Structural Gender Equality and Federal Sentencing Outcomes: A Test of the Ameliorative and Backlash Hypotheses

Abstract

While previous research has examined gender disparities in sentencing, most explanations focus on individual-level differences. We argue that structural gender equality has an important influence on gender disparities as well. Drawing from previous research on victimization, we provide a test of the ameliorative and backlash hypotheses. Using federal sentencing data from 1999-2003, we demonstrate how measures of structural gender equality contextualize the relationship between gender and sentencing. Our analyses suggest that structural gender equality is important for understanding the relationship between gender and sentencing, but different measures of gender equality lead to distinct patterns.

Keywords

courts judges quantitative research gender equality sentencing

One of the most robust findings in the sentencing literature is that women are generally sentenced with more leniency than men. Numerous theoretical explanations have attempted to demonstrate both the generality of this finding (Albonetti, 1991, 1997; Doerner & Demuth, 2010, 2014; Steffensmeier, Kramer, & Streifel, 1993; Steffensmeier, Ulmer, & Kramer, 1998), and also the nuances behind it (see Koons-Witt, 2002; Rodriguez, Curry, & Lee, 2006; Tillyer, Hartley, & Ward, 2015). More specifically, this body of research tends to agree that women are generally met with leniency in sentencing because they are deemed less culpable, less dangerous (e.g., less violent and less likely to recidivate), and carry with them more mitigating considerations (e.g., dependent children) which warrant less punitive sentences. Other research has indicated that leniency is reserved for only those women who adhere to socially accepted gender roles (Daly, 1989; Farnworth & Teske, 1995; Rodriguez et al., 2006) and those who do not are sentenced more similarly to men.

Although individual-level studies of gender disparity in sentencing are prevalent, research that examines how aggregate-level variables might foster gender disparity in sentencing are much more rare (see Farrell, Ward, & Rousseau, 2010; Ryon, 2013, for examples). Criminologists have generally reserved the application of aggregate-level gender equality processes for two forms of female victimization: homicide and rape. These studies suggest that gender equality may affect victimization in one of two ways. First, the ameliorative hypothesis suggests that as women make strides toward equality, rates of victimization should fall (Bailey & Peterson, 1995; Whaley, 2001; Whaley & Messner, 2002; Whaley, Messner, & Veysey, 2013). The accumulation of resources serves as a protective factor, and as such, women are better able to defend themselves because men exert less power over women. As an alternative, the backlash hypothesis (Russell, 1975) suggests that as women move toward equality, men lash out against them in an attempt to maintain the status quo. This represents an attempt to exert social control on women as a penalty for entrenching upon men’s societal advantage. These hypotheses have been tested extensively to predict victimization, but little research has applied them to examine more formalized types of social control, such as criminal sentencing.

This article attempts to fill an important gap in the sentencing literature by investigating whether and how aggregate-level measures of gender equality might influence gender disparity in sentencing. Using multiple measures of gender equality, we argue that structural gender equality has an important influence on gender disparities in sentencing outcomes. Moreover, different measures of gender equality lead to distinct patterns; thereby some types of gender equality produce an ameliorative effect while other types produce a backlash effect.

Gender Disparity in Sentencing

A large body of research has examined differences in the types of sentences meted out to men and women. Broadly, the results indicate that women benefit from leniency at the sentencing stage (Belknap, 2007; Daly & Bordt, 1995; Doerner & Demuth, 2014; Nowacki, 2017). This individual-level disparity is generally explained through the focal concerns (Steffensmeier et al., 1993; Steffensmeier et al., 1998), judicial paternalism (Daly, 1989; Moulds, 1980), and selective chivalry (Farnworth & Teske, 1995) perspectives.

The focal concerns perspective suggests that judges sentence offenders based on three criteria: blameworthiness, danger to the community, and practical constraints and consequences (Albonetti, 1991; Steffensmeier et al., 1993; Steffensmeier et al., 1998). Blameworthiness relates to an offender’s level of accountability or culpability, where less culpable offenders are more likely to encounter leniency during sentencing. Danger to the community equates to how damaging the offense was to the community, and the likelihood of recidivism. Finally, practical constraints and consequences relate to other issues that judges consider, such as likelihood of victimization if incarcerated, available prison space, familial considerations, and correctional costs.

Previous research offers support for the focal concerns perspective in explaining gender disparities at the individual level. Women are generally sentenced more leniently than men, often because they are deemed less blameworthy, less likely to recidivate, and practical constraints mitigate sentences dealt to them (Doerner & Demuth, 2014; Embry & Lyons, 2012; Farnworth & Teske, 1995). Furthermore, multiple studies find that women are less likely to receive custodial sentences (Armstrong, 1999; Bishop & Frazier, 1992; Spohn, 1999), and when they do, the sentences are shorter (Curran, 1983; Griffin & Wooldredge, 2006; Moulds, 1980). Judges may extract information from presentence reports to assess blameworthiness, which may result in more lenient treatment of many women (Jeffries, Fletcher, & Newbold, 2003).

Another explanation of individual-level gender disparity in sentencing is the judicial paternalism (or chivalry) perspective. This perspective suggests that judges sentence women to protect them. Often, this practice leads to more lenient sentencing, but other times, it does not. Thus, paternalism does not necessarily conflate with leniency (Daly, 1989). Daly’s (1989) qualitative interviews suggest that it is not women, but the children of those offenders, whom judges are protecting. More specifically, several judges indicate that they consider child care among the most important mitigating factors of all. Moreover, the interviews suggest that nonfamilied women are not treated more leniently than nonfamilied men, so the distinction is presence of children, not gender (Daly, 1989). Several studies that focus on the juvenile system find support for the notion of noncongruence between paternalism and leniency (Carr, Hudson, Hanks, & Hunt, 2008; Feld, 2009; Spivak, Wagner, Whitmer, & Charish, 2014; Tracy, Kempf-Leonard, & Abramoske-James, 2009). In short, punitive responses toward girls may reflect paternalism, even when they do not simultaneously reflect leniency.

In contrast, the selective chivalry hypothesis suggests that leniency is reserved for some women, but not others (Belknap, 2007; Crew, 1991; Farnworth & Teske, 1995; Rodriguez et al., 2006; Spohn, 1999). Specifically, women who commit crimes that violate socially accepted gender roles (e.g., violent offenses) are less likely to benefit from chivalrous sentencing decisions (see Rodriguez et al., 2006). Previous research finds support for the selective chivalry hypothesis (Bickle & Peterson, 1991; Koons-Witt, 2002; Rodriguez et al., 2006; Spohn, 1999; Tillyer et al., 2015). Thus, chivalry appears to influence sentencing for some women, but its influence depends on family roles and responsibilities (Bickle & Peterson, 1991), presence of a dependent children (Koons-Witt, 2002; Spohn, 1999), type of offense (Rodriguez et al., 2006), and criminal history (Tillyer et al., 2015).

Thus, previous research offers important insights on how gender at the individual level influences sentencing outcomes. Overall, these perspectives offer different interpretations as to whether women receive less harsh sentences because of criminological factors (such as focal concerns about blameworthiness) alongside mitigating factors (such as parental status), and whether mitigating factors are uniformly applied to all women. We offer another dimension to understanding gender disparities in sentencing by examining how aggregate-level gender equality influences sentencing outcomes. In the next section, we address how gender is a multidimensional concept and structural gender equality can be measured in a variety of ways. We then draw from previous scholarship on female victimization to discuss how structural gender equality contributes to understanding sentencing.

Structural Gender Equality

Gender is important for understanding various criminological and criminal justice outcomes. While research often examines individual-level differences between men and women (Belknap, 2007; Bontrager, Barrick, & Stupi, 2013; Daly & Bordt, 1995; Doerner & Demuth, 2014; removed; Spohn, 1999; Steffensmeier & Demuth, 2006), gender can also be conceptualized as structural (Risman, 2004). Gender as structure shifts attention away from solely examining differences in men and women offenders and instead examines multiple dimensions of gender ranging from individual to interactional to institutional (Risman, 2004). As such, applying a gendered structure framework to sentencing outcomes could explain the differences between men and women offenders. At the interactional level, gender could affect how judges and other actors interpret blameworthiness and mitigating factors differently for men and women. In this article, we provide another dimension for understanding gender and sentencing by focusing on the institutional or structural level. In particular, we focus on district-level gender equality measures as an important context that influences sentencing outcomes. Previous research on victimization has incorporated structural gender equality (DeWees & Parker, 2003; Vieraitis, Britto, & Kovandzic, 2007; Whaley et al., 2013), and we extend this body of research to examine sentencing. Moreover, we identify important differences between economic and educational measures of gender equality, and how they influence sentencing outcomes.

One of the most standard measures of structural gender equality is female labor force participation. Labor force participation indicates equality, where higher rates of labor force participation suggest that women have more opportunities that are similarly available to men. A broad measure of female labor force participation, however, can be limited because it says nothing about the type of opportunities available to women (Acker, 2006). In short, labor force participation is a necessary, but not sufficient, condition for accumulation of economic power, status, and equality (Blumberg, 1984). When women are not afforded the same opportunities as men, it is unlikely that similar opportunities for employment will be available to them. Another important measure of gender equality is educational attainment (Browne & Misra, 2003). One of the keys to applying structural gender equality to social control requires understanding what this concept captures.

While conceptualizing gender equality is important, it is also important to understand these indicators of gender inequality as a symptom, not a cause, of patriarchy (Chesney-Lind, 2006; Hunnicutt, 2009; Hunnicutt & Broidy, 2004). The reason that gender equality can explain distinct outcomes, such as violence against women and state-sponsored formal social control against women (e.g., sentencing), is because structural inequality indicates a larger, ideological concept reflecting large-scale oppression of women. Thus, where women have more relative economic power, they may face less oppression (Jackson, 2015), both in the form of violence and formal controls.

In measuring structural gender equality, it is important to note distinct trends with regard to employment and education. After steady increases in female labor force participation for much of the 20th century, this trend plateaued by the 1990s (Cotter, Hermsen, & Vanneman, 2001; Lee, 2014; Wharton, 2015). In this regard, gender inequality is still quite institutionalized within the economic realm. On the contrary, gender equality in educational attainment shows a different pattern. Women outpace men in the proportion of college degrees received (Buchmann, DiPrete, & McDaniel, 2008; Jacobs, 1996; Wharton, 2015). In this regard, it may appear that gender equality has increased within the educational realm (or may even favor women). However, there are a few important caveats. First, while women surpass men in the number of college degrees received, there is still marked gender segregation in college major (Bobbitt-Zeher, 2007; Wharton, 2015). Moreover, in regard to gender equality, educational attainment influences economic outcomes (Jacobs, 1996). For instance, Bobbitt-Zeher (2007) finds that college major is an important explanatory variable that influences the gender wage gap. Thus, even with higher levels of educational attainment, women have not gained parity with men in terms of wages or labor force participation. These different patterns of structural gender equality raise important concerns about measurement. Moreover, as we discuss in the next section, there are competing hypotheses regarding the influence of structural gender equality that are relevant to understanding sentencing outcomes.

Structural Gender Equality, Victimization, and Sentencing

Little research has explored the relationship between aggregate measures of gender equality and sentencing outcomes (see Ryon, 2013, for a notable exception), so we use theoretical perspectives from cognate literatures. Specifically, the victimization literature has examined the influence of gender equality extensively. This literature has generated two primary perspectives: the ameliorative and backlash perspectives. The ameliorative, and similarly situated economic marginalization, perspective suggests that as women make strides toward economic equality, they fare better (Bailey & Peterson, 1995; Heimer, 2000). In the victimization literature, this suggests that as women move toward gender equality, homicide victimization rates among them should decrease. Conversely, the backlash perspective suggests that as women move toward gender equality, they are actually at a higher risk of victimization (Brownmiller, 1975; Russell, 1975; Williams & Holmes, 1981). This is because when men feel that their advantage in society is threatened, they are more likely to lash out against women, who are perceived as representing that threat. Thus, the backlash perspective is seemingly the gender analogue to the minority-group threat perspective for race/ethnicity (see Blalock, 1967).

The ameliorative perspective has been widely tested in both the homicide and rape literatures, and the empirical research generally finds support for it. Specifically, Vieraitis, Kovandzic, and Britto (2008) find that absolute status is linked to intimate partner homicide, where female victimization rates decline as absolute status improves. Moreover, Xie, Heimer, and Lauritsen (2012) find that absolute increases in female labor force participation, income, and education are associated with decreases in intimate-partner violence.

Conversely, however, other studies fail to find support for the ameliorative hypothesis. Neither Vieraitis et al. (2007), Reckdenwald and Parker (2010), nor Brewer and Smith (1995) find a link between gender equality and female homicide victimization rates. Moreover, Titterington (2006) finds that socioeconomic, political, and legislative controls mediate away many of the general indicators of female homicide. Finally, Steffensmeier and Haynie (2000) find that structural variables influence female homicide rates, but not in ways substantially different from male homicide rates.

On the other side of the coin, a number of studies find support for the backlash perspective. Broadly, the backlash perspective predicts that as the relative status of females increases, so will female victimization rates. DeWees and Parker (2003) find support for this hypothesis, as they found that gender disparity in educational attainment, occupation, and income predicted higher rates of female homicide. Moreover, Pridemore and Freilich (2005) find a positive relationship between gender equality and female homicide victimization, and Whaley and Messner (2002) find support for the backlash hypothesis for homicides against women in the south. In a study examining the link between gender socioeconomic equality and rape rates, Peterson and Bailey (1992) find that SMAs with more women in managerial and professional positions have higher rates of rape, and Bailey (1999) finds that places with more gender equality tend to have higher levels of rape as well. Finally, Vieraitis and Williams (2002) find support for the backlash perspective in the case of White, but not Black women. In short, advances in gender equality also have the potential to harm women.

Most existing research examines the ameliorative and backlash perspectives using cross-sectional data. Some recent research adds additional layers of sophistication into these hypotheses, examining elements of time and degree of equality. In particular, Whaley (2001) proposes a refined theory of gender inequality, where she argues that gender equality has a backlash effect in the short term, but an ameliorative effect in the long term. Using data from 1970-1990 in 109 U.S. cities, she finds support for this refined theory. Moreover, Vieraitis, Britto, and Morris (2015) use Census data from 1980-2000 to examine female homicide victimization and found evidence of an ameliorative effect over time for the total homicide rate and when the victim and offender were friends, but not in stranger, family, or intimate homicides. Temporal elements of inequality are important, but in this article, we focus specifically on incorporating structural gender inequality into research on sentencing outcomes. As few studies of sentencing have attempted to address either the backlash or ameliorative perspectives, we offer an important contribution by examining whether these perspectives are applicable to sentencing outcomes. Moreover, we offer important nuance by incorporating multiple measures of structural gender inequality.

In an effort to contextualize the roles of these perspectives further, Whaley et al. (2013) position the ameliorative and backlash perspectives not as competing, but instead as complementary hypotheses. They argue that both processes operate at once, but the relative strength depends on levels of equality. They find that backlash processes are stronger at lower levels of equality, but ameliorative processes are more powerful at high levels of equality. Taken together, this body of research suggests that both the ameliorative and backlash perspectives are useful avenues to investigate the relationship between gender equality and criminological outcomes. While, to date, much of this literature focuses on rape and homicide victimization, it also seems appropriate as an application for sentencing outcomes.

Current Study

The current study advances understanding of gender disparity in federal sentencing outcomes by incorporating measures of aggregate-level gender equality. By using multiple measures of gender equality, we extend previous research on structural variables and sentencing disparities. In doing so, we draw from victimization studies that suggest gender equality can influence rates of homicide and rape against women and apply this framework to social responses to crime. Specifically, previous research has found that gender equality can have either an ameliorative effect (beneficial to women) or backlash effect (detrimental to women). Based on these theoretical perspectives, our study posits the following hypotheses:

Hypothesis 1: Gender equality has an ameliorative effect on sentencing women, where indicators of gender equality correspond with sentencing outcomes that are more lenient.

As women make strides toward equality, they receive particularly lenient sentences because egalitarian social attitudes will benefit women.

Hypothesis 2: Gender equality has a backlash effect on sentencing women, where indicators of gender equality correspond with sentencing outcomes that are more punitive.

As women make strides toward equality, they receive especially punitive sentences as a method of gendered social control.

Data and Method

We draw the data for this research from multiple sources. We draw the case-level variables from the population of female offenders in the Monitoring of Federal Criminal Sentences database from 1999-2003. The United States Sentencing Commission (USSC) compiles these data annually and they contain data on all offenders from each of the 94 federal districts.¹ These data are well suited for this study because they allow for quantitative analysis of a large number of offenders across social context. We merge these data with aggregate-level measures from the 2000 Census.² Together, these data sources allow us to examine the relationship between aggregate- and individual-level measures of offender characteristics, gender equality, and sentencing outcomes. Following previous research, offenses involving noncitizens and immigration offenses are deleted from the analyses, because they differ in important ways from the sentences meted out to citizens (see Demuth, 2002; Doerner & Demuth, 2010). This leaves us with a final sample of 22,571 women.

Dependent Variable

The dependent variable in our study is the sentence length measured in months.³ This variable is highly skewed, so we follow convention and take the natural log to normalize the distribution. Sentence length is capped at 460 months, representing a life sentence.

Independent Variables

We include several variables to estimate the effect on sentence length. We include multiple measures of gender, particularly at the structural level. We also include individual-level variables for both legal and extralegal measures. At the aggregate level, we also control for a number of district variables.

Gender variables

We estimate the effects of several measures of aggregate-level gender equality on sentencing outcomes. We capture economic equality using a labor force participation measure. Specifically, we constructed a ratio variable of women to men (16 and older) participating in the labor force within each district. This variable was calculated as the percent of the labor force that is female divided by the percent of the labor force that is male. We also include a more nuanced measure of labor force participation creating a ratio of women to men participating in managerial, professional, and related occupations. Our final measure of structural gender equality captures educational attainment. We constructed a ratio variable of women to men with at least a bachelor’s degree within each district. This variable was calculated as the percent of females 25 and older with a bachelor’s degree or higher divided by the percent of males 25 and older with a bachelor’s degree or higher. For these ratio measures of gender equality, a value of “1” indicates gender parity while a value that is less than “1” indicates gender inequality where men outpace women and values greater than “1” indicate the converse where women outpace men.

Legal variables

Most contemporary studies of federal sentencing control for a number of legally relevant variables (see Engen & Gainey, 2000, for example). In particular, these studies include measures of criminal history and offense severity. While the USSC data offer measures of both final offense level and criminal history, scholars have suggested using minimum presumptive sentence in lieu of offense severity score (see Ulmer, Light, & Kramer, 2011). This variable captures the combination of these two important legally relevant variables. Some studies further advocate for controlling for criminal history score in addition to minimum presumptive sentence, suggesting that criminal history exerts an influence on sentencing outcomes above and beyond its calculation in the presumptive sentence variable (Doerner & Demuth, 2010; Ulmer, 2000). We measure presumptive sentence as the minimum guideline sentence in months. Furthermore, we control for criminal history score, which ranges from 1 to 6, where offenders with lower scores have a limited criminal history, and those with higher scores have more extensive prior records. In addition, we control for the following legally relevant variables: mode of conviction, whether the defendant was detained prior to trial, and whether the defendant received a substantial assistance downward departure. Each of these variables are dummy-coded (e.g., the trial variable is coded as “1” if the defendant went to trial, and “0” otherwise).

Extralegal variables

It is also possible that a number of extralegal variables will exert effects on sentencing outcomes net of legally relevant variables, so we include controls for race/ethnicity, age, and education. Race/ethnicity is dummy coded for Black and Hispanic offenders with White offenders serving as the reference category. Education is also dummy coded, where offenders with a college degree or graduate degree are coded as “1.” Finally, age and number of dependent children are measured as continuous variables.

District-level variables

At the district level, we control for the percentage of households below the poverty line, the rate of cases going to trial, and the natural log of the caseload measured as number of cases divided by number of judges (following Feldmeyer & Ulmer, 2011). Finally, while not shown in the results tables, all models include controls for the year in which the offender was sentenced.

Analytic Strategy

Because we are attempting to estimate the effects of both individual- and aggregate-level variables, ordinary least squares (OLS) regression techniques are insufficient for these analyses. Instead, we employ linear mixed models, which estimate individual-level (Level 1) and district-level (Level 2) variables at once. Moreover, this technique also produces more efficient standard errors, controls for correlation between districts, and allows for random slopes (see West, Welch, & Galecki, 2007). For these reasons, linear mixed models are more appropriate for this research than OLS techniques.

Results

We present descriptive statistics in Table 1. These statistics indicate that the average sentence received by women is less than 29 months. Roughly 30% of the women in our data were Black with another 25% Hispanic and the mean age was about 34. Approximately 21% of these women received substantial assistance departures, and 41% were detained prior to sentencing.

Table 1.

Descriptive Statistics (N = 22,571).

	M	SD	Minimum	Maximum
Dependent variable
Sentence length (in months)	28.83	37.79	0	428
Level 1 independent variables
Black	0.30	0.46	0	1
Hispanic	0.25	0.43	0	1
Age	34.51	10.54	17	84
Education	0.05	0.23	0	1
No. of dependent children	1.42	1.50	0	15
Criminal history	1.64	1.23	1	6
Presumptive minimum sentence	37.03	45.40	0	360
Trial	0.03	0.18	0	1
Substantial assistance departure	0.21	0.40	0	1
Presentence detention	0.41	0.49	0	1
Level 2 independent variables
Female:male labor force participation	0.82	0.04	0.74	0.94
Female:male professional occupations	1.13	0.14	0.82	1.42
Female:male educational attainment	0.99	0.09	0.85	1.43
% living below poverty line	14.04	4.32	6.91	24.78
Trial rate	95.13	1.85	88.55	99.16
Caseload	418.95	330.54	108	2,333.75

We begin our analysis by running an intercept-only model (Table 2). This allows us to determine the proportion of variance explained at the district level. The intraclass correlation is .085, which suggests that more than 8% of the variance in sentence length is explained at the district level.

Table 2.

Intraclass Correlations for Sentence Length Models.

Intercept	0.229*** (.036)
Residual	2.456*** (.022)
Intraclass correlation	0.085*** (.012)

Note. Standard errors in parentheses.

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

Results from the individual-level regression model are presented in Table 3. Many of the control variables included in the model conform to expectations from previous research. Specifically, women who have more extensive criminal records and commit more serious offenses, women who go to trial, and women who are detained prior to sentencing all receive sentences that are more punitive. Conversely, women who receive substantial assistance downward departures receive sentences that are more lenient.

Table 3.

Multilevel Model for Sentence Length.

	b (SE)	b (SE)
Level 1 variables
Criminal history	0.045*** (0.005)	0.045*** (0.005)
Presumptive minimum sentence (ln)	0.900*** (0.016)	0.899*** (0.016)
Black	0.013 (0.016)	0.012 (0.016)
Hispanic	−0.004 (0.021)	−0.002 (0.021)
Age	0.002** (0.001)	0.002** (0.001)
No. of dependent children	−0.003 (0.004)	−0.002 (0.004)
Education	0.002 (0.026)	0.002 (0.026)
Substantial assistance downward departure	−0.697*** (0.038)	−0.696*** (0.038)
Trial	0.184*** (0.024)	0.183*** (0.024)
Pretrial detention	0.270*** (0.028)	0.270*** (0.029)
Intercept	−0.105 (0.074)	4.318*** (1.086)
Level 2 variables
Female:male labor force participation		−1.711** (0.597)
Female:male professional occupations		0.376** (0.138)
Female:male educational attainment		0.744** (0.250)
% living below poverty line		0.0001 (0.005)
Trial rate		0.043*** (0.010)
Caseload (ln)		−0.014 (0.045)
Intercept	0.040*** (0.009)	0.027*** (0.005)
Residual	0.713*** (0.080)	0.713*** (0.080)

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

Hypotheses 1 and 2 are competing hypotheses that respectively suggest that gender equality has an ameliorative effect (Hypothesis 1) or a backlash effect (Hypothesis 2) on sentencing outcomes. To test these hypotheses, Table 3 presents results that illustrate how gender equality influences sentencing of women. Results indicate that both the ameliorative and the backlash hypotheses receive partial support, but they differ across measures of gender equality. Specifically, the ameliorative hypothesis is supported, in that increases in the ratio of women to men in the labor force correspond with more lenient sentences for women offenders (b = −1.711).⁴ Conversely, the results support the backlash hypotheses such that increases in the ratio of women to men working in professional occupations (b = .376) and the ratio of women to men earning a bachelor’s degree or greater corresponds with more punitive sentences in a given district (b = .744).

To better illustrate the differences between measures of structural gender equality and patterns of amelioration or backlash, we also present figures to diagram the relationship to sentencing outcomes. As illustrated in Figure 1, women tend to receive the most punitive sentences in districts with the lowest rates of female labor force participation, but those sentences become increasingly lenient as women’s representation in the labor force increases. Figure 2 shows that women are sentenced more punitively in districts where more women are working in professional occupations relative to men. Moreover, Figure 3 shows that women are sentenced more punitively in districts where the ratio of women to men who hold at least a bachelor’s degree is greater. In short, both hypotheses receive support, but the specific measure of gender equality is consequential.

Figure 1.

Labor force participatoin and adjusted predictions for sentence length (with 95% confidence intervals).

Figure 2.

Professional occupations and adjusted predictors for sentence length (with 95% confidence intervals).

Figure 3.

Educational attainment and adjusted predictions for sentence length (with 95% confidence intervals).

Discussion and Conclusion

A wide array of studies in the sentencing literature attempt to explain gender disparity in sentencing outcomes. The majority of these studies focus on individual characteristics to explain these disparities. In contrast, this study examined how structural measures of gender equality influence sentencing of women. Broadly, results from this study suggest not only that structural gender equality influences sentencing outcomes for women in important ways but also that the relationship between structural equality and gender depends on the specific type of equality and how it is measured.

The main contribution of this article was the use of multilevel analyses, which estimated both individual and district-level variables together, and the use of multiple measures of gender equality. Using this method, we extended research on gender disparities by testing whether district-level gender equality produced an ameliorative or backlash effect on federal sentencing outcomes. As illustrated in Figures 1 through 3, our results provide compelling insights on the effects of structural gender equality on sentencing. Our measure of female labor force participation produced an ameliorative effect on sentencing outcomes (e.g., more lenient sentences in districts with a larger share of women participating in the labor force). Conversely, our measures of employment in professional occupations and women’s educational attainment produced a backlash effect (e.g., more punitive sentences in districts with larger proportions of women earning at least bachelor’s degrees). While these results may seem contradictory, it is important to situate these results within the larger patterns of gender equality. While women’s educational attainment has reached parity with men (Buchmann et al., 2008; Jacobs, 1996; Wharton, 2015), female labor force participation plateaued (Cotter et al., 2001; Wharton, 2015). Similar to educational attainment, the percent of women within the professional occupations outpaces the percentage of men. This is due primarily to the classification of education and health care occupations as professional (Fronczek & Johnson, 2003). In this respect, the ratio measure of professional occupations presents a similar pattern as educational attainment whereby women outpace men because of the predominance of women within certain feminized occupations, and there is still a substantial wage gap between men and women employed within professional occupations (Fronczek & Johnson, 2003).

In this respect, our results provide support for the claims that ameliorative and backlash effects may both be present (Whaley, 2001; Witt & Witte, 2000). For backlash to occur, there needs to be some threat to the patriarchal status quo. As women reach parity with, and even surpass, men in terms of college degrees and employment in professional occupations, this could be interpreted as a true and significant threat. Therefore, it is not surprising that our results indicate that professional and educational gender equality has a backlash effect on women’s sentencing outcomes. On the contrary, female labor force participation stagnated. Within the economic realm, women have not gained as much parity with men, and therefore, the female labor force may not be as threatening to the status quo. Maintenance of the status quo is more likely to produce an ameliorative or paternalistic reaction to women offenders. As such, gender inequality is already firmly entrenched within the economic realm, so there may be less of a need to reestablish patriarchal social control of women via more punitive criminal sentencing.

While this study provides an examination on how structural gender equality might influence how women are treated in the criminal justice system (see also Ryon, 2013), future research should develop this line of inquiry even further. More refined measures of structural gender equality can further explain the extent to which social control measures are taken against women when, as a group, they are making strides toward equality. Moreover, as suggested by Whaley et al. (2013), it is important to examine the temporal dimension of structural gender equality. For instance, future research can further distinguish how distinct measures of gender equality vary over time and whether a backlash effect or ameliorative effect is conditioned by larger temporal patterns.

It is also important to examine this question at smaller units of analysis. One limitation of our study is that, by necessity, we aggregated county-level data into larger districts. This undoubtedly introduces some measurement error into our Level 2 measures. By examining this question at the state court level, researchers could use more spatially precise measures of gender equality. Finally, our study does not contain judicial characteristics. While we incorporated measures of macro-level gender equality, the data did not provide information on the interactional level of gender dynamics. It would be interesting to know whether women judges sentence women offenders differently in districts with different levels of gender equality. Thus, future studies should attempt to gather data on individual characteristics of judges, and see how these characteristics, specifically gender, interact with sex of offender and aggregate measures of equality (see Steffensmeier & Herbert, 1999).

Future research can continue to build from our results, but this study provides an important step in linking variations in structural gender equality to federal sentencing decisions. While there is a growing literature on contextual effects and sentencing, this literature has been largely silent with respect to gender equality, both structurally and individually. Moreover, gender should be treated as a multilevel concept with implications at the individual, interactional, and institutional levels. At the same time, scholars must recognize that measurement decisions for gender equality can influence conclusions. In particular, our results point to the relevance of both the ameliorative and backlash perspectives for understanding how aggregate gender equality influences sentencing outcomes.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Notes

Author Biographies

Jeffrey S. Nowacki is an assistant professor in the Department of Criminology & Criminal Justice at Southern Illinois University. His research interests include federal and state sentencing outcomes, police organizations, and gender and crime. His recent research has appeared in Crime & Delinquency, Criminal Justice Policy Review, and Criminology & Criminal Justice.

Elena A. Windsong is a lecturer in the Department of Criminology & Criminal Justice at Southern Illinois University. Her research interests include the intersectionality of race and gender and community and place dynamics. Her research has been published in International Journal of Social Research Methodology, The Social Science Journal, and Society & Natural Resources.

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