RESEARCH SERIES ON THE RECIDIVISM OF

FEDERAL GUIDELINE OFFENDERS 

RELEASE 1

MEASURING RECIDIVISM:

THE CRIMINAL HISTORY COMPUTATION

OF THE FEDERAL SENTENCING GUIDELINES

A COMPONENT OF THE

FIFTEEN YEAR REPORT 

ON THE U.S. SENTENCING COMMISSION’S

LEGISLATIVE MANDATE

May 2004
RUBEN CASTILLO

Vice Chair

 

WILLIAM K. SESSIONS, III

Vice Chair

 

JOHN R. STEER

Vice Chair

 

RICARDO H. HINOJOSA

Commissioner

 

MICHAEL E. HOROWITZ

Commissioner

 

MICHAEL E. O’NEILL

Commissioner

 

EDWARD F. REILLY, JR. (Ex officio)

 

DEBORAH J. RHODES

(Ex officio)

Commission Staff

Linda Drazga Maxfield

Miles Harer*

Timothy Drisko

Christine Kitchens

 Sara Meacham

*while serving at the U.S. Sentencing Commission
1

USSG Ch. 4, Pt. A, intro. comment. The U.S. Sentencing Commission, 1987, 4, cites the U.S. Supreme Court

ruling in Graham v. West Virginia, 224 U.S. 616,623 (1912) that states “the repetition of criminal conduct aggravates

the guilt and justifies heavier penalties when they are again convicted.”

2

U.S. Sentencing Commission, 1987, 41.

3

U.S. Sentencing Commission, 1987, 43; Hoffman and Beck 1997, 1. 

4

USSG Ch. 4, Pt. A, intro. comment

When sentencing offenders under the guidelines, federal judges reference two axes

of a sentencing table to determine the appropriate sentencing range (prior to

consideration of a warranted departure). The vertical axis of the sentencing table

contains 43 “offense levels” designed to quantify the seriousness of the instant offense. Along the

horizontal axis lie six “criminal history categories” (CHCs) designed to quantify the extent and

recency of an offender’s past criminal behavior. The table cell in which the offense level and the

criminal history level intersect displays the minimum and maximum number of months for an

offender’s recommended sentence. 

Inherent in using the horizontal axis of the sentencing table is the notion that prior criminal

behavior warrants incremental punishments: the more extensive an offender’s criminal history, the

harsher the sentence should be. This notion is formally justified in terms of culpability (just

punishment), deterrence, incapacitation, and limited rehabilitation potential.1 The Commission

recognizes the importance of measuring accurately such prior criminal behavior and future

recidivism risk, thus improving the goals of crime control.2 

In developing the guidelines’ Chapter Four criminal history component, the first U.S.

Sentencing Commissioners evaluated several preexisting prediction tools. Due to pressing

congressional deadlines, the guidelines’ criminal history measure did not emanate from its own

direct empirical evidence. Instead, the chosen criminal history instrument combined elements from

the already validated U.S. Parole Commission’s “Salient Factor Score” and the “Proposed Inslaw

Scale.”3 It was reasoned that the Salient Factor Score’s high predictive power would transfer, at

least in part, to the nascent guidelines’ criminal history measure. While the new guidelines’ criminal

history measure combined elements of the preexisting Salient Factor Score and the Proposed Inslaw

Scale, it also added elements aimed at achieving philosophical and practical concerns specific to

sentencing federal defendants. The Sentencing Commission currently uses the criminal history

measure as a tool to measure offender culpability, to deter criminal conduct, and to protect the public

from further crimes of the defendant.4

Since the time the sentencing Guidelines Manual was first released, the introductory section
5

Ibid.

6

Betsey, 1989; Swenson, 1990; Wilkins, 1990; Schmidt and Garner, 1991.

7

Hoffman and Beck 1997, 192. 

Page 2 of 38

of Chapter Four has always stated that in order to protect society from known criminals, the criminal

history measure should take into account culpability (i.e., harsher punishments for offenders with

aggravated prior criminal backgrounds) and recidivism (i.e., the likelihood of re-offending).

Furthermore, the way in which the sentencing guidelines account for culpability and recidivism

should be “consistent with the extant empirical research” and should incorporate “additional data

insofar as they become available in the future.”5

 The Commission’s intention to follow these

directions is reflected in its early research agenda, as well as in staff resources spent on preliminary

recidivism projects conducted in the early years of the Sentencing Commission.6

In fact, however, a delay in beginning a vigorous validity test of the criminal history measure

was unavoidable. Such a project had to await the simple passage of time. In order to conduct a

recidivism study, enough federal offenders would have to be convicted and then released to the

community. Once released, a research design requires that offenders sentenced under the guidelines

be in the community and at risk of recidivating for a follow-up period of several years. 

As such, a fair assessment of the guidelines’ new criminal history measure would require

time: time for the guidelines to adjust to its early constitutional/legal challenges, time for judges to

become familiar with the guideline process, time for the stabilization of a system of data collection

from district courts, and time for adequate observation periods to elapse so that offenders with prison

sentences as long as five years would have been released into the community for at least two years.

Nonetheless, even given these necessary time requirements, the delayed validation of the criminal

history measure generated concern among judicial personnel and sentencing practitioners that the

criminal history measure might not accurately reflect recidivism risk, may be too complex to apply

correctly, fail to reflect differences in past criminal behavior among federal offenders, and sustain

disparate sentencing practices the Sentencing Reform Act aimed to diminish.7

This report serves as a “performance review” of criminal history’s predictive ability. Much

like performance reviews for employees, the performance review of the criminal history measure

includes a discussion of areas where performance is in need of improvement, is satisfactory, or is

exceeding expectations. As such, it assesses the predictive power of the criminal history measure,

determining whether it predicts better than random chance, and, if so, by how much. Emanating

from this performance analysis, the reports in the recidivism project series examine the recidivism

contributions of current criminal history components and suggest modifications or changes to

improve predictive accuracy. 

Using data collected from guideline federal offenders sentenced in fiscal year 1992, the

current study examines in detail the predictive statistical power of the criminal history measure,

responds to the Sentencing Commission’s initial intentions, fills the void of empirical evidence

about the criminal history measure, and addresses current criticisms of the CHC.
8

Due to alien deportation following conviction for criminal behavior, it is difficult to measure recidivism of

noncitizens convicted of federal crimes. A companion report in this recidivism project series, “Recidivism of Fiscal Year

1995 Noncitizen Federal Offenders,” addresses citizenship issues in recidivism research.

9

Details on the structure, methodology, and statistical techniques of the analysis are documented in the project’s

companion report, “Background and Methodology of the U.S. Sentencing Commission’s 2003 Recidivism Study.”

10While the two year recidivism follow-up period limit was set at June 1, 2001, the actual collection of FBI

“RAP” sheet data did not occur until October 2001. The four month delay between June and October assured that

“RAP” sheet data would reflect all events prior to June 1. The intervening months accounted for administrative

processing time to update fingerprint card information on the FBI datafile, thus minimizing any potential bias due to the

states’ differential schedules for reporting data to the FBI.

Page 3 of 38

A. The Recidivism Project 

The recidivism study data are composed of a stratified, random sample of 6,062 U.S.

citizens8 who were sentenced under the federal sentencing guidelines in fiscal year

1992.9 Data on criminal behavior prior to the federal instant offense, as well as

demographic and offender characteristics, were collected from the federal pre-sentence reports

(PSRs) and other court documents submitted to the Sentencing Commission by U.S. district courts.

Prison release date information was extracted from the SENTRY datafile of the Federal Bureau of

Prisons in the U.S. Department of Justice. Recidivism information was obtained from the “RAP”

sheet data of the Federal Bureau of Investigation’s Criminal Justice Information Services Division

office. 

 

The sample of offenders in the recidivism analysis represents the total 28,519 sentenced

federal citizen offenders in the Commission’s fiscal year 1992 datafile where the following “two

year window inclusion” conditions were met:

• a pre-sentence report (PSR) from a fiscal year 1992 sentencing was

submitted to the Sentencing Commission;

• a “RAP” sheet was located on the FBI datafile; and

• for offenders receiving prison sentences, the release from prison

occurred at least two years prior to June 1, 2001. 

Given the project schedule for “RAP” sheet data collection in October 2001, an offender had

to have been released from prison by June 1, 1999. Because the sampled offenders were sentenced

in fiscal year 1992 (i.e., between October 1, 1991 and September 30, 1992), prison sentences as

long as seven years are available for study.10 The selection of these specific dates reflects the

Commission’s specific interest in recidivism impacts of mandatory minimum sentences. The effect

of five year mandatory minimum sentences can be analyzed using the project’s sampling strategy.

With the recidivism study’s prison release deadline established as June 1, 2001, there were
11This figure represents the sum of the sample offenders who by June 1, 2001, were still in prison (8.9%) or who

had died (0.4%). 

12The percentage of offenders in each CHC who were released from prison and met the two year window

inclusion conditions are: CHC I–91.3 percent; CHC II–85.6 percent; CHC III–83.7 percent; CHC IV–80.9 percent; CHC

V–73.8 percent; and CHC VI–49.6 percent.

13Disposition information was obtained from the FBI’s criminal history record (“RAP sheet”). 

Page 4 of 38

offenders in the sample who either were still in prison on this date, or had at this time been released

from prison for less than two years. Exhibit 1 illustrates the impact of the study definition on

offender inclusion in the study. For the entire recidivism sample, 9.3 percent of sample offenders

are not in the analysis because they had not finished serving their prison time.11 In addition, for the

entire recidivism sample, an additional 5.4 percent were released from their prison terms but did not

have a two year “at risk” window of recidivism opportunity by June 1, 2001. Even with these

limitations, however, 85.3 percent of the total recidivism sample is included in the analysis reported

here. Not included in the analysis are those offenders with sentences roughly longer than seven

years who are disproportionately found in the higher CHCs, particularly CHC VI.12

B. Recidivism Definitions

Recidivism results are presented using two substantive definitions. The first, or

“primary,” definition includes the first occurring of any one of the following three

types of events during the offender’s initial two years back in the community: 

• a re-conviction for a new offense; 

• a re-arrest with no conviction disposition information available on the

post-release criminal history record;13 or

• a supervision revocation (probation or post prison supervision). 

The second “re-conviction only” recidivism definition limits the recidivism definition to

re-conviction events during the two year follow-up period. As such, under this secondary definition,

recidivism is measured as the first occurring re-conviction for a new offense during the initial two

years back in the community.

 

The use of two different recidivism definitions addresses the state of post-release criminal

behavior records. The recidivism literature recognizes that the FBI offender “RAP” sheets are the

most accurate and readily available data source for repeat criminal behavior. However, “RAP”

sheets can contain errors or partial information. For example, “RAP” sheets only contain

information on offenses for which offender fingerprints were obtained. Additionally, depending on

the reporting policies and practices of local jurisdictions, arrest dispositions may not always be

transferred to the FBI for inclusion on “RAP” sheets. “RAP” sheets will under report actual criminal
14Spohn and Holleran, 2002. The methodological arguments supporting various empirical definitions of

recidivism are contained in the companion project report, “Background and Methodology of the U.S. Sentencing

Commission’s 2003 Recidivism Study.”

15Quinsey, Harris, Rice, and Cormier, 1998; Swets, Dawes, and Monahan, 2000. 

16The AUC methodology is summarized in Appendix A, with a detailed explanation in the project’s companion

report, “Background and Methodology of the U.S. Sentencing Commission’s 2003 Recidivism Study.”

Page 5 of 38

behavior, and will under report convictions resulting from arrests. For this reason, recidivism

studies (including all of the Commission’s previous recidivism study reports) commonly cite

recidivism rates separately for what is termed here the “primary” and “re-conviction” definitions.

However, these same studies also argue that, compared to the “re-conviction” definition, the

“primary” recidivism definition is a more reliable and valid measure for the probability of actual reoffending because of its inclusiveness and its high association with actual re-offending.14 Also,

because these two definitions are strongly correlated, the research findings resulting from analyses

using the two definitions are similar and lead to nearly identical conclusions, although the magnitude

of their comparative findings differs.

C. Methodology

I

n this study, three techniques are used to analyze recidivism rates of federal offenders.

The three techniques are tabular analysis, area measurement under the receiver operating

characteristic curve, and survival analysis with hazard modeling. All three are accepted

methods for evaluating recidivism. By using all three methods, the Commission is able to show the

degree to which the criminal history components predict recidivism.15

Tabular analyses provide “yes” or “no” rates of recidivism for offender characteristics. For

example, exhibits in this report compare recidivism rates across many characteristics, such as gender

(male rates versus female rates) or educational level (those without high school diplomas, those

graduating high school, or those with college). Tabular analyses permit straightforward comparison

between characteristics, but can be misleading. They do not provide information about intervening

influences that may in fact mitigate or enhance an association. For example, if offenders of one race

are more likely to finish high school than are offenders of another race, the confounding effects of

race and level of education cannot be separated from a tabular analysis. In essence, tabular analyses

cannot claim that race determines or causes the educational achievement. Tabular analyses simply

show whether offenders of a particular race are more likely to finish high school. In other words,

tabular analyses do not explain why some people of a particular race are more likely to finish high

school.

Measuring the area under the curve (AUC)16 is an established technique associated with

receiver operating characteristic curve analysis. It provides the probability that the offender’s prior

criminal history is able to predict who does recidivate and who does not. The AUC statistic ranges

from a value of 0.5 (indicating no predictive power for recidivism) to a value of 1.0 (indicating 100
17The survival (or hazard modeling) methodology is summarized in Appendix B, with a detailed explanation

in the project’s companion report, “Background and Methodology of the U.S. Sentencing Commission’s 2003

Recidivism Study.”

Page 6 of 38

percent accuracy in predicting recidivism). The greater the AUC statistics, the better is the

predictive power of the prior criminal history model. 

The third method used to evaluate recidivism in this study, survival analysis,17 makes a

further important contribution. Survival analysis measures the ability of criminal history to predict

how rapidly offenders recidivate during a follow-up period. An important strength of survival

analysis is its ability to incorporate explanatory variables into the prediction model, thus measuring

the statistical significance of their independent effects on recidivism.

The analysis below uses all three of these methodological techniques to present and evaluate

the current predictive power of the guidelines’ current Criminal History Category (CHC) and

criminal history point measures.

D. Recidivism Prediction Results

The sections below present the recidivism prediction statistics for the guideline criminal

history measures. The analysis uses recidivism data tabulations, survival analysis, and

AUC outcomes for both the CHC and criminal history point measures. Exhibits 2

through 8 cited in this section appear at the end of the report. 

1. Criminal History Category

Exhibit 2 (located at the end of the

report) presents the guidelines’ recidivism

rates by CHC for both the primary and reconviction recidivism definitions. The

sidebar graphic to the right displays the

numerical data of Exhibit 2 for the primary

recidivism definition. Recidivism risk

increases with each CHC: guideline

offenders in higher CHCs are more likely

to re-offend within two years of release

from prison or upon entering probation

status. Under the primary recidivism

measure, offenders in CHC I have a

substantially lower risk of recidivating

within two years (13.8%) than do offenders in CHC VI (55.2%). 

Percent Recidivating Within Two-Years (Primary Definition),

by Criminal History Category

Recidivism Sample 2003

0

10

20

30

40

50

60

I II III IV V VI

Criminal History Category

Percent Recidivating

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. citizens), 2003, weighted data.
18Recall that the three components of the primary recidivism definition are: a re-conviction; a re-arrest with no

conviction disposition information available on the post-release criminal history record; and a supervision revo-cation

(during either probation or post-prison supervision).

19However, analysis using only one of the three primary definitions components is not recommended. Using

only one element of the definition reduces the sample size of recidivating offenders, which subsequently increases the

rate of error in the indicator chosen to measure the underlying risk for re-offending. Further, analysis using only one

recidivism event type (re-convictions only, for example) may find that certain predictors are not statistically significant

both because one vent type provides a lower base rate for recidivism and because one event type will provide a more

error prone measure of the underlying risk for re-offending. 

Page 7 of 38

Exhibit 3 focuses on the three components of the primary recidivism definition and illustrates

the distribution of recidivating activity type.18 The level of recidivism under each individual primary

recidivism definition element is proportional across the CHCs. Supervision violations are the largest

type of recidivism behavior, accounting for an average 45 percent of recidivism across the CHCs.

Arrests without known conviction dispositions account for an average 33 percent of recidivism

events across the CHCs. Finally, new offense re-conviction accounts for an average 22 percent of

recidivism events across the CHCs. Similar proportions of behavior types across the CHCs suggest

that the results from a recidivism analysis conducted on any one recidivism element alone will

produce results similar to those obtained using the combined three elements of the primary

recidivism definition.19

2. Criminal History Points

Exhibit 4 appears at the end of

the report, but its data are displayed in

the sidebar graphic to the right. The

data reflect the primary recidivism

definition. Offenders with 20 or more

criminal history points are all grouped

into the last category. As the data in

Exhibit 4 illustrate, approximately 0.9

percent of offenders in the recidivism

study have 20 or more criminal

history points.

Criminal history points

represent the purest form in which the

guidelines measure recidivism risk.

The CHC is not as pure in its form

because the CHC is an aggregation of points into one of only six categories. Therefore, the sum of

criminal history points is the primary source for evaluating the predictive ability of the current

criminal history Chapter Four provisions. Not surprisingly, therefore, when predicting the primary

Percent Recidivating Within Two Years (Primary Definition),

by Criminal History Points

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20+

Criminal History Points

Percent Recidivating

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. citizens), 2003, weighted data.

Recidivism Sample 2003
20Observed deviations from linearity may be due to insufficient sample size or the correlation of recidivism with

other variables not included in criminal history points(e.g., age or gender).

21The survival (or hazard modeling) methodology is summarized in Appendix B, with a detailed explanation

in the project’s companion report, “Background and Methodology of the U.S. Sentencing Commission’s 2003

Recidivism Study.” The recidivism survival curves presented in Exhibit 5 were generated using a proportional hazard

model with the primary recidivism measure regressed on dummy variables representing each of the CHCs. CHC I is

the comparison category. This method is described by several experts on survival modeling: Allison 1995; Hosmer and

Lemeshow 1999; and Klein and Moeschberger 1997.

Page 8 of 38

measure of recidivism, criminal history points also perform well.20

Recidivism rates for number of criminal history points also follow the upward positive linear

slope trend seen with the recidivism rates of the CHCs. In general, as the number of criminal history

points increases, the risk of recidivating within two years increases.

3. Survival Analysis

This subsection of the report focuses on survival analysis methodology and its recidivism

findings. Exhibit 5 displays cumulative survival curves for the first two years at risk by CHC.21 One

way to conceptualize this methodology is to imagine federal offenders in the community at risk of

committing a recidivating act. As time passes, one or another offender recidivates. The longer the

time at risk, the greater the total number of offenders recidivating. A line drawn over time would

slope upward to the right, thereby showing the cumulative percent of offenders who had recidivated

by each day. This “survival curve” graphs the cumulative probability over time of offenders

recidivating. The curves from the recidivism survival analysis have a distinct advantage in showing

what happens over each day during the two year follow-up period and the total cumulative percent

of each CHC who have recidivated at any point in time.

The survival curves in Exhibit 5 show the cumulative percentage of offenders recidivating

in each CHC for each day, starting with the first day at risk. For example, for CHC II offenders at

one year (365 days), approximately 13.4 percent had recidivated or conversely, 86.6 percent had not

recidivated, during the first year (365 days) at risk in the community. Visually following the CHC

II’s survival curve past 365 days, the curve continues to slope upward. By two years (730 days) the

percentage of CHC II offenders recidivating increases to 24.0 percent – almost doubling the rate

from the one-year time point for those in CHC II. 

The survival curve data in Exhibit 5 show the same pattern of recidivism rates as observed

of Exhibit 2. Recidivism rates are lowest in CHC I and rise for each increasing CHC. However, two

findings from the survival analysis are noteworthy.

The first noteworthy finding is the striking similarity of the proportional rate increases from

CHC I through CHC V. The ratio of the survival curve lines is almost constant: the lines on their

graph appear nearly equally spaced as they fan out over the number of days from one to 730. This
22In this statistical analysis, CHC I is used as the comparison category. 

23p<0.05 

24p<0.05 

25Here the AUC statistic technically provides the probability that a prior criminal history measure (i.e., the CHC

itself, or the sum of criminal history points) for a randomly selected recidivist will be greater than the same measure for

a randomly selected non-recidivist. In the discussion, such a significant relationship is referred to as recidivism

prediction.

Page 9 of 38

“stepping stone” appearance suggests that the first five CHCs clearly delineate recidivism risk by

CHC. 

A second noteworthy finding is the almost nonexistent difference between the survival

curves of CHC V and CHC VI. The lines lie nearly on top of each other, although they do begin

to diverge slightly (with CHC VI rates slightly higher) after approximately 600 days at risk. 

Statistical tests22 for differences between the CHCs confirm the visual analysis. The

difference in predictive accuracy between CHC V and CHC VI is not statistically significant,23 while

the differences between the other categories are statistically significant.24 The statistical results of

this modeling are reported in the appendix, and specify that there is no significant difference

between CHC V and CHC VI in predicting recidivism. This finding is, however, somewhat

misleading, because offenders sentenced under the career offender guideline (§4B1.1) and the armed

career criminal guideline (§4B1.4) can be assigned to criminal history category VI, even if they have

fewer than 13 criminal history points, the minimum number of points otherwise needed for an

offender to be placed in category VI. Approximately 145 offenders in the weighted recidivism two

year follow-up sample had fewer than 13 criminal history points, but were assigned to criminal

history category VI for sentencing. When the hazard model using criminal history categories

predicting days until recidivism was rerun for criminal history categories assigned based only on

criminal history points, the statistical tests show that all categories are significantly different from

one another, including categories V and VI. The results indicate that category VI offenders have

higher recidivism rates than offenders in category V. In sum, it appears that assigning offenders to

criminal history category VI, under the career criminal or armed career criminal guidelines, is for

reasons other than their recidivism risk. The survival analyses described here will be explored

further in forthcoming papers. 

4. Area Under the Curve (AUC) Analysis

Earlier, in this report’s methodology section, an introduction to AUC analysis appears. The

AUC method reports prediction power strength25 for a given criminal history measure, and also

permits comparisons of prediction power over alternative measures. This subsection of the report

presents the recidivism prediction power of the CHC and of the sum of criminal history points.

Analyses for both the primary and re-conviction definitions appear. 
26The random model defines the likelihood of recidivism at 50 percent – similar to tossing a coin.

27Statistical tests show that the ROC for criminal history points is significantly larger at the .05 level than

criminal history categories when predicting the primary recidivism measure.

Page 10 of 38

AUC Criminal History Category Predictions

Exhibit 6 shows the AUC curve generated using the CHC measure to predict the primary

recidivism definition within a two year follow-up. The AUC is 0.6786, which is significantly

(p<0.05) different from the random model.26 It is clear that the CHC measure is a significantly

better predictor of recidivism than chance. 

AUC Criminal History Points Predictions 

As discussed earlier, criminal history points are the underlying pure measure of the

guidelines’ Chapter Four computation. The criminal history points are aggregated into the CHCs,

and in so doing, would be expected to lose some of the precision in their predictive power.

Therefore, it is appropriate to examine criminal history points as the better predictor of recidivism

when assessing Chapter Four’s predictive power. 

To gauge criminal history points’ predictive ability, Exhibit 7 graphically displays the AUC

analysis for the recidivism measure of criminal history points. Again, the AUC analysis predicts

recidivism during the first two years at risk. The AUC is 0.6992, which is a statistical (p<0.05)

improvement over the random model. The conclusion is that the current criminal history points

measure is a predictor of recidivism risk. As expected, however, note that the AUC for CHC

(0.6786 from Exhibit 6) is less than the AUC for criminal history points (0.6992 from Exhibit 7).

This ordering indicates that the criminal history points measure is more predictive27 and

demonstrates that information regarding recidivism risk is indeed lost when criminal history points

are converted into CHCs. 

E. Summary of Recidivism Prediction Results

Both CHCs and criminal history points predict recidivism, regardless of whether

recidivism is measured with the primary definition or by a re-conviction only

definition. Exhibit 8 summarizes the prediction for the four empirical groupings that

result from two recidivism definitions (primary recidivism measure and re-conviction recidivism

measure) and two criminal history measures (CHC and criminal history points). All four

combinations statistically predict recidivism and result in the same analytical conclusions.

However, the most predictive result occurs when criminal history points are the measure

used to predict the primary recidivism measure. The AUC for this combination is the highest, at

0.6979. This finding confirms the expectation that the criminal history points measure represents
28Errors in recidivism prediction occur when a model predicts recidivism for an offender and in fact the offender

does not recidivate (a Type I error, denoted as "), or when a model does not predict recidivism for an offender and in

fact the offender does recidivate (a Type II error, denoted as $). The advantages of the primary recidivism measure in

minimizing Type I and Type II errors is discussed in a recidivism project companion report, “Background and

Methodology of the U.S. Sentencing Commission’s 2003 Recidivism Study.”

29Harer 1994.

Page 11 of 38

the most accurate predictive instrument. Recall also that the primary recidivism definition has been

shown to be the more statistically error free measure of recidivism, minimizing prediction errors.28

The analysis indicates that criminal history points have a greater prediction power than do

the CHCs. However, the difference in predictive power does not argue for the abandonment of the

CHC axis of the sentencing table. Even though the prediction power difference between the two

criminal history measures is significant, the CHC provides a simplicity and efficiency that argues

for its continued use in the sentencing process. The absolute level difference of prediction power

may have a statistically significant import, but the difference is, in fact, relatively small and justifies

the practical significance of only a small handful of CHCs.

F. Recidivism and Offender Characteristics

The sections below summarize the variation in recidivism rates based on offender

characteristics. The tabular analyses show association between characteristics, but do

not imply causation. The recidivism rate patterns observed are typical of those

reported in most prior federal offender recidivism research,29 and reveal associations potentially

relevant to policy discussions concerning the guidelines’ criminal history measures. Exhibits 9

through 13 cited in this section appear at the end of the report. 

1. Gender

Overall, women recidivate at a lower rate than men. Exhibit 9 depicts the percentage of male

and female offenders who recidivate within two years of returning to the community. Of the males

in the study sample, 24.3 percent recidivate, 75.7 percent do not. Of the females in the study

sample, 13.7 percent recidivate, 86.3 percent do not. Again, the rates for males and females increase

in the higher CHCs. The difference between male and female rates, however, is not constant. In

CHC I through CHC IV, there is never greater than approximately a five percentage point difference

between male and female rates. However, in CHC V and CHC VI, the difference between the rates

jumps to approximately 15 percentage points. 
Page 12 of 38

2. Age at Sentence

Recidivism rates decline relatively consistently as age increases. Generally, the younger the

offender, the more likely the offender recidivates. Exhibit 9 illustrates the age recidivism trend of

the study sample. Among all offenders under age 21, the recidivism rate is 35.5 percent, while

offenders over age 50 have a recidivism rate of 9.5 percent.

3. Race and Ethnicity

Exhibit 9 illustrates that the race of the offender is associated with recidivism rates. Overall,

Black offenders are more likely to recidivate (32.8%) than are Hispanic offenders (24.3%). White

offenders are the least likely to recidivate (16.0%). 

4. Employment Status

Exhibit 10 shows that those with stable employment in the year prior to their instant offense

are less likely to recidivate (19.6%) than are those who are unemployed (32.4%). The difference

between the employed and unemployed recidivating declines in the higher CHCs, until offenders

in CHC VI have virtually the same recidivism rate regardless of their employment status in the year

prior to their instant offense. 

5. Educational Attainment

Exhibit 10 shows recidivism rates for offenders with different educational backgrounds.

Overall, offenders with less than a high school education are most likely to recidivate (31.4%),

followed by offenders with a high school education (19.3%), offenders with some college education

(18.0%), and offenders with college degrees (8.8%). One exception is seen in CHC V where

recidivism rates for offenders with a college education (73.3%) are higher than rates for offenders

with less than a high school education (50.6%).

6. Marital Status

Offenders who have never been married are most likely to recidivate (32.3%), as shown in

Exhibit 10. Those who are married are slightly less likely to recidivate (13.8%) than are those who

are divorced (19.5%). 
30Zone D in the sentencing table of Chapter Five Part A requires that, absent departure, the minimum sentence

term be satisfied with a sentence of imprisonment (§5C1.1(f)). In contrast, sentence terms in Zones B and C can be

satisfied in part with alternative confinement, and sentence terms in Zone A do not require confinement nor

imprisonment. Within any given CHC, the sentence terms of guideline ranges in Zone D have the greatest length in the

sentencing table, ranging from offense levels 13 and higher for CHC I, to offense levels 6 and higher for CHC V. 

Page 13 of 38

7. Illicit Drug Use

Exhibit 10 demonstrates a relationship between illicit drug use and recidivism. Overall,

offenders using illicit drugs within one year prior to their instant offense have a higher recidivism

rate (31.0%) than those not using illicit drugs (17.4%). This finding does not hold for CHC V

offenders.

8. Guideline Instant Offense Level

There is no apparent relationship between the sentencing guideline final offense level and

recidivism risk. Exhibit 11 illustrates the percentages of offenders who recidivated within each

offense level grouping. The recidivism rates are essentially the same, regardless of the offender’s

offense severity under the sentencing table. This relationship is consistent with the principle that

the guideline offense level is not designed to predict recidivism, while the criminal history

computation is designed to predict recidivism.

9. Guideline Applied for Instant Offense

Exhibit 11 relates the recidivism rate and the guideline driving the sentence for the instant

offense. Overall, offenders sentenced under the guidelines for robbery, §2B3.1 (41.2%) and

firearms, §2K2.1 (42.3%) are most likely to recidivate. Offenders sentenced in fiscal year 1992

under fraud, §2F1.1 (16.9%), larceny, §2B1.1 (19.1%), and drug trafficking, §2D1.1 (21.2%) are

overall the least likely to recidivate. Noteworthy data are seen in the patterns across the CHCs.

Even though fraud and larceny offenders had lower recidivism rates in CHC I and II, the recidivism

rates for these offenses exceed 50 percent in CHC V and CHC VI and appear sometimes comparable

to the recidivism rates for robbery and firearms at the higher CHCs. Additionally, except in CHC

I, drug trafficking offenders have the lowest, or second lowest, rate of recidivism across the CHCs.

10. Sentence Type: Probation, Alternatives, or Prison

Exhibit 12 reports that, overall, offenders are most likely to recidivate (25.6%) when their

sentence is a straight prison sentence. Recall that straight prison sentences fall within Zone D of the

sentencing table,30 with greater instant offense seriousness implicit. Of those offenders sentenced

to a probation only sentence, 15.1 percent recidivate. Offenders serving a sentence of probation

combined with confinement alternatives have a similar rate of 16.7 percent. 
31Recall the discussion in section A, noting that due to their typically longer prison confinements, fewer of the

offenders sampled from the higher CHCs had been released and included in the presented statistics. Follow-up

recidivism studies that include more offenders with longer sentences may uncover relationships that differ from those

observed at this time.

Page 14 of 38

11. Length of Sentence Imposed for Instant Offense

Exhibit 12 displays the relationship between the length of the instant offense prison sentence

and recidivism rates. The overall trend shows that recidivism has an “inverted U” shape.

Recidivism is comparatively low for the lowest sentences (less than six months, or probation), peaks

with mid-length sentences (lengths of roughly six months to two years) and then drops for the

longest sentences.31 Among the individual CHCs, however, no discernable trend emerges, although

“inverted U” shapes are recognizable in CHC III, CHC V, and CHC VI. Note that for some CHCs,

offenders spending “0” time in prison (i.e., probation only or probation with alternative confinement

alternatives) have comparatively high recidivism rates. In fact, in CHC I, CHC III, CHC V, and

CHC VI, the recidivism rates for “0” prison lengths are often higher than those of offenders who

received a minimum prison sentence (one to six months). 

12. Departure Status

Panel three of Exhibit 12 displays recidivism rates of offenders by departure status. Overall,

offenders receiving a substantial assistance departure have the lowest recidivism rate (17.9%).

Recidivism rates for sentences within the guideline range and downward departures sentences are

similar (23.3% and 23,0%, respectively). Looking at differences among the CHCs themselves, only

the lower CHCs (I, II, III, and IV) have the lowest recidivism rates in substantial assistance

departure cases. Downward departure offenders only in the higher CHCs (III, IV, V, and VI) have

recidivism rates below the rates for within guideline offenders.

13. Type of Recidivating Event

Exhibit 13 uses a different format from the preceding exhibits. The exhibit reports the

distribution of first recidivating events in each CHC. The exhibit addresses the kinds of events that

constitute recidivism, and provides this information for all offenders and for each separate CHC. 

Overall, probation revocations account for the highest percentage (20.8%) of recidivating

events. Across all recidivating offenders, one in five committed a probation violation. However,

this finding is in part an artifact of the guideline sentencing structure. There are greater numbers of

offenders in the lower CHCs, and those in the lower CHCs are more likely to receive a non-prison

sentence, and thus be under probation supervision. It is not surprising, therefore, that only for CHC

I, CHC II, and CHC III are probation revocations the most common recidivating events.

For similar reasons, supervision revocations, which are overall the second most frequent

recidivating offenses, are the most frequent recidivating events for CHC IV, CHC V, and CHC VI.
Page 15 of 38

Those in the higher CHCs are more likely to receive a prison sentence, and thus be under supervised

release when returning to the community. 

Among the non-supervision re-offending events, almost one in every ten events is a violent

crime, and their frequency is comparable across the CHCs. Drug trafficking crimes comprise almost

nine percent (8.8%) of recidivating events, with their likelihood decreasing from 11.2 percent in

CHC I to 4.1 percent in CHC VI. In contrast, larceny rates increase from a low of 6.9 percent in

CHC I to a high of 14.9 percent in CHC VI.

G. Conclusion

Testing of the guidelines’ criminal history measure’s predictive power shows that the

aggregate Chapter Four provisions are performing as intended and designed.

Recidivism rates rise as criminal history points increase and as CHCs increase. The

empirical evidence shows that criminal history as a risk measurement tool has statistically

significant power in distinguishing between recidivists and non-recidivists. Not surprisingly,

criminal history points predict better than the CHC. This is because points comprise the raw

criminal history calculation. The CHC measure sums the number of points into aggregate

categories. For example CHC III contains offenders with four, five, or six criminal history points.

It is logical that the points themselves are more predictive than the summed measure. However, the

statistically significant difference in prediction between the points and sum measures may not have

policy significance. The practical and efficiency advantages of having a small set of CHCs in the

sentencing table overwhelm the small absolute increase in predictive power.

The findings in this analysis suggest a number of additional topics that will be addressed in

the companion reports from the recidivism project. One example focuses on offenders with minimal

prior criminal history. Not only do they have substantially lower recidivism rates, but they are

considered less culpable under the guidelines. Possible sentencing reductions for “first offenders”

are supported by the recidivism data and would recognize their lower re-offending rates. At the

other end of the scale, this analysis finds no statistical difference between the recidivism rates of

offenders in CHC V and CHC VI. One conclusion is that these two categories could be combined

with no loss of predictive power. Even with equal recidivism rates, however, practitioners and

policy makers may identify a culpability difference between offenders in CHC V and CHC VI. Such

a culpability distinction might argue for maintaining these two different CHCs.

The recidivism rates associated with offender or offense characteristics also highlight

important relationships. One such relationship concerns offense seriousness and recidivism. There

is no correlation between recidivism and guidelines’ offense level. Whether an offender has a low

or high guideline offense level, recidivism rates are similar. While surprising at first glance, this

finding should be expected. The guidelines’ offense level is not intended nor designed to predict

recidivism. Other sets of interesting relationships are those between prior illicit drug use and

recidivism, or offender education level and recidivism. If, as the data indicate, abstinence from

illicit drug use, or high school completion, reduces recidivism rates, then rehabilitation programs
32USSG Ch. 4, Pt. A, intro. commentary states that some explanatory variables, such as age and drug abuse,

were not incorporated into Chapter Four as recidivism predictors because of policy concerns. However, the guidelines

leave open their possible later consideration, stating that the factors were “not included here at this time.”

Page 16 of 38

to reduce drug use or to earn high school diplomas may have high cost-benefit values. Even further,

the relationships between recidivism rates and instant offense types are noteworthy. Robbery and

firearms offenders have overall higher recidivism rates compared to fraud, larceny, or drug

trafficking offenders. However, in the higher CHCs, the recidivism rates for larceny and fraud

offenders reach levels almost comparable to the rates for robbery and firearms offenders.

Investigations using the recidivism data suggest that there are several legally permissible

offender characteristics which, if incorporated into the criminal history computation, are likely to

improve predictive power. Analyses in the recidivism project’s report series address the rationale

and value of various proposals to modify the current Chapter Four computation in order to increase

predictive power. Offender age32 is a pertinent characteristic. Data from this report indicate that

younger offenders are more likely to recidivate than older offenders. Indeed, the U.S. Parole

Commission’s recidivism predictor, the salient factor score, does incorporate offender age, thus

improving its predictive power. A companion report from the recidivism project explores this issue

in detail and compares the predictive power of the salient factor score with the guidelines’ Chapter

Four computation.
Page 17 of 38

References

Allison, Paul D.

 1995 Survival Analysis Using the SAS System: A Practical Guide. Gary, NC: SAS

Institute Inc.

Beck, Allen J. and Bernard E. Shipley

 1989 Recidivism of Prisoners Released in 1983. Bureau of Justice Statistics Special

Report. Washington, D.C.: Department of Justice.

Betsey, Charles

 1989 Future Plans for Research Memorandum, May 22. Washington, D.C.: United States

Sentencing Commission. 

Blumstein, Alfred and Jacqueline Cohen

 1979 Deterrence and Incapacitation. Report of the National Academy of Sciences Panel

on Research on Deterrent and Incapacitative Effects. Washington, D.C.: National

Academy Press.

Brame, Robert, Raymond Paternoster, Paul Mazerolle, and Alex Piquero

 1998 Testing for the Equality of Maximum-Likelihood Regression Coefficients Between

Two Independent Equations. Journal of Quantitative Criminology 14:245-261.

Copas, John B., Peter Marshall, and Roger Tarling

 1996 Predicting reoffending for Discretionary Conditional Release. Home Office

Research Study 150. London: Home Office. 

Delong, E. R., D. M. Delong, and D. L. Clarke-Pearson

 1988 Comparing the Areas under Two or More Correlated Receiver Operating Curves: A

Nonparametric Approach. Biometrics 44:837-845. 

Hanley, J. A. and B. J. McNeil

 1982 The Meaning and Use of the Area under a Receiver Operating Characteristic (Roc)

Curve. Radiology 143:26-36.

Hanley, J. A. and B. J. McNeil

 1983 A Method of Comparing the Areas under Receiver Operating Characteristic Curves

Derived from the Same Cases. Radiology 148:26-36.
Page 18 of 38

Harer, Miles D.

 1994 Recidivism Among Federal Prison Releasees in 1987: A Preliminary Report.

Washington, D.C.: Federal Bureau of Prisons, U.S. Department of Justice.

Henegan, Sharon

 1999 Presentation in the panel on Post-Release Supervision at the National Association of

Sentencing Commissions, 1999 Annual Conference. Salt Lake City, Utah.

Hoffman, Peter B.

 1994 Twenty years of operational use of a risk prediction instrument: the United States

Parole Commission’s Salient Factor Score. Journal of Criminal Justice, Vol. 22, No.

6:477-494. 

Hoffman, Peter B. and James Beck

 1997 The Origin of The Federal Criminal History Score. Federal Sentencing Reporter

Vol. 9, No. 4:192-196. 

Hosmer, David W. Jr. and Stanley Lemeshow

 1999 Applied Survival Analysis: Regression Modeling of Time to Event Data. New

York: Wiley.

Jones, Mark and Barbara Sims

 1997 Recidivism of Offenders Released from Prison in North Carolina: A Gender

Comparison. The Prison Journal 77:335-348.

Klein, John P. and Malvin L. Moeschberger

 1997 Survival Analysis: Techniques for Censored and Truncated Data. New York:

Springer. 

Langan, Patrick A. and David J. Levin

 2002 Recidivism of Prisoners Released in 1994. Bureau of Justice Statistics Special

Report. Washington D.C.: Department of Justice. 

Maltz, Michael D.

 1984 Recidivism. Orlando, FL.: Academic Press.

Quinsey, Vernon L., Grant T. Harris, Marnie E. Rice, Catherine A. Cormier

 1998 Violent offenders: Appraising and Managing Risk. Washington, D.C.: American

Psychological Association. 

Schmidt, Annesley K., and Joel H. Garner.

 1991 Recidivism of Federal Prisoners Released in 1978: A Five Year Follow-Up (March

6). Washington, D.C. : United States Sentencing Commission. 
Page 19 of 38

Roberts, Julian V. and Loretta Stalans

 1997 Public Opinion, Crime and Criminal Justice. Boulder, CO: Westview Press. 

Schmidt, Peter and Ann Dryden Witte

 1988 Predicting Recidivism Using Survival Models. New York: Springer-Verlag.

Sphon, Cassia and David Holleran

 2002 The Effect of Imprisonment on Recidivism Rates of Felony Offenders: A Focus on

Drug Offenders. Criminology 40:329-357. 

Swenson, Win

 1990 Future Commission Tasks Memo, April 25. Washington, D.C.: United States

Sentencing Commission.

 

Swets, J. A., Dawes, R. M., and Monahan, J.

 2000 Better Decisions Through Science. Scientific American, 283(4), 82-87.

United States Sentencing Commission

 2002 Guidelines Manual. Washington, D.C.: United States Sentencing Commission.

United States Sentencing Commission

 1987 Supplemental Report on the Initial Sentencing Guidelines and Policy Statements,

June 18. Washington, D.C.: United States Sentencing Commission.

United States Sentencing Commission

 2003 Variable Codebook: Criminal History and Recidivism Study, 2001 Data Collection

from PSR. Washington, D.C.: United States Sentencing Commission.

Wilkins, William Jr.

 1990 Letter to Congressman Schumer Regarding Commission Projects, March 16.

Washington, D.C.: United States Sentencing Commission. 
Page 20 of 38

*Cases with RAP Sheets and PSRs, weighted.**As of June 1, 2001.***End of two year recidivism window of opportunity is June 1, 2001.Source: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.

Recidivism 

Sample*

(n=28,519)

Sentenced to 

Probation:

(n=7,618)

Sentenced to 

Prison:

(n=20,901)

Within 

Follow Up: 

(n=7,618)

Re

(n

R

(n

Recid

Within 

Follow Up:

(n=16,717)

Outside of

Follow Up:

(n=1,532)

Two-Year Recidivism 

Follow-Up***

Released:

(n=18,249)

Still in Prison:

(n=2,531)

Deceased:

(n=121)

Prison Status**

(100% of sample) (90.7% of sample) (85.3

73.

3%

prison

26

.7%

probation

The

24,335 

meet t

this rec

Exhibit 1

Chronological Flow Chart of Recidivism Sample

Recidivism Study 2003
Page 21 of 38

Exhibit 2

Recidivism Rates, by Criminal History Category for 

Primary and Re-Conviction Recidivism Definitions

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Total Category I Category II Category III Category IV Categ

Percent

Recidivating

Percent

Recidivating 

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Per

Recid

TOTAL1 24,335 15,429 2,857 2,844 1,359 

Primary RecidivismDefinition2

Did Recidivate 22.1 13.8 24.0 34.2 44.6 Did NOT Recidivate 77.9 86.2 76.0 65.8 55.4 

Re-Conviction RecidivismDefinition3

Did Recidivate 6.3 4.0 8.1 9.0 11.5 Did NOT Recidivate 93.7 96.0 91.9 91.0 88.5 

 1 Number of offenders with a 24 month period at risk of recidivating following either initiation of probation (for offenders receiving probation-only sentences) or release from 

 receiving confinement sentences).

2 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.3 Re-conviction recidivism definition based solely on the offender’s re-conviction, excluding any supervised release/ probation violations or re-arrests.

 SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
Page 22 of 38

0

102030405060

I II III IV V VI

Criminal History Category 

Percent Recidivating 

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. citizens), 2003, weighted data.

Exhibit 3

Two Year Primary Recidivism Measure Components,

by Criminal History Category

Recidivism Study 2003
Page 23 of 38

Exhibit 4

Two Year Recidivism Rates for Number of Criminal History Points:

Primary and Re-conviction Recidivism Definitions

Recidivism Study 2003

Number of

Criminal

History Points N

Percent

Recidivating

Primary 

Definition

Percent

Recidivating

“Re-conviction”

Definition

TOTAL 24,335 22.1 6.3

0 points 12,562 11.8 3.6

1 point 2,888 22.5 5.5

2 points 1,274 21.9 7.5

3 points 1,552 26.2 8.5

4 points 1,141 30.2 8.7

5 points 847 34.0 9.6

6 points 921 38.7 8.7

7 points 499 43.3 12.6

8 points 464 43.7 10.5

9 points 431 47.5 11.2

10 points 300 50.9 13.7

11 points 279 53.1 14.0

12 points 249 48.7 14.0

13 points 131 56.8 12.0

14 points 139 63.4 18.1

15 points 136 64.3 18.6

16 points 87 54.2 19.3

17 points 80 53.9 19.7

18 points 73 34.3 11.5

19 points 60 47.4 3.5

20 points or more 222 59.3 18.5

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, 

weighted data. Missing data are excluded, unless specified.
Page 24 of 38

0

10

20

30

40

50

60

0 100 200 300 400 500 600 700 800

Days at Risk

Cumulative Percent Recidiva

I

II

III

IV

V

VI

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. citizens), 2003, weighted data.

One Year Two Years

Exhibit 5

Two Year Cumulative Recidivism Rates:

Primary Recidivism Definition

Recidivism Study 2003
Page 25 of 38

0.00 0.25 0.50 0.75 1.00

Sensitivity

0.00 0.25 0.50 0.75 1.00

1 - Specificity

Exhibit 6

Criminal History Category:

ROC Curve for Two Year Primary Recidivism Definition

Recidivism Study 2003

ROC Area = 0.6786 

Standard Error = 0.0041 

95% Confidence Interval = 0.67064 to 0.68657

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
Page 26 of 38

Exhibit 7

Criminal History Points:

ROC Curve for Two Year Primary Recidivism Definition

Recidivism Study 2003

ROC Area = 0.6992

Standard Error = 0.0041 

95% Confidence Interval = 0.69113 to 0.70721

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data. 0.00 0.25 0.50 0.75 1.00

Sensitivity

0.00 0.25 0.50 0.75 1.00

1 - Specificity
Page 27 of 38

Exhibit 8

Summary of Predictive Power of Current Criminal Measures:

Criminal History Category and Criminal History Points 

Two Year Statistics Using Different Recidivism Definitions

Recidivism Study 2003

Instrument and 

Recidivism Definition

Area

Under

Curve

(AUC)

95%

Confidence

Interval

Standard

Error

Primary Recidivism Definition

Criminal History Category

Measure

0.6786* (0.6706, 0.6866) 0.0041

Criminal History Points Measure 0.6992* (0.6911, 0.7072) 0.0041

Re-Conviction Recidivism Definition 

Criminal History Category Measure 0.6396* (0.6256, 0.6537) 0.0072

Criminal History Points Measure 0.6510* (0.6366, 0.6654) 0.0073

*Significantly different from 0.5 at p<0.05. 

SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
Page 28 of 38

Exhibit 9

Primary Definition Recidivism Rates1 for General Demographic Variables, by Criminal History 

 Gender, Age at Sentencing, and Race

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Demographic Characteristics

Total Category I Category II Category III Category IV Categ

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Per

Recid

TOTAL2 24,335 15,429 2,857 2,844 1,359 

Gender

Female 13.7 10.0 23.6 30.7 40.0 Male 24.3 15.2 24.1 34.7 45.0 

Age at Sentence

Under 21 35.5 29.5 35.6 54.7 64.3 21 – 25 31.9 22.3 29.1 42.7 55.1 26 – 30 23.7 13.3 27.3 33.6 43.9 31 – 35 23.8 14.6 22.7 32.7 42.7 36 to 40 19.7 12.1 23.2 29.4 33.1 41 to 50 12.7 6.9 13.3 24.5 45.3 Over 50 9.5 6.2 13.9 19.8 21.0 

Race

White 16.0 8.9 18.9 27.8 42.8 Hispanic 24.3 18.9 22.9 36.0 28.1 Black 32.8 23.7 31.4 41.6 48.0 Other3 26.4 15.5 35.9 58.3 39.6 

1 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.

 2 Number of offenders with a 24 month period at risk of recidivating following either initiation of probation (for offenders receiving probation–only sentences) or release fro

 offenders receiving confinement sentences).

3 “Other” race category includes Native Americans and Asians.

 ‡ Indicates fewer than 10 sample subjects. Findings may not be statistically significant. SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data. 
Page 29 of 38

Exhibit 10

Primary Definition Recidivism Rates1 for General Demographic Variables, by Criminal History 

Employment, Education, Marital Status, and Illicit Drug Use

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Demographic Characteristics

Total Category I Category II Category III Category IV Categ

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Per

Recid

TOTAL2 24,335 15,429 2,857 2,844 1,359 

Employment Status3

Unemployed 32.4 20.6 26.8 39.4 48.0 Employed 19.6 12.7 23.3 32.1 43.1 

Educational Attainment4

Less Than High School 31.4 21.3 31.3 38.5 49.8 

 High School 19.3 10.6 21.8 32.5 40.1  Some College 18.0 13.9 17.8 29.0 39.0  College Graduate 8.8 7.1 6.5 18.5 34.6 

Marital Status

Never Married 32.3 22.7 32.3 44.6 46.9 Legal Marriage 13.8 9.8 13.9 25.1 40.0 Divorced 19.5 9.8 23.3 27.2 44.0 Other5 22.9 12.9 23.1 31.4 45.1 

Illicit Drug Use6

No Illicit Drug Use 17.4 10.8 21.2 31.5 40.2 Illicit Drug Use 31.0 21.9 27.5 37.6 49.6 

1 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.2 Number of offenders with a 24 month period at risk of recidivating following either initiation of probation (for offenders receiving probation-only sentences) or release from 

 receiving confinement sentences).

3 Employment status during the year prior to the instant offense. “Employed” includes alternative forms of employment and “Unemployed” includes missing values.4 Educational Attainment at the time of the instant offense.5 “Other” marital status category includes “Co-habitating,” “Widowed,” and “Separated.”6 Illicit drug use during the year prior to the instant offense. Missing values counted as “No”illicit drug use.

 ‡ Indicates fewer than 10 sample subjects. Findings may not be statistically significant.
Page 30 of 38

Exhibit 11 

Primary Definition Recidivism Rates1 for Instant Offense Characteristics, by Criminal History 

Instant Offense Level and Primary Sentencing Guidelines

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Offense Characteristics

Total Category I Category II Category III Category IV Categ

Percent

Recidivating 

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Per

Recid

TOTAL2 24,335 15,429 2,857 2,844 1,359 

Instant Offense Level

01 – 08 22.5 15.1 29.8 37.6 44.1 09 – 10 22.5 9.6 18.3 45.4 51.0 11 – 12 21.7 8.7 38.0 39.1 50.8 13 – 16 22.2 14.8 23.5 37.4 39.5 17 – 21 27.3 17.5 25.7 37.5 44.1 22 – 25 22.8 13.3 22.5 33.3 40.3 26 – 30 20.7 18.9 19.7 19.2 39.5 31 – 43 17.5 11.1 12.2 22.4 30.6 

Primary Sentencing Guideline

§2D1.1 (drug traf.) 21.2 16.7 19.8 26.1 37.7 §2F1.1 (fraud) 16.9 9.3 26.3 33.8 42.3 

§2B1.1 (larceny) 19.1 11.6 37.9 56.6 43.0 §2K2.1 (firearms) 42.3 23.7 26.8 44.1 53.0 §2B3.1 (robbery) 41.2 33.7 31.4 38.8 57.1 All Other Guidelines 20.5 12.6 23.6 34.0 44.3 

1 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.2 Number of offenders with a 24 month period at risk of recidivating following either initiation of probation (for offenders receiving probation-only sentences) or release fro

 offenders receiving confinement sentences).

3 The sentence imposed for the offender’s instant offense, presented in months.

 SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
Page 31 of 38

Exhibit 12 

Primary Definition Recidivism Rates1 for Instant Offense Characteristics, by Criminal History 

Type of Sentence, Length of Sentence, and Departure Status

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Offense Characteristics

Total Category I Category II Category III Category IV Categ

Percent

Recidivating 

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Percent

Recidivating

Per

Recid

TOTAL2 24,335 15,429 2,857 2,844 1,359 

Type of Sentence

Fine Only 1.2 0.0‡ 0.0‡ 0.0‡ 100.0‡ 

Probation Only 15.1 12.7 21.8 36.4 42.9 

Probation + Alternatives 16.7 13.3 29.4 37.2 44.1 

Prison + Alternatives 18.3 12.1 26.3 32.5 57.1 Prison Only 25.6 14.8 23.6 34.0 44.2 

Length of Sentence3

0 15.7 12.8 25.2 36.8 44.4 01 – 05 14.3 7.4 32.7 31.2 66.7 

06 – 11 27.1 17.2 25.0 45.5 42.3 12 – 23 28.4 16.5 27.2 40.4 51.0 24 – 59 26.8 14.0 22.9 34.4 44.6 60 or More 22.7 15.1 19.1 22.3 36.6 

Departure Status

Within Guideline 23.3 14.6 25.5 37.0 45.8 Upward Departure 27.8 0.0‡ 14.3‡ 22.2 44.4 

Substantial Asst. 17.9 11.5 19.0 24.7 40.2 Other Downward 23.0 16.7 27.3 29.8 39.2 

1 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.

2 Number of offenders with a 24 month period at risk of recidivating following either initiation of probation (for offenders receiving probation-only sentences) or release fro

 offenders receiving confinement sentences).

3 The sentence imposed for the offender’s instant offense, presented in months.

 ‡ Indicates fewer than 10 sample subjects. Findings may not be statistically significant.

 SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
Page 32 of 38

Exhibit 13

Offenders’ Recidivating Offense Under the Primary Recidivism Definition,1

by Criminal History Category

Recidivism Study 2003

CRIMINAL HISTORY CATEGORIES

Total Category I Category II Category III Category IV Categ

Percent Percent Percent Percent Percent Per

TOTAL PERCENT 100.0 100.0 100.0 100.0 100.0 Recidivating Offense Type2

Probation Rev. 20.8 24.1 21.8 20.8 17.8 Supervision Rev. 18.6 14.0 17.4 19.6 25.1 Fraud 4.8 5.9 5.1 5.1 2.5‡ 

Drug Possession 5.6 5.2‡ 2.6‡ 8.8 4.5 

Drug Trafficking 8.8 11.1 9.3 7.4 7.6 Larceny 7.7 6.9 8.0 5.6 5.7 DUI 4.9 6.1 6.0 5.0 2.3‡ 

Serious Violent Offense3 11.7 9.6 13.5 12.2 16.3 

Other 17.1 17.1 16.3 15.5 18.2 

(N=5,377)4 (N=2,128)4 (N=687)4 (N=974)4 (N=606)4 

1 Primary recidivism definition based on offender’s re-arrest, including supervised release/ probation violations, re-arrest, or re-conviction.2 Offense types only for offenders’ who had a first recidivism event during the 24 month recidivism follow-up period after either initiation of probation (for offenders receiv

 release from confinement (for those offenders receiving confinement sentences).

 3 “Serious Violent Offense” category includes re-arrests for the following offense types: homicide, kidnaping, robbery, sexual assault, aggravated assault, domestic violence

4 Number of offenders who recidivated in the given CHC. 

 ‡ Indicates fewer than 10 sample subjects. Findings may not be statistically significant. SOURCE: U.S. Sentencing Commission, FY1992 Recidivism Sample (U.S. Citizens), 2003, weighted data.
33This is a simplified explanation. For the sake of clarity, additional issues involving “true negatives” and “false

negatives” are not considered. 

34On the graph, the false positive rate is plotted along the x axis and the true positive rate (called the

“sensitivity”) is plotted along the y axis. The false positive rate is defined as 1 minus the specificity.

Page 33 of 38

Appendix A

Evaluating Predictive Power with the

Area Under the Receiver Operating Characteristic Curve

Determining the area under a specific geometric curve is an established technique for

measuring the predictive power of a measurement instrument. This technique is well

established in diagnostic testing for health disorders when a procedure is used to

determine the presence of a disease. If the diagnostic test accurately predicts the presence of the

disease, this is a “true positive” result. However, the researcher must also know how often a

diagnostic test indicates that a disease is present when in fact the disease is not present. This type

of prediction error is called a “false positive” result. The best prediction tool maximizes true

positives and minimizes false positives.33 The technique that analyzes prediction accuracy uses a

graph that maps the “true positive” rate against the “false positive” rate. The curve of this graph is

called a receiver operating characteristic (ROC) curve. The area under the curve provides a measure

of the predictive power of the prediction instrument. The paragraphs below illustrate how this

technique is used.

ROC analysis is a straightforward application of this technique to recidivism research. Here

the measurement instrument is the offender’s prior criminal history. For this example, assume that

the instrument measures criminal history points. The points are used to predict recidivism. The

analysis must show how often the measure of criminal history points accurately predicts recidivism,

compared to how often the measure of criminal history points predicts recidivism incorrectly. 

Graphically, the data is shown in a rectangular box with the area being one unit, as shown

in the figure below. A diagonal line is drawn from the lower left to the upper right corners, bisecting

the rectangle into two equal parts. The area below the diagonal line measures one half the area of

the box (i.e., 0.5 units) and represents “random” prediction, or no predictive power at all. Any

observation along the diagonal line is equally likely to be a true positive or a false positive. This

line, and the area of the rectangle below it, is the comparison point for the recidivism model. 

When the observed true positive and false positive recidivism data are plotted in this

rectangular box,34 they form a line that curves upward from the diagonal line. This curved line is the

ROC curve. The higher the ROC line curves above the diagonal line (and toward the top and left

side of the rectangle), the greater the area under its curve. The area under the ROC curve represents

the predictive power of the criminal history model. Because the ROC curve is higher than the
Page 34 of 38

diagonal line, the area under the ROC curve is greater than the area under the diagonal line. Thus,

as represented in the figure, the ROC curve improves upon random chance prediction.

The statistic used to measure the predictive power of the criminal history model is the

“AUC”: the Area Under the Curve. The greater the AUC, the better the predictive power of the

measurement instrument. 

The AUC has several desirable qualities as a prediction power gauge. First, and in the

context of prior criminal history predicting recidivism, the AUC is interpreted as the probability that

a randomly chosen known recidivist will have more prior criminal history than a randomly chosen

known non-recidivist. Therefore, the AUC ranges from 0.5 to 1.0. 

• With an AUC of 0.5, a randomly chosen known recidivist has a 50 percent

chance of having more prior criminal history than a randomly chosen nonrecidivist. This would mean that there is no relationship between recidivism

and prior criminal history. 

• With an AUC of 1.0, 100 percent of the randomly chosen recidivists will

have more prior criminal history than the randomly chosen non-recidivists,

meaning that prior criminal history predicts recidivism perfectly. With an

AUC of 1.0, all recidivists would have more prior criminal history than all

non-recidivists. 

AUCs between 0.5 and 1.0 indicate a “better than random” predictive accuracy, but a “less
35The introductory comments of USSG Ch.4, Pt. A states that “while empirical research has shown that other

factors are correlated highly with the likelihood of recidivism, e.g., age and drug abuse, for policy reasons they were not

included here at this time.”

36Hanley and McNeil, 1982; Hanley and McNeil, 1983, Delong, Delong, and Clarke-Pearson, 1988. The ability

to compare different criminal history models makes AUC an important tool in assessing impacts of changes to the

guidelines’ Chapter Four provisions.

Page 35 of 38

than perfect” predictive accuracy. For the hypothetical example appearing in the figure above, the

AUC of 0.67 indicates that 67 out of 100 times, randomly chosen recidivists and non-recidivists are

compared, a recidivist would have more prior criminal history than a non-recidivist. This indicates

that while the extent of prior criminal history is a strong predictor of recidivism, it is not a perfect

predictor. The prediction power of a criminal history measure might be improved by adding or

changing its components. However, both legal and policy factors guide the types of characteristics

that can be part of a prediction model.35

The AUC statistic has three additional desirable properties. It is insensitive to the base rate

of recidivism. It can be used across multiple predictive instruments to compare statistically

significant prediction power differences.36 Finally, the AUC can be graphically represented allowing

visual comparisons among multiple curves calculated from different prediction instruments.
37Allison, 1995; Hosmer and Lemeshow, 1999; Klein and Moeschberger, 1997.

Page 36 of 38

Appendix B

Evaluating Predictive Power with Survival Analysis

S

urvival analysis, also called hazard rate analysis, is a method that can be used to

evaluate the predictive ability of the guidelines’ criminal history measure. Survival

analysis measures the ability of criminal history to predict how rapidly offenders

recidivate during the follow-up period.37 The concept of crimes committed per unit of time has its

roots in the incapacitation literature, where great effort was expended to identify offenders who

committed crimes frequently. These repeat offenders were individuals committing multiple crimes

in a given period, such as daily, monthly, or yearly. By identifying frequent offenders, they could

be targeted for longer prison terms than offenders who commit crimes less frequently.

Incapacitation literature argues that the selective targeting of frequent offenders would result in a

more efficient use of prison resources as a means of crime reduction. 

Survival analysis is used here to examine how well criminal history predicts the number of

crimes offenders will commit during a specific follow-up period. The example below illustrates its

strengths.

Assume a researcher has recidivism data for 200 offenders who were released from prison

all on the same day. Assume that all 200 offenders recidivate during the first two years back in the

community. 

• The first 100 of the offenders all recidivate on the last day of the two year

follow-up, the 730th day. Thus, 100 crimes were committed on day 730. The

daily rate of crime for this group is 100 crimes divided by 730 days, or

0.1370 offenses per day. 

• The second 100 of these offenders all recidivate on the first day of the

follow-up period. Thus, 100 crimes were committed on day one. The daily

rate of crime for this group is 100 crimes divided by one day, or 100 offenses

per day. 

These two groups have different speeds of offending. Within 730 days, the first group is

expected to commit 100 crimes. During the same 730 day period, however, the second group is

expected to commit 73,000 crimes (assuming, of course, that none are apprehended).

Although an extreme example, it illustrates the concern of crime control advocates with

respect to their goal of protecting the public. This approach argues that criminal history must have
38AUC denotes the Area Under the Curve analysis from the ROC curve, described in Appendix A.

39Companion reports in the recidivism project series perform these analyses such as, whether the guideline

provision of “recency points” (§4A1.1(e)) adds significantly to predictive power over and above other criminal history

components.

Page 37 of 38

the predictive power to identify offenders who pose a greater risk to public. Survival analysis allows

for this evaluation of the prior criminal history measures. 

Survival analysis and the AUC38 provide different perspectives on recidivism risk. The AUC

measures how well prior criminal history predicts whether offenders recidivate or not during a fixed

follow-up period. Given the 100 percent rate of recidivism for both groups in the example, the AUC

would be identical when using criminal history to predict recidivism separately for each group of

offenders. The AUC is not sensitive to the speed at which offenders recidivate and therefore, does

not gauge how well criminal history predicts the frequency of re-offending. 

In contrast, survival models are sensitive to the speed of recidivism and therefore provide

a way to test how well criminal history measures this important aspect of re-offending risk. The

survival curves in Exhibit 5 show, from a survival analysis perspective, the rapidity of recidivism

for each day of the two year follow-up period, for each of the six CHCs (I through VI). Offenders

in CHCs V and VI recidivate most rapidly and therefore if not apprehended, are likely to offend

more frequently during the two year follow-up period than are offenders in CHC I. 

Survival analysis has another advantage over the AUC when used to evaluate criminal

history’s ability to predict recidivism. Multiple regression methods for survival analysis have been

developed that allow simultaneous evaluation of the independent contribution to predictive power

of the various criminal history components.39 The proportional hazard model estimates presented

below test whether each individual CHC contributes uniquely when predicting the rapidity of

recidivism. As discussed in the report, CHCs I, II, III, and IV have independent and significant

effects on predicting recidivism. However, the impact of CHC V and CHC VI are not statistically

different. This finding is, however, somewhat misleading, because offenders sentenced under the

career offender guideline (§4B1.1) and the armed career criminal guideline (§4B1.4) can be assigned

to criminal history category VI, even if they have fewer than 13 criminal history points, the

minimum number of points otherwise needed for an offender to be placed in category VI.

Approximately 345 offenders in the weighted recidivism two year follow-up sample had fewer than

13 criminal history points, but were assigned to criminal history category VI for sentencing. When

the hazard model using criminal history categories predicting days until recidivism was rerun for

criminal history categories assigned based only on criminal history points, the statistical tests show

that all categories are significantly different from one another, including categories V and VI.

Results indicating that category VI offenders have higher recidivism rates than offenders in category

V. In sum, it appears that assigning offenders to criminal history category VI, under the career

criminal or armed career criminal guidelines, is for reasons other than their recidivism risk. The

survival analyses described here will be explored further in forthcoming papers. 
Page 38 of 38

Proportional Hazard Model Estimates for

Criminal History Category Variables

Two Year Primary Recidivism Definition

Each CHC is analyzed as a binomial variable with Category I as the Comparison Category.

Statistical Tests for Differences Between

Criminal History Category Dummy Variable Coefficients

 Parameter Standard Hazard

Variable Estimate Error Pr > ChiSq Ratio

CAT_II 0.62261 0.04613 <.0001 1.864

CAT_III 1.04420 0.04024 <.0001 2.841

CAT_IV 1.38747 0.04410 <.0001 4.005

CAT_V 1.61343 0.05522 <.0001 5.020

CAT_VI 1.68928 0.04796 <.0001 5.416

Likelihood Ratio 2131.0946 <.0001

 Hypotheses Tests for Equality between

Successive Criminal History Category Coefficients

 Wald

 Label Chi-Square DF Pr > ChiSq

 CAT_II = CAT_III 64.0514 1 <.0001

 CAT_III = CAT_IV 46.5174 1 <.0001

 CAT_IV = CAT_V 12.7208 1 0.0004

 CAT_V = CAT_VI 1.3180 1 0.2510