Long-time readers of this site know that one of my pet projects is finding a way to use the statistics generated by this National Police Misconduct Statistics and Reporting Project (NPMSRP) to do more than just give us a hint at how prevalent police misconduct is in the United States. One of the experimental models I’ve come up with is a “Police Misconduct Index” (PMI) which is a composite average of different types of police misconduct related statistics that could help predict how likely police misconduct rates for an area might rise or decline in the future.
The PMI works on the assumption that as the likelihood of potential consequences for acts of misconduct rise the prevalence of misconduct falls… and that the opposite holds true in that a perceived lack of risk for misbehavior invites a higher rate of misconduct. So, as a basis of this model, we calculate the PMI from a set of statistics that examine the current relative rate of misconduct, how state or local governments respond to reports of police misconduct, and how effective those responses are.
Now, the PMI chart that I use for this computation is a bundle of useful information in and of itself. Within the chart is the estimated rate of police misconduct along with the percentage of reports that result in disciplinary action, criminal prosecution, and ratio of criminal prosecutions to criminal convictions…. all of that info packed into a small table.
But, there was something missing from the PMI. It was the effect that transparency, or the lack thereof, might have on the rate of police misconduct and the propensity for that rate to rise or fall within a given area. After all, the saying goes, bad things thrive in the secrecy of darkness and ignorance. So, I had to find some way to include how much transparency is allowed into police misconduct records by state law for each state.
Fortunately, the Suffolk University Law School’s Media and Communications Law Society and The Reporters Committee for Freedom of the Press came to the rescue with a list of Open Records laws for each state that also includes info on how those laws apply to police disciplinary records disclosure… which I’ve used to add another set of data to the PMI that corresponds with how open police misconduct records are within each state.
So, here’s the PMIv3 chart:
For those who aren’t familiar with the old chart and it’s naming conventions, here’s a rundown of the columns:
State – The abbreviation code for each state
PMR – The Police Misconduct Rate, which is a per capita ratio of officers out of 100k officers involved in police misconduct reports tracked by the NPMSRP.
PMDPR – The percentage of officers tracked that are subject to either/both disciplinary action and/or criminal charges.
PMCPR – The percentage of officers tracked that are prosecuted.
PMConR1 – The percentage of officers prosecuted who are convicted.
PMConR2 – The percentage of officers reported who are convicted.
FOI – The open records laws as they apply to public access to police misconduct records which can be either Open, Partial, Discretionary, or Closed.
PMI – The Police Misconduct Index which is an inverse predictive number, a lower number indicates a greater likelihood of increased police misconduct rates in the future.
Color coding: Dark red includes the 5 worst for each category, light red is worse than average, clear is 2 closest to average, light green is better than average, dark green includes the 5 best per category. FOI designations use a different, self explanatory, color code at the moment.
Now, the astute who read the 2009 Police Misconduct Statistical Report we published might note that the PMI has not yet been modified by the FOI as I’ve not yet determined how much the lack of transparency might affect the likelihood of a police misconduct rate change over time. I need trending data to see just how effective the old PMI is at predicting future rates and that will give me some more data with which I can then determine if the FOI does affect this rate and, if so, how much it might do so by looking at the deviations. I figure I might be able to do that after the 2010 Q2 report.
In any case, as you can see, even discounting the usefulness or potential efficacy of this predictive Police Misconduct Index, the process of generating the PMI gives us a lot of useful information that’s fairly difficult to find elsewhere.
In any case, I just thought I would share one of the things I’ve been working on in the background. Let me know what you think of it and if you have any suggestions on what you would like to see included that might also have an effect on future misconduct rate trends.