Moral Communities and Institutional Misconduct: A Reassessment of Religious Contextual Influences on Inmate Behavior

Abstract

Most of the research into religious influences on inmate misconduct has been undertaken with respect to personal religious participation. However, the religious environment of prisons offers potential influences on inmate behavior by providing social control and support. Currently, the literature is mixed regarding the association between religious context and inmate behavior. This article reconciles the divergent findings regarding religious contexts, and integrates recently emergent directions in religious contextual research. Using nationally representative data of inmates in state and federal prisons, I estimate multilevel models of several different forms of misconduct. The results indicate a relationship between religious context and each form of misconduct, highlighting the role of cultural contexts on inmate behavior.

Keywords

prison religion religious context inmate misconduct rule violations

Introduction

Maintaining an orderly environment remains one of the foremost challenges facing correctional administrators. Not surprisingly, social science researchers have shown great interest in understanding individual and structural determinants of inmate behavior (Celinska & Sung, 2014; Jiang, Fisher-Giorlando, & Mo, 2005; Morris & Worrall, 2014; Morris, Carriaga, Diamond, Piquero, & Piquero, 2012; Steiner, Butler, & Ellison, 2014; C.-Y. Williams & Porter 2016; Wooldredge, Green, & Pratt, 2001; Worrall & Morris, 2012). Within this vein of research, religion is one factor that has caught the attention of social scientists. The literature regarding religious practice and inmate misconduct tends to be illustrative of an inverse relationship (Camp, Daggett, Kwon, & Klein-Saffran, 2008; Duwe, Hallett, Hays, Jang, & Johnson, 2015; Jang, Johnson, Hays, Hallett, & Duwe, 2017; Kerley, Matthews, & Blanchard, 2005), particularly so among inmates who view God as forgiving and engaged with the world (Jang, Johnson, Hays, Duwe, & Hallett, 2018). Religious practice at the individual level has comprised the focal point of this line of inquiry. Since religion at a contextual level has been linked to social control with respect to several different forms of delinquency (Adamczyk & Palmer, 2008; Eitle, 2011; Lee & Bartkowski, 2004; Regnerus, 2003; Rivera, Lauger, & Cretacci, 2018), the religious context of correctional settings presents a potentially salient correlate of inmate behavior.

Although a limited literature has begun to assess the association between religious context and inmate behavior, divergent results, largely a product of differences in measurement (Jiang et al., 2005; Sturgis, 2010), have resulted in an ambiguous understanding of this relationship. Contemplation of religious environmental influences on inmate behavior is further compounded by emerging research indicating that the impact of religious context operates in a nonlinear fashion (Drakeford, 2019). The present study aims to address contrasting results present in the literature, in part through integrating a nonlinear approach. Presented results advance understanding of inmate behavior by highlighting the importance of cultural contexts.

Empirical and Theoretical Considerations

Perspectives on Inmate Misconduct

Explanations of inmate misconduct have typically been grounded within two theoretical perspectives, deprivation and importation. Deprivation theory posits that inmate behavior is a function of the deprivation of liberty, security, material possessions, relationships, and autonomy (Sykes, 1974). In essence, inmate behavior, including misconduct, results from the manner by which inmates adapt to the deprivation of various conduits of well-being. A contrasting view is provided by importation theory, which postulates that inmate behavior is informed by prison culture that is constituted of the values, norms, and behavioral patterns that are derived from criminal subcultures brought into prison by inmates (Irwin & Cressey, 1962).

Recent studies have begun to shift to a more integrative approach based on general strain theory (GST) in explaining misconduct among prison inmates (e.g., Morris et al., 2012; C.-Y. Williams & Porter 2016). The foundation of GST is that strains emanating from negative relationships and experiences lead to delinquency as a form of coping (Agnew, 1992). Application of the GST framework to prison misconduct has identified three forms of strain: denial of positive goals and stimuli, denial of positive material goods, and exposure to noxious environments (Blevins, Litwan, Cullen, & Jonson, 2010). Moreover, there are different forms of coping with the strains of imprisonment including deviant coping, negative affective states, and more conventional forms of coping. Thus, GST sees inmate misconduct as a form of deviant coping in response to the stress of incarceration (Blevins et al., 2010). The cultural and social context of prison presents implications for inmate misconduct by imposing a potential constraint through social control as well as lowering the disposition to engage in misconduct by providing alternative forms of coping.

Religious Context and Inmate Misconduct

The religious environment is one potential cultural factor that may be salient for inmate misconduct. There are two potential pathways connecting religious context to inmate misconduct. First, the religious context of prisons offers social control of deviant behavior. This contention is consistent with the moral communities hypothesis, which posits that social control resulting from religion is largely a function of whether religion “is accepted by the majority as a valid basis for action” (Stark 1996, p. 164) rather than whether individuals themselves are religious. In other words, religion acts as a property of social groups. This is a point that has found empirical support with respect to research taking an ecological approach in showing the influence of moral communities on outcomes such as homicide (Lee & Bartkowski, 2004) and criminal sentencing (Ulmer, Bader, & Gault, 2008). An additional proposition of the moral communities hypothesis is that religious context moderates the effect of personal religiosity on delinquency. That is, religious individuals are less likely to engage in delinquency, “but only in communities where the majority of people are actively religious” (Stark 1996, p. 165). Although prior research has supported this cross-level interaction between the individual and social domains of religion relative to a variety of outcomes (Eitle, 2011; Finke & Adamczyk, 2008; Regnerus, 2003; Stroope & Baker, 2018), there is little evidence that personal religiosity is moderated by the religious context of prisons, a finding that likely arises due to ascribed social identity resulting from involuntary membership in prison society (Drakeford, 2019; Peterson, 2010).

Second, the religious context of prison can discourage misconduct by facilitating alternative forms of coping through the promotion of social support among inmates. Participation in religious activity is associated with higher quality social relationships, and greater social integration and support (Bradley, 1995; Ellison & George, 1994), a consideration that largely explains the tendency of religious individuals to report better mental health (Nooney & Woodrum, 2002). Social support such as that associated with religion comprises a vital component in processes of coping with chronic stressors (Kawachi & Berkman, 2001; Thoits, 1995). Consistent with the promotion of social integration and interaction, religious inmates are expected to have higher levels of available social support in highly religious prisons. As social ties commonly transcend religious boundaries (Olson & Perl, 2011; Vargas & Loveland, 2011), there is the potential of spillover effects onto nonreligious inmates given that many religionists offer social support to those holding a variety of religious beliefs (Schafer, 2015), a point supported by the tendency of inmate social networks to resemble social networks in other settings (Schaefer, Bouchard, Young, & Kreager, 2017).

Thus far, a limited literature into religious contextual influences on inmate behavior has produced contradictory results. In particular, Jiang et al. (2005) reported an inverse relationship between facility-level participation in religious activity and inmate rule violations. Their results showed that inmates in more highly religious prisons report fewer total rule violations, with findings that were consistent across violent and drug/property violations. Sturgis (2010), using the same data as Jiang et al., presented a contrasting argument by asserting that religious context is not a salient predictor of inmate misconduct. These disparate findings are undergirded by differences in the measurement of religious context. Jiang et al. uses the percentage of inmates reporting religious activity as measurement of religious context, whereas Sturgis measures it using the mean time spent in religious activities among inmates within the same facility.

Reconciliation of these divergent results relative to differences in measurement can be informed through the consideration of the moral communities hypothesis. As the focal point of the moral communities hypothesis is concerned with whether the majority of a population participates in religious activity, measurement of religious context based on proportion of inmates participating in religious activity represents a more optimal means of testing the effect of religious context on inmate misconduct. The capacity of mean time spent in religious activity to reliably assess religious context is further confounded in that higher levels of commitment among the religious would be expected in situations of higher social tension, a situation that would be expected in less religious contexts (Stark & Finke, 2000). The preceding point highlights a key limitation in using mean values to assess religious context, primarily that measures such as mean values are sensitive to extreme values.

Clarity of this point is provided by considering the example of two prisons, each comprised of 100 inmates. In one prison, 50 inmates are religious and each spend 1 hr in religious activity per week. In the other prison, only 25 inmates are religious, but they each spend 2 hr in religious activity per week. Although there is a significant disparity between these prisons in the proportion of inmates who are religious, they share the same mean time spent in religious activity. Because the locus of the moral communities revolves around there being a religious majority, religiously induced social control is expected to be more prevalent in the first prison as there are more religious inmates engaging in the social control of shared religious norms even though the inmates on average spend the same amount of time participating in religion. Thus, the use of mean time spent in religious activities represents a flawed approach to assessing religious context.

New Directions in Religious Contextual Research

Consideration of an emergent line of research regarding religious contextual influences on inmate mental health further confounds the conceptualization and measurement of religious context in carceral settings. Drakeford (2019) reported that the relationship between religious context and mental health is curvilinear, whereby inmates in more homogeneously religious or secular prisons tend to report fewer symptoms of mental distress. Her central argument revolved around the importance of a coherent social identity, whether religiously or secularly based. This social identity perspective provides implications for inmate misconduct given that social identity informs the course of social interaction among inmates (Hogg, 2011). In other words, having a shared set of beliefs with the coinciding norms of behavior, like that provided by religion (Ysseldyk, Matheson, & Anisman, 2010), is salutary to well-being as it provides guidance into how inmates are to behave.

Drakeford’s finding that homogeneity relative to the dichotomy between religious and secular environments is associated with lower mental distress suggests a common elemental basis to the social groups that tend to emerge in secular prisons, otherwise there would have been little variation in mental distress relative to religious concentration in secular institutions. This point presents considerable implications for the consideration of religious contextual influences on inmate behavior in that secularly grounded prison social groups, such as gangs, can also promote social control of misconduct centered on shared norms. Although it may seem antithetical to consider social groups such as gangs as being inversely related to misconduct given the well-documented association between gang affiliation and prison violence (DeLisi, Berg, & Hochstetler, 2004; Gaes, Wallace, Gilman, Klein-Saffran, & Suppa, 2002; Griffin & Hepburn, 2006), social groups such as prison gangs provide governance of behavior along with mechanisms of sanctioning (Skarbek, 2012; 2014). As facilitators of black markets, prison gangs have a strong incentive to maintain order, or to at least make sure misconduct occurs out from under the watchful eye of correctional staff, to help ensure the purveyal of contraband (Skarbek, 2014).

One key point that emanates from the preceding discussion is that the relationship between religious context and misconduct is very likely to differ across different forms of misconduct due to differences between religiously and secularly informed moral codes. For instance, while religion, at the individual and contextual levels, offers protective effects against substance use (Adamczyk & Palmer, 2008; Rivera et al., 2018), other groups that form the basis of social identity may have norms that promote or tolerate substance use. Prison gangs represent one such form of social belonging that offers potential impacts to substance use as control of drug markets within prisons stands as a central function of such gangs (Skarbek, 2014). Similarly, gang influence is positively related to violent misconduct (DeLisi et al., 2004; Gaes et al., 2002; Griffin & Hepburn, 2006). In this vein, potential differences in religious contextual influences on inmate behavior result from differences in what constitutes acceptable behavior across different social identities. As a result, the nature of the association between religious context and inmate misconduct in mostly secular facilities remains unclear.

Summary of Research Expectations

The present study aims to clarify the divergent results present in the extant literature regarding the relationship between religious context and inmate misconduct by extending nascent lines of inquiry regarding the influence of religious concentration on inmate mental health to consideration of inmate misconduct. A review of the literature documents differences in the assessment of religious contexts present in prior studies, and identifies new directions in research into religious contextual influences in correctional settings. In particular, recent research indicates that homogeneity arising from a strong social identity stands as an integral component of contextual influences on inmate well-being. These results, in concert with the moral communities hypothesis and social support function of religion suggest that religious context will provide broad social control and alternative coping processes, a point that leads to the first hypothesis.

Hypothesis 1: Increases in religious concentration will be inversely associated with inmate misconduct in mostly religious prisons.

As outlined above, secularly based social groups also offer potential social control, although differences in norms indicate that the relationship between religious concentration and inmate misconduct should not be consistent across different forms of misconduct. In the instance that secularly based social groups do provide social control of misconduct, there should be a positive relationship between religious concentration and misconduct in mostly secular prisons. Given that the norms promulgated by secularly grounded social groups such as prison gangs tend to be more accepting of drug use and violence, there should be little social control of these behaviors in mostly secular facilities. Accordingly, increases in religious concentration could deter these behaviors, a point considered by the second hypothesis.

Hypothesis 2: There will be either an inverse relationship or no relationship between religious concentration and drug, alcohol, or assault misconduct in mostly secular prisons.

Although secularly based social groups such as prison gangs can promote forms of misconduct such as drug or alcohol use or assaults, the nonetheless have an incentive to maintain an ordered environment to help facilitate black markets. Accordingly, for forms of misconduct other than assault or drug/alcohol violations social control can originate from such social groups, which is reflected in the third hypothesis:

Hypothesis 3: There will be an inverse relationship between religious concentration and forms of misconduct other than assault or drug/alcohol violations in mostly secular prisons.

The literature review mentioned that the cross-level interaction between personal and contextual religiosity comprises a focal point in the moral communities hypothesis. As pointed out, though, emergent research has shown that this moderating effect does not appear to be present in correctional settings with respect to inmate mental health. As inmates remain involuntary members of prison society, this lack of moderation should also be present with respect to misconduct, which leads to the fourth hypothesis.

Hypothesis 4: The association between religious concentration and misconduct will not vary between religious and nonreligious inmates

Method

Data

The current study draws upon the 2004 Survey of Inmates in State and Federal Correctional Facilities (SISFCF; U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics, 2004), a cross-sectional, nationally representative survey of the U.S. inmate population that has been collected on an occasional basis since 1974. The 2004 wave, the most recent collection, was administered by the Bureau of the Census on behalf of the Bureau of Justice Statistics.

A dual-stage sampling strategy was employed whereby prisons were selected based upon the stratification of prisons by sex, with adjustments included to account for differences in size of prison population. Nonresponse among prisons was low, as 39 of the 40 (97.5%) federal and 287 of the 289 (99.3%) state prisons agreed to participate. Sampling proceeded into the second stage with the random selection of inmates. Those inmates who agreed to participate (federal: 86.7%; state: 89.8%) completed the survey through face-to-face computer-assisted personal interviews, resulting in a sample of 18,185 (3,686 federal; 14,499 state) inmates.

Measures

Outcome variables

SISFCF respondents were asked if they had “been written up or found guilty of breaking prison rules.” Respondents who answered affirmatively were then asked a series of subsequent questions concerning their rule violations over several domains of infractions. On the basis of these questions, four dichotomous outcome indicators were created.¹ The first outcome measure indicated whether respondents had any rule infractions (1 = has rule infractions [for all four outcomes]). The other three measures indicated rule violations across three categories of rule violations: drug/alcohol (drug or alcohol possession, use, or sale), assault (physical and verbal assaults of correctional staff or other inmates), and, following Steiner and Wooldredge’s (2008) approach, other nonviolent infractions (escape or attempted escape, being out of place, disobeying orders, and possession of weapons, stolen property, or contraband).

Key determinants

Two measures were used to assess religious practice. SISFCF asked respondents whether they had participated in religious activities within the preceding week. Respondents who provided an affirmative answer were subsequently asked how long they had spent participating in religious activities. A dichotomous indicator was created to indicate whether respondents had spent at least 1 hr in religious activities (1 = at least 1 hr of religious activities). A facility-level measure of religious concentration was then calculated to assess the percentage of each respective facility’s respondents who reported at least 1 hr of religious activity and rescaled by a factor of 0.1 (e.g., a value of 3 indicates 30% reported engaging in religious activity).

Covariates

Potentially confounding factors were controlled through the inclusion of covariates assessing a variety of inmate and prison characteristics. Dichotomous measures indicated violent offender (1 = violent offender), property offender (1 = property offender), drug offender (1 = drug offender), whether inmates had received visits within the previous month (1 = received visits), placed telephone calls within the preceding week (1 = had phone calls), or participated in educational or job training programs during their current incarceration term (1 = participated). Respondents were asked whether they had been intentionally injured since their admission to prison, resulting in an indicator of having experienced victimization (1 = did experience). Continuous variables assessed age (in years) and time served (logarithm of months served). Categorical variables indicated race and ethnicity (White non-Hispanic [reference], Black non-Hispanic, Hispanic, other non-Hispanic) and sentence length (less than 2 years [reference], 2-5 years, 5 years and 1 month-10 years, more than 10 years, life or death sentence, not sure). Prison-level covariates included dichotomous indicators of federal facilities (1 = federal) and sex of inmates housed (1 = female). Responses were aggregated by facility into continuous measures of mean inmate age (in years), race and ethnicity (percent of inmates in each racial/ethnic category), average sentence length (log of mean number of months), and percent of inmates reporting victimization, job training participation, and educational training participation.

Analytic Strategy

As the locus of the analysis comprises consideration of contextual variables, I use multilevel binary logit models to produce multivariate estimates. Multilevel models employ random effects that allow parameters to differ across clusters (i.e., prisons), which addresses the violation of the assumption of independence that would be inherent in single-level models. As a result, multilevel models provide more accurate results by managing the bias that would be present in single-level modeling (Hox, 2010). To adjust for potential biases resultant from sampling and nonresponse, SISFCF supplied weights were rescaled for multilevel analysis (Carle, 2009) using the PWIGLS module for Stata 14.2 (Chantalla, Blanchette, & Suchindran, 2011). To ease the interpretability of the results, coefficients have been exponentiated into odds ratios (ORs).

Results

As noted in Table 1, approximately half the respondents (49.9%) reported rule violations. Other nonviolent violations were the most commonly reported (35.3%), followed by assaults (18.9%) and drug/alcohol violations (7.7%). Likewise, close to half the respondents (51.1%) reported religious participation, while inmates, on average, resided in prisons that were near-evenly mixed between religious and secular inmates (53.4%).

Table 1.

Descriptive Statistics.

	M / %	SD
Inmate-level variables (n = 15,831)
Any rule violations	49.85	—
Assault violations	18.92	—
Drug/alcohol violations	7.66	—
Other nonviolent violations	35.69	—
Age	35.34	10.51
Race
White non-Hispanic	35.18	—
Black non-Hispanic	40.42	—
Hispanic	18.56	—
Other non-Hispanic	5.84	—
Sentence length
0-23 months	19.97	—
24-60 months	28.12	—
61-120 months	22.46	—
More than 120 months	30.37	—
Life or death sentence	7.76	—
Not sure	1.32	—
Log of time served	3.47	1.23
Victimization	15.06	—
Violent offender	45.06	—
Property offender	17.52	—
Drug offender	24.28	—
Received visit	29.97	—
Had telephone call	85.01	—
Job training participation	28.17	—
Educational program participation	32.42	—
Religious participation	51.12	—
Prison-level variables (n = 326)
Federal facility	11.96	—
Female facility	21.47	—
Mean age	35.69	3.63
Racial composition
White non-Hispanic	35.75	15.99
Black non-Hispanic	40.02	18.60
Hispanic	18.12	15.28
Other non-Hispanic	6.12	6.42
Log of mean time served	3.87	0.56
Victimization	13.86	10.29
Violent offenders	42.35	22.82
Job training participation	28.33	14.75
Educational program participation	33.37	14.79
Religious concentration	53.43	13.52

Analysis proceeded with the estimation of multilevel binary logit models to account for an array of potentially confounding factors. The first set of models, presented in Table 2, assesses the relationship between religious concentration and odds of violating rules. Odd-numbered models (1, 3, 5, 7) include only the religious concentration term. Statistically significant relationships were found with respect to overall violations (OR = 0.889; p < .01), assault (OR = .892; p < .001), and drugs and alcohol (OR = .876; p < .01). As prior literature (Drakeford, 2019) and the bivariate analysis presented above suggest a curvilinear relationship, the next set of models (Models 2, 4, 6, 8) included a religious concentration squared term. For two outcomes, statistically significant relationships were found whereby religious concentration is related to increased odds of having rule infractions (overall OR = 1.507, p < .05; other nonviolent OR = 1.809, p < .01), although the results of the squared term indicates that this effect diminishes as religious concentration increases (overall OR = 0.951, p < .01; other nonviolent OR = 0.942, p < .01).

Table 2.

Multilevel Logit Models Predicting Rule Violations.

	Overall				Assault				Drugs/Alcohol				Other nonviolent
	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6		Model 7		Model 8
	OR		OR		OR		OR		OR		OR		OR		OR
Inmate-level covariates (n = 15,831)
Age	0.957	***	0.957	***	0.950	***	0.950	***	0.964	***	0.964	***	0.955	***	0.955	***
Race^a
Black non-Hispanic	1.089		1.089		1.431	***	1.431	***	0.859		0.859		1.023		1.023
Hispanic	0.745	***	0.744	***	1.017		1.017		0.812		0.812		0.717	***	0.716	***
Other non-Hispanic	0.974		0.974		0.966		0.967		0.989		0.988		1.064		1.064
Sentence length^b
24-60 months	1.205	*	1.200	*	1.237		1.230		0.943		0.945		1.254	*	1.248	*
61-120 months	1.323	**	1.317	**	1.232		1.227		0.979		0.980		1.325	**	1.321	**
More than 120 months	1.467	***	1.457	***	1.209		1.201		1.112		1.115		1.519	***	1.510	***
Life or death sentence	2.074	***	2.060	***	1.302		1.294		1.742	*	1.748	*	1.978	***	1.963	***
Not sure	1.100		1.085		1.220		1.204		0.241		0.242		0.812		0.798
Log of time served	1.836	***	1.837	***	1.929	***	1.929	***	2.732	***	2.732	***	1.756	***	1.757	***
Victimization	3.801	***	3.802	***	4.948	***	4.948	***	1.630	***	1.630	***	2.396	***	2.396	***
Violent offender	0.934		0.935		1.089		1.090		0.915		0.815		0.877		0.879
Property offender	1.006		1.008		1.011		1.014		0.946		0.946		0.971		0.973
Drug offender	0.891		0.892		1.015		1.016		0.874		0.873		0.805	**	0.806	**
Received visit	0.895	*	0.893	*	0.887	*	0.885	*	1.048		1.049		0.928		0.926
Had telephone call	0.759	***	0.755	***	0.665	***	0.663	***	0.820		0.821		0.792	**	0.787	**
Job training participation	1.600	***	1.601	***	1.169	**	1.169	**	1.516	***	1.517	***	1.455	***	1.457	***
Educational program participation	1.360	***	1.359	***	1.136	*	1.136	*	1.066		1.066		1.315	***	1.314	***
Religious participation	0.879	**	0.879	**	0.845	**	0.846	**	0.811	**	0.811	**	0.840	***	0.840	***
Prison-level covariates (n = 326)
Federal facility	0.498	***	0.491	***	0.853		0.844		2.005	**	2.023	**	0.390	***	0.382	***
Female inmates	1.687	***	1.817	***	2.034	***	2.140	***	0.816		0.781		1.641	***	1.797	***
Mean age	1.021		0.014		0.987		0.984		0.986		0.988		1.017		1.010
Racial composition^a
Black non-Hispanic	0.998		0.997		1.000		0.999		1.006		1.006		1.001		0.999
Hispanic	0.990	**	0.990	***	0.993	*	0.993	*	0.990	*	0.990	*	0.991	**	0.991	**
Other non-Hispanic	0.997		0.995		1.007		1.006		1.022		1.022	*	0.993		0.991
Log of mean time served	0.756	*	0.777		1.052		1.066		0.784		0.777		0.835		0.859
Victimization	1.013	*	1.011	*	1.016	***	1.016	**	1.001		1.002		1.010		1.008
Violent offenders	2.426	**	2.523	**	2.780	**	2.897	**	3.794	**	3.731	**	1.803		1.887	*
Job training participation	1.001		1.002		0.998		0.999		1.006		1.006		0.998		0.999
Educational program participation	1.013	***	1.012	***	1.001		1.001		0.992		0.993		1.019	***	1.019	***
Key determinants
RC	0.889	**	1.507	*	0.892	***	1.177		0.876	**	0.722		0.972		1.809	**
RC × RC			0.951	**			0.973				1.019				0.942	**
Constant	0.507		0.176	*	0.082	***	0.046	***	0.018	***	0.027	***	0.165	**	0.047	***

Note. OR = odds ratio; RC = religious concentration.^aas compared to White non-Hispanic.

as compared to 0-23 months.

p < .05. **p < .01. ***p < .001.

To more fully elucidate this relationship, I present graphical representations (Figures 1 and 2). Figure 1 presents the predicted probability of an inmate having any or other nonviolent rule violations over the range of religious concentration values in the sample. With respect to having any rule violations, there is a modest increase in predicted probability that coincides with increases in religious concentration at the low end of the range of values. However, the increased probability of having any rule violations as religious concentration increases at the low range is not statistically significant, indicating a threshold effect to this relationship. Changes in probability of having any rule violation associated with a marginal increase in religious concentration do not become statistically significant until a level of 48% religious concentration. At religious concentration values of 48% and above, the probability of having any rule violation decreases from 52.2% to 17.2%, a 67% decrease. The relationship between religious concentration and predicted probability of having other nonviolent rule violations appears similar to that of having any rule violations, with the exception that there was no threshold effect observed. Increased religious concentration is associated with a statistically significant higher predicted probability of having other nonviolent infractions in more secular prisons, before leveling off then significantly decreasing in mostly religious prisons. Figure 2 indicates that the predicted probability of having other nonviolent rule violations increases from 23.3% (at 20% religious concentration) to a high of 33.5% in prisons equally comprised of religious and nonreligious inmates. As religious concentration increases from this point, the predicted probability of other nonviolent rule infractions decreases to 10.7%.

Figure 1.

Inmate misconduct relative to religious concentration.

Figure 2.

Inmate misconduct and religious context.

The relationship between drug/alcohol violations and religious concentration is presented in Figure 2. As shown in Figure 2, there is a consistent decrease in the predicted probability of having drug or alcohol violations that coincides with increases in religious concentration. At a religious concentration of 20%, the predicted probability of having a drug or alcohol violation is 0.055. As religious concentration increases through the full range, the predicted probability of a violation decreases to 0.020. A similar effect is observed with respect to assault violation (also in Figure 2). As indicated, there is an inverse relationship between the predicted probability of having an assault violation and religious concentration. Inmates in prisons at the low end of the scale have an expected probability of having an assault violation of 0.167. For inmates in fully religious prisons, the expected probability of having an assault violation is 0.075, a 55% decrease.

To assess whether the effect of religious context on inmate misconduct varied by personal religiousness, I then estimated a series of models incorporating interactive terms between religious concentration and personal participation in religious activity. The results of this analysis are presented in Table 3, which features two models for each category of rule violations. Odd-numbered models (9, 11, 13, 15) examine religious concentration linearly, whereas even-numbered models (10, 12, 14, 16) include interactions between religious concentration squared and inmate participation in religious activities. As indicated in Table 3, no moderating effects were found, indicating that the effect of religious context on inmate misconduct does not appear to vary between religious and nonreligious inmates.²

Table 3.

Multilevel Logit Models Predicting Rule Violations.

	Overall				Assault				Drugs/alcohol				Other nonviolent
	Model 9		Model 10		Model 11		Model 12		Model 13		Model 14		Model 15		Model 16
	OR		OR		OR		OR		OR		OR		OR		OR
Inmate-level covariates (n = 15,831)
Age	0.957	***	0.957	***	0.950	***	0.950	***	0.964	***	0.964	***	0.955	***	0.955	***
Race^a
Black non-Hispanic	1.089		1.089		1.430	***	1.431	***	0.859		0.857		1.023		1.023
Hispanic	0.745	***	0.744	***	1.017		1.016		0.813		0.810		0.717	***	0.717	***
Other non-Hispanic	0.972		0.974		0.963		0.965		0.988		0.985		1.062		1.063
Sentence length^b
24-60 months	1.206	*	1.201	*	1.237		1.231	***	0.943		0.947		1.254	*	1.248	*
61-120 months	1.322	**	1.317	**	1.232		1.227	***	0.979		0.981		1.326	**	1.321	**
More than 120 months	1.468	***	1.457	***	1.209		1.202		1.113		1.117		1.520	***	1.510	***
Life or death sentence	2.075	***	2.059	***	1.303		1.294		1.743	*	1.748	*	1.979	***	1.964	***
Not sure	1.100		1.085		1.218		1.204		0.240		0.240		0.811		0.798
Log of time served	1.867	***	1.838	***	1.930	***	1.931	***	2.733	***	2.737	***	1.757	***	1.757	***
Victimization	3.800	***	3.800	***	4.944	***	4.942	***	1.628	***	1.625	***	2.394	***	2.396	***
Violent offender	0.934		0.935		1.089		1.087		0.915		0.909		0.877		0.879
Property offender	1.007		1.007		1.013		1.012		0.948		0.942		0.972		0.974
Drug offender	0.891		0.892		0.016		1.015		0.874		0.870		0.805	**	0.806	**
Received visit	0.895	*	0.893	*	0.887	*	0.885	*	1.048		1.049		0.928		0.926
Had telephone call	0.759	***	0.755	***	0.664	***	0.664	***	0.819		0.823		0.791	**	0.786	**
Job training participation	1.600	***	1.600	***	1.169	**	1.169	**	1.518	***	1.517	***	1.456	***	1.457	***
Educational program participation	1.360	***	1.360	***	1.136	*	1.136	*	1.066		1.068		1.315	***	1.314	***
Prison-level covariates (n = 326)
Federal facility	0.498	***	0.491	***	0.851		0.844		2.003	**	2.019	**	0.389	***	0.382	***
Female inmates	1.697	***	1.804	***	2.048	***	2.124	***	0.820		0.770		1.649	***	1.805	***
Mean age	1.021		1.014		0.987		0.984		0.986		0.987		1.017		1.010
Racial composition^a
Black non-Hispanic	0.998		0.997		1.000		0.999		1.005		1.006		1.001		1.000
Hispanic	0.990	**	0.990	***	0.993	*	0.993	*	0.990	*	0.990	*	0.991	**	0.991	**
Other non-Hispanic	0.997		0.995		1.007		1.006		1.022		1.022	*	0.993		0.991
Log of mean time served	0.757	*	0.777		1.052		1.063		0.784		0.768		0.835		0.859
Victimization	1.013	*	1.011	*	1.016	***	1.016	**	1.001		1.002		1.010		1.008
Violent offenders	2.436	**	2.515	**	2.790	**	2.829	**	3.807	***	3.761	**	1.808		1.891	*
Job training participation	1.001		1.002		0.999		0.999		1.006		1.006		0.998		0.999
Educational program participation	1.013	***	1.012	***	1.001		1.001		0.992		0.993		1.019	***	1.018	***
Key determinants
RP	1.064		0.525		1.051		0.447		0.927		0.193		0.985		1.230
RC	0.907	*	1.298		0.912	*	0.980		0.887	*	0.504	*	0.988		1.958	**
RP × RC	0.963		1.256		0.958		1.343		0.974		1.910		0.969		0.860
RC × RC			0.966				0.993				1.061				0.935	**
RC × RC × RP			0.977				0.969				0.934				1.014
Constant	0.468		0.247		0.075	***	0.069	***	0.017	***	0.062	**	0.155	**	0.039	***

Note. OR = odds ratio; RP = religious participation; RC = religious concentration.

as compared to White non-Hispanic.

as compared to 0 to 23 months.

p <.05. **p < .01. ***p < .001.

Discussion and Conclusion

The present study examined the role of religious context on inmate misconduct through the extension of a nascent line of research regarding the role of religious context on inmate mental health. It was hypothesized that increases in religious concentration would be inversely associated with inmate misconduct in mostly religious prisons. This hypothesis was supported by results indicating a consistent decreased probability of having rule violations relative to increases in religious concentration in mostly religious prisons for each of the outcomes. The finding is emblematic of religion’s role in social control processes. Indeed, religious context has been linked to social control of behaviors such as homicide (Lee & Bartkowski, 2004), gambling (Eitle, 2011), theft, and other minor forms of delinquency (Regnerus, 2003). These results indicate that religious context-based social control extends into carceral settings; that is, social control of several forms of misconduct appears to be present in prisons in which religion “is accepted by the majority as a valid basis of action” (Stark 1996, p. 164). Contrary to the moral communities hypothesis (Stark, 1996), the effect of religious context on misconduct was found to be consistent between religious and nonreligious inmates. The involuntary nature of social belonging in inmate societies has previously been noted (Peterson, 2010). In this way, the cultural contexts of prisons results in an ascribed social identity (Drakeford, 2019) whereby inmates are bound to norms regardless of their personal attachment to the social groups that propagate them.

In addition to social control, the religious environment of prisons offers a potentially supportive environment that can promote alternative forms of coping, a premise steeped in the GST framework (Agnew, 1992) that sees inmate misconduct as a deviant form of coping with the stress of imprisonment (Blevins et al., 2010). Social integration, support, and relationships associated with religious practice (Bradley, 1995; Ellison & George, 1994) are integral to coping with chronic stress (Kawachi & Berkman, 2001; Thoits, 1995), such as incarceration. The tendency of social relationships to cross-religious boundaries (Olson & Perl, 2011; Vargas & Loveland, 2011), in concert with the capacity of social support to transcend religious boundaries (Schafer, 2015), indicates that elevated levels of social support expected among religious inmates can result in higher social support for nonreligious inmates as well. The ability to discern the extent to which this result is a product of social control or support is inhibited by the design of the study. Specifically, SISFCF respondents were not asked questions pertaining to their social networks or perceived social support. Future research focusing on how religious contexts influence inmate social networks can help disentangle the social support and social control aspects of this relationship.

It was further hypothesized that the relationship between religious concentration and misconduct would be less consistent in mostly secular prisons, which was supported by the findings. With respect to assault and drug/violations, there was an inverse relationship with religious concentration in mostly secular prisons. The consistent decrease in the predicted probability of having assault or drug/alcohol violations coinciding with increases in religious concentration indicate that secularly based forms of social belonging in prisons are tolerant, or even promotive, of assault as well as drug and alcohol use. This is not an altogether unexpected finding, given the established link between involvement in secularly based prison groups such as gangs and violence (DeLisi et al., 2004; Gaes et al., 2002; Griffin & Hepburn, 2006). Furthermore, prison gangs operate in large part to facilitate the distribution of drugs through black markets (Skarbek, 2014).

This finding is contrasted by the finding that other nonviolent forms of misconduct are positively related to religious concentration in mostly secular prisons. More specifically stated, inmates in prisons in which less than half of the inmates participate in religious activity have lower odds of having other nonviolent rule violations at lower levels of religious concentration. This points to a social control function of secularly based prison social groups. That is, while secular-based prison groups appear to promote the incidence of assaults and drug/alcohol use, they do provide social control against other forms of misconduct. Prison gangs present a strong form of social belonging, and this result is reflective of those gangs’ incentive to ensure an orderly environment to help facilitate black markets (Skarbek, 2014). However, whether this is what is occurring cannot be definitively ascertained as SISFCF does not contain the data necessary to do so. Future researchers should work to identify and understand secularly based forms of social belonging among inmates, as well as the interplay between these forms of belonging and those that are religiously based.

This study is not without limitations. Specifically, the cross-sectional nature of the data inhibit the ability to ascertain causality. It could be that the cultural environment of prisons influences the enforcement of prison rules. For example, an alternative explanation is that highly religious prisons may not be so vigorous in seeking out rule breakers. Collection of longitudinal data would go a long way toward addressing this limitation; unfortunately there is a dearth of longitudinal data of inmate populations. Second, SISFCF measurement of religious practice is limited, a particular concern given that the presented findings depend upon the existence of a social element to religious practice among inmates. Religion is a complex, multidimensional construct that encompasses a wide variety of public and private behaviors, beliefs, and identities. Future efforts at collecting data concerning religious practice among inmates should aim to incorporate a more fully developed conceptualization of religious practice.

In spite of these limitations, the present study contributes findings on the importance of cultural contexts on inmate behavior. The religious context of prison is salient for several different forms of misconduct. Presented results highlight the need for prison administrators to consider the cultural environment of their institutions when implementing strategies aimed at reducing inmate misconduct. In addition, social scientists assessing the impact of religious contexts should consider nonlinear conceptualizations of religious contextual variables.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Leah Drakeford is a PhD candidate at Louisiana State University. Her research focuses on well-being in correctional settings. Some of her recent work has appeared in outlets such as Society and Mental Health and Journal of Correctional Health Care.

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