Time to Apprehension and the Correlates of Warrant Closure

Abstract

To understand how offenders are caught, past research has focused on case closures, which combines the identification and apprehension of a fugitive. However, there is a gap in applied research concerning duration to apprehension and variation in time to capture by crime. This study examined the days to close arrest warrants using administrative data containing 1.3 million cases. A Cox proportional hazards model demonstrated that sex crimes involving contact or encompassing child pornography/exploitation, kidnapping, sex offender registration violations, and warrants involving assaults or an armed/dangerous notation had the strongest relationships to warrant closure. The results illustrate the prioritizing of cases involving sex offenders and violent offenders, as well as underscoring a need for future research on time to warrant closure.

Keywords

warrants NCIC fugitives sex offenses Cox proportional hazards model

Determining where to allocate resources and on whom to focus apprehension efforts is a constant struggle among law enforcement. The number of active warrants contained in the National Crime Information Center (NCIC) database has remained relatively stable throughout the past decade at approximately two million (Bierie, 2014; Craun & Detar, 2015). New warrants are added everyday as other fugitives are apprehended and their warrants closed. Decisions are made daily on how to allocate resources to track down fugitives on a case-by-case basis. However, on a national level, little is known about how long it takes to find and apprehend a fugitive, and there is a paucity of information about variation in arrest times across the types of crimes for which warrants are issued. This study advances knowledge of this topic by focusing on the time between identification and apprehension, aspects of the law enforcement cycle that are largely unexplored in previous research.

Understanding the length of time between issuing an arrest warrant and apprehending a fugitive is important for multiple reasons. First, law enforcement may more accurately determine when a fugitive investigation has become stagnant to make informed decisions about obtaining extra resources or outside assistance from other agencies. An additional reason for underscoring the importance of time to arrest is that offenders frequently commit new crimes from the time a warrant is issued to the time that they are arrested. Preventing fugitives from serially offending by reducing the time to apprehension is a top priority for law enforcement. In fact, the U.S. Sentencing Commission (2012) reflects the magnitude of this concern in their sentencing guidelines by deeming that a sex offender who commits an additional sex crime while failing to register is eligible for an upward departure in the disseminated sentence. Finally, this type of knowledge can provide victims a clear expectation as to the likelihood of a perpetrator being arrested.

Background

Identifying Offenders

The process of arresting an offender for a crime and ending fugitive status starts with identifying the offender. This initial step is fraught with complexity. In one study, researchers used a national database of DNA collected from various crime scenes to determine that the percentage of offenders who committed serial crimes were not yet identified by law enforcement. Lammers (2014) found that 74% of offenders that were linked by DNA as serial criminals had not been identified and arrested. Furthermore, this DNA linking method provided sufficient data to estimate that these offenders were responsible for an average of three crimes for which they had not yet been arrested. A Cox proportional hazards model was used to determine that within this sample, approximately 50% of the previously unidentified offenders were arrested within 10 years (Lammers, 2014). A different sample of unidentified offenders produced an estimate that 65% would be arrested within 10 years (Lammers & Bernasco, 2013; Lammers, Bernasco, & Elffers, 2012). The number of crimes an unidentified offender committed had a positive relationship with the time to detection, whereas there was a negative relationship between the more specialized an offender was with his or her crime type and time to detection (Lammers et al., 2012).

Case Clearance

After a person has been detected and identified as the main suspect for a crime, that suspect must be charged with a crime, arrested, and turned over to the court system for prosecution to meet the FBI’s (2011) definition of “cleared.” The vast amount of research predicting how fast criminals are apprehended examines clearance rates. However, many past studies do not examine aspects of the process related to opening and closing warrants and do not refer to cases where suspects have never been identified. In certain contexts, this may be due to lack of access to restricted law enforcement data. This implies some danger of inflating arrest rates because cleaned and processed administrative data that are examined post hoc are more likely to contain aggregate information that does not include cases that are missing full arrest or court dispositions. Studies that examine the cycle of warrant to arrest may produce more accurate base rates that can be used to inform law enforcement policy.

It also is important to take into account the fact that there is wide variability in the speed of clearance, if crimes are cleared at all. In 2010, 47% of violent crimes were cleared by arrest, whereas only 18% of property crimes were cleared (FBI, 2011). In a recent study examining the clearance rates of warrants involving nonlethal violence, Roberts (2008) found that the odds of clearing cases decreased precipitously 2 days after the violent incidents occurred. In reviewing the time to clearance for murders, researchers determined that in a national sample of cases, most (more than 75%) were cleared within a week (Regoeczi, Jarvis, & Riedel, 2008).

Beyond determining how fast clearances occur, researchers have investigated the correlates of quick clearance rates. Most of this research focuses on factors about the victims and victim–offender relationships (Briggs & Opsal, 2012; Lyons & Roberts, 2014; Regoeczi et al., 2008; Roberts, 2007, 2008; Roberts & Lyons, 2009;). Although understanding these relationships is important for law enforcement investigators for the purpose of identification, this line of research may not provide findings that are generalizable to the second stage of case clearance—apprehension.

Exploring clearance rates provides critical information for understanding the administration of justice. However, it is important to point out that this perspective combines the process of identifying, apprehending and prosecuting a suspect into one concept. In their work on the arrest likelihood of geographically mobile offenders, Lammers and Bernasco (2013) methodologically equated identified offenders to arrested offenders, which is not always the case. Bierie (2014) found that 5% of active warrants in NCIC were issued prior to 2001 and 45% were issued at least 2 years before the study date. Further research by Lammers (2014) acknowledged that focusing on the data of arrested offenders could lead to biased results because those who managed to evade detection and arrest would not be included in the statistical analyses.

The presented empirical body of work reveals the need to separate the two concepts of detection and arrest. Most importantly, when data allow for it, analyzing the time from warrant issue to closure is key to presenting a complete picture of the arrest cycle. The current research examines the timing of apprehensions for those offenders where an arrest warrant was issued, specifically, the time to close arrest warrants as measured in days between the warrant entry date in the NCIC database and the close date of the warrant. This research presents two explicit research questions that guide the investigation.

Research Question 1: What is the average number of days to close a warrant from date of issue in the United States?

Research Question 2: What factors about the offense and the offender affect how quickly a warrant is closed?

Method

Data

This study examined all warrants in the NCIC Wanted Persons file from January 1, 2014 to September 14, 2014 that were not subsequently labeled as “canceled” (N = 1,373,995). The Wanted Persons file is maintained by the FBI and contains summary information about each warrant, the extradition limitation associated with the warrant, the offender for whom the warrant was issued, and relevant miscellaneous notes (e.g., warnings for law enforcement, contact information of the issuing jurisdiction, and other associated warrants and crimes). Throughout the United States, law enforcement agencies can add warrants and query NCIC data so that warrants entered into the database by one jurisdiction can be viewed and acted upon by all participating agencies nationwide. Within this dataset, there were 8,447 unique originating agency identifiers (ORI), which indicate the jurisdiction responsible for the warrant in NCIC.

Measures

Dependent variable

The variable of main interest was the number of days needed to close a warrant from date of issue. This was calculated by subtracting the cleared date from the warrant date for all warrants that were not closed by a cancelation code. All cases that were still open on September 14, 2014 were right censored at 257 days (n = 614,872; 44.8% of the sample).

Independent/control variables

Utilizing previous research on warrants to guide the variable selection, the analyses focused on four sets of independent variables to determine whether they affected time to arrest: the type of offense, the notation of the offender as armed/dangerous, extradition limitations of the warrant, and the seriousness of the infraction (felony/misdemeanor; Bierie, 2014; Craun & Detar, 2015). NCIC offense codes were used to categorize offenses into general offense categories as outlined by the NCIC manual. Further distinctions were made with sex crimes so it was possible to determine whether the crime was a sex crime involving contact, without contact, involving pornography and the exploitation of children, commercially based sex crimes, and sex offender registrations violations (i.e., failure to register). Offense categories were determined by either the current warrant code, or by the original offense if the current warrant was for a court offense (e.g., parole violation). For example, a current parole violation warrant with an original offense of burglary would be marked as both burglary for the original crime and a court offense for the parole violation. Both types of crimes were included as law enforcement had this information and it may have factored into prioritizing the offender’s warrant.

Previous work determined that approximately 3% of warrants nationwide note that the offender may be armed and dangerous (Craun & Detar, 2015). To measure whether an offender was considered armed/dangerous, a binary variable was created to flag those warrants where the words armed or dangerous were entered by the issuing ORI in the miscellaneous field.

Extradition limitations are important to consider when examining time to case closure as a warrant could remain open due to the fact that a fugitive fled to a different jurisdiction and the issuing ORI refused to extradite from that jurisdiction. Warrants listed in NCIC must provide the limits to their extradition so that law enforcement officers in other jurisdictions know whether an ORI is willing to fund the return of an offender. Within the NCIC data, there are six extradition choices: no extradition, full extradition, limited extradition, adjacent states only extradition, extradition limitation decision pending, and extradition pending. The extradition codes within NCIC also indicate the severity of the infraction. Extradition codes entered as letters indicate a misdemeanor warrant whereas numerals indicate that it is a felony warrant.

Although not the focus of this article, it would be negligent to conduct an analysis measuring time to close warrants without considering demographics. Previous work has demonstrated how demographics can influence police–offender interactions and recidivism patterns (Bierie, Detar, & Craun, 2013; Craun & Detar, 2015; Craun, Detar, & Bierie, 2013; Durose, Cooper, & Snyder, 2014; James, Vila, & Daratha, 2013). Therefore, race, gender, and age of the offenders are included as control variables.

Finally, as a control, a binary variable measuring whether an address was documented within NCIC was included in the multivariate model. It is important to note that this variable only measures whether an address was available to law enforcement, not whether the address was a viable lead to where an offender was found. Table 1 shows the univariate distribution of the variables, along with their bivariate relationships to the days to close.

Table 1.

Descriptive Statistics.

	Total sample	Average days to close
Average days to close	31.7 days (40.4)
Felony warrant	57.6%	29.1
Noted armed/dangerous	3.1%	26.1
Address is documented	32.3%	31.0
Criminal offense listed on warrant^a
Homicide	0.5%	16.2
Kidnapping	0.2%	22.8
Assault	9.4%	25.8
Robbery	2.2%	23.2
Sex crime–contact	1.8%	15.7
Sex crime–no contact	0.1%	22.0
Sex offender registration violation	0.7%	27.7
Sex crime–pornography/exploitation	0.2%	12.9
Sex crime–commercial activity	0.3%	35.0
Burglary	5.8%	28.7
Property crime	22.8%	33.2
Drugs	18.4%	32.7
Public order	1.2%	31.7
Court	59.0%	34.1
Escape	1.5%	28.0
Weapon offenses	1.9%	25.8
Crimes against people	0.7%	25.7
Other	9.4%	33.4
Extradition
Full	30.5%	27.7
Limited	13.5%	33.0
Extradition arrangement pending	0.2%	28.4
Extradition decision pending	0.3%	20.1
Adjacent	8.0%	30.9
None	47.2%	34.7
Gender
Female	24.2%	32.3
Male	75.8%	31.5
Race
Black	34.4%	33.4
White	63.6%	30.8
Asian	0.7%	31.3
Native American/Alaska Indian	1.4%	32.5
Missing	3.0%	32.6
Age at warrant	33.4 years (11.0)

Criminal offense listed on the warrant will total over 100% as the variable is coded off of the warrant offense or the criminal offenses. For example, the warrant could have been counted as a court-based warrant (failure to appear) and for the original crime.

Analytic Plan

The presented analysis investigates the relationship between warrant characteristics, the armed/dangerous designation, and offender characteristics in respect to the number of days it takes to close an arrest warrant. This was accomplished by fitting a Cox proportional hazards model that controlled for clustering within ORIs. Doerner and Doerner (2012) found that clearance rates can be partially explained by the number of sworn law enforcement personnel and an agency’s law enforcement expenditures. Even though the current study focuses on time to arrest rather than clearance rates, the finding by Doerner and Doerner suggests the necessity of controlling for clustering at the ORI level when taking into account arrest rates post warrant issuance. As expected, a one-way ANOVA model illustrated significant clustering (intraclass correlation = .12, p < .001) of the dependent variable at the ORI level. Therefore, utilizing the cluster option was necessary. To determine the relative impact of variables within the model as a whole, Z scores provided standardization and a measure of strength of impact. In addition, hazard ratios were calculated to measure the relative risk of arrest for the subsets of the sample.

The Cox model was fit as follows:

h_{i} (t) = h_{o} (t) \times e^{β_{1} X_{i 1} + \dots + β_{k} X_{i K}},

where h_o(t) represents the baseline hazard of arrest at time t when all coefficients are zero. The hazard of arrest for case i (h_i) at any given time (t) in the study window is a function of the proportional effect (β _k ) of the covariates (X_ik) on the hazard.

Results

Research Question 1: What is the average number of days from the date of issue to close a warrant in the United States?

For those warrants that were closed within the study timeframe, the average number of days from warrant date until close date was 31.7 days with a standard deviation of 40.4 (Table 1). Using the Kaplan-Meier method for estimation purposes, it was found that the probability of a warrant still being open at 30 days past the warrant date was 61.3% while it decreased to 50.3% at 60 days. By the end of the study period (257 days), the likelihood that a warrant was still open was 29.6%. Figure 1 is a graphic description of the Kaplan-Meier survival estimates. There is a clear decrease in the slope of warrant closures over the study period. One can see that the longer a warrant remains open, the likelihood of it being closed at any one point in time becomes lower. For example, from 50 days to 100 days post warrant entry, the percentage of warrants still remaining open declines from 53.1% to 42.4%, a decrease of 10.7%. However, from 100 days to 150 days post warrant entry, the probability falls from 42.4% to 36.4%, a 6% decrease.

Research Question 2: What factors about the offense and the offender affect how quickly a warrant is closed?

Figure 1.

Kaplan-Meier survival estimate for time to close warrants.

This study focused on the variables that measured the crime committed by the offender, the seriousness of that crime (whether the warrant was for a felony or misdemeanor), extradition limits, and whether the warrant labeled the offender as armed/dangerous. Utilizing Z scores as a standardization estimate of the relative strength of each of the variables on the number of days to close a warrant, it was found that warrants for child pornography/exploitation had the strongest relationship to days to warrant close (see Table 2). Warrants for pornography had a hazard ratio of 2.24; in other words, at any point during the study window, the daily arrest rate was 124% higher for child pornography warrants as compared with other types of warrants. Many other criminal offenses were also related to how quickly a warrant was closed. Sexual crimes both with and without contact, sexual offender registration violations, assault, kidnapping, and homicide all had significantly higher chance of arrest. Public order related crimes were unique among statistically significant offenses; these crimes on average took a longer time to close (hazard ratio = 0.68.). In this sample, the public order arrest crime code appeared as somewhat of a catch-all, capturing crimes from the trafficking of food stamps and disrupting public services to conspiracy. Warrants where the offender was labeled as armed/dangerous had a higher likelihood of being closed at any one point in time as compared with those offenders who were not designated as armed/dangerous (hazard ratio = 1.35). This hazard ratio indicates that warrants with the armed/dangerous notation have a 35% increase in probability of being closed. Interestingly, the severity of the warrant measured by felony or misdemeanor classification had no impact on the speed of closure (p = .80).

Table 2.

Cox Regression Measuring Days to Close Warrants (n = 1,296,666).

	β	Hazard ratio	Robust SE	z	p
Felony warrant	.01	1.01	.05	0.25	.80
Armed/dangerous	.30	1.35	.08	3.94	<.001
Address is documented	.15	1.17	.04	4.12	<.001
Criminal offense(s) listed on warrant
Homicide	.29	1.34	.10	2.93	.003
Kidnapping	.33	1.39	.05	6.95	<.001
Assault	.30	1.35	.03	11.37	<.001
Robbery	.20	1.21	.05	3.58	<.001
Sex crime–contact	.46	1.58	.05	9.49	<.001
Sex crime–no contact	.26	1.29	.06	4.06	<.001
Sex offender registration violation	.26	1.30	.04	6.29	<.001
Sex crime–pornography/exploitation	.81	2.24	.06	13.16	<.001
Sex crime–commercial activity	−.10	0.90	.11	−0.98	.33
Burglary	.13	1.14	.03	4.36	<.001
Property crime	.04	1.04	.02	1.47	.14
Drugs	.06	1.06	.03	2.15	.03
Public order	−.39	0.68	.18	−2.15	.03
Court	−.08	0.92	.04	−1.91	.06
Escape	.16	1.17	.05	2.93	.003
Weapon offenses	.19	1.20	.07	2.68	.007
Crimes against people	.08	1.08	.09	0.86	.39
Other	.01	1.01	.04	0.33	.74
Extradition^a
Full	.20	1.23	.06	3.47	.001
Limited	.09	1.09	.06	1.57	.12
Arrangement pending	.31	1.37	.12	2.45	.01
Decision pending	.32	1.37	.14	2.22	.03
Adjacent	.19	1.21	.05	3.57	<.001
Male	−.03	.97	.01	−4.74	<.001
Race^b
Black	−.14	.87	.02	−6.51	<.001
Asian	−.29	.75	.05	−6.41	<.001
Native American	−.02	.98	.04	−0.34	.73
Age at warrant	−.03	.97	.00	−14.0	<.001
Age-squared	.001	1.01	.00	12.89	<.001

Note. 8,246 clusters at the ORI level, p < .001. SE = standard error.

Reference group is No Extradition. The Wald test was significant at p = .001.

Reference group is White. The racial category Race-Missing was dropped due to collinearity. The Wald test was significant at p < .001.

As compared with those warrants where the ORI will not extradite, warrants with full extradition and warrants with adjacent states only extradition limits were more likely to be closed earlier (hazard ratio = 1.23 and 1.21, respectively). Warrants with limited extradition represented the only extradition category that did not significantly differ in likelihood of closure compared with warrants with no extradition (p = .12). In a related vein, when an address is documented in NCIC there was a statistically significant increase in the speed at which a warrant is closed (p < .001).

Moving past examining the offense itself that necessitated a warrant, offender demographic characteristics also affected the likelihood of closure. Warrants for male offenders demonstrated a lower average rate of closure than warrants for female offenders (p < .001). Offender race was a significant predictor of days to close (p < .001). Asian offenders and African American offenders’ warrants represented a lower average rate of closure than those of White offenders. Hazard ratios demonstrate a 25% decrease in the probability that a warrant was closed for an Asian offender as compared with a White offender, and a 13% decrease for an African American offender as compared with a White offender. Age had a positive curvilinear relationship with days to close (p < .001).

Controlling for the other variables in the model, Figure 2 illustrates the projected survival probability for warrants for four major crime types: assault, pornography/child exploitation, sexual crime involving contact, and homicide. The criminal offenses with the highest Z scores from the Cox proportional hazard model were chosen due to the strength of their relationship with days to warrant close (pornography, assault, and sexual crimes that involve contact). Homicide was chosen as a reference for the graph, as research has demonstrated that the public sees homicide as one of the most serious, harmful, and morally depraved crimes an offender can commit (Warr, 1989), and it is routinely listed as one of the most costly crimes when you consider direct, indirect, and governmental costs (Wickramasekera, Wright, Elsey, Murray, & Tubeuf, 2015). Warrants for child pornography/exploitation immediately show the steepest decline and display a consistently lower survival function over time than assault, homicide, or sex crimes involving contact.

Figure 2.

Projected time to close for assault, child pornography, sexual crimes with contact, and homicide warrants (n = 1,296,666).

Discussion

The purpose of the presented analysis was to investigate time to warrant closure for an audience of law enforcement, criminal justice researchers, and practitioners to promote a fundamental understanding of this process at the national level. This knowledge as well as information on which crimes and offender characteristics lead to quicker close times can provide for a better informed distribution of resources. Expenditures can be focused on certain types of criminals for whom empirical analysis has demonstrated an increased difficulty in quickly apprehending. In the 9-month study period, it was found that the average time to close was 31.7 days. As illustrated in Figure 1, the time to close was not uniform across the study because warrants tend to be closed quickly and then lose traction over time. For example, 17.4% of all warrants that were closed were done so within the first 7 days and 9.4% were closed in the second week.

When differentiating by crime, the numbers presented from this research differ from what was found with clearance rates for crimes. Regoeczi and colleagues (2008) found that most murderers (77.8%) were identified and arrested within a week, whereas in the current sample, 34.6% of homicide warrants were closed within a week of the warrant entry date. This is less than half of what Regoeczi and colleagues found. However, it is important to note that homicide warrants were closed substantially faster than non-homicide warrants within the current sample. To put these differences into perspective, one must take into consideration that case clearances cannot necessarily be compared directly with warrant clearances as criminals can be identified and arrested without warrants being issued and prior to data being entered into NCIC.

Beyond homicide, the results demonstrate that some warrants are likely to be closed in a shorter time span than others. The sex crime category that encompasses child pornography and child exploitation had the strongest positive relationship to days to close a warrant. It is possible that these offenders are easier to capture as law enforcement would have utilized Internet Protocol (IP) addresses to determine a location where the possession of obscene material was occurring, hence apprehension would occur more quickly than with other crimes. Even so, approximately 16% of pornography/exploitation warrants were still open at the end of the 9-month study period. There are other crimes in addition to the pornography crimes that also have significant and high hazard ratios indicating strong relationships to days to close a warrant, including assault, sex crimes involving contact, kidnapping, and sex offender registration violations. The strength of the associations of these particular crimes may be due to their perceived seriousness by those in the criminal justice community. This may also reflect the attention given to such cases by the media and the general public. Kidnapping, crimes ending in victim death, and rape are considered to be the most serious crimes an offender can commit according to previous research that surveyed law enforcement and court officials (McCleary, O’Neil, Epperlein, Jones, & Gray, 1981; Pontell, Granite, Keenan, & Geis, 1985), and the perceptions of crime seriousness are similar when surveying the general public (Kwan, Chiu, Ip, & Kwan, 2002). Child pornography consumption and distribution are also seen as severe crimes in the eyes of the general public. Mears, Mancini, Gertz, and Bratton (2008) found that almost 70% of the general public supported jail time for those accessing child pornography, while almost 90% supported jail for distributing pornography. Although the current data did not allow for separating pornography warrants involving minors from those involving general possession and distribution due to small subsamples (general pornography = 0.03% of total; pornography involving minors = 0.02% of total), we recommend that future studies do so given larger sample sizes.

Moving on, we found that those warrants that allowed for full extradition of an offender and extradition limits to surrounding states had higher hazard ratios as compared with those warrants that provided no possibilities for extradition. This augments the basic understanding that fleeing the issuing jurisdiction can reduce how quickly an offender is apprehended. Outside empirical research has found that if an offender commits crimes across multiple police jurisdictions, that offender is less likely to be caught than if offending within one jurisdiction (Lammers & Bernasco, 2013), although subsequent research found no difference in the average distance between crimes and detection (Lammers, 2014). Although future research can further clarify whether there is a discrepancy between the relationships among distance, jurisdiction, and offender detection, it is not out of line to suggest that law enforcement agencies consider outside assistance to expedite the apprehension process. For warrants with full or adjacent state extraditions, it may be possible that requesting assistance from a multi-agency task force or law enforcement entity with arrest powers across jurisdictions may help close these warrants more quickly than if one jurisdiction searches independently. Future work will need to test the empirical validity of this suggestion.

Limitations/Directions for Future Research

The most noteworthy limitation to the current work is that the predictive model of days to close contains no measures of criminal history. One of the most consistent and strongest predictors of recidivism is criminal history (Gendreau, Little, & Goggin, 1996; Steiner, Makarios, & Travis, 2015). In an often cited study from the Bureau of Justice Statistics, three quarters of prisoners across 30 states were rearrested for a new crime within 5 years of release, and more than half were within the 1st year (Durose, Cooper, & Snyder, 2014). Therefore, it would seem that law enforcement would assess individuals with a longer criminal history as being high risk, while giving less priority to those warrants by first-time offenders. Furthermore, it may be that individuals who are more likely to reoffend are at greater risk of coming into contact with law enforcement due to increased criminal activity and are consequently more likely to be identified and arrested for outstanding warrants. For these reasons, factoring criminal history into a survival analysis is the next crucial step to understanding how long warrants remain open.

Moreover, there were no available measures to assess the organizational characteristics of the ORIs. Rather the only consideration of agency-level factors was controlling for clustered standard errors. Previous research has demonstrated that agency-level characteristics, such as the number of sworn law enforcement, affect clearance rates (Doerner & Doerner, 2012). Future research could use multilevel modeling to incorporate both offender-level factors and agency-level factors, such as the number of officers per 1,000 residents, agency expenditures, and the crime rate for an ORI into one model measuring time to close warrants. Another perspective that has been used to account for the unexplained variation in arrest rates across agencies and regions is the shared frailty framework (Tiedt & Sabol, 2015). Shared frailty allows for the calculation of the latent effects of unobservable factors, as long as there are existing clusters that can be identified in the data. For the purpose of these analyses, the assumption could be made that warrants issued for distinct crimes within the same jurisdiction share a group effect and that the unknown heterogeneity in the data in terms of time to close warrants can be accounted for by modeling this effect.

Next, the analyses demonstrated that warrants with full extradition, including those with extradition in adjacent states, had an increased probability of closure. This underscores that inter-jurisdictional communication and legal transfers of authority across states get results. However, this research was unable to examine the underlying support mechanisms for extradition. The possibility exists that wealthier counties and municipalities have the means for financing law enforcement efforts outside of everyday caseloads. Future research might explore the relationships among extradition, local resources, and the administration of justice.

In the current study, there were no data that captured how an arrest transpired. The most common reason for interactions between police and the public is due to traffic stops, with one study providing an estimate of 17.7 million traffic stops occurring in 2008 (Eith & Durose, 2011). Contrast these unplanned interactions with the effort expended by the U.S. Marshals Service (2016) in clearing 125,000 warrants in one fiscal year through focused investigations. It is crucial to determine whether the relationships in the model hold while controlling for those arrests that came about unintentionally through a traffic stop or some other unplanned police interaction. This information would provide a more complete picture of the time to close warrants and resources used by law enforcement to apprehend fugitives.

Finally, in respect to NCIC, the data contain no identifier for Hispanic/Latino ethnicity. This means that justice services cannot be evaluated for this community, despite the fact that individuals of Hispanic and Latino origin make up 16.3% of the national population (U.S. Census Bureau, 2016). Furthermore, participation in NCIC is voluntary. Although the FBI (2010) houses the data, the responsibility for the entry and removal of arrest warrants lies with the warrant issuing agency. Therefore, the current sample does not allow for generalizations to those jurisdictions that do not participate in NCIC and may only use a local database to house warrants. Future research should determine whether these findings are replicable within these localized systems.

Due to limited access to the NCIC database by researchers outside of law enforcement, empirical research is sparse in understanding warrants at a fundamental level. Those in the criminal justice field can now have a basic understanding of the speed at which the administration of justice begins from the standpoint of days to arrest. With this foundation of knowledge, best practices can be developed so that individuals with warrants are arrested before committing additional crimes that may be undetected by law enforcement and are an incremental detriment to society.

Footnotes

Acknowledgements

The authors would like to express their gratitude to Eliza Edgar, Paul Detar and David Bierie at U.S. Marshals for their valuable feedback while drafting the manuscript.

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.

Author Biographies

Sarah W. Craun, PhD, is currently the research coordinator for the National Center for the Analysis of Violent Crime within the Federal Bureau of Investigation. Her work to date covers topics such as sex offender registration, violence against law enforcement, and secondary traumatic stress.

Andrew D. Tiedt, PhD, is a statistician at the U.S. Marshals Service. A social demographer by training, he has published on issues ranging from recidivism of released prisoners to aging and mental health. His current research focuses on sex offender registration, recidivism and law-enforcement related topics.

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