prison disciplinary tickets: a test of the deprivation and importation models

Pergamon Journal of Criminal Justice, Vol. 25, No. 2, pp. 103-113, 1997

Copyright © 1997 Elsevier Science Ltd Printed in the USA. All rights reserved

0047-2352/97 $17.00 + .00

PII S0047-2352(96)00054-2

PRISON DISCIPLINARY TICKETS" A TEST OF THE DEPRIVATION AND IMPORTATION MODELS

L I Q U N C A O

Department of Sociology, Anthropology, and Criminology Eastern Michigan University Ypsilanti, Michigan 48197

JIHONG ZHAO

Department of Criminal Justice University of Nebraska at Omaha

Omaha, Nebraska 68182

STEVE VAN DINE

Bureau of Research Ohio Department of Correction

Columbus, Ohio 43229

ABSTRACT

Competing models of prison rule violation exist in criminology. The deprivation model proposes that inmate rule infraction is the product of the stressful and oppressive conditions within the prison itself In contrast, the importation model argues that characteristics of individuals that predate confinement, such as race and gender, are critical factors in determining modes of inmate adjustment. Individual-level data from the Ohio correctional bureau are used to evaluate the efficacy of these two models. The results of analyses support the importation model over the relative deprivation model. The implications of the study are discussed within the limitations of the data. © 1997 Elsevier Science Ltd

INTRODUCTION

After more than a decade of sustained growth, the American prison population reached a record high of one million in 1994 (U.S. Department of

Justice, 1995). This recent development high- lights a task confronted by prison officials: maintaining peace and order in increasingly overcrowded correctional facilities for the safety of both officers and inmates. In this regard, dis-

103

104 L. CAO et al.

ciplinary actions against prisoners are often considered as the primary tool for keeping peace in an institutional environment (Flanagan, 1980).

Disciplinary actions are important for management because they are an indicator of individual inmates' adjustment to the institutional environment (Proctor, 1994). This official approach helps to define a line between what is and is not acceptable behavior in the process of inmates trying to adapt themselves in the "cap- tive environment" (Sykes, 1958). Theoretically speaking, using disciplinary tickets to test models of deprivation and importation may produce information that is potentially useful for prison management to better administrate prisons. Re- search focusing on the institutional disciplinary violation of inmates, therefore, has both theoretical importance and practical implications.

Disciplinary citations are also important for inmates because, in many prisons, their record of citations is considered in reclassification decisions, use of the disciplinary cell, placement in custody level, and assigning work tasks. Fur- ther, this record is available to the parole board for consideration in determining whether to release an inmate from prison (Gottfredson, 1979; Flanagan, 1982).

This article attempts to evaluate the effectiveness of the two prevailing criminological models explaining inmates' institutional behavior pattern: the deprivation model and the importation modelJ In doing so, it is hoped that the existing literature can be advanced in several ways. First, the current article assesses the ex- planatory power of the deprivation model and the importation model on the number of disciplinary tickets. Research can be found in support of both models. The deprivation model, however, has seldom been tested with individual-level data, and its empirical relevance at this level has yet to be established.

Second, a statewide intake sample of both male and female prisoners is used. A sample from a state prison system with both males and females is rare. The effect of gender has infre- quently been examined in a pooled (both male and female) sample at the state level. Third, because the sample of the current article includes only people admitted into the state prison system in a two-month span, it circumvents the prob-

lem of the temporal pattern suffered by many of the previous studies on prison rule violation.

Fourth, the number of independent variables in the model is more extensive than in previous research. Although various factors have been identified to influence prison rule violation, they have been assessed in different studies over time, and have not been included in one study. This research will estimate these independent variables simultaneously in one equation. Fifth, the effects of these independent variables on minor and severe rule violations will be examined separately. The issue of the severity of prison rule infractions has been largely ignored by the existing literature (Light, 1990), even though the social determinants of the two may be different. Sixth, the dependent variables are disciplinary violations, which encompass a broader range of behavior problems than more narrowly defined violence on which most of the previous studies have focused. As a result, the implication of the this research is potentially more useful for prison management.

Finally, an appropriate regression technique-- the Tobit model--is employed in the analysis. The primary advantage of this technique is that it takes into consideration the nonnormally distributed dependent variable, which is due to the sizable proportion of inmates who did not receive any rule violations during the observation period.

THEORETICAL MODELS EXPLAINING INMATE BEHAVIORS

Scholars have endeavored to explore and identify models associated with variations of inmate behavior within the prison environment. In this regard, two models stand out: the deprivation model and the importation model.

The deprivation model proposes that inmate aggression is the product of the stressful and oppressive conditions within the prison itself (Goff- man, 1961; Sykes, 1958; Sykes and Messinger, 1960). As a result, prison-specific variables are singled out to predict inmate adjustment. Some studies measure absolute deprivation, such as overcrowding, visiting patterns, involvement in prison programs, and stringency of rule en-

Prison Disciplinary Tickets 105

forcement (Barak-Glantz, 1985; Cooke, 1989; Ellis, Grasmick, and Gilman, 1974; Gaes and McGuire, 1985; Silberman, 1988; Light, 1990). Because the pains of imprisonment are gener- ally constant and ubiquitous, stress per se is an inadequate explanation for prison disturbances (Wilsnack, 1976). Consequently, other studies of prison violence (Akers, Hayner, and Gruninger, 1977; Gaes and McGuire, 1985; McCorkle, Mi- ethe, and Drass, 1995) have focused on relative deprivation (i.e., changes in levels of deprivation).

Further, previous studies have measured the relative deprivation at the aggregate, institutional level (Akers, Hayner, and Gruninger, 1977; El- lis, Grasmick, and Gilman, 1974; Gaes and McGuire, 1985; McCorkle, Miethe, and Drass, 1995), and none have tested the deprivation model explicitly at the individual level. The current article examines the effectiveness of the deprivation model at the individual level. Three independent variables are derived from the deprivation model to capture inmates' sense of relative deprivation.

The first of these three is the level of security in a prison. In Ohio, there are three levels of security for most prisons: maximum/close, medium, and minimum. Differences among levels of security are reflected by a number of factors. The ratio between correctional officers and inmates, for example, varies mainly depending on the level of security: The higher the level of security is rated, the more correctional officers are assigned to work there. Further, high security means an increase in the control exercised with respect to inmate movement in prison, free time, and other privileges. At the aggregate level, McCorkle, Miethe, and Drass (1995) found that security levels are positively related to assault- ive behavior. Building on previous studies that showed the importance of environmental factors on misconduct in prison (McCorkle, Mi- ethe, and Drass, 1995; Proctor, 1994; Wright, 1991), it is hypothesized that inmate misconduct is related to the level of security, with inmates exposed to higher levels of security having more rule violations than those with lower levels of security.

The second variable of the deprivation model is the indeterminate sentence. This variable has not been widely used in the past literature.

Goodstein (1982) investigated the relationship between types of sentences and prison violence, and found no differences in inmates' adjustment to prison between those with indeterminate and those with determinate sentences. Theoretically speaking, those with determinate sentences do not have much incentive to behave well. In contrast, those with indeterminate sentences should expect early release through good behavior. Thus, rule violations should differ between these two groups since repeated rule violations--an indicator of maladjustment in prison--would make those inmates with indeterminate sentence look "bad" when going for their scheduled early release hearing by the parole board.

The third variable of the relative deprivation model is the length of each inmate's sentence. Basing their argument on Wheeler (1961), Ak- ers, Hayner, and Gruninger (1977) treated this variable as a measure of deprivation and they hypothesized that those having longer sentences would feel more deprived and, thus, would be more likely to violate rules than those with shorter sentences. Gaes and McGuire (1985) also re- garded this variable as an indicator of relative deprivation and found that the percentage of time remaining until the inmate's release date is negatively related to the number of infractions between inmates. This relationship will be tested again in the current study.

The competing model, the importation perspective, was initially proposed by Irwin and Cressey (1962). According to this model, even in a total institutional environment, which was supposed to insulate offenders from the outside influence, individual inmates' own distinctive traits and social histories external to the prison situation remain important in their adaptation to the new situation. Characteristics of individuals that predate confinement--that is, attributes that are "imported" into prison settings, such as the nature of offense and race--are critical factors in determining modes of inmate adjustment. Tests of the importation model have used factors such as race, age, and the nature of offense to account for variations in adjustment across institutions (Akers, Hayner, and Gruninger, 1977; Ellis, Grasmick, and Gilman, 1974; Gaes and McGuire, 1985; Poole and Regoli, 1983; Wright, 1991).

106 L. CAO et al.

The current article includes twelve variables derived from the importation model. These variables are age, education, gender, race, employment, marital status, history of mental illness, history of substance abuse, the number of violent offenses, the county of crime commitment, juvenile incarceration history, and adult incarceration experience.

For the first six demographic variables, there is lack of consensus in the existing literature as to their effects on prison rule violation, except for age (Adams, 1992; Akers, Hayner, and Gruninger, 1977; Gaes and McGuire, 1985; Lindquist, 1980; MacKenzie, 1987; Wooldredge, 1991). This lack of consensus could be due to three primary factors. First, samples used in most previous research were limited in scope, usually a group of inmates from several prisons or the entire population in one prison; most of the studies were conducted in either New York or California. Utilization of a sample from a state prison system is rare (for an exception, see Flanagan, 1980). Conclusions based on single prison studies are potentially limited because they may either downplay the role of these demographic variables on disciplinary violation if the sample is drawn from a minimum security prison, or they may overstate the effects of variables if the sample is drawn from a maximum security prison.

Second, the unit of analyses (aggregate vs. individual levels), the measurement of the key dependent variables (violence vs. rule violation), and independent variables differ across studies. Some of the variables, such as security levels--a potential contributor to disciplinary reports at the individual level--have seldom been examined (U.S. Department of Justice, 1989). Other variables, such as gender, have rarely been examined in a pooled (both male and female) sample. Still others, such as employment and marital status, are not always con- trolled.

Third, the statistical methods employed in most previous studies are limited because the analysis uses either a noncausal comparison approach or an ordinary least squares (OLS) model to test the association between infractions and various characteristics of inmates. The commonly operationalized measure of institu-

tional adjustment--rule violations--may not be normally distributed, however. A survey of rule violators in state prison systems, for example, found that about one-half of the inmates had no rule violations in a given year (out of 450,000 total prison population in the United States); this pattern was consistent between 1979 and 1986 (U.S. Department of Justice, 1989). Conse- quently, the dependent variable has a large number of observations clustered at zero. Further, the number of rule violations cannot assume a negative value. The ordinary least squares model, therefore, is an inappropriate technique for such a situation (Gaes and McGuire, 1985; Goetting and Howsen, 1986; Greene, 1993; Maddala, 1983; Tobin, 1958).

Researchers have recently become more in- terested in the effects of mental illness and substance abuse. Toch and Adams (1989) found that inmates who have a history of mental health treatment show higher infraction rates, while Flana- gan (1983) and Zamble and Porporino (1988) reported a relationship between drug use and rule violation. These relationships will be examined in the current article again.

Finally, this study's model also includes the variables of the county in which offenders committed their crimes, the number of violent offenses, and two previous incarceration experiences (juvenile and adult incarcerations). MacKenzie (1987) found that inmates from large urban areas are more deviant than those from rural environments. Previous research also indicates that the nature of offenses is related to infractions within the prison (Flanagan, 1983; Ru- back, Carr, and Hopper, 1986; Toch and Adams, 1989); violent offenders tend to have higher prison infraction rates than nonviolent offenders. Goetting and Howsen (1986) found that prior incarceration increased inmates' infraction rates, while Wolfgang (1961) suggests that prior prison experience facilitates inmates' adjustment. The current study will test these relationships again.

METHODS AND MEASURES

Data for this article are from an intake study in the Ohio Penitentiary System. In the mid-


1980s, confronted with the beginning trend of an increase in incarceration, the Bureau of Re- search at Ohio Department of Correction conducted this research project attempting to pro- file the characteristics of its prison population. The sample included all convicts admitted to the twelve Ohio State Prisons during September and October of 1985. A total of 1,722 male and female prisoners were processed in the intake. Excluding those who were released within twelve months because of the completion of their sentences, being paroled, or various early release programs, the final analysis only included those who served more than twelve months. The re- sulting total sample size is 883 inmates.

In this way, two problems of the temporal pattern in receiving disciplinary tickets are cir- cumvented. The first problem of the temporal pattern of rule violations suggests that the number of tickets is a function of the time: the longer one stays in prison, the more likely it is that he/she would receive a ticket. Many previous studies survey inmates at one point in time without any control over when prisoners were admitted into the system; they mix together inmates incarcerated for twenty years or more with those having served only one or two days.

The second problem of the temporal pattern concerns studies that arbitrarily set their observation period for one year. Although offenders in these studies must be in prison for at least one year, many inmates in the sample have been incarcerated for a much longer period. This research, however, must address the problem that disciplinary infraction rates are relatively high at the start of the prison sentences and peak within the first six to nine months of incarceration; thereafter, infraction rates show a steady downward trend over one's stay in prison (Toch and Adams, 1989). Because all of the inmates in the sample at the current study were admitted within two months in 1985 and their records were all based on their first year's stay in prison, the above two problems with the temporal order are, thus, eliminated.

Table 1 presents the sample characteristics in terms of means and standard deviations of the ten variables in this study. Also, the percentage distributions for Class II and Class III tickets and for security levels are reported.

TABLE 1

SELECTED VARIABLE MEANS AND STANDARD DEVIATIONS

Standard Mean Devation Cases

Dependent variables Class II ticket 2.360 4.281 883

No ticket (0) 47.2% 417 One ticket 15.2% 134 Two tickets 10.3% 91 Three tickets 6.1% 54 Four tickets 5.2% 46 Five to thirty-seven

tickets a 15.0% 141 Class III ticket .990 2.239 883

No ticket (0) 69.3% 612 One ticket 10.8% 95 Two tickets 7.8% 69 Three tickets 3.6% 32 Four tickets 2.3% 20 Five to twenty-one

tickets a 6.2% 55

Independent variables Age at admission 29.999 9.133 883 Race (non-White = 1) .465 .499 883 Claimed education 10.777 2.024 883 Gender (male = 1) .931 .254 883 Marriage (single = 1) .623 .485 883 Security level 2.110 .651 883

Minimum security (1) 16.3% 144 Medium security (2) 56.4% 498 Maximum/close (3) 27.3% 241

Indeterminate sentence (yes = 1) .797 .402 883

aTo save space, the detailed distribution of six tickets and above is omitted, and, in regression analysis, the actual number is used.

Dependent Variables

The dependent variables are two types of disciplinary violations. The first type of disciplinary violations is represented by the number of Class II tickets filed by staff members on inmates for rule violations during the observational period. In the Ohio prison system, a Class II ticket is issued for severe violations, defined as inmate behaviors that pose a threat to the security of an institution, such as disobeying or- ders, fighting, and possession of contraband. The second type of disciplinary violations is measured by the number of Class III tickets, defined as a misconduct that disturbs the peace and order of an institution, such as bad work and horseplay. It also includes a large number

108 L. CAO et al.

of "reduced" Class II violations. Overall, these two types of reports capture both severe and minor rule violations in an incarcerated environment.

During the twelve-month observational period, slightly more than one-half of the inmates had at least one Class II ticket and about 30 percent had at least one Class III ticket (see Ta- ble 1). These figures are quite consistent with data at the national level in any given year between 1979 and 1986 (U.S. Department of Jus- tice, 1989).

Independent Variables

The deprivation model As discussed earlier, three variables are derived from the deprivation model. The first is the level of security. It is hypothesized that people in the higher level of security would be more restricted and monitored more closely than those in the lower level of security. They are expected to violate the prison rule more frequently because of more deprived conditions in higher security environments. In Ohio, there are three levels of security: close/ maximum, 2 medium, and minimum security. The security levels in this analysis are coded into three ordinal levels: 3 = maximum/close security; 2 = medium security, and 1 = minimum security. Table 1 also reports the percentage distribution of the inmates in each security level.

Second, the indeterminate sentence is a dummy variable with 1 = indeterminate sentence and 0 = determinate (or fiat) sentence. 3 About four- fifths of the inmates (79.7 percent) in this sample are serving an indeterminate sentence. Third, because indeterminate sentences carry an upper and a lower limit, the sentence length is calcu- lated by the following formula: (maximum + minimum)/2. Further, because this variable was marked by a problem of skewness (quite a few people were sentenced to 100-450 years in prison), it is regrouped into an ordinal variable with those serving fewer than five years as 1, five to ten as 2, eleven to fifteen as 3, sixteen to twenty-four as 4, twenty-five to forty-nine as 5, and more than fifty years as 6.

The importation model According to the importation perspective, differences in rule violations between individuals within prison mainly are due to the differences that existed before the inmates entered the prison. Six demographic variables are included in this analysis. The first is age, which is coded as the inmate's age at admission. Moreover, MacKenzie (1987) observed that the relationship between age and problem behavior in prisons was not linear because be- havioral problems of inmates peaked in late teens and early twenties. To capture this curvelinear relationship, a new variable--age square--is created. Since age and age square are highly correlated, the following formula is used to keep them mutually exclusive: age square = (age - mean of age2).

The next five variables are gender, race, edu- cational attainment, employment, and marital status. Males are coded as 1, and females, serving as the reference group, are coded as 0. About 7 percent of the sample are females. With regard to race, Hispanics (they are seven- teen or 1.9 percent of the total sample) and Afri- can Americans are placed into one group and are coded as 1, Caucasians are coded as 0. Nearly one-half of the sample (46.5 percent) are non-Caucasians. Education is measured by the inmate's self-claimed education in years. It ranges from no formal education (0) to twenty- two year's formal education, with a mean of 10.8 (see Table 1). Employment codes those who worked full-time prior to the current sentences as 1 (20 percent of the sample); others as 0. For marital status, those (62 percent of the sample) who were single, divorced, and sepa- rated at admission are coded as 1; those who were married or in common law marriage as 0.

In addition, rule-violation differences may also be explained by histories of mental illness and substance abuse, the number of violent offenses, the county of crime commitment, and prior juvenile and adult incarceration histories. These six variables are, thus, included in the analysis. The history of mental illness is a dummy variable with 1 = having such a history and 0 = no such history. The history of substance abuse codes those with no such history as 0, those with only alcoholic abuse as 1, and


those with drug abuse and/or alcoholic abuse plus drug abuse as 2. The number of violent offenses measures the number of times an inmate was convicted of violent offenses as an adult. It ranges from no such conviction (74 percent) to two such convictions (7 percent) with a mean of .336. Nelson (1989), after comparing different measures, concludes that most measures of criminal history are equally good: The simplest method works as well as the most complicated measures. It seems reasonable to expect that the number of violent offenses is positively associated with institutional infractions.

The variable county codes those who committed their crimes in the three largest counties (Cuyahoga, Franklin, Hamilton) as 1 and all others as 0. These counties contain the largest metropolitan areas in Ohio: Cleveland, Colum- bus, and Cincinnati. Finally, the prior incarceration histories are represented by the number of times as offender was incarcerated as a juvenile and the number of incarcerations as an adult.

RESULTS

Table 2 presents the results using Tobit regression analysis for the number of Class II tickets--violations of prison rules that pose a more serious threat to the security of an institution. Two equations are presented. In Equation One, only three variables of the deprivation model are examined. None of these independent variables are significantly related to the number of severe rule violations. Further, in Equation Two, when the variables from the deprivation model are put together with variables derived from the importation model, none of them are statistically significant.

In contrast, five out of thirteen variables in the importation model are able to predict the number of Class II tickets (see Equation Two of Table 2). As expected, an increase in age de- creases the chances of getting a Class II ticket. This relationship, however, is nonlinear. The positive sign of the quadratic effect of age on severe rule violation indicates that the drop in rule violation is sharp for inmates between sev- enteen and twenty-seven years old, but its ef-

TABLE 2

THE DEPRIVATION AND IMPORTATION MODELS ON THE NUMBER OF CLASS II TICKETS: THE RESULTS OF THE

TOBIT REGRESSION

Equation One Equation Two Independent Variables b S.E. b S.E.

Indeterminate sentence (yes = 1) 1.298 .670 - 1.013 .636

Security level .547 .411 .488 .399 Sentence length - . 367 .209 - .098 .191 Age - .386"* .037 Age square ,006"* .002 Education - .231" .108 Gender (male = 1) -3 .190"* .772 Race (non-

Caucasian = 1) 1.379"* .437 Employment (yes = 1 ) - . 140 .507 Marriage (single = 1 ) .365 .340 Mental illness - . 773 .525 Substance abuse - .192 .266 Offense - .279 .363 County .392 .415 Juvenile incarceration 1.037" .502 Adult incarceration .312 .307 (Constant) -1 .273 .802 16.608"* 2.264

"0.01 <- p < 0.05. **p -< 0.01.

fects diminish thereafter. That is, the chances of rule violations are about equal for people over the age of twenty-seven. Further, education is inversely related to the probability of getting a Class II ticket. Being female, however, increases the probability of rule violation. Finally, non-Caucasians are significantly more likely to get a Class II ticket than Caucasians. The rest of the variables----employment, marriage, mental illness, substance abuse, previous violent offenses, counties, and prior incarceration histories----do not have an appreciable effect on the severe rule violations.

To discern whether the sources of minor and severe prison rule violations are similar, the competing models also are analyzed for minor rule violations. The results of the Tobit regression analysis for the number of Class III tickets--misconduct that disturbs the peace and order of an institution--are reported in Table 3. Like the analysis of severe rule violation, only variables of the deprivation model are examined in Equation One. None of them significantly influence the number of Class III tickets. Further,

110 L. CAO et al.

TABLE 3

THE DEPRIVATION AND IMPORTATION MODELS ON THE NUMBER OF CLASS III TICKETS: THE RESULTS OF THE

TOBIT REGRESSION

Equation One Equation Two Independent Variables b S.E. b S.E.

Indeterminate sentence (yes = 1)

Security level Sentence length Age Age square Education Gender (male = 1) Race (non-

Caucasian = 1) Employment (yes = 1) Marriage (single = 1) Mental illness County Substance abuse Offense Juvenile incarceration Adult incarceration (Constant)

.596 .666 -1 ,217 .648 - .151 .413 - . 030 .405

.047 .203 ,215 .194 - .315"* .038

.008** .002 -.061 .116

.642 .851

-3 .058" * .814

.472 .443 - .844 .525

.889* .450

.095 .558

.179 .422 -.438 .271

.459 .374 -.610 .522 -.269 .322 6.298** 2.364

"0.01 -< p < 0.05. **p -< 0.01.

in Equation Two, when the variables from the importation model are introduced into the model, the three variables from the deprivation model remain statistically insignificant.

The independent variables of the importation model--age, age square, and marital status-- significantly affect the likelihood of Class III tickets. As expected, an increase in age at admission reduces the number of minor prison rule infractions. The relationship is nonlinear: The quadratic effect shows that the decrease mainly occurs before the age of twenty-seven, and after that age, there is little difference in rule infractions. Further, being single increases one' s chances of a Class III ticket, again consistent with the importation model's prediction. Other variables of the importation model---education, gender, race, employment, mental illness, substance abuse, county, the number of violent offenses, and prior incarceration h i s t o r i e s ~ o not have significant effects on the number of minor rule infractions. It seems, therefore, that the deprivation model at the individual level

does not fare well empirically either with the minor or severe prison rule violations.

DISCUSSION AND CONCLUSION

Data from the Ohio Bureau are used to test the competing criminological models of deprivation and importation with minor and severe rule infractions in prison as the dependent variables. The analyses do not support the deprivation model as an explanation of inmates' rule breaking. None of the three variables derived from the model are significantly related to either severe or minor rule violations. Despite the statewide sample of the current study, use of more appropriate statistical techniques, more refined measures, and the control of time incarcerated, it should be noted that the analysis of the deprivation model is limited to individual- level data, and that some measures of the deprivation model, such as the gang activity within prisons and staff/management characteristics, are not included. Jackson and Ammen (1996), for example, recently have reported that African American correctional officers are less punitive over time in their attitudes then Caucasian officers. Future studies may incorporate more macro- level institutional variables and more refined measures for a full test of the deprivation model in predicting inmates' behavior patterns. The empirical results of the current study, however, are consistent with Wooldredge (1991:6), who concluded, after a careful review of the literature, that the deprivation model "may no longer be applicable to an understanding of inmate behavior because of the changes in institutions and life- styles which have occurred over the past 30 years."

At the individual level, the importation model appears to be supported over the deprivation model. Many of its variables, such as age at admission, education, gender, marriage, and race, are significant in predicting either one or both types of rule infractions, although other variables are found to be insignificant in predicting inmates' behavior in prison. The directions of these significant effects are also consistent with the previous research on these relationships (El- lis, Grasmick, and Gilman, 1974; Flanagan,

Prison Disciplinary Tickets 1 l 1

1983; Goetting and Howsen, 1986; Light, 1990; Lindquist, 1980; MacKenzie, 1987; Poole and Regoli, 1980; Proctor, 1994; Tischler and Mar- quart, 1989). These results can only be general- ized to those who stay in the Ohio prison system for more than twelve months.

Several other important findings of this research warrant attention. First, the current analyses confirm that age at admission is the most consistent and reliable predictor of both minor and severe rule violations in prison (Adams, 1992; MacKenzie, 1987; Wooldredge, 1991), and they also confirm MacKenzie's (1987) finding that the relationship between age and prison rule breaking is nonlinear. A significant positive quadratic effect of age on rule violations indicates that violations are frequent before age twenty, and then drop sharply thereafter; inmates age twenty-seven or over appear to possess similar likelihoods of engaging in problem behavior.

Second, the results indicate the importance of differentiating the severity of rule violations. It seems that the sources of minor and severe rule infractions differ. The models predicting minor and severe rule violations share only one vari- ab le -age . Other significant predictors vary: marital status predicts minor rule infractions, but its effect on severe rule breaking is insignificant; in contrast, education, gender, juvenile incarceration history, and race only predict the probability of the severe rule violation. Future studies need to consider this issue in more detail.

Third, with a large number of independent variables in the models of the current study, the findings are potentially more robust than those reported in previous studies that included fewer independent variables. It is noticed that employment, histories of mental illness and substance abuse, the county in which offenders committed their crimes, violent offenses, prior incarceration experiences, and marital status do not affect the number of Class II tickets. The insignificant effect of prior violent offenses on the severe rule violations in prison is particularly noteworthy. Future studies are needed to further validate the findings.

Finally, the significant impact of race and gender on severe rule violations, but not on minor

violations, merits discussion. The prison is largely hidden from the public scrutiny (Ramirez, 1983: 414). Racial discrepancies between rule violation and its reporting have been noticed in some empirical studies (see Light, 1990). Hewitt, Poole, and Regoli (1984), for example, found that inmates are much more extensively involved in rule-breaking than is usually presumed from official institutional records, and even though prison guards observe nearly the same number of violations, they report very few of the violations that they observe. Noticing a similar pattern, Ramirez (1983) made a distinction between the commission of misconduct and the apprehension of misconduct. Although prison guards' differential ticketing of inmates by race cannot be disregarded fully when interpreting the findings of the current study, the insignificance of race in predicting minor rule infractions suggests that Ohio correctional officers are not bi- ased in this domain of conduct. There is at least some reason to conclude, therefore, that the race effect for more serious infractions--where pre- sumably discretion would be less wide--is not merely an artifact of racial discriminations in administering disciplinary tickets. Additional results from this analysis, such as age and education, enhance the confidence in the validity of the findings on racial effects.

As for gender, the finding that females are more likely to violate prison rules than males seems to challenge the image of docile female inmates. Although it contradicts the finding of Goetting and Howsen (1986), it is consistent with Lindquist (1980) and partially consistent with Tischler and Marquart (1989). Lindquist (1980) found that female offenders committed significantly more disciplinary offenses than males. Tischler and Marquart (1989) reported that while females and males on the whole did not differ on the total number of offenses committed, females in the maximum security prison had a significantly higher number of total reported infractions than their male counterparts. Further, the current study's finding is consistent with recent studies of serious female criminal behavior, which report that women are taking more assertive roles in deviance (Decker et al., 1993; Sommers and Baskin, 1993; Alarid et al.,

112 L.CAOet al.

1996). More detai led research, such as what types of pr ison rules females are more l ikely to

break than males, is needed before f i rm theoretical conc lus ion can be reached.

In sum, this research indicates that the im-

portat ion model appears to be a more adequate

model at the indiv idual level in predict ing in-

mates ' behavior pattern than the depr ivat ion model . This f inding is impor tant because the

deprivat ion model predicts few, if any, individ-

ual differences in the pr ison envi ronment , whi le

the importa t ion model argues that inmate conduct is an ex tens ion of the cultural and struc-

tural differences in individuals beyond the

pr ison walls. Pr ison management , thus, is urged

to pay closer at tention to inmates ' indiv idual differences and design t reatment programs, i f

any, based on these indiv idual differences. The

data of the current study, however , are l imited

to those who stayed in the Ohio prisons for

more than twelve months , and all possible mea-

sures f rom the depr ivat ion model have not been

exhausted. The f indings should be interpreted

wi th in these l imitations. Previous studies also have poin ted out the impor tance of jur isdic-

t ional differences (Dilul io, 1987; Light, 1990; Petersi l ia and Honig, 1980; Wooldredge, 1991).

Future studies, thus, may extend the effort of

the current study to other states, and may evaluate the reclassif ication process to see whether

the n u m b e r of discipl inary tickets can predict

inmates ' transfers of securi ty l e v e l s - - a decis ion

made every six months in O h i o - - a n d predict parole decisions.

ACKNOWLEDGMENTS

The authors wish to thank Francis T. Cullen for his help- ful comments on the earlier version of this article. The views expressed here are entirely those of the authors, and any er- rors of analysis and/or interpretation are attributable to the authors alone.

NOTES

1. Akers, Hayner, and Gruninger (1977) called the deprivation model the "functional" or "situational response" model, while Gibbons (1968) and Ellis, Grasmiek, and Gil- man (1974) called it the "institutional product" model. For the importation model, Gibbons (1968) and Ellis, Grasmick, and Gilman (1974) called it the "diffusionist" model.

2. Because only seven people (or .8 percent of the total sample) are in the maximum security prison, they were merged into those in the close security prison.

3. In 1985, the Ohio sentencing system was in a transi- tional period. Some inmates were sentenced under the "new law" (i.e., under the new Sentencing Guidelines--a determinate sentence), while others were under the "old law" (an indeterminate sentence).

REFERENCES

Adams, K. (1992). Adjusting to prison life. In Crime and justice: A review of research, ed. M. Tonry. Chicago: The University of Chicago Press.

Akers, R. L., Hayner, N. S., and C-runinger, W. (1977). Pris- onization in five countries: Type of prison and inmate characteristics. Criminology 14:527-54.

Alarid, L. F., Marquart, J. W., Burton, V. S., Cullen, F. T., and Cuvelier, S. J. (1996). Women's roles in serious offenses: A study of adult felons. Justice Quarterly 13:431-54.

Barak-Glantz, I. L. (1985). The anatomy of another prison riot. In Prison violence in America, eds. M. Braswell, S. Dillingham, and R. Montgomery, Jr. Cincinnati, OH: Anderson Publishing Co.

Cooke, D. (1989). Containing violent prisoners: An analysis of the Barlinnie special unit. British Journal of Criminol- ogy 29:129-43.

Decker, S., Wright, R., Redfern, A., and Smith, D. (1993). A woman's place is in the home: Females and residential burglary. Justice Quarterly 10:143-62.

DiIulio, J., Jr. (1987). Governing prisons. New York: Free Press.

Ellis, D., Grasmick, H. G., and Gilman, B. (1974). Violence in prisons: A sociological analysis. American Journal of Sociology 80:16--43.

Flanagan, T. J. (1980). Time served and institutional misconduct: Patterns of involvement in disciplinary infractions among long-term and short-term inmates. Journal of Criminal Justice 8:357-67.

Flanagan, T. J. (1982). Discretion in the prison justice system: A study of sentencing in institutional disciplinary proceedings. Journal of Research in Crime and Delin- quency 19:216-37.

Flanagan, T. J. (1983). Correlates of institutional misconduct among state prisoners. Criminology 21:29-39.

Gaes, G. G., and McGuire, W. J. (1985). Prison violence: The contribution of crowding versus other determinants of prison assault rates. Journal of Research in Crime and Delinquency 22:41-65.

Gibbons, D. C. (1968). Society, crime, and criminal ca- reers. Englewood Cliffs, NJ: Prentice-Hall.

Goetting, A., and Howsen, R. (1986). Correlates of prisoner misconduct. Journal of Quantitative Criminology 2:49-67.

Goffman, E. (1961). Asylums: Essays on the social situation of mental patients and other inmates. Garden City, NY: Anchor Books.

Goodstein, L. (1982). A quasi-experimental test of prisoner reactions to determinate and indeterminate sentencing. In Coping with imprisonment, ed. N. Parisi. Beverly Hills, CA: Sage Publications.

Gottfredson, D. M. (1979). Parole board decision making: A study of disparity reduction and the impact of institutional behavior. Journal of Criminal Law and Criminol- ogy 70:77-88.

Prison Disciplinary Tickets 1 13

Greene, W. H. (1993). Econometric analysis. 2d ed. Engle- wood Cliffs, NJ: Prentice-Hall.

Hewitt, J. D., Poole, E. D., and Regoli, R. M. (1984). Self- reported and observed rule-breaking in prison: A look at disciplinary response. Justice Quarterly 1:437-47.

Irwin, J., and Cressey, D. (1962). Thieves, convicts, and the inmate culture. Social Problems 10:142-55.

Jackson, J. E., and Ammen, S. (1996). Race and correctional officers' punitive attitudes toward treatment programs for inmates. Journal of Criminal Justice 24:153-66.

Light, S. C. (1990). Measurement error in official statistics: Prison rule infraction data. Federal Probation 54:63-68.

Lindquist, C. A. (1980). Prison discipline and the female offender. Journal of Offender Counseling, Services, and Rehabilitation 4: 305-18.

MacKenzie, D. L. (1987). Age and adjustment to prison. Criminal Justice and Behavior 14:427-47.

Maddala, G. (1983). Limited dependent and qualitative variables in econometrics. New York: Cambridge Uni- versity Press.

McCorkle, R. C., Miethe, T. D., and Drass, K. A. (1995). The roots of prison violence: A test of the deprivation, management, and "not-so-total" institution models. Crime and Delinquency 41 : 317-31.

Nelson, J. F. (1989). An operational definition of prior criminal record. Journal of Quantitative Criminology 5:333-54.

Petersilia, J., and Honig, P. (1980). Theprison experience of career criminals. Santa Monica, CA: Rand Corporation.

Poole, E. D., and Regoli, R. M. (1980). Race, institutional rule breaking and disciplinary response: A study of dis- cretionary decision making in prison. Law and Society Review 14:931-46.

Poole, E. D., and Regoli, R. M. (1983). Violence in juvenile institutions: A comparative study. Criminology 21:213-32.

Proctor, J. L. (1994). Evaluating a modified version of the federal prison system inmate classification model: An as- sessment of objectivity and predictive validity. Criminal Justice and Behavior 21:256-72.

Ramirez, J. (1983). Race and the apprehension of inmate misconduct. Journal of Criminal Justice 11:413-27.

Ruback, R., Carr, T., and Hopper, C. (1986). Perceived control in prison: Its relation to reported crowding, stress,

and symptoms. Journal of Applied Social Psychology 16:376-86.

Silberman, M. (1988). Dispute mediation in the American prison: A new approach to the reduction of violence. Pol- icy Studies Journal 16:522-32.

Sommers, I., and Baskin, D. R. (1993). The situational con- text of violent female offending. Journal of Research in Crime and Delinquency 30:136-62.

Sykes, G. (1958). The society of captives. Princeton, NJ: Princeton University Press.

Sykes, G., and Messinger, S. L. (1960). The inmate social system. In Theoretical studies in social organization of the prison, ed. R. Cloward. New York: Social Science Research Council.

Tischler, C. A., and Marquart, J. W. (1989). Analysis of disciplinary infraction rates among female and male inmates. Journal of Criminal Justice 17:507-13.

Tobim J. (1958). Estimation of relationships for limited dependent variables. Econometrica 26:24-36.

Toch, H., and Adams, K. (1989). Coping: Maladaptation in prisons. New Brunswick, NJ: Transaction.

U.S. Department of Justice. (1989). Prison rule violators: Special report. Washington, DC: U.S. Department of Jus- tice.

U.S. Department of Justice. (1995). Prisoners in 1994. Washington, DC: U.S. Department of Justice.

Wheeler, S. (1961). Socialization in correctional communi- ties. American Sociological Review 26:694-712.

Wilsnack, R. W. (1976). Explaining collective violence in prisons: Problems and possibilities. In Prison violence, eds. A. K. Cohen and R. G. Bailey. Lexington, MA: Lex- ington Books.

Wolfgang, M. E. (1961). Quantitative analysis of adjustment to the prison community. Journal of Criminal Law, Criminology, Police Science 51:608-18.

Wooldredge, J. D. (1991). Correlates of deviant behavior among inmates of U.S. correctional facilities. Journal of Crime and Justice 14:1-25.

Wright, K. N. (1991). A study of individual environmental, and interactive effects in explaining adjustment to prisons. Justice Quarterly 8:217--42.

Zamble, R., and Porporino, F. (1988). Coping, behavior, and adaptation in prison inmates. New York: Springer-Verlag.

prison disciplinary tickets: a test of the deprivation and importation models

Documents