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Crime Clearance and Temporal Variation in Police Investigative Workload: Evidence from National Incident-Based Reporting System (NIBRS) Data

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Abstract

Objectives

Police workload’s relationship with crime clearance has been studied widely. In the challenging environment now facing police, even small and possibly temporary changes in investigative workload could harm clearance. However existing workload-clearance research either used only a yearly average that obscures temporal variability in caseload, or explored proxy rather than direct measures of workload’s short-term variation. Our improved workload measures capture caseload’s daily changes as crimes are reported, cleared, or remain uncleared but reach the end of active investigation. We examine relationships between clearance and both long- and short-term variability in workload.

Methods

Using NIBRS and LEMAS data, we calculated between-agency (typical or long-term) and time-varying, within-agency (daily fluctuating or short-term) workload measures. We used these and other agency/jurisdiction- and incident-level variables in multi-level survival analysis of clearance by arrest for serious violent incidents from 2007 NIBRS.

Results

Both workload measures were significantly and negatively related to the clearance hazard rate; higher long- and short-term workloads are associated with reduced chance of a case being cleared. The estimated relationship between longterm workload and clearance became progressively stronger (more negative) as the crime incident’s legal seriousness decreased. However, estimates indicated greater sensitivity of the clearance hazard to short-term workload fluctuations for more serious crimes, though the workload-clearance relationship remained negative for all crime types.

Conclusion

Crime clearance should be considered by police agency planners when addressing workload through staffing decisions. Refinement of our workload measures will require additional information, and should be considered in future agency- and incident-level data collection.

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Article 28 January 2023

Ian T. Adams, Scott M. Mourtgos, … Geoffrey P. Alpert

Notes

  1. The Federal Bureau of Investigation defines a case as cleared by arrest if police arrested, and processed for further prosecution, at least one person. However, crime incidents can also be cleared by “exceptional means” when arrest of a suspect is not possible for reasons beyond police control, such as death of the offender, declined prosecution, denied extradition, and victim’s refusal to cooperate with the investigation.

  2. For the current data, more than 90 % of ever-cleared incidents were cleared within 30 days. As police are surely aware of this, it is reasonable to assume for our analysis of workload that police view uncleared cases past 30 days as not warranting significant active investigative effort. To check the sensitivity of these results to this assumption, we repeated the multivariate analyses using 90- and 180-day time limits to calculate workload. (96 % of ever-cleared cases were cleared within 90 days, and 98 % within 180 days.) The results using these longer time limits were similar to those for the 30-day limit in terms of statistical significance and direction of the independent variables’ coefficients.

  3. Less serious offenses, especially property crimes, tend not to be very actively investigated, due to their sheer volume and typical lack of witnesses and evidence (Eck 1983; Greenwood et al. 1977; Jang et al. 2008).

  4. We began this process with December 2, 2006, so as to include incidents from 2006 that could still be active and contributing to caseloads in January 2007, using zero as the number of active cases on 12/01/06. Of course this would not provide accurate active caseloads for dates in December 2006, but the active caseloads would be correct starting with January 1, 2007, the first day of direct interest for our analysis.

  5. The included states were Arkansas, Colorado, Connecticut, Delaware, Iowa, Idaho, Illinois, Kansas, Louisiana, Massachusetts, Michigan, North Dakota, New Hampshire, Ohio, Oregon, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Wisconsin, and West Virginia.

  6. A Cox model for the hazard rate hij(t) at time t (since the incident) for incident i in agency j, including agency-level predictors, time-invariant incident-level predictors, and a time-varying incident-level predictor, can be expressed as

    $$h_{ij} \left( t \right) = h_{0} \left( t \right)\exp \left( {\alpha_{j} + \sum\nolimits_{k} {\beta_{k} X_{ijk} } + \gamma W_{ij} \left( t \right) + \sum\nolimits_{p} {\delta_{p} Z_{jp} } } \right)$$

    h0(t) is a baseline hazard rate (that does not differ by incident and is not further specified), αj is the random intercept term associated with agency j (here assumed to be drawn from a normal distribution), Xijk is the value of time-invariant incident-level predictor k and βk is its associated coefficient, Wij(t) is the time-varying incident-level variable (here, the incident-level daily workload measure; some of our analyses below extend this with interaction terms) and γ its associated coefficient, and Zjp is the time-invariant agency-level predictor p with associated coefficient δp. Many event times (days to clearance or censoring) were equal in these data; the EXACT treatment of ties in PROC PHREG is very slow in such a large data set, so we used the EFRON approximation. The expression above gives the multiplicative model for hij(t); it can be rewritten as an additive model for log hij(t). Estimated coefficients b in Tables 1 and 2 below refer to the log hij(t) form, while the hazard ratios eb refer to the hij(t) form.

  7. Number of sworn officers is more appropriate than number of investigators, as all sworn officers likely contribute to investigation and clearance (Cordner 1989; Chaiken 1975). Patrol officers may identify witnesses at crime scenes, write initial reports on incidents, and search for and arrest suspects. Nevertheless, we explored the sensitivity of the results by repeating analyses using workload measures based on number of investigators. 14 agencies did not report the number of investigators in LEMAS, leaving 93 agencies in our data. The number of investigators varied between 4 and 477 with mean 64.92 and standard deviation 77.12, and correlated .75 with the number of sworn officers. We also repeated analyses using workload measures based on the combined number of sworn and non-sworn officers (counting part-time officers as before); non-sworn officers such as crime analysts and evidence technicians could be directly involved with investigation. The number of non-sworn officers varied between 0 and 711.5 with mean 104.01 and standard deviation 121.21, and the number of sworn officers correlated .93 with the combined number of sworn and non-sworn officers. Results using these alternative workload measures were the same in terms of statistical significance and direction of association as those reported here. Note also that for the rare occasions on which a small agency had zero active caseload for a day, we substituted a value of 0.1 (representing 40 % of the contribution of a single aggravated assault) before calculating the log ratio.

  8. Recall that the between-agency workload measure is the typical log ratio of active weighted cases to sworn officers; this mean value is equivalent to a ratio of 0.04 active weighted (homicide-equivalent) cases per sworn officer. The standard deviation value can be interpreted as roughly a multiplier of 2 for the ratio, so high workload agencies might have had four times greater workloads than average.

  9. In the language of multi-level models, our approach is like centering a Level 1 variable by its Level 2 mean, with a model that includes both the centered Level 1 variable and its Level 2 mean (Kreft et al. 1995).

  10. This standard deviation value refers to deviations in the log scale, so it is equivalent to a multiplier of 1.055 in the caseload/officer ratio, suggesting within-agency workload deviations of roughly 11 % above or below the agency’s typical workload. Within-agency differences were therefore meaningful, but of course generally smaller than between-agency differences.

  11. We also considered operational budget per crime incident, but excluded it because it introduced high collinearity, and may mainly reflect police employees’ salaries rather than investigative resources (Liska et al. 1985).

  12. Those 13 questions included whether agency used digital imaging on (1) fingerprints, (2) facial recognition and (3) suspect composites, used computers for (4) crime analysis, (5) crime investigation, (6) in-field communication, (7) intelligence gathering, and (8) inter-agency information sharing, owned computerized files for biometric data for use with (9) facial recognition systems, (10) fingerprints, and (11) gangs, (12) had in-field access to computers, and (13) had access to an automated fingerprint identification system (AFIS).

  13. Those 12 questions included whether (1) more than half of new recruits or (2) in-service sworn personnel participated in at least 8 h of community policing training, whether the agency (3) maintained a mission statement that included a community policing component, (4) actively engaged in SARA-type problem-solving projects on their beats, (5) conducted a citizen police academy, (6) maintained or created a formal, written community policing plan, (7) gave patrol officers responsibility for specific geographic areas/beats, (8) included collaborative problem-solving projects in the evaluation criteria of patrol officers, (9) upgraded technology for community policing, (10) partnered with citizen groups to develop community policing strategies, (11) conducted or sponsored a survey of citizens, and (12) maintained a community policing unit with full time personnel.

  14. In addition to our interest in this interaction, seriousness of incidents likely influences investigative resource allocation, so it is potentially important in its own right. Different types of offenses may also provide different information and physical evidence. For example, there is closer physical contact between victim and offender during forcible sexual offenses than would be usual during other violent offenses.

  15. Offender characteristics also have substantial theoretical importance. However in these data, missing information on the offender’s demographic characteristics almost always indicated non-clearance, so we did not include offender’s demographics. (This close correspondence of missing information with non-clearance was not evident for other incident-level variables.)

  16. We adopted the NIBRS categorization of victim-offender relationship. “Family” includes the victim as child, in-law, stepparent, stepchild, stepsibling, and other family member of the offender. “Friend and acquaintance” includes offender as friend, acquaintance, neighbor, babysitter, boyfriend, girlfriend, child of boyfriend/girlfriend, ex-spouse, employee, employer, and otherwise known to the victim.

  17. Uncleared incidents had more information missing than cleared incidents: 20 % of uncleared incidents contained missing information for at least one independent variable, compared to only 10 % of cleared incidents. However, a strong association between item-missing information and non-clearance was only observed for victim-offender relationship, and not for other independent variables. The percentage of non-clearance among cases with missing victim-offender relationship is similar to that of cases with a reported “unknown” relationship; perhaps many cases that should be recorded as “unknown” victim-offender relationship have no value reported.

  18. Regarding the possibility that missing information is truly unknown to police, we conducted two sensitivity analyses. First, we repeated the analyses with a special category of “missing” for each independent variable. Second, we repeated the analysis leaving out all cases with missing information. Those sensitivity analyses were similar to our main analysis in terms of statistical significance and estimated coefficients of the workload variables.

  19. Variance inflation factor (VIF) scores indicated no collinearity problem for the analysis.

  20. States’ and police agencies’ haphazard participation in NIBRS, and the further filtering of those not in LEMAS, means that our set of agencies is not a random sample from a defined population. Hypothesis tests therefore might be seen as descriptive rather than providing classical interpretation. Of course similar concerns apply in a variety of other contexts, so this caution is not unique to linked NIBRS-LEMAS data.

  21. As the workload measure is a log, and the coefficients b in Table 1 refer to the log hazard rate, a 10 % increase in workload corresponds to multiplication of the hazard rate by 1.10−.440 = .959 (for the between-measure) and 1.10−.441 = .959 (for the within-measure), or a 4.1 % decrease.

  22. 2007 LEMAS only records information on whether or not full-time sworn officers are assigned to special units, not the exact number of assigned personnel.

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  1. Department of Sociology, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI, 53201, USA

    Aki Roberts & John M. Roberts Jr.

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Appendix

Appendix

See Tables 3 and 4.

Table 3 Descriptive statistics for workload and agency/jurisdiction-level variables (107 agencies)
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Table 4 Descriptive statistics for incident-level variables (before missing value imputation)
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Roberts, A., Roberts, J.M. Crime Clearance and Temporal Variation in Police Investigative Workload: Evidence from National Incident-Based Reporting System (NIBRS) Data. J Quant Criminol 32, 651–674 (2016). https://doi.org/10.1007/s10940-015-9270-9

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