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The Influence of Investigative Resources on Homicide Clearances

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Abstract

Objectives

This paper investigates the influence of case characteristics and investigative resources on homicide clearance rates.

Methods

We extend a previous evaluation of a problem-oriented policing project intended to improve homicide clearance rates in Boston. Data were collected on N = 465 homicide incidents that occurred between January 1, 2007 and December 31, 2014. Confirmatory factor analyses are used to identify latent variables representing investigative resources, initial crime scene results, and subsequent investigative actions and forensic testing. The effects of these investigative factors on homicide clearances net other covariates were estimated using mixed effects logistic regression models. Mediation analysis was then used to decompose the total, direct, and indirect effect of investigative resources on homicide clearances. Exploratory group comparisons were examined to distinguish investigative differences in gang and drug homicides relative to non-gang and non-drug homicides.

Results

Investigative resources, crime scene results, and subsequent investigative actions and forensic testing were found to increase the likelihood of homicide case clearance controlling for other covariates. Investigative resources were found to produce both direct and indirect impacts on homicide clearances mediated through its positive influence on initial crime scene results and subsequent investigative actions and forensic testing. Clearance through follow-up investigation was more difficult for gang and drug homicide cases when compared to other homicide cases.

Conclusion

While inherited case characteristics matter, enhanced investigative resources and improved practices increase homicide clearances. Beyond investments to improve investigations, gang and drug homicides remain particularly difficult to clear due to a lack of physical evidence and witness cooperation.

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Notes

  1. An offense is “cleared by arrest” or solved for crime reporting purposes when at least one person is: (1) arrested; (2) charged with the commission of the offense; and (3) turned over to the court for prosecution. An offense is also counted as cleared by arrest if certain “exceptional” conditions pertain, including suicide of the offender; double murder; deathbed confession; offender killed by police or citizen; confession by offender already in custody; extradition denied; victim refuses to cooperate in prosecution; warrant is outstanding for felon but prior to arrest the offender dies of natural causes or as a result of an accident, or is killed in the commission of another offense; or, handling of a juvenile offender either orally or by written notice to parents in instances involving minor offenses where no referral to juvenile court is customarily made. https://ucr.fbi.gov/crime-in-the-u.s/2014/crime-in-the-u.s.-2014/offenses-known-to-law-enforcement/clearances/main (accessed July 29, 2017).

  2. For instance, in 2015 (the most recent year data are available), U.S. law enforcement agencies cleared 61.5% of homicides reported to the FBI Uniform Crime Reports program. https://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s.-2015/offenses-known-to-law-enforcement/clearances/clearances (accessed July 29, 2017).

  3. Generally, about 21 percent of Index Crimes were cleared by arrest during the 1970 s according to the FBI’s Uniform Crime Reporting program. Source: U.S. Bureau of Justice Statistics, Sourcebook of Criminal Justice Statistics Online, http://www.albany.edu/sourcebook/pdf/t4202007.pdf (accessed June 30, 2017).

  4. For instance, for total counts of physical evidence collected per investigation, r = .90 for coders 1 and 2, r = .92 for coders 1 and 3, and r = .89 for coders 2 and 3. For all correlations p < .05, this suggested a high degree of agreement among the coders.

  5. This June 1, 2017 clearance rate is slightly larger than the March 1, 2016 50.3% clearance rate (234 of 465) reported by Braga and Dusseault (2018). The clearance rate increase is due to indictments of offenders in 16 homicide cases awaiting grand jury dispositions at the time the previous study was completed.

  6. Since they are not directly measured, latent variables do not have intrinsic variances. For the factor loadings in Table 2, the variances of the latent variables were constrained to equal “1” for ease of interpretation (StataCorp, 2015). These coefficients can be interpreted as correlations that range from “0” to “1”.

  7. Since our endogenous variables had different frequency distributions, we retained items that had “good” loadings on their respective factors by applying the more stringent cut-offs recommended by Tabachnick and Fidell (2013) and Comrey and Lee (1992): 0.32 (poor), 0.45 (fair), 0.55 (good), 0.63 (very good) or 0.71 (excellent). We also considered the suggestion of Guadagnoli and Velicer (1988) to regard a factor as reliable if it has four or more loadings of at least 0.6 regardless of sample size. All three factors had four or more loadings greater than 0.6.

  8. The CFI assesses the fit of a user-specified solution in relation to a more restricted, nested baseline model in which the covariances among all input indicators are fixed to zero or no relationship among variables is posited (Brown 2006, p. 84). The CFI coefficient value ranges from 0 to 1.00 with values greater than 0.90 indicating a reasonably good fit of the hypothesized model (Hu and Bentler 1999). RMSEA takes the error of population approximation and degrees of freedom into account and measures the lack of fit of the hypothesized model to the population covariance matrix. SRMR is estimated in a similar to RMSEA but does not penalize model complexity. As a general rule of thumb, SRMR and RMSEA results of 0.05 or less indicates a close approximate fit of the model (Hu and Bentler 1999).

  9. The inverse of homicide detective response time was used in the confirmatory factor analyses to ensure this variable moved in the same direction as the other covariates being considered for inclusion in the investigative resources latent variable.

  10. A diversity of other criminal justice agencies were called upon to support homicide investigations in varied ways. These included corrections agencies such as the Massachusetts Department of Correction and Suffolk County House of Correction providing recorded phone calls while suspects, victims, and other persons of interest were in prison and/or jail and Massachusetts Probation Service providing GPS information on individuals sentenced to electronic monitoring terms; federal law enforcement agencies such as the US Secret Service enhancing acquired videos and the Federal Bureau of Investigation restoring deleted information from seized computers; and local police departments providing intelligence on prior criminal activities and associates of victims and suspected offenders who lived in other jurisdictions.

  11. Witnesses identified at the initial homicide scene ranged from individuals who provided information that generally moved the investigation forward by confirming basic facts about what had happened (e.g., “a white van pulled up and a man in a mask jumped out and fired three shots at the victim” or “I heard five shots, looked out my window, and saw two black males running from the corner towards the park”) to individuals who could positively identify suspects through detailed physical descriptions or by providing offender names. In essence, all were “eyewitnesses” with varying degrees of relevant information on the homicide event.

  12. These included officers who may have been on-location when a homicide occurred or shortly thereafter, officers who knew the victim prior to the homicide and had information on what may have happened based on prior disputes or ongoing criminal activity, or officers who acquired information from informants or other civilians at the scene who were not comfortable sharing information directly with homicide detectives.

  13. These included witnesses who were first identified at the scene who were brought in for subsequent interviews (sometimes viewing a photo line-up of possible suspects), individuals who witnessed the homicide who were not first identified at the scene, and other civilians who had relevant knowledge of the homicide or the events that precipitated the homicide.

  14. The BPD has 12 districts that provide policing services across Boston’s neighborhoods: A-1 serving Downtown, Beacon Hill, and Chinatown; A-15 serving Charlestown; A-7 serving East Boston; B-2 serving Roxbury and Mission Hill; B-3 serving Mattapan and parts of North Dorchester; C-6 serving South Boston; C-11 serving most of Dorchester; D-4 serving Back Bay, Fenway, and South End; D-14 serving Allston and Brighton; E-5 serving West Roxbury and Roslindale; E-13 serving Jamaica Plain; and E-18 serving Hyde Park.

  15. Given the salience of police district activities to homicide investigations, we believed that police districts were most appropriate representations of spatial variations in police action and neighborhood contexts. U.S. Census units did not provide the same precision in accounting for varying police activities across Boston. Nevertheless, we also ran a mixed effects regression model that used N = 181 Census tracts as proxies for neighborhoods instead of police districts. The results of the mixed effects model with Census tracts did not significantly differ from the version with police districts. However, the police district model fit the data much better than the Census tract model. As presented below in Table 3, the log pseudolikelihood was − 226.698 for the police district model and − 329.712 for the Census tract model.

  16. Our decision to use random effects were primarily driven by theoretical considerations. Our mixed effects regression model confirmed that homicide clearances did significantly vary across police districts. For instance, our models estimated a 95% confidence interval for the population police district variance that did not capture zero and ranged from .009 to 1.277. As a sensitivity analysis, we also ran our impact evaluation models as single-level fixed effects logistic regressions with police districts entered as dummy variables. The results did not significantly differ from the mixed effects logistic regressions reported here.

  17. In this first stage of the mediation analysis, we used standard Ordinary Least Squares regression models with control variables, fixed effects for BPD district, and robust standard errors clustered by BPD district. Hierarchical linear models add an “unobserved” component via the random effects; standard OLS regressions avoid the problem of adding a portion of the error to the prediction or eliminating the error from the residualization.

  18. As Table 3 demonstrates, there was a statistically-significant curvilinear relationship between victim age and the probability that a homicide was cleared (age + age2). We investigated the curvilinear relationship further by adding a cubic term to the quadratic equation. However, the cubic term was not statistically significant and its addition caused age2 to no longer be statistically significant, controlling for the other covariates: age = .7511 (.0828), p = 0.009; age2 = 1.0052 (.0027), p = 0.063; age3 = .999 (.0001), p = .183.

  19. There was very little circumstance overlap among the killings included in the combined gang and drug homicide category used in this exploratory analysis. Gang homicides involving drug dealing conflicts characterized only 21 of the 305 homicides (6.9%) in the combined gang and homicide category between 2007 and 2014.

  20. As suggested by one of the senior investigators, homicides committed with knives, blunt instruments, hands/feet, ligatures, and other non-firearm weapons often result in direct contact between victims and offenders. This contact increases the amount of physical evidence left at the scene (such as fingerprints, DNA, and fibers) that can be collected and analyzed.

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Acknowledgements

This research was supported by funds provided by the U.S. Bureau of Justice Assistance (Award #2011-DB-BX-0014) and the Rappaport Institute for Greater Boston. We would like to thank Boston Mayor Martin Walsh, Boston Police Commissioner William Evans, Deputy Chief of Staff Desiree Dusseault, former Boston Police Commissioner Edward Davis, former Chief of Staff Sharon Hanson, Superintendent Gregory Long, Lieutenant Detective Darrin Greeley and the men and women of the BPD homicide unit for their valuable assistance in the completion of this research. We also would like to thank Robert Apel of Rutgers University for his advice on the statistical analyses presented here. Points of view in this document are those of the authors and do not necessarily represent the official position of the U.S. Bureau of Justice Assistance, City of Boston, or the Boston Police Department.

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  1. School of Criminology and Criminal Justice, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Anthony A. Braga, Brandon Turchan & Lisa Barao

  2. School of Criminal Justice, Rutgers University, Newark, NJ, USA

    Brandon Turchan

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Braga, A.A., Turchan, B. & Barao, L. The Influence of Investigative Resources on Homicide Clearances. J Quant Criminol 35, 337–364 (2019). https://doi.org/10.1007/s10940-018-9386-9

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