GENDER AND IMPRISONMENT DECISIONS

GENDER AND IMPRISONMENT DECISIONS

DARRELL STEFFENSMEIER The Pennsylvania State University

JOHN KRAMER The Pennsylvania State University

CATHY STREIFEL Purdue University

Guidelines sentencing data from Pennsylvania for the years 1985-1987 are analyzed to assess the influence of gender on judges’ imprisonment decisions. These data provide detailed in formation on offense severity and prior record, permit statistical controls for other variables thought to affect imprisonment decisions, cover a fairly comprehensive list of common-law offenses (with adequate sample size), and contain judges’ dispsitional- departure reasons for sentences outside the guidelines schema. The data-analyzed with additive and interactive models-indicate that gender (net of other factors) has a small effect on the likelihood of imprisonment toward lesser jailing of female defendants but has a negligible effect on the length-of-imprisonment decision. Observations and interview responses from selected judges help to clarijj the ways in which judges’ sentencing practices are gender linked. Together, the statistical and the qualitative data suggest that the sentencing practices of judges are driven by two main concerns, blameworthiness (e.g.. as indicated by prior record, type of involvement, remorse) and practicality (e.g., as indicated by childcare responsibility, pregnancy, emotional or physical problems, availability of adequate jail space). Based on our findings, we suspect that when men and women appear in (contemporary) criminal court in similar circumstances and are charged with similar offenses, they receive similar treatment. A major question from a policy perspective is, when gender disparities in sentence outcomes do arise, are the disparities warranted or unwarranted?

The issue of gender differences in judicial sanctioning has received increasing empirical attention over the past two decades. While some researchers have found few differences, a fairly persistent finding has been that adult female defendants are treated more leniently than adult male defendants. The gender differences in treatment are more often found in sentencing or imprisonment decisions than in case dismissals and convictions. Considered together, the studies substantiate the widely held belief that female defendants receive more lenient treatment (apparently) because of judicial paternalism,

CRIMINOLOGY VOLUME 31 NUMBER 3 1993 411

412 STEFWENSMEIER ET AL.

the social costs to children and families of sending women to prison, or the view that female defendants are less dangerous and more amenable to rehabilitation than male defendants (Daly, 1987; Steffensmeier, 1980). Some writers have even suggested that the much smaller rate of incarceration within female versus male populations is due in part to the greater lenience accorded the female defendant (Pollak, 1950; Simon, 1975).

The above conclusions are limited because of shortcomings in the existing research. For example, Steffensmeier (1980) suggests that the gender finding may be an artifact of inadequate attempts to control for seriousness of offense and prior record, two variables known to be robustly correlated with sentencing decisions. Spohn et al. (1987) criticize the existing research for failing to control for defendants’ race, especially since some research suggests that black female defendants are sentenced more harshly than white female defendants (Klein and Kress, 1976; Kruttschnitt, 198G81; but see Curran, 1983; Mann, 1984; Spohn et al., 1985). Still other writers have noted that the research suffers from a small number of cases and offenses that limit the selection of analytic techniques and the generalizability of findings (Nagel and Hagan, 1983; Steffensmeier, 1980).

This article examines gender differences in imprisonment decisions using statewide data from Pennsylvania for the years 1985-1987. We distinguish imprisonment decisions relative to two critical stages of judicial decisionmaking-whether to imprison and length of term. As we describe below, the Pennsylvania data are exceptionally well suited for assessing whether decisions concerning imprisonment are unduly lenient toward female offenders. Besides this central hypothesis, we examine whether women who commit serious crimes receive comparatively harsher treatment, and whether black female offenders receive comparatively harsher treatment than white female offenders. Some writers contend that judges regard serious crime as the prov- ince of men and thus sentence more harshly those women who commit the more serious, or masculine-type, offenses (Temin, 1976). Other writers insist that judges view black female offenders less paternalistically and are more punitive toward them than toward white female offenders (Klein and Kress, 1976).

PRIOR RESEARCH Table 1 provides a breakdown of post-1960 studies that examine gender

differences in imprisonment decisions, include at least rudimentary controls for legally relevant variables, and focus on common-law offenses.1 While not

1. Because our concern is with imprisonment decisions, we do not review the studies that examine gender differences at earlier stages of the criminal justice process, such as charging or plea negotiation (for reviews, see Albonetti, 1991; Nagel and Hagan, 1983). Also, we do not consider the research on the sentencing of white-collar defendants (see,

GENDER AND IMPRISONMENT DECISIONS 41 3

necessarily exhaustive, the list is a ledger of the major “scientific” studies in the area.

Note that Table 1 does not include a number of genderhentencing studies that we judged as seriously flawed because they lacked a control for prior record (Alabama Law Review Special Project, 1975; Cousineau and Veevers, 1972; Figueira-McDonough, 1974; Heilbrun, 1982; Kempinen, 1983; Kritzer and Uhlman, 1977; Nagel and Weitzman, 1971; Rhodes, 1977). We also omitted studies in which the interpretation of findings is uncertain because the variables entered into the regression equation were not identified (Simon and Sharma, 1979) or because the dependent variable was ambiguous (Ghali and Chesney-Lind, 1986).2 Finally, we excluded studies that included gender as a control variable but did not focus specifically on gender effects (e.g., Peterson and Hagan, 1983; Miethe and Moore, 1986; Orsagh, 1985).3

We draw several conclusions from Table 1. First, while some researchers report no differences (Kruttschnitt and Green, 1984), most report that adult female defendants are treated more leniently than adult male defendants. Female defendants, in particular, are less likely to be incarcerated.

Second, nearly all the studies included in Table 1, even those published in recent years, are based on data sets dating back to the 1960s or 1970s. In light of recent changes in the legal system, the findings from these studies may no longer apply. The past few decades have witnessed greater concern for equal application of the law, increasing professionalization and bureaucra- tization of criminal justice agencies, and a move toward more determinate sentencing procedures. Those factors have helped to (a) establish more uni- versal standards of decisionmaking and (b) reduce the importance of statuses ascribed to the “client” as criteria in decisionmaking (Steffensmeier, 1980). Because of these trends, for example, it is believed that race and class disparities in legal and criminal justice outcomes have been reduced.

Third, the shortcomings in the research to date vary from one study to another, but they include (a) crude measures of the nature of the offenses adjudicated; (b) “weak” controls for defendant’s prior record; (c) small number of cases and offenses; (d) failure to analyze the sentencing decision as two separate outcomes-the decision to incarcerate or not, and length of term imposed on those imprisoned; and (e) absence of contextual analysis to assess

e.g., Wheeler et al., 1982, who report mixed results regarding the effects of gender on sentencing outcomes).

Regarding the studies we have omitted from Table 1, while most report gender effects favoring female defendants (e.g., Nagel and Weitzman, 1971), several studies report either an absence of gender effects (Ghali and Chesney-Lind, 1986) or effects favoring male defendants (e.g., Alabama Law Review Special Project, 1975).

Note also that Orsagh’s (1985) measure of prior record- “offenses reported in the offender’s file”-is puzzling. It is not known whether “offenses” designate prior arrests or prior convictions, nor how “offenses” were classified for purposes of the analysis.

2.

3.

Tabl

e 1.

M

ultiv

aria

te S

tatis

tical

Stu

dies

of

Gen

der

Effe

cts

on I

mpr

ison

men

t D

ecis

ions

P,

P

Sam

ple

Size

: Total

(No.

Fem

ales

) St

udy

(Dat

a Pe

riod)

Gre

m,

1960

(195

6-57

) 1,

437

(91)

Baa

b an

d Fe

rgus

on,

1,72

0 (9

8)

1967

(196

5-66

)

Mou

lds,

198

Od (1

974)

30

,344

(2,4

17)

Farr

ingt

on a

nd M

om

s,

408

(110

) 19

83 (1

979)

Fraz

ier

et a

l., 1

983

Gru

hl e

t al.,

198

4

Kru

ttsch

nitt,

198

0-81

Kru

ttsch

nitt

and

Gre

en,

1984

(1

965-

80)

son.

198

4 (1

969-

77)

( 196

8-69

)

(197

2-73

)

(197

7-80

)

(197

2-76

)

Zing

ralf

and

Thom

p-

Spohn

et a

l., 1

985

Mye

rs a

nd T

alar

ico,

19

86 (5

,123

) ( 1

976-

82)

309

(108

)

10,7

12 (6

10)

1,02

7 (5

23)

2,92

3 (1

,558

)

9,46

4 (5

47)

29,9

65 (

1,96

5)

16,7

98 (5

,123

)

Gen

der

Effe

ctb

Prio

r R

ecor

d'

offe

nse

Seve

rity

In/O

ut

Sent

ence

Leng

th

scale

Prio

r co

nvic

tions

(0

,1,2

,3,4

+)

Prio

r fe

lony

con

vic-

tio

ns

Prio

r m

isde

mea

nor

conv

ictio

ns

Prio

r in

carc

erat

ions

Pr

ior

conv

ictio

ns

Con

vict

ions

in p

ast

two

Y- Age

at f

irst c

onvi

ctio

n Pr

ior

arre

sts

Prio

r ar

rest

sco

re

Years

in p

rison

Prio

r ar

rest

s

Prio

r se

nten

ces (

prio

r vs

. no

prio

r)

Prio

r pr

ison

sen

tenc

es

N/AB

offe

nse

type

(fe

lony

vs

. mis

dem

eano

r)

offe

nse

type

(12

offense

dum

my

vari-

ab

les)

offe

nse

type

dum

my

varia

bles

) of

fens

e ty

pe (3

off

ense

offense

type

(fel

ony

vs. m

isde

mea

nor)

offense

grav

ity s

core

(r

ange

: 1-9

9)

Max

imum

sta

tuto

ry

pena

lty fo

r 3

offe

nses

Maximum

stat

utor

y pe

nalty

for

3 offenses

offense t

ype

(8 o

ffens

e du

mm

y va

riabl

es)

offense

type

(12

offe

nse

dum

my

vari-

ab

les)

offense

type

(6 o

ffen

se

dum

my

varia

bles

) M

idpo

int o

f range

of

stat

utor

y pe

nalty

N/A

N/A

N/A

N

/A

Mod

erat

e ef

fect

Smal

l effe

ct

N/A

No

effe

ct

N/A

Smal

l effe

ct'

Smal

l effe

ct

N/A

N/A

N/A

N

/A

N/A

N/A

N/A

N/A

Mod

erat

e ef

fect

e

N/A

Smal

l dfe

ct

No

effe

ct

Smal

l effe

ct

3 m 2 2: E 3

Smal

leff

ect

No

effe

ct

N/A

N/A

E

Smal

l effe

ct

N/A

3

r N

/A

No

effe

ct

N/A

Tabl

e 1

(con

tinue

d)

__

__

__

_~

~

~ ~~

Gen

der

Effe

ctb

0

U

Sam

ple

Size

:

!! 5 E E

Stud

y (D

ata

Perio

d)

Tot

al (N

o. F

emal

es)

Prio

r R

ecor

d'

offe

nse

Seve

rity

In/O

ut

Sent

ence

Len

gth

scale

Dal

y. 1

987

(197

3)

474

(101

) Pr

ior

felo

ny c

onvi

ctio

ns

Otfe

nse t

ype

(5 o

ffen

se

Smal

l eff'

ecth

Sm

all e

ffect

N

/A

John

ston

et a

l., 1

987

2.49

0 (1

,251

) Fi

rst-t

ime

offe

nder

s' O

tfens

e typ

e (8

ind

ex

Mod

erat

e ef

fect

Sm

all e

ffec

t N

/A

Cum

an,

1983

(196

5-66

. 54

3 (2

71)

Prio

r ar

rest

s Se

verit

y sc

ale

(Sel

- N

/A

N/A

Sm

all e

ffec

t

Cro

yle,

198

3 (1

973-

77)

506 (4)

Prio

r co

nvic

tions

Se

verit

y sc

ale

N/A

N

/A

Tjad

en a

nd T

jade

n, 1

98 1

771

(92)

Pr

ior

conv

ictio

ns

offe

nse

type

(e.g

., lar

- Sm

all e

ffect

N

/A

N/A

dum

my

varia

bles

)

(197

9-83

) O

ffenS

eS)

1975

-76)

lin

-Wol

fgan

g)

~0

eff-

t U

(1

978)

ce

ny, a

ssau

lt)

cd

' All

of th

e st

udie

s use

d an

ex

post

fact

0 co

llect

ion

of p

rior r

ecor

d in

form

atio

n ba

sed

on p

roba

tion

or p

rison

reco

rds.

Whe

ther

prio

r rec

ord

info

rmat

ion

was

used

by th

e se

nten

cing

ju

d~

e is th

eref

ore

unkn

own.

AU o

f the

stud

ies u

sed

test

s of s

tatis

tical

sig

nific

ance

to d

eter

min

e w

heth

er th

ere

is a

gen

der e

ffec

t. T

he fa

irly

larg

e sam

ple

size

s in

man

y of

the

stud

ies f

requ

ently

pro

duce

d sm

all

gend

er e

ffec

ts th

at t

urne

d ou

t to be s

tatis

tical

ly s

igni

fican

t. D

epen

dent

var

iabl

e in

clud

es b

oth

the

idou

t an

d se

nten

ce le

ngth

dec

isio

ns.

Incl

udes

thei

r m

easu

res b

ut d

id n

ot c

ontro

l Sim

ulta

nwus

ly f

or p

rior

reco

rd a

nd o

ffen

se se

verit

y.

Gen

der e

ffec

t for

fel

onie

s but

not

for

mis

dem

eano

rs.

3 2 m

2: c3 U

Gen

der d

ect f

or s

ampl

e in

clud

ing

blac

ks o

nly.

6

Prio

r ar

rest

s and

prio

r in

carc

erat

ions

wer

e a

varia

ble

only

for

the

pris

on s

ampl

e (3

,710

). T

he b

ulk

of th

eir

anal

ysis

did

not

inc

lude

a c

ontro

l fo

r pr

ior

reco

rd.

' Ana

lysi

s is

limite

d to

def

enda

nts

tech

nica

lly p

rocs

scd as f

irst-t

ime

offe

nder

s; h

owev

er, a

n un

know

n nu

mbe

r of

def

enda

nts

had

prio

r fe

lony

con

vict

ions

. whi

ch t

he a

utho

rs

Gen

der e

ffec

t is

redu

ced

to n

onsi

gnifi

canc

e whe

n va

riabl

es re

pres

entin

g de

fend

ant's

fam

ily s

ituat

ion

are

incl

uded

in t

he m

odel

.

indi

cate

pre

sum

ably

was

kno

wn

by t

he court a

t se

nten

cing

. 2 Z rn

416 STEFFENSMEIER ET AL.

possible interaction effects of legal and extralegal factors on gender-sentencing practices, particularly seriousness of offense and defendant’s race.

The major shortcoming of the existing research is the inadequate controls for legally relevant variables, such as offense seriousness and prior record. Some studies used offense categories, such as larceny or burglary, as the proxy for offense gravity (e.g., Daly, 1987; Tjaden and Tjaden, 1981); others used broad groupings, such as violent versus property or felony versus misdemeanor (e.g., Frazier et al., 1983; Green, 1960). Neither approach offers a rigorous control for offense gravity because the offense categories typically are graded statutorily into two or more levels of offense seriousness. This failure to differentiate grades of severity within an offense category or group- ing may easily confound gender-based comparisons in sentencing outcomes because female offenders tend to commit the less serious forms of crime within the broad category (Steffensmeier and Allan, 1990).

Regarding prior record, some studies have used prior arrests, prior prison sentences, or even years in prison as a proxy, but the appropriate indicator is prior convictions. The latter is the only indicator of prior record that is authorized in penal statute as a criterion for judicial decisionmaking. Other study limitations include collapsing prior record into a simple dichotomy of prior versus no prior record (e.g., Zingraff and Thompson, 1984); limiting the analysis to first-time felony offenders but offering the caveat that the data do not clearly distinguish between those who had prior felony convictions and those who did not (Johnston et al., 1987); and including measures for both offense gravity and prior record but confounding the results by controlling for these variables one at a time rather than simultaneously (Moulds, 1980). Critically, none of the studies described in Table 1 offers a weighted prior record score that takes into account, as sentencing judges typically do, the gravity of the defendant’s prior convictions. (See discussion below of the Pennsylvania sentencing data.)

It also is significant that many studies listed in Table 1 did not analyze the critical decision about incarcerating the convicted offenders ( idout decision), and very few studies examined the length of prison sentences. Rather, the dependent variable was a single ordinal scale that encompasses several different sentencing options (e.g., whether to incarcerate, sentence length). But combining separate sentencing decisions into a single ordinal scale is mislead- ing because decisions about the type and duration of punishment are concep- tually and empirically distinct phenomena (Myers and Talarico, 1987; Spohn et al., 1981-82; Wheeler et al., 1982). The effect that various offender attributes, such as gender, have on sentencing outcomes may depend strongly on the decision being made.


PENNSYLVANIA DATA For several reasons, the Pennsylvania data are exceptionally well suited for

a study of gender differences in imprisonment decisions. First, the state enacted a sentencing guidelines system in 1982 that takes into account the legally relevant variables of offense severity and prior record, so that the Pennsylvania data are more likely to reflect accurately the impact of these two variables on sentencing than is true of sentencing statistics from most other jurisdictions. For example, after adoption of the guidelines, the Penn- sylvania State Police and the Pennsylvania Association of District Attorneys worked diligently to speed the process of obtaining police rap sheets and to improve the accuracy of the information contained in the rap sheets. In effect, the enactment of a sentencing guidelines system standardized the cal- culation and presentation of the defendant’s prior record to the court, improved the likelihood that information about prior record will be collected and recorded accurately, and ensured that sentencing judges would be informed of the defendant’s prior record and conviction-offense gravity score.

Such a situation did not characterize sentencing practices in Pennsylvania prior to passage of the guidelines structure. It also does not characterize the irregular availability in many states of criminal history information at the sentencing hearing, or when presented, its consideration by the court.4 Often, information on criminal history requires responses from the state police or the Federal Bureau of Investigation. Such responses take weeks in many jurisdictions, and courts frequently prefer to sentence the offender promptly rather than wait for the criminal history report. Additionally, state criminal history reports often fail to provide the final disposition of the case, so that the court does not know if in fact the defendant was convicted. In Penn- sylvania, this recordkeeping process has improved dramatically because the courts have been required to consider the prior record information systematically in applying the sentencing guidelines.

One option used by researchers lacking criminal history data has been to

4. An analysis by the Pennsylvania Commission on Sentencing of information available to, or used by, sentencing judges in Pennsylvania prior to enactment of the guidelines found that prior record information was available at time of sentencing in only about 50% of cases. Frequently, prior record information was simply not requested by the judge. This situation was due largely to the lack of an institutionalized state repository that accurately compiled prior record information on convicted felons and made that information easily available to the judges. A number of other states have recently instituted, or are considering instituting, a guidelines-type structure that emphasizes prior record. Personal commu- nication with officials in some of these states (e.g., Delaware, Minnesota, North Carolina, Washington, Wisconsin) revealed similarly high levels (roughly 40%-50%), so that unavailability of accurate prior record information appears to be commonplace. Appar- ently, the haphazard provision of prior record information in sentencing decisions is the “norm” in states where such information is not explicitly mandated and where a statewide apparatus for compiling such information does not exist.

41 8 STEFFENSMEIER ET AL.

survey ex post facto probation or prison records for information on the defendant’s prior record and then to merge that information with sentencing outcomes-as was done in virtually all of the studies listed in Table 1. This does not satisfactorily solve the problem, however, because one still does not know whether the sentencing judge in fact was aware of the information compiled in the defendant’s dossier. Frequently, the criminal history information in a probationer’s or a prisoner’s dossier is self-reported by the sentenced felon during an intake interview conducted when the defendant undergoes classification for entry into a state’s prison system. In many respects, due to the recent enactment of a sentencing guidelines system in a few states in addition to Pennsylvania, researchers can better assess the effects of prior record on sentence outcomes because they have access to more detailed criminal history information and the information is provided in a structured format to the court.

A second advantage of the Pennsylvania data (again due partly to changes instituted with the implementation of the guidelines system) is that the state’s recordkeeping includes refined classifications not only of prior record but of offense severity as well. The set of offenses is well defined and seriousness is measured with some precision, so that extraneous variation within offense type is limited. In fact, in Pennsylvania some offenses with the same statutory grade are subdivided and given different severity scores by a broad-based panel composed of judges, legislators, defense attorneys, and district attorneys. Previous research has had to rely on broadly defined statutes as the basis for classifying offenses.

Third, while Pennsylvania now operates with a guidelines sentencing structure, it is a comparatively “loose” one that permits significant judicial discretion (Tonry, 1987). In addition, the criminal code endorses several sanction philosophies (for deterrence and restitution as well as for rehabilitation and retribution), so that opportunities for case, court, and community contexts to affect sentencing are enhanced (Kramer and Scirica, 1986). Also, there is in fact considerable variation in the sentences imposed, both within and across crime categories (Pennsylvania Commission on Sentencing, 1990).

Fourth, there are 67 counties and 59 judicial districts in the state. As via- ble political and social entities, counties vary markedly in demographic, political, economic, and social composition. Prior research typically assumes that the significance of gender on sentence outcomes is not influenced by the con- text within which the sentencing occurred. But this is a dubious assumption (see, e.g., Eisenstein et al., 1988).

Fifth, the large number of cases (61,294) allows for the consideration of a broad range of offenses, from the least serious to most serious. Small sample size has prevented most prior research on the issue of gender bias in sentencing from adequately exploring the role of severity and type of offense in sentencing.

GENDER AND IMPRISONMENT DECISIONS 419

We are not aware of any previous studies on the gender/imprisonment issue that encompass all the attributes described above, which makes our study a significant advance over prior research (see Table 1). Nonetheless, our study does warrant some caveats. First, we focused only on sentences imposed on convicted, noncapital offenders. Thus, we did not address whether gender bias exists in earlier processing or prosecution stages (e.g, in charging) or whether bias exists in capital sentencing. Prior research, nonetheless, does tend to find that the strongest gender effects (when they occur) are manifest at the sentencing stage and that the inclusion of process variables in sentencing models contributes little, if anything, to explained variation in sentencing outcomes (Curran, 1983; see reviews in Albonetti, 199 1; Eisenstein et al., 1988). Moreover, as Weisburd et al. (1990) point out, the practical limitations of research make it virtually impossible to account for all the selection processes that operate in the criminal justice system, such as the decision to prosecute or the decisions that occur in bargaining over conviction. In this sense, some degree of uncorrected sampling bias is common to every study. Second, although we do control for important contextual variables (see below), their inclusion in our analysis is far from exhaustive.

PROCEDURES

In this study Pennsylvania guidelines sentencing data are analyzed for 61,294 cases from 1985-1987. Cases involving several offenses with few female defendants (e.g., kidnapping, rape offenses) are excluded, as are cases in which information on one or more of the predictor variables was missing. The purpose of the guidelines, which apply to any offender convicted of a felony or serious misdemeanor after July 21, 1982, is to establish sentencing standards in which the severity of the convicted offense and the offender’s criminal history are the major determinants of sentencing decisions (Kramer and Scirica, 1986). Guideline sentences are established for each combination of offense severity/criminal history in the form of a sentencing matrix. Under the guidelines, dispositional or durational departures from the presumptive sentences are permissible, but the judge is requested to justify any departure from the guidelines with written statements outlining the circumstances behind the departure. (As we discuss later, the matter of dispositional departures contributes to our explanation of the gender differences in sentencing in the Pennsylvania data.) Taken together, the Pennsylvania guidelines represent a systematically crafted sentencing system, yet they afford ample oppor- tunity for the intrusion of sentencing disparity (Tonry, 1987).

The data for this study are based on the monitoring system developed by the Pennsylvania Commission on Sentencing. Each sentence given for a separate criminal transaction must be reported to the sentencing Commission. To our knowledge the Pennsylvania data offer the richest information in the


country for analyzing judges’ imprisonment decisions. Besides gender, the independent variables we use in the analysis include a combination of legally prescribed variables, offender characteristics such as race and age, and contextual factors. Coding of these variables is straightforward and is presented in Table 2.

The legally prescribed variables include the severity of the convicted offense (Severity) and criminal history score (History). We measured severity of the current conviction using a 10-point scale developed by the commission and by a dummy-variable procedure. The 10-point seventy scale ranks each statutory offense on the scale; for certain offenses, such as burglary, it subdi- vides the statutory classification into multiple ranks depending on the specific circumstances of the crime (Kramer and Scirica, 1986).5 The dummy-variable procedure across 20 offense categories provides an even more rigorous control of seriousness of offense (see discussion below).

We used a weighted, seven-category scale developed by the commission to measure criminal history. The criminal history score measures the number and severity of the defendant’s past convictions. All felonies, as well as misdemeanors punishable by at least one year of incarceration, are included. Misdemeanors (punishable by up to five years in Pennsylvania) may total no more than two points on the criminal history score: felonies add one, two, or three points each, depending on their severity.6

One anomaly of the commission’s measure of prior record is that the “no prior” category (i.e., a “0” prior score) includes defendants with no prior convictions whatsoever, as well as some defendants with a prior conviction either as a juvenile or an adult. A “0” prior score designates those defendants with no prior convictions or with only one prior misdemeanor conviction, whereas a “1” score indicates those defendants with either a felony-3 conviction as an adult (a felony-2 counts two points and a felony-1 three points in

5 . The scale of offense severity used by the sentencing commission ranges from 1 (minor theft) to 10 (murder in the second degree). The ranking of misdemeanors/felonies on this scale is consistent with offense rankings employed in other scales of crime seriousness. Also, by way of special subdivision of specific offenses (e.g., robbery-1 vs. robbery-2), offense severity includes whether there was victim injury and the degree of injury.

We also developed several additional measures of criminal history: (a) the number of current convictions at the time of sentencing; (b) a dummy variable representing whether the defendant had a violent criminal history; (c) dummy variables representing whether the defendant had a juvenile prior record only, an adult prior record only, or both; and (d) dummy variables representing a combination of the above (e.g., whether the defendant had a violent vs. nonviolent juvenile record or a violent vs. nonviolent adult record). We found that these prior record measures had very little impact on sentence outcomes beyond the seven-category prior record score and that they had no significant effects on the regression coefficients for gender or the other variables. Thus, because a large number of cases had missing information on these measures (which would result in a substantially reduced sample size), we excluded the alternative measures of prior record from the models reported here.

6.


Table 2. Description of Variables

Independent Variables Legally Prescribed

Seventy

History

Offense

Race Gender

Offender Characteristics

Age Contextual Factors

Workload Type of Disposition

% Urban % Black

% Republican 70 15-19

Dependent Variables Probation vs. JaiVPrison Probation/Jail vs. Prison Jail vs. Prison

Sentence Length

Description

Severity of the convicted offense: 10-category ordinal scale with a range of 1 (least serious) to 10 (most serious)

Criminal history score: 7-category ordinal scale with a range of 0 (no prior record) to 6 (prior record)

Twenty dummy-coded offenses*

Binary: coded 1 if black, 0 if white Binary: coded 1 if male, 0 if female In years

No. of cases receivedho. of judges in county Binary: coded 1 if trial, 0 if plea % of county population living in urban areas % of county population that is black % of county population aged 15-19 years % of county registered voters registered Republican

Binary: coded 1 if incarcerated, 0 otherwise Binary: coded 1 if state incarceration, 0 otherwise Binary: coded 1 if state incarceration, 0 if county

Midpoint between minimum and maximum in months incarceration

The dummy offense variables are involuntary manslaughter, robbery felony 1, robbery felony 2, robbery felony 3, aggravated assault, simple assault, arson, weapons offenses, burglary (7), burglary (6), burglary (9, theft-felony, theft-misdemeanor, retail theft felony, retail theft other, forgery felony 2, forgery felony 3, drug felony, and terroristic threats. Drug misdemeanor was excluded as the contrast level.

the prior record scoring) or conviction as a juvenile for a felony- 1 or a felony- 2 offense. Two misdemeanor convictions as an adult also result in a “1” prior score. Thus having a “prior record” within the Pennsylvania guideline system involves a record of prior criminality that is more serious, perhaps, than that found in other sentencing studies.

Regarding the contextual variables, investigators have identified urbaniza- tion and proportion of the population that belongs to minority groups (racial mix) as important contextual factors of the social environment that may affect criminal sentencing (Benson and Walker, 1988). Contextual factors also may include differences in organization and caseload processing among courts, such as caseload and type of disposition (Hagan and Bumiller, 1983). As a contextual factor to measure conservatism of the social environment, we used “percent Republican” (Friedman, 1975; Steffensmeier, 1976). We also


included in the initial regression runs the crime rate of the county, the unem- ployment rate, and median income levels. However, these variables were highly collinear and redundant with percent black, so we omitted them from the analysis reported here.

Sentencing can be thought of as a two-stage process, involving first a decision about whether to imprison and second, if incarceration is selected, a decision about the length of sentence. Thus, we employ two dependent variables: incarcerated versus not incarcerated ( idout decision) and length of sentence. For the dependent variable involving the i d o u t decision, we conducted the analysis using three alternative measures where,

1. “In” refers to confinement in either county jail or state prison, and “out” refers to any combination of nonconfinement options (probation, fines, restitution, or suspended sentence-hereafter called probation). This is the traditional measure of the in/out decision that we designate as “jail/prison versus probation.” 2. “In” refers to confinement in state prison versus all other, including jail, probation, and so on. This measure is designated as “prison versus jail/probation. ” 3. “In” refers to confinement in state prison and “out” refers to confinement in county jail, and is designated as “prison versus jail.” This part of the analysis is conducted on a reduced sample that excludes all defendants who received a sentence of probation, fine, restitution, or suspended sentence.

Since a sentence of “county jail” time is viewed typically as less stigma- tizing and less punitive than “state prison” time (Kramer and Scirica, 1986), it is important that confinement in state prison be distinguished from other sentencing options. Based on a legalistic model of sentencing, we would expect that the effects of prior record and offense severity on sentencing will be particularly strong when measures (2) and (3) above are employed, since incarceration in a state prison tends to be reserved for the more serious and/ or repeat offenders.

We used ordinary least squares (OLS) regression and logistic regression to analyze the in/out decision. Each of these procedures has been employed in recent studies of sentencing, and we display the results from each procedure for comparative purposes. The two methods revealed similar patterns, but we rely mainly on OLS in discussing our findings because of its familiarity to most readers and its more straightforward interpretation. Also, the OLS procedure allows for the assessment of the relative contribution of each independent variable to total explained variation-a procedure not possible with logistic regression. We used OLS for the analysis of the length-of-prison term decision because that outcome was a continuous variable.’

7. In other analyses (available from the authors on request), we included a correction


FINDINGS

Descriptive statistics and bivariate correlations of the variables included in the analysis are available on request from the authors. The major results are that males are more likely to be incarcerated (r = .16 for jaiVprison vs. probation) and to receive lengthier jail/prison sentences (r = .08), but that male defendants have somewhat higher offense severity scores on average than females (r = . 1 1) as well as lengthier prior records of offending (r = .14). In turn, the two legal variables (Severity, History) are correlated strongly with sentence outcomes (r’s ranging from .30 to .43), whereas gender and the other variables in the model are correlated only weakly with sentence outcomes.

Additionally, as is found more generally in sentencing studies, the bivariate analysis reveals that offense severity and prior record have large effects on sentence outcomes and thus are important statistical controls for estimating gender effects. As for the other independent variables, their effects were very small and relatively trivial, and for that reason are not discussed in the text. Also, we focus our discussion on the traditional measure of the in/out decision, jail/prison versus probation.

IN/OUT DECISION

We began by estimating the effects of gender on sentencing while controlling for the effects of all other variables. We estimated two separate models for each dependent variable-one employing the dummy-variable procedure as a control for offense seriousness and the other employing the severity scale. The offense variables include 20 dummy variables, and misdemeanor drug offenses are excluded from the analysis as the reference category. We rely mainly on the dummy-variable procedure in describing our results. It offers a more stringent control for offense seriousness since each of the 10 categories in the offense severity scale includes several specific types of offenses. How- ever, we also display the results for the offense severity index when it is employed in the regression models because it provides an overall indicator of

term for selection effects in the regression equation for length of sentence. Sample selection bias is a concern in the modeling of length of sentence, since that decision follows the initial decision about whether to incarcerate. To correct for this potential bias, we used a two- stage estimation procedure recommended by Berk (1983). The results indicated that the predicted probability of exclusion from one stage to the next (the “hazard rate”) was very highly correlated with offense seriousness, a significant predictor in each of the models. Because of its collinearity with offense seriousness and because the inclusion of the correction term did not significantly affect the magnitudes, signs, or p values of the variables in the “partitioned” models, we do not include it in the analyses reported here. The high collinearity between the correction factor and other variables in the model appears to be a commonplace problem in research on sentencing (see, e.g., Benson and Walker, 1988).


the effects of offense seriousness (as compared with surveying 20 dummy variable coefficients). We also controlled for year (Year) to partial the effects of trending.

The results are displayed in Table 3 and Appendix 1 (in which we report the individual OLS regression coefficients and odds ratios from the logit analysis for each of the 20 offense dummy variables). Because the sample size is so large, tests of statistical significance are not very meaningful. Thus, to identify “predictive” or substantive significance, we calculated each variable’s net contribution to total explained variance, or R2. After controlling for the effects of the other variables, we ask the following: How much does gender contribute (in terms of explained variance) to the judge’s incarceration decision?

A comparison of the full and reduced models reveals that the bulk of the explained variation in each of the alternative measures of the in/out decision is accounted for by the seriousness of the crime committed by the offender and the length of the offender’s prior record. As offense gravity and prior record scores increase, so does the harshness of the sentences imposed. For the traditional in/out measure (jail/prison vs. probation), type of offense has a net contribution to R2 of 10% and prior record has a net contribution of 8%. Together, these two variables account for about three-fourths of total R (about .26). When the two alternative measures of the traditional idout decision are considered, prior record and offense type also account for a large share of the explained variance. The major difference is that prior record becomes the more powerful predictor of variation in the idou t decision when the principal distinction in sentence outcome is state imprisonment versus other sentencing options. Also, it is for the prison versus jail/probation con- dition that the model as a whole explains the most variation (R = .3392).

Next, we reestimated the models replacing the offense dummy variables with the offense severity scale (Severity). Across all three in/out decisions, moving from one level of severity to the next results, on average, in a 9% greater likelihood of being incarcerated. Again, offense severity and prior record account for most of the explained variation. On the other hand, regardless of which control for offense severity is used, gender contributes only a fraction of a percent to explained variation in each of the three in/out classifications. The effect of gender is somewhat greater when the traditional way of defining in/out is the dependent variable than when “in” refers only to incarceration in a state prison. When the dependent variable is jail/prison versus probation, male defendants, on average, are 12% more likely to be incarcerated than females, net of all other variables, whereas male and female defendants have an almost equal probability of being incarcerated when the definition of “in” is restricted to state imprisonment.

These findings provide modest support for the gender bias model, which

Tabl

e 3.

R

esul

ts fr

om O

LS a

nd L

ogis

tic R

egre

ssio

n A

naly

sis

for

Each

of

the

Four

Ind

epen

dent

V

aria

bles

0

!z !2 2 ?I! %

Urb

an

--.@I3

.009

--.0

15

1.02

--

.001

,0

02

-.010

1.

01

-.001

.0

01

-.00

4 1.

00

-.W

*

.ooO

Ts

Pris

on/J

ail

vs.

Prob

atio

n Pr

ison

vs.

JaiV

Prob

atio

n Pr

ison

vs.

Jail

OLS

L

ogis

tic

OL

S L

ogis

tic

OL

S L

ogis

tic

Sent

ence

Len

gth:

OL

S

Con

trib

utio

n C

ontr

ibut

ion

Con

trib

utio

n C

ontr

ibut

ion

to R2

Bet

a O

dds

Rat

io

b to

R2

Bet

a O

dds

Rat

io

b to

Rz

k z U

Inde

pend

ent V

aria

ble

b to

R2

Bet

a O

dds

Rat

io

b

,081

,1

25

,434

1.

54

4.48

,1

08

His

tory

,0

69

,075

,4

58

1.58

,0

80

,143

,5

27

1.69

O

ffen

se T

ype

-a

,102

-

-

-

,107

-

-

-

.097

-

-

-

.251

Rac

e ,0

84

.M)8

,4

29

1.54

,035

.001

,3

27

1.45

.044

,002

,2

86

1.33

.6

95

.ooO

-.002

,0

02

--.0

13

1.01

.m

* .m

,0

02'

1.00

,0

02

.001

,0

10

1.01

,1

46

,002

A

ge

,118

,0

06

,701

2.

02

8.36

,008

Typ

e of

Dis

posi

tion

,066

,0

02

--.4

97

1.64

,1

13

,007

,8

27

2.29

W

orkl

oad

-,m1

.ooO

-.m

* 1.

00

-.m

* .m

-.

m*

1.00

-.m

* .m

-.

m*

1.00

-.0

02*

.m

% B

lack

-.0

01

.ooO

-.010

1.

01

-.00

4 ,0

04

-.033

1.

03

-.00

5 .0

05

-.031

1.

03

,013

. .m

%

15-

19

.006

,0

01

.045

1.

05

.oOl'

.m

,021

' 1.

02

,003

' .OO

O ,0

28'

1.03

-.0

33*

.ooO

% R

epub

lican

,0

01

,001

,0

04

1.00

--.@

I2 ,0

06

-.026

1.

03

-.00

5 .0

04

-.028

1.

03

.021

.m

1.

21

,001

Y

ear

,014

.ooO

,039

. 1.

03

,006

.m

,0

041

1.00

.0

04*

.m

,003

1.

00

[Sev

erity

lb

[.092

.I

00

.496

1.6

41

[.086

,0

73

,696

2.

011

[.086

,0

74

,550

1.7

31

(7.5

4 .I5

91

c3

U

R2 =

,25

63

X2 =

12,

511.

2 R2

= ,

3392

X

z =

20,

300.

6 R2 =

,29

05

X2 =

956

6.5

Rz =

,43

54

M

Gen

der

,119

.0

06

372

1.77

,0

03.

.ooO

,153

1.

17

-.020

.ooO

-.093

* 1.

10

-1.6

4 .OO

O

cd 2

N

61,2

94

61,2

94

33,5

27

34,8

47

Sam

ple

size

is so

larg

e th

at m

ost c

oeff

icie

nts a

re s

tatis

tical

ly s

igni

fica

nt.

An

desi

gnat

es th

ose

coef

ficie

nts NO

T si

gnif

ican

t at

p 1.

05.

E a

Res

ults

for d

umm

y va

riab

le a

re p

rese

nted

in A

ppen

dix

1. b

Effe

ct o

f 10

-cat

egor

y off

ense

sev

erity

scor

e on

the

depe

nden

t va

riab

le w

hen

this

var

iabl

e is

in t

he m

odel

inst

ead

of t

he o

ffen

se d

umm

y va

riab

le.

Z m


predicts that judges are reluctant to incarcerate female defendants. Appar- ently, the key ingredient here is “incarceration” because the already weak relationship becomes nonexistent when incarceration is defined as state prison rather than jail or probation. According to some writers, female defendants are somewhat less likely to be incarcerated because of the financial and emotional hardships for the families to which they are connected (Daly, 1987; Steffensmeier, 1980). But these findings also are consistent with expectations derived from a legalistic model of sentencing-the effects of offense type and prior record, already strong across all the in/out measures, are particularly strong when incarceration in a state prison is distinguished from other sentencing options.

The logistic regression procedure produces similar results.8 For the traditional in/out decision, the odds ratio of 1.77 for gender indicates that the odds of males being incarcerated versus not being incarcerated are 1.77 times higher than the odds of females being incarcerated versus not being incarcerated. Or, the odds of males being incarcerated versus females is about 1.8 versus 1.0. As with OLS regression this effect diminishes considerably when the definition of “in” is restricted to state imprisonment. The odds ratios for criminal history and offense severity indicate that each additional increase in the criminal history score, or the offense severity score, results (on average) roughly in a 60% greater likelihood of being incarcerated when the traditional measure of in/out ( jail/prison versus probation) is employed.

Finally, we conducted separate regression analyses (both OLS and logistic) for each of the 20 offenses, for the traditional in/out measure.9 The results, shown in Table 4, reveal that gender has a small-to-moderate effect on the in/ out decision that is quite consistent across offense types. That is, the effect is not greater for specific kinds of offenses-robbery compared with forgery, for example.

In surn, both OLS and logistic methods indicate that female defendants are less likely than male defendants to be sentenced to jail or prison. Both methods also reveal that the effect of gender on imprisonment decisions is weak

8. Note, however, that the OLS procedure slightly underestimates the gender effect, as well as the effect of some of the other variables. Analysts have noted (e.g., see Hanushek and Jackson, 1977) that the OLS regression coefficient multiplied by four should equal the coefficient obtained with logistic regression if the two procedures produce exactly identical results. In this case, the OLS coefficient for gender multiplied by four equals .476, whereas the logistic regression coefficient is ,572. However, when the coefficients obtained from the two procedures are compared across all of the independent variables, the patterns are identical; that is, the variables with higher OLS coefficients also have higher logistic regression coefficients and vice versa.

For space reasons, we show the effects pertaining to gender only in Table 4. Results for the full regression models are available from the authors upon request.

9.

Tabl

e 4.

Ef

fect

s of

Gen

der

on S

ente

nce

Out

com

e by

Offe

nse

Type

Ja

iVPr

ison

vs.

Prob

atio

n

OLS

Lo

git

Sent

ence

Len

gth

OLS

No.

of

Con

tribu

tion

Con

tribu

tion

N Fe

mal

es

b to

R2

Bet

a Odds R

atio

b

to R2

-

-

-

Invo

lunt

ary

Man

slau

ghte

r 171

25

,027.

.m

,149

1.16

,918.

.001

R

obbe

ry F

elon

y 1

2.428

73

,141

,011

1.340

3.82

22.800

.005

Rob

bery

Fel

ony 2

1,611

88

,190

,012

,981

2.67

6.030.

.002

Rob

bery

Fel

ony 3

1.311

70

,077

.00

I ,396

1.49

3.110'

,002

Agg

rava

ted

Ass

ault

3.41 1

404

.I93

,017

,855

2.35

5.420

,003

Sim

ple

Ass

ault

5,047

482

,098

.0

03

,538

1.71

,541.

.Ooo

A

rson

449

61

,146

,014

.983

2.71

-4.460'

.001

W

eapo

ns

2,291

162

,093

.002

.408

1.50

-.014*

.Ooo

B

urgl

ary (7)

I.008

38

,182

.009

I .020

2.77

- 6.270

.00 I

B

urgl

ary (6)

4.796

189

,078

.002

,573

1.77

- 1.670.

.Ooo

B

urgl

ary (5)

4.792

112

.256

,006

1.230

3.42

-4.970.

.m

Crim

inal

Tre

spas

s Fe

lony

2

1,11

0 42

.I95

,005

,941

2.56

4.460'

,001

C

rimin

al T

resp

ass

Felo

ny 3

772

45

.082

,001

,442

1.56

- 1.4

40.

,001

Th

eft

Felo

ny 3

6,013

517

,122

,005

,526

1.69

- 1.4

00.

,001

Th

eft

Mis

dem

eano

r 5,

609

77 1

,129

,007

,617

1.85

- 1.020.

,001

R

etai

l The

ft Fe

lony

2,675

1.178

,101

.0

1 I

,567

1.76

--.745.

,001

Ret

ail T

heft

Oth

er

3,309

1.461

,085

.006

,405

1.50

,089'

.Ooo

Fo

rger

y Fe

lony

2

990

288

.I07

,009

,552

1.74

--.397.

.Ooo

Fo

rger

y Fe

lony

3

2,052

697

.I27

,013

.614

1.85

2.190

,004

Drug F

elon

y 6,585

1,179

,133

.01 I

,6

41

I .90

- 3.070

.003

Drug M

isde

mea

nor

4,314

753

.03 I

,0

01

.2Ol

1.22

- 8.750

.009

Sam

ple

size

is s

o la

rge

that

mos

t co

effic

ient

s are

sta

tistic

ally

sig

nific

ant.

An

desi

gnat

es th

ose

coef

ficie

nts N

OT

sig

nific

ant a

t p

2.0

5.


compared with the effects of criminal history and offense severity. Ascertain- ing whether the gender effect is substantively significant is not straightforward, however. On the one hand, the gender variable adds only a fraction of a percent to explained variation across most offenses. On the other hand, male offenders have, on average, about 12% greater likelihood of being incarcerated than female offenders and the odds ratio favoring females is 1.77. We return to this issue later when we examine judicial departures from the guidelines schema and assess their dampening effect on the relationship between gender and the in/out decision.

LENGTH O F SENTENCE

For defendants who were incarcerated, we employed multiple linear regression procedures to analyze the relationship between the independent variables and length of sentence. We used regression diagnostics (Belsley et al., 1980) to detect influential observations (outliers), and we inspected the data for multicollinearity. Because of the large sample size, we also considered each variable’s net contribution to R2.

The results are displayed in the right-hand column of Table 3. As with the in/out decision, type of offense and the defendant’s prior record weigh over- whelmingly in judicial decisionmaking concerning length of sentence. The two variables together contribute 36% to explained variation, net of all other variables, or roughly 82% of total R2. On the other hand, the defendant’s gender accounts for only a small fraction of a percent (.0002) of the variation after the other variables are controlled, so that it plays a very small role in decisions about sentence length. In fact, the negligible effect that does exist is a negative one, which indicates that male defendants, on average, receive slightly shorter sentences (about 1.6 months) than their female counterparts. 10

Next, we reestimated the model using the offense severity scale as a control for offense seriousness. Again, the variables representing offense seriousness and prior record account for most of the explained variation. Although the effect of gender is statistically significant, its contribution to explained variation in sentence length is nil-less than one-tenth of a percent.

Finally, we ran the regressions separately for each offense. The results, shown in Table 4, confirm the patterns described above. Across all offenses,

~ ~~~~ ~

10. Interviews with sentencing judges reveal that one reason for the slightly longer incarceration terms for female offenders is that judges are sometimes reluctant to sentence female defendants to county jails but instead prefer to incarcerate them in the state correctional institution for women. The latter requires a sentence of two or more years instead of a sentence of 23 months or less, which will lead to incarceration at a county jail. The state correctional institution for women is seen as having more adequate staff and facilities as compared with many county jails, which typically lack jail space or adequate staffing to accommodate female offenders.


gender contributes very little to total explained variation. Also, despite the large sample size, the gender effect fails to reach statistical significance for 16 of the 20 offense categories. The variable that has by far the largest impact on length of term is offender’s prior record.

INTERACTION EFFECTS

So far, we have conducted multivariate analyses that include a number of legal and extralegal variables of potential relevance to sentencing decisions, but we have examined only additive models. The additive model is premised on the assumption of constancy in the influence of gender across levels of other variables and does not allow for the possibility that the effect of gender may be conditioned by other variables. Instead, an interactive model may be needed to explore interactive effects that can obscure substantial gender differences in sentencing. Thus, in addition to having explored the main effects of gender, we estimated interactive models to determine whether legal and contextual attributes have different effects across gender.

We were concerned in particular with whether any unique gender interactive effects exist by race or by offense severity, as some researchers have pro- posed. Are judges more punitive toward black than white women and/or is the gender differential in sentencing greater for minor than for serious crimes? Regarding race x gender, some writers propose that leniency is directed more toward white than black female defendants, on grounds that the chivalry and other protections of traditional gender stereotyping are not accorded to low-income black women, who are overrepresented in court dockets (Klein and Kress, 1976; Spohn et al., 1985). Regarding offense seriousness, some commentators hypothesize that women are more likely to receive favorable outcomes when the courts are responding to defendants charged with less serious offenses, on grounds that women committing serious crimes depart too far from traditional gender role expectations, and preferential treatment ceases (Nagel and Hagan, 1983). But other writers speculate that leniency toward female defendants will be more manifest in serious offenses (felonies) than in minor offenses (misdemeanors), on grounds that minor offenses involve a routinization of the criminal justice process that is ultimately reflected in relatively standardized sentence lengths, while serious offenses permit more discretion (involve a larger range of possible sentence lengths) and receive more careful attention to all (including extralegal) aspects of the case (Zingraff and Thompson, 1984).

We began by repeating the above analysis, estimating separate models for male and female defendants. Table 5 shows the results for the traditional in/ out measure and for sentence length. (The results are similar when the two alternative measures of the in/out decision are used.) For both sentence outcomes-the in/out decision and sentence length-the results are generally similar across the gender-specific models. For male and female defendants,


seriousness of offense and prior record account for the largest share of explained variation in sentence outcomes (from about 70% to 95% of R2 across all comparisons). Further, comparing the unstandardized regression coefficients across models, the legal variables-seriousness of offense and prior record-have similar effects on the sentence outcomes of defendants of both sexes.

There are, however, small interactive effects involving gender x race and gender x offense severity when sentence length is the dependent variable. Regarding gender x race, among male defendants, race has a negligible effect on sentence length; among female defendants, however, black female defendants receive prison sentences that, on average, are about three months longer than white female defendants. Regarding offense severity, proportionate increases in offense seriousness tend to increase the sentence length for male more so than female defendants (about four months).

Looking back at Table 4 helps to clarify the gender x offense severity interaction. There are larger gender effects for robbery felony-1 and small (but statistically significant) gender effects for aggravated assault, forgery felony-3, and drug violations. Note, however, that the gender effect is negative for drug violations, which means that female defendants receive slightly longer sentences than male defendants. The large gender effect for robbery felony-1 (i.e., a robbery involving threatened or actual serious bodily injury) results in a substantially longer sentence-about 22 months longer on averagefor male than female defendants. The swamping effect of the extra-long sentences for robbery helps explain the overall interactive effect observed earlier: that offense seriousness tends to increase the sentence length for male more so than female defendants.

To decipher further the gender x offense severity interaction, we partitioned the sample into three levels of offense seriousness: (a) conviction offense had a severity score of 7 or higher (felony-1 offenses), (b) conviction offense had a severity score between 4 and 6 (felony-2/3 offenses), and (c) conviction offense had a severity score of 3 or lower (misdemeanors).

We found a small gender effect favoring females for felony-1 offenses (b = .19, p < .Ol), whereas the gender effect favored males for misdemeanors (b = - 1.44, p < .01) and felony-2/3 offenses (b = .75, p < .01). Recall, however, that the only strong interactive effect occurs when we isolate the more serious of the felony-1 offenses (e.g., a robbery involving serious injury and aggravated assault) and that the interactive effect favors female defendants.

Thus, once judges have decided to incarcerate the defendant, female defendants receive slightly shorter prison sentences when the conviction is for a serious felony, whereas they receive slightly longer jail or prison terms when the conviction is for minor offenses (see note 10). It is important, nonetheless, that the differences across all three levels of offense seriousness are small and substantively quite trivial.

Tabl

e 5.

R

esul

ts fr

om M

ultip

le R

egre

ssio

n A

naly

sis

of S

epar

ate

Mod

els

for

Mal

e an

d Fe

mal

e D

efen

dant

s Q

Mal

e Fe

mal

e M

ale

Fem

ale

U > 2: U 2 B E 2 3

Pris

on/J

ail

vs.

Prob

atio

n Se

nten

ce L

engt

h

Con

tribu

tion

Con

tribu

tion

to R2

b to

R2

Con

tribu

tion

Con

tribu

tion

-

-

-

-

Inde

pend

ent V

aria

ble

b to

R2

b to

R2

b

His

tory

.0

67

,077

,0

96

,103

4.

55

.I09

3.

30

.085

O

ffens

e Ty

pe

-

-

-

.085

-

.262

-

.I12

Rac

e .08 1

.0

05

,109

.009

,314

. .m

3.

61

.006

- ,0

02

,002

- ,0

02

,002

,1

47

,001

.1

52

.003

A

ge

Type

of

Dis

posi

tion

,062

,0

01

,107

.0

03

8.56

,009

4.3 1

.0

03

Wor

kloa

d - .ooo

.ooo

.OM

) .oo

o - ,00

2' .oo

o - .009

,001

70

Urb

an

- .0

03

.009

- ,0

03

.009

-.017

.oo

o .080

,004

70 Bl

ack

-.001

.oo

o - ,0

03

,002

,0

39

.OM

) - .2

00

,005

70 1

5-19

,0

06

,001

.m

a*

.ooo

-.113

* .ooo

,352

. .ooo

% R

epub

lican

.0

01

,001

.a

ll*

.ooo

.028

.ooo

- ,0

39.

.ooo

Year

,014

.0

01

,016

,0

01

1.38

.00 I

- ,4

33.

8 z .m

M

U

M

[Sev

erity

] [.0

93

.lo6

,0

84

.080

] [7

.95

,168

3.

73

.OE I

] R

2 ,2

39

,213

,4

45

,230

N

53,1

76

8.1

17

31,6

55

3,19

1

Sam

ple

size

is so

lar

ge th

at m

ost c

oeff

icie

nts a

re s

tatis

tical

ly si

gnifi

cant

. An

desi

gnat

es th

ose

coef

ficie

nts N

OT

sign

ifica

nt a

t p 2

.05.

P

w n


Finally, we estimated models that included all possible gender interactive terms to examine the contributions of the interactive terms to explained variation. Across all the dependent variables (for the i d o u t and length-of-term decisions), the interactive terms as a whole contribute less than a percent to the total R2. The gender x race interaction contributes only .0003 percent to explained variation, compared with .0007 for gender x offense seriousness.

Taken together, we conclude that an interactive model does not produce an improvement of fit over the additive model. For the i d o u t and sentence length decisions, the effects of the independent variables are virtually identical for male and female defendants.

JUDICIAL DEPARTURES

So far we have determined that, net of other factors, gender has a negligible effect on length of sentence, but that female defendants are less likely to be jailed than male defendants. Although gender contributes very little to explained variation (less than a percent) in the decision whether to imprison, females are about 12% less likely than males to be incarcerated- a pattern that is generally consistent across a broad range of offenses (the major excep- tion is drug violations).

Some additional data on sentencing practices in Pennsylvania are available that help to explain this 12% difference. First, as noted earlier, the state’s sentencing system allows judges to depart from the guidelines schema when they strongly deem it appropriate. When they do depart, judges are asked to justify in writing their reasons for departing. For our purposes, we are inter- ested in dispositional departures in which the judge sentences an offender to probation rather than to jail or prison when the guidelines call for some incarceration. We ask, to what extent does the gender effect described above stem from differences in the propensity of judges to grant “departure” sentences to female more so than male defendants? Second, we interviewed a number of judges in the state regarding their departure decisions.

We began by distinguishing the departures by gender and found that a higher percentage of female defendants (29% of 2,308 cases) received departure sentences than male defendants (15% of 23,359). We next partitioned the sample to exclude all departure sentences, and found that the gender coefficient is reduced when the departure sentences are removed. The 12% lesser likelihood of incarceration is reduced to 9% (b = .094) so that the gender effect in the in/out decision that we observed above is partly attributable to departures from the guidelines and judges rejecting the guideline recommen- dation for incarceration in favor of less punitive treatment of female defendants.

We then examined the judges’ official justifications for their departure sentences. Unfortunately, the analysis here must remain suggestive because


some judges gave multiple reasons, some gave perfunctory ones (e.g., plea agreements), and others did not give any reasons at all for their departure sentences. In order of importance, there were five justifications for departure sentences that favored female defendants: 1 1

1. defendant has a nonviolent prior record (e.g., a high prior record score that consists solely of property offending), 2. defendant has mental or health problems (e.g., jailing would over- burden the jail staff and would harm rather than help the defendant), 3. defendant is caring for dependents or is pregnant (e.g., jailing would not protect the community in the long term and would be inhumane, risky, and possibly costly), 4. defendant played a minor role in the crime or was only an accomplice, and 5 . defendant showed remorse (e.g., “felt bad about what she/he had done”).

Together, the departure reasons indicated that judges viewed female defendants as less “dangerous,” as less culpable than their male codefendants, and as having more responsibilities and ties to the community (e.g., taking care of dependents).

Following our examination of departure reasons, we focused on those cells in the guidelines schema where the judge could impose either an “in” sentence or an “out” sentence, with the “out” defined as the minimum. Appen- dix 2 displays the distribution of sentence outcomes of male and female defendants across those cells in the guidelines schema where an “out” penalty is possible. About 60% of the cases (37,791 of 61,294) fell into those cells, and the percentage of defendants receiving an “out” decision is generally higher for females. The “out” percentage is larger among female defendants in 11 of 14 comparisons (excluding the cells which have fewer than 15 female cases and which typically involve defendants with higher prior record scores). A separate regression analysis conducted on those cells revealed that female defendants are about 9% less likely to be jailed/imprisoned in situations in which judges can impose an “out” sentence and not depart from the guidelines framework.

The next step in our analysis, which took advantage of an ongoing evaluation of the guidelines schema in Pennsylvania, involved interviews with a number of judges in the state regarding the “stickiness” of the gender effect in the in/out decision.12 We also observed a number of sentencing hearings.

11. The majority of reasons listed by the judges did not favor one sex over the other, including victim characteristics, crime not premeditated, old criminal record, offender cooperated with police, jail or prison overcrowding, and “no reasons.”

The Pennsylvania Commission on Sentencing has under way an evaluation of the guidelines that is focused in particular on the issue of disparity in sentencing and the

12.


The judges’ responses overlapped the departure reasons (summarized earlier) but added richer detail regarding the sentencing approaches of judges. Con- siderations pertaining to child care, remorse, criminal involvement, threat to society, and so forth were offered as explanations for the gender disparity. The judges viewed the lesser jailing of female defendants as warranted disparity and as sensible. According to the judges, even when they exhibit similar criminality “on paper,” male and female defendants frequently differ in degree of blameworthiness and in the practical effects of the in/out decision on the community and the correctional system. The comments of two judges, one female and the other male, reflect the overall pattern of responses. The female judge noted,

Maybe some judges do give women [defendants] a break because they feel sorry for the woman or because she has children. But the main thing for me is that sometimes you’re comparing “apples and oranges.” A woman coming before you in court may have the same prior record score or the same offense score as a man but her score involves all property offenses-no violent priors at all. And many times the woman’s part in the offense is small, more the follower than the leader. I don’t know where they find these guys but some of these woman get hooked up with such losers you can hardly imagine it. Another thing that doesn’t show up [in the official record] is that the women I see in court fairly often have health problems or mental problems. What are we going to do? The jails can’t handle that.

A lot of female cases I see, they seem to be under mitigated circumstances. A lot of times they get involved because of inducements from boyfriends and spouses, [they] come to court as a codefendant with a male who is the primary offender. A big thing here, too, is the women are more likely to show some remorse; they actually feel bad about what they did. The other thing is that the women are the care providers for children. Very often men are not in that circumstance. With a lot of the women that come before the court, there’s no b a c k - u p a parent or a spouse to care for the children. This is very much a circumstance that is relevant legally. Whatever else you do, you have to consider the interests of the children. The court first and foremost, all the courts in Penn- sylvania, always consider themselves the protector and guardians of children. This is protection of the community, really protecting the future of our community, too.

A number of judges noted, however, that female drug offenders were unlikely to get “a break” because they are “every bit as likely to get into

The male judge observed,

impact of the guidelines on prison overcrowding. The evaluation includes interviews with judges and courtroom observations.


trouble again as are male druggies.” Recall that judges in fact did sentence female drug offenders to slightly longer jail/prison terms than male drug offenders. 13 Finally, while the judges generally dismissed the significance of chivalry or paternalism in their own decisionmaking, several judges stated that such considerations may influence some of their colleagues.

SUMMARY A N D IMPLICATIONS Our study builds on prior research about the influence of gender on impris-

onment decisions by incorporating more exact controls for offense seriousness and prior record, by including a large number of cases and offenses, by parti- tioning the sentencing decision, and so forth. We found (net of other variables) that the primary determinants of judges’ imprisonment decisions are the type or seriousness of the crime committed and the defendant’s prior record, not the defendant’s gender (or, for that matter, age, race, or other back- ground/contextual variables). Specifically,

1. Gender has a small-to-moderate effect on the i d o u t “incarceration” decision favoring female defendants. Males are about 12% more likely to be jailed or imprisoned. The female advantage in the i d o u t decision is reduced to 9% when dispositional departures are partitioned. 2. Departure reasons and interview responses of judges suggest, in many instances, that the lesser jailing of female defendants is based on legally relevant considerations (e.g., prior record that is nonviolent, played minor role in the offense). In other instances, the judges’ reasons for their lesser jailing of female defendants (i.e., childcare responsibilities, physical or mental problems, showed remorse) can be construed as justified even though they may violate strict legal criteria. 3. Gender has no effect on the length-of-sentence decision (females receive slightly longer sentences for minor offenses but receive slightly shorter sentences for serious offenses). 4. These patterns hold across levels of offense seriousness and by race.

That our findings differ somewhat from those reported in previous research can be interpreted in a number of ways. Compared with earlier times (e.g., 1960s and 197Os), contemporary sentencing practices may have become more gender neutral due to a more bureaucratic criminal justice system and the

13. Our findings are consistent with some prior research (e.g., Alabama Law Review Special Project, 1975) but are at odds with Peterson and Hagan’s (1984) study of federal offenders (1973-1977 data), which concluded that male drug defendants were sentenced more harshly. However, (a) Peterson and Hagan’s drug offense classifications were too broad to distinguish adequately serious from more minor forms of drug trafficking and (b) their data did not distinguish between accomplice and leader roles, a distinction that is crucial when federal drug cases are analyzed. It is quite likely that female defendants in the federal sample, in fact, were involved in the less serious forms of drug trafficking and played minor roles (Steffensmeier and Allan, 1990).


greater concern about equal application of the law in reducing sentencing disparities of any kind, including gender. If this is the case, the previous findings on the effect of gender on sentencing outcomes are time bounded (or historically situated) but equally valid.

Or, it may be that our results are state or circumstance specific and derive from the particular structure of sentencing guidelines in Pennsylvania, which lead to greater equality by removing some of the discretion of judges. While similarly conducted research in other jurisdictions is obviously needed, it is worth noting that Pennsylvania’s guidelines schema reflects a national trend toward more determinate sentencing systems. Since the mid- 197Os, roughly one-half of the states have adopted comprehensive sentencing reform. Several states (e.g., California) have adopted legislatively written guidelines: 14 states and the federal government have created sentencing commissions mandated to establish sentencing guidelines. 14 Many other states are considering such a model. Part of the stimulus for this movement is the serious overcrowding in state prisons and the state’s desire to establish a sentencing policy that ensures the prudent use of limited prison space. Research involving guidelines systems, therefore, is generalizable to many states currently, and it provides information to many other states that are likely to adopt the guideline system during this decade. Moreover, because states without guidelines have varying mixtures of mandatory and nonmandatory sentences, along with good time and other mechanisms to change the length of incarceration, generalizability among nonguideline states may be risky.

We believe the differences in findings between our study and most prior research are due partly to the tighter controls for prior record and offense seriousness we employed. First, while earlier studies can be faulted for not adequately controlling for offender and offense characteristics that legitimately enter into judicial decisionmaking, several of the better-designed studies also report weak or negligible gender effects (e.g., Curran, 1983; Kruttschnitt and Green, 1984). For example, although limited to a single jurisdiction, Kruttschnitt and Green concluded,

Our analyses suggest that we should question, or at least qualify, the commonly held assumption that women are treated with relative leniency by the criminal justice system (p. 550).

It appears, moreover, that the studies with the fewest controls are likely to report large gender effects, whereas those studies with more adequate controls find small, if any, gender effects (see Table 1).

14. Besides Pennsylvania, the states are Florida, Kansas, Louisiana, Maryland, Min- nesota, North Carolina, Ohio, Oregon, South Carolina, Tennessee, Texas, Washington, and Wisconsin. Also, Michigan, Utah, and Virginia have enacted judicially created guidelines, and Arkansas and Vermont have initiated legislative discussion about the implementation of a guidelines system.


Second, some evidence to support the view that the differences are due to improved methodology is presented in Table 6, which compares the effects of gender on imprisonment decisions in conditions involving different measures of prior record and offense gravity (i.e., measures that have been employed in much of the prior research). The findings are clear-cut and convincing. The fairly strong relationship between gender and imprisonment decisions that exists under conditions of “weak” controls for legally relevant variables is substantially reduced for the in/out decision and eliminated in the case of sentence length when more precise controls for prior record and offense gravity are employed.

Table 6. Effects of Gender on Imprisonment Decisions Under Conditions of Different Controls for Prior Record and Offense Gravity (OLS and logit results)

In/Out: Length-of-Term Conditions OLS (Odds) (in months) Violent vs. Nonviolent .21 (2.33) 7.1 Felony vs. Misdemeanor .19 (2.20) 5.9 Violent vs. Nonviolent .20 (2.32) 7.2 All Other Controls Felony vs. Misdemeanor .18 (2.31) 6.2 All Other Controls

Prior vs. No Prior* All Other Controls

Prior vs. No Prior* All Other Controls

Violent vs. Nonviolent .15 (1.91) 4.3

Felony vs. Misdemeanor .15 (1.91) 3.7

Pennsylvania Controls for .12 (1.17) - 1.6 Prior Record & Offense Gravity

All Other Controls Departure Sentences Partitioned .09 (1.10) -

* Recall that the lowest “prior” in the Pennsylvania guidelines schema is a weighted score (see “Methods” section) and, therefore, it is not directly comparable to measures of prior record used in previous research. Thus, we do not display separately the coefficients for the prior vs. no prior breakdown.

In light of these findings, more statistical analyses of criminal court decisions in other localities are needed in order to determine if, after introducing suitable controls for legally relevant variables, significant effects remain concerning gender and imprisonment decisions. Cumulative research in this area should be embedded in refined or precise breakdowns of offense severity and prior record. This means, among other things, that rather than simply col- lecting data on legal variables such as prior record from probation or prison

43 8 STEFFENSMEIER ET AL.

files that are conveniently available and then assuming (sometimes wrongly) that judges are so enlightened, researchers should ascertain carefully whether in fact judges are cognizant of such information and how they treat it. More so than our study, moreover, such statistical studies should seek to include a broader range of contextual variables, such as amount and quality of jail/ prison space available, financial resources available to defendant, and victim characteristics or victim-offender interaction.

Second, observational studies and interviews of judges are also needed in order to assess more fully whether judges make ad hoc decisions based on an offender’s gender rather than on offense and offender characteristics that legitimately enter judicial decisions. The qualitative data involving the departure reasons and the interview responses of judges suggest two important conclusions. First, judges are guided by two focal concerns in their sentencing decisions: concerns about blameworthiness and practical considerations. Second, from a policy perspective, the observed gender differences in sentencing outcomes may be viewed as warranted or justified disparities.

The two focal concerns intertwine and contribute in a number of ways to small gender disparities in sentencing, particularly at the in/out phase. For example, the departure reasons stated by Pennsylvania judges suggest that even when there is official sameness in offense severity or prior record scores, the criminality and criminal history of male and female defendants may be quite different, that is, one is comparing “apples and oranges” in blameworthiness. The interview and departure responses also suggest the importance of pragmatic considerations, including the concern of judges about social costs to children of sending women to prison and the organizational demands of incarcerating pregnant women or women with physical or mental health problems. In Pennsylvania, as in most states, jail space is less available for female offenders, and the jails that house female offenders are poorly staffed and provide fewer opportunities for rehabilitation. Several of the judges we interviewed said they were reluctant to sentence female offenders to local jail facilities because they were in such “bad shape” that incarceration would entail extra-harsh punishment. The legal relevance or proper place of these sorts of considerations in judicial decisionmaking is open to debate. For our purposes, they underscore the need for research that investigates judges’ per- spectives in sentencing female (and male) offenders. We know very little about the ways in which judges make judgments about defendants, whether their justifications for a particular sanction are gender linked (but not due to sex or gender per se), and whether would-be disparities by gender are justified. Certainly, as interviewers and observers, we routinely found ourselves agreeing with the judge’s decision and viewing it as “wise” or warranted.

Finally, our findings both augment the concerns of other researchers who have addressed the gender-sentencing issue and broaden our understanding of sentencing practices. Specifically, the findings bolster Steffensmeier’s (1 980)


contention that the frequent finding of gender bias may be an artifact of inadequate controls for seriousness of offense and prior record, and they bolster Daly (1987) and Steffensmeier’s (1980) arguments that gender differences in court outcomes reflect a concern with maintaining the family unit and the high priority accorded to women’s care-taking role. But the findings also suggest that gender differences in courtroom outcomes reflect “real” differences between male and female defendants in criminal involvement, in danger or threat to society, and in the extent of health problems or mental disorder (see Steffensmeier and Allan, 1990, for a review of female crime). To the extent that these gender-linked considerations are viewed as legitimate ante- cedents of judicial decisionmaking, an overall pattern of more lenient outcomes for women may still be defined as warranted and ought not necessarily be construed as gender “bias.”

CONCLUSION

It is widely assumed that female defendants receive preferential criminal justice outcomes. But the methodological limitations of the existing research, along with the ambiguity in the findings reported, cloud the issue. The results of our study indicate that more complete information on offense profiles, prior criminal histories, and the status of female defendants vis-a-vis childcare responsibilities and physical-mental health may eliminate the small-to- moderate gender disparity often observed in analyses of judicial decisionmaking. There obviously is a great need for research on other localities that is based on data sets that not only include stringent statistical controls but also reflect contemporary sentencing practices. It is disappointing that “old” data from the 1970s remain the basis for (nearly all) published analyses of gender- based differences in sentencing. Those data will not reflect the potentially important role played by a more bureaucratic criminal justice system and the greater concern about equal application of the law in reducing sentencing disparities of any kind, including gender. Today, there also is a greater availability of jail facilities for female offenders, a factor in the past that attenuated the jailing of women.

Our findings suggest that the sentencing practices of judges are driven by two focal concerns, blameworthiness (e.g., as indicated by prior record, type of involvement) and practicality (e.g., as indicated by childcare responsibility, pregnancy, emotional or physical problems, availability of jail space). If understanding of the gender-sanctioning relationship is to be enhanced, researchers will have to develop data sets that capture these sorts of variables in their statistical analysis. Should they do so, we suspect they will find that when men and women appear in (contemporary) criminal court in similar circumstances and are charged with similar offenses, they receive similar treatment.


REFERENCES

Alabama Law Review Special Project A study of differential treatment accorded female defendants in Alabama criminal courts. Alabama Law Review 27:676746.

1975

Albonetti, Celesta 1991 An integration of theories to explain judicial discretion. Social Problems

381247-266.

Baab, George and William Ferguson 1967 Texas sentencing practices: A statistical study. Texas Law Review

45:47 1-503.

Belsley, David A., Edwin Kuh, and Roy E. Welsch 1980 Regression Diagnostics, Identifying Influential Data and Sources of Col-

linearity. New York: John Wiley & Sons.

Benson, Michael L. and Esteban Walker 1988 Sentencing the white collar offender. American Sociological Review

53:294302.

Berk, Richard 1983 An introduction to sample selection bias in sociological data. American

Sociological Review 48:386-398.

Incarceration as a response to crime: The utilization of Canadian prisons. Canadian Journal of Criminology and Corrections 14: 10-3 1.

Measuring and explaining disparities in felony sentences: Courtroom workgroup factors and race, sex, and socioeconomic influences on sentence severity. Political Behavior 5: 135-153.

Cousineau, David and Jane E. Veevers 1972

Croyle, James 1983

Curran, Debra A.

Daly, Kathleen

1983 Judicial discretion and defendant’s sex. Criminology 2 k4l-58.

1987 Discrimination in the criminal courts: Family, gender, and the problem of equal treatment. Social Forces 66: 152-175.

Gender, race, and discrimination research: Disparate meanings of statistical “sex” and “race” effects in sentencing. Unpublished paper, University of Michigan, Ann Arbor.

Daly, Kathleen and Rebecca Bordt 1993

Eisenstein, James, Roy Fleming, and Peter Nardulli 1988 The Contours of Justice: Communities and Their Courts. Boston: Little,

Brown.

Farrington, David P. and Allison M. Morris 1983 Sex, sentencing, and reconviction. British Journal of Criminology

23~229-248.

Figueira-McDonough, Josefina 1985 Gender differences in informal processing: A look at charge bargaining and

sentence reduction in Washington, D.C. Journal of Research in Crime and Delinquency 22: 101-1 33.


Frazier, Charles E., E. Wilbur Bock, and John C. Henretta 1983 The role of probation officers in determining gender differences in sentencing

severity. Sociological Quarterly 24:305-3 18.

The Legal System: A Social Science Perspective. New York: Russell Sage.

Gender bias and the criminal justice system: An empirical investigation. Sociology and Social Research 70: 164-1 7 1.

Sentencing practices of criminal court judges in Philadelphia. American Journal of Corrections (July-August):32-35.

Gruhl, John, Susan Welch, and Cassia Spohn Women as criminal defendants: A test for paternalism. Western Political Quarterly 37:456467.

Hagan, John and Kristin Bumiller Making sense of sentencing: A review and critique of sentencing research. In Alfred Blumstein, Jacqueline Cohen, Susan Martin, and Michael Tonry (eds.), Research on Sentencing: The Search for Reform. Vol. 2. Washing- ton, D.C.: National Academy Press.

Friedman, Lawrence

Ghali, Moheb and Meda Chesney-Lind

1975

1986

Green, Edward 1960

1984

1983

Hanushek, Eric A. and John E. Jackson

Heilbrun, Alfred

1977 Statistical Methods for Social Scientists. New York: Academic Press.

1982 Female criminals: Behavior and treatment within the criminal justice system. Criminal Justice and Behavior 9:341-35 1.

Gender differences in the sentencing of felony offenders. Federal Probation Johnston, Janet, Thomas Kennedy, and I. Gayle Shuman

1987 5 1~49-55.

Kempinen, Cynthia 1983 Changes in the sentencing patterns of male and female defendants. Prison

Journal 63:3-11.

Klein, Done and June Kress 1976 Any woman’s blues. Crime and Social Justice 5:3&49.

Complex policy choices-The Pennsylvania Commission on Sentencing. Federal Probation 50: 15-23.

Kramer, John H. and Anthony J. Scirica 1986

Kritzer, Herbert and Thomas Uhlman 1977 Sisterhood in the courtroom: Sex of judge and defendant in criminal case

disposition. Social Science Journal 4:77-88.

1980-81 Social status and sentences of female offenders. Law and Society Review Kruttschnitt, Candace

15~247-265.

Kruttschnitt, Candace and Donald Green 1984 The sex sanctioning issue: Is it history? American Sociological Review

49~541-551.


Mann, Coramae 1984 Race and sentencing of female felons: A field study. International Journal

of Women’s Studies 7:160-172.

Miethe, Terance D. and Charles A. Moore 1986 Racial differences in criminal processing: The consequences of model

selection on conclusions about differential treatment. Sociological Quarterly 27~2 17-237.

Moulds, Elizabeth 1980 Chivalry and paternalism: Disparities of treatment in the criminal justice

system. In Susan Datesman and Frank Scarpitti (eds.), Women, Crime, and Justice. New York: Oxford University Press.

Myers, Martha A. and Susette M. Talarico

Nagel, Ilene and John Hagan

1987 The Social Contexts of Criminal Sentencing. New York: Springer-Verlag.

1983 Gender and crime: Offense patterns and criminal court sanctions. In Michael Tonry and Norval Morris (eds.), Crime and Justice: An Annual Review of Research. Vol. 4. Chicago: University of Chicago Press.

Nagel, Stuart and Lenore Weitzman

Orsagh, Thomas

1971 Women as litigants. The Hastings Law Journal 23:171-198.

1985 The judicial response to crime and the criminal: A utilitarian perspective. Journal of Quantitative Criminology 1 :369-386.

Pennsylvania Commission on Sentencing 1990 Sentencing in Pennsylvania: Annual Report. University Park: Pennsylvania

Commission on Sentencing.

Peterson, Ruth and John Hagan Changing conceptions of race and sentencing outcomes. American Sociologi- cal Review 49:56-70.

1984

Pollak, Otto 1950 The Criminality of Women. Philadelphia: University of Pennsylvania Press.

A study of sentencing in Hennepin County and Ramsey County district courts. Journal of Legal Studies 6:333-353.

Rhodes, William 1977

Simon, Rita J.

Simon, Rita J. and Narvin Sharma

1975 Women and Crime. Boston: Lexington Books.

1979 The Female Defendant in Washington, D.C., 1974 and 1975. Washington, D.C.: Institute for Law and Social Research.

Spohn, Cassia, John Gruhl, and Susan Welch 1981-82 The effect of race on sentencing: A reexamination of an unsettled question.

1985

1987

Law and Society Review 16:71-88. Women defendants in court: The interaction between sex and race in convicting and sentencing. Social Science Quarterly 66: 178-1 85. The impact of ethnicity and gender of defendants on the decision to reject or dismiss felony charges. Criminology 25:175-192.


Steffensmeier, Darrell 1976 1980

Advocates of law and order. Criminal Justice and Behavior 3:273-285. Assessing the impact of the women’s movement on sex-based differences in the handling of adult criminal defendants. Crime and Delinquency 23:344356.

Gender, age, and crime. In Joseph Sheley (ed.), Handbook of Criminology. Belmont, Calif.: Wadsworth.

Discriminatory sentencing of women offenders: The argument for ERA in a nutshell. In Laura Crites (ed.), The Female Mender. Lexington, Mass.: Lexington Books.

Differential treatment of the female felon: Myth or reality? In Marguerite Warren (ed.), Comparing Male and Female Offenders. Beverly Hills, Calif.: Sage.

Sentencing guidelines and their effects. In Andrew von Hirsch, Kay A. Knapp, and Michael Tonry (eds)., The Sentencing Commission and Its Guidelines. Boston: Northeastern University Press.

Class, status, and the punishment of white-collar criminals. Law and Society Inquiry 50:223-243.

Sentencing the white collar offender: Rhetoric and reality. American Sociological Review 47641-659.

Differential sentencing of women and men in the U.S.A. International Journal of the Sociology of Law 12:401-413.

Steffensmeier, Darrell and Emilie Allan 1990

Temin, Carolyn 1976

Tjaden, Patricia and Claus Tjaden 1981

Tonry, Michael 1980

Weisburd, David, Elin Waring, and Stanton Wheeler 1990

Wheeler, Stanton, David Weisburd, and Nancy Bode 1982

Zingraff, Matthew and Randall Thompson 1984

Darrell Steffensmeier is Professor of Sociology at The Pennsylvania State University, University Park. His research interests include the sociology of law, organized crime, and the structural covariates of crime (including race, gender, and age). His recent book The Fence: In the Shadow of Two Worlds, was the recipient of the 1987 Award of Outstanding Scholarship of the Society for the Study of Social Problems. During 1990-91 he served as project director and principal writer of the 1990 Report-Organized Crime in Pennsylvania: A Decade of Change. He is currently conducting a NSF-sponsored study of men and women lower-court judges in Pennsylvania.

John Kramer is Associate Professor of Sociology at The Pennsylvania State University. He is also the Executive Director of the Pennsylvania Commission on Sentencing. His research interests include the sociology of law, sentencing and sentencing disparity. As Executive Director of the Commission on Sentencing he is actively involved in writing, monitoring and evaluating Pennsylvania’s sentencing guidelines. He is currently conducting a BJA funded study of structured sentencing.

Cathy Streifel earned her Ph.D. at The Pennsylvania State University in 1989, and currently is Assistant Professor of Sociology at Purdue University. Her articles on gender and


crime and age and crime have been published in leading criminology and sociology jour- nals. In addition to her continuing study of gender and crime, Streifel's current research efforts involve analyses of judicial decisionmaking and the crime-alcoholism relationship (especially among women offenders).

App

endi

x 1.

R

esul

ts fo

r O

ffen

se D

umm

y V

aria

bles

~~

JaiV

Pris

on v

s. Pr

obat

ion

~~

~~

Pris

on v

s. Ja

iVPr

obat

ion

Pris

on v

s. Ja

il

Invo

lunt

ary

Man

slau

ghte

r R

obbe

ry F

elon

y 1

Rob

bery

Fel

ony

2 R

obbe

ry F

elon

y 3

Agg

rava

ted

Ass

ault

Sim

ple

Ass

ault

Ars

on

Wea

pons

B

urgl

ary

(7)

Bur

glar

y (6

) B

urgl

ary

(5)

Cri

min

al T

resp

ass

Felo

ny 2

C

rim

inal

Tre

spas

s Fe

lony

3

The

ft F

elon

y 3

The

ft M

isde

mea

nor

Ret

ail

The

ft F

elon

y 3

Ret

ail

Thef

t O

ther

Fo

rger

y Fe

lony

2

Forg

ery

Felo

ny 3

D

rug

Felo

ny

ba

,437

,5

88

,525

,4

13

,450

,0

97

,531

,2

43

,535

,4

77

,356

,2

40

,239

.2

86

,159

,4

07

,143

,3

46

,246

,4

21

-

Odd

s R

atio

7.7

57.9

16

.3

8.3

9.1 1.6

16.8

3.

3 20

.9

14.7

5.

6 3.

1 2.

7 3.

9 2.

1 7.

8 2.

1 5.

0 3.

0 8.

1

b ,240

,6

32

,272

,1

43

,238

,0

15

.359

,1

23

,371

.2

68

,181

,0

78

,050

,1

29

,042

,0

95

.w

,139

,1

09

,162

-

Odd

s R

atio

15.8

12

2.7

16.4

6.

8 14

.3

1.2

27.1

5.

7 27

.1

14.6

8.

6 3.

6 2.

7 6.

0 2.

3 4.

7 1.

1 6.

2 5.

1 8.

3

b ,354

,6

49

,332

,1

95

,322

,446

,2

23

.43 I

,3

32

,255

.I

38

.085

,2

05

,089

,1

21

--.0

11

,202

,1

92

.22 I

-

-.019

Odd

s R

atio

7.2

39.3

6.

1 2.

9 5.

9 1.

3 10

.9

3.4

10.2

6.

1 4.

0 2.

0 1.4

3.1 1.5

1.9

1.5

3.0

2.9

3.5

Sent

ence

Le

ngth

b 8.

880

52.6

00

7.93

0 0.

927

9.75

0 -9

.400

20

.300

- 3.

970

20.6

00

12.7

00

4.14

0 -3

.960

- 8.

690

- 1.

990

-6.7

40

- 8.

730

- 10

.900

-0

.938

-2

.99

1.81

NO

TE

: M

isde

mea

nor

drug

vio

latio

ns w

ere

excl

uded

as

the

cont

rast

lev

el.

a U

nsta

ndar

dize

d re

gres

sion

coe

ffic

ient

from

OL

S re

gres

sion

ana

lysi

s.

Odd

s ra

tio f

rom

log

istic

reg

ress

ion

anal

ysis

.

m

App

endi

x 2.

Fr

eque

ncy

Perc

enta

ge o

f M

ales

and

Fem

ales

Not

Inc

arce

rate

d fo

r C

ells

with

“O

ut”

Min

imum

s (c

ell t

otal

s of

all

case

s in

par

enth

eses

)

5 F

=

1,54

9 64%

M =

5,9

41

51%

(1

4.00

9)

4 F

=

228

76%

F

=

20

41%

F

=

18

69%

M =

1,

331

64%

M =

119

44

%

M =

17

1 43

%

(2,3

82)

(321

) (4

28)

A

F =

6

16%

M =

2,5

28

61%

M =

245

49

%

M =

33

6 43

%

M

=

142

40%

M

=

77

21%

M

=

29

16%

M

=

80

11%

P .z 3

F =

80

7 75

%

F =

61

60

%

F =

41

52

%

F =

20

51

%

F

=

6 32

%

F =

0

1 8 2

F =

85

9 79

%

F =

48

64%

F =

28

47

%

F =

12

50

%

F =

13

65

%

F =

0

F =

4

27%

M =

2,

636

70%

M =

200

52

%

M =

25

6 47

%

M

=

83

36%

M

=

100

43%

M

=

16

73%

M

=

68

16%

(5,1

34)

(0‘9

(868

) (3

90)

(382

) (1

89)

(769

) s

(4.8

44)

(463

) (6

w

(255

) (2

53)

(125

) (446)

I F

=

520

85%

F

=

34

69%

F

=

20

47%

F

=

7 64%

F =

10

59

%

F =

8

89%

F

=

11

38%

M =

2,

191

78%

M =

14

3 60

%

M =

28

6 68

%

M

=

93

56%

M

=

99

55%

M

=

27

38%

M

=

139

43%

(3,4

10)

(289

) (4

67)

(197

) ( 1

97)

(81)

(3

5 1)

0 1

2 3

4 5

6

Prio

r R

ecor

d Sc

ore

Not

e: T

otal

N =

37.

791

3 ? r

GENDER AND IMPRISONMENT DECISIONS

Documents

Transcript of GENDER AND IMPRISONMENT DECISIONS