The Influence of Prison Gang Affiliation on Violence and Other Prison Misconduct 

March 9, 2001

Gerald G. Gaes, Susan Wallace, Evan Gilman, Jody Klein-Saffran, and Sharon Suppa*

Federal Bureau of Prisons

*During most of the data collection and analysis phase of this project, all of the co-authors were

employed by the Bureau of Prisons (BOP). Currently, Gerald G. Gaes, Evan Gilman, and Jody

Klein-Saffran are BOP employees. Sue Wallace works for the U. S. Government Accounting

Office, and Sharon Suppa works for the Department of Justice, Criminal Division. The opinions

expressed in this paper are solely those of the authors and do not necessarily represent the

opinions or policies of officials of the Federal Bureau of Prisons or U.S. Department of Justice. 

We wish to express our appreciation to Miles Harer and Larry Karacki for their contributions to

the conceptualization of many of the ideas expressed in this paper.
Abstract

Most of the empirical research and practically all of the fieldwork conducted on gangs has been

devoted to street gangs. In this paper, automated data from the Bureau of Prisons were used to

evaluate the contribution of prison gang affiliation to violent and other forms of misconduct

within the institution. We also examined a measure of gang embeddedness to see if, similar to the

street gang research, it can be shown that core members of a prison gang were more likely to

commit violent and other kinds of misconduct than more peripheral members. A composite

measure of gang misconduct is also presented to represent the threat that a particular gang poses

to prison order. The “threat index” is model-based and provides a graphical representation of the

relative magnitude and heterogeneity of threat posed by different gang affiliations. 
1

The Influence of Prison Gang Affiliation on Violence and Other Prison Misconduct 

Although prison gangs are a pervasive problem in many correctional jurisdictions, there has been

very little empirical analysis of the impact of prison gang membership on violent and other kinds

of prison misconduct. In this paper, we provide such an analysis. We also develop a model-based

“threat index” that can be used to portray a composite representation of the impact of a gang on

prison order. 

Most of our knowledge on prison gangs is based on responses to surveys of institutions or

jurisdictions (American Correctional Association, 1993; Camp and Camp, 1985; Knox, 2000).

Information collated from these survey responses specify, among other things, the extensiveness

of gangs, the problems they cause, and the strategies jurisdictions use to monitor and suppress

gang activity. There have been a few in-depth studies of prisons and their respective systems that

have featured the growth of prison gangs as one aspect of a broader analysis. Crouch and

Marquart (1989) described the emergence of prison gangs in the power vacuum that occurred in

the Texas prison system during the Ruiz era. Jacobs (1977) analyzed the ascendancy of Chicago

street gangs into powerful and controlling groups in Stateville penitentiary in the mid-1970's.

Irwin (1980) formulated some of the following reasons for the evolution of prison gangs in

California: changes in prisoner culture; increasing numbers of violent, poor, minority, state-raised

youth to a level of concentration that they could no longer be controlled by older, less violent

inmates; the radicalization of the prison population in conjunction with external political

movements; and, what he characterized as the repressive response by prison officials to the
2

attempt by inmates to share in prison decision-making. Both Crouch and Marquart, and Jacobs

described the political, legal, administrative, and social context that served as a backdrop for the

elevation of prison gangs into their positions of influence. There has been no field study of prison

gangs analogous to the rich literature of field research on street gangs (Decker and Van Winkle,

1996; Fleisher, 1998; Hagedorn, 1988; Moore, 1978, 1991; Padilla, 1992; Sanchez-Jankowski,

1991; Thrasher, 1927; Vigil, 1988).

The empirical evidence on the relationship between prison gang affiliation and misconduct is

rather limited. Ralph and Marquart (1991) and Fong, Vogel, and Buentello (1992) provided data

on violent offenses, primarily gang-related, prior to, during, and after the “war years,” 1984 to

1985, in the Texas prison system. According to Fong et al., (1992) of the 25 homicides in 1984,

20 were gang-related. Twenty-three of the 27 homicides in 1985 were gang-related. In 1972, 30

of 36 murders in the California prison system were committed by one gang–the Mexican Mafia

(Fong et al., 1992 p. 68). Huff and Meyer (1997) reported that three gangs emerged as the riot

organizers and negotiators during the 1993 Lucasville, Ohio riot in which one correctional officer

and nine inmates were killed.

Shelden (1991) found that gang members in a Nevada prison had more total arrests, more juvenile

court referrals, more felony arrests, and were more likely to have used a weapon in their last

crime than a sample of inmates not in a gang. Shelden also found that gang members were far

more likely to commit prison rule infractions, especially drug and fighting infractions, than nongang inmates. However, Shelden did not control for individual risk factors, other than gang
3

participation that predispose inmates to prison misconduct. The Ohio Department of

Rehabilitation and Correction (1998) conducted a similar study showing that gang members were

more likely to commit prison infractions than non-gang members. However, even though they

measured other background individual differences between gang and non-gang members, those

variables were not used as controls in the comparison between gang and non-gang rates of

misconduct. Although there is much qualitative and anecdotal evidence, and there have been

specific periods in the history of some correctional systems that gang violence has been extremely

high, what is lacking is systematic research to demonstrate whether gang membership increases

the likelihood of violence or other forms of prison misconduct above the factors that predispose

inmates toward violence. 

The state of prison gang literature is analogous to the early street gang literature prior to studies

that established that street gang membership does indeed increase delinquency and criminal

behavior above the level that would be found among delinquent individuals not considered to be

members of gangs. There has been a consistent finding that in impoverished and marginal

communities gang membership increases the likelihood of delinquency (Esbensen and Huizinga,

1993; Fagan, 1989; Spergel, 1990; Thornberry et al., 1993). Sampson (1986) evaluated the

impact of neighborhood and individual levels of SES on police contacts and court referrals.

Although it was not a primary consideration of the study, he found that delinquent peer affiliation

and not gang membership accounted for police contact. Esbensen and Huizinga (1993) found that

even though delinquents who became street gang members had high delinquency rates prior to

their gang involvement, their delinquency rates increased during their gang affiliation and dropped
4

off when they left the gang. More recent work has shown that gang membership does increase

self-reported and court-reported delinquency above and beyond the effect of having delinquent

friends, which itself is a strong predictor of delinquency (Battin, Hill, Abbott, Catalano, and

Hawkins, 1998). 

Testing the impact of gang membership on violent misconduct presupposes one can distinguish a

gang affiliation from a peer relationship, or that one can identify a gang member at all. Klein

(1998) makes this point about street gangs, characterizing them as informal social groups. Thus,

establishing membership is one essential criterion to our understanding. Recent work by Curry

(2001) showed that in a small youth cohort (429 youth), police-identified gang members were a

subset of individuals who self-reported gang involvement. The data also showed that 43.6 percent

of the police-identified gang members did not identify themselves as either delinquents or ganginvolved. In the present research, we were unable to separately measure self- and official-gang

identification. In fact, the official measure of gang identity we used has as one component, selfidentification.

Klein (1998) has also addressed what we should count as gang-related crime. The member-based

definition implies that any crime committed by the gang is an instance of gang crime. Those who

adopt the motive-based definition, count only those crimes that further the goals or interests of

the gang. We adopted the member-based definition of misconduct rather than the motive-based

definition. We counted any instance of violent or other misconduct committed by a gang member. 

The data did not allow us to sort out motive from member-based misconduct.
5

Defining gang membership can be a controversial issue. There has been a debate in the street gang

literature whether delinquency and criminal activity should be a prerequisite for defining a group

as a gang (Klein, 1995). Even the definition of membership can be elusive. A youth’s affiliation

with a gang can be very short term or quite marginal; someone can move in and out of a gang

continually (Esbensen and Huizinga, 1993; Thornberry, et al., 1993). We suspect the boundaries

between members and nonmembers is less permeable in prison than on the streets, although we

have no evidence either way. However, the definition of a gang member for the purpose of this

study was established by Bureau of Prisons (BOP) policy. Gangs have been defined based on their

actual or perceived threat to the orderly management of the prison. Specific gang membership

was based upon identifying signs, symbols, correspondence, prior official records, associations, or

self admission of inmates; however, there was no prerequisite that a prison gang member had to

be involved in prison misconduct. The present study allows us to evaluate the relationship of this

kind of operational definition of gang and gang membership with criminal behavior and rule

violations within the prison setting. 

We used data from automated information systems of the Bureau of Prisons. In addition to the

identification of specific gang membership, the Bureau of Prisons uses a three-tiered system to

establish gang identity differentiating between members, suspects, and associates1. This threetiered system is used to distinguish how embedded a particular prisoner is in the gang. A member

is viewed as a full-fledged, core gang member. In a few gangs, this means “blood in, blood out.”

Someone has to kill to become a member; someone has to be killed to “leave” the gang. A

suspect is thought to be a gang member whose credentials have not been fully established. An
6

associate is someone whose actions indicate he is conducting business or looks out for the

interests of a gang but has not joined the gang, or by virtue of one’s race, ethnicity, residence, or

cultural background cannot join the gang2. All other inmates, except inmates associated with

organized crime, were unaffiliated.

Currently, this three-tiered system only applies to the most organized and “menacing” gangs in the

BOP, officially called “disruptive groups”; however, a two-tiered classification exists for all other

gang members. They are either suspects or associates. If this identification system is meaningful,

we would expect that members would be most likely to commit violent misconduct, suspects, the

next most likely, and associates the third most likely when compared to unaffiliated inmates after

controlling for other variables that predispose an inmate to commit violence. This embeddedness

hypothesis is based on the supposition that the more entrenched a gang member is, the more likely

he will use violence to carry out the mission of the gang and to enforce the gang’s hegemony over

other gangs and the unaffiliated inmate population. There is an analog in street gang research

which has shown that core members are more likely to be engaged in delinquency and violence

than fringe members (Klein, 1995; Spergel, 1995).

To test the impact of gang membership on violence, we will present both a model which

incorporates specific gang affiliation and a second model which uses the member, suspect,

associate, classification system3. The former model’s weakness is that not all gangs will be

associated with increased violent activity since some gangs are more benign than others. The

latter model’s weakness is that the classification system representing the level of embeddedness of
7

a gang member is partially confounded with the fact that the full embeddedness classification only

applies to the most menacing gangs.

A second purpose of this research was to try to identify a model-based “threat index” that could

be used to characterize the propensity of a gang toward violence and other forms of misconduct.

Such an index is already in use by BOP authorities; however, it is based upon the unadjusted rates

of serious misconduct. We developed an approach based on a multivariate statistical model that

incorporated gang affiliation in the prediction of different forms of misconduct. 

METHODS

RESEARCH DESIGN AND TIME FRAME

A referent population was chosen representing sentenced male inmates in the custody of the

Federal Bureau of Prisons on March 1, 1997 who were housed in facilities operated by the

Bureau. The Bureau also has jurisdiction over other inmates held in contract facilities; however,

the recording of misconduct and gang membership in those facilities is not as consistent or

systematic. The sample was restricted to men for whom we could determine a security-custody

score, a measure of misconduct risk that we define later. This restriction would exclude primarily

pre-trial inmates who had not yet received a sentence and a small number of inmates who were

sentenced but whose security-custody score had not yet been determined. Women were excluded

because there were so few with a gang affiliation and few with violent misconduct, the primary
8

dependent variable in this analysis4. There were 82,504 male inmates in this sample, and 7,445

(9.02 percent) were considered gang affiliated. We obtained misconduct data for the year

following the reference date. If an inmate left the system, his risk period was adjusted. While this

cross-section prevents us from analyzing certain causal relationships, such as whether gang

identification precedes or follows from increased misconduct, it does provide a rich data set of

inmates and gang members with variable amounts of time in prison and variable amounts of time

left to serve. Both of these factors have been found to be quite important in analyzing prison

misconduct (Toch and Adams, 1988).

DEPENDENT VARIABLES

All of the dependent variables in the analyses were based upon a finding of guilt in an

administrative hearing conducted by Bureau of Prisons staff. There is a policy statement

governing the procedures of a disciplinary hearing. The hearing officer receives special training

and has no other duties except conducting hearings. We used two dependent measures of

violence. The first, violent misconduct, consisted of the following behaviors: homicide or

attempted homicide, aggravated assault, setting a fire, possessing a dangerous weapon, rioting,

encouraging others to riot, taking hostages, possessing a hazardous tool, fighting, threatening

bodily harm, extortion/blackmail, using martial arts or boxing skills, and simple assault. If an

inmate committed any of these types of misconduct, he was recorded as having committed a

count of violent misconduct. Although violent misconduct is the primary concern of prison

officials with respect to maintaining prison order (Bottoms, 1999), of particular concern is
9

whether the violent misconduct results in injury or harm to the victim. Thus, we had a measure of

serious violence based on whether the violent act resulted in an injury or whether a weapon was

used in commission of the violence regardless of whether the victim was an inmate or staff

member.

Weapons categories included: bodily fluids, hands, legs, and lethal objects. Injury could be

anything from minor to fatal. Only 8 percent of staff suffered an injury, while 62 percent of

inmates suffered an injury from violent attacks. While most of the inmate related injuries were

minor and were treated at the prison (45.7 percent), 11.1 percent required treatment at an outside

hospital and were considered moderate, 4.2 percent were life threatening (major), and 1.0 percent

were fatal. 

When a victim was attacked, weapons were used 49.5 percent of the time against staff and 86.1

percent of the time against inmate victims. The weapon of choice against inmates or staff was

hands or fists. However, the second most likely weapon to be used against inmates was a solid or

blunt object, while the second most likely weapon to be used against staff was bodily fluids.

Stabbing (7.0 percent) and cutting objects (4.3) were much more likely to be used against inmates

than staff (1.2 and 1.0 percent respectively). 

In addition to violent misconduct, we examined other misconduct categories including: drug,

accountability, property, security, sexual misconduct, and other. Although violent and drug

misconduct are considered the most serious with regard to the safety and security of inmates and
10

staff, other forms of misconduct can indicate systemic problems in the maintenance of prison

order. Accountability misconduct was based on prohibited acts related to the inmate’s being in

unauthorized areas or his unwillingness to follow orders including work assignments5. Property

misconduct included prohibited acts related to theft, gambling, and property damage. Security

misconduct included serious escape attempts and behavior designed to disrupt the orderly running

of the institution. Sexual misconduct included sexual misbehavior and exposure. Other

misconduct was a catchall for anything else that was not included in the above categories. We also

did an analysis of the total amount of misconduct committed by gang members and other inmates

during the study period. 

For each inmate in the study, we were able to record the number of occurrences of a misconduct

category in the year following the reference date. The mean, standard deviation, minimum, and

maximum values for these count variables appear in Table 1. Table 1 also contains descriptive

statistics on variables indicating the inmate’s history of committing these specific kinds of

misconduct. In each analysis, we incorporated the history of a specific kind of misconduct into the

structural model as an independent variable. For the models evaluating violent and serious violent

misconduct we used the history of violent misconduct as a covariate, since the history of serious

violent misconduct was not available prior to the reference period.

BACKGROUND VARIABLES

All of the variables used as background covariates in these analyses were culled from the
11

automated records of the Bureau of Prisons. Most of these variables have been used in other

analyses of prison misconduct (Harer and Langan, 2000) and are known to be related to violent

misconduct. Table 2 lists the background variables, the mean, standard deviation, minimum,

maximum, and reference category if the variable was a dummy code. 

The security-custody score is a continuous scale that is used by the Bureau of Prisons to classify

inmates. The higher the score, the more likely an inmate is expected to commit violent

misconduct. Inmates with higher security-custody scores are placed in prisons that have more

procedural controls (e.g. controlled movement, pass system, areas out-of-bounds) and more

internal and external physical security (e.g. fence alarms, razor wire, double fences, guard

towers). The security-custody score is based on criminal history, sentence length, severity of the

commitment offense, history of violence (most of which occurred during prior offenses), escape

history, drug abuse history, adjustment in prison, and categories of prison misconduct. A great

deal of empirical work has gone into assessing and improving this measure (Harer and Langan,

2000). 

Citizenship was entered as a series of dummy variables with U.S. citizenship as the referent.

Citizens of Columbia and Mexico were distinguished for several reasons. One reason is because

they comprise the two largest citizen groupings other than the U.S. A second reason for entering

Mexican citizenship was because many gangs are composed of Mexican inmates both of U.S. and

Mexican origin and we wanted to disentangle the influence of Mexican nationality from the

influence of the gangs composed of Mexican Nationals. A third reason for this citizenship
12

distinction was that these two citizenship groups were hypothesized to be involved in a high

proportion of drug trafficking, and we wanted to test the relationship of citizenship to drug

trafficking and drug use within prison. A small number of inmates (1.1 percent) had missing

citizenship values and these were represented with a dummy variable coded as 1 if the data were

missing, 0 otherwise. 

Ethnicity, age, and race were recorded. The referent for Hispanic inmates was Non-Hispanics.

White inmates were the referent for African American, Native American, and Asian races. Age

was transformed using the natural logarithm. The amount of time already served by an inmate was

recorded and was also transformed by taking its natural logarithm. 

The security level of the prison in which an inmate was housed on the referent day was recorded.

Theoretically there are two competing influences of prison security level. Higher security levels

result in higher levels of custody staff and more rigorous security procedures to prevent violence.

Recent evidence by Berk and de Leeuw (1999) demonstrated the inhibiting influence of greater

security procedures on violence in the California prison system. However, because of inmate

classification assignments, the higher prison security levels are composed of inmates who are

more likely to commit violence. This should serve to increase violence at these security levels.

The prison security levels were coded as dummy variables using the lowest security level,

minimum, as a referent. The administrative security level is composed of jails and medical centers.

We coded a dummy variable (Security Level -- Florence/Marion) if the inmate’s institution was

ADX Florence or USP Marion because these institutions house the most dangerous inmates.
13

However, the security procedures are extremely tight including 24 hour lock-down for many of

the inmates.

Finally, days at risk was the number of days for the 1-year follow-up period an inmate was still in

the custody of the Bureau. Inmates who died or were released were censored and their risk days

were adjusted to the appropriate value.

GANG VARIABLES

Variables representing gang affiliation, length of time in the gang, and the variables measuring

how embedded an individual was in the gang are represented in Table 3. The mean, standard

deviation, minimum and maximum values, and reference category for dummy variables are listed

in Table 3. Time in the gang is represented in months. All of the gang participants have as their

referent anyone not in a gang, namely someone who is unaffiliated. Gang embeddedness

represented by the variables member, suspect, associate, and organized crime also have as their

referent the unaffiliated inmates. Organized crime members were given their own special category

because of their allegiance to their organization.

STATISTICAL MODEL

Since the dependent variable in each model was the number of occurrences of misconduct in one

year, a count model was chosen to analyze the data. A negative binomial regression model was
14

selected since a Poisson regression model assumes that the conditional variance is equivalent to

the conditional mean and this is rarely true in practice (Long, 1997, p. 230)6. For each model, we

conducted a test of overdispersion that is a likelihood ratio test of the differences in the log

likelihood between the negative binomial and Poisson regression models. This is actually a test of

whether an error term is needed in the structural model relating the expected count of violence to

the background and gang variables. The statistical program STATA 6.0 was used which allowed

us to use days-at-risk as the variable exposure period to control for different risk periods for each

inmate. 

RESULTS

SPECIFIC GANG AFFILIATION

For each dependent variable, we adopted the same analytical strategy. We first tested the

relationship between the background variables and the dependent variable in the absence of any

information about gang affiliation. STATA provides a likelihood ratio test of overdispersion

indicating whether the negative binomial model is a better fit to the data than the Poisson allowing

us to choose the appropriate model. If the model was statistically significant, we added time in the

gang, an indicator of multiple gang affiliation, and dummy variables representing the specific gang

affiliation. To test whether the additional gang specification variables informed our understanding,

we conducted a P

2

 test of twice the difference in the log likelihood values of the model with the

background variables and the model with additional gang affiliation variables7. For example, for
15

the model assessing the impact of background variables on violence, the global likelihood ratio P

2

of all the parameters was 4,604.7 (df=17), p < 0.000. The pseudo R2 was .119. The

overdispersion test showed that a negative binomial model was more appropriate than the

Poisson. The additional gang variables added important information to the prediction of violence.

The P

2

 was 171.88 (df=29), p < .001. This same approach was repeated for all 9 dependent

variables. With the exception of sexual misconduct, in every case, the model relating background

variables to the dependent variable was significant, a negative binomial regression was more

appropriate than a Poisson, and the additional gang variables statistically improved the fit of the

model8. Because sexual misconduct was so rare -- only .61 percent of the sample committed any

sexual misconduct in one year -- we also used a complementary log log regression. The sexual

misconduct was dichotomized and then analyzed using STATA’s cloglog procedure (STATA

Reference A-G, 1999 P. 217-224). The complementary log log distribution is intended for rare

events (Agresti, 1990). All of the models did converge; however, the model which added specific

gang variables did not improve the fit above the baseline model to warrant interpreting the gang

coefficients. 

The main purpose of our analyses was to test the implication of gang affiliation and the

background variables were used to control for potential differences in individual inmates that

previous research has shown to be related to violent and other misconduct. To simplify our

presentation of all of these models, we symbolically depict only the gang coefficients since the

background coefficients were secondary to our purpose. Later in this paper, we indicate in a more

substantively meaningful way, the relative influence of the background and the gang variables. 
16

The influence of specific gang affiliation on all forms of prison misconduct is represented in Table

4. The gang variables (time in the gang, multiple gang affiliation, and specific gang membership)

are depicted in the rows of Table 4. The prison misconduct categories are represented in the

columns. Thus, each column represents the results of a model in which we added specific gang

affiliation to the models containing only the background characteristics. An “I” in Table 4

indicates that a Wald test ( p<.05) demonstrated that the coefficient was significant and increased

the predicted count of misconduct. A “D” indicates the variable was significant and was

associated with a decrease in the predicted misconduct. Blank cells indicate the variable was not

significant. 

As can be seen in Table 4, specific gang affiliation was associated with an increase in the

probability of violence for 20 of the 27 gangs, and an increase in serious violence for 18 of the 27

gangs. Gang affiliated inmates were more likely to be involved in drug, property, accountability,

and, to some extent, other misconduct as well. The analog R2 values at the bottom of the table

show that there was actually a slightly better fit of the serious violence model than the model

examining violence. The worst fitting model was for property misconduct. Time spent in the gang

decreased the amount of violence, while having more than one affiliation was not significant. No

gang was associated with a lower probability of violence relative to the unaffiliated inmates.

However, many gangs were no more violent than their unaffiliated peers. Furthermore,

membership in some of the gangs such as the Texas Syndicate and the Mexakanemi was

associated with increases in almost all forms of misconduct. 
17

GANG EMBEDDEDNESS

We used the same analytical strategy to test the gang embeddedness hypothesis. Instead of using a

specific gang affiliation, dummy variables representing the degree of gang embeddedness

(member, suspect, associate) were used. Once again, the unaffiliated inmates were used as the

referent. These models also incorporated time in the gang and the multiple gang affiliation

variable. Rather than evaluate every dependent variable, we examined violence, serious violence,

drug, and total misconduct.

The results of these analyses are shown in Table 5. As with the specific gang affiliations, in this

model, time in the gang was significant and decreased the probability of violent misconduct.

The three gang affiliation variables were all significant and indicated a higher likelihood of violent

misconduct compared to unaffiliated inmates with similar background characteristics. Once again,

the fit for the serious violence model was the best. The embeddedness hypothesis implies that the

member coefficient should be greater than the suspect coefficient, which should be higher than the

associate coefficient. This was in fact the case. To establish statistical differences among the

coefficients, we conducted a Wald test of the difference in those coefficients. For the violence

analysis, a Wald test of the coefficients showed that the member coefficient was statistically

different from the suspect coefficient, P

2

 = 33.19 (df=1), p < 0.000. The member coefficient was

statistically different from the associate coefficient, P

2

 = 37.7 (df=1), p < 0.000. Finally, the

suspect coefficient was statistically different from the associate coefficient, P

2

 = 5.18, (df=1), p<

0.023, confirming the embeddedness hypothesis.
18

Because gang embeddedness is partially confounded with gang affiliation in these data, we ran a

model that included only those gangs for which someone could be classified as a member, suspect,

or associate. As mentioned before, these gangs are called disruptive groups and are supposed to

be the most structured and organized gangs. The count of violent misconduct was the dependent

variable and we used the same specification as the previous violence model except that we

excluded the variable representing organized crime because it was not a disruptive group. The

reduced sample size was 82,442 and included all unaffiliated inmates and the inmate affiliates of

the 6 prison gangs that were considered disruptive groups. The model yielded a likelihood ratio P

2

of 3,938, df = 20, p < 0.000, pseudo R2 = .120. The variables of interest were member, suspect,

and associate. Member was significant, z = 8.911, p < 0.000. Suspect was also significant, z =

3.00, p < 0.003; however, associate was not significant, z = 0.100, p < .920. Thus, when

compared to unaffiliated inmates, members and suspects were more likely to commit violent

misconduct; however, associates, those thought to be least embedded in these highly organized

gangs, were no more violent than their unaffiliated peers at least for those gangs identified as

disruptive groups.

Since time in the gang was consistently negatively related to all forms of misconduct, it deserves a

discussion. We can only speculate post hoc why we found a negative relationship. It is possible

that the time-in-the-gang result represents burnout -- prolonged gang activity becomes so stressful

that longer periods of activity reduce violent behavior. Since we controlled for age of the inmate,

it is unlikely that longer gang participation represents an aging out phenomenon. Another

possibility is that the time-in-the-gang phenomenon is a proxy for the suppression effect of greater
19

scrutiny and custody of gang members. The longer an inmate is in a gang, the more likely prison

authorities will elevate their security. Since we did control for security-custody score and their

institution security level this explanation is also less satisfactory, however, still tenable. Yet, a

third possibility is that the longer one is in a gang, the more likely one will become a leader or

“supervisor” and therefore will be more likely to give orders related to violent and other

misconduct rather than commit the misconduct oneself. Of course, this explanation would have to

account for violent misconduct committed by members, suspects and associates. Irrespective of

whether one was a member, suspect, or associate, the longer one was in a gang, the less likely he

committed violent misconduct. Since the ranks of the gang leadership came from members or

suspects, and they were more likely to commit almost any kind of misconduct than unaffiliated

inmates, one would have to speculate, that the leadership positions from within members and

suspects were composed of inmates who had spent the longest time in the gang. A final

explanation may have to do with initiation rituals. New gang members have to prove loyalty by

assaulting rival gang members or fighting other inmates. Thus, gang members with shorter

durations of time in the gang represent those who are in the process of demonstrating their

loyalty. While we cannot support one explanation to the exclusion of the other, the fact that time

in the gang is consistently associated with lower misconduct should be explored further and

replicated in other prison gang research to inform our understanding of this phenomenon.

CHANGES IN THE DISCRETE PROBABILITY OF VIOLENT AND TOTAL

MISCONDUCT ASSOCIATED WITH GANG CLASSIFICATION VARIABLES
20

To gain further substantive clarification of gang affiliation, we used several algorithms developed

by J. Scott Long (2000) to examine how the discrete probability of violent, serious violent, drug,

and total misconduct changes as a result of the background and gang embeddedness variables.

Instead of evaluating the expected count, we constructed a binary variable for whether a

misconduct episode occurred during the one-year risk period. We analyzed the data using

STATA’s probit regression routine and then applied Long’s interpretation algorithms “prchange,”

and “prvalue”. 

The LR P

2 , degrees of freedom, , p-value, and pseudo R2

 for the probit regression of violence,

serious violence, drug, and total misconduct are reported at the bottom of Table 6. All of the

probit regressions were significant. The structural relationships for the probit model were the

same as they were for the negative binomial. All of the variables significant in the negative

binomial model were significant in the probit and all the variables which were not significant in the

negative binomial model were not significant in the probit. Because both the probit and the

negative binomial models are nonlinear, changes in the dependent variable depend on the level of

all other variables in the model, in addition to the value of the particular variable in which one is

interested.

Long’s prchange interpretation algorithm is designed to hold all other variables at a level of one’s

choosing (e.g. mean, median, or a particular value) while the variable of interest is varied in a

particular way and the discrete change in probability of the depended variable is recorded. While

Long uses a number of different variations in changing the variable of interest, we report only
21

two. In the results reported here, we hold all other variables at their mean level. In the case of the

dummy variables, we report the discrete changes in probability as the dummy variables change

from 0 to 1. In the case of continuous variables, the Long procedures use various ranges, but here

we report the values associated with varying the continuous variables from - 1⁄2 standard deviation

to + 1⁄2 standard deviation about each variable’s mean. In Table 6, we report discrete changes in

the probability of misconduct associated with gang embeddedness for violent infractions, serious

violent infractions, drug violations, and total misconduct. The same conventions were adopted for

all of the dependent variables. Only the statistically significant variables (p<.05) are indicated in

the table.

Table 6 contains a great deal of information on the substantive meaning of both the background

variables and gang embeddedness. Examining the continuous variables, a one standard deviation

about the mean of the security custody score was associated with an increase in the probability of

violence of .082, while a one standard deviation increase in the natural log of age was associated

with a decrease in the probability of violence of .066. Gang embeddedness has a profound

influence on the risk of violence holding all other variables at their mean. A gang member

increases the probability of violence by .207, serious violence by .079, drug misconduct by .059,

and total misconduct by .259 in the reference year. Suspects and associates also increase these

kinds of misconduct quite dramatically, much more than background characteristics such as race,

and ethnicity, and citizenship.

GRAPHICAL REPRESENTATION OF A COMPOSITE GANG THREAT INDEX
22

We also computed the discrete change in the probabilities of misconduct associated with specific

gang membership to create a model-based, composite gang threat index. The discrete changes in

probability in violent, serious violent, and drug misconduct associated with specific gang

affiliation are graphically depicted in a stacked bar chart in Figure 1. Although these probabilities

are, strictly speaking, not additive, the stacked bar does convey the relative problem behaviors

associated with gang membership in each of the gangs. It also quickly conveys the type of

misconduct in which gangs specialize and whether that affiliation increases or decreases the

probability of misconduct relative to unaffiliated inmates. For example, even though the Border

Brothers had a quite high discrete change in the probability of violent misconduct, relative to

affiliation with the Texas Syndicate, or inmates designated as being in Multiple Gangs-None

Monitored9, they had a lower probability of serious violent misconduct. Affiliation in the Mexican

Mafia lowered the probability of drug misconduct. Affiliates of the Texas Syndicate were less

likely than the Border Brothers to commit violent misconduct but more likely to commit drug

misconduct. This graphical depiction of the threat index allows one to quickly inspect, not only

the severity of the collective gang threat, but the composition of the threat as well. Furthermore,

this alternative index is model-based and has a straightforward interpretation. The composite gang

threat index shows the relative probability of violent, serious violent, and drug misconduct for the

referent year while simultaneously controlling for background factors of the individual gang

participants that predispose them to misconduct. The graphical depiction also demonstrates the

heterogeneity in the effect gang affiliation has on forms of misconduct.

CONCLUSIONS
23

As far as we know, this paper represents the first comprehensive multivariate analysis of the effect

of gang affiliation on prison misconduct. Similar to previous research on street gangs, we found

that membership does increase violent and almost all other forms of prison misconduct, whether

these misbehaviors are rule infractions or actual crimes. We have demonstrated that gang

affiliation increases the likelihood of violent and other forms of misconduct, even after controlling

for individual characteristics of inmates that prior research has established are associated with a

violent predisposition. Thus, after controlling for a measure of violent risk (security-custody

classification), a previous history of violence, and other background factors, gang affiliation

increases the probability of violence and other misconduct. 

Secondly, we have also demonstrated that gang-embeddedness that distinguishes whether

someone is a core or more peripheral member of a gang, was also related to the level of violence.

Core members were more likely than more peripheral affiliates to commit violent misconduct. And

furthermore, the more peripheral members (suspects and associates) were more likely to commit

violent misconduct than their unaffiliated peers. This also is analogous to findings in the street

gang literature (Klein, 1995; Spergel, 1995).

While this study was prospective, it was not longitudinal with respect to gang affiliation.

Therefore, we must qualify some of the causal conclusions. Although we controlled for factors

that predispose inmates toward violence and other forms of misconduct, it is possible that these

predisposing indicators became elevated after an inmate joined a gang. Another possibility,

although we think less likely, was that inmates were more likely to be designated as gang
24

members, suspects, or associates because of their tendency to misconduct, and therefore, what we

were observing was the result of labeling rather than some gang phenomenon? Mitigating this

interpretation is the fact that gang membership within the Bureau of Prisons is based upon signs,

symbols, and intelligence about gang communications rather than the misconduct of an individual. 

 

The consistent finding that time in the gang reduces the amount and probability of misconduct

bears further study. Is this gang burnout or quite the opposite? Is it, in fact, evidence that the

longer one is an affiliate, the more one takes on a leadership position, and orders, rather than

commits gang misconduct. Or, is it that the longer one is in the gang, the more authorities

respond by monitoring and suppressing one’s activities. Since the meaning of this phenomenon is

quite important for gang intervention strategies, it bears further study.

The model-based gang threat index provides an alternative assessment of composite gang activity.

The graphical representation also showed that there was a great deal of heterogeneity in

aggregate measures of gang misconduct after controlling for factors that would predispose

individuals to misconduct. Any jurisdiction that can track similar information could develop such a

model-based threat index to monitor gang activity.

While this analysis does yield information on the influence of individual gang affiliation on

misconduct, it does not indicate the influence of institutional gang composition on misconduct. To

what extent is individual misconduct influenced by the number and proportion of gang affiliates in

the institution. Do institutions which have larger gang membership have more violence than other
25

institutions? Is there a specific aggregate composition of certain types of gangs or certain levels of

embeddedness that influences misconduct? Are there certain gang antagonisms that result in

higher individual level violence? To answer these questions we will conduct further analyses to

disentangle institutional gang composition from individual gang affiliation using hierarchical linear

models (Bryk and Raudenbush, 1992). 
26

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32

Table 1. Descriptive statistics on misconduct count variables and variables involving the inmate’s history of these

kinds of misconduct, N=82,504. DV represents a dependent variable and IV represents an independent variable. 

Variable

Percent

Having at

Least One

Incident Mean Std. Dev. Min. Max.

Violent Misconduct (DV) 5.1% 0.070 0.373 0 15

Serious Violent Misconduct (DV) 1.7% 0.021 0.201 0 18

History of Violent Misconduct (IV) 0.308 1.243 0 57

Drug Misconduct (DV) 5.4% 0.073 0.354 0 11

History of Drug Misconduct (IV) 0.309 1.179 0 30

Accountability Misconduct (DV) 12.1 % 0.187 0.693 0 42

History of Accountability Misconduct (IV) 0.703 1.896 0 59

Property Misconduct (DV) 6.6 % 0.081 0.342 0 14

History of Property Misconduct (IV) 0.259 0.781 0 39

Security Misconduct (DV) 4.8 % 0.056 0.275 0 9
33

History of Security Misconduct (IV) 0.172 0.547 0 16

Sexual Misconduct (DV) 0.6 % 0.007 0.112 0 8

History of Sexual Misconduct (IV) 0.027 0.234 0 19

Other Misconduct (DV) 5.3 % 0.067 0.335 0 16

History of Other Misconduct (IV) 0.240 0.852 0 32

All Misconduct (DV) 25.9 % 0.484 1.248 0 61

History of Any Misconduct (IV) 1.812 4.116 0 134
34

Table 2. Summary statistics on the background variables used in the analyses, N=82,504.

Variable Mean Std. Dev. Min. Max.

Referent for

Dummy Vars.

Security Custody Score 6.545 5.916 0 34 --

Citizenship – Columbian 0.041 0.197 0 1 U.S.

Citizenship – Missing 0.011 0.105 0 1 U.S.

Citizenship – Mexican 0.081 0.273 0 1 U.S.

Citizenship – Other 0.099 0.299 0 1 U.S.

Hispanic 0.245 0.430 0 1 Non-Hispanic

Age (Natural Log) 3.575 0.276 2.833 4.489 --

Time Served (Natural Log) 3.192 1.226 0 5.951 --

Race -- Asian 0.014 0.116 0 1 Caucasian

Race -- African American 0.407 0.491 0 1 Caucasian

Race – Native American 0.017 0.127 0 1 Caucasian

Florence/Marion 0.008 0.087 0 1 Minimum
35

Security Level – Administrative 0.079 0.270 0 1 Minimum

Security Level – High 0.116 0.320 0 1 Minimum

Security Level – Medium 0.324 0.470 0 1 Minimum

Security Level – Low 0.286 0.452 0 1 Minimum

Days at Risk 322.953 89.873 1 364 –
36

Table 3. Descriptive Statistics for Gang Variables (all inmates, n=82,504).

Variable Mean

Std.

Dev. Min. Max.

Referent for Dummy

Vars.

Time-in-the-gang (Months) 2.748 9.870 0 52 --

Multiple Gang Affiliation 0.004 0.064 0 1 One or None

Southeast Asian Org. Crime 0.001 0.038 0 1 Unaffiliated

Aryan Brotherhood 0.002 0.045 0 1 Unaffiliated

Black Guerilla Family 0.001 0.027 0 1 Unaffiliated

Mexican Mafia 0.003 0.051 0 1 Unaffiliated

Texas Syndicate 0.002 0.044 0 1 Unaffiliated

Organized Crime 0.005 0.069 0 1 Unaffiliated

Dirty White Boys 0.003 0.055 0 1 Unaffiliated

Mexakanemi 0.004 0.061 0 1 Unaffiliated

Netas 0.002 0.045 0 1 Unaffiliated

White Supremacy Groups 0.005 0.073 0 1 Unaffiliated

Bloods 0.005 0.080 0 1 Unaffiliated

Crips 0.014 0.117 0 1 Unaffiliated

Black Gangster Disciples 0.006 0.080 0 1 Unaffiliated

Border Brothers 0.001 0.030 0 1 Unaffiliated

Latin Kings 0.005 0.067 0 1 Unaffiliated
Table 3. Descriptive Statistics for Gang Variables (all inmates, n=82,504).

Variable Mean

Std.

Dev. Min. Max.

Referent for Dummy

Vars.

37

Vice Lords 0.002 0.048 0 1 Unaffiliated

Drug Cartel No Other 0.002 0.044 0 1 Unaffiliated

Drug Cartel Other 0.005 0.068 0 1 Unaffiliated

Anti-Government 0.002 0.042 0 1 Unaffiliated

Motorcycle 0.004 0.060 0 1 Unaffiliated

Prison Gangs Modern 0.001 0.026 0 1 Unaffiliated

Jamaican Posse 0.002 0.046 0 1 Unaffiliated

New York Street Gangs 0.001 0.038 0 1 Unaffiliated

Misc City 0.011 0.105 0 1 Unaffiliated

DC Crews 0.001 0.022 0 1 Unaffiliated

 Multiple Gangs – None Monitored 0.001 0.020 0 1 Unaffiliated

Multiple Gangs – Monitored 0.001 0.036 0 1 Unaffiliated

Member 0.005 0.074 0 1 Unaffiliated

Suspect 0.069 0.253 0 1 Unaffiliated

Associate 0.014 0.118 0 1 Unaffiliated

Organized Crime 0.001 0.038 0 1 Unaffiliated
38

Table 4. Summary table of specific gang coefficients which increase (I) the expected count of a specific kind of

misconduct and those coefficients which decrease (D) the expected count of the target misconduct.

Variable

Viol. Serious

Viol.

Drug Acct. Prop. Secur. Sex

Misc.

Other All 

Time-in-thegang (Months)

D D D D D D D

Multiple Gang

Affiliation 

Southeast

Asian Org.

Crime

I

Aryan

Brotherhood

I I I I I

Black Guerilla

Family

I

Mexican

Mafia

I I I

Texas

Syndicate

I I I I I I I I

Organized

Crime 

I I D I I I

Dirty White

Boys

I I I I I I

Mexakanemi I I I I I I I

Netas I

White

Supremacy

Groups

I I I I I I I

Bloods I I I I
Table 4. Summary table of specific gang coefficients which increase (I) the expected count of a specific kind of

misconduct and those coefficients which decrease (D) the expected count of the target misconduct.

Variable

Viol. Serious

Viol.

Drug Acct. Prop. Secur. Sex

Misc.

Other All 

39

Crips I I I I I I I

Black

Gangster

Disciples

I I I I I I

Border

Brothers

I I I I I I I

Latin Kings I I I I I

Vice Lords I I I

Drug Cartel

No Other

I I

Drug Cartel

Other

AntiGovernment

I I I

Motorcycle I I I

Prison Gangs

Modern

I I I

Jamaican

Posse

I I I

New York

Street Gangs

I I

Misc City I I I I I I I I

DC Crews
Table 4. Summary table of specific gang coefficients which increase (I) the expected count of a specific kind of

misconduct and those coefficients which decrease (D) the expected count of the target misconduct.

Variable

Viol. Serious

Viol.

Drug Acct. Prop. Secur. Sex

Misc.

Other All 

40

Multiple

Gangs – None

Monitored

I I

Multiple

Gangs –

Monitored

I I I I I

Model Fit

Statistics

Pseudo R2

LR P2=

4776.6

(df=47),

p<0.000

.124

LR P2=

4439.5

(df=47),

p<0.000

.136

LR P2=

4587.4

(df=47),

p<0.000

.112

LR P2=

6234.6

(df=47),

p<0.000

.078

LR P2=

1992.1

(df=47),

p<0.000

.043

LR P2=

1741.2

(df=47),

p<0.000

.050

1

LR P2=

3413.5

(df=47),

p<0.000

.084

LR P2=

11958

(df=47),

p<0.000

.081

1. Model converged; however, gang variables did not significantly increase the fit of the model.
41

Table 5. Summary table of specific gang coefficients which increase (I) the expected count of a specific kind of

misconduct and those coefficients which decrease (D) the expected count of the target misconduct.

Variable

Viol. Ser.

Viol.1

Drug All 

Time-in-the-Gang (Months) D D D D

Multiple Gang Affiliation 

Member I I I I

Suspect I I I I

Associate I I I I

Organized Crime I

Model Fit Statistics

Pseudo R2

LR P

2=

4783.6

(df=23),

p<0.000

.124

LR P

2=

4345.2

(df=23),

p<0.000

.135

LR P

2=

4519.5

(df=23),

p<0.000

.110

LR P

2=

11903

(df=23),

p<0.000

.081
42

Table 6. Discrete changes in the probability of different types of misconduct for variables holding

constant all other variables at their mean. The discrete changes in probability are indicated only for

statistically significant variables (p<.05).

Dependent

Variables:

Violent Serious Violent Drug Total

Predictor

Variables

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2

Security

Custody

Score

.082 .005 .018 .068

Number of

Prior Total

Acts

.044 .003 .017 .091

Citizenship

–

Columbian

-.034 -- -.027 -.060

Citizenship

– Missing

-- -- -- --

Citizenship

– Mexican

.051 .005 -.005 --

Citizenship

– Other

.043 -- -.021 --

Hispanic .015 -- -- --

Age

(Natural

Log)

-.066 -.005 -.014 -.068

Time

Served

(Natural

Log)

-- -- -- -.021

Race --

Asian

-- -.005 -.019 --
Table 6. Discrete changes in the probability of different types of misconduct for variables holding

constant all other variables at their mean. The discrete changes in probability are indicated only for

statistically significant variables (p<.05).

Dependent

Variables:

Violent Serious Violent Drug Total

Predictor

Variables

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2

43

Race --

African

American 

.019 -- -.019 .010

Race –

Native

American

.045 .004 -- --

Florence/M

arion

-.064 -.005 -.036 -.200

Security

Level –

Administrati

ve

.110 .011 -.012 -.025

Security

Level –

High

.060 .004 -.008 -.079

Security

Level –

Medium

.044 .003 -- -.024

Security

Level – Low 

-- -- -- -.026

Time-in-thegang

(Months)

-.024 -.002 -.005 -.026

Multiple

Gang

Affiliation 

-- -- -- --

Riskdays .071 .003 .010 .061
Table 6. Discrete changes in the probability of different types of misconduct for variables holding

constant all other variables at their mean. The discrete changes in probability are indicated only for

statistically significant variables (p<.05).

Dependent

Variables:

Violent Serious Violent Drug Total

Predictor

Variables

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

Dummy

Variables

Continuous

Vars.

0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2 0 -> 1 - + SD/2

44

Member .207 .079 .059 .259

Suspect .156 .014 .034 .129

Associate .102 .009 .022 .114

Model Fit

Statistics

LR P2= 5,068.32

(df=23), p<0.000,

pseudo R2=.152.

LR P2=2,167.30

(df=23), p<0.000 ,

pseudo R2=.156.

LR P2=4,937.34

(df=23), p<0.000 ,

pseudo R2=.142.

LR P2= 11283(df=23),

p<0.000 , pseudo

R

2=.120.
45

-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 

Probability of Misconduct in One Year

Drug Cartel Other

Netas

DC Crews

Anti-Government

New York Street Gangs

Bloods

Organized Crime 

Crips

Motorcycle

Black Guerilla Family

Black Gangster Disciples

White Supremacy Groups

Jamaican Posse

Prison Gangs Modern

Violent SerViol Drug

Figure 1 Model-Based Misconduct
46

-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 

Probability of Misconduct in One Year

Drug Cartel No Other

Southeast Asian Org. Crime

Misc City

Vice Lords

Mexican Mafia

Latin Kings

Multiple Gangs – Monitored

Mexakanemi

Dirty White Boys

Multiple Gangs, None Monitored

Aryan Brotherhood

Texas Syndicate

Border Brothers

Violent SerViol Drug

Figure 1. Model-Based Misconduct
47

1. Only certain staff at an institution are allowed to enter the member, suspect, associate label into

the computer-based file. These are staff most familiar with the policy governing these designations

and whose job it is to ascertain and validate the use of these labels.

2. Many gangs have racial, ethnic, citizenship, residence, or cultural prohibitions on who can be a

gang member. Thus, certain inmates may be doing the gang’s business but that person is

precluded by his background from becoming a gang member. 

3. We could not test these variables simultaneously, since they are linearly dependent.

4. Of the 6,736 women in the original sample, 133 had a gang affiliation distributed over 21

different gangs. There were only 189 women who had a violent misconduct.

5. The complete list for every misconduct coded in any one of these categories is available from

the first author.

6. We also experimented with hurdle or zero-inflated regression models which allow the analyst to

develop a structural model that determines whether any misconduct might occur and a second

structural model associated with the count of misconduct. Although these models almost always

converged and yielded statistically significant results, we had no real theoretical basis for

specifying the different structural models. In the interest of parsimony, we abandoned the hurdle

models in favor of the negative binomial models. 

7. This likelihood ratio test is implemented in STATA’s lrtest ado file. This difference -2(L1 - L0)

is distributed as P

2

 with d0 - d1 degrees of freedom (STATA Reference H-O, 1999 P. 246-250).

8. Chi-square values and their associated probabilities demonstrating the fit of each model are

available upon request from the first author.

9. Some gangs are closely monitored by staff while others are not. Inmates designated as being in

this category were not closely monitored.

Endnotes