .68a .27 -.54 .22 .44 .41 .64a .16 .78a .8oa .40 -.34 .12 .4oa .19 .39 -.005 .6sa . 7oa .62 -.22 NOTE: Missing data for the independent varia- bles were coded as 0. In the regression including all three states, 36 cases were excluded because of missing data for the dependent variable. For simi- lar reasons, 6 cases were excluded in California, 10 cases were excluded in Michigan, and 20 cases were excluded in Texas. ap < .05.

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THE RAND INMATE SURVEY: A REANALYSIS 207 TABLE A.3 Frequency Distribution of Offenders by Predicted ant] Self-Reported Offense Rates for Specific Subgroups Self-Reported Offense Rate Predicted Offense Rate Low Medium High Reanalysis with All Cases (N = 886) Low193 4122 Medium193 10988 High65 67108 California Robbers (N = 166) Low26 20 Medium35 139 High23 2632 Michigan Robbers (N = 142) Low38 6 Medium25 2422 High8 5 Texas Robbers (N = 114,, Low32 113 Medium22 1114 High3 711 California Burglars (N = 151) Low31 42 Medium35 1611 High10 1725 Michigan Burglars (N = 113) Low20 3~ Medium29 1711 High12 71C Texas Burglars (N = 200) Low46 15. Medium47 2821 High9 522 Only Cases with Unambiguous Responses (N = 568) Low149 3414 Medium126 7259 High29 3649 Six-Variable Scale (N = 886, Low231 6030 Medium178 103102 High43 5487

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208 CRIMINAL CAREERS AND CAREER CRIMINALS TABLE A.4 Parameters for the Incapacitation Model According to Estimates from the Reanalysis Predicted Offense Rate Parameter SymbolLowMediumHigh Total Number of offenders N95,50016,47310,611 Average annual offense rate A.98.120.8 Probability of arrest and convic tion q.030303 Probability of incarceration given conviction J.86.86.86 Probability of prison given incar ceration p.12.27.46 Average jail term in years s1.01.01.0 Average prison term in years S3.8924.6924.075 Average time served in years S1.3471.9972.415 Incarcerated population R2,8654,9425,942 13,749 Fraction of time free r197.70.44 Total crime C83,37293,40297,112 273,886 APPENDIX B.: DESCRIPTION OF QUESTIONS USED TO CONSTRUCT THE SEVEN VARIABLES The following information provides an overview of how the seven variables se- lectec3 for the scale used in the Green- wood report were constructed. All vari- ables are coded either 1 (yes) or 0 (no or missing). Short variable labels are user] in the following descriptions. 1. Prior Conviction Official records for most prison inmates contained information on the number of past convictions for several crime types. This variable was coclecl 0 if a convicted robber (or burglar) had no prior convic- tions in his record for robbery (or bur- glary) and 1 if one or more of the defining convictions were in his record. 2. Incarcerated Before Arrest A question on the survey asked inmates to indicate the months that they were in jail or in prison on their calendars. The percentage of possible "street time spent imprisoned was calculated, and in- mates with more than 50 percent were coded 1. (Greenwood user] Rand's mini mum estimate of street months for his calculations; thus, more inmates were coded 1 for this variable in his analysis than in the reanalysis.) 3. Convicted Before Age 16 The survey asked, "How old were you when you were first convicted of a crim- inal offense (an adult or juvenile convic- tion, other than a traffic violation)?" In- mates who reported a first conviction at age 15 or younger were coiled 1. 4. Juvenile Incarceration The survey asked, "Were you ever sent to a statewide orfe~leral juvenile institu- tion?" Inmates who respondecl "yes" were codecl 1. 5. Recent Drug Use The survey askocl, "During the months when you were using drugs, how often would you say you usually used each of the drugs listed below?" The drug types were: heroin/methaclone, barbiturates/ clowners/"reds," and amphetamines/up- pers/"whites"; the response categories were: did not use at all, few times a month, few times a week, every clay, or more than once a clay. This variable was

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THE RAND INMATE SURVEY A REANALYSIS coded 1 if inmates responded that they used heroin or barbiturates at all. 209 offenders and 8.43 years in prison for high-rate robbers can be calculated as follows: 6. Juvenile Drug Use The survey asked about drug use of the Low Medium High Total following types before age 18: marijua na, LSD/psyche(lelics/cocaine, uppers/ downers, heroin; frequency levels were: often, sometimes, just once or twice, never. According to Greenwood's defini tion only the uppers/clowners and heroin responses are relevant, but Rand's com puter code indicates that the LSD/ psychedelics/cocaine category was also included. In the reanalysis this variable was coded as "yes" if inmates used her oin or uppers/clowners either sometimes or often as juveniles, and "no" otherwise. 7. Unemployed Before Arrest The survey askect, "During how many of the street months on the caTenclar cticI you work?" The percentage of street months spent working was calculated, and inmates who worked less than 50 percent of the time were cocled 1. APPENDIX C: CALCULATION OF POTENTIAL INCAPACITATIVE EFFECTS USING DATA REPORTED BY GREENWOOD Greenwood (1982:74) found the maxi mum incapacitative effects using the fol Towing hypothetical policy: "low- and mectium-rate offenders are sentenced to jai! and high-rate offenders are sentenced to prison for terms of increasing length." He also states that"none of . . . the sen tence lengths for high-rate offenders Lis] increased by more than a factor of 2" (p. 791. This means that the end point in his graph (Figure 5.1) represents an expected sentence length of 8.43 years (twice the 50.6 months reported in Table 5.1, p. 77~. Using this information and the data in Table 5.1, the maximum incapacitative effect for a sentencing policy that assumes l-year jail terms for low- and medium-rate Number of Offendersa 49,714 11,895 9,028 c . ''b Time Free (71) Incarcerated PopulationC (R) Total Crimed (C) .95 .79.13 2,486 2,4987,854 12,838 94,457 94,91036,148 225,515 aThese estimates are reported in Greenwood (p. 77) with the exception of the low-rate offender estimate, which was corrected by Cohen and con- firmed by Abrahamse (1984, personal communica- tion). bUsing equation for ~ reported in Greenwood (p. 7s) ~ = 1/[1 + (2.0)(0.03)(0.86)(1)] = 0.95 EM = 1/[ 1 + ( 10.1)(0.03)(0.86)(1)] = 0.79 AH = 1/[1 + (30.8)(0.03)(0.861(8.43)] = 0.13 CUsing equation for Ri reported in Greenwood (p. 7s) RL = 49,714(1 - 0.95) RM = 11,895(1 - 0.79) RH = 9,028(1 - 0.13) dUsing equation for Ci reported in Greenwood (P- 75): CL = 49,714(0.95)(2.0) CM = 11,895(0.79)(10.1) CH = 9,028(0.13)(30.8) Under the current sentencing policy, the estimated incarcerated population is 13,930 (Table 5.1, p. 77) and the estimated number of robberies is 259,917 (corrected figure; Abrahamse, 1984, personal communica- tion). Percent decrease in incarceration: 1 - (12,838/13,930) = 8 percent Percent decrease in robbery: 1-(225,515/259,917) = 13 percent REFERENCES Avi-Itzhak, B., and Shinnar, J. 1973 Quantitative models in crime control. Jour- nal of Criminal Justice 1 :18~217. Blackmore, J., and Welsh, J. 1983 Selective incapacitation: sentencing accord

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