The author(s) shown below used Federal funds provided by the U.S. 

Department of Justice and prepared the following final report: 

Document Title: Reconsidering Domestic Violence Recidivism: 

Individual and Contextual Effects of Court 

Dispositions and Stake in Conformity

Author(s): John Wooldredge ; Amy Thistlethwaite 

Document No.: 188509 

Date Received: 06/28/2001 

Award Number: 95-IJ-CX-0102 

This report has not been published by the U.S. Department of Justice. 

To provide better customer service, NCJRS has made this Federallyfunded grant final report available electronically in addition to

traditional paper copies.

 

Opinions or points of view expressed are those 

of the author(s) and do not necessarily reflect 

the official position or policies of the U.S. 

Department of Justice.
RECONSIDERING DOMESTIC VIOLENCE RECIDIVISM: INDIVIDUAL AND 

CONTEXTUAL EFFECTS OF COURT DISPOSITIONS AND STAKE IN 

CONFORMITY 

a 

Project Report Submitted to the National Institute of Justice 

PROPERTY OF 

National Criminal Justice Reference Service (NCJRS) 

Box 6000 /../ - 

S~kvilk, MD 20849-5000 

John Wooldredge 

Amy Thistlethwaite 

Division of Criminal Justice 

University of Cincinnati 

Cincinnati, OH 4522 1-0389 

PO Box 210389 

October 3, 1999 

NIJ Grant # 95-IJ-CX-0102 

This document is a research report submitted to the U.S. Department of Justice. This report has

not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
Acknowledgments 

The authors acknowledge with gratitude the guidance provided 

by our project monitor at NIJ, Angela Moore Parmley, throughout 

the course of this project. 

We would like to thank the following people for their 

contributions to data collection for this project: 

-- David Gibbs, Department of Pretrial Services, Hamilton 

County Justice Center, Cincinnati, Ohio 

-- Mark Carozza, Institute for Policy Research, University of 

Cincinnati 

0 -- Melissa Winston, Division of Criminal Justice, University of 

Cincinnati 

-- Mark Schweikert, Court Administrator, Court of Common Pleas, 

Municipal Court, Cincinnati, Ohio 

We would also like to thank the Center for Criminal Justice 

Research at the University of Cincinnati for housing the project. 

Supported under award No. 95-IJ-CX-0102 from the National 

Institute of Justice, Office of Justice Programs, U.S. Department 

of Justice. Points of view in this report are those of the 

authors and do not necessarily represent the official position of 0 

This document is a research report submitted to the U.S. Department of Justice. This report has

not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
the U.S. Department of Justice. 0 - 

Executive Summary 

Mandatory arrest policies in cases of misdemeanor domestic 

violence were implemented in many jurisdictions across the U.S. 

during the 1980s. Chapter 3 under title IV of the Crime Bill 

clearly states that mandatory arrest policies should be 

encouraged for domestic violence offenders (1 994). However, the 

"true" success of arrest alone in preventing or delaying 

recidivism remains unknown (Sherman 1992, 1993a). Furthermore, 

any effectiveness of arrest may actually hinge on whether 

arrestees are actually prosecuted and convicted, and whether they 

e are placed on probation and/or serve a jail sentence. 

Unfortunately, nothing is known about the effectiveness of 

particular court dispositions for preventing further domestic 

violence. This knowledge is important for more effective policies 

related to the control of domestic violence. 

This report presents the results from a study of the 

effectiveness of various court dispositions for preventing, 

reducing, and delaying domestic violence across a sample of 3,662 

suspects arrested for misdemeanor domestic violence in Hamilton 

County (Cincinnati), Ohio between August 1993 and May 1996. The 

main effects of court dispositions as well as how these effects 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
may be conditioned by informal social controls (stake in 

conformity) is examined. Like many other urban jurisdictions in 

the U.S., Hamilton County has a mandatory arrest policy for 

misdemeanor domestic violence suspects when police officers 

determine that there is an immediate threat to the victim. 

Although there is always some discretion involved in the arrest 

decision, this policy results in an 80 percent arrest rate for 

all such calls to the police. 

All arrestees in the sample were tracked until June 1998. 

Both varied (up to 57 months) and fixed (2 year) follow-up 

periods are examined. Information collected from non-anonymous 

arrest reports, intake interview forms, court records, and the 

U.S. decennial census led to the construction of a rich data 

set including numerous statistical control variables as well as 

measures stemming from individual- and aggregate-level theories 

of stake in conformity @e., informal social control). The 

unique micro- and macro-level data permitted the following: 

1. Empirical tests of the effectiveness of court dispositions 

for reducing the prevalence and incidence of recidivism as 

well as for delaying recidivism among misdemeanor arrestees, 

controlling for legal and social dimensions of risk. 

2. An examination of relationships between recidivism and 

0 individual- and aggregate-level measures of stake in 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
conformity. 

3. Analyses of the conditioned effects of court dispositions by 

stake in conformity on recidivism. 

4. The construction of maps depicting the geographic distribution 

of domestic violence across Cincinnati. 

5. A descriptive analysis of the time until recidivism (in 

months) for suspects falling into each disposition group. 

Of the suspects who remained in the jurisdiction for the 

duration of the study, 17 percent were re-arrested for domestic 

violence during the study period. As much as one-fourth of the 

suspects were not even living with their partner at the time of 

arrest, 40 percent were unemployed, and 18 percent were receiving 

no financial support (including welfare). Two-thirds of the 

sample had not held a job for at least a year, 96 percent did not 

have college degrees, and nearly two-thirds had previous 

convictions (3 5 percent having been incarcerated previously for 

an offense other than domestic violence). Roughly 16 percent were 

female, over one-third were 35 years or older (versus only 23 

percent under age 25), and 60% are African-American. 

Regarding the analyses of court dispositions, support is 

found for the importance of filing charges against suspects in 

order to delay re-arrest, even if the cases ultimately result in 

subsequently dropped charges or trial acquittals. Also, offender 

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programs appear to have a suppression effect on the prevalence 0 

and incidence of re-arrest across the sample. Finally, persons 

arrested while old charges are still pending are significantly 

more likely to recidivate, suggesting that a failure to process 

cases with greater speed may also increase recidivism. 

Regarding the individual- and aggregate-level measures of 

stake in conformity, only neighborhood-level stake maintains its 

statistical significance in the multivariate analysis. When 

including both levels of stake in conformity in the same model, 

the aggregate-level measure not only prevails in strength but 

also renders a non-significant relationship involving offender 

stake. 

The significance of aggregate-level stake in conformity is 

consistent with studies of other types of predatory crimes 

revealing higher violent crime rates as well as higher 

individual-level likelihoods of violent crime in more socially 

"disorganized" neighborhoods. Further inspection of geographic 

maps of the distribution of residences for domestic violence 

arrestees reveals a preponderance of arrests closer to the 

central business district of Cincinnati. The heavy concentration 

in census tracts closest to the city' major thoroughfares (1-7 1 , 

1-74 and 1-75) is also consistent with the idea that Doorer 

neighborhoods with more transient populations often correspond 

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with higher rates of predatory crime. 

The analysis of the conditioned effects of court 

0 

dispositions by offender and neighborhood stake in conformity 

yields support for the idea that some dispositions are more 

effective for persons with higher levels of individual stake, but 

the same is not true for persons residing in neighborhoods with 

larger proportions of higher stake residents. Specific findings 

for conditioned effects include the following: 

1. Offender programs are more effective for reducing the 

prevalence and incidence of re-arrest among offenders with 

higher individual-levels of stake in conformity. 

2. Split sentences (probation and jail) are more effective for 

reducing the prevalence and incidence of re-arrest as well as 

for delaying re-arrest among offenders with higher individuallevel stake. 

3. Prosecuting offenders from lower-stake neighborhoods 

significantly reduces the incidence of re-arrest for domestic 

violence. 

4. The prevalence and incidence of re-arrest are lower for 

offenders from lower-stake neighborhoods serving split 

sentences. 

5. The prevalence and incidence of re-arrest are actually higher .- 

a for offenders from higher-stake neighborhoods serving jail or 

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split sentences, perhaps a consequence of a greater 

willingness of residents in these neighborhoods to call the 

police regarding domestic disputes, or possible labeling 

effects occurring among offenders less familiar with legal 

intervention. 

The analysis of time to re-arrest reveals that, regardless 

of the follow-up period examined, sentences of jail alone have a 

greater incapacitative effect compared to probation alone and 

probation combined with jail. In other words, even though 

suspects who go to jail are typically worse risks compared to 

those receiving probation, sentences of jail serve to at least 

0 delay recidivism longer through its incapacitative effect. 

Although suspects whose cases are ignored end up with some 

of the highest recidivism likelihoods in the sample overall, 

recidivists falling in this group actually have longer delays to 

re-arrest compared to other disposition groups (offender programs, 

probation, jail, and probation combined with jail). While it is 

possible that the factors leading to inaction on the part of 

prosecutors are related to this delay, the fact remains that a 

very high proportion of the group ultimately recidivate for 

domestic violence. 

Similar patterns of delay can be observed for suspects whose 

@ cases are dismissed, acquitted, and who undergo the offender 

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program. However, unlike those whose cases are ignored, these 

suspects have among the lowest recidivism likelihoods in the 

sample. In short, the risk evaluations of these individuals made 

by prosecutors and judges seem to be fairly well-informed. 

The results provide several consistencies with previous 

research on the effectiveness of arrest for reducing domestic 

violence recidivism. First, just as the main effects of arrest 

were not statistically significant in most of the arrest studies, 

most of the main effects of court dispositions are not 

significant as well. Second, the significant individual-level 

interactions tapping offender program and split sentences support 

previous findings related to formal and informal social controls. 

Finally, the findings for the conditioned effects of court 

0 

dispositions suggest that formal and informal controls should be 

considered together when understanding domestic violence 

recidivism. 

The contradictions with the arrest literature include higher 

likelihoods and rates of re-arrest for offenders in particular 

disposition groups with higher (aggregate-level) stake in 

conformity, the lack of an escalation effect for lower-stake 

offenders serving the same sentences that appear to "work" for 

higher-stake offenders at the individual-level, and a suppression __ 

effect for offenders from lower-stake neighborhoods who are 

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subjected to particular dispositions . 0 Table of Contents 

Introduction .................................................. 1 

Background .................................................... 4 

Informal Social Controls and Stake in Conformity ............. 10 

Considering Contextual Effects of Stake in Conformity ........ 15 

Methods ...................................................... 19 

Sample and Data ........................................ 19 

Variables .............................................. 22 

Analytical Procedures for Examining the 

Prevalence and Incidence of Re-arrest .................. 33 

Analytical Procedures for Examining 

a Time to Re-arrest ...................................... 35 

Results and Discussion ....................................... 36 

Zero-Order Relationships ............................... 41 

Life Tables of Time to Re-arrest for 

Domestic Violence ...................................... 44 

Multivariate Findings for Unconditional Effects ........ 51 

Individual-level Conditional Effects ................... 61 

Aggregate-level Conditional Effects .................... 63 

Collective Efficacy and Calls to the Police ............ 65 

Labeling Effects in Higher Stake Communities ........... 66 

Implications for Current Knowledge 

and Future Research .................................... 67 

Conclusions and Policy Implications .......................... 71 

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author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
References ................................................... 75 m 

List of Tables and Figures 

Figure 1 : Distribution of Domestic Violence Arrestees in 

Cincinnati, OH .................................... 3 8 

Figure 2: Distribution of Domestic Violence Arrestees in 

Hamilton County, OH ............................... 39 

Table I 

Summary of Findings of the Minneapolis Domestic 

Violence Experiment and Subsequent Replications: Same 

Victim ............................................... 5 

Table 2 

Variables and Frequencies (Means and Standard Deviations 

Reported for IntervalRatio Scales) .................. 24 

0 Table 3 

Zero-order Main Effects of Predictors on the Incidence 

of and Time to Re-arrest for Domestic Violence 

(Unstandardized Fixed Effects Reported) ............. 42 

Table 4a 

Survival Analysis of Time to Re-arrest After Sentence 

Completion: Cumulative Proportions Not Recidivating 

(Cumulative Survival Rates; Pooled N = 3,662) ....... 45 

Table 4b 

Survival Analysis of Time to Re-arrest After Sentence 

Completion: Standard Errors of Cumulative Survival 

Rates ............................................... 46 

Table 5a 

Survival Analysis of Time to Re-arrest After Sentence 

Completion: Proportions Exposed to Risk Recidivating a During Specific Month (Hazard Rates; 

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Pooled N = 3,662) ................................... 47 

Table 5b 

Survival Analysis of Time to Re-arrest After Sentence 

Completion: Standard Errors of Hazard Rates ......... 48 

Table 6 

Hierarchical Bernoulli Models Predicting Prevalence of 

Re-arrest for Domestic Violence (Fixed Effects 

Reported) ........................................... 53 

Table 7 

Hierarchical Poisson Models Predicting Incidence of 

Re-arrest for Domestic Violence (Fixed Effects 

Reported) ........................................... 54 

Table 8 

Pooled Survival Models Predicting Months to Re-arrest for 

Domestic Violence (Cox Regression Coefficients a Reported) ............................................ 5 5 

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Introduction 

Domestic assault is the most common form of violence 

encountered by the police (Sherman 1992). More women are injured 

by the hands of a spouse or partner than any other source (Zorza 

1992). Despite such trends, domestic assault historically has 

become an issue of public concern during periods when crime has 

been rampant. Efforts to criminalize domestic violence and 

subsequent efforts to enforce these laws thus have tended to 

occur in times of social turmoil when criminal behavior has been 

attributed to a breakdown in law and order (Pleck 1989). 

Police response to the problem of domestic violence has 

a traditionally been one of non-intervention. Help for domestic 

violence victims was left up to private organizations and 

charities. Beginning in 1970 and lasting until 1983, however, 

several jurisdictions established legislation intended to modify 

official and societal responses to the problem of domestic 

violence (Buzawa and Buzawa 1985). Despite such legislative 

trends, evidence suggests that during this time the police 

remained largely apathetic toward domestic violence (Zorza 1992). 

In the early 1980s a research study was undertaken that 

would help to dramatically alter traditional police responses to 

domestic violence. In 1983 Lawrence Sherman and Richard Berk 

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(1 984) conducted a controlled experiment in Minneapolis to 

determine which police response--arrest, separation, or 

mediation--would yield the largest decrease in domestic violence 

recidivism. The experiment found that arrested offenders were 

less likely to recidivate compared to offenders who were either 

separated or counseled by the police (Sherman and Berk 1984). 

Drawing from the experiment's conclusion, mandatory arrest 

policies were widely adopted across jurisdictions in the United 

States, and arrest became the predominant response to domestic 

violence (Sherman et al. 1992). 

It was not until the original experiment was replicated five 

years later that questions were raised as to the effectiveness of 

mandatory arrest policies. Experiments conducted in Milwaukee, 

Omaha, and Charlotte revealed that arrest actually escalated the 

occurrence of domestic violence (Dunford, Huizinga, and Elliott 

1990; Hirschel et al. 1990; Sherman et al. 1992). Researchers 

began examining differences across jurisdictions and found that 

extra-legal factors such as employment and marital status either 

increased or decreased the likelihood that arrest would deter 

this behavior. In addition, the social structure of a community 

(proportion of unemployed males or unmarried couples) was 

hypothesized to determine how residents react to arrest (Sherman 

et al. 1992). 

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Whether arrest deters domestic violence offenders remains 

unknown (Sherman et al. 1992). Further, the effectiveness of 

arrest could depend upon what happens to the offender after 

arrest. Offenders who are arrested are not necessarily 

prosecuted, convicted, and sentenced. While the relationships 

between time spent in custody upon arrest, prosecution, and 

domestic violence recidivism have been examined (Berk et al. 

1992; Hirschel et al. 1992; Sherman et al. 1992), no studies 

exist that explore the possible relationship between other (and 

more punitive) sanctions and recidivism. 

This study is not a replication of prior studies examining 

the effectiveness of arrest on domestic violence recidivism. 

Rather, the study is intended to expand upon prior research 

examining the separate and combined effects of formal (legal) and 

informal social control on domestic violence recidivism. The 

study examines both the main effects of different types of formal 

social control (prosecution, conviction, and various sentences) 

and informal social control processes on domestic violence 

recidivism. In addition, interaction effects between formal and 

informal social control processes are examined as well. 

Interactions have been examined in previous research (e.g., Berk 

et al. 1992; Dunford, Huizinga, and Elliott 1990; Sherman et al. 

0 1992), but have been limited to analyzing the interactions 

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between arrest and employment or martial status. This study 

builds upon the existing body of research by exploring additional 

informal social control variables such as education, length of 

employment, and family ties. The present study hrther builds 

upon past research by examining the effects of these social 

control processes at the individual level as well as the 

contextual effects of community characteristics on individual 

behavior. Aggregate-level measures (proportion of college 

educated individuals, proportion of employed individuals, 

proportion of financially independent residents, etc.) are 

included to determine the extent that these neighborhood 

characteristics influence an individual's propensity to 

recidivate and/or increase or decrease the effectiveness of 

formal social control. 

Background 

Results from the Minneapolis Domestic Violence Experiment 

revealed that arrest, compared to separation and counseling, 

produced a significantly lower likelihood of recidivism for 

domestic violence (Sherman and Berk 1984). The experiment was 

replicated six times in five different cities: Omaha, Charlotte, 

Milwaukee, Colorado Springs, and Miami-Dade County. Yet only the 

study of Dade County yielded statistically significant main 

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effects of arrest on re-arrest for domestic vioIence involving e 

the same victim (Garner, Fagan, and Maxwell 1995). Findings from 

the arrest studies are summarized in table 1. 

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Table 1. Summary of Findings of the Minneapolis Domestic 

Violence Experiment and Subsequent Replications: Same 

Victim 

Findings City 

Colorado MiamiMinneapolis Omaha Charlotte Milwaukee Springs Dade 

6 month 

deterrent 

effect 

official 

measures yes 

6 month 

deterrent 

effect 

victim 

interviews yes 

@ 6-12 month 

escalation 

effect 

official 

measures no 

6- 12 month 

escalation 

effect 

victim 

interviews * 

escalation 

effect for 

unemployed * 

deterrent 

effect for 

employed * 

no 

slight 

Yes 

no 

Yes 

Yes 

no 

no 

Yes 

no 

* 

* 

1 of2 

Yes 

no 

no 

* 

* 

a * no relationship reported 

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These studies provide mixed support for the applicability of 

the additive deterrence hypothesis (Grasmick and McLaughlin 1978) 

to an understanding of specific deterrence among domestic 

violence offenders (Sherman and Smith 1992). Although the 

additive hypothesis was originally derived under general 

deterrence theory, Sherman and Smith (1 992) recognized that legal 

sanctions could maintain significant effects on recidivism 

independent of informal social controls. 

The conflicting results across studies prompted speculation 

as to why arrest appears to "work" for some offenders and in some 

cities. One possible explanation is that the mixed findings could 

be a result of variation in formal social controls implemented by 

these courts after arrest (Garner, Fagan, and Maxwell 1995; 

Sherman and Smith 1992; Zorza 1992). Formal social control here 

refers to actions taken by the criminal justice system in 

response to crime (e.g., arrest, prosecution, and sentencing). 

More punitive dispositions may result in lower recidivism if 

individuals are deterred by such punishments. Any type of 

disposition that forces a person to do something that (s)he 

otherwise would not do can be considered a punishment (Packer 

1968), but different dispositions provide varying degrees of 

inconvenience (Black 1976). For arrestees, not filing charges - - 

would be the mildest inconvenience to a suspect, followed by 

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subsequently dropped charges (due to some time spent in custody), 

prosecution and conviction (due to time spent in court), and 

sentencing (due to time spent serving a sentence). Therefore, the 

studies of jurisdictions providing less punitive legal sanctions 

after arrest may have produced no significant findings for arrest 

effects. This suggests that analyses of court dispositions may 

yield support for the additive (specific) deterrence hypothesis. 

Considering court sanctions is particularly important now that 

many jurisdictions (including Cincinnati) operate under a 

mandatory arrest policy for cases of domestic violence, so 

variation in the decision to arrest has been reduced considerably 

0 in some jurisdictions. 

Another possible explanation for the inconsistent findings 

of the arrest studies involves the variation in how offenders 

were assigned to the treatment groups across those studies 

(Sherman, Schmidt, and Rogan 1992; Sherman and Smith 1992). 

Questions concerning whether arrest decisions had been 

consistently randomized within the experiments prompted concerns 

that the results could have varied if arrested suspects differed 

significantly in their levels of risk across studies. This led to 

interest in sub-group analyses of data from some of the studies 

in order to examine whether arrest was more effective for groups 

varying in social dimensions of risk (e.g., employed versus 

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unemployed persons). An analysis of whether the effects of legal 

sanctions are conditioned by levels of informal social controls 

constitutes a test of two other deterrence hypotheses applied to 

an understanding of specific deterrence: conditional deterrence, 

predicting that legal sanctions are only effective for deterring 

individuals who are committed to more conventional societal 

values (Sherman and Smith 1992; Tittle and Logan 1973; Williams 

and Hawkins 1986), and replacement deterrence, predicting that 

legal controls only deter persons who lack effective informal 

social controls (Grasmick and McLaughlin 1978; Sherman and Smith 

1992; Silberman 1976). 

Underlying the sub-group analyses of the data from 

Minneapolis, Omaha, Milwaukee, and Colorado Springs was the idea 

that individuals with a higher "stake in conformity'' may be more 

likely deterred from committing additional acts of domestic 

violence subsequent to an arrest for domestic violence. This 

framework was borrowed from Jackson Toby (1 957). According to 

Toby, persons differ with respect to their stake in conformity in 

that some have little to lose by committing crimes. As such, 

persons with lower stake in conformity are less likely to be 

deterred by formal sanctions. Applying Toby's idea to arrest 

effects on recidivism, investigators speculated that employed and 

a 

0 married individuals each have a higher stake in conformity 

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(compared to unemployed and unmarried persons) and, as a 

consequence, have more to lose through formal criminal justice 

sanctions. Therefore, these individuals may be more likely 

deterred through arrest. 

Findings emerged from the sub-group analyses that support 

the stake in conformity hypothesis. Using the data from Colorado 

Springs, Omaha, and Milwaukee, Berk and his colleagues (1 992) 

created risk categories for offenders who were either employed or 

serving in the military. As hypothesized, the authors found a 

conditional deterrent effect of arrest for "good risk" 

individuals; that is, for individuals who were either employed or 

serving in the military at the time of arrest (Berk et al. 1992). 

Interestingly, an escalation effect was found for unemployed 

offenders, introducing the possibility of a labeling effect of 

legal sanctions for this sub-group (Berk et al. 1992). A reanalysis of the Milwaukee data also revealed that employed, 

married, and high school graduate offenders were all less likely 

to be involved in repeat offenses as measured by "hotline" 

reports made by the police to a battered women's shelter (Sherman 

and Smith 1992). It should be noted, however, that the findings 

for marital status in other re-analyses were not always in the 

hypothesized direction (Garner, Fagan, and Maxwell 1995). 

Nonetheless, these observations suggest that the stake in 

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conformity hypothesis offers a plausible explanation for the a 

inconsistent results stemming from the arrest studies. They also 

support the conditional hypothesis of specific deterrence while 

refuting the replacement hypothesis (Sherman and Smith 1992). 

Differences in the findings for the main effects of arrest 

on recidivism could have also resulted fiom variation in social 

structure across the cities examined. Similar to the argument 

involving individual-level stake in conformity, the social 

structure of a community could influence the deterrent effects of 

criminal justice sanctions for particular offenders (Sherman, 

Schmidt, and Rogan 1992; Sherman 1993b). According to Sampson and 

Wilson (1 993, explanations of criminal behavior tend to rely 

solely on individual-level characteristics. However, several 

structural characteristics of neighborhoods (e.g., economic 

levels, family disorganization) have been found to correlate 

significantly with individual-level delinquency or adult 

criminality (Elliott et al. 1996; Gottfiedson, McNeil, and 

Gottfiedson 1991; Gunn et al. 1993; Sampson and Wilson 1995; 

Simcha-Fagan and Schwartz 1986). Therefore, not only may 

individuals with a higher stake in conformity feel that they have 

more to lose by committing crime, but also individuals residing 

in communities comprised of a significant proportion of higherstake individuals may be deterred because of the possible stigma 

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faced if caught committing a crime. This would be consistent with 

the conditional hypothesis of specific deterrence. 

From a theoretical perspective, the application of Toby’s 

thesis and the speculation regarding contextual effects of 

community-level stake in conformity represent interesting 

extensions of sociological theory to an important criminal 

justice issue. This perspective provided the framework for the 

current investigation. 

Informal Social Controls and Stake in Conformity 

Since Durkheim (1933) first mentioned the concept of 

informal social control in his theory of anomie, the multiple 

dimensions of informal control have been called different terms 

by different criminologists. However, these terms all have in 

common an emphasis on two elements: direct and indirect social 

control (Kornhauser 1978). A breakdown in either may be 

sufficient to explain deviant behavior. Direct control involves 

attempts made by such social institutions as the family, school, 

and work to restrain an individual’s behavior through 

supervision, whether through restrictions placed directly on an 

individual’s behavior by others, or involvement in conventional 

activities and pastimes that limit opportunities to engage in 

a deviance (Hagan, Simpson, and Giilis 1987; Hirschi 1969; Nye 

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1958; Reckless 1961 ; Reiss 195 1 ; Wells and Rankin 1988). In 

contrast, indirect social control involves an internalization of 

conventional values emerging out of socialization and 

interdependence (Durkheim 1933; Hirschi 1969; Reckless 1958; 

Reiss 195 1 ; Shaw and McKay 1 942). The strength of social 

relationships may provide an explanation of criminal behavior. 

Further, persons who are strongly connected to informal social 

control institutions (i.e., the family, work, or school) are 

provided with the necessary social and psychological resources, 

such as conformist self-identity and emotional support, which to 

draw upon when faced with the temptation of criminal behavior 

(Hagan, Simpson, and Gillis 1987; Braithwaite 1989; Sampson and 

Laub 1993). 

Control theory per se, grew out of the work of Shaw and 

McKay from the Chicago school. According to their theory of 

social disorganization, one of the consequences of 

disorganization is a breakdown in informal social control (Shaw 

and McKay 1942). Institutions such as the family, school, and 

church lose their ability to exercise informal social control 

over the individual members of a neighborhood (Shaw and McKay 

1942). Parents are unable to meet their children's needs and lose 

their ability to effectively socialize their children. 

The first sociologist after Shaw and McKay to pick up on the 

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idea of crime resulting from a breakdown in control was Albert 

Reiss in 195 1. Reiss (1 95 1) argued there are two types of 

control: personal and social. Personal or internal control refers 

to the ability of an individual to resist meeting his or her 

needs in ways that conflict with society's norms and values. 

Social or external control refers to the ability of social 

institutions and groups to make rules and norms binding on the 

individual members. Delinquency is a product of a breakdown in 

either type of control or both (Reiss 195 1). 

Jackson Toby (1 957) introduced his stake in conformity 

hypothesis relatively soon after Reiss' control theory was 

proposed. Toby's hypothesis was in response to claims made by 

social disorganization theorists that neighborhood factors such 

as high rates of poverty, transience, and heterogeneity explain 

rates of delinquency (Shaw and McKay 1942). According to Toby, 

such factors are insufficient in their explanation because they 

fail to explain why some individuals residing in socially 

disorganized areas do not become delinquent. Persons differ with 

respect to their stake in conformity. Some have more to lose by 

committing crimes. As such, individuals with a higher stake in 

conformity are more likely to be deterred by formal sanctions. 

Other control theories were introduced after Toby proposed 

his stake in conformity thesis. Hirschi's (1 969) social bond 

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theory is perhaps one of the most well known, and a particular 

aspect of this theory maintains a close conceptual linkage to 

Toby's thesis. Hirschi's social bond theory asserts that 

conformity can be explained by an individual's bond to society. 

This bond is comprised of four related elements, but if an 

individual is weak on one element the bond will be weak overall 

(Hirschi 1969). Moreover, a weakness of any one element is 

sufficient to explain delinquency. 

The elements of social bond include attachment, commitment, 

involvement and belief. Attachment refers to the extent an 

individual is emotionally invested in his or her family and 

friends. Commitment refers to the extent an individual is 

invested in conventional activities. Involvement refers to the 

amount of time and resources an individual invests in 

conventional activities and with his or her family and friends. 

Finally, belief is the element that refers to the extent to which 

an individual subscribes to and adopts the rules and norms of 

society (Hirschi 1969). 

0 

Hirschi's concept of commitment fits well with Toby's 

concept of stake in conformity. Hirschi's observation that a weak 

link in the bond contributes to a weak bond overall implies that 

individuals with lower stakes in conformity (lower commitment) 

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should maintain weak social bonds. Although Hirschi's social bond 

theory was originally offered as an explanation for delinquency, 

Sampson and Laub (1993) put forth a control theory to explain 

adult criminal behavior that integrates Hirschi's social bond 

theory with factors emergent over an individual's life course. 

Whereas the research on Hirschi's social bond theory as a whole 

tends to suggest only moderate to weak relationships between 

social control factors and delinquent involvement, Sampson and 

Laub (1 993) found that adult social bonds in the form of job 

stability and marital attachment are strong predictors of 

criminal behavior. This bond can even be significant enough to 

0 negate the effects of prior delinquent involvement. 

Aside from the specific parallel to Hirschi's concept of 

commitment, Toby's stake in conformity hypothesis emulates other 

control theories as well with the idea that persons with greater 

stake in conformity are insulated from the enticements of illegal 

behavior. Applying his thesis to delinquency, Toby noted that a 

juvenile's stake in conformity depends in part on a family's 

socio-economic status as well as the juvenile's success in 

education. This is why education (whether a high school graduate) 

and employment (whether or not) were previously examined by the 

arrest researchers in the context of Toby's hypothesis (Berk et 

al. 1992; Sherman and Smith 1992). However, additional dimensions 

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of these factors such as college degrees and type of employment 

(skilled versus unskilled) are also relevant to the broader 

concepts of educational achievement and socio-economic status 

applied to adults. Other individual-level attributes such as 

financial independence (not receiving public assistance) and 

residential stability are pertinent as well. Regarding the 

latter, Sampson (1 992) observed that stability in residence 

increases an individual's sense of attachment to their community 

which increases an individual's friendships. This may, in turn, 

contribute to an individual's investment in conformity. 

The dimensions of informal social control were originally 

applied to an understanding of primary delinquency and adult 

criminality. Yet Toby's stake in conformity thesis may also apply 

to an understanding of recidivism in the population of offenders 

[as discussed by Berk et al. (1992), Dunford, Huizinga, and 

Elliott (1 990), Pate, Hamilton, and Annan (1 991), and Sherman and 

Smith (1 992)]. In short, offenders with higher stakes in 

conformity may be less likely to recidivate. As noted by the 

arrest researchers, an offender's stake may also mediate the 

effects of legal sanctions on recidivism. Punishments may either 

decrease, increase, or have no effect on crime depending upon the 

context in which the sanction is administered. Toby (1 957) argues 

a 

0 that persons with a significant stake in conformity may feel that 

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they have more to lose through formal sanctioning and are thereby 

more likely to be deterred. Therefore, more punitive sanctions 

may be more effective for reducing recidivism among offenders 

with higher stakes. 

Considering Contextual Effects of Stake in Conformity 

Informal social controls may operate at both the individualand aggregate-level. The idea that informal social control 

processes operate on an aggregate level and that variations in 

these processes can explain rates of crime and delinquency forms 

the foundation of social disorganization theory (Shaw and McKay 

1942). Poverty and transience can result in a neighborhood 

becoming "disorganized" (Shaw and McKay 1942). A consequence of 

disorganization is that the neighborhood -- an institution of 

social control -- can no longer exercise control over residents 

either directly through supervision or indirectly by transmitting 

conventional values (Kornhauser 1978). Crime and delinquency may 

flourish in such a neighborhood. Shaw and McKay also noted that 

disorganized neighborhoods contain high rates of repeat 

offenders. 

@ 

Social disorganization theory as well as subsequent 

ecological theories of crime and delinquency were put forth as 

aggregate-level theories to explain rates of crime and 

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delinquency. Structural characteristics of a community such as 

family disruption, residential mobility, and neighborhood socioeconomic status were variables found to be related to the crime 

rate. However, it has been hypothesized that the structural 

characteristics of a neighborhood can produce contextual effects 

on the behavior of individuals (Sampson 1991; Sherman 1992). A 

recent body of research renders support for examining the 

structural characteristics of a neighborhood as influences on the 

behavior of individuals (Elliott et al. 1996; Gottfredson, 

McNeil, and Gottfredson 1991; Gum et al. 1993; Simcha-Fagan and 

Schwartz 1986). 

Sampson (1 991) examined the contextual effects of community 

characteristics on individual behavior. He found that an 

individual's length of residence as well as community residential 

stability increase an individual's attachment to a community by 

increasing an individual's friendships. This, in turn, is 

correlated with self-reported victimization. Pertinent to this 

study, Sampson (1 991) observed that neighborhood characteristics 

may shape how parents relate to their children (i.e., through 

"family management practices"). An extension of this idea is that 

these characteristics may also influence how parents/partners 

relate to each other. 

0 

Toby (1957) himself observed that stake in conformity may 

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also operate at the aggregate-level to influence crime. Despite 

his criticism of social disorganization theory, Toby argued that 

his thesis might also explain differences in crime rates between 

neighborhoods. Specifically, communities with larger proportions 

of "higher stake" residents should have lower crime rates, 

representing an effect beyond simply adding individual-levels of 

stake in conformity among residents of a community. Larger 

proportions of higher stake individuals may effectively insulate 

potential offenders from engaging in deviance because of the 

negative stigma associated with legal sanctions by larger numbers 

of residents. Although Toby's observation relates to aggregate 

crime rates, Sherman (1992) extended this idea to possible 

contextual effects on individual-level likelihoods of recidivism. 

Consistent with Toby's idea, neighborhood levels of stake in 

@ 

conformity should have a main (negative) effect on recidivism 

among offenders. Moreover, consistent with Sherman's (1 992) idea, 

the effects of legal sanctions should also be mediated by 

aggregate-level stake in conformity. Existing research on 

domestic violence recidivism lends some support for this idea. 

Colorado Springs had a higher employment rate compared to 

Milwaukee, and a greater deterrent effect of arrest was found in 

Colorado Springs (Sherman 1992). Upon reviewing the inconsistent 

findings from the replication studies on arrest, Sherman (1992) 

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hypothesized that informal social control processes, such as 

employment, could also operate at the aggregate-level. That is, 

the higher the proportion of employed individuals residing in a 

community, the greater an individual's stake in conformity might 

be. These individuals may be more likely deterred through formal 

sanctioning (Sherman 1992). Sherman's hypothesis ties into 

Sampson's finding regarding community attachment. The argument 

can be made that the structural characteristics of a neighborhood 

influence an individual's attachment to their community and that 

the stronger an individual's attachment is to their community, 

the greater will be that individual's stake in conformity. The 

present study tests the ecological hypothesis by examining 

variation in census tract characteristics that reflect variation 

0 

in aggregate-level dimensions of stake in conformity. 

Some of the dimensions of aggregate-level stake in 

conformity focused on here can also be tied to various aspects of 

more recently developed ecological theories of crime. For 

example, the structural characteristics of a community may also 

influence individual criminal behavior by having an impact on 

"social capital,'' or the capabilities and efforts of individuals, 

families, and groups to solve commonly experienced problems 

(Coleman 1990; Hagan 1994). Social inequality coupled with a 

declining economy generates a process of "capital inequality" 

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caused by residential segregation, race-linked inequality, and 

concentrations of poverty (Hagan 1994). These processes of 

capital disinvestment discourage the creation of social capital 

where persons are less likely to invest in other individuals and 

the larger community (Coleman 1990; Hagan 1994). 

The interactions of persons participating in social groups 

are a source of social capital. The consideration of residential 

stability in the present study fits well with this idea. Hagan, 

MacMillan, and Wheaton (1 996) argue that residential stability 

offers (indirectly) an indication of the amount of social capital 

within a community because people need time to establish personal 

and social networks. According to Coleman (1 990), social 

organization creates social capital and allows individuals to 

achieve goals that they would not be able to attain on their own. 

Applying this idea to the other dimensions of stake in conformity 

considered here, the facilitation of goal attainment may be 

further enhanced if social networks involve persons with higher 

socio-economic status. 

0 

The aggregate-level dimensions of stake in conformity 

related to employment and financial independence also overlap 

somewhat with Sampson, Raudenbush, and Earls' (1 997) concept of 

concentrated disadvantage and the hypothesized effects of poverty 

0 and unemployment (in addition to other aspects of disadvantage) 

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on collective efficacy, or the willingness of individuals to help 

other residents of their neighborhoods. Although not a study of 

crime per se, Sampson and his colleagues speculated that 

victimization likelihoods may be lower for persons in less 

disadvantaged neighborhoods because of greater collective 

efficacy. 

Methods 

Sample and Data 

The sample consists of all persons arrested for misdemeanor 

domestic violence in Hamilton County, Ohio during two time 

periods: August 1, 1993 to October 3 1, 1993 (the pilot study), 

and January 1, 1995 to December 3 1, 1996 (the grant study). Those 

arrested between November 1993 and December 1994 are not included 

a 

because the county adopted a data base system for record keeping 

beginning in 1995. The original paper documents (including the 

information necessary to construct all of the independent 

variables for the model) for persons arrested prior to 1995 were 

unavailable by the time the grant project began. However, this 

did not prevent following up all of the cases included in the 

pilot study for every month after their original arrest. This is 

because a different data base was used to access the re-arrest 

0 data. It is a limited data base that does not include information 

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aside from the types and dates of re-arrests. 

0 

Selecting all arrests for these periods provides 3,954 

unique individuals (due to repeat offending). For persons with 

multiple arrests, the earliest ones were coded as the initial 

arrests and all subsequent ones were coded as re-arrests for 

domestic violence. Available for analysis are 3,662 cases since 

292 persons left the jurisdiction and could not be tracked. 

The individual-level data were compiled from arrest reports, 

intake interview forms, and court records. The census tract data 

were obtained from the 1990 U.S. Census of Population and 

Housing. Intake interview data provided the demographic and legal 

characteristics of suspects and their initial arrests, court data 

provided the dispositions, and arrest data provided the dates and 

types of all re-arrests until May 3 1 , 1998. This permitted an 

analysis of a fixed two year follow-up period for 3,110 suspects 

in the sample. The data allow for a survival analysis of varied 

follow-up periods (from 17 to 58 months) for all 3,662 suspects. 

0 

Hamilton County has 2 17 census tracts, one of which is 

completely commercial @e., no personal residences exist within 

the tract). Census tracts are created by local committees 

appointed by the Census Bureau to approximate what local 

residents perceive their neighborhood to be (Gunn et al. 1993). 

0 They are also created to be as homogeneous as possible in terms 

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of socio-demographic characteristics. e 

Census tracts have been criticized as not being 

theoretically meaningful in terms of providing data on those 

factors believed to mediate the effects of community structural 

characteristics (Sampson and Groves 1989). However, they have 

been shown to provide valid measures of the types of community 

structural characteristics examined here (Gottfredson, McNeil, 

and Gottfredson 1991). Further, a study conducted by Simcha-Fagan 

and Schwartz (1 986), which compared measures derived from census 

data with data collected from interviews with community 

residents, revealed that the two measures were highly correlated 

in the expected direction. For these reasons, census tract data 

were used to construct the aggregate-level measure of stake in 

conformity. Also, aggregating across census tracts to create 

neighborhoods would result in a loss of internal homogeneity that 

is provided by the census tracts. 

0 

It must be noted that cases were not randomly assigned to 

various disposition groups, bringing the applicability of 

statistical inference into question (Berk and Freedman 1995). 

Short of obtaining a sample where every individual has the same 

selection probability, Berk and Freedman (1995) call for a more 

careful focus on the research questions that can be addressed by 

such data. For example, the results of the present study can be 

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used to forecast findings of future studies of the topic. If 

future studies yield consistency with some or all of the findings 

presented here, this would enhance the external validity of 

particular findings. This is usefbl for specifically defined 

populations from which the sample is drawn, as is the case for 

the present study (Le., the population of persons arrested for 

misdemeanor domestic violence in an urban jurisdiction). 

A form of cross-validation may also help to reduce concerns 

over these types of data (Berk and Freedman 1995). By dividing a 

large sample into two or more groups drawn randomly fiom the 

larger sample, findings can be compared across the sub-samples ' for consistency (although this does not provide a complete 

solution since the sub-samples still come fiom the same study). 

This was done for the present study by ordering the cases by 

social security numbers and systematically selecting every other 

case in order to create two sub-samples. The same analyses were 

conducted for each sub-sample, confidence intervals were 

constructed for parameter estimates, and an analysis was 

performed on whether the estimates from one sub-sample fell into 

the intervals fiom the other sub-sample. Only two percent of the 

estimates differed significantly between the two sub-samples, a 

figure that could have been expected by chance alone. This 

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supports the idea that, at a minimum, the findings presented here 

can be used to forecast those ffom future studies of the topic. 

Inferences to the population of interest may also be more 

sound with longitudinal data, statistical controls for risk, and 

predictors that do not create collinearity problems such as 

unstable parameter estimates and large standard errors. All of 

these elements characterize the study presented here. 

Variables 

The measures and univariate descriptives are presented in 

table 2. Three dependent variables were examined based 

on recommendations by Blumstein, Cohen, and Nagin (1 978) for 

a sentencing research: whether a person was re-arrested for any 

domestic violence during a fixed, two year follow-up period 

beginning after the sentence (if any) had been completed 

(N=3,1 lo), the number of re-arrests for domestic violence during 

the same follow-up, and the number of months between the end of 

an offender's sentence and re-arrest for domestic violence during 

a varied follow-up spanning 17 to 58 months (N=3,662). [These 

types of measures were examined in a re-analysis of the arrest 

data conducted by Garner, Fagan, and Maxwell (1 995)]. The 

incidence measure was collapsed into four categories due to the 

heavily skewed distribution of re-arrests. The descriptives in 

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table 2 reflect all 3,662 suspects. e 

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Table 2. Variables and Frequencies (Means and Standard Deviations e Reported for Interval/Ratio Scales) 

Variable 

Category 

Categories Frequencies 

Dependent 

Re-arrested for d.v. 

during the O=no 2,662 

2 year follow-up 1 =yes 448 

Number of re-arrests 0 = none 2,662 

2 year follow-up 2 = two 71 

24 

for d.v. during 1 =one 353 

3 = three or more 

Months to re-arrest 

for d.v. during 

varied follow-up* range: 1-35 n = 589 

M = 9.77 

s = 9.29 

Case Processing and Outcome 

Nocharges 0 = no 3,438 

filed 1 =yes 224 

Charges 

dropped 

Acquitted at 

trial 

Offender 

program 

0 = no 2,013 

1 =yes 1,649 

0 = no 3,427 

1 =yes 23 5 

O=no 3,416 

1 =yes 246 

Probation with or O=no 3,113 

without a fine 1 =yes 549 

Jail with or 0 = no 3,379 

without a fine 1 =yes 283 

Probation +jail with O=no 3,551 

or without a fine 1 =yes 111 

Charges pending 0 = no 3,179 

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at arrest 1 =yes 483 0 

continued.. . 

* Analysis of this dependent variable included all cases in the 

sample, but the univariate descriptives reflect recidivists 

only. 

Table 2. (Continued) 

Variable 

Category 

Categories Frequencies 

Stake in Conformity 

Individual-level 

s.i.c. (sum of 

z-scores for.. .) 

High school 

degree 

Bachelors 

degree 

Employed 

Employed in 

skilled job 

Not receiving 

public assistance 

Same residence at 

least five years 

Aggregate-level 

s.i.c. (sum of 

z-scores for ...) 

Proportion high 

school graduates 

M = 0.00 

s = 3.00 

range: -7.3-8.4 n = 3,662 

O=no 502 

1 =yes 3,160 

0 =no 3,512 

1 =yes 150 

O=no 1,574 

1 =yes 2,088 

O=no 3 ,OO 1 

1 =yes 66 1 

O=no 654 

1 =yes 3,008 

0 = no 3,104 

1 =yes 558 

M = 0.00 

s = 4.60 

range: -14.6-9.1 n = 216 

M = 0.72 

range: 0.22-0.97 s = 0.15 

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Proportion college M = 0.20 I) graduates range: 0.00-0.69 s = 0.16 

Proportion M = 0.93 

pop. employed range: 0.52-1.00 s = 0.07 

Proportion in M = 0.24 

skilled occupation range: 0.04-0.49 s = 0.10 

Proportion not 

receiving public M = 0.89 

assistance range: 0.35-1 .OO s = 0.13 

Proportion same residence past 5 years range: 0.13-0.75 s = 0.1 I 

M = 0.54 

continued.. . Table 2. (Continued) 

Category 

Variable Categories Frequencies 

a 

Control Variables 

Male 

Age 

(in years) 

# prior convictions 

for violent 

misdemeanors 

Ever incarcerated 

for other than d.v. 

Not living with 

spouse andor 

children at 

arrest 

O=no 585 

1 =yes 3,077 

M = 32.2 

range: 18-90 s= 9.5 

M = 0.56 

range: 0-13 s= 1.11 

0 = no 2,395 

1 =yes 1,267 

0 = no 2,692 

1 =yes 970 

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Some of the published reports on arrest focused only on 

recidivism involving the same victim @.e., Omaha, Charlotte, 

Minneapolis, and Miami), and many were limited to six month 

follow-up periods. Sherman and Smith (1 992) subsequently 

criticized the use of the same victim measure due to its exclusion of 

domestic violence against other persons, and Reiss (1 985) noted 

that focusing only on the same victim ignores possible 

displacement effects. This led to the construction of measures 

tapping re-arrest for domestic violence against any victim. 

There is a potential problem with using arrests as measures 

of offense behavior and recidivism in that arrest data do not tap 

undetected crime. Sherman, Schmidt, and Rogan (1992) observed 

that the use of official measures yielded the same conclusions as 

victim interviews in 5 of the 7 arrest studies (5 of the 6 study 

sites) regarding the deterrent effects of arrest. While this 

lends greater credence to the use of arrest data, the two arrest 

studies yielding conflicting results reflect some potential for 

bias. 

From a different perspective, using arrest data might be 

problematic if some of the arrests do not meet the standard of 

legal guilt. However, focusing only on convictions fails to 

account for an even greater number of cogent cases that are 

0 dismissed for any given reason (especially domestic violence 

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cases). Research comparing arrests and convictions suggests that 

arrests are a preferable measure of recidivism (Maltz 1984). 

There are seven dummy variables tapping the entire range of 

court dispositions for the sample (see table 2), and "no charges 

filed" is excluded from the complete model for purposes of 

estimation and interpretation. Results for the other six 

disposition measures should then be interpreted relative to not 

filing charges (or "doing nothing" with those arrested). 

Differences between the coefficients for the dispositions 

included in the model can also be examined to determine whether 

some dispositions are "better" or "worse1' than others. This can 

be done by constructing 95 percent confidence intervals around 

the coefficient estimates and seeing whether the estimates for 

other coefficients fall outside these intervals. Those falling 

outside can be treated as significantly different. 

A dichotomous measure of whether suspects had old charges 

pending at arrest is also included in the complete model as a 

somewhat crude measure of the speed of case processing although 

this variable is not a "disposition" per se. 

The variable labeled ''offender program'' refers to a two day 

counseling session related to domestic assault. Some of these 

offenders may have also served time on probation and/or in jail, 

but the number of those who did were too limited to permit a 

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separate (reliable) examination of such sentences. 

A measure of sentence length (in months) was originally 

examined but excluded from the model because of its high 

correlation with the dummy variable tapping split sentences and 

its highly skewed distribution. Moreover, results for such a 

measure do not offer much insight into effective policies 

designed to reduce domestic violence recidivism because of its 

limited distribution for convicted misdemeanants. For example, 

whether offenders should be sentenced to one versus two months 

seems trivial. 

The individual- and aggregate-level measures of stake in 

conformity each consist of the same six items measured at 

different levels. These items tap education, employment, public 

assistance, and residential stability (see table 2). This was 

done in order to determine the relative importance of the same 

dimensions measured at different levels. Measures tapping 

different dimensions at each level would lead to the question of 

whether the results for one level versus another were due to the 

greater importance of a particular level or simply the difference 

in measures between the two levels (Langbein and Lichtman 1978). 

The choice of items was determined in part by available census 

data (e.g., the U.S. Census Bureau defined residential stability 

0 in 1990 as residing at the same residence for 5 or more years), 

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and available individual-level data (e.g., public assistance * 

recipients were known although actual income was unavailable). 

Therefore, it should be noted that the data set includes a number 

of additional individual- and aggregate-level variables that can 

be manipulated to create other measures of informal social 

control not included in the complete model examined for the 

analysis. 

The redundancy in the education measures (Le., including 

two variables at each level tapping high school and college) is 

justified on three grounds: Sherman and his colleagues found that 

arrestees with high school degrees were less likely to be rearrested compared to those without high school degrees; a 

bachelors degree may provide an effect beyond having a high 

school degree; and only 4 percent of the sample had bachelors 

degrees (further justifying the inclusion of "high school degree"). 

@ 

A composite measure including all six items was constructed 

at each level. A measure including aspects of education, 

employment, public assistance, and residential stability seems to 

be a better reflection of the relatively broad concept of stake 

in conformity. Also, the addition of six measures to the complete 

individual-level model creates problems of multicollinearity 

within some of the census tracts, and the six aggregate-level 

measures are all strongly correlated (R > 0.60). These problems 

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are avoided with the composite measures. Finally, tests for 

interactions between court dispositions and the separate measures 

of stake in conformity would not be independent of each other due 

to the significant correlations between these items. 

Equal weight was given to each element of the composite 

measure by summing the standard scores for these items within 

each level. The zero-order correlation between the individual 

and aggregate factors is 0.52 (R), indicating that each factor 

accounts for 27 percent of the variation in the other. The items 

included in these factors are offered only as proxy measures for 

Toby's thesis since they are really structural antecedents to the 

concept of stake in conformity rather than stake in conformity 

processes in and of themselves. 

The individual-level measure of stake in conformity reflects 

offenders only whereas the aggregate-level measure reflects the 

population of offenders and non-offenders. This may explain the 

smaller range for the individual measure relative to the 

aggregate measure. Yet the significant variation that does exist 

in the individual measure (see table 2) suggests that the concept 

of stake in conformity may help to distinguish non-recidivists 

from recidivists. This is consistent with the applications of the 

thesis by Berk et al. (1992), Dunford, Huizinga, and Elliott 

(1990), Pate, Hamilton, and Annan (1991), and Sherman, Schmidt, 

. 

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and Rogan (1 992). 

Some of the stake in conformity measures examined, such as 

education and employment, can be considered structural 

antecedents of informal social control, not informal social 

control processes in and of themselves. These variables are only 

offered as proxy measures for Toby's stake in conformity thesis. 

Hindelang (1 973) noted that these types of variables do not tap 

elements of social control theory directly due to the social 

psychological nature of these elements. For example, to say that 

a person who has lived longer in one neighborhood is more 

"invested" in that neighborhood is not necessarily true, and the 

degree of this investment cannot be known without understanding 

the individual's perception of a neighborhood. Therefore, these 

measures are not perfect measures of the theoretical concepts. It 

is assumed, however, that there is overlap between the two (i.e., 

persons residing longer in particular neighborhoods are more 

likely to be invested in those communities). 

Two measures of criminal history are included in the models 

as controls for risk of re-offending. For length of prior record, 

seven ratio measures were examined in order to identi@ the one 

that absorbs the most variation in re-arrests. These measures 

included the number of prior convictions for all misdemeanors, 

e number of prior convictions for violent misdemeanors, number of 

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prior convictions for all felonies, number of prior convictions 

for violent felonies, number of prior convictions for all violent 

crimes, number of prior convictions for all felonies and 

misdemeanors, and number of prior convictions for all felonies, 

misdemeanors, and petty misdemeanors. Of these measures, prior 

convictions for violent misdemeanors is the best predictor. Prior 

incarceration for offenses other than domestic violence is also 

included because of the idea that the risk of domestic violence 

is somewhat higher for persons with histories of other criminal 

offending [see Bart01 (1 996) for a review of this literature]. 

Whether an individual was living with hisher spouse or 

0 

partner at the time of the arrest that brought himher into the 

sample is included as a control variable even though some might 

consider it to be an aspect of stake in conformity. This measure 

is used instead of marital status due to its stronger zero-order 

correlations with the outcome measures. While living arrangements 

may be an important aspect of stake in conformity in studies of 

other types of crime, in the context of understanding domestic 

violence it is treated here as a control variable because it taps 

variation in the opportunity to victimize one's partner. 

A suspect's race was considered as a control variable but 

was dropped because of its non-significant relationships with the 

outcome measures in the complete model (despite significant zeroThis document is a research report submitted to the U.S. Department of Justice. This report has

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order correlations at the 0.05 probability level). e 

Analytical Procedures for Examining the Prevalence and Incidence 

of Re-arrest 

Hierarchical non-linear modeling was used for the analysis 

of the prevalence and incidence of re-arrest during the two year 

fixed follow-up period. This procedure is appropriate due to the 

inclusion of variables measured at two different levels of 

analysis. The statistical software used was HLM 4.04 (Bryk, 

Raudenbush, and Congdon 1996). The dichotomous dependent variable 

was examined using a non-linear model with a Bernoulli 

distribution whereas the limited interval scale was examined 

using a non-linear model with a Poisson distribution. 

Hierarchical non-linear modeling is capable of estimating 

these types of models because of its ability to (a) center the 

individual- and aggregate-level stake in conformity measures (and 

interactions including these measures) to provide unique 

estimates of their separate effects, (b) weight the individuallevel analysis to correct possible problems associated with 

differences in selection probabilities for the individual-level 

cases stemming from differences in the census tract of residence, 

(c) weight the aggregate-level analysis to correct 

heteroscedasticity resulting from unequal numbers of individual- 

@ level cases within each aggregate, and (d) provide two-stage 

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estimation to generate a reliable test of the aggregate-level 

null hypothesis (i.e., basing the test on the number of 

aggregates rather than the number of individual-level cases). 

For a discussion of alternative methods of estimation and their 

relative weaknesses, see Kreft and DeLeeuw (1 998). 

For the analysis of interactions between stake in conformity 

and the legal variables, there is an option in the HLM 4.04 

software to estimate interaction effects between variables 

measured at level 2 (the aggregate-level) and those measured at 

level 1 (the individual-level). Such tests are not an option for 

interactions consisting solely of level 1 measures, so product 

terms for interactions between the individual-level stake in 

conformity measure and the legal variables were added to the 

complete models. At either level, a significant interaction 

coefficient would indicate a stronger relationship between a 

particular disposition and recidivism for higher or lower levels 

of stake in conformity compared to "doing nothing" with those 

particular offender groups. For example, split sentences may 

correspond with significantly less recidivism for higher stake 

persons compared to not filing charges against those particular 

individuals. 

HLM 4.04 provides estimates of sigma-square (variance in an 

a outcome measure existing within the aggregates at level 1) and 

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tau00 (variance existing between the aggregates at level 2). 

These two statistics should provide the information necessary to 

calculate improvements in explained within-group variance and 

explained between-group variance as one moves from an 

unconditional model (with no predictors) to more complex models. 

However, unlike the printout for linear analyses, the printout 

for non-linear analyses reports the change in tau but not the 

change in sigma-square. The latter value presented in the 

printout is always the within-group variance from the 

unconditional model. This makes it difficult to calculate a 

statistic reflecting the fit of the whole model. Therefore, the 

only model statistic presented is the proportion of the betweengroup variance explained by each model. A Science article by 

Sampson, Raudenbush, and Earls (1 997) includes non-linear models 

estimated with the sohare. They also present only this model 

statistic. 

Analytical Procedure for Examining Time to Re-arrest 

The third outcome measure of re-arrest is the number of 

months that elapsed between the end of an offender's sentence and 

a re-arrest for domestic violence during the varied follow-up 

period including all suspects (see table 2). Unfortunately, there 

is no statistical technique available that provides a two-stage a 

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estimation technique (such as hierarchical modeling) for rightcensored data. Therefore, a pooled event history analysis with 

Cox regression was used to estimate the relative effects of the 

predictors in the complete model on time to re-arrest. Interaction 

0 

terms for both levels of analysis were created by constructing 

product terms for court dispositions by stake in conformity. The 

results for the aggregate-level main and interaction effects from 

this analysis should therefore be interpreted with caution. 

However, we believe that the results for the individual-level 

effects of court dispositions and stake in conformity are valid. 

Life table analysis was also used to describe the time to 

recidivism for each disposition group. Both event history 

analysis and life tables are appropriate for right-censored data 

and unequal follow-up periods (Allison 1984). The "life table" 

is generally used to follow the experiences of a closed cohort of 

persons born in the same year over time (Keyfitz 1977). It is 

used in mortality research, where the death of a cohort member 

constitutes a ''decrement" to the life table. For this study, 

recidivists constitute the decrements to the life table and the 

cohorts being followed over time include persons receiving 

identical dispositions. 

Two life table measures were compared across disposition 

groups: the cumulative survival (non-recidivism) rate, and the 

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hazard rate of recidivism. In demography, a hazard rate is an 

age-specific decrement rate defined as the probability that an 

individual who has survived to a given age will "exit" the cohort 

during that specific age interval (Kalbfleisch and Prentice 

1980). The hazard rate examined here is a time-specific 

recidivism rate, describing the probability of recidivism during 

month b for an individual who has not yet recidivated before 

reaching month b. Although the follow-up spanned 58 months for 

some offenders, everyone re-arrested for domestic violence was 

re-arrested by the thirty-fifth month. 

Results and Discussion 

Figure 1 displays a map of the geographic distribution of 

persons arrested for domestic violence within the city limits of 

Cincinnati. A map of the distribution of domestic violence for 

Hamilton County is also displayed in figure 2. These maps are 

presented for two reasons. First, they help to describe the 

distribution of domestic violence in a major metropolitan area. 

Second, these maps provide an appreciation for the variation that 

exists between census tracts in the distribution of domestic 

violence (particularly figure 1). Yet despite the relatively 

broad distribution of domestic violence in figure 1, note the 

heavier pockets of arrests closer to the central business a 

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district of Cincinnati as well as areas closest to the city's 

major thoroughfares (I-71,I-74 and 1-75). These pockets are 

consistent with the ecological hypothesis that poorer areas with 

more transient populations coincide with higher rates of 

predatory crime. 

Regarding recidivism for domestic violence, roughly 14 

percent of the 3,110 suspects followed for at least two years 

were re-arrested for domestic violence. Of the 3,662 suspects in 

the larger sample, 16.1 percent were re-arrested at least once 

during the varied follow-up. 

Other univariate descriptives for the sample are also worth 

reviewing, some of which are not displayed in table 2. Roughly 

one-quarter of the sample were not even living with the victim at 

the time of arrest. Over 40 percent of the sample were also 

unemployed, and nearly 18 percent were receiving no financial 

support upon arrest. The vast majority of the sample (96 percent) 

did not have college degrees and only one-third held jobs for at 

least a year. 

A review of the other criminal history measures explored for 

the study indicated that a majority of the suspects (63 percent) 

have prior records. A significant percentage of the sample have 

been incarcerated previously for an offense other than domestic 

0 violence (35 percent), suggesting that many of these offenders 

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are somewhat eclectic in their criminal activities. 

While a clear majority of the suspects are male, 16 percent 

are female. Over one-third of those arrested were 35 years or 

older at the time of arrest whereas 23 percent were under age 25. 

Finally, African-Americans constitute 60 percent of the sample. 

Approximately 35 percent of the variance in the prevalence 

of re-arrest exists between census tracts whereas 65 percent 

exists within these tracts (at the individual-level). Similarly, 

for the incidence of re-arrest, the figures are 38 percent 

(between-group variance) and 62 percent (within-group). 

Zero-order Relationships 

Table 3 presents zero-order main effects of the predictors 

on the incidence of (number of re-arrests) and time to re-arrest 

for domestic violence. Relationships involving the prevalence of 

re-arrest are not presented because they are not significantly 

different from those for incidence. Results for the measure of no filed 

charges are presented because it is only necessary to leave this 

variable out of the complete model for estimation purposes. 

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Table 3,Zero-order Main Effects of Predictors on the Incidence 

of and Time to Re-arrest for Domestic Violence a 

(Unstandardized Fixed Effects Reported) 

Predictor 

Dependent Variables 

# rearrests Months to re-arrest 

(Poisson) (Cox Regression) 

Individual-level 

Control Variables 

Male 0.593** 

Age -0.022* * 

# priors 0.307** 

Other than dv inc. 0.434** 

No family contact -0.817** 

Case processing and Outcome 

No charges filed 0.336* 

Charges dropped -0.104 

Acquitted -0.402* 

Offender program -0.553** 

Probation 0.595* 

Jail 0.396** 

Probation+j ail 0.879** 

Pending charges 1.038** 

Stake in Conformity -0.056** 0 

0.760** 

-0.023** 

0.228** 

0.428** 

-0.788* 

0.476* * 

-0.122 

-0.264 

-0.414* 

-0.010 

0.229 

0.633** 

0.889** 

-0.055** 

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Aggregate-level a 

Stake in Conformity -0.036** -0.032** 

N 3,110 3,662 

* p < 0.05; ** p < 0.01 

Results for the court disposition variables reveal that rearrests are fewer and time until re-arrest is longer for suspects 

who (a) have charges filed against them (versus those who do 

not), (b) undergo the domestic violence offender program, and (c) 

do not have old charges pending at the time of arrest. However, 

suspects convicted and sentenced to probation and jail are rearrested more often and more quickly compared to others. Also, 

suspects who are not acquitted at trial, serve probation, and 

serve jail time were re-arrested more often during the follow-up 

period. 

@ 

The relationships involving trial acquittals, the offender 

program, probation, jail, and split sentences may simply reflect 

a lack of controls for risk (where offenders who are more likely 

to recidivate are also more likely to be convicted at trial and 

receive harsher sentences whereas the "better risks" are more 

likely to be acquitted or forced to undergo the offender 

program). However, the relationship involving filed charges is 

0 consistent with the idea that "doing something" may be better 

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than "doing nothing." e 

Both levels of stake in conformity are significant 

predictors in the hypothesized directions of the incidence and 

delay of re-arrest (p < 0.01). Moreover, the magnitudes of these 

effects are comparable between the two levels of analysis. This 

suggests that re-arrests are fewer and time until re-arrest is 

longer for suspects who (a) have higher levels of education, (b) 

live longer at the same residence, (c) are employed, (d) work in 

skilled occupations, and (e) are financially independent. These 

results suggest that controlling for these social dimensions of 

risk may be important in order to obtain valid estimates of the 

main effects of court dispositions and recidivism likelihoods. 

All of the statistical control variables are significantly 

related to the outcome measures (most at the 0.01 level of 

statistical significance). These results reinforce the importance 

of recognizing the possible relevance of these variables in 

related studies. 

The life tables of time to re-arrest for different 

disposition groups provide an interesting complement to the 

results for the zero-order relationships involving court 

dispositions. For this reason, and because the life tables do not 

control for the other dimensions of risk included in the 

multivariate analyses, these tables are presented and discussed 

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first before turning to the findings for the complete models. a 

Life Tables of Time to Re-arrest for Domestic Violence 

Tables 4 and 5 present the cumulative survival rates and 

hazard rates (respectively) of re-arrest during the follow-up 

period for each disposition group and the pooled sample. The 

follow-up period spanned 58 months for some offenders even though 

everyone re-arrested for domestic violence during the follow-up 

was re-arrested by month 35. In short, the survival rate for 

month 35 was the same as for month 58. This is interesting in and 

of itself because it suggests that three year follow-ups of these 

offenders may include 100 percent of all "failures." 

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Table 4a. Survival Analysis of Time to Re-arrest After Sentence 

Completion: Cumulative Proportions Not Recidivating e 

(Cumulative Survival Rates; Pooled N = 3,662) 

II 

II 

II Court Disposition 

/Pooled/ Off. Proba- Prob. Chrgs 

MonthlSamplelIgnore Dismiss Acquit Prog. tion Jail +Jail Pend. 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 

.965 .983 .985 .979 .986 .9 18 .944 ,836 .902 

.954 .954 .973 .963 .975 .913 .937 ,836 .87 1 

.938 .917 .962 .959 .955 .909 .904 .801 .839 

.928 .892 .950 .947 .95 1 .908 .891 .801 .8 12 

.919 .888 .936 .939 .944 .903 .884 .80 1 .793 

.911 .875 .927 .935 .930 .901 .871 .801 .789 

.906 .863 .921 .93 1 .927 .899 .868 .801 .785 

.903 .858 .918 .923 .927 .899 .868 .784 .781 

.898 .858 .912 .919 .927 .898 .855 .784 .769 

.896 .854 .909 .919 .923 .896 .852 .784 .765 

.892 .850 .903 .919 .923 .896 .848 .784 .757 

.889 .842 .899 .919 .923 .896 .842 .784 .750 

.886 .829 .894 .919 .923 396 .838 .784 .738 

.883 .829 .890 .915 .920 .896 .835 .784 .730 

.879 .809 .887 .911 .920 .889 .835 .784 .726 

.876 .800 .884 .911 .920 .888 .828 .784 .722 

.874 .800 .882 .907 .917 .888 .828 .784 .718 

.870 .796 .877 .891 .917 .888 .828 .784 .710 

.868 .792 .874 .887 .913 .888 .828 .784 .707 

.866 .792 .871 .883 .913 .888 .828 .784 .703 

.864 .784 .869 .883 .913 .884 .828 .784 .703 

.862 .780 .867 .879 .9 13 .884 .825 .775 .703 

.860 .775 .864 .875 .913 .884 .815 .775 .703 

.856 .771 .860 .871 .906 383 .8 15 .775 .691 

.851 .763 354 .867 .899 .883 .812 .775 .683 

.849 .755 .850 .863 .899 .883 .812 .775 .679 

.845 .75 1 .846 .859 .896 .883 .805 .775 ,675 

.843 .746 .843 .859 .896 .883 .802 .775 .671 

.84 1 .746 .840 .855 .892 .883 .802 .775 .668 

.839 .746 .837 .851 .892 .883 .799 .775 .664 

.837 .738 .835 .851 .892 .883 .799 .775 .664 

.837 .738 .834 .851 .892 .883 .799 .775 .660 

.836 .73 8 333 .85 1 .892 .883 .799 .775 .660 

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34 336 .738 333 351 .892 .883 .799 .775 .660 

@ 35 336 .738 333 351 392 .883 .799 .775 .660 

Table 4b. Survival Analysis of Time to Re-arrest After Sentence 

Completion: Standard Errors of Cumulative Survival 

Rates 

II 

I1 

II Court Disposition 

[Pooled[ Off. Proba- Prob. Chrgs 

MonthJSamplelIgnore Dismiss Acquit Prog. tion Jail +Jail Pend. 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 a 28 

.ooo .ooo .ooo 

.002 .008 .002 

,003 .014 .004 

.003 .019 .004 

.004 .021 .005 

.004 .021 .006 

.004 .022 .006 

.004 .023 .006 

.004 .023 .006 

.004 .023 .007 

.004 .024 .007 

.005 .024 .007 

.005 .024 .007 

.005 .025 .007 

.005 .025 .007 

.005 .026 .007 

.005 .027 .008 

.005 .027 .008 

.005 .027 .008 

.005 .027 .008 

.005 .027 .008 

.005 .028 .008 

.005 .028 .008 

.005 .028 .008 

.005 .028 .008 

.005 .028 .008 

.005 .029 .008 

.005 .029 .008 

.005 .029 .009 

.ooo .ooo .ooo .ooo .ooo .ooo 

.009 .008 ,011 .012 .035 .018 

.011 .010 .012 .013 .035 .020 

.011 .014 .012 .017 .038 .022 

.013 .014 .012 .018 .038 .023 

.014 ,015 .012 .018 .038 .024 

.015 .017 .012 .019 .038 .024 

.015 .017 .012 .020 .038 .024 

.016 .017 .012 .020 .039 .025 

.017 .017 .012 .020 .039 .025 

.017 .017 .013 .021 .039 .025 

.017 .017 .013 .021 .039 .025 

.017 .017 .013 .021 .039 .026 

.017 .017 .013 .021 .039 .026 

.017 .018 .013 .021 .039 .026 

.018 .018 .013 .021 .039 .027 

.018 .018 .013 .022 .039 .027 

.018 .018 .013 .022 .039 .027 

.020 .018 .013 .022 .039 .027 

.020 .018 .013 .022 .039 .027 

.020 .018 .013 .022 .039 ,027 

.020 .018 .013 .022 .039 .027 

.021 .018 .013 .022 .040 .027 

.021 .018 .013 .023 .040 .027 

.021 .019 .013 .023 .040 .028 

.021 .020 .013 .023 .040 .028 

.022 .020 .013 .023 .040 .028 

.022 .020 .013 .023 .040 .028 

.022 .020 .013 .023 .040 .028 

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29 .005 .029 .009 .022 .020 .013 .023 .040 .028 

30 .005 .029 .009 .022 .020 .013 .023 .040 .028 

3 1 .006 .029 .009 .022 .020 .013 .023 .040 .028 

32 .006 .029 .009 .022 .020 .013 .023 .040 .028 

33 .006 .029 .009 .022 .020 .O 13 .023 .040 .028 

34 .006 .029 .009 .022 .020 .O 13 .023 .040 .028 

35 .006 .029 .009 .022 .020 .O 13 .023 .040 .028 

Table 5a. Survival Analysis of Time to Re-arrest After Sentence 

Completion: Proportions Exposed to Risk Recidivating 

During Specific Month (Hazard Rates; Pooled N = 3,662) 

I1 

I1 

II Court Disposition 

IPooledl Off. Proba- Prob. Chrgs 

MonthlSamplelIgnore Dismiss Acquit Prog. tion Jail +Jail Pend. 

0 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 a 23 

.ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo 

.034 .016 .014 .020 .013 .OS5 .057 .178 .lo2 

.012 .030 .012 .O 16 .010 .005 .007 .OOO .035 

.O 16 .039 .011 .004 .021 .003 .035 .042 .036 

.O 1 1 .027 .012 .012 .003 .001 .014 .OOO .033 

.010 .004 .014 .008 .007 .005 .007 .OOO .024 

.008 .014 .010 .004 .014 .001 .015 .OOO .004 

.004 .O 14 .005 .004 .003 .OO 1 .003 .OOO .005 

.003 .004 .003 .008 .OOO .OOO .OOO .02 1 .005 

.005 .OOO .006 .004 .OOO .001 .015 .OOO .015 

.002 .004 .002 .OOO .003 .OO 1 .003 .OOO .005 

.004 .004 .006 .OOO .OOO .OOO .003 .OOO .O 1 0 

.003 .009 .004 .OOO .OOO .OOO .007 .OOO .O 10 

.004 .O 14 .005 .OOO .OOO .OOO .003 .OOO .O 1 5 

.003 .OOO .004 .004 .003 .OOO .003 .OOO .O 1 0 

.004 .025 .003 .004 .OOO .007 .OOO .OOO .005 

.003 .010 .003 .OOO .OOO .001 .007 .OOO .005 

.002 .OOO .003 .004 .003 .OOO ,000 .OOO .005 

.004 .005 .005 .017 .OOO .OOO ,000 .OOO .010 

.002 .005 .003 .004 .003 .OOO .OOO .OOO .005 

.OO 1 .OOO .003 .004 .OOO .OOO .OOO .OOO .005 

.002 .O 10 .002 .OOO .OOO .003 .OOO .OOO .OOO 

.002 .005 .002 .004 .OOO .OOO .004 .O 1 1 .OOO 

.003 .005 .003 .004 .OOO .OOO .O 12 .OOO .OOO 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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24 .004 .005 .005 .004 .007 .OO 1 .OOO .OOO .016 

25 .005 .010 .006 .004 .007 .OOO .004 .OOO ,011 

26 .003 .010 .004 .004 .OOO .OOO .OOO .OOO .005 

27 .003 .005 .004 .004 .003 .OOO .008 .OOO .005 

28 .002 .005 .004 .OOO .OOO .OOO .004 .OOO .005 

29 .002 .OOO .002 .005 .003 .OOO .OOO .OOO .005 

30 .002 .OOO .004 .005 .OOO .OOO .004 .OOO .005 

3 1 .001 .011 .002 .OOO .OOO .OOO .OOO .OOO .OOO 

32 .001 .OOO .002 .OOO .OOO .OOO .OOO .OOO .005 

33 .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo 

34 .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo 

3 5 .ooo .ooo .ooo .ooo .ooo .ooo .ooo .ooo ,000 

Table 5b. Survival Analysis of Time to Re-arrest After Sentence 

Completion: Standard Errors of Hazard Rates 

II 

II 

II Court Disposition 

[Pooled( Off. Proba- Prob. Chrgs 

MonthlSamplelIgnore Dismiss Acquit Prog. tion Jail +Jail Pend. 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 a 20 

.ooo .ooo .ooo 

.003 .009 .003 

.001 .012 .002 

.002 .014 .002 

.001 .011 .002 

.001 .005 .003 

.001 .008 .002 

.001 .009 .002 

.001 .005 .001 

.001 .ooo .002 

.OOO .005 .001 

.001 .005 .002 

.001 .007 .001 

.001 .009 .001 

.001 .ooo .001 

.001 .012 .001 

.001 .007 .001 

.ooo .ooo .001 

.001 .005 .002 

.OOO .005 .001 

,000 .ooo .001 

.ooo .ooo .ooo .ooo .ooo .ooo 

.009 .008 .012 .013 .042 .019 

.007 .007 .003 .005 .OOO .012 

.ooo .010 .002 .012 .022 .012 

.007 .004 .OOO .007 .OOO .011 

.006 .006 .003 .005 .OOO .010 

.004 .007 .002 .008 .OOO .004 

.004 .004 .002 .004 .OOO .004 

.006 .OOO .OOO .OOO .016 .004 

,004 .OOO .002 .008 .OOO .008 

.OOO .004 .002 .004 .OOO .004 

.OOO .OOO .OOO .004 .OOO .006 

.OOO .OOO .OOO .005 .OOO .007 

.OOO .OOO .OOO .OOO .OOO .008 

.004 .004 .OOO .004 .OOO .007 

.004 .OOO .004 .OOO .OOO .005 

.OOO .OOO .002 .006 .OOO .005 

.004 .004 .OOO .OOO .OOO .005 

.009 .OOO .OOO .OOO .OOO .007 

.004 .004 .OOO .OOO .OOO .005 

.004 .OOO .OOO .OOO .OOO .005 

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not been published by the Department. Opinions or points of view expressed are those of the

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21 .ooo .008 

22 .OOO .005 

23 .OOO .005 

24 .001 .005 

25 -001 .008 

26 .001 .006 

27 .001 .OOO 

28 .001 .006 

29 .001 .OOO 

30 .001 .OOO 

31 .001 .006 

32 .001 .OOO 

33 .ooo .ooo 

34 .ooo .ooo 

35 .ooo .ooo 

.001 

.oo 1 

.001 

-001 

.002 

.oo 1 

.oo 1 

.001 

.001 

.oo 1 

.001 

.001 

.001 

.ooo 

.001 

.ooo 

.004 

.004 

.004 

.ooo 

.004 

.ooo 

.ooo 

.004 

.004 

.ooo 

,000 

.ooo 

.ooo 

.ooo 

.ooo .002 .ooo .ooo .ooo 

.OOO .OOO .004 .011 .OOO 

.OOO .OOO .007 .OOO .OOO 

.006 .002 .OOO .OOO .009 

.006 .OOO -004 .OOO .007 

.OOO .OOO .OOO .OOO .005 

.004 .OOO .006 .OOO .005 

.OOO .OOO .004 .OOO .005 

.004 .OOO .OOO .OOO .005 

.OOO .OOO .004 .OOO .005 

.ooo .ooo .ooo .ooo ,000 

.OOO .OOO .OOO .OOO .005 

.ooo .ooo .ooo .ooo .ooo 

.ooo .ooo .ooo .ooo .ooo 

.ooo .ooo .ooo .ooo .ooo 

Table 4a presents the cumulative survival rates (i.e., 

cumulative proportions not recidivating throughout the follow-up) 

for each group, and table 4b displays the corresponding standard 

errors of those rates. Similarly, tables 5a and 5b present the 

hazard rates (i.e., proportions at risk recidivating during 

specific months) and standard errors, respectively. The number of 

persons falling into each group is presented at the bottom of 

each table. If a person recidivated before a sentence was served 

completely, (s)he was included in the percentage of recidivists 

falling into the first month of the follow-up period. This 

permits an examination of the incapacitative effects of some 

dispositions versus others. The discussion that follows stems 

from carefd examination of these rates in conjunction with their 

e standard errors in order to determine significantly different 

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estimates of recidivism across disposition groups. 

The cumulative survival rates presented in the last row of 

table 4a in conjunction with their standard errors indicate that 

offenders with pending charges had the highest recidivism rate 

(the lowest cumulative survival rate) of any group, with 34 

percent recidivating by the last time period. This is consistent 

with the significant zero-order main effect for pending charges 

discussed previously. The next lowest cumulative survival rate 

corresponds with the group whose cases were ignored (no charges 

filed). The survival rate for the group is 74 percent, a figure 

that is significantly lower than the survival rates for all 

remaining groups except offenders serving jail and split 

sentences. This is also consistent with the zero-order 

relationship involving no filed charges. Finally, the survival 

a 

rates for offenders undergoing the offender program (89 percent) 

and probation (88 percent) are significantly lower than for 

offenders sent to jail (80 percent) and serving split sentences 

(78 percent). For the groups aside from offenders with pending 

charges, anywhere from 11 percent (offender program) to 26 

percent (no filed charges) recidivated by the end of the followUP. 

Offenders whose charges were pending and those receiving * split sentences of probation and jail were the quickest to 

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recidivate, with 20 percent or more being re-arrested by the end 

of month 7. The hazard rates in table 5a also indicate much 

higher risks of recidivism occurring during the early months of 

follow-up for these groups. Although suspects sentenced to jail 

alone were typically worse risks compared to most others in the 

sample, sentences ofjail alone served to at least delay 

recidivism longer through its incapacitative effect. Table 5a 

reveals a ten percent difference in risk during the first month 

between those sentenced to jail versus probation and jail. 

Suspects whose cases were ignored ended up having some of 

the highest recidivism likelihoods in the sample, but recidivists 

from this group actually had longer delays to re-arrest compared 

to those receiving probation, jail, and split sentences. The 

hazard rates for this group are significantly higher for later 

periods (e.g., months 15,21,26, and 3 1) compared to all other 

@ 

groups. It is possible that the factors leading to inaction on 

the part of prosecutors were related to this delay, but the fact 

remains that a very high proportion of the group ultimately 

recidivated for domestic violence (26 percent), and risk was 

significantly greater in later periods for this group alone. 

Similar patterns of delay can be observed for suspects whose 

cases were dismissed, acquitted, and sentenced to either the 

0 offender program or probation. However, unlike those whose cases 

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were ignored, the offender program and probation groups had among 

the lowest recidivism likelihoods in the sample. In short, the 

risk evaluations of these individuals made by prosecutors and 

judges seemed to be fairly well-informed. Note the significantly 

higher hazard rates for the more severe disposition groups during 

the early periods of follow-up. Month by month, these more severe 

disposition groups have consistently lower cumulative survival 

rates compared to the ''better risks." Note the 11 percent 

difference in survival rates by the end of month 24 for 

probationers versus those sentenced to probation and jail. This 

difference was established after only two years and was 

maintained throughout the remainder of the follow-up. 0 

Multivariate Findings for Unconditional Effects 

Table 6 presents the results for the models predicting 

whether a suspect was re-arrested for domestic violence during 

the fixed, two year follow-up period. Table 7 displays the 

findings for the number of re-arrests for domestic violence 

during the same period. Table 8 presents the findings for the 

event history analysis of months to re-arrest for all offenders 

in the sample followed over varying periods of time. Each table 

includes three models: Model 1 presents the results for main 

effects only, model 2 presents both main effects and individualThis document is a research report submitted to the U.S. Department of Justice. This report has

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level interactions only, and model 3 presents main effects and e 

interaction effects at both levels. All three models are 

presented so the similarities and differences between 

coefficients can be compared from one stage of the analysis to 

the next. A review of the estimates for main effects (in 

conjunction with their standard errors) reveals that they are 

relatively stable across the models even with the addition of the 

interaction terms. 

Table 6. Hierarchical Bernoulli Models Predicting Prevalence of 

Re-arrest for Domestic Violence (Fixed Effects Reported) 

Predictor 

Constant 

Individual-level 

Control Variables 

Male 

Age 

# priors 

Other than dv inc. 

No family contact 

Case Processing 

Charges dropped 

Acquitted 

Offender program 

Probation 

Jail 

Probation+jail 

Pending charges 

Model 1 Model 2 Model 3 

Coeff. SE Coeff. SE Coeff. SE 

- 1.997 -1.993 -2.020 

0.688** 0.121 0.730** 0.172 0.735** 0.170 

0.219** 0.035 0.239** 0.046 0.235** 0.047 

0.083 0.069 0.120 0.085 0.123 0.085 

-0.017** 0.004 -0.022** 0.007 -0.021** 0.007 

-0.051 0.139 -0.970* 0.383 -0.929* 0.385 

-0.285 0.150 -0.290 0.158 -0.172 0.257 

-0.311 0.192 -0.385 0.233 -0.271 0.354 

-0.493* 0.216 -0.592* 0.266 -0.655* 0.365 

0.153 0.250 -0.298 0.355 -0.040 0.419 

-0.044 0.213 -0.185 0.270 0.177 0.359 

0.372 0.278 -0.035 0.406 0.571 0.444 

0.800** 0.161 0.842** 0.162 0.921** 0.284 

. 

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Stake in Conformity -0.038** 0.013 -0.005 0.046 -0.004 0.047 

Interactions 

Stake (x) Dropped -0.039 0.043 -0.037 0.045 

Stake (x) Acquitted -0.071 0.074 -0.071 0.074 

Stake (x) Program -0.144* 0.070 -0.145* 0.071 

Stake (x) Probation 0.141 0.093 0.136 0.093 

Stake (x) Jail 0.044 0.068 0.036 0.068 

Stake (x) Pending 0.017 0.054 0.018 0.054 

Stake (x) Prob.+jail -0.240* 0.1 18 -0.256* 0.133 

Aggregate-level 

Stake in Conformity -0.031** 0.007 -0.027** 0.008 -0.028** 0.008 

Interactions 

Stake (x) Dropped 

Stake (x) Acquitted 

Stake (x) Program 

Stake (x) Probation 

Stake (x) Jail 0 Stake (x) Prob.+jail 

Stake (x) Pending 

0.026 0.024 

0.031 0.036 

0.041 0.042 

0.042 0.049 

0.080* 0.038 

0.145** 0.051 

0.014 0.026 

% between-group 

variance explained 51.592 64.648 82.3 13 

* p < 0.05; ** p < 0.01 

Table 7. Hierarchical Poisson Models Predicting Incidence of 

Re-arrest for Domestic Violence (Fixed Effects Reported) 

Predictor 

Model 1 Model 2 Model 3 

Coeff. SE Coeff. SE Coeff. SE 

Constant -1.582 -1.513 -1.928 

Individual-level 

Control Variables 

Male 0.527** 0.161 0.513** 0.179 0.505** 0.172 

I# priors 0.229** 0.023 0.221** 0.025 0.226** 0.027 

0 Age -0.025** 0.004 -0.022** 0.006 -0.019** 0.006 

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Other than dv inc. 0.169* 0.076 0.107 0.072 0.1 14 0.074 

No family contact -0.909** 0.283 -1.051** 0.359 -1.039** 0.371 

Case Processing 

Charges dropped -0.190 0.121 -0.257 0.138 0.057 0.203 

Offender program -0.506** 0.188 -0.654** 0.223 -0.61 1* 0.294 

Probation 0.289 0.224 0.162 0.219 0.238 0.361 

Jail -0.107 0.182 -0.166 0.229 0.330 0.283 

Probation+jail 0.364 0.211 0.166 0.362 0.630 0.346 

Acquitted -0.255 0.181 -0.418 0.211 -0.161 0.281 

Pending charges 0.888** 0.143 0.739** 0.126 0.880** 0.202 

Stake in Conformity -0.024* 0.012 0.020 0.037 0.024 0.039 

Interactions 

Stake (x) Dropped -0.047 0.037 -0.048 0.040 

Stake (x) Acquitted -0.076 0.065 -0.084 0.064 

Stake (x) Program -0.150** 0.055 -0.155** 0.056 

Stake (x) Probation 0.071 0.042 0.111 0.047 

Stake (x) Jail -0.021 0.064 -0.034 0.061 

Stake (x) Prob.+jail -0.15 1 * 0.075 -0.153* 0.073 

-0.028 0.050 -0.022 0.048 

Stake in Conformity -0.025** 0.007 -0.025** 0.007 -0.071** 0.020 

Interactions 

Stake (x) Dropped 

Stake (x) Acquitted 

Stake (x) Program 

Stake (x) Probation 

Stake (x) Jail 

Stake (x) Prob.+jail 

Stake (x) Pending 

0.043* 0.018 

0.050 0.026 

0.050 0.031 

0.027 0.040 

0.085** 0.032 

0.125** 0.039 

0.023 0.018 

% between-group 

variance explained 73.789 84.156 94.702 

* p < 0.05; ** p < 0.01 

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Table 8. Pooled Survival Models Predicting Months to Re-arrest for 

e Domestic Violence (Cox Regression Coefficients Reported) 

Model 1 Model 2 Model 3 

Predictor Coeff. SE Coeff. SE Coeff. SE 

Individual-level 

Control Variables 

Male 0.63** 0.17 0.64** 0.17 0.65** 0.17 

# priors 0.18** 0.03 0.19** 0.03 0.18** 0.03 

Other than dv inc. 0.16 0.09 0.14 0.10 0.15 0.10 

No family contact -0.79* 0.36 -0.77* 0.36 -0.72* 0.36 

Age -0.02** 0.01 -0.02** 0.01 -0.02** 0.01 

Case Processing 

Charges dropped -0.24 0.15 -0.25 0.15 -0.12 0.22 

Offender program -0.44* 0.22 -0.45* 0.22 -0.29 0.29 

Acquitted -0.37 0.22 -0.37 0.22 -0.32 0.31 

0 Jail Probation -0.26 0.27 -0.23 0.28 -0.05 0.38 

-0.16 0.19 -0.14 0.20 0.09 0.28 

Probation+jail 0.34 0.27 -0.08 0.38 0.40 0.42 

Pending charges 0.69** 0.13 0.69** 0.13 0.82** 0.18 

Stake in Conformity -0.04* 0.02 -0.03 0.05 -0.03 0.05 

Interactions 

Stake (x) Dropped -0.01 0.05 -0.01 0.06 

Stake (x) Acquitted -0.03 0.08 -0.02 0.08 

Stake (x) Program -0.07 0.08 -0.07 0.08 

Stake (x) Probation 0.12 0.09 0.12 0.09 

Stake (x) Jail 0.05 0.07 0.05 0.08 

Stake (x) Pending 0.03 0.05 0.02 0.05 

Stake (x) Prob.+jail -0.21* 0.10 -0.23* 0.11 

Aggregate-level 

StakeinConformity -0.01 0.01 -0.01 0.01 -0.04 0.02 

Interactions 

Stake (x) Dropped 0 Stake (x) Acquitted 

0.02 0.03 

0.001 0.04 

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Stake (x) Program 

Stake (x) Jail 

Stake (x) Probation 

Stake (x) Prob.+jail 

Stake (x) Pending 

0.03 0.04 

0.03 0.05 

0.04 0.03 

0.13* 0.05 

0.02 0.02 

Model Chi-square 179.956 195.600 206.274 

(14 df) (21 df) (28 df) 

* p < 0.05; ** p < 0.01 

Some observations related to the control variables are worth 

making before turning to the findings for court dispositions. All 

of the statistical control variables except prior incarceration 

for other crimes are significantly related to the two outcome 

measures in at least eight of the nine models. In conjunction 

with the relationships involving the composite measures of stake 

0 in conformity, the results reinforce the notion that controlling 

for these types of variables is important for obtaining valid 

estimates of relationships between court dispositions and 

recidivism . 

Aside fiom a suspect's sex and age, the number of prior 

convictions for violent misdemeanors yields one of the strongest 

relationships in the model. A separate analysis revealed that 

this control variable alone renders a non-significant 

relationship between the prevalence of re-arrest and whether a 

suspect had ever been incarcerated previously for offenses other 

than domestic violence. This suggests that a history of less 0 

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serious criminal behavior may be a better predictor of re-arrest 

for domestic violence than a history of more serious criminal 

behavior. This was also suggested in a separate analysis of the 

bivariate relationships involving the different measures of prior 

record, where the relationships involving misdemeanors are 

stronger than those for felonies. 

Re-arrests were also less likely, less fiequent, and were 

delayed longer for persons not living with their original victim 

at the time of the original arrest. From a substantive 

perspective, this is consistent with the earlier contention that 

marital status and living arrangements should be treated 

separately from the concept of stake in conformity in the context 

of domestic violence. Given the unique offense examined, persons 

living with their partners have greater opportunities to engage 

in domestic violence due to their physical proximity. This may 

have implications for the "escalation" effects for married 

offenders found in some of the arrest studies (summarized by 

Garner, Fagan, and Maxwell 1995). 

Regarding the main effects of court dispositions on the 

outcome measures, recall that the dummy disposition variable left 

out of the model is the measure of no filed charges. The results 

for the remaining disposition variables in each model, aside fiom 

@ pending charges, therefore reflect differences fiom "doing 

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nothing" with arrested suspects. 

Results for the unconditional effects of the legal variables 

reveal that only one of the six disposition variables maintains a 

statistically significant difference from "doing nothing" for bo 

of the outcome measures. That is, the prevalence and incidence of 

re-arrest are lower among persons who went through the offender 

program. Recall that the cumulative failure rate for persons with 

no charges filed was 24 percent versus only 11 percent for 

offenders who went through the program. Yet aside from this 

significant difference in the likelihood of re-arrest, even after 

controlling for risk, no other disposition appears to be any 

better than not filing charges for significantly reducing rearrests. Moreover, comparisons of differences between the 

coefficient estimates (in conjunction with their standard errors) 

for the dummy disposition measures included in the models reveal 

no other significant differences for either outcome measure. The 

majority of main effects for the dispositions are therefore 

consistent with five of the seven arrest studies where the main 

effect of arrest was not statistically significant for predicting 

the prevalence andor incidence of re-arrest (Garner, Fagan, and 

Maxwell 1995). While doing something may be better than doing 

nothing with these offenders, the gains of the offender program -- 

over other (more restrictive) dispositions appear to be modest. 

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author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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This set of findings provide no support for the additive 

deterrence hypothesis. Although the offender program might have 

an effect on recidivism, this particular disposition should not 

be treated as a punishment intended to "deter" future offending. 

Rather, it is the only disposition examined that falls under a 

rehabilitative ideology. The program is intended to prevent 

future violence through counseling, not punishment. 

In contrast to the dummy variables tapping case 

dispositions, the measure of pending charges is a statistically 

significant (and relatively strong) predictor of all three 

dependent variables (p < 0.01). Given that this variable is 

statistically significant even when controlling for prior record 

of violent misdemeanors and prior incarceration, this reinforces 

the earlier contention that the speed of case processing may be 

an important factor in reducing domestic violence recidivism. 

This is also consistent with the finding from the National Pretrial Reporting Program (1 992) that persons released after arrest 

for other types of crimes were significantly more likely to be 

re-arrested while waiting for trial compared to those whose cases 

were processed more expediently (Walker 1998). In short, faster 

processing may help to reduce the likelihood and frequency of rearrest. Whether this is because the celerity of case processing 

contributes to a deterrent effect or because faster processing 

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reduces opportunities for victimization by separating offenders 

from their victims more quickly cannot be answered with the 

available data. 

e 

Results for the main effects of the stake in conformity 

measures presented in the first models of tables 6 and 7 indicate 

that both factors are significant predictors (in the predicted 

directions) of both outcome measures (p < 0.05). The magnitude of 

each estimate is also virtually identical to the other in model 

1. However, the addition of the interactions involving stake in 

conformity at the first level serve to render non-significant 

main effects for the individual measure on both dependent 

variables, whereas the addition of the interactions at the second 

level does not have the same effect on the aggregate measure in 

either model. These results suggest that neighborhood levels of 

stake in conformity maintain significant unconditional effects on 

domestic violence recidivism whereas offender levels of stake do 

not. This observation supports the relevance of ecological 

theories, particularly a contextual theory of stake in 

conformity, for an understanding of repeat offending among 

adults. The applicability of an individual-level theory of stake 

in conformity is more limited in the context of main effects on 

recidivism only. 

0 

The results for aggregate-level stake in conformity 

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presented in table 8 are quite different from those in tables 6 

and 7. However, recall that the pooled survival analysis may not 

provide valid estimates of the aggregate-level relationships 

examined. Yet the results for offender stake are consistent with 

those in the other two tables, suggesting that the main effect of 

a 

offender stake is not important for predicting domestic violence 

recidivism. 

The significance of the aggregate-level measure in tables 6 

and 7 is consistent with studies of other types of predatory 

crimes revealing higher violent crime rates as well as higher 

individual-level likelihoods of violent crime in lower socioeconomic neighborhoods (e.g., Elliott et al. 1996; Gottfredson, 

McNeil, and Gottfredon 199 1 ; Gunn et al. 1993; Sampson 199 1 ; 

Simcha-Fagan and Schwartz 1986). The findings for model 3 in both 

tables are also consistent with the criminological literature on 

the relative importance of aggregate- versus individual-level 

predictors of criminal behavior. When including both levels of 

predictors in the same model with the interactions, the 

aggregate-level measure of stake in conformity prevails in 

significance and magnitude whereas the individual-level measure 

becomes weaker (in table 7) and non-significant (in tables 6 and 7). 

0 

A qualification to the contextual main effects is that the 

causality from neighborhood characteristics to domestic violence 

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cannot be tested directly. This leaves open the possibility that e 

persons more likely to engage in domestic violence may possess 

characteristics that lead them to reside in areas with particular 

characteristics. For example, Sampson, Raudenbush, and Earls 

(1 997) observed that single persons and African-Americans may 

gravitate towards neighborhoods with lower socioeconomic status, 

contributing to higher concentrations of poverty at the 

aggregate-level. Nonetheless, these aggregate-level relationships 

provide incentive for research that can test the causal 

relationships directly. 

As previously discussed, several aspects of the specific 

dimensions of aggregate-level stake considered here overlap 

conceptually with various aspects of social capital and 

concentrated disadvantage applied to an understanding of crime. 

While it is up to future research to delineate finer operational 

distinctions between the three sets of theoretical concepts, the 

significant contextual results provide incentive to do so. 

Individual-level Conditional Effects 

Results for the prevalence and incidence of re-arrest reveal 

significant interactions involving individual-level stake in 

conformity with the offender program and split sentences. The 

same is true for split sentences only in the analysis of time to 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
re-arrest. Persons with higher stake in conformity who undergo 

the offender program are significantly less likely to be rearrested and were re-arrested less often compared to higher-stake 

persons whose cases are ignored. (This represents an effect 

beyond the significant main effect for all offenders in the 

sample). The same holds true for higher-stake persons serving 

split sentences in the analyses of all three outcome measures. 

e 

Comparing the interaction for offender program with those 

for the other individual-level interactions, the program 

corresponds with a significantly lower incidence of re-arrest for 

higher-stake persons when compared with higher-stake individuals 

serving probation only. On the other hand, the coefficient for 

split sentences in each of the three tables is not significantly 

different from any other interaction. 

Further analysis reveals that higher-stake persons serving 

split sentences have a lower prevalence of re-arrest (compared to 

doing nothing with these types of offenders), whereas lower stake 

persons serving these sentences are neither more or less likely 

to be re-arrested. The finding of "no effect'' for lower-stake 

individuals does not contradict the negative coefficient for the 

interaction given that a non-linear analysis was performed. 

The findings for the individual-level interactions support __ 

@ the hypothesis that formal controls may be more effective for 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
individuals with higher stake in conformity. However, the e 

observations offered in the arrest literature that formal 

sanctions may actually make matters worse for lower-stake 

offenders is not supported with the analysis of court 

dispositions. Only the results for higher-stake individuals are 

consistent with the previous arrest studies which included 

interactions between arrest and measures of stake in conformity. 

Garner, Fagan, and Maxwell (1 995) observed that when there was a 

significant interaction between stake in conformity and arrest, 

with the exception of marital status, the results generally 

favored higher-stake individuals in that arrest appeared to have 

a greater suppression effect on re-arrest for this group. 

Researchers who found this significant link observed that arrest 

might have a conditional effect for higher-stake persons. 

Similarly, the offender program and split sentences may be more 

effective for persons with higher (individual) stake in 

conformity. 

Aggregate-level Conditional Effects 

For the discussion of the aggregate-level interactions 

involving court dispositions and stake in conformity, only the 

findings for the prevalence and incidence of re-arrest are 

e interpreted due to the methodological reasons aforementioned. 

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author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
The interactions involving the aggregate-level measure of 

stake in conformity with jail and split sentences are 

statistically significant predictors of both outcome measures. 

However, unlike the individual-level interactions, the 

coefficients are opposite in sign to those hypothesized. Jail and 

split sentences correspond with higher likelihoods of re-arrest 

as well as more re-arrests for persons living in census tracts 

with larger proportions of higher-stake residents (compared to 

the same types of individuals who have no charges filed against 

them). Also, prosecuting suspects and subsequently dropping 

charges corresponds with a higher incidence of re-arrest for 

suspects residing in "higher-stake" areas compared to not filing 

any charges against these types of individuals. 

The finding for split sentences is particularly interesting 

in light of the significant negative relationship involving the 

same interaction measured at the individual-level. Further 

analysis revealed that split sentences have no appreciable effect 

on re-arrests for persons in lower-stake census tracts, yet a 

significant positive effect for those residing in higher-stake 

tracts. For jail sentences, the additional analysis revealed that 

offenders residing in higher-stake census tracts who serve jail 

sentences are re-arrested significantly more often than those 

who have no charges filed against them, whereas offenders in 

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not been published by the Department. Opinions or points of view expressed are those of the

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lower-stake tracts serving these sentences are re-arrested e 

significantly less often. Similar relationships exist for 

prosecution with subsequently dropped charges. 

Sherman and Smith (1 992) found support for a labeling 

(escalation) effect of arrest on re-offending for persons with 

lower individual levels of stake in conformity. However, the 

results presented here for the conditioned effects of court 

dispositions by both individual and aggregate levels of stake in 

conformity refute the applicability of a labeling (escalation) 

effect for lower-stake offenders when formal intervention occurs 

and sanctions are applied. On the contrary, prosecution and jail 

may actually have a suppression effect on arrest for individuals 

residing in lower-stake tracts. This provides some support for 

the replacement hypothesis applied to specific deterrence. That 

is, prosecution and jail sentences only appear to work 

effectively for offenders in low stake communities. 

0 

The findings presented here suggest that both individuallevel and contextual theories of stake in conformity are 

applicable to an understanding of domestic violence recidivism. 

However, these applications are quite different from each other. 

The absence of significant main effects for individual-level 

stake in conformity on the prevalence and incidence of domestic 

violence recidivism suggest that the applicability of the 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
individual-level theory is limited to the role of offender stake 8. 

in conditioning the effects of legal sanctions (in the direction 

of greater effectiveness for offenders with greater stake). On 

the other hand, the significant main effects of aggregate-level 

stake in conformity on recidivism in conjunction with conditioned 

effects that favor offenders in lower stake neighborhoods while 

generating escalation effects for those in higher stake 

neighborhoods suggest a broader yet more complicated application 

of the contextual theory. Although it would be inappropriate to 

provide substantive meaning to ex-post facto hypotheses related 

to the contextual findings for conditioned effects, such 

hypothesizing is useful in. the context of specific directions for 

future research. 

Collective Efficacy and Calls to the Police 

Given the non-experimental nature of the data for the 

present study, it is logical to expect that higher risk offenders 

are more likely to receive the more severe dispositions including 

jail and split sentences, the sanctions that correspond with 

higher recidivism for offenders in higher stake neighborhoods. As 

noted previously, some aspects of the aggregate-level measure of 

neighborhood stake in conformity overlap with the concept of 

concentrated disadvantage introduced by Sampson, Raudensbush, and a 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
Earls (1997). Sampson et al. found that less disadvantaged 

neighborhoods maintain higher levels of collective efficacy, or 

the willingness of individuals to help other residents of their 

neighborhoods. If offenders receiving more severe sanctions are 

more likely to recidivate, it is possible that neighbors in 

higher stake communities are more likely to get involved in these 

disputes (including calling the police) if these communities 

maintain higher levels of collective efficacy. Surveys of 

offenders asking who notified the police about their particular 

incidents would provide more insight into this possibility. 

Labeling Effects in Higher Stake Communities 

The results for the significant aggregate-level interactions 

also do not refute the possibility of a labeling effect of more 

severe sanctions for offenders living in higher-stake tracts, 

especially since no such effect appears for offenders in lowerstake tracts. The literature by Sherman, Berk, and their 

colleagues focuses heavily on the possible role of labeling 

theory in some of the individual-level conditioned effects they 

found related to arrest for lower stake offenders. That is, lower 

stake offenders who were arrested also recidivated more than 

those not arrested. However, a provocative comment from Manski 

and Nagin (1 998) provides a possible explanation for why some of a 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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the court sanctions examined here might correspond with more 

recidivism among offenders in higher-stake communities. 

Discussing the applicability of labeling theory to an 

understanding of juvenile court sanctions and the risk of reoffending, they noted how labeling theory predicts that any 

effects of stigmatization for juveniles tend to occur during the 

first few encounters with police and juvenile court, after which 

a deterrent effect may actually occur with additional 

interactions. A similar situation may exist with offenders in 

higher-stake areas who have less experience with the police and 

the criminal court. Such offenders who are arrested and 

sanctioned by the court may initially undergo a labeling process 

and, possibly, a short-term escalation in their criminal 

behavior. Offenders residing in lower-stake areas, on the other 

hand, may not be as susceptible to the labeling process due to 

their lack of relative deprivation in social status before and 

after formal intervention, and because they may have more 

experience with the system. 

Pursuing this idea in future research would require survey 

data on offenders' perceptions of formal treatment. This ties in 

with Sherman's (1 992) observation that offenders' perceptions of 

justice may influence the effects of formal sanctions on future 

behavior. Those perceiving a sanction as fair are more likely to 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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believe in the legitimacy of the law and may be less likely to 

break it (Paternoster et al. 1997; Tyler 1990). However, 

perceptions of fairness may be influenced by stake in conformity 

(Scheff and Retzinger 1991), but not necessarily in the direction 

of favoring offenders in higher-stake communities. Perceptions of 

injustice may be enhanced among offenders residing in higherstake communities who receive more punitive dispositions if these 

individuals are more resentful of formal intervention (due to 

more privileged lifestyles compared to those living in lowerstake communities). This could ultimately damage their respect 

for the system and initially increase the likelihood of re- 

@ offending. 

Implications for Current Knowledge and Future Research 

Despite the focus on contradictions with previous research 

findings related to arrest and stake in conformity, the present 

study of court dispositions actually yields several 

consistencies. First, just as the main effects of arrest were not 

statistically significant in five of the seven arrest studies, 

most of the main effects of court dispositions were not 

significant as well. Second, the significant individual-level 

interactions tapping offender program and split sentences support 

e previous findings related to formal and informal social controls. 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
Finally, the findings for the interactions suggest that formal 

and informal controls should be considered together when 

understanding domestic violence recidivism. 

The contradictions with previous findings include higher 

likelihoods and rates of re-arrest for offenders in particular 

disposition groups with higher (aggregate-level) stake in 

conformity, the lack of an escalation effect for lower-stake 

offenders serving the same sentences that appear to "work" for 

higher-stake offenders at the individual-level, and a suppression 

effect for offenders from lower-stake neighborhoods who are 

subjected to particular dispositions. Aside from differences in 

research design, these contradictions could have resulted from 

differences between arrest and the formal sanctions examined 

here. Also, in contrast to the arrest studies, the focus here is 

on a population of persons already arrested. Finally, previous 

research did not include the contextual analyses which generated 

most of the inconsistencies found here. 

The present study also provides a couple of new insights 

related to the main effects of formal interventions on domestic 

violence recidivism. First, in contrast to the Minneapolis and 

Dade County findings supporting the additive (specific) 

deterrence hypothesis related to arrest, this study finds support 

0 for a treatment effect of an offender program on recidivism. 

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author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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Second, the finding that suspects were more likely to recidivate 

for domestic violence if charges from previous arrests were still 

pending also suggests that the speed of case processing may be 

important for reducing the likelihood of domestic violence 

recidivism. Perhaps a pre-occupation with the quality and length 

of sentences has taken attention away from the importance of 

efficiency in case processing. 

0 

The significant main effect of living arrangements is also 

important since the idea of "opportunity" is central to many 

criminological theories. In the context of understanding domestic 

violence recidivism, an offender's living arrangements may not 

adequately reflect his or her attachment to immediate family 

members. Nonetheless, this operational concept applied to 

domestic violence offenders may tap into an interaction between 

impulsivity and opportunity that is more reflective of 

Gottfredson and Hirschi's concept of low self-control (1 990). Of 

course, further research is needed to test this idea. 

The dramatic differences in how individual- and aggregatelevel theories of stake in conformity apply to domestic violence 

recidivism suggest that each should be considered to provide a 

more comprehensive understanding of the topic. These differences 

also indicate that findings at one level cannot be inferred to 

possible results at the other level. The main effects of offender 

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stake are non-significant while support is found for the 

conditioned effects of legal sanctions by offender stake in the 

predicted directions. By contrast, the main effects of 

neighborhood stake are significant in the predicted directions 

although some of the conditioned effects are significant in the 

opposite hypothesized directions. Moreover, at the individuallevel, the conditioned effects neither decrease or increase 

recidivism for lower stake offenders whereas some of the 

conditional effects at the aggregate-level actually appear to 

benefit offenders in lower stake neighborhoods. This provides an 

interesting (although somewhat complicated) picture of the role 

of neighborhood stake in conformity in predicting recidivism. 

Future research examining the relevance of collective efficacy 

and labeling may provide additional insight into the contextual 

findings presented here. Support for either of these theories 

would further extend the application of sociological theory to 

the topic. 

The predominant focus on conditional effects is not intended 

to trivialize the significant main effect found for aggregatelevel stake in conformity. This finding extends previous 

criminological research yielding support for the main effects of 

neighborhood characteristics on crime in general to an 

understanding of recidivism among a fairly large population of 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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offenders. The significance of the aggregate dimension of stake 

in conformity is also consistent with studies of other predatory 

crimes revealing higher violent crime rates as well as higher 

individual-level likelihoods of violent crime in more socially 

"disorganized" neighborhoods. A map of the geographic 

distribution of persons arrested for domestic violence in 

Cincinnati (see figure 1) reveals a preponderance of arrests 

closer to the central business district and areas closer to the 

city's major thoroughfares. This is consistent with the idea that 

poorer areas with more transient populations coincide with higher 

rates of predatory crime. 

a 

The methodological contributions of the the present study 

include more rigorous measures of stake in conformity compared to 

those offered previously, and a contextual analysis of the 

conditioned effects of court dispositions by aggregate measures 

of stake in conformity. Despite the limitations of non-random 

assignments to the disposition groups examined, cross-validation 

checks and statistical controls for risk and opportunity served 

to enhance the validity of the findings. At a minimum, this study 

provides a methodology that can be replicated in other 

jurisdictions in addition to results that can be used as 

forecasts in future research. 

0 

The role of collective efficacy in higher stake 

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neighborhoods, labeling effects of formal intervention, and 

perceptions of justice are all worthwhile pursuits in future 

studies of the topic. At a minimum, the results presented here 

provide a forecast for future research and a context in which 

these hypotheses can be tested. 

a 

Conclusions and Policy Implications 

The findings from the study presented here reinforce the 

importance of filing charges against suspects in order to delay 

re-arrest, even if the cases ultimately result in subsequently 

dropped charges or trial acquittals. Also, offender programs 

appear to have a suppression effect on the prevalence and 

0 incidence of re-arrest regardless of individual- or aggregatelevels of stake in conformity. Finally, persons arrested while 

old charges are still pending are significantly more likely to be 

re-arrested, are re-arrested more often, and are re-arrested 

faster. These results suggest that a failure to process cases 

with greater speed may also increase recidivism. 

The findings for conditional effects of court dispositions 

suggest that certain dispositions may contribute to the 

prevention of further domestic violence among certain types of 

offenders arrested for misdemeanor domestic violence. 

Specifically, offender programs appear to work even better @ 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

Justice.
(beyond the significant main effects) for reducing the prevalence 0 - 

and incidence of re-arrest among offenders with higher 

individual-levels of stake in conformity. Split sentences also 

work for these types of offenders to reduce the prevalence and 

incidence of re-arrest as well as for delaying re-arrest. 

Considering aggregate-level conditional effects of court 

dispositions, prosecuting suspects from lower-stake neighborhoods 

as well as sending convicted offenders from these areas to jail 

appear to have suppression effects on re-arrest. However, jail 

and split sentences also appear to escalate recidivism for 

offenders from higher-stake neighborhoods. This last finding 

could be the result of a greater willingness of residents in 

these neighborhoods to call the police regarding domestic 

disputes, or possible labeling effects occurring among offenders 

less familiar with legal intervention. 

0 

The analysis of time to re-arrest reveals that, regardless 

of the follow-up period examined, sentences of jail alone have a 

greater incapacitative effect compared to probation alone and 

probation combined with jail. In other words, even though 

suspects who go to jail are typically worse risks compared to 

those receiving probation, sentences of jail serve to at least 

delay recidivism longer through its incapacitative effect. 

Although suspects whose cases are ignored end up with some 

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not been published by the Department. Opinions or points of view expressed are those of the

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of the highest recidivism likelihoods in the sample overall, 

recidivists falling in this group actually have longer delays to 

re-arrest compared to other disposition groups (offender program, 

probation, jail, and probation combined with jail). While it is 

possible that the factors leading to inaction on the part of 

prosecutors are related to this delay, the fact remains that a 

very high proportion of the group ultimately recidivate for 

domestic violence. 

Similar patterns of delay can be observed for suspects whose 

cases are dismissed, acquitted, and who undergo the offender 

program. However, unlike those whose cases are ignored, these 

suspects have among the lowest recidivism likelihoods in the 

sample. In short, the risk evaluations of these individuals made 

by prosecutors and judges seem to be fairly well-informed. 

0 

The results presented here indicate that sentencing policies 

designed to reduce domestic violence recidivism need not place 

unrealistic demands on probation officers and jail resources. For 

example, the significance of the main and conditional effects of 

the offender program over other (more severe) dispositions 

suggest that many offenders can be successfully dealt with in a 

relatively expedient fashion. Even the results for a suspect's 

living arrangements at the time of arrest underscore the 

0 importance of restraining orders for reducing opportunities for 

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not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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violence. 

Another recommendation follows from the results for the 

contextual analysis. The idea that the types of sentences for 

higher-risk offenders do not matter is not supported here. 

Pursuing charges against these individuals in addition to 

sentences of jail appear to be effective for reducing recidivism 

among these offenders. On the other hand, reducing violence among 

offenders living in higher-stake neighborhoods needs more careful 

consideration. More punitive dispositions may not be successful 

for these individuals. In some cases, more severe sentences may 

escalate the problem. This does not mean that such offenders 

should necessarily be treated more leniently, but it does 

underscore the need for additional research on the lifestyle 

characteristics of these offenders that may mediate the effects 

of legal controls. For example, the lower divorce rates in higher 

socio-economic neighborhoods could lead to greater opportunities 

for these offenders to repeat their assaults. Perhaps a greater 

use of restraining orders and victim counseling would be 

effective, but more research is needed on what works for these 

types of offenders. 

a 

This document is a research report submitted to the U.S. Department of Justice. This report has

not been published by the Department. Opinions or points of view expressed are those of the

author(s) and do not necessarily reflect the official position or policies of the U.S. Department of

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