Victim-offender racial dyads and clearance of lethal and nonlethal assault

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and Delinquency Journal of Research in Crime

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2009; 46; 301 originallyJournal of Research in Crime and DelinquencyAki Roberts and Christopher J. Lyons

Nonlethal AssaultVictim-Offender Racial Dyads and Clearance of Lethal and

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Victim–Offender Racial Dyads and Clearance of Lethal and Nonlethal AssaultAki RobertsChristopher J. LyonsUniversity of New Mexico

Previous clearance research provides an incomplete test of theories empha-sizing the role of both victim and offender status in police discretion. Using National Incident Based Reporting System (NIBRS) data, we investigate the impact of both victim’s and offender’s race, and, in particular, victim– offender racial dyads on homicide clearance by arrest, using event history (survival) analysis, so that time to clearance and censoring are considered. We also compare models for homicide clearance with those for aggravated assault. For homicides, results indicate that incidents with non-white offend-ers are more likely to be cleared by arrest than those with white offenders, regardless of victim’s race. In contrast, for aggravated assault, dyads are important: incidents involving white victims and offenders are most likely to be cleared, with incidents involving non-white parties least likely to be cleared. Furthermore, the impact of victim–offender racial dyads on clear-ance is smaller for homicide than for aggravated assault.

Keywords: race; clearance; NIBRS; racial dyad

The United States clears by arrest a lower percentage of homicide incidents than do other industrialized countries.1 Furthermore, U.S. homicide clear-

ance rates have decreased over the past few decades. Social scientists recog-nize that low and declining clearance rates, especially for serious crimes such as homicide, may have deleterious consequences for law and society more

301

Authors’ Note: We would like to thank the journal’s editor and anonymous reviewers and Felipe Gonzales, Gary LaFree, John M. Roberts, Howard Waitzkin, and attendees of the University of New Mexico Department of Sociology’s presentation series for helpful comments.

Article Journal of Research in Crime and Delinquency

Volume 46 Number 3August 2009 301-326© 2009 The Author(s)

10.1177/0022427809335168http://jrc.sagepub.com



302 Journal of Research in Crime and Delinquency

broadly. Poor clearance rates may reduce the deterrent effect of the criminal justice system, public trust in police, and morale among police officers (Riedel and Jarvis 1998). Uncleared homicide incidents also traumatize vic-tims’ families and increase fear of victimization (Riedel and Jarvis 1998).

Spurred by the decline of homicide clearance over the past few decades, researchers have studied factors that influence clearance by arrest. Such inquiry has potential policy relevance for improving declining clearance rates, but studying clearance also provides a window into police behavior and particularly the allocation of investigative effort. Researchers have directed attention to the relative importance of discretionary versus nondiscretionary case characteristics in determining police efficacy in clearing homicides. Is crime clearance a function of legally relevant characteristics of a case—that is, characteristics that influence the availability of investigative evidence leading to an arrest? Or, as conflict theories of crime such as Black’s (1976) theory of law and social reaction theory (e.g., Swigert and Farrell 1977) suggest, is clearance also a product of extralegal factors, such as the racial status of victims and offenders? An emerging body of research has consistently found that evidentiary factors tend to affect homicide clearance rates more than racial characteristics of the victim. At the incident level, hypotheses predicting police discretion leading to the devaluing of non-White victims have received little support.

However, previous research has failed to examine the possible effects of offender’s race and victim–offender racial dyads in clearing homicide and therefore has provided an incomplete test of theories that emphasize the role of both victim and offender status in police discretion (e.g., Black 1976; Swigert and Farrell 1977). In the current study, we build on previous research on homicide clearance in a number of ways. First, we examine clearance rates with data from the National Incident-Based Reporting System (NIBRS). Apart from providing detailed incident-level information, NIBRS data allow for examining the effects of offender’s race. Second, because we include offender information, we are able to test hypotheses suggested by Black (1976) about the relevance of victim and offender racial dyads. Third, we compare models of homicide clearance to models for a less serious crime—aggravated assault—to determine the degree to which legal seriousness acts to limit police discretion. Finally, instead of the usual logistic regression typical in crime clearance research, we employ event history analyses to consider the length of time to clearance, increasing the accuracy of estimates (Lee 2005; Regoeczi, Jarvis, and Riedel 2008; Roberts 2007, 2008b).



Roberts, Lyons / Victim–Offender Racial Dyads 303

Theoretical Perspectives and Previous Findings on Homicide Clearance

Two perspectives offer contrasting explanations for the processes that lead to homicide clearance by arrest. The first prioritizes nondiscretionary, evidence-based factors over extralegal factors such as ascriptive attributes of the victim and offender. The nondiscretionary view contends that strong organizational and public pressure to clear homicides—perceived as the most serious of crimes—leads law enforcement to respond to all homicide cases with maximum investigative effort, regardless of victim and offender characteristics. Accordingly, homicide clearance is primarily a function of whether investigators have enough evidence to make an arrest and not a choice of how vigorously to investigate. The quality and quantity of evidence available to officers may depend mostly on situational factors beyond police control, including the presence of witnesses and their willingness to provide information to the police (Cooney 1994), the relationship between victim and offender, and the physical contact between victim and offender (with the potential for DNA or other evidence). Research has consistently shown that the use of a firearm (vs. a contact weapon) decreases the likelihood of clearance, whereas increased familiarity between offender and victim improves odds of clearance (Addington 2006; Alderden and Lavery 2007; Lee 2005; Litwin and Xu 2007; Puckett and Lundman 2003; Regoeczi, Kennedy, and Silverman 2000; Riedel and Rinehart 1996; Roberts 2007). Indeed, the decline in homicide clearance rates during the postwar period coincided with increases in the proportion of gun and stranger homicides (Riedel and Jarvis 1998). Furthermore, lower homicide clearance rates compared to other industrialized countries such as Japan may be because of the greater prevalence of guns in American society (Roberts 2008a). Other research has suggested that homicides committed concurrently with another felony can also lead to lower clearance rates, presumably because such incidents tend to have a random “hit and run” nature that impedes investigation (Litwin 2004).

Black’s (1976) seminal work on the behavior of law offered a contrasting perspective. Black and others (e.g., Cooney 1994) have posited that the “quantity of law,” one aspect of which is the allocation of effort and resources to clear a case, varies with the status of parties. Although the mechanisms linking victim status to investigative effort are not completely clear in Black’s work (for a thorough critique, see Greenberg 1983), Black implied that perceptions of seriousness on the part of the police and the




general public play an important role. At the incident level, homicides involving higher status victims may be perceived as more serious and therefore draw more organizational attention, whereas incidents involving lower status victims should attract less law and have a lower likelihood of clearance. Although Black refers to status primarily in socioeconomic terms, he clearly views racial identity (as well as gender and age) as a proxy for socioeconomic status (Black 1976:20).

Incident-level homicide clearance research has typically found either no effect of victim’s status (age, gender, or race) or effects opposite that implied by Black’s theory.2 For instance, most studies have found that homicide incidents with younger victims have more chance of clearance than do those involving older victims (Addington 2006, 2007; Litwin 2004; Litwin and Xu 2007; Puckett and Lundman 2003; Regoeczi et al. 2000; Regoeczi et al. 2008; Riedel and Rinehart 1996). Homicide clearance also appears either equally likely for male and female victims (Addington 2006, 2007; Litwin 2004; Litwin and Xu 2007; Puckett and Lundman 2003; Riedel and Rinehart 1996; Roberts 2007; Wellford and Cronin 1999) or more likely for female victims (Addington 2006, 2007; Lee 2005; Litwin and Xu 2007; Regoeczi et al. 2000; Regoeczi et al. 2008). Of the studies that examined the effect of victim race, only Addington (2007), Alderden and Lavery (2007), Lee (2005), and Marché (1994) found clear evidence that crime clearance is less likely for non-White victims.3

The Role of Offender Characteristics in Homicide Clearance

Does this body of evidence refute Black’s ideas on the relevance of status for homicide clearance? Before this can be determined conclusively, the theory’s propositions must be investigated to the full extent. We note that the literature on homicide clearance has focused mostly on victim characteristics while neglecting the potential effects of offender characteristics, in particular the offender’s race. In contrast to the predicted positive relationship between victim’s status and law, Black (1976:25) predicted the quantity of law to vary “inversely with the offender’s rank.” All else equal, Black suggested that offenses committed by non-White offenders are viewed as more serious by the police (and the rest of society), receive more attention, and therefore have more chance of clearance than do similar incidents committed by White offenders. Black’s general predictions




about offender racial status are consistent with those of social reaction theorists (Swigert and Farrell 1977) who have argued that stereotypes of the “deviant” held by individuals and the community influence how law enforcement responds to criminal incidents. Because non-White (and particularly African American) offenders are generally viewed as greater threats to society, police are more willing and/or more pressured to invest effort in pursuing such offenders (D’Alessio and Stolzenberg 2003). As very few homicide clearance studies have included information on offender racial status, these basic hypotheses remain largely untested.

Furthermore, Black (1976:28) clearly posited that the quantity of law is a function of not simply victim or offender racial status alone but also the combination of victim and offender racial status. Black suggested a specific order of clearance likelihood based on victim–offender racial dyads. Crime incidents with White victims and non-White offenders, which he referred to as “upward deviance,” should attract the most police attention and effort and be the most likely cleared. The second most serious combination occurs between White individuals, followed by criminal incidents between non-White individuals. Last, crime incidents between non-White victims and White offenders, termed “downward deviance,” are likely viewed as the least serious and should therefore be the least likely cleared (Black 1976).

Intuition about the salience of victim–offender racial dyads is prominent in criminal justice research. Research in other areas has found that victim–offender racial dyads are important predictors of various outcomes, particularly capital punishment sentencing and execution, in patterns supportive of Black’s thesis. For instance, Blacks committing upward interracial homicide (e.g., Blacks who kill Whites) are more likely to be sentenced to death than are Whites who kill Whites, Blacks who kill Blacks, or Whites who kill Blacks (Baldus and Woodworth 2003; Baldus, Woodworth, and Pulaski 1990; Radelet and Pierce 1991). Furthermore, victim race and offender race also interact to influence the likelihood of capital execution, with execution most likely when Black and Hispanic offenders kill Whites (Jacobs et al. 2007). Jacobs et al. (2007) attributed the effect of upward deviance to the increased media exposure and public response to non-White on White homicides, which is consistent with the idea that upward deviance engenders stronger reactions from the criminal justice system and perhaps the larger public. Despite the centrality of victim–offender racial dyads to both Black’s theory and research findings on other criminal justice outcomes, researchers have not investigated how victim race and offender race combine to influence homicide clearance rates.




The exclusion of offender race and victim–offender racial dyads likely stems in large part from limitations inherent in the Supplementary Homicide Reports (SHRs) used in previous homicide clearance studies. Because SHRs do not include information on arrest, researchers use the availability of offender information as a proxy for clearance. For example, Regoeczi et al. (2000) defined an incident as uncleared if SHRs did not indicate any information about the offender (age, race, and gender or the number of offenders). Marché (1994) and Riedel and Rinehart (1996) used similar proxies. With clearance measured by the presence of offender characteristics, offender information is by definition absent for all uncleared incidents, and it is impossible to determine how the likelihood of clearance differs by offender characteristics. It is important to note, however, that availability of offender information does not guarantee clearance by arrest (Addington 2006). Data from the 2000 to 2005 NIBRS, for instance, revealed that 34 percent of homicide incidents with offender information known to police and recorded in NIBRS were not cleared by arrest.

Given these limitations, other homicide clearance researchers have relied on more detailed data from police reports in a single city or county (Alderden and Lavery 2007; Lee 2005; Litwin 2004; Litwin and Xu 2007; Puckett and Lundman 2003) or a limited number of cities (Wellford and Cronin 1999). Among this research, only Wellford and Cronin (1999) examined offender as well as victim characteristics in homicide clearance. They found differences in clearance by offender’s race, but the nature of those differences changed depending on the model specification. Wellford and Cronin did not consider victim–offender racial dyads.

Recent clearance studies by Addington (2006, 2007), Regoeczi et al. (2008), and Roberts (2007) have used NIBRS data, which cover jurisdictions in about half the states. Maxfield (1999) discussed the many potential advantages of NIBRS over other official crime statistics sources, such as SHRs. Most relevant to clearance research is that NIBRS data provide information on whether a suspected offender was arrested for the crime (during the NIBRS year and the follow-up arrest reporting period). Furthermore, NIBRS data record offender information even when the case was not cleared by arrest, allowing examination of the impact of offender characteristics on the clearance outcome (Chilton and Jarvis 1999). However, Addington (2006, 2007), Regoeczi et al. (2008), and Roberts (2007) focused only on the impact of victim characteristics and did not examine offender’s race or victim–offender racial dyads.




Current Research

The current study attempts to investigate more thoroughly propositions related to Black’s theory of law and social reaction theory by examining the impact of victim’s and offender’s race and, in particular, victim–offender racial dyads on homicide clearance by arrest. It also compares models of homicide clearance to those for a less serious crime—specifically, aggravated assault. Gottfredson and Hindelang (1979) argued that legal crime seriousness, determined by degree of bodily injury or financial loss, is the principal predictor of criminal justice outcomes. According to Kalven and Zeisel’s (1966) “liberation hypothesis,” discretion based on extralegal factors should increase as legal seriousness decreases. Thus, victim–offender racial dyads may matter more for aggravated assault clearance than for homicide clearance.

The current study also improves on previous research by modeling the relevance of time to clearance via event history (survival) analysis. Although the event history approach has been used increasingly in recent clearance studies (Lee 2005; Regoeczi et al. 2008; Roberts 2007, 2008b), most previous homicide clearance research employed logistic regression, with the dependent variable taking a value of 1 when an incident was cleared within one year and 0 when it was not. Thus, incidents taking almost a year to clear are treated the same as those that are cleared in a few days. Event history analysis takes fuller advantage of the data by examining the timing of clearance. The event history method also formally recognizes censoring—the possibility that an incident could still clear after the end of the study period. The event history approach thus permits a more satisfactory investigation of the relationship between independent variables and clearance.

Data and Method

Data

Data on incident characteristics and clearance information come from NIBRS. NIBRS is the largest-scale incident-level data set collected by the FBI and was developed to eventually replace Uniform Crime Reports (Dunn and Zelenock 1999). Although police departments in only slightly more than half of the states currently report data to NIBRS, the number of participating police agencies is growing. For each crime incident in participating jurisdictions, NIBRS data provide information such as type and number of offenses involved, victim and offender demographic




characteristics (age, gender, and race), circumstances of the crime incident (weapon use, time of the incident, and victim’s injury), victim–offender relationship, and whether (and when) an arrest was made.

There are three main reasons why NIBRS data are the best available for the current analysis. First, offender information from the relatively early stage of police investigation is recorded in the offender segment; arrest information (whether at least one person was arrested during the study period) is separately recorded in the arrestee segment. NIBRS thus reports offender information even when the case was not cleared by arrest (Chilton and Jarvis 1999). For example, 28 percent of homicide and 48 percent of aggravated assault incidents with offender information recorded in NIBRS in 2005 did not clear by arrest. In contrast to SHR data, the availability of offender information is not totally dependent on clearance by arrest, and it is possible to examine the impact of offender characteristics on the likelihood of clearance. Second, NIBRS data give information on the time between incident and arrest, allowing event history analysis of time to clearance. NIBRS records information on whether a suspected offender was arrested for the crime incident during the NIBRS year and the following year. Thus, incidents are tracked for at least one year, and the maximum time to clearance is two years (730 days). Third, NIBRS contains data for crimes of varying severity, allowing comparison of factors affecting clearance of homicide and aggravated assault.

The current analysis examines 2,798 murder and non-negligent mans-laughter homicides (cleared or not cleared by arrest) taken from 2000 to 2005 NIBRS data.4 We pool homicide data across multiple years because of the relative infrequency of homicide. We also examine 44,667 aggravated assault incidents from the 2005 NIBRS. All data cover jurisdictions in 30 states and the District of Columbia.5 We exclude incidents with multiple victims and/or offenders from the analysis because it is difficult to determine the victim–offender racial dyads in such incidents.

Furthermore, we follow Puckett and Lundman’s (2003) and Alderden and Lavery’s (2007) practice in excluding “quick clearance” incidents. An example of quick clearance is an incident in which a husband kills his wife, then calls police and waits at the crime scene to be arrested. Such incidents require very little investigative work and therefore provide little potential for the exercise of police discretion based on victim–offender racial dyads in investigation and arrest.6 We employ Puckett and Lundman’s definition of quick clearance as “incidents cleared by arrest in the same day.” In all, 58.3 percent of homicide arrests and 32.4 percent of aggravated assault




arrests happened after the day the incident took place. Table 1 shows the frequency distribution of time to arrest.

As with SHR and other official data, NIBRS records are subject to missing information. In all, 35.8 percent of homicide incidents and 20.0 percent of aggravated assault incidents contained missing information in at least one of the independent variables used in this study. Most of the missing information pertains to offender characteristics. Although techniques for dealing with missing data, such as multiple imputation (Riedel and Regoeczi 2004), are increasingly common in criminological research, missing data imputation makes less sense for testing hypotheses derived from Black’s theory of law and/or social reaction theory. We argue that the police can exercise discretion based on offender status and/or victim–offender racial status dyads only if they are aware of this information.7 In the absence of such information, the combination of victim and offender status cannot directly influence police investigative behavior. Therefore, for the main analyses, we focus only on clearance of incidents in which investigators know victim and offender race rather than impute racial information for incidents in which such information is unknown to the police. Nevertheless, to explore the sensitivity of results, we repeated the analyses with missing values imputed for all variables via multiple imputation (Riedel and Regoeczi 2004) and discuss these results immediately after the results without imputation.

Table 1Frequency Distribution of Time to Arrest for Cleared Incidents

Homicides Aggravated Assaults

Time to Clearance Frequency Percent Frequency Percent

Same day 1,324 42.0 24,418 68.0One day 559 18.0 3,363 9.02 to 7 days 554 17.0 3,003 8.08 to 30 days 324 10.0 2,699 7.031 to 90 days 189 6.0 1,528 4.091 to 180 days 120 4.0 644 2.0181 to 364 days 78 2.0 355 1.0365 to 547 days 26 1.0 97 .27548 to 730 days 2 .06 9 .02Total arrests 3,176 36,116Total arrests after one day 1,852 58.0 11,698 32.0




Event History Analysis

We employ continuous-time event history (survival) analysis (Allison 1995; Regoeczi et al. 2008) in the models below. Table 1 indicates that there is substantial variation in time to clearance for both homicide and aggravated assault incidents. The event history approach considers timing of crime clearance and thus uses much more of the potential information in the data than logistic regression. Event history analysis also explicitly recognizes censoring for uncleared incidents. There is a small but nonzero chance of incidents being cleared after the end of the arrest-reporting period (covering the NIBRS year and the following one year). The event history approach neither assumes that such cases will never clear nor arbitrarily treats them as cleared as of some date. Instead, the known length of time until an uncleared incident was censored (at the end of the arrest-reporting period) is part of the information used to estimate effects of independent variables. By using both information on time to clearance for cleared incidents and information on time until censoring for uncleared incidents, event history models improve the accuracy of estimates for the effects of independent variables on clearance (Allison 1995). Event history analysis tends to produce different results from the usual logistic regression on clearance data. For example, Regoeczi et al. (2008) found that in conventional logistic regression analysis victim’s race had a statistically significant impact on homicide clearance (homicide incidents with White victims were more likely to be cleared), but the statistical significance disappeared when time to clearance and censoring were considered via event history analysis. Regoeczi et al. concluded that clearance researchers should explicitly model time to clearance via event history analysis.

A continuous-time event history approach is appropriate for data indicating the time to clearance (Allison 1995). We use the Cox regression approach, which combines the proportional hazards model with partial likelihood estimation methods. A major advantage of the Cox approach is that it does not require specification of exactly how the hazard rate (the instantaneous probability of an event occurring) depends on the passage of time (Allison 1995). Other methods rely on an assumed probability distribution of event times, and it may be difficult to decide which distribution is most appropriate. Formally, the Cox model describes the relationship between the covariates and the hazard rate.8 Parameter estimates rely on the observed event (clearance) times and times to censoring (for uncleared incidents) and can be obtained in SAS PROC PHREG. Although these times are inputted as the dependent variable, the analysis treats




cleared and censored incidents differently. Also, note that the hazard rate is inversely related to the clearance time: Incident characteristics that imply a high hazard rate imply a relatively short time to clearance, and characteristics implying a low hazard rate imply a relatively long time to clearance. The hazard ratio exp.(i) represents the difference in the hazard resulting from a difference of 1 unit in Xi and is analogous to an odds ratio in logistic regression. Clearance data contain tied event times because two or more incidents can clear in the same number of days. With the number of days representing a grouping of the possible continuous times to clearance, there is actually some precise but unknown ordering of the apparently tied event times. Partial likelihood methods use the ordering of event times to estimate effects of covariates on the hazard, so some method of dealing with ties is necessary. Allison (1995) reviewed different methods for ties; this research used the EXACT method in PROC PHREG.

Dependent Variable

The model described above is for the hazard rate, but in practice the time to clearance or censoring can be viewed as the dependent variable. Time to clearance is the number of days between incident and arrest for cleared incidents, and time to censoring is the number of days between incident and the end of the arrest-reporting period for uncleared (censored) cases. As noted above, NIBRS reports arrest information during the NIBRS year and the following year, so the maximum value for the dependent variable is 730 days (two years).

Independent Variables

The combination of victim–offender racial status is the main independent variable. Following the order suggested by Black (1976), we classify victim–offender racial dyads into four categories: White victim and non-White offender (dyad 1), White victim and White offender (dyad 2), non-White victim and non-White offender (dyad 3), and non-White victim and White offender (dyad 4). According to Black, dyad 1 incidents (upward deviance) should attract the most police attention and investigative effort and are most likely to be cleared, followed by dyad 2, dyad 3, and finally dyad 4 incidents (downward deviance). Non-White includes African Americans, Native Americans, Asians, and other racial categories, although African Americans made up most of the non-White category in these data.9 We note that NIBRS data do not permit identification of Hispanic ethnicity




for offenders. In addition to race/ethnicity, we include information on victim’s and offender’s gender and on victim’s age.10 We follow Addington (2006) and Roberts (2007) and categorize victim’s age into 0 to 12 years (reference category), 13 to 59 years, and 60 years and older. Previous research has indicated that homicide incidents with children are usually most likely to be cleared (Addington 2006; Alderden and Lavery 2007; Litwin 2004; Litwin and Xu 2007; Puckett and Lundman 2003; Regoeczi et al. 2000; Riedel and Rinehart 1996).

Situational characteristics that may determine the quantity and quality of evidence available to the police include type of weapon (contact [knife, blunt objects, hands] vs. noncontact [firearm, poison, drugs]), presence versus absence of concomitant felony, victim–offender relationship (family member, friend or acquaintance, or stranger), and the time of day in which the incident occurred.11 We classify incidents by time of day (7:00 a.m. to 9:59 p.m. vs. 10:00 p.m. to 6:59 a.m.) because incidents occurring between 7:00 a.m. and 9:59 p.m. are likely more visible and exposed than are incidents occurring during late night or early morning hours. Because of the potential presence of more physical evidence, witnesses, or information, we hypothesize greater likelihood of clearance for incidents involving a contact weapon, no concomitant felony, or an offender known to the victim and for incidents occurring during high visibility and exposure hours.

For the comparative analysis of aggravated assault clearance, we add severity of victim injury (no or minor injury vs. major injury).12 Aggravated assault incidents involving seriously injured victims are likely considered more serious by the police and the community and thus are expected to clear more readily than otherwise similar aggravated assaults. Furthermore, according to the liberation hypothesis, extralegal factors such as victim–offender racial dyads should matter less for homicides than for aggravated assaults. The appendix presents descriptive statistics for all variables (Appendix A) and the number of cleared and uncleared homicide and aggravated assault incidents by victim–offender racial dyad (Appendix B).

Results

Homicide Clearance

The first columns of Table 2 show the results of the event history analysis for homicide. Parameter estimates indicate that homicide incidents with non-White offenders and White victims (dyad 1—the reference category) have the greatest risk of clearance, followed by incidents with




both non-White victims and offenders (dyad 3), then by incidents with both White victims and offenders (dyad 2), and finally by incidents with White offenders and non-White victims (dyad 4).

Table 2Coefficients and Hazard Ratios for Homicide and Aggravated Assault

Clearance by Arrest from Event History Analysis

Homicides (n = 2,798)Aggravated Assaults

(n = 44,667)

b SEHazard Ratio b SE

Hazard Ratio

Victim–offender racial dyadsa

White on White (dyad 2) –.189 .086** .828 .154 .033** 1.167 Non-White on non-White

(dyad 3)–.058 .084 .944 –.493 .034** .611

White on non-White (dyad 4) –.262 .156* .769 –.082 .062 .921Victim’s gender Female –.163 .061** .850 .156 .021** 1.169Offender’s gender Female –.007 .081 .993 –.191 .025** .826Victim’s ageb

Age 0 to 12 .232 .119* 1.262 –.137 .075* .872 Age 13 to 59 .066 .089 1.068 –.038 .061 .963Weapon typec

Contact .408 .051** 1.504 .207 .028** 1.230Concomitant offense Present .088 .098 1.092 .330 .034** 1.391Victim–offender relationshipd

Family .264 .074** 1.302 .846 .030** 2.330 Friends or acquaintances .342 .057** 1.408 .468 .029** 1.597Timee

High visibility or exposure hours

–.043 .048 .958 –.088 .019** .916

Victim’s injuryf

Major injury — — — .431 .020** 1.539

a. Referent is non-White on White (dyad 1).b. Referent is age 60 and older.c. Referent is noncontact weapon.d. Referent is stranger and unknown to police.e. Referent is low visibility or exposure hours.f. Referent is minor or no injury.*p < .05, one-tailed. **p < .01, one-tailed.




A likelihood ratio test comparing the full model to the model with racial dyad variables removed reveals statistically significant differences in homicide clearance across racial dyads (p < .0001). Table 2 indicates statistically significant differences in hazard rates between dyad 1 (the reference category) and each of dyad 2, dyad 3, and dyad 4. To assess the significance of differences among dyad 2, dyad 3, and dyad 4, we repeated analyses with different choices of reference categories. From these analyses, the difference between dyad 1 and dyad 3 is not statistically significant, so those two scenarios are essentially equal with respect to clearance. Similarly, the difference between dyad 2 and dyad 4 is not statistically significant. However, the difference between the dyad 1 and dyad 3 pair and the dyad 2 and dyad 4 pair is statistically significant. (Recall that the order of highest to lowest estimated clearance odds is dyad 1, dyad 3, dyad 2, and dyad 4.) Thus, the two racial dyads involving non-White offenders (dyad 1 and dyad 3) are significantly more likely to be cleared than the two dyads involving White offenders (dyad 2 and dyad 4), but for neither pair is there a significant difference by victim’s race. This suggests that homicide incidents with non-White offenders appear more likely to be cleared than those with White offenders, regardless of the victim’s race. In that sense, it seems that examining racial dyads for homicide is not necessary because the pattern of results for the dyads is explained by offender’s race alone.

To verify this finding, we conducted an analysis with separate variables for victim’s and offender’s race instead of the racial dyad categories. The first columns of Table 3 show the effects of victim’s and offender’s race separately on homicide clearance hazard rates. (Results for other independent variables are not shown because they are very similar to those in Table 2.)

Table 3Coefficients and Hazard Ratios for Homicide and Aggravated Assault

Clearance by Arrest from Event History Analysis

Homicides (n = 2,798) Aggravated Assaults (n = 44,667)

b SEHazard Ratio b SE

Hazard Ratio

Victim’s race Non-White –.062 .072 .940 –.426 .030** .653Offender’s race Non-White .193 .073** 1.213 –.214 .029** .807

Note: Includes controls for all nonrace variables in Table 2 (estimates not shown).*p < .05, one-tailed. **p < .01, one-tailed.




The results confirm the findings from the earlier analysis using victim–offender racial dyads. According to the first column of Table 3, victim’s race does not have a statistically significant impact on homicide clearance hazard rates, controlling for offender’s race and other variables. However, offender’s race does significantly affect homicide clearance: Hazard rates of clearance for homicide incidents with non-White offenders are greater than those for incidents with White offenders. These results are only partially consistent with the hypothesis derived from Black (1976), as the quantity of law appears to be inversely related to offender’s race but not related to victim’s race.

Aggravated Assault Clearance

To compare homicide to an offense of lesser severity, the latter columns of Table 2 show results for aggravated assault. As for homicide clearance, a likelihood ratio test indicates statistically significant (p < .0001) differences in hazard rates for aggravated assault across victim–offender racial dyads. However, the estimated clearance hazard order is different for aggravated assault. Aggravated assault incidents between Whites (dyad 2) are most likely to be cleared, followed by interracial incidents (dyad 1 and dyad 4) and then incidents between non-Whites (dyad 3). Analyses with different reference categories (as above for homicide) indicate that the difference in hazard rates between the two kinds of interracial incidents (dyad 1 and dyad 4) is not statistically significant but that other differences are.

The analysis with separate variables for victim’s and offender’s race (the latter columns of Table 3) finds that both victim’s race and offender’s race significantly affect aggravated assault clearance. Estimated hazard rates are much greater when the victim or offender is White. This is consistent with the results for the racial dyads, in which clearance hazard rates are highest for incidents involving White victims and offenders (dyad 2) and lowest for incidents involving non-White victims and offenders (dyad 3).

Comparing the estimates for victim and offender racial status in Table 2 reveals that differences in clearance hazard rates across racial dyads are smaller for homicide than for aggravated assault. For homicide, the hazard rate for the dyads with the lowest estimated hazard (dyad 4) is 23 percent lower (calculated by 1 – e(–.262 – 0)) than that of the dyads with the greatest (dyad 1). But for aggravated assault, the difference of lowest (dyad 3) from highest (dyad 2) is 48 percent (calculated by 1 – e(–.493 – .154)). These calculations suggest that the impact of victim–offender racial dyads on clearance varies by the seriousness of the crime: Victim–offender racial dyads seem to matter more for less serious offenses.13




Effects of Control Variables

The estimates for other independent variables in Table 2 also reveal different patterns for homicide and aggravated assault. For instance, victim’s gender significantly influences both homicide and aggravated assault clearance hazard rates, but in different directions. For homicides, incidents with female victims have a 15 percent lower clearance hazard rate than do incidents involving male victims. In contrast, aggravated assaults with female victims have a 17 percent greater clearance hazard rate. On the other hand, aggravated assaults with female offenders have a lower risk of clearance. We find statistically significant differences by victim’s age for both homicide and aggravated assault. Consistent with most previous studies, homicide incidents with child victims (i.e., younger than 13 years old) have the greatest clearance hazard rate. In stark contrast, aggravated assault incidents with child victims were least likely to be cleared.

As in previous research, homicides and aggravated assaults with contact weapons and known offenders had the greatest risk of clearance, probably because those incidents produce more information and physical evidence for investigation (Addington 2006; Litwin 2004; Puckett and Lundman 2003; Riedel and Jarvis 1998; Roberts 2007). However, type of weapon influences homicide clearance more strongly than it does aggravated assault clearance. Although use of a contact rather than a noncontact weapon increases clearance hazard rates by about 50 percent for homicide, contact weapons increase aggravated assault clearance hazard rates by only 23 percent for aggravated assault. Physical evidence provided by a contact weapon appears less necessary in solving aggravated assaults, perhaps because investigators can collect more useful information from a surviving victim.

Furthermore, we find that other incident characteristics thought to influence the quantity and quality of evidence, namely, the presence of a concomitant offense and time of incident, influence the clearance hazard rates for aggravated assault but not homicide. However, the signs of these estimates for aggravated assault are unexpected. Aggravated assault incidents with a concomitant offense and those occurring during low visibility and exposure hours have greater clearance hazard rates than those without a concomitant offense or those occurring during higher visibility and exposure hours. Note, though, that the magnitude of the time of day effect is small (an 8 percent difference in estimated hazard rates for high vs. low visibility hours).14 Finally, consistent with arguments about the role of crime seriousness in clearance (Gottfredson and Hindelang 1979), the severity of the injury’s impact on clearance hazard rates for aggravated assault is positive and statistically significant as expected.




Sensitivity Analyses with Imputed Missing Values

We reasoned earlier that police could exercise discretion based on victim–offender racial dyads only if they are aware of this information. Thus, we focused exclusively on clearance of incidents in which investigators knew the victim’s and the offender’s race rather than imputing racial information for incidents in which such information was unknown to police. Nonetheless, we also report sensitivity analyses with missing values imputed for all variables via multiple imputation (Riedel and Regoeczi 2004; Roberts 2007, 2008b).15

Details of the results are available on request; here, we summarize them briefly. For both homicide and aggravated assault, results with missing data imputed are identical in terms of signs of estimates, but for homicide clearance there are slight differences in statistical significance. The estimated order (from highest to lowest clearance hazard rate) of the victim–offender dyads for homicide clearance is the same in the two analyses (dyad 1 > dyad 3 > dyad 2 > dyad 4), but with missing data imputed the only statistically significant differences are between non-White on White (dyad 1) and the other racial dyads. As in the results without imputation, the analysis using victim’s and offender’s race separately with imputation found that homicide incidents with non-White offenders have a significantly greater clearance hazard rate than do incidents with White offenders. However, unlike in the analysis without imputation, victim’s race also has a statistically significant impact on homicide clearance: Clearance hazard rates are significantly higher for homicide incidents with White victims than for those with non-White victims. Although the ordering of dyads with imputed missing values is still not perfectly consistent with Black’s (1976) proposition, the significant impacts of both victim’s and offender’s status provide stronger support for the idea that quantity of law decreases with offender’s status and increases with victim’s status.

Discussion and Conclusion

This study contributes to the crime clearance literature both by investi-gating the relevance of victim–offender racial dyads and by comparing homicides and aggravated assaults. Event history analyses indicate different effects of victim–offender racial dyads depending on the type of crime. Controlling for other incident-level variables, homicide incidents with non-White offenders are more likely to be cleared than are incidents with White offenders, regardless of the victim’s race. This finding is consistent with




Black’s (1976) proposition that offender’s status is inversely related to likelihood of arrest as well as with the focus of social reaction theorists (Swigert and Farrell 1977) on racialized notions of the typical criminal. The results provide some support for the argument that because non-White homicide offenders are perceived as greater threats to society, the police are more willing to invest effort in pursuing such offenders (D’Alessio and Stolzenberg 2003; Swigert and Farrell 1977). Such pressure might be stronger for homicide than for lesser offenses because homicide investigations receive more public or media attention (Puckett and Lundman 2003). Results from the analysis with imputed missing values are very similar (and the differences support Black’s proposition even more). For aggravated assault, the results indicate that incidents between Whites were most likely to be cleared, followed by interracial incidents. Aggravated assault incidents between non-Whites were least likely to be cleared. These results could indicate police devaluation of incidents involving people from lower social positions and reluctance to investigate such incidents for less serious offenses that receive less public attention (Eitle, Stolzenberg, and D’Alessio 2005).

Of course, greater likelihood of clearance for homicide incidents with non-White offenders could be because of factors other than police discretion. For instance, witnesses or other third parties may be more willing to cooperate with police in homicide incidents with a “typical” offender. Similarly, patterns in aggravated assault could indicate a difference in the amount of citizen (victim and/or witness) cooperation in incidents involving minorities (Cooney 1994). Crime incidents between non-Whites might receive less citizen cooperation with police investigation than those between Whites, perhaps because of higher levels of police distrust and fear of retaliation among minorities (Riedel and Jarvis 1998). Fear of retaliation might also be higher for aggravated assault incidents than for homicide incidents. Victims and witnesses may be aware that aggravated assault offenders are less likely to be convicted and receive a long sentence than are homicide offenders and will therefore be returning to the community sooner.

Despite their advantages over other data sources for studying clearance, NIBRS data do not permit precise testing of whether racial variation in clearance is because of police discretion or witness cooperation. Future research should further investigate these mechanisms, using data that provide more detailed information. For example, data from police reports or interviews may allow researchers to include the level of witness cooperation in their analyses. Although it would of course be difficult to




collect such information for as many jurisdictions as are present in NIBRS data, it would be interesting to determine if the impact of race diminishes after including witness cooperation variables. If so, racial differences in crime clearance may be in large part because of differences in witness cooperation rather than solely reflecting police discretion based on race.

Other limitations of NIBRS are important to consider. NIBRS is not a nationally representative sample of crime incidents. Only slightly more than half of the states currently participate in NIBRS, and not all agencies in participating states report data to NIBRS; in general, large police departments are less likely to adopt NIBRS than are smaller agencies (Addington 2006; Maxfield 1999). Thus, the results of the current analysis should be interpreted as estimates of the effects of victim–offender racial dyads on homicide and aggravated assault clearance in small to midsized American cities. NIBRS participation is increasing, and results of future studies will be more generalizable as more police departments—especially those in large and more crime-ridden cities—participate.

Furthermore, NIBRS, like most official sources of crime data, does not record some theoretically important incident-level information, including victim’s and offender’s social class, the race of the main investigator, and more detailed racial/ethnic categories for offender. The current analysis measured victim’s and offender’s status by race. Although race and social class are generally correlated, and although Black (1976:20) clearly viewed race as a proxy for class, class may affect clearance rates independently, or class may interact with racial status in important ways. Furthermore, the investigator’s race could interact with victim–offender racial dyads, as investigators of different races may exercise discretion differently. If such information were available and reliable, research could examine, for instance, whether White investigators more effectively solve incidents between Whites than those between non-Whites. The current analysis also could not examine victim–offender dyads based on more detailed categories of race and ethnicity. For example, criminologists are increasingly interested in crime involving Hispanics. NIBRS distinguishes Hispanic ethnicity for victims but not for offenders. Thus, a satisfactory use of Hispanic ethnicity in classifying offender-victim racial dyads is impossible. NIBRS data collection is already costly and time-consuming (Maxfield 1999), so it may not be realistic to hope that the FBI will collect more incident-level information. However, if NIBRS data collection procedures are ever revised, scholars of criminology and criminal justice should strongly encourage expanded collection of theoretically important incident-level data.




It would also be useful to investigate the impact of victim–offender status combination on clearance over time. Litwin and Xu (2007) examined Chicago homicide data between 1966 and 1985. They found that the effects of extralegal factors on homicide clearance were different in different time periods, whereas factors related to evidence and information had similar effects over time. At present, NIBRS data have not been collected long enough to support such analysis, but as collection efforts continue it will be possible to study the effect of various incident-level variables (including victim–offender racial combination) over time.

Even in light of these limitations, the current research makes an important contribution to homicide clearance research by showing the importance of offender’s race. Were we to solely focus on victim’s status, as in previous homicide clearance research, we would conclude from the results without imputed missing data that Black’s (1976) thesis on racial status and the quantity of law, and related theses on the stigmatization of minority offenders, are not supported. But the impact of offender’s race on homicide clearance offers at least partial support for Black’s theory of law and related social reaction theories. Studies that do not explore offender status risk an incomplete interpretation of how racial status may structure clearance outcomes. The analyses also show smaller differences across victim–offender racial dyads for homicide than for aggravated assault clearance. This supports the liberation thesis (that discretion based on extralegal factors decreases as crimes increase in legal seriousness) and reinforces the importance of considering legal seriousness in explanations of clearance outcomes. We hope these results will encourage continued research into the impact of victim–offender dyads based on race and other status dimensions (e.g., gender and age) on clearance, including further work on nonlethal offenses such as rape and robbery.




Appendix A Descriptive Statistics

Homicides (n = 2,798)Aggravated Assaults

(n = 44,667)

Frequency Percent Frequency Percent

Clearance by arrest (after one day)

Cleared 1,852 66 11,698 26 Not cleared 946 34 32,969 74Victim–offender racial dyads Non-White on White (dyad 1) 254 9 4,296 10 White on White (dyad 2) 1,175 42 18,798 42 Non-White on non-White (dyad 3) 1,284 46 20,138 45 White on non-White (dyad 4) 85 3 1,435 3Victim’s gender Female 815 29 20,502 46 Male 1,983 71 24,165 54Offender’s gender Female 297 11 9,133 20 Male 2,501 89 35,534 80Victim’s age 0 to 12 180 6 2,051 5 13 to 59 2,394 86 41,590 93 Older than 60 224 8 1,026 2Weapon Contact 1,143 41 35,650 80 Noncontact 1,655 59 9,017 20Concomitant felony Present 160 6 2,683 6 Absent 2,638 94 41,984 94Victim–offender relationship Family 786 28 16,001 36 Friends or acquaintances 985 35 17,993 40 Stranger or unknown to police 1,027 37 10,673 24Time High visibility or exposure hours 1,612 58 28,307 63 Low visibility or exposure hours 1,186 42 16,360 37Victim’s injury No or minor injury — — 32,611 73 Major injury — — 12,056 27



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Notes

1. In 2002 the homicide clearance rate was 64 percent in the United States, compared to 95 percent in Japan, 96 percent in Germany, and 81 percent in England and Wales. Data sources are Sourcebook of Criminal Justice Statistics (Bureau of Justice Statistics, United States), Police Crime Statistics (Federal Criminal Police Office, Germany), White Paper on Crime (Ministry of Justice, Japan), and Crime in England and Wales (Home Office, England and Wales).

2. For a detailed review of homicide clearance research, see Riedel (2008). 3. Litwin and Xu (2007) also found a statistically significant impact of victim’s race on

homicide clearance, but the direction and statistical significance of effects varied by time period.

4. Typically, the definition of a cleared case is one in which police identified or arrested at least one person and processed the person for further prosecution; however, crime incidents can also be cleared by “exceptional means” when arrest of a suspect is not possible because of reasons beyond police control, such as death of the offender, declined prosecution, denied extradition, and victim’s refusal to cooperate with the investigation. In the National Incident-Based Reporting System (NIBRS), 6.0 percent of all homicide clearances, and 8.5 percent of aggravated assault clearances, were exceptional. A large majority (83.3 percent) of the excep-tionally cleared homicide cases in NIBRS were because of the death of the offender, and almost all (96.9 percent) exceptionally cleared aggravated assaults were because of declined prosecution or the victim’s refusal to cooperate. Although victim–offender racial dyads might play some role in exceptional clearance outcomes, very different criminal justice processes are involved in clearance by arrest and clearance by exceptional means (Riedel and Boulahanis 2007). We therefore focus only on clearance by arrest.

5. The 30 states included Arizona, Arkansas, Colorado, Connecticut, Delaware, Georgia, Idaho, Iowa, Kansas, Kentucky, Louisiana, Maine, Massachusetts, Michigan, Montana, Nebraska, New Hampshire, North Dakota, Ohio, Oregon, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, and Wisconsin.

6. Homicide incidents between Whites (dyad 2) are slightly overrepresented in quick compared to nonquick clearances, with the reverse pattern for incidents with non-White offenders (dyad 1 and dyad 3). For aggravated assaults, the distributions of victim–offender racial dyads are similar in quick and nonquick clearances.

7. This contrasts with the usual research setting of, say, counting how many crimes were truly interracial. There, it is sensible to impute, for example, a missing value of victim–of-fender racial dyads. But the key to theories emphasizing discretion and the importance of race is what is known or believed by police, so it is a distortion to impute a value that the police did not know.

8. Writing the clearance hazard rate for incident i at time t as hi(t), the model is,

with (t) representing the unspecified time dependence (Allison 1995). 9. Sensitivity analyses, available on request, using dyads constructed with Whites and

African Americans only (with other racial groups excluded) produced virtually identical results to those reported below (using White and non-White) for both homicide and aggravated assault clearance.

loghiðtÞ=aðtÞ+XK

k= 1

Xikbk




10. NIBRS requires an exact age for the offender, which is difficult to collect from wit-nesses or a dying victim, especially for stranger homicide incidents. It is less difficult to obtain information on offender’s race and gender in such incidents. Because offender’s age is not the target variable in the current analysis, we exclude it from the analysis presented below. Sensitivity analyses, available on request, reveal that when offender’s age is included for both homicide and aggravated assault, results are the same in terms of statistical significance and direction of other effects.

11. We adopt NIBRS categorizations for victim–offender relationship. Family includes the victim as child, spouse (including common-law spouse), parent, sibling, grandparent, grand-child, in-law, stepparent, stepchild, stepsibling, and other family member of the offender. Friend and acquaintance includes offender as friend, acquaintance, neighbor, babysitter, boy-friend, girlfriend, child of boyfriend or girlfriend, ex-spouse, employee, employer of the vic-tim, and otherwise known.

12. Major injuries included broken bones, possible internal injury, loss of teeth, severe laceration, and unconsciousness.

13. We further explored the degree to which seriousness conditions the effects of incident characteristics on the likelihood of clearing aggravated assaults via separate event-history models for aggravated assaults involving serious injury and for assaults not involving serious injury. For both types of aggravated assault, incidents between Whites (dyad 2) have the great-est clearance hazard rate and incidents between non-Whites (dyad 3) have the least. However, the estimated difference in lowest from highest hazard rate is larger for aggravated assaults without a seriously injured victim than for those with a seriously injured victim. This suggests that victim–offender relationship matters more for less serious incidents, the same conclusion we reach in our comparison between homicide and aggravated assault.

14. We repeated the analysis with different specifications of incident time of day, such as within (between 8:00 a.m. and 6:59 p.m.) versus outside (between 7:00 p.m. and 7:59 p.m.) approximate business hours. Results did not change for aggravated assault, but the effect of time of day became statistically significant for homicide clearance. Thus, for both homicide and aggravated assault, incidents within approximate business hours have less risk of clear-ance than those outside approximate business hours.

15. Multiple imputation is a standard method for dealing with missing data that uses infor-mation from the observed data to impute missing values; SAS includes a multiple imputation routine, PROC MI (Riedel and Regoeczi 2004). For 10 imputed data sets created in PROC MI, we used PROC MIANALYZE to combine event history results from PROC PHREG. Reported estimates reflect the average of estimates across the imputed data sets, and estimated standard errors include variability across the imputed sets as well as the usual uncertainty in parameter estimates.

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Aki Roberts is assistant research professor in the Department of Sociology, University of New Mexico. Her interests include quantitative methods, crime clearance, National Incident-Based Reporting System data, Japanese crime, police networks, and crime trends. She published articles on crime clearance in Homicide Studies and Journal of Criminal Justice in 2008.

Christopher J. Lyons is assistant professor in the Department of Sociology, University of New Mexico. His interests include intergroup relations, discrimination, community dynamics, and crime. His recent research on the community-level antecedents of racially motivated crime is published in American Journal of Sociology and Social Forces.

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Victim-offender racial dyads and clearance of lethal and nonlethal assault