Posted: April 24th, 2025
Discussion 5 Senior Capstone CJUS-4110
Based on what you read in the Haynes article, do you think the courtroom workgroup is good or bad for defendants? Why? If bad, what do you think can be done to solve it?
Courtroom Workgroups
and Sentencing
The Effects of Similarity,
Proximity, and
Stability
Stacy Hoskins Haynes
Barry Ruback
Pennsylvania State University
Gretchen Ruth Cusick
Chapin Hall Center for Children and University of Chicago
Sentencing decisions are the product of a group of courtroom actors, primarily
judges and district attorneys. Although the structure of the courtroom work-
group and the interdependencies among members are assumed to be important
determinants of sentencing decisions, the degree of this importance and the
specific mechanisms through which workgroups affect these decisions have
not been investigated. This study used data from the Pennsylvania Commission
on Sentencing (PCS) for the years 1990 to 2000 to examine how three social
psychological aspects of courtroom workgroups (similarity, proximity, and
stability) affect sentencing decisions. Results indicated (a) that workgroups
generally had very high levels of similarity in terms of race, gender, and polit-
ical party but lower levels of similarity in terms of age, college education, and
law school education and (b) that proximity and stability were generally high.
Controlling for individual, case, and distal contextual factors, workgroup fac-
tors affected the decision to incarcerate, the decision to impose fines, and the
decision to impose restitution. In particular, proximity increased the use of
economic sanctions relative to incarceration and stability was associated with
a decrease in the imposition of economic sanctions. Similarity had inconsistent
effects.
Keywords: courtroom workgroups; sentencing; contextual factors; incar-
ceration; economic sanctions
Sentencing decisions represent the culmination of investigative, legal,
practical, and policy determinations. Although sentencing is primarily a
function of two factors—the severity of the offense and the offender’s prior
Crime & Delinquency
Volume 56 Number 1
January 2010
126
-161
© 2010 SAGE Publications
10.1177/0011128707313787
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hosted at
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Haynes et al. / Courtroom Workgroups and Sentencing 127
record—individual, case, and contextual effects can contribute to variations
in sentencing outcomes. In addition, the relationships between individuals
involved in sentencing—the judge, prosecutor, and defense attorney—may
be important because of shared beliefs about what constitutes appropriate
sentencing. These individuals, who together form the courtroom work-
group, share a common task environment and work together to achieve the
common goal of disposing of cases.
This study uses hierarchical modeling techniques to examine whether
and how sentences are affected by three aspects of courtroom workgroups:
similarity (i.e., the degree to which workgroup members share the same
characteristics), proximity (i.e., the location of workgroup members’
offices in relation to one another), and stability (i.e., the number of years
workgroup members worked together in the same jurisdiction). These three
quantitative measures of the proximal context (i.e., characteristics of the
individuals responsible for the handling of cases), along with measures of
individual, case, and distal contextual factors (i.e., characteristics of the
jurisdiction in which cases are processed), are used to investigate the deci-
sion to incarcerate, the decision to impose fines, and the decision to impose
restitution. In addition, this study examines how workgroup factors affect
statutory implementation, specifically restitution decisions before and after
a 1995 statutory change making restitution mandatory.
Overview of Sentencing Research
Legally relevant factors, particularly crime seriousness and offense
history, are the most important determinants of sentencing outcomes
(Hofer, Blackwell, & Ruback, 1999; Myers & Talarico, 1987), but studies
have also examined how offender characteristics, judge characteristics, and
contextual characteristics affect sentencing.
Offender Characteristics
Although some studies have found that extralegal factors such as the
offender’s race/ethnicity, gender, and age have little effect on sentencing,
Authors’ Note: This research was supported by grants from the National Institute of Justice
(Grant No. 97-CE-VX-0001) and the Pennsylvania Commission on Crime and Delinquency and
by funds from the Center for Research on Crime and Justice at Penn State University. The points
of view expressed in this article do not necessarily reflect the opinions of the Department of
Justice, the Pennsylvania Commission on Crime and Delinquency, or the Pennsylvania
Commission on Sentencing. We thank Cynthia Kempinen for her thoughtful comments on an
earlier draft.
128 Crime & Delinquency
independent of legally prescribed variables such as the severity of the
offense and the offender’s prior record (Konecni & Ebbesen, 1982), other
studies have found that offender characteristics contribute significantly to
sentencing outcomes (Steffensmeier, Ulmer, & Kramer, 1998). Studies
have focused primarily on three offender characteristics: race/ethnicity,
gender, and age. Regarding race/ethnicity, studies indicate that Black and
Hispanic offenders receive harsher sentences than White offenders (Kramer
& Ulmer, 1996; Steffensmeier & Demuth, 2000). Although Black and
Hispanic offenders are generally more likely to be incarcerated than White
offenders, they do not necessarily receive lengthier sentences (Spohn,
2000). With regard to gender, evidence suggests that the criminal justice
system treats female offenders more leniently than male offenders (Hofer
et al., 1999; Steffensmeier et al., 1998). Regarding age, evidence suggests
that both older and younger offenders receive more lenient sentences than
offenders in the middle of the age distribution (Steffensmeier, Kramer, &
Ulmer, 1995). In sum, research suggests that although offender characteris-
tics contribute significantly to sentencing outcomes, the effects are small
when compared to legally relevant factors.
Judge Characteristics
Most studies that have examined the effects of decision-maker charac-
teristics on sentencing outcomes have focused primarily on judges. Judges’
socialization experiences, which are a product of their political affiliation,
demographic characteristics, and educational experiences, influence their
later values, attitudes, beliefs, and ideologies. For example, conservative
judges embrace a more punitive stance toward crime and are more likely to
attribute an individual’s criminal involvement to personal choice, whereas
liberal judges focus more on rehabilitating the offender and are more likely
to believe that factors external to the individual are responsible for their
involvement in crime (Carroll, Perkowitz, Lurigio, & Weaver, 1987).
In addition to political beliefs, some research suggests that judges’ race
and gender affect sentencing. Findings regarding the effects of race on sen-
tencing are mixed regarding the imposition of incarcerative sentences. For
example, some studies find that minority judges are more likely to incar-
cerate offenders (Steffensmeier & Britt, 2001), whereas others find that
minority judges are somewhat less punitive (Johnson, 2006). With regard to
gender, studies consistently find greater similarities than differences in how
male and female judges sentence offenders (Gruhl, Spohn, & Welch, 1981).
However, some evidence suggests that female judges are somewhat harsher
Haynes et al. / Courtroom Workgroups and Sentencing 129
in their sentencing decisions, particularly toward repeat Black offenders,
because they are more strongly influenced by offender characteristics and
prior record than are male judges (Steffensmeier & Hebert, 1999).
In sum, there appear to be no consistent findings regarding the effects of
individual judges’ characteristics on sentencing. Rather than continue to focus
on the characteristics of individual judges, it might make sense to examine
how those characteristics relate to those of people with whom judges interact.
Contextual Characteristics
The differential weighting of individual and case factors is probably the
result of both the judge who handed down the decision and the jurisdiction
in which the case was processed. Thus, in addition to examining how judi-
cial discretion affects the decision-making process, studies have examined
how the jurisdiction conditions that discretion. The court context refers to
both the proximal context—the characteristics of the individuals responsi-
ble for the handling of cases—and the distal context—the characteristics of
the jurisdiction in which cases are processed.
Studies consistently find that otherwise similar offenders receive differ-
ent sentences in rural and urban areas. Research at both the state and fed-
eral levels finds that offenders sentenced in large urban courts receive less
severe sentences than offenders sentenced in small rural courts (Hofer et al.,
1999). Because urbanization correlates with other court and county vari-
ables, however, its effect depends on the offense, the specific sentencing
outcome under consideration, and the attributes and behavior of offenders
(Myers & Talarico, 1986, 1987; Olson, Weisheit, & Ellsworth, 2001).
A county’s political ideology (i.e., the extent to which residents are con-
servative or liberal) is important because judges’ decisions generally reflect
the views of their constituents. Thus, judges in more conservative jurisdic-
tions tend to impose longer sentences, particularly on repeat offenders and
offenders convicted of more serious offenses (Eisenstein, Flemming, &
Nardulli, 1988). Furthermore, a study of 337 jurisdictions in seven states
revealed that male offenders and Black offenders received longer sentences
in more conservative political environments (Helms & Jacobs, 2002).
In sum, although sentencing studies have examined legally relevant fac-
tors, offender characteristics, judge characteristics, and contextual factors,
many of these studies have examined only one type of factor (typically
offender characteristics). There is a need for studies that test all of these fac-
tors simultaneously. Moreover, there is a need to acknowledge the interplay
of these factors. This study does that with regard to judges, by analyzing
how judges relate to the other primary member of the courtroom work-
group, the district attorney.
A Social Psychological Framework for Studying
Courtroom Workgroups
According to Eisenstein and his colleagues (Eisenstein et al., 1988;
Eisenstein & Jacob, 1977; Nardulli, Eisenstein, & Flemming, 1988), court-
room workgroups consist of individuals who share a common workplace,
who interact in the performance of their jobs, and whose collective purpose
is to dispose of cases. Those researchers conducted two studies that inves-
tigated how courtroom workgroup traits, offender characteristics, and case
factors affect the disposition process. In their first study, Eisenstein and
Jacob (1977) examined more than 4,000 cases in felony courts in Baltimore,
Chicago, and Detroit. Ten years later, Eisenstein et al. (1988) conducted a
more extensive study that further explored how courtroom workgroups
dispose of cases. In this second study, the researchers analyzed data on
nearly 7,400 defendants and 300 judges, prosecutors, defense attorneys,
and other participants in three medium-sized criminal courts in each of
three states: Illinois, Michigan, and Pennsylvania.
On the basis of these studies, Eisenstein and his colleagues concluded
that differential patterns of sentencing occur because courtroom work-
groups perceive offenders and cases differently (Eisenstein & Jacob, 1977;
Eisenstein et al., 1988). Furthermore, because sentencing is a shared deci-
sion, the structure of the courtroom workgroup and the interdependencies
among members explain much of the variance in sentencing outcomes
across jurisdictions. The results suggested that factors such as the degree of
familiarity among workgroup members, the context in which the court is
located, and the county legal culture (i.e., workgroup members’ shared
beliefs about both interpersonal relations and the manner in which cases
should be disposed of) affect workgroup behavior.
The notion of workgroups is powerful, but there have been few quantita-
tive studies that have examined how they affect sentencing, and there have
been no real tests of their importance relative to individual and case charac-
teristics. Moreover, Eisenstein and his colleagues’ research focused on
courtroom workgroups in only 12 medium-sized counties, thereby neglect-
ing how workgroups across the entire range of counties in a state—from
small rural counties to very populous urban counties—might affect sentenc-
ing (Eisenstein & Jacob, 1977; Eisenstein et al., 1988).
130 Crime & Delinquency
Haynes et al. / Courtroom Workgroups and Sentencing 131
A further limitation of the research on workgroups is that it ignores the
social psychological literature on small groups, particularly work relating
to the formation of groups and the manner in which groups carry out tasks.
Three factors from the social psychological work are relevant to Eisenstein
et al.’s research on courtroom workgroups: similarity (i.e., the degree to
which individuals share the same characteristics), proximity (i.e., spatial
closeness), and stability (i.e., interacting with the same individuals over
time). We chose to focus on these variables because similarity and proxim-
ity are two of the most important predictors of group formation. Thus, not
surprisingly, because most group members come from the same social net-
work there tends to be considerable overlap in members’ knowledge, expe-
riences, and perspectives (Jehn, Northcraft, & Neale, 1999). Furthermore,
stability affects group performance by reducing uncertainty about others’
intentions and probable behavior. We expected that quantitative opera-
tionalizations of all three social psychological aspects of courtroom work-
groups—similarity, proximity, and stability—would affect sentencing outcomes.
Similarity
Eisenstein et al. (1988) observed that members of courtroom work-
groups are similar to each other. Given that similarity, they should like one
another, as people are attracted to others who have similar attitudes, beliefs,
and personal characteristics (Byrne, 1971; Newcomb, 1961), and they value
the contributions of similar others more than the contributions of dissimilar
others (Hinds, Carley, Krackhardt, & Wholey, 2000).
In a courtroom workgroup, there are several possible dimensions of sim-
ilarity that might be relevant to how the workgroup operates, including
demographic characteristics (race, gender, and age), background (college
and law school education), and beliefs (political party). Although numerous
studies in the organizational literature have examined how differences in
age, gender, race, job tenure, and education affect performance (Jehn &
Bezrukova, 2004; Tsui, Porter, & Egan, 2002), few studies have examined
the role of these factors in sentencing. Our first step was to determine the
similarity of workgroups, that is, the extent to which workgroup members
(judges and district attorneys) share the same characteristics. Second, we
wanted to determine whether that degree of similarity is related to sentenc-
ing decisions. In general, we expected that similarity among workgroup
members would be relevant primarily for decisions that involve discretion.
In contrast, for decisions that are straightforward or automatic, workgroup
132 Crime & Delinquency
similarity should not be very important. Furthermore, although we expected
all three types of similarity to be important, we expected education to
matter more than demographic characteristics because research shows that
over time age and gender become less relevant (Jehn et al., 1999).
Proximity
Individuals who are physically closer to one another generally interact
more and are therefore more likely to like each other than are individuals who
are more distant from one another (Festinger, Schachter, & Back, 1950).
Because organizational settings provide such proximity, they create opportu-
nities for individuals to form interpersonal network ties (McPherson &
Smith-Lovin, 1987). Consistent with these findings, Eisenstein and his col-
leagues (1988) observed that courtroom workgroups are highly dependent on
their environments. The geography of the courthouse (e.g., the availability of
informal meeting places) influences the structure of court communities and
the opportunities available for workgroup members to interact. These oppor-
tunities lead to greater familiarity, which, in turn, facilitates cooperation and
improves the decision-making process.
It is likely that proximity relates to whether there are opportunities for
workgroup members to discuss alternative sanctions. When workgroup
members’ offices are in the same building, for example, there are more
opportunities for workgroup members to come into contact with one
another outside the courtroom. These informal meetings may affect sen-
tencing outcomes because they provide workgroup members with opportu-
nities to discuss the general appropriateness of different sanctions and, we
believed, would make intermediate punishments like economic sanctions
more likely to be imposed.
Stability
Workgroup members become more familiar with one another when they
have more interactions over time, which are likely to increase when there
are few changes in workgroup personnel. The stability of a group’s mem-
bership determines the familiarity among members (Goodman & Leyden,
1991) and their patterns of communication (Katz, 1982). These factors
exert a significant effect on the group’s performance because groups com-
posed of more familiar individuals generally perform better than groups
composed of less familiar individuals (Guzzo & Dickson, 1996).
Workgroup stability also reduces uncertainty about others’ intentions and
probable behaviors. Because groups are generally more productive when
members’ behaviors are predictable, individuals typically prefer to work with
others whose personalities and work practices are familiar to the rest of the
group (Hinds et al., 2000). This preference explains why Eisenstein and
Jacob (1977) found that workgroup members who were more familiar with
one another were more likely to dispose of cases by plea bargaining.
We expected that stability would affect the imposition of economic sanc-
tions. Members of more stable workgroups have more joint experience with
offenders and are therefore probably more realistic about offenders’ ability
to pay economic sanctions. Furthermore, we expected that stability would
affect statutory implementation. A statutory change is likely to be success-
fully implemented when it is consistent with individuals’ beliefs and prac-
tices and when the individuals and organizations responsible for its
implementation are both capable and willing to transform the policy into
practice (Goggin, 1986). In 1995, Pennsylvania implemented a statutory
change mandating that courts order restitution for those victims who had
suffered a physical injury or monetary loss as the direct result of a crime
(18 Pa. C.S.A. §1106). The statute required judges to impose full restitution
regardless of the offender’s ability to pay. Studies have found that the
statute had an effect, as courts in Pennsylvania ordered restitution more
often for offenses after the statutory change (Ruback, Ruth, & Shaffer,
2005; Ruback, Shaffer, & Logue, 2004). We expected that decisions by
more stable workgroups would be more consistent across the pre– and
post–statutory change periods than would decisions by less stable work-
groups. That is, we expected more stable workgroups to be less likely to
change their rate of imposition of restitution following the statutory change.
The Current Study
The current study used statistical techniques appropriate for multilevel
data to examine individual, case, and contextual effects on sentencing
outcomes across an entire state. Furthermore, we built on the work of
Eisenstein and his colleagues (Eisenstein & Jacob, 1977; Eisenstein et al.
1988) by examining how three quantitative measures of the courtroom
workgroup—similarity, proximity, and stability—affect the decision to
incarcerate, the imposition of fines, and the imposition of restitution. We
focused on these three sentencing outcomes because they range in type
(i.e., one incarcerative sanction and two economic sanctions), severity, and
amount of discretion. We also examined how workgroup characteristics
Haynes et al. / Courtroom Workgroups and Sentencing 133
134 Crime & Delinquency
affected courts’ response to a statutory change that made restitution manda-
tory. Courtroom workgroups may consist of judges, prosecutors, defense
attorneys, probation officers, police officers, and administrative personnel.
Although all of these individuals play some role in the disposition process,
our data are limited to judges and district attorneys, individuals who are
employed by the state, who have a more permanent role in the courtroom
workgroup, and who handle the most cases.1
For each of the analyses, we first analyzed all 67 Pennsylvania counties.
Then we conducted those same analyses excluding Allegheny (Pittsburgh
)
and Philadelphia Counties, the two largest urban areas in the state, because
urban areas are likely to be highly formal and bureaucratic and decision
making is more likely to be based primarily on legal factors (Dixon, 1995).
By excluding these counties, we could determine whether the effects of
workgroups are stronger when only the less bureaucratized counties are
examined.
Method
We used hierarchical modeling techniques to examine individual, case,
and contextual (both proximal and distal) effects on sentencing outcomes.
Because criminal cases are nested within different counties, similarities
among cases at the county level are likely to occur (i.e., cases are likely to
be more similar within counties than across counties). This similarity
means that residual errors tend to be correlated within counties, which vio-
lates the ordinary least squares assumption of independent error terms and
risks the misestimation of standard errors. Hierarchical modeling tech-
niques address this problem of dependence by partitioning the variance
within and between units of analysis. Thus, multilevel models are appro-
priate for this study because they allow simultaneous tests for individual
and contextual effects on the dependent variable. The sentencing data
were analyzed using the hierarchical linear modeling (HLM) program of
Raudenbush and Bryk (2002).
The county-level (level-2) data for this study consisted primarily of
contextual information (i.e., proximal and distal characteristics) from The
Pennsylvania Manual (Commonwealth of Pennsylvania) for the years 1989 to
2000, the 1990 and 2000 U.S. Censuses, and the 1990 and 1995 Uniform
Crime Reports (Federal Bureau of Investigation). We collected additional
information from the Martindale-Hubbell Law Directory (Martindale-
Hubbell, 2003), the Pennsylvania District Attorneys Association (2004), and
personal communications with court personnel in all 67 Pennsylvania
Haynes et al. / Courtroom Workgroups and Sentencing 135
counties. The case-level (level-1) data consisted of sentencing information
(i.e., individual and case characteristics) from the Pennsylvania Commission
on Sentencing (PCS) for the years 1990 to 1994 and 1996 to 2000.2
Contextual Variables
The contextual variables included in this study were measures of both
the proximal context and the distal context. The proximal context refers to
characteristics of the courtroom workgroup (i.e., characteristics of the indi-
viduals responsible for the handling of cases), whereas the distal context
refers to characteristics of the county in which the court is located (i.e.,
characteristics of the jurisdiction in which cases are processed).
Proximal context. Three measures of the proximal context were included
in these analyses: similarity among workgroup members, proximity of
workgroup members’ offices in relation to one another, and stability of
workgroup membership. The information for these variables came primar-
ily from The Pennsylvania Manual (Commonwealth of Pennsylvania) for
the years 1989 to 2000.
The first set of variables included six measures of workgroup member
characteristics: race, gender, age, political party, and college and law school
location. Race was a dichotomous variable coded 1 for Whites and 0 for
nonwhites. Gender was coded 1 for males and 0 for females. Age was a
continuous variable that represented a workgroup member’s age (in years)
in 1990. Political party was a dichotomous variable coded 1 for Republicans
and 0 for Democrats. College and law school location were coded 1 for
Pennsylvania schools and 0 for non-Pennsylvania schools. We used these
variables to create separate measures of similarity for each workgroup
member characteristic. For all of the variables except age, similarity was
whether the variable had the same value for each judge and the county dis-
trict attorney (see Table 1). For example, if the district attorney was White
(as they all were), our measure of race similarity reflected the percentage of
judges who were White. Furthermore, if the district attorney attended col-
lege in Pennsylvania, our measure of college similarity reflected the per-
centage of judges who attended Pennsylvania colleges. Our measure of age
similarity reflected the percentage of judges who were within 5 years of the
district attorney’s age. For each type of similarity, we computed the per-
centage similarity for each year and then averaged across the 11-year
period for each of the 67 counties.
136 Crime & Delinquency
Table 1
Description of the Coding Scheme
Variable Coding Mean SD
County level
Urbanization % of the population living in an urban area 45.30 27.03
Economic climate % of the population living below poverty line 11.28 3.58
Political climate % of voters who voted for Republican candidate 49.46 7.83
Crime rate Part I offenses per 100,000 population 2,411.72 1,047.56
Gender composition % of males in the population 48.80 1.17
Stability Number of years worked together 3.77 1.48
Proximity 1 = offices in the same building, 0.75 0.44
0 = offices not in the same building
Race similarity % of judges the same 99.14 3.58
race as the district attorney
Gender similarity % of judges the same gender 90.24 19.56
as the district attorney
Age similarity % of judges within ± 5 years 32.84 25.95
of the district attorney’s age
College similarity % of judges who went to 45.06 37.01
college in Pennsylvania
Law school similarity % of judges who went to law 41.38 37.99
school in Pennsylvania
Political party similarity % of judges the same political 68.45 30.58
party as the district attorney
Case level
Prior record High prior record 0.18 0.38
(dummy variables) Low prior record 0.22 0.41
No prior record (reference) 0.60 0.49
Offense severity Range 0.07-1.00 0.31 0.18
Type of offense Person offense 0.21 0.41
(dummy variables) Property offense 0.35 0.48
Drug offense 0.20 0.40
Traffic offense 0.14 0.35
Other offense (reference) 0.09 0.29
Type of disposition Jury trial 0.02 0.15
(dummy variables) Bench trial 0.04 0.19
Plea (reference) 0.94 0.24
Type of sentence Prison 0.13 0.34
(dummy variables) Jail 0.42 0.49
Probation (reference) 0.45 0.50
Fines 1 = fines imposed, 0 = fines not imposed 0.55 0.50
Restitution 1 = restitution imposed, 0.36 0.48
0 = restitution not imposed
Offender race 1 = White, 0 = non-White 0.62 0.49
Offender gender 1 = male, 0 = female 0.84 0.37
Offender age Age at time of offense 30.32 9.58
The second variable was a measure of proximity that controlled for
whether the president judge (the administrative head of the county court
elected by all of the judges) and district attorney had offices in the same
building. This was a dichotomous variable coded 1 if their offices were in
the same building and 0 if their offices were not in the same building.3 In
all of the counties except Allegheny (Pittsburgh) and Philadelphia, all
judges are in the same building as the president judge. In these two coun-
ties, we coded that the president judge and the district attorney did not have
offices in the same building.
The final variable was a measure of workgroup stability that represented
the number of years the judge and county district attorney worked together
within the same jurisdiction. For each county, we aggregated across all
judges to compute the average workgroup stability for each year and then
averaged across the 11-year period. For any given year, the average work-
group stability ranged from 0 to 14 years. For any given judge, the number
of years he or she worked with the county district attorney ranged from
0 to 22 years.
Distal context. We included five variables representing the social, politi-
cal, and criminal justice context of each county. Three variables came from
the 1990 and 2000 U.S. Censuses. The first variable, a measure of urbaniza-
tion, was the percentage urban population within each county. The second
variable, a measure of the county’s economic climate, was the percentage of
the population living below the poverty level. The third variable, the percent-
age of males within each county, controlled for the individuals most prone to
engaging in crime. We calculated each of these variables separately using the
1990 (pre–statutory change) and 2000 (post–statutory change) census data.
We then computed the average percentage urban population, percentage liv-
ing below poverty, and percentage male for the years 1990 to 2000.
The fourth variable came from The Pennsylvania Manual (Commonwealth
of Pennsylvania, 1989/1990-1999/2000). This variable, a measure of the
county’s political climate, was the average percentage of county voters who
voted for the Republican candidate in the 1992 and 1996 presidential elec-
tions and the 1994 and 1998 gubernatorial elections. For the restitution
analyses only, results from the 1992 presidential election and the 1994
gubernatorial election controlled for the political climate during the
pre–statutory change period and results from the 1996 presidential election
and the 1998 gubernatorial election controlled for the post–statutory
change period. We also estimated correlations between voter registration
and (a) the average percentage of county voters who voted for the Republican
Haynes et al. / Courtroom Workgroups and Sentencing 137
138 Crime & Delinquency
candidate in the 1992 and 1996 presidential elections and the 1994 and
1998 gubernatorial elections (.91), (b) the average percentage of Republican
voters during the pre–statutory change period (.89), and (c) the average per-
centage of Republican voters during the post–statutory change period (.90).
The fifth variable, taken from the 1990 and 1995 Uniform Crime Reports
(Federal Bureau of Investigation) was an indicator of county crime rates
based on reported Part I offenses per 100,000 population. For the restitution
analyses only, the 1990 crime rate controlled for the pre–statutory change
period and the 1995 crime rate controlled for the post–statutory change
period.
Sentencing Variables
Under Pennsylvania law, judges are required to submit a Guideline
Sentence Form for most misdemeanor and felony convictions in the state.
This form provides information about offender characteristics, the severity of
the conviction offense, the offender’s prior convictions, the sentence recom-
mendation, the type of disposition, and the sentence imposed. For each crim-
inal sentence, we included only the most serious offense because it is the best
approximation of the crime for which the offender was sentenced. Because
individuals could appear in the data more than once during a single year,
we excluded second and subsequent offenses within each year to eliminate
the problem of correlated errors. After removing these cases from the data,
the final sample consisted of 549,942 cases for the years 1990 to 2000.
In addition to this data set, we also compiled a data set over the same
time period that included only restitution-eligible cases (i.e., cases with an
identifiable victim, other than the state, who suffered a quantifiable loss).
The final sample of restitution-eligible cases consisted of 117,543 cases in
1990 to 1994 (before the statutory change making restitution mandatory)
and 93,610 cases in 1996 to 2000 (after the statutory change).
Three sentencing decisions served as the dependent variables. The deci-
sion to incarcerate offenders was a dichotomous variable coded 1 if incar-
cerated and 0 if not incarcerated. The decision to impose fines and the
decision to impose restitution were dichotomous variables coded 1 if
imposed and 0 if not imposed. To control for level-1 factors, we also
included three measures of individual offender characteristics (race,4
gender, and age;5 see Table 1) and four variables measuring legally relevant
aspects of the case (offense type, offense severity,6 prior record,7 and mode
of disposition;8 see Table 1).
Our analyses predicting the decision to incarcerate included two controls
for the overall severity of the sentence: fines and restitution. For the remain-
ing analyses, we included dummy variables for jail and prison, with nonin-
carcerated individuals serving as the reference group. We used this
distinction because Holleran and Spohn (2004) found that studies including
a total incarceration variable (i.e., one that combines jail and prison into a
single response category) reach different conclusions regarding the corre-
lates of sentencing decisions. Including this variable allowed us to test
whether offenders incarcerated in jail or prison were less likely than nonin-
carcerated offenders to have economic sanctions imposed. Our analyses
predicting the imposition of fines included controls for restitution, jail, and
prison and our analyses predicting the imposition of restitution included
controls for fines, jail, and prison. Table 1 summarizes our coding scheme.
Results
The results are in three parts. First, we present descriptive information
about Pennsylvania judges and district attorneys, county courtroom work-
groups, and the overall sample of 549,942 cases. Second, we present results
from the multilevel logistic models and describe how offender characteristics,
case characteristics, and the social context, including the courtroom work-
group variables, affected sentencing decisions regarding incarceration and
fines. Third, we present analyses of restitution decisions before and after
the 1995 statute making restitution mandatory. For all three sentencing
decisions—the decision to incarcerate, the imposition of fines, and the impo-
sition of restitution—we estimated one model using all 67 Pennsylvania
counties and one model using all but Allegheny and Philadelphia Counties.9
Descriptive Analyses
Judges. Across the 11-year period 1990 to 2000, there were 599 judges,
most of whom were White (92%) and male (85%). They ranged in age from
32 to 71, with a mean and median age of 51. Most judges were Democrats
(59%), and most had attended Pennsylvania colleges (70%) and Pennsylvania
law schools (72%).10
District attorneys. All of the 125 district attorneys included in the sam-
ple were White and most were male (95%). They ranged in age from 28 to
68, with a mean and median age of 41. Most district attorneys were
Haynes et al. / Courtroom Workgroups and Sentencing 139
140 Crime & Delinquency
Republicans (71%), and most had attended Pennsylvania colleges (54%)
and Pennsylvania law schools (53%).11
Courtroom workgroups. In most Pennsylvania counties, the president
judge and district attorney had offices in the same building (75%). The
average workgroup stability ranged from 1.3 to 9.1 years (M = 3.8; Mdn =
3.5 years). Although most courtroom workgroups exhibited high race sim-
ilarity (99%), gender similarity (90%), and political party similarity (68%),
the average age similarity was much lower (33%). Furthermore, counties
had slightly more college similarity (45%) than law school similarity
(41%). Five law schools in Pennsylvania accounted for 63% of all judges.
Five law schools, four in Pennsylvania, accounted for 53% of all district
attorneys.
Allegheny and Philadelphia Counties, the two largest counties in
Pennsylvania, accounted for more than one quarter of the cases in our sam-
ple. Because these counties are very different from the rest of the state, we
also examined how workgroup characteristics in these counties compared
to those in other counties. Table 2 describes workgroup characteristics
across all 67 Pennsylvania counties, as well as separately for Allegheny and
Philadelphia Counties and for the remaining 65 counties. Compared to the
rest of the state, workgroups in Allegheny and Philadelphia Counties had
lower race similarity, gender similarity, and political party similarity but
had higher college similarity, law school similarity, and stability.
Offender sample. Most offenders were White (62%) and male (84%) and
had a mean age of 30 years (Mdn = 29). Most offenders had no prior record
(60%). Fines were imposed in 55% of all cases, and restitution was
imposed in 36% of all cases. Overall, 42% of offenders were sentenced to
jail and 13% of offenders were sentenced to prison.
Multivariate Analyses
Random coefficients models. We began the multivariate analyses by esti-
mating baseline models that included all case-level (level-1) variables and
their random coefficients. That is, we allowed the effects (i.e., slopes) of
both case and offender characteristics to vary across counties. We estimated
these models to determine whether judges in different counties differen-
tially weighted case and offender characteristics when making sentencing
decisions.
Haynes et al. / Courtroom Workgroups and Sentencing 141
All of the variance components were significant for the decision to
incarcerate and for the decision to impose fines. For the decision to impose
restitution during the years 1990 to 1994, all of the variance components
except bench trial were significant. Therefore, all of the coefficients except
bench trial were allowed to vary (i.e., treated as random) in the subsequent
models. For the years 1996 to 2000, all of the variance components except
high prior record were significant for the decision to impose restitution.
Therefore, in the subsequent models, all of the coefficients except high
prior record were treated as random.
The significant variance components for the intercepts mean that there
were significant differences between counties in the mean proportion of
Table 2
Description of Workgroup Characteristics Across All 67 Pennsylvania
Counties, Including Separate Descriptions for Allegheny and
Philadelphia Counties and for the Remaining 65 Counties
Allegheny and
67 Counties Philadelphia Counties 65 Counties
Variable Mean Median Mean Median Mean Median
Stabilitya 3.8 3.5 5.0 5.0 3.7 3.5
(1.5) (2.3) (1.5)
Proximityb 0.7 1.0 0.0 0.0 0.8 1.0
(0.4) (0.0) (0.4)
Race similarityc 99.1 100.0 83.9 83.9 99.6 100.0
(3.6) (10.0) (2.0)
Gender similarityc 90.2 100.0 56.3 56.3 91.3 100.0
(19.6) (40.1) (18.
2)
Age similarityc 32.8 31.8 32.5 32.5 32.9 31.8
(26.0) (12.3) (26.3)
College similarityc 45.1 45.5 62.7 62.7 44.5 45.5
(37.0) (3.9) (37.5)
Law school similarityc 41.4 36.4 72.7 72.7 40.4 32.1
(38.0) (6.8) (38.2)
Political party similarityc 68.5 72.7 46.3 46.3 69.1 72.7
(30.6) (44.3) (30.3)
Note: Numbers in parentheses are standard deviations.
a. Refers to the number of years workgroup members worked together in the same jurisdiction.
b. A dichotomous variable coded 1 if the president judge and district attorney had offices in
the same building and 0 otherwise.
c. Similarity refers to the percentage agreement between judges and district attorneys on that
dimension.
cases in which incarceration was ordered, fines were imposed, and restitu-
tion was imposed. The significant variance components for the case-level
predictors mean that judges weighted these offender and case characteris-
tics differently across counties. Furthermore, the size of the variance com-
ponents indicates how much variation exists for each factor across counties.
For example, factors related to the case (e.g., offense severity and the impo-
sition of fines) had larger variance components than offender demograph-
ics (e.g., race and gender). This finding means that judges used case
characteristics less consistently than offender characteristics when making
sentencing decisions.
Incarceration. Table 3 presents the main effects of 13 case characteris-
tics, 4 offender characteristics, 5 distal context effects, and 8 proximal con-
text effects. The odds of incarcerating an offender were significantly greater
for offenders with low or high prior records (compared to offenders with no
prior records); for offenders who committed more serious crimes; for
offenders convicted of person, drug, or traffic offenses (compared to
offenders who committed other offenses); for offenders convicted at a
bench or jury trial (compared to offenders who pleaded guilty); for cases in
which fines were not imposed; for non-White offenders; for male offend-
ers; and for older offenders. The odds of incarcerating an offender were
significantly lower for offenders who committed property offenses com-
pared to those who committed other offenses. Offenders were more likely
to be incarcerated in rural counties and in counties with a lower percentage
of people living below the poverty level.
To determine the overall significance of the eight workgroup variables,
we conducted a Wald test. The results indicated that, as a whole, the work-
group variables contributed significantly to the prediction of the odds of
incarceration beyond what was explained by the offender, case, and distal
context variables (χ2 = 39.782, p < .001). Five workgroup variables affected
the decision to incarcerate. Offenders were more likely to be incarcerated
in counties with less gender similarity, less age similarity, more college
similarity, less law school similarity, and less political party similarity. A
second model excluding Allegheny and Philadelphia Counties revealed the
same general pattern.
Each of the predictor variables must be considered separately because
they have different scales (i.e., proximity is a dichotomous variable, stabil-
ity is measured in years, and the similarity measures could range from 0%
to 100%). Apart from race similarity (which ranged from 77% to 100%), all
of the similarity measures ranged from 0% to 100%.
142 Crime & Delinquency
(text continues on p. 146)
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Although the significant odds ratios may appear to differ only slightly
from 1, in fact, the effects of the significant variables are large. For
example, consider gender similarity, age similarity, college similarity, law
school similarity, and political party. The odds ratio of 0.996 for gender
similarity indicates that for each 1% increase in gender similarity, the odds
of incarceration decreased by 0.4%. Thus, the odds of incarceration for a
workgroup with the highest level of gender similarity (100%) were 33.0%
lower than the odds of incarceration for a workgroup with the lowest level
of gender similarity (0%).12 The odds ratio of 0.996 for age similarity indi-
cates that for each 1% increase in age similarity, the odds of incarceration
decreased by 0.4%. Thus, the odds of incarceration for a workgroup with
the highest level of age similarity were 33.0% lower than the odds of incar-
ceration for a workgroup with the lowest level of age similarity. The odds
ratio of 1.003 for college similarity indicates that for each 1% increase in
college similarity, the odds of incarceration increased by 0.3%. Thus, the
odds of incarceration for a workgroup with the highest level of college sim-
ilarity were 34.9% higher than the odds of incarceration for a workgroup
with the lowest level of college similarity. The odds ratio of 0.997 for law
school similarity indicates that for each 1% increase in law school similar-
ity, the odds of incarceration decreased by 0.3%. Thus, the odds of incar-
ceration for a workgroup with the highest level of law school similarity
were 26.0% lower than the odds of incarceration for a workgroup with the
lowest level of law school similarity. The odds ratio of 0.996 for political
party similarity indicates that for each 1% increase in political party simi-
larity, the odds of incarceration decreased by 0.4%. Thus, the odds of incar-
ceration for a workgroup with the highest level of political party similarity
were 33.0% lower than the odds of incarceration for a workgroup with the
lowest level of political party similarity.
We estimated a second model that compared offenders incarcerated in
prison to offenders incarcerated in jail. The results (not presented) showed
that no workgroup factors significantly affected the decision to incarcerate
offenders in prison rather than jail. This finding also held after excluding
Allegheny and Philadelphia Counties from the model.
Fines. The odds of fines being imposed were significantly greater for
offenders who committed less serious crimes, for offenders convicted of
drug or traffic offenses (compared to offenders convicted of other offenses),
for nonincarcerated offenders (compared to offenders incarcerated in prison
or jail), for cases in which restitution was imposed, and for male offenders.
The odds of fines being imposed were significantly lower for offenders
146 Crime & Delinquency
with high prior records (compared to offenders with no prior records) and
for offenders convicted of person or property offenses (compared to offend-
ers convicted of other offenses). Fines were more likely to be imposed in
rural counties. These results were essentially the same after excluding
Allegheny and Philadelphia Counties from the model.
The Wald test revealed that, as a whole, the eight workgroup variables
contributed significantly to the prediction of the imposition of fines beyond
what was explained by the case, offender, and distal context variables (χ2 =
40.290, p < .001). Three workgroup variables significantly affected the
decision to impose fines: stability, proximity, and college similarity. More
stable workgroups were less likely to impose fines. The odds ratio of 0.858
for stability indicates that each 1-year increase in stability corresponds to a
14.2% decrease in the odds of fines being imposed. Thus, the odds of fines
being imposed for a workgroup with the highest level of stability (9.1
years) were 69.7% lower than the odds of fines being imposed for a work-
group with the lowest level of stability (1.3 years). More stable workgroups
may have been less likely to impose fines because workgroup members had
developed their own “going rates” that were lower than those of less stable
workgroups.
In terms of proximity, workgroups were more likely to impose fines in
counties where the president judge and the district attorney had offices in
the same building. The odds ratio of 1.351 indicates that the odds of fines
being imposed were 35.1% greater when the offices were in the same build-
ing. Fines were also more likely to be imposed in counties with greater col-
lege similarity. The odds ratio of 1.004 for college similarity indicates that
for each 1% increase in college similarity, the odds of fines being imposed
increased by 0.4%. Thus, the odds of fines being imposed for a workgroup
with the highest level of college similarity were 49.1% higher than the odds
of fines being imposed for a workgroup with the lowest level of college
similarity.
Analyses of Restitution
We conducted two sets of analyses of the restitution decision. The first
set of analyses comprised the years 1990 to 1994, when judges had com-
plete discretion about whether to impose restitution. The results presented
in Table 4 show that the odds of receiving an order of restitution were sig-
nificantly greater for offenders who committed more serious crimes, for
offenders convicted of a property offense (compared to a person offense),13
for White offenders, and for female offenders. The odds of receiving an
Haynes et al. / Courtroom Workgroups and Sentencing 147
148 Crime & Delinquency
order of restitution were significantly lower for offenders with high prior
records (compared to offenders with no prior records) and for offenders
convicted at a jury trial (compared to offenders who pleaded guilty). In
terms of workgroup factors, only law school similarity significantly
affected the decision to impose restitution, such that greater law school sim-
ilarity significantly decreased the odds of receiving an order of restitution.
The odds ratio of 0.996 for law school similarity indicates that for each per-
centage point increase in law school similarity, the odds of restitution being
imposed decreased by 0.4%. Thus, the odds of restitution being imposed
for a workgroup with the highest level of law school similarity were 33.0%
lower than the odds of restitution being imposed for a workgroup with the
lowest level of law school similarity.
The second set of analyses we conducted comprised the years 1996 to
2000. Consistent with the multivariate analyses for 1990 to 1994, the odds
of receiving an order of restitution were significantly greater for offenders
who committed more serious crimes, for offenders convicted of a property
offense, for White offenders, and for female offenders. In contrast to the
pre–statutory change period, restitution was significantly more likely for
offenders sentenced to prison and for older offenders. Restitution was sig-
nificantly less likely for offenders convicted at a bench trial (compared to
offenders who pleaded guilty). Furthermore, stability and law school simi-
larity, which were marginally significant when all 67 counties were
included in the model, reached significance after excluding Allegheny and
Philadelphia Counties from the analysis. The odds ratio of 0.996 for law
school similarity indicates that for each percentage unit increase in law
school similarity, the odds of restitution being imposed decreased by 0.4%.
Thus, the odds of restitution being imposed for a workgroup with the high-
est level of law school similarity were 33.0% lower than the odds of resti-
tution being imposed for a workgroup with the lowest level of law school
similarity. The odds ratio of 0.938 for stability indicates that each 1-year
increase in stability corresponds to a 6.2% decrease in the odds of restitu-
tion being imposed. Thus, the odds of restitution being imposed for a work-
group with the highest level of stability were 49.9% lower than the odds of
restitution being imposed for a workgroup with the lowest level of stability.14
To test whether stability affected the imposition of restitution after the
enactment of the mandatory statute, we also conducted all of the above
analyses including as predictors the 1990 to 1994 county rate of imposition
of restitution and the interaction of this rate with stability. Neither of these
variables was significant in these additional analyses. Consistent with the
(text continues on p. 152)
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fact that few workgroup variables affected the decision to impose restitu-
tion, the Wald test revealed that, as a whole, the workgroup variables did
not significantly affect the odds of restitution being imposed during either
the pre–statutory change period (χ2 = 12.244, p = .14) or the post–statutory
change period (χ2 = 13.351, p = .10). We also conducted z tests comparing
the logistic coefficients for the workgroup variables before and after the
imposition of the mandatory statute (Paternoster, Brame, Mazerolle, &
Piquero, 1998). The results (not presented) indicated that there were no sig-
nificant differences between the workgroup variables during the pre–statutory
change period and the post–statutory change period.
To investigate whether our results were robust with respect to whether the
district attorney headed a small or large office, we also estimated models
using only those counties with five or fewer full- or part-time assistant dis-
trict attorneys (n = 34). We had to exclude the race similarity variable from
these analyses because it equaled 100% across all counties (i.e., all judges
and district attorneys were White). For all three sentencing outcomes—the
decision to incarcerate, the imposition of fines, and the imposition of
restitution—the results (not presented) were generally consistent across
all 67 counties and across all counties with five or fewer full- or part-time
assistant district attorneys.
Discussion
This study has several strengths; it examined multiple decisions in a
large number of cases from an entire state over a long time period and con-
trolled for a large number of factors at both individual and contextual lev-
els. Thus, there is reason to believe the findings are valid.
Overall, the results suggest that although crime seriousness and offense
history were the most important predictors of sentencing outcomes, work-
group factors affected the decision to incarcerate, the decision to impose
fines, and the decision to impose restitution. Although traditional indicators
of the distal context (e.g., the percentage of the population living below
poverty, the percentage of Republican voters, and the crime rate) generally
did not predict sentencing decisions, courtroom workgroup characteristics
did affect sentencing. Thus, when correct statistical models are employed,
it may be that the context operates through proximal factors such as work-
group similarity, rather than through distal factors such as poverty. Previous
studies may have failed to find evidence of contextual effects because they
focused primarily on characteristics of the distal context. Future research
152 Crime & Delinquency
needs to examine both proximal factors (i.e., characteristics of the individ-
uals responsible for the handling of cases) and distal factors (i.e., charac-
teristics of the jurisdiction in which cases are processed).
The Makeup of Workgroups
We expected that workgroup characteristics would differ substantially
between counties. In general, however, there was little or no variation in the
proximity between workgroup members’ offices or in the race and gender
of workgroup members. This lack of variation may explain why some
workgroup variables did not have larger effects on sentencing outcomes.
Overall, most workgroup members were White and male and attended both
Pennsylvania colleges and Pennsylvania law schools. Workgroups are
probably even more similar in states with fewer colleges and law schools.
We also expected that the workgroups in the two large urban counties
would be different from the workgroups in the remaining counties.
Consistent with that expectation, we found that more workgroup members
were non-White, female, and Democratic in the two large urban counties
than in the rest of the state. One reason that workgroup stability was higher
in these counties was because Philadelphia County had only one district
attorney between the years 1990 and 2000.
The fact that workgroups generally had high levels of similarity proba-
bly means that the groups tended to function better, as heterogeneity gen-
erally leads to less communication, more formal communication, and more
miscommunications. Members of homogeneous workgroups are more
likely to communicate with each other and to have informal communica-
tions with each other because they are more likely to have shared interests.
Furthermore, because they are more likely to share the same values, atti-
tudes, beliefs, and ideologies, they are less likely to misunderstand each
other. These findings explain Eisenstein and his colleagues’ (Eisenstein &
Jacob, 1977; Eisenstein et al., 1988) observation that homogeneous work-
groups are more likely to dispose of cases through consensus. However,
there is a downside to high levels of homogeneity, in that a lack of diversity
can lead to inflexibility and less innovation (Levine & Moreland, 1998).
Future research needs to examine these benefits and costs in more depth.
The Effect of Workgroup Factors on Decision Making
Overall, workgroup factors affected the decision to incarcerate, the
imposition of fines, and the imposition of restitution, sometimes in the
Haynes et al. / Courtroom Workgroups and Sentencing 153
154 Crime & Delinquency
same direction and sometimes in opposite directions. Moreover, workgroup
characteristics had different effects, depending on the type of sentencing
decision. That workgroup factors were significant at all, given the relatively
small number of counties and the large number of variables that we con-
trolled for, suggests that the effects are meaningful.
Furthermore, we found these effects even though Pennsylvania is a sen-
tencing guidelines state. Pennsylvania’s presumptive sentencing guidelines,
which apply to both misdemeanors and felonies, were intended to structure
judicial discretion and to reduce unwarranted disparities in sentencing. Under
the guidelines, judges’ sentencing decisions are determined primarily by the
severity of the offense and the offender’s prior record, but judges may depart
from the guidelines after considering mitigating and aggravating circum-
stances. Because Pennsylvania’s sentencing guidelines allow more discretion
than do other guideline systems, there are more opportunities for extralegal
factors to influence judges’ sentencing decisions (Kramer & Ulmer, 1996).
The fact that workgroup characteristics mattered even in a state with rela-
tively structured sentencing implies that the effects of these factors may be
even more robust in states where judges’ discretion is less limited.
Type of sentence. Compared to incarceration and the imposition of resti-
tution, workgroup characteristics had the strongest effect on the decision to
impose fines. One reason for this difference may be that judges have the
most discretion regarding fines. Sentencing guidelines and mandatory sen-
tencing probably play a more important role in the decision to incarcerate
than in the decision to impose fines. Furthermore, increased concern for
victims may lead to greater agreement among workgroup members about
the imposition of restitution, particularly after the 1995 statute making
restitution mandatory. Regarding the imposition of fines, however, work-
group members may have more flexibility.
Because the effects of workgroup characteristics may differ depending
on the mode of disposition, we also estimated models using only cases
resolved by bench or jury trial. The results indicated that in general, work-
group factors were no longer significant. That is, workgroup characteristics
that were significant in the overall sample no longer mattered when only
bench and jury cases were considered. This finding suggests that work-
group factors as we measured them matter least for more serious crimes
(i.e., the ones most likely to go to trial).
Similarity. Across the three sentencing decisions that we modeled, law
school similarity appeared to be the most important workgroup characteristic,
in that greater similarity led to less punishment (i.e., a lower probability of
incarceration and a lower probability that restitution would be imposed).
This finding suggests that workgroup members’ early socialization experi-
ences influence their later values, attitudes, beliefs, and ideologies. In other
words, judges and district attorneys likely share similar professional social-
ization experiences that, in turn, affect their sentencing practices.
Unlike all of the other types of similarity, college similarity increased
the probability of incarceration and the probability of imposing fines. It
may be that college similarity is associated with greater punitiveness
because workgroup members attended Pennsylvania colleges and therefore
probably have stronger ties to the state and have beliefs that are more
reflective of their communities.
One possible explanation for this effect is based on two arguments:
(a) Pennsylvania colleges are characterized by less diversity of opinion and
(b) the effects of living for 4 years in that environment are long-lasting.
With regard to the first point, the diversity of Pennsylvania colleges, it is
plausible that workgroup members who attended Pennsylvania colleges
were less likely to be exposed to new ideas. With regard to the second point,
that the college environment has a long-lasting effect, the best evidence is
a study of students at Bennington College conducted by Newcomb from
1935 to 1939. Newcomb found that the students, most of whom came from
conservative family backgrounds, became more liberal during their time in
college, and this change was especially true for students who were more
involved in campus activities. Decades later, these women continued to be
more liberal than their peers who went to comparable, but less liberal col-
leges (Newcomb, 1963).
Proximity. Greater proximity was associated with a lower likelihood of
incarceration and a greater likelihood of fines and, before the statutory
change mandating restitution, a greater likelihood of restitution. This pat-
tern suggests that when judges and district attorneys are in the same loca-
tion there is more likely to be a trade-off between incarceration and
economic sanctions, a realistic strategy that may be more common when
decision makers see each other more often. However, such efficiency may
not always result in greater justice, if, for example, restitution is less likely
to be imposed.
Stability. Greater stability was associated with a lower likelihood of
imposing fines and, after the statutory change, a lower likelihood of imposing
restitution. More stable workgroups may have been less likely to impose
Haynes et al. / Courtroom Workgroups and Sentencing 155
economic sanctions because workgroup members have more experience
with offenders, particularly with offenders’ (in)ability to pay. With regard
to restitution, more stable workgroups may have been less likely to impose
restitution because they were more resistant to the statutory change making
restitution mandatory, which is consistent with Eisenstein et al.’s (1988)
argument that entrenched groups are less likely to adopt new laws.
Type of county. Contrary to our expectations, even though workgroups
in large urban counties differed from workgroups in rural, suburban, and
small urban counties (i.e., workgroups in large urban counties exhibited
lower race similarity, gender similarity, and political party similarity), the
effects of workgroup characteristics on sentencing outcomes were gener-
ally consistent across all 67 Pennsylvania counties. This consistency sug-
gests that workgroup factors operate in the same way across all types of
counties.
Implications
In general, greater similarity within a workgroup decreased the odds of
an offender being incarcerated and the odds of restitution being imposed. In
this study, greater similarity means that most workgroup members were
White, male, and older. Furthermore, greater similarity means that most
workgroup members attended both Pennsylvania colleges and Pennsylvania
law schools. More similar workgroups may have been less likely to incar-
cerate offenders because workgroup members are more likely to agree about
possible alternatives to incarceration.
More similar workgroups may have been less likely to impose restitution
because these individuals may be more traditional in their views about the
criminal justice system. Orders of restitution to crime victims may have
been a relatively new idea to these individuals. Because a statutory change
is less likely to be implemented when it is inconsistent with the existing
beliefs and practices of the group and when the individuals responsible for
its implementation are either unable or unwilling to transform the policy
into practice (Goggin, 1986), more similar workgroup members (i.e., older
males) may have been less likely to impose restitution because it conflicted
with their beliefs about what considerations should be given to crime vic-
tims. It would be interesting to see whether, as workgroups become more
diverse in terms of their demographic makeup, there is a change in the
imposition of economic sanctions and in the implementation of statutory
change.
156 Crime & Delinquency
Haynes et al. / Courtroom Workgroups and Sentencing 157
In sum, then, the demographic and background characteristics of judges
and district attorneys affect both their sentencing patterns and how they
implement statutory change. We examined three social psychological indi-
cators of the proximal context—similarity, proximity, and stability—that
affected the decision to incarcerate, the decision to impose fines, and the
decision to impose restitution. Because characteristics of the proximal con-
text also affected the implementation of the mandatory statute, these find-
ings help us understand how policy is implemented.
Future Research
Eisenstein and his colleagues (1988) suggested that courtroom work-
groups consisted primarily of a judge, prosecutor, and defense attorney. A
major limitation of this study is that we were able to collect information
about only Pennsylvania judges and district attorneys. Nevertheless, the
fact that we found significant effects by examining only a subset of indi-
viduals implies that the effects may be even stronger if additional work-
group members are examined. Thus, future research should examine in
other states how workgroup members, such as defense attorneys and assis-
tant district attorneys, might affect the results presented in this study.
Studies should also consider the role of probation officers, because these
individuals may determine the types of information available to judges and
other workgroup members and therefore affect how offenders proceed
through the criminal justice system (Carter & Wilkins, 1967; Konecni &
Ebbesen, 1982). Information about these additional workgroup members
would provide a more complete understanding of how courtroom work-
groups affect the decision-making process.
Notes
1. Most Pennsylvania counties have five or fewer full- or part-time assistant district attor-
neys (zero = 9%, one = 10%, two = 13%, three = 8%, four = 6%, and five = 5%; National
Prosecutors Survey, 2001). Thus, most district attorneys handle cases (i.e., depending on the
size of the county and the amount of administrative work that is required, most district attor-
neys handle a full caseload).
2. We excluded data from 1995 because they are incomplete (Pennsylvania Commission
on Sentencing, 1997).
3. We created this variable using addresses provided by the PCS.
4. We grouped all non-White offenders into a single category. In practice this grouping is
not problematic, as the percentage of non-Black offenders in this category is small. For
example, in the full data, 5.6% of offenders were Hispanic, 0.2% were Asian, 0.5% were other,
158 Crime & Delinquency
and 3.4% were unknown. By grouping these individuals in the non-White category, we are
being conservative in our estimates (i.e., some of these individuals may be more similar to
Whites than to Blacks).
5. The final sample included only those offenders who were 18 years of age or older.
6. The range of possible offense gravity score (offense severity) values reported by the
PCS was 0 to 10 for the years 1990 to 1994 but increased to 13 under the 1994 guidelines and
to 14 under the 1997 guidelines. We created a variable that was comparable across years and
that captured the relative proportionality of the seriousness of the offense within each time
period by dividing the seriousness of the offense by the number of possible values for that time
period.
7. The PCS assigns a point value to offenses that it then uses to calculate an offender’s
prior record score. Because there were changes in the values assigned to particular offenses
over the course of this study, we created a variable that was comparable across years. Within
each year we recoded prior record into three categories: no prior record, low prior record, and
high prior record. Offenders with no prior record served as the reference group.
8. We included dummy variables for bench and jury trials, with guilty pleas serving as the
reference group. Approximately 18% of cases were missing data on the mode of conviction
variable. Rather than lose such a substantial number of cases, missing data were coded as
guilty pleas for two reasons. First, of the nonmissing cases, only 6% resulted in a bench or jury
trial conviction. Second, tests were conducted to determine whether there was a significant dif-
ference between missing cases and plea, jury, or bench trials based on the offender’s age,
gender, race, offense severity, offense type, and prior record. Results showed that missing
cases were significantly different from cases resulting in bench and jury trial convictions based
on these other variables but were not significantly different from plea outcomes.
9. Because multilevel modeling techniques are not appropriate for use with only two
counties, we could not estimate a separate model for these two counties or compute a statewide
model containing an interaction term for these 2 versus the remaining 65 counties.
10. Approximately 10% of judges were missing political party information, and approxi-
mately 5% of judges were missing information regarding the college and/or law school
attended. As a result, we created two variables for each measure: one with missing coded 0
and one with missing coded 1. In terms of political party, 0 means Democratic and 1 means
Republican. In terms of college and/or law school attended, 0 means non-Pennsylvania school
and 1 means Pennsylvania school. In our analyses, we used the variables with missing coded
0 because we believed this recoding provided more conservative estimates. When missing was
coded 1, 49% were Democrats, 75% attended Pennsylvania colleges, and 76% attended
Pennsylvania law schools. When we conducted all of our analyses with missing coded as 1,
the results were essentially the same as those presented in the tables.
11. Approximately 23% of district attorneys were missing information regarding the col-
lege and/or law school attended. As with the judges, we created two variables for each mea-
sure: one with missing coded 0 (non-Pennsylvania school) and one with missing coded 1
(Pennsylvania school). When missing was coded 1, 77% of district attorneys attended
Pennsylvania colleges and 76% attended Pennsylvania law schools. When we conducted all of
our analyses with missing coded as 1, the results were essentially the same as those presented
in the tables.
12. We computed the difference between workgroups with the highest and lowest levels of
gender similarity as follows: 0.996100 = 0.670, 1 – 0.670 = 0.330 (33.0%), where 0.996 is the
odds ratio and 100 is the range of the gender similarity variable.
Haynes et al. / Courtroom Workgroups and Sentencing 159
13. In the restitution data, which excluded drug, traffic, and other crimes, offense type was
coded 1 for property crimes and 0 for person crimes.
14. For the years 1996 to 2000, the average stability ranged from 1.2 years to 12.0 years.
Thus, we computed the difference between workgroups with the highest and lowest levels of
stability using a range of 10.8 years (rather than 7.8 years, as in the previous analyses).
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Stacy Hoskins Haynes is a PhD student at Pennsylvania State University. Her research inter-
ests include victimization, sentencing and the courts, and criminal justice policy.
Barry Ruback is a professor of crime, law, and justice and sociology at Pennsylvania State
University. His research focuses on victimization and on decision making in the legal system.
Gretchen Ruth Cusick is a senior researcher at Chapin Hall Center for Children and a
research associate at the University of Chicago. She received her PhD from Pennsylvania State
University in the Department of Sociology and Crime, Law, & Justice. Her research interests
include crime during the transition to adulthood, the dual involvement of youth in the juvenile
justice and child welfare systems, and the service system involvement of formerly incarcer-
ated youth.
Haynes et al. / Courtroom Workgroups and Sentencing 161
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