RURALITY AND UNEMPLOYMENT RATES: COVARIATES TO INTIMATE
PARTNER HOMICIDE?
Kimberly Mansfield
B.S., California State University, Sacramento, 2006
THESIS
Submitted in partial satisfaction of the requirements for the degree of
MASTER OF SCIENCE in
CRIMINAL JUSTICE at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2010

Approved by:
RURALITY AND UNEMPLOYMENT RATES: COVARIATES TO INTIMATE
PARTNER HOMICIDE?
A Thesis by
Kimberly Mansfield
__________________________________, Committee Chair
Jennie Singer, Ph. D.
__________________________________, Second Reader
David Swim, Ph. D.
____________________________
Date ii

Student: Kimberly Mansfield
I certify that this student has met the requirements for format contained in the University format manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for the thesis.
__________________________, Graduate Coordinator ___________________
Yvette Farmer, Ph. D. Date
Division of Criminal Justice iii

Abstract of
RURALITY AND UNEMPLOYMENT RATES: COVARIATES TO INTIMATE
PARTNER HOMICIDE? by
Kimberly Mansfield
Intimate partner homicides (IPH’s) have been decreasing in the United States for the last four decades. There has been considerable research into the reasons for this decline. All the evidence has lead researchers to focus on a few key areas including gender, marital status, race, location of residence, and economic deprivation. This study focuses on location of residence, being either a rural or urban county, and unemployment rates in those counties in California for the years 1990-2000. Data on IPH’s were taken from the original study completed by William Wells and William DeLeon-Granados (2002) and data on unemployment rates were taken from the Bureau of Labor Statistics website.
Rural counties had significantly lower rates of IPH and significantly higher rates of unemployment. Although controls were not used, it appears there is enough evidence to show that rural counties have a characteristic, namely collective efficacy, that reduces
IPH despite high unemployment rates.
_______________________, Committee Chair
Jennie Singer, Ph. D.
_______________________
Date iv

ACKNOWLEDGMENTS
Thank you to my husband and daughter who have been very patient in the last few months and occupying themselves while I was fixed to the computer. v

TABLE OF CONTENTS
Page
Acknowledgments..................................................................................................................... v
List of Tables .......................................................................................................................... vii
Chapter
1. INTRODUCTION ………………………………….…………………………………….1
Purpose of the Study…………………………………………………………………1
2. LITERATURE REVIEW ................................................................................................... 5
Decreasing Homicide Rates ......................................................................................... 5
Marital Status ............................................................................................................... 7
Gender.. ...................................................................................................................... 11
Race….. ..................................................................................................................... 14
Urban/Rural Areas ...................................................................................................... 19
Economic Deprivation ............................................................................................... 25
3. THEORY .......................................................................................................................... 33
Contemporary Movements......................................................................................... 36
Support for Social Disorganization Theory ............................................................... 37
Criticisms of Social Disorganization Theory ............................................................. 40
Policy Implications .................................................................................................... 41
Social Disorganization Theory and This Study ......................................................... 43
4. METHODOLOGY ........................................................................................................... 47
5. ANALYSIS AND RESULTS ........................................................................................... 51
Limitations ................................................................................................................. 56
Appendix A. Definitions ...................................................................................................... 60
Appendix B. Overview of a Single County From 1990-2000 .............................................. 62
References …………………………………………………………………………………...63
vi

1.
2.
3.
4.
LIST OF TABLES
Page
Table 1 Summary of Data………
………………….…….…………………….. 51
Table 2 Five Highest Unemployment Rates……
……………………………...... 54
Table 3 Five Lowest Unemployment Rates……….
…………………………….. 54
Table 4 Five Highest IPH Rates……………………….………………….….. 55 vii

1
Chapter 1
INTRODUCTION
Purpose of the Study
Homicide rates in the United States (US) have been decreasing over the last 30 years. This is especially true of intimate partner homicides (IPH’s), which are homicides committed by an intimate partner only, but is broadened to include spouses, boyfriends/girlfriends and casual lovers. This is different from interpersonal homicides, which include matricide, patricide, infanticide, sibling homicides, and spousal
(husband/wife) homicide. There are a number of factors that are known to be correlated with IPH’s, and they will be examined more thoroughly in the literature review, but each will be briefly explained here also.
Marriage can increase or decrease IPH’s. Women and men who are bound to their partners by marriage may endure more violence than partners in a less formal relationship. If a woman is not married she may have an easier time moving out and away from her abuser (Mouzos & Shackelford, 2004). If a man is not married and feels threatened, he may be more willing to end the relationship and move on to another person who will be less threatening (Mouzos & Shackelford, 2004). On the other hand, studies have found unmarried women were more likely to be victims of IPH (Browne &
Williams, 1993; Lauritsen & Schaum, 2004). This could be because an unmarried woman is exposed to more men and thus increases her likelihood of being exposed to violence than a woman who is only with her husband.

2
Gender seems to be related to the motive for committing homicide. Women tend to commit IPH to protect their families from a real or perceived threat (Browne, 1987;
Puzone, Saltzman, Kresnow, Thompson, & Mercy, 2000). They often say they felt it was their last hope to prevent harm to themselves or their children. Wilson and Daly (1992) report that men tend to commit IPH when they feel a real or perceived threat to their manhood or they feel emasculated (as cited in Wells & DeLeon-Granados, 2004). In fact, women who made more money than their partners had higher IPH victimization rates than those women who made less than their partners (Dugan, Nagin, & Rosenfeld,
1999).
Race is also a statistically significant factor in IPH’s. Black females have the highest IPH victimization rate of any other category of people (Dugan, Nagin, &
Rosenfeld, 2003; Parker, 2001; Yates, Pillai, & Berry, 2008). Hispanic men and women statistically have the lowest IPH victimization rates (Cunradi, Caetano, Clark, & Schafer,
2000). Although, race is closely related to numerous other variables and has an important part; it is not a deciding factor in the statistics of IPH.
Location of residence, whether rural or urban, is another statistical factor in the occurrence of IPH. As with other crimes, rural areas seem to have less IPH’s than urban areas (Bureau of Justice Statistics, 2004; Lauritsen & Schaum, 2004). This variable also appears to be closely intertwined with other variables such as economic deprivation, police presence, and racial composition of the area (Barnett & Mencken, 2002; Lee,
Maume, & Ousey, 2003; Osgood & Chambers, 2000; Wells & DeLeon-Granados,
2002b).

3
Finally, economic deprivation seems to be a strong factor in rates of IPH.
Poverty, income inequality and unemployment rates converge for a stressful mix, placing strain on relationships and leaving partners to feel trapped or threatened. Poverty makes people feel isolated and dehumanized (Barnett & Mencken, 2002; Kovandzic, Vieraitis,
& Yeisley, 1998; Spano & Nagy, 2005). With those constant feelings, empathy for others seems to decrease and negative consequences are less feared. Unemployment can leave a man feeling emasculated and worthless so he lashes out at his partner to make up for those feelings (Shihadeh & Ousey, 1998).
A major gap in the research relating to IPH factors is the complete lack of studies about how living in rural or urban areas and unemployment rates affect IPH’s. Most studies of crime have traditionally focused on urban areas, perhaps because the numbers are greater in urban areas. More recently, researchers have begun comparing urban and rural areas, and why crimes are committed at different rates. However, to date there are no studies which focus on IPH’s in rural compared to urban counties, nor whether or not unemployment rates appear to play a role. Therefore, this study will try to answer the question: Are urban counties more likely to have higher IPH rates than rural counties when unemployment rates are high?
Social disorganization theory was used to help answer the research question. This theory states that when community members are able to work together to create a common set of values; the community as a whole will have fewer crimes than those communities with members who do not work together for common values. The theory also states that impoverished neighborhoods will have more crime, because they lack

social structures which allow neighbors to bond and set values. The contemporary reshaping of the theory includes the concept of collective efficacy, which is social cohesion among neighbors. The policy implications of social disorganization theory are in practice in many law enforcement agencies throughout the state of California with community-oriented policing tactics. There is a general belief that rural communities have higher levels of connectedness than urban communities, but until recently studies have not compared the two types. Since poverty and unemployment are believed to augment crime rates, and collective efficacy abates crime; it is important to try to determine if one of these factors can overcome the other.
4

5
Chapter 2
LITERATURE REVIEW
The purpose of this study is to further the understanding of previous findings of decreasing rates of intimate partner homicide. Unemployment rates in rural and urban
California counties were compared to intimate partner homicide (IPH) rates in rural and urban California counties in this study.
Decreasing Homicide Rates
Homicide rates have been decreasing over the last several decades (Browne &
Williams, 1993; Dugan et al., 1999; 2003; Greenfield et al., 1998; Parker, 2001; Puzone et al., 2000; Rosenfeld, 2000; Serran & Firestone, 2004; Wells & DeLeon-Granados,
2004). In 1992, the US homicide rate dropped to its lowest point in almost 30 years with less than six homicides per 100,000 population (Blumstein & Wallman, 2000; Fox, 2000;
Grogger, 2000). And as Raphael and Winter-Ebmer (2001) point out, violent and property crime rates declined between 1993 and 1998, as well as the unemployment rate which declined to a 30-year low of four and one-half percent.
Some studies have hypothesized about what caused the decrease. For instance,
Grogger (2000) argues that violent crime rates rose substantially between the mid 1980’s and the early 1990’s, then by 1997 dropped due to an increase in wages and a decline in the crack market. Crack cocaine is an illegal substance that is supported by illegal jobs and leads to illegal means of obtaining the substance. He opines that when the legal job market declined, the illegal jobs attracted the unemployed. A greater number of sellers

6 led to higher sales. Sales competition and absence of funds from employment along with the need for a means by which to obtain crack led to more violence surrounding the crack market. As the labor market improved those with the illegal crack-selling jobs quit high risk jobs to work in the legal sector. Likewise, as legal wages increased, Grogger believes that crimes committed to obtain crack such as theft, robbery, burglary, homicide and other violent crimes are due to the buyers’ lack of legal paychecks. Therefore, when legal jobs are obtained by users of crack, they are able to buy crack without obtaining money illegally and they commit less crime (Grogger, 2000).
Snyder and Finnegan (1997) cite the aging out of young criminals as the reason for decline (as cited in Rosenfeld, 2000). They assert that statistically 60% of homicide victims and almost 50% of homicide offenders are under the age of 25. However, in the last two to three decades the population of ages 25 and over has become larger than the population of those under 25 years old, thus making the statistic given by Snyder and
Finnegan even more intriguing.
Intimate partner homicide (IPH) is the killing of a spouse, boyfriend/girlfriend, lover, or otherwise intimate partner, by the other intimate partner. It is sometimes included in the definition of domestic homicide. Reasons for or factors involved in committing IPH are, however, different from reasons for committing homicide in general.
For example, homicides in general may be committed to obtain money or valuables, to obtain status or notoriety, out of anger, or in retaliation. On the other hand, the commission of IPH’s seems to be related to marital status, gender, race, living in rural or urban areas, and levels of economic deprivation. This review will briefly discuss marital

7 status, gender, and race, and delve deeper into the research by more thoroughly reviewing intimate partner homicide rate differences in rural and urban areas along with the factor of economic deprivation.
Marital Status
A short review of marital status as related to intimate partner violence and intimate partner homicide is discussed here since it is clearly an important variable.
However, how it is correlated is beyond the scope of this study. There are studies supporting two theories of marital status and intimate partner violence and homicide.
Some studies found that as marriage rate decreases, the rate of intimate partner homicide decreases, while other studies find the opposite to be true. Clearly, there are intervening variables.
Intimate partner homicide, in many cases, occurs as the result of a victim attempting to leave the abuser. Sometimes the abuser acts on a belief that the partner is trying to leave. In a preliminary study, 17 of the 20 women who responded to a survey by DeKeseredy and Joseph (2006) were victims of physical violence when they tried to leave their partners. Some were threatened with death if they were to leave. Campbell et al.’s (2003) review of records showed that 79% of female IPH victims were physically abused by the same person who killed them.
Studies regarding emergency room use have yielded similar results. In an evaluation of rural area emergency room use, 29% of study patients admitted to being abused in their current relationships, and feared more violence if they were to try to leave

8
(Krishnan, Hilbert, & Pase, 2001). Interestingly, only three to four percent of those patients admitted that the visit to the emergency room at the time of the study was due to or related to abuse. Most claimed the visit was due to an injury sustained in another way.
Furthermore, over one-third of the study participants said they had been abused in past relationships, and 17% believed their current partners had a history of being abusive
(Krishnan et al., 2001). While only a small percentage of victims admitted to being current victims of abuse, the low reporting number might be due to concern over stigma attached to abuse, or could be due to patient perception that health care professionals are not empathetic to their situation. In a different study of records of emergency room use,
47% of femicide victims were seen in the healthcare system at sometime in the year before they were killed (Campbell, 2004).
Statistics of intimate partner homicide rates decreased in conjunction with decreased rates of all other homicides. Dugan, Nagin, and Rosenfeld (1999) and
Rosenfeld (2000) argued that for the past twenty years, reduction in exposure to violent relationships is responsible for the decline in IPH rates in the U.S. Additionally, they found support for the lower IPH rates due to women’s higher overall economic status and greater access to domestic violence services such as shelters, hotlines, and systems for education and raising awareness about domestic violence. Rosenfeld (2000) cited the US
Census Bureau statistics published in 1984 and 1998 to support his theory that fewer women being married was positively related to a lower incidence of IPH’s. In women between the ages of 20 and 24, the statistics demonstrate an increase of 20% for those who have never been married, up from 50% in 1980, to 70% in 1996. For ages 25-29,

9 those women who have never been married increased from 20 to 40% in the period from
1980 to 1996. Finally, only ten percent of women between ages 30-34 in 1980 had never been married, while by 1996 the figure doubled to 20%. As the number of marriages decreased so did the number of IPH’s, lending support for their argument of reducing violent relationship exposure will reduce IPH’s. Looking specifically at women perpetrators revealed that older cohabitating women and younger married women were more likely than their counterparts to commit IPH (Mouzos & Shackelford, 2004). These studies suggest that marriage locks victims and offenders together so that escape from a violent partner may be harder than for unmarried couples.
Conversely, other countries’ studies suggest that marriage has a protective effect on IPH rates for men and women, and all age ranges. From 1976 to 1987 in the US, IPH rates declined for married victims, while it increased in the unmarried (Browne &
Williams, 1993). In the US, men living with, rather than married to them are ten times more likely to be victims of IPH. In Australia, unmarried men are 16 times more likely to be victims than the married study participants (Mouzos & Shackelford, 2004). A study of marriage and homicide rates in Russia found similar results, unmarried men were more likely to be victims of IPH than married men (Pridemore & Shkolnikov, 2004). Another study in Michigan from 1990 to 1999 found single women with children are twelve times more likely to be victims of intimate partner violence than married women with children
(Lauritsen & Schaum, 2004). Another U.S. study used trends in IPH stratified by relationship type during the period 1976 to 1995 and found spousal homicide rates decreased overall, with rates decreasing more rapidly for husbands than for wives

10
(Puzone et al., 2000). They also learned there was a dramatic rate decrease for boyfriends, a non-statistically significant decrease for girlfriends, and a decrease in rates for ex-wives, which was only half the rate of the ex-husbands’. Further analysis found male victims were older than their partners, while the opposite applied for female victims. These studies propose that people in marriages may be more committed to and thus less likely to harm their partners.
Numerous studies have examined race and marriage as they relate to intimate partner homicide. For example, Parker (2001) studied black and white families and found divorce directly and significantly affects family-related homicide rates for both groups. Dugan, Nagin, and Rosenfeld (2003) determined that married black women were protected by domestic violence laws, not single black women. In another study completed in 1999, they found a positive correlation between marriage rates and husband-/wife-perpetrated homicide rates. The laws and domestic violence resources reduce the rates of death for white married women, while increasing the rates of death for black unmarried women (Dugan et al., 1999, 2003).
When determining how marital status relates to IPH rates an obvious point is made: gender is an important risk factor. The decision to commit homicide is partially due to societal pressures and partially due to feelings of helplessness. These feelings of helplessness are displayed differently for men and women and can often be resolved by one of the partners leaving the relationship. This is why marriage can mitigate or exaggerate circumstances surrounding intimate partner homicide.

11
Gender
Gender and intimate partner homicide rates were studied in the original study by
Wells and DeLeon-Granados (2002b), so a short review of the literature is done here.
However, it is important to note they found significant drops in victimization rates for females (49%) and males (61%) starting from the year 1987 and ending in 2000.
Rosenfeld (2000) further supports Wells and DeLeon-Granados, finding a decrease of intimate partner homicide rates for all gender-race groups; black males, white males, and black females had the largest decrease at 50-75% from 1980-1995 while white females had a comparatively negligible decrease of 20-33%.
Enlightened theories about different motives for men and women to commit IPH have come from recent research (Browne, 1987; Johnson, Li, & Websdale, 1998).
According to Wilson and Daly (1992), men commit IPH due to jealousy and perceived threats to their power and control (as cited in Wells & DeLeon-Granados, 2004).
According to Browne (1987) and Puzone et al. (2000), women commit IPH due to threats to the safety of their families. Adler’s (2002) study furthered the previous studies, finding that women killed their husbands to protect themselves and their families, while men killed “out of desperation” (p.877).
Looking more into motives for committing IPH, examining female victims may help explain the motives. Dayton, Ohio, and Campbell (1992) determined female victims of IPH were abused physically by their partners prior to their death in over half of the cases and nearly 80% of male IPH victims abused their partners prior to their own deaths
(as cited in Goetting, 1995; see also Nicolaidis et al., 2003). In accordance with the

12 former study, researchers found that a risk factor for intimate partner homicide is prior violence sustained by or distributed by the other partner (Browne, 1987; Campbell, Glass,
Sharps, Laughon, & Bloom, 2007; Chimbos, 1978; Greenfield et al.,1998; Henning,
Renauer, & Holdford, 2006; Nicolaidis et al., 2003; Simmons, Lehmann, & Collier-
Tenison, 2008; Swatt & Ni, 2006).
Details of women offenders of IPH were discovered, which furthers the understanding of motives for committing IPH. Women planned the killing of their husbands, preparing for the next battering they are to endure. Rarely did they express remorse. More often they were relieved, when discussing the murders (Adler, 2002).
Further, women who kill intimate partners tend to be older, life-experienced women, in contrast to those who commit other types of homicides, young, inexperienced women living in poverty. A study by Roland (2002) reviewed police documents and statistics in
Chicago from1870-1930 and found a surprising increase in rates of women killing their husbands. Women in Chicago from 1875 to 1920 committed homicide at an alarming rate, a rate higher than ever before seen. Of their victims, relatives and lovers were victims almost 80% of the time. They compared the period of 1870 to 1907 and 1926 to
1930 and found the rate increased almost two and a half times from ten percent to 24% of family homicides having the wife as the offender. By comparison, in the same periods, husband offending dropped from 45% to 31%. Concurring with Roland’s research, Adler
(2002) found men only killed relatives and lovers 27% of the time. More often the homicide was against other people in their lives. Although women were less likely to commit homicide; when they did, it was at a rate of 3.5 times and 1.8 times higher than

13 men when killing a spouse and lover, respectively (Adler, 2002). Adler also found that all but one husband was a “wealthy” victim and the same was true for their murderous wives. Sixty percent of the women in the study were over 30. However, he does speculate as to why “wealth” or age might be factors in IPH.
Other countries, such as England and Wales, have found similar statistics. The
Homicide Index of England and Wales between 1995 and 1999 found 44% of female homicide victims were killed by current or former partners, while this is true for only 7% of males (Brookman & Maguire, 2005). The finding implies societal pressures in the US are similar for men and women in other countries.
In an effort to understand and possibly prevent IPH’s , Dugan, Nagin, and
Rosenfeld (1999) studied the effects of domestic violence shelters and other societal interventions, such as hotlines, police responses, and law, on homicide rates after the interventions were put into place. They found domestic violence shelters and other interventions affected the victimization rates for females only slightly, but greatly affected male victimization rates. The finding makes sense when considering the previously discussed reasons that women commit intimate partner homicide. If women are able to remove themselves and their children from the threatening situation they do not resort to violence, and the male partners are left alive. On the other hand, when women utilized the shelters, their partners who were no longer able to control the women took final control by committing murder. It should be noted, however, the interventions were effective at different rates for different races. The effect of shelters and other

14 interventions on various races and economic status levels will be further discussed throughout this section.
Race
Race and intimate partner homicide rates were also studied in the original study by Wells and DeLeon-Granados (2002b) so another short review is done here. Race too has been studied to see if it is a factor in intimate partner homicide rates. Although the relationship of race and intimate partner homicide is reported, it may not have a direct correlation. There are other factors such as resources to reduce domestic violence, marital status, and levels of economic deprivation that are more strongly correlated.
Wells and DeLeon-Granados (2002b) studied race and its relation to IPH. White victimization rates dropped 51% for females and 63% for males across a longitudinal study lasting 13 years from 1987-2000. The number of black female victims reduced by
63% and an incredible 86% for black males from the beginning of the study to the end.
Wells and DeLeon-Granados (2002b) segregated their study participants based on their living in rural or urban counties. Participants living in urban counties had higher rates of white female and Hispanic male victimization rates than rural counties. In rural counties,
IPH was such a “rare” event for Hispanic and black, men and women that statistically significant results were not reported. It is important to keep in mind the reductions found may have been due to resources to reduce domestic violence that became available to the community members during the study period.

15
Domestic violence resources, such as shelters and hotlines, and laws affect various races differently. Dugan, Nagin, and Rosenfeld (1999) determined only married black women were protected by domestic violence laws, not single black women.
Further, domestic violence laws and changing police practices, such as mandatory arrests and restraining orders, have resulted in lower homicide rates for married white females and higher homicide rates for unmarried black females. It is unknown why some of the groups had positive results from the resources and laws while the other groups did not reap the benefits. Research done to identify factors that may reduce intimate partner violence compared blacks and whites who received mediation to blacks and whites who did not have mediation for their crimes of intimate partner violence (Yates et al., 2008).
In the group without mediation, blacks were more likely to recidivate than whites. By contrast, for the group that had mediation, the only subjects to recidivate were white.
Another study on the effects of domestic violence laws and resources showed that death rates reduced for white married women, but increased for black unmarried women
(Dugan et al., 1999, 2003).
Divorce is another variable that was studied for its effect on IPH’s. In 1990, both black and white husbands’ victimization rates decreased more than black and white wives’ rates when compared to a baseline IPH rate. For unmarried couples, black girlfriends’ victimization rates decreased while white girlfriends’ rates actually increased compared to a baseline IPH rate (Parker, 2001). Dugan, Nagin, and Rosenfeld (1999) had similar results. They found a positive correlation between marriage rates and husband/wife-perpetrated homicide rates. When stratifying for race the number of black

16 or white husband victims decreased more than the number of black or white wives.
Interestingly, victimization rates for black girlfriends decreased while victimization rates for white girlfriends increased. Overall, victimization rates for males decreased at a more pronounced rate than for woman across all relationship types and blacks’ rates decreased more than whites regardless of relationship types (Puzone et al., 2000).
Just as they are affected differently by marriage and domestic violence laws and resources, blacks and whites are also affected differently by economic deprivation.
Parker (2001) found black intimate partner homicides were not significantly impacted by economic deprivation in a sample of 164 US cities with populations of 100,000 or more in 1990. Conversely, the white intimate partner homicide rate was significantly and positively impacted by economic deprivation, which is consistent with previous research
(Messner & Golden, 1992; Parker, 2001; Parker & McCall, 1997). Further, the Parker and McCall study revealed a negative association between job accessibility and black homicides, particularly in acquaintance homicide and IPH. That is to say, the fewer jobs available, the more homicides are committed by and against blacks.
Domestic violence rates are higher in economically disadvantaged communities than in affluent communities for both whites and blacks. In fact, when comparing the rates of domestic violence in predominately white neighborhoods to the rates of domestic violence in predominately black neighborhoods with equally impoverished economic levels, race is a non-factor (Benson, Wooldredge, Thistlethwaite, & Fox, 2004). The study supports the theory that race is not correlated with domestic violence when controlled for economic levels. However, another study found that race is, in fact,

17 correlated with domestic violence in equal economic levels. Living in impoverished neighborhoods puts blacks, whites, and Hispanics at a statistically significant high risk of intimate partner violence, and is even higher for blacks than whites or Hispanics (Cunradi et al., 2000). And, looking more specifically at the results in male-on-female intimate partner violence, Hispanic couples had lower rates when household incomes were higher.
They also found white women in poor neighborhoods were more likely to commit intimate partner violence against their partners. Moreover, black men and women were equally at risk for intimate partner violence when they lived in impoverished neighborhoods, especially when the community lacked social organization. Interestingly,
Cunradi, Caetano, Clark and Schaefer’s (2000) study revealed male unemployment was related to lower levels of intimate partner violence with black couples. The reason for this is unknown, but Cunradi, Caetano, Clark and Schafer postulate it may be related to the high rates of incarceration black males face.
More research found black non-specific homicide rates are three times higher than white homicide rates (Messner & Golden, 1992; Parker, 2001; Parker & McCall, 1997).
Interestingly, income inequality has no effect on the commission of homicides by blacks
(Harer & Steffensmeier, 1992; Messner & Golden, 1992; Peterson & Krivo, 1993), while it has a positive effect on the commission of homicides by whites (Harer &
Steffensmeier, 1992; Messner & Golden, 1992). In concert with the former studies, more recent study of black and white homicide rates and economic deprivation found poverty, unemployment, and income inequality all caused whites more than blacks to commit murder (Parker & McCall, 1997; Shihadeh & Ousey, 1998). Sampson and Wilson (1995)

18 argue that black crime rates are higher due, in part, to the concentration of blacks in socially and economically disadvantaged communities (as cited in Benson et al., 2004; see also Parker & McCall, 1997).
Some studies have reviewed the racial differences in unemployment rates, crime, and homicide rates (Messner & Sampson, 1991; Shihadeh & Ousey, 1998; Smith, 1990;
Smith, Devine, & Sheley, 1992). Smith, Devine, and Sheley (1992) created a study to determine the impact of unemployment on various age and racial groups when evaluating crimes of homicide, robbery, and burglary. They considered unemployment from two aspects: a) the rate of unemployment in the community and b) the change in employment status at the individual level. The results were recorded as measurements of unemployment and how they affected crime rates in all age groups and races. When evaluating property crimes they found whites were more likely to become perpetrators if they lost their jobs. Furthermore, being unemployed increased crime rates for whites more than blacks. They concluded that if whites lived in social and economic levels in common with the social and economic levels of blacks, white crime rates would be a significantly higher percent than the percentage currently seen in the black population.
When looking specifically at homicide rates, they noted an indirect link to unemployment rates. When employment opportunities decrease, and police presence increases, the effects are that the unemployment rate is more closely linked to homicide rate.
In the US, most large cities have high concentrations of various races, while rural areas are inhabited by a single race, such as only whites, or only Hispanics. Multiple

19 races living in close proximity under stressful situations can lead to higher rates of intimate partner violence and IPH, as the next section will discuss.
Urban/Rural Areas
Another aspect of Wells and DeLeon-Granados’s (2002b) study of IPH rates was the urban and rural setting of the perpetrators. Location of residence has been determined to be a factor in the study of IPH. In their original study, they found that in urban counties the victimization rates for white females and males is higher than the rates in rural counties. In rural counties, intimate partner homicide victimization occurred rarely for Hispanic or black men and all races of women. The low victimization rate for women may be because, in rural areas, shelter availability was related to lower rates of IPH’s.
Based upon a number of studies dedicated to studying crime in cities, crime has been traditionally thought of as a largely urban problem. More recent studies have found that rural areas can be crime-ridden as well. In the US there is less personal and property crime in rural areas than in urban areas (Bureau of Justice Statistics, 2004). Furthermore, living in a city in the US exposed one to a higher risk of stranger violence than living in a rural area; however, the rate of intimate partner violence is equal in both settings
(Lauritsen & Schaum, 2004). The US Department of Justice (1998) reported violent crime rates increased in rural areas (as cited in Spano & Nagy, 2005).
One example of a study of rural areas and crime is that of Barnett and Mencken
(2002). They used county-level data to determine how socioeconomic status and population change affect crime in rural areas. While population stability was related to a

20 lower incidence of property crime, that relationship did not apply to violent crimes. They also found stability alone cannot overcome the negative effects of economical hardships in urban areas. In rural counties with stable residents, unlike their metropolitan counterparts, there is a positive effect on crime, both property and violent (Barnett &
Mencken, 2002).
Osgood and Chambers (2000) found small differences in crime rates between rural and urban areas for many crimes. Often there is simply just a lag in time for crimes that go up in the city to move to the surrounding rural areas and go up in incidence there as well. The studies of Blumstein and Wallman (2000) and Fox (2000) both noted increases and decreases in violence started in larger cities, and within one to three years the changes were reflected in the smaller cities. Although, property crime and violent crime rates are strongly related to the location of residence, whether urban or rural
(Kposowa, Breault, & Harrison, 1995), but homicide was not as strongly correlated.
The divergence of crime between rural and urban areas in the US has been noted in other countries as well. Lee, Maume, and Ousey (2003) compared the average homicide rates in 1990-1992 for Washington, DC and other non-metropolitan counties.
Even though Washington, DC was ranked highest among metropolitan counties/cities, they found five rural counties had higher homicide rates. In Australia, the Bureau of
Crime Statistics and Research found rural areas have higher concentrations, or hot spots, of violent crime while urban areas are more concentrated with property crimes
(Carrington & Scott, 2008). Further, the study determined domestic violence was almost six times more likely in rural areas (39 hot spots) than in coastal, urban communities

21
(seven hot spots) and almost ten times more likely in metropolitan Sydney (four hot spots).
Absence of social resources designed to reduce the effects of violence may explain why homicides occur frequently in rural areas. An argument is made that timely access to medical facilities mitigates the outcome of personal attacks while the lack of access to medical facilities in rural areas leads to an outcome of homicide in contrast to injury only (Nicolaidis et al., 2003). Injuries may have been non-lethal with the timely and appropriate care. Fatality of the injuries in rural areas is promoted by geographic distance between the site of the assault and the medical facilities as well as transportation issues with the injured residents. In 1980-85 urban areas saw 100 more hospital closures than rural areas (Mullner & McNeil, 1986). Unfortunately, the overall number of hospital closures affects areas differently. Statistics alone do not fully describe the impact that even one hospital closure has on the surrounding community. For example, in urban areas, there is a higher concentration of hospitals within a small geographic area.
In urban areas if one of the five hospitals closes, there are four others a short distance away from which to choose; thus, the impact is not as great. In rural areas the one hospital in the community may be the only one within hundreds of miles. Its closing eliminates the possibility that local residents will receive emergency care to abate the effects of assault and battery. Geographic isolation places hospital care further out of reach. The absence of timely and competent medical care complicates the problems of intimate partner violence even more. When studying rural area emergency room use, it has been found that 29% of patient-participants admitted to being abused by their

22 partners in their current relationships (Krishnan et al., 2001), with many not admitting to abuse because they are afraid of sharing personal information and being victims of personal gossip and ridicule. Not only is there difficulty in accessing medical care, but rural settings also have a lack of resources for preventing and reducing domestic violence. Wells & DeLeon-Granados (2002a) note domestic violence resources “are more readily available in areas with greater overall socioeconomic resources” (p. 5), which generally translates to wealthier urban areas rather than economically disadvantaged urban or rural areas.
Studies of intimate partner violence and its relation to rural and urban communities support rural settings both reduce and increase the likelihood that household violence will occur. One study’s statistics showed that rural women have lower rates of intimate partner violence than urban and suburban women (Rennison & Welchans, 2000).
In another study, researchers found women in rural areas are battered equally as often as women living in urban areas (Bachman, 1994; Bachman & Saltzman, 1995). Abundant reasons exist for why rural women are at high risk for being battered and/or murdered by their intimate partners. First, as previously noted, they are isolated geographically
(Websdale, 1998). This may be by design of the aggressor. Batterers isolate their victims to better control them. Some tactics used by rural abusers are not as effective for urban abusers due to geographic proximity. For example, a rural perpetrator can monitor or disable the vehicle, remove the phone receiver upon leaving the home, or shoot a firearm to scare the victim without fear of being investigated (Websdale, 1998). Second, rural victims are socially isolated (Nicolaidis et al., 2003; Websdale, 1998). The isolation

23 keeps the victims from social resources. Being away from family and friends that could help the victims escape the violence is another tactic by which the victims are controlled.
Phone service was less common for rural than urban residents, at the time this study was completed (Websdale, 1998). The combination of segregation from friends and family and lack of phone service serve to keep the victim far away from help. A qualitative study of urban and suburban women was conducted to determine what factors, if any, were thought to be helpful in reducing intimate partner violence. Both groups of women believed close relationships with neighbors and their vigilance in looking out for each other reduced the likelihood of violence by their partners (Burke, O'Campo, & Peak,
2006).
Rural and urban crime and intimate partner homicide rates have been extensively studied in recent years, however few studies have examined the relationship between crime and IPH (Gallup-Black, 2005; Jennings & Piquero, 2008; Kovandzic et al., 1998;
Krishnan et al., 2001; Williams & Flewelling, 1988). Gallup-Black (2005) examined the rates of intimate partner and familial homicide for urban and rural residents in the U.S.
She found intimate partner homicide rates were higher in rural counties and, from 1976 through 1999, actually increased while intimate partner homicide rates decreased in metropolitan and non-metropolitan areas. She also compared unemployment rates and employment in part-time versus full-time jobs and found that the employment rates were lower in rural areas than in the urban areas from 1980 through 1999, except for the time period from 1990 to 1994. Johnson (2000) and Websdale (1998) found similar results.
Furthermore, one of the reasons for high homicide rates in metropolitan areas and low

24
IPH rates in rural and metropolitan areas was that close-knit ties with other community members reduced the likelihood that an intimate partner would commit murder.
However, they also found that victims were not likely to report domestic violence because they did not want to embarrass themselves or their partners in front of their families and friends. Thus, close community relationships can be both positive and negative for crime deterrence.
Although rural setting were examined in the original study by Wells and DeLeon-
Granados (2002b), a second study furthered their research by examining the effect of a rural setting and unemployment rates in each county in California from 1990-2000. The second study built on their original research and that of others (Balkwell, 1990; Barnett &
Mencken, 2002; Blau & Golden, 1986; Brookman & Maguire, 2005; Fowles & Merva,
1996; Kovandzic et al., 1998; Kposowa et al., 1995; Land, McCall, & Cohen, 1990; Lee et al., 2003; Levitt, 2001; Osgood & Chambers, 2000; Peterson & Krivo, 1993; Smith et al., 1992) in discussing how these factors interact to affect IPH rates. Social disorganization theory explains why rural and urban areas differ in relation to crime rates.
According to the theory, rural residents are more closely tied to each other. Close ties in rural neighborhoods create an extended family of neighbors who look out for each other and prevent crime.
Social disorganization theory proposes that social integration and solidarity inhibit the impulse of other community members to commit crimes. When integration and solidarity are not present crime rates increase. Thus, close knit rural communities have less crime than their urban counterparts, but when the rural community is not

25 integrated the community members are more isolated. Under these conditions crime rates are actually higher than urban communities (Barnett & Mencken, 2002). In Wilkinson’s
(1984) study of homicide rates, he found that they were higher in rural areas than in urban areas. He concluded that contrary to numerous other research studies, the diffusion of the population over large geographic areas disengages community bonds and higher homicide rates result. Spano and Nagy (2005) also found social isolation to be a risk factor for adolescents in rural communities to participate in violent crimes, robbery and assault victimization. In other words, rural settings more than urban settings depend on social solidarity to deter crime. If absent the isolation of rural settings offers more opportunity for crime promotion.
Economic Deprivation
Economic problems such as poverty, income inequality or unequal levels of income for differing racial groups, and unemployment seem to be strong predictors of crime, especially when looking at intimate partner violence, homicide, and intimate partner homicide. This is true regardless of race or residential settings for victims and offenders. Studies and researchers have differing conclusions regarding the strength of the relationship. Sampson and Lauritsen (1990) completed a comprehensive review of homicide studies in the US and found almost every study linked economic deprivation with higher levels of violence, especially homicide (see also Blau & Blau, 1982; Messner
& Rosenfeld, 1999; Parker & McCall, 1997). Braithwaite (1979) notes criminological literature tends to suggest that frustration caused by economic deprivation is expressed by

26 violence against those people who are the closest to the frustrated victim of the deprivation (as cited in Parker, 2001).
It has been hypothesized that economic deprivation is a key factor in crime rates.
This is especially true when examining homicide rates and situations of social isolation
(Barnett & Mencken, 2002; Kovandzic et al., 1998; Messner, 1982, 1983; Parker &
McCall, 1999; Parker, 1989; Spano & Nagy, 2005; Williams & Flewelling, 1988).
Poverty and crime have been positively associated through multiple studies (Messner &
Tardiff, 1986; Patterson, 1991), but how they are related depends on the type of crime being studied. Barnett and Mencken (2002) discovered “socioeconomic hardship leads to violent crime even in the most stable non-metropolitan counties” (p. 387). Studies consistently found the more economically deprived the members of cities, metropolitan areas, and states, the higher the homicide rates (Land et al., 1990). Additionally, data reported by Bureau of Justice Statistics (2007) found simple assault and aggravated assault occur at a rate three times higher for those with annual family incomes of less than $7500 than those with $75,000 and above (as cited in Maston, 2010). The assault rate on victims who are closely acquainted with their offenders is six to seven times higher for the lowest income families compared to the highest income families (Maston,
2010). Moreover, the lower the income for a man, the more likely he is to engage in domestic violence. Violence against their wives is their way of asserting their manhood on their wives (Smith, 1990). On the other hand, in a study of only low-income women, income levels and unemployment were not associated with increased occurrences of intimate partner violence or recurring intimate partner violence (Sonis & Langer, 2008).

27
However, it should be noted that these findings may be due to the fact that all the study participants had low-incomes and minor differentiations between household income levels and those with low-paying jobs does not appear to be statistically significant. As discussed, economic deprivation affects most crime rates and the greater the level of deprivation the more deleterious the effect on crime rates.
Ethnicity is also a determinant in how severely economic deprivation affects crime rates. Sampson and Wilson (1995) concluded that black crime rates are higher due, in part, to the concentration of blacks in socially and economically disadvantaged communities (as cited in Benson et al., 2004). Those living in impoverished neighborhoods are at a higher risk of intimate partner violence, and the statistical significance is higher for blacks than for whites or Hispanics (Cunradi et al., 2000). They also discovered that Hispanic couples have an inverse relationship between household income and male-on-female intimate partner violence. This finding supports to the theory that other community variables can mitigate the relationship between poverty and
IPH. White women living in impoverished neighborhoods were more likely to commit intimate partner violence against their mates. Male perpetrated violence was not associated with household income nor living in low-income neighborhoods (Cunradi et al., 2000). More results identified black men and black women as equally at risk for intimate partner violence when they lived in impoverished neighborhoods. This was especially true when the community lacked social organization. Yet another study concluded that poverty did not affect the homicide rates of blacks for homicides committed by family or intimates (Peterson & Krivo, 1993). Interestingly though,

28
Cunradi et al. (2000) found that in black couples male unemployment was related to lower levels of intimate partner violence. Other studies found income inequality has no effect on the commission of homicides by blacks (Harer & Steffensmeier, 1992; Messner
& Golden, 1992; Peterson & Krivo, 1993), while it has a positive effect on the commission of homicides by whites (Harer & Steffensmeier, 1992; Messner & Golden,
1992; Parker & McCall, 1997).
Domestic violence rates are higher in economically disadvantaged communities than in affluent communities for both whites and blacks. In fact, when comparing the rates of domestic violence in predominately white neighborhoods to rates in predominately black neighborhoods with the same economic levels, race is a non-factor
(Benson et al., 2004; Parker & McCall, 1997). In another study, Parker (2001) found black intimate partner homicides were not significantly impacted by economic deprivation in a sample of 164 US cities with populations of 100,000 or more. On the other hand, white intimate partner homicide rates were significantly and positively impacted by economic deprivation. These findings are consistent with previous research completed by (Messner & Golden, 1992; Parker & McCall, 1997). It was Parker (2001) who realized a negative association between job accessibility and black homicides, particularly in acquaintance and intimate partner homicides. Another researcher (Ousey,
1999) found similar results when he concluded that homicide rates were associated with poverty, unemployment, and income inequality and differentiated the rates for whites and blacks. He found the effects of poverty, unemployment and income inequality were greater for whites than blacks in their offending rates. Interestingly, Chamlin (1989)

29 found that economic deprivation and structures which create opportunity in the local community were significant influences on rates of intra-racial homicides for both blacks and whites (as cited in Parker & McCall, 1999). Although race is obviously related to crime rates and economic disadvantage, it is not the only important factor.
Access to community resources designed to deter crime is another important factor when examined in relation to crime rates. Much as constructive community resources for women are absent in rural areas, so too are they missing for women who live in poverty (Cunradi et al., 2000). Messerschmidt (1986) found that poverty is the strongest predictor of intimate partner violence against women (as cited in Benson et al.,
2004; see also Sonis & Langer, 2008; Wilkinson, 1984). This is supported by studies showing higher income households had less violence than lower income households, when examining the crime of violence against women (Cattaneo & Goodman, 2005;
Lauritsen & Schaum, 2004). Women in Los Angeles, CA living in homeless shelters were compared to women living in low-income housing. Researchers found the rate of abuse by intimate partners was higher, in most cases doubled, for sheltered women than those in low income housing (Wenzel, Tucker, Hambarsoomian, & Elliott, 2006).
Furthermore, Fleury, Sullivan and Bybee (2000) followed 278 women over a three year time period after they exited a domestic violence shelter. The women were given choices of the types of interventions in which they would like to participate, including social support, health, education, finances, employment, and legal areas. Interestingly, the women who chose employment assistance looked for work associated with legal and financial jobs. This suggests that the women were interested in earning money to leave

30 their violent environments as well as learning how to protect themselves from abuse.
Perhaps if more resources and interventions are available and accessible for the victims of intimate partner violence, especially in poverty-stricken areas, the rates of violence could be reduced dramatically.
Examining only the relationship between homicide and economic deprivation a copious number of studies determined poverty is also a strong determinant for homicide
(Balkwell, 1990; Blau & Golden, 1986; Fowles & Merva, 1996; Kposowa et al., 1995;
Lee et al., 2003). The same results were found in studies in other countries as well. For example, Brookman and Maguire (2005) reviewed the Homicide Index from England and
Wales and found data supporting that both victims and offenders of homicide were more likely to come from lower socio-economic levels and more often than not men are killed by strangers or acquaintances while women are killed by their partners/spouses or expartners/ex-spouses. Likewise, in the European Union homicide rates increased by almost one percent at a time of increasing unemployment rates (Stuckler, Basu, Suhrcke,
Coutts, & McKee, 2009). And in a self-report study of 60 female prisoners in South
Africa who were serving time for murdering their intimate partners, twelve women listed financial difficulties as being large or very large contributing factor to the murder
(Pretorius & Botha, 2009). The relationship between economic deprivation and homicide is not just a phenomenon seen in the US.
In examining the various types of economic deprivation and the relationship to homicide rates, the research results differ. It should be noted, however, that in numerous studies, homicide rates were not distinguished between street-type homicide and

31 domestic-type homicide. The rates of street homicide would be expected to be less if unemployed people spent less time outside the home with nothing to occupy their time.
It is unclear whether unemployment and free time impacts domestic homicides. One study found there is a negative correlation between unemployment rates and homicides
(Patalinghug, 2009), two studies found a positive correlation (Kposowa & Breault, 1993;
Land et al., 1990), and three studies found no correlation (Levitt, 2001; Osgood &
Chambers, 2000; Raphael & Winter-Ebmer, 2001).
Unemployed offenders were significantly more likely to commit IPH than those who were employed (Campbell et al., 2003). Further, the relationship between unemployment rates and intimate partner violence was examined (O'Campo et al., 1995).
They found the risk of violence was higher in neighborhoods where there were high unemployment rates than in neighborhoods of low unemployment. Kovandzic et al.
(1998) found income inequality and poverty are independently related to homicide rates in the cities they studied. They postulate poverty is directly related to the homicide rate because people lacking material possessions will have a greater desire to gain possessions illegally if legitimate methods of attainment are believed to be unavailable. Their study further found that income inequality increased the chances that impoverished people would be willing to use illegitimate methods for balancing the inequality, even choosing homicide.
A final factor in the relationship between unemployment and homicide is gender.
As discussed in the gender section, a males’ honor is the motivator for murder and violent crime (Dykstra, 2009). Traditionally, men are the providers for their families. When

32 unemployment prevents them from providing for their families they feel pressure from within, from their families, and from their communities. This pressure can lead to homicide, especially intimate partner homicides. Thus, homicide rates are indirectly affected by the number of jobs available. Shihadeh and Ousey (1998) found that when the number of low-skill jobs are reduced levels of unemployment and economic deprivation rise, as do homicide rates. And at an extreme level, intimate partner homicide is also more likely when one of the partners is unemployed. Women whose partners are unemployed are at a higher risk of intimate partner violence than those whose partners are employed (Kantor & Jasinski, 1998). A similar finding was made by
Cunradi et al. (2000) where couples living in higher poverty neighborhoods (where 20% or more of residents lived below the Federal poverty line) were at higher risk of violence towards each other than couples not living in those settings.
Gender, marital status, race, urban/rural areas, and economic deprivation are all variables related to the commission of crime and specifically to the commission of IPH.
This study looks very specifically at urban and rural counties in California and their corresponding IPH rates and unemployment rates. Based on the literature review it can be said the relationships between community members in urban and rural communities affects the willingness of community members to commit IPH. Unemployment rates appear to have an impact on the way people react to and interact with others in their homes and communities. To help explain why this is, social disorganization theory is used and is discussed thoroughly in the next chapter on theory.

33
Chapter 3
THEORY
Shaw and McKay’s (1969) theory of social disorganization states that communities where there are no shared values among the members cannot solve problems amongst themselves. Such communities are more likely to be plagued with crime. This theory removes the individual’s responsibility for crime, where the prevalent theories of the time placed blame; and puts the responsibility on the community group.
Shoemaker (1996), drawing from Shaw and McKay (1969), explains the set of assumptions on which the theory is based. First, it is assumed that social factors are the primary cause of crime and delinquency. Crime is caused primarily by the group of people around the delinquent, not the delinquent himself. Second, societal structures are absent or in disarray. Structures and institutions that control the group are absent or are too weak and unorganized to function in a way that promotes peaceful coexistence.
Third, because of absence of strong societal structures, people live in a state of confusion and uncertainty which makes them more susceptible to unacceptable or delinquent behavior. Fourth, lower classes are more susceptible to the disarray’s effects. In other words, communities in which there are high rates of poverty, joblessness, and blue collar jobs are more likely to propagate delinquency. Fifth, delinquency results from the breakdown of community controls. An important note is the individual is not necessarily prone to live in a broken, unorganized state, it is the societal environment which causes the individual’s failure. Sixth, disorganization in the community is the result of rapid immigration, major demographic shift, and urbanization. A demographic shift can be a

34 change of majority race, or majority age groups. This idea is similar to Park and Ernest
Burgess’s concept of concentric zones (Williams & McShane, 2004), which says the closer to the center of the city the more crime. The sixth assumption has lead most of the research on this theory to be situated in urban areas. Finally, socially disorganized areas go through a fundamental change in which the community and its members engender crime. Attempts to change community core values to more socially acceptable values are almost impossible. The problem is compounded as those who are accepting of crime move in, and law-abiding neighbors move out of the community (Vold, Bernard, &
Snipes, 2002). Further along the economic lines, Shaw and McKay (1969) believed impoverished neighborhoods would be more crime-ridden because they lack the ability to create stable social structures which discourage criminality.
Although Shaw and McKay (1969) link economic status with “uniformity, consistency, and universality of conventional values and attitudes with respect to child care, conformity to law, and related matters” (p. 170). However, when describing areas of higher delinquency, it can be concluded that the two concepts of economic status and community uniformity are not necessarily related. That is to say, higher economic status neighborhoods do not necessarily have consistent values and morals among the members.
In fact, in some urban neighborhoods where there are strong social ties, efforts of outside forces to undermine social control are thwarted by the residents’ ties and interconnectedness, notwithstanding low economic levels (Wilson, 1996). It can be inferred that Shaw and McKay (1969) place more weight on the concept of consistent morals and values among neighborhood members as they related to delinquency, than on

35 the concept of the community’s economic status. For example, they state, “thus, within the same community, theft may be defined as right and proper in some groups and as immoral, improper, and undesirable in others” (p. 171). The reason the discrepancy is so powerful is that the children in the neighborhood social group must use their best judgments in their decisions of which actions are appropriate for varying situations.
Consistency and reliability in consequences are easier for children to understand and their decisions can be based on the certainty of the results.
It is theorized that social ties and informal controls can counteract many of the variables of social disorganization such as poverty (Bellair, 2000; Markowitz, Bellair,
Liska, & Liu, 2001; Sampson, Raudenbush, & Felton, 1997). Nevertheless, social organization can lead to greater crime rates, or at least that is the perception of its members. Pattillo (1998) like Shaw and McKay decades earlier, studied Chicago neighborhoods and found some of the social networks helped fight crime and delinquency. Other social networks facilitated crime because of their members, gang members and drug dealers. Community members were more aware of the crime occurring in their neighborhoods due to their close ties. Their anxiety about crime and fears of being victimized were higher than in neighborhoods where there was more social disorganization (Covington & Taylor, 1991). Therefore, it can be said that social organization and poverty interact to form positive and negative associations.
The primary assumptions of social disorganization theory have been supported by numerous researcher studies. For example, it is believed that social disorganization is caused by many factors including residential instability (Kornhauser, 1978; Osgood &

36
Chambers, 2000), low socio-economic status (Bursik & Grasmick, 1993; Land et al.,
1990), racial/ethnic heterogeneity (Kornhauser, 1978; Land et al., 1990; Osgood &
Chambers, 2000; Sampson, 1986; Sampson & Groves, 1989), disruption in family structure (Blau & Blau, 1982; Land et al., 1990; Sampson & Groves, 1989), and income inequality (Land et al., 1990). As more studies found both support and lent doubt to the assumptions of social disorganization theory, it evolved.
Contemporary Movements
In the development of contemporary social organization theory, Kornhauser
(1978) argued against Shaw and McKay’s injection of cultural conflict and strain into the elements of the theory. Instead, she believed that social control processes occurring at a community-level were essential in determining how social processes affect crime. Thus, she and Sampson, Raudenbush, and Earls (1997) developed the concept of collective efficacy. Collective efficacy is generally defined as trust and intervention for the common good, or social cohesion among neighbors. In agreement with Kornhauser,
Bursik and Grasmick (1993) affirm the effectiveness of social control “is determined by the extensiveness and density of the formal and informal networks within the neighborhood that bind the residents together as a social community” (p.4).
Further development of the theory by Bursik and Grasmick (1993) places relationships in the communities at three levels: intimate, informal groups; parochial systems of control; and relationships between communities. First, intimate, informal groups include families, friends, and neighbors. In this group, if collective efficacy can

37 be established, crime can be reduced. Also, the relationship of the intimate, informal group is arguably the most influential type of relationship. The breadth and strength of these groups is a strong predictor of their effectiveness on the control of crime (Bursik,
1988). Second, parochial systems of control include school, churches, and co-workers
(Bursik & Grasmick, 1993). This system builds on the intimate, informal group and broadens the control to more formal arenas with more community members. Third, relationships between communities allow the local community to obtain resources which are allocated by agencies in other communities (Bursik & Grasmick, 1993). This relationship is mostly absent in disadvantaged neighborhoods. With regard to domestic homicide, resources may include safe houses, law enforcement, and therapeutic interventions. In all levels of relationships, the structural characteristics of neighborhoods help determine the neighborhood collective efficacy, which, in turn, determines the type and amount of crime tolerated (Sampson, Morenoff, & Felton, 1999).
Support for Social Disorganization Theory
Support for social disorganization theory is plentiful. Kornhauser (1978) and
Bursik and Webb (1982) completed studies which found disorganization among neighborhood social entities explains high delinquency rates. Social disorganization reduces cohesion which increases crime and the perception of crime, which further decreases cohesion (Markowitz et al., 2001). Collective efficacy has been shown to reduce crime in general (Sampson et al., 1999; Sampson et al., 1997), to reduce homicide rates in neighborhoods of high concentrations of poverty (Morenoff, Sampson, &

38
Raudenbush, 2001), and reduce violence in Chicago neighborhoods in 1995 (Sampson et al., 1997). High levels of poverty coupled with low cohesion and low social control between neighbors predicts higher homicide rates in urban neighborhoods (Morenoff et al., 2001). On the other hand, in communities where social ties are strong, community members have reduced levels of fear of crime (Ross & Jang, 2000; Sun & Triplett, 2008).
Relationship strength between community members can be weakened when crime is present, which leads to further disorganization. Personal victimization increases fear more than property victimization. Personal victimization lowers social control and the strength of the three relationships noted by Bursik and Grasmick. Further, personal victimization, more than property victimization, encourages the exodus of more residents.
An exception is the case of domestic homicide, where neighbors are less effected by the murders because of the personal nature of the crime (Kubrin, 2003; Kubrin & Weitzer,
2003). Domestic homicide is restricted in the choice of victim. Only the partner if effected, other members of the community are not potential victims. Kposowa, Breault, and Harrison’s (1995) findings also support Bursik and Grasmick’s adaptation of social disorganization theory in that church membership, divorce, and population shifts were all found to be significantly related to violent and property crime. A study by Osgood and
Chambers (2000), confirmed social disorganization theory in that the first level of relationship in a community, family, is critical in either keeping delinquency at bay, or having crime run rampant throughout the community.
Interestingly, studies in several countries either support or erode social disorganization theory’s assumption that more crime occurs in areas of lower classes.

39
For example, an Argentinean study by DeFleur (1967) found higher crime and delinquency rates in rural areas, where there are concentrations of poverty conditions (as cited in Shoemaker, 1996). This is in contrast to American studies, such Grattet’s (2009) study of bias or prejudice crime, which found that when poverty and residential turnover were constant, bias crimes are still affected by demographic dynamics, which in turn finds higher crime rates in urban areas. Ebbe (1989) studied a city in Nigeria which both supported and disproved some assumptions of social disorganization theory. His study found communities with high levels of informal social control had the lowest rates of delinquency, supporting the second assumption that crime occurs when societal structures are absent and, the third assumption, the lack of societal structures leads to confusion as to acceptable behavior. However, his study did not support the fourth assumption, that lower classes are more susceptible to the effects of the disarray. The lower income areas in his study were well organized and had low levels of delinquency. Osgood and
Chambers (2000) did not find statistically significant relationships between delinquency rates and poverty rate or unemployment. Some crimes had either a slight or negative relationship to poverty. Unemployment was associated with higher arrest rates, but the standard errors were too large to be significant. They conclude that it is not poverty per se that produces social disorganization, but rather associations of poverty with other structural factors that weaken systems of social relationships in a community (p. 107).

40
Patillo (1998) discovered the black middle-class neighborhoods of Chicago had high levels of social organization which allowed for gang members and drug dealers to control the neighborhoods and actually increased crime. Even as studies supported or concluded that some of the assumptions from the theory were false, they also criticized parts of the theory. It was the inability to support all six assumptions that lead to the adaptations of the theory as noted previously.
Criticisms of Social Disorganization Theory
One criticism of social disorganization theory is that it does not adequately take into consideration the significance of cultural factors (Shoemaker, 1996). It is argued that the type of crime committed varies by culture and ethnicity. Homicide is strongly associated with population concentration of Hispanics, but in non-Hispanic neighborhoods poverty is associated with homicide (Curry & Spergel, 1988). Jonassen
(1949) points out that Shaw and McKay’s own data support a marked difference in delinquency rates for various ethnicities (as cited in Shoemaker, 1996). In fact, Shaw and
McKay (1969) later make note of the low delinquency rates of Asians in others’ studies.
Another criticism of this theory is that the concepts are poorly defined. For example, it is unclear how the site of the crime fits in the operationalization of crime.
When crime statistics are evaluated, is crime counted where the offender lives or where the offense takes place? Some researchers say the definition is unnecessary because offenders commit crimes in the neighborhoods in which they reside (Fabrikant, 1979;
Sutherland & Cressy, 1978), particularly when examining felonies such as murder, rape,

41 and robbery. Another ambiguity occurs when differentiating between neighborhoods which breed criminals and those which attract criminals. Social disorganization theory argues criminal activities and criminals themselves are tolerated because of weakened social bonds in the neighborhoods, preventing delinquents from shunning the crimes and encouraging other delinquents to come to the neighborhood and commit more crime.
Finally, it would be advantageous to learn whether social ties or social controls have the greater impact on preventing crime. The inconsistent operational definitions of key concepts in social disorganization studies may explain why there is incongruence with conclusions about how social control and social ties affect crime rates. Although social disorganization theory was developed so that the word “crime” was loosely defined as
“delinquency”, keeping the terms vague has allowed the theory to be used to predict a wide variety of crimes, such as property crimes, robbery, and homicide.
Policy Implications
Policy implications based on the assumptions of social disorganization theory abound. This theory posits that fostering stronger bonds between community residents, with community committees and groups to create social structures, can deter crime.
Contemporary ideas of policing such as community-oriented and problem-oriented policing groups are a testament to the strength of positive peer pressure. Local and statewide law enforcement agencies in California such as Sacramento Police Department
(sacpd.org), Los Angeles Police Department (lapdonline.org), California Highway Patrol
(chp.ca.gov), San Francisco Police Department (sf-police.org), Shasta County Sheriff

42
(co.shasta.ca.us), and San Diego County Sheriff (sdsheriff.net), to name a few, have used the contemporary policing methods to promote bonds in the communities and reduce crime through interactions. Officers and deputies work with community members to identify problems and brainstorm ways to reduce the opportunities and incentives to commit crime. The groups continue to work collaboratively to maintain or adjust crime prevention strategies until they mutually agree that the community is able to control crime on its own.
Another policy implication is to find ways to create jobs or to offer job training in local communities which are largely poverty-stricken. The theory suggests that local programs empower community members, hold them accountable to themselves and their neighbors, and give them a sense of belonging and responsibility. Together with governmental agencies private organizations have created just such programs. They teach community members job skills and assist them with obtaining legitimate jobs. The thought is that having a legitimate job reduces the amount of time spent idle, and thus reduces criminal ventures. Aside from bringing money into the community, jobs place co-workers into social groups where they can share socially-acceptable morals.
Examples of these programs currently in place are People Reaching Out, Urban League, and Regional Occupational Programs.
Employment in the community increases the chance that a resident will conform to community goals and morals. The motivation to commit criminal acts is reduced by the negative opinions of their peers. An obligation of the employee to the employer to fulfill daily duties makes them less likely to engage in activities that have the potential to

43 inhibit their capacity to work. For example, people with daily work schedules would be less likely to risk their jobs by committing a crime that would cause them to go to jail.
Also, the employed have the esteem of the community as respectable members. Thus, they are motivated to maintain their personas by not becoming involved in criminal activities.
Social Disorganization Theory and This Study
Until recently, studies of social disorganization theory, and other criminological theories, were focused only on urban, densely populated cities, all but ignoring the unique culture of rural communities. Social disorganization theory states that in neighborhoods with high levels of disorganization among its members crime goes unchecked. It is generally believed that rural neighborhoods have higher levels of collective efficacy and cohesiveness than urban areas, which supports lower levels of crime. As noted above, most studies of crime using social disorganization theory focus on urban areas, leaving researchers to wonder, in rural areas where social organization is high, do the theory’s principles still apply?
Interestingly, the origin of the concept of social disorganization came from the rural communities of Poland, yet it was not until 1984 when Wilkinson compared homicide rates with poverty in rural communities that rural communities become a focus of study. Numerous recent studies support the applicability of social disorganization theory in rural areas (Barnett & Mencken, 2002; Kposowa & Breault, 1993; Kposowa et

44 al., 1995; Osgood & Chambers, 2000; Petee & Kowalski, 1993). Also, Arthur (1991) sampled 13 Georgia counties to apply social disorganization theory in a rural context.
Contemporary social disorganization theory argues that urbanization is partially responsible for low levels of collective efficacy. In spite of this, rural areas do have high homicide rates. In fact, in some studies homicide rates are higher in rural areas than in urban areas (Wilkinson, 1984). It is hypothesized that higher crime levels in rural rather than urban areas is due to an absence of social integration (Barnett & Mencken, 2002). It should be noted, social solidarity, or collective efficacy, can be positive if present, but without it crime can destroy the neighborhood, especially in rural areas.
Another assumption of social disorganization theory is that poverty increases crime. As previously discussed, studies tested the effects of median income, resident mobility, and neighborhood composition as it relates to crime rates. However, Suttles
(1968) determined the key to supporting or disproving a theory is to test the intervening variables to delinquency or crime and community structures, of which only few prior studies were able to consider (as cited in Sampson & Groves, 1989). Miles-Doan (1998) found in her study of Duval County, Florida that neighborhoods with high concentrations of unemployed males had “dramatically higher rates of spouse and intimate violence than other neighborhoods” (p.637). Along this vein, Wilson (1987) studied the concept of
“concentrated disadvantage”, or high levels of poverty in a small area, and believes in his study the prevalent measures of poverty were insufficient to demonstrate the key issues of disorganization. In this, he explains that just labeling a neighborhood “poor” does not account for the other underlying issues of inability to access resources, absence of valid

45 role models, substandard educational systems, and incapacity of the family members to supervise children. All of these issues compound the problems of poverty, creating concentrated populations of disadvantaged citizens and socially isolated citizens.
According to Miles-Doan (1998), few studies use the six assumptions of social disorganization theory to explain domestic violence. This may be because due to its privacy in engagement, intimate violence is suspected to be devoid of environmental factors such as delinquency, community resources, and cohesion (Peterson & Krivo,
1993). In applying social disorganization theory to domestic violence, Benson et al.
(2004) posit when individuals in neighborhoods with low or no collective efficacy are involved in domestic disputes, neighbors are less likely to call the police or personally intervene because they are expected to mind their own business. In these neighborhoods, the residents may have negative opinions about domestic violence offenders, but they will not openly disapprove of their neighbors’ actions. The covert disapproval allows the offender to continue the violence without fear of others intervening. Button’s (2008) study of acceptance of family violence in communities found that when the community is crime-ridden, the acceptance of violence is higher. Interestingly, when she investigated the concept of spanking as an example of family violence, she found that the community generally accepted the action more when there was little violence in the community.
Essentially the personal, intra-family violence of spanking was considered an acceptable way to control violence outside of the home. Acceptance of family violence was also determined by whether or not the involved individuals felt it was acceptable to them personally even when their neighbors were opposed to violence (Button, 2008).

46
In Anderson’s (1999) book “Code of the Street”, he argues poverty-stricken adults tended to fight more often, which lead to more violence, and left them to their own devices to solve their problems. Without social resources such as police, safehouses, hotlines, spouses and partners feel hopeless. They handle their anger and fear in the way with which they are familiar. Compared to homicides committed due to fighting between strangers or acquaintances and homicides committed in the process of other crimes domestic homicides are hypothesized to be least correlated with economic disadvantage.
This study used social disorganization theory to hypothesize about the effects of levels of unemployment on interpersonal homicide rates. The study also compared unemployment and homicide rates in urban and rural counties in the state of California for the period of 1990-2000. It was further argued that high levels of unemployment compound other problems of poverty. To do this, community structures such as urban versus rural counties were compared with their IPH rates along with their corresponding unemployment rates. The results of this study can then serve as a basis for further research to further support or weaken social disorganization theory as an explanation for the cause of crime.

47
Chapter 4
METHODOLOGY
This is a nomothetic quantitative study of IPH and it’s correlating variables. That is the study does not fully explain all the reasons for IPH rates in rural and/or urban areas, rather it uses gathered data and statistics in an attempt to explain how unemployment rates affect IPH rates differently in different types of counties. Using data from the work of Wells and DeLeon-Granados’s 2004 collection of data, this study looks at intimate partner homicide rates between rural and urban areas in California in the years 1990 through 2000 when unemployment rates are considered.
There are two independent variables, unemployment rates in California in the years 1990 through 2000 and counties in California (rural or urban), and two dependent variables, counties in California (rural or urban), and IPH rates in California in the years
1990 through 2000, depending on the hypothesis. For conceptual and operational definitions of the variables, see appendix A. The relationship between independent and dependent variables will be analyzed through secondary data analysis.
The county’s label of rural or urban is based on the US Census Bureau’s definition of rural and urban communities (operational definition in appendix A).
Concisely put, the US Census Bureau defines rural communities as having a population of less than 2,500, while urban communities have populations of 2,500 or greater
(Websdale, 1998). Although this definition may not be the best for all situations, Bealer,
Willits, and Kuvlesky (1965) explain using a definite number definition is utilized in

48 quantitative research because it is “precise, convenient, and easily operationalized” (p.
260).
The original data on homicide was taken from California Department of Justice
(CDOJ), Criminal Justice Statistics Center. CDOJ data is collected from all reporting law enforcement agencies in California and includes all homicides known to law enforcement. The data included detailed information, such as the county in which the homicide occurred and the relationship between the victim and that offender. The original authors excluded cases with multiple victims, victims younger than 16, and justifiable homicides, while leaving only IPH’s to be included in their data. The IPH data included cases where a report was written, whether or not an arrest was made. This study uses data collected in the original study by Wells and DeLeon-Granados. The IPH rates are calculated per 100,000 people to allow the ability to compare between counties, regardless of population size.
Data on unemployment rates was obtained from the Bureau of Labor Statistics website. The Bureau of Labor Statistics obtains its data through a Current Population
Survey (CPS). CPS data is collected on the labor force status of the civilian noninstitutionalized population and is a sample of 50,000 households which data are surveyed by telephone and personal interviews. Completion of the survey is voluntary and the information is confidential. Each month approximately four percent of the individuals declined to be surveyed. Further, approximately three to four percent of eligible households are not included due to the inability to make contact. Labor force

49 data is gathered monthly. As reported by Bureau of Labor Statistics, all of the known population of California over the age of 16 was included in its statistics.
The population is gathered from all counties in California for the years 1990 through 2000 with a total of 638 cases (58 counties over 11 years). The state of
California was selected, in part, due to its diversity in rural and urban populations, its varying levels of economic status, and its easy access to data. A sample of the population was created using stratified random sampling with a random start (7), with strata of urban and rural counties, making the sample size 91 (n=91). This technique was chosen because the entire population was known and as Babbie (2007), explains, it allows for a greater level of representativeness among the strata as well as decreasing the sampling error. The sample of counties in California was 91 (n=91), of which 53 were urban counties and 38 were rural counties.
Counties as a unit of analysis are used for this study as it was in the original study by Wells and DeLeon-Granados. The advantages to using larger units of analysis, are mathematical ease with less cumbersome numbers, and a larger overall picture, are explained. First, counties are common units of analysis for government agencies, which permits data to be drawn from these sources without degeneration of the numbers.
Second, there is precedent in finding county-levels as an appropriate unit of analysis for general crime rates (Barnett & Mencken, 2002; Land et al., 1990; Lee et al., 2003;
Osgood & Chambers, 2000) and homicide rates (Gallup-Black, 2005; Kposowa &
Breault, 1993). Third, there are different social structures for rural, urban, and suburban counties which would be lost if isolated cities, entire states, or selective metropolitan

50 statistical areas were used for units of analysis (Nielsen & Anderson, 1997). Given these advantages, there are also some marked disadvantages. First, counties are large and heterogeneous. Second, in this study the counties are labeled as either rural or urban, no other labels such as suburban are considered. This may further lump heterogeneous populations into an all encompassing label. Third, rural areas are more susceptible to outliers due to the smaller overall population size. The killing of one family of three can skew the statistics for the entire year. This study is not particularly plagued by the latter issue as an intimate relationship generally involves one partner. It should be noted that rural research has typically relied upon county levels of analysis because arrest data and population characteristics data are available from public resources in units of analysis, and because counties tend to have established internal structures of government and economics, making their statistics more reliable.

51
Chapter 5
ANALYSIS AND RESULTS
The analysis was originally going to be restricted to data for the year 2000 only.
However, due to the absence of IPH’s in rural counties in 2000, the years included were expanded to 1990 through 2000 to try to capture a better view of IPH’s in rural and urban counties. For the eleven-year time frame there are a total of 29,525 recorded IPH’s.
There are a total of 638 cases (a case is a county in a particular year- 58 counties for eleven years), most of which had no IPH's. Yet of the 374 urban cases, 146 (39%) had a least one IPH while only 6 of the 263 (2%) rural cases had a least one IPH.
The mean IPH rate is 8.43 per 100,000 population for all 638 cases, rural and urban. Interestingly, the mean IPH rate for rural cases is 0.89, while for urban cases it is
372.57 per 100,000 population. The rural IPH numbers range from 0 to 100 per case
(sd= 8.74, s
2
= 152.49) and the urban IPH numbers range from 0 to 1703 per case (sd=
213.8, s 2 = 45,711.9), and the total population of cases range is the same (σ= 167.97, σ 2 =
28,213.6).
Table 1 Summary of Data
County type
Number of
IPH’s
Average
IPH/county
Variance of
IPH’s
Standard deviation of
IPH’s
Average unemployment rate per 100,000
Rural
Urban
212
29412
0.89
372.57
152.49
45,711.9
8.74
213.8
11,595.15
8,376.96
Both 29,525 8.43
28,213.6
167.969
9,599.27
The mean unemployment rate for all cases, rural and urban, is 9,599.27 per 100,000 population, with urban cases being below the mean at 8,376.96 and rural cases well above the mean at 11,595.15.

52
Turning to the hypotheses:
Hypothesis 1: Rates of IPH will be lower in rural counties than in urban counties. The relative stability of the rural population engenders social ties that are stronger than those for urban communities. Stable populations allow familiarity with other members and their activities. It increases the opportunity to chastise through intimate, informal groups and parochial systems of control. (H a
: µ r
< µ u
)
Null hypothesis : Rates of IPH with be equal in rural and urban counties (H
0
: R
IPHrual
=
R
IPHurban
)
To test this hypothesis a sample of 91 cases was taken from the population of 638 cases using stratified random sampling with a random start. Of the 38 rural counties sampled none (0%) had even one IPH. Of the 52 urban cases sampled 18 (34.6%) had an
IPH. Thus the original hypothesis is accepted and the null hypothesis is rejected.
Social disorganization theory is supported by this research. It should be noted, however, that collective efficacy was no specifically tested. Rural communities have in place a series of informal controls which deter crime, such as IPH’s, from occurring as frequently as in urban communities. Urban communities have an overwhelming number of people in a compact area without informal controls binding them to the community.
As in the sixth assumption of social disorganization theory, the disorganization in the community is the result of rapid immigration, major demographic shift, and urbanization.
The belief that rural neighborhoods have higher levels of collective efficacy and cohesiveness than urban neighborhoods and are thus able to better control crime seems to

53 be appropriate in this case, even though “collective efficacy” was not tested. In this test,
IPH’s were lower in rural counties than urban counties even when correcting for the major differences in population numbers.
Hypothesis 2: Rates of unemployment are positively correlated to IPH rates in rural communities. Unemployment rates break down the third system of control (see the theory chapter), relationships between communities, by reducing the community’s ability to obtain outside resources by having lower economic means. Prior research has shown generally positive results when implementing community resources for women to escape intimate partner violence, such as hotlines, shelters, and increased policing and sentences for domestic violence offenses (Brookman & Maguire, 2005; Campbell, 2004; Campbell et al., 2007; Campbell et al., 2003; Cattaneo & Goodman, 2005; Cunradi et al., 2000;
Davies, 2008; Dobash & Dobash, 2000; Dugan et al., 1999; Henning et al., 2006;
Simmons et al., 2008; Wells & DeLeon-Granados, 2002a, 2004; Wenzel et al., 2006).
There are 108 cases over the eleven-year time period with 100 or more IPH’s and the average unemployment rate is 7,587.69 per 100,000 population in these 108 cases.
However, the average unemployment rate for all 638 cases is 9,599.27 per 100,000 population. In other words, the cases with a high number (over 100) of IPH’s have an average unemployment rate that is lower than the average unemployment rate of all cases. This finding is in contrast to the findings suggested by the second hypothesis.

54
Table 2 Five Highest Unemployment Rates
County and year
Unemployment rate per
100,000
IPH rate per
100,000
Type of county
Imperial- 1992
Imperial- 1996
Imperial- 1993
Imperial- 1995
Imperial- 1994
30,954.9
29,918.3
29,496.1
29,269.9
26,338.5
0
0
0
0
0 rural rural rural rural rural
Table 3 Five Lowest Unemployment Rates
County and year
Unemployment rate per
100,000
IPH rate per
100,000
Type of county
San Mateo-
1999
Marin- 1998
San Mateo-
1998
Marin- 1990
San Mateo-
1990
1,983.75
2,350
2,457.71
2,527.7
2,630.95
0.25
0
51.05
0
0.54
urban urban urban urban urban
Tables 2 and 3 provide an overview of the five highest and five lowest unemployment rates for all 638 cases. Interestingly, the cases with the highest unemployment rates are all rural and had no IPH’s. On the other hand, the cases with the lowest unemployment rates are all urban and only two of the five cases had no IPH’s, while one of the cases had over 51 IPH’s per 100,000 population! This suggests that another factor that would explain these discrepancies missing from the data used in this study. Social disorganization theory states this result is to be expected in rural and urban communities.

55
Table 4 Five Highest IPH Rates
County and year
Kings- 1993
Madera- 1994
Madera- 1996
Madera- 1999
Humboldt- 1993
IPH rate per
100,000
244.09
206.28
191.96
189.29
167.71
Unemployment rate per
100,000
Type of county
15,336 rural
15351.5
urban
14230 urban
11651 urban
9960.59
rural
Unemployment rate above/below average for county type
Above
Above
Above
Above
Below
Further, looking specifically at rural counties, only two cases have over 100 IPH’s.
Those cases’ average unemployment rate was 12,648.3 per 100,000 population while the overall average unemployment rate for rural cases is 11,595.15. In the rural cases, the higher the unemployment rate the higher the IPH’s. Of the five highest IPH rates for all cases, only one of the cases, rural Humbolt county in 1993, had an unemployment rate which was lower than the average unemployment rate for rural counties (see Table 4).
Thus the data suggest unemployment rates are positively correlated with IPH rates. Rural communities have higher levels of collective efficacy which can deter crimes from occurring. Urban residents lack close ties with their neighbors and thus crime control is reduced. Although collective efficacy was not specifically tested in this study, the conflicting results from highest and lowest unemployment rates and the concomitant IPH rates suggests there is another variable affecting the relationship between independent and dependent variables.

56
Limitations
There are some obvious limitations in this study. First, use of controls was outside the scope of this study. It is possible that there are unknown confounding variables which may be affecting each other in ways unknown by the researcher. For this reason it is suggested future research examine what, if any, other variables intervene in the relationship between IPH rates and unemployment rates in rural and urban counties.
Second, the data analyzed in this study were secondary data. The data were not originally collected for the purposes of the study. Further, each data source has its own set of limitations. Law enforcement data have numerous limitations, in and of themselves. Accuracy and completeness of the data are only as good as its source. In other words, some departments may keep coherent and organized records, while others may not. When the department records are inaccurate or crimes are reported incorrectly then the data is not as valid as it otherwise would be. However, it should be noted that homicide is a crime that is most likely to be reported and cleared, or solved, and investigated extensively. Also, as is true with many criminal offenses, more affluent areas are not patrolled as intensely has poverty-stricken areas, therefore the statistics could be skewed to appear that poor populations commit more crimes than rich populations. Again, since homicide is considered to be one of the most heinous crimes it is reported and solved more often than other crimes regardless of the affluency of the neighborhood. Therefore, the two strongest validity issues with crime statistics are actually weak in the study of homicides. As a final note, there are cases in which the

57 victim is actually murdered, but because of lack of evidence their death is considered to be caused by unknown or natural causes instead of homicide.
The data taken from Bureau of Labor Statistics are aggregated with the county being the unit of analysis not individuals. Results can only be applied to counties, not the individuals who reside in the counties. Also, the unemployment rate reported by the
Bureau of Labor Statistics excludes those who have given up on finding a job and are currently unemployed. The people who are under-employed, or working below their competency level are also left out of this statistic. These problems lead to a question of validity in unemployment measurement. It should, however, be noted that although there are serious validity issues, the Bureau of Labor Statistics’ unemployment statistics are still widely accepted and used by numerous researchers.
Finally, counties may contain numerous, distinct communities the diversity of which is lost in the use of county level data. The definition of community has not been specifically operationalized. Some studies use cities, some use blocks, and others use “a relatively small locality that reasonably approximates the concept of ‘local community’”
(Sampson & Groves, 1989, p. 783). Communities vary dramatically from one area, city, county, and country to another. In L.A., California, for example, a community may be the area that is controlled by the notorious gang, the Bloods. In Chicago, Illinois it is “a collection of people and institutions occupying a subsection of a larger community” and
“composed of geographically contiguous census tracts and internally homogeneous on key census indicators” (Sampson et al., 1997, p. 919). And in England a community is based on an average sized ward (Office of Population Censuses and Surveys, 1984, as

58 cited in Sampson & Groves, 1989). For this reason, among others, many studies in the
US use US census data on counties as their definition of “communities”. It is likely the counties are spatially dependent on neighboring counties and crime occurring in adjacent areas affects each other. The spatial dependence argument notes that social interactions occur across artificial lines denoting a county border. However, as this study reviewed only interpersonal homicide in which the partners live together, in most cases, the crime between the victim and the offender occurred in the same household. Thus, spatial dependence is not an issue for this study.
In conclusion, this study found that living in rural or urban counties is correlated with IPH rates. Unemployment rates are not related to IPH when living in rural counties.
Social disorganization theory suggests this is due to social cohesion in rural neighborhoods which deters IPH’s from occurring. Previous studies of intimate partner violence supports the conclusion that close relationships among community members and societal interventions can help the victims escape harmful relationships before murder is committed. It may be worthwhile to investigate the usefulness of support programs in urban counties that help neighbors feel connected and noticed. Cohesion could work for urban neighborhoods as collective efficacy works in rural neighborhoods.

APPENDICES
59

APPENDIX A
Definitions
Concept Conceptual definition
Operational definition
Intimate partner homicide
Unemployed
A murder committed by a wife, husband, boyfriend, girlfriend, homosexual partner, or other intimatelyrelated couple (sexual or otherwise) against the partner. The rate is based per 100,000 population.
Calculation of the rate is the number of intimate partner homicides divided by the total population multiplied by 100,000.
Same
People who are not currently working; people who are working, but below their level of expertise; people who are currently taking unemployment benefits from the government
Persons 16 years of age and over who are not employed during the reference week
(week in which the questionnaire was completed), were available for work, and made efforts to find employment during the 4-week period ending with the reference week.
Those person who were waiting for a recall to a job where they were laid off were not required to have been looking for work to be considered unemployed.
Calculations of the rate is the number of unemployed people divided by the total population multiplied by 100,000.
60

Rural county A county in which most of the land is used for agriculture; a county lacking a large metropolitan city; a county in California in which the population of most of its consisting communities is less than
2,500 people.
Urban county
A county in which most of the land contains buildings or housing; a county having large numbers of people living in or around one or more large metropolitan cities; a count in California in which the population of most of its consisting communities is 2,500 or more people.
A county defined by the US Census Bureau to be rural
A county defined by the US Census Bureau to be urban
61

62
APPENDIX B
Overview of a Single County From 1990-2000
Although this data are not referred to in the study, an overview of Los Angeles county for all eleven years is shown because it far outnumbers any other county in the number of
IPH’s. This cross-section of data is interesting because of its large population size and contrast between IPH’s and unemployment rates. Notice the large number of IPH’s every year, while the unemployment rate is only above average in the three-year period between 1992 and 1994. This snapshot of a single county is in agreement with previous studies examining IPH rates in urban counties (Lauritsen & Schaum, 2004).
Overview of a Single County From 1990-2000
Year of
LA cases IPH rate
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
IPH’s Unemployment rate
33.42
1512
29.27
1305
29.74
1322
39.22
1703
23.29
1001
26.06
1116
37.14
1606
15.87
702
13.66
621
15.35
703
8.65
405
5,836.07
8,016.51
9,878.97
9,993.09
9,277.52
8,029.74
8,251.39
6,909.81
6,590.47
5,900.95
2848.2

63
REFERENCES
Adler, J. (2002). "I loved Joe, but I had to shoot him": Homicide by women in turn-ofthe-century Chicago. Journal of Criminal Law & Criminology, 92 (3/4), 867.
Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city . New York: W.W. Norton.
Arthur, J. (1991). Socioeconomic predictor of crime in rural Georgia. Criminal Justice
Review, 16 (1), 29-41.
Babbie, E. (2007). The practice of social research (11th ed.). Belmont: Thomson
Wadsworth.
Bachman, R. (1994). Violence against women: A national crime victimization survey report.
Bachman, R., & Saltzman, L. (1995). Violence against women: Estimates from the redesigned survey .
Balkwell, J. W. (1990). Ethnic Inequality and the Rate of Homicide. Social Forces, 69 (1),
53-70.
Barnett, C., & Mencken, F. (2002). Social disorganization theory and the contextual nature of crime in nonmetropolitan counties. Rural Sociology, 67 (3), 372-393.
Bealer, R., Willits, F., & Kuvlesky, W. (1965). The meaning of rurality in American society: Some implications of alternate definitions. Rural Sociology, 30 (3), 255-
266.
Bellair, P. (2000). Informal surveillance and street crime: A complex relationship.
Criminology, 38 (1), 137-169.
Benson, M. L., Wooldredge, J., Thistlethwaite, A. B., & Fox, G. (2004). The Correlation between Race and Domestic Violence is Confounded with Community Context.
Social Problems, 51 (3), 326.
Blau, J. R., & Blau, P. M. (1982). The Cost of Inequality: Metropolitan Structure and
Violent Crime. American Sociological Review, 47 (1), 114-129.
Blau, P. M., & Golden, R. M. (1986). Metropolitan Structure and Criminal Violence. The
Sociological Quarterly, 27 (1), 15-26.

64
Blumstein, A., & Wallman, J. (Eds.). (2000). The recent rise and fall of American violence . New York: Cambridge University Press.
Brookman, F., & Maguire, M. (2005). Reducing homicide: A review of the possibilities.
Crime, Law and Social Change, 42 (4-5), 325.
Browne, A. (1987). When battered women kill . New York: Free Press.
Browne, A., & Williams, K. (1993). Gender, intimacy and lethal violence. Gender and
Society, 7 (1), 78-98.
Bureau of Justice Statistics. (2004). Criminal victimization in the United States-
Statistical tables index. Retrieved 24 August 2009: http://www.ojp.usdoj.gov/bjs/pub/pdf/cvus/previous/cvus57.pdf
.
Burke, J., O'Campo, & Peak, G. (2006). Neighborhood influences and intimate partner violence: Does geographic setting matter? Journal of Urban Health: Bulletin of the New York Academy of Medicine, 83 (2), 182-194.
Bursik, R. (1988). Social disorganization and theories of crime and delinquency:
Problems and prospects. Criminology, 26 (2), 519-551.
Bursik, R., & Grasmick, H. (1993). Neighborhoods and crime: The dimensions of effective community control . New York: Lexington Books.
Bursik, R., & Webb, J. (1982). Community Change and Patterns of Delinquency. The
American Journal of Sociology, 88 (1), 24-42.
Button, D. (2008). Social Disadvantage and Family Violence: Neighborhood Effects on
Attitudes about Intimate Partner Violence and Corporal Punishment. American
Journal of Criminal Justice : AJCJ, 33 (1), 130.
Campbell, J. C. (2004). Helping Women Understand Their Risk in Situations of Intimate
Partner Violence. [Article]. Journal of Interpersonal Violence, 19 (12), 1464-
1477.
Campbell, J. C., Glass, N., Sharps, P. W., Laughon, K., & Bloom, T. (2007). Intimate partner homicide. [Article]. Trauma, Violence & Abuse, 8 (3), 246-269.
Campbell, J. C., Webster, D., Koziol-McLain, J., Block, C., Campbell, D., Curry, M. A., et al. (2003). Risk factors for femicide in abusive relationships: results from a multisite case control study. American Journal of Public Health, 93 (7), 1089-
1097.

65
Carrington, & Scott. (2008). Masculinity, Rurality And Violence. The British Journal of
Criminology, 48 (5), 641.
Cattaneo, L., & Goodman, L. (2005). Risk factors for reabuse in intimate partner violence: A cross-disciplinary critical review. Trauma, Violence & Abuse, 6 (2),
141-175.
Chimbos, P. (1978). Marital violence: A study of interspouse homicide . San Francisco: R
& E Research Associates.
Covington, J., & Taylor, R. B. (1991). Fear of Crime in Urban Residential
Neighborhoods: Implications of Between- and Within-Neighborhood Sources for
Current Models. The Sociological Quarterly, 32 (2), 231-249.
Cunradi, C., Caetano, R., Clark, C., & Schafer, J. (2000). Neighborhood poverty as a predictor of intimate partner violence among white, black, and Hispanic couples in the United States: A multilevel analysis. Annals of Epidemiology, 10 (5), 297-
308.
Curry, G. D., & Spergel, I. (1988). Gang homicide, delinquency, and community.
Criminology, 26 , 381-405.
Davies, P. (2008). Looking Out a Broken Old Window: Community Safety, Gendered
Crimes and Victimizations. Crime Prevention and Community Safety, 10 (4), 207.
DeKeseredy, W., & Joseph, C. (2006). Seperation and/or divorce sexual assault in rural
Ohio. Violence Against Women, 12 (3), 301-311.
Dobash, R. E., & Dobash, R. (2000). Evaluating criminal justice interventions for domestic violence. Crime & Delinquency, 46 , 252-270.
Dugan, L., Nagin, D., & Rosenfeld, R. (1999). Explaining the decline in intimate partner homicide: The effects of changing domesticity, women's status, and domestic violence resources. Homicide Studies, 3 (3), 187-214.
Dugan, L., Nagin, D., & Rosenfeld, R. (2003). Exposure reduction or retaliation? The effects of domestic violence resources on intimate-partner homicide. Law &
Society Review, 37 (1), 169-198.
Dykstra, R. R. (2009). Lies, Damned Lies, and Homicide Rates. [Article]. Historical
Methods, 42 (4), 139-142.

66
Ebbe, O. (1989). Crime and Delinquency in Metropolitan Lagos: A Study of 'Crime and
Delinquency Area' Theory. [Article]. Social Forces, 67 (3), 751.
Fabrikant, R. (1979). The Distribution of Criminal Offenses in an Urban Environment: A
Spatial Analysis of Criminal Spillovers and of Juvenile Offenders. American
Journal of Economics and Sociology, 38 (1), 31-47.
Fleury, R., Sullivan, C., & Bybee, D. (2000). When ending the relationship does not end the violence. Violence Against Women, 6 (12), 1363-1383.
Fowles, R., & Merva, M. (1996). Wage inequality and criminal activity: An extreme bounds analysis for the United States, 1975-1990. Criminology, 34 (2), 163.
Fox, J. (Ed.). (2000). Demographics and US homicide . New York: Cambridge University
Press.
Gallup-Black, A. (2005). Twenty years of rural and urban trends in family and intimate partner homicide: Does place matter? Homicide Studies, 9 (2), 149-173.
Goetting, A. (1995). Homicide in families and other special populations . New York:
Springer.
Grattet, R. (2009). The Urban Ecology of Bias Crime: A Study of Disorganized and
Defended Neighborhoods. Social Problems, 56 (1), 132.
Greenfield, L., Rand, M., Craven, D., Klaus, P., Perkins, C., Ringel, C., et al. (1998).
Violence by intimates: Analysis of data on crimes by current or former spouses, boyfriends, and girlfriends .
Grogger, J. (Ed.). (2000). An economic model of recent trends in violence . New York:
Cambridge University Press.
Harer, M., & Steffensmeier, D. (1992). The Differing Effects of Economic Inequality on
Black and White Rates of Violence. Social Forces, 70 (4), 1035-1054.
Henning, K., Renauer, B., & Holdford, R. (2006). Victim or Offender? Heterogeneity
Among Women Arrested for Intimate Partner Violence. Journal of Family
Violence, 21 (6), 351.
Jennings, W. G., & Piquero, A. R. (2008). Trajectories of non-intimate partner and intimate partner homicides, 1980-1999; The importance of rurality. Journal of
Criminal Justice, 36 (5), 435-443.

67
Johnson, B., Li, D., & Websdale, N. (1998). Florida mortality review project: Executive summary .
Johnson, R. (2000). Rural health response to domestic violence: Policy and practice issues. Retrieved 24 August 2009, from U.S. Department of Health and Human
Services: http://ruralhealth.hrsa.gov/pub/domviol.htm
Kantor, G., & Jasinski, J. (1998). Dynamics and risk factors in partner violence .
Thousand Oaks: Sage.
Kornhauser, R. (1978). Social sources of delinquency: An appraisal of analytical models .
Chicago: University of Chicago Press.
Kovandzic, T., Vieraitis, L., & Yeisley, M. (1998). The structural covariates of urban homicide: Reassessing the impact of income in equality and poverty in the post-
Reagan era. Criminology, 36 (3), 569-599.
Kposowa, A., & Breault, K. (1993). Reassessing the structural covariates of U.S. homicide rates: A county level study. Sociological Focus, 26 , 27-46.
Kposowa, A., Breault, K. D., & Harrison, B. M. (1995). Reassessing the Structural
Covariates of Violent and Property Crimes in the USA: A County Level Analysis.
The British Journal of Sociology, 46 (1), 79-105.
Krishnan, S., Hilbert, J., & Pase, M. (2001). An examination of intimate partner violence in rural communities: Results from a hospital emergency department study from
Southwest United States. Family Community Health, 24 (1), 1-14.
Kubrin, C. (2003). Structural Covariates Of Homicide Rates: Does Type Of Homicide
Matter? Journal of Research in Crime and Delinquency, 40 (2), 139-170.
Kubrin, C., & Weitzer, R. (2003). New directions in social disorganization theory.
Journal of Research in Crime and Delinquency, 40 (4), 374-402.
Land, K. C., McCall, P. L., & Cohen, L. E. (1990). Structural covariates of homicide rates: Are there any invariances across time and social space? The American
Journal of Sociology, 95 (4), 922-963.
Lauritsen, J., & Schaum, R. (2004). The social ecology of violence against women.
Criminology, 42 (2), 323.

68
Lee, M., Maume, M., & Ousey, G. (2003). Social isolation and lethal violence across the metro/nonmetro divide: The effects of socioeconomic disadvantage and poverty concentration on homicide. Rural Sociology, 68 (1), 107-131.
Levitt, S. (2001). Alternative strategies for identifying the link between unemployment and crime. Journal of Quantitative Criminology, 17 (4), 377-390.
Markowitz, F., Bellair, P., Liska, A., & Liu, J. (2001). Extending social disorganization theory: Modeling the relationships between cohesion, disorder, and fear.
Criminology, 39 (2), 293-320.
Maston, C. (2010). Criminal victimization in the United States, 2007- Statistical tables.
Retrieved 11 March 2010, from Bureau of Justice Statistics: http://bjs.ojp.usdoj.gov/index.cfm?ty=pbdetail&iid=1743
Messner, S. (1982). Poverty, inequality, and the urban homicide rate. Criminology, 20 (1),
103-114.
Messner, S. (1983). Regional differences in the economic correlates of the urban homicide rate. Criminology, 21 (4), 477-488.
Messner, S., & Golden, R. (1992). Racial inequality and racially disaggregated homicide rates: An assessment of alternative theoretical explanations. Criminology, 30 (3),
421-447.
Messner, S., & Rosenfeld, R. (1999). Social structure and homicide: Theory and research . London: Sage.
Messner, S., & Sampson, R. (1991). The sex ratio, family disruption, and rates of violent crime: The paradox of demographic structure. Social Forces, 69 (3), 693-713.
Messner, S., & Tardiff, K. (1986). Economic inequality and levels of homicide: An analysis of urban neighborhoods. Criminology, 24 , 297-318.
Miles-Doan, R. (1998). Violence between Spouses and Intimates: Does Neighborhood
Context Matter? Social Forces, 77 (2), 623-645.
Morenoff, J., Sampson, R., & Raudenbush, S. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology,
39 (3), 517.

69
Mouzos, J., & Shackelford, T. K. (2004). A comparative, cross-national analysis of partner-killing by women in cohabiting and marital relationships in Australia and the United States. [Article]. Aggressive Behavior, 30 (3), 206-216.
Mullner, R., & McNeil, D. (1986). Rural and urban hospital closures: A comparison.
Health Affairs, 5 (3), 131-141.
Nicolaidis, C., Curry, M., Ulrich, Y., Sharps, P., McFarlane, J., Campbell, D., et al.
(2003). Could we have known? A qualitative analysis of data from women who survived an attempted homicide by an intimate partner. Journal of General
Internal Medicine, 18 (10), 788-794.
Nielsen, F., & Anderson, A. (1997). The Kuznets curve and the great U-turn: Income inequality in U.S. counties, 1970-1990. American Sociological Review, 62 , 12-33.
O'Campo, P., Gielen, A., Faden, R., Xue, X., Kass, N., & Wang, M. (1995). Violence by male partners against women during the childbearing year: A contextual analysis.
American Journal of Public Health, 85 (8-1), 1092-1097.
Osgood, D., & Chambers, J. (2000). Social disorganization outside the metropolis: An analysis of rural youth violence. Criminology, 38 (1), 81-115.
Ousey, G. (1999). Homicide, structural factors, and the racial invariance assumption.
Criminology, 37 (2), 405-426.
Parker, K. (2001). A Move Toward Specificity: Examining Urban Disadvantage and
Race-and Relationship-Specific Homicide Rates. Journal of Quantitative
Criminology, 17 (1), 89.
Parker, K., & McCall, P. (1997). Adding another piece to the inequality-homicide puzzle:
The impact of structural inequalitz on racially disaggregated homicide rates.
Homicide Studies, 1 (1), 35-60.
Parker, K., & McCall, P. (1999). Structural conditions and racial homicide patterns: A look at the multiple disadvantages in urban areas. Criminology, 37 (3), 447-477.
Parker, R. (1989). Poverty, subculture of violence, and type of homicide. Social Forces,
67 (4), 983-1007.
Patalinghug, E. (2009). Identifying the Link between Unemployment and Crime in the
Philippines*. International Journal of Comparative and Applied Criminal Justice,
33 (1), 83.

70
Patterson, B. (1991). Poverty, income inequality, and community crime rates.
Criminology, 29 , 755-826.
Pattillo, M. E. (1998). Sweet Mothers and Gangbangers: Managing Crime in a Black
Middle-Class Neighborhood. Social Forces, 76 (3), 747-774.
Petee, T., & Kowalski, G. (1993). Modeling rural violent crime rates: A test of social disorganization theory. Sociological Focus, 26 (1), 87-89.
Peterson, R., & Krivo, L. (1993). Racial Segregation and Black Urban Homicide. Social
Forces, 71 (4), 1001-1026.
Pretorius, H. G., & Botha, S. (2009). The cycle of violence and abuse in women who kill an intimate male partner: A biographical profile. South African Journal of
Psychology, 39 (2), 242-252.
Pridemore, W., & Shkolnikov, V. (2004). Education and Marriage as Protective Factors
Against Homicide Mortality: Methodological and Substantive Findings from
Moscow. Journal of Quantitative Criminology, 20 (2), 173.
Puzone, C., Saltzman, L., Kresnow, M., Thompson, M., & Mercy, J. (2000). National trends in intimate partner homicide, United States, 1976-1995. Violence Against
Women, 6 (4), 409-426.
Raphael, S., & Winter-Ebmer, R. (2001). Identifying the Effect of Unemployment on
Crime. Journal of Law and Economics, 44 (1), 259-283.
Rennison, C., & Welchans, S. (2000). Intimate Partner Violence.
Roland, C. (2002). Homicides among Chicago families: 1870-1930. Journal of Criminal
Law & Criminology, 92 (3/4), 899.
Rosenfeld, R. (Ed.). (2000). Patterns in adult homicide: 1980-1995 . New York:
Cambridge University Press.
Ross, C., & Jang, S. J. (2000). Neighborhood disorder, fear, and mistrust: The buffering role of socal ties with neighbors. American Journal of Community Psychology,
28 (4), 401-420.
Sampson, R. (1986). Crime in cities: The effects of formal and informal social control .
Chicago: University of Chicago Press.

71
Sampson, R., & Groves, W. (1989). Community Structure and Crime: Testing Social-
Disorganization Theory. The American Journal of Sociology, 94 (4), 774-802.
Sampson, R., & Lauritsen, J. (1990). Violent victimisation and offending (Vol. 3).
Washington: National Academy Press.
Sampson, R., Morenoff, J., & Felton, E. (1999). Beyond Social Capital: Spatial
Dynamics of Collective Efficacy for Children. American Sociological Review,
64 (5), 633-660.
Sampson, R., Raudenbush, S., & Felton, E. (1997). Neighborhoods and Violent Crime: A
Multilevel Study of Collective Efficacy. Science, 277 (5328), 918-924.
Serran, G., & Firestone, P. (2004). Intimate partner homicide: A review of the male proprietariness and the self-defense theories. Aggression & Violent Behavior,
9 (1), 1-15.
Shaw, C., & McCKay, H. (1969). Juveline delinquency and urban areas: A study of rates and delinquency in relation to differential characteristics of local communities in
American cities (Revised ed.). Chicago: University of Chicago Press.
Shaw, C., & McKay, H. (1969). Juvenile delinquency and urban areas: A study of rates of delinquency in relation to deifferential characteristics of local communities in
American cities (Revised ed.). Chicago: University of Chicago Press.
Shihadeh, E., & Ousey, G. (1998). Industrial restructuring and violence: The link between entry-level jobs, economic deprivation, and black and white homicide.
Social Forces, 77 (1), 185-206.
Shoemaker, D. (1996). Theories of delinquency: An examination of explanations of delinquent behavior (3rd ed.). New York: Oxford University Press.
Simmons, C., Lehmann, P., & Collier-Tenison, S. (2008). Men's Use of Controlling
Behaviors: A Comparison of Reports by Women in a Domestic Violence Shelter and Women in a Domestic Violence Offender Program. Journal of Family
Violence, 23 (6), 387.
Smith, M. D. (1990). Sociodemographic Risk Factors in Wife Abuse: Results from a
Survey of Toronto Women. The Canadian Journal of Sociology / Cahiers canadiens de sociologie, 15 (1), 39-58.
Smith, M. D., Devine, J., & Sheley, J. (1992). Crime and unemployment: Effects across age and race categories. Sociological Perspectives, 35 (4), 551-572.

72
Sonis, J., & Langer, M. (2008). Risk and Protective Factors for Recurrent Intimate
Partner Violence in a Cohort of Low-Income Inner-City Women. Journal of
Family Violence, 23 (7), 529.
Spano, R., & Nagy, S. (2005). Social guardianship and social isolation: An application and extension of lifestyle/routine activities theory to rural adolescents. Rural
Sociology, 70 (3), 414-427.
Stuckler, D., Basu, S., Suhrcke, M., Coutts, A., & McKee, M. (2009). The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. [doi: DOI: 10.1016/S0140-6736(09)61124-7]. The Lancet, 374 (9686),
315-323.
Sun, I. Y., & Triplett, R. A. (2008). Differential perceptions of neighborhood problems by police and residents. Policing, 31 (3), 435.
Sutherland, E., & Cressy, D. (1978). Criminology (10th ed.). Philadelphia: Lippincott.
Swatt, M. L., & Ni, H. (2006). Exploring the Difference Between Male and Female
Intimate Partner Homicides. [Article]. Homicide Studies, 10 (4), 279-292.
Vold, G., Bernard, T., & Snipes, J. (2002). Theoretical Criminology (5th ed.). New York:
Oxford University Press.
Websdale, N. (1998). Rural woman battering and the justice system: An ethnography .
Thousand Oaks: Sage Publications.
Wells, W., & DeLeon-Granados, W. (2002a). Analysis of unexamined issues in the intimate partner homicide decline: Race, quality of victim services, offender accountablility, and system accountability, final report. NCJRS , 1-50.
Wells, W., & DeLeon-Granados, W. (2002b). Intimate Partner Homicide in California,
1987-2000 .
Wells, W., & DeLeon-Granados, W. (2004). The Intimate Partner Homicide Decline:
Disaggregated Trends, Theoretical Explanations, and Policy Implications.
[Article]. Criminal Justice Policy Review, 15 (2), 229-246.
Wenzel, S., Tucker, J., Hambarsoomian, K., & Elliott, M. (2006). Toward a more comprehensive understanding of violence against impoverished women. Journal of Interpersonal Violence, 21 (6), 820-839.

73
Wilkinson, K. (1984). Rurality and patterns of social disruption. Rural Sociology, 49 (1),
23-36.
Williams III, F., & McShane, M. (2004). Criminological Theory (4th ed.). New York:
Garland Publishing, Inc.
Williams, K., & Flewelling, R. (1988). The social production of criminal homicide: A comparative study of disaggregated rates in American cities. American
Sociological Review, 53 (3), 421-431.
Wilson, W. (1987). The truly disadvantaged: The inner city, the underclass, and public policy . Chicago: University of Chicago Press.
Wilson, W. (1996). When work disappears: The world of the new urban poor . New York:
Random House, Inc.
Yates, D., Pillai, V., & Berry, P. (2008). Mediation Verses Arrest Approaches to
Domestic Assault: Policy Implications for Addressing Domestic Abuse Among
Under-Educated and Jobless Offenders. American Journal of Criminal Justice :
AJCJ, 33 (2), 282.