THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF SOCIOLOGY AND CRIMINOLOGY

SENTENCING DISPARITY AND STRUCTURAL INEQUALITY BETWEEN PERPETRATORS OF DOMESTIC VIOLENCE: WHO GOES FREE?

MARY LAWRENCE SPRING 2018

A thesis submitted in partial fulfillment of the requirements for baccalaureate degrees in Sociology and Criminology with honors in Sociology

Reviewed and approved* by the following:

Laurie Scheuble Teaching Professor of Sociology Thesis Supervisor

Stacy Silver Associate Professor of Sociology and Human Development and Family Studies Honors Advisor, Sociology and Criminology Director, Undergraduate Program in Sociology Honors Adviser

* Signatures are on file in the Schreyer Honors College. i

ABSTRACT

I examine the relationship between racial differences of perpetrators of domestic violence and their outcomes in the criminal justice system. I look for differences in likelihood of arrest, being charged with a misdemeanor or a felony, and sentence. Data come from police records in

San Diego in 1998-1999 (N=384). Findings show that race was significant only in the arrest and charging phase, but not in the sentencing phase. Hispanics were more likely to be arrested and charged with a felony as compared to Whites. I found that other variables were significant in the sentencing phase including the ages of the suspect and the victim and the nature of their relationship.

Keywords: domestic violence, structural inequality, sentencing, arrest.

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TABLE OF CONTENTS

LIST OF TABLES ...... iii

ACKNOWLEDGEMENTS ...... iv

Chapter 1 Literature Review ...... 1

Introduction ...... 1 Legal History of Domestic Violence ...... 2 Theory ...... 3 Structural Inequality ...... 4 Perpetrators ...... 4 Gender ...... 5 Feminist Theories ...... 5 Duluth Theory ...... 7 Race ...... 8 Whites ...... 9 Blacks ...... 10 Hispanics ...... 10 Other Races ...... 11 Age 12 Abuser-Victim Relationships ...... 13 Weapons and Violence ...... 14 Alcohol and Drugs ...... 15 Presence of Children ...... 16 Length of Relationship ...... 17 Hypotheses ...... 18

Chapter 2 Methods ...... 20

Data 20 Independent Variables ...... 21 Control Variables ...... 21 Dependent Variables ...... 24 Analysis ...... 25

Chapter 3 Findings ...... 27

Chapter 4 Discussion ...... 32

Chapter 5 Conclusion ...... 39

Limitations ...... 39 Suggestions for future research ...... 39 iii

Contributions to the field ...... 40

Appendix A Internal Review Board Approval ...... 49

BIBLIOGRAPHY ...... 50 iv

LIST OF TABLES

Table 1: Descriptive Statistics ...... 41

Table 2: Logistic regression presenting the effects of independent and control variables on whether or not the suspect was arrested. N=298 ...... 42

Table 3: Logistic regression presenting the effects of independent and control variables on whether or not the suspect was charged with a felony. N=298 ...... 43

Table 4: Logistic regression presenting the effects of the independent and control variables on whether or not the suspect was charged with a misdemeanor. N=298 ...... 44

Table 5: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to jail. N=71 ...... 45

Table 6: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to complete a domestic violence program. N=71 46

Table 7: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to pay a fine. N=71 ...... 47

Table 8: Logistic regression presenting the effects of the independent and control variables on whether or not the suspect was sentenced to probation. N=71 ...... 48

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ACKNOWLEDGEMENTS

I would like to thank Laurie Scheuble, my thesis supervisor, and Caren Bloom-Steidle for giving me the direction to pursue this subject of research. Thank you for the guidance, encouragement, and feedback throughout this entire process. I would also like to thank Stacy

Silver, my honors advisor for guiding me through the process of becoming a Schreyer Scholar.

Your assistance in the beginning of my undergraduate studies was vital to my success as a student. Thank you to the faculty and staff in the department of Sociology and Criminology for your guidance over the past four years. I would also like to thank my family, my parents Alita and Kevin, and my sister Aggie for the unconditional love and support.

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Chapter 1

Literature Review

Introduction

Since the 1970s, there has been considerable variation in both the statutory law and sentencing guidelines (expectations) for domestic violence suspects (Fagan 1996). In order to continue progress toward ending domestic violence, it is important to examine how policies have affected perpetrators’ treatment in the criminal justice system. My research focuses on examining the sentencing trends from San Diego, one of the first cities to develop a domestic violence taskforce (Pennell and Burke 2002) that enforced a no-drop policy. No-drop policies are rooted in evidence based prosecution, meaning that if there is sufficient evidence to prosecute the suspect, the prosecutors will proceed with the case even if the victim does not want their abuser to face charges. This data set is ideal for my study as it contains demographic details, such as age and race of both the perpetrator and the victim as well as other factors that may have influenced sentencing and the sentencing decision itself (Pennell and Burke 2002). In this research, I examine four main research questions: Are White males charged and convicted of domestic violence crimes less than men of color? Are perpetrators punished more harshly depending on the relationship between them and their victim? How does the age of suspect and victim, and their alcohol and drug use influence sentencing? Finally, does the number of violent acts that occurred during the abuse influence sentencing? 2 It is important to understand how and why perpetrators are sentenced because those working in the criminal justice system need to know if policies currently in place are applied fairly to all suspects of domestic violence. According to the National Coalition Against Domestic Violence, domestic violence crimes are one of the least reported crimes, and often after they are initially reported, the victim chooses to drop the charges (NCADV 2017). Victims drop these charges for a multitude of reasons, making research about domestic violence crimes more difficult to study

Because the incident is not categorized as a crime (Davis and Smith 1995). Identifying and analyzing sentencing trends in domestic violence cases is further complicated because most cases do not make it into a courtroom unless they have escalated to serious violence.

Legal History of Domestic Violence

Views of domestic violence as a private matter have persisted in the United States despite the passage of laws changing the behavior from familial and personal to the public domain. Maryland enacted the first law criminalizing domestic violence and by the turn of the nineteenth century, public views about the appropriateness of domestic violence began to change. Family courts were established and social workers were included as counseling became part of the problem-solving process. The next phase of change in the treatment of domestic violence in the U.S. occurred during second wave of feminism. These changes focused on protection of abused women and police intervention. In the l960’s and 1970’s the definition of domestic violence was changed to include emotional and psychological abuse in addition to physical abuse (Schneider, 2008). In the 1990’s, mandatory arrest laws became widespread although controversy exists about the effectiveness of the laws, the short-term length of deterrence on offender behaviors, and the effects on victims and their children (Zelcer 2014). During this time period,

Congress passed the Violence Against Women Act (VAWA) which recognized that the legal system did 3 not serve abused women (Weissman, 2013). VAWA however became a stagnant document when

Congress refused to reauthorize it and include changes in the knowledge base brought about through research and theory. Although VAWA was important in that it highlighted the legal system’s failures in the treatment of battered women, it did not lead to significant changes in the treatment of victims of domestic violence. Schneider (2008) stated that even if there has been a change in statutes, judges may still show bias. Consequently, the legal system continues to treat victims differentially based on ascribed and achieved characteristics. My research examines these effects by focusing on both victim and offender background variables and the treatment of offenders by the legal system.

Theory

When examining the relationship between domestic violence and the criminal justice system, it is imperative to take a holistic approach in evaluating data since a number of theories are applicable to the analysis of the treatment of abused women. Three main theories work symbiotically to help better understand the treatment of abused women and the treatment of their abusers within the criminal justice system. Structural inequality emerges as the most applicable.

The question I seek to answer is whether or not the justice system disproportionately charges and sentences individuals of color to harsher sentences than Whites. In order to do this, it is necessary to distinguish the difference between the structural inequality between races, and the structural inequality between men and women (Hudson 2006). This analysis requires an understanding of how structural inequality impacts the perpetrator at the arrest and sentencing stage. It is also necessary to focus on the structural inequality between races and genders, both overall and within the criminal justice system. Two other theories that also aid in the understanding of domestic violence are: feminist theory which focuses on the treatment of 4 women overall and the Duluth theory with a focus on the origins of the justice system intervening in domestic violence cases. The Duluth Model was revolutionary in its attempts to rehabilitate perpetrators of domestic violence instead of simply incarcerating them, as early laws against domestic violence tended to include short sentences for perpetrators and high rates of recidivism.

Structural Inequality

Perpetrators

Structural inequality focuses on the latent barriers perpetrators face within society. People most affected by the inequality engrained in the structure of the criminal justice system are racial minorities and those of low socioeconomic status (Sampson and Lauritsen 1997). Since the 19th century, racial disparities in the criminal justice system have received considerable empirical attention. Findings consistently show that Blacks are disproportionately arrested and incarcerated as compared to Whites (Hindelang 1978) and that being non-white can lead to an individual being targeted by law enforcement and punished more severely (Sampson and Lauritsen 1997).

At the sentencing stage, Hispanic and Black defendants are more likely than White defendants to receive sentences that include incarceration (Demuth 2000). Minorities experience inequitable treatment by law enforcement officials through both subliminal and overt bias. For example, the penalty for having possession of crack cocaine is much more severe than for possession of cocaine. Research shows that minorities are much more likely to possess crack cocaine, while White people are more commonly caught with powder cocaine. This is just one 5 example of the way legislation can be and is written to foster the kind of structural inequality minorities face (Schrantz and McElroy 2000).

Gender

To effectively analyze how and why domestic violence perpetrators are sentenced, it is imperative to look further into the structure of abusive relationships. Some social researchers argue that gender is a form of structural inequality (Stark 2007). Stark asserts that this inequity makes women more vulnerable to coercive control than men. Other researchers believe it is more than just gender which creates this disparity. Men and women who experience domestic violence are unequally situated in their marriages in terms of economic dependency on a partner, and this dependency affects their likelihood of marital dissolution (Anderson 2007). In this research, I hypothesize that the type of relationship between the partners will affect suspect treatment at each phase of the criminal justice system. Suspects in a marital relationship may be more likely than their counterparts to receive harsher punishment since the relationship is codified and subject to greater scrutiny.

Feminist Theories

Gendered structural inequality is one of the assumptions on which feminist theory is based. Some theorists argue that the disparity between genders is not rooted in biology, but rather through the process of socialization and laws in which this structural inequality takes place

(Rennison 2005). Despite the number of different feminist theories, there is consistency between them in how the causes and effects of domestic violence are interpreted. Over time, there has 6 been up to 12 variants of “feminist theory” (Rennison 2005). Not all of these theoretical explanations cover sexual violence, but there are common elements within these different theories. Feminist theories conceptualize domestic violence as a sociopolitical issue regarding how men and women are acculturated into their roles of masculinity and femininity (Zosky

1999). Liberal feminists take the position that the goal is to reform laws and practices to bring them in line with those laws and practices that respond to stranger violence (Hopkins 2005).

Liberal feminist theory also underscores the notion of masculinity and femininity and gender roles ascribed to males and females. Cultural feminists argue that the economic, political and judicial institutions are masculinist in practice to the extent that the rules under which they function exclude women’s unique voices and lived experiences. They argue that treating men and women as equals would result in more oppression when the baseline is inherently male

(Hopkins and Koss 2005). Another prong of feminist theory is radical feminism. Radical feminists argue that the root of the male domination over women has direct relations to sex and sexuality. Sexual assault and domestic violence is just an extreme end to this continuum of domination (Curran and Rennzetti 2001).

Marxist and socialist feminists look at the economic sphere of women’s work to explain inequality. To a Marxist feminist, the only way to liberate women from this oppression would be to overthrow the existing economic order (Ollenburger and Moore 1998). Crenshaw, an intersectional feminist, argues that women of color are often reluctant to call the police, due to a general unwillingness among people of color to subject their private lives to the scrutiny and control of a police force that is frequently hostile and often prejudiced (Crenshaw 1991). 7 Feminist theory, in its many variations, focuses on the existing differences between the genders with respect to a woman’s opportunity to succeed in society. These differences are developed through the process of socialization of the victim, the abuser, and members of society.

There is some discussion within the legal literature about whether or not the ideology of treatment of victims of domestic violence represents a gendered view in that mandatory arrests of domestic violence suspects is based in the belief that women are not rational beings and cannot take care of themselves (Miccio 2005). I expect that type of relationship between the all-male suspects and all-female victims will result in different treatment by the criminal justice system. I expect my findings to show that abusers in marital relationships will receive harsher penalties than those in non-marital relationships as the act of male dominance occurs in the context of a socially approved relationship where men and women should function as a unit.

Duluth Theory

The Duluth (MN) Domestic Abuse Intervention Program started in 1980 after a newsworthy episode of intimate partner violence occurred in the city of Duluth, Minnesota. This was one of the first criminal justice movements to hold offenders accountable (Pence 1993) by giving batterers an opportunity to rehabilitate themselves prior to being put in jail. This theory asserts that perpetrators of domestic violence might be able to be rehabilitated and thus nonviolent toward their significant other. This taskforce was created so that intimate partner violence could be stopped before a relationship becomes dangerous enough where someone ends up seriously injured or dead. After this initiative, more perpetrators were charged with domestic violence related crimes. This influx of offenders lead courts to decide how best to deal with 8 perpetrators must be an incidence of domestic violence initiates both criminal proceedings and civil proceedings (Epstien 1999). Courts that deal with specific problems, such as domestic violence courts, orient themselves to not only personal deterrence, but ultimately cultural transformation and the general deterrence that follows new norms (Mirchandani 2008). Judges and many laypeople however write off domestic violence as a “family matter” minimizing the seriousness of domestic violence in the family unit (Epstien 1999). This construct relates to my analysis due to the impact of a judge throwing out a case that is not deemed “threatening or serious” enough, resulting in the escalation of violence in that relationship. If the violence does continue to worsen, then the couple is likely to return to court with more serious injuries thus resulting in a more serious crime such as a felony rather than a misdemeanor or other charge with the concomitant more serious penalties.

I am using the name Duluth Theory to refer to all court mandated domestic violence programs, both criminal and civil. An eighteen-month study found that almost two thirds of the men who completed a twelve-month batterer education and combined groups, and were available for further interviewing, were reported to be nonviolent. They also found that longer programs did not prove any more effective (Edleson and Syers 1991). This relates to the sentencing aspect of my dependent variables. I expect that, due to bias in the system, minorities will be more likely than Whites to be sentenced to domestic violence education programs.

Race

Race is one of the pillars of structural inequality, but how exactly does race effect suspects and defendants in the criminal justice system? The U.S. Sentencing Commission found 9 that sentences for Black men are twenty three percent longer than those for White men. (US

Sentencing Commission (USSC 2010). Others found that, even after controlling for legal facts that pertain to the case such as the seriousness of the crime, Blacks were still given longer sentences than Whites (Mustard 2001). As a result, minorities view the criminal justice system as less legitimate than Whites do because of anticipated inequity in the system (Roque 2011). The roots of racial disparity include higher crime rates, inequitable access to resources, legislation that disproportionately affects minorities, and overt bias (Schrantz and McElroy 2000). The racial disparities research lends support to my hypotheses that Black men are more likely to be arrested and charged more often, and sentenced more harshly as compared to White men.

Whites

In 2016, Brock Turner was convicted of sexual assault crimes that could have put him in jail for up to fourteen years. The judge took pity on this wealthy, White, promising Ivy League athlete and sentenced him to six months in jail (Barbery 2016). This is important as it is a case study example of the inequality of sentence based on the race of the abuser.

Another layer of complexity in which race plays a role is economic distress. Whites have a more favorable labor market available to them, and since economic distress is something that heightens the rates of domestic violence, Whites are therefore less vulnerable to domestic violence on the basis of economic distress (Leguizamon, Leguizamon, and Howden 2017). The majority of research done on domestic violence prior to the 21st century is concerned mostly with white women, while today scholars take many races and ethnicities into account when examining rates of intimate partner violence. 10 Blacks

Another case worth noting is the 2014 case in Ferguson, Missouri. Michael Brown was a young black man who had stolen a pack of cigarillos from a convenience store. A police officer pulled over Brown and a co-suspect and there was an altercation between the officer and Brown.

In the end, the police officer shot and killed Michael Brown at close range (Buchanan, New York

Times 2015). Sampson and Lauritsen (1997) argue that racism in the justice system is not overt, but more likely to be subliminal and caused by the war on drugs and moral panic. Black and

Hispanic defendants are consistently found to receive higher sentences as compared to Whites

(Demuth 2000).

According to the National Crime Victimization Survey, Blacks are more likely to experience intimate partner violence than Whites (Rennison and Planty 2003). Black women, in particular, report higher levels of abuse, in addition to the higher incidence rates of abuse, as compared to women of other races (West 2004). Within American culture, there is a common stereotype that Black women are too strong willed and Black men are weak. This ideology has set up Black men to assert dominance in intimate partner relationships (Collins 2005). This relates to structural inequality between genders, as when there is a struggle for power there is a greater likelihood of coercive control, sometimes to the point of force (Stark 2007).

Hispanics

There are similar stereotypes in Latino culture as in African American culture. The concept of machismo is a set of behaviors exhibited by Hispanic males as being strong, superior, and dominant in relationships (Marrs Fuchsel, Murphy and Defresne 2012). This concept may 11 contribute to the high rates of domestic violence victimization among Hispanics (Edelson,

Hokoda and Ramos-Lira 2007). Similarly, the concept of marianismo, a set of behaviors exhibited by women who act out their roles as a nurturer and caregiver of their husband and children, is present within Hispanic culture. Women are submissive and do everything they can to keep their family intact, even in a domestic violence situation (Marrs, Fuchsel, Murphy and

Defresne 2012).

Latina women, however, are less likely to utilize formal public services such as health care and legal services, despite their increased vulnerability to intimate partner violence (Alvarez and Fedock 2016). Therefore, the data involving victimization may not be representative for this group. A study done in 2007 argues that Latinos lack of need of formal public service shows that they have still maintained community ties, and may be seeking refuge with a community member or family (Grossman and Lundy 2007). Based on the data I am analyzing for this research, I hypothesize that Hispanic male suspects are more likely to be arrested and charged in comparison to White men. I also expect Hispanic suspects to be sentenced more harshly than

White men. In additional to theoretical explanations for this, the cultural generalizations of a hyper-masculine culture among Latinos is consistent with this hypothesis.

Other Races

Not only are Blacks and Hispanics disproportionately affected by the criminal justice system, but other races, including recent immigrants, have also faced the brunt of bias

(Khandelwal 2003). Since 9/11 and the Patriot Act, Middle Eastern men experienced increased police scrutiny and excessive government intrusion (Abraham 2005). Other studies have 12 identified race and ethnicity effects among other groups. A study conducted in 2000 by Tjaden and Theonnes found that American Indian/Alaskan Native women experienced the highest rates of lifetime victimization involving rape, physical assault, and stalking as compared to other women. The second highest rates of lifetime victimization are of mixed race women (Tjaden and

Theonnes 2000).In the realm of domestic violence, Middle Eastern women are less likely to contact police to avoid investigation in fear of being deported (Raj et al. 2004). Middle Eastern cultures also facilitate a patriarchal power imbalance within the family. This supports my hypothesis that men of other races are more likely than White men to be arrested and charged.

This also supports my hypothesis that men of other races will receive sentences that are harsher than the sentences of White suspects.

Age

Approximately 2 million U.S. women are severely assaulted by male partners each year

(Myers and Jacobo 2005). Girls and young women ages 16-24 experience the most domestic violence (Rennison 2001). In a comparative study examining three age cohorts, young women ages 12-24, middle aged women ages 25-54, and mature women ages 55 and older, Rennison

(2003) found that intimate partner violence rates and victims’ ages are inverted. Younger females experience intimate partner violence at significantly higher rates than middle-aged women, and middle-aged women experience significantly higher rates of intimate partner violence than older women (Rennison and Rand 2003). These findings show that younger women are more likely to be victimized than older women, but it is unclear if the abuse is reported and addressed by the police due to the fact that these data are from a victimization survey. Because this research was 13 drawn from victimization research, and research surrounding age and sentence is sparse, I cannot conclusively hypothesize how suspects are treated on the basis of age. In this research, I am using a null hypothesis and stating that the age of both the suspect and the victim will not affect arrest, charge or how they were sentenced.

Abuser-Victim Relationships

Research suggests that intimate partner violence exists across all types of relationships

(Logan, Walker, Jordan, & Leukefeld, 2006). Couples that are in a relationship, but not yet married, have higher rates of intimate partner violence than those in more codified relationships

(Caetano et al. 2002). Couples or ex- couples are more likely to report abuse by the other due to the fact that they are less invested in the relationship than married couples (Stets 1991). Johnson

(1996) found that cohabiting couples have the highest level of violence, but the longer they have cohabited, the more similar the violence levels are to married couples. In my research, knowing the nature of the relationship between the suspect and victim is important because it can pinpoint when the victim is in the most danger throughout the course of a partnership.

Aggressive behavior is often influenced by age and socioeconomic status. People who are in a relationship and cohabiting tend to be younger and have a lower socioeconomic status making them more susceptible to act out aggressively (Stets 1991). Research has shown that the most dangerous time for a victim is when they are trying to exit the abusive relationship

(Wooldredge and Thistlethwaite 2006). Additional sources support that stalking increases after a woman attempts to exit the relationship, as the abuser is using different means to continue the intimate partner violence (Logan, Leukefeld, & Walker 2000). This supports my hypothesis that 14 relationship between suspect and victim is relevant as I expect that suspects who abuse former significant others will be or are the most likely to get arrested and charged with a crime.

Weapons and Violence

The two most significant factors a woman considers when deciding to leave an abusive relationship are if her abuser keeps a weapon inside the house, and what formal action will be taken by the criminal justice system, such as an arrest or attempted prosecution (Stroshine and

Robinson 2003). Weapon use in domestic violence incidents are a great cause for concern. When looking at all crimes, and not just those of domestic violence incidents, the Bureau of Justice

Statistics reported that victimization by a weapon was reported more for Hispanics and Blacks than for Whites (Perkins 2003). Folkes, Hilton, and Harris (2012) found that an abuser with access to firearms is different than an abuser without access to firearms. They found that having a firearm makes abusers more intimidating to their victim and more likely to seriously harm them. Men who used weapons also tend to commit more serious violent offenses without weapons (Folkes, Hilton, Harris 2012). When it comes to fatality, weapon choice matters.

Kellerman and Mercy (1992) found that women are more than twice as likely to be shot by an intimate partner as shot or killed in any other way by a stranger. Intimate partner violence ends in fatality in one percent of cases, meaning that most incidence of intimate partner violence is ongoing non-fatal abuse (Sorenson 2006).

In approximately 25% of domestic violence cases, a weapon is used. However, in less than 1 percent of cases is that weapon a firearm. Kernsmith and Craun (2008) found that neither suspect drug or alcohol use nor relationship type were significantly related to the use of weapons 15 in a domestic violence incident. In cases where there was the presence of a weapon, but the weapon was not a firearm, the weapon itself was usually an item commonly found in the house that is not a part of the assailant’s body (Folkes, Hilton, Harris 2012). Results of another study suggest that while firearms may contribute to the escalation of violence, the impact of possessing the firearm is only as strong as the intent to harm (Philips and Maume 2007). This relates to my research as I hypothesize that using a weapon or an object used as a weapon will influence the likelihood of an arrest, increase the severity of a charge, and increase the severity of the sentence as compared to other types of altercations including hitting, threats, and name calling.

Alcohol and Drugs

Alcohol and drugs are known for diminishing inhibitions and are associated with violent behavior, both in general and in an intimate partner setting. A perpetrator with alcohol and drug abuse issues also has a higher incidence of domestic violence assaults (Coker, Smith, McKeown, and King 2000). Alcohol may be a contributing factor of domestic violence, but it is not a direct causal relationship (Leonard 2001). This suggests that the abuser may act violently while intoxicated, but it does not take the intoxication to facilitate the violence. There is mixed support for this view among intimate partner violence researchers. Other researchers argue that alcohol does play a functional role in intimate partner violence, but there are many other factors in addition to alcohol that can contribute to aggression (Klostermann and Fals-Stewart 2006).

Severe aggression is 11 times more likely when a batterer has been consuming drugs or alcohol as compared to when a batterer has not been consuming drugs or alcohol (Fals-Stewart

2003). Sixty percent of that violence is expressed within two hours of the abuser consuming 16 drugs or alcohol. Leonard (2001) found that who is using the substance is important to consider.

If the perpetrator is using drugs or alcohol, the violence is not treated as an excuse, but if the victim is using drugs or alcohol, it may be used as an excuse for the aggression. One study reports that women whose partners abuse alcohol are 3.6 times more likely to be physically assaulted by their partners than women whose partners do not abuse alcohol (Kyriacou et al.

1999). This relates to my research because I predict that alcohol and drugs will increase the likelihood of abuse, and therefore will impact how the suspect is arrested, charged, and sentenced.

Presence of Children

Exposure to domestic violence can have a wide range of negative effects on children.

There is extensive research on how children suffer from exposure of domestic violence, but there is not a sufficient amount of data on how the presence or absence of children effect the perpetrator’s behavior. Approximately 10 to 20 percent of children are exposed to intimate partner violence each year, and one third of children are exposed to domestic violence at some point in their childhood or early adolescence (Carlson 2000).

In North Carolina, district courts judges are often required to assess, grant, or deny domestic violence protective orders. These judges maintain that the presence of children play a key role in deciding whether or not to grant a domestic violence protective order. If a child is present during a violent incident, they are much more likely to grant the order, but there is a fine line as judges fear that false complaints are made in attempt to get sole custody of the child

(Agnew-Brune et al. 2017). Other researchers argue that while a child may be exposed to 17 domestic violence from their fathers, maintaining contact is often encouraged by courts, especially when there is insufficient evidence that the violence occurred from both mother and child accounts of the incident (Macdonald 2016).

Because the majority of research relating to children’s presence and absence in intimate partner violence households has been focused on child maltreatment and private civil family court, I will test the null hypothesis. There is insufficient data on the role of children in domestic violence households and how suspects are treated in a criminal court setting. I hypothesize that the absence or presence of children will not affect a suspect’s likelihood of charge, arrest, or influence their sentence.

Length of Relationship

In addition to the abuser-victim relationship type, the length of their relationship is an important factor to consider. These two subcategories are not mutually exclusive as the length of a relationship may be a predictor of the nature of that relationship. However, the difference in violence between cohabiting couples and married couples are only significant when the cohabiting relationship is three years or less in length (Johnson 1996). Often younger women in relationships are screened for domestic violence, but older women who may be divorced or widowed are not. Wolkenstein and Sterman (1998) conducted a study on older women who sought services for anxiety, depression, and other mental health issues. Older women seeking mental health services were assessed for both child abuse and intimate partner abuse, and 87 percent of these women were found to be victimized in both their childhood and adult life

(Wolkenstein and Sterman 1998). 18 The pattern of intimate partner violence tends to increase in severity as well as escalate in frequency over time (Fagan et al. 1984). The length of the relationship may not tell us as much about domestic violence, as socialization has changed throughout the years. Victims of domestic violence differ depending on their age. Older victims are more likely to be long-term abuse survivors, but may think this behavior is appropriate for a submissive, well behaved wife (Straka and Montminy 2006). This relates to my body of research as I predict that shorter relationships with younger victims are more likely to be taken seriously by the criminal justice system. I predict that relationships that lasted less than three years are more likely to have higher arrest rates, more serious charges, and longer sentences than relationships that lasted longer than three years.

Hypotheses

H1: White perpetrators are less likely to be charged with a crime than Black, Hispanic, and perpetrators of other races.

H2: White perpetrators are less likely to be convicted of a crime than Black, Hispanic, and perpetrators of other races.

H3: White perpetrators are more likely to have a more lenient sentence than Black, Hispanic, and perpetrators of other races.

H4: Race of the victim will not affect sentencing of the perpetrator.

H5: Age will have no effect on arrest, charge, or sentence. 19 H6: The relationship between the perpetrator and the victim will influence the suspect’s arrest, charge, and sentence.

H7: Increased number of violence related behaviors will increase the likelihood of an arrest, increase the severity of a charge, and increase the severity of the sentence.

H8: Alcohol and drugs will affect how the suspect is arrested, charged, and sentenced.

H9: Presence of children will not affect a suspect’s likelihood of charge, arrest, or influence their sentence.

H10: Length of relationship will affect how the suspect is arrested, charged, and sentenced.

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Chapter 2

Methods

Data

I examined the causes of arrest and resulting sentences for suspects of domestic violence crimes. This data were collected via official police records on a sample of domestic violence cases in San Diego, California (Pennell & Burke 2002). The data were collected between 1998 and 1999 with the goal of evaluating the implementation of the specialized domestic violence units in San Diego County (Pernell & Burke 2002). Information was included for perpetrators of both sexes. I am utilizing the part of the original study which contains case tracking data including demographics of the suspect and victim, the nature of the relationship between victim and suspect, arrest and sentencing decisions, among other variables. The data were gathered from a sample of the case tracking system where reported cases of domestic violence were followed.

Response rate statistics are not applicable to these data since they were compiled from existing data. My analysis focuses only on male perpetrators with female victims. For my analyses, the final N is 384. The variable with the largest number of missing cases is suspect’s age with 1.8 percent of the cases missing this information. 21 Independent Variables

In my research, I examine two main independent variables: race of the suspect and the race of the victim. Race is categorized as White (1=yes, 0=no), Black (1=yes, 0=no), Hispanic

(1=yes, 0=no) and other races (1=yes, 0=no). The percentage of suspects that are White is 58.2 percent. About 12 percent of the suspects are Black. The percentage of suspects that are Hispanic is 23.6 percent. Other races which contains every race that falls outside of White, Black and

Hispanic. Asians were included in the other category due to the small number of Asians in the data set. Asians and those of other races make up 6.2 percent of the suspects. The percentage of victims that are White are 60 percent, 9.1 percent of the victims are Black. The percentage of victims that are Hispanic is 24.2 percent and 6.5 percent of victims are of other races. Due to the small sample size particularly in the sentencing analyses and the small number of minorities, race was recoded into two categories: White (0) and minority (1).

Control Variables

I have identified several control variables that have been identified in previous research as having an influence on both the occurrence of domestic violence and the treatment of suspects by the legal system Included are both suspect and victim age, the nature of their relationship, level of violence during the domestic violence event, alcohol and drug involvement in both the suspect and the victim, the presence or absence of children, and the length of the relationship. Behaviors included in the measure of level of violence included different categories of abuse such as if the victim was assaulted with a piece of property, if the victim was harmed through bodily force, if 22 the victim was verbally abused, and if the victim was threatened by the suspect. Unfortunately, many of these categories had a small number of cases.

Age of victim and suspect were included as control variables in the analysis. The median age of the suspect was 34.44 years (std. deviation =10.45) and the median age for the victim was

33.00 (std. deviation=9.74) Relationship between the suspect and victim is divided into three groups: victim and suspect were married or formerly married, if the victim and suspect were significant others and if the victim and suspect were former dating partners. About forty-six percent of participants were spouse’s or former spouses. About twenty-six percent of participants were significant others and seventeen percent were former dating partners. For the analysis, the relationship variable was made into a dummy variable with each category of relationship represented by a 1 (the suspect is in that kind of relationship) or a 0 (the suspect is not in that category of relationship). The comparison group is former dating partners.

The involvement of alcohol and drugs by the suspect was recoded into a dummy variable of 1 and 0. One indicates that there was admitted or apparent use of drugs/alcohol, and 0 if use is unknown or undetectable. Thirty-two percent of suspects were known to use drugs during the altercation. The victim was known to use drugs during the altercation at a rate of thirteen percent.

The presence of children is also recoded into a dummy variable as 1 if there were children present at the time of the altercation, and 0 if there were none. Forty-one percent of cases had children present during the incident. Length of relationship was broken down into four categories. These increments are less than one year, one to five years, six to ten years, and ten years and above. There was fourteen percent in the category of less than one year, thirty-nine percent in the category of one to five years, twenty-one percent between six and ten years, and 23 thirteen percent in more than ten years. This only accounts for eighty-seven percent of cases as there is thirteen percent of this particular data unknown or missing.

In analyzing weapon involvement, the data was recoded from the categories of guns, knives, and other weapons to a general dummy variable of weapon involvement represented by a 1, and no weapon involvement represented by a 0. Six and a half percent of altercations included the use of a weapon. For property that would not fall under the category of a weapon, but is being used as one, such as a phone, was also recoded into a dummy variable of 1 meaning there was property involved and 0 meaning there was no property involved. Sixteen percent of altercations included property being used as a weapon. Body interaction, which would consist of hitting and any type of physical contact, was recoded into dummy variables, 1 meaning there was physical contact, 0 if there was no physical contact. There was a seventy-eight percent occurrence of physical contact during an altercation. Verbal abuse includes non-threatening types of abuse, this would consist of insults, put downs and other types of emotional abuse. This was recoded into a dummy variable of 1, there was verbal abuse, and 0 meaning there was no verbal abuse. Twelve percent of victims claim there was verbal abuse. Verbal abuse is different than threats because threats are an intention of danger to another person. Threats were recoded into 1 meaning there was a threat of harm, and 0 there was no threat of harm. Victims were threatened at the rate of fifteen percent in this dataset.

Because of the small number of cases in each of the violent interaction categories, I created a scale of violence. This allows me to test the effect of level of violence during the interaction and the effect on the dependent variable without focusing on events that have only a small number of cases. On this scale, 5 the highest number, indicated the most violent interaction with the use of 24 a weapons, four was assigned to the category of hitting, slapping and shoving, three represents violence with property including throwing a telephone, two is threatening language, 1 is verbal abuse and 0 means none of these occurred during the interactions. The median violence level is

4.0.

Dependent Variables

My dependent variables include: was the suspect is charged with a crime, what type of crime they were charged with, and what was the sentence they received. The first dependent variable I examine is whether or not the suspect is arrested (1=yes, 0=no). About fifty-two percent (52%) of the suspects were arrested. The second dependent variable is the degree of the crime. I have these categorized under felony (1= yes, 0=no), misdemeanor (1=yes, 0=no), and other (1=yes, 0=no). About forty percent (40%) of the suspects were charged with a felony, thirty-eight percent (38%) with a misdemeanor and twenty-two percent (22%) with another charge.

Next, I examine the relationship between the independent and control variables and whether the suspect plead guilty. Of those suspects whose cases went to court, about seventy-five percent of the suspects plead guilty. The final dependent variable I examine is the penalties that a suspect could receive. I coded this into four categories: jail, probation, restitution/fine, and domestic violence program. About seventy percent (70%) of the suspects received a jail sentence; Eighty-two percent (82%) received probation, twenty-one percent (21%) received a fine, fifteen percent (15%) were sentenced to a work furlough and twenty-two percent (22%) were sentenced to participate in a domestic violence program. These add to more than one 25 hundred percent as a suspect could have received more than one sentence. In order to further the relationship between race, age, and relationship, I created a simple additive scale of the four types of penalties. The descriptive statistics for all of the variables included in the analysis are shown in Table 1.

[Table 1 about here]

Analysis

In analyzing this data, I used descriptive statistics, correlations, and logistic regressions.

Descriptive statistics provide simple summaries about the sample and the measures. It includes the type of variable, the variable name, the mean, and the standard deviation. A correlation is a single number that describes the degree of relationship between two variables between the numbers 0 and 1. I also used logistic regression when the dependent variable had only two response categories (0=event did not occur) (1=event occurred). When the dependent variable in the analysis is a binary variable, logistic regression is preferred over linear regression analysis as a binary outcome violates some of the statistical assumptions underlying linear regression. The logistic regression coefficients used here are odds ratios. For each unit of the independent variable, the odds ratio coefficient (OR) estimates how much the odds of being in one category of the outcome rather than in the other differs. For example, assume the dependent variable is whether or not the suspect has been arrested with being arrested coded as 1 and not being arrested coded as 0. The independent variable of interest is suspect’s race which was created in a series of two category variables (Black yes=1 no=0; Hispanic, 1=yes, no=0 etc). Odds ratios

(OR) are all positive and vary around 1.0. An OR of 1.00 means being Black has no effect on 26 whether or not suspects were arrested. An OR of 2.00 would mean that Blacks would be twice as likely to be arrested as compared to Whites (the comparison group). An OR of 0.5 would mean they would be half as likely or 50% less likely. 27

Chapter 3

Findings

Table 2 presents the relationship between suspect and victim race, suspect and victim age and the suspect and victim relationship status on the dependent variable of whether or not the suspect was arrested. Model 1 presents the effect of only the race of the suspect and victim.

None of the main independent variables by themselves are statistically significant, however there are two races effects that are close to being significant. As compared to White suspects, Hispanic suspects were 2.1 times more likely to be arrested, and those of other races were 3.3 times more likely to be arrested (p < 0.1). Model 2 presents the relationship with the control variables added.

Hispanic suspects were 2.9 times more likely to be arrested as compared to White suspects (p <

0.05). Another variable is not statistically significant, but worth noting is other races are 3.6 times more likely to be arrested as compared to White suspects (p < 0.1). Several control variables were significant. Suspects who had the altercation with a significant other were 3.4 times more likely to be arrested as compared to suspects who had an altercation with former dating partners (p < 0.01). Suspects who were married or formerly married to their victim were

2.2 times more likely to be arrested as compared to a former dating partner (p < 0.05). Another significant variable is if the suspect was using drugs/alcohol during the incident they were 1.9 times more likely to be arrested than if there were no drugs or alcohol involved (p < 0.05).

Suspects were also 1.4 times more likely to be arrested with each increase on the violence scale

(p < 0.01).

[Table 2 about here] 28

The findings for the logistic regression analysis of the independent variables of suspect and victim race, suspect and victim age and relationship of the suspect and victim with the dependent variable of whether or not the suspect was charged with a felony are presented in

Table 3. Model 1 presents the effect of only the race of the suspect and victim. Only one of the independent variables was statistically significant. Hispanic suspects were 2.5 times more likely to be charged with a felony as compared to White suspects (p < 0.05). Model 2 presents the relationship with the control variables added. The effect of being a Hispanic suspect remain statistically significant, but the likelihood of them being charged with a felony has increased to

2.8 times more likely as compared to Whites (p < 0.05). Four control variables were significant: victim age, suspect age and the different relationships the suspect had with the victim. For each increase in the victims’ year of age, suspects were 1.1 times more likely to be charged with a felony (p < 0.01). The youngest victim in the data set was 15. For victims who are ten years older

(25 years of age), there is an 11 times greater chance that the suspect will be charged with a felony. For each increase in the suspects’ year of age, suspects were 4 percent less likely to be charged with a felony (p < 0.05). Suspects who had the altercation with a significant other were

8.2 times more likely to be charged with a felony as compared to suspects who had an altercation with former dating partners (p < 0.01). Additionally, suspects who were married or formerly married were 4.3 times more likely to be charged with a felony as compared to their counterparts

(p < 0.01).

[Table 3 about here]

The relationship between suspect and victim race, suspect and victim age and partner status of the suspect and victim with whether or not the suspect was charged with a misdemeanor 29 is presented in Table 4. None of the main effects of race of suspect and victim are statistically significant as shown in Model 1. Model 2 presents the relationship with the control variables added. Victim’s age is again statistically significant (p < 0.05). For each increase in years of victims age, suspects are 4 percent less likely to be charged with a misdemeanor. As compared with a 15-year-old victim, suspects charged with abusing a 25-year-old victim are 40 percent less likely to be charged with a misdemeanor. For each increase in years of the suspects age, they were 1.0 times more likely to be charged with a misdemeanor (p < 0.05). Suspects who had the altercation with a significant other were 79 percent less likely to be charged with a misdemeanor as compared to suspects who had an altercation with former dating partners (p < 0.01). Suspects who had the altercation with their spouse or former spouse were 68 percent less likely to be charged with a misdemeanor in comparison to former dating partners (p < 0.01). With every increase in increment of time in the relationship, the suspect is 25 percent less likely to be charged with a misdemeanor (p < 0.05). Suspects who were using drugs or alcohol were 53 percent less likely to be charged with a misdemeanor when compared with someone who did not use drugs or alcohol during the incident.

[Table 4 about here]

When analyzing the case disposition, I was left with 80 cases to analyze. The majority of the cases never when to trial due to plea bargaining practices. Of those who actually went to court only 8 (10%) were found to be not guilty. Often in the criminal justice system, plea bargaining keeps suspects out of the courtroom. Since this data is so restrictive and the number of cases is so low, predictors of guilt cannot be examined. 30 My final series of analyses focuses on the case disposition phase and an examination of race effects on whether or not the suspect got jail time, a fine, probation, or sentenced to a domestic violence education program. Only 71 suspects went on to the case disposition phase. A small N reduces the chance of obtaining statistical significance and consequently, for these analyses, I use .1 as my level of statistical significance. In the penalty analysis, I have categorized Whites as 0 and minorities as 1 because of the small number of cases

Table 5 presents the relationship between suspect and victim race, suspect and victim age and relationship with if the suspect received any jail time as a part of their conviction. There were no effects from the independent variable of suspect and victim race on the likelihood of jail time. Only two of my control variables impacted the effect of jail time. With each increment of time in length of relationship (every 5 years) the suspect is 51 percent less likely to be sentenced to jail (p < 0.1). Additionally, with every increase on the violence scale suspects were 14 percent less likely to be sentenced to jail (p < 0.05).

[Table 5 about here]

The relationship between suspect and victim race, suspect and victim age and partner status of the suspect and victim with whether or not the suspect was sentenced to complete a domestic violence program is presented in Table 6. The main effect of suspect and victim race had no statistically significant effect (p < 0.1) on the likelihood of a suspect being sentenced to a domestic violence program. However, the nature of the relationship was statistically significant when determining if the suspect was to be sentenced to a domestic violence program. As compared to an ex-significant other, a suspect in a relationship with a significant other was 92 31 percent less likely to receive a sentence that involved a program on intimate partner violence (p <

0.1).

[Table 6 about here]

Table 7 presents the findings for the logistic regression analysis of the independent variables of suspect and victim race, suspect and victim age and relationship of the suspect and victim with whether or not the suspect was required to pay a fine as a part of his sentence. There were no statistically significant race effects on whether or not the suspect was required to pay a fine. In this case, the only control variables that had a significant effect on a fine is suspect’s and victim’s age. As the suspect age increased, the suspect was 0.04 times more likely to be given a fine with each passing year (p < 0.05). As the victims age increased, the suspect was 12 percent less likely to get a fine (p < 0.01).

[Table 7 about here]

Table 8 presents the relationship between suspect and victim race, suspect and victim age and relationship with if the suspect received any time on probation as a part of their conviction.

There were no significant (p < 0.1) race effects on probation. Victim and suspect age were both significant when determining if probation was a part of the conviction. As the victims age increased,the suspect is 26 percent less likely to be assigned to probation (p < 0.05). As the suspects age increased, they were 1.5 times more likely to be assigned r.

[Table 8 about here]

32 Chapter 4

Discussion

The goal of this study was to address the central question, how are perpetrators of domestic violence treated in the criminal justice system and what influences the treatment?

Domestic violence is a serious problem in society, but to better understand the impact of it on structural inequality in society, it is important to continue to examine the presence or absence of racial inequality within the criminal justice system. Research which focuses on racial inequality must be continually conducted to monitor changes and the effectiveness of domestic violence policies. While there are many interacting variables in predicting who gets punished, it is important to examine causal independent variables including background characteristics of the victim and suspect in order to determine which variables increase the likelihood that a suspect will end up being arrested and severity of sentence. The purpose of this study is to add to the research about racial inequality in the justice system, to determine the effect of race and other suspect and victim characteristics on sentencing decisions in domestic violence cases.

My research shows that, in the arrest phase, Latino suspects as compared to White suspects, were more likely to be charged with the crime of domestic violence. Additionally, people of “other” races as compared to Whites were more likely to be charged. This finding supports my first hypothesis (H1) which states that Whites are less likely to be arrested as compared to people of other races. Hispanics report higher rates of domestic violence victimization, but victimization is not always indicative of rates of arrest (Edelson, Hokoda and

Ramos-Lira 2007) because minority groups, due to issues of trust in law enforcement, are less likely to call for police assistance than Whites. Peck (2015) finds support for this idea in a meta- 33 analysis study of prior research focusing on negative perceptions of the police by race of the respondent. The findings show that, overall, minorities were significantly more likely than

Whites to hold negative perceptions of the police and this may result in a decreased likelihood of minorities seeking help from the police in a domestic violence situation. The racial differences in arrest are also consistent with structural inequality theory because differential access to social rewards are built into the norms, laws and values of society including the criminal justice system.

My second hypothesis (H2) focuses on race and other control variables and the likelihood of conviction for domestic assault. Since the greater majority of suspects pleaded guilty or entered into a plea bargain, the charge they were given became the conviction. It is common in the criminal justice system for the prosecutor and the defense attorney, or the defendant themselves, to enter into plea deals as opposed to going to trial. This means entering a guilty plea for lesser charges instead of being found guilty by a judge or jury of the most serious charge.

When it comes to the type of charge given to the suspects, Hispanic men were more likely to be charged with a felony as compared to White men. Latino culture and norms may be contributing factors as to higher rates of intimate partner violence because it is seen as common and expected in their culture, this higher rate of victimization may lead to more police intervention (Edelson,

Hokoda and Ramos-Lira 2007). Messing, Becerra and Ward-Lasher (2015) found that Latinas value trust in local law enforcement more than they fear deportation. This could mean that

Hispanics are likely to report violent crime even if they are undocumented.

While my main variable in this study was race effects on arrest and sentencing, it was not found to have a significant effect on sentencing. My third hypothesis (H3) that Whites were more likely to receive a more lenient sentence as compared to minorities was not supported. This could 34 suggest that the most overt racism only occurs at the arrest phase when officers are deciding whether or not to charge the suspect. The race of the victim had no effect on the arrest or sentence of the suspect, which supports my fourth hypothesis (H4). Research suggests that minority women experienced higher rates of domestic violence victimization as compared to

White, but were less likely to report to authorities (Rennison and Planty 2003). This lack of reporting may reduce any racial differences of victim since these women may not be present in police and sentencing data as compared to victimization data. It is important that rates of reporting can vary by minority group and this can influence the data gathered by police and other law enforcement agencies.

Interestingly, other variables did influence sentencing decisions as the suspect moved through the criminal justice system. Age of both the suspect and the victim were found to be significant predictors of both type of charge and sentencing. Due to the inconsistency in research findings on age, domestic violence, and punishment, I examined these variables but did not develop a directional hypothesis (H5). Victimization research supported the notion that younger women are more likely to be victims of domestic violence as compared to older women

(Rennison and Rand 2003). Judges often punish suspects more harshly if their victim is younger

(Rennison 2003) and this is consistent with my findings. Older suspects received harsher punishment than younger suspects. Older suspects with young victims were the most likely to receive a harsh punishment. This would suggest that judges felt the need to protect younger women against the dangers of domestic violence more than older women experiencing domestic violence. 35 The abuser-victim relationship is also important in both the arrest and sentencing stage.

My sixth hypothesis (H6) is a directional hypothesis that focuses on the kind of perpetration- victim relationship and the influence on arrest and sentencing of the suspect. I find that abusers were more likely to be arrested if the couple had been formerly dating rather than if they were currently dating. This supports the research that it is more dangerous for the victim after they have exited or attempted to exit the relationship (Wooldredge and Thistlethwaite 2006). In the sentencing stage, the results suggest that judges try and rehabilitate abusers that have intentions of reuniting with their victim. When compared with ex-dating partners, I find that perpetrators who are in a relationship or married to their victim were more likely to be sentenced to a domestic violence program than their counterparts This supports the idea that couples or ex- dating partners are less invested in the relationship (Stets 1991). This could mean that judges are less motivated to attempt to rehabilitate the suspect if the couple no longer intends on staying together. This may indicate that judges view cases of domestic violence as independent events and specific to a partnered relationship and not that some individuals are more violent than others independent of the relationship. The opposite pattern was found for perpetrators sentenced to probation. If the suspect was married to their victim, they were less likely to receive probation as compared to former dating partners. This may be due to a lack of involvement from the victim or, if they are married, the wife may be less likely to pursue charges and punishment against her spouse.

When factoring in levels of violence, the effects are found in the arrest phase, and in the sentencing phase. This is partially supportive of my seventh hypothesis (H7). In the arrest phase, the higher the level of violence in the altercation, the greater the likelihood that the suspect will be arrested. This is a common practice in criminal justice not only in the realm of domestic 36 violence, as men who use weapons in domestic violence also use weapons in other crimes and are more likely to be arrested (Folkes, Hilton, Harris 2012). Much of the research in this area focuses primarily on firearms, however in domestic violence relationships there are often blurred lines on what constitutes as a weapon. Often there are household items being used as weapons such as phones, knives, or hammers. In my research, I find that a small percent of the domestic violence interactions involved the use of a gun and a much larger percent included the use of household items that have the potential to inflict bodily harm. So, although research does show that there is a relationship between perpetrator access to firearms and severity of victim injury

(Zeoli, Malinksi and Turchan 2016), it is important to consider the role of other kinds of weapons in domestic violence situations as they too can result in victim injury.

In the sentencing phase, violence matters most as to the likelihood of the perpetrator receiving jail time. The higher degree of violence, the more likely the suspect is to be sentenced to jail time. This may be relative to the amount of danger the suspect puts the victim in both during and after the domestic violence event, and if removing the suspect would allow the victim to seek help or increase the safety of the victim (Stroshine and Robinson 2003).

Alcohol and drug involvement matters in the arrest phase, and the charge phase, but not in the sentencing phase, which partially supports my eighth hypothesis (H8). If the suspect was known to be using drugs or alcohol at the time of the incident, there was a higher chance that they will be arrested. The role of alcohol is a highly debated topic in the domestic violence literature as some scholars argue that alcohol is important to consider because it plays an active role in the act of violence (Klostermann and Fals-Stewart 2006). Others researchers argue that the violence would happen without the alcohol and the effect may be spurious (Leonard 2001). 37 Suspect drug/alcohol use also influences the likelihood of receiving a misdemeanor charge. I find alcohol/drug use was not significantly related to whether or not the perpetrator is charged with a felony, however, if the suspect was intoxicated/high, they are more likely to be charged with a misdemeanor. This may indicate that law enforcement is concerned not only about the domestic violence itself, but also substance abuse issues which endanger the community. Additionally, since severe aggression is 11 times more likely when a batterer has been consuming drugs, the influence of alcohol could have been used as a qualifier, justifying more misdemeanor charges, and less felony charges (Fals-Stewart 2003).

Due to the lack of literature surrounding the presence of children and punishment outcome, my ninth hypothesis a null hypothesis (H9). The presence of children did not have a significant effect on any of the dependent variables, supporting the null hypothesis. Since family specific courts are a relatively new concept, I expect that future research will be able to provide data with higher levels of reliability on how the presence of children influence law enforcement and judicial decisions.

The length of the relationship had an effect on what charge the suspect received and their sentence. This is partially supportive of my tenth hypothesis (H10). The longer the couple was together in their relationship, the less likely the perpetrator was to be charged with a misdemeanor. In instances where couples have engaged in intimate partner violence for a long time, local police may not intervene as often since older women do not always categorize their abuse as dangerous (Straka and Montminy 2006). There is also a connection with length of relationship and jail time. The longer the relationship has gone on, the less of a chance for the suspect to be sentenced to spend time in jail. 38 My findings support the notion that there is structural inequality between perpetrators by race. Feminist theories become the most relevant in regards to age, as the social norms ascribed to younger women are different than those ascribed to older women. Older women are less likely to report abuse because they have a harder time identifying the difference between abuse and dominance (Straka and Montminy 2006). Duluth theory supports the assertion that batterers can be rehabilitated (Pence 1993). My research supports that some judges believe this and assign suspects to domestic violence programs, but only if they are married or still in a relationship when the abuse occurs. 39

Chapter 5

Conclusion

Limitations

The most significant limitation in this study is the data sample itself. The data were collected in 1998-1999 which may not be representative of domestic violence today.

Additionally, while there was plenty of data in determining the arrest levels and what the suspect was charged with, there were only 80 cases to examine for sentencing analysis. Since 90 percent of cases are resolved in plea bargains and not in trials, there was not sufficient data about what occurred after the arrest phase. Another limitation to consider is the literature. When intimate partner violence started to be addressed in the 1970s, the focus was on white middle-class women. It is only in recent literature where women of other races are considered in the literature.

The literature has also changed over the years as gender roles and expectations have changed.

The laws pertaining to domestic violence have also changed over time which can influence what happens to suspects in all phases of the arrest to sentencing system (Schneider 2018; Weismann

2013).

Suggestions for future research

It is important to continue to conduct research on racial disparities in the criminal justice system. There is a great deal of empirical evidence that shows that minorities have a higher 40 likelihood of being arrested and receiving harsher sentences as compared to Whites (Alexander,

2012). This pattern is an example of the structural inequality in the criminal justice system. In order to monitor the treatment of minorities, it is vital that research continues to examine these inequalities with the hopes that appropriate changes can be made to reduce disparities in the treatment of minorities in both the criminal justice system and society as a whole.

Contributions to the field

Domestic violence is a crime that effects people of all ages, races, and socioeconomic backgrounds. In order to assess the efficacy of domestic violence policy, it is central to understand how the criminal justice system handles these cases. My research exemplifies the complexity of domestic violence and the criminal justice system, and points to needed directions in future research focusing on the impact of age and the abuser-victim relationship. The findings from my research also pose new questions including: What is the most important characteristic when judges decide how to sentence a suspect? How closely do judges follow sentencing guidelines and is this influenced by suspect characteristics? As research is done and policies change, our society becomes better equipped to deal with the atrocity that is domestic violence.

41

Table 1: Descriptive Statistics

Variable Name N Minimum Maximum Mean Std. Deviation

Independent Variables Suspect Race White 307 0 1 0.5537 0.49791 Black 307 0 1 0.1238 0.32987 Hispanic 307 0 1 0.2704 0.44487 Other 307 0 1 0.0521 0.22263 Victim Race White 307 0 1 0.5831 0.49386 Black 307 0 1 0.0879 0.28368 Hispanic 307 0 1 0.2606 0.43967 Other 307 0 1 0.0684 0.25285

Control Variables Suspect Age 302 16 81 34.7 10.38 Victim Age 307 15 70 32.75 10.04 Relationship Status Ex Significant Other 308 0 1 0.1857 0.38947 Significant Other 308 0 1 0.3766 0.48533 Ex Spouse/Spouse 308 0 1 0.4365 0.49676 Length of Relationship 308 0 4 2.0584 1.1848 Presence of Children 308 0 1 0.4058 0.49185 Suspect Drug Use 308 0 1 0.3182 0.46653 Victim Drug Use 308 0 1 0.1331 0.34025 Violence Scale 308 0 5 3.1786 1.24411

Dependent Variables Suspect Arrested 298 0 1 0.5 0.50081 Charge Felony 298 0 1 0.4224 0.49477 Charge Misdemeanor 293 0 1 0.3663 0.4826 Sentence Jail 72 0 1 0.7222 0.45105 DV Program 72 0 1 0.2222 0.42866 Fine 72 0 1 0.75 0.43605 Probation 72 0 1 0.8056 0.39855

42

Table 2: Logistic regression presenting the effects of independent and control variables on whether or not the suspect was arrested. N=298

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (Whites are comparison group) Black 0.387 1.477 0.544 0.468 1.597 0.59 Hispanic 0.744 2.104 + 0.428 1.077 2.935 * 0.453 Other 1.195 3.303 + 0.647 1.283 3.606 + 0.69 Victim Race (Whites are comparison group) Black 0.106 1.112 0.628 0.013 1.013 0.664 Hispanic -0.237 0.789 0.425 0.366 0.694 0.446 Other 0.575 1.778 0.554 0.673 1.959 0.593 Victim Age 0.03 1.031 0.021 Suspect Age -0.007 0.993 0.021 Relationship Status (Former partner comparison group) Significant Other 1.225 3.404 ** 0.389 Ex Spouse/Spouse 0.798 2.222 * 0.375 Length of Relationship 0.055 0.117 1.056 Presence of Children 0.035 1.036 0.276 Suspect Drug Use 0.649 1.913 * 0.296 Victim Drug Use -0.673 0.51 0.426 Violence Scale 0.325 1.385 ** 0.11 Constant -0.241 -3.156 *** Nagelkerke R2 0.044 0.171 + = p < 0.1 * = p < 0.05 ** = p < 0.01 *** = p < 0.001

43 Table 3: Logistic regression presenting the effects of independent and control variables on whether or not the suspect was charged with a felony. N=298

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (Whites are comparison group) Black 0.777 2.175 0.55 0.452 1.572 0.623 Hispanic 0.897 2.452 * 0.427 1.016 2.763 * 0.454 Other 0.966 2.628 0.598 7.41 2.097 0.648 Victim Race (Whites are comparison group) Black -0.078 0.925 0.636 0.153 1.165 0.695 Hispanic -0.176 0.839 0.423 -0.1 0.905 0.444 Other -0.616 0.54 0.583 -0.442 0.643 0.63 Victim Age 0.064 1.066 ** 0.023 Suspect Age -0.045 0.956 * 0.022 Relationship Status (Former partner comparison group) Significant Other 2.106 8.218 ** 0.469 Ex Spouse/Spouse 1.456 4.29 ** 0.453 Length of Relationship 0.156 1.169 0.122 Presence of Children 0.04 1.041 0.283 Suspect Drug Use 0.131 1.14 0.302 Victim Drug Use -0.237 0.789 0.422 Violence Scale 0.138 1.148 0.115 Constant -0.642 *** -3.517 *** Nagelkerke R2 0.051 0.204 * = p < 0.05 ** = p < 0.01 *** = p < 0.001

44 Table 4: Logistic regression presenting the effects of the independent and control variables on whether or not the suspect was charged with a misdemeanor. N=298

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (Whites are comparison group) Black -0.599 0.549 0.583 -0.419 0.658 0.645 Hispanic -0.643 0.526 0.445 -0.61 0.543 0.481 Other -0.654 0.52 0.649 -0.129 0.879 0.698 Victim Race (Whites are comparison group) Black -0.05 0.951 0.674 -0.06 0.942 0.734 Hispanic -0.046 0.955 0.44 0.049 1.05 0.472 Other -0.537 0.585 0.578 -0.833 0.435 0.631 Victim Age -0.045 0.956 * 0.023 Suspect Age 0.046 1.047 * 0.022 Relationship Status (Former partner comparison group) Significant Other -1.538 0.215 ** 0.398 Ex Spouse/Spouse -1.137 0.321 ** 0.369 Length of Relationship -0.0286 0.752 * 0.124 Presence of Children -0.381 0.683 0.288 Suspect Drug Use -0.755 0.47 * 0.322 Victim Drug Use -0.015 0.985 0.468 Violence Scale 0.083 1.087 0.115 Constant -0.211 1.375 * Nagelkerke R2 0.038 0.195 * = p < 0.05 ** = p < 0.01

45 Table 5: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to jail. N=71

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (White = 0, Minority = 1) 0.444 1.559 0.816 0.683 1.979 1.109 Victim Race (White = 0, Minority = 1) 0.479 1.615 0.832 1.261 3.529 1.167 Victim Age 0.065 1.067 0.06 Suspect Age -0.074 0.929 0.072 Relationship Status (Former partner comparison group) Significant Other 0.83 2.294 1.143 Ex Spouse/Spouse 0.173 1.188 1.099 Length of Relationship -0.706 0.494 * 0.356 Presence of Children -1.015 0.363 0.746 Suspect Drug Use 1.195 3.303 0.803 Victim Drug Use -3.666 0.026 1.225 Violence Scale -0.15 0.86 ** 0.338 Constant 0.477 2.8 Nagelkerke R2 0.052 0.382 * = p < 0.1 ** = p < 0.05

46 Table 6: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to complete a domestic violence program. N=71

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (White = 0, Minority = 1) -1.369 0.254 1.186 -0.636 0.529 1.496 Victim Race (White = 0, Minority = 1) 1.469 4.345 1.179 0.751 2.118 1.526 Victim Age -0.032 0.969 0.063 Suspect Age 0.022 1.023 0.068 Relationship Status (Former partner comparison group) Significant Other -2.572 0.076 * 1.28 Ex Spouse/Spouse 0.466 1.594 0.917 Length of Relationship 0.312 1.366 0.331 Presence of Children 0.133 1.142 0.743 Suspect Drug Use -0.67 0.512 0.734 Victim Drug Use 0.096 1.101 1.388 Violence Scale -0.236 0.789 0.333 Constant -1.209 -0.298 Nagelkerke R2 0.043 0.359 * = p < 0.1 ** = p < 0.05

47

Table 7: Logistic regression presenting the effects of independent and dependent variables on whether or not the suspect was sentenced to pay a fine. N=71

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (White = 0, Minority = 1) -0.187 0.829 0.847 -0.367 0.693 0.951 Victim Race (White = 0, Minority = 1) 0.101 1.107 0.829 0.161 1.175 0.964 Victim Age -0.123 0.885 * 0.072 Suspect Age 0.185 0.035 ** 0.088 Relationship Status (Former partner comparison group) Significant Other 0.255 0.783 0.924 Ex Spouse/Spouse -0.074 0.939 0.967 Length of Relationship -0.166 0.609 0.325 Presence of Children 0.654 0.326 0.666 Suspect Drug Use -0.604 0.354 0.652 Victim Drug Use 0.756 0.438 0.974 Violence Scale -0.197 0.538 0.319 Constant 1.142 0.003 Nagelkerke R2 0.001 0.168 * = p < 0.1 ** = p < 0.05

48 Table 8: Logistic regression presenting the effects of the independent and control variables on whether or not the suspect was sentenced to probation. N=71

Model 1 Model 2 B Exp(B) S.E. B Exp(B) S.E. Suspect Race (White = 0, Minority = 1) -0.543 0.581 0.9 -0.051 0.95 1.141 Victim Race (White = 0, Minority = 1) 0.358 1.431 0.869 -0.875 0.417 1.205 Victim Age -0.281 0.755 ** 0.129 Suspect Age 0.413 1.511 ** 0.157 Relationship Status (Former partner comparison group) Significant Other 1.538 4.657 1.114 Ex Spouse/Spouse -0.318 0.728 1.097 Length of Relationship 0.407 1.503 0.411 Presence of Children 0.207 1.23 0.776 Suspect Drug Use 0.467 1.595 0.859 Victim Drug Use -0.117 0.89 1.259 Violence Scale -0.572 0.564 0.444 Constant 1.558 -1.682 Nagelkerke R2 0.008 0.408

49 Appendix A

Internal Review Board Approval

NOT HUMAN RESEARCH

Date: October 25, 2017 From: Jodi Mathieu, IRB Analyst To: Mary Lawrence

Type of Submission: Initial Study

Title of Study: Sentencing Disparity and Structural Inequality Between Perpetrators of Domestic Violence: Who Goes Free?

Principal Mary Lawrence Investigator:

Study ID: STUDY00008262

Submission ID: STUDY00008262

Funding: Not Applicable

The Office for Research Protections determined that the proposed activity, as described in the above-referenced submission, does not meet the definition of human subject research as defined in 45 CFR 46.102(d) and/or (f). Institutional

Review Board (IRB) review and approval is not required.

The IRB requires notification and review if there are any proposed changes to the activities described in the IRB submission that may affect this determination. If changes are being considered and there are questions about whether IRB review is needed, please contact the Office for Research Protections.

This correspondence should be maintained with your records. 50

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ACADEMIC VITA

Academic Vita of Mary E. Lawrence [email protected]

Education:

Major(s) and Minor(s): B.A. Criminology, B.A. Sociology June 2014 - Present Honors: Sociology Graduation: Spring 2018

Thesis Title: Sentencing Disparity and Structural Inequality Between Perpetrators of Domestic Violence: Who Goes Free? Thesis Supervisor: Laurie K. Scheuble

Work Experience:

HUDDIL Interdisciplinary Lab, Bennett Pierce Prevention Research Center Date: Fall 2017 – Present, University Park PA Title: Research Assistant Description: • Use human-centered techniques to redesign and package an anti- bullying/anti-teen dating violence program utilized by Penn State Athletes Take Action • Partner with the Centre County Women’s Resource Center to analyze survey data • Collaborate in an interdisciplinary lab with social and health science, and engineering students Supervisor’s Name: Emily Waterman

Magisterial District Court 15-1-04 Judge Marian Vito Date: May 2017 – August 2017, West Chester PA Title: Legal Intern Description: • Observed court proceedings for criminal, civil, and non-traffic matters • Composed a fact guide for legal secretaries to aid in understanding legal language used in statutory text books • Founded a tracking program to detect danger zones for West Chester University students Supervisor’s Name: Marian Vito

Teaching Assistant SOC/CRIM 406 Sociology of Deviance Date: Fall 2016 – Spring 2017, University Park PA

Description: • Teaching assistant for Sociology of Deviance for two semesters • Monitored quizzes administered using the clicker system, graded paper quizzes • Ran review sessions for midterm exam and final exam Supervisor’s Name: Timothy Robicheaux

Schindler Law Group LLC Date: May 2015 – August 2016, Kennett Square PA Title: Legal Intern Description: • Prepared case summaries, interrogatory responses and drafted basic motions to support attorneys • Created and maintained client files improving accessibility and ensuring files are up to date • Conducted legal research to be applied to relevant client matters Supervisor’s Name: Thomas K. Schindler

Rovito Law LLC Date: May 2016 – August 2016, West Chester PA Title: Administrative Assistant Description: • Organized client files ensuring files are up to date and easily accessible • Observed courtroom proceedings for family law matters, such as divorce, custody and settlement conferences • Managed QuickBooks and performed bookkeeping duties Supervisor’s Name: Alita A. Rovito

Leadership:

Penn State Mock Trial Date: August 2015 – May 2017, University Park PA Title: Tournament Director May 2016 – May 2017 Description: • Invited and organized teams to attend the Happy Valley Invitational, and Regional Tournaments • Coordinated all aspects of the above tournaments involving 30+ competitive teams; each comprised of 10 competitors per team Title: Judging Coordinator May 2015 – May 2016 Description: • Recruited attorneys and judges to volunteer at Penn State’s Happy Valley Invitational, and Regional Tournaments

Title: Captain August 2015 – May 2017 Description: • Recognized as All-Regional Outstanding Witness • Qualified for and served as a team leader at ORCS in Spring 2017 Supervisor’s Name: Efrain Marimon

International Education: Study Abroad: Amsterdam, The Netherlands (Dutch Criminal Justice CRIM 499)

Awards:

Distinguished Graduating Senior in Sociology, 2018