Disparities in Sex-Specific Arrest Rates: Does Offense Type and Neighborhood Context Matter?

Abstract

We investigate differential effects of neighborhood structure on the arrest rates of men versus women. Given potential disparities in the use of discretion by offense severity, we disaggregate crime to aggravated assaults, burglaries, and drug offenses. We employ negative binomial regression models to predict the number of arrests by sex for each crime type, and test for significant differences within and between sex across offense severity. We find few differences within and across sex, however, levels of disorder and the racial composition of a neighborhood are important structural factors in understanding arrests by sex and across offense type. Neighborhood composition is associated with differential rates of arrest by sex and across offense severity, which has implications for gender disparities in the criminal justice system.

Keywords

neighborhood race disorder sex disparities gender disparities police arrests

Introduction

Studies of sex-specific arrest rates often focus on individual-level characteristics such as race (Kochel et al., 2011; Stolzenberg et al., 2004), differences in self-control (Gottfredson & Hirschi, 1990), or delinquent peers (Zimmerman & Messner, 2010). Other scholarship examines policing behaviors, such as those that tend to target young men, as an explanation for disparities in arrests (e.g., Riksheim & Chermak, 1993; Sherman, 1980). Recent research, however, has begun looking at how neighborhood structural factors influence arrest patterns generally (Chamberlain et al., 2021; Huff, 2021; D. A. Smith, 1986; D. A. Smith & Visher, 1981) and by sex specifically (Crum & Ramey, 2023; Huff, 2021). Much of this research focuses on violent offenses (Johnson & Olschansky, 2010; Sobol, 2010; Sobol et al., 2013), while other studies focus specifically on patterns of domestic violence (Busch & Rosenberg, 2004; Chesney-Lind, 2002; DeLeon-Granados et al., 2006).

Notably missing are studies looking at structural correlates of sex-specific arrests for less serious crimes. Police have greater discretion to arrest for less serious crimes such as drug possession, where subjective factors such as sex may be more likely to influence the likelihood of arrest. Further, the influence of sex on arrest may interact with the type of neighborhood in which the police-suspect interaction occurs. For example, in established or low-crime neighborhoods, sex may be more salient, and officers may be less likely to arrest women in line with chivalry theory (Visher, 1983); this may be particularly true for low-level offenses. Whereas in other areas, such as disadvantaged or high crime ones, sex is less influential because officers may be less sympathetic to residents in these neighborhoods generally and arrests ensue regardless of suspect sex (Klinger, 1997). This suggests that gender disparities in arrests may be driven, in part, by the seriousness of the crime and the broader ecological context in which the arrests occur.

Therefore, the current study examines whether arrest rates for men or women are differentially affected by neighborhood structure, and whether offense severity mitigates any between sex differences. To address these questions, we use data from Aldan,¹ a pseudonym for a representative mid-sized city in the South. We focus on three offense types ranging in severity—aggravated assault, burglary, and drug arrests—and examine how structural factors such as poverty, residential instability, and racial composition affect overall and sex-specific arrest rates. In doing so, we are able to assess the impact of neighborhood structure on within and between sex differences in arrests.

Literature Review

Outside of mandatory arrest requirements (e.g., in response to a warrant, or domestic violence), officers have discretion as to how to respond in any police-citizen interaction. These decisions are based on a range of factors (Herbert, 1998; Klinger, 1997), including individual or incident characteristics (e.g., suspect and officer race/ethnicity or sex, crime severity, or the number of victims), and neighborhood conditions (e.g., disadvantage; Fagan & Davies, 2000; Lum, 2011; D. A. Smith, 1986). The majority of these interactions are for less serious offenses (Engel et al., 2019), and the situations often involve equivocal evidence that does not decisively indicate whether or not to make an arrest. This means that officers may consciously or subconsciously consider external factors, such as the community context, when determining how to use their discretion.

Neighborhood Context and Arrests

Crime is not evenly distributed across the neighborhoods of a city, rather, it tends to concentrate in a small number of locations (e.g., Weisburd, 2015). These areas tend to have high poverty, residential instability, single-parent households, racial/ethnic heterogeneity (Krivo & Peterson, 1996; Sampson, 2006, 2012), and disorder (Silver & Miller, 2004; Wilson & Kelling, 1982). These factors hinder interactions between residents and reduce informal social control mechanisms. With weakened informal control, these neighborhoods become increasingly reliant on formal actors, such as the police, to resolve issues such as crime (e.g., Bursik & Grasmick, 1993). As a consequence, police may be deployed in greater numbers to these neighborhoods (Tomaskovic-Devey et al., 2004; Warren et al., 2006), which in turn translates into greater opportunities to detect criminal activity, especially low-level crimes such as drug dealing that tend to occur in public (Engel et al., 2012; G. Rengert et al., 2000). This implies that targeted enforcement in disadvantaged neighborhoods contributes to higher arrest rates for lower-level offenses when compared to more affluent or stable communities that are patrolled less frequently. From this perspective, higher arrest rates in disadvantaged neighborhoods are associated with the size of the police presence as opposed to any differential behaviors by police in certain communities.

Alternative arguments suggest that police alter their behaviors based on the neighborhood (Holmes et al., 2008; Johnson & Olschansky, 2010; Lum, 2011; B. W. Smith & Holmes, 2003). Klinger’s (1997) negotiated order and patrol perspective asserts that the social and ecological characteristics of the area influences police response. Officer vigor, or the amount of legal authority used by an officer to address an incident, varies depending on the overall level of crime in a patrol area. Because officers in these areas are expected to address a large number of criminal incidents, they may do so less vigorously in high crime areas due to resource constraints. This suggests that officers in high crime areas may strategically focus their enforcement efforts on more serious crimes, and less vigorously enforce lower-level offenses. Further, Klinger (1997) asserts officers’ willingness to enforce the law can be influenced by whether they perceive the neighborhood to be “deserving” of service. Officers working in high crime communities may become more cynical, ultimately viewing crime as a normal feature of the neighborhood and its residents less deserving of police protection. Indeed, research has shown that officers make fewer misdemeanor arrests in socially disorganized areas (Johnson & Olschansky, 2010) and Huff’s (2021) recent research finds evidence that arrests are less likely in high crime areas.

Relatedly, officers may infer that more impoverished or disorderly neighborhoods have higher crime, regardless of whether this is in fact the case (Logan & Stults, 1999; Lum, 2011; Quillian & Pager, 2001). Prior research has found evidence of place-based stereotyping or the use of perceptual shorthand to label locations less deserving of police protection, and this is especially likely to occur when these places have both high concentrations of minority residents and crime (Hurwitz & Peffley, 1997; Quillian & Pager, 2001; G. F. Rengert & Pelfrey, 1997). These perceptions may contribute to differential enforcement in line with Klinger’s (1997) theory and result in lower rates of arrests for minor crimes. However, contrary to Klinger (1997), Sobol et al. (2013) found that officers were more vigorous in making arrests in high crime neighborhoods.

Neighborhoods and Sex-Specific Arrest Rates

Particularly when it comes to less serious crimes, neighborhood characteristics drive who is arrested, with some neighborhood factors associated with disproportionate rates of arrests of minority residents (Bolger & Lytle, 2018; Chamberlain et al., 2021; Gaston, 2019a). One dominant perspective, racial threat, argues that arrests of minority suspects would be greater in predominantly minority communities because police use arrests to exert dominance over a community that is seen as threatening (Gaston, 2019a; Parker et al., 2005). Alternatively, the race-out-of-place perspective posits that arrests of minority suspects increase when the individual’s race/ethnicity is out-of-sync with the racial/ethnic composition of the community (Gaston, 2019a; Gaston, 2019b). However, the theories behind race-specific differentials in rates of arrest are not applicable when considering sex. For example, it is unlikely that a “sex-out-of-place” scenario would exist in the vast majority of neighborhoods. As such, we have to consider alternative explanations that may explain differential rates of arrests between men and women.

Women are more likely to engage in crime in certain types of neighborhoods. Indeed, prior research has consistently found that neighborhood disadvantage is associated with higher rates of offending for both men and women (Boggess et al., 2018; Chamberlain et al., 2021; Schwartz, 2006a, 2006b; Steffensmeier & Haynie, 2000a, 2000b), though the magnitude of the effects may vary. More recently, however, women may be engaging in the types of crime that make them more susceptible to arrests, namely violent crime (Lauritsen et al., 2009; Zimmerman & Messner, 2010). As women become increasingly involved in more serious crimes, their likelihood of arrest may also increase, as officers are more likely to make arrests for serious offenses relative to minor ones (Brown & Frank, 2006). This suggests that when it comes to more serious offenses (e.g., aggravated assault and robbery), the rate of arrests between men and women will be similar. Indeed, Stolzenberg and colleagues (2004) examined a large sample of incidents from NIBRS, and found that there was no sex difference in the probability of arrests of forcible rape and robbery, and for simple assault, the probability of arrest was only 9 % lower for women than men. However, (Crum & Ramey, 2023) studied different factors predicting booked arrests for low-level offenses (cannabis possession, drug paraphernalia, shoplifting, criminal damage, and non-DUI-traffic), and found no discernible differences in the odds of booking between men and women for all crimes except shoplifting (in which female suspects had 34.2% lower odds of being booked into jail). Notably, they found that community factors were not really related to the decision to book arrests regardless of sex (Crum & Ramey, 2023).

Alternatively, shifts in policing strategies may specifically impact women’s arrest rates. In an effort to increase proactive enforcement, less serious forms of violence have been increasingly criminalized. For instance, police may perceive and classify less serious forms of interpersonal aggression or minor incidents of physical violence as more serious offenses, resulting in harsher penalties such as arrests (Schwartz et al., 2009). Police have adopted more proactive zero-tolerance or order maintenance policing policies with regard to disorder and incivilities (Garland, 2012; Mastrofski et al., 2000). Evidence has shown that the consequences of the over-policing of disorder results in the disproportionate arrest of minorities (The Sentencing Project, 2018). But it is also likely that the aggressive policing of disorderly behaviors such as panhandling or prostitution may result in the increased arrests of women too.

Additionally, the implementation of mandatory arrest policies for intimate partner violence may specifically affect women (Chesney-Lind, 2002; S. L. Miller, 2001; Roark, 2016). While these policies were primarily established to protect women from abusive male partners, evidence suggests that the arrests of women increased as a result of domestic disputes (Chesney-Lind, 2002; S. L. Miller, 2001; Roark, 2016). Many police departments adopted dual-arrest strategies, where both parties are arrested when the identity of the aggressor is unclear (Roark, 2016; Schwartz et al., 2009). In such cases, women are arrested who traditionally would not have been arrested on a domestic violence call.

There are likely situations, however, where men are more likely to be arrested than women. Findings from police stop, question, and frisk (SQF) stops may have analogous implications for male arrests. Research on SQF suggests that there may be a lower threshold of suspicion for people of color to be stopped (Gaston, 2019a; Gelman et al., 2007), although they are less likely than Whites to be arrested as a consequence of the incident. This suggests that officers may be relying on extra-legal factors or stereotypes instead of suspicion of illegal activity when deciding who to stop. Gaston (2019b) found that police interpret the clothing and mannerisms of Black residents differently, and are more likely to perceive their behaviors as criminal. Similarly, Terrill and Mastrofski (2002) found that non-white, male, poor, and younger suspects were more likely to be treated with force during police stops (regardless of their behavior); and, controlling for offense serious, Sobol et al. (2013) found that officers responded more vigorously to suspects who were male, young, and disrespectful.

Research suggests that officers may be more likely to perform discretionary stops for minor crimes when these incidents occur in mixed or Black neighborhoods (Gaston, 2019a). Mixed or Black neighborhoods are also associated with officers engaging in more aggressive police behavior. In a series of simulation studies examining the interaction between attire, neighborhood context, and race, Kahn et al. (2017) found that Black individuals in dangerous neighborhoods were more likely to be “shot” by police relative to Whites in the same neighborhood, and Blacks in stereotypically threatening clothing (i.e., hoodies) faced more shooter bias than Black individuals wearing more neutral attire (Kahn & Davies, 2017). Analogously, law enforcement officers may have similarly lower thresholds for stops based on sex. For example, police may rely on sex- or gender-based stereotypes, such that most offenders are men or that men are more aggressive to infer that men’s general demeanors or attitudes are suspicious, leading to more male arrests.

Further, officers may interpret dress and behaviors differentially by sex, and that the interpretation may interact with the larger structural characteristics of neighborhoods. The suspicion of women may, in part, be assessed on the basis of their dress and their adherence to stereotypical “appropriate” feminine behaviors. For example, early research by Visher (1983) found that officers were more likely to extend leniency to women who projected feminine images and behaviors. Her work replicates even earlier conclusions by DeFleur (1975), who showed that officers’ leniency toward women suspected of drug violations was contingent on their feminine demeanor. This suggests that the likelihood of arrests for women may be driven, in part, by their personal characteristics more so than their suspected involvement in illegal activity. Notably, however, the interpretation of suspect behaviors may vary based on the larger context. Women who are dressed provocatively may be stereotyped as prostitutes when they are in a high-crime or disadvantaged areas where prostitution is more common. Indeed, prior research has shown that police attention to street prostitution is particularly high in neighborhoods where prostitution is generally deemed unacceptable (Krüsi et al., 2016; Larsen, 1992).

However, these arrest patterns might also vary by offense severity. Prior research has shown that higher priority calls are more likely to result in arrests than less serious ones (Rief & Huff, 2023). Notably, however, Rief and Huff (2023) found this to only be the case between Priority 1 calls (the most serious) and Priority 2 calls (the middle level of seriousness). In fact, they found a 13% higher probability of arrest for Priority 3 calls (the least serious). While Rief and Huff (2023) accounted for a wide array of officer characteristics, they did not assess suspect characteristics or the context in which the arrest occurred. The anomalous higher likelihood of arrest for the least serious crimes may be, in part, driven by officer discretion of these suspect and neighborhood characteristics.

Current Study

The current study investigates the relationships between neighborhood context and sex-specific arrests. A large body of research (see Kochel et al., 2011) has examined differential rates of arrest by race, but less attention has been paid to sex. Further, we disaggregate by offense severity to assess differences across violent (aggravated assaults), property (burglary), or drug offenses. We have three major objectives: (1) assess the impact of neighborhood structural characteristics on overall suspect arrests for aggravated assault, burglary, and drug crime; (2) test for significant differences of structural effects on male versus female arrests across crime type; and (3) assess within sex differences across levels of crime severity. Based on research showing that neighborhood factors influence police decision-making (e.g., Sobol et al., 2013), including race-specific arrests (e.g., Gaston, 2019a, 2019b), and evidence that officers may be more likely to use their discretion for the benefit of women (Brown, 1981; M. R. Smith et al., 2006) and less serious incidents (e.g., Farrell, 2015; Huff, 2021), we expect that neighborhood context generally will have a greater impact on the arrest of women as opposed to men, and that within sex, neighborhood factors will affect arrests for less serious crimes to a greater extent than more serious ones regardless of sex. We conduct a series of negative binomial models using data from Aldan, a pseudonym for a mid-sized city in the South. We employ seemingly unrelated estimation using the post estimation command to look for significant differences across and within sex.

Data and Methods

Data

To assess the impact of neighborhood structure on within and between gender disparities in arrests for serious and non-serious crimes, we use 2010 through 2015 data from the city of Aldan, a pseudonym for a mid-sized, representative city located in the US South. Aldan had a population of approximately 250,000 in 2010; demographically, 69% of the population was White, 24% Black, and 6.6% were Hispanic residents (U.S. Census, 2010). We obtained individual-level arrest data for all assaults, burglaries, and drug related crimes. The data include the demographic data of the suspect such as sex, race, and age, as well as the street address where the incident occurred. The address data were geocoded and then summed to obtain 2010 census block group level counts of total aggravated assault, burglary, and drug arrests. To capture neighborhood structure, we use data from the American Community Survey 5-year estimates (2010–2014). In 2010, there were 222 block groups in Aldan, but we excluded 3 block groups due to low residential population and an additional 6 block groups were dropped because of missing census data. The final sample includes 213 block groups.

Dependent Variables

Our outcome variables are the total number of arrests for aggravated assaults, burglaries, and drug crimes (possession and sales). We also disaggregate each arrest type by sex, thus each arrest-type was coded such that three possible outcomes exist: total arrests, total arrests for men, and total arrests for women. We pool our data across all years to increase the prevalence and variability of arrests across sex, particularly for women who are much less prevalent relative to men. During the study period, the Aldan Police Department made a total of 2,670 arrests for aggravated assault, 3,556 arrests for burglary, and 17,697 arrests for drugs. There was no missing data: the sex of the suspect was recorded for all arrest incidents. Men make up the vast majority of arrests in all three categories: 68% of arrestees for aggravated assaults, 88% for burglaries, and 81% for drug arrests.

Independent Variables

To examine the effect of neighborhood structure on sex-specific arrests, we include block group-level variables associated with social disorganization and consequently levels of police deployment. We include several variables capturing the overall sociodemographic structure of a neighborhood. To capture disorder in the neighborhood, we account for all calls for service related to drug use, public drunkenness, and disorderly behavior. We measure concentrated disadvantage as a principal components analysis comprised of the following measures: (1) percent of residents below the poverty line; (2) median household income (reverse coded); (3) percent of residents with a Bachelor’s degree (reverse coded); and (4) the percent of single parent households with children. We include the percent of residents who moved in the last year as an indicator of residential instability. To potentially account for physical disorder, we include the percent of vacant housing units and the percent of owner-occupied housing units. We account for the racial composition of a neighborhood by including a measure of percent Black residents.² Finally, we include a measure of ethnic heterogeneity in a neighborhood using the Herfindahl Index to capture the degree of racial mixing in a neighborhood (Hipp et al., 2009; Sampson & Graif, 2009).

Descriptive statistics for all variables used in the analysis are shown in Table 1. There is no evidence of multicollinearity, as all VIF scores are well below 10, the commonly specified cutoff in macro-level research (Kennedy, 2003).

Table 1.

Summary Statistics, Aldan Neighborhoods N = 213.

Variables	M	SD	Minimum	Maximum
Number of arrests
Aggravated assault	12.54	15.33	0	109
Burglary	16.69	17.41	0	207
Drugs	83.08	131.44	0	1230
Aggravated assault, males	8.61	10.97	0	93
Burglary, males	14.76	15.14	0	176
Drugs, males	67.66	109.62	0	1034
Aggravated assault, females	3.79	5.20	0	30
Burglary, females	1.73	2.55	0	28
Drugs, females	15.43	23.56	0	196
Neighborhood characteristics
Percent Black	26.05	32.85	0	100
Ethnic heterogeneity	25.68	16.38	0.00	61.61
Concentrated disadvantage	0.00	1.00	−3.02	3.14
Percent poverty	17.32	12.74	0	62.50
Median HH income (in $1,000s)	48.36	21.45	1.14	145.14
Percent residents with bachelors degree	28.09	16.88	0	79.77
Percent single parent HH with kids	17.68	15.88	0	76.13
Residential instability	16.00	11.21	0	61.22
Percent vacant units	17.21	11.81	0	61.68
Percent owner occupied units	61.92	24.03	0	100

Analytical Plan

We employ negative binomial models to assess how neighborhood factors differentially predict the overall and sex-specific number of aggravated assaults, burglaries, and drug arrests. Depending on the model, we use both the overall population as an offset measure for the combined analysis, and the sex-specific population for the sex-specific models. The exposure variable is log transformed and constrained to a coefficient of one, to account for the potential population at risk of arrest. Essentially, this estimates the outcome measure as an overall arrest rate, and an arrest rate for each sex for each crime type.³

To account for the non-independence of arrests for men and women occurring in the same neighborhood, we use seemingly unrelated estimation using the post estimation command suest in Stata (Zellner, 1962). This is an established technique used when comparing subgroups within neighborhoods or cities (Chamberlain et al., 2021; Steffensmeier & Haynie, 2000a). The suest command combines the estimation results from each equation into a single parameter vector with a simultaneous covariance matrix; in doing so, it corrects the standard errors prior to determining statistical significance. By employing the suest command, we can also conduct Wald tests, which enable significance testing of parameters across models.

Results

We conduct a series of analyses. First, we assess the impact of general neighborhood structural characteristics on total arrests by crime type. Second, we examine between sex across the three arrest types. Third, we then examine the differential effects of neighborhood structural characteristics across arrest type, but within sex.

Neighborhood Structure and Arrests

Table 2 displays the effects of neighborhood structure on arrests. In general, we find few direct predictors of block group arrests. Disorder is positive and significant for each arrest type and has the largest effect on drug arrests. For each additional 10 disorder-related calls for service, there is a 2.8% increase in arrests for assault (Model 1; b = 0.0028, IRR = 1.0028, p < .01), a 2.4% increase in burglary arrests (Model 2; b = 0.0024, IRR = 1.0024, p < .01), and a 5.3% increase in drug arrests (Model 3; b = 0.0053, IRR = 1.0053, p < .01). To determine whether the effects of disorder are significantly different across arrest type we use Wald tests. Wald tests reveal that the magnitude of these effects is significantly different across arrest types: the impact of disorder on drug-related arrests is significantly greater than its effect on assaults (W2) or burglary (W3). There are no differences in the effects of disorder on assault versus burglary (W1).

Table 2.

Negative Binomial Regression Results Predicting Overall Aggravated Assault, Burglary, and Drug Arrests with Measures of Neighborhood Context, Aldan Block Groups.

	Model 1 aggravated assault		Model 2 burglary		Model 3 drug		Wald tests
	Model 1 aggravated assault		Model 2 burglary		Model 3 drug		W1	W2	W3
Variables	b	IRR	b	IRR	b	IRR	AA/B	AA/D	B/D
Disorder	0.0028** (0.0006)	1.0028	0.0024** (0.0008)	1.0024	0.0053** (0.0009)	1.0053		***	***
Percent Black	0.0115** (0.0016)	1.0116	0.0037* (0.0017)	1.0037	0.0101** (0.0020)	1.0102	***		***
Ethnic heterogeneity	−0.0004 (0.0033)	0.9960	−0.0009 (0.0035)	0.9991	−0.0020 (0.0036)	1.0020
Disadvantage	0.3662** (0.0718)	1.4422	0.2018** (0.0698)	1.2236	0.2815** (0.0907)	1.3251	*
Residential instability	0.0102* (0.0052)	1.0103	0.0028 (0.0057)	1.0028	0.0069 (0.0061)	1.0069
Percent vacant units	0.0112* (0.0052)	1.0113	0.0066 (0.0058)	1.0066	0.0071 (0.0062)	1.0071
Percent owner occupied units	−0.0010 (0.0028)	0.9990	−0.0017 (0.0032)	0.9983	−0.0019 (0.0031)	0.9981
Constant	−5.8455** (0.2649)	0.0029	−4.7398** (0.3255)	0.0087	−4.1809** (0.3110)	0.0153

Note. Robust standard errors in parentheses

***

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

The percent of Black residents in a neighborhood is associated with increases in arrests for all three crime types: a 1% increase in the percent Black population is associated with an additional 1.2% arrests for assaults, 0.4% burglary arrests, and 1.1% drug arrests. Wald tests report that the size of these effects is significantly greater for assaults (W1) and drugs (W2), compared to burglary. Similarly, concentrated disadvantage and residential instability are associated with an increase in arrests across all crime types, but only the effect of disadvantage is significantly greater for assaults relative to burglary (W1).

Differentiating Effects of Neighborhood Structure on Arrests for Men and Women

Next, we disaggregate our arrest measures by sex to determine whether neighborhood structure differentially impacts arrests between men and women (Table 3). This allows us to unmask between-sex differences that may have been obscured when looking at arrests in the aggregate. As in the aggregate models, calls-for-service related to disorder is significantly associated with increases in arrests for men and women across all crime types (Table 3, Models 4, 5, and 6). However, Wald tests reveal that the effect of disorder is only significantly different for drug offenses, with the impact of disorder being greater for drug arrests for women (Model 6; b = 0.0062, IRR = 1.0062, p < .01) relative to men (Model 6; b = 0.0048, IRR = 1.0048, p < .01). There are also some interesting differences in arrests between sex associated with the percent Black residents in a neighborhood. As the percentage of Black residents in a neighborhood increases there are more arrests for assault for men (Model 4; b = 0.0108, IRR = 1.0109, p < .01) and women (Model 4; b = 0.0181, IRR = 1.0183, p < .01), and this effect is significantly greater for women. However, for the remaining crime types, it is only associated with an increase in burglary (Model 5; b = 0.0058, IRR = 1.0058, p < .01) and drug (Model 6; b = 0.0135, IRR = 1.0136, p < .01) arrests for men. Wald tests reveal that the effect sizes are only statistically different for drug arrests. Residential instability is only significantly related to increases in female burglary, and Wald tests reveal this effect is significantly greater than the null effect for men (Model 5; W5). There are no significant between sex differences in the remaining factors, though concentrated disadvantage is associated with an increase in the arrest rates for both men and women across all offense types.

Table 3.

Negative Binomial Regression Results Predicting Male and Female Aggravated Assault, Burglary, and Drug Arrests with Measures of Neighborhood Context, Aldan Block Groups.

Variables	Model 4 aggravated assault				Model 5 burglary				Model 6 drug				WALD tests
	Model 4 aggravated assault				Model 5 burglary				Model 6 drug				W4	W5	W6
	Male		Female		Male		Female		Male		Female		AA M/F	B M/F	D M/F
	b	IRR	b	IRR	b	IRR	b	IRR	b	IRR	b	IRR	AA M/F	B M/F	D M/F
Disorder	0.0027** (0.0006)	1.0027	0.0022** (0.0006)	1.0022	0.0021** (0.0007)	1.0021	0.0027** (0.0009)	1.0027	0.0048** (0.0009)	1.0048	0.0062** (0.0010)	1.0062			***
Percent Black	0.0108** (0.0020)	1.0109	0.0181** (0.0024)	1.0183	0.0058** (0.0018)	1.0058	0.0050 (0.0031)	1.0050	0.0135** (0.0021)	1.0136	0.0024 (0.0023)	1.0024	*		***
Ethnic heterogeneity	−0.0027 (0.0038)	0.9973	0.0037 (0.0042)	1.0037	−0.0009 (0.0039)	1.0009	−0.0099* (0.0047)	0.9901	−0.0032 (0.0039)	0.9968	−0.0006 (0.0042)	0.9994
Disadvantage	0.3800** (0.0848)	1.4623	0.3043** (0.1024)	1.3557	0.1847* (0.0722)	1.2029	0.2697** (0.1027)	1.3096	0.2938** (0.0896)	1.3415	0.2185* (0.1031)	1.2442
Residential instability	0.0097 (0.0063)	1.0097	0.0082 (0.0066)	1.0082	−0.0009 (0.0064)	0.9991	0.0183* (0.0087)	1.0185	0.0049 (0.0067)	1.0049	0.0050 (0.0068)	1.0050		*
Percent vacant units	0.0103 (0.0056)	1.0104	0.0174** (0.0063)	1.0176	0.0076 (0.0059)	1.0076	−0.0029 (0.0077)	0.9971	0.0064 (0.0066)	1.0064	0.0112 (0.0067)	1.0111
Percent owner occupied units	−0.0021 (0.0035)	0.9979	−0.0011 (0.0031)	0.9989	−0.0032 (0.0035)	0.9968	0.0018 (0.0038)	1.0018	−0.0031 (0.0034)	0.9969	−0.0023 (0.0035)	0.9977
Constant	−5.2666** (0.3289)	0.0052	−6.7628** (0.3371)	0.0012	−3.9856** (0.3639)	0.0186	−6.6324** (0.3955)	0.0013	−3.5299** (0.3369)	0.0293	−5.0871** (0.3613)	0.0062

Note. Robust standard errors in parentheses

***

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

Differentiating Effect of Neighborhood Structure Within Sex Across Arrest Type

Finally, we examine whether there are meaningful differences in neighborhood structure within sex but across arrest type. Table 4 presents the results for arrests for men. Consistent with previous models, disorder, the percent of residents who are Black, and disadvantage are all positively associated with increased arrests across all offense types. When examining the Wald tests, disorder exhibits a greater effect on drug arrests relative to burglary or assaults (W8 and W9). For percent Black, the magnitude of effect is greater for assaults and drugs relative to burglary (W7 and W9) while the impact of disadvantage is greater for assaults compared to burglary (W7).

Table 4.

Negative Binomial Regression Results Predicting Male Aggravated Assault, Burglary, and Drug Arrests with Measures of Neighborhood Context, Aldan Block Groups.

	Model 7 aggravated assault		Model 8 burglary		Model 9 drug		Wald tests
	Model 7 aggravated assault		Model 8 burglary		Model 9 drug		W7	W8	W9
Variables	b	IRR	b	IRR	b	IRR	AA/B	AA/D	B/D
Disorder	0.0027** (0.0006)	1.0027	0.0021** (0.0007)	1.0021	0.0048** (0.0009)	1.0048		***	***
Percent Black	0.0108** (0.0020)	1.0109	0.0058** (0.0018)	1.0058	0.0135** (0.0021)	1.0136	*		***
Ethnic heterogeneity	−0.0027 (0.0038)	1.0027	−0.0009 (0.0039)	0.9991	−0.0032 (0.0039)	1.0032
Disadvantage	0.3800** (0.0848)	1.4623	0.1847* (0.0722)	1.2029	0.2938** (0.0896)	1.3415	*
Residential instability	0.0097 (0.0063)	1.0097	−0.0009 (0.0064)	0.9991	0.0049 (0.0067)	1.0049
Percent vacant units	0.0103 (0.0056)	1.0104	0.0076 (0.0059)	1.0076	0.0064 (0.0066)	1.0064
Percent owner occupied units	−0.0021 (0.0035)	0.9979	−0.0032 (0.0035)	0.9968	−0.0031 (0.0034)	0.9969
Constant	−5.2666** (0.3289)	0.0052	−3.9856** (0.3639)	0.0186	−3.5299** (0.3369)	0.0293

Note. Robust standard errors in parentheses

***

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

For women (Table 5), we find some notable differences within sex across severity of crime type. An increase in disorder is associated with an increase in arrests for women across all crime types, but the magnitude of this effect is significantly greater for drug arrests compared to those for assaults and burglaries (W11 and W12). The percent Black residents is only associated with an increase in arrests for assault, and this is significantly different from the null effects detected for burglary and drug arrests (W10 and W11). Ethnic heterogeneity is associated with a decrease in burglary only, and this effect is significantly greater relative to the null effect for assault (W10). Finally, an increase in the percent of vacant units in a neighborhood increases the likelihood that women will be arrested for aggravated assaults, and this effect is significantly different from the null effects for burglary (W10).

Table 5.

Negative Binomial Regression Results Predicting Female Aggravated Assault, Burglary, and Drug Arrests with Measures of Neighborhood Context, Aldan Block Groups.

	Model 10 aggravated assault		Model 11 burglary		Model 12 drug		Wald tests
	Model 10 aggravated assault		Model 11 burglary		Model 12 drug		W10	W11	W12
Variables	b	IRR	b	IRR	b	IRR	AA/B	AA/D	B/D
Disorder	0.0022** (0.0006)	1.0022	0.0027** (0.0009)	1.0027	0.0062** (0.0010)	1.0062		***	***
Percent Black	0.0181** (0.0024)	1.0183	0.0050 (0.0031)	1.0052	0.0024 (0.0023)	1.0024	***	***
Ethnic heterogeneity	0.0037 (0.0042)	1.0037	−0.0099* (0.0047)	0.9901	−0.0006 (0.0042)	1.0015	*
Disadvantage	0.3043** (0.1024)	1.3557	0.2697** (0.1027)	1.3096	0.2185* (0.1031)	1.2442
Residential instability	0.0082 (0.0066)	1.0082	0.0183* (0.0087)	1.0185	0.0050 (0.0068)	1.0050
Percent vacant units	0.0174** (0.0063)	1.0176	−0.0029 (0.0077)	0.9971	0.0112 (0.0067)	1.0113	**
Percent owner occupied units	−0.0011 (0.0031)	0.9989	0.0018 (0.0038)	1.0018	−0.0023 (0.0035)	0.9977
Constant	−6.7628** (0.3371)	0.0012	−6.6324** (0.3955)	0.0013	−5.0871** (0.3613)	0.0062

Note. Robust standard errors in parentheses

***

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

Discussion

This study examined the impact of neighborhood structural characteristics on sex-specific arrests, and whether offense severity mitigates any differences within and between sex. We assessed the extent to which neighborhood structure, especially factors associated with social disorganization and disorder, might differentially impact arrests for men and women for assault, burglary, and drug offenses. In general, we find few differences. However, two factors—the percent of Black residents and neighborhood disorder—consistently accounted for variation in arrests within and between sex, and across offense severity.

First, the racial composition of a neighborhood is an important factor driving arrest patterns, but has differential effects between sex depending on offense type. In the baseline models, the percent of Black residents exhibited the greatest effect on arrests for assaults compared to arrests for either burglaries or drugs. A slightly different pattern emerged when arrests were disaggregated by sex: the percent Black population had a significantly greater effect on drug arrests for men compared to women. Racial stereotypes link Black Americans to social problems such as crime and poverty (Bobo & Kluegel, 1997; Loury, 2009), but Black men are most pervasively linked to illicit drug use and drug sales (Welch, 2007). These implicit biases may alter how officers interact with and formulate arrest decisions when interacting with suspects in predominantly Black neighborhoods, especially men. Prior research has found that discretionary stops for drugs are more likely to occur in Black or mixed neighborhoods relative to White neighborhoods (Gaston 2019a), and that officer misconduct is more likely to occur in poor, minority neighborhoods (Kane, 2002; 2005; Weitzer, 2010). This suggests that in predominantly Black neighborhoods, officers may also take sex into account when determining whether to make an arrest for a drug crime. Indeed, the neighborhood Black population was not a significant predictor of burglary or drug arrests for women. This may mean that officers perceive men and women in predominantly Black communities differently. For men, the degree to which men are stereotyped as criminals, may drive arrests. Indeed, prior research has found that men, particularly Black men, are disproportionately stopped and arrested by police, especially in neighborhoods characterized by high rates of disorder and disadvantage (Fagan & Davies, 2000). Future research should further disaggregate by sex and race to determine whether these sex-specific patterns for drug arrests hold true for Black versus White men and women.

In contrast, the percent Black population had a significantly greater effect on arrests for assaults for women relative to men. As women’s participation in crime steadily increases—especially for more serious crime (Lauritsen et al., 2009; Prison Policy Initiative, 2019)—sex differences may be leveling off. The number of women living in poverty continues to rise, and women may be increasingly using violence as they become enmeshed in street culture (Miller, 1998). In doing so, women may employ violence to establish or maintain reputations to appear “tough” or “hard” (Miller & Mullins, 2006), or to protect themselves from predatory or abusive men (Steffensmeier & Allan, 1996). Violence may be more prevalent in Black communities where a culture of violence is more common (Anderson, 1990). At the same time, police have increasingly implemented proactive policing policies aimed at reducing violence (Schwartz et al., 2009), especially intimate partner violence (IPV). These often include mandatory arrest policies aimed at removing male abusers, but a collateral consequence of these policies is that women may also be arrested when police are unable to determine which party is the aggressor (McCormack & Hirschel, 2021; Roark, 2016; Schwartz et al., 2009). Notably, although historically tense relations between Black communities and the police often translate to a reduced willingness to involve authorities in more minor disputes—such as domestic ones—many of these dual arrest policies involve proactive policing strategies that put officers in neighborhoods actively looking for (and discouraging) crime regardless of whether residents have initiated the contact. These proactive policies may offset reticence among residents in disadvantaged neighborhoods to report to police (Berg & Loeber, 2011; Goudriaan et al., 2006; Jacobs & Wright, 2006). Notably, a recent study analyzing IPV incidents found that Black female victims were more likely to engage in self-defense during these incidents, and that these incidents had a higher likelihood of being reported to the police (Powers & Bleeker, 2023). Further, some work suggests that neighborhood factors differentially predict domestic violence assaults relative to simple or violent ones when disaggregating by assault type (Boggess et al., 2022). Future work should disaggregate type of aggravated assaults and assess whether IPV arrests are driving these relationships.

Second, the amount of disorder in a neighborhood is associated with an increase in arrests across all offense types, but has a significantly greater effect on drug arrests. This pattern is consistent across all models, suggesting that disorder is a key factor in arrest decisions. Disorder and low-level crimes have been increasingly scrutinized by public officials, leading to the targeted enforcement of these types of crimes (Fagan & Davies, 2000; Howell, 2009). This type of targeted enforcement may have a spillover effect for drug arrests, since drug dealing and illicit activity are more likely to occur in neighborhoods characterized by high levels of disorder and disadvantage (Engel et al., 2012; G. Rengert et al., 2000). Further, since disordered conditions are more common in disadvantaged neighborhoods, informal social control may be relatively weak, forcing residents to increasingly rely on formal actors, such as the police, to address issues related to crime and disorder (Boggess & Maskaly, 2014; Schaible & Hughes, 2012).

Notably, disorder has a qualitatively greater effect on drug arrests for women relative to men. Although early research suggests that officers may be more lenient toward women suspected of drug violations based on their adherence to feminine behaviors (DeFleur, 1975; Visher, 1983), our results do not suggest that women benefitted from this phenomenon. This finding was somewhat unexpected given the strong stereotypes applied to men, particularly Black men, regarding drug use and violence (Welch, 2007). Given that disorder is more likely to occur in disadvantaged neighborhoods, and more drug arrests take place in disordered areas generally (Weisburd & Mazerolle, 2000), officers may be less tolerant of minor drug infractions which may increase the risk of arrest for women to a greater extent. Future research should assess how conditions of disorder and disadvantage collectively influence officer behavior toward men and women.

Third, neighborhood structural conditions were also important factors in determining the likelihood of arrest across different levels of offense severity, but opposing patterns emerged within sex. For men, disorder and percent Black were most impactful for drug arrests compared to burglary or assault. This suggests that men are much more vulnerable to arrests for disorder-related behaviors such as drugs when the offense occurs in a neighborhood more likely to be targeted for police enforcement—namely, a minority neighborhood associated with high levels of disorder. For women, the percent of Black residents in a neighborhood was particularly robust for increasing the risk of arrest for assault. Police have less discretion for more serious crimes such as assault, and the racial composition of a neighborhood may diminish an officer’s willingness to defer arrest. Some research suggests that minority women or women living in disadvantaged neighborhoods may be more likely to call the police to resolve issues within the private sphere (Hutchison et al., 1994). This may increase the risk of arrest for women in Black neighborhoods, especially if these calls are related to domestic disputes. Collectively, structural factors matter differently when sex is constant, but offense severity varies.

Although this study makes several important contributions to the literature regarding the impact of neighborhood structure differential arrests within and across sex and offense type, there are some limitations that should be acknowledged. First, while prior research has used 911 calls for service to capture disorder (Boggess & Maskaly, 2014; Chamberlain et al., 2021; O’Brien & Sampson, 2015), it is largely driven by residents’ willingness to contact the police. This suggests that there may be discrepancies based on how the resident interprets disorder; indeed, research suggests that disorder is subjective (Wallace, 2011; Wallace et al., 2015). Second, we rely on official police arrest reports, which only capture the number of individuals who were arrested, and does not provide information on suspects who were questioned and released. While having such data would make for an interesting avenue of research, most research on disparities in arrests (Chamberlain et al., 2021; Chappell et al., 2006; Huff, 2021; Lum, 2011; D. A. Smith & Visher, 1981) cannot account for individuals who were stopped and not arrested. Third, due to low base rates, especially for burglary and aggravated assault,⁴ we were unable to incorporate race and sex in this study, and recommend that future studies disaggregate by race, sex, and crime. Research has shown differential effects of neighborhood conditions by race and sex in offending (Boggess et al., 2018; Chamberlain et al., 2021; Steffensmeier & Haynie, 2000a), which suggests that there may also be differential impacts on arrests. Fourth, we are unable to directly measure the presence of police in a given neighborhood, and recommend that future studies should incorporate a measure of police intensity. Finally, our focus on a single city in the South may not be generalizable to other cities or in other regions.

Conclusion

Neighborhood structure is an important driver of offending and arrest rates. Prior research consistently shows an impact on race, but we extend this work across sex and by offense severity. We find that disadvantage is a strong consistent predictor of arrests for both men and women across offense type. But when we look at differences, the percent of Black residents is associated with more drug arrests for men and assault arrests for women. Further, neighborhood disorder has important implications for arrests across crime type and sex, but is especially impactful for female drug arrests. This suggest that officer behavior may vary based on neighborhood composition, particularly the racial composition. As other work suggests (Gaston, 2019a; Ousey & Lee, 2008; D. A. Smith, 1986; Stewart et al., 2009), law enforcement may use race as a proxy for crime overall, and in doing so, may create gender disparities in arrests across different crime types. The racial composition of a neighborhood may make women more vulnerable to arrest for serious crimes, whereas police may be more inclined to arrest men for less serious crimes, such as drugs, based on implicit bias toward men in these neighborhoods as criminals. These differential patterns of arrests across neighborhood type may have implications for the perpetuation of gender disparities in the criminal justice system. This may particularly be the case for less serious crimes, where we find most of the between-sex differences.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iDs

Alyssa W. Chamberlain

Lyndsay N. Boggess

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, and 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.

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, and gentrification and economic investment. Her work has appeared in Criminology, the Journal of Quantitative Criminology, and Crime & Delinquency.

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