Gender Sentencing of Rural Property Offenders in Iowa

Abstract

During recent decades, sentencing patterns of offenders based on gender have been the topic of criminological and sociological research. Particular attention has been given to the lenient sentencing of female offenders compared to male offenders. Several research studies have found that gender affects the sentencing process in ways that are advantageous to female offenders, such as lower incarceration rates and shorter prison sentences. Despite the existing research on gender and sentencing, there are still several specific areas that need to be studied regarding this topic, such as property crimes and rural areas. The current research adds to the existing literature by using data collected from five rural counties in Iowa to examine sentencing patterns of nonviolent property crimes in rural areas. Contrary to existing literature, results found gender did not have an effect on the decision to incarcerate, the sentence length, or the fine amount.

Keywords

gender bias sentencing disparity rural crime

Gender-based sentencing patterns have been the topic of criminological and sociological research during recent decades, both before and after the implementation of sentencing structures. The issue of sentencing discrepancies arises when offenders are given different sentences for the same type of offense. The discrepancy is usually based on an extralegal variable, a variable that legally cannot be used and is not directly related to the criminal situation (e.g., class, race, or gender of the offender). Continuous sentencing discrepancies have led to the development of sentencing guidelines, which have been adopted in many states, limiting the amount of discretion judges have regarding sentencing decisions. Despite these limitations, there are still discrepancies between different groups of offenders.

Particular attention has been given to the lenient sentencing of female offenders compared to male offenders. Several studies have found that gender affects the sentencing process in ways that are advantageous to female offenders in terms of lower incarceration rates and shorter prison sentences (Britt, 2009; Bushway & Phiel, 2001; Crow & Gertz, 2008; Doerner & Demuth, 2010; Feldmeyer & Ulmer, 2011; Franklin & Fearn, 2008; Freeborn & Hartmann, 2010; Freiburger, 2011; Haynes, 2011; Myers, 1989; Peterson & Hagan, 1984; Spohn & Beichner, 2000; Stacey & Spohn, 2006). The leniency female offenders receive in regards to sentencing can be seen in violent, drug and property crime, although the degree of impact varies across crime type (Rodriquez, Curry, & Lee, 2006).

Despite the history of literature in this area, there are several variable relationships in sentencing discrepancies that have not been directly tested. These include specific types of crimes, such as property crime, and the size of cities used in the sample. Several of the previous studies have used large urban cities in their data collection (Gruhl & Welch, 1984; Spohn, 1999; Spohn & Beichner, 2000; Wooldredge, 2010), leaving out rural areas. Also, while there is research on violent crime and drug offenses, there is very little existing research on property offenses. Research on property crime and rural areas would be useful to determine if there is a link between gender, property offenses, rural crime, and sentencing discrepancies. If a link between the variables is found, results from the study could contribute to policy implications regarding sentencing structures. The current study will examine the relationship between sentencing and nonviolent property crimes in rural areas through the lens the chivalry/paternalism theory, which posits females will be treated more leniently than males.

Chivalry/Paternalism Theory

The chivalry/paternalism theory is based on the societal beliefs some legal officials, specifically male legal officials, may have concerning females and female offenders, such as being protective and benevolent toward females (Herzog & Oreg, 2008). In the case of sentencing, stereotypes of female defendants that judges may harbor are often developed out of the concepts of chivalry and paternalism, steeped heavily in perceptions of appropriate femininity (Curry, Lee, & Rodriquez, 2004). These beliefs are responsible for the lenient sentencing approach toward female offenders for two possible reasons. First, chivalry implies that men are unwilling to inflict harm on women (Moulds, 1978). Second, paternalism implies an accepted attitude that women are weak, submissive, childlike, and defenseless, and therefore need to be protected and are not fully responsible for their criminal actions (Herzog & Oreg, 2008; Moulds, 1978). Although these concepts are independent of each other, paternalism has been argued as the more insidious pattern (Moulds, 1978), they are difficult to distinguish from one another due to their similar outcomes (Crew, 1991). As a result, they are often seen as one perspective of how females are viewed by criminal justice officials and are often used to justify the advantageous treatment of women (Herzog & Oreg, 2008; Moulds, 1980).

According to the chivalry/paternalism theory, there are three general propositions regarding gender and criminal sanctions in America. First, it is believed that if females adhere to traditional sex roles, they will receive a more lenient sentence than male offenders. This is because females who adhere to traditional sex roles are seen as weak, submissive, and defenseless and therefore need to be taken care of, leading to lighter sentences (Crew, 1991). Measures of sex roles include gender as well as employment status. For females, being unemployed is seen as adhering to sex roles, as they are economically dependent (Crew, 1991; Kruttschnitt & Savolainen, 2009). In addition, traditional sex roles may dictate women should be unemployed to ensure proper attention is given to their role as a wife, as well as a mother to her dependent children, which leads to the second proposition.

Second, the sanctioning of female offenders will be affected by extralegal variables such as sex-related status attributes, for example, being a parent. However, the sanctioning of male offenders will not be affected by these same variables; females who are mothers will be treated more leniently than males who are fathers (Crew, 1991; Daly, 1989; Koons-Witt, 2002). Although offenders of both genders who support dependents may be treated advantageously, mothers who are often primary caregivers are given more leniency than fathers (Daly, 1989). The sentencing of fathers may take into account the caregiver role of the mother of offender’s dependent children, while the sentencing of mothers may focus solely on her as possible caregiver (Daly, 1989).

Finally, the nature and seriousness of the offense will have a stronger impact on female offenders than male offenders, simply because the type of offense committed can be an indicator of conformity to sex roles (Crew, 1991). Offenses which conform to female sex roles include nonviolent offenses such as embezzlement or larceny because they are not seen as threatening or dangerous (Bernstein, Leung, & Schultz 1977; Rodriquez et al., 2006). In addition, the motivation behind these crimes may be traced back to sex role status of being a mother. The crime may be justified as a mother needing more money to raise her children, for example. Conversely, violent crimes are considered unfeminine and challenge feminine sex roles. The offender who commits such a crime runs contradictory to the views of women held by sentencing officials and may result in equal or harsher punishments than male counterparts (Crew, 1991; Rodriquez et al., 2006).

In addition, no analogous sex role criteria has been hypothesized or applied to the sentencing of male offenders. This may be the case as sex roles assigned to males that may shape criminal activity have very little to do with other statuses such as being a husband or father (Kruttschnitt, 1984). The preferential treatment for sentencing is based on the degree that criminal justice officials perceive the female as fitting stereotypes as a good woman, by being a mother and a wife, statuses that may influence criminal activity (Griffin & Wooldredge, 2006; Kruttschnitt, 1984).

The chivalry/paternalism theory can be applied to all stages of the criminal justice process from arrest decisions by police officers (Visher, 1983), case-processing decisions (Farnworth & Teske, 1995; Spohn & Spears, 1997), prosecutor’s decision to file felony charges (Spohn, Gruhl, & Welch, 1987), and to sentencing decisions (Crew, 1991; Daly, 1989; Freiburger, 2011; Rodriquez et al., 2006; Spohn & Spears, 1997; Stacey & Spohn, 2006; Wu & Spohn, 2010). Although the conclusions were not substantially large, most of these studies found support for the chivalry/paternalism theory, such that female offenders were treated more leniently than male offenders (Britt, 2009; Crew, 1991; Daly, 1989; Farnworth & Teske, 1995; Freiburger, 2011; Haynes, 2011; Rodriquez et al., 2006; Spohn et al., 1987; Spohn & Spears, 1997; Stacey & Spohn, 2006; Visher, 1983; Wu & Spohn, 2010).

Gender and Sentencing Discrepancies

Literature on gender and sentencing discrepancies spans several decades, and looks at several specific relationships (Albonetti, 1991; Blackwell, Holleran, & Finn, 2008; Curry et al., 2004; Daly, 1989; Doerner & Demuth, 2010; Freiburger, 2011; Peterson & Hagan, 1984; Stacey & Spohn, 2006; Ulmer & Kramer, 1996; Wu & Spohn, 2010). Throughout the literature, general patterns find females being treated advantageously over males, although magnitudes may vary (Daly & Bordt, 1995). This lenient treatment is seen in the lower likelihood of incarceration for females and longer lengths of prison sentences for males (Bourassa & Andreescu, 2009; Daly, 1989; Doerner & Demuth, 2010; Feldmeyer & Ulmer, 2011; Freeborn & Hartmann, 2010; Haynes, 2011; Jeffries, Fletcher, & Newbold, 2003; LaFrentz & Spohn, 2006; Myers, 1989; Peterson & Hagan, 1984; Spohn & Beichner, 2000; Stacey & Spohn, 2006; Ulmer, Eisenstein, & Johnson, 2010; Wu & Spohn, 2010). This sentencing pattern has held for drug offenders (Freiburger, 2011; Spohn, 1999; Rodriquez et al., 2006) and violent offenders (Curry et al., 2004; Franklin & Fearn, 2008; Rodriquez et al., 2006), as well as property offenders, discussed later (Freiburger, 2011; Nagel & Johnson, 1994; Rodriquez et al., 2006).

Several additional sentencing patterns involving gender have been documented. These relationships include race, age, and employment. Repeated research involving gender, race, and sentencing has found that while generally females are treated more leniently than males, when race is involved, a hierarchy appears. White women are treated most leniently of the race/gender combinations, while non-White men are treated the harshest (Bourassa & Andreescu, 2009; Daly, 1989; Freeborn & Hartmann, 2010; Mustard, 2001). The age of the offender was more influential in the sentencing of male than female offenders. Mixing age with race, the influence of race among male offenders depended on the individual’s age. Race has more of an impact on the sentencing decisions for younger males than older males; young, Black males are treated the harshest of any race–age–gender category (Doerner & Demuth, 2010; Steffensmeier, Ulmer, & Kramer, 1998). In addition, employment status and gender had a negative impact on sentence length. Being male and employed reduced sentence severity, while being female and unemployed reduced sentence severity (Crew, 1991).

Property Offenses, Gender, and Sentencing Discrepancies

Although there is a wealth of existing literature on gender and sentencing, there is a lack of evidence specifically regarding property crime, gender, and sentencing outcomes. Several studies have looked at violent offenses (Curry et al., 2004; Franklin & Fearn, 2008) and drug offenses (Bourassa & Andreescu, 2009; Myers, 1989; Peterson & Hagan, 1984; Spohn, 1999; Stacey & Spohn, 2006; Steffensmeier, Kramer, & Streifel, 1993) but very few have focused on property offenses. Studies that address property crimes compare outcomes from various types of offenses including violent and drug offenses (Freiburger, 2011; Nagel & Johnson, 1994; Rodriquez et al., 2006). However, these studies may not only overshadow nonviolent property offenses, perhaps the least glamorous type of crime, they also ignore rural populations.

Rodriquez et al. (2006) looked at criminal sentences for three types of offense: violent, drug, and property offenses in Texas, a state with an indeterminate sentencing structure. Property offenses included in their study were burglary, larceny/auto theft, and forgery/fraud. They found that on the whole, property offenders had higher chances of receiving a deferred adjudication compared to violent offenders, who received prison sentences more often. For property offenders, the odds of incarceration of male offenders were 2.79 times higher than female offenders (Rodriquez et al., 2006). In addition, the difference of sentence length was 3.14 years between male and female offenders, with males serving a longer sentence than females.

Nagel and Johnson (1994) compared sentencing decisions of two property offenses, embezzlement and larceny. They found that females convicted of embezzlement are more likely than males to receive downward departures for extralegal reasons such as their gender. In 1992, the rate of downward departure was 9.8% for female embezzlers and 7.5% for male embezzlers. This difference of 2.3% is actually a decrease from the 1991 difference of 3.4% between the downward departure rates for female and male offenders (Nagel & Johnson, 1994). For larceny charges, female offenders were more likely than male offenders to be given downward departures, and were substantially more likely than male offenders to receive a shorter sentence within the given ranges.

Most recently, Freiburger (2011) compared in/out sentencing decisions for drug and property offenders in Pennsylvania. Two models were presented for each offense type, and similar patterns were found across all four models. In all four models, male offenders were more likely to be incarcerated than female offenders. However, only the models presented for drug offenders found the gender pattern to be statistically significant. Unlike previous research (Nagel & Johnson, 1994; Rodriquez et al., 2006), gender differences for sentencing patterns of property offenses were not statistically significant (Freiburger, 2011).

Importance of Current Study

Based on the previous discussion of literature, it is clear there is a dearth of studies that examine property offenses, in addition to rural settings. This study will add to existing literature of sentencing discrepancies by targeting several rarely studied relationships, such as nonviolent property offenses and rural populations. Studying sentencing patterns in rural settings is important due to the drastic difference between urban and rural culture. For one, judges in urban settings are likely to see a higher number of offenders for nonviolent property offenses. Seeing a large number of offenders may cause the judge to rely on a “perceptual shorthand” developed from generalizations based on variables, such as gender, to reach sentencing decisions to save time, which may account for the gender discrepancies found in urban areas (Steffensmeier et al., 1998). Rural judges are likely to see a smaller number of offenders with greater amounts of time between cases, preventing them from facing the same time constraints as urban judges. As a result, it is unknown how the judge’s personal bias may affect sentencing decisions. In addition, this study adds an additional sentencing variable of fine amount, akin to sentence length for offenders not incarcerated. While there is evidence of sentencing disparities regarding sentence length, very little research has examined other sentencing outcomes, such as fine amounts. Examining patterns of fine amounts is necessary due to criminal classification of nonviolent property offenses, most of which are simple misdemeanors that carry a fine as the punishment.

This study is based on the chivalry/paternalism theory, which claimed female offenders would be treated more leniently than male offenders. Therefore, the hypothesis for this research is that female offenders committing nonviolent property offenses will be treated more leniently than male offenders. This means that female offenders are less likely to be incarcerated, to have shorter sentences when they are incarcerated, and to have smaller fine amounts than their male counterparts.

Materials and Method

Sample

Data for this project were collected from five small rural counties in Iowa. Butler, Grundy, Guthrie, Keokuk, and Sac counties in Iowa each have less than 15,000 residents and are considered to be strictly rural counties. These counties were chosen to due to their similar demographics, including sex and race ratios, average age, cost of living index and median age, to allow for combination of results. In addition, five counties geographically diversified the sample, making it possible to generalize the conclusions to rural Iowa.

A total of 507 cases comprised the sample for this research. Each case represented a theft, larceny, fraud or embezzlement offense that occurred in Butler, Grundy, Guthrie, Keokuk, or Sac county between January 1, 1998, and December 31, 2008. As shown in Table 1, 37% of the offenders were female, and 95% were White, which is not surprising given the rural nature of the sample. Two-thirds had at least a high school degree or General Education Development (GED), although more than half (57%) were unemployed at the time of arrest. In addition, more than 70% of the offenses were classified as simple misdemeanors, leading to 77% of offenders not being incarcerated.

Table 1.

Variable Distributions.

Variable	N	Percentage
Age		µ = 30.9 years (s = 9.72)
Gender
Male	319	62.9
Female	188	37.1
Race
Caucasian	461	95.6
Non-Caucasian	21	4.4
Education
Less than high school	106	35.1
High school, GED, or beyond	196	64.9
Employment
Unemployed	119	57.2
Employed at least part time	89	42.8
Marital status
Single or divorced	299	68.2
Married	139	31.7
Offense class code
Simple misdemeanor	363	71.6
Serious misdemeanor	32	6.3
Aggravated misdemeanor	44	8.7
Class D felony	61	12.0
Class C felony	7	1.4
Total prior convictions		µ = 3.3 (s = 3.01)
Prior property convictions		µ = 1.6 (s = 2.11)
In/out
Incarcerated	114	22.5
Nonincarcerated	393	77.5
Sentence length		µ = 888.86 (s = 1,024.46)
Fine amount		µ = US$135.25 (s = US$203.48)

Dependent Variable

The sentencing process for this research was based off previous research, and was considered a two-stage process (Rodriquez et al., 2006; Steffensmeier et al., 1993; Steffensmeier et al, 1998). The first stage was the decision to incarcerate, which was a dichotomous variable and tested using a binary logistic regression model. The second stage was the length of the sentenced, measured in days, which was tested using an ordinary least squares (OLS) regression model. In addition, because nonviolent property crimes were included in the study, a third sentencing variable was added: the amount of the fine. The most common crime included in the data set was theft, often a misdemeanor carrying a fine and very little jail time. Therefore, a large majority of offenders were fined for their offense instead of being incarcerated. The dollar amount of the fine was tested using an OLS regression model.

Independent and Control Variables

Gender, coded male or female, was the independent variable for this study. Control variables included in this study included both legal and extralegal variables. Legal variables included prior criminal record and severity of the offense. The criminal record of the offender was determined by the total number of prior convictions (for any offense) on their record, and was recorded for the data as that raw number. In addition, the total number of prior property convictions was included as a legal control variable. The severity of the offense was based on the criminal class code of the offense, such as “simple misdemeanor,” “serious misdemeanor,” or “Class D Felony” offenses. Extralegal variables included the offender’s age, marital status, and education level.¹ Employment status was also included as an extralegal variable as a measure of traditional sex roles, specifically for the models split by gender.

Results

Bivariate Relationships

Before the multivariate models are presented, bivariate relationships explore possible differences between male and female offenders and their current offense severity, prior criminal history, as well as sentencing outcomes. The results of the independent sample t- and χ²-tests are presented in Table 2. Although the general pattern of current offense severity was similar for male and female offenders, there were a few statistically significant differences between male and females. Females were statistically more likely to commit a simple misdemeanor offense than males (77.1% vs. 68.3%; χ² = 4.493; p < .05), while males were more likely to commit a Felony D offense than females, although the difference was only marginally significant (13.8% vs. 9.0%; χ² = 2.522; p < .10. The difference between the average number of total prior convictions for males and females is also statistically significant, although it was much stronger (p < .01). Differences between male and female offenders and the average in/out decision for offenders were marginally statistically significant, with less females being incarcerated than males (18.6% vs. 24.8%; χ² = 2.565; p < .10), and fewer males being sentenced in nonincarcerated manners than females (75.2% vs. 81.4%; χ² = 2.565; p < .10). There were no differences in the mean sentence length and fine amount given to male and female offenders.

Table 2.

Bivariate relationships.

	Male	Female
Current offense severity
Simple misdemeanor (χ² = 4.493**)	68.3%	77.1%
Serious misdemeanor (χ² = 1.174)	7.2%	4.8%
Aggravated misdemeanor (χ² = 1.173)	9.7%	6.9%
Felony D (χ² = 2.522*)	13.8%	9.0%
Felony C (χ² = 1.224)	0.9%	2.1%
Average total prior convictions (t = 2.619**)	3.83 (SD 3.9)	2.96 (SD 2.5)
Incarceration decision
In (χ² = 2.565*)	24.8%	18.6%
Out (χ² = 2.565*)	75.2%	81.4%
Average sentence length in days (t = 0.402)	913.86 (SD = 957.29)	832.43 (SD = 1175.17)
Average fine amount in dollars (t = 0.771)	142.13 (SD = 15.18)	123.26 (SD = 18.53)

p < .10. **p < .05.

The results of the bivariate relationships surmise that gender does influence the likelihood of incarceration, although how gender plays a role is unclear from these tests. This relationship is easy to understand as other bivariate tests show that male and female offenders commit different offenses based on severity, and have different prior criminal histories. Presumably the group of offenders who commit more serious offenses and have longer prior records would be more likely to be incarcerated as offenders who commit more serious offenses and/or have more extensive prior records have an increased likelihood of incarceration. Gender was not found to influence sentence length or fine amount.

Multivariate Relationships

Two types of models were used to examine the sentencing data. Logistic regression models were constructed using binary in/out sentencing data as the outcome variable to determine the significance of gender on the decision to incarcerate the offender. The full model included both the legal and extralegal control variables (see Table 3). It was predicted that female offenders would be treated more leniently than male offenders at the in/out stage, leading to a decreased likelihood of incarceration. The results from Table 3, however, indicate that the offender’s gender does not have a significant influence on this sentencing outcome, unlike the results of the bivariate test. In addition, other extralegal variables such as race, age, education, marital status, and employment status also do not significantly influence the in/out sentencing decision. In fact, the only variables that significantly increased the likelihood of incarceration were two of the legal variables, the total number of prior convictions (b = 1.119; p < .10) and the severity of the offense (b = 2.659; p < .01).

Table 3.

Logistic Regression of In/Out Decision (N = 507).

Variable	Coefficient (SE)	Exp(b)
Constant	−3.347 (0.794)	0.035
Extralegal characteristics
Sex	−0.146 (0.291)	0.864
Race	−0.184 (0.583)	0.832
Age	−0.003 (0.014)	0.997
High school education	0.318 (0.403)	1.374
Married	0.214 (0.291)	1.239
Employed	0.039 (0.277)	1.040
Legal characteristics
Total prior convictions	0.112 (0.063)	1.119*
Prior property convictions	−0.033 (0.083)	0.968
Class code of current charge	0.978 (0.109)	2.659**
−2 Log likelihood	380.315
Nagelkerke R²	0.323
Hosmer and Lemeshow	X² = 33.538**

p < .10. **p < .01.

Next, two OLS regression models were constructed to determine the effect gender may have on the length of the sentence and the amount of the fine. Both models included the same legal and extralegal variables as the logistic regression model (see Table 4). Once again, the hypothesis regarding leniency toward female offenders in the form of shorter sentences and lower fines was not supported in the data. The results in Table 4 show that not only is gender not significantly related to sentence length, it is also not significantly related to the amount of the fine. Both results mirror the bivariate relationship previously tested. Similar to the in/out decision, the only variable in the model that significantly increases the sentence length is the severity of the offense as measured by the criminal class code (b = 0.713; p < .01).

Table 4.

OLS Regression.

Sentence length (n = 114)			Fine amount (n = 290)
Variable	B (SE)	β	B (SE)	β
Constant	−209.991 (361.875)		−214.984 (25.201)
Extralegal characteristics
Sex	128.860 (146.659)	0.068	−4.774 (8.056)	−0.011
Race	−413.229 (254.283)	−0.126	38.219 (19.181)	0.038*
Age	0.391 (7.280)	0.004	0.028 (0.412)	0.001
High school degree	−58.298 (212.054)	−0.021	−0.012 (11.620)	.000
Married	−18.583 (151.108)	−0.010	−6.472 (8.406)	−0.015
Employed	−117.883 (143.258)	−0.066	−4.867 (7.874)	−0.011
Legal characteristics
Total prior convictions	−26.963 (27.781)	−0.113	−0.101 (2.030)	−0.001
Prior property convictions	44.140 (39.792)	0.129	−1.789 (2.566)	−0.697
Class code of current charge	518.972 (57.495)	0.713**	244.229 (4.806)	0.960**
R²	0.516		0.906
F test	10.66**		299.537**
DF	99		289

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

The results for determining the amounts of the fine are similar to the results of the sentence length and the in/out decision (see Table 4). Severity of the offense significantly increased the amount of the fine (b = 0.960; p < .01). There was one interesting finding in the model for fine amount not seen in the other two models. Race was significantly related to the fine amount (b = 0.038; p < .05), indicating White offenders were given higher fines than non-White offenders. The significance of race regarding the fine amount was the only extralegal variable related to sentencing outcomes in all three models. As expected, legal variables such as the severity of the offense and the prior criminal history of the offender significantly increased the likelihood of incarceration as well as the sentence length and fine amount.

In addition to running the full models, models split by gender were designed to examine how individual variables may predict sentencing outcomes based on gender. Table 5 presents the results of two logistic regression models, one for male and one for female, examining how legal and extralegal variables influence the likelihood of incarceration. Similar to the previous in/out decision model in Table 3, none of the extralegal characteristics are significantly related to the in/out decision for both male and female offenders. The only characteristic that is statistically significant for both groups is the severity of the offense (b = 2.567; p < .01). Although the relationships are not significant, it is interesting to note the direction of several extralegal characteristics for female offenders are reversed for male offenders.

Table 5.

Logistic Regression of In/Out Decision Split by Gender.

Male offenders (n = 227)			Female offenders (n = 121)
Variable	Coefficient (SE)	Exp(β)	Coefficient (SE)	Exp(β)
Constant	−5.172 (1.192)		−5.406 (2.106)
Extralegal characteristics
Race	0.929 (0.804)	2.532	0.865 (1.603)	2.374
Age	0.008 (0.022)	1.008	0.009 (0.033)	1.009
High school degree	0.219 (0.766)	1.245	−0.462 (1.014)	0.630
Married	0.683 (0.441)	1.981	−0.625 (0.652)	0.535
Employed	−0.111 (0.410)	0.895	0.506 (0.662)	1.659
Legal characteristics
Total prior convictions	0.110 (0.075)	1.117	0.202 (0.182)	1.224
Prior property convictions	−0.076 (0.103)	0.927	0.034 (0.197)	1.035
Class code of current charge	1.215 (0.177)	3.370**	0.943 (0.220)	2.567**
β−2 log likelihood	174.989		79.845
Nagelkerke R²	0.409		0.384
Hosmer and Lemshow	X² = 20.362*		X² = 8.805

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

OLS regression models were constructed to determine the effect individual predictors may have on the length of the sentence and the amount of the fine for male and female offenders. Table 6 presents the results of the OLS regression for length of sentence for the offenders sentenced to incarceration. For male offenders, the race of the individual (b = −0.167; p < .10) as well as the severity of the offense (b = 0.748; p < .01) were significantly related to the length of the sentence. White male offenders were given shorter incarceration sentences than non-White male offenders. For female offenders, no statistically significant relationships were found. Results from both the male and female analyses should be taken lightly as the sample size for both models was quite small (53 males and 31 females) leading to abnormally large standard errors.²

Table 6.

OLS Regression for Sentence Length Split by Gender.

Male offenders (n =53)			Female offenders (n = 21)
Variable	B (SE)	β	B (SE)	β
Constant	−270.575 (431.415)		2,316.224 (2,368.580)
Extralegal characteristics
Race	−458.728 (277.745)	−0.167*	−1,394.589 (1,602.663)	−0.220
Age	6.014 (7.614)	0.086	−34.033 (35.176)	−0.237
High school degree	141.031 (323.347)	0.045	−311.999 (1128.041)	−0.081
Married	−111.790 (171.761)	−0.071	328.365 (714.994)	0.115
Employed	33.478 (145.181)	0.023	−416.319 (855.995)	−0.154
Legal characteristics
Total prior convictions	−20.948 (22.808)	−0.135	−32.281 (204.041)	−0.074
Prior property convictions	22.822 (34.108)	0.097	26.321 (213.213)	0.054
Class code of current charge	485.428 (67.018)	0.748**	501.756	0.520
R²	0.628		0.457
F test	9.293**		1.264
DF	52		20

p < .10. **p < .1.

Table 7 presents the results from the OLS regression model for the amount of the fine as punishment. For male offenders, the race of the offender (b = 0.056; p < .10) and severity of the offense (b = 0.946; p < .01) were statistically related to the amount of the fine. White offenders were given a larger fine than non-White male offenders. For female offenders, all three legal characteristics were statistically related to the amount of the fine. Unlike the rest of the models included in the research, total prior convictions (b = 0.107; p < .05) and number of prior property convictions (b = −0.113; p < .05) were related to the outcome variable. Similar to male offenders, the severity of the offense was strongly related to the amount of the fine for female offenders (b = 0.954; p < .01).

Table 7.

OLS Regression for Fine Amount Split by Gender.

Male offenders (n = 143)			Female offenders (n = 70)
Variable	B (SE)	β	B (SE)	β
Constant	−189.303 (37.047)		−214.720 (56.200)
Extralegal characteristics
Race	49.589 (26.139)	0.056*	27.281 (44.963)	0.020
Age	−0.857 (0.728)	−0.035	0.414 (0.847)	0.016
High school degree	−2.579 (22.790)	−0.003	−21.616 (24.083)	−0.033
Married	2.171 (14.513)	0.004	−22.083 (15.547)	−0.047
Employed	−17.773	−0.040	7.316 (15.730)	0.015
Legal characteristics
Total prior convictions	−1.622 (2.684)	−0.027	10.469 (5.303)	0.107**
Prior property convictions	0.245 (3.482)	0.003	−12.596 (5.827)	−0.113**
Class code of current charge	242.059 (7.673)	0.946***	238.357 (8.767)	0.954***
R²	0.890		0.944
F test	135.227***		128.042***
DF	142		69

p < .10. **p < .05. ***p < .01.

Discussion

This research was based on the chivalry/paternalism theory, which posits that female offenders would be treated advantageously when being sentenced. This type of sentencing pattern has been demonstrated in previous research that found female offenders were less likely to be incarcerated than male offenders, and served shorter sentences when they were incarcerated (Britt, 2009; Bushway & Phiel, 2001; Crow & Gertz, 2008; Doerner & Demuth, 2010; Feldmeyer & Ulmer, 2011; Franklin & Fearn, 2008; Freeborn & Hartmann, 2010; Freiburger, 2011; Haynes, 2011; Myers, 1989; Peterson & Hagan, 1984; Spohn & Beichner, 2000; Stacey & Spohn, 2006). Based on previous sentencing patterns and using sentencing data from rural property offenses, it was hypothesized that the gendered sentencing pattern would be present. Sentencing data from Iowa, however, found gender and measures of traditional sex roles did not have a significant effect on sentencing. This lack of effect was seen for the in/out decision, sentence length, and fine amount. The results of this research contradict existing literature, even literature using property offenses. Nagel and Johnson (1994) and Rodriquez et al. (2006), for example, found that female property offenders were treated more leniently than male offenders in both sentencing outcomes and prison term lengths.

What makes the lack of gender influence on sentencing decisions more interesting is the relationship between the chivalry/paternalism theory, traditional sex roles, and the type of crimes used. Recall that two of the three general propositions regarding this theory discuss adherence to traditional sex roles, and the seriousness of the criminal offense (Crew, 1991). If females follow traditional sex roles (i.e., are financially dependent on males), and are nonviolent offenders they will receive more lenient sentence than male offenders due to their perceived weakness, submissiveness, and defenselessness (Crew, 1991). Given that nonviolent property offenses may be considered a crime of traditional female sex roles, females, especially unemployed females, should have been treated more leniently than males. The failure to find support for this notion may be explained by the type of crime and setting used in this study.

Other studies that have tested gender and sentencing discrepancies have used samples from metropolitan areas (Gruhl & Welch, 1984; Spohn, 1999; Spohn & Beichner, 2000; Wooldredge, 2010). Metropolitan areas tend to have a larger number of disposition cases, leading to larger samples. This means judges in big cities are going to sentence more offenders during a short period of time, which may lead to inconsistencies in sentencing outcomes based on time constraints and relying on the stereotypes mentioned in the chivalry/paternalism theory. Judges in rural settings, however, are expected to see fewer offenders less often, leading to fewer inconsistencies in sentencing outcomes. Rural judges may not face the same time constraints and may sentence the offender based on criminal factors rather than stereotypes attached to types of offenders.

Previous research that found sentencing discrepancies based on gender used different offenses, such as violent or drug offenses (Myers, 1989; Peterson & Hagan, 1984; Stacey & Spohn, 2006). These offenses can be classified as more dangerous offenses, based on criminal class code, that lead to harsher punishments such as incarceration. The property offenses used in this study were considerably less dangerous offenses, based on criminal class code, which lead to punishments such as fines and probation. Out of the 507 cases, only 74 cases (15%) had a sentence of jail or prison time. The other 433 cases (85%) had a sentence of fine, community service, probation, or other nonincarceration sentences. Having such a small amount of cases that resulted in incarceration could explain the lack of significant relationship between gender and sentencing. When most of the cases ended in a fine or probation, there was no need to treat some offenders, specifically female offenders, more leniently.

In addition, the development of sentence structures or guidelines continues, steadily decreasing the amount of judicial discretion at the sentencing stage of the criminal justice process (Harmon & O’Brien, 2011). In theory, these sentencing structures should narrow sentencing gaps between groups for not only males and females, but in the case of race as well. However, the extent and direction of the effect these guidelines have on sentencing discrepancies remains mixed. While Jacobs (2003) argued the narrowing of gender-related sentencing discrepancies was related to mandatory minimum sentencing laws, other research has concluded in a contradictory manner finding gender-related discrepancies still existed (Harmon & O’Brien, 2011; Koons-Witt, 2002).

There were several limitations of this study, which include where the sample was drawn from, issues with missing data, and variable-related issues such as gender and race. The sample is one of the most significant limitations to this study. The entire sample was drawn from one state, Iowa. Despite using counties from various parts of the state, the selected counties may not be representative of rural counties in different states in different regions. Due to differences between the regions, the findings from this study should be used with caution. With that in mind, the results of the study are still important to the existing literature because this is the first study to examine both rural settings and property offenses. In addition, the lack of significant findings opens a discussion regarding sentencing discrepancies, and rural populations and the differences between rural and urban sentencing patterns, as mentioned previously.

A final limitation discussed includes the methodology of data collection, which affected the gender gap of the sample, as well as the eligibility of cases for the sample. The gap between male and female offenders developed in a later stage in the data collection when the introduction of Iowa Corrections Offender Network (ICON) identification numbers occurred. Not all cases included in the sample had an ICON ID number and therefore their criminal and personal background information could not be obtained. These cases were then discarded from the final sample. The problem with this data collection method is the final sample already has a criminal background, making it very hard to obtain data from first time offenders. In addition, the gender gap occurs because more male offenders have a criminal background than female offenders and were therefore more likely to be included in the final sample.

Future research would benefit from use larger and more diversified samples. Using rural sentencing from more than one region allows for stronger a culturally diversified sample and better generalizability. Also, pulling from multiple regions would allow for comparisons of sentencing patterns. In addition, having a larger sample size allows for stronger statistical power, which leads to sufficient evidence to support or fail to support the hypothesis of the research. A larger sample may also provide additional variables such as dependent children to further explore the role of being a parent when examining gender-based sentencing discrepancies.

Despite the strong presence of gender effects in previous research, sentencing data from rural Iowa failed to find a gender effect for property crimes. Female offenders were not treated more leniently than male offenders as predicted. The lack of significant findings in this study should not be generalized outside the state of Iowa, and does not suggest that there will not be a gender effect in future rural sentencing studies. These results, however, do show a need for more research in rural settings. While gendered sentencing patterns regularly occur in metropolitan settings, very little is known about sentencing patterns from rural areas. Given the lack of influence of additional extralegal variables such as race, age, marital status, employment status, and education level on sentencing decision, it may be plausible that rural sentences are based more heavily on legal factors.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Notes

Author Biography

Maria D H Koeppel is a PhD candidate in criminal justice at Sam Houston State University. Her research areas of interest include sociological aspects of crime such as gender and sexual orientation, and victimization. Currently she is working with the Crime Victims Institute where she has worked on projects examining short- and long-term consequences of victimization.

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