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OCR for page 55
Dimensions of
Active Criminal Careers
Participation in criminal activity is ob-
viously restricted to a subset of the popu-
lation. For the subset who do become
offenders, our focus turns to the fre-
quency, seriousness, and duration oftheir
criminal careers. This chapter provides
some empirical estimates ofthose dimen-
sions of active criminal careers and also
examines some factors that may be asso-
ciated with active offending, such as age
of initiation, drug use, and employment
history.
INDIVIDUAL FREQUENCY RATES
Individual offending frequencies are a
funciamental feature of the criminal ca-
reer. The offense rate for individual of-
fenders, A, reflects the frequency, or in-
tensity, of offending by individuals who
are actively engaging in crime (i.e., active
offenders). Despite the importance of A,
research that statistically characterizes
the intensity of offending for large num-
bers of offenders is relatively recent.
Short of having offenders maintain daily
Togs of their criminal activity, two main
approaches are available to measure fre
55
quency rates for active offenclers: retro-
spective self-reports of prior criminal ac-
tivity and officially recorded arrest
histories. Each approach has its own lim-
itations ant! sources of error. The main
problems with self-reports stem from se-
lection biases in the subsample of of-
fenders who are willing to provide self-
reports and errors in responses arising
from erroneous recall or intentional mis-
representation. The main problem in us-
ing official arrest records aside from er-
rors in the records themselves is that
arrests typically occur for only a small
number of the crimes that are committed.
The limitations of each approach require
that various assumptions, albeit clifferent
ones, be made in order to draw inferences
about individual crime rates from the
available data. Because ofthe basic differ-
ence in the sources of error, however,
the two approaches can serve as impor-
tant tests and cross-validations for each
other.
Two sources provide explicit estimates
of inclividual offense rates for active of-
fenders: self-report estimates developed
at the Rand Corporation and official
OCR for page 56
56
record estimates developed at Carnegie-
Mellon University. Both estimates of A
are limited to samples of adult males who
have committed serious offenses.
The Rand Corporation estimates are
baser! on surveys of male inmates serving
sentences in state prisons (Peterson and
Braiker, 1980; Chaiken ant! Chaiken,
1982a), and the most recent survey also
includes inmates serving sentences in To-
cal jails. The samples are thus restricted
to offenders whose current offense or
prior criminal record are serious enough
to have warranted incarceration. The fre-
quency estimates are based on inmate
self-reports of counts of crimes they com-
mitted in an observation period preced-
ing the current incarceration.
The Carnegie-Mellon studies using of-
ficial arrest records are based on samples
of adult arrestees selected because they
had at least one arrest for an index offense
(other than larceny) during the years sam-
pled. Arrest frequency estimates are de-
veloped from the history of arrests re-
corded prior to the arrest leading to their
selection. This design excludes offenders
who engage exclusively in minor of-
fenses. While females are not explicitly
exclucled, the arrestee samples are com-
posed predominantly of adult mates.
In addition to total rates, crime-specific
frequencies can be estimated for specific
offense types. In computing the mean
frequency for a given crime type during
any observation period, only "active" of-
fenders those who commit at least one
offense or experience one arrest for that
crime type- are included in the calcula-
tions. Therefore, mean frequencies differ
from "incidence rates," which relate the
total number of crimes to the size of the
entire sample, including those with no
crimes of that type.
The Rand and Camegie-Mellon stud-
ies are different from the much larger
body of research on participation in of-
fending, which is based on self-reports or
CRIMINAL CAREERS AND CAREER CRIMINALS
official records of offending or of deviance
for juveniles sampled from a general pop-
ulation. Results from that body of re-
search are therefore dominated by com-
mon minor offenses such as vandalism
and simple assault. This difference in
design reflects the different focus of the
studies, in one case on the scope of cle-
viance found in a broad population, and
in the other case on the intensity of seri-
ous offending by more continuously ac-
tive adult offenders. By focusing on
subsets of offenders who are active in
serious offense types, these studies can
yield frequency estimates for serious of-
fenses.
In aclclition to focusing on active of-
fenders, the studies also restrict the cal-
culation of A to periods when offenders
are at risk of committing offenses in the
community. The time that an offender is
incapacitated through incarceration or
Tong-term hospitalization (e.g., more than
a 1-month stay) is excluded from the time
at risk. The resulting estimates of A reflect
the frequency of offending that occurs
while an offender is criminally active in
the community. It is the rate of offending
that wouIcl be expected if the offender
were not incarcerated.
While not reporting explicit estimates
of frequency rates for active offenders, a
number of other studies do report esti-
mates of population incidence rates mea-
surecl by offenses per capita (Wolfgang,
Figlio, and Sellin, 1972; Elliott et al.,
1983; Farrington, 1983b, 19841. These in-
cidence rates reflect the frequency of of-
fenses (or arrests) in a total population at
risk of offending, while A is calculated for
active offenders only. Such reported inci-
dence rates reflect the combined effects
of participation in crime in a population
and individual frequency rates for active
offenders. An incidence rate measured by
crimes per capita in a total sample can be
partitioned between participation rates
and frequency rates:
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DIMENSIONS OF ACTIVE CRIMINAL CAREERS
Incidence _
rate (C)
=
Total number of crimes
committed by sample
Total number of persons
in sample
Number of persons who
commit crimes in sample
Total number of persons
in sample
Total number of crimes
committed by sample
x
Number of persons who
commit crimes in sample
Current participation rate (d)
x Mean individual frequency rate (A)
This partition indicates that the com-
monly reporter! incidence rates, when
combined with estimates of participation
rates, can be user! to develop estimates of
indiviclual frequency rates. The general
strategy is to divide incidence rates by
participation rates for the same measure-
ment period to yield frequency rates for
the active subset in the population. With
this approach, frequency rates can be es-
timated for a broader range of offender
samples, including juveniles. However,
these estimates of frequency are inflated
by the requirement of at least one event
(offense or arrest) during the measure-
ment period] reflected in current partici-
pation rates, but adjustments for this up-
ward bias are possible (see Cohen,
Appendix B).i Also, values of A estimated
iEstimates obtained directly from data in the
Rand and Carnegie-Mellon studies (Blumstein and
Cohen, 1979; Peterson and Braiker, 1980; Chaiken
and Chaiken, 1982a; Cohen, 1983) are also vulner-
able to this upward bias: to be considered active in
an offense type, offenders must have at least one
event for that offense type. In the Carnegie-Mellon
studies, this arrest may occur any time in a career; in
the Rand studies the criterion event must occur
within the observation period prior to the current
incarceration. As indicated in Cohen (Appendix B),
however, this upward bias is small when the length
of time during which the required event may occur
is long-as in the Carnegie-Mellon studies, or when
frequency rates are high as in the Rand studies.
57
from incidence rates typically do not dis-
tinguish between time incarcerated and
time at risk of offending in the commu-
nity, and so they understate frequency
rates while offenders are free in the com-
munity. For the samples of juveniles that
are the basis for most available incidence
rate estimates, however, these underesti-
mates are likely to be small because in-
carceration is infrequent, and when it
occurs, the length of time served is usu-
ally shorter than found among adults.
Arrest Frequencies (it) by Crime
Type
The Camegie-Mellon estimates of
mean individual arrest frequencies for
active adult offenders (,u) have been ob-
tained directly from longitudinal official-
record data (Blumstein ant] Cohen, 1979;
Cohen, 19811. The analysis inclucled only
offenders whose first arrest as an adult
occurred! before age 21 and who had a
subsequent arrest in the sampling period
7-10 years later. Arrest frequencies were
estimated for the period between the two
required arrests. This requirement of an
arrest prior to and another arrest follow-
ing the estimation period was intended to
limit the analysis to arrestees who-with
reasonable certainty were active in
criminal careers during the estimation pe-
riod. Since inclusion of the required ar-
rests would lead to artifactually inflating
the estimate offs, the required arrests
were excluded from the analysis. Fur-
thermore, when calculating arrest fre-
quencies for any particular offense type,
say, robbery, only offenders in the sample
with at least one arrest for robbery are
included.2
Since the required one arrest for an offense type
may occur during the estimation period, the result-
ing crime-specific frequency estimates may be in-
flated somewhat. To have restricted the required
arrest for each offense type to the bounding periods
before and after the estimation period would have
OCR for page 58
58
CRIMINAL CAREERS AND CAREER CRIMINALS
TABLE 3-1 Mean Individual Arrests Rates from Official Arrest Histories (arrests
per offender per year free)
Mean Individual Arrest Rates (,u)
Washington, Detroit Brooklyn
D.C., SMSA Philadelphia (New York City)
Offense Type Adultsa Adultsa Juvenilesb AdultsC
Robbery .23 .20 .23
Aggravated assault .19 .18 .13 (violent
Burglary .26 .20 indexer
Larceny .27 .22 .41 (property
Auto theft .14 .14 indexes
Index .598 .33
Total (any type excluding traffic) 1.078 .s6 .84 1.2
NOTE: Reported arrest rates are based on samples of 100~00 active offenders per offense type, except
for violent index offenses and robbery for Philadelphia juveniles, for which the samples number 49 and
29 active offenders, respectively.
aArrest rates were estimated by offense type only for offenders with at least one arrest for an offense
type some time during their arrest histories. Only arrests prior to the sampling years and after the first
arrest were considered. When computing ,u for 1973 arrestees in Washington, D.C., only the most serious
charge was considered (Blumstein and Cohen, 1979~; for 1974 to 1977 arrestees in the Detroit SMSA, all
charges recorded for an arrest were counted (Cohen, 1981~. This will inflate ,u for individual offense types
in Detroit somewhat relative to those estimated for Washington, D.C.
bDerived for the panel by Cohen (Appendix B) from data on juvenile arrest histories of males born in
1945 and residing in Philadelphia between ages 10 and 18 (Wolfgang, Figlio, and Sellin, 1972~. Rates are
not adjusted for time served and are thus not rates while free. Violent index offenses include murder,
rape, aggravated assault; property offenses include burglary, larceny, and auto theft.
CDerived for the panel by Cohen (Appendix B) from data on the adult (16 or older) arrest histories of
adults arrested in Brooklyn in 1979 (McGahey, 1982~.
Arrests for murder and rape are excluded from computation of individual index arrest rates in
Washington, D. C.; in 1973 those crimes accounted for 7.3 percent of all adult arrests for index offenses.
The reported rates in Washington, D.C., are simple averages of "index" rates and total rates found for the
five offender types with at least one index arrest (i.e., robbers, aggravated assaulters, burglars, larcenists,
and auto thieves).
The mean arrest rates for adult ar-
restees in their 20s between 1966 and
1973 in Washington, D.C., ant! in the
Detroit Standard Metropolitan Statistical
Area (SMSA) are reported in Table 3-1.
The lowest values of ,u are found for auto
theft and aggravated assault, with active
offenders in these offense types averaging
about one arrest every 5 to 7 years. In
Washington, active offenders in robbery
and burglary experience about one arrest
so limited the sample sizes that estimating fre-
quency rates would have been impractical. Further-
more, since the measurement period in which the
required arrest may occur is long (ranging from 7 to
10 years), the upward bias will be very small.
for these offense types every 4 years;
Detroit arrests for these same offense
types occur about once every 5 years.
Individual arrest rates for active offend-
ers are generally higher in Washington
than in the Detroit SMSA. Including all
crime types, adult arrestees in Washing-
ton experience just over 1 arrest per year,
while the average for adult arrestees in
Detroit is half that rate, only .56 arrest per
year. A similar difference is found for
index offenses: arrestees in Washington
experience an average of.59 index arrest
per year compared with only .33 index
arrest per year for Detroit arrestees.
Very similar estimates of ,u were ob-
tained in an analysis by Cohen (Appendix
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Representative terms from entire chapter:
active offenders
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
B) of the arrest histories of active juvenile
offenders in a 1945 birth cohort of boys in
Philadelphia (Wolfgang, Figlio, and
Sellin, 19721. On the basis of reported
incidence and participation rates, active
mate juvenile offenders are arrested at
average annual frequencies of .23 for rob-
bery, .13 for the other violent index of-
fenses (murder, rape, and aggravated as-
sault), and .41 for property index offenses
(burglary, larceny, and auto theft). Active
offenders among Philadelphia juveniles
experience an average of .84 arrest per
year. In another estimate from incidence
and participation rates (Cohen, Appendix
B), active offenders in a sample of adult
arrestees in Brooklyn, New York
(McGahey, 1982), are arrested for all of-
fense types at a mean annual rate of 1.2
per year free.
The accuracy of the estimates of A,
· 1 1
especially in comparisons across Jurisclic-
tions, depends in large part on the com-
pleteness of the arrest history data. As
indicated in Cohen (Appendix B), nonre-
cording rates vary substantially across
jurisdictions: in the Detroit SMSA, less
than half of all arrests reported in local
police statistics are recorded in the arrest
history data, while in Washington, D.C.,
recording in arrest histories is almost
complete.3
The failure to record large numbers of
arrests leads to two countervailing biases:
an undercount of total arrests, which bi-
ases estimates of,u downward, and an
undercount of low-rate active offenders,
which biases estimates of ,u upward (see
Cohen, Appendix B). The accuracy of
arrest frequency estimates thus depends
on the relative strengths ofthe two biases.
Incompleteness of the arrest history data
3A major factor in Me low recording rates for Me
Detroit SMSA is a policy in the city of Detroit to
only forward arrest reports win known dispositions.
However, this nonrecording of many arrests in De-
~oit does not affect the estimates of q, which are
based on aggregate statistics.
59
could be a factor contributing to the Tower
values of,u estimated for arrestees in the
Detroit SMSA than for Washington, D.C.,
arrestees, especially for less serious of-
fense types, which are more likely to be
subject to greater discretion in terms of
being recorded in the central repository.
Offending Frequencies (A) by Crime
Type
This section discusses estimates of A
that have been obtained in either of two
ways: by adjusting estimates of,u to infer
A, using aggregate arrest probabilities, or
by surveying samples of offenders to ob-
tain self-reports of their offending fre-
quencies.
Inferring A from ,u
Individual arrest rate estimates like
those in Table 3-1 can be used in combi-
nation with assumptions about the arrest
process to generate estimates of individ-
ual frequency rates for crimes committed.
Assuming that the individual crime rate
(
60
CRIMINAL CAREERS AND CAREER CRIMINALS
TABLE 3-2 Estimates of Arrest Risk per Crime (q) by Offense
Estimates of q from Estimates of q from Self-Reports
Aggregate Data
California,
Washington, Detroit Michigan, and
Offense Type D.C.a SMSAa Californiab Texas CombinedC
Robbery .069 .043 .21 (armed .21 (business)
robbery) .16 (personal)
Aggravated assault .111 .062 .10 .24
Burglary .049 .038 .07 .06
Larceny .026 .030 .02 (theft)
Auto theft .047 .015 - .11
aProbability of arrest per crime is estimated from aggregate data on reported arrests (A), reported
crimes (C), the rate of victims reporting crimes to the police (r), and the average number of offenders per
crime incident (O) (Blumstein and Cohen, 1979; Cohen, 1981~. A, C, and r are based on data available
from local police statistics and from victimization survey data for Washington, D.C., and the State of
Michigan; O is estimated from national victimization data available in Reiss (1980a).
bThe probability of arrest per crime is estimated from the ratio of self-reported arrests to self-reported
crimes in a survey of inmates of California prisons in 1976. The estimates from inmates are weighted to
reflect the estimated average probability of arrest per crime faced by street offenders (Peterson and
Braiker, 1980:Table 2, 236-2371.
CThe probability of arrest per crime is estimated from the ratio of self-reported arrests to self-reported
crimes in a survey of state prison inmates in California, Michigan, and Texas in 1978. The estimates
reflect the arrest risk per crime faced by an incoming cohort of state prison inmates for respondents in the
three states combined (Petersilia, 1983:Table 4.4~.
event, O. The final estimate of the prob-
ability of arrest per crime of type i (as
proposed by Blumstein and Cohen, 1979)
· ~
IS given Dy
qi =
Al/O
Cllr
Table 3-2 presents estimates of the ar-
rest risk per crime (q) derived from aggre-
gate data in Washington, D.C., and the
Detroit SMSA. The probability of arrest
per crime is highest for aggravated assault
in both jurisdictions, at about 1 arrest for
every 10 to 20 offenses committed, or
more than 5 percent; the risk of arrest for
other offense types is generally less than
5 percent. The arrest probability is also
generally higher in Washington than in
Detroit. This higher arrest risk per crime
in Washington contributes to the higher
individual arrest rates found in that city
(Table 3-l).
Alternative estimates of q from self-
reported arrests and self-reportec! crimes
by inmates are also presented in Table
3-2. With the exception of larceny (theft),
the arrest risk estimated from the inmate
self-reports is somewhat higher than that
from aggregate data. In part, this differ-
ence reflects the nature of the offenses
surveyed: armed] robbery and serious as-
saults were more likely to involve identi-
fications by victims that would increase
the risk of arrest for these crimes. Also,
the arrest risk may have been inflated by
the exclusion of individuals who reported
no arrests (see Peterson and Braiker,
1980:237~.
Table 3-3 presents inclividual offending
frequencies estimated from arrest fre-
quencies for adults. For most offense
types, the inclividual frequency rates es-
timatecl in Washington and Detroit are
comparable in magnitude, with mean in-
diviclual rates of 3.5 to 4.5 robberies, 2 to
3 aggravated assaults, and 5 to 6 burglar-
ies. The largest difference in A between
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
the two jurisdictions is found for auto
theft, with a mean rate of only 3 auto
thefts committed per year free by offend-
ers in Washington compared with more
than 9 by offenders in Detroit. Total fre-
quency rates are Tower in Detroit than in
Washington: active adult offenders are
estimated to commit from 9 to 13 index
offenses per year free in the two jurisdic-
tions.
The accuracy of these estimates of A
derived from ofI?icial arrest histories de-
pends fundamentally on the adequacy of
estimates ofthe arrest risk per crime, q. In
adclition to concern about the accuracy of
the average value of q that is used, the
TABLE 3-3 Mean Individual Offending
Frequencies Estimated from Arrest
Histories for Adult Arrestees (crimes
committed per active offender per year
free)
Mean Individual
Offending Frequencies,
A
Offense Type
Robbery
Aggravated assault
Burglary
Larceny
Auto theft
All index
Washington,
D.C.,
Adultsa
Detroit
SMSA
Adultsb
3.4
1.7
5.7
10.9
3.0
13.2
4.7
2.9
5.3
7.3
9.3
8.7
NOTES: Sample sizes for specific offense
types range from 100 to 300 active offenders.
In computing A for all index offenses, the arrest
probabilities for individual offense types are
weighted by the distribution of offense types
found in the aggregate. Murder and rape are
excluded from the computation of rates in Wash-
ington, D.C.; in 1973 those offenses accounted for
7.3 percent of all adult arrests for index offenses.
The Washington, D.C., index rate reported here is
a simple average of index rates computed for each
of the five offender types with at least one index
arrest (i.e., robbers, aggravated assaulters, bur-
glars, larcenists, and auto thieves).
aEstimates from Blumstein and Cohen (1979~.
bDerived from data of Cohen (1981, 1983~.
61
estimates of- A may also be clistorted by
failure to aclequately address variation in
q among offenders. Even after controlling
for offense type, this heterogeneity in q is
especially problematic if A ant] q for an
offense are systematically related to one
another. If A and q are negatively relatecl,
with high-rate offenders less likely to be
arrested for each crime, use of a single,
homogeneous value of q for all offenders
will result in an underestimate of A. Cor-
responclingly, if A and q are positively
related, A will be overestimated. Such
relationships might arise directly because
the same offenders are skflIfuT both at
committing crimes and avoiding detec-
tion (a negative relationship) or because
of police practices that target apprehen-
sion efforts at high-rate offenders (a posi-
tive relationship). Alternatively, A and q
might be relatecl indirectly because they
both vary systematically with other of-
fender attributes. If A and q are relatecI,
the failure to adequately control for vari-
ations in q will confound individual dif-
ferences in A with differential police
practices reflected in q. While knowledge
about the variation in q and its relation-
ship to A is crucial to developing
improved estimates of A from arrest histo-
ries, available results suggest that esti-
mates ofthe average value of A clerived by
assuming A and q to be independent are
not likely to be seriously in error.
The Rand Inmate Surveys
Two surveys of sentenced prisoners (in
1976 ant! in 1978) provide estimates of
inclividual crime rates for active adult
male offenders (Peterson ant! Braiker,
1980; Chaiken and Chaiken, 1982a). The
estimated rates are based on self-reports
of the number of offenses committed dur-
ing an observation period] prior to the start
of the current incarceration. The most
striking feature of these estimated fre-
quency rates is the highly skewed distri
62
button of rates across individuals. Figure
3-1 presents Me distribution for robbery
only, but it is illustrative of the clis~ibu-
tion of frequency rates found for other
offenses. The distribution is character-
ized by a large number of offenders com-
mitting offenses at Tow rates and a small
number committing offenses at very high
rates. In this example, among incoming
prisoners who commit robbery (i.e., in
60
50
4J
cot
to
~40
9
~2
o
CC
E
o 30
CD
4,
Cat
E
-
O 20
4J
cat
c'
cot
10
o
CRIMINAL CAREERS AND CAREER CRIMINALS
mates who report at least one robbery
during the 1 to 2 years prior to incarcera-
tion), We mean frequency rate per of-
fencier is 43.4 robberies committed per
year of sweet time. Half of these offend-
ers, however, committed fewer than 4
robberies each per year free, while about
5 percent committed more than 180 rob-
beries per year free. A distinguishing fea-
ture of these skewed distributions is that
r
Median= 3.75
~Mean = 43.4
25 50 75 100 125
150 175
1 80+
Individual Robbery Frequency per Year of Street Time, ~
FIGURE 3-1 Distribution of robbery frequency among incoming inmates. Source:
Derived from data in Visher (Volume II) reanalysis of Rand inmate survey data.
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
TABLE 3-4 Mean Individual Frequency Rates (A) from Surveys of California
Prison Inmates (offenses committed per person per year free)
First
63
Inmate
Survey, Second Inmate Survey, 1978
1976a Incoming Chaiken/
Resident Prisoners Visher Rolph Adjusted
Prisoners (Min-Max~b ReanalysisC Reanalysis Ratese
Robbery 5.2 49-74 42.4 38.9 21.8
Assault 7.1-7.6 N.A. 7.5 N.A.
Shot/cut 2.0
Threatened 3.2
Aggravated 2.8
Burglary 14.2 116-204 98.8 114.6 44.6
Motor vehicle theft 3.9 38-102 N.A. 28.7 N.A.
Forgery 4.9 6~94 N.A. 52.5 N.A.
Fraud 11.4 156~202 N.A. 48.1 N.A.
Drug deals 115.0 927-1681 N.A. 849.9 N.A.
NOTE: Sample sizes for specific offense types are generally in the range of 100~50 active offenders,
except for fraud and motor vehicle theft in the second inmate survey of incoming prisoners, with 69 and
87 active offenders, respectively. In general, the offense type categories in the second survey were more
inclusive than those in the first survey (except assault). For example, the first survey includes only Brined
robberies; the second survey includes all robberies.
aThe first inmate sample was drawn randomly from the resident population at five California prisons
(Peterson and Braiker, 1980:Table 10a).
bThe second inmate sample was drawn to reflect cohorts of incoming prison and jail inmates in three
states: California, Michigan, and Texas. Minimums and maximums for the mean frequency rates were
estimated. These rates for California prison inmates are reported as a range in the table; from Chaiken and
Chaiken (1982a:Tables A3-A141.
CThe original data from the second inmate survey were reanalyzed for the panel as described in Visher
(Volume II). The rates reported from this reanalysis include both prison and jail inmates in California; the
rates for prisoners alone will be slightly higher.
The rates are adjusted downward to reflect the offender's frequency averaged over spurts in activity
and quiescent periods; from Chaiken and Rolph (1985:Appendix).
eUsing the Visher estimates, high estimates of A are truncated at the 90th percentile value before
calculating the final adjusted means.
most offenders committed crimes at rates
well below the mean.
The original frequency rates from the
two surveys of inmates published by the
Rand Corporation differ substantially in
magnitude, with much higher values of A
estimated from the second inmate survey.
Table 34 compares various estimates of
mean frequency rates for selected offense
types. Except for assault the only of-
fense that is not defined more broadly in
the second] survey-even the minimum
mean rates estimated in the second sur-
vey are at least seven times higher than
the mean rates for incoming prisoners
estimated in the first survey. Many factors
contribute to the substantial differences
between the rates reported from the two
surveys. For various methoclological rea-
sons~iscussed in detail in Cohen (Ap-
pendix B) and summarized here-there is
reason to believe that the originally pub-
lished estimates from the second survey
are inflated, while the rates from the first
survey are underestimatecI.
The principal differences between the
surveys relate to the length of the obser-
vation periods and the response formats
64
used to elicit counts of the number of
crimes committed during that observa-
tion period. In the first survey, the obser-
vation period was the 3 years preceding
the current incarceration. In the second
survey, the observation period ranged
from 1 to 2 years depending on when in
the calendar year the arrest leading to the
current incarceration occurred: the later
in the calendar year, the longer the avafl-
able observation period preceding that
arrest. The longer observation period in
the first survey could contribute to under-
estimates of A if memory recall problems
led to greater underreporting of offenses
in the more distant and longer observa-
tion period and if the time that offenders
were active in an offense type was over-
estimated by a failure to account for pos-
sible initiation and termination of careers
sometime during the longer observation
period. Imprecision in the frequency re-
sponse categories in the first survey, es-
pecially for high frequencies, could also
contribute to underestimates.
Other factors could lead to possible
overestimates of A in the second survey.
By directly requesting a respondent's
own estimate of usual frequency rates,
the question eliciting counts of crimes
committed on the second survey is in-
tended to provide greater precision for
high rates of offending. The increased
complexity of the rate response items-
which required separate responses on (1)
the most appropriate time interval for
gauging their offending frequencies (e.g.,
monthly, weekly, or dafly); (2) the num-
ber of crimes committed during that time
period; (3) the number of months in
which crimes were committed at this
usual rate; and (4) total months free dur-
ing the observation period greatly in-
creased respondent problems in answer-
ing these items. The resulting 35 to 40
percent ambiguous responses were likely
to have been a factor in the computation
CRIMINAL CAREERS AND CAREER CRIMINALS
of minimum and maximum rates for each
respondent in the original analysis of the
second inmate survey. In a reanalysis of
the survey data, Visher (Volume II)
adopts an alternative to the extreme val-
ues represented by the minimum and
maximum estimates: in cases with ambig-
uous responses, she relies on information
available in the responses of unambigu-
ous respondents to develop a single esti-
mate for each respondent. As indicated in
Table 3-4, Visher's estimates are close to
Rand's minimum estimates for the second
survey.
However, even Rand's minimum esti-
mates and the alternative Visher esti-
mates from the second survey are much
higher than the estimates from the first
survey. Certain structural features of re-
sponse items, especially reliance on
crime counts in small time intervals (e.g.,
monthly, weekly, or dally counts), could
lead to overestimates of A. When offend-
ing is irregular over the entire observa-
tion period with periods of high levels of
activity interspersed with periods of Tow
levels of activity, applying frequencies
found in short high-activity periods to the
entire observation period will overstate
the frequency rate. In a reanalysis of the
data from the second inmate survey,
Chaiken and Rolph (1985) found evi-
dence of such spurts in offending, with
periods of high activity clustered just
prior to the current incarceration. Re-
spondents with short street times are thus
especially vulnerable to overestimates
since their observation periods are more
likely to be limited to periods of spurts in
activity. To account for these spurts in
activity, the estimated rates were ad-
justed downward to reflect an estimate of
an offender's frequency averaged over
active and quiescent periods. As indi-
cated in Table 34, this adjustment re-
duces the original minimum frequency
by as much as 25 percent for some offense
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
types; only burglary and assault are unaf-
fected by the adjustment for short-term
spurts in activity.
Even with the adjustment for spurts, a
few individuals are estimated to commit
crimes at very high rates averaging one
or more crimes every day. The mean
frequency is very sensitive to these few
very high-rate offenders, and thus vulner-
able to serious overestimates arising from
errors in the estimates] rates for those
offenders. To reduce the impact of the
high-rate offenders, the mean A can be
reestimated by identifying a maximum
limiting value of A and assigning that
value to all offenders whose estimated
rates exceed it. Cohen (Appendix B: Ta-
ble 16) uses the Visher estimates to illus-
trate the changes in mean value of A when
different upper limits are used: using the
90th percentile as a limit for robbery, for
example, the maximum value of A is 71.3
robberies for the three states combined.
The 10 percent of active robbers with
individual rates above that limit each
commit an estimated average of 346.3
robberies per year. When the 90th per-
centile value of 71.3 is assigned to the
high-rate robbers, the mean A decreases
from 43.4 to 14.3 per year. When the 90th
percentile is used for burglary, the mean
A is similarly reduced by more than half,
from 79.0 to 36.7 per year. A similar pro-
ceclure was used to estimate the acIjusted
rates for California prison inmates that
appear in Table 3-4.
The maximum limit adjustments to the
estimates from the second survey reduce
the mean frequency rates to values that
are much closer to those estimates! from
the first survey. For incoming inmates,
the mean rates for the various offense
types (other than drugs and burglary)
most likely fall in the range of 5 to 15
offenses committed per active offender
per year free. The lower rates in this
range are characteristic of violent crimes,
65
and the higher rates are characteristic of
property crimes. The rates for burglary
are even higher: between 15 and 40 bur-
glaries per active offender per year free.
National Youth Survey
Individual offense frequencies are also
estimated from self-reported offenses in
the annual National Youth Survey. Inci-
dence rates (offenses per capita) are com-
bined with current participation rates (of-
fenders per capita) reporter! by Elliott et
al. (1983) to yield] estimated offending
frequencies for active offenders (see dis-
cussion by Cohen in Appendix B). An-
nual incidence rates for male youths are
between .5 and 1.0 for serious offense
types, while annual participation rates are
between 5 and 20 percent over the 5
years of the annual survey. When esti-
mates are restricted to male youths who
are active, mean annual frequencies by
offense type are 4.4 felony assaults, 8.4
robberies, and 7.1 felony thefts commit-
ted per year per active offender. Youthful
male offenders who are active in index
offenses are estimated to commit an aver-
age total of 7.6 index offenses per year.4
Estimates of A
As shown in Table 3-5, there is reason-
able convergence among various esti-
mates of A that are derived by applying
different estimation techniques to data
from different jurisdictions and with dif-
ferent offender attributes. The frequen-
cies derives! from the arrest histories of
4These rates are adjusted to remove the upward
bias introduced by the requirement that all active
offenders commit at least one crime of an offense
type during the 1-year observation periods. The
rates do not account for any time served by active
offenders. However, if the amount oftime served by
youthful offenders is small, the underestimates of A
will also be small.
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
active juvenile offenders, the changing
mix of offenders alone conic! produce the
observed increases in average serious-
ness on successive contacts. Appropriate
controls for record length are needed to
assess the role of selection effects in the
observed escalation for juveniles.
TERMINATION AND LENGTH OF
CRIMINAL CAREERS
The findings that participation in crim-
inal activity is more widespread among
teenage males than among adult mates,
that A is relatively stable over age for
offenders who do remain active, but that
there is a decline with age in aggregate
arrest measures suggest that many crimi-
nal careers must be very short, ending
after only brief ventures into crime as
teenagers. At the same time, however,
many offenders do continue careers be-
yon(1 the teenage years. Consequently,
certain critical questions about the dura-
tion of criminal careers emerge, particu-
larly about the length of typical careers
and how to prospectively distinguish
short careers from long careers. Also, for
already active offenders, there are ques-
tions about the process of terminating
their careers, especially about the ex-
pectec! time remaining in an offender's
criminal career as of a particular time
the residual career length. Variations in
career length with crime type and with
attributes of offenders may be important
factors in distinguishing persisters-
those with Tong careers from other of-
fenders.
The answers to these questions have
implications for attempts to modify crim-
inal careers. A finding that career length
is related to identifiable attributes of of-
fenders, for example, may serve as a basis
for distinguishing among offenders who
have different career lengths. At one
level, such variations in the base levels
for duration across offenders shouts! be
85
taken into account in evaluating treat-
ment programs so that existing differ-
ences among offenders are not mistakenly
interpreted as effects of treatment. At an-
other level, the variations may be used!
in selecting offenders for intervention
programs. A strong relationship between
legitimate employment and termination
of criminal careers, for example, may sug-
gest greater attention to employment fa-
cilitation as a useful policy intervention.
Alternatively, the factors associated with
longer residual careers might influence
the selection of offenders for incarcera-
tion for reasons of incapacitation, since
the effectiveness of incapacitation is di-
minishec! if an offencler's career termi-
nates while he is incarcerated.
Types of Studies
Depending on the attributes of Me data
usecl, analyses have addressed career ter-
mination at very different levels.6 Partly
because ofthe partition between juvenile
and adult justice systems, much research
on criminal careers has focused exclu-
sively either on juveniles or on aclults.
These studies have of necessity taken the
passage from juvenile to adult status as a
partition for analysis, signaling the end of
juvenile careers or the start of adult ca-
reers. Other researchers, recognizing the
potential continuity in offending between
juvenile and adult periods, have followed
juvenile samples into the early adult pe-
riods and report data on the juvenile/adult
6Besides the analyses discussed here, earlier
studies have reported termination rates for groups of
subjects followed for long periods following juve-
nile court contact (e.g., Glucck and Glucck, 1940),
release on parole (e.g., Glueck and Glueck, 1943), or
other types of release (e.g., Christiansen et al., 1965;
Soothill and Gibbens, 1978~. Because of the wide
range of ages in the samples and the methods used,
those studies do not provide estimates of career
length (measured in years), of the probability of
persistence of juveniles into adult criminality, or of
the probability of a "next" arrest.
86
link in participation between these two
periods.
Other studies have examined the se-
quence of events in individual careers in
more detail, providing estimates of termi-
nation probabilities after each arrest. In
these analyses, career length is character-
ized by the number of arrests in a history.
Important questions in these studies are
how termination probabilities change
with the accumulation of further arrests,
what the expected number of future ar-
rests is at any point in a career, and
whether there are any bases for prospec-
tively identifying the "persisters," who
go on to have Tong records with large
numbers of arrests or crimes.
The third approach to career length
focuses on the actual duration of criminal
careers, estimating the time that elapses
between the first and last crimes commit-
ted. In analyzing incapacitative sentenc-
ing policies, variation in residual career
lengths at the time of sentencing is espe-
cially important because incarceration
that extends beyond the end of a career
has no incapacitative effect.
Persistence by Delinquents into Adult
Careers
Consistent evidence is available from
various research settings that 30 to 60
percent of juvenile delinquents known to
the police or juvenile courts persist as
adult offenders with at least one arrest or
conviction as an adult for an index or
felony offense (McCord, 1978, 1982; Shan-
non, 1978, 1982a; Polk et al., 1981; Far-
rington, 1983a; Wolfgang, Thornberry,
and Figlio, 1985; see also a review by
Langan and Farrington, 19831. As shown
in Table 3-13, follow-up studies of delin-
quent and nondelinquent juveniles indi-
cate that a much smaller fraction of
nondelinquents are arrested as adults.
The fraction classified as persisters into
adult careers increases when the expo
CRlMINAL CAREERS AND CAREER CRIMINALS
sure period is lengthened by observing
adults to older ages, when broader do-
mains of crime are used, and when the
measures are based on arrests instead of
convictions. For three birth cohorts in
Racine, Wisconsin, for example, Shannon
(1982a) reports that 31,44, and 54 percent
of mates with police contacts for nontraf-
fic offenses before age 20 were arrested
again as adults by ages 21, 26, and 32,
respectively. If the cohorts are in fact
similar in terms of their tendency to per-
sist into adult careers, the differences in
persistence must reflect the effects of ear-
lier cutoff ages for the more recent co-
horts. Reflecting the relationship be-
tween persistence and domain of crime, a
study by Shaw in 1947 (cited in Langan
and Farrington, 1983) reports that, of
1,336 males appearing in Chicago juve-
nile court for the first time in 1930, 66
percent were arrested as adults by age 31
for a felony or misdemeanor but only 46
percent were arrested for a felony.
When comparable measures and proce-
dures are available, demographic varia-
tions in juvenile-to-adult persistence mir-
ror variations in overall participation
rates. Among the mate delinquents in the
1945 Philaclelphia cohort followed from
age 18 to 30, about twice as many non-
whites (54 percent) as whites (28 percent)
were arrested for nontragic offenses as
adults. For young female offenders in the
three Racine cohorts, Shannon (1982a)
reports persistence rates after age 20 in
nontragic offenses of 20, 29, and 34 per-
cent compared with rates of 31,44, and 54
percent, respectively, for male offenders.
One implication of these findings is that
differences in sample composition with
respect to these demographic attributes
would substantially affect juvenile-to-
adult persistence rates in different sam-
ples.
A juvenile record is a strong indicator of
later adult offending, and the strength of
this relationship increases as the juvenile
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
TABLE 3-13 Persistence of Delinquents into Aclult Careers
87
Study
Offenses Criminal
Sample Examined Event
Non
delinquents
mange tor warn Laud with Adult
Adult Careers Careers
Events (percent) (percent)
Age Delinquents
~r .. , · ~it.
Shannon 356 males born All nontrafficArrests (police 21 to 32 54 36
(1982a: in 1942 and (including contacts as
Table 2) "residingcon- suspicion juveniles)
tinuously"a in and inves
Racine, Wis., ligation)
to 1974
As above for (Same as (Same as 21 to 26 44 15
740 males above) above)
born in 1949
As above for (Same as (Same as 21 31 3
1,114 males above) above)
born in 1955
Farrington Cohort of 411 Indictable Conviction 18 to 25 71 16
(1983a) London boys offenses
aged 25 in
1980
McCord and 506 boys from All nontragic Conviction 18 to 52 18
McCord Cambridge (adult end median
(1959:92) and Somer- juvenile) age
ville, Mass.; 28.5
median age
10.5 in 1939
McCord (Same as All nontragic Conviction 25 to 36 11
(1978:285) above) as juve- median
niles; age 47
"serious
. ,,
crimes
against
persons or
property,
as adults
Polk et al. 1,227 boys from All nontragic Arrests
(1981) Marion
County,
Ore.; high
school soph
omores in
1964
Wolfgang, 975 Philadel- All nontraffic Arrests (police 18 to 30
Thornberry, phia boys contacts as
and Figlio born in 1945 juveniles)
(1985:348)
18 to 30 49 22
51 18
aNever absent from Racine for a period exceeding 2 years.
record becomes longer. As shown in
three cohort follow-up studies in Table
3-14, the fraction of members with adult
criminal records is lowest 16 to 18 per-
cent in the data of Farrington (1983a) and
Wolfgang, Thornberry, ant! Figlio
(1985 - for members with no juvenile
records. This fraction rises sharply with
the presence of just one police contact in
a juvenile record and continues to rise
88
CRIMINAL CAREERS AND CAREER CRIMINALS
TABLE 3-14 Persistence into Adult Careers, by Length of Juvenile Record
Study
Sample
Criminal Event
Juveniles Becoming
Cutoff Adult Offenders (%)
Age for Number of Juvenile
Adult Arrests
Offending 0 1 2
3 4 5+
16 64 71 92
Farrington
(1983a)
Wolfgang,
Thornberry,
and Figlio
(1985:348)
Shannon
(1982a)
Cohort of 411 London
boys aged 25 in
1980
10% follow-up sample
of cohort of boys
born in 1945 and
residents of Phila-
delphia from ages
10 to 18
Males and females
born in 1942 and
residing continu-
ously in Racine,
Wis., to 1974
Same as above for
males and females
born in 1949
Same as above for
males and females
born in 1955
Convictions for in-
dictable offense
25
Arrests for nontragic 22
offenses (police
contacts as juve-
niles)
Arrests for any of-
fense including
traffic (police con-
tacts as juveniles)
(Same as above) 26
(Same as above) 21
18 38 45 55 68 78
32 47 71 89 90 89 93
41 58 75 79 89 98
26 44 59 70 78 85
with each additional police contact in the
juvenile record. Thus, while the precise
fraction persisting into aclult criminal ca-
reers varies by jurisdiction, by domain of
crime, and by the criterion used for char-
acterizing the adult record (e.g., arrests or
convictions), there is strong evidence that
the existence of a juvenile delinquency
career foreshadows adult criminal ca-
reers.
Even though juvenile delinquents are
far more likely than nondelinquents to
become adult offenders, 40 to 50 percent
of adult offenders do not have records of
juvenile police contacts: because nonde-
linquent juveniles greatly outnumber de-
linquent juveniles, even though a smaller
fraction of the nonclelinquents become
adult offenders, their great numbers lead
to a substantial contribution of adult of-
fenders. Thus, for a sample of the 1945
Philadelphia cohort followed to age 30, in
which B1s was 35 percent, nondelin
quents macle up 65 percent of the entire
sample and 41 percent of adult arrestees
in the sample, even though only 18 per-
cent of nondelinquents were arrested as
adults, compared with 51 percent of the
delinquents (see Table 3-13~. Similarly,
nondelinquents accounted for 47 percent
of adult arrestees in the 1942 Racine co-
hort (followed to age 32) and 49 percent of
adult offenders in the Cambridge study.
These findings from prospective studies,
based on official records, are consistent
with studies of retrospective self-reports;
of 755 incarcerated robbers and burglars
providing usable responses in the second
Rand inmate survey, 67 percent reported
not having been convicted of any offense
before age 16 (Greenwood, 1982:Table
4.4~. This result, previously noted by
Shannon (1982a) has significant policy
implications for targeting crime control
efforts on juvenile offenders: despite the
much higher likelihood of continued of
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
fending as adults by juvenile delin-
quents, substantial proportions of adult
offenders will not be prospectively iden-
tifiable as juvenile delinquents.
Measuring Career Length by Number
of Arrests
Attention was initially drawn by
Wolfgang, Figlio, and Sellin (1972) to
analysis of career length as measured by
number of arrests. They noted that
"chronic" juvenile offenders those who
were arrested five or more times by age
Manmade up only 6 percent of the Phil-
adelphia 1945 cohort, or 18 percent of all
arrestees in the cohort, but accounted for
52 percent of all arrests of cohort mem-
bers. Thus, any social intervention that
could reduce participation by the chronic
offenders could have a significant crime
control impact. In retrospective analyses
comparing chronic offenders with those
whose juvenile careers terminated with
fewer arrests, Wolfgang, Figlio, and
Sellin (1972) reported that chronics were
more likely than others to be nonwhite;
within each race category were more
likely to be of low socioeconomic status;
and within each race-status category were
distinguished from other arrestees bY
more family moves, lower mean lids,
fewer school grades completed, and more
school discipline problems. The authors
reported that chronic offenders were ar-
rested for more serious offense types than
other offenders and began their delin-
quency careers earlier, as measured by
the age of first arrest. Their longer period
of exposure to risk before turning 18
artifactually creates greater opportunity
. · ~
for early starters to become curon~cs, out
later analyses controlling for exposure
time (Barrett and Lofaso, 1985; Cohen,
Appendix B) suggest that the result is also
attributable to a genuinely higher level of
activity by early starters, as measured by
annual arrest frequency, ,u.
89
The basic finding that a small number
of extraordinarily active offenders ac-
count for a disproportionately large share
of total arrests attracted the interest of
scholars and practitioners and stimulated
efforts to understand offenders' tem~ina-
tion pattems. This problem has often
been pursued by examining persistence
probabilities of at least one more event
after each event in a criminal history.7 As
indicated in Table 3-15, between one-
half and two-~irds of first offenders are
rearrested (Philadelphia and Racine) or
reconvicted (London). After each subse-
quent event, the persistence probability
increases, reaching a plateau range of .7
to .9 by the fours event. This same gen-
eral pattern has been found in various
settings, win only minor variations due
to differences in Me criminal event (arrest
or conviction) and the domain of offenses
(indictable offenses in London and all
non~affic offenses in Philadelphia and
Racine).8
Blumstein and Troika (1980) noted that
Me Philadelphia data are consistent with
a constant persistence probability of .72
Actually, the research has often computed termi-
nation probabilities following each arrest, usually in
the context of analyses of crime-type switching be-
tween arrests. Following any arrest, however, per-
sistence probability is simply the complement ofthe
termination probability.
8Police contacts in the Racine cohorts include
substantial numbers of contacts for investigation
and suspicion involving nothing more than police
stops for questioning. This broader domain of of-
fense types accounts for the higher participation and
persistence rates in those cohorts. Persistence prob-
abilities have also been computed for two Colum-
bus, Ohio, samples: juveniles arrested at least once
for a violent offense before age 18 (Hamparian et al.,
1978), and adults arrested at least once for robbery,
murder, assault, or rape (Miller, Dinitz, and Conrad,
1982~. Because the arrest causing inclusion in the
sample may not have been the first arrest, persis-
tence probabilities for those samples are not compa-
rable to the probabilities for the other samples
described in Table 3-15, and are therefore not re-
ported there.
go
CRIMINAL CAREERS AND CAREER CRIMINALS
TABLE 3-15 Conditional Persistence Probabilities for Males from Contact (k- 1)
to Contact k
Contact
Number Philadelphia Philadelphia Racine, Wis., Cohorts
(k) Cohort Ia Cohort IIb LondonC 1942 1949 1955
1 .35 .33 .70 .68 .59
2 .54 .63 .69 .72 .68
3 .65 .67 .74 .78 .77 .76
4 .72 .73 .69 .78 .81 .80
5 .72 .73 .76 .83 .81 .89
6 .74 .72 .69 .91 .83 .89
7 .79 .81 .91 .86 .84 .90
8 .77 .73 .90 .88 .88 .87
9 .80 .78 .92 .88 .87
10 .83 .86 .82 .92 .92
11 .79 .87 .94 .90
12 .80 .85 .93 .92
13 .73 .82 .90 .94
14 .88 - .89 .90 .96
15 .70 - .79 .94 .98
aData from Wolfgang, Figlio, and Sellin (1972:163).
bData from A. Barnett, 1985, personal communication.
CData from Farrington (1983a).
Data from Shannon (1981:169~. Contacts for investigation and suspicion but not for traffic offenses are
included in computation of the persistence probabilities reported here.
after the third arrest. All offenders with
more than three arrests can be expected
to have an average of 2.57 subsequent
arrests [.72/~1 - .721] no matter how many
prior arrests they have. Thus, the number
of previous arrests alone would not be
sufficient to prospectively distinguish
chronic offenders from other offenders
who have at least three arrests. There is a
need for research on correlates of persis-
tence probabilities.
The tendency for persistence probabil-
ities to increase to a common limit as the
number of arrests or convictions increases
can be interpreted as reflecting a devel-
opmental process, in which persisters
gradually become more strongly commit-
ted to illegal behavior (or less well suited
for legal employment) as their criminal
careers progress. An altemative account
(Blumstein, Farrington, and Moitra, 1985)
poses a model of offender heterogeneity
in which some offenders are "clesisters,"
with relatively low persistence probabil
ities and others are "persisters," with rel-
atively high persistence probabilities. As
more careers of Resisters end after each
arrest, the remaining sample of offenders
is increasingly composed of persisters
with their higher persistence probabiTi-
ties. This moclel of population heteroge-
neity represents a reasonable alternative
to moclels of a homogeneous population
in which career parameters change as
individual offenders' careers progress.9
Another problem in interpreting the
findings on desistance is the cutoff of
observations at a specific age. Arrest-free
9In Blumstein, Farrington, and Moitra (1985),
persisters were distinguished from desisters by the
following characteristics observed at ages ~10:
"troublesomeness" as assessed by peers and teach-
ers; "conduct disorder" and "acting out" as rated by
teachers and social workers; a "deprived back-
ground" (in terms of income, social class, housing,
family size, and neglect); criminal parents; low
nonverbal IQ; and poor parental child-rearing prac-
tices.
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
intervals at the end of the observation
period do not necessarily indicate termi-
nation of criminal careers. Offenders who
are arrested at rate ,u per year will have
average time intervals of length 1/,u years
between successive arrests. Thus, crime-
free intervals as Tong as 1/,u years will not
be uncommon for active offenders.~° Er-
roneously attributing the absence of fur-
ther events near the end of the observa-
tion period to career Resistance rather
than to the random time between events
in a still active career will leac! to over-
statements of Resistance, called "false de
. ,,
slstance.
For example, in the case of the analysis
by Wolfgang, Figlio, ant! Sellin (1972),
some portion of arrestees who are not
considered chronic offenders by age 18
would accumulate additional arrests if
they were followed after age 18. There-
fore, truncation of observations at age 18
undoubtedly biases downward the esti-
mated proportion of cohort members with
five or more arrests. Barnett and Lofaso
(1985) attempter! to measure this bias by
estimating individual arrest rates for the
312 youths with five arrests by age 17.
They estimated that even if there were no
Resistance in this group, 43 of these
youths would be expected to have no
further arrests between their fifth and the
cutoff age of 18. That number is quite
close to the 51 youths actually observed
to have no more arrests. This similarity
suggests that the true Resistance rate is
very small and that the Resistance rate of
28 percent reported for the chronics prob-
ably exceeds the actual rate substantially.
Similar false Resistance among youths
with less than five arrests by age 18 prob-
ably led to an understatement of the num-
ber of chronic offenders in the cohort.
Thor example, under an assumption that arrests
occur according to a Poisson process with annual
rate A, the chance of an arrest-free interval of at least
1/,u years is given by e-~, or 37 percent.
91
The problem of false Resistance high-
lights the ambiguity inherent in the char-
acterization of chronic offenders in terms
of numbers of arrests without reference to
exposure time. For example, a cohort
member first arrested at age 15 would
have to be arrested at twice the annual
rate of one first arrester! at age 12 in order
to accumulate five arrests by age 18.
"Chronics" can be early starters, high-
rate offenders, or offenders with espe-
cially Tong careers.
Measuring Career Length in Years
Analysis of career length measured in
years is relatively rare. Estimates of total
career length in three major studies range
between 5 ant! 15 years (Shinnar ant!
Shinnar, 1975; Greenberg, 1975; Greene,
1977~. Shinnar and Shinnar (1975) esti-
mate<3 total careers to be 10 to 15 years
based on aggregate data on the time be-
tween first ant] current adult arrests of 5
years for all offenders and 10 years for
recidivists reporter! for a sample of of-
fenders from the FBI computerized crim-
inal history file. This estimate is depen-
clent on the statistical assumptions made
to infer total career length from the partial
career length observed for active offencI-
ers. Also, because the data include only
first arrests as adults, the 5- to 10-year
arrest careers observed for aclults were
arbitrarily inflated to 10 to 15 years to
include both juvenile careers and the ac-
tive period before the first aclult arrest
(Shinnar and Shinnar, 1975:597~. There is
also some concern about the representa-
tiveness of the arrestee sample, which
preclominantly includes persons arrested
for a fecleral offense.
Greenberg (1975:561~62) used a sim-
ple approximation to estimate the average
length of careers for index offenses. If ,u is
the average number of index arrests per
year for an offender and N is the total
number of adclitional lifetime index ar
92
rests experienced after the first arrest,
then T = Nix is the average career length
for index offenses. Using estimates off =
.5~i ant] N = 2.5,~2 Greenberg calculated
the average index career length to be 5
years.
Following a method outliner] in Shin-
nar and Shinnar (1975), Greene (1977:
Chapter 3) applied a life-table approach
(derived from survival moclels) to the age
distribution of arrestees in a single year to
estimate the total length of aclult criminal
careers. Using data on aclult inclex ar-
restees in Washington, D.C., in 1973 and
assuming that they were all criminally
active at age 18, he estimated the mean
adult career length for index offenses to
be 12 years. This career length estimate,
however, was acknowledged to be quite
sensitive to late starters who begin their
careers after age 18; failure to exclude
these late starters leads to overestimates
of career length. This career length esti-
mate includes only adult careers, so time
as a juvenile offender wouIc3 be added to
estimate overall career length.
More recently, Blumstein and Cohen
(1982) user! life-table methods to provide
estimates of residual career length- the
expected time remaining in careers con
iiThis crude estimate of,u, based on the total
number of intervals between index arrests divided
by the time between the first and most recent index
arrests derived from the FBI report on criminal
careers in 1965 (Federal Bureau of Investigation,
1966), is almost identical to the more recent esti-
mates off for index offenses, reported earlier in this
chapter, based on analysis of arrest histories for
active offenders.
i2In a simulation of criminal careers using crime-
specific recidivism probabilities (which were held
constant to some age and then declined to zero at
some later age) and an empirically derived crime-
type switch matrix for recidivists, Blumstein and
Larson (1969:222-226) estimated the total number
of subsequent index arrests after the first to be
between 2.2 and 2.9 for different initial index of-
fense types, which was Greenberg's source for his
estimate of N.
CRIMINAL CAREERS AND CAREER CRIMINALS
ditional on the time already elapsed in
careers. The analysis used data on the
histories of incliviclual arrestees to adjust
the more conventionally reported age dis-
tribution of arrests and to estimate the
relationship between residual career
length and age. The pattern displayed in
Figure 3-4 led Blumstein and Cohen
(1982) to characterize the career in terms
ofthree segments: a"break-in" period (I),
a "stable" period (II), and a "wear-out"
period] (III). For aclult careers beginning
at age 18, the total aclult career is esti-
matecl to average 5.6 years for inclex of-
fenses. This is very close to the approxi-
mation for aclult inclex careers provided
by Greenberg (19751. As time in the ca-
reer elapses, the pattern in residual ca-
reer length is consistent with the hetero-
geneous population mode! of persistence
in arrests. Over the first 10 to 12 years of
the break-in period of index careers,
mean residual career length increases
from 5 to 10 years, a pattern consistent
with increasing dropout of Resisters from
the offender population. After the 12th
elapsed year (or around age 30 for 18-
year-old starters), residual career length
remains fairly stable at about 10 addi-
tional years for each of the next 10 years,
perhaps representing the mean residual
career length for persisters. Finally, per-
haps reflecting "burnout" by persisters in
the wear-out period, the resiclual career
begins to clecTine at about age 41 (or after
23 years in active aclult careers). Furler
research is needled to discover the extent
to which this decline is due to greater
mortality found among active offenders
than among the general population, dif-
ferential incarceration at biller ages, phys-
ical "bumout," or other reasons.
The three-periocl pattern for resi(lual
career lengths has important implications
for incapacitation policies appliecl to
older, more established offenders. The
sharp decline in aggregate arrest rates by
age 30 has conventionally been inter
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
14
;' 1 2
10
8
a, 6
4
._
cr
~2
car
a,
o
1
_\
11
Time Already
2 7 12 17 22
. . .
. . .
20 25 30 35 40 45 50 55 60
111
inaCareer,a-18
27 32
37 42
Age, a
FIGURE 34 Variation in mean residual career length (TR) with time already in a
career (18- to 20-year-old starters only). Source: Blumstein and Cohen (1982:Fig-
ure 12~.
preted to mean that incarceration wouIcl
be wasted on 30-year-old offenders be-
cause they are about to terminate their
careers. The findings for residual career
lengths, however, suggest that such inter-
pretations may be wrong. The few per-
sistent offenders who begin their aclult
careers at 18 and remain criminally active
into their 30s appear to represent prime
candidates for incarceration.
There are also variations in residual
career length by crime type. Estimates of
crime-specific residual career lengths re-
fer to the average period during which
offenders engage in a particular crime
type. For example, the residual career
length for robbery refers to the remaining
period cluring which a robber continues
to commit robberies. The same offender
could similarly have a residual burglary
career or an index offense career. When
the techniques laid out by Blumstein and
Cohen (1982) are used, distinct career
93
length patterns are evident for property
and violent offenses. For the property
crimes of burglary, auto theft, and rob-
bery analyzecl separately and as a
group-patterns of residual career length
are similar to that shown in Figure 34 for
all index offenses. In terms of career
length, then, robbery is perhaps best
viewer! as a property crime from the per-
spective of the offender, even though it is
a violent crime from the perspective of
the victim. In contrast, for the serious
violent offenses of murder, rape, and ag-
gravated assault, resiclual career lengths
are on average longer, ant! offenders who
are arrested for these crime types are less
likely than property offenders to drop out
during the early years of their careers.
Thus, older offenders often have Tong
careers marked by arrests for violent of-
fenses, especially aggravated assault.
These crime-specific career length pat-
tems suggest that persisters are found
94
widely among violent offenders. Among
property offenders, persisters are less
widely found, but those who do remain
active as aclult property offenders in their
30s are likely to continue committing
property crimes for another 10 years.
CONCLUSION
Some of the most important conclu-
sions from the review of research on crim-
inal careers relate to factors that are well
known to be associated with aggregate
crime rates (C) or aggregate arrest rates
(A - factors such as age, race, ant! sex.
Most prior literature has not distin-
guished whether such factors are associ-
ated with participation in offending or
with frequency, and the literature has
implicitly suggested that the association
is equally strong with both. As the last
chapter showed, these demographic vari-
ables are associated with participation;
however, results from research on active
offenders indicate that those variables are
only weakly related to inclividual fre-
quency. Thus, the demographic groups
most often found to be associated with
offending-young, black, and male dif-
fer predominantly in the fraction of their
base population who become involved in
offending. To the extent that criminal jus-
tice officials use their knowledge of the
demographic correlates of aggregate rates
to make judgments about the future crim-
inaTity of individual offenders, those judg-
ments are likely to be incorrect. Even
though appreciably different fractions of
the various demographic subgroups be-
come involved in crime, those who do
participate seem to be much more similar
across the demographic categories.
The distribution of inclividual offencI-
ing frequencies (A) is highly skewed over
the population of active offenders. The
median offender engages in only a few
crimes per year, but the most active 10
percent of offenders commit crimes at
CRIMINAL CAREERS AND CAREER CRIMINALS
rates that may exceed 100 per year. At
virtually all stages of criminal careers, the
factors that distinguish the hi~hest-rate
offenders are still only incompletely
known, but certainly include the follow-
~ng:
,, ,
· high frequency of prior offending;
· early onset of delinquency as a juve-
nile;
· drug use, measured either currently
or over time; and
· unstable employment in the recent
Offenders engage in a great diversity of
crime types, with a somewhat greater
tendency for offenders to repeat the same
crime or to repeat within the group of
property crimes or the group of violent
crimes. For samples of juvenile offenders,
later arrests tent! to be for more serious
offenses than earlier arrests, but it is clif-
ficult to determine how much of that
tendency is a consequence of more seri-
ous offenders committing a larger number
of offenses or of the same indivicluals esca-
lating the seriousness of their offenses as
their careers progress. Adult offenders who
are arrested more than once do not, on
average, escalate to more serious crimes as
their criminal careers progress.
Research on the length of criminal ca-
reers indicates, first, that careers are rea-
sonably short, averaging about 5 years for
offenders who are active in index offenses
as young adults. In the first 10 to 12 years
of adult careers, resiclual careers (i.e., the
time still remaining in careers) increase
from 5 years for 18-year-olds who commit
FBI index offenses to an expected 10
years for index offenders still active in
their 30s. This increase probably occurs
because of early career termination in the
early years by many offenders, leaving
the offender group more clensely popu-
latec! with offenders who have longer av-
erage career lengths. Offenders with the
longest resiclual career length (TR) are
DIMENSIONS OF ACTIVE CRIMINAL CAREERS
those who were active in careers at age 18
ant! who are stflT active in their 30s. TR
does not begin to decline rapidly until
active offenders reach their 40s.
These insights into the structure of re-
sidual career length contradict a widely
held view that derives from aggregate
statistics. These show low aggregate ar-
rest rates (A) by individuals in their 30s
95
ant! have been assumed to reflect high
termination rates in those years. Individ-
ual-leve} analysis ofthe variation in resid-
ual career length with age suggests that
offenders who started young and who re-
main active into their 30s are few but
have the lowest termination rates and so
are probably the most confirmed offend-
ers.
