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Appendix B Research on Criminal Careers: Individual Frequency Rates and Offense Seriousness Jacqueline Cohen INTRODUCTION The level of crime experienced in a soci- ety varies with both the participation by individuals (b or d) in that society and the frequency of offending by active offenders (A). Increases in crime may be clue to in- creases in either the participation rate or the frequency of offending. Distinguishing among the different dimensions of criminal career's has implications both for our under- stancling ofthe factors contributing to crime and for efforts to control crime. The characterization of criminal careers invoked here assumes that offending is not pervasive throughout a population, but rather is generally restricted to a subset of individuals who are actively committing crimes cluring some period oftime. It is also assumed that the constituents of the subset of active offenders vary with time as some inclividuals become criminally active (onset of careers) and others terminate their crim- inal activity. Uncler this characterization, The author would like to thank Arnold Barnett, Alfred Blumstein, David Farrington, and Jeffrey Roth for their helpful comments on an earlier ver- sion of this paper. 292 the defining attribute of offenders is com- mission of at least one crime. Participation, the subject of Appendix A, refers to the size of the criminally active offender subset cluring some observation pe- riod. This subset of active offenders includes both new offenders (first offense occurs dur- ing the observation period) and persisting offenders (criminal activity began in an ear- lier period and continues into the current observation period). Participation rates dur- ing any observation period will thus depend on the number of individuals who become offenders and how long they remain crimi- nally active. The longer criminal careers are, the greater will be the contribution of persist- ers to participation in any observation period. The subset of active offenders in any observation period is distinguished by hav- ing a positive frequency of committing crimes (e.g., five crimes per year per active offender). Beyond the requirement of at least one offense for active offenders, fre- quency rates may vary substantially across active offenders, with some offenders hav- ing very high rates and others low rates of offending. Frequencies may also vary over time for an individual. Individual offenders who have the highest frequencies will con- tribute most to total crimes.

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APPENDIX B: RESEARCH ON CRIMINAL CAREERS Many different offense types may contrib- ute to an individual's frequency. Individual offenders, for example, may vary in the scope of their offending, from "specialists" (who engage predominantly in only one type of offense or one group of closely related offenses) to "generalists" (who en- gage in a wide variety of offense types). The degree of specialization may also vary across offense types; some offense types may be committed exclusively by special- ists, while others are routinely committed as part of an offender's varied mixture of offense types. The mix of offenses commit- ted by any offender may also vary as of- fending continues individual offenders may become either more or less special- ized, or increase or decrease the serious- ness of their offending. If there are consis- tent patterns of change in the mix of offenses, then commission of serious of- fenses may be characteristic of certain peri- ods during criminal careers (e.g., later ca- reers may be periods of more serious criminal activity). The various aspects of individual crimi- nality participation, career length, fre- quency, and crime mix will affect the con- tribution of individual offenders to the total volume of crime experienced at any time. Offending may be widespread, with many offenders each committing crimes at rela- tively low rates; in this event, individual offenders contribute very little to the total volume of crime. Altematively, individual 293 same individuals commit crimes over longer periods of time, and these persisters are major contributors to total crime. This appendix provides a critical review of the emerging body of research that em- pirically characterizes various dimensions of individual offending. Because of its scope and volume, the full range of the literature is beyond the reach of a single paper. Nar- rowing the focus of this review builds on a natural partition of the various dimensions of criminal careers. Participation delimits the subset of active offenders in a popula- tion; this dimension of criminal careers is addressed in Appendix A. This appendix focuses on the progress, or course, of indi- vidual offending during criminal careers, as measured by frequency rates and offense seriousness. Frequency rates are addressed first, fol- lowed by offense seriousness. In reviewing the research findings, special attention is given to their validity in light of various methodological concerns. In many in- stances, frequencies or offense seriousness are not addressed directly in the reported results, and whenever possible, available data have been reanalyzed in order to pre- sent results on frequency rates and offense seriousness in comparable terms. INDIVIDUAL OFFENDING FREQUENCIES FOR ACTIVE OFFENDERS , , frequencies may be high and participationIndividual offending frequencies, A, are a low; individual offenders would then befundamental feature of individual criminal responsible for a larger portion of total crimes. Career lengths may be short or lone. If careers are characteristically short, then there is likely to be a large turnover of active offenders as individuals quickly ter minate careers and new individuals be come criminally active. In this event, new offenders would be major contributors to crime. Also, with short careers, current par ticipation levels may be relatively low, while cumulative participation (all individ uals who were ever criminally active) is more widespread in the population. If crim inal careers are characteristically long, the careers. Despite the importance of A in estimating the magnitude of offending dur- ing~criminal careers, research that statisti- cally characterizes the intensity of offend- ing for large numbers of ordinary offenders is relatively recent. Much of the early re- search on individual criminal careers con- sisted of biographical or autobiographical studies. While such case studies provided interesting and often insightful reports on the individuals studied, there was little in ~Some of the classics among these studies are Booth (1929), Shaw (1930, 1931), Sutherland (1937), and Martin (19521.

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294 dication that the individuals were represen- ~tive of a larger group of offenders. Indeed, We subjects were more likely chosen for Weir fascinating uniqueness than for Weir representativeness. More recently, a large body of research has examined the attributes of large sam- ples of offenders. This research includes both studies of self-reported delinquency and studies using official records, such as arrest histones.2 Because this research has been largely motivated by interest in the causes and prevention of crime, it has fo- cused on identifying the correlates social, economic, psychological, and o~erwise- of offending. This research has typically developed estimates of participation (i.e., We prevalence of offenders) or of continued offending in different population subgroups. Estimates of the intensity of offending by identified offenders, A, are rarely provided. A related body of literature attempts to de- velop topologies of offenders win similar social or psychological at~ibutes.3 2The self-report literature is extensive and in- cludes over 100 studies. A partial bibliography is available in the review of the National Council on Crime and Delinquency (1970~. A critical review of much of this research is found in Reiss (1973) and Hindelang, Hirschi, and Weis (19791. The following represent only a small sample of the available re- search in this area: Reiss and Rhodes (1959), Hirschi (1969), Gold (1970), Waldo and Chiricos (1972), Williams and Gold (1972), Elliott and Voss (1974), Elliott and Ageton (1980), Hindelang, Hirschi, and Weis (1981), Elliott et al. (1983~. A recent review of participation measures, including those based on self-reports, is available in Visher and Roth (Appen- dix A). Analyses of official records typically involve lon- gitudinal analysis of large samples of criminal rec- ords. Among such studies are Glueck and Glueck (1937, 1940), McCord and McCord (1959), Robins (1966), Wolfgang, Figlio, and Sellin (1972), West and Farrington (1973, 1977), Robins, West, and Heganic (1975), Robins and Wish (1977), McCord (1978), Farrington and West (1981), Hindelang, Hirschi, and Weis (1981), Famngton (1983b, 1984~. 3See Warren (1971) and Gibbon,s (1975) for re- views of the topology literature. Examples of typol- ogy research are found in Kinch (1962), Gibbons (1965), Hurwitz (1965), Roebuck and Quinney (1967), and Davies (1969~. CRIMINAL CAREERS AND CAREER CRIMINALS Recent interest in the crime control ef- fects of incapacitation has underscored the importance of developing estimates of A. Recognizing the impact of variability in A on estimates of incapacitative effects, the National Research Council Panel on Deter- rent and Incapacitative Effects (Blumstein, Cohen, and Nagin, 1978:80) made the fol- lowing recommendation: Empirical investigation should also be directed at estimating the parameters measuring the level of individual criminal activity, especially the indivicI~al grime rates ... and career lengths.... Furthermore since estimates of the incapacita- tive effect are sensitive to variations in these parameters, these estimates should not be re- stricted to highly aggregated population aver- ages. They should be disaggregated by crime type and demographic group and should reflect the statistical distribution of the parameters. Recent studies in two research pro- grams~ne at the Rand Corporation and the other at Carnegie-Mellon University have begun to provide explicit, disaggre- gated estimates of A. That research is re- viewed in this section, in particular the very different approaches used and the resulting estimates of A. A number of other studies provide estimates of participation rates and aggregate incidence rates for a study popu- lation. These data provide a basis for devel- oping estimates of A for the studied popula- tions. The results of these new analyses are also reported below. Throughout this review of estimates of A, various methodological issues in the mea- surement of A are discussed and suggestions are made for further research in this area. The section begins with a discussion of the distinction between A, the main interest here, and more commonly available esti- mates of aggregate incidence rates. Distinguishing Individual Frequency Rates from Aggregate Incidence Rates Individual frequency rates, A, apply only to active offenders. This restriction distin- guishes A from the more commonly avail- able measure of aggregate incidence rates, which reflect the frequency of offenses, or arrests, in the general population. Aggre

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APPENDIX B.: RESEARCH ON CRIMINAL CA0ERS gate incidence rates are exemplified by the annual crime rates and arrest rates reported by the Federal Bureau of Investigation. The key feature distinguishing A from aggregate incidence rates is the population base on which the estimates are calculated. In calculating A, only individuals with at least one offense, or arrest, are included in the population base. Estimates of A thus reflect the average frequency of offending for individuals who are actively committing crimes. Aggregate incidence rates, by con- trast, apply to a total population. The popu- lation at risk includes offenders and nonof- fenders alike. Aggregate incidence rates reflect the com- bined contribution of participation rates for offenders in a population, ~ or b, and individ- ual frequency rates, A or A, for active of- fenders. Consider, for example, estimates of aggregate arrest rates for some population i: Aggregate = Number of arrests of persons arrest in population i rate for population i Number of persons in population i This aggregate measure can be partitioned between the participation rate for offenders (~) and the frequency rate for those of- fenders (,u): Aggregate Number of persons arrest arrested in population i rate for Number of persons in population i population i Number of arrests of persons in population i Number of persons arrested in population i = Participation rated x Frequency rated (1) The conceptual distinction between par- ticipation rates and individual frequency rates has important implications for the evaluation of incapacitative effects. The crime control potential of incapacitation hinges on the magnitude of an individual's offending frequency, A. This is the expected 295 number of crimes averted by incapacitating an offender. Aggregate incidence rates in- clude rates of zero for nonoffenders, who are not vulnerable to incarceration, except in the rare cases of wrongful conviction. Aggregate incidence rates, therefore, would seriously underestimate the crime reduc- tion achieved by incapacitation. Likewise, the impact of incapacitation policies on prison populations depends on the partici- pation rates of offenders in a population. The more widespread that offenders are in a population, the greater will be the potential increases in prison populations as a result of incapacitation strategies. To the extent that A exceeds one offense per offender, aggre- gate incidence rates will overstate the prev- alence of offenders. In this event, use of aggregate incidence rates in place of partic- ipation rates would lead to overestimates of the potential impact of increased incapaci- tation on prison populations. Accurate esti- mates of the tradeoffs between increases in prison population and reductions in crime through alternative incapacitation policies depend critically on having separate esti- mates of participation and frequency rates. The partition of aggregate incidence rates into participation rates, on the one hand, and individual frequency rates for active offenders, on the other, may also be useful in evaluating the effectiveness of other crime conko1 policies. To date, evaluations of deterrent and rehabilitative effects have relied almost exclusively on aggregate out- come measures.4 To the extent that partici 4Recidivism rates are a special variant of aggre- ~ate incidence rates. While restricted to a popula- tion of identified offenders, the prospective per- foll~ance of this population is the combined result of the level of continued participation by active offenders and the magnitude of individual fre- quency rates for those who remain criminally active. In particular, failure to recidivate during a follow-up period may occur because some offenders end their criminal careers altogether, or because some offend- ers who do remain criminally active do so at low frequency rates. In the latter case, extending the length ofthe follow-up period increases the likelihood of observing eventual recidivism; in the former case, recidivism will never occur no matter how long the follow-up period.

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296 pation rates and frequency rates are dif- ferentially affected by deterrence or reha- bilitation policies, important effects on these component parts may be obscured in the aggregate measures. Analyses of effects on the partitioned measures may provide valuable insights for improving the crime control effectiveness of deterrence and re- habilitation policies. It may be, for example, that different strategies will be more effec- tive if they are targeted on selected popula- tion subgroups. Review of Estimates of Individual Frequency Rates The main interest in this subsection is empirically based estimates of A. Relatively few studies provide explicit estimates of A, and they are limited to samples of serious adult offenders. Three such studies are re- viewed in this section. Indirect estimates of A, derived expressly for the panel from other published data on current participa- tion rates and aggregate incidence rates, are also reviewed. The indirect estimates have been developed for a wider variety of study populations. The studies that provide explicit esti- mates of A are summarized in Table 1. Separate rates are generally estimated for individual offense types, and total rates are presented for larger offense classes. Of- fense-specific frequencies reflect the aver- age number of offenses committed when that offender is active in that offense type. Active offenders are distinguished by hav- ing at least one offense (or one arrest) for a crime type. Individuals with no offenses of a particular type during the observation period are excluded in the computation of rates for that offense type. By this criterion, the earliest self-report survey, the Rand survey of 49 prison in- mates (Petersilia, Greenwood, and Lavin, 1977), is properly excluded from consider- ation. The offending rates reported in that study apply to the total sample of 49 of- fenders; rates of zero for inmates who re- ported no offenses of a given type are in- cluded in the reported rates. As the di- rect precursor of the two later Rand inmate CRIMINAL CAREERS AND CAREER CRIMINALS surveys, however, the study is included here. All of the studies summarized in Table 1 are based on samples of adult offenders. The samples are also generally restricted to more serious, or more criminally active, subsets of adult offenders. The three self- report surveys, all by the Rand Corporation, are based on surveys of inmates serving sentences in state prisons and, in one study, inmates in local jails. These inmate samples are thus restricted to offenders whose cur- rent offense or prior criminal record was serious enough to have warranted a sen- tence of incarceration. The two studies using official arrest rec- ords are based on samples of adult ar- restees. While potentially including a broader range of offenders, these studies also focus on offending by a more serious subset of offenders. To enter the sample, an offender must have had at least one arrest for a serious index offense (murder, rape, robbery, aggravated assault, burglary, or auto theft) during the sampling period. This criterion excluded offenders who engaged exclusively in minor offenses or who were never arrested for a serious index offense. By focusing on subsets of offenders who are active in serious offense types, studies of frequency rates can develop estimates of A in those serious offense types. Because they are generally quite rare, these more serious offense types have often been excluded from surveys of general population samples. The studies that provide estimates of A for serious offenders are in direct contrast to the much larger body of research on participa- tion in offending, which is typically based on self- or official reports of offending and deviance for juveniles sampled from a gen- eral population, and which is therefore dominated by the more common minor of- fenses, such as vandalism or simple assault. This difference in design reflects the dif- ferent focus of the studies, in the first in- stance, the intensity of serious offending by more continuously active offenders, and in the second, the scope of deviance found in a broad population. In addition to focusing on active offend- ers, the studies in Table 1 also restrict the

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302 calculation of frequency rates to periods when the offenders were at risk of commit- ting offenses in the community. Time when the offender was incapacitated through in- carceration or long-term hospitalization (e.g., more than a 1-month stay) was ex- cluded from the time at risk. The resulting frequencies reflect the intensity of offend- ing while an offender is criminally active in the community, A, the rate of offending that would be expected ' if the offender were never incarcerated. The estimates of A are to be distinguished from the effective rates for offenders, A*. In estimating the reduction in crime associated with different periods of incarceration, the appropriate quantity is the individual's active offending frequency. This is the rate at which crimes would be committed if the offender were not incarcer- ated. Any time spent incarcerated will re- duce the annual active rate to yield the effective rate for offenders. For example, an offender may commit crimes at a rate of 10 per year while he is free in the community. If this offender is incarcerated for 6 months, however, he can only commit crimes at rate 10 for the 6 months he is free. His active rate is 10 offenses per year, but his effective rate during the entire year is only 5, since he was only actively committing crimes in the community for half of the year. The effective offending rate reflects the reduction in the potential level of crime as a result of current incapacitation policies. Be- cause effective rates are already discounted by current incapacitation levels, using the effective rate instead of the active rate would lead to underestimates of the total crime reduction associated with increases in incapacitation. When the effective annual offending rate is used, incarceration during all of the following year would be incor- rectly estimated to avert only five offenses. This fails to include the additional five of- fenses that would have occurred had the offender not been incarcerated for one-half year. On the basis of the offender's offend- ing frequency while free, incarceration for a full year can be expected to avert 10 of- fenses. CRIMINAL CAREERS AND CAREER CRIMINALS Estimating A from Self-Reports: The Rand Inmate Surveys Survey of Habitual Offenders. The study of 49 habitual offenders by Petersilia, Greenwood, and Lavin (1977) laid the groundwork for later Rand surveys of larger samples of inmates. The original 49 inmates were chosen as exemplars of serious, recid- ivistic offenders. To be included in the sample, an inmate had to be currently serv- ing a sentence for at least one armed rob- bery conviction and have at least one prior sentence of incarceration. Through per- sonal interviews, the inmates were asked about their frequency of offending and prior criminal record (arrests, convictions, and incarcerations) for nine offense types, as juveniles, as young adults (before their first incarceration as adults), and prior to the start of the current sentence. In addition, they were asked about other aspects oftheir personal histories, including family circum- stances, school and employment experi- ences, drug and alcohol use, personal moti- vations for crime, and styles of committing crimes (e.g., the amount of planning and preparation, use of accomplices). The findings on average levels of offend- ing over time for the 49 offenders, including offenders who were active in an offense and those who were not, are summarized in Table 2. As one reads down the table, the offense classes become more inclusive; the total rate includes offending in any of the nine offense types surveyed. Except for vi- olent offenses, the reported monthly rates declined markedly as offenders got older. The anomalous slight increase in monthly rates with age for violent offenses is attrib- uted by the authors to the sampling crite- rion that required an atoned robbery prior to the current incarceration, which marks the end of the adult period (Petersilia, Green- wood, and Lavin:27~. As indicated above, all 49 offenders are included in a rate, whether or not they were active in that offense type. The opposite trends across age observed for violent of- fenses and all other offense types illustrate

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408 her of prior arrests for that same type found in a career, or using some weighted statistic for that offense type that gives greater weight to more recent occurrences of the offense type. Reliance on Official Records All of the studies ot ottense switching reviewed here relied on official-record data on sequences of police contacts or arrests for samples of offenders. This dependence on official-recorc! data arises from the re- quirement for data that document the exact sequences of offense types over time" in- formation that is readily available in official records. The picture of offense switching that emerges from analyses of official-record data, however, confounds patterns of of- fense switching by offenders with patterns of law enforcement, especially by the po- lice. As noted earlier, the offense types observed on successive police contacts or successive arrests will vary with the levels of police effectiveness in apprehending of- fenders for different offense types. Offense types with higher detection and apprehen- sion rates will be overrepresented among official contacts compared with their repre- sentation in successive crimes committed. If enforcement rates vary substantially for different offense types, the patterns of switching observed in official-record data will provide a distorted view of offense switching between actual crimes commit- ted. This confounding effect is recognized in virtually all studies of offense switching, and the studies are careful to note that the reported results apply to successive official contacts for offenders. The variability in arrest risk for different offense types is illustrated in Table 53. Based on data for the United States, the ratio of arrests to reported crimes (in column 3) varies from a low of .12 for auto theft to a high of.42 for aggravated assault. The ratio of arrests to reported crimes alone, how- ever, is an inadequate estimate of the chance of arrest for a crime for an indi- vidual. Crimes committed but not reported to the police are not included, and arrests CRIMINAL CAREERS AND CAREER CRIMINALS sometimes include arrests of more than one individual for the same crime incident. The number of reported crimes can be adjusted for nonreporting by using the re- porting rates for various offense types avail- able from national surveys of criminal vic- timization. A further adjustment for multiple offenders per crime incident is also available in these national surveys.36 The adjusted estimates of the probability that any individual offender is arrested for a crime committed, whether reported to the police or not, are shown in the last column in Table 53. The risk of arrest is highest for offenses involving direct contact between offenders and victims (robbery and aggra- vated assault) and lowest for property of- fenses without contact. The final arrest risk for the various offense types is generally low, averaging only 1 arrest for every 20 crimes committed. De- snite the reduction in arrest risk after the adjustments, there is still a threefold differ- ence between the highest risk (aggravated assault) and the lowest risk (larceny and auto theft). Variability in arrest risk for dip ferent offense types is thus a very real con- cern in analyses of offense switching that rely on official contacts only. Two strategies are available for dealing with distortions in offense-switching pat- terns that arise from use of official-record data. The first is to expand the scope of self-reports of offenses committed to in- clude data on the actual sequence of dif- ferent offense types. To date, such data on sequences of crimes actually committed have been unavailable. It is only recently Mat self-report studies have begun to col- lect data on frequency of offending during a reporting period. Collecting data on the sequence of offense types will require 36The adjusted arrest risk for a crime of offense type k, qk, is given by Arrests of Type k rk qk = X- Reported Offenses of Type k k where rk is the rate of reporting offenses to the police by crime victims and k is the average number of offenders per crime incident.

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APPENDIX B.: RESEARCH ON CRIMINAL CAREERS 409 TABLE B-53 Probability of Arrest for a Crime, Adjusted for Nonreporting to the Police and Multiple Offenders per Crime Proportion Offense Type Reported Offenses in U.S., 1980a Arrests in UPS., 1980 Ratio of of Total Arrests to Offenses Probability Number of of Arrest for Offenders a Crime, A, Reported Reported per Crimg for Individual Offenses to Police- Incident- Offenders Robbery548,809146,270 Aggravated assault654,957277,470 Burglary3,759,193513,300 Larceny7,112,6571,191,900 Auto theft1,114,651138,300 .27 .42 .14 .17 .12 .57 .54 .51 .27 .69 2.3 2.6 1.6 1.6 1.8 .07 .09 .04 .03 .03 federal Bureau of Investigation (1981:Table 1). bFederal Bureau of Investigation (1981:Table 24). bureau of Justice Statistics (1982a:Table 89). dReiss (1980b:Table 2). panel designs that include data collection from the same sample of offenders at fre- quent intervals. Depending on the antici- pated rates of individual offending, monthly or perhaps even weekly reports may be required. Given that the focus of the research is offense seriousness, the strategy of repeated and frequent self-reports is best limited to samples of known ollenders. Those offend- ers might be identified from self-reported offenses in a more widely used screening instrument, or through arrest or police con- tact associated with an offense. As noted earlier, self-report studies involving re- peated and frequent reporting will be costly, will require a reasonably long-term commitment-of at least several years- to data collection, and will involve difficult logistics in order to maintain contact throughout the study with samples whose members are likely to be uncooperative and mobile. While the self-report approach is certainly possible, the various implementa- tion problems in addition to the large sam- ple sizes required to estimate switching patterns make pursuit of this research strategy all the more difficult. The data re- quirements are somewhat less demanding if repeated self-reports are used to estimate changes in offense mix during successive reporting periods. Such analyses would not require data on the exact sequence of of- fense types and smaller samples of offend- ers would be adequate. An alternative strategy for analyzing the actual sequence of crimes committed builds on the current reliance on official-record data, extending it to address offense switch- ing between crimes actually committed. As we learn more about the links between individual offending and the criminal jus- tice selection process, we will be better able to model the selection process. By incorporating models of the selection proc- ess with readily available official-record data, we can begin to draw inferences about the switching process for undetected crimes that intervene between official-record events. This inferential strategy has begun to be employed with some success in stud- ies of individual crime rates based on offi- cial-record data. Biases Associated with Sample Selection The most obvious biases arising from the sampling process are distortions introduced by the sampling event itself. These are most

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410 likely to arise when sampling is based on some threshold of seriousness in offense types. The analyses of criminal histories for a sample of prison inmates by Frum (1958) and incarcerated juveniles by Smith and Smith (1984) are excellent illustrations of this problem. The samples were drawn from among inmates in state correctional facilities. Since all sample members were incarcerated for the last arrest in their rec- ords, that last event was likely to be for a serious offense type or to follow a record of repeated convictions for serious offense types. The sampling strategy of using incar- cerated offenders, and the failure to exclude the last offense type from the analysis, were no doubt major factors contributing to the findings of escalation toward more serious offense types over the course of criminal careers and of the tendency for some of- fenders to specialize in serious offense types. When sample selection is based on a seriousness threshold, it is essential that the sampling event be excluded from analysis of offense-switching patterns. Failure to ex- clude the necessarily more serious sam- pling event will bias estimates of offense .. . . switching patterns toward these more serious events. It was precisely to avoid such biases that the more serious sampling event was excluded from analyses of adult arrestees by Moitra (1981) and Blumstein, Cohen, and Das (1985~. Sample selection in Rojek and Erikson (1982) and Bursik (1980) was based on ei- ther processing by the juvenile court or an adjudication as a delinquent in the juvenile court. Given that the discretion to resolve juvenile cases informally is available to both the police and to intake officers at juvenile court, the formal involvement of the juvenile court likely increases the seri- ousness of the sampling event in both sam- ples. The sampling event was not excluded from either analysis. The sampling event, however, was not restricted to the last event in the record; it could appear anywhere in the record, depending on the age of the offender during the sampling period. This distribution of the sampling event over dif- ferent points in a record limits the biasing CRIMINAL CAREERS AND CAREER CRIMINALS effect toward more serious events at the end of the record. On the other hand, it may be responsible for findings of stationarity over transitions as intermittent escalations in se- riousness associated with the sampling event are randomly distributed over indi- vidual arrest histories, obscuring any pat- terns over time that may otherwise exist. Two strategies are available to control for distortions arising from the sampled event. First, the sampled event can be excluded from the analysis entirely. This strategy is especially appropriate when the sampled event falls at the end of arrest history data. The alternative is to include the sampled event in the analysis, but to limit the anal- ysis to similar events. Thus, in the two juvenile court samples, analysis of offense switching would be limited to contacts processed by juvenile court (in the case of Rojok and Erikson, 1982) or to offenses that were adjudicated delinquent (in Bursik, 1980~. In this way, the sampling event is indistinguishable from other events in the analysis. This strategy of only analyzing events similar to the sampling event was employed in the study of offense switching by juveniles in the Philadelphia cohort (Wolfgang, Figlio, and Sellin, 1972~. Like the adult analyses, this study of juveniles is free of biases associated with the sampling event. Aside from biases introduced into the switching process by the sampling event, the sampling process itself selectively lim- its the population of offenders who are stud- ied. All analyses of offense switching re- quire at least one official-record event (police contact, arrest, juvenile court proc- essing, juvenile court adjudication, convic- tion, or incarceration). Those that exclude Resistance require at least two contacts for each offender. The switching patterns ob- served thus apply most accurately to sub- sets of offenders with official records. Even if the criminal justice selection process was completely random, offenders with official contacts would be a random sample of all offenders only if all offenders are homoge- neous in offending. Any variability in of- fending (e.g., higher frequency rates for some offenders compared with others, or

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APPENDIX B.: RESEARCH ON CRIMINAL CAREERS N longer criminal careers) would increase the representation of more active offenders in the sample. Because their greater criminal activity increases their exposure to risk, of- fenders with higher frequencies and longer criminal careers are more likely to experi- ence an official contact and thus are more likely to be found in samples.37 Most anal- yses of offense-switching patterns, there- fore, reflect offense switching for the more active offenders who are found in the sam- ple and may not apply to all offenders. Role of Frequency Rates, Career Length, and Incapacitation in Switching Patterns Frequency rates, career length, and time spent incarcerated vary for different offense types. As reported in the review of fre- quency rates above, individual crime rates are higher for property crimes and lower for violent crimes. Analyses of the length of criminal careers (Blumstein and Cohen, 1982) report an opposite relationship: shorter average careers in property crimes and longer average careers in violent crimes. Time spent incarcerated is also likely to be longer for violent crimes than for property crimes. These differences can affect the switching patterns observed, es- pecially when observation periods are lim- ited in length. In particular, offense types that occur at high rates, and thus involve short intervals between events and short periods of incarceration, are likely to be more prevalent as switching destinations. Conversely, switching to offense types that involve longer average intervals between events and longer periods of incarceration is likely to be underrepresented, especially when observation periods are short. The possible distorting effect of the dis 37This tendency to oversample more active of- fenders holds under a variety of conditions. The only exception is those instances in which selection risk per offense committed is strongly inversely related to individual offending patterns win high- rate or long-career offenders having a much lower risk of official contact per offense than low-rate or short-career offenders. 4~] tribution in the various offense types was evident in the analysis of specialization for adults. Without controlling for differences in the distribution of offense types, burglary and larceny have the largest diagonal switching probabilities of all offense types for adults, which suggests greater special- ization in these offense types by adult of- fenders. Examination of the column marginals for these offense types, however, reveals that switching to these offense types is higher generally. Thus, the tendency to specialize in burglary or larceny is not es- pecially great relative to the generally higher frequency of switching to burglary and larceny as the next offense type. Con- versely, even apparently small diagonal val- ues may reflect significant specialization when switching to an offense type is gener- ally quite rare. The distribution in different offense types is explicitly controlled in all analyses in which the observed frequency of switches is tested against a model of complete independence in switching. Dif- ferences in the distribution of the offense types are reflected directly in the frequency of switches expected in an independent process. Variations in the number of events in a criminal history are one indication of dif- ferent levels of offending and differences in incapacitation experiences. Most directly, differences in the number of events will reflect variations in individual frequency rates and in career length. High-rate of- tenders are more likely to accumulate large numbers of events, as are offenders who remain criminally active for long periods of time. Extended periods of incarceration during careers. bv contrast. will limit the c , , , number of events in a career. Because lev- els of offending and incapacitation experi- ences may also be associated with the of- fense types found in a record, differences in the number of events can affect analyses of offense-switching patterns. This potential source of bias in analyses of switching was illustrated most dramati- cally in the earlier examination of escalation effects. Without controls for differences in the number of arrests for different individ- uals, average seriousness appeared to de

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412 cline with each additional arrest for adults. The analysis, however, was not based on the same sample of individuals at each ar- rest. Offenders with only a few arrests con- tributed to the average seriousness of early arrests, but seriousness on successive ar- rests was increasingly based on offenders with larger numbers of arrests. Thus, the observed decline in seriousness could re- flect differences among offenders, and not a change as individual offending progresses. The key role of population heterogeneity was confirmed when controls for this sam- ple-selection effect were introduced. Con- trolling for the number of arrests in a his- tory, average seriousness was generally stable on successive arrests for adults. Av- erage seriousness, however, was lower for adult offenders who had larger numbers of arrests. Variations in record length among offend- ers is a similar concern in estimating switch- ing probabilities more generally. More ac- tive offenders, with their larger numbers of arrests, will contribute disproportionately to estimates of a single, summary transition matrix that combines all offense switches together. To the extent that switching pat- terns vary with record length among of- fenders, the combined matrix estimate will be biased to reflect the pattern of offenders who have long records. This potential bias is partially controlled by estimating sepa- rate transition matrices for each offense switch; variations in switching with record length will be evident in the variability (nonstationarity) across the separate matri- ces. These separate matrices, however, are subject to the same sample-selection biases affecting average seriousness. Successive matrices are based on an increasingly more selected sample of offenders those with larger numbers of arrests. Thus any trends in switching observed over successive ma- trices may reflect population heterogeneity and not a progression in switching during individual criminal careers. The potential role of selection effects in successive transition matrices was illus- trated in the reanalysis above of the data on juvenile offenders in Pima County. The reanalysis found nonstationarity in switch- ing probabilities from juvenile status of CRIMINAL CAREERS AND CAREER CRIMINALS tenses, with more Resistance on early tran- sitions compared with later transitions, and more switches to personal and "other" crimes on later transitions compared with early transitions. This pattern suggests an escalation in seriousness for status offend- ers. The successive transition matrices, how- ever, were not estimated using the same sample of offenders on each transition. Of- fenders with only a few police contacts were selected out of the analysis through early Resistance. Thus, the apparent trend to more serious offending for status offend- ers may reflect a selection effect in which status offenders with a small number of contacts were also less serious offenders. These less serious offenders, however, only entered the estimates of early transition matrices. Later transition estimates were based increasingly on status offenders who had larger numbers of police contacts. If these more active status offenders were also more serious offenders generally, the trend to more serious offending observed on suc- cessive transitions would reflect this popu- lation heterogeneity and not a tendency to move to more serious offenses for individ- ual status offenders. As in the analysis of trends in average seriousness, the effects of this form of pop- ulation heterogeneity can be explored by estimating successive transition matrices af- ter controlling for the number of arrests in a history. This, however, places increased de- mands on the sample size necessary for analysis. Population heterogeneity, especially with respect to record length, represents a strong competing hypothesis in accounting for differences in offense patterns observer! in the studies reviewed here. In comparing adult and juvenile offenders, for example, greater specialization was observed for adult offenders than for juvenile offenders. In the juvenile years, offender samples may consist of some casual offenders whose of- fending is exploratory and ends quickly and of other more committed offenders who are specialized in their offending. As explor- atory offenders leave offending in the juve- nile years, adult samples would consist more heavily of committed, specialized of

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APPENDIX B: RESEARCH ON CRIMINAL CAREERS fenders. In this event, the difference in specialization for adults and juveniles might arise from differences across offend- ers and not from a developmental process toward greater specialization as offenders get older. Sorting out these rival hypotheses requires analyses of offense-switching pat- terns for a common sample of offenders who begin offending as juveniles and persist into adulthood. Aside from the potential distortions asso- ciated with variations in the number of events in criminal histories, variations in the length of observation periods may also affect the switching pattern observed. Of- fenders' frequency rates and career lengths, as well as their incarceration experiences, all affect the length of intervals between events. Inter-arrest intervals, as notecl, will be sho* when individual arrest rates are high. When individual arrest rates are low, by contrast, or when long periods of incar- ceration are likely to substantially reduce the time at risk for subsequent arrests, inter- arrest intervals are more likely to be long, and these intervals will only be observed in longer careers. To the extent that frequency rates, career length, and incarceration risk vary across different offense types, the asso- ciated differences in intervals between events for different offense types can affect the mix of offense types observed in switch- ing data. In particular, offense types charac- terizec] by short inter-event intervals are more likely to be observed when observa- tion periods are short. ~. Correspondingly longer observation periods are required if offense types characterized by longer inter- event intervals are to be adequately repre- sented. Variations in inter-arrest intervals for dif- ferent offense types may affect the estimates of transition matrices. All the analyses of offense switching reviewed here have sup- pressec] differences in the time intervals between events. Switching events were de- fined by the occurrence of a next arrest (or police contact), and switching events were aggregated regardless of the differences in the time interval to that event. The pattern of switching among offense types, however, may vary with the length of inter-arrest intervals. 4~3 Building on the differences in frequency rates and career lengths observed for dif- ferent offense types, for example, it might be expected that switches to property of- fenses-with their higher individual fre- quencies and short careers would be more likely when the intervals between events are short. Conversely, when intervals are long, greater switching to violent offenses- with their lower frequencies ant] longer careers- would be expected. The data on offense switching between arrests for adults in Washington, D.C., and Michigan are user] here to explore these potential differences. The estimated transi- tion probabilities for selected offense types for Washington, D.C., arrestees are pre- sented in Table 54. The significance of differences in switching was assessed using the ASRs of Goodman's (1962, 1968) contin- gency table approach. Taking one offense type at a time, a test was made of whether switching patterns from that offense were independent of the length of the interval to the next arrest. Although not presented here, results similar to those for Washing- ton, D.C., arrestees were also found for both whites and blacks in the Detroit and south- ern Michigan samples. Systematic variations in switching were found with differences in the length of in- tervals between arrests. Consistent with the lower frequency and longer careers in ag- gravated assault, the most persistent differ- ence was an increased tendency to switch to aggravated assault as the length ofthe inter- val between arrests increased (indicated by a shift from negative to positive ASRs). Switches to robbery, with its higher fre- quency rate and shorter careers, were more likely after short intervals (indicated by a shift from positive to negative ASRs). A decline in specialization as intervals in- creased was also observed for robbery and burglary (indicated by a shift from positive to negative ASRs). - Alternatives to Simple Markov ModeZs A simple Markov property was invoked in several analyses of offense-switching pat- terns. Under this Markov assumption, of- Sense switching Lepers, at most, on the

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414 CRIMINAL CAREERS AND CAREER CRIMINALS TABLE B-54 Variations in Offense-Type Switching with Length of Interval Between Arrests for Washington, D.C., Arrestees Prevalence Offense Length of Probability of of Offense Type on Interval Offense Type on k + 1st Arrest Type on kth kth Between Aggravated Arrest Arrest Arrests Assault Robbery Burglary (percent) Aggravated <1 year.300 .086.059 11.6 assault (NS)a (2.7)**(NS) (x2 = >1 year and.269 .070.065 16.9 51.03,* <2 years(NS) (NS)(NS) 33 d.f.) >2 years and.259 .026.043 18 <4 years(NS) (-2.1)*(NS) >4 years.385 .049.045 19.3 (3.0)** (-1.8)(NS) Robbery <1 year.109 .312.074 10.4 (-2.7)** (3.7)***(NS) (x2 = >1 year and.133 .301.062 10 57.03,** <2 years (NS) (NS) (NS) 33 d.f.) >2 years and .143 .214 .071 8 <4 years (NS) (NS) (NS) >4 years .195 .134 .101 10 (3.0)** (-4.4)*** (NS) Burglary <1 year .071 .083 .316 10 (-3.8)*** (NS) (4.8)*** (x2 = . . 68.96,*** >1 year and .113 .056 .185 11.3 33 d.f.) <2 years (NS) (NS) (-2.6)** >2 years and .067 .067 .225 14.2 <4 years (NS) (NS) (NS) >4 years .180 .087 .174 12.5 (4.7)*** (NS) (-3.5)*** pOnly ASRs significant at the .10 level or better (two-tailed test using standard normal distribution) are reported in parentheses. All other nonsignificant values are indicated by NS. *Significant at the .05 level. **Significant at the .01 level. ***Significant at the .001 level.

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APPENDIX B: RESEARCH ON CRIMINAL CAREERS offense type of the current arrest. The lim- ited tests available for assessing the ade- quacy of the Markov assumption suggest that offense switching is not adequately modeled as a first-order Markov chain. De- pendence on prior offense types appears to extend beyond the current offense type and results in greater specialization than would be expected in this simple Markov model. The tendency for observations to bunch on the diagonals of transition matrices has been observed in a variety of social pro- cesses, most notably residential migration and status mobility. Failure of simple Markov models in these processes is often attributed to population heterogeneity, and a variety of alternative modeling strategies have been proposed (see, for example, Singer and Spilerman, 1978, for a discus- sion of the various approaches). In its most common form, the population is presumed to vary in its tendency to stay in the same state on successive transitions. In the case of offense switching, offenders would vary with respect to offense specialization. At one extreme, some offenders might be highly specialized and thereby have a high likelihood of repeatedly engaging in the same offense type. At the other extreme would be generalists, whose offending would vary randomly over many different offense types. Various alternative models have been proposed to address population heterogene- ity satisfactorily. Many ofthese models pre- serve the Markov property for switching within different population subgroups, but specify different transition matrices for each subgroup. The non-Markov aggregate tran- sition matrix reflects the combined effect of these separate Markov transition processes. The simplest, and one of the earliest, ap- proaches to population heterogeneity was the "mover-stayer" model first introduced by Blumen, Kogan, and McCarthy (1955~.38 If this model is applied to offense switch- ing, the population of offenders would be 38Various later extensions and tests of the mover- stayer model are available in the research literature; see, for example, Goodman (1961), White (1970), and Spilerman (1972b). 4~5 TABLE B-55 Distr ibution of Youths with Over Half of Their Police Contacts in a Single Offense Category White (percent) Nonwhite (percent) Personal injury Personal property Impersonal property Other ~ No n specialization" Total 1.5 1.5 34.3 28.4 34.3 100 (N = 134) 1.5 0.9 32.5 14.9 50.1 100 (N = 335) NOTE: Cook County Juveni le Court s able of youths with at least five police contacts. SOURCE: Bursik (1980:Table 5). divided into two groups-the "stayers," who would always repeat the same offense type, and the "movers," who would switch among offense types according to a common Markov transition process. Switching by both groups can easily be combined and various predictions of expected future switching patterns for the aggregate popu- lation are available. The finding of specialization in a variety of offender samples suggests that this parti- tion of offenders into specialists and gener- alists may be a fruitful approach to model- ing offense switching. While there is evidence of specialization in all samples, some offenders seem to be more likely to specialize than others. As indicated in Ta- ble 55, specialization within aggregate of- fense categories was widespread among Cook County juvenile offenders. For of- fenders with at least five police contacts as juveniles, one-half of the nonwhites and two-thirds of the whites in the sample had over 50 percent of all their contacts in a single offense category. The distribution of specialists in different offense categories reflects the relative distribution over these offense categories generally. On the basis of data on adult arrestees in Washington, D.C., the proportion of spe- cialists varies considerably for different of- fense types. As indicated in Table 56, spe

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416 CRIMINAL CAREERS AND CAREER CRIMINALS TABLE B-56 Proportion of Specialists Found Among Adult Arrestees in Washington, D.C. Offense Type of Arrest Number of in 1973 Arrestees Percent with Prior Arrests for Index Offenses Percent Specialists Among Those with Prior Index Arrests Percent Specialists Among All Arrestees Murder277 65.0 - 21.1 13.7 Rape253 63.3 22.0 13.9 Robbery1,230 65.2 53.4 34.8 Aggravated assault1,930 59.6 57.0 34.0 Burglary902 55.5 44.5 24.7 Auto theft496 52.4 35.7 18.7 NOTE: "Specialists" are arrestees with prior arrests for the same charge as the sampled arrest in 1973. For arrestees with only one prior arrest for an index offense, that one prior index arrest is for the same charge as the sampled arrest. With two or more prior index arrests, the preponderance of the prior arrests are for the same charge as the 1973 arrest. Under the "predominance" criterion, about one-half of all index arrests in a record--including the 1973 sampled arrest-- must be for the current charge. For a record with a total of 3, 4, or 5 index arrests, including the sampled arrest, at least 2 must be for the current charge. For a total of 6 or 7 index arrests, at least 3 must be for the current charge. More generally, if n is equal to the number of prior index arrests of any type, and m is equal to the number of prior index arrests of the same type as the current arrest, a person satisfies the "specialist" criterion if for n > 3, m > (n - 1)/2 for n odd or m > (n - 2)/2 for n even, and for n = 2, m > n/2 = 1. SOURCE: Derived from data in Cohen (1982:Table 3-3). cialists within an offense type were most often found among offenders arrested for robbery and aggravated assault. One-third of all arrestees in these offense types had prior records ant! a predominance of arrests for the same offense type. Specialists in robbery and aggravated assault represented over one-half of those arrestees who had any prior arrests for index offenses. Special- ists were least prevalent in murder and rape, accounting for only 14 percent of all arrestees for these offense types. This lower prevalence of specialists was not due to a lower likelihood of any prior arrests. Two- thircls of the arrestees for murder and rape had prior arrests for index offenses, but less than one-quarter of those recidivistic ar- restees were specialists in those offense types. A similar mix of specialists and general- ists was evident among respondents to the second Ranc! inmate survey. As in~licatecl in Table 57, diversity in offending was very common; most inmates indicated that they committed several different offense types during the observation period. Never~e

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APPENDIX B.: RESEARCH ON CRIMINAL CAREERS less, more than one-quarter of all respon- dents reported that they committed only one offense type. Only robbery was rarely committed as a sole offense type. Even among the category of"Iow-level robbers," 64 percent of the respondents (N = 153) also reporter] that they committed burglary and theft crimes during the 1- to 2-year observation period. The simple mover-stayer model and vari- ations ofthe model that permit a continuous distribution of differences among offenders (see, for example, Spilerman, 1972b) rest on an assumption of population heterogeneity. 4~7 The transition process varies across the population, but within any subgroup the transition process is invariant over time. An alternative explanation offered for the ten- dency of switching processes to bunch on the diagonals explicitly incorporates vari- ability in the process with time. This is most often Lone by allowing for duration clepen- dence in the switching process. In analyses of residential migration and status mobility, duration depenclence re- flects a phenomenon of cumulative inertia (McGinnis, 1968), whereby the probability of remaining in the same state increases as TABLE B-57 Combinations of Offense Types Committed by Respondents to the Second Rand Inmate survey Offenses Reported During Observation Period Combinations a b Drug Robbery Assault- Burglary Theft- Deals Number of Respon- Per dents cent Violent predators (robbery-assault drug deals) Robber-assaulters Robber-dealers Low-level robbers Mere assaulters Burglar-dealers Low-level burglars Property and drug offenders 0 Low-level property offenders Drug dealers Totals + + + + ? ? ? ? O o o o + o + o o + ?? + o o ? + 306 ? 0 160 ? + 188 240 105 199 171 + O O ?? O 128 0 0 0 + 0168 112 1,777 O O O + 15.0 7.8 9.2 11.8 5.1 9.8 8.4 6.3 8.2 5.5 87.1 NOTE: + Respondents commit this crime by definition. 0 Respondents do not commit this crime by definition. ? Respondents may or may not commit this crime; analysis shows that nearly all in this category do. ?? Respondents may or may not commit this crime; analysis shows that most in this category do not. Assault includes homicide arising out of assault or robbery. bTheft includes auto theft, fraud, forgery, and credit card crimes. CThe remaining 12.9 percent did not report committing any of the offense types surveyed. Respondents with missing data (150 out of 2,190) were excluded in calculation of the percentages. SOURCE: Chaiken and Chaiken (1982a:Table 2.5).

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418 time already spent in that state increases. Switches to a different state are more likely the shorter the duration in any state. In offense switching, duration dependence is reflected in variations in switching patterns with increases in the length of the intervals between arrests. The preliminary analysis of the role of different intervals (Table 54) suggests that duration dependence may be a factor in offense switching. Contrary to the cumulative inertia observed in studies of mobility, however, repeating the same of- fense type seems to be more likely when intervals between arrests are short. The tendency to specialize appears to decrease for longer intervals. A variety of modeling strategies have been proposed for incorporating duration dependence. These include expanding the state space to include duration explicitly as a defining attribute (Cox and Miller, 1965; McGinnis, 1968), introducing inde- pendent variables into Markov chain mod- els (Coleman, 1964; Spilerman, 1972a), and semi-Markov processes (Ginsberg, 1971~. (See Hoem, 1972, for a review of various models that incorporate duration depen CRIMINAL CAREERS AND CAREER CRIMINALS dence.) These approaches may be fruitfully applied to analyses of offense switching as well. The analytic treatment of offense switch- ing is currently in the earliest stages of development. Only the simplest first-order Markov models have been explored, and then in very limited ways. Analysis in this area may gain substantially from the many developments in modeling already avail- able in other fields, especially the treatment of mobility processes in demography and sociology. Attempts to model offense switching may also benefit from expanding the process to include consideration of the role of intervening, but undetected, of- fenses in the observed switching process between arrests. Such models would char- acterize switching between arrests in terms of the basic switching process between of- fenses committed and the selection process that transforms some offenses into observed arrests. Alternatives to Markov fo,,~ula- tions, with their limited focus on successive events, might also be fruitfully explored to accommodate the role of prior history in future offense seriousness.