Intimate Partner Homicide - SAGE Journals - Sage Publications

Intimate Partner Homicide Relationships to Alcohol and Firearms

Journal of Contemporary Criminal Justice Volume 25 Number 1 February 2009 67-88 © 2009 Sage Publications 10.1177/1043986208329771 http://ccj.sagepub.com hosted at http://online.sagepub.com

Darryl W. Roberts University of Maryland School of Nursing

An overwhelming proportion of intimate partner (IP) homicide perpetrators are under the influence of substances when the crime occurs, and alcohol consumption is a strong predictor of intimate terrorism of women. In IP homicide, female victims are twice as likely to die from a gunshot wound as from stabbing, strangling, or other methods; and firearm ownership is shown to increase the likelihood of IP homicide by a factor of 5.38. Compiled from publicly available data sources, the present study analyzes a database of all lethal events occurring in the U.S. from 1985 to 2004. Using a panel of counties and negative binomial regression, the influences of alcohol and firearms, controlling for other variables, on IP homicide and IP homicide by firearm are estimated. Alcohol consumption and firearm ownership increase both the incidence rates of IP homicide and IP homicide by firearm. However, highly restrictive firearms carry laws also increase the incidence of IP homicide. IP homicide is strongly influenced by alcohol and firearms availability, but some types of firearms carry laws might be counterproductive in decreasing the incidence of this crime. Keywords: intimate partner homicide; alcohol; firearms; panel studies

omicide is a crime of men (Batton, 2004; Fox & Zawitz, 2006; Silverman & Kennedy, 1987; Wolfgang, 1967; Zahn & Sagi, 1987). Men perpetrate seven of eight homicides and are the victims in three of four homicides. The homicide victimization rate was more than 3 times higher for men (8.7 per 100,000) than for women (2.4 per 100,000) in 2004 (Fox & Zawitz, 2006). More than 65% of all male-perpetrated homicides also had a male victim. The smallest homicide category is that of female-on-female homicides, which account for 2.4% of all homicides in the United States (Fox & Zawitz, 2006; Glass, Koziol-McLain, Campbell, & Block, 2004). However, more than 30% of homicides committed by women have an intimate partner (IP) as victim (Jensen, 2001). In fact, women are the perpetrators of 34.7% of IP homicides1 but only 11.3% of the remaining categories of homicide (Fox & Zawitz, 2006). IP homicide is the only type of lethal violence in which the principal victims are women. Since 1976, the distribution of IP homicide has changed from a near 50-50

H

Author’s Note: The author could not have completed this research without the aid of Dr. Dave Marcotte, University of Maryland Baltimore County; and Drs. Carolyn Yocom and Judy Ozbolt, University of Maryland School of Nursing. Additionally, the author thanks Dr. James Alan Fox of Northwestern University for providing access to his exceptional data files. 67

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Journal of Contemporary Criminal Justice

split between the sexes to one in which three quarters of the victims are female. In 2004, 1,301 women were killed by their male partners, which accounts for one third of all women’s homicides. In that same year, there were considerably fewer male IP homicide victims (n = 515) (Fox & Zawitz, 2006). The Federal Bureau of Investigation (FBI) and National Center for Health Statistics (NCHS) currently classify IP homicide as a form of criminal homicide that is counted along with nonintimate (NI) homicide in reporting of a jurisdiction’s homicide incidence. This classification has a noteworthy impact on law enforcement methods (Brannan, 2006; Buzawa & Buzawa, 2002; National Institute of Justice, 1995; Task Force on Local Criminal Justice Response to Domestic Violence, 2005; Wisconsin Department of Justice, 2002) that goes beyond the scope of the current article. In a recent 20-year national panel study, I found significant differences between NI homicide and IP homicide, suggesting that a separate classification for IP homicide might be appropriate (Roberts, 2009). The current article reports on important findings from that study relating to the relationships between alcohol and firearms accessibility and the county-level incidence of IP homicide. The aim for the current article is to answer the following questions: 1. Do IP homicide incidence rates vary with the county’s quantity of alcohol purchases (a proxy for alcohol consumption)? 2. Do firearm restrictions increase, decrease, or have no effect on IP homicide? 3. Does IP homicide incidence vary with the prevalence of firearm ownership?

Background and Significance The criminology literature correctly refers to homicide as a rare event. According to Fox and Zawitz (2006), the U.S. homicide rate in 2006 was 6.1 per 100,000 persons. Thus, IP homicide, which accounts for 7% of all homicides, is very rare. However, this level of rarity does not negate the importance of understanding as much as possible about IP homicide to develop and apply policy alternatives focused on eliminating it altogether. Perhaps it is due to its rarity that social scientists and criminologists should focus additional attention on IP homicide, because as Campbell and colleagues (Campbell, 1994, 1995; Campbell et al., 2003) and Websdale and colleagues (Websdale, 2003; Websdale, Sheeran, & Johnson, 2004) have noted, IP homicide is the most preventable form of lethal violence. Additionally, the rarity of IP homicide does not diminish its impact. Consider that in 2004 alone more Americans lost their lives to violence at the hands of intimates (1,816 deaths) than lost their lives to violence at the hands of enemy forces in Iraq during the first 30 months of the war (1,798 deaths) (see the U.S. Department of Defense Web site, http://www.defenselink.mil). Furthermore, male IP homicide perpetrators are significantly more likely to commit suicide after the crime (Koziol-McLain et al., 2006;

Roberts / Intimate Partner Homicide 69

Milroy, 1995; Starzomski & Nussbaum, 2000). This highly replicated finding speaks to the significant effect IP homicide has on the perpetrator and the community. Campbell, her mentees, and her colleagues have published several papers identifying IP homicide risk factors (Campbell, 1994, 1995; Campbell & Soeken, 1999; Campbell et al., 2003). The most well-known risk factor is a prior history of IP violence. Additional factors that increase IP homicide risk include estrangement, male partner’s unemployment, firearm ownership, and presence of stepchildren (Bullock & Cubert, 2002). Stalking is present in 70% to 90% of IP homicides (McFarlane, Parker, Soeken, Silva, & Reed, 1999). Walsh and Hemenway (2005) concluded that illicit drug and alcohol use do not significantly correlate with IP homicide but that nearly 70% of perpetrators were under the influence of drugs or alcohol when the crime took place. Additionally, many researchers report a strong relationship between pregnancy and IP homicide risk (Campbell et al., 2003; Chang, Berg, Saltzman, & Herndon, 2005; Decker, Martin, & Moracco, 2005; Frye, 2001), particularly among teens (Krulewitch, Roberts, & Thompson, 2003). Forced sex during pregnancy also increases IP homicide risk (Bullock & Cubert, 2002). Furthermore, mental illness among both victims and perpetrators increases IP homicide risk (Bourget, Gagne, & Moamai, 2000; Campbell & Soeken, 1999; Jurik & Winn, 1990; Rosenbaum, 1990). Whereas the aforementioned change in sex distribution of IP homicide victims from half men and half women to three women for every man presents a disturbing trend, both men and women have benefited from a significant decrease in IP homicide incidence over the past three decades (Fox & Zawitz, 2006). In 1976, there were 2,957 IP homicide deaths in the United States. By 2004, that number decreased to 1,816 deaths. The decrease in IP homicide deaths is consistent with the overall decrease in homicide rates since they peaked in 1980 at 10.4 per 100,000 persons. A breakdown of this decrease by sex shows that in 1980 male homicides peaked at 16.6 per 100,000, dropping to 9.6 per 100,000 in 2005;2 whereas female homicides peaked at 4.4 per 100,000 and dropped to 2.5 per 100,000 in that year (Fox & Zawitz, 2006). The reason for the decrease is somewhat elusive; however, Blumstein, Rivara, and Rosenfeld (2000) present a convincing argument that suggests that the 1980 homicide spike was a function of the demographic peak caused by baby boomers who reached the highest crime age (late teens to early 20s) in the late 1970s and early 1980s. By the late 1980s, this cohort matured sufficiently that violent crime rates decreased. These consistent decreases in violent crime rates overall and in homicide in particular seem to support Blumstein et al.’s position. The recent plateau in homicide rates is consistent with U.S. Census Bureau (2006) reports indicating that the growth rate in the U.S. population has also hit a plateau. Whereas Blumstein et al.’s (2000) position provides a strong argument to explain the decrease in homicide overall, it does not sufficiently explain the decrease in IP homicide. In general, homicide perpetration peaks among persons who are in their

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20s; however, IP homicide perpetration peaks among persons who are somewhat older, usually older than 35 years (Paulozzi, Saltzman, Thompson, & Holmgreen, 2001). Therefore, Blumstein et al.’s hypothesis suggests that IP homicide would have peaked in about 1995, but that spike in IP homicide did not occur.

Alcohol and Firearms in IP Homicide Alcohol. Gyimah-Brempong and Racine (2006) found a direct correlation between alcohol accessibility and crime, particularly homicide, at the census-tract level in Detroit. Concomitantly, Gorman, Zhu, and Horel (2005) found that alcohol accessibility contributed to an increase in violent crime but only contributed 6% to the overall variance. However, as Pollack, Cubbin, Ahn, and Winkleby (2005) found, the contribution of alcohol to violent crime might not correlate directly with violent crime in poorer neighborhoods. Instead, their Southern California study suggests that heavier drinking is actually found in more affluent neighborhoods, concluding that although violent crime is related to relative deprivation, it might not be mediated by alcohol. Although alcohol accessibility at the local level might have a questionable effect on lethal violence, many studies suggest that consumption is related to many lethal acts. Perpetrators and victims of homicide are more likely than those for other crimes (or the population in general) to have used alcohol at the time of the crime (Hiroeh, Appleby, Mortensen, & Dunn, 2001). Shaw et al.’s (2006) United Kingdom study found that as many as 58% of homicide perpetrators and 50% of victims used alcohol at the time of the crime. Walsh and Hemenway (2005) found that substance use does not have a significant overall influence on IP homicide; however, an overwhelming proportion (70%) of IP homicide perpetrators were under the influence of substances when the crime occurred. Furthermore, Frye, Manganello, Campbell, Walton-Moss, and Wilt (2006) found that partner’s use of alcohol is a strong predictor of intimate terrorism3 of women, a form of IP violence that is closely associated with IP homicide. Firearms. Local, national, and international studies show significant relationships between firearms and lethal violence. In Miami, Nielsen, Martinez, and Rosenfeld (2005) found that more than three quarters of homicides were associated with firearms and that homicides were 8.5 times more likely to involve a firearm than any other weapon. Furthermore, they found that homicide offenders were significantly more likely to be male if the homicide involved a firearm. In IP homicide, female victims are twice as likely to die from a gunshot wound as from stabbing, strangling, or other methods. Bullock and Cubert (2002) found that firearm ownership increased the likelihood of IP homicide by a factor of 5.38. In nonfatal IP violence, the firearm is still the man’s weapon of choice (Sorenson, 2006). However, women are much more likely to kill their male intimate with a knife


(Swatt & He, 2006). Cross (2005) reported that this might be due to a male-female differential in firearm access, with men having a higher level of accessibility. Frye and colleagues (2006) stated that firearms access also increases the likelihood of intimate terrorism. This presents the issue of access to firearms, which is an issue of some debate among social scientist and criminologists. Krug, Powell, and Dahlberg (1998) suggested that the United States has higher firearm-related death rates than other highincome countries because firearms are more accessible in the United States. In support of this position, Miller and colleagues (Miller, Azrael, Hepburn, Hemenway, & Lippmann, 2006; Miller, Lippmann, Azrael, & Hemenway, 2007) found that decreased levels of firearm ownership correlate directly with decreased numbers of gunshot wound suicides. Furthermore, they found that twice as many individuals died of gunshot wounds in states with high firearm ownership than in states with low firearm ownership.

Other Influences on IP Homicide Besides alcohol and firearms, several other influences on IP homicide can be measured directly or by proxy at the county level on a national scale. Among these influences are time of year, region of the country, urban/rural distribution, race, age, and economic factors (e.g., income, poverty, unemployment). Time of year. There is a very small literature regarding seasonality of IP homicide. Among the available reports, Harries (1997) found a distinguishable summer peak in IP homicide. Tjaden and Thoennes (2000b) also reported a significant summer peak. So far, no researcher has found a statistically significant winter pattern similar to that seen in NI homicide (Harries, 1997). Geography. Many researchers cite the American South as a region of increased lethal violence incidence (Corzine & Huff-Corzine, 1994; Gastil, 1971; Hackney, 1969; Harries, 1997; Vollum & Titterington, 2001). However, others believe that the western states have higher lethal incidences (Gallup-Black, 2005). Farmer and Tiefenthaler (2003) stated that IP violence, a well-known predictor of IP homicide, tends to occur where local attitudes are more permissive of violence. In the United States, they reported that the lowest incidence of IP violence is in the Midwest, New England, and the South. Social isolation is another influence on IP homicide. Gallup-Black (2005) and Campbell and colleagues (2003) cited the geographical isolation in rural areas as a contributing factor for social isolation. Social isolation allows IP violence perpetrators in rural areas to abuse with less fear of police or neighbor intervention. Gallup-Black suggested that this isolation, combined with fear of retribution by the perpetrator, contribute to underreporting of IP violence and a subsequently higher rate of IP

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homicide in rural areas.4 In further support of this, the Bureau of Justice Statistics reported that during the period from 1976 to 2005 IP homicide rates fell the most in urban and suburban areas but fell the least in rural area (Fox & Zawitz, 2006). Additionally, Gallup-Black (2005) noted that IP homicide correlated inversely with population density in rural areas. However, she also reported that IP homicide correlated directly with population density in urban areas. This inversion of correlation might also be due to social isolation, which increases not only as population density decreases in rural areas but also as population increases above our capacity for association in densely populated urban areas (Cubbin, Pickle, & Fingerhut, 2000; Singh & Siahpush, 2002; Tigges, Browne, & Green, 1997). Gallup-Black added that socioeconomic distress factors, including poverty, substance abuse, crime, and unemployment, correlated directly with IP homicide in metropolitan areas. Sinauer, Bowling, Moracco, Runyan, and Butts (1999) conducted a statewide study of female homicide victims using North Carolina medical examiner data for the period 1988 to 1993. They found that whereas the rate of homicide among rural females was lower (5.56 per 100,000 women, 95% confidence interval [CI] = 4.047.09) than it was among intermediate (7.16, 95% CI = 6.52-7.8) or urban victims (6.40, 95% CI = 5.84-6.96), rural female homicide victims were significantly more likely to be killed by an intimate (p = .027). Their findings regarding population density are consistent with Gallup-Black’s (2005) results and with similar explanations. Demographics. By far, Black males have the highest homicide victimization and perpetration rates, whereas White females have the lowest rates in both categories (Fox & Zawitz, 2006; Zahn & Sagi, 1987). Furthermore, one of the most predictive risk factors for IP violence is race (Rennison, 2003; Tjaden & Thoennes, 2000a; Zepp, 1996). However, when income is controlled, White women are more likely to experience IP violence than are Black women (Farmer & Tiefenthaler, 2003). Furthermore, in comparison to Blacks, Whites are more often the victims (56.5% White vs. 41.3% Black) and the perpetrators (54.3% White vs. 43.6% Black) of IP homicide. The demographics of intimates involved in IP violence differ somewhat by gender and outcome of the violent episode (lethal or nonlethal). The peak incidence of nonfatal victimization of women by intimate partners occurs between the ages 16 and 24 years (Rennison, 2001). In fatal intimate violence, the demographics remain similar for both genders, but the peak age is strikingly higher for both men and women at ages 35 to 49 years (Fox & Zawitz, 2006; Rennison, 2001). This higher age has been consistently reported for IP homicide incidence in the United States (Roth, 1999). Economics. Whereas both IP homicide and NI homicide occur more often among persons of lower socioeconomic status, IP homicide victims and perpetrators have higher incomes than do victims and perpetrators of NI homicide (Kung, Liu, & Juon,


1998). Additionally, IP violence and homicide rates tend to be higher in areas with high levels of poverty, high divorce rates, and recent large increases in local population levels (Campbell et al., 2003; Farmer & Tiefenthaler, 2003; Tjaden & Thoennes, 2000a). Bullock and Cubert (2002) found that unemployment of male intimate partners increased the risk of IP homicide by 4.42 times.

Data and Method The literature shows IP homicide is in many ways related to accessibility and use of alcohol and firearms. In addition, previous studies show IP homicide influenced by temporality, relative geography, demographics, and seasonality. The variables tested in this study include state-level firearm ownership, laws restricting firearms access, and alcohol consumption. Additionally, the study controls for several countylevel demographic features. Furthermore, it considers the state’s relative geography, as well as the time of year in which the violent acts occurred. In the subsequent sections of this article, I will present the results of a portion of a 20-year panel study of IP homicide. The larger study from which these data come investigates the value of defining IP homicide as a separate form of lethal violence that is substantially different from either NI homicide or suicide.

Data The dependent variables for the ensuing analyses are IP homicide counts and IP homicide by weapon type counts by county for the United States from 1985 to 2004. The weapon types are firearm; knife; or other, which includes strangulation, poisoning, and so on. The independent variables include alcohol purchases in gallons per capita, percentage firearm ownership, and state firearm waiting periods and carry laws. As shown in the literature review, other variables predict IP homicide at the county level. The impact of many of these variables on IP homicide is established in the literature, and thus they are useful as controls in the current study (see complete list and type of data in Table 1). To answer the research questions listed above, I used a panel data set with variables collected from several disparate sources covering the period 1985 to 2004. The panel participants include all counties in the United States, aggregated into 280 jurisdictions with populations of 100,000 persons or more. The county or county aggregate (described below) constitutes the unit of analysis. In recent years, there has been a steady decline in homicide incidence. Maltz (1998) warned researchers against using short-term trends in their studies, as such trends could contribute to the fallacy of regression to the mean. The error is that researchers mistake short-term trends as indications of significant changes in outcomes,

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Table 1 Variables used in study Variable Dependent Variable IP homicide IP homicide by firearm (IPF) Independent Variables Alcoholic beverages Firearm ownership Firearm waiting period Firearm carry law Control Variables Region (descriptive) Division (multivariate)

Month Population Population density Males per 100 females Population 65 White males White females Single mom Poverty rate Income Unemployment

Variable Description (source)

Raw count of intimate partner homicides (FBI SHR) Raw count of intimate partner homicides using a firearm (FBI SHR) Gallons of alcoholic beverages purchased per capita (NIAAA) Percent of population owning a firearm (NRA) Categorical variable for waiting period before firearm purchase: none, 24-hours, 2-7 days, >7 days or banned (Police Foundation) Categorical variable for firearm carrying restrictions: shall issue, may issue, own-not carry, banned (Police Foundation) Geographical region: Northeast, Midwest, South, West (US Census) Geographical division: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, Pacific (US Census) Month of year Total population in county or county aggregate (US Census) Categorical breakdown of persons per square mile: 0-500, 501-1,000, 1,001-2500, 2,501-5,000, 5,001-10,000, >10,000 (US Census) Number of males per hundred females (US Census) Population under 18 years old (dichotomous): national mean, 1 SD >mean (US Census) Population over 65 years old (dichotomous): national mean, 1 SD >mean (US Census) Proportion of population white male (US Census) Proportion of population white females (US Census) Proportion of single mother households (CPS) Percent of persons below poverty line in county (CPS) County per capita income (categorical): national mean, 1 SD mean (CPS) Unemployment rates for persons aged over 16 years (CPS)

when in reality they are only a return to a long-term mean. Thus, he recommended considering long-term trends, which give a broader perspective important in avoiding regressing to the mean. In the current study, I used 20 years of data to reduce the likelihood of committing that error.

Data Sources Homicide data. The homicide data source is the FBI’s Supplementary Homicide Reports (SHR). These reports are a voluntary addendum to the Uniform Crime


Reports (UCR), which each police (reporting) jurisdiction in the United States must submit monthly. These SHR data include more in-depth information about homicide incidents, including month and year of homicide; name of reporting agency; population; age, race, and sex of victims and perpetrators; relationship between victim(s) and offender(s); weapon used; and crime circumstances. Since 1976, agencies voluntarily contributed SHR data accounting for nearly 91% of homicides (FBI, 2007). Alcohol purchases data. The National Institute for Alcohol Abuse and Alcoholism (NIAAA) employs the Alcohol Epidemiologic Data System to compile several national-level data sets. In addition, they report state level annual per capita purchases of alcoholic beverages in gallons (Lakins, LaVallee, Williams, & Yi, 2007). Firearms restrictions data. The National Rifle Association (NRA) is the source for state firearms laws. This resource gives several characteristics of gun laws. From this resource, I generated a categorical variable representing the restrictions (0 = “shall issue,” 1 = “may issue with limitations,” 2 = “own but not carry,” 3 = “handguns banned”). Oftentimes, municipalities and counties enact firearm restrictions as well; however, limitations of my data required me to account for state laws only (National Rifle Association Institute for Legislative Action, 2007). Firearm ownership data. The Police Foundation reports state-level firearm ownership rates per 100,000 persons (Cook & Ludwig, 1996). These data were only available for 1996; however, they do provide an indication of the prevalence of gun ownership at the midpoint of the data set. County demographic data. The county population, geographical area (i.e., area in square miles), employment, income, racial and sex distributions, home occupancy, and other demographic data are taken from the U.S. Census’s 1990 and 2000 decennial census Summary File 2. The Census Bureau combined these data with the same years’ Current Population Survey (CPS) data. The CPS provides the best information available on the employment characteristics of the U.S. labor force using a sample representative of the civilian noninstitutional population. The CPS data include employment status earnings, hours of work, age, sex, race, marital status, educational attainment, occupation, industry, and class of worker (U.S. Census Bureau, 2006).

The Panel The United States has 3,143 counties. The study from which this report comes compares homicide and suicide; therefore, in creating the data set, I combined homicide data from the FBI and suicide data from the Center for Disease Control and

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Prevention (CDC). These agencies differ appreciably in their reporting rules for publicly accessible lethal violence data with regard to how they identify counties reporting lethal violence. Because the CDC has the most rigorous rules set, I used its reporting format to obtain the most reliable panel. For example, the first several years of the CDC data reported suicides for all U.S. counties. However, during the study period, the CDC limited suicide reports to only counties with large populations, finally adopting a policy of identifying suicides by county name only for those counties with populations over 100,000 people. It identified the remaining counties with a rural marker that I combined into county aggregates. This practice provided 230 regular county reporters plus 50 county aggregates, giving 280 counties reporting in each year.5 The county aggregates’ data include the sums of the demographic data, crime data, and other count data, as well as means of ratio data (e.g., per capita income, firearm ownership rate, etc.). The panel counties6 did not report lethal violence in every month of the 240month panel; however, they provided sufficient data to obtain 54,041 county months of reports or an average of 225 county reports per month. Miami-Dade County formed from Dade County and parts of neighboring jurisdictions during the panel period. Because of the difficulty this presented in identifying the sources of certain lethal violence numbers, I omitted these counties altogether. I will instead rely on the several other urban jurisdictions within Florida to represent the state’s urban characteristics. This omission reduced the number of observations to 54,037 countymonths.

Statistical Methods To aid in identification of trends in the data, I summarized interval-level and ratiolevel variables, including independent and control variables, for the entire panel period at the individual data level. I also summarized these data by year, region, and season. After presenting descriptive results, I present multivariate results of the county panels using IP homicide models depicted below. The dependent variables are counts of lethal events (IP homicide and IP homicide by weapon type) that counties have a constant but dissimilar chance of experiencing. Therefore, panel random-effects Poisson regression models are appropriate to test the predictors of these events (Cameron & Trivedi, 1986). However, most counties have zero values in most months of the panel, giving the panel an overdispersed nature. To overcome this overdispersion, I used panel random-effects negative binomial models (NBM). The NBM is a Poisson model that is standardized to relax the assumption that the distribution mean is equal to the distribution variance (Campbell, Jones, Dienemann, Kub, Schollenberger, et al., 2002). Before applying the NBM model, I calculated a Poisson regression with a goodness-of-fit χ2 test. The χ2 was very large in each case, which indicated that the dependent variable is


overdispersed. Furthermore, the models had very large, negative log likelihoods, which further support overdispersion. The NBM coefficients, being Poisson coefficients, convert easily into incidence rate ratio (IRRs).7 IRRs of less than 1 decrease the dependent variable by a factor of that ratio. For example, an IRR of variable X is .98261; thus, a one-unit increase in X causes a decrease in the dependent variable by 1.739% (calculated by 100 × [.98261 – 1]%). IRRs greater than 1 infer that a one-unit increase in X causes a subsequent increase in the dependent variable by that factor. If X is a dummy variable, the change from 0 to 1 has the same effect as stated above. Stata 9.2 calculates the dispersion as a within-group effect only, assuming a random effects model (StataCorp, 2005). Because the hypotheses for this model group by county, random effects is the appropriate model. Additionally, I smoothed the decennial population changes by factoring in the linear slope of change between the two censuses; therefore, the populations were not consistent within each county, preventing population weighting. Because of the rural county aggregation methods described above, population weighting might inappropriately bias the results toward urban counties.

Results Results reported below are at the national level; however, the rates reported differ somewhat from commonly used individual level rates, because I report county mean rates (CMR) for the period stated. Regional, divisional, state, and county-level results are also available from the author by request.

Descriptive Results The national county mean firearm ownership rate is 33%, with the Midwest and the South regions having the highest rates (39%), the Northeast having the lowest (21%), and the West in the middle with 32%. Annual alcohol purchases average 2.33 gallons per capita nationally. The West had the highest per capita purchases with 2.54 gallons, followed by the Northeast (2.33 gallons), South (2.24 gallons), and Midwest (2.21 gallons). Overall, there were 46,784 IP homicides (CMR = 0.09 per 100,000 persons). Of those, 26,498 were committed using firearms, 10,380 using knives, and 9,906 using other means. IP homicide peaked in 1986 with 2,953 events and reached its nadir in 2003 with 1,788 events (see Figure 1). The regional distributions of IP homicides are quite interesting. The IP homicide incidence in the South is twice as high as it is in the West (21,988 events compared to 10,158 events). However, the IP homicides using firearms had a higher incidence rate in the South (13,550 events) than in the other three regions combined (12,948 events). Additionally, IP homicides using

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Figure 1 National Annual Intimate Partner Homicide Counts by Weapon Used

2,000 1,500 1,000 500

Firearm

Knife

20 03

20 01

19 99

19 97

19 95

19 93

19 91

19 89

19 87

19 85

0

Other

knives were twice as high in the South as in the West, which has the second highest incidence (see Figure 2).

Multivariate Results Using the NBM equation elucidated above, I calculated the effect of alcohol and firearms first on IP homicide overall and again on IP firearm (IPF) homicides (see Table 2). On the overall IP homicide data, alcohol sales, firearm laws, and firearm ownership rates have both expected and unexpected effects. Expectedly, a 1 percent increase in alcohol sales increases the incidence of IP homicide by 1.2 times. Additionally, a 1 percent increase in firearm ownership increases IP homicide incidence by 10.8 times. Firearms laws have some unexpected results. Compared to a “no wait” locality, areas with a 2- to 7-day firearm waiting period have a lower IP homicide incidence by 52.9%. Longer and shorter waiting periods are not significantly different from the reference category. Interestingly, compared to a “shall issue” law, a more restrictive “may issue” law correlates with a 1.7-fold increase in IP homicide. Stronger restrictions are no different from the reference category. The regional distributions of IP homicide are also of some interest. However, to gain a more granular perspective on the geography of IP homicide, I used the nine divisions of the country (see Figure 3 for a breakdown of divisions) in lieu of the four


Figure 2 Regional Distribution of Intimate Partner Homicide by Weapon Used

25,000 20,000 15,000 10,000 5,000 0 northeast

midw est Firearm

south Knife

w est

Other

regions presented in the descriptive results. I selected the Northeast division, which has the lowest IP homicide rate (0.046 per 100,000—data not shown), as the reference category. Compared to the Northeast, the East South Central division is the only one that correlates with a reduced incidence of IP homicide (36.3% lower). Alternatively, the East North Central, South Atlantic, West South Central, and Pacific divisions all correlate with increases in incidence, which range from 1.8 to 2.6 times higher. None of the other geographical divisions significantly differed from New England (see Table 2 for selected results.) For IPF homicides, the results are similar, but in many ways unexpected (see Table 3). First, increasing alcohol purchases by one gallon per capita had nearly the same result in IPF homicides as seen in the overall events above. In addition, like in the overall results, increasing firearm ownership by 1 percent increases firearmrelated IP homicide incidence, but by 6.3-fold—much less than in the overall IP homicide findings. A 2- to 7-day waiting period for firearm purchase decreases the incidence of IPF homicides, but this time, the decrease is only 28%. In a highly unexpected finding, longer waits (>1 week) and outright bans significantly increase the incidence of IPF homicides by 1.6 times. Even more unexpected is the finding that carry laws have no significant effect on IPF homicides. IPF homicides responded much differently to census divisions than did IP homicides overall. The West North Central and East South Central divisions are no

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Table 2 Results of Random-Effects Negative Binomial Regression for Intimate Partner (IP) Homicide Overall IP Homicide Alcohol sales (gallons per capita) Firearm ownership (%) Firearms (no wait)—reference Firearms (24-hour wait) Firearms (2-7 day wait) Firearms (>1 week wait) Firearms (“shall issue”)—reference Firearms (“may issue”) Firearms (banned) New England—reference Middle Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific Number of observations = 54,037 Number of groups = 280 Group variable (i): county Wald χ2 (37) = 1,356.4 Log likelihood = –55,661.6 p > χ2 = 0 Likelihood-ratio test vs. pooled: EQUATION(01) = 7,473.1 p ≥ EQUATION = 0

Incidence Rate Ratio (IRR)

SE

z

1.1974 10.7760

0.043 5.918

4.99*** 4.33***

0.5630 0.4708 0.7448

0.179 0.053 0.142

–1.80 –6.70*** –1.54

1.7128 0.9621

0.216 0.212

4.27*** –0.17

1.2759 1.7961 0.9933 2.0981 0.6373 2.3959 1.3292 2.6378

0.214 0.343 0.204 0.355 0.136 0.471 0.255 0.486

1.45 3.06** –0.03 4.38*** –2.11* 4.45*** 1.48 5.27***

*p ≤ .05. **p ≤ .01. ***p ≤ .001.

different from the reference region. However, the remaining regions all have IRRs that are significantly higher than New England. These IRRs range from 1.7 times higher in the Middle Atlantic division to 3.3 times higher in the West South Central division (see Table 3 for selected results—complete regression results are available from the author).


Figure 3 Census Bureau Regions and Divisions With State FIPS Codes

Source: Figure courtesy U.S. Census Bureau. Available from http://www.census.gov/geo/www/us_ regdiv.pdf. Note: *Prior to June 1984, the Midwest Region was designated as the North Central Region.

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Table 3 Results of Random-Effects Negative Binomial Regression for Intimate Partner Firearm (IPF) Homicides IPF Homicide Alcohol sales (gallons per capita) Firearm ownership (%) Firearms (no wait)—reference Firearms (24-hour wait) Firearms (2-7 day wait) Firearms (>1 week wait) Firearms (“shall issue”)—reference Firearms (“may issue”) Firearms (banned) New England—reference Middle Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific Number of observations = 54,037 Number of groups = 280 Group variable (i): county Wald χ2 (37) = 1,396.5 Log likelihood = 40,338.8 p > χ2 = 0 Likelihood-ratio test vs. pooled: EQUATION(01) = 4,395.26 p ≥ EQUATION = 0

Incidence Rate Ratio (IRR)

SE

z

1.2135 6.2562

0.053 3.111

4.40*** 3.69***

0.5755 0.7182 1.5577

0.176 0.076 0.280

–1.81 –3.13*** 2.46*

1.1202 0.8608

0.128 0.190

1.00 –0.68

1.7180 2.2991 1.2674 2.8924 1.4798 3.2944 2.1239 2.3810

0.265 0.398 0.237 0.463 0.298 0.599 0.378 0.422

3.51*** 4.81*** 1.27 6.63*** 1.94 6.56*** 4.23*** 4.90***

*p ≤ .05. ***p ≤ .001.

Discussion The purpose of the current study was to answer three questions: 1. Do IP homicide incidence rates vary with the county’s quantity of alcohol purchases (a proxy for alcohol consumption)? 2. Do firearm restrictions increase, decrease, or have no effect on IP homicide? 3. Does IP homicide incidence vary with the prevalence of firearm ownership?

Each question has inherent policy implications that I hoped to inform with this study. Because of the regional differences in this type of crime, it is unlikely that a national


approach would be as effective as approaches at the state and local levels. Furthermore, because the firearm is the weapon of choice for IP homicide, I chose to focus on that method of killing, thus hoping to give policy makers useful information about it. Alcohol sales increase IP homicide generally and IPF homicides specifically in this panel study. This finding is consistent with those of many investigations relating alcohol to IP violence and homicide. What makes this a particularly useful finding is that I used an easily accessible parameter over a 20-year national panel that supported other studies focused specifically on urban areas (Campbell et al., 2003; GyimahBrempong & Racine, 2006), which provided consistent results. Thus, this finding contributes to the generalizability of the earlier studies. Furthermore, my findings lend a modicum of causality to the contribution of alcohol use to IP homicide found by Walsh and Hemenway (2005), who reported that nearly three in four IP homicide perpetrators were using substances at the time of the crime. Policy makers could use this parameter in concert with other more individualized statistics to predict times of increased need for availability of IP violence resources. In support of Bullock and Cubert’s (2002) findings, IP homicide generally and IPF homicides increased significantly in counties with higher percentages of firearm ownership. However, I did not expect firearm accessibility to have the smaller effect on IPF homicides. At first, I believed that this might be due to the differential access to firearms by sex suggested by Cross (2005). However, in similar regressions using IP homicide by knives and IP homicide by other means as dependent variables (data not shown), there is no significant relationship with firearm ownership. Therefore, these results suggest that women threatened by firearms in high ownership areas are not responding with lethal knife violence. Combined with the finding that a 2- to 7-day waiting period decreases IP homicide incidence (overall and by firearm), this result suggests a useful policy direction. Together, they suggest that IP homicide is committed using firearms already in the household, but they are prevented by the “cooling off” period provided by the 2- to 7-day wait for those who do not already possess firearms. The increase in incidence in counties with longer waits or bans might suggest that firearms used in these areas were obtained in the secondary or illegal markets. Overall, this suggests that laws providing for such “cooling off” periods work but that bans and long waits might be counterproductive. The finding that carry laws have no significant effect on IPF homicide goes beyond my ability to conjecture, except to suggest that firearm accessibility related to intimate lethal violence in the United States is not affected by carry laws. Finally, consistent with the research cited in the literature review, the South and the West had the highest incidence of IP homicide and IPF homicide. The notable exception to the literature is the East South Central division, which is the only division with an IP homicide incidence that is significantly lower than New England. Perhaps these southern states promote a culture that minimizes social isolation or one that is less permissive of IP violence than is found among their neighbors.

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Additionally, the Middle Atlantic and Mountain divisions, although having IP homicide IRRs not significantly higher than New England, have significantly higher IRRs for IPF homicides. This finding is not easily explained. The Mountain division has a much higher than average firearm ownership rate (41%), which could contribute to the finding; however, the Middle Atlantic division has a much lower than average rate (22%). Furthermore, the Mountain division has vast rural areas that could contribute to social isolation, which contributes to IP homicide, but the IP homicide IRR is not significantly higher there. Similarly, the Middle Atlantic division has a large number of urban centers that could also contribute to social isolation, but again, the IP homicide IRR is not significant. Further investigation at the state or county level might offer more clues. Overall, policy makers interested in this area of research would do well to investigate the differences between their domains and the East South Central division, as this division stands out as the only one with a protective IP homicide IRR.

Conclusion In this study, I investigated the relationships between alcohol, firearms, and IP homicide. Several of the results were consistent with those of other studies, others were different, and still others were so unexpected as to go beyond conjecture. The results of this study suggest that alcohol purchases are a reliable predictor of IP homicide and IPF homicide. Furthermore, the variable provides a good proxy for consumption might be very useful in future research. Additionally, firearm ownership and laws restricting firearm access, all of which vary greatly across the country, provide useful and reliable predictors of lethal intimate violence. However, users of these measurements should be mindful of their geographical limitations. Generalizing from one part of the United States to another could cause problems for policy makers. This study has many limitations. While the data set used for this study is the most comprehensive county-level panel data set available for this purpose, it is intended for use at the county level or above. Generalizing these findings to the individual level could be problematic. Furthermore, proxies used in this study to measure anomie, such as alcohol purchases as a proxy for alcohol abuse, could give unreliable signals. I encourage readers to use these results cautiously. Future studies focused on individual-level data will add greatly to current knowledge of the influences of alcohol and firearms on IP homicide.

Notes 1. The term intimate partner (IP) does not have a standard definition. For this study, I use Browne, Williams, and Dutton’s (1999) definition, which includes “current or former dating, cohabiting, commonlaw, and formally married heterosexual couples” (p. 56). This definition excludes homosexual intimates.


Although I believe that IP homicide within the homosexual cohort is an issue worthy of further investigation, the small numbers of these crimes within the data set present a research challenge that exceeds the scope of the present study. 2. The lowest homicide rate for men occurred in 2000, when the rate dropped to 9.0 per 100,000. However, I am showing the latest available rates to maintain consistency. 3. Frye, Manganello, Campbell, Walton-Moss, and Wilt (2006) stated, “The critical difference between intimate terrorism and situational couple violence is proposed to be that the former is characterized by the efforts of one partner, typically the male, to systematically control the other partner, typically the female” (p. 1287). 4. Fox and Zawitz (2006) and Sinauer, Bowling, Moracco, Runyun, and Butts (1999) reported similar findings. 5. Maryland is an excellent example of how I aggregated panel members: Maryland has 24 countylevel jurisdictions of which 19 merge to form one county aggregate (Rural Maryland, listed as 24999). The 5 remaining counties are populous enough to remain intact (Anne Arundel County 24003, Baltimore County 24005, Montgomery County 24031, Prince George’s County 24033, Baltimore City 24510). 6. To facilitate reading, I refer to counties and county aggregates as counties. 7. To facilitate incidence rate ratio (IRR) conversion, Stata 9.2 permits direct specification of IRR in the model.

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Darryl W. Roberts, MS, RN, is a public health informatics specialist and an assistant professor at the University of Maryland School of Nursing. His primary research area is intimate partner homicide; however, he also collaborates on other public health studies. He is a doctoral candidate in public policy at the University of Maryland Baltimore County. His dissertation uses a 20-year national panel to identify patterns and prevalence in lethal violence.