Not so Simple: Examining the Gendered Nature of Intimate Partner Assault Victimizations

Abstract

Although prior research has established a relationship between neighborhood structure and intimate partner violence (IPV), much of this literature focuses on serious IPV, despite the fact that less serious forms of IPV comprise the majority of IPV instances. Further, women are far more likely to experience all types of IPV victimization relative to men, especially simple assault, yet the relative importance of neighborhood structure across gradations of IPV and victim sex is relatively unexplored. We use data from Los Angeles, CA, and disaggregate IPV assaults across victim sex to examine whether neighborhood factors have differential effects on simple and aggravated IPV. We find differential effects of neighborhood structure by victim sex, especially for simple assault. Most notably, we find that neighborhood racial composition has significantly greater effects on females relative to male victims of simple IPV assault, while residential stability is protective of women more so than men. Only the percent of vacant or owner-occupied housing was specific to male simple IPV victimization. This suggests that neighborhood-based interventions should differ depending on the type of IPV being targeted.

Keywords

intimate partner violence neighborhoods neighborhood race victimization gender disparities

Neighborhood structural characteristics are important factors in understanding the spatial clustering of violence (Chamberlain & Hipp, 2015; Krivo & Peterson, 1996; Morenoff et al., 2001). Prior research largely focuses on nonlethal violence such as robberies (Martinez et al., 2008), gang-related homicides (Mares, 2010; Valasik et al., 2017) and other forms of street violence between strangers (Avakame, 1998; Bellair, 2000; Kubrin, 2003; Miethe & McDowall, 1993). A growing body of research has begun to link neighborhood context with violence between individuals who are known to each other (Beyer et al., 2013; Caetano et al., 2010), including intimate partner violence (IPV) (Beyer et al., 2015; Copp et al., 2015; Pinchevsky & Wright, 2012). This research shows that structural factors such as disadvantages (Benson et al., 2003; Benson et al., 2004; Pinchevsky & Wright, 2012) and racial and ethnic concentration (Benson et al., 2004; Wright & Benson, 2010, 2011) are associated with increased IPV rates.

However, prior research linking neighborhood correlates of crime to IPV has almost exclusively focused on lethal or aggravated violence (for exceptions, see DeMaris et al., 2003; Stueve & O’Donnell, 2008), despite the fact that most interpersonal incidents are less serious (Catalano, 2013). Nearly two-thirds of IPV incidents involve simple assault relative to one third that can be considered serious violent crimes (Catalano, 2013). Thus, the narrow focus on serious IPV and community context may obfuscate important nuances between different degrees of severity of intimate partner violence, limiting our understanding of how neighborhood conditions impact the most common types of IPV—simple assault. Indeed, prior work has shown that neighborhood factors differentially affect more serious aggravated assaults versus simple or domestic assaults (Boggess et al., 2022). This suggests that neighborhood factors relevant to aggravated IPV may differ from those relevant to simple IPV.

Further, theories of victimization that consider the broader conditions of a neighborhood have largely neglected the possibility of gender differences at the macro level (Smith & Chiricos, 2003). Men and women may utilize neighborhood resources differently, such as reliance on social ties for support and protection from IPV, which could explain gender differences in victimization (Broidy & Agnew, 1997). For instance, women may be especially vulnerable in more disadvantaged neighborhoods, where they are more isolated and social support is weak (Van Wyk et al., 2003; Warner & Roundtree, 1997). Indeed, McKinney et al. (2009) showed that neighborhood poverty is associated with female IPV but not male IPV, although it may be moderated by the couple’s race and neighborhood disadvantage (Cunradi et al., 2000). The effects of structure, particularly on women, may be further compounded by the fact that women’s exposure to poverty and disadvantage has risen, and this is especially the case for women of color (Heimer, 2000; Lindhorst & Mancoske, 2003). This suggests that rates of IPV may be even more pronounced in these communities for women. However, the extent to which these factors differ across sex and IPV severity is relatively underexplored in criminology.

Despite these insights, little research has assessed how neighborhood structure might affect IPV rates across sex and offense severity. This is notable given the sheer distribution of IPV incidents, which are disproportionately less serious and more likely to be perpetrated against women (Catalano, 2013). Therefore, in the current study, we extend our understanding of IPV and neighborhood structure in two ways. First, we disaggregate IPV incidents to capture the level of seriousness of the event. In doing so, we are able to assess the influence of structure on the most common form of IPV—simple assault—and differentiate neighborhood risk factors relative to more serious forms of IPV (Boggess & Chamberlain, 2020; Pinchevsky & Wright, 2012; Wright & Skubak Tillyer, 2017). Second, we contrast the effects of neighborhood structure and IPV offense type across victim sex. To address these questions, we use data from the city of Los Angeles and the American Community Survey to better understand the interplay between different types of IPV victimization, victim sex, and neighborhood structure.

Literature Review

Neighborhood Characteristics and IPV

To understand neighborhood patterns of crime, research has primarily drawn from the social disorganization perspective (Chamberlain & Hipp, 2015; Chamberlain et al., 2021; Hipp, 2007; Kubrin et al., 2022; Peterson & Krivo, 2010). Social disorganization theory postulates neighborhood structural characteristics, such as low socioeconomic status, racial/ethnic heterogeneity, and residential instability, impede interaction between residents and hinder the formation of social ties (Kornhauser, 1978; Shaw & McKay, 1942). Consequently, residents in these neighborhoods have a decreased capacity to maintain effective informal social control leading to increased crime and violence (Bursik & Grasmick, 1993; Kornhauser, 1978). The theory is also often extended to examine victimizations, finding similar patterns related to disadvantage, residential instability, racial/ethnic heterogeneity, and violent victimization rates, although not always in line with expectation or theory (Gilbert & Wright, 2023; Sampson & Groves, 1989; Tillyer & Walter, 2019).

Neighborhood characteristics associated with social disorganization have also been linked to IPV, especially economic disadvantage (Benson et al., 2003, 2004; Browning, 2002; Wright & Benson, 2010, 2011). Economic strain can create stress between couples or within families, which can exacerbate IPV. For example, Benson et al. (2003, 2004) found an association between disadvantage and IPV, while Morgan and Jasinski (2017) determined that concentrated disadvantage increased rates of domestic violence (broadly defined) in Chicago census tracts but was not significantly associated when assessing larger units of analysis (i.e., Illinois counties). Other scholars have found an association between the neighborhood prevalence of female-headed households (Lauritsen & Schaum, 2004), racial/ethnic heterogeneity (Bouffard & Goodson, 2017), and residential stability (Li et al., 2010) and IPV.

Within disadvantaged neighborhoods, residents tend to be isolated (DeMaris et al., 2003; Tigges et al., 1998), strained (Jewkes, 2002; Yonas et al., 2011), and segregated from social support networks (Benson et al., 2003), all of which can increase the prevalence of IPV (Jasinski & Williams, 1998). In this way, resource-deprived neighborhoods lack reporting mechanisms, such as residents reporting suspicious activity or intervening when problems arise, and this unwillingness to engage can intensify IPV in a neighborhood (Miles-Doan, 1998). Additionally, because disadvantaged neighborhoods often also have high rates of residential instability, social connections are even harder to establish; as a result, residents may be unaware of IPV occurring in their neighborhood (Browning, 2002; Wright & Benson, 2011; Wright & Skubak Tillyer, 2017). The lack of social connections in disadvantaged neighborhoods is especially deleterious for women in violent relationships since they have smaller social networks to provide social support compared to women in nonviolent relationships (Katerndahl et al., 2013).

Neighborhoods and IPV Severity

Research has long examined macro-level influences on lethal versus nonlethal violence (Dalve et al., 2021; Hipple et al., 2020) or even different forms of lethal violence (Kubrin, 2003; Valasik et al., 2017). This research has found that neighborhood structure does not uniformly predict subtypes of violence or different levels of crime seriousness. For example, Kubrin (2003) found neighborhood characteristics were differentially associated with distinct types of homicide, such as domestic, felony, and general altercation. While disadvantage was positively related to all homicide types, some structural factors, such as residential instability, were inconsistent across homicide types, while others, such as family structure, had no effect on any homicide type.

Even less research has compared differences between IPV and other forms of nonlethal violence. Recently, Boggess and colleagues examined the different effects of neighborhood structure across aggravated, domestic, and simple assaults and found important differences between all three assault types (Boggess et al., 2022). In general, their findings largely showed significant differences between aggravated and either simple or domestic assaults and fewer differences between simple and domestic violence, which may reflect the large percentage of domestic incidents that are less serious in nature. Notably, concentrated disadvantage and the percent of single-parent households were associated only with increases in the rate of domestic assaults and not simple or aggravated assaults, while residential stability showed a crime-suppressing effect on aggravated and simple assaults but not domestic ones. This suggests that structural factors may play a more salient role in more serious forms of aggressive violence. However, Boggess and colleagues were unable to differentiate between serious and less serious forms of domestic violence. Thus, it is unclear whether the effect of neighborhood structure on these distinct forms of IPV will mirror prior findings, especially since IPV-related acts are distinct from more general forms of violence that may be perpetrated by strangers or associates.

IPV can also be disaggregated into simple and aggravated versions, but the extent to which neighborhood context may impact these different types of domestic violence is limited, making it difficult to draw conclusions. Some evidence suggests that the influence of neighborhood context on violence increases as relational distance increases. Miles-Doan (1998) found that neighborhood characteristics are not as effective in explaining IPV as compared to violence between other types of relations, such as other family members, friends, or acquaintances. Other evidence suggests that neighborhood structure is more predictive of serious forms of violence between intimates. In a study of couples in the National Survey of Families and Households, DeMaris et al. (2003) estimated neighborhood effects on categorical outcomes of no violence, physical aggression, and intense male violence. They found that neighborhood disadvantage significantly increased the likelihood of intense violence as compared to no violence, but there was no significant difference between the likelihood of no violence or physically aggressive victimization.

Further, neighborhood conditions may condition how residents respond to different types of IPV. Wright and Skubak Tillyer (2020) examined the interrelationship between different types of IPV, collective efficacy, and social ties. They found loose neighborhood social ties influenced IPV victimizations, but only on the lower end of IPV severity. They suggest neighborhood residents may not consider less severe IPV incidents necessary to intervene in, as it does not impact the welfare of the neighborhood at large.

Neighborhoods and IPV Victimization by Sex

While neighborhood structural characteristics are linked to IPV, they may differentially influence who is being victimized. Criminological research consistently demonstrates women experience IPV more commonly than men, including more severe forms of IPV (Catalano, 2013; Larsen & Hamberger, 2015). Survey data shows that female victims often experience multiple forms of IPV victimization, while male victimization is largely relegated to forms of physical violence (Black et al., 2011). Women also have a higher lifetime prevalence of severe physical violence by an intimate (23.2%) relative to men (13.9%) (Smith et al., 2017). Neighborhood factors may play a role in explaining the prevalence of female victims.

For example, a lack of employment opportunities for men may contribute to increased IPV against women (Yonas et al., 2011). When personal financial strain, such as unemployment or underemployment, is compounded by existing neighborhood deprivation, this may increase IPV. Likewise, in low socioeconomic status neighborhoods, women may be more vulnerable to IPV as they likely lack the ability to access household or other resources that could decrease their victimization risk (Fox et al., 2002). Given that IPV victimization is prevalent in disadvantaged neighborhoods (Benson et al., 2000, 2003), women living in these neighborhoods may engage in IPV against male partners to shield themselves from future victimization (Caldwell et al., 2009). Caldwell et al. (2009) found that women who engaged in IPV adopted a tough guise, which may be a way for women living in disadvantaged communities to manage living in challenging situations. Indeed, women living in poverty have a greater need for self-protection from predatory or abusive men (Steffensmeier & Allan, 1996).

Women may also have more effective coping strategies compared to men (Broidy & Agnew, 1997) and may use their social networks for social support or childcare (Anderson, 1990; Venkatesh, 2009). Thus, the disruption of these networks or the inability to access them may be more detrimental for women compared to men. Further, single mothers who are working multiple jobs or nontraditional hours are less likely to have the time to build social ties in the neighborhood that could prevent or provide help for women experiencing IPV (Messner & Sampson, 1991; Sampson, 1985).

However, male IPV victims may be less inclined to request help from social ties and networks or report the victimization to police as it conflicts with the norms of masculinity (Machado et al., 2016), which may be more critical in disadvantaged neighborhoods (Anderson, 1999). In a similar vein, male social ties may even increase IPV victimization risk as they promote engaging in risky behaviors such as drug use or risky sexual behavior (Policastro & Daigle, 2019). Coupled with this, disadvantaged neighborhoods often provide more opportunities to engage in risky behaviors, which can further exacerbate IPV risk. Thus, while research has begun to explore how neighborhood effects are associated with IPV victimization across sex, missing from this research is whether neighborhood effects can explain differences in the severity of IPV across sex (DeMaris et al., 2003; Tigges et al., 2010).

Data and Methods

Data

Building upon prior research (Boggess & Chamberlain, 2020; Boggess et al., 2022), we investigate the impact of neighborhood structural characteristics on within and between-sex disparities across gradations of IPV. We use crime incident data from 2014 through 2018 from the city of Los Angeles. Incident-level data on IPV-related aggravated and simple assaults were obtained from the Los Angeles Police Department. The data include demographic data of the victim, including the age and sex. The data also included the street address where the incident occurred to the nearest 100 block¹ and the associated longitude and latitude of the address. The data were summed and then aggregated into 2010 tract-level counts of total domestic aggravated assaults and total domestic simple assaults. To capture neighborhood structural characteristics, we use 5-year estimates from the American Community Survey (2014–2018). In 2010, there were 1,011 tracts in the city of Los Angeles. We excluded 12 tracts because they lacked crime data and 12 tracts because they lacked complete census data for our variables of interest. Finally, we dropped 2 tracts because they had a population of fewer than 500 residents. The final sample includes 985 tracts.

Dependent Variables

Our outcome variables are the total number of incidents for aggravated and simple assaults perpetrated by intimates. We disaggregate each incident type by victim sex; thus, each incident was coded three ways: total incidents, female victims, and male victims. We pool data across all years to increase the prevalence and variability of incidences across sex, especially for male victims. During the study period, the Los Angeles Police Department reported a total of 12,452 aggravated assaults and 102,224 simple assaults, which were aggregated into tracts. Of these, there were 9,644 aggravated assaults in which the victim was female and 2,802 in which a male was the victim.² For simple assaults, 81,903 incidents involved a female victim, while there were 20,283 incidents in which a male victim was involved. Females make up the vast majority of the victims in both categories: 77% of victims for aggravated assaults and 80% for simple assaults.

Independent Variables

We include a number of neighborhood structural variables to assess the effects of social disorganization and disadvantage on IPV and sex-specific victimization. We measure concentrated disadvantage as a principal components analysis comprised of the following measures: (a) percent of residents below poverty; (b) median household income (reverse coded); (c) percent of residents receiving public assistance; (d) percent of residents with a Bachelor’s degree (reverse coded); and (e) the percent of residents unemployed. Higher values indicate greater disadvantage. We include the percent of residents who moved in the last year as an indicator of residential instability. We also include the percent of vacant housing units and the percent of owner-occupied housing units. We also include the Gini coefficient, which captures relative inequality, measures of percent Black residents, and the percent of Hispanic residents.

Descriptive statistics for the variables used in the analysis are found in Table 1. The variables were evaluated for multicollinearity, and there is no evidence to suggest that this is a problem. All of the variance inflation factor (VIF) scores are well below 10, which is the commonly specified cutoff for macro-level research (Kennedy, 2003).

Table 1.

Summary Statistics, LA Neighborhoods N = 985.

Variables	Mean	SD	Min	Max
IPV simple assaults	61.74	49.29	0	455
IPV aggravated assaults	11.32	11.33	0	127
IPV aggravated assaults—female victims	8.79	9.03	0	100
IPV aggravated assaults—male victims	2.52	2.83	0	27
IPV simple assaults—female victims	49.12	41.69	0	364
IPV simple assaults—male victims	12.59	9.43	0	91
Disadvantage	0.00	1.00	-3.10	3.98
Percent poverty	19.06	11.96	0	84.62
Median income (reverse coded)	−64,364.69	35,631.95	−250,001	−8,264
Percent public assistance	4.09	3.71	0	28.73
Percent Bachelor’s degree (reverse coded)	−32.64	21.93	−86.46	−0.73
Percent unemployed	4.69	2.11	0	14.50
Percent Black	8.59	12.03	0	81.06
Percent Hispanic	48.03	28.82	1.60	98.51
Residential instability	88.2	7.74	38.41	98.85
Percent female-headed households	13.75	9.42	0	100
Percent vacant units	6.43	4.39	0	36.52
Percent owner-occupied units	35.22	24.36	0	94.43
Gini coefficient	0.45	0.06	0.2622	0.89

Note. IPV = intimate partner violence.

Methods

We utilize negative binomial regression models to determine how neighborhood structural characteristics might differentially predict overall and sex-specific counts of IPV aggravated and simple assaults. For the overall models, we use the total tract population as an offset measure, whereas for the sex-specific models, we employ the sex-specific tract population. The exposure variable is log-transformed and then constrained to one, enabling us to account for the potential population at risk for victimization. This allows us to estimate the outcome measure as an overall victimization rate and a victimization rate for each sex for each type of IPV assault.

Since the risk of victimization for men and women in the same neighborhood is not independent, we use seemingly unrelated estimations. This is an established technique used when comparing subgroups within neighborhoods or cities (e.g., Chamberlain et al., 2021; Steffensmeier & Haynie, 2000). By employing seemingly unrelated estimation, we are able to combine the results from each equation into a single parameter vector with a simultaneous covariance matrix, thus correcting the standard errors when calculating statistical significance. Additionally, we conduct Wald tests to assess significant differences in parameters across models.

Results

We first assess the impact of neighborhood characteristics on overall victimization for simple and aggravated domestic violence (DV) assault. Second, we examine the effects of structure on victimization across sex. Third, we test whether neighborhoods differentially affect victimization across crime types but within sex.

Neighborhood Effects on Simple Versus Aggravated Domestic Assaults

The results for the aggregate models are presented in Table 2 below. The overall pattern of results is fairly similar between the two types of IPV assault, with a few important differences. Disadvantage is positive and significant for both simple and aggravated assault. Taking the exponential of the b coefficient gives us the incident rate ratio (IRR) for a one-unit increase in the independent variable. A one standard deviation in disadvantage results in a 32% increase in the rate of simple assaults (Model 1; b = 0.2752, IRR = 1.3168, p < .001), and a 47% increase in the rate of aggravated assaults (Model 1; b = 0.3857, IRR = 1.4706, p < .001). We employ Wald tests to determine whether the effects of disadvantage are significantly different across crime types. Results indicate that disadvantage has a greater effect on aggravated assaults compared to simple assaults (W1).

Table 2.

Negative Binomial Regression Results Predicting Simple and Aggravated IPV with Measures of Neighborhood Context, LA Tracts.

Variables	Model 1 IPV Simple Assault				Wald Tests
	IPV Simple Assault		IPV Aggravated Assault		W1
	b	IRR	b	IRR	SA/AA
Disadvantage	0.2752** (0.0455)	1.3168	0.3857** (0.0593)	1.4706	***
Percent Black	0.0145** (0.0016)	1.0146	0.0182** (0.0020)	1.0184	**
Percent Hispanic	0.0057** (0.0012)	1.0057	0.0042** (0.0016)	1.0042
Residential stability	−0.0084 (0.0050)	0.9916	−0.0093 (0.0060)	0.9907
Percent female-headed households	0.0031 (0.0034)	1.0031	0.0041 (0.0040)	1.0041
Percent vacant units	0.0198** (0.0045)	1.0200	0.0213** (0.0061)	1.0215
Percent owner-occupied units	−0.0027* (0.0011)	0.9973	0.0002 (0.0014)	1.0002	***
Gini coefficient	−0.0517 (0.3364)	0.9496	−0.2658 (0.4612)	0.7666
Constant	−4.0089** (0.4192)	0.0182	−5.6673** (0.5254)	0.0035

Note. Robust standard errors in parentheses. AA = Aggravated Assault; IPV = intimate partner violence; SA = Simple Assault.

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

The racial composition of a neighborhood is also positively associated with IPV-related assaults: a 1% increase in the percent of Black residents in a neighborhood is associated with a 1.5% increase in the rate of DV simple assaults (Model 1; b = 0.0145, IRR = 0.0146, p < .001), and a 2% increase in DV-aggravated assaults (Model 1; b = 0.0182, IRR = 0.0184, p < .001). Wald tests indicate that the effect sizes are significantly different (W1). An increase in the percent of Hispanic residents is also positively and significantly associated with an increase in the rate of both simple (Model 1; b = 0.0057, IRR = 1.0057, p < .001) and aggravated assaults (Model 1; b = 0.0042, IRR = 0.0042, p < .001), but there are no differences in the effects of the percent Hispanic on simple versus aggravated assaults.

Finally, our measures of guardianship are associated with DV assaults. An increase in the percentage of vacant units in a neighborhood results in a 2% increase in the rate of simple assaults (Model 1; b = 0.0198, IRR = 0.02, p < .001) and a 2.1% increase in the rate of aggravated assaults (Model 1; b = 0.0213, IRR = 0.0215, p < .001). Wald tests indicate that these effects are not significantly different. An increase in the percent of owner-occupied units is related to a decrease in simple assaults (Model 1; b = −0.0027, IRR = 0.9973, p < .05) but has no effect on the rate of aggravated assaults (Model 1; b = 0.0002, IRR = 1.00, ns). Wald tests reveal that this difference is significant (W1). Percent female-headed households and the inequality were not significantly associated with either assault type.

Differentiating Neighborhood Effects on Simple Versus Aggravated Domestic Assault Victimization Between Men and Women

In Table 3, we disaggregate our crime incident measures by sex to determine whether neighborhood structure differentially affects victimization between men and women. Model 2 presents the between-sex results for IPV-related simple assault, while Model 3 presents the results for IPV-related aggravated assault. Several notable differences emerge that were previously obfuscated in the baseline models and are captured in the results for the Wald tests (W2 and W3). The pattern of results suggests significant variance in victimization between men and women for simple assault but a pattern of invariance associated with aggravated assault. As in the aggregate models, disadvantage is associated with an increase in simple and aggravated assault for both men and women (Table 3, Models 2 and 3). Wald tests reveal that there are no significant differences in the effect of disadvantage on the rate of victimization for men and women across crime types (W2).

Table 3.

Negative Binomial Regression Results Predicting Male and Female Simple and Aggravated IPV with Measures of Neighborhood Context, LA Tracts.

Variables	Model 2 IPV Simple Assault				Model 3 IPV Aggravated Assault				Wald Tests
	Male		Female		Male		Female		W2	W3
	b	IRR	b	IRR	b	IRR	b	IRR	SA M/F	AA M/F
Disadvantage	0.1821** (0.0489)	1.1997	0.2121** (0.0795)	1.2363	0.3428** (0.0751)	1.4089	0.3709** (0.0736)	1.4490
Percent Black	0.0097** (0.0017)	1.0097	0.0155** (0.0017)	1.0156	0.0181** (0.0027)	1.0183	0.0181** (0.0020)	1.0183	***
Percent Hispanic	0.0021 (0.0013)	1.0021	0.0070** (0.0013)	1.0070	0.0019 (0.0022)	1.0019	0.0055** (0.0016)	1.0055	***	†
Residential stability	−0.0042 (0.0049)	0.9958	−0.0159** (0.0054)	0.9842	−0.0137 (0.0075)	0.9864	−0.0105 (0.0059)	0.9896	***
Percent female-headed households	−0.0030 (0.0029)	1.0164	0.0163 (0.0106)	1.0039	0.0039 (0.0048)	1.0039	0.0054 (0.0072)	1.0054
Percent vacant units	0.0271** (0.0055)	1.0275	0.0147** (0.0054)	1.0148	0.0221** (0.0083)	1.0223	0.0210** (0.0068)	1.0212	**
Percent owner-occupied units	−0.0056** (0.0012)	0.9944	−0.0024* (0.0012)	0.9976	0.0001 (0.0018)	1.0001	−0.0003 (0.0014)	0.9997	***
Gini coefficient	−0.1035 (0.3951)	0.9017	−0.5302 (0.4612)	0.5885	−0.0064 (0.6299)	0.9936	−0.4359 (0.5190)	0.6467
Constant	−4.8203** (0.4350)	0.0081	−2.9192** (0.4894)	0.0540	−6.0417** (0.6848)	0.0024	−5.1178** (0.5410)	0.0060

Note. Robust standard errors in parentheses. AA = Aggravated assault; IPV = intimate partner violence; SA = Simple assault.

†

p < .1. *p < .05. **p < .01. ***p < .001.

The percent of Black residents in a neighborhood increases the rate of victimization among men and women for both DV simple and aggravated assaults; however, Wald tests reveal that the effect of Black residents is only significantly different for simple assault. As the percentage of Black residents in a neighborhood increases, there are more victimizations among women (Model 2; b = 0.0155, IRR = 1.0156, p < .001) compared to men (Model 2; b = 0.0097, IRR = 1.0097, p < .001). The percent of Hispanic residents in a neighborhood is also associated with an increase in simple assaults for females only (Model 2; b = 0.0070, IRR = 1.0070, p < .01), and this difference is significantly greater. For aggravated assaults, it is only associated with an increase in victimization among women (Model 3; b = 0.0055, IRR = 1.0055, p < .001), and Wald tests indicate that this effect approaches significance from the null effect for men. Notably, the percentage of Hispanic residents is the only structural characteristic that is potentially different across sex-aggravated assaults.

While residential stability was not significantly associated with assaults in the aggregate models (Table 2), it is negatively associated with female victimization for simple assault (Model 2; b = −0.0159, IRR = 0.9842, p < .001), but not for male victimization. Wald tests indicate that this is a significant difference (W2). Vacant units are associated with increases in victimization for males and females for both assault types, but Wald tests reveal these effects are only significantly different for simple assaults, having a greater impact on male victimization (Model 2; b = 0.0271, IRR = 1.0275, p < .01; b = 0.0147, IRR = 1.0148, p < .01) . Finally, the percent of owner-occupied units is only significantly related to decreases in victimization for simple assaults but has a greater effect for male victimization (Model 2; b = −0.0056, IRR = 0.9944, p < .01) compared to female victimization (b = −0.0024, IRR = 0.9976, p < .05); Wald tests reveal this effect is significantly greater (W2).

Differentiating Neighborhood Effects Within Sex Across Simple Versus Aggravated Domestic Assault

Finally, we examine whether there are meaningful differences in neighborhood structure within sex but across crime (Table 4). Model 4 displays the results for incidents involving female victims, while Model 5 presents the findings for male victims. For women, consistent with previous models, disadvantage, the percent of residents who are Black, and the percent of residents who are Hispanic are all positively associated with increased victimization among women for both DV simple and aggravated assaults. When examining Wald tests, disadvantage and the presence of Black residents have a greater effect on the rate of aggravated assaults compared to simple assaults against women (W4). Residential stability decreases the rate of simple assaults against women, and this approaching significance difference when compared to the null effect associated with aggravated assaults (W4). Finally, while the percent of vacant units is associated with a decrease in both simple and aggravated assaults, the effect sizes are not significant (W4).

Table 4.

Negative Binomial Regression Results Predicting Simple and Aggravated IPV within Sex with Measures of Neighborhood Context, LA Tracts.

	Model 4 Females				Model 5 Males				Wald Tests
	DV Simple		DV Aggravated		DV Simple		DV Aggravated		W4	W5
Variables	b	IRR	b	IRR	b	IRR	b	IRR	F SA/AA	M SA/AA
Disadvantage	0.2121** (0.0795)	1.2363	0.3709** (0.0736)	1.4490	0.1821** (0.0489)	1.1997	0.3428** (0.0751)	1.4089	***	**
Percent Black	0.0155** (0.0017)	1.0156	0.0181** (0.0020)	1.0183	0.0097** (0.0017)	1.0097	0.0181** (0.0027)	1.0183	*	***
Percent Hispanic	0.0070** (0.0013)	1.0070	0.0055** (0.0016)	1.0055	0.0021 (0.0013)	1.0021	0.0019 (0.0022)	1.0019
Residential stability	−0.0159** (0.0054)	0.9842	−0.0105 (0.0059)	0.9896	−0.0042 (0.0049)	0.9958	−0.0137 (0.0075)	0.9864	*
Percent female-headed households	0.0163 (0.0106)	1.0164	0.0054 (0.0072)	1.0054	−0.0030 (0.0029)	1.0039	0.0039 (0.0048)	1.0039
Percent vacant units	0.0147** (0.0054)	1.0148	0.0210** (0.0068)	1.0212	0.0271** (0.0055)	1.0275	0.0221** (0.0083)	1.0223
Percent owner-occupied units	−0.0024* (0.0012)	0.9976	−0.0003 (0.0014)	0.9997	−0.0056** (0.0012)	0.9944	0.0001 (0.0018)	1.0001	*	***
Gini coefficient	−0.5302 (0.4612)	0.5885	−0.4359 (0.5190)	0.6467	−0.1035 (0.3951)	0.9017	−0.0064 (0.6299)	0.9936
Constant	−2.9192** (0.4894)	0.0540	−5.1178** (0.5410)	0.0060	−4.8203** (0.4350)	0.0081	−6.0417** (0.6848)	0.0024

Note. Robust standard errors in parentheses. AA = Aggravated Assault; SA = Simple Assault.

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

For males (Model 5), disadvantage and the percent of Black residents are associated with increases in both DV simple and aggravated assaults against men, and these structural characteristics are significantly greater for aggravated assaults compared to simple assaults. Similar to the results for women, while the percent of vacant units is significantly associated with an increase in male victimization for simple and aggravated assaults, there are no significant differences between these effects. Finally, the percent of owner-occupied units decreases the rate of DV simple assaults perpetrated against males, and this effect is significantly greater relative to the null effect for DV-aggravated assaults.

Discussion

While prior research has linked neighborhood structural characteristics to higher rates of IPV, much of this research focuses exclusively on lethal or serious violent crime. Although important insights have been gleaned from this work, the impact of neighborhood conditions on less serious forms of IPV has been largely ignored. This is a notable omission since most IPV-related incidents involve less serious forms of violence. To address this gap, we investigate whether neighborhood structural characteristics function similarly across IPV simple and aggravated assault and whether these effects vary within and across victim sex. Overall, we find that the effect of neighborhood structural factors varies between simple and aggravated IPV, especially between men and women, though some within-ex differences were also detected. We discuss our findings below.

First, in the aggregate models that are not sex specific, we largely find that neighborhood structural characteristics operate similarly for simple and aggravated IPV, although the magnitude of the effects is generally greater for aggravated IPV. While neighborhood disadvantage and the percentage of Black residents were significantly associated with an increase in both IPV types, the effect was larger on aggravated assaults. This suggests that when not accounting for victim sex, neighborhood factors may be more influential on more serious forms of IPV. This is consistent with previous research that finds neighborhood disadvantage and the racial composition of a neighborhood are associated with IPV (e.g., Powell, 2008). Disadvantaged neighborhoods and neighborhoods with concentrations of minority residents may have weakened social ties (Pinchevsky & Wright, 2012; Rountree & Warner, 1999), blunting the effectiveness of social supports that may help reduce IPV occurrences. For instance, Pinchevsky and Wright (2012) found that the utility of family support in reducing IPV was weakened in disadvantaged neighborhoods and that some relationships were actually associated with more frequent victimization. Similarly, disadvantaged neighborhoods and neighborhoods with a high prevalence of minority residents often hold views of legal cynicism (Sampson & Bartusch, 1998), where resident distrust of the police is high, decreasing the likelihood that crimes are reported (Kirk & Matsuda, 2011). Emery et al. (2011) found IPV desistance was higher in neighborhoods where trust in law enforcement was greater, suggesting residents may perceive IPV acts will be penalized formally by police.

Second, we find differences in the association between neighborhood structural characteristics and the severity of IPV between sexes. Notably, these differences were largely in terms of simple IPV assault, which was masked in the aggregate models. For instance, the racial composition of a neighborhood has differential effects across sex for simple assault IPV. The percent Black residents has a greater effect on IPV, simple assaults perpetrated against women relative to men. This may reflect elements of a street code in which men establish dominance and reputation through acts of violence against other men (e.g., Anderson, 1999); when it comes to women victims, however, the severity of violence may be muted. Women in these neighborhoods may be more inclined to use violence against their male partners to protect themselves. Indeed, in an analysis of IPV incidents in the U.S., Powers and Bleeker (2023) found that Black female victims had a greater likelihood of engaging in self-defense during these incidents, and as a result, these incidents were more likely to be reported to the police. Likewise, the percent of Hispanic residents is associated with female victimizations for simple assault. The role of culture may explain this relationship: in Hispanic culture, the male gender role, machismo, is associated with dominance, authority, and strength, while the female gender role, marianismo, is associated with being subservient (Castillo et al., 2010). Previous research has found that marianismo values may be a factor in domestic violence victimization (Da Silva et al., 2021), as this gender role promotes acquiescent reactions to victimization experiences (Derks & Heessels, 2011). Similarly, marianismo ideals promote the familial institution and women being forgiving, which can increase the likelihood of staying in abusive relationships (De Faria, 2021). Thus, gender roles and cultural norms between sexes can amplify women’s victimization, specifically in Hispanic and Black neighborhoods.

We also find evidence that residential stability significantly reduces the number of women victims of simple IPV. To the degree that residential stability reflects stronger social ties, previous research has found social support was associated with less IPV victimization of women but not men (Richards & Branch, 2012). Residential stability and social ties are vital sources of social support for women who experience IPV victimization (Katerndahl et al., 2013). Whereas the same protective effect may not apply to male victims if the street code or culture of masculinity reduces the likelihood of seeking support through neighbors or social networks (Machado et al., 2016). Finally, once disaggregating by victim sex, the effect of disadvantage was no longer significantly different between aggravated and simple IPV. This means that disadvantage impacts simple and aggravated IPV similarly, regardless of the sex of the victim.

Other neighborhood variables, such as the percent of vacant or owner-occupied housing, were specific to male simple IPV victimization. This suggests that male victims may be more vulnerable when they are physically isolated from others, as opposed to the social isolation that often accompanies female victims. One possibility is that female perpetrators of IPV are more careful about the stigma associated with abuse and are therefore more strategic when engaging in violence against male victims. Indeed, a higher presence of owner-occupied units suggests there are more individuals who will live in the neighborhood longer and be more involved in neighborhood activities (Rohe & Stewart, 1996).

Finally, neighborhood characteristics are better able to explain differences in victimization severity only when looking between victim sex but looking at IPV severity within sex, the impact of neighborhood factors is less variable. This suggests that policies targeted at reducing IPV should consider offense severity and the sex of the victim in tandem. In our study, neighborhoods with greater residential stability had fewer simple assaults perpetrated against females. One mechanism to enhance stability in neighborhoods is to promote homeownership and broader investment into the community vis-à-vis home mortgage loans. Indeed, Boggess and Chamberlain (2020) found that even small monetary investments through home mortgage loans decreased IPV, particularly in disadvantaged neighborhoods. The city of Los Angeles developed the Low-Income Purchase Assistance Program, which provides down payment, acquisition, and closing cost assistance to low-income households purchasing their first home (Los Angeles Housing Department, 2023). The implementation of these types of policies has the potential to significantly reduce less serious forms of IPV, which are much more pervasive across communities.

Limitations

While the current study explored the influence of neighborhood structural characteristics on IPV simple and aggravated assault both within and across sex, there are some limitations that should be acknowledged. First, we rely on official police incident reports, which only capture officially reported incidents. IPV is often unreported or may not result in an arrest (Flores et al., 2020). While the recent implementation of dual arrest policies might mitigate this to some extent (Hirschel et al., 2021; Roark, 2016), we are likely not capturing the totality of incidents that occur and cannot account for those that are not reported to police. Thus, our data may undercount the number of IPV incidents occurring. Second, we are unable to capture the sex composition of the victim-offender dyad. While prior research has examined this relationship in the context of other forms of violent crime (Chamberlain et al., 2021), information on the suspect was not available. Future research should include this information, particularly as more IPV research broadens its scope to examine violence between same-sex partners. Third, we did not account for the simultaneous impact of victim sex and race. Given the differential effects of neighborhood racial composition on victims by sex and by severity of IPV, future studies should delve more deeply into the roles of culture and gender norms to explain the higher prevalence of simple IPV on women relative to men in predominantly Black or Hispanic communities. Finally, our study is cross-sectional in nature because we had to pool data across years to obtain sufficient power. As a result, our ability to draw causal inferences is limited.

Conclusion

In sum, we find that neighborhood factors are important to understanding patterns of male and female IPV victimization by offense severity. In general, we find that most neighborhood variables, but particularly racial composition and disadvantage, significantly increase simple and aggravated IPV victimization. When we disaggregate by victim sex, however, we uncover striking differences, particularly the simple assault IPV victimization of women. Indeed, our results show that racial composition—whether predominantly Black or Hispanic—increases female simple assault IPV victimization. Whether this reflects cultural norms in such communities is up to future research to explore, but scholars and policymakers should, moving forward, continue to acknowledge how race, gender, and violence are intertwined in communities.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Alyssa W. Chamberlain

Notes

Author Biographies

Alyssa W. Chamberlain is an Associate Professor in the School of Criminology and Criminal Justice at Arizona State University. Her research focuses on neighborhood dynamics related to changes in social and demographic structure, housing, inequality, and the spatial distribution of crime. She also examines issues related to prisoner reentry and recidivism and how incarceration and reentry both affect and are affected by community structure.

Lexi Gill is a doctoral student in the Department of Criminology at the University of South Florida. She earned her Master of Science in Criminal Justice and Criminology from the University of Missouri—Kansas City. She is a graduate research assistant on a Smart Policing Initiative grant. Her research interests include communities and crime, policing, and social network analysis.

Lyndsay N. Boggess is an Associate Professor in the Department of Criminology at the University of South Florida. Her research focuses on communities and crime, primarily how crime affects and is affected by neighborhood change, racial/ethnic composition, gentrification, and economic investment. Her work has appeared in Criminology, the Journal of Quantitative Criminology, and Crime & Delinquency.

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